Mediterranean Desertification
Mediterranean Desertification: A Mosaic of Processes and Responses Edited by N.A. Geeson
King’s College, University of London, UK C.J. Brandt
King’s College, University of London, UK and J.B. Thornes
King’s College, University of London, UK
Copyright 2002
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2001046911
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Contents
List of Contributors
ix
Preface
xv
Part 1
Thematic Issues
1
Section I
Introduction
3
Chapter 1
The Evolving Context of Mediterranean Desertification J.B. Thornes
5
Section II
Climate, Processes and Responses
13
Chapter 2
Extreme Climatic Events over the Mediterranean M. Conte, R. Sorani and E. Piervitali
15
Chapter 3
Potential Effects of Rising CO2 and Climatic Change on Mediterranean Vegetation C.P. Osborne and F.I. Woodward
33
Use of NOAA-AVHRR NDVI Data for Climatic Characterization of Mediterranean Areas Giovanni Cannizzaro, Fabio Maselli, Luciano Caroti and Lorenzo Bottai
47
Section III
Land Use, Processes and Responses
55
Chapter 5
The Effect of Land Use on Soil Erosion and Land Degradation under Mediterranean Conditions C. Kosmas, N.G. Danalatos, F. L´opez-Berm´udez and M.A. Romero D´ıaz
57
Chapter 4
Chapter 6
Agro-pastoral Activities and Land Degradation in Mediterranean Areas: Case Study of Sardinia G. Enne, G. Pulina, M. d’Angelo, F. Previtali, S. Madrau, S. Caredda and A.H.D. Francesconi
71
Chapter 7
Landscape Protection from Grazing and Fire N.S. Margaris and E. Koutsidou
83
Chapter 8
Bioengineering Principles and Desertification Mitigation J.N. Quinton, R.P.C. Morgan, N.A. Archer, G.M. Hall and A. Green
93
vi
Contents
Section IV
Physical Processes and Responses
Chapter 9
Differing Responses of Greek Mediterranean Plant Communities to Climate and the Combination of Grazing and Fire A. Dalaka, E. Papatheodorou, G. Iatrou, T. Mardiris, J. Pantis, S. Sgardelis, C. Lanara Cook, T. Lanaras, M. Argyropoulou, K.J. Diamantopoulos and G.P. Stamou
Chapter 10
Vegetation Cover Assessment in Mediterranean Semi-arid Landscapes F.J. Garc´ıa-Haro, J. Meli´a, M.A. Gilabert and M.T. Younis
Chapter 11
The Impact of Rock Fragments on Soil Degradation and Water Conservation B. van Wesemael, J. Poesen, C. Kosmas, N.G. Danalatos and J. Nachtergaele
107
109
119
131
Chapter 12
Aridification in a Region Neighbouring the Mediterranean ´ am Kert´esz, Tam´as Husz´ar, D´enes L´oczy, B´ela M´arkus, J´anos Mika, Ad´ Katalin Moln´ar, S´andor Papp, Antal S´antha, L´aszl´o Szalai, Istv´an T´ozsa and Gergely Jakab
147
Chapter 13
Soil Salinization in the Mediterranean: Soils, Processes and Implications L. Postiglione
163
Section V
Tools for Exploring Desertification
175
Chapter 14
Environmentally Sensitive Areas in the MEDALUS Target Area Study Sites A.C. Imeson and L.H. Cammeraat
177
Investigation on Environmental Characteristics to Underpin the Selection of Desertification Indicators in the Guadalent´ın Basin L.H. Cammeraat, A.C. Imeson and L. Hein
187
Chapter 15
Chapter 16
MEDRUSH: A Basin-scale Physically Based Model for Forecasting Runoff and Sediment Yield M.J. Kirkby, R.J. Abrahart, J.C. Bathurst, C.G. Kilsby, M.L. McMahon, C.P. Osborne, J.B. Thornes and F.I. Woodward
203
Part 2
Regional Studies
229
Section VI
The Guadalent´ın Basin, South-east Spain
231
Chapter 17
Natural Resources in the Guadalent´ın Basin (South-east Spain): Water as a Key Factor ´ and F. Belmonte F. L´opez-Berm´udez, G.G. Barber´a, F. Alonso-Sarria Serrato
Chapter 18
Local and Regional Responses to Global Climate Change in South-east Spain C.M. Goodess and J.P. Palutikof
233
247
Chapter 19
Chapter 20
Chapter 21
Chapter 22
Contents
vii
The Impact of Land Abandonment on Regeneration of Semi-natural Vegetation: A Case Study from the Guadalent´ın J.A. Obando
269
Lithology and Vegetation Cover Mapping in the Guadalent´ın Basin as Interpreted through Remote Sensing Data M.T. Younis, J. Mel´ıa, M.A. Gilabert, F.J. Garc´ıa-Haro and A.J. Bastida
277
Changing Social and Economic Conditions in a Region Undergoing Desertification in the Guadalent´ın Asunci´on Romero D´ıaz, Pedro Tobarra Ochoa, Franc´ısco L´opez-Berm´udez and Gonzalo Gonz´alez-Barber´a Management Plan to Combat Desertification in the Guadalent´ın River Basin L. Rojo Serrano, F. Garc´ıa Robredo, J.A. Mart´ınez Artero and A. Mart´ınez Ruiz
289
303
Section VII
The Agri Basin, Southern Italy
319
Chapter 23
General Description of the Agri Basin, Southern Italy F. Basso, E. Bove and M. del Prete
321
Chapter 24
The Agri Valley – Sustainable Agriculture in a Dry Environment: Crop Systems and Management F. Basso, M. Pisante and B. Basso
331
Chapter 25
Soil Erosion and Land Degradation F. Basso, M. Pisante and B. Basso
347
Chapter 26
Social and Economic Conditions of Development in the Agri Valley E. Bove and G. Quaranta
361
Chapter 27
Characterization of Soil Hydraulic Properties in a Desertification Context Alessandro Santini and Nunzio Romano
369
Chapter 28
Aspects of Forestry in the Agri Environment Agostino Ferrara, Vittorio Leone and Malcolm Taberner
385
Chapter 29
Modelling Large Basin Hydrology and Sediment Yield with Sparse Data: The Agri Basin, Southern Italy J.C. Bathurst, J. Sheffield, C. Vicente, S.M. White and N. Romano
397
Section VIII Conclusions
417
Chapter 30
419
Emerging Mosaics J.B. Thornes
Glossary
429
Index
433
List of Contributors
R.J. Abrahart
School of Geography, University of Nottingham, University Park, Nottingham NG7 2RD, UK
F. Alonso-Sarr´ıa
Laboratorio de Geomorfolog´ıa, Universidad de Murcia, Campus de “La Merced”, c/Santo Cristo 1, E-30001 Murcia, Spain
M. d’Angelo
Centro Interdipartimento di Ateneo NRD (Nucleo di Ricerca sulla Desertificazione), Dipartimento di Scienze Zootecniche, Universit`a degli Studi di Sassari, Facolt`a de Agraria, Via de Nicola, I-07100, Sassari, Italy
N.A. Archer
Division of Environmental and Applied Biology, Biological Sciences Institute, University of Dundee, Dundee DD1 4HN, UK
M. Argyropoulou
Department of Biology, Aristotele University of Thessaloniki, GR 540 06 Thessaloniki, Greece
B. Basso
Dipartimento di Produzione Vegetale, Universit`a degli Studi della Basilicata, Via Nazario Sauro 85, 85100 Potenza, Italy
F. Basso
Dipartimento di Produzione Vegetale, Universit`a degli Studi della Basilicata, Via Nazario Sauro 85, 85100 Potenza, Italy
A.J. Bastida
Departamento de Geolog´ıa, Universtitat de Val`encia, Spain
J.C. Bathurst
Water Resource Systems Research Laboratory, School of Civil Engineering and Geosciences, University of Newcastle upon Tyne, Newcastle upon Tyne NE1 7RU, UK
F. Belmonte Serrato
Laboratorio de Geomorfolog´ıa, Universidad de Murcia, Campus de “La Merced”, c/Santo Cristo 1, E-30001 Murcia, Spain
L. Bottai
FMA, Via Einstein 36, 50023 Campi Bisenzio, Firenze, Italy
E. Bove
Dipartimento Tecnico-Economico perla Gestione del Territorio Agricolo-Foresstale, Universit`a degli Studi della Basilicata, Via Nazario Sauro 85, 85100 Potenza, Italy
L.H. Cammeraat
IBED-Fysische Geografie en Bodemkunde, Universiteit van Amsterdam, Nieuwe Achtergracht 166, NL 1018 WV Amsterdam, The Netherlands
G. Cannizzaro
TelespazioSpA, Via Tiburtina 965, 00156 Rome, Italy
S. Caredda
Centro Interdipartimento di Ateneo NRD (Nucleo di Ricerca sulla Desertificazione), Dipartimento di Scienze Zootecniche, Universit`a degli Studi di Sassari, Facolt`a de Agraria, Via de Nicola, I-07100, Sassari, Italy
L. Caroti
CeSIA-Accademia dei Georgofili, Logge Uffizi Corti 1, 50122 Firenze, Italy
x
List of Contributors
M. Conte (deceased)
Formerly at Istituto Fisica Atmosfera CNR, PZA L. Sturzo 31, 00144, Rome, Italy
A. Dalaka
Department of Biology, Aristotele University of Thessaloniki, GR 540 06 Thessaloniki, Greece
N.G. Danalatos
Department of Agriculture, University of Thessaloniki, 38221 Volos, Greece
M. del Prete
Dipartimento di Produzione Vegetale, Universit`a degli Studi della Basilicata, Via Nazario Sauro 85, 85100 Potenza, Italy
K.J. Diamantopoulos
Department of Biology, Aristotele University of Thessaloniki, GR 540 06 Thessaloniki, Greece
G. Enne
Centro Interdipartimento di Ateneo NRD (Nucleo di Ricerca sulla Desertificazione), Dipartimento di Scienze Zootecniche, Universit`a degli Studi di Sassari, Facolt`a de Agraria, Via Enrico de Nicola, 9-07100, Sassari, Italy
A. Ferrara
Dipartimento di Produzione Vegetale, Universit`a degli Studi della Basilicata, Via Nazario Sauro 85, 85100 Potenza, Italy
A.H.D. Francesconi
Centro Interdipartimento di Ateneo NRD (Nucleo di Ricerca sulla Desertificazione), Dipartimento di Scienze Zootecniche, Universit`a degli Studi di Sassari, Facolt`a de Agraria, Via Enrico de Nicola, 9-07100, Sassari, Italy
F. Garc´ıa Robredo
Fundaci´on Universidad Empresa de Murcia, Escuela de Negocios de la Regi´on de Murcia, Campus de Espinardo, 30100 Espinardo (Murcia), Spain
F.J. Garc´ıa-Haro
Remote Sensing Unit, Universitat de Val`encia, Dr Moliner 50, 46100-Burjassot, Val`encia, Spain
M.A. Gilabert
Remote Sensing Unit, Universitat de Val`encia, Dr Moliner 50, 46100-Burjassot, Val`encia, Spain
G. Gonz´alez-Barber´a
Departamento de Coservacion de Suelos y Agua, CEBAS-CSIC, Campus Universitario de Espinardo, Apartado 4195, 30080 Murcia, Spain
C.M. Goodess
Climatic Research Unit, University of East Anglia, Norwich, Norfolk NR4 7TJ, UK
A. Green
National Soil Resources Institute, Cranfield University, Silsoe, Bedford MK45 4DT, UK
G.M. Hall
National Soil Resources Institute, Cranfield University, Silsoe, Bedford MK45 4DT, UK
L. Hein
FSD, PO Box 570, NL 6700 AN Wageningen, The Netherlands
T. Husz´ar
Dept of Physical Geography, Geographical Research Institute, Hungarian Academy of Sciences, H-1112 Budapest, Hungary
G. Iatrou
Department of Biology, Aristotele University of Thessaloniki, GR 540 06 Thessaloniki, Greece
A.C. Imeson
IBED-Fysische Geografie en Bodemkunde, Universiteit van Amsterdam, Nieuwe Achtergracht 166, NL 1018 WV Amsterdam, The Netherlands
List of Contributors
xi
G. Jakab
Department of Physical Geography, Geographical Research Institute, Hungarian Academy of Sciences, H-1112, Budapest, Hungary
´ Kert´esz A.
Department of Physical Geography, Geographical Research Institute, Hungarian Academy of Sciences, H-1112, Budapest, Hungary
C.G. Kilsby
Water Resource Systems Research Laboratory, School of Civil Engineering and Geosciences, University of Newcastle upon Tyne, Newcastle upon Tyne NE1 7RU, UK
M.J. Kirkby
School of Geography, University of Leeds, Leeds LS2 9JT, UK
C. Kosmas
Laboratory of Soil Chemistry, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
E. Koutsidou
Department of Environmental Studies, University of the Aegean, “Xenia” Building, 81100 Mytilini, Lesvos, Greece
C. Lanara Cook
Department of Biology, Aristotele University of Thessaloniki, GR 540 06 Thessaloniki, Greece
T. Lanaras
Department of Biology, Aristotele University of Thessaloniki, GR 540 06 Thessaloniki, Greece
V. Leone
Dipartimento di Produzione Vegetale, Universit`a degli Studi della Basilicata, Via Nazario Sauro 85, 85100 Potenza, Italy
D. L´oczy
Department of Physical Geography, Geographical Research Institute, Hungarian Academy of Sciences, H-1112, Budapest, Hungary
F. L´opez-Berm´udez
Department of Physical Geography, Laboratorio de Geomorfolog´ıa, Universidad de Murcia, Campus de “La Merced”, c/Santo Cristo 1, E-30001 Murcia, Spain
S. Madrau
Centro Interdipartimento di Ateneo NRD (Nucleo di Ricerca sulla Desertificazione), Universit`a degli Studi di Sassari, Facolt`a de Agraria, Via de Nicola, I-07100, Sassari, Italy
T. Mardiris
Department of Biology, Aristotele University of Thessaloniki, GR 540 06 Thessaloniki, Greece
N.S. Margaris
Department of Environmental Studies, University of the Aegean, “Xenia” Building, 81100 Mytilini, Lesvos, Greece
B. M´arkus
Department of Physical Geography, Geographical Research Institute, Hungarian Academy of Sciences, H-1112, Budapest, Hungary
J.A. Mart´ınez Artero
DGCONA, Ministerio de Medio Ambiente, Avda. Alfonso X El Sabio 6, 30008 Murcia, Spain
A. Mart´ınez Ruiz
Fundaci´on Universidad Empresa de Murcia, Escuela de Negocios de la Regi´on de Murcia, Campus de Espinardo, 30100 Espinardo (Murcia), Spain
F. Maselli
IATA-CNR, P. le delle Cascine 18, 50144 Firenze, Italy
M.L. McMahon
Infocom (UK) Ltd, York Science Park, York, UK
J. Meli´a
Remote Sensing Unit, Universitat de Val`encia, Dr Moliner 50, 46100-Burjassot, Val`encia, Spain
J. Mika
Department of Physical Geography, Geographical Research Institute, Hungarian Academy of Sciences, H-1112, Budapest, Hungary
xii
List of Contributors
K. Moln´ar
Department of Physical Geography, Geographical Research Institute, Hungarian Academy of Sciences, H-1112, Budapest, Hungary
R.P.C. Morgan
National Soil Resources Institute, Cranfield University, Silsoe, Bedfordshire MK45 4DT, UK
J. Nachtergaele
Laboratory for Experimental Geomorphology, Katholieke Universiteit Leuven, Belgium
J.A. Obando
Department of Geography, Kenyatta University, PO Box 43844, Nairobi, Kenya
C.P. Osborne
Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK
J.P. Palutikof
Climatic Research Unit, University of East Anglia, Norwich, Norfolk NR4 7TJ, UK
J. Pantis
Department of Biology, Aristotele University of Thessaloniki, GR 540 06 Thessaloniki, Greece
E. Papatheodorou
Department of Biology, Aristotele University of Thessaloniki, GR 540 06 Thessaloniki, Greece
S. Papp
Department of Physical Geography, Geographical Research Institute, Hungarian Academy of Sciences, H-1112, Budapest, Hungary
E. Piervitali
CRATI s.c.r.l., Universit`a della Calabria, Rende (CS), Italy
M. Pisante
Dipartimento di Produzione Vegetale, Universit`a degli Studi della Basilicata, Via Nazario Sauro 85, 85100 Potenza, Italy
J. Poesen
Laboratory for Experimental Geomorphology, Katholieke Universiteit Leuven Redingenstraat 16, B-3000 Leuven, Belgium
L. Postiglione
Faculty of Agriculture, University of Naples Federico II, via Universit´a, 100, 80055 Portici (NA), Italy
F. Previtali
Dipartimento di Scienze dell’Ambiente e del Territorio, Universit`a di Milano–Biocca, Milano, Italy
G. Pulina
Centro Interdipartimento di Ateneo NRD (Nucleo di Ricerca sulla Desertificazione), Dipartimento di Scienze Zootecniche, Universit`a degli Studi di Sassari, Facolt`a de Agraria, Via de Nicola, I-07100, Sassari, Italy
G. Quaranta
University of Basilicata–DITEC, Via Macchia Romana, I-85100 Potenza, Italy
J.N. Quinton
National Soil Resources Institute, Cranfield University, Silsoe, Bedfordshire MK45 4DT, UK
L. Rojo Serrano
DGCONA, Ministerio de Medio Ambiente, Gran V´ıa de San Francisco 4, 28005 Madrid, Spain
N. Romano
Department of Agricultural Engineering, Division for Land and Water Resources Management, University of Naples “Federico II”, Via Universita’, 100, 80055 Portici (Naples), Italy
M.A. Romero D´ıaz
Department of Physical Geography, University of Murcia, Campus de “La Merced”, c/Santo Cristo 1, E-30001 Murcia, Spain
List of Contributors
xiii
A. S´antha
Department of Physical Geography, Geographical Research Institute, Hungarian Academy of Sciences, H-1112, Budapest, Hungary
A. Santini
Department of Agricultural Engineering, Division for Land and Water Resources Management, University of Naples “Federico II”, Via Universita’, 100, 80055 Portici (Naples), Italy
S. Sgardelis
Department of Biology, Aristotele University of Thessaloniki, GR 540 06 Thessaloniki, Greece
J. Sheffield
Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, USA
R. Sorani
Servizio Meteorologico dell’Aeronautica, Rome, Italy
G.P. Stamou
Department of Biology, Aristotele University of Thessaloniki, GR 540 06 Thessaloniki, Greece
L. Szalai
Department of Physical Geography, Geographical Research Institute, Hungarian Academy of Sciences, H-1112, Budapest, Hungary
M. Taberner
c/o Institute for Environment and Sustainability, Ispre, Italy
J.B. Thornes
Department of Geography, King’s College London, the Strand, London WC2R 2LS, UK
P. Tobarra Ochoa
Department of Fundamentals of Economical Analysis, University of Murcia, Spain
I. T´ozsa
Department of Physical Geography, Geographical Research Institute, Hungarian Academy of Sciences, H-1112, Budapest, Hungary
B. van Wesemael
D´epartement de G´eographie, Universit´e Catholique de Louvain, Place Louis Pasteur 3, B-1348 Louvain-la-Neuve, Belgium
C. Vicente
C/Cafetos #4, Col. Campestre, Cordoba, Veracruz 93653, Mexico
S.M. White
Institute of Water and Environment, Cranfield University, Silsoe, Bedfordshire, MK45 4DT, UK
F.I. Woodward
Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK
M.T. Younis
Remote Sensing Unit, Universitat de Val`encia, Dr Moliner 50, 46100-Burjassot, Val`encia, Spain.
Preface
Desertification has been recognized as one of the biggest problems facing the European Mediterranean countries. By desertification we mean land degradation resulting from various factors, including climatic variation and human impact, and it is the long history of human intervention, from Classical times onwards, that has particularly shaped the landscape here. Water resources have been exploited unsustainably, resulting in chemical pollution, salinization and exhaustion of aquifers. As economic activity has flourished in coastal areas so abandonment and degradation of land in the interior, previously sustained by traditional farming practices, have continued. Portugal, Spain, Italy and Greece are all now signatories to the United Nations Convention to Combat Desertification and implementation of the convention within national and regional action plans will require further organization of research and monitoring. The European Commission has funded a number of projects within the Environment Programme (DGXII), aimed at improving the understanding of the whole range of desertification issues. This book is based on the results of one of those projects, MEDALUS II, where 44 different universities and other institutions combined their expertise to clarify the processes of desertification operating in the Mediterranean environment, and the responses to those processes. Scientists of many disciplines, ranging from remote sensing to microbiology, researched climate, land use and the physical processes within soil and vegetation systems in order to design tools to describe and monitor desertification. Part 2 of this book describes how these processes and tools have been applied specifically. The regional studies illustrate how the application of remedial action cannot usually be uniform, but must respect the mosaic of physical environments and social and historical variations that interact within the geographical space of two of the target areas: the Guadalent´ın Basin of south-east Spain, and the Agri Valley of southern Italy. The editors feel privileged to have had the opportunity to work with the MEDALUS projects and to edit this book. All the authors should feel very proud of the unique spirit of co-operation that the projects have engendered. Each individual contribution makes up a part of the mosaic of our current knowledge, and the years of work behind this achievement are very much appreciated. Nichola Geeson Jane Brandt John B. Thornes Department of Geography, King’s College, University of London, UK November 2001
PART 1
THEMATIC ISSUES
Section I
Introduction
1
The Evolving Context of Mediterranean Desertification
J.B. THORNES
Department of Geography, King’s College London
1 INTRODUCTION In the last 10 years, the issue of desertification has not only become more widely recognized, both internationally and regionally, but the social and political framework has changed dramatically in a way that makes a change in the research approach crucial. It is the purpose of this chapter to outline these changes in order to set the context for further assessment of the problem. There have been a number of major syntheses that reflect the wider consciousness and appraisal of the problem. Despite these changes, the UNEP (United Nations Environment Programme) definition of desertification as “land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors including climatic variations and human activities” remains as helpful today as it was in 1990 (UNEP 1990). Bearing in mind that “land” means the terrestrial bioproductive system that comprises soil, vegetation, other biota and the ecological and hydrological processes that operate within the system, the definition is particularly relevant. “Land degradation” means reduction and loss of the biological and economic productivity caused by land-use change, or by a physical process or a combination of the two. If anything, it would be useful to incorporate the rural depopulation implied in the French language usage, especially in a European context, where desertion of rural areas has been stressed as a pivotal problem in European Agricultural Reform. More light was spread on the problems of desertification in southern Europe by the conference held jointly by the Directorate General for Research of the European Commission and the Greek Government from 29 October to 1 November 1996. The proceedings have been published in two volumes (Balabanis et al. 1999, 2000). Another source is the documentation arising from the Concerted Action on Mediterranean Desertification, funded by the Research Directorate under Framework V and published in three volumes (Burke and Thornes 1998, in press a, b). A further important contribution, in addition to the publication of the two major books on the MEDALUS Project (Brandt and Thornes 1996; Mairota et al. 1998), is van der Leeuw’s brilliant synthesis of the Archaeomedes Project (van der Leeuw 1998).
2 AGENDA 21 AND SUSTAINABILITY At the international level, the UNCED Rio Conference of 1992 urged signatory nations to “reposition their economies, their societies and their collective purpose to maintain all life on earth, peacefully, equitably and with sufficient wealth to ensure that all are content in their survival” (O’Riordan and Voisey 1998, p. xiii). In Europe, this requirement was foreseen in the Fifth Environmental Plan, a precursor to the Rio Conference’s position on sustainability. Although progress has been relatively slow in some European countries and almost non-existent in others, the plan anticipates a level of public empowerment in environmental matters that will, in the longer term, enlighten environmental affairs. In Portugal, the establishment of Environmental Protection Associations at four different Mediterranean Desertification: A Mosaic of Processes and Responses. Edited by N.A. Geeson, C.J. Brandt and J.B. Thornes 2002 John Wiley & Sons, Ltd
6
Mediterranean Desertification
levels (Reibeiro and Rodrigues 1998) has strong affinities with the Land Care approach of Australia, in its emphasis on community and end-user involvement. This bodes well for the future contemplation of measures against desertification. Greece has been somewhat slower to act, according to Greek authors (Fousekis and Lekakis 1998), but the difficulties are the same: the lack of familiarity with, and acceptance of, consultation of the people; the late development of institutions of government, especially those concerned with environment; and the shortage of basic data that are required for decision making at the local level. Another major change since the start of the MEDALUS Project has been the shift in the Common Agricultural Policy (CAP) as a result of changing public awareness of the failures of the agricultural price support system and, specifically, the negotiations in 1992 of the General Agreement on Tariffs and Trade (GATT).
3
AGRI-ENVIRONMENTAL MEASURES AND AGENDA 2000
Perhaps the largest socio-economic change to occur in Europe that may be expected to have a bearing on the desertification problem is the Cork Declaration. In this, Commissioner Fischer stated his determination to reform the CAP into a more broadly based rural policy, integrating environmental issues. This was to bring to an end 40 years of price support and potentially affect an area of 141 million hectares, 44% of the total land surface of the 15 European Union states and probably change the landscape of Europe forever. There is a close link here to Agenda 21, because the need for sustainable agriculture is one of the key forces driving the reform of the CAP. This reform is called Agenda 2000. Here sustainable use means “The use of components of biological diversity in a way and at a rate that does not lead to the long-term decline of biological diversity, thereby maintaining its potential to meet the needs and aspirations of present and future generations” (according to the International Convention on Biodiversity, Rio 1992). In their assessment of the new CAP proposals, Birdlife International (1997) described the old CAP as “the engine of destruction in the countryside”. The productionist philosophy, with its link to regional and national development, led to the intensification of agriculture after the Second World War, through increased mechanization, fertilizer application and the promotion and extension of irrigation, that was so notable in the Mediterranean, leading to the conversion of dry farming to dense, fast-growing, heavily fertilized and pest-treated crops. It also led to a sharp increase in the demand for irrigation water and massive extraction of groundwater resources (see below). The original CAP (arising in the earliest days of the Community from the Treaty of Rome) was “an outdated, expensive, inefficient, inequitable and environmentally-damaging collection of policies that by 1992 was in need of further reform”. This was urgent for several reasons: • • • •
the proposed enlargement of the European Community; the requirement to meet the needs of the Uruguayan round of GATT and to prepare for the next round of World Trade negotiations with an emphasis on the reduction of trade-distorting subsidies; the commitment at the Rio Conference to promoting sustainable agriculture and protecting and enhancing the natural environment, as well as helping to meet the needs of rural communities; public demand for economic reform, relating to the budgetary costs and the economic inefficiency of the CAP.
The Commission decided to follow the McSharry approach to reforms by reducing support payments to world levels and replacing production incentives with direct payments. For example, the sharp cuts in beef and cereal prices are designed to allow food to be exported into world markets without subsidy enabling an enlarged EU to sell off its surpluses in these commodities. The East European countries that are joining will need to develop their agricultural systems in a sustainable way while meeting the needs of their rural communities. It is too early to see the outcomes of these policy shifts, which tend to be obscured by short-term fluctuations, such as the rise in grain prices that enabled export without subsidy in 1995–1996. The
The Evolving Context of Mediterranean Desertification
7
increased harvest in 1996 and the subsequent fall in world grain prices have reintroduced the need for export subsidies. The potential for significant increases in production brought some difficulties in meeting GATT limits on subsidized exports, requiring a significant increase in the set-aside rates. What is clear is that rural depopulation remains an important issue. A key objective of Agenda 2000 is to maintain the viability of rural communities by maintaining employment and incomes in rural areas through sustainable long-term use of resources. According to Birdlife International (1997), the number of people employed in agriculture in the 12 member states of the EU declined from 16.3 million in 1970 to 7.0 million in 1994, falling from 13.5% to 5.5% of total employment. At the same time, farm sizes and agricultural production have increased, resulting in increased levels of subsidies going to smaller numbers of farmers. As employment in agriculture continues to decline, the benefits of the (original) CAP are becoming less apparent. This valuable appraisal goes on to say that “Europe’s rural development problems cannot be addressed by support for agricultural production alone. They require a more integrated approach to rural policy, which places agriculture within the context of the whole rural economy” (Birdlife International 1997, p. 19). It is hard to disagree with this view and it must be added that the failure to address the most severe crisis in southern Europe, land degradation, highlights this lack of an integrated approach. The Agenda 2000 reforms are a great opportunity to couple economic regulations with environmental reforms. This has been done directly, to some extent through the “extensification measures” and indirectly through Environment Impact Directives. The ideal agri-environmental programme would, among other things, provide opportunities for all farmers to manage land for erosion mitigation rather than allow them to pass externalities (such as reservoir siltation) to the tax payers.
4 LAND ABANDONMENT It is often claimed that land abandonment invariably leads to land degradation and desertification, partially at least through the failure to maintain agricultural terraces. However, as Baudry (1991) points out, land abandonment is not a new phenomenon. It has been constantly occurring in Europe since 1950 and has been widespread in eastern North America since 1920. Rather than simply blame land abandonment on European Union policy, we need to know better what lessons can be learned from history. In the Mediterranean, there have been phases of strong outward migration. These have been both local (such as the impact of the Phylloxera plague on vines in the Spanish Alpujarra in the early years of the 20th century), and regional (as in the out-migrations for employment from southern Spain to northern Europe in the mid-20th century). Land abandonment does not necessarily mean that land is no longer used, either by agriculture or any other rural economy; it means a change in land use from the traditional or recent pattern to another, less intensive pattern. Nevertheless, we need to be able to identify how the landscape will change in relation to our knowledge of the erosion risk. Perhaps it is self-evident that the land at greatest risk is most likely to be abandoned. There are two sides to the coin: land abandonment occurs either because of external stresses and/or because of its inherently low productive capacity. Land abandonment occurs as a result of external driving forces, such as market changes, or internal changes that are “intrinsic”, for example if the system crosses some invisible threshold, such as the critical soil depth for plant growth. Once crossed, the tendency is for change to be negative, self-reinforcing and irreversible. Over the years, farming practice has brought the farming systems more stability, making them more resilient to changes. It is claimed that the mixed tree–grass–herb–grazing system of Extremadura, Spain (the dehesas) is highly stable to change because of its need for very low external inputs, its high biological diversity and the highly partitioned tree and herb layer (Bernaldez 1991). On the other hand, ecosystems are more unstable and susceptible to change when there is a strong competition between components. Thus Thornes (1990) was able to demonstrate the low stability in Mediterranean ecosystems where plants and soils compete for water, a situation that can lead to catastrophic changes as a result of small changes in the inputs and outputs (rainfall and grazing take-off, respectively). Progressive slow degradation
8
Mediterranean Desertification
can move the system towards an unstable state without the dangers being recognized. The trick is to identify the “position” of the threshold in state-space, so that trajectories towards instability can be recognized. The trajectory towards instability becomes apparent over time. After fire, it often takes 8–10 years before the pre-fire equilibrium between vegetation cover and sediment yield is reestablished. Unfortunately, abandonment and the associated neglect often bring the system rapidly to a threshold that, when crossed, may lead to irreversible erosion. Abandonment after ploughing results in a succession that requires about 20 years to reach equilibrium as a mature ecosystem under the prevailing grazing. Alpha diversity increases with succession and niche amplitude tends to diminish, the new plant species becoming specialists of increasingly narrower habitats (Pineda et al. 1981). Traditional sylvo-pastoral systems are subjected to either increases or decreases in grazing pressures. The former leads to destruction of natural pastures and the replacement of valuable grasses and legumes by unpalatable nitrophilous vegetation as has occurred at the MEDALUS field site in north-west Lesvos Island, Greece, observed by Kosmas et al. (1998). Replacement of nutritious herbs by rough pasture has also been described in Spain (De Miguel 1989). If this “matoralization” process proceeds unchecked, it eventually induces a decrease in biological diversity and a decrease in stability, as described by Naveh and Whittaker (1974), and an increase in fire risk.
5
WATER RESOURCES
Problems of water resources are inextricably bound to, but not synonymous with, desertification. As land degradation occurs, soil storage capacity is reduced, runoff increases and erosion thresholds are passed. The high inter-annual variability of rainfall moves Mediterranean soils inexorably towards the thresholds of land degradation as the pressure on vegetative cover increases through lack of soil moisture. The gathering pace of confidence in the observation of the existence of global warming and revised estimates by the ICCP indicate more difficult times ahead for hillslope hydrology as systems dry out. MEDALUS research suggests significant reductions in the biomass of grass and bushlands in areas having more than seven rain-free months per year in the Iberian Peninsula, as temperatures and atmospheric CO2 rise (Diamond and Woodward 1998), and estimates made by the Spanish Ministerio de Obras Publicas indicate important (17–20%) reductions in the flow of major Spanish rivers. Even accepting the scope for errors in these model estimates, the contemporary data already show that the supply of water for river flow replenishment and aquifer recharge is decreasing. In Mediterranean regions with average rainfalls of less than 300 mm per year, high inter-annual variability and high summer temperatures, there is a more or less continuous threat of water scarcity. In meteorological droughts this is caused by failure of precipitation, as has occurred in Italy, Greece and Spain in the last two decades of the last century. The whole of Italy was affected by severe drought during 1988–1999. A sequence of three years with low rainfall were accompanied by high temperatures; snow depths were also considerably reduced, with lower snowfalls than normal, combined with high temperatures. Large areas of Greece are susceptible to drought, notably eastern Greece and some Aegean islands. Catchments are often small and underlain by highly permeable karstic formations. There was an extended drought in the Athens area from 1987 to 1993, when rainfall was only 50% of normal, including two extremely dry years (1989/90 and 1991/2) that were the most severe over the last century. Most of Spain, except the north-west coast, was severely drought affected in the years 1990–1996. An analysis of seasonal rainfall (Institute of Hydrology and ISPRA 1999) indicates that the rainfall deficit was generally concentrated in winter and spring. Autumn rainfall was normal or above average and summer rainfall fairly regular. Mean percentage departure from normal rainfall exceeded −20% in the southern part of the country, which was worst affected. The drought reached its maximum coverage in September 1994 and August 1995 when rainfall reached −25%, and over two-thirds of Spain was affected. MEDALUS research by Goodess and Palutikof (Chapter 18) demonstrates the close coupling of the Atlantic Ocean pressure differences between the Azores High and the Iceland Low, on the one
The Evolving Context of Mediterranean Desertification
9
hand, and pressure fields over the Mediterranean that are linked to rainfall aberrations on the other. Earlier, Turkes (1996) showed, by the analysis of normalized rainfall patterns, that anticyclonic activity affected Turkey more frequently over the period 1973–1993. The abrupt decrease in rainfall since the early 1970s has been attributed to the northward shift of the Polar front, resulting from a more easterly extension of the drought-dominated subtropical anticyclone extending from the Azores to the eastern Mediterranean. According to the Institute of Hydrology/ISPRA report (1999), a study by Reynard et al. (1997) concluded, inter alia, that • there is a general tendency for an increase in annual average runoff in northern Europe and a decrease in southern Europe of over 30% in some areas; • the greatest sensitivity to change is in the drier parts of southern and eastern Europe; • before the 2050s there could be a substantial reduction in snowfall that would alter the current temporal distribution of river flows by reducing or eliminating the spring peak and substantially increasing winter flows in central and eastern Europe. In addition to the impacts of meteorological drought, the public perception of desertification has been heightened by water resource shortage arising from anthropologically induced water problems, including: • the huge and continuing rise in demand for water to meet the needs of tourism growth, which has locally caused salinization because aquifers have been drawn down, as in the case of Benidorm, Spain; • a number of major floods, whose magnitude and time-to-rise have almost certainly been affected by vegetation removal and soil erosion, but whose impact has resulted from the failure of planning measures to provide flood plain zoning; • the heavy reliance in Mediterranean countries on irrigation for agricultural production: in Greece, 80% of water is used for irrigation, in Italy 50%, in Spain 68% and in Portugal 52%; • the continued rise in the demand for irrigation water, which has led to a reversion to engineeringtype solutions. An example of the latter is the National Hydrological Plan of Spain, which foresees the transfer of water from the lower Ebro to both Catalonia (Barcelona) and Murcia. There has been a bitter debate by the people of Aragon who claim that the water needs for the poorer areas of Aragon are also exacerbating underdevelopment. There is a crisis of democracy because the central government has had to try to balance out the needs of the wet north and the dry south. In 1998, in the severe drought, the existing transfer canal taking water from the River Tajo in Castilla la Mancha to the River Segura in Murcia failed to stave off the impacts of drought in Murcia, where large numbers of fruit trees were lost. The Tajo–Segura Trasvase (transfer canal) has a capacity for transferring 6 × 108 m3 year−1 and the Spanish government ordered the diversion of a further 5.5 × 107 m3 to “save” Murcia. It is against this background that the current bitter row over transfer from the Ebro to Murcia is being waged. At a demonstration in the Aragon city of Zaragoza, two-thirds of the population of Aragon turned out to protest against the projected transfer, instead of letting the water flow to the Ebro delta and the irrigated lands around Tortosa. Meanwhile Barcelona is negotiating with France for water from the Rhˆone. Water quality deterioration is adding to the environmental crisis that has been confused and compounded with desertification and coupled to the issues of sustainability and the defence of rural areas. Again, the effects of productionist agriculture are evidently the major causes and any action taken to mitigate desertification through regulatory measures in an integrated catchment context will have to address the water quality problem (Foster 2000). The flux of fertilizer returns in water in the northern states is three times greater than in the southern states and contributes 73% of the total. Of the national amounts, the largest returns are of irrigation water (Egypt and Italy) and power station cooling water (France).
10
6
Mediterranean Desertification
A MOSAIC AND A PALIMPSEST
One of the major difficulties facing this planning operation is the fact that the Mediterranean landscape is one of the most complicated in the world. Over space, conditions rarely remain the same for more than a kilometre or two because of local variations in topography, soils, land use, climate and surface water conditions. Another source of variety is that almost every municipality bears the imprint of national, regional and local constraints throughout history. The challenge for those concerned with planning for environmental sustainability in a local Agenda 21, including desertification and land degradation, is threefold: • • •
to identify the local-scale causes of desertification and its manifestations, and develop suitable sensitive indicators to do this; to understand the historical development of the problem, also at different time-scales; to develop regulations that, far from being applicable to the whole of Europe, are sufficiently flexible to accommodate the local variations in history and conditions in the hope that this will facilitate implementation and contribute towards successful outcomes from the interventions.
Given the multiple pressures on national and regional governments from the International Convention, from the European Community and from national and local pressure groups, rural planning has shifted sharply into focus. With it has come the need for empowerment of local people in finding and negotiating optimal strategies to meet these legally binding requirements (Thornes 1998).
REFERENCES Balabanis P, Peter D, Ghazi A and Tsogas M (1999) Mediterranean Desertification. Research Results and Policy Implications, Volumes 1 and 2. Plenary Session Papers, European Commission, Directorate General for Science, Research and Development, EUR 19303, Brussels. Baudry J (1991) Ecological consequences of grazing, extensification and land abandonment. Role of interactions between environment, society and techniques. Options Mediterraneennes, Serie Seminaires 15, 13–19. Bernaldez FG (1991) Ecological consequences of the abandonment of traditional land use systems in central Spain. Options Mediterraneennes, Serie Seminaires 15, 23–29. Birdlife International (1997) A future for Europe’s Rural Environment: Reforming the Common Agricultural Policy. Birdlife International European Community Office, Brussels, p. 55. Brandt CJ and Thornes JB (1996) Mediterranean Desertification and Land Use. John Wiley, Chichester. Burke S and Thornes JB (1998) Volume 1, Actions taken by national governmental and non-governmental organisations to mitigate desertification in the Mediterranean; Volume 2, Thematic review (in press); Volume 3, Summary (in press). Concerted Action on Mediterranean Desertification. European Commission, Directorate General for Science, Research and Development, EUR 18490EN, Brussels, p. 349. De Miguel JM (1989) Estructura de un sistema silvopastoral de dehesa. PhD thesis, Universidad Complutense de Madrid, Facultat de Ciencias (in Spanish). Diamond S and Woodward I (1998) Vegetation modelling. In P Mairota, JB Thornes and N Geeson (eds) Atlas of Mediterranean Environments in Europe: The Desertification Context . John Wiley, Chichester, pp. 68–69. Foster S (2000) Sustainable groundwater exploitation for agriculture: current issues and recent initiatives in the developing world. Papers of the Groundwater Project, Madrid, Marcelin Botin Foundation, Series A, No. 6. Fousakis P and Lekakis J (1998) Adjusting to the changing reality: the Greek response. In T O’Riordan and H Voisey (eds) The Transition to Sustainability: The Politics of Agenda 21 in Europe. Earthscan, London, pp. 214–229. Institute of Hydrology (UK) and ISPRA 1999. Workshop on Drought and Drought Mitigation. Space Applications Institute, Ispra, Varese, Italy, February 1999. Kosmas C, Bakker M, Bergkamp G, Detsis V, Diamantopoulos J, Gerontidis St, Imeson A, Levelt O, Maranthianou M, Oortwijn R, Oustwoud Wijdnes D, Poesen J, Vandevkkerckhove L and Zaphirou Th (1998). Mairota P, Thornes JB and Geeson N (eds) (1998) Atlas of Mediterranean Environments in Europe: The Desertification Context. John Wiley, Chichester. MEDALUS III Meeting, Lesvos, 24–28 April 1998. MEDALUS Lesvos Field Guide. Laboratory of Soils and Agricultural Chemistry, Agricultural University of Athens.
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Naveh Z and Whittaker RH (1974) Structural and floristic diversity of shrublands and woodlands in northern Israel and other Mediterranean areas. Vegetation 41, 171–190. O’Riordan T and Voisey H (eds) (1998) The Transition to Sustainability: The Politics of Agenda 21 in Europe. Earthscan, London, pp. 214–229. Pineda FD, Nicolas JP, Ruiz M, Peco B and Bernaldez FG (1981) Succession, diversite at ampliyude de niche dans les paturages du centre de la Peninsula Iberique. Vegetation 47, 267–277 (in Spanish). Rebeiro T and Rodrigues V (1998) The evolution of sustainable development strategies in Portugal. In T O’Riordan and H Voisey (eds). The Transition to Sustainability: The Politics of Agenda 21 in Europe. Earthscan, London, pp. 202–214. Reynard NS, Hulme M, Conway D and Faulkner D (1997) In NW Arnell (ed.) The Impact of Climatic Change on Hydrological Regimes and Water Resources in Europe. Final Report to EC DCXII. Thornes JB (1990) The interaction of erosional and vegetational dynamics in land degradation: spatial outcomes. In JB Thornes (ed.) Vegetation and Erosion. John Wiley, Chichester, pp. 41–55. Thornes JB (1998) Mediterranean desertification and Di Castri’s fifth dimension. Mediterraneo 12/13, 149–166. Turkes M (1996) Meteorological drought in Turkey: an historical perspective 1930–1993. Drought Network News 8(3). UNEP (1990) Desertification revisited: proceedings of an ad hoc consultative meeting on the assessment of desertification. UNEP/DC/PAC, Nairobi, pp. 289–294. Van der Leeuw S (1998) The Archaeomedes Project – Understanding the Natural and Anthropogenic Causes of Land Degradation and Desertification in the Mediterranean Basin. European Commission, Directorate General for Science, Research and Development, EUR 18181EN, Brussels.
Section II
Climate, Processes and Responses
2
Extreme Climatic Events over the Mediterranean
M. CONTE,1 R. SORANI2 AND E. PIERVITALI3 1
Istituto Fisica Atmosfera CNR, Rome, Italy Servizio Meteorologico dell’Aeronautica, Rome, Italy 3 Universita` della Calabria, Rende (CS), Italy 2
1 INTRODUCTION Violent meteorological phenomena, including strong winds, heavy precipitation and intense thermal conditions, may lead to events such as floods and forest fires, with disastrous consequences to land cover and land use. The resulting damage, particularly to agricultural settlements, can lead to abandonment and degradation of once cultivated land. In the Mediterranean region the normal climate includes sparse rainfall and high temperatures, so that extreme meteorological events can have a big impact, destroying the fragile balance between climate, soils and vegetation. A small increase in aridity may be enough to prevent regeneration of vegetation, and cause soil erosion and salinization. In this way extreme climatic events are an agent of desertification, in a wider context. In this chapter attention is directed to some extreme meteorological events, covering large areas but having heavy consequences locally. “Meteorological bombs”, heat waves and precipitation patterns, particularly extreme rainfall episodes, have been studied.
2 THE METEOROLOGICAL BOMB IN THE MEDITERRANEAN 2.1 Introduction and Definition
Several studies have been devoted to meteorological “bombs” in the last few years because of the serious damage attributed to them. Strong winds, intense precipitation and resultant floods are generally associated with these “bombs”. T. Bergeron defined a very rapidly deepening extratropical low as “a depression in which the central sea-level pressure falls at a rate of 1 hPa h−1 or more for a period lasting at least 24 hours”. As Bergeron’s definition referred to the latitude of 60 ◦ N, a geostrophically equivalent rate can be obtained for a latitude ϕ by multiplying this rate by sin ϕ/sin 60◦ . The resulting critical rate, denoted as 1 bergeron by Sanders and Gyakum (1980), varies from 28 hPa 24h−1 at the pole to about 9 hPa 24h−1 at 20 ◦ N, which is the southern limit at which the phenomenon has been observed. In the Mediterranean, applying the geostrophic correction, the critical value of 1 bergeron is obtained with a deepening of 20 hPa 24h−1 at the extreme northern boundary, and of 14 hPa 24h−1 at the deep southern limit of the basin. An average value of 17 hPa 24h−1 is the critical value for an average latitude of 38 ◦ N. Sanders and Gyakum (1980) described this explosive extratropical cyclogenesis as a meteorological “bomb”. General case studies of “meteorological bombs” were examined by Mansfield (1974), Bosart (1981), Anthes Keiser (1979) and Mullen (1983). Specifically in the Mediterranean there have been studies by Bassani (1983), Capaldo et al. (1980) and Karakostas and Flocas (1983). In addition, a synoptic-dynamic climatology of the “bomb” was developed by Sanders and Gyakum Mediterranean Desertification: A Mosaic of Processes and Responses. Edited by N.A. Geeson, C.J. Brandt and J.B. Thornes 2002 John Wiley & Sons, Ltd
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Mediterranean Desertification
(1980) for part of the northern hemisphere, but not including the Mediterranean. In this chapter a synoptic climatology of this extreme meteorological system in the Mediterranean Basin is presented for the 31-year period 1965–1995. Using various data sources, 101 “bomb” events from this time period have been examined. 2.2
Mechanism of Development of the ‘‘Bomb’’ in the Mediterranean
A synoptic but accurate analysis of the 101 events studied indicated that for the most part (93%) the “bombs” occurred, in broad outlines, following only two fundamental types of meteorological development. In the first type many elements pointed out by Karakostas and Flocas (1983) are recognizable. In broad terms the “bomb” develops from an interaction between a baroclinic, open long wave and an unstable short wave. In addition, the resulting cyclonic vorticity, the upper air temperature advection, and the sensible and latent heat exchange support the rapid and intense deepening of the cyclone. During the analyses carried out for this synoptic climatology over the Mediterranean region, it was observed that, in several situations, the effect of the Alpine barrier can be very important in initiating a small-scale cyclogenesis, known as “cyclogenesis in the lee of the Alps”. This can interact with a wave at a larger scale and degenerate to form a “bomb”. In other words, the function of the short wave of the Karakostas and Flocas mechanism may be enhanced by the Alps or assumed by cyclogenetic factors to be related to the Alps. This type of development of a “bomb” is hereafter denoted as KF (after Karakostas and Flocas 1983). The second type of development follows the dynamics reported in the work of Capaldo et al. (1980). In this case the “bomb” originates from the interaction between a middle-latitude depression at synoptic scale, deeply penetrated into the Mediterranean, and a depression of African origin, sometimes at sub-synoptic scale. Often the interaction can be an effective intrusion of a smallscale African depression into a larger scale low-pressure area drawn from middle latitudes. In this process the low-level jet-stream and the intense baroclinicity have very important roles, related to the strong thermal contrast between the two systems of quite different origins. Other relevant features, especially in the initial stage of the development, are the very intense upper air vorticity due to a marked closeness of a branch of the polar jet-stream to a branch of the subtropical jet-stream and the release of sensible and latent heat. This type of growth of a “bomb” is hereafter denoted as CC (after Capaldo et al. 1980). In both types of development the fact that the Mediterranean sea surface temperatures (SST) are higher than Atlantic sea surface temperatures seems to be of relevant importance. The “bomb” is essentially a meteo-marine phenomenon. 2.3
The Calendar and the Geographical Distribution of the ‘‘Bombs’’ in the Mediterranean for the Period 1965–1995
Annual calendars of the incidence of “bombs” occurring in the Mediterranean region in the period 1965–1995 were compiled, and an example for 1965 is shown in Table 2.1. The date of maximum intensity is reported, as well as a value for that intensity in bergeron, and the geographical location of the centre of the “bomb”. An indication of the mechanism of development (CC, KF or other) is also given. The complete set of calendars (1965–1995) is available from the authors. Figure 2.1 shows the geographical distribution of the “bomb” events between 1965 and 1995 within location quadrilaterals, (2◦ latitude) × (3◦ longitude). Three particular areas of incidence appear: the Corsican–Sardinian Sea and the central and southern Thyrrenian Sea; an area including the central and southern Adriatic Sea and the northern Ionian Sea; and an area including the Aegean Sea. This is in accordance with observations over sea areas by Sanders and Gyakum (1980), who stated that explosive developments of “bomb” type occur over a wide range of sea surface temperatures (SST), but frequently near and a little south of their strongest gradients. Actually the three preferential areas in the Mediterranean are situated a little south of moderate or strong gradients of SST.
17
Extreme Climatic Events over the Mediterranean Table 2.1 Calendar of ‘‘bomb’’ development in the Mediterranean region in 1965
Year
Date of maximum activity
1965
Location of “bomb” centre
21 Jan 4 Feb 9 Feb 20 Apr 12 Nov
1.29 1.25 1.25 1.10 1.25
40N-12E 36N-32E 41N-12E 43N-14E 41N-05E
11 Dec 31 Dec
1.29 1.06
40N-18E 39N-21E
long.N
Intensity (bergeron)
3 1 1
2
3 6
4
3 7
12
7
5 6
5
4
Mode of development
KF KF KF KF Frontal cyclogenesis in a western flux CC CC
40
1
1
1
6
1
7 1
1
9 1
1
1
30 0
lat.W
1
10
2
3
10
20
19
22
15
30
7
7
10
lat.E
1
3
Figure 2.1 Geographical distribution of ‘‘bomb’’ events between 1965 and 1995 within location quadrilaterals (2◦ latitude) × (3◦ longitude) 2.4 Simple Statistical Distribution of the Mediterranean ‘‘Bombs’’
The monthly distribution of all cases of Mediterranean “bomb” is reported in Figure 2.2, showing that the phenomenon is much more frequent in the cold winter season. Only two summer events were observed in 31 years. Most of the winter “bombs” were associated with KF or CC development. No “bomb” developed during summer associated with CC, since in this season the African depressions are very infrequent. Figure 2.3 indicates that the KF mechanism occurs more frequently than does CC development. Only 7 cases out of 101 are due to developments other than KF and CC. The influence of the Alps in initiating the process of development of a “bomb” has also been determined, by the examination of the cyclogenesis. Mediterranean low-pressure areas, which can degenerate into “bombs”, either arrive in the basin from external regions, generally from the Atlantic, or originate in the basin, in particular in association with the orographic effect of the Alps. Figure 2.3 shows that the Alps play a significant role. The cyclogenesis of about 42% of the “bombs” is influenced by the Alps. Our analysis has also shown that the frequency of “bomb” development is higher in months during which the air temperature is lower than normal. This is probably due to the fact that
18
Mediterranean Desertification 25
Number of "bombs"
20
15
10
5
1
2
3
4
5
6
7
8
9
10
11
12
Month
Figure 2.2 Monthly distribution of ‘‘bomb’’ cases identified over the Mediterranean during the period 1965–1995
80 70
Number of "bombs"
60 50 40 30 20 10 0 KF mechanism
CC mechanism
Other mechanism
cases influenced by the Alps cases not influenced by the Alps
Figure 2.3 Distribution of type of development of ‘‘bombs’’ identified over the Mediterranean (1965–1995): either KF (after Karakostas and Flocas 1983), CC (after Conte et al. 1986) or an alternative mechanism
19
Extreme Climatic Events over the Mediterranean
in cold periods the difference between the SST and the air temperature is higher than at other times. A high temperature difference favours a transfer of sensible heat and water vapour from the sea to the atmosphere, thus increasing the energy available for the development of intense cyclogeneses. 2.5 The ‘‘Bomb’’ in the Framework of Large-scale Mediterranean Atmospheric Circulation
The annual number of meteorological “bombs” is shown in Figure 2.4, with a clear negative trend over the whole period, and a shift in the mean since 1982. The difference between the mean of the period 1965–1981 and the mean of the period 1982–1995 is statistically significant at the 95% level (Student’s t-Test). The observed shift should not be attributed to inhomogeneity in the data series, since all data have been recorded by the Meteorological Service of the Italian Air Force, which also performs stringent data quality controls. Colacino and Conte (1993a) investigated the behaviour of the height of the 500 hPa level over the Mediterranean. It is well known that the evolution of this level represents very well the physical situation of the whole atmosphere in a large area, such as the Mediterranean Basin. The analysis concluded that a positive trend is present in the 500 hPa height, over most of the Mediterranean, during the past 45 years. The height of the 500 hPa level appears to have particularly increased during the 1980s. High values of the 500 hPa are related to high frequency and persistence of anticyclones, i.e. high pressure systems that should prevent or limit the cyclogenetic activity. As a consequence, we would expect that the annual frequency of the “bombs”, which are very severe cyclonic systems, should also be reduced by the increased anticyclonic patterns in the Mediterranean. This conclusion appears to be supported by the regression analysis between the annual numbers of “bombs” and the 500 hPa height: these two series are negatively correlated, with a correlation coefficient of −0.7. 8 7
Number of events
6 5 4 3 2 1 0 1960
1965
1970
1975
1980
1985
1990
1995
2000
Year
Figure 2.4 Number of Mediterranean ‘‘bombs’’ per year, showing how the mean annual number of events has changed since 1982
20
Mediterranean Desertification
2.6 The Meteorological ‘‘Bomb’’, Damage and Desertification The intense wind, torrential precipitation and floods associated with the “bombs” cause severe damage, and therefore research into their behaviour is important. One example of a particularly disruptive meteorological “bomb” occurred over the southern Tyrrhenian Sea on 25 October 1973. A deep depression, with intense precipitation, thunderstorms and winds, affected the northern coast of Sicily in particular, where the whole of Palermo Harbour was badly damaged. From an economical point of view the negative impact was evaluated in hundreds of billions of Italian lira (Lauteri et al. 1974). Impacts on a lesser scale frequently affect agricultural settlements. Damage to crops and soil erosion may be enough to ruin farmers, causing them to abandon their land and find alternative employment elsewhere. Land abandonment leading to land degradation and desertification in the relatively dry and hot environment of southern Europe is a widespread problem. 2.7 Conclusions on the Meteorological ‘‘Bomb’’ From the discussion above we can draw the following conclusions:
(i) Meteorological “bombs” are not unusual over the Mediterranean Basin during the winter season. (ii) “Bombs” can be produced by two different dynamic processes, and the process proposed by Karakostas and Flocas (1983) appears to occur more frequently than that proposed by Capaldo et al. (1980). (iii) Statistical analysis indicates that the greatest number of events occurs in the central Mediterranean, while a secondary maximum is found over the Aegean Sea. This distribution suggests a possible role of Alpine orography in triggering these events. (iv) In recent years an increase of the atmospheric pressure over the central and western basin has been recorded. This appears to be associated with a trend in which the annual number of “bombs” has been reduced. (v) Meteorological “bombs” can have serious impacts on agricultural settlements, damaging the terrain and reducing crop production.
3
HEAT WAVES IN THE CENTRAL MEDITERRANEAN BASIN
3.1 Introduction During the warm season (from June to September) over large areas of the Mediterranean Basin, the air temperature sometimes increases up to several degrees above the normal value. These hot spells can either be sudden and very intense, but of short duration (3–5 days), or more gradual and less intense, but of long duration (i.e. 10 days or more). Studies regarding such phenomena over Greece and surrounding regions of the eastern Mediterranean have been made by Karapiperis and Mariopoulos (1956), who defined these thermal events as “heat waves”, and more recently by Metaxas and Repapis (1978) and Metaxas and Kallos (1980). For the central Mediterranean Basin a synoptic study was carried out by Conte (1986), who examined some specific cases. This chapter presents research that analyses all heat waves that occurred over the central Mediterranean during the period 1950–1995. The mechanisms of their development are outlined, essentially from the point of view of synoptic meteorology. A simple statistical presentation of all events has also been carried out. The study was focused on the central Mediterranean area, but, since the patterns leading to the heat waves are of western origin, most of them also influence the Iberian Peninsula, southern France and the coastal areas of north Africa and other Mediterranean countries. 3.2 Definition of the Short- and Long-lasting Heat Waves We define a short-lasting heat wave as a sudden and disruptive increase of air temperature, which, in three separate reference stations located in southern Italy, reached temperatures from 7 ◦ C to 15 ◦ C above the normal monthly mean computed for the period 1951–1980. This event usually has a
Extreme Climatic Events over the Mediterranean
21
Table 2.2 Incidence of short-term heat waves over the central Mediterranean (1950–1995), showing that the highest frequency occurs in July and August
June 19–21/1972 24–27/1982
July 04–07/1952 19–21/1956 11–13/1962 23–27/1962 03–05/1965 20–23/1967 08–11/1968 01–04/1981 30–01 Aug/1982 03–07/1985 23–27/1985 24–28/1987 04–08/1988 26–29/1992 03–06/1993
August
September
12–15/1952 11–13/1960 25–28/1960 03–06/1963 13–15/1963 03–05/1967 07–09/1970 06–08/1971 03–05/1981 20–24/1985 14–17/1989 03–07/1992
07–11/1962 11–14/1970 03–05/1974 03–06/1988 21–25/1995
Table 2.3 Incidence of longer term heat waves over the central Mediterranean (1950–1995)
June 16–28/1950 10–27/1952 09–22/1966 14–26/1970 08–17/1981 02–13/1983 19–30/1990 01–10/1993
July 14–23/1964 16–29/1969 07–16/1974 01–20/1982 13–02 Aug 1983 07–16/1984 11–21/1993 02–15/1994 15–28/1995
August 16–31/1967 01–14/1969 12–23/1971 27 July–12/1980 08–18/1981 27 July–16/1986 13–28/1987 25 July–14/1988 17–03 Sep/1991 25 Jul/08 1994
September 08–17/1951 15–28/1961 14–27/1975 19–27/1983 08–24/1987 08–18/1992
duration of about 3–5 days, and encompasses all of Italy, also reaching Corsica, Malta, the Adriatic side of the former Yugoslavia, Albania, part of Greece and North Africa. A list of all the heat waves of this type that occurred in the period 1950–1995 is shown in Table 2.2. The total number of events was 34, with 131 days influenced by these heat waves, and a mean duration of the phenomenon of about four days. In contrast, the long-lasting heat waves give rise to a gradual air temperature increase, with temperatures that are about 5 ◦ C higher than the normal monthly mean over most of the central basin, and lasting for 10 days or more. Table 2.3 shows that in the study period there were 33 events that influenced, with a mean duration of about 14 days, 462 days of the warm summer season. 3.3 Mechanism of Development of Heat Waves
Short-term Heat Waves The mechanism of development of short-term heat waves is outlined using a simple composite analysis of 12 events lasting four days, which is the mean duration of this kind of phenomenon,
22
Mediterranean Desertification
and a synoptic analysis of the meteorological patterns of an intense event that occurred during 24–27 June 1982. Figure 2.5 shows the pattern of the tropopause/maximum windspeed on 25 June 1982, in which the Subtropical Jet Stream (STJ) appears to be largely north of its normal position, which is in the southern sector of the basin. Following the divergence–vorticity relationship (Palmen and Newton 1969), on the right-hand side of the Jet Streak (the band of the maximum windspeed) a strong upper air convergence gives rise to downward vertical motion, which, in turn, produces warming by adiabatic compression of the atmosphere and, thus, the heat wave. When the STJ returns to its normal position the temperatures return to normal values. Since the latitudinal oscillations of the STJ occur rapidly and are short term, the associated heat waves have the same characteristics.
(a)
275
275
PJ 250
80 0 10
250
225 60
STJ
225 200
175 200 (b)
0
0
0
0 D
0.3
0.2 0
0.1
0.1
0
Figure 2.5 (a) Heat wave development. Tropopause/maximum wind pattern on 00 UTC of 25 June 1982. STJ, Subtropical Jet Stream; PJ, Polar Jet Stream; heights of tropopause (- - - - ) in hPa; isotachs in knots ( ). (b) Vertical motion (Pa s−1 ) on 06 UTC of 25 June 1982. Data from the European Centre for Medium Range Forecasting (ECMWF). Reproduced by permission from Societe` Italiane di Fisice
23
Extreme Climatic Events over the Mediterranean
Longer term Heat Waves The mechanism of development of the long-lasting heat wave is briefly analysed, taking as an example a case that occurred in July 1983. In this type of event the atmospheric circulation in the Euro-Atlantic region, outlined using the pattern at the 500 hPa level, is always characterized by a socalled “omega” pattern (due to its resemblance to the last letter of the Greek alphabet) moving very slowly from west to east (Figure 2.6). The southerly winds blowing between the western depression of the “omega” and the central anticyclonic ridge progressively invade the western and central parts of the Mediterranean. With this southerly flux there is usually associated a horizontal advection of very warm air masses moving from north Africa, which invade the basin, producing the increase in air temperature.
Comparison of Short-term and Longer Term Heat Waves All cases in Tables 2.2 and 2.3 were identified using both the temperature increases recorded in the three reference stations (as well as information for the whole central Mediterranean) and synoptic analyses similar to those outlined above for the two typical cases (Conte 1986). The clear similarities between meteorological patterns associated with the different situations permitted us to group events into short-term and longer term cases. In summary, the main difference between the two heat wave mechanisms is as follows: the shortterm heat waves are essentially determined by downward motion, although the adiabatic compression is exerted on a dome of warm air of African type. The longer term events are produced essentially by horizontal motions, especially if temporary and brief incursions of the STJ can reinforce the phenomenon by adiabatic compression. 3.4 Some Statistical Considerations on Mediterranean Heat Waves
During the study period (1950–1995) the number of summer-time days influenced by a short or longer term heat wave was 586. Since the total number of days in June, July, August and September (summer season) in the whole 46 years is 5612, about 10% of the summer period in the central Mediterranean was influenced by warming due to heat waves. This is an appreciable percentage and indicates that the phenomenon is not infrequent or exceptional and it should probably be considered as a feature of the Mediterranean summer. Table 2.4 summarizes the monthly distribution of the events, the number of days influenced by heat waves and their relative percentages. Two-thirds of the total number of events occur in July and August. The number of days affected by heat waves shows a difference between July and August. Short-term events tend to be more frequent in July, with more long-term events in August. The number of heat wave days in each year of the study period is reported in Table 2.5. The linear trend gives an increase, with a value of 0.4 days year−1 and the polynomial smoothing of seventh order indicates a 20-year oscillatory pattern. Interesting patterns do occur, and recently Colacino and Conte (1993a) detected in the pressure field over the central and western Mediterranean an oscillation having the Hale period (i.e. about 22 years), together with a clear increasing trend. This Table 2.4 Summary of heat wave events (1950–1995)
Month
No. of short-term events
No. of longer term events
Total number of events
% per summer month
No. of days affected by events
% per total summer days affected
June July August September
2 15 12 5
8 9 10 6
10 24 22 11
15 36 33 16
109 183 198 96
19 31 34 16
Total
34
33
67
100
586
100
58
15 JULY 46
L
58
L
82 H
82
52
64
64 70
24
8 JULY L
64 70
70 76
70
82
64
76
H
L 58
82
76 70
L
L
82 76 88
L
88
H
H
94 22 JULY
94 29 JULY
L 52
64 H
52
52
L
70
64
58
64
58 70
64
82 76
88
94 H
82
H 94
H
L
82
70
76 L
76
L
76 88
88 88
H 94
Figure 2.6 Development of the ‘‘omega pattern’’ at the 500 hPa level, producing the long-term heat wave of July 1983. Reproduced by permission from Societe` Italiane di Fisice
25
Extreme Climatic Events over the Mediterranean Table 2.5 Number of heat wave days during the years of the study over the study area
Year
No. of heat wave days
Year
No. of heat wave days
Year
No. of heat wave days
Year
No. of heat wave days
Year
No. of heat wave days
1950 1951 1952 1953 1954 1955 1956 1957 1958 1959
13 10 26 0 0 0 3 0 0 0
1960 1961 1962 1963 1964 1965 1966 1967 1968 1969
7 14 13 7 10 3 14 23 4 28
1970 1971 1972 1973 1974 1975 1976 1977 1978 1979
20 15 3 0 13 14 0 0 0 0
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989
17 28 27 42 10 15 21 38 30 4
1990 1991 1992 1993 1994 1995
12 18 20 25 29 19
pattern of the pressure field was associated with an oscillatory, but progressive increase over the Mediterranean of the persistence of anticyclonic systems, particularly of the Azores anticyclone, over the last fifty years. Heat waves are strongly connected with anticyclonic patterns. A comparison of the number of days influenced by heat waves and the 500 hPa heights over Cagliari, Sardinia, which is in the core of the central basin, shows a general agreement between the two data sets, with low values during the 1950s and the 1970s and higher values in the 1960s and the 1980s. The correlation coefficient for the two data sets is 0.64, which is statistically significant at the 95% level. The heat waves produce very warm and dry environmental conditions, and prolonged drought can lead to desertification (Palutikof et al. 1996). In addition, dryness and high temperature can exacerbate ideal conditions for forest fires to rage out of control over large areas. Fires destroy crops, both forestry and agricultural, and can also destroy the ecological balance within the vegetation and fauna. Colacino and Conte (1993b) examined the pattern of forest fires in the Mediterranean region in connection with the number of heat waves. The number of heat waves recorded in the period 1980–1985 was about 70% higher than in the period 1970–1975, and a similar increase was recorded in the extension of forest burned in the regions of the Mediterranean Basin, for which data are available. Unfortunately more and more fires are started deliberately rather than naturally, but it is evident that the aridity of the soil and vegetation, and the warming associated with heat waves, play an important role in maintaining and extending the fires. Finally, it must be remembered that the heat waves can influence the health of the population, and mortality is often enhanced during these events. A study carried out for the heat wave of 13 July–2 August 1983 indicated that during and immediately after the warmest days, the number of deaths in Rome was 450 more than the normal average seasonal value (Todisco 1987). 3.5 Conclusions on Heat Waves
The following conclusions have been drawn: (i) (ii) (iii) (iv) (v)
There exist two different types of heat wave: the first very intense and of short duration, the second less intense but of longer duration. These two heat wave types are associated with different meteorological patterns. In the study period (1950–1995) the total number of heat waves was 67: 34 short-term events and 33 long-term episodes. Heat waves occur in summer, most frequently in August. Analysis of heat wave events during the study period suggested a 20-year oscillatory pattern, with a superimposed trend of increasing incidence.
26
Mediterranean Desertification
(vi)
(vii)
4
4.1
This behaviour is similar to that found for the same period for atmospheric pressure in the western and central Mediterranean and a connection with the anticyclones in the basin is probable. The impacts of heat waves are important because they may contribute to drought, desertification and forest fires, and may negatively influence the health of the population.
SPACE – TIME PRECIPITATION PATTERNS IN THE WESTERN AND CENTRAL MEDITERRANEAN BASIN AND ANALYSIS OF EXTREME CASES Introduction
Many examples of climatological research are at present focused on climate evolution in association with anthropogenic enhancement of the greenhouse effect. Studies concerning the trend of increased air temperatures and the impact of this increase include those of Jones et al. (1986) and Hansen and Lebedeff (1987). Particular attention is devoted to the hydrological cycle and to the precipitation regime because a reduction in precipitation limits water resources, while increased events of intense rain could cause more frequent floods. Papers published on this subject at a global scale (Bradley and Groismann 1989; Diaz et al. 1989; Vinnikov et al. 1990) indicate that an increase in precipitation has been recorded at latitudes higher than 50 ◦ N, while at lower latitudes an opposite pattern is found. However, the regional analyses do not confirm this general picture, and give contradictory results (Kutiel 1991; Ben-Gai et al. 1994; Beniston et al. 1994; Groismann and Easterling 1994; Lettenmaier et al. 1994; Norsallah and Balling 1996). Several studies have been carried out in the Mediterranean Basin, but some do not refer to the recent period (Maheras 1988) and only a few papers give quantitative results (Palutikof et al. 1994). Here analysis of the trends of yearly and seasonal precipitation in the central and western Mediterranean is given for the period 1951–1995. The study area is subdivided into three latitudinal belts: northern (>42 ◦ N), central (38 ◦ N–42 ◦ N) and southern ( eucalyptus > wheat > shrub land > olives. In the Mediterranean the topography of a region exerts a powerful influence on settlement patterns and land-use practices, as well as being a contributory factor in soil erosion. The physiography Mediterranean Desertification: A Mosaic of Processes and Responses. Edited by N.A. Geeson, C.J. Brandt and J.B. Thornes 2002 John Wiley & Sons, Ltd
58
Mediterranean Desertification
of the Mediterranean area includes a diverse array of land-forms, a large proportion of which are dominated by sparsely vegetated upland zones (Perez-Trejo 1992). Such a physiographic relief presents ideal conditions for the generation of water erosion on slopes, resulting in loss of soil productivity and desertification. The slope aspect greatly influences the temperature of the local environment, which in turn affects evaporation and subsequently vegetation growth and resilience. A greater slope inclination also influences infiltration rates and accelerates runoff and sediment loss. The steepest slopes generate mass movements, such as landslides and mudflows. The vegetation patterns that cover a landscape affect the soil in all its dynamics, including water redistribution over and within the soil, and microbiological activity. Biotic interactions occur which generate and maintain soil structure in the upper soil through the process of aggregation. Aggregation is a strong determinant of the soil’s hydrological and biological characteristics (Imeson 1984), and affects erosional response. Extensive Mediterranean areas cultivated with rainfed crops such as cereals, vines, almonds and olives are mainly confined to hilly lands with shallow soils very sensitive to erosion. These areas become vulnerable to erosion and desertification because the reduced vegetation cover means less protection from raindrop impact during heavy rains (Faulkner 1990), the reduction of infiltration rates due to compaction from heavy machinery (Fullen 1985), and the formation of a surface crust (Morin and Benyamini 1977; Casenave and Valentin 1992; Romero D´ıaz et al. 1998). Many authors have demonstrated that in a wide range of environments, both runoff and sediment loss decrease exponentially with increasing percentage of vegetation cover (Elwell and Stocking 1976; Lee and Skogerboe 1985; Francis and Thornes 1990). Without vegetation, all the runoff energy is directed to soil erosion and the removal of the detached material over various distances. Thus, vegetation and land use are in that respect of paramount importance, controlling the intensity and the frequency of overland flow and surface wash erosion (Bryan and Campbell 1986; Mitchell 1990). Large-scale deforestation of semi-arid areas accompanied by intensive cultivation and overgrazing has resulted in accelerated erosion and the formation of badlands with very shallow soils. Erosion rates measured in Mediterranean badlands vary widely from 0.4 to 1.7 mm year−1 (Yair et al. 1982; Benito et al. 1992). However, even greater erosion rates have been reported elsewhere, as in the Trevelez river basin in Spain in which an average soil loss value of 2.4 mm year−1 has been measured (Martin-Vivaldi and Jimenez Olivencia 1992). In critical semi-arid areas of Spain, soil loss of 12 mm year−1 or 200 t ha−1 year−1 has been reported (L´opez-Berm´udez 1990). These values can easily be surpassed during heavy rainfall events occurring occasionally over the Mediterranean (Alias-Perez and Ortiz-Silla 1986; L´opez-Berm´udez et al. 1991; Romero D´ıaz et al. 1995).
2
HISTORICAL EVOLUTION OF LAND USE IN THE MEDITERRANEAN REGION
The Mediterranean must be the region of the world most badly affected by human-induced degradation over thousands of years. The evidence of degradation is very clear, with only relict patches of the indigenous forest cover remaining and entire landscapes no longer able to sustain any cultivation. Accelerated soil erosion is as old as farming. Two early leaders of the US Conservation Service, Hugh Bennett and Clay Lowdermilk, wrote in the 1938 Yearbook of Agriculture: “soil erosion began when the first heavy rain struck the first furrow turned by a crude implement of tillage in the hands of prehistoric man. It has been going on ever since, wherever man’s culture of the earth has bared the soil to rain and wind” (USDA 1938). Soil erosion was first reported by Homer in The Iliad. Greek hillsides were originally forested and covered by a fertile soil mantle, which, however, was rather shallow and vulnerable to erosion. Upland grazing and farming probably began around the middle of the second millennium and began the initial damage to forests. Several thousands of years of exploitative agriculture have greatly contributed to a dramatic reduction of agricultural productivity in the region, something that had already been mentioned by Plato, who, speaking for Attica in the 4th century BC (Critias III), noted the occurrence of massive floods and landslides, the disappearance of forests and the denudation of cattle pasture. This description provides us with one of the earliest recorded examples of degradation and desertification, but also implicates climatic as well as anthropogenic causes. Two centuries earlier, Solon had already advocated discontinuing grain
Effect of Land Use on Soil Erosion and Land Degradation
59
cultivation on the sloping lands of Attica, and recommended planting olives and grapes instead. His advice was echoed in the 4th century BC by Theophrastus in his “Cause of Plants”. Considering the effects of land use on erosion and particularly the positive effects of olive groves (see below), one realizes how suitable this early land-use change plan of Solon was. However, neither man’s advice addressed the root cause of the problem, which was not the choice of the crop as such but the process of erosion and the failure of the ancient Greeks to control it. Additional historical evidence relating to the effects of degradation on vegetation can be traced to Roman times when land degradation resulted in the creation of large pastoral estates. Wherever Romans established their dominion, they repeated the same pattern of extensive forest clearing, over-cultivation and overgrazing of land to satisfy the avaricious demands of their centre of power (Hillel 1991). Land-use changes in the Mediterranean during recent history are mainly due to physical and technical factors as well as socio-economic reasons. Particular land uses have been related to specific population behaviours, spatial distribution changes, and pressure over natural resources. The region has suffered important transformations since the middle of the 19th century, when the agricultural development really began. Land mismanagement stimulated by demographic dynamics resulted in shifting of the agricultural population (and activities) to marginal areas unsuitable for agriculture. Human impact on the landscape was increasingly negative through conventional large-scale extensive agriculture, negatively affecting soil properties and enhancing the erosion processes. The extension of cultivated areas at the expense of forest land implies high ecological alterations due to deforestation and the break-up of the original equilibrium between cultivation, grazing and forestry. Short-term capital investment and intensive cultivation have often resulted in land degradation. Land profits are usually not invested for land conservation measures, but are simply reinvested for cultivating another area. The most significant change in the current land-use distribution in Mediterranean Europe is the increasing intensification of agricultural land in terms of mechanization, extensive use of agro-chemicals, and irrigation. The Guadalent´ın Basin in south-eastern Spain may serve as an example for demonstrating the impacts of land transformation changes and population evolution on land degradation (Barbera et al. 1997). The basin is characterized by the greatest hydrological deficit in the Iberian Peninsula and also in Europe. Historically the lack of water resources and the pressure for land-use change have been constant factors. Land-use changes have been related to specific population behaviour and spatial distribution, and pressure on natural resources, often as a response to economic demands. The Guadalent´ın has suffered significant transformation since the latter half of the 19th century, when agricultural development began. Since then agricultural activities and some mining have seriously affected the rural landscape and the whole environment in general. Population evolution in the Guadalent´ın Basin has been analysed from the middle of the 18th century and indicates substantial interaction of population dynamics with land-use changes (L´opez-Berm´udez et al. 1995). On the other hand, extreme climatic events typical for the region have also exerted an important socio-economic influence. Soil erosion cannot be considered as a human-induced disaster of only recent times (Wise 1982). Archaeological and geomorphological evidence from the badlands in southern Spain shows that the basic physical properties such as drainage patterns and degree of slope have been in place for some 4000 years. In the hilly Guadalent´ın Basin, human-induced land degradation has been particularly due to intensive cereal cropping, grazing and exploitation of Quercus forest resources (Figure 5.1). Inappropriate agricultural practice and management in relation to soil properties, topography and climate have stimulated economically based political decisions that have resulted in the migration of people and their agricultural activities to marginal areas with poor soils not necessarily suitable for agriculture. Another negative human impact on the landscape has been through conventional largescale extensive agriculture using mechanization, weakening soil properties in relation to weathering and erosive processes. Due to economic reasons and also as a response to soil degradation, large areas then had to be abandoned or used only for grazing. The following discussion focuses on the impacts of precipitation and land use on erosion rates based on an extensive database collected in various northern Mediterranean sites, located in Portugal, Spain, France, Italy and Greece. These sites represent a variety of landscapes under a variety of land
60
Mediterranean Desertification Dry land
Irrigated land
Forested land
1850
1981
60
Area (%)
50 40 30 20 10 0 1755
Year
Figure 5.1 Changes in land-use types in the Guadalent´ın Basin (Spain) since 1755 ´ (Lopez-Berm udez et al. 1995) ´
uses typical for the Mediterranean region, such as agricultural land cultivated with rainfed cereals, vines, olives, eucalyptus plantation or under natural vegetation (shrub land).
3 3.1
LAND USE AND EROSION RATES The Impact of Vegetation and Surface Soil Conditions
The effects of soil surface conditions and percentage vegetation cover are of paramount importance to rainwater runoff and sediment loss. These effects were clearly demonstrated across a hillslope catena, where land use was the only dependent parameter and where all other factors, e.g. weather, soils and topography, remained almost standard. The hillslope (gradient 14.5–16.2%) is formed on a sandstone formation near Athens (southern Greece). The climate of the area is Thermo-Mediterranean with an average air temperature of 17.8 ◦ C. The average annual precipitation is 495 mm with more than two-thirds (71%) falling between November and April. The following soil-surface conditions/land uses were studied, all being typical for Mediterranean environments: • • • • •
Olive grove under semi-natural conditions, with winter-annual understorey vegetation. The soil surface was sufficiently protected from raindrop impact by the ground cover (including the plant residues). No ploughing of the soils took place for more than 20 years. Vine cultivation with moderate inputs involving sufficient weed control. The soil was ploughed parallel to the contours. Surface roughness was estimated at 14 cm, and clod/furrow angle at 30◦ . Bare land abandoned for 2.5 years, without any vegetation (kept bare by controlling weeds) and with an average soil surface roughness of 4 cm. Land abandoned for 2.5 years. The soil contained large rock fragments (15 cm average diameter) partially embedded in the soil surface, and covering 17.8% of it. The average soil-surface roughness was 4 cm. Land with annual vegetation and abandoned for 2.5 years. The soils were under natural vegetation (no weeding) with an average soil-surface roughness of 4 cm.
Four rainfall events (27.5, 24.9, 28.5 and 18.2 mm) inducing incipient ponding fell between the end of November and late January 1994, and runoff volumes were measured from the different plots. There was considerable variation in the total runoff, reflecting the enormous importance of surface conditions on runoff generation and land degradation (Figure 5.2). The presence of annual vegetation and the plant residues covering about 90% of the soil surface in the olive grove prevented
61
Effect of Land Use on Soil Erosion and Land Degradation 12
(a)
Runoff (mm)
9
6
3
0
27.5
24.3
28.5
18.2
Rainfall events (mm) (b) 64.10
Sediment loss (t km−2)
olives 48.07 vines bare
32.05
rock fragments annual vegetation
16.02
0.00
27.5
24.3
28.5
18.2
Rainfall events (mm)
Figure 5.2 (a) Rainfall runoff and (b) sediment loss measured during four rainfall events under different land uses and surface soil conditions
the formation of surface sealing and minimized the velocity and volume of runoff water. A total runoff of only 1.0 mm was measured from four rainfall events (Figure 5.2(a)). An intermediate water runoff (16.3 mm) was measured on the plots of the abandoned land where annual vegetation had been allowed to grow. In contrast, the lack of vegetation cover in the plots kept bare, or in the vineyard, favoured much greater volumes of runoff, with values of 22.6 mm and 21.0 mm, respectively, from the four rainfall events. The greatest runoff (30.3 mm) was generated from the bare soil containing rock fragments (cobbles), at a rate even higher than the bare, stonefree soil. Figure 5.2(b) shows how the total sediment loss varied according to land use after each of the four ponding rainfall events. The sediment loss was at a maximum (203.3 t km−2 ) on the soil containing rock fragments at the soil surface. In contrast, the abandonment of the olive grove for a long time, and thus the presence of annual vegetation and plant residues on the soil surface, was responsible for
62
Mediterranean Desertification
the drastic reduction of soil loss to negligible values (0.1 t km−2 ). Therefore, under olives grown like this, further degradation of the land is very restricted. Rock fragments on the soil surface appeared to play the most important role against erosion, especially during particularly heavy showers (i.e. heavier than those mentioned previously). A plant cover of 48% growing after abandonment of the land for 2.5 years reduced the total soil loss due to erosion by 35% as compared to the stone-free bare soil. Contour ploughing of the soils under vines also significantly decreased runoff and sediment loss. The absence of vegetation and the low aggregate stability (mean aggregate size equal to 0.6 mm) favoured surface sealing and increased runoff and sediment loss. 3.2
The Combined Effects of Land Use and Climate
The considerable variation in total runoff and sediment loss measured at various field sites along the northern Mediterranean reflect the great importance of total rainfall as well as land use on runoff generation and sediment loss, and therefore soil erosion. A number of runoff plots were installed at eight different sites (Figure 5.3), mainly formed on sedimentary rocks, i.e. schist, slates and phyllites, limestones, marls, sandstone-marls or unconsolidated alluvial deposits with slightly gravelly to gravelly, coarse to moderately fine-textured soils. In the following paragraphs, the effect of total rainfall on runoff and sediment loss is compared for the six study sites kept under the same land use.
Cereals Rainfed cereals, particularly wheat and barley, are widespread on the Mediterranean uplands. However, in some years the prevailing weather conditions during the growing period of these crops may be so adverse that the soils remain bare, creating favourable conditions for overland flow and erosion. Any loss of soil volume from these marginal lands greatly reduces the potential for biomass production, ultimately leading to desertification. Desertification at present threatens only the shallow and severely eroded soils. This threat, however, may expand to the majority of soils due to the adverse effects of global climatic change. Figure 5.4 indicates that the total annual runoff from the fields under rainfed cereals is positively related to the annual rainfall. It appears that runoff is a very small portion of the total rainfall (less than 1.5%) if the latter does not exceed 380 mm. However, amounts of rainfall greater than
Var Roussillon Petralona
Vale Formoso EI Ardal
Spata Santa Lucia
Rambla Honda
Figure 5.3 Location of the eight experimental field sites where erosion rates under various land uses were studied 1, Vale Formoso (Portugal); 2, El Ardal (Murcia, Spain); 3, Rambla Honda (Almeria, Spain); 4, Roussillon (Pyrenees, France); 5, Var (Pyrenees, France); 6, Santa Lucia (Sardinia, Italy); 7, Spata (Athens, Greece); 8, Petralona (Thessaloniki, Greece)
Effect of Land Use on Soil Erosion and Land Degradation Vale Formoso
(a) 300
El Ardal
63
Petralona
Y = −3.83 − 0.12∗X + 0.00056∗X 2
Runoff (mm year −1)
R = 0.82, n = 65 200
100
0 100
300
500
700
900
Rainfall (mm year −1) (b)
Portugal
Sediment loss (g m−2 year −1)
120
Spain
Greece
Y = −12.7 + 0.046∗X + 0.000083∗X 2 R = 0.60, n = 65
90
60
30
0 100
300
500
700
900
Rainfall (mm year −1)
Figure 5.4 (a) Rainwater runoff and (b) sediment loss versus total annual rainfall measured at three Mediterranean sites under rainfed wheat (Kosmas et al. 1997)
700 mm generated runoff volumes of up to 24% of the total precipitation. Most runoff events under Mediterranean conditions occur in the period from early October to late February. The rains falling during this period are of high intensity and long duration, and the soils cultivated with rainfed cereals are not sufficiently covered and protected from raindrop impact. As Figure 5.4 shows, in Mediterranean areas with a total precipitation of less than 280 mm, sediment loss is really not a threat. Sediment loss increases with increasing rainfall and may fluctuate between about 15 and 90 t km−2 year−1 for the range of 280–700 mm rain per year (Kosmas et al. 1997). Inbar (1992) reported a value of 20 t km−2 year−1 for the Catalunya area of Spain with an annual precipitation of 600–700 mm which is less than the values measured in wheat fields in wet years. Despite the wide variability existing, the obtained data show an increasing trend of sediment loss with increasing annual precipitation. The area cultivated with cereals around the northern Mediterranean is currently diminishing following a decline in the market prices for cereals, the rising cost of fertilizers and fuel, and the increased frequency of dry years. Most uplands with shallow soils have already been abandoned, and this abandonment seems likely to continue in the future.
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Vines The available data suggest that vine cultivation creates conditions for increased water runoff and sediment loss. This is because the soils cultivated with vines remain almost bare during autumn, winter and early spring due to the removal of annual vegetation by ploughing or the application of pesticides (to control weeds). Very high runoff rates have been measured in Spata (Greece) and Roussillon (France) with values of up to 31.8% of the annual precipitation, which reached 850 mm. The greatest runoff occurred typically in winter when the soil was wet and characterized by a low sorptivity and infiltration rate (Danalatos 1993). Infiltration was further diminished by the high compaction of the plough layer. It should be noted that the soils under vines are usually ploughed twice in mid-spring and are treated once or twice a year with herbicides. As the soils are very susceptible to dispersion, and rainfall intensities can be extremely high in the area (e.g. rainfall of 700 mm in one day was recorded at the Roussillon site in 1947, and 185 mm at Spata in 1994 in one day with a maximum intensity of 335 mm h−1 ), soil crusting occurs very often after ploughing, creating favourable conditions for overland flow and erosion. As with rainwater runoff, the greatest volumes of sediment loss were measured under vines, ranging from 67 to 460 t km−2 year−1 . These values greatly exceed those measured in fields cultivated with wheat. Data for sediment yields are not available from the Roussillon site. Further experiments and data are required in order to establish a clear trend of runoff and sediment loss in relation to annual rainfall along the whole northern Mediterranean.
Eucalyptus Eucalyptus cultivation, especially for pulp production, is very important, covering more than 500 000 ha in both Spain and Portugal and about 70 000 ha in Italy. Available data on soil erosion under eucalyptus plantations, which have been collected at Rio Santa Lucia (Sardinia), suggest that, as with vines, eucalyptus creates conditions for increased overland flow and erosion. Eucalyptus plantations are dense and dark and create adverse conditions for the growth of understorey annual or perennial vegetation so that the soil remains almost bare during the whole year. The total annual runoff under eucalyptus measured over a period of four years ranged from 0.6% to 8.2% of the annual precipitation, which varied from 171 mm to 564 mm. Figure 5.5 illustrates that the average sediment loss ranged from 1.4 to 65.6 t km−2 year−1 (SD = 1.2–46.8 t km−2 year−1 ) for the same precipitation range, demonstrating a serious erosion hazard for any soil reforested with eucalyptus as compared to the soils left under natural vegetation
Runoff (mm year −1)
Sediment loss (g m−2 year −1)
Runoff and sediment loss
70 60 50 40 30 20 10 0 171
453
473
564
Rainfall (mm year −1)
Figure 5.5 Runoff and sediment loss measured in hilly areas cultivated with eucalyptus at Rio Santa Lucia, Sardinia, over a four-year period (Kosmas et al. 1997)
Effect of Land Use on Soil Erosion and Land Degradation
65
(Aru and Barrocu 1993). These erosion rates are generally lower than those measured from soils under vines and generally higher than those measured under wheat. Runoff and sediment loss may be expected on any cultivated Mediterranean upland area, but especially where the soil is left bare for large parts of the year. If, in addition, heavy cultivation machinery is used, soil aggregate stability and organic matter content are decreased. This further increases the likelihood of soil erosion.
Olives Olive groves cover an appreciable part of the Mediterranean hilly areas. Where they grow as seminatural vegetation, annual vegetation and accumulating plant residues provide a high soil-surface cover, occasionally up to 90%, so preventing surface sealing and minimizing the velocity of the runoff water. Figure 5.6 shows that runoff in excess of 5% of the total rain, and sediment loss greater than 5.3 t km−2 year−1 never occurred under olive groves monitored for four years in southern Greece (Spata, Athens). Thus, the presence of annual vegetation and plant residues on the soil surface allows negligible soil loss and olives can play a big part in protecting Mediterranean uplands from further degradation and desertification. In fact, large areas around the Mediterranean region have been covered with olive trees since ancient times but many now grow untended as the prices obtained for olive oil have declined and made harvesting uneconomic in some places. Land-use planning should recognize the benefit of growing olives for the conservation of the soil before advocating alternative crops, such as eucalyptus, for only a short-term profit. Olives show a particularly high adaptation and resistance to long-term droughts and support a remarkable diversity of flora and fauna, greater than in some natural ecosystems (Margaris et al. 1995). The olive groves can be considered as a natural forest highly adapted to dry Mediterranean conditions, with lower vulnerability to fire than pine or eucalyptus forests, and protecting hilly areas from desertification in many ways. Much Mediterranean upland has been terraced for cultivating cereals, vines, olives and other crops (Figure 5.7). In many cases the stonewalled terraces are hundreds or even thousands of years old. Sometimes individual crescent-shaped terraces have been carefully constructed for individual trees. Soil was removed from other places to fill these terraces. This type of conservation management requires high labour costs to maintain the terraces in good repair. In the last few decades the value of such terraces for an agricultural return has markedly declined because of difficulties associated with poor accessibility and the limited use of labour-saving machinery. Many of these
Sediment loss (g m−2 year −1)
Runoff (mm year −1)
Runoff and sediment loss
10 8 6 4 2 0 349
453
508
575
Rainfall (mm year −1)
Figure 5.6 Runoff and sediment loss versus total annual precipitation measured between 1991 and 1994 at an olive grove under semi-natural conditions in Greece (Spata, Athens)
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Mediterranean Desertification
Figure 5.7 Terraced olive grove well protected from erosion on the island of Lesvos
areas have been abandoned and if the stonewalling is allowed to collapse, removal of the retained soil by the runoff water can happen very quickly. Unfortunately maintaining such abandoned terraces appears a very expensive practice in labour terms compared to most other alternatives for soil erosion control.
Shrub Land Through the First and Second World Wars much upland was cleared of natural vegetation and cultivated mainly for cereal production to ensure sufficiency of cereal supply to the local populations. In the first years of cultivation, the production was fairly good, but very soon soil degradation reached serious levels, productivity started to decline dramatically and so did the local population and agriculture in those uplands. Land abandonment has continued until recent times, so that more areas are left under semi-natural vegetation. At least this is not accelerating soil erosion. Vegetation cover in the abandoned areas is variable and depends on the amount and distribution of rainfall. Figure 5.8 demonstrates the tendency of increasing overland flow with decreasing annual rainfall. Of course there is an inevitable variation between the different experimental sites, which is attributed to soil-surface properties, slope grade and length, and rainfall intensity and duration. Vegetation cover is crucial to runoff generation and alters throughout the Mediterranean uplands depending on climatic conditions and the period of the year. In areas such as southern Spain (Almeria, Murcia), with an annual precipitation lower than 280 mm year−1 and high evapotranspiration rates, the soil water available to plants is drastically reduced and therefore the soil remains relatively bare, favouring overland flow. Runoff reached values up to 10% of the total rainfall at the rather dry Almeria site. The available data show a peak of runoff with an annual precipitation total of 280–300 mm. The relationship between annual sediment loss and precipitation shows a trend of increasing loss with decreasing precipitation as long as the latter exceeds 280–300 mm year−1 . If annual precipitation falls below this range, then erosion decreases with increasing aridity. Inbar (1992) reported similar trends for different watersheds in the coastal area of Israel. Data for sediment loss from the wetter experimental sites of Petralona (northern Greece) and Rio Santa Lucia (Sardinia), having an average annual precipitation of 464 and 448 mm, respectively, showed the lowest values, ranging from 13.8 to 0.5 t km−2 year−1 . Sediment loss increases if one moves from areas of higher precipitation to areas of lower precipitation (such as Murcia and Almeria, southern Spain). The maximum value of sediment loss was recorded in Almeria (21.5 t km−2 year−1 ), associated with an annual precipitation of 282 mm. Under drier climatic conditions, sediment loss is greatly reduced to values similar to those measured in the relatively wetter sites.
Effect of Land Use on Soil Erosion and Land Degradation
67
20
Runoff (mm year −1)
n = 18
15
10
n = 21
5
n =8
n = 12
0
n = 17
100−200 200−300 300−400 400−500 500−600 Rainfall (mm year −1)
Sediment loss (g m−2 year −1)
20
15
10
5
0
100−200 200−300 300−400 400−500 500−600 Rainfall (mm year −1)
Figure 5.8 Annual runoff and sediment loss versus rainfall measured in shrub lands at various experimental sites across the northern Mediterranean region (Kosmas et al. 1997)
3.3 Land Abandonment
There is a growing interest in the evolution of abandoned dry lands (Gordon et al. 1981; RuizFlano et al. 1992), which are now marginal from an environmental and socio-economic point of view (Esteve et al. 1993). The abandoned fields may show quite different evolutions depending on various environmental and land-use features. Some of these, especially soil type, water availability and the type of previous and post-abandonment land use, could play a more important role in some places than others. A wide variety of situations are generated, so that it is difficult to predict future evolution. The evolution of vegetation types depending on age of abandonment shows clear tendencies: the predominance of annuals in the fallow land and the field abandoned for five years, and a progressive decrease of annuals until they barely appear by the time scrub lands have developed. Shrubs and herbaceous perennials show the opposite behaviour, though in a less pronounced way and with
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Mediterranean Desertification
different time-scales. It can take more than 20 years for shrub lands with a high percentage of ground cover to develop. Martinez-Fernandez et al. (1996) studied the effects of land abandonment in eight abandoned fields in Murcia (Spain) with 1–30 years since abandonment, N–S exposure, similar present-day conditions and limestone substratum. The pedological property most related to vegetation dynamics was found to be the organic matter content. This factor also has important implications in soil degradation processes. Organic matter content shows a clear relationship with age of abandonment, which confirms a tendency already pointed out in previous studies (Martinez-Fernandez et al. 1994). The agricultural use of these fields led to a loss of organic matter content, being usually less than 1% under actual crops close to these abandoned fields. The results show that, after the abandonment of agricultural practices, an evident recovery of this factor may be detected, even in the early stages. The organic matter content gradually reverts to the situation before agricultural use. Recovery is helped if the abandoned field has a northern exposure, which loses soil moisture less readily than a field with a southern exposure. The post-abandonment uses of the abandoned fields have major importance in their evolution. Moderate grazing has a minor effect on the partial rejuvenation of the communities, visible through the maintaining of intermediate successional stages and high diversity index. These results may be of interest for all Mediterranean semi-arid areas showing similar environmental conditions, in which the abandonment practices are relevant. This is also true of sediment production. Generally, the greatest soil losses occur during the months with the highest rainfall, especially those with high hourly intensities, such as happen frequently in the month of October. However, in other months with significant rainfall, such as April and November 1984, November and December 1985, and January and April 1986, there was hardly any sediment production. Such observations confirm that the degree of correlation between rainfall, runoff and sediment production is low in semi-arid south-east Spain (Romero D´ıaz et al. 1998). Other controls are still only partly understood (Fisher et al. 1985), such as annual variations in the content and retention of soil moisture, biomass production, the incorporation of organic material, the quantities of soluble anions and cations, conductivity, etc. All these factors play an important role in the complex relations between rainfall, runoff and sediment production.
REFERENCES Alias-Perez LJ and Ortiz-Silla R (1986) Proceedings of XIV National Soils Meeting. Spanish Sciences Society of the Soil (CSIC), University of Murcia, Spain. Aru A and Barrocu G (1993) The Rio Santa Lucia catchment area. In Mediterranean Desertification and Land Use, MEDALUS Final Report. Commission of the European Communities. Contract number EPOC-CT900014-(SMA), pp. 533–559. Barbera GG, L´opez-Berm´udez F and Romero D´ıaz MA (1997) Cambios de uso del suelo y desertificac en el Mediterraneo: El caso del Sureste Iberico. In JM Garcia-Ruiz and P Lopez Garcia (eds) Accion humana y desertification en ambientes semiaridos. Instituto Pirenaico de Ecologia, Zaragoza, pp. 9–39. Benito G, Gutierrez M and Sancho C (1992) Erosion rates in Badland areas of the Central Ebro Basin (NESpain). Catena 19, 269–286. Bryan RB and Campbell IA (1986) Runoff and sediment discharge in a semi-arid drainage basin. Zeitschrift f¨ur Geomorphologic 58, 121–143. Casenave A and Valentin C (1992) A runoff capability classification system based on surface features criteria in semi-arid areas of West Africa. Journal of Hydrology 130, 231–249. Danalatos NG (1993) Quantified analysis of selected land use systems in the Larissa region, Greece. PhD thesis, Agricultural University of Wageningen, Wageningen. Douglas I (1969) Sediment yields from forested and agricultural lands. Proceedings of the Symposium on The Role of Water in Agriculture. University of Wales (Aberystwyth) Memorandum No. 12, E1–E22. Elwell HA and Stocking MA (1976) Vegetal cover to estimate soil erosion hazard in Rhodesia. Geoderma 15, 61–70. Esteve MA, Calvo F, Ibernon M, Gimenez A, Palazon JA and Ramirez-Diaz L (1993) Tierras marginales en ecosistemas semiaridos del Sureste Iberico: descriptores, relacion con los factores fisicos y aplicaciones a la gestion ambiental. Problematica Geoambiental y Desarrollo. V Reunion Nacional de Geologia Ambiental y Ordenacion del Territorio, Murcia, pp. 777–786.
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Faulkner H (1990) Vegetation cover density variations and infiltration patterns on piped alkali sodic soils: implications for the modelling of overland flow in semi-arid areas. In JB Thornes (ed.) Vegetation and Erosion, Processes and Environments. John Wiley, Chichester, pp. 317–346. Fisher GC, Romero D´ıaz A, L´opez-Berm´udez F, Thornes JB and Francis F (1985) Vegetation litter production and effects in an eroding Mediterranean ecosystem, Mula, SE Spain. IX Coloquio de Geografos Espanoles, Murcia, Poniense, V2. Francis CF and Thornes JB (1990) Runoff hydrographs from three Mediterranean vegetation cover types. In JB Thornes (ed.) Vegetation and Erosion, Processes and Environments. John Wiley, Chichester, pp. 363–384. Fullen MA (1985) Soil compaction, hydrological processes and soil erosion on loamy sands in East Shropshire, England. Soil and Tillage Research 29(6), 17–29. Gordon M, Guillerm JL, Poissonet J, Poissonet M, Thiault M and Trabaud L (1981) Dynamics and management of vegetation. In FDi Castri, DW Goodal and R Specht (eds) Mediterranean-type Scrublands. Elsevier, Amsterdam, pp. 317–344. Hillel D (1991) Deforesting the earth. In D Hillel (ed.) Out of the Earth, Civilization and the Life of the Soil . University of California Press, Berkeley and Los Angeles, pp. 175–185. Imeson A (1984) An eco-geomorphological approach to the soil degradation and erosion problem. In R Fantechi and NS Margaris (eds) Desertification in Europe. Proceedings of the Information Symposium in the EEC Programme on Climatology. Reidel, Dordrecht, pp. 153–168. Inbar M (1992) Rates of fluvial erosion in basins with a Mediterranean type climate. Catena 19, 393–409. Kosmas C, Danalatos N, Cammeraat LH, Chabart M, Diamantopoulos J et al. (1997) The effect of land use on runoff and soil erosion rates under Mediterranean conditions. Catena 29, 45–59. Langbein WB and Schumm SA (1958) Yield of sediment in relation to mean annual precipitation. American Geophysical Union Transactions 39, 1076–1084. Lee CR and Skogerboe JG (1985) Quantification of erosion control by vegetation on problem soils. In Al Swaify, WC Moldenhauer and A Lo (eds) Soil Erosion and Conservation. Soil Conservation Society of America, Ankeny, IA, pp. 437–444. L´opez-Berm´udez F (1990) Soil erosion by water on the desertification of a semi-arid Mediterranean fluvial basin: the Segura basin, Spain. Agriculture, Ecosystems and Environment , 33(2), 129–145. L´opez-Berm´udez F, Thornes JB, Fisher G and Francis C (1984) Erosion y Ecologia en la Espana semiarida (Cuenca de Mula, Murcia). Cuadernos de Investigacion Geografica 10(1–2), 113–126. L´opez-Berm´udez F, Romero D´ıaz MA and Martinez-Fernandez J (1991) Soil erosion in semi-arid Mediterranean environment. El Ardal experimental field (Murcia, Spain). In M Sala, JL Rubio and JM Garcia-Ruiz (eds) Soil Erosion Studies in Spain. Geoforma Ediciones, Logrono, pp. 137–152. L´opez-Berm´udez F, Sancez-Fuster MC and Romero D´ıaz A (1995) Incidencia de los modelos de gestion socioeconomica (siglos XIX y XX) en la degradacion del suelo en el Campo de Lorca (Cuenca del Guadalentin, Murcia). Papeles de Geografia 22, 5–18. Universidad de Murcia. Margaris N, Koukoutsidou E, Giourga Ch, Loumou A, Theodorakis M and Hatzitheodoridis P (1995) Managing desertification. In MEDALUS II Project 3, Managing Desertification, EV5V-CT92-0165, pp. 83–110. Martinez-Fernandez J, Romero D´ıaz MA, L´opez-Berm´udez F and Martinez-Fernandez J (1994) Parametros estructurales y funcionales de Rosmarinus officinalis en ecosistemas mediterraneos semiaridos. Studia Oecologica, 10–11, 309–316. Martinez-Fernandez J, Romero D´ıaz MA and Belmonde-Serrato F (1996) Evolution of vegetation and pedological characteristics in fields with different age of abandonment: a case study in Murcia (Spain). In JL Rubio and A Calvo (eds) Soil Degradation and Desertification in Mediterranean Environments. Geoforma Ediciones, Logrono, pp. 279–290. Martin-Vivaldi MC and Jimenez Olivencia Y (1992) Estudio de la erosion en la cuenca del Rio Trevelez (Granada). In F L´opez-Berm´udez C Conesa-Garcia and A Romero D´ıaz (eds) Estudios de Geomorfologia en Espana. Sociedad Espanola de Geomorfologia, Murcia, pp. 93–103. Mitchell DJ (1990) The use of vegetation and land use parameters in modelling catchment sediment yields. In JB Thornes (ed.) Vegetation and Erosion, Processes and Environments. John Wiley, Chichester, pp. 289–314. Morin J and Benyamini Y (1977) Rainfall infiltration into bare soils. Water Resources Research 13, 813–817. Newson MD (1985) Forestry and water on the uplands of Britain – the background of hydrological research and options for harmonious land use. Journal of Forestry 79, 113–120. Patton PC and Schumm SA (1975) Gully erosion, North-western Colorado: a threshold phenomenon. Geology 3, 83–90. Perez-Trejo F (1992) Desertification and Land Degradation in the European Mediterranean. European Commission, EPOCH programme, Directorate General XII, Science, Research and Development, EUR 14 850.
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Reed LA (1971) Hydrological and sedimentation of Corey Creek and Elk Run Basins, North-Central Pennsylvania. US Geological Survey Water Supply Paper. Rodriguez-Aizpeolea J, Perez-Badia R and Cerda-Bolinches A (1994) Colonizacion vegetal y produccion de escorrentia en bancales abandonatos: Vall de Gallinera. Alacant. Cuaternario y Geomorfologia. Romero D´ıaz MA, L´opez-Berm´udez F, Thornes JB, Francis CF and Fisher GC (1988) Variability of overland flow erosion rates in a semi-arid Mediterranean environment under matorral cover in Murcia Spain. Catena, supplement 13, 1–11. Romero D´ıaz A, Barbera GG and L´opez-Berm´udez F (1995) Relaciones entre erosion del suelo, precipitacion y cubierta vegetal en un medio semiarido del sureste de la peninsula iberica. Lurralde 18, 229–243. Romero D´ıaz A, L´opez-Berm´udez F and Belmonte Serrato F (1998) Erosion y escorrentia en el Campo Experimental de “El Ardal” (Murcia). Nueve anos de experencias. Papeles de Geografia 26, 129–144 Ruiz-Flano P, Garcia-Ruiz JM and Ortigosa L (1992) Geomorphological evolution of abandoned fields. A case study in the Central Pyrenees. Catena 19, 301–308. USDA (1938) Soils and Men, 1938 Yearbook of Agriculture. USDA, Washington, DC. Williams KF and Reed LA (1972) Appraisal of stream sedimentation in the Susquehanna River basin. US Geological Survey Water Supply Paper. Wise SM (1982) How old are the badlands? A case study from south-east Spain. In R Bryan and A Yair (eds) Badland Geomorphology and Piping. GeoBooks, Norwich, pp. 259–277. Yair A, Goldberg P and Brimer R (1982) Long term denutation rates in the Zin-Havarim badlands of northern Negev, Israel. In R Bryan and A Yair (eds) Badland Geomorphology and Piping. GeoBooks, Norwich, pp. 279–291.
6
Agro-pastoral Activities and Land Degradation in Mediterranean Areas: Case Study of Sardinia
G. ENNE,1 G. PULINA,1 M. D’ANGELO,1 F. PREVITALI,2 S. MADRAU,1 S. CAREDDA1 AND A.H.D. FRANCESCONI1 1
Dipartimento di Scienze Zootecniche, University of Sassari, Sassari, Italy Dipartimento di Scienze dell’Ambiente e del Territorio, Universita` di Milano–Biocca, Milano, Italy 2
1 INTRODUCTION Livestock farming is one of the main agricultural activities in the Mediterranean Basin, both in terms of the numbers of people employed and in its distribution throughout the region. There are about 100 million livestock units (LSU) of herbivores in the countries of the Mediterranean Basin, 53.8% of which are in Europe, 23.2% in Africa and 23% in Asia (FAO 1995). Most LSU are ruminants (cattle, sheep and goats) and their main feeding source is natural or cultivated pastures grazed directly. The international scientific community has recognized that agro-pastoral activities are clearly one of the main causes of land degradation in the Mediterranean. In southern Spain, Greece and Portugal, wide areas intensively exploited by small ruminants have already reached a severe level of land degradation. The spread of agro-pastoral activities in most Mediterranean countries and the increased grazing pressure are also related to past European Union (EU) policies, which favoured the uncontrolled development of modern agricultural practices. Those policies provided a system of guaranteed prices and subsidies to farmers for the production of meat and wheat which resulted in the cultivation of marginal areas. In addition, the Common Agricultural Policy (CAP) led to a steep rise in productivity by encouraging mechanization. As has been shown by several studies, this production-orientated model protected farmers against the economic consequences of environmental degradation and also removed their responsibility toward environment management (Buller 1992). Fortunately, since the beginning of 1992, all State Members of the EU have focused their attention on a new concept of environmental sustainability, as laid out by the Treaty of Maastricht. The EU concerns about the rural environment, land abandonment and its consequent degradation have led to it supporting environmentally friendly practices. For example, Council Regulation Number 2078/92 concerns aid to encourage agricultural production methods with a low impact on the environment, while Number 2080/92 concerns aid to encourage afforestation. Furthermore, in order to give scientific assistance to the political decisions on land degradation issues, the EU has promoted several research programmes, including MEDALUS, aimed at understanding the major causes of land degradation, and developing schemes to mitigate and prevent land degradation.
2 SARDINIA: AN ISLAND THREATENED BY LAND DEGRADATION Sardinia is one of the Italian regions most threatened by land degradation. Although it is a relatively low-lying region (the highest peak being Punta la Marmora, 1834 m a.s.l.), which does not exceed Mediterranean Desertification: A Mosaic of Processes and Responses. Edited by N.A. Geeson, C.J. Brandt and J.B. Thornes 2002 John Wiley & Sons, Ltd
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the upper limit of vegetation, the unproductive lands, excluding urban and coastal areas and inland waters, represent about 12% of the total area. These are distributed over the whole island as a result of the land use through the centuries, which has always been agro-pastoral to a great extent. Today, about 85% of Sardinian land is used for agriculture (ISTAT 1976, 1982, 1992), and livestock farming is one of the main economic activities. There are about 622 835 LSU of herbivores, of which 50% are dairy sheep (Table 6.1). This fact has greatly influenced land use in Sardinia: meadows and pastures are intensively grazed, and both wooded areas and arable land are cultivated to provide forage and other animal feeding sources. Agro-pastoral activities are a major cause of fires. A detailed analysis of the causes of forest fires shows that more than 90% of the total number of fires are started deliberately, and are historically and traditionally related to human activities (Figure 6.1). Fire has been considered an important practical and economical tool for clearing lands for grazing. Land fragmentation and the heterogeneity of land cover, typical of Mediterranean environments, have in many cases favoured fire propagation from grasslands to shrublands and wooded areas, thus compromising forested ecosystems (Figure 6.2). Although in the Mediterranean Basin fire has always been present in the ecosystem, promoted by hot dry periods common in the Mediterranean climate and the particularly inflammable characteristics of typical Mediterranean vegetation (Molina 1996), in the last 50 years its occurrence has dramatically increased, and is now a major factor of desertification. The destruction of the vegetation cover and the effects on the underlying soil (Chandler et al. 1983) result in an increased erosion risk. In Table 6.1 Grassland, livestock and stocking rate evolution (1971–1991)
Year
1971 1981 199l a
Agricultural land (ha)
2 159 245 2 047 811 2 050 731
Grassland (ha)
1 613 279 1 497 503 1 539 224
Livestock number (LSU)a Cattle
Sheep
Goats
Total
273 050 287 798 286 840
215 323 226 714 313 129
25 070 22 463 22 867
513 443 536 875 622 835
LSU = 450 kg live weight (1 cattle; 10 sheep; 10 goats).
Figure 6.1 Typical aspect of an over-exploited pasture in Sardinia. Overgrazing and the frequent use of fires are among the main causes of land degradation
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Agro-pastoral Activities and Land Degradation in Sardinia
Figure 6.2
Erosive processes in an area recently affected by fire
Table 6.2 Number of fires and areas affected by fires in Sardinia (1982–1996)
Year No. of fires Wooded area (ha) Pasture (ha) Other areas (ha) Total area (ha)
1982–1986
1987–1991
1992–1996
Annual average (1982–1996)
14 406 64 720 217 621 17 299 299 640
16 111 35 694 150 246 12 562 198 502
16 438 48 537 128 324 14 429 191 290
3 130 9 930 33 079 2 953 45 962
Source: RAS (1996). Sardinia, during the period 1982–1996, an area of 689 432 ha of land was swept by fire (Table 6.2). The mean annual area affected by fire amounts to about 46 000 ha, about 72% of which is pasture. With particular reference to woodlands, during the period 1989–1993 about 1.6% of the total area was annually swept by fire; when compared to the European Mediterranean average (1%) (EEC 1996), this datum shows the significant incidence of this phenomenon in Sardinia. These are the main reasons why Sardinia has been considered a representative study area for the impact of grazing systems on desertification processes. On the island there are two main forage systems: agro-silvo-pastoral activities in hilly and mountainous areas and cereal–dairy sheep farming on the plains and low hills. Between these two systems there are various intermediate conditions. The traditional agro-pastoral system, based on pasture with or without fertilization and with shortterm forage crops, is very common in hilly and mountainous areas of Sardinia. This system makes it very difficult for forage availability and animal feed requirements to coincide (Caredda et al. 1992). The green forage production is mainly concentrated in spring, while the maximum dairy sheep feed requirements are concentrated in autumn–winter, at the end of ewe pregnancies and at the beginning of lactation. Generally, this problem is solved either by vertical transhumance or, more often, by feeding sheep with hay and concentrates. During the 1950s and 1960s, a reduction of the traditional rotation of cereal-grazed fallow brought about the abandonment of arable lands and different land-use management. As a consequence, the
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Figure 6.3 Maquis clearing in favour of cultivated pastures can result in severe surface erosion when carried out on unsuitable lands
balance between crop and pasture was altered, leading to an increased abundance of low-growing palatable species on the abandoned land and a reduction in forage availability. Unplanned and irrational grazing patterns (autumn overgrazing and/or spring undergrazing) caused a progressive reduction of palatable species and an increase in bare soil areas, which generated soil losses, particularly on slopes. During the 1970s and 1980s an increase in sheep milk prices led to a large increase in sheep numbers in lowlands and hilly areas. In order to increase forage availability on natural pastures, farmers utilized different agronomic techniques which, particularly on slopes, tended to lead to erosion. For example, inadequate machines (e.g. scrapers) were used to clear the ground of stony and shrubby vegetative cover and trees, and removed the topsoil irreversibly. Generally, forage intensification is based on cultivation of short-term forage crops that are grazed in winter and harvested as hay or grain in late spring. However, because of the lack of sufficient flat surfaces, forage crops are also cultivated on slopes, often by ploughing across rather than parallel to the contours, and without adopting soil conservation practices. As a consequence, gullying due to runoff after autumn rainfalls is frequently observed in hilly regions (Figure 6.3). Indeed, the risk of soil erosion on hillslope areas is concentrated at the end of the summer and autumn, when frequent and intense rains coincide with the soil being recently ploughed or bare, and very vulnerable to the strength of rainwater (Rivoira et al. 1989; Roggero et al. 1995). Even though agro-pastoralism, especially overgrazing, has often been considered as the main cause of land degradation, Seligman (1996) maintains that “among all the factors that contribute to landscape degradation in the Mediterranean Basin, high stocking rates must be placed low on the list”. In order to contribute to the clarification of this matter, this chapter mainly deals with the general effects of agro-pastoral activities on vegetation and soil degradation, and presents some experimental results of the MEDALUS II research carried out in Sardinia on this topic. Finally, practical implications of irrational grazing practices on soil fertility are examined and some guidelines on proper land management in an agro-pastoral context are proposed.
3
EFFECTS OF GRAZING ON VEGETATION AND SOIL
In agro-pastoral systems, animals, plants and environment interact with each other in a complex manner, and are directly affected by human activities. The common belief that grazing is always
Agro-pastoral Activities and Land Degradation in Sardinia
75
detrimental to vegetation is a result of the confusion between grazing and overgrazing, with only the latter being destructive to plants and soil. Mathematical models have been devised to aid the understanding of the complex interactions of the diverse components that make up the soil–animal–plant interaction system (Doucet and Sloep 1992). Rational management should encourage certain positive influences of grazing on vegetation and soil resources. Indeed, grazing delays maturation of the vegetation (Vallentine 1990), keeping plants in a vegetative, forage-producing state. Grazing also stimulates growth and regrowth by its pruning effect, maintaining optimum leaf area index (LAI = total leaf area per unit ground area), improving the nutritive value of available forage, and reducing excessive accumulations of standing dead vegetation and mulch. This reduces the vegetation biomass which, if allowed to build up, provides the undergrowth that favours outbreaks of fires and their propagation during the hot dry periods of summer. On the other hand, other common agro-pastoral activities can have negative effects. Soil is easily compacted due to trampling by livestock, and the soil can become bare due to overgrazing or ploughing. If fire is used to destroy vegetation to clear land for pasture, valuable organic matter is also lost from the soil. An understanding of the effects of grazing on vegetation and soil is fundamental for the development of a rational grazing management strategy within a more sustainable agro-pastoral context. 3.1 Grazing Effects on Vegetation
Grazing involves biting, pulling and breaking off plant parts, which causes defoliation, or even pulling entire plants out of the ground, if they are not well rooted. Furthermore the trampling and treading of the vegetation may damage the stand. Seed dispersal, internally through the animal digestive system, or externally by temporary attachment to animal hair, fleece or hooves, is an ecological factor affecting a perennial forage stand, but the impact will range from favourable to unfavourable, depending upon the plant species and site being affected (Vallentine 1990). Covering some parts of the vegetation with faeces and urine is another effect of grazing. Manure spots are generally avoided by animals visiting later, even though the nutritive quality of the affected forage (particularly rich in nitrogen) may be better than that on adjacent ground. Rejection is presumably on grounds of palatability based on smell or taste, and perhaps designed to avoid recycling internal parasites (Van Soest 1994). Measurement of the leaf area index (LAI) is a very useful indicator of how forage responds to grazing. Undergrazing allows overgrowth and shading by senescent foliage, which reduces photosynthesis and increases respiration. Optimum grazing pressure improves the effective LAI, whereas higher pressures, which result in excessive defoliation and a related decrease in forage yield, diminish it (Van Soest 1994). In addition to the reduction in leaf area per plant by grazing, the thinning out of grass species, which is a consequence of the selective feeding action of the animals or of the exposure of roots to the cutting edge of the hooves, can have a strong impact on final LAI and quality of forage. Plant recovery from defoliation depends not only on the available carbohydrate reserves but also on the quantity of the remaining foliage and its photosynthetic capacity. The rate of development of new foliage and photosynthetic capacity of new leaves is also important (Caldwell 1984). Generally, the lower the level of reserve carbohydrates, the more important the remaining leaf area is in promoting regrowth. Also, while perennial forage plants are influenced by the conditions in current and preceding years, which affect their root reserves and spring regrowth, annual forage plants are not. The supply of nutrients needed for the regrowth of annual forage plants after defoliation is primarily dependent on the remaining leaf area rather than storage compounds (Vallentine 1990). The optimum rational grazing management strategy should allow the maximum level of defoliation that will still maintain sustainable forage production and animal response. The definition of the optimum moment for grazing, the optimum frequency and duration of grazing, and the intensity of defoliation, by using a proper stocking rate, are of great importance (Brandano and Rossi 1975).
76 3.2
Mediterranean Desertification Grazing Effects on Soil
The negative effects of grazing on the soil are felt directly, through trampling, and indirectly, through the reduction of vegetation cover and removal of organic matter from the soil (Pulina et al. 1995a). The treading of soil by grazing animals may be detrimental, causing soil compaction, surface horizon disruption, reduction of infiltration, creation of terracing on steep slopes, development of animal trails, and thus erosion (Vallentine 1990). The degree of impact the animal treading has on a specific site depends on the interaction between vegetation, soil, weather and animals. Soil compaction by hooves causes a reduction in soil porosity which reduces water infiltration and percolation in the soil, leading to increased water runoff and erosion on sloping terrain, and a tendency to hydromorphism or to stagnation on flat terrain. Soil compaction depends both on the characteristics of the animals’ behaviour, such as their tendency to walk, run or jump, or to graze in groups, and on agro-pastoral interventions, such as the presence of concentration areas (shade of trees, areas protected from predominant winds, drinking places and artificial feeding places, etc.). Compaction depends not only on the stocking rate but also on the specific pressure of the hooves per square centimetre. For instance, a calculation based on hoof area and body weight of various animals has estimated an average pressure per unit area of 0.47 kg cm−2 for sheep, 0.98 kg cm−2 for cattle and 1.01 kg cm−2 for donkeys (Pulina et al. 1995a). The destruction of the soil surface by penetration of hooves is more likely to occur when soils are wet, where there are clay-textured soils and where there is poor vegetation cover. Other factors that may accentuate damage to soil properties include allowing grazing in the wet winter months, high stocking rates at any time, or a preponderance of cattle rather than lighter animals. The creation of terracing on sloping terrain, and of trails on flatter terrain, is a result of the routes chosen by animals while grazing and being transferred from one pasture to another. Trails, which become areas of bare soil, are created in direct proportion to stocking rates and in inverse proportion to the availability of forage. At times these trails may become a high proportion of the total pasture area, especially at waiting points near gates and in areas with a high movement of animals, such as near drinking places. These areas may suffer from significant wind erosion during the dry season. Overgrazing may remove part of the vegetation cover which, in its turn, brings about an increase in raindrop impact and surface soil crusting, and a decrease in organic soil matter, aggregate stability, and water infiltration rates (Blackburn 1983, 1984). All these effects may cause increased water runoff, reduced soil water content, and increased erosion. Organic matter in the soil is an important component of soil fertility and essential for the maintenance of good soil structure, which can counteract the erosive action of water and wind. The removal of organic matter by animals is due to an imbalance between the amount of dry matter they consume and the dry matter that returns to the soil in the form of faeces and urine (Pulina et al. 1995b). It is not easy to estimate the quantity of organic matter actually returned and incorporated into the soil by animals. Organic matter restitution is efficient only during wet seasons, when faeces are easily incorporated into the soil. At this time it is soft as a consequence of the animals’ intake of fresh grass with a high moisture content. On the other hand, in dry seasons, the faeces are much drier as a result of feeding on dry stubble and some internal body defence mechanisms that protect animals from wasting water. Dry faeces can remain on the soil surface for months and are likely to be completely oxidized.
4
THE CASE STUDY OF SARDINIA
In order to evaluate the effect of agro-pastoral activities on land degradation in Sardinia, a series of laboratory and field experiments were conducted under the aegis of the MEDALUS II project. A two-year experiment (1994–1995) was carried out on the main factors influencing cattle grazing behaviour. Initially the experimental site in north-western Sardinia at the Astimini-Fiume Santo basin (Figure 6.4), a region characterized by semi-arid climate (mean annual rainfall is 544 mm over a 39-year period), was overstocked (20 heifers of Limousine × Bruno Sarda cross occupying 10 ha with an initial stocking rate of 450 kg ha−1 ) (Enne et al. 1996). The average slope in this hilly area
77
Agro-pastoral Activities and Land Degradation in Sardinia Experimental area Stintino E A
Porto torres
Sardinia (Italy)
H
B
G I
Astimini-fiume santo basin
km 0
F
C
D
Experimental area 5
10 Legend Enclosure limits Sub-catchment and sub-areas limits
Subarea Surface (ha) A B C D E F G H I
Figure 6.4
0.40 1.87 1.02 1.49 0.35 0.84 1.46 0.69 1.08
Variables Aspect N NE N N W W NW N NW
Slope
Land cover
Soil
0.7 kg kg−1 ). The effect of rock fragment content on the bulk density of the fine earth is shown by plotting BDfe after 192.5 mm of cumulative rainfall versus rock fragment content (Figure 11.3(b)). BDfe is only affected by the presence of rock fragments for Rm (a)
20
15
10 0
5
10
15
20
25
30
35
40
45
50
55
60
65
0
5
10
15
20
25
30 cm
35
40
45
50
55
60
65
(b) 24 22
cm
20 18 16 14 12 10
Figure 11.2 Evolution of soil surface roughness and compaction of freshly tilled topsoils. Measured cross-sections before (solid line) and after (gray line) 192.5 mm rainfall for (a) soils without rock fragments and (b) soils with gravels (rock fragment content by mass (Rm) = 0.52 kg kg−1 ; after van Wesemael et al. 1995a). Reproduced by permission of John Wiley and Sons Ltd
134
Mediterranean Desertification (a)
0.8
Random roughness (cm)
0.7 0.6 0.5 0.4 0.3 0.2 0
0.1
0.2
0.3
0.4 0.5 Rm (kg kg−1)
0.6
0.7
0.8
(b) 2000 1750
BD (kg m−3)
1500 1250 1000
BDfe BDt
750 500 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Rm (kg kg−1)
Figure 11.3 The role of rock fragments in reducing physical degradation of topsoils. (a) Surface roughness and (b) bulk density of topsoils containing various rock fragment contents. Random roughness, fine earth bulk density (BDfe) and total bulk density (BDt) of bare soils exposed to 192 mm of cumulative rainfall are plotted (after van Wesemael et al. 1995a,b)
values in excess of 0.3. Poesen et al. (1994) demonstrated that soil erosion decreases exponentially with surface cover. Hence, the effectiveness of a rock fragment cover is highest at low rock fragment contents. Therefore, the role of rock fragments at the soil surface is more important than that of rock fragments in the soil profile, since the latter mainly protect the soil structure at high rock fragment contents (i.e. from Rm > 0.3 upwards; cf. Figures 11.3(a) and (b); Poesen and van Wesemael 1995).
3
SOIL EROSION BY WATER
Rock fragments play an important role in controlling overland flow and sediment loss (Poesen et al. 1994). The effects of different sizes, amounts and positions of rock fragments on water erosion were studied under field conditions in experimental plots. Thirty experimental plots, 2 m × 5 m each, were
Impact of Rock Fragments on Soil Degradation
135
Figure 11.4 Experimental plots for studying the effect of rock fragments on soil erosion and water conservation (Photo by C. Kosmas, May 1994)
installed on a hillslope (slope gradient of 17%) located 30 km east of Athens (Figure 11.4). The site is characterized by a thermo-mediterranean climate (UNESCO-FAO 1963), with an average annual air temperature of 17.8 ◦ C and an annual precipitation of 496 mm. The soil is a well-structured, dark, stony calcareous clay loam, formed on marl interbedded with sandstones. It is classified as a typic calcixeroll according to the Soil Taxonomy (Soil Survey Staff 1975). Rock fragments (>1.5 cm) were completely removed from the upper 40 cm of the soil. Then rock fragments of two sizes, classified according to Miller and Guthrie (1984) as coarse gravel with 4.4 cm average diameter (range 1.9–6.7 cm), and cobbles with 14.6 cm average diameter (range 9.4–18.9 cm), were either incorporated into the upper 15 cm of soil or partially embedded in the soil surface. Most plots were kept bare, and in the rest natural vegetation was allowed to grow, dominated by winter annuals and perennials of the following species: Avena fatua, Aegilops ovata, Sinapis arvensis, Echium sp. and Thymus capitatus. Each plot was enclosed by trenches to divert runoff originating upslope. Runoff from the plots was drained into covered metal containers along the lower side of each plot. The containers were cleared of sediment after each runoff event. Additional equipment was installed in seven plots, representing various treatments, to automatically measure and sample runoff with tipping buckets. The data were recorded on a data logger at 5-min intervals. The volumetric soil moisture content was measured at weekly intervals and after every rainfall event at four depths (5–10 cm, 15–20 cm, 20–30 cm, and 30–40 cm) using a neutron and gamma probe. 3.1 Runoff Generation
Soils containing rock fragments exhibit various effects on runoff generation. Generally, large rock fragments (cobbles) cause greater runoff than smaller fragments (coarse gravel). Over five rainfall events with maximum intensity ranging from 21 to 50 mm h−1 , the largest amounts of runoff were generated from bare soils containing abundant large rock fragments, either partially embedded in the surface or incorporated in the upper soil part (Figure 11.5). This is in line with results from laboratory experiments reported by Poesen and Lavee (1991). Soils containing abundant gravel on the surface exhibited a variable effect depending on the rainfall characteristics during individual events: they generated small amounts of runoff and soil loss under rainfalls of high intensity and low duration, but generated more runoff and soil loss under low-intensity rainfalls. Thus, the runoff collected from such soils is even lower than the runoff collected from bare soils in the events with the highest intensities (31 mm h−1 in Figure 11.5). There is a protective effect of coarse gravel at the
136
Mediterranean Desertification 12 surface cobbles
Runoff (mm)
9
incorporated cobbles surface gravel
6
incorporated gravel stone-free
3 vegetation
0
21
31
100 90 Sediment loss (g m−2)
80 70 60 50 40 30 20 10 0
31 21 Rainfall intensity (mm h−1)
Figure 11.5 Runoff and soil loss from erosion plots with various soil surface conditions (after Moustakas et al. 1995)
surface; it probably prevents surface sealing, and this counteracts and eventually exceeds the runoff generated on the impervious rock fragments themselves. As expected, soils protected by a vegetation cover generate less runoff than bare soils. Among the vegetative treatments, the stony soil had the highest biomass production and vegetation cover, and thus generated the least runoff. The higher biomass production in stony soils has been attributed to the generally higher water conservation in such soils (Danalatos et al. 1995). A natural vegetation cover of 48% restricted runoff rates even more; in some cases, the runoff volume was even less than half of the volume measured on a bare soil, depending on the duration and the intensity of the rain. 3.2
Soil Loss
Rock fragments on the soil surface appear to play an important role in the protection against erosion, especially during the heaviest showers. Soil loss is greater from soils containing cobbles than from soils with coarse gravel (Figure 11.5), which is also in line with results reported by Poesen and Lavee (1991). A soil rich in rock fragments with a vegetative cover of 48% reduced the total soil loss by 74% during the rainfall with the highest intensity (Moustakas et al. 1995).
Impact of Rock Fragments on Soil Degradation
137
Coarse gravel on top of the (bare) soil surface has a variable effect on soil loss and erosion. Soil loss is greatly reduced as compared to the stone-free soil during rainfall events with high intensity and short duration, but generates appreciable sediment loss during rainfall with low intensity and long duration. The soil loss varied according to the characteristics of the rainfall. In the stone-free, bare soil it ranged from 2.7 to 38.7 kg m−2 , whereas in the soil with 23% coarse gravel on the surface the soil loss varied from 13.2 to only 19.2 kg m−2 because of the protective effect of the rock fragments against raindrop impact. The ratio of sediment mass to runoff varied from 2.4 to 8.1 kg m−2 mm−1 when averaged for five events, depending on the amount, size and position of rock fragments. In particular, this ratio was 2.4–2.9 for the vegetative plots and 3.0 for the bare soil with coarse gravel on the surface. Among the rest of the bare soils, the ratio was 3.7 for the stone-free bare soil and fluctuated between 4.8 and 8.1 for the other soils containing rock fragments. The ratio in the plots with coarse gravel on the surface was the lowest among the bare soils and slightly higher than in the soils with a vegetation cover.
4 WATER CONSERVATION 4.1 Evaporation Under Laboratory Conditions
Rock fragments influence evaporation from bare soils by changing the soil–atmosphere interface. Numerous efforts have been undertaken to modify the topsoil characteristics (mulching, tillage) in order to create a thin dry topsoil that reduces evaporation. However, little attention has been paid to the role of topsoil stoniness with respect to evaporation. Soil columns with various rock fragment contents simulating a stony plough layer were left to evaporate at a constant evaporative demand. The conditions during the experiments are summarized in Table 11.1. Two evaporation runs were carried out with initial water contents typical for the end of the wet season and the dry season in a Mediterranean environment: • Soils at field capacity: moist silt loam soils (gravimetric moisture content: 0.2 g g−1 ) with different river gravel contents were subjected to 24 mm rain. This condition simulates the beginning of the growing season when excess rainfall has infiltrated to greater depth. • Air-dry soils with different gravel contents received 20 mm of rain. This condition simulates a dry period in which a limited amount of rain falls. For soils at field capacity, the initial total soil (it ) and fine earth (ife ) water contents decrease with increasing rock fragment content (Table 11.1). This decrease can be explained by the drainage of excessive moisture and the limited retention of moisture in stony soils (Childs and Flint 1990; Poesen and Lavee 1994; van Wesemael et al. 1995b). In the case of air-dry soils, it decreases slightly with rock fragment content, but ife increases with rock fragment content due to the concentration of an equal amount of rainfall in a smaller volume of fine earth (Table 11.1). After 10 days, clear differences in cumulative evaporation between soil columns with different rock fragment contents could be observed. These differences correspond to the differences in initial fine earth water contents (Figure 11.6 and Table 11.1). For the soils at field capacity, cumulative evaporation decreases with rock fragment content (Figure 11.6(a)), whereas for the air-dry soils cumulative evaporation increases with rock fragment content (Figure 11.6(b)). A rock fragment mulch reduces evaporation compared to a non-stony soil in both experiments (Figure 11.6). These experiments illustrate the ambivalent impact of rock fragments with respect to evaporation rates. During the wet period (winter), when soils are at field capacity, excess precipitation can penetrate below 25 cm (the depth to which evaporation losses are largely restricted; Hanks and Ashcroft 1980). Soils containing rock fragments have a lower fine earth water content in their top layer due to the small water retention capacity of stony soils. Therefore, evaporation rates are smaller in soils containing rock fragments compared to stone-free soils. The high efficiency of a rock fragment mulch under wet conditions in reducing evaporation losses has already been reported by Bond and Willis (1969), Hillel (1980) and Kamar (1994). During dry periods, an equal amount of rain
138
Mediterranean Desertification Table 11.1 Set-up of the three sets of laboratory experiments with mean air temperature, relative humidity and evaporative demand. Figures in parentheses are minimum and maximum values
P = 24 mm, soil at field capacity Temperature: 20.1 ◦ C (16–28 ◦ C) Relative humidity: 77.8% (58–95%) 7.71 mm day−1 (5.6–10.1 mm day−1 ) Evaporative demand (Eo ): Rv (m3 m−3 )
it (m3 m−3 )
ife (m3 m−3 )
0 0.19 0.35 0.53 mulch
0.38 0.30 0.23 0.10 0.37
0.38 0.37 0.35 0.21 0.37
P = 20 mm, air-dry soil Temperature: Relative humidity: Evaporative demand (Eo ):
18.5 ◦ C (15–23 ◦ C) 77.6% (57–94%) 9.24 mm day−1 (6.3–11.7 mm day−1 )
Rv (m3 m−3 )
it (m3 m−3 )
ife (m3 m−3 )
0 0.16 0.30 0.46 mulch
0.14 0.12 0.12 0.11 0.16
0.14 0.14 0.17 0.20 0.16
P , total rainfall amount; Rv , rock fragment content by volume; it , total soil water content at the start of evaporation; ife , fine earth water content of the fine earth at the start of evaporation. Mulch is a 5-cm-thick continuous gravel layer. All figures represent mean of duplicates. that falls on a dry soil is concentrated in a smaller volume of fine earth with increasing rock fragment content, thus leading to a higher fine earth water content. Hence, evaporation rates increase with fine earth water content and rock fragment content. It should be kept in mind that evaporation during the laboratory experiments was caused by convection rather than by a combination of radiation and convection, and the soil columns were rather short. The following section will discuss the pertinence of these laboratory experiments to water conservation under field conditions. 4.2
Evaporation Under Field Conditions
Soil moisture storage was measured in the upper part (5–15 cm) of a stone-free soil and the same soil covered by coarse gravel (28%) and by cobbles (18%) under field conditions for one year (Figure 11.7). It can be seen that soil water content was generally higher in the cobbly soil, pointing to greater water conservation by the cobbles for most of the study period. Only after the end of July did the water storage in the cobbly soil decrease sharply and show values lower than both the control
139
Impact of Rock Fragments on Soil Degradation
Cumulative evaporation (mm)
(a) 60 field capacity; P = 24 mm
50 40 30 20 10 0 0
Cumulative evaporation (mm)
(b) 24 22 20 18 16 14
5
10
15 Days
20
25
30
air-dry soil; P = 20 mm
12 10 8 6 4 2 0
Rv = 0 0
5
Rv = 0.16 10
Rv = 0.30 15
Rv = 0.46 20
25
mulch 30
Days
Figure 11.6 Cumulative evaporation depth from soil columns containing different contents of rock fragments. Rv is rock fragment content by volume. Experiments were carried out in the laboratory with (a) soils at field capacity subjected to 24 mm rainfall and (b) air-dry soils subjected to 20 mm rainfall (after van Wesemael et al. 1996). Reprinted from Journal of Hydrology 182, B. van Wesemael, J. Poesen, C.S. Kosmas, N.G. Danalatos and J. Nachtergaele, Evaporation from cultivated soils containing rock fragments, 65–82. Copyright 1996, with permission from Elsevier Science
and the gravelly soils for the rest of the dry and hot period (Figure 11.7). This is apparently due to a much greater heating of the rock fragments at that period (Danalatos et al. 1995). Conversely, the soil containing coarse gravel had the lowest water storage and therefore the highest evaporation losses throughout the wet period and the period of moderate drought. Only during the dry and hot summer were values higher than those of the soil with cobbles embedded in the surface (Figure 11.7). Data on soil moisture loss obtained from the weighing lysimeters demonstrated that the presence of cobbles on the soil surface is extremely important, especially the first day after a rainfall or irrigation event. As Figure 11.8(a) illustrates, heating of the cobbly soil during daytime in summer resulted in a great loss of water as compared to the loss from the stone-free soil. In the following days, the rate of water loss remained almost the same in both lysimeters due to the formation of a desiccated layer, drastically reducing the evaporation loss. Conversely, the presence of cobbles
140
Mediterranean Desertification cobbles
25
free of RF Soil water (mm)
20
gravel
15 10 heavy rainfall
5 0 120
M
J
J
A 220
S
O
N
D
J
320 Time (days)
F
M
420
A
Month 520
Figure 11.7 Evolution of the soil moisture stored in the 5–15 cm soil layer for soils with rock fragments of different sizes on the soil surface (after Kosmas et al. 1995) (a) Dry period irrigation 21 mm
Soil weight (kg)
243 241 239 237 235 233 231
232
233
234
235
236
249
250
(b) Wet period
Soil weight (kg)
242 irrigation 21 mm
240 238 236 234 245
246 cobbles 28%
247 248 Time (days) stone-free
Figure 11.8 Changes in soil weight with time measured in lysimeters with a stone-free soil and a soil with rock fragments, during a period of (a) high and (b) moderate evaporative demands (after Kosmas et al. 1995)
141
Impact of Rock Fragments on Soil Degradation
reduced the evaporation during the wet period the first day after irrigation as compared to the stonefree soil (Figure 11.8(b)). These field experiments are in agreement with the ambivalent role of rock fragments illustrated in the laboratory. Increasing rock fragment cover is associated with decreasing evaporative water loss during periods of no to moderate drought, such as from late fall to early summer, but with an increased evaporation during the dry and hot months. Stony soils are generally warmer during daytime and cooler at night than soils free of rock fragments. In the warmest month (July) the diurnal amplitude reached 14.3 ◦ C in the stone-free soil and 24.1 ◦ C in the stony soils under climatic conditions prevailing in the region of Attica. Considering that maturation of rainfed crops occurs in late spring, rock fragments appear to be very important in conserving appreciable amounts of soil moisture for growing plants in late spring, when rain can be scarce in the Mediterranean region. Water conservation in stony soils supports considerable biomass production, and protects extensive hilly lands from desertification. 4.3 Water Vapour Adsorption
Rock fragments present on the soil surface restrict evaporation as well as water vapour adsorption by the soil by reducing the soil–atmosphere interface. Daily fluctuations in soil moisture tension measured in the upper 3–5 cm soil layer and in patches that were free of rock fragments (a transect between two rock fragments), were greater than those measured under the cobbles of the same soil (Figure 11.9). In soil patches under rock fragments, the maximum and minimum values of soil moisture tension occurred one or two hours later than in soil patches free of rock fragments. During the day, fluctuations in soil moisture tension in stone-free soils were about twice those of stony soils, while during the night soil moisture tensions reached almost the same value in both cases (Figure 11.9). This points to the importance of rock fragments in conserving soil moisture from evaporation under Mediterranean conditions. Rock fragments restrict evaporation losses during the day, whereas during the night, water adsorbed as water vapour by the stone-free soil–air interface is transmitted and protected from evaporation under the rock fragments. This water storage can be of great importance for rainfed crops throughout the Mediterranean. 4.4 Biomass Production
The presence of rock fragments on the soil surface (i.e. stone mulches) is extremely important in dry years in order to conserve appreciable amounts of soil water and prevent large areas from
Soil moisture tension (kPa)
30 28 26 24 22 20 351
353
357
355
359
361
Time (days) Under RF
Free of RF
Figure 11.9 Changes in soil moisture tension with time measured in soil patches free of rock fragments (solid line) and patches under rock fragments (dashed line) in the same soil (after Kosmas et al. 1995)
142
Mediterranean Desertification
desertification. Despite their normally low productivity, stony soils formed on conglomerates and shale–sandstones may supply appreciable amounts of previously stored water to the stressed plants and, therefore, secure a substantial biomass production even during extremely dry years (Kosmas et al. 1993). Soils formed on parent materials free of rock fragments such as marl are, despite their considerable depth and high productivity during normal and wet years, very susceptible to desertification. Such soils are unable to support any vegetation during particularly dry years due to adverse soil physical properties and the absence of gravel and stone mulching. Investigations into the relationship between biomass production and evapotranspiration rate, taking into account the rock fragment cover, were conducted along catenas of central and northern Greece. Rock fragments were removed from a number of plots in distinct landscape positions after the sowing of wheat, and the total aboveground biomass production of wheat was measured at the end of the growing period of cereals and compared with that where cobbles remained on the soil. The presence of cobbles on the soil surface conserves appreciable amounts of soil water by surface mulching, which results in increased biomass production, particularly in dry years, by preventing desiccation of the soil. After removing all rock fragments from the surface of 32 plots, biomass production of rainfed cereals decreased by 2–30%. In an attempt to relate measured biomass production to the actual evapotranspiration (ETa ) and to the percentage of rock fragment cover (Rc), the ETa was calculated from its maximum value (ETm ) and the momentary soil moisture content in the root zone using a simple water balance model based on Doorenbos and Pruitt’s (1977) methodology. The maximum crop evapotranspiration rate (ETm ) was calculated from the potential evapotranspiration rate (ETp ) and the crop (leaf area) coefficient of wheat according to Doorenbos and Pruitt (1977). Finally, the potential evapotranspiration rate was calculated from daily values of maximum and minimum air temperature, sunshine duration, air humidity and wind speed, according to Penman (1948; modified by Frere 1979). It was found that the relative biomass production (RBP) of rainfed wheat could be related to the relative evapotranspiration rate (ETa /ETm ) and the percentage of rock fragment cover, according to the following empirical relation (Kosmas et al. 1995): RBP = 0.97 + 0.54∗ ln(ETa /ETm ) + 0.035∗ ln(Rc)
R = 0.90
n = 52
(1)
The relative biomass was determined from the measured biomass production divided by the maximum value estimated for each landscape position and parent material under conditions of no water deficit (Kosmas et al. 1993). The equation above is valid only when a soil water deficit occurs (ETa /ETm < 1) during the growing period, which is normally the case under Mediterranean conditions. In the case that there is no water deficit, rock fragments negatively affect biomass production due to the combined effect of a reduction in effective rooting depth and a decreased soil volume available for adsorption of nutrients.
5
A PRACTICAL EXAMPLE OF GRAVEL MULCHING
Few studies have investigated the effect and behaviour of a gravel mulch in the field. Gale et al. (1993) describe the application of gravel mulches in the loess belt of north-west China, and Caldas and Salguero (1988) report mulching with lapilli on the Canary Islands. These studies remain descriptive and lack experimental data. Fieldwork was carried out in the vineyards of an alluvial fan in the upper Rhˆone valley in Switzerland (Nachtergaele et al. 1988). An artificial gravel mulch of 20 cm was applied to most vineyards totalling approximately 10 km2 . Although mean annual precipitation in the Upper Rhˆone Valley is amongst the lowest in Switzerland (597 mm year−1 ), irrigation water is readily available from mountain streams. An inquiry amongst the wine-growers revealed that the thermal properties are higher ranked than the hydraulic characteristics of the mulch. Since the role of rock fragments on water conservation and soil erosion has already been discussed in previous sections, we will concentrate here on the thermal properties of a rock fragment mulch. Soil temperature at 3 cm below the soil surface is constantly higher for the topsoil with a mulch compared to the topsoil without a mulch (mean difference: 0.7 ◦ C; Figure 11.10). The difference in
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Impact of Rock Fragments on Soil Degradation 27
25
26 20
24 15
23 22
10
21 20
Rainfall depth (mm)
Temperature (°C)
25
5
19 18
0 Time (Julian days)
ˆ Figure 11.10 Soil temperature measured in July 1994 at 3 cm depth in vineyards of the Rhone Valley (Switzerland). The solid line represents values under a gravel mulch and the dashed line represents the control situation without a gravel mulch. Vertical bars indicate rainfall depth (after Nachtergaele et al. 1998). Reprinted from Soil and Tillage Research 46, J. Nachtergaele, J. Poesen and B. van Wesemael, Application and efficiency of gravel mulches in southern Switzerland, 51–59. Copyright 1998, with permission from Elsevier Science
soil temperature between the treatments is affected by soil moisture status. During and immediately after a rainy period, the temperature difference is much less than in drier conditions (Figure 11.10). These results are in agreement with the findings of Childs and Flint (1990) and Gras (1994), indicating that the presence of non-porous rock fragments in a dry soil profile increases thermal diffusivity (i.e. the ratio of thermal conductivity to heat capacity). Measurements of the soil surface temperature also indicate that there is a systematic temperature increase due to the mulch cover (mean increase 2.2 ◦ C; Nachtergaele et al. 1998). The implications of the higher soil and soil surface temperatures for the mulched vineyards are: (i) plant roots are protected from low temperatures at night or during spring; (ii) plants and fruits receive an extra amount of radiation.
6 CONCLUSIONS Rock fragments appear to have a profound impact on physical soil degradation, soil erosion, soil moisture conservation, plant growth and thermal properties under Mediterranean conditions. Rock fragments at the soil surface preserve the surface roughness of freshly tilled soils during rainfall even at low rock fragment contents. At high rock fragment contents a skeleton structure will develop in the topsoil which prevents soil compaction. Maximum runoff and erosion are expected in soils containing large amounts of cobbles partially embedded or incorporated in the soil. However, soils containing abundant gravel on the surface may show various effects. They generate small amounts of runoff and cause little soil loss under rainfalls of high intensity and short duration, but greater runoff and soil loss under low-intensity rainfalls. Cobbles have a beneficial effect on soil moisture conservation under conditions of moderate water stress such as those prevailing in spring and early summer, which is the most crucial period for winter crops. Later in the summer, their effect becomes negative because they cause greater heating of the soils. This is not harmful, however, since in that period (late summer to early autumn) only irrigated crops may survive or give reliable yields. The presence of rock fragments can be very valuable, particularly in dry years, by conserving appreciable amounts of water stored in previous rainy periods or adsorbed at night, thus protecting large areas from degradation and eventual desertification. It
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is believed that both coarse gravel and cobbles in soils are important for their combined positive effects on erosion rates (coarse gravel) and soil moisture conservation (cobbles).
REFERENCES Abrahim YB and Rickson RJ (1989) The effectiveness of stubble mulching in soil erosion control. Soil Technology 1, 115–126. Bertuzzi P, Rauws G and Courault D (1990) Testing roughness indices to estimate soil surface roughness changes due to simulated rainfall. Soil and Tillage Research 17, 87–99. Bond JJ and Willis WO (1969) Soil water evaporation: surface residue rate and placement effects. Soil Science Society of America Proceedings 33, 445–448. Caldas FE and Salguero TMK (1988) Mulch farming in the Canary Islands. In N Wichiro (ed) Agro-geology in Africa. Commonwealth Science Council Technical Publication 226, pp. 242–256. Childs SW and Flint AL (1990) Physical properties of forest soils containing rock fragments. In SP Gessel, DS Lacate, GF Weetman and RF Powers (eds) Sustained Productivity of Forest Soils. Proceedings of the 7th North American Forest Soils Conference, University of British Columbia, Faculty of Forestry Publication, Vancouver, pp. 95–121. Danalatos NG, Kosmas C, Moustakas N and Yassoglou N (1995) Rock fragments: II. Their effect on soil properties and biomass production. Soil Use and Management 11, 121–126. De Ploey J and Poesen JW (1985) Aggregate stability, runoff generation and interrill erosion. In KS Richards, RR Arnett and S Ellis (eds) Geomorphology and Soils. George Allen and Unwin, London, pp. 99–120. Doorenbos J and Pruitt WO (1977) Guidelines for predicting crop water requirements. Irrigation and Drainage Paper 24, FAO, Rome. Frere M (1979) A method for the practical application of the Penman formula for the estimation of potential evapotranspiration and evaporation from a free water surface. FAO, AGP: Ecol./1979/1. FAO, Rome. Gale WJ, McColl RW and Xie Fang (1993) Sandy fields traditional farming for water conservation in China. Journal of Soil Water Conservation 48, 474–477. Gilley JE, Finker SC and Varvel GE (1986) Runoff and erosion as affected by sorghum and soybean residue. Transactions of the American Society of Agricultural Engineers 29, 1605–1610. Gras R (1994) Sols Caillouteux et Production V´eg´etale. Institut National de la Recherche Agronomique, Paris. Hanks RJ and Ashcroft GL (1980) Applied Soil Physics. Springer-Verlag, New York. Hanson CT and Blevins RL (1979) Soil water in coarse fragments. Soil Science Society of America Journal 43, 819–820. Hillel D (1980) Introduction to Soil Physics. Academic Press, London. Jennings GD and Jarrett AR (1985) Laboratory evaluation of mulches in reducing erosion. Transactions of the American Society of Agricultural Engineers 28, 1466–1470. Kamar MJ (1994) Natural use of stone and organic mulches for water conservation and enhancement of crop yield in semi-arid areas. Advances in GeoEcology 27, 163–179. Kemper WD, Nick AD and Corey AT (1994) Accumulation of water in soils under gravel and sand mulches. Soil Science Society of America Journal 58, 56–63. Kosmas C, Danalatos NG, Moustakas N, Tsatiris B, Kallianou Ch and Yassoglou N (1993) The impacts of parent material and landscape position on drought and biomass production of wheat under semi-arid conditions. Soil Technology 6, 337–349. Kosmas C, Yasoglou N, Moustakas N and Danalatos N (1995) Field site: Spata. In Mediterranean Desertification and Land Use, Basic Field Programme, Phase 2 . Final report of MEDALUS II-Project 1, contract EV5VCT92-0128, MEDALUS Office, Thatcham, UK. Le Bissonnais Y (1996) Aggregate stability and assessment of soil crustability and erodibility: 1. Theory and methodology. European Journal of Soil Science 47, 425–438. Lopez PR, Cogo NP and Levien R (1987) Erosion reduction effectiveness of types and amounts of surfaceapplied crop residues. Revista Brasileira de Ciencia do Solo 11, 71–75. Magier J and Ravina I (1984) Rock fragments and soil depth as factors in land evolution. In JD Nichols, PL Brown and WJ Grant (eds) Erosion and Productivity of Soils Containing Rock Fragments. Special Publication no. 13, Soil Science Society of America, Madison, Wisconsin, pp. 13–30. Meyer LD, Johnson CB and Foster GR (1972) Stone and woodchip mulches for erosion control on construction sites. Journal of Soil Water Conservation 27, 264–269. Miller FT and Guthrie RL (1984) Classification and distribution of soils containing rock fragments in the United States. In JD Nichols, PL Brown and WJ Grant (eds) Erosion and Productivity of Soils Containing Rock Fragments. Soil Science Society of America, Madison, Wisconsin, pp. 1–6.
Impact of Rock Fragments on Soil Degradation
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Moustakas N, Kosmas C, Danalatos NG and Yassoglou N (1995) Rock fragments. I: Their effect on runoff, erosion and soil properties under field conditions. Soil Use and Management 11, 115–120. Nachtergaele J, Poesen J and van Wesemael B (1998) Application and efficiency of gravel mulches in southern Switzerland. Soil and Tillage Research 46, 51–59. Onstad CA, Wolf ML, Larson CL and Slack DC (1984) Tilled soil subsidence during repeated wetting. Transactions of the American Society of Agricultural Engineers 27, 733–736. Parry WL and Carter TR (1991) Climatic changes and future land use potential in Europe. In R Fantechi, G Maracchi and ME Almeida-Teixeira (eds) Climatic Change and Impacts: A General Introduction. Commission of the European Communities, Directorate-General Science, Research and Development, Report EUR 11 943 EN. Penman HL (1948) Natural evaporation from open water, bare soils and grass. Proceedings of the Royal Society 193, 120–145. Peters DB (1960) Relative magnitude of evaporation and transpiration. Agronomy 52, 536–538. Poesen J (1990) Conditions for the evacuation of rock fragments from cultivated upland areas during rainstorms. In DE Walling, A Yair and S Berkowicz (eds) Proceedings of the Jerusalem Workshop on Erosion, Transport and Deposition Processes. IAHS Publication 189, IAHS Press, Wallingford, pp. 145–160. Poesen J and Ingelmo-Sanchez F (1992) Runoff and sediment yield from topsoils with different porosity as affected by rock fragment cover and position. Catena 19, 451–474. Poesen J and Lavee H (1991) Effects of size and incorporation of synthetic mulch on runoff and sediment yield from interrills in a laboratory study with simulated rainfall. Soil and Tillage Research 21, 209–233. Poesen J and Lavee H (1994) Rock fragments in topsoils: significance and processes. Catena 23, 1–28. Poesen J and van Wesemael B (1995) Effects of rock fragments on the structural collapse of tilled topsoils during rain. In E Derbyshire, T Dijkstra and IJ Smalley (eds) Genesis and Properties of Collapsible Soils. NATO Advanced Science Institute Series Vol. 468, Kluwer Academic, Dordrecht, pp. 333–343. Poesen JW, Torri D and Bunte K (1994) Effects of rock fragments on soil erosion by water at different spatial scales: a review. Catena 23, 141–166. Ravina I and Magier J (1984) Hydraulic conductivity and water retention of clay soils containing rock fragments. Soil Science Society of America Journal 48, 736–740. R¨omkens MJ, Wang JY and Darden RW (1988) A laser microreliefmeter. Transactions of the American Society of Agricultural Engineers 31, 408–413. Soil Survey Staff (1975) Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys. USDA-SCS Agric Handbook 436. US Government Print Office, Washington, DC. UNESCO-FAO (1963) Bioclimatic map of the Mediterranean zone. Explanatory notes. Arid Zone Research XXI. FAO, Rome. Van Wesemael B, Poesen J, de Figueiredo T and Govers G (1995a) Effects of rock fragments on soil surface roughness evolution during rainfall. Earth Surface Processes and Landforms 21, 399–441. Van Wesemael B, Poesen J and de Figuiredo T (1995b) Effects of rock fragments on physical degradation of cultivated soils by rainfall. Soil and Tillage Research 33; 229–250. Van Wesemael B, Poesen J, Kosmas CS, Danalatos NG and Nachtergaele J (1996) Evaporation from cultivated soils containing rock fragments. Journal of Hydrology 182, 65–82.
12
Aridification in a Region Neighbouring the Mediterranean
´ ´ AM ´ ´ ´ HUSZAR, ´ ´ ´ ´ ´ AD KERTESZ, TAMAS DENES LOCZY, BELA MARKUS, JANOS MIKA, ´ ´ ´ ´ ´ ´ ´ TOZSA KATALIN MOLNAR, SANDOR PAPP, ANTAL SANTHA, LASZLO SZALAI, ISTVAN AND GERGELY JAKAB
Department of Physical Geography, Geographical Research Institute, Hungarian Academy of Sciences, Budapest, Hungary
1 INTRODUCTION AND OBJECTIVES Interpretation of temperature, precipitation and potential evaporation anomaly patterns, and the scenarios of regional climate change based on the General Circulation Model, generally suggest that climate modification may be predicted for the northern part of the Mediterranean region as well as the south. Hungary, lying in the heart of the Carpathian Basin, among the countries of central and eastern Europe, is a flat, low-lying country, and faces some severe problems. The effects of three main climatic influences felt in Hungary (Mediterranean, continental and Atlantic) may become modified and result in a changed, more difficult, climate. The main desertification problems in Hungary have always been connected with drought. Dry periods, ranging from several years up to 25 years, have, in the past, led to serious water deficits and water imbalances affecting natural systems and land resource production systems. The term aridification was introduced to characterize the increasing dryness (aridity) of the climate as a result of global climate change and its environmental consequences. To consider these possible consequences of global climate change, an aridification research programme was launched within the framework of the MEDALUS II project. Objectives included climatological investigations to explore the impact of global climate change on the climate of Hungary. Some test areas were studied in detail, and the physical processes of aridification were examined and tested. It has yet to be shown whether medium- and short-term oscillations do indicate a tendency towards a warmer and dryer climate. Changes in soil properties, water reserves and vegetation were studied in areas considered most environmentally sensitive. Special attention was paid to water budget parameters. Recent groundwater level changes have been monitored, and future trends predicted. Soil moisture dynamics in soil profiles, and the impacts of groundwater level changes on soil processes were studied. The species composition of the natural vegetation of the central Great Hungarian Plain was evaluated to provide further climatic change parameters. Remote sensing and GIS techniques were used to map land-use changes between 1975 and 1991, and the trends that emerged were considered from environmental, agricultural and economic viewpoints.
2 CLIMATE CHANGE IN HUNGARY As in other parts of Europe, the meteorological record for recent years in Hungary (Bussay et al. 1995) shows major deviations from long-term mean values, from data available since 1881. For Mediterranean Desertification: A Mosaic of Processes and Responses. Edited by N.A. Geeson, C.J. Brandt and J.B. Thornes 2002 John Wiley & Sons, Ltd
148 Temperature (°C)
Mediterranean Desertification
12 10.2 9 800
Precipitation (mm)
700 600 500 400 300 200 100 0 1881
1891
1901
1911
1921
1931 1941 Year
1951
1961
1971
1981
1991
Figure 12.1 Trends of annual mean temperature and annual precipitation for Budapest, 1881–1991 (Matyasovszky 1995)
example, the precipitation deficits observed in the summer of 1994 (when rainfall was only 43% of the long-term average) were followed in the next year by the almost unprecedented low October precipitation of 3 mm and then record rainfall amounts in December 1995. Figure 12.1 shows the long-term trends of annual mean temperature and annual total precipitation for Budapest. From this, and with data from other meteorological stations, changes in other climatic variables can be deduced, and scenarios of climate change can be suggested. To test a hypothesis of increasing aridification since 1900, time series of monthly mean temperatures and precipitation between 1900 and 1990 were analysed using records for 16 stations across Hungary (17 for precipitation). The locations of the stations are shown in Figure 12.2. Although they are not geographically evenly spaced, the stations do seem to represent the full range of climate experienced over Hungary (Matyasovszky 1995; Moln´ar and Mika 1997). The inhomogeneity of temperature data series can lead to some uncertainty when establishing trends. In the present survey, the data series was homogenized applying Szentimrey’s (1994) data correction procedure, which relies on an undistorted reference (the temperature series for Kremsm¨unster, Austria). The inhomogeneities are due to the changes of the measurement frequency of meteorological data in Hungary in the middle of the 1960s. The station of Kremsm¨unster, with an undisturbed data series, is located near to the Hungarian border. The time series for 1900–1990 was divided into two intervals: 1900 to 1949, and 1950 to 1989, and these intervals were also analysed for trends of climatic change. 2.1
Temperature Trends
There are definite suggestions of climatic warming over Hungary since 1900. In areas of colder climate, annual mean temperatures were 0.2–0.3 ◦ C higher than the long-term average during the interval 1950–1989. For stations with the warmest climate the increase was even more remarkable (+0.3–0.5 ◦ C). Monthly mean temperatures also showed an increasing trend. The change in January (coldest month) temperatures can be illustrated with the examples of Ny´ıregyh´aza where there was a
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Aridification in a Region Neighbouring the Mediterranean 16
16
18 1
2 18
16 3
4 14
7
18 5
16
6 8 9
10
12
11 18
N
14
18 13
20
W
E S
18
16
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15 20
Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
0
40
80 km
Location Mosonmagyaróvár Sopron Eger Nyíregyháza Szombathely Pápa Budapest Debrecen Zalaegerszeg Keszthely Kecskemét Túrkeve Kalocsa Szarvas Pécs Baja Szeged Kiskunfélegyháza groundwater well
Figure 12.2 Locations of meteorological stations (and a groundwater well) across Hungary. Precipitation data are available for all stations since 1881, and temperature data for all stations except Eger. July isotherms (◦ C) are also shown
rise from −2.7 to −2.4 ◦ C, and of Debrecen where there was a rise from −2.2 to −1.8 ◦ C. Warmer areas experiencing warming in January included P´ecs (from −0.4 to −0.1 ◦ C) and Kecskem´et (from −0.5 to −0.1 ◦ C). July (warmest month) mean temperatures do support the warming tendency, but not so clearly. At the station with the coldest climate (Zalaegerszeg) July mean temperature was 20.0 ◦ C in the study period up to 1949, but 19.7 ◦ C after 1949. Regarding the two warmest areas of Hungary, similar trends were observed. The mean July temperature in the centre of the Great Hungarian Plain (Kecskem´et) between 1900 and 1949 was 23.1 ◦ C and between 1950 and 1989 it
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was 22.9 C. From studying the means of other months, it became clear that most of the warming has been associated with rising temperatures in the winter half of the year. Annual temperature range has decreased at every meteorological station, although not at the same rate. The biggest changes were observed along the Danube (Kalocsa: −1.0 ◦ C), and at Zalaegerszeg (−0.9 ◦ C). The nationwide mean change in annual temperature range was found to be −0.56 ◦ C. This may indicate reduced continentality of the Hungarian climate. In conclusion, we suggest that the 1950–1989 temperatures are several tenths of degrees higher than those measured between 1900 and 1949. The annual mean temperatures of the 16 stations increased, on average, by 0.3 ◦ C during the period 1950–1989. Milder winters and the reduced annual range experienced in this region may be associated with global warming. 2.2
Precipitation Trends
At stations with the lowest annual precipitation, there has been a decrease in precipitation since 1949. The degree of change, however, varies greatly, e.g. for Szarvas (from 585 mm to 495 mm), and T´urkeve (from 544 mm to 534 m). Both these stations are on the Great Hungarian Plain. Data from the stations with the highest annual precipitation (western Hungary) also show a clear reduction in rainfall of 40–65 mm, e.g. Zalaegerszeg (from 740 mm to 701 mm) and Sopron (from 715 mm to 657 mm). Mean monthly precipitation figures confirm the tendency towards decreasing precipitation since 1949. Figure 12.3 shows that the pattern of rainfall distribution throughout the year has changed considerably. In March there has been a decrease, except for two stations (Szombathely and T´urkeve), where no change was detected. There was a significant drop in precipitation for all the 17 stations studied in April (mean change of −7 mm) and also May in most regions (about −4 mm). June was the only month when an increase in precipitation was found for all 17 stations. The average for the first half of the century was 68.8 mm in this month, becoming 78.1 mm in recent decades. In July and August no change was observed. In September all stations recorded a mean monthly decrease 90.00
Monthly average precipitation (mm)
80.00 70.00 60.00 50.00 40.00 30.00 20.00
1900−1949 1950−1989
10.00 0.00 Jan
Feb
Mar
Apr
May
Jun Jul Month
Aug
Sep
Oct
Nov
Dec
Figure 12.3 A comparison of monthly precipitation between the intervals 1900–1949 and 1950–1989, average values for 17 stations
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Aridification in a Region Neighbouring the Mediterranean 80 1900−1949 1950−1989 70
Precipitation (mm)
60 50 40 30 20 10 0 1
2
3
4
5
6
7
8 9 10 11 Station number
12
13
14
15
16
17
Figure 12.4 Differences in October mean precipitation between the intervals 1900–1949 and 1950–1989; averages for 17 stations across Hungary
in precipitation. The mean fall was from 53.4 mm to 42.8 mm. The decrease was even greater in October, from 55.3 mm to 38.1 mm (Figure 12.4). In September the effect was mainly uniform across Hungary but in October, three particular areas with a climate of Mediterranean character have shown a reduction in autumn rainfall. The distribution of annual and monthly precipitation has more anomalies than the corresponding distributions of temperature. It is common to find any month without rain, or alternatively, there may be high rainfall in any month of the summer (200–300 mm) associated with intense storms. 2.3 Statistical Significance of Precipitation and Temperature Trends A statistical T-test was applied to the results of linear regression analysis, to establish which stations have shown statistically significant trends for warming and precipitation distribution changes, at the 95% level. Table 12.1 lists the temperature changes for 16 stations using the basic data. Table 12.2 is for the same stations but the data have been adjusted and corrected to conform with the Kremsm¨unster series. In Table 12.1, if the trends are averaged, an increase of only 0.003 ◦ C year−1 is found. However, where the corrected data are shown, in Table 12.2, all 16 stations showed a moderate warming tendency of at least 0.010 ◦ C year−1 . For each station studied, a trend of decreasing annual precipitation was found – trends that were statistically significant for 12 stations out of the total 17 at the 95% significance level. An average precipitation decrease of −0.917 mm year−1 (from Table 12.3) is similar to the findings of Koflanovits-Adamy and Szentimrey (1986). Ambr´ozy et al. (1990) studied change over a 84-year period (1901–1984) and claim that over the Great Hungarian Plain the first decades of the 20th century were characterized by increased humidity, followed by a long period of little change, and then evidence of drought. The range amounts to almost 10% of annual precipitation, meaning that the oscillations of the mean annual precipitation in a dry period can be 10% of the long-term average. The main concern is that reduced annual precipitation and rising temperatures in Hungary are leading to increased aridity.
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Table 12.1 Temperature trends for 16 meteorological stations in Hungary (1900–1990) (non-corrected database)
Number
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Location of meteorological station in Hungary
Temperature trend (◦ C year−1 )
Statistical T value
Statistically significant at 95% level
Mosonmagyar´ov´ar Sopron Ny´ıregyh´aza Szombathely P´apa Budapest Debrecen Zalaegerszeg Keszthely Kecskem´et T´urkeve Kalocsa Szarvas P´ecs Baja Szeged Mean for 16 stations
+0.004 +0.003 +0.003 +0.003 −0.003 +0.010 +0.002 −0.003 +0.002 −0.003 +0.002 −0.000 −0.001 −0.006 +0.003 −0.010 +0.003
1.740 1.724 1.301 1.436 1.448 5.599 0.836 1.337 0.836 1.291 0.992 0.212 0.622 2.632 1.335 4.446
no no no no no yes no no no no no no no yes no yes
Table 12.2 Temperature trends 1900–1990 (corrected database)
Number
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Location of meteorological station in Hungary
Temperature trend (◦ C year−1 )
Statistical T value
Statistically significant at 95% level
Mosonmagyar´ov´ar Sopron Ny´ıregyh´aza Szombathely P´apa Budapest Debrecen Zalaegerszeg Keszthely Kecskem´et T´urkeve Kalocsa Szarvas P´ecs Baja Szeged Mean for 16 stations
0.010 0.010 0.011 0.010 0.011 0.011 0.011 0.011 0.010 0.011 0.010 0.011 0.010 0.011 0.011 0.010 0.0105
5.307 5.174 5.532 5.372 5.330 5.806 5.707 5.307 5.490 5.332 4.795 5.727 4.635 5.545 5.718 4.519
yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes
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Aridification in a Region Neighbouring the Mediterranean
Table 12.3 Precipitation trends for 17 meteorological stations across Hungary (1900–1990)
Number
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Location of meteorological station in Hungary Mosonmagyar´ov´ar Sopron Eger Ny´ıregyh´aza Szombathely P´apa Budapest Debrecen Zalaegerszeg Keszthely Kecskem´et T´urkeve Kalocsa Szarvas P´ecs Baja Szeged Average
Precipitation trend (mm year−1 )
Statistical T value
Statistically significant at 95% level
−0.61 −1.22 −0.47 −1.02 −1.41 −0.27 −1.09 −0.47 −0.56 −0.39 −0.86 −0.98 −0.43 −0.79 −2.30 −0.71 −0.91 −0.918
1.953 3.518 1.618 3.086 4.432 0.745 3.309 1.358 1.526 0.994 2.892 2.980 1.436 2.754 5.765 2.070 3.002
no yes no yes yes no yes no no no yes yes no yes yes yes yes
For calculating the frequency of warm and dry years (using the method of Tar 1992), a sample meteorological station was selected and the time series was divided into 10-year intervals. The database included corrected annual and monthly mean temperatures (Szentimrey 1994) and annual and monthly precipitation sums for a total of 110 years (1881–1989). Each year with a mean annual temperature higher than or equal to the 110-year average was defined as a warm year, and each year lower than average, defined as a cold year. The same classification was made for precipitation (as wet and dry years). Categories of humid and dry years were defined.
3 ARIDIFICATION PROCESSES 3.1 Groundwater Level Changes
A major impact of changes toward a drier climate is the depletion of groundwater reserves. This has been studied in one of the most severely affected regions of Hungary, on the Danube–Tisza interfluve (Husz´ar et al. 1996). The database analysed derives from the observation well network operated by the Research Centre for Water Resources Development (VITUKI). A most serious aspect of the aridification trend here is extremely reduced infiltration into the soils and reduced recharge of groundwater. In the 1980s, significantly diminished autumn and winter precipitation only allowed infiltration (of insignificant amount) on two occasions. According to hydrologists (P´alfai 1991), a combined effect of several factors is responsible for falling groundwater levels: lower precipitation and increased evaporation explain about 50% of the drop, but the extraction of confined groundwater for drinking water supply (25%), afforestation and other land-use changes (10%), drainage regulation (7%), direct extraction of free groundwater as well as reduced recharge from the neighbouring hills and from the Danube (6%) are also significant factors. In the 1980s and early 1990s the deficit in autumn and winter precipitation and diminished infiltration led to a reduction of peaks on the annual groundwater graph (P´alfai 1995). The changes in
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101.00
m a.s.l.
100.00
99.00
98.00
97.00
96.00 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1952 1958 1980 1984 1968 1972 1978 1980 1984 1988 1992 Year
Figure 12.5 Fall in groundwater levels on the Danube–Tisza interfluve illustrated by the ´ ´ long-term (1950–1993) curve of monthly average groundwater levels in the Kiskunfelegyh aza observation well (no. 883)
the well shown in Figure 12.5 clearly show both the gradual fall of the annual average groundwater table level and the reduced seasonal range which is a consequence of inadequate winter rain infiltration. Three representative SSE–NNE-aligned cross-sections of the Danube–Tisza interfluve (perpendicular to the strike of the interfluve ridge) were analysed. Each included 12 to 15 observation wells, at around 5 to 7 km intervals along the cross-section. Groundwater levels in the early 1990s were compared to the average of the 1960s, when infiltration was above the long-term average level, and therefore this decade most certainly preceded the beginning of aridification. Through the geographical interpolation of groundwater well observation records, a map of changes in the annual mean groundwater level was constructed (Figure 12.6). It shows that in some of the most susceptible, rapidly drained areas (loess-mantled as well as sand regions), 2–4 m falls in the water table are common. With falling groundwater levels, soil moisture contents also reduced considerably during the 1990s. For instance, in spring 1990 in some sections of the Danube–Tisza interfluve the uppermost 1 m of soil had only 60–70% soil-moisture reserves, as opposed to the long-term average of 100% field capacity. In 1992, in the same area, the 0–0.5 m topsoil contained less than 15% moisture, which is below the wilting-point of most agricultural crops (P´alfai 1996). Before the wet winter of 1995, the winter precipitation deficit had maintained a decreasing trend of relative moisture content in the topsoil for 12–15 years. The drought also involves water level falls in ponds traditionally used to irrigate crops. Then confined groundwater reserves suffer from increased water use for irrigation. The levels have recently sunk more than 20 m at some locations (Ber´enyi and Erd´elyi 1990). The area affected is virtually the same as in the case of free groundwater. After the depletion of the Quaternary aquifer of the alluvial fan, the Pliocene aquifers come into use and their pressure conditions are also now being affected.
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No change 4 dS m−1 >4 dS m−1 15%
1000 altitude (metres)
Alm en
R. d
ara
Fieldsite
kilometres
Figure 15.1 Location map of the study area. 1 and 2 indicate the locations of Figures 15.2 and 15.3 respectively
730
SSW
3A/B
N
Altitude (m)
720 Limestone Marls Response unit boundary
710
1B 1C
700
1A
690 680
2
2
1B
1A 0
200
400
600
800
1000
1200
Distance (m)
˜ Figure 15.2 Cross-section of the Canada de Cazorla area, indicating the lithology and response unit boundaries
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7
8
6
S
1A
2
5 4
750
Altitude (m)
a b
700
c 650
d
600 lim
m
ar l
300
m
um ps gy
ne
200
l+
to
100
ar
es
ne
0
ne
lim
500
m
to es
l+ to es
ne
lim
to
ly
es
ar
ar
m
lim
550
400
500
600
800
700
900
Distance (m)
Figure 15.3 Cross-section of the Alquer´ıa area, indicating the lithology and response unit boundaries: a, response unit number; b, response unit boundary; c, barrier for water with high threshold value; d, direction of surface water flow
1A 1B 1B
1C
2
1C
1B
3B 3A
3A 1B 1A
˜ Figure 15.4 High-resolution digital aerial photograph mosaic of the Canada de Cazorla plateau and its surrounding active pediment surfaces. A label indicates the individual response units. Broken lines are response unit boundaries. The position of the cross-section shown in Figure 15.2 is indicated (thick white line). The photo is orientated to the north and the distance over the lower side of the photograph is approximately 500 m
types, as shown for the Ca˜nada de Cazorla area in Figure 15.4. Detailed information on the test sites can be found in Cammeraat and Imeson (1998, 1999) and in Imeson and Cammeraat (2000).
4
´ REGION FUNCTIONS IN THE ALQUERIA
Environmental sensitivity to desertification involves the loss of soil and nutrients, but also changes in biodiversity or landscape fragmentation. Sensitivity is influenced by land use, which is on the one
191
Selection of Desertification Indicators
hand dominated by the properties and resources of the area and on the other hand by the driving socio-economic forces. Therefore an analysis is essential if we are to understand impacts on the environment, to assess gains or losses of natural and socio-economic functions, and how indicators can be used to monitor changes in this. An analysis of the goods and services provided by the environment (see e.g. De Groot 1992; Constanza et al. 1997) in the Alquer´ıa region is based on the following functions: agricultural production, animal husbandry, supply of water, hunting, nature conservation, and the supply of wood and other materials. 4.1 Agriculture
Wheat is the most important crop in the area, of which over 80% is grown without irrigation. The extent of this crop is decreasing as a result of increases in other crops. Almonds are an important crop and their area of cultivation is rapidly expanding in the area with stony soils. Olives are also a traditional rain-fed crop, but the area with irrigated olives is increasing. A fast expansion of irrigated crops such as melons, broccoli, peppers, lettuce and tomatoes can be found on the more marly soils. In Table 15.1, the total gross yields in tonnes per hectare and pesetas per hectare are presented for the major crops. 4.2 Animal Husbandry and Hunting
Sheep and goat production is an important economic activity in the Guadalent´ın Basin. Although the number of herds has greatly decreased over the last 20 years, the number of animals is thought to have remained constant (120 animals km−2 ). An increasing number of large pig farms are present in the research area, both bio-industry farms and free-range pig farms. This activity is not dependent on a particular environment and is not affected by land degradation. However, free-range pig farms are built on both former cultivated and cleared semi-natural fields. Soil degradation, both by wind and water erosion, is affecting these areas, as they are kept free of vegetation. Hunting is an important function of the area. Game includes wild boar, partridge and rabbits. Most of the study area is classified as hunting area, and a hunting licence costs about 1000 pts ha−1 year−1 . Because the forests provide more shelter and food for animals, the forest is expected to have a higher value for hunting than esparto grass (Stipa tenacissima). The valleys and ramblas are also important for hunting because the animals depend on them for drinking water. Table 15.1 Yields of the major crops in the Alquer´ıa region (1997)
Crop Wheat (not irrigated) Wheat (irrigated) Almonds (irrigated) Almonds (not irrigated) Olives (irrigated) Tomatoes (irrigated) Melons (irrigated) Peppers (irrigated)
Yield (t ha−1 year−1 )
Yield (pts kg−1 )
Yield (103 pts ha−1 year−1 )
EU subsidy (103 pts ha−1 year−1 )
0.8–1.5 3–5 2–4 0.7–0.9
30 30 225a 225a
24–45 90–150 450–900 158–203
5 5 40 40
2–4 30 20 10
80 25 30 75 (wet)
160–320 750 600 750
? – – –
a (Shell + nut). The yield expressed as pts ha−1 year−1 is a gross yield. It does not include labour costs and investments, e.g. in irrigation equipment. Subsidies are not included in the figures. (100 pts = 0.601 euros)
192 4.3
Mediterranean Desertification Water Supply
Water is the most important limiting factor both for agricultural activities and in the natural environment. Interviews with farmers showed that water is considered by far the most important limiting factor for agriculture in the area. Annual rainfall is highly variable, and in years with low rainfall, non-irrigated crops cannot be harvested. The irrigated crops have a high water demand (Table 15.2). For example, in the case of almonds, the water supplied by irrigation is approximately 400 mm year−1 . Drinking water for the nearby villages of Zarcilla de Ramos and La Parroquia/Fuensanta comes from karst springs in the nearby mountains. The various farms depend on natural private wells, which usually have an output of only a few litres of water per minute (e.g. Cortigo de Alquer´ıa: 3 dm3 min−1 baseflow at the end of dry season, but has permanently fallen dry since the autumn of 1998). For irrigation, water comes from irrigation canals, groundwater sources (deep drilled wells) and reservoirs such as the Embalse de Puentes. The area has a high diversity in flora, influenced by variations in altitude and humidity, and endangered animals are also present. The greatest diversity of plants can be found at locations with a somewhat wetter microclimate, e.g. in the ramblas, on concave slopes, or in areas that receive runoff from uphill. The economic value and the nature conservation value of the landscape units are presented in Table 15.3. The figures are average values for the units; in some cases a range is given.
Table 15.2 Water demand of irrigated crops
Crop
Water supplied by irrigation (m3 ha−1 year−1 )
Wheat Almonds Olives Melons Tomatoes
3500–4000 4000 4500 5500 7000
Source: Comarcal (1997).
Table 15.3 Socio-economic functions of the landscape units. The natural value is presented as a relative value
Landscape unit
Irrigated agriculture Non-irrigated agriculture Abandoned meadow Esparto Forest Reforested Valleys a
Huntingc Total gross Agriculturea Grazingb Value for nature (×1000 pts) (×1000 pts) (×1000 pts) economic value conservation (×1000 pts) (relative scale) 100–750 24–45
– 2–4
– –
– – – – –
2–4 2–4 – – 2–4
– 0.5–1 1–2 0.5–1 1–2
100–750 25–50 2–4 2.5–5 1–2 0.5–1 3–6
? ? ? + +++ + +++
Gross value excluding labour costs and investment costs (irrigation equipment, machinery, etc.). Excluding labour costs. c The distinction between the landscape units is based on estimation of the relative value of the units for foraging of the hunted animals. b
Selection of Desertification Indicators
193
5 SENSITIVITY TO SOIL DEGRADATION 5.1 Indicators and Response Units
Prior to the degradation assessment the area was subdivided into several response units. This was done by applying the methodology tested in two representative training sites (Ca˜nada de Cazorla and Alquer´ıa; Figures 15.2 and 15.3). In these training sites many indicators have been used to characterize the individual response units. For an extended discussion of the indicators applied to the training sites, see Imeson and Cammeraat (2000). Examples are worked out for the Alquer´ıa area, concentrating on the fine- and intermediate-scale indicators, and are given in Table 15.4. The next step was to evaluate these indicators for each of the response units. The stability and resilience of the response units was evaluated from the scores in Table 15.4. The results are shown in Table 15.5 where the scores for the three classes are summed. Indicators with a good score received a weight of 3, intermediate scores received a weight of 2 and poor scores received a weight of 1. The final scores were summed and converted to a relative scale of 1 to 100. They are shown in the last column of Table 15.5 and are also given a ranking number in descending order of vulnerability per response unit. This enables a characterization for larger areas, as the whole area of study can be characterized in response units. In Figure 15.5, the southern slope of the Alquer´ıa hill is visible, showing different response units, corresponding with the profile of Figure 15.3. From the top of the valley to the bottom, the units are 5 (bare limestone), 4 (narrow band of shrubs), 2 (marl slopes with esparto cover) and 1A (cultivated area). 5.2 Soil Erosion
Erosion measurements have been carried out at many different sites in the Mediterranean, including in the Guadalent´ın and neighbouring areas. It is very hard if not impossible to directly translate these literature data to the Guadalent´ın as the actual values depend very much on local differences in slope, soil and vegetation cover, erodibility and erosivity. Furthermore, many of these data come from bounded plot experiments which are especially limited in value for semi-natural areas (Romero D´ıaz et al. 1999) and which have been maintained for periods that were too short to cover the high temporal variation in Mediterranean precipitation. Also, the application of the Universal Soil Loss
Figure 15.5 Alquer´ıa hill showing different response units, and different typical indicator characteristics
194
Mediterranean Desertification
Table 15.4 Physical and biological indicators of ecosystem function and structure relevant for soil and water conservation applied to the response units (adapted and extended from Imeson and Cammeraat 2000)
Indicator
Good
Intermediate
Poor
Well-defined small flow lines and associated deposits Some displacement also of larger organic debris Few micro-terraces, stones moved
Numerous flow paths and associated deposits Extreme movement during each event
Physical indicator Flow paths
Litter
Little evidence of water movement from unit In place
Rainwash
No evidence
Crusting and sealing Exposure of tree and shrub roots Surface cover Rills Gullies
None or very limited
Soil conservation dams
Crusting obvious, reducing infiltration Some
Significant movement of large stones and exposure of roots Hard crusts strongly reducing infiltration Abundant exposure
>0.5 dams ha−1
Incomplete protection Occasionally present Few but not very active 1 and u > 0), we have u (1−a)/a exp(−u2 /2) v q = y (1−a) − uf (u) + f (v) dv √ i0 x0 b−1 2π 0 u exp(−u2 /2) S (2−2a) 2 √ = y (1 + u )f (u) − K(i0 x0 )2 2π (2−2a)/a u 2 exp(−v /2) v + √ dv (6) − vf (v) b−1 2π 0
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MEDRUSH: A Basin-Scale Physically Based Model
Equations (5) and (6) show that, at low infiltration rates, there is net overland flow produced everywhere, so that discharge increases linearly downslope, and sediment discharge as the square of the distance (for a given gradient). As infiltration exceeds mean rainfall, only occasional bursts of rain produce overland flow, and accumulate only over short stretches of the slope, so that water and sediment discharge flatten off with distance progressively closer to the divide. This is in accord with observed behaviour on semi-arid to arid slopes, as has been noted by Dunne and Aubry (1986) and Yair and Lavee (1982) among others. For a rough surface, the discharge squared term, q 2 = [(i − f )x]2 in equation (4) for sediment discharge must be replaced by the modification of equation (3). The forecast sediment discharge is thus replaced by a linear combination of the two terms shown in equations (5) and (6), which provides an estimate of the roughness dependence. The equations for water discharge are unchanged, provided that our initial assumption of constant overland flow routing velocity is observed. Combining the equations gives S0 h0 q Sh = + (7) K(i0 x0 )2 K(i0 x0 )2 i0 i0 x0 where Sh indicates the sediment discharge for roughness h0 . Equation (7) shows that the concentration of flow within roughness elements produces increases in sediment discharge everywhere. Increases are greatest at rainfalls that are low relative to infiltration, and greatest near the top of the slope. Increases in infiltration lead to a reduction in sediment transport that is greatest downslope. Thus days when the soil is dry may lead to deposition downslope, while smaller rainfalls after wetter antecedent conditions may be associated with similar rates of sediment transport overall, but with downslope incision. Increases in microtopographic roughness give increases in sediment transport everywhere, but these increases are least downslope. The effect of roughness decreases, however, as storm size increases, and the roughness elements are drowned out. Subsurface flow is calculated using TOPMODEL (Beven and Kirkby 1979), but with allowance for downslope differences in the amount of water percolating into the saturated zone (Kirkby 1986). The hourly time step is broken down into variable increments to maintain computational stability during intense rainfall events. Where necessary, exfiltrating saturation overland flow (return flow) is also added to the Hortonian overland flow described above. This is calculated from the intersection of the rough (microtopography) surface with the mean saturated deficit level within the soil. The relationships between the flow components are shown schematically in Figure 16.2. 2.3 Grain-size Effects
The effect of changes in grain size with travel downslope may similarly be integrated over the frequency distribution, at least to a first, linear approximation. Many grain-size distributions are approximately log-normal in form, so that the source distribution at a point, before transport, may be expressed in the following form: ... 2 1 d − 1 d (8) p(d, 0) = √ exp − 2 σ σ 2π ...
where d is the grain size in phi units, d = − log2 (grain size in millimetres), and d and σ are the mean and standard deviation of the distribution. Assuming that travel distance is inversely proportional to grain size to the mth power, then the mean travel distance for size d is ... ... x = x0 2(d− d )m (9) ...
where x0 is the mean travel distance for the mean diameter d . For moderate travel distances, at which the source material is not exhausted, an inverse exponential (or Ŵ (1)) distribution of travel distances is appropriate (Kirkby 1991), giving a rate of deposition
210
Mediterranean Desertification Fractal distribution of intra-hour intensities
Hourly rainfall Infiltration rate
Infiltration capacity
Unsaturated infiltration store
Hortonian overland flow
Key Water flow Causality Flow
Percolation rate Surface roughness
Saturated subsurface store
Store
Subsurface flow referred to mean surface
Intersections with depressions in microtopographic surface
Return flow (exfiltration)
Reduced subsurface flow
Figure 16.2 Schematic relationships between overland and subsurface flow components in the MEDRUSH model
at distance x, for grain size d, of
x ... 1 x ... 1 exp − ... = 2(d− d )m exp − 2( d −d)m x x x0 x0 ...
(10)
Combining these expressions, the mean grain size of the transported material in phi units is given as ... ... x −1 (11) d (x) = d (0) + mσ 2 ln(2) x0 ...
where d (x) indicates the mean after travel distance x. This approximation shows that the mean grain size is coarsened close to the source, unchanged at the mean travel distance and finer at greater distances. Summing and weighting across the distribution of deposition given in equation (9), the mean grain size of the transported material as a whole is properly conserved. It is assumed here that the phi standard deviation (σ ) is preserved during transport. 2.4
Sediment Transport in General
Sediment transport of all kinds is modelled as an erosion-limited process. This is similar in principle to its inclusion in the MEDALUS I catena model, and has been described in greater detail in Kirkby (1992), although set there in the context of integration of storm impacts over longer periods. This approach allows fine sediment transport to be effectively limited by supply, while coarse sediment transport is limited by a limited travel distance, and is essentially flux-limited. In this approach, sediment transport is governed by the continuity equation, and constrained by a sedimentation balance. For each individual process, the rate is determined by two quantities, the rate of detachment D and the travel distance h, both of which generally vary with rainfall, flow and/or surface conditions. ∂S S dw S ∂z = − =D− (12) − ∂t ∂x w dx h
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211
where S is the actual sediment transport per unit width, w is the local flow strip width, z is elevation, and x is the distance measured down the flow strip. The first part of this equation represents the continuity equation, taking due allowance for flowstrip convergence or divergence. The second part of the equation is the sedimentation balance, in which the rate of detachment, D, is balanced against the rate of sedimentation, S/ h. Clearly the capacity transport rate C = Dh. Where the travel distance, h, is small, then the sediment transport is close to its capacity, and where h is very large relative to the flow-strip length, removal is essentially supply-limited, following the equation ∂z =D (13) − ∂t 2.5 Sediment Transport by Wash Processes
Wash processes are considered to include rainsplash, rainflow and rillwash. In the first two of these processes, detachment is by raindrop impact, and in the third by flow traction. In rainsplash, travel is by aerial saltation, and in the last two processes travel is within the overland flow. We therefore need to specify two processes of detachment and two processes of travel. Raindrop detachment is modelled as independent of gradient and grain size, varying as the square of rainfall intensity. Detachment is limited by flow depth beyond about 6 mm. The attenuation is modelled as D = (1 + y) exp(−y) (14) D0 where D0 is the detachment on a bare surface and y is the ratio of flow depth, z, to attenuation depth, z0 . A given average flow depth, z¯ , may be converted to an actual flow depth relative to the roughness elements, by solving the following equation for z:
z 1 z′ 2 1 ′ dz′ (15) (z − z ) exp − z¯ = √ 2 h0 h0 2π −∞ Summing for equation (14) over this distribution, the overall efficiency of detachment is
∞ z ′ D z − z′ 1z2 1 1 ′ 1 + exp − √ dz + = √ D0 2 h20 z0 h0 2π z h0 2π −∞ 1 z′ 2 z − z′ exp − dz′ × exp − z0 2 h
(16)
Using this relationship, it may be shown that the attenuation depth controls the decay with depth for smooth surfaces. For surfaces with a roughness greater than the attenuation depth, the dominant effect is the exposure of significant unsubmerged areas due to the concentration of flow in the depressions. Movement of splashed material takes place both downslope and laterally. For erosion of the hillslope as a whole, the downslope direction is relevant. The travel distance may be calculated if all or part of the momentum of a raindrop is transferred to an underlying particle, and this impulse is used to project particles equally at all vertical and horizontal projection angles. On a gradient this leads to a net mean downslope travel distance, x, of n dR 4 vT2 2 (17) x= π g d where vT is the raindrop terminal velocity, dR is its diameter, d is the grain diameter, 2 is the slope gradient, and n is an exponent that takes the value of 2 for d < dR , and 6 otherwise.
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Mediterranean Desertification
This expression contains rainfall-related terms that have already been included in the detachment process, leaving the gradient and grain-size terms as the independent components of travel distance. For momentum transfer to an average detached layer, the detachment component, D0 , is D0 =
vT2 2g
The exponent, n, is controlled by the way in which momentum is transferred. If the raindrop is smaller than the grain, then the whole of its momentum is transferred. If larger than the grain, then only a cylindrical cross-section impacts the grain, the remainder of the drop falling around it. Clearly for a range of raindrop sizes and velocities, and partial grain overlaps, there is a cross-over zone between these behaviours, and travel distance can be approximated, ignoring constants, as follows: 2 (18) x= 2 t (1 + t 4 ) where t is the ratio d/dR . This expression gives a smooth transition in the region of the raindrop diameter, dR , and a maximum grain transport (defined as xd 3 ) for d = 0.77 dR . In the cross-slope direction, splash is a critical process in softening microtopographic roughness. At low roughness, the driving gradients increase linearly with roughness, h0 , but beyond a critical point, hc , lateral gradients encourage rill-wall collapse, which has been identified as an important process (de Ploey 1983). Summed over relevant grain sizes, the contribution of rainsplash to roughness reduction may be expressed in the approximate form h0 dh = −µh 1 + (19) dt hc where µ has the depth dependence characterized by equation (16). We will return to the issue of roughness generation below. Flow detachment, Dc , is modelled through a threshold power, in the following form: Dc ∝ q2 −
(20)
where q is the overland flow discharge per unit width, and is the detachment threshold. Thresholds may be set by turf strength or grain characteristics according to surface conditions. For Mediterranean conditions, grain thresholds are widely relevant, with components for grain friction, cohesion and corrections for steep gradients. An appropriate form (Kirkby et al. 1993) is d2 tan φ d+ c (21) = 0.06c4 tan φ − 2 d where 4 is the ratio of submerged grain to water density (≈1.65), φ is the angle of grain friction (≈35◦ ), c is the overland flow routing velocity, and dc is the grain size for minimum traction threshold (≈0.1 mm). Travel distance in rill and inter-rill flows is taken as proportional to flow discharge per unit width, q. Thus the total transporting capacity in rill flow, for example, is given as q(q2 − ) = 2 q2 − q (22) C = Dc h c ∝ The summation takes place, as above, by integrating over the full range of flow depths within the distribution of microtopography. Rillwash is also selective in modifying microtopography. In an erosive or depositional event, the deepest flow strands are most strongly affected, while high areas above the flow level are unaltered. The overall effect can be summarized by the change in the standard deviation of microtopographic elevation, which is our parameter for describing surface roughness.
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213
2.6 Changes in Surface Roughness Over Time
In a rillwash event, sediment transport is dominated by the q 2 term in equation (22). Over a short distance downslope, discharge increases more or less linearly with distance, so that the rate of erosion is proportional to d(q 2 )/dx ∼ q. In other words, local erosion within the microtopography is proportional to flow depth. Summing across the microtopography, we obtain z 1 z′ 2 λ ′ (z − z ) exp − dz′ = λ¯z E= √ 2 h h 2π −∞ ∞ 1 1 z′ 2 ′2 ′ 2 h = √ (z + E) exp − dz′ 2 h h 2π z z 1 z′ 2 1 ′ ′ 2 dz′ (23) [z + E − λ(z − z )] exp − + √ 2 h h 2π −∞ where E is the local integrated erosion rate, z¯ is the mean overland flow depth, h′ is the modified roughness, calculated from the definition of variance, and λ is a measure of the local erosional intensity ( µ may therefore be taken as a primary condition for the presence of active rilling. The balance of factors in this roughness equilibrium is equivalent to the conditions for stability described by Smith and Bretherton (1972) in theory, and by Dunne and Aubry (1986) for Kenya in the field. In a changing erosional environment, the roughness is constantly adjusting to current conditions. For much of the time roughness is very slowly decreasing, with bursts of regeneration in storms. Rates of change of roughness vary with roughness and erosional intensity, including the equilibrium relationship with zero rate of change. Down the length of a slope catena, we expect the erosional intensity to be low near the divide, to increase downslope, and perhaps to change to deposition (negative intensity) near the slope base. The equilibrium line suggests that there should be a corresponding increase in roughness with intensity, falling to lowest roughnesses in the depositional area. Using these order of magnitude values, it can be shown that responses to storm events may be rapid, but that in general equilibration to average conditions takes several hundred years, and is completed sooner where the change is towards lower roughness (i.e. in depositional environments) than where roughness is increased. 2.7
Construction of Sub-basins and Representative Flow Strips
Following the construction of suitable digital elevation models for the Agri catchment, an automated procedure for surface water routing and subdivision of the catchments was undertaken using GRASS modules written for the purpose. These modules calculate the accumulated upslope drainage area and principal flow direction at each point to create sub-basin raster maps and a river network vector map for flows within and between sub-basins. Two different types of sub-basin are produced, some covering headwater areas (leaf-type sub-basins) and others containing one or more through-flowing streams (stem-type sub-basins). Sub-basins are also selected with a threshold size that increases with stream order, to provide greater detail in the catchment headwater areas. Flow paths and networks are accumulated by sorting all cells in the DEM in altitude order, and applying a multiple flow direction algorithm to share the outflow between all lower neighbours. The merits of alternative weighting schemes, and ways of ensuring that streams follow their thalwegs are discussed by Quinn et al. (1991). Here a cubic weighting of gradients to the eight neighbouring points has been used, and this has been found as a satisfactory compromise which gives strong dominance to thalweg flow paths, but still allows some distribution of flow on fan and other divergent flow areas. The flow direction vector is then drawn in the direction of the neighbouring cell with the highest accumulated upslope drainage area value. For large flat areas, this procedure has been implemented by creating a tree of drainage directions, working upstream from the lowest exit point. Finally, the catchment outlet point is located by following the flow direction map to the edge of the catchment, and then recursively ascending against the principal flow direction to accumulate drainage area. Where the total accumulated area first exceeds the threshold associated with main-stem stream order, it defines the position as the mouth of a sub-basin, and each sub-basin is tagged to avoid re-use and link it to the catchment network structure, using the “segment ordering” system of Shreve (1967, cited in Gregory and Walling 1973). This scheme, using a principal flow direction, is a compromise between the single- and multi-path analyses, and has the advantage of providing unambiguous subbasin definition. This scheme was used to generate 208 sub-basins for the Agri Basin, ranging in
MEDRUSH: A Basin-Scale Physically Based Model
215
1) Section 1 (top) 2) Section 2 3) Section 3 4) Section 4 5) Section 5 (foot)
Figure 16.3 (top to foot)
Division of the Agri catchment into sub-basins and representative catena sections
size from 4.70 to 28.83 km2 , with the majority in each instance being of a similar proportion, as shown in Figure 16.3. The principal flow strips for each sub-basin were also extracted automatically within GRASS. At the same time, a relative strip width figure is calculated for each cell in that particular track according to local surface curvature, based on accumulated upslope drainage area and downslope distance from the start of the track. This figure therefore provides a quantitative estimate of both convergence and divergence on the track. The representative width, w, is approximated as 0 da − 1 dx (28) 1/a w = exp dx z where a is the accumulated upslope drainage area, and x is the distance from the top of the slope. These paths, which as stated can start at both sub-basin boundaries and at the occasional internal within-area peaks, perforce will be of different lengths. Thus the next step is to normalize the data set for each sub-basin such that the overall lengths and total drop match that of the longest principal strip. Normalized values are then averaged, taking the median value in each case, to produce a representative hillslope profile, which in combination with the mean width figure at each point produces the representative flow strip. The dimensions of the representative flow strips are scaled up to match the real length and drop of the longest strip in each sub-basin. The reason for using the longest flow strip is that, given an idealized basin, it is the centre strip that is most representative of the sub-basin as a whole. The final product from this exercise is therefore a set of representative flow strips on which the MEDRUSH hillslope model can be run. A number of strategies were tried to create a suitable representative flow strip. None is fully satisfactory, and the best are only considered adequate for estimating short-term, and therefore relatively minor, changes in the sub-basin. To transfer data to the representative flow strip, mean values are calculated for each required input value, thus all equivalent parts of the slope are modelled using equivalent input statistics. Mean values were used to ease the computational load, since median values are more time-consuming to compute, and mean values are considered sufficient to provide acceptable input data at this level of spatial generalization. To update the spatial database from the hillslope models, as they evolve, three strategies have been tried, each with some positive features, although none is fully satisfactory (Kirkby 1999). One proposal is to transfer changes in each variable on the basis of common values of area drained per
216
Mediterranean Desertification
unit contour length (referred to as unit area below); a second is to use the wetness index (the ratio of unit area to local gradient); and a third is to use elevation. There are theoretical reasons for preferring each of these in certain ideal circumstances, but all tend to change steadily down-catchment, so the differences between them are not large in relation to the errors except over periods longer than those for which MEDRUSH has been designed.
3
THE VEGETATION GROWTH MODEL
The model simulates processes of primary productivity and evapotranspiration in stands of vegetation, and focuses on vegetation functions that are likely to be involved in mediating responses to atmospheric and climate change (see Osborne et al. 2000). In particular, responses of canopy gas exchange to rising CO2 are considered important, and are explored elsewhere in this volume (Chapter 3). A generic vegetation model is applied to groups of plant species by using a different set of key model input variables for each. 3.1
Plant Functional Types
The use of plant functional types in landscape-scale models is preferable to modelling at the species level, because of the reduced complexity required and the scarcity of data available for most individual species (Smith et al. 1993). Four functional types are currently simulated: evergreen sclerophyllous shrubs, drought-deciduous shrubs, perennial tussock grasses and winter annual grasses. Functional types are defined according to their strategies for surviving summer drought. Sclerophyllous shrubs have tough, evergreen leaves, which have adaptations for minimizing water loss and damage due to high temperatures, and remain physiologically active throughout the summer drought period (Table 16.1). In contrast with the other functional types, they tend to be deep-rooted and many reach depths of over 5 m, often allowing access to water throughout the summer (Specht 1988; Table 16.1). Despite this, they tend to conserve water through stomatal closure, and can remain physiologically active at low soil water potentials (Archibold 1995; Table 16.1). In contrast, drought-deciduous shrubs “avoid” the summer drought, becoming dormant after leaf abscission at Table 16.1 Comparison between Mediterranean plant functional types that are simulated by the MEDRUSH vegetation model
Life history Leaf phenology Plant life span Drought adaptations Rooting depth Wilting point Growth during drought? Primary productivity Photosynthetic rate Respiration rate Storage capacity
Evergreen sclerophyllous shrub (e.g. Pistacea lentiscus)
Droughtdeciduous shrub (e.g. Anthyllis cytisoides)
Perennial tussock grass (e.g. Stipa tenacissima)
Winter annual grass (e.g. Vulpia ciliata)
Evergreen
Deciduous
Deciduous
Perennial
Perennial
Facultatively deciduous Perennial
Deep Low Yes
Shallow High No
Shallow Low Opportunistic
Shallow High No
Low Low High
High Low High
Low Low Moderate
High High Low
Annual
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the beginning of the summer, which may be triggered by low soil water potential or phenological cues such as day length (Margaris 1975; Smith et al. 1997; Table 16.1). Leaves tend to be intolerant of drought and physiological activity ceases at a relatively high water potential (Clark et al. 1998; Table 16.1). The leaves of perennial tussock grasses persist and growth continues throughout the summer period, provided that sufficient water is available (Pugnaire and Haase 1996; Pugnaire et al. 1996; Table 16.1). In common with sclerophyllous shrubs, stomatal closure and morphological adaptations of leaves tend to minimize water loss in transpiration and damage due to high irradiance, and tussock grasses can remain physiologically active at low soil water potentials (Pugnaire and Haase 1996; Pugnaire et al. 1996; Table 16.1). Winter annual grasses avoid drought by completing their life-cycle before or at the start of the summer dry season (Clark et al. 1998; Table 16.1). While their productivity during the wet season is high, drought resistance is low, and physiological activity ceases at a relatively high soil water potential (Table 16.1). 3.2 Model Functions
Model functions are summarized below and in Figure 16.4, and are described in detail by Osborne et al. (2000). Further applications of this model are presented elsewhere (Woodward and Osborne 2000; Osborne and Woodward 2001). The vegetation model requires only climate data, CO2 concentration and soil water potential as inputs, and predicts biomass, net primary productivity (NPP), leaf area index (LAI), evapotranspiration and litter production. Canopy photosynthesis provides carbohydrate for growth, and is calculated as a linear function of absorbed solar radiation (Figure 16.4; Monteith 1972; Haxeltine and Prentice 1996). Photosynthetic rate varies in response to atmospheric CO2 concentration, air temperature and soil water potential. Increases in photosynthesis which occur at high CO2 concentration interact with solar radiation and temperature, according to functions that were developed using a biochemical model of canopy photosynthesis (Wilks et al. 1995). Respiration consumes carbohydrate, and is partitioned between maintenance and growth processes, the former being dependent on temperature (Figure 16.4;
CO2
Photosynthesis a
CH2O
Maintenance b respiration
CO2
Storage
Biomass
Growth NPP
Rain H2O
Soil H2O
Evapotranspiration c
Litter + Fruit
H2O Vapour
Figure 16.4 Overview of vegetation model processes. Flow of matter is shown by the solid arrows; pools of matter are highlighted in bold boxes (CH2 O = simple carbohydrates); and model processes are enclosed by normal boxes. Atmospheric CO2 , climate and soil influence model processes through (a) the response of canopy photosynthesis to atmospheric CO2 concentration, temperature and soil water potential; (b) the temperature-dependence of maintenance respiration; (c) changes in evapotranspiration via effects of air temperature, vapour pressure deficit (VPD) and soil water availability, and the response of canopy stomatal conductance to CO2 concentration, temperature, VPD and soil water potential. Productivity and evapotranspiration models interact via soil water potential (dashed arrow)
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Thornley 1970). Carbohydrate is partitioned between storage and new growth (NPP; Figure 16.4) of leaves, woody stems, woody roots, fine roots and reproductive tissues in fixed proportions, which vary according to phenology. Winter annuals have no storage capacity, and neither grass functional type has woody tissues. Three phenological stages are distinguished for each functional type: a period of vegetative growth, in the winter or spring, when a large proportion of canopy and root growth occurs; reproductive growth, during the late spring or summer, when secondary thickening of wood also occurs in shrub functional types; and a period of dormancy, in the autumn and winter for sclerophyllous shrubs, and the summer for drought-deciduous shrubs and winter annuals. There is no dormancy in tussock grasses because of the opportunistic nature of growth. Rates of abscission and the death of organs are calculated using coefficients that relate to longevity and vary with phenological stage. However, litter production increases when storage is low and starvation of tissues occurs. Evapotranspiration is calculated using a modified version of the Penman–Monteith equation, which was developed for sparse canopies, and partitions water loss between canopy and soil surfaces according to net radiation at each (Shuttleworth and Wallace 1985; Shuttleworth and Gurney 1989). Canopy stomatal conductance is estimated following the approach of Jarvis (1976), and varies in response to atmospheric CO2 concentration, air temperature, soil water potential and vapour pressure deficit. Stomatal closure in response to CO2 concentration is assumed to be linear, following the general response of C3 plants (Morison 1985; although see Osborne and Woodward, Chapter 3 in this volume). The evapotranspiration model interacts with the vegetation productivity model via its effect on soil water potential (Figure 16.4), and influences the distribution of soil water, modifying infiltration, subsurface flow and other physical processes within MEDRUSH. 3.3
Vegetation Model Testing
The model has been tested for sites throughout the Mediterranean Basin, using the method of Mitchell (1997) and Mitchell and Sheehy (1997), where the deviation of model predictions from observations is compared with a standard that is set using independent criteria, and in advance of the comparison. Simulations of biomass, NPP and LAI were tested using published observations for Mediterranean sclerophyllous shrub vegetation at 18 sites (Figure 16.5). Observations were summarized as a mean for each site, and simulations were carried out for each using mean climate data from a nearby meteorological station (M¨uller 1982). The model was run to equilibrium using a daily timestep for primary productivity and an hourly timestep for evapotranspiration. The precision of model predictions was assessed by comparison with an estimate of the 95% confidence interval for observations. In ten observations of biomass made at two different sites in the Mediterranean, the 95% confidence limits were, on average, ±43% of the mean, varying between ±25% and ±75% (Trabaud 1991; Puigdef´abregas et al. 1996). The confidence interval tended to increase on a relative basis when biomass was less than 200 g m−2 . We therefore estimated confidence limits to be approximately ±50% of the mean value for biomass, NPP and LAI. Confidence limits for values of biomass and NPP less than 200 g m−2 were estimated as ±100 g m−2 , and for LAI less than 1.0, estimated as 0.5 m2 m−2 . Eight out of 14 predictions of biomass (57%), 4 out of 5 predictions of NPP (80%) and 10 out of 13 predictions of LAI (77%) were within our estimate of the 95% confidence interval for observations (Figure 16.6). Model predictions tended to be closest to observations for sites in the western Mediterranean Basin, in Portugal, Spain and France, and furthest from observations for sites in the eastern Mediterranean Basin, in Italy and Greece (Figure 16.5). Results also suggested a negative bias in model predictions at the most productive sites, many of which were located in the eastern Mediterranean (Figure 16.6). However, model predictions tended to be too high for the Rambla Honda site in south-east Spain, where productivity was very low (Figure 16.6). Success of the vegetation model in predicting biomass, NPP and LAI of sclerophyllous shrubs therefore varied between sites, but was generally good, especially for the western Mediterranean, where nearly 90% of observations (n = 9 for biomass and LAI) were predicted within the estimate of their 95% confidence limits.
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(a)
Error (kg m−2)
(b)
2
Biomass
0 −2 −4 −6
0
2
4
6 −2)
(c)
Error (g m−2 year −1)
Observation (kg m 300 NPP 100 −100 −300
0
100
200
300 −2
Observation (g m (d)
400
500
year−1)
Error (m2 m−2)
4 LAI
2 0 −2 −4
0
1 2 3 Observation (m2 m−2)
4
Figure 16.5 (a) Locations of sites within the Mediterranean Basin used to test the vegetation model. Simulated values of (b) above-ground biomass, (c) above-ground net primary productivity (NPP) and (d) leaf area index (LAI) for Mediterranean sclerophyllous vegetation at each site were compared with published observations (Sources: Specht 1969; Lossaint 1973; Rapp and Lossaint 1981; Catarino et al. 1981; Rambal and Leterme 1987; Malanson and Trabaud 1988; Specht 1988; Tsiouvaras 1988; Merino et al. 1990; Trabaud 1991; Valentini et al. 1991; Arianoutsou and Paraskevopoulos et al. 1992; Pitacco et al. 1992; Diamantopoulos et al. 1993; ´ Harrison et al. 1993; Rambal 1993; Paraskevopoulos et al. 1994; Puigdefabregas et al. 1996; ´ Lopez-Berm udez et al. 1996; Rambal et al. 1996; Scarascia-Mugnozza et al. 1996) ´
4 THE CHANNEL ROUTING COMPONENT In the model the basin is divided into sub-basins of varying size and shape but typically larger than 5 km2 . Connections between sub-basins are provided by the river links or reaches and each reach accepts lateral inputs from the hillslope component (i.e. overland, subsurface baseflow and tributary
Mediterranean Desertification Biomass
2000 0 −2000 −4000 −6000
0
2000 4000 6000 Observed Biomass (g m−2)
300 NPP
200 100 0 −100 −200 −300
Error (Observed-Simulated)
Error (Observed-Simulated)
Error (Observed-Simulated)
220
0
100 200 300 400 Observed NPP (g m−2 y−1)
500
4 LAI 2 0 −2 −4
0
1
2 3 4 Observed LAI (m2 m−2)
5
Figure 16.6 Error in model simulations, calculated as the difference between model predictions and observations, for: above-ground biomass (kg m−2 ); above-ground NPP (g m−2 year−1 ); LAI (m2 m−2 ). Positive errors indicate an overestimation, and negative errors an underestimation, compared with observations. Dotted lines delimit an estimate of the 95% confidence limits for observations (see text for explanation). Symbols distinguish sites located in different countries (see map above)
flows) and inputs from upstream. The channel component was required to route water and sediment (by size fraction) along the channel network from the sub-basins to the basin outlet on the main river network and to simulate discharge at the outlet and at any point along the network. 4.1
Water Flow Routing
The routing scheme is required to be fast, to be computationally simple and to deliver a distributed output. The first two requirements are satisfied by using linear transfer functions derived from analytical solutions of the convection–diffusion equation. In connection with the third, two routing modes
MEDRUSH: A Basin-Scale Physically Based Model
Cascade mode
221
Direct mode
Figure 16.7 Cascade and direct routing modes for the MEDRUSH channel flow component, illustrated for a system of channel reaches
have been developed (Figure 16.7): a cascade system, routing from reach to reach and providing a spatially distributed output; and a direct, superposition scheme in which the discharge at each link is routed directly to the outlet. The direct scheme is faster and simpler than the cascade scheme but provides discharge at the outlet only and does not therefore allow sediment transport modelling.
Derivation of Transfer Functions The transfer functions describe the characteristic time of water flow through each reach. A parcel or impulse of water enters the head of the reach and the function provides the percentages of the total parcel which arrive at the reach outlet in given time intervals, e.g. 0% in hour 1, 10% in hour 2, 45% in hour 3. A range of functions, different for each reach, are required to allow for spatial and temporal variation in the routing time at the reach scale. A linear solution of the convection–diffusion approximation to the Saint Venant equations ∂Q ∂ 2Q ∂Q +C − D 2 = Cq ∂t ∂x ∂x
(29)
is used, where Q is channel discharge; C is a convection or celerity coefficient; D is a diffusion coefficient; q is lateral discharge per unit distance; t is time; and x is distance along the channel. Analytical solutions to impulse (parcel) inputs to a river reach can be found for two cases: upstream point input and uniformly distributed lateral input. Integration of these impulse responses provides pulse responses, equivalent to transfer functions. The discharge out of the reach is then given by Qout (t) =
M 2 j =1 i=1
H (j, i)Qin (j, t − i + 1)
(30)
where Qout is discharge out of the reach; Qin is discharge into the reach; H is the transfer function; i is the time index of the transfer function; j is input type (lateral or point); and M is memory (length) of the transfer function.
Parametrization Celerity, C, and diffusivity, D, are related to the discharge and channel characteristics by C=
1 dQ B dy
(31)
D=
Q 2BSf
(32)
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where Q is discharge; B is channel width; Sf is friction slope; and y is depth. Using the Manning or Ch´ezy resistance relationship, it can be shown that the celerity varies as αU , where U is flow velocity and 1.5 < α < 1.666 depending on channel geometry and the resistance relationship used. To linearize equation (29), C must be constant. Velocity–discharge (U –Q) curves are therefore discretized for each reach to define ranges through which C may be considered constant. A transfer function is then calculated for each value of C (corresponding to a U –Q range). The transfer function to be used in the simulation changes when the discharge into the reach changes to a different U –Q range. Ten U –Q ranges are discretized at each reach to ensure smooth transitions between functions.
Calculations of C and D for Cascade Routing To calculate celerity and diffusivity at each reach, a number of parameters are required, determined as follows: 1. characteristic discharges, including the mean annual flood, estimated by interpolation and drainage area weighting; 2. channel bankfull width, calculated using the between-site equation of hydraulic geometry for width (Leopold and Maddock 1953); 3. friction slope, Sf , approximated by the mean channel bed slope over the reach, calculated from a digital elevation model (DEM) of the basin; 4. the Manning resistance coefficient, n, determined from formulae or tables. The mean flow velocity, U , is calculated for the 10 discharge ranges using the Manning relationship, with the simplifying assumption of a rectangular channel. C and D are then calculated from equations (31) and (32). Parametrization of the channel routing component requires a digitized river network, normally to be obtained using network node information (position coordinates and elevation) supplied from the automated subcatchment division program operating on the catchment DEM (see section 2.7). The channel transfer functions are then calculated from values of C and D derived from the network node spacings, mean channel slopes and estimated channel dimensions. 4.2
Channel–Aquifer Interaction Groundwater effects are represented by a “bank storage element” which exchanges water with the channel according to the relative head difference. The bank element has the area L × W and contains groundwater in a matrix of porosity θ with head ha (see Figure 16.8 for an explanation of terms). Flow between the channel and the bank element is specified by the relative heads and a user-defined transmissivity. The exchange discharge Qb is calculated as
Qb = k(hc − ha )
(positive out of the channel)
Qb ≤ 0 if d = 0
(limited by available water)
(33)
and ha changes through each timestep as ha1 = ha2 + Qb θ dt/Lw
(34)
where ha1 and ha2 are the values at the end and start of the timestep respectively. The discharge Qb is then supplied to the channel routing component at the next timestep as a lateral input (which may be positive or negative) and routed downstream. 4.3
Sediment Routing
The sediment routing scheme runs in step with the flow routing scheme, to solve the equation for conservation of sediment mass. An upwind difference scheme was developed, operating on the
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Qb d
zb
hc
w w = width of bank element (m) (user defined) L = length of channel element (m) (user defined) Qc = channel discharge (m3s−1) (routing model) zb = bed elevation (m) (user defined) ha = aquifer head (m) (calculated) hc = channel head (m) (calculated = d + zb) d = channel water depth (m) (calculated from Qc by a stage-discharge relationship) k = transmissivity (m2s−1) (user defined) Qb = exchange discharge
Figure 16.8
L
ha
Qc
Schematic diagram of the MEDRUSH channel–aquifer interaction model
channel reaches in descending order of elevation. An adaptive time weighting has been used, dependent on the Courant number at each reach and timestep. This ensures stability, whilst minimizing numerical dispersion, which is already present owing to the water flow transfer function method. For each sediment size fraction and each reach, a semi-implicit finite-difference mass balance gives An+1 Lcn+1 − An Lcn = Qnup + Qnl − An Vsn cn+σ 4t
(35)
where A is flow cross-sectional area; L is reach length; c is volumetric concentration of sediment transport; Qup is volumetric rate of upstream input of sediment; Ql is net volumetric rate of input from sources (such as bank erosion, overland flow, infiltration into the bed); Vs is sediment particle velocity; 4t is timestep; σ is a time weighting factor (σ = 0 gives an explicit scheme, σ = 1 gives an implicit scheme); and n indicates the time level. The last group of terms on the right-hand side represents the rate at which sediment leaves the bottom end of the reach. The scheme allows for two particle sizes, fine and coarse. The fine fraction moves at the water velocity, while the coarse fraction moves more slowly. Transport is limited by a capacity rate; excess sediment falls to the bed, and may be re-suspended if discharge increases. The transport capacities and coarse sediment velocity are pre-computed in order to reduce program running time, and are held in a look-up table referenced by the channel discharge. 4.4 Verification of Routing Schemes
Channel Routing The channel routing component is designed to accept lateral inputs from each MEDRUSH subbasin and to route these inputs along the channel network. A full test of the component therefore requires data on the inputs as well as the corresponding discharges along the channel. However, measurements of lateral inputs along an entire network are not generally available for large basins. The component (not including the channel–aquifer interaction model) was therefore tested using artificial data and data generated by other models.
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Good agreement was obtained in comparisons of the analytical channel routing solution with the numerical solution of the MIKE11 hydraulic software package (DHI 1989) for an idealized single reach case. Good agreement was also obtained between MEDRUSH and SHETRAN discharge simulations for the 300-km2 upper South Tyne catchment in England, using lateral input data generated by SHETRAN. Application of the routing model to the channel network of the Agri target basin in southern Italy, using artificial input data and a range of input conditions, likewise produced satisfactory discharge simulations, with a physically reasonably level of dispersion. (For a description of SHETRAN and the Agri Basin, see Chapter 29.) The tests with the Tyne and Agri channel networks showed the model to behave as expected as the values of the routing parameters C and D vary. Crucially, reasonable values of C may be derived from estimates of the water flow velocity and measurable channel properties. However, there is a need for field data to provide a basis for checking such parametrization. Values of D are more difficult to derive but the sensitivity of the scheme to D is low. The transfer function approach has also proved to be robust and very fast. For example, a one-year simulation for the full Agri channel network was completed in less than one minute on a SUNSparc20 workstation.
Sediment Routing The sediment routing scheme was tested on the Agri channel network using artificial inputs designed to invoke the full range of possible conditions in the model. Sediment pulses were satisfactorily routed and mass conservation was observed exactly. In general, the sediment routing scheme is of similar robustness and speed to the flow routing scheme. Its main limitation is the dispersion introduced by considering each channel reach as a computational element for the finite-difference scheme. The scheme assumes immediate mixing of inputs at a reach, which is more realistic for short rather than long reaches. Channel reaches should therefore be less than about 5 km in length for the false dispersion introduced to become a second-order error.
5
IMPLEMENTATION
MEDRUSH has been implemented in C++, within the GRASS GIS, using Borland Turbo C++ compilers on Windows and UNIX platforms. Some features are only available within UNIX. A detailed manual, describing parameter requirements, set-up procedures and program modules, is available through the MEDALUS website, at http://www.medalus.demon.co.uk.
6 1.
CONCLUSIONS
The MEDRUSH model contains many innovative features, which have been integrated through collaboration between modelling groups specializing on hillslope runoff processes, eco-physiology and channel hydrology. 2. The sub-basin model contains a flow-strip model for water and sediment routing which provides strong interactions with soil and vegetation as they change over time, both seasonally and in the longer term. This is a critical component to allow forecasting in a global change context. The representative flow-strip concept is acceptable for distributing short-term changes, but would not be satisfactory for longer term (>100 year) forecasts. 3. Simulations of vegetation productivity and canopy size showed good agreement with observations made throughout the Mediterranean region, giving confidence in these key model variables. The vegetation model is grounded firmly in plant eco-physiology, and therefore provides a mechanistic basis for plant sensitivity to climate, soil properties and atmospheric CO2 . However, it remains computationally straightforward, thereby allowing the relatively rapid simulation of many vegetation patches for long time intervals. 4. The channel water flow and sediment transport routing component was subjected to a thorough verification programme. Comparison of test simulations with alternative model solutions
MEDRUSH: A Basin-Scale Physically Based Model
5.
225
and with expected mathematical performance showed excellent agreement. Through its analytical solution the component is fast, robust and flexible, it retains a firm physical basis and it incorporates an innovative approach to flow routing. Verification of model performance to date has primarily been at the level of individual components, through a clear physical understanding of each component process. Attempts to validate the model for large catchments have proved impracticable, and further development of the model is likely to concentrate on smaller catchments, and consequently with timesteps shorter than the 1-hour increments currently used. These methods are being applied to 10–150 km2 subcatchments, in both the Agri and Guadalent´ın (south-east Spain) catchments.
ACKNOWLEDGEMENTS The following contributed significantly to the development of the MEDRUSH channel component: Dr John Ewen (University of Newcastle upon Tyne), Dr Pascal Lardet and Douglas Clark (both former members of WRSRL, University of Newcastle upon Tyne). Most of the work reported here was funded within the MEDALUS II project, by the European Commission under its Environment Programme, contract numbers EV5V-CT92-0128/0164/0165 and 0166, and this support is gratefully acknowledged.
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Kirkby MJ (1999) Translating models from hillslope (1 ha) to catchment (1000 km2 ) scales. In B Diekkr¨uger, MJ Kirkby and U Schr¨oder (eds) Regionalization in Hydrology. IAHS Publication 254, pp. 1–12. Kirkby MJ, Baird AJ, Lockwood JG, McMahon MD, Mitchell PJ, Shao J, Sheehy JE, Thornes JB and Woodward FI (1993) MEDALUS Final report. Kirkby MJ, Baird AJ, Diamond SM, Lockwood JG, McMahon ML, Mitchell PJ, Shao J, Sheehy JE, Thornes JB and Woodward FI (1996) The MEDALUS slope catena model: a physically based process model for hydrology, ecology and land degradation interactions. In JB Thornes and J Brandt (eds) Mediterranean Desertification and Land Use. John Wiley, Chichester, pp. 303–354. Leopold LB and Maddock T, Jr. (1953) The hydraulic geometry of stream channels and some physiographic implications. US Geological Survey Professional Paper 252, US Government Printing Office, Washington, DC. L´opez-Berm´udez F, Romero D´ıaz A and Mart´ınez-Fern´andez J (1996) The El Ardal field site: soil and vegetation cover. In CJ Brandt and JB Thornes (eds) Mediterranean Desertification and Land Use. John Wiley, Chichester, pp. 169–188. Lossaint P (1973) Soil–vegetation relationships in Mediterranean ecosystems of Southern France. In F di Castri and HA Mooney (eds) Mediterranean Type Ecosystems Origin and Structure, Chapman & Hall, London, pp. 199–210. Malanson GP and Trabaud L (1988) Vigour of post-fire resprouting by Quercus coccifera L. Journal of Ecology 76, 351–365. Margaris NS (1975) Effect of photoperiod on seasonal dimorphism of some Mediterranean plants. Berichte der Schweizerenischen Botanischen Gessellschaft 85, 96–102. Merino O, Martin MP, Martin A and Merino J (1990) Successional and temporal changes in primary productivity in two mediterranean scrub ecosystems. Acta Oecologia 11, 103–112. Mitchell PL (1997) Misuse of regression for empirical validation of models. Agricultural Systems 54, 313–326. Mitchell PL and Sheehy JE (1997) Comparison of predictions and observations to assess model performance: a method of empirical validation. Applications of Systems Approaches at the Field Level. Volume 2. Proceedings of the Second Annual Symposium on Systems Approaches for Agricultural Development, held at IRRI, Los Ba˜nos, Philippines, 6–8 December 1995 (eds Kropff MJ, Teng PS, Aggarwal PK, Bouma J, Bouman BAM, Jones JW, Van Laar HH), Kluwer Academic, Dordrecht, pp. 437–451. Monteith JL (1972) Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology 9, 747–766. Morison JIL (1985) Sensitivity of stomata and water use efficiency to high CO2 . Plant, Cell and Environment 8, 467–474. M¨uller MJ (1982) Selected Climatic Data for a Global Set of Standard Stations for Vegetation Science. Dr W. Junk Publishers, The Hague. Osborne CP and Woodward FI (2001) Biological mechanisms underlying recent increases in the NDVI of Mediterranean shrublands. International Journal of Remote Sensing 22, 1895–1907. Osborne CP, Mitchell PL, Sheehy JE and Woodward FI (2000) Modelling the recent historical impacts of atmospheric CO2 and climate change on Mediterranean vegetation. Global Change Biology 6, 445–458. Paraskevopoulos SP, Iatrou GD and Pantis JD (1994) Plant growth strategies in evergreen-sclerophyllous shrublands (Maquis) in central Greece. Vegetatio 115, 109–114. Pitacco A, Gallinaro N and Giulivo C (1992) Evaluation of the actual evapotranspiration of a Quercus ilex L. stand by the Bowen ratio–Energy Budget method. Vegetatio 99–100, 163–168. Pugnaire FI and Haase P (1996) Comparative phenology and growth of two perennial tussock grass species in a semi-arid environment. Annals of Botany 77, 81–86. Pugnaire FI, Haase P, Incoll LD and Clark SC (1996) Response of the tussock grass Stipa tenacissima to watering in a semi-arid environment. Functional Ecology 10, 265–274. Puigdef´abregas J, Alonso JM, Delgado L, Domingo F, Cueto M, Guti´errez L, L´azaro R, Nicolau JM, S´anchez G, Sol´e A, Videl S, Aguilera C, Brenner A, Clark SC and Incoll LD (1996) The Rambla Honda field site: interactions of soil and vegetation along a catena in semi-arid southeast Spain. In CJ Brandt and JB Thornes (eds) Mediterranean Desertification and Land Use. John Wiley, Chichester, pp. 137–168. Quinn P, Beven KJ, Chevallier P and Planchon O (1991) The prediction of hillslope flow paths for distributed hydrological modelling using digital terrain modes. Hydrological Processes 5, 59–79. Rambal S (1993) The differential role of mechanisms for drought resistance in a Mediterranean evergreen shrub – a simulation approach. Plant, Cell and Environment 16, 35. Rambal S and Leterme J (1987) Changes in aboveground structure and resistances to water uptake in Quercus coccifera along a rainfall gradient. In JD Tenhunen, FM Catarino, OL Lange and WC Oechel (eds) Plant Response to Stress. Functional Analysis in Mediterranean Ecosystems. Springer-Verlag, Berlin and Heidelberg, pp. 191–200.
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Rambal S, Damesin C, Joffre R, M´ethy M and Lo Seen D (1996) Optimization of carbon gain in canopies of Mediterranean evergreen oaks. Annales des Sciences Foresti`eres 53, 547–560. Rapp M and Lossaint P (1981) Some aspects of mineral cycling in the garrigue of southern France. In F di Castri, DW Goodall and RL Specht (eds) Ecosystems of the World 11. Mediterranean-type Shrublands. Elsevier Scientific, Amsterdam, pp. 289–301. Scarascia-Mugnozza GE, De Angelis P, Matteucci G and Valentini R (1996) Long-term exposure to elevated [CO2 ] in a natural Quercus ilex L. community: net photosynthesis and photochemical efficiency of PS II at different levels of water stress. Plant, Cell and Environment 19, 643–654. Shuttleworth WJ and Gurney RJ (1989) The theoretical relationship between foliage temperature and canopy resistance in sparse crops. Quarterly Journal of the Royal Meteorological Society 116, 497–519. Shuttleworth WJ and Wallace JS (1985) Evaporation from sparse crops – an energy combination theory. Quarterly Journal of the Royal Meteorological Society 111, 839–855. Smith SD, Monson RK and Anderson JE (1997) Physiological Ecology of North American Desert Plants. Springer-Verlag, Berlin and Heidelberg. Smith TM, Shugart HH, Woodward FI and Burton PJ (1993) Plant functional types. In AM Solomon and HH Shugart (eds) Vegetation Dynamics and Global Change. Chapman & Hall, London, pp. 272–292. Smith TR and Bretherton FP (1972) Stability and the conservation of mass in drainage basin evolution. Water Resources Research 8(6), 1506–1529. Specht RL (1969) A comparison of the sclerophyllous vegetation characteristic of mediterranean type climates in France, California, and southern Australia. II. Dry matter, energy, and nutrient accumulation. Australian Journal of Botany 17, 293–308. Specht RL (1988) Mediterranean-Type Ecosystems: A Data Source Book. Kluwer Academic, Dordrecht. Thornley JHM (1970) Respiration, growth and maintenance in plants. Nature 227, 304–305. Trabaud L (1991) Fire regimes and phytomass growth dynamics in a Quercus coccifera garrigue. Journal of Vegetation Science 2, 307–314. Tsiouvaras CN (1988) Long-term effects of clipping on production and vigor of Kermes Oak (Quercus coccifera). Forest Ecology and Management 24, 159–166. Valentini R, Scarascia-Mugnozza GE, De Angelis P and Bimbi R (1991) An experimental test of the eddy correlation technique over a Mediterranean macchia canopy. Plant, Cell and Environment 14, 987–994. Wilks DS, Wolfe DW and Riha SJ (1995) Simple carbon assimilation response functions from atmospheric CO2 , and daily temperature and shortwave radiation. Global Change Biology 1, 337–346. Woodward FI and Osborne CP (2000) The representation of root processes in models addressing the responses of vegetation to global change. New Phytologist 147, 223–232. Yair A and Lavee H (1982) Factors affecting the spatial variability of runoff generation over arid hillslopes, southern Israel. Israel Journal of Earth Sciences 31, 133–143.
PART 2
REGIONAL STUDIES
Section VI
The Guadalent´ın Basin, South-east Spain
17
Natural Resources in the Guadalent´ın Basin (South-east Spain): Water as a Key Factor
1 ´ ´ ´ 2 F. ALONSO-SARRIA ´ 1 AND F. LOPEZ-BERM UDEZ, G.G. BARBERA, 1 F. BELMONTE SERRATO
1 2
Laboratorio de Geomorfolog´ıa, Universidad de Murcia, Spain CEBAS-CSIC, Campus Universitario de Espinardo, Murcia, Spain
1 INTRODUCTION The Guadalent´ın Basin is located in south-eastern Spain, and covers an area of 3300 km2 (see Plate 2 in the colour plate section). The climate is semi-arid, this being one of the driest areas of Europe, with high inter-annual variability in rainfall. The Guadalent´ın River has an extremely irregular flow, which can change within hours from a dry channel to catastrophic floods. The relief is variable: there are two wide plains surrounded by mountains reaching 500–2100 m. Natural vegetation is severely limited by climate, and most of the semi-natural ecosystems are shrublands of diverse types, although in the mountains there are Pinus halepensis forests. Desertification is a complex set of processes that results in degradation of the land, with a loss of productive value. Much attention has been paid to the local causes and effects of these processes, such as deforestation, overgrazing and soil erosion. There are also off-site effects of these primary processes, such as changes in the hydrological dynamics of channels, floods and sedimentation. However, less attention has been paid to the global relationship between the development of socioeconomic systems and the progress of desertification. In this chapter such a relationship is studied in the Guadalent´ın Basin, by attempting to synthesize some relevant aspects of environmental conditions, the constrictions imposed on the historic development of socio-economic systems, and how both aspects affect the natural resources of the basin. The relationship between the human society and its environment is reflected in the land use system. Here we introduce the ways in which this system has been modelled through time, and the key factors in these processes. The objective has been to isolate the most relevant aspects in order to characterize the main problems associated with desertification in the basin. Socio-economic systems and natural systems have strong links that interact with and influence each other. The way in which socio-economic systems have evolved has been closely related to environmental factors, but also according to changing politics and technology. Historical changes in land use in this basin are discussed in relation to climate and the exploitation of water resources.
2 THE ADVERSE CLIMATIC CONDITIONS OF A SEMI-ARID BASIN In the Guadalent´ın Basin aridity is constant, with less than 300 mm of annual rainfall and 900–1000 mm of potential evapotranspiration (PET) throughout most of the territory. The extreme variability in rainfall is characterized by long periods of drought and sudden extreme torrential precipitation events causing soil erosion. Mediterranean Desertification: A Mosaic of Processes and Responses. Edited by N.A. Geeson, C.J. Brandt and J.B. Thornes 2002 John Wiley & Sons, Ltd
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Mediterranean Desertification
In spite of this generally semi-arid environment, mostly with an annual deficit of more than 300 mm, the Guadalent´ın Basin, like many of the larger Mediterranean semi-arid basins, has a sub-humid sector at higher altitude which provides most of the water supply for the basin. With regard to this variability, the Guadalent´ın Basin can be divided into three sectors (L´opez Berm´udez et al. 1998), which are outlined in Figure 17.1. • • •
The western sub-humid sector in the highest areas of Sierra de Mar´ıa (2000 m a.s.l.) has a positive water balance (negative hydraulic deficit). This area covers 0.4% of the territory. The higher relief of the northern sector (Sierra Espu˜na and Sierra del Cambr´on, 1400–1500 m a.s.l.) and the upper part of the basin upstream of Puentes Reservoir support a semi-arid sector with hydraulic deficits lower than 300 mm year−1 . These areas cover 16.8% of the territory. The largest sector is arid, with annual hydraulic deficits exceeding 300 mm. This sector includes the eastern and south-eastern sectors and most of the valley floor, covering 82.8% of the territory.
The temporal variability of rainfall totals is extreme. This variability produces a characteristic alternation between humid and dry periods that is best studied through a long time series, such as the Murcia 1862–1997 series (Figure 17.2). Drought periods can be considered as climatic hazards
4200
Deficit > 300 mm year −1
4190 Deficit < 300 mm year −1
4180 4170
Positive balance
4160 560 570 580 590 600 610 620 630 640 650 660
Figure 17.1 Water balance in the Guadalent´ın Basin. The scales on the axes are UTM (Universe Transverse Mercator) coordinates
800
Rainfall (mm)
600
400
200
0 1860 1870 1880
1890
1900
1910
1920
1930 1940
1950
1960 1970
1980
1990
Year Annual rainfall
Moving average (5 yr)
Trend
Figure 17.2 Annual precipitation for Murcia (1862–1997), showing the decreasing trend
235
Natural Resources in the Guadalent´ın Basin Table 17.1 Basic climatic characteristics of the Guadalent´ın Basin
Station Alcantarilla Aledo Alhama de Murcia Do˜na In´es Librilla Lorca Puerto Lumbreras Puentes Totana Zarzilla de Ramos Zarzadilla de Totana Mar´ıa Topares V´elez Rubio
H
YP
D
Tav
Tmax
Tmin
PET
72 620 760 786 168 335 465 450 225 652 861 1200 1192 838
321 528 448 329 343 261 295 265 259 550 359 391 408 391
69 41 45 26 50 38 46 36 67 31 23 67 30 43
17.3 14.5 15.0 14.1 18.5 18.1 17.2 17.8 17.3 16.1 17.7 11.4 12.5 13.5
23.9 19.7 19.7 21.0 25.0 24.5 24.4 24.7 22.7 24.2 22.9 17.1 16.8 19.3
10.7 10.2 10.2 7.2 12.0 11.8 10.0 10.9 11.9 8.0 12.5 5.8 8.2 7.7
904 795 795 774 981 837 899 942 894 843 932 669 713 728
H , height (m a.s.l.); Y P , annual precipitation (mm); D, precipitation days; Tav , average temperature (◦ C); Tmax , average maximum temperature (◦ C); Tmin , average minimum temperature (◦ C); PET, potential evapotranspiration (mm). After Garc´ıa de Pedraza and Reija Garrido (1994). with very fuzzy spatio-temporal limits, because of (a) their inherent variability and (b) the subjectivity of their evaluation. In this century, a large number of drought periods have occurred. The most remarkable were those of 1911–1913, 1925–1928, 1934–1941, 1944–1945, 1952–1953, 1955–1956, 1963–1964, 1978–1979, 1981–1984 and 1993–1995. There has been a clear trend of decreasing annual rainfall during the period monitored. Table 17.1 provides temperature and rainfall information for the main weather stations in the Guadalent´ın. Data were available to estimate average precipitation volume per day. However, daily precipitation has a far from Gaussian distribution, and it is better fitted to a General Extreme Values distribution (Alonso-Sarr´ıa 1995). This means that a high percentage of the yearly rainfall falls during a few very intense events. These high-intensity rainfall events can occur in either humid or dry periods. The main cause of a drought period is a low circulation index for the middle troposphere, causing the eruption of low pressure cells in the Mediterranean that result in high-intensity rainfall events. Alternatively, local breeze and valley winds, originating due to land–sea pressure gradients, favour the formation of warm and humid air masses that can rise as they reach higher relief, and release precipitation (Mill´an and Estrela 1994). The combination of these processes produces high-intensity rainfall events. With these climatic conditions, and taking into account the relief characteristics of the Guadalent´ın Basin, the hydrology is dominated by channels that are usually dry, but experiencing episodic flash floods. The specific drainage network of the Guadalent´ın Basin has a considerable influence on the origin and paths of extreme flash floods due to its morpho-structural configuration, with high relief and steep slopes, in the tributary sub-basins and a flat and subsident valley floor. A high number of flash flood events have been recorded at the Puentes Reservoir (located in the centre of the Guadalent´ın River headwaters). The most severe incidents in the last 200 years were those of 1802, 1830, 1831, 1838, 1846, 1860, 1943, 1948, 1973 and 1982. In the 20th century, 23 significant flood events were recorded in the Guadalent´ın Basin.
3 IRRIGATION AS AN EARLY RESOURCE TO OVERCOME ARIDITY The combination of low precipitation, high potential evapotranspiration, and the infrequency of rainfall events, seriously constrains the ecosystem productivity in the Guadalent´ın Basin. It is clear
236
Mediterranean Desertification
that even under undisturbed conditions, primary production is naturally low, limiting the development of the most structurally complex terrestrial ecosystems such as forests. Aridity is a major factor for these ecosystems and has strongly influenced the way in which human societies have exploited the land. In turn, the environment has influenced the human population evolution. The spatial distribution of the present land uses (Figure 17.3(a)–(d)) is a result of the interaction between environmental conditions, human evolution and technological capabilities and advances. It can be observed from Figure 17.3(c) that the main area covered by irrigated crops is located in a belt in the centre of the basin, following an E–W direction. This corresponds to the valley bottom along the Guadalent´ın River. The main land uses at present include huerta (a Spanish word to name a complex of herbaceous crops of different species including little orchards of irrigated trees), citrus trees, almond trees, and greenhouses. Dry crops are grown near the borders of irrigated land and on the plains surrounding the valley bottom, although in the southern part of the basin they can also be grown on mountains composed of soft siliceous metamorphic rocks. The main crops are barley and almond trees. Finally, seminatural ecosystems are mainly located in the mountains and their margins. Shrublands are more extensive than forests. The former are complex but most are dominated by Stipa tenacissima (a perennial tussock herb), Rosmarinus officinalis and Anthyllis cytisoides. The distribution of Stipa is deeply influenced by humans as it was used for fibre production for centuries. Forests are dominated by Pinus halepensis, in part as a result of afforestation policies over the last 150 years. The Guadalent´ın Basin has been exploited by humans for many years, particularly with the early development of agriculture and pastoralism in Neolithic times, about 5000 BP (Walker 1979; Camel-Avila 1998; L´opez-Berm´udez and Mariscal 1996). In the context of the naturally low ecosystem productivity, the use of water as a basic production factor appeared very early in the human exploitation of the basin. For example, in the Argar culture (about 3500 BP), an autochthonous culture of south-eastern Spain in the Bronze Age, two types of villages are found in the basin: lowland and highland villages. In lowland villages irrigation schemes were a major characteristic, although alongside other activities. In highland villages the people seemed to rely more on hunting and stockbreeding for their livelihood (Ayala Juan 1991). The extensive use of irrigation in the Argar period is even more remarkable if one takes into account that there is evidence that the climate was once more humid than at present (Ayala Juan 1991). Irrigation is a characteristic that has been maintained throughout the history of the basin, but continuously increased. It is well known that in Roman and Arabic times irrigation schemes were widely improved and enlarged. After the Christian Reconquest, in the 13th century, most of the agriculture was concentrated close to the town of Lorca, mainly on areas irrigated by the Guadalent´ın River. The total ploughed area (irrigated and non-irrigated) is estimated to have been about 10 000 ha in the first third of the 14th century (Torres Fontes 1994). During the 13th and 14th centuries, the territory of Lorca was virtually free of agriculture and nearly uninhabited, except in a small area surrounding the town. Stock-breeding was the most important occupation. During the 16th and 17th centuries, once the last Muslim kingdom of Granada had been reconquered in 1492, there was a slow expansion of agriculture, mainly in the valley close to Lorca town and the nearby hillslopes, where people took advantage of the irregular irrigation from boqueras. In 1550, the population of the central part of the Guadalent´ın Basin was estimated at 8000 inhabitants (Figure 17.4), about 4 people km−2 , 92% of whom were employed in agriculture (Lemeunier 1980). Agricultural production in 1550 and 1750, as an indicator of change in land use during that period, is shown in Table 17.2. Land use was changing to a subsistence economy based on cereal and on sheep products. The distribution of land turned over to dryland cereal production affected the land available for grazing, and semi-natural ecosystems became important for pasture. By 1635, irrigated lands were estimated to cover about 9150 ha, and these sustained most of the agricultural production. By 1694, the irrigated area was estimated to be 10 000 ha (Gil Olcina 1980), so the increase in the
Natural Resources in the Guadalent´ın Basin
237
(a)
Forests
0− 5% 5− 20% 20− 40% 40− 60% 60− 80% 80−100%
(b)
Shrublands
0 −5% 5− 20% 20− 40% 40−60% 60−80% 80−100%
Figure 17.3 Distribution of the main land use types over the Guadalent´ın Basin, 1996: (a) forests; (b) shrublands; (c) irrigated crops; (d) dryland crops
area of irrigated lands was slow but continuous. However, without further technology, only basic channel building for irregular, or even ephemeral, flow diversion towards the fields was possible, and this kind of development of food production and subsequent human population increase was severely limited (Figure 17.4).
238
Mediterranean Desertification
(c)
Irrigated crops
(d)
Dryland crops
0 −5% 5 −20% 20−40% 40−60% 60−80% 80−100%
0 −5% 5 − 20% 20−40% 40−60% 60−80% 80−100%
Figure 17.3 (Continued)
4
EXPANSION OF AGRICULTURE
In the previous section it was shown that irrigation has been an important factor since prehistoric times in determining land use in the Guadalent´ın Basin. Lack of technological knowledge limited the extent of irrigation, but the low population density was also due to political factors. Then came the chance to expand irrigation into areas not previously used for agriculture. The 18th century marked a huge change in land use around Lorca. Crown lands were sold to the town, and then they were progressively privatized and ploughed. Between 1700 and 1775 the increase in the area of agricultural land was relatively slow, but between 1775 and 1790 the process was accelerated. Between 1771 and 1807, it is estimated that 33 000 ha were transformed into agricultural
239
Natural Resources in the Guadalent´ın Basin 80
Population (× 1000)
70 60 50 40 30 20 10
0 1500
1550
1600
1650
1700
1750
1800
1850
1900
Year
Figure 17.4 Evolution of the human population in the central sector of the Guadalent´ın Basin (1500–1900). Sharp increases occurred, first when Crown lands were sold off, and later, when cereal production became dominant
Table 17.2 Relative value of agricultural production in the central sector of the Guadalent´ın Basin in the 16th, 18th and 20th centuries
Product
1550
1750
1997
Cereal Legumes Vegetables Fruits Wine Olive oil Barrillaa Wool and meat Other
67% – 2 mm)
Size class and particle diameter
Pebble Gravel Little Coarse Fine gravel sand sand Upper part 0–25 cm 25–50 cm Middle part 0–25 cm 25–50 cm Lower part 0–25 cm 25–50 cm
Silt
Carbonate pH Organic Total Total Total as CaCO3 matter N P2 O5 K2 O (ppm) (ppm)
Clay
3.7 2.3
1.9 1.2
3.0 2.2
6.84 4.39
13.21 40.25 39.60 13.46 40.25 41.90
16.3 20.6
7.8 8.0
1.81 1.31
0.18 0.12
0.22 0.21
1.08 1.02
3.7 5.3
2.4 2.4
3.6 3.6
7.71 6.67
18.69 23.55 50.05 16.13 27.95 49.25
17.3 22.0
8.0 8.1
1.61 1.26
0.17 0.12
0.22 0.23
1.02 1.40
3.9 11.2
1.9 1.8
1.9 3.0
4.72 5.37
11.68 34.90 48.70 12.78 30.80 51.05
11.5 12.5
8.0 7.9
1.53 1.31
0.13 0.12
0.16 0.17
1.44 1.44
Mean values based on 100 soil samples.
Table 25.2 Physical and chemical characteristics of the soil in the second experimental field
Sand Silt Clay Carbonate pH Organic matter Total N Available P2 O5 a Exchangeable K2 Ob (ppm) (ppm) (%) (%) (%) (%) (%) (%) 44.9 20.7 34.4 a b
5.5
Olsen method. Ammonium acetate method.
7.7
1.4
0.1
56
342
351
Soil Erosion and Land Degradation
3. harrowing (H) 4. no tillage (NT) In the period 1990–1995 different tillage methods were used: 1. harrowing (H) 2. ploughing at 40 cm depth + harrowing (P + H) 3. scarifying at 50 cm depth + ploughing at 20 cm depth + harrowing (S + P + H) 4. scarifying at 50 cm depth + harrowing (S + H) A random distribution method was used to distribute combinations of tillage method and crop to the plots in the experimental field. The leaf area index (LAI) of two crops (chickpea and durum wheat) cultivated in a biennial rotation was determined during the 1993–1995 period in order to evaluate the relationship between vegetation cover and soil erosion. LAI was determined by collecting plant leaves of 0.10 m2 with 3 replications every 15 days from the beginning of germination (second half of March) to harvest, and then measuring leaf area with an Area Meter LI COR mod. LI-3100. The climatic data were collected using a multifunction Kampus station equipped with sensors to measure rainfall, wind direction and velocity, soil and air temperature, humidity and radiation. The thermopluviometric trend of the experimental plots was determined by looking at the results over 25 years, starting in 1970. Figure 25.2 is a climate diagram of the experimental field according to the method of Walter and Leigh (1960). The diagram is drawn with a temperature scale equal to twice the rainfall scale, to indicate the extent of the period of insufficient water availability for plant growth. On the diagram this is when the rainfall curve is below the temperature curve, between May and September. The climate diagram reflects a typical Mediterranean climate with relatively low temperatures and abundant rainfall during the autumn–winter period and with a dry summer with scarce rainfall and high temperatures. Most rainfall fell during spring (mean March value 68 mm) and winter (mean December value 73 mm). Rainfall was much lower during the summer period, particularly during July, the hottest month (mean 21 ◦ C), with 28 mm of rainfall on average. Therefore, the best growth conditions are limited to two periods, from the last days of winter to the end of spring, and autumn whenever temperatures are high enough for plant growth and there is sufficient water. 90
45 671.8 mm
60
30
45 15
30
13.7 °C
Precipitation (mm)
Temperature (°C)
75
15 0
J
F
M
A
M
J J Month
A
S
O
N
D
0
Figure 25.2 Walter and Leigh’s climate diagram for the experimental area, Guardia Perticara, showing means for 1970–1997
352
Mediterranean Desertification Table 25.3 Rainfall registered during 1970–1995 in the experimental fields
Years
1970/71 1971/72 1972/73 1973/74 1974/75 1975/76 1976/77 1977/78 1978/79 1979/80 1980/81 1981/82 1982/83 1983/84 1984/85 1985/86 1986/87 1987/88 1988/89 1989/90 1990/91 1991/92 1992/93 1993/94 1994/95
Rainfall (mm)
No. of events with runoff
Total
Autumn–Winter
Superficial
Deep
764.2 773.4 839.2 653.0 545.4 611.8 730.3 652.5 580.7 526.9 469.3 467.2 421.0 704.9 1073.2 606.0 512.4 569.4 610.0 429.0 811.0 618.0 774.6 737.9 661.2
446.0 685.9 686.6 354.1 418.6 335.8 573.9 372.8 324.3 388.6 368.0 367.7 244.2 509.0 713.2 396.0 317.0 407.0 278.0 201.0 622.0 335.0 575.4 527.0 342.0
18 22 19 14 7 10 8 12 11 10 21 23 19 23 16 17 16 18 12 30 27 27 12 26 24
– – – – – – – – – – – – – – – – – – – – 15 8 7 13 12
Table 25.3 shows that rainfall varied between 421 and 1073 mm year−1 during the period 1971–1995. The mean value was 650 mm during the whole 25 years, which is quite different from the mean value of 820 mm recorded during the previous 50 years (1920–1970). A particular change over the period 1971–1995 was that precipitation was concentrated mainly during the autumn–winter period, which during 1976–1977 accounted for 78.58% of the total annual precipitation. The meteorological trend in the two-year (1993–1995) experimental period reflected the trend for the full 25 years of observation. In particular, rainfall was 737.9 mm in the first year and 661.2 mm in the second; rainfall distribution during the seasons was similar to the total period except for March 1994 which was characterized by no rainfall and August 1995 when rainfall was much greater than the average, with 97.6 mm against an average 29.5 mm over 25 years. The lowest temperature of −3 ◦ C was recorded in March 1994 and January 1995, while the hottest temperatures were recorded in July, reaching 35 ◦ C during the first year and 32 ◦ C in the second.
Results and Discussion The effect of direction of tillage on soil erosion The results (Table 25.4) show that soil water flow control was more effective in the layout along contour lines than in the layout according to slope. Soil loss was related to number of incidences and intensity of the rainfall during the year. During 1971–1980, the average volume of soil loss for the layout following the contour lines was 1.25 t ha−1 while it was 1.54 t ha−1 for the same period in the layout running downslope. This
353
Soil Erosion and Land Degradation Table 25.4 Influence of surface layouts on soil losses (1971–1980)
Layouts
1971–1973
Following contours Following slope
1974–1976
1976–1980
1971–1980
Total (t ha−1 )
Mean (t ha−1 year−1 )
Total (t ha−1 )
Mean (t ha−1 year−1 )
Total (t ha−1 )
Mean (t ha−1 year−1 )
Total (t ha−1 )
Mean (t ha−1 year−1 )
4.90 5.79
1.63 1.93
3.36 4.15
1.12 1.38
4.28 5.41
1.07 1.35
12.54 15.35
1.25 1.54
Table 25.5 Influence of different crops on soil losses (1971–1980, on 1000 m2 plots)
Crops
1971–1973
1976–1980
1971–1980
Total (t ha−1 )
Mean (t ha−1 year−1 )
Total (t ha−1 )
Mean (t ha−1 year−1 )
Total (t ha−1 )
Mean (t ha−1 year−1 )
Total (t ha−1 )
Mean (t ha−1 year−1 )
6.87 6.18 5.49 4.65
2.29 2.06 1.83 1.55
7.01 5.43 3.85 2.74
2.34 1.81 1.28 0.91
6.42 5.00 – –
1.43 1.11 – –
20.30 16.61 9.34a 7.39a
2.03 1.66 1.56 1.23
3.46
1.15
1.26
0.42
1.40
0.31
6.12
0.61
Horsebean Durum wheat Sweet vetch Mixed hay field (with alfalfa) Natural pasture a
1974–1976
Period 1971–1976.
indicates that tillage following the contours reduced soil erosion by 23.2% compared to tillage following the direction of slope. The effect of different crop systems on soil erosion The influence of different crops on soil loss was also considerable (Table 25.5). Natural pasture always showed the lowest soil loss values, the mean value for the period 1971–1980 being 0.6 t ha−1 year−1 . Horsebean for seed was the crop associated with the highest soil loss among the crops used in the experiment. Although there were large differences between soil losses for individual study periods, it is clear that there is a series of increasing erosion risk as follows:
natural pasture < mixed meadow < Spanish esparcet < durum wheat < horsebean (Barbieri and Basso 1973; Basso and Linsalata 1983; Postiglione et al. 1983). Similar results were found for the 60 m2 Wischmeier plots for the same crops. Here the slope angle was lower, and so soil loss was also lower. The mean values for soil loss recorded during the 10 years of observation ranged from 0.18 t ha−1 year−1 with natural pasture, to 1.15 t ha−1 year−1 with horsebean for seed (Marzi et al. 1983). The effect of different tillage systems on soil erosion With regard to the influence of different tillage systems on the amount of runoff and soil loss, it was found that the mean soil loss after conventional tillage (ploughing followed by harrowing) was 1.14 t ha−1 year−1 during the period 1976–1980. After minimum tillage (harrowing and chemical weed control) the mean soil loss amounted to 1.40 t ha−1 year−1 . This is a 19% difference in soil loss (Postiglione et al. 1990). During the following 10-year observation period, particular attention was paid to the influence of different tillage systems and crops. From the comparison of four different tillage systems applied on a layout according to slope, and cultivated with durum wheat, it emerged that the tillage systems influenced both volume of surface runoff and the amount of soil loss. After ploughing at 40 cm
354
Mediterranean Desertification
depth, the turbidity of runoff water was higher, whereas the amount of runoff was smaller. In contrast, turbidity was much lower from the unploughed soil, but the amount of runoff was higher. Consequently there was a higher soil loss for the non-tillage system (1.35 t ha−1 year−1 was the mean value for the period 1981–1984) than after ploughing at 40 cm depth (1.12 t ha−1 year−1 ). Decreasing intermediate values were found after roto-tillage and ploughing at 20 cm depth (Basso et al. 1986). These results were subsequently confirmed (Tables 25.6 and 25.7) by the trials carried out during the following four-year period, both for durum wheat and horsebean. For the latter crop, as in the previous trial, much greater soil loss was measured. Furthermore, ploughing was found to have a positive effect on weed control, helping to increase horsebean yield, due to weaker competition (Basso et al. 1987). During the years 1990–1993, tillage systems that were slightly different from those used previously were compared (Table 25.8). Roto-tillage alone or scarifying at 50 cm depth plus harrowing showed smaller amounts of soil loss compared to scarifying plus ploughing at 20 cm depth with Table 25.6 Hydrological data measured from experimental plots under different tillage methods under continuous durum wheat (average 1981–1984)
Hydrological parameter
Direct drilling
Rotavation
Ploughing at 20 cm
Ploughing at 40 cm
Runoff (mm) Runoff coefficient (%) Turbidity (g l−1 ) Soil losses (t ha−1 )
62.66 15.43 2.16 1.35
54.20 13.36 2.26 1.23
48.60 11.93 2.43 1.17
40.10 9.86 2.80 1.12
Table 25.7 Hydrological data measured from experimental plots under different tillage methods (annual mean) Hydrological parameters
Horsebean for seed (1984–1985, 1986–1987)
Durum wheat (1985–1986, 1987–1988)
Direct Rotavation Ploughing Ploughing Direct Rotavation Ploughing Ploughing drilling at 20 cm at 40 cm drilling at 20 cm at 40 cm Runoff (mm) Runoff coefficient (%) Turbidity (g l−1 ) Soil losses (t ha−1 )
106.90 13.65
85.45 10.85
77.70 9.85
73.85 9.20
67.10 11.50
58.55 10.00
52.65 8.95
45.25 7.70
3.65 4.15
3.75 3.27
4.60 3.66
5.15 3.88
2.80 1.73
2.85 1.63
3.05 1.59
3.60 1.58
Table 25.8 Hydrological data measured from experimental plots under different tillage methods (average over 1990–1993)
Hydrological parameters
Horsebean Runoff (mm) Soil losses (t ha−1 ) Durum wheat Runoff (mm) Soil losses (t ha−1 )
Rotavation
Scarifying at 50 cm depth
Scarifying at 50 cm + ploughing at 40 cm
Ploughing at 40 cm depth
19.9 0.76
13.8 0.73
14.2 0.92
15.9 0.91
19.8 0.39
17.1 0.41
17.0 0.48
18.6 0.55
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Table 25.9 Soil organic matter and total nitrogen values of the Guardia Perticara experimental field
Depth (cm)
Organic matter (%) Direct
0–10 10–20 20–40 40–50
2.01 1.57 1.42 0.80
Rotavation
1.80 1.49 1.23 0.68
Total nitrogen (%)
Ploughing 20 cm
40 cm
1.50 1.54 1.26 0.95
1.43 1.25 1.37 1.27
Direct
1.42 1.22 1.09 0.81
Rotavation
1.45 1.15 1.05 0.91
Ploughing 20 cm
40 cm
1.04 1.02 1.01 0.94
1.03 0.98 0.99 0.96
Mean values based on 20 soil samples. harrowing or ploughing at 40 cm depth alone. This was true both for durum wheat and for horsebean, the latter showing greater soil loss. Organic matter and total nitrogen were determined in all experimental plots at the end of the trials. Organic matter content (Table 25.9) of the surface layers was higher in the plots that had been left unploughed for years, whereas it had decreased in the plots treated with roto-tillage and in those ploughed at 20 cm depth, and was lowest in those ploughed at 40 cm depth. The 0–10 cm layer had the highest value (2.01%) after no ploughing, and 1.43% after ploughing at 40 cm depth. Similar trends were measured for total nitrogen content (Postiglione et al. 1990).
Superficial and Deep Runoff Superficial runoff occurred after 27 individual rainfall events in the year 1993–1994, and after 23 events during the year 1994–1995. An event was defined as rainfall that fell within a six-hour interval (Linsalata et al. 1983). Of all the rainfall events, 48% produced superficial runoff for both durum wheat and horsebean during the year 1993–1994 compared to 43.2% in the following year. The rainfall events resulting in runoff were divided up according to the period in which they occurred each year. The years were then divided into four periods with respect to the dates on which the main cropping technique was carried out and to a measure of vegetation cover (LAI): • Period 1 was characterized by little or no vegetation cover from the date on which the main cropping technique was performed to the date when LAI values were ≤0.5. This was at the beginning of spring, when the low winter soil temperatures slow plant growth down. • Period 2 included almost all of spring, when vegetation cover was at a maximum, with LAI values >0.5. • Period 3 was when the crops were ripening and becoming senescent rather than growing. The period between the LAI dropping to below 0.5 to the time of harvest was the first 10 days of July for both crops in both years. • Period 4 was between harvest and the first tillage operations to clear crop residues (during the first half of October). These divisions did not indicate differences between the crops. The distribution of the rainfall events in both years gave similar results, with the greatest number of events resulting in superficial runoff occurring during the first period. In general, the occurrence of superficial runoff was infrequent and only after a few events did it exceed 1 mm resulting during the first and last periods (Figure 25.3). The first rainfall event after tillage resulted in low runoff values from the chickpea plot in both years. The durum wheat residues probably played a part in this. Likewise, the rainfall events that occurred just before harvest (events 24 and 25) indicated the more positive effect of vegetation cover with durum wheat compared
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Chick-pea
700
−1
Soil eroded (kg ha )
600 500 400 300 200 100 0 1993−94
1994−95
1993−94
1994−95
Wheat 1993−94
1994−95
10
Runoff (mm)
8 Harrowing Scarifying + Harrowing
6
Scarifying + Ploughing + Harrowing Ploughing + Harrowing
4 2 0 superficial runoff
deep runoff
superficial deep runoff runoff
Chick-pea 1993−94
1994−95
10
Runoff (mm)
8 6 4 2 0 superficial runoff
deep runoff
superficial runoff
deep runoff
Figure 25.3 Surface, deep runoff and soil losses for wheat and chickpea during the 1993–1995 period
to chickpea. In fact, the latter showed higher runoff probably due to the poor vegetation cover before harvest compared to the vegetation cover of durum wheat. The effect of crop presence on runoff during the two-year period was negligible since the most runoff was recorded when there was no or little vegetation cover (70% for durum wheat and 67% for chickpea). Scarifying (S) + ploughing (P) + harrowing (H) produced the lowest amount of runoff for both crops (10.2 mm for durum wheat and 12.7 mm for chickpea). Periods b and c resulted in
Soil Erosion and Land Degradation
357
limited superficial runoff in both years; a mean value of 0.95 mm was obtained for both crops during 1993–1994 while in the following year the mean superficial runoff was 0.92 mm for durum wheat and 1.7 mm for chickpea. Thirteen deep runoff events occurred for durum wheat and eight for chickpea during 1993–1994. Twelve events occurred for both crops during the following year. These events occurred at the same time or just after the heaviest rainfall events. The measured volumes were greater than those that had occurred in the preceding years with durum wheat and horsebean on the same experimental field in response to the greater volume and intensity of rainfall (De Falco et al. 1994; Pisante et al. 1994). The vegetation structure of the crops also influenced runoff near harvest time. Most rainfall events were only associated with limited soil erosion and only those that occurred during summer and the beginning of autumn caused significant erosion. This is probably due to the high intensity of the rainfall that occurred during these events (between 11.9 and 52.0 mm h−1 ) falling on the sparse vegetation cover. Only one event in each year resulted in large amounts of eroded soil during the winter period, recorded in February 1994 (event 17) and March 1995 (event 37), corresponding to very high runoff measurements. Recorded rainfall intensity during these periods was only between 2 and 4.6 mm h−1 , therefore only superficial runoff produced a high amount of eroded soil. Erosion from under durum wheat gave lower values, with a two-year mean value of 1.131 t ha−1 compared with 1.617 t ha−1 . The lowest values were recorded for durum wheat with tillage methods that excluded ploughing (0.863 t ha−1 for S + H and 0.938 t ha−1 for H). The lowest values for chickpea were found with S + H (1.069 t ha−1 ) and P + H (1.248 t ha−1 ).
3 CONCLUSIONS From the results above it is clear that slope angle is a significant factor for soil erosion in the experimental fields, and carrying out tillage following the land contours rather than tillage downslope is important in reducing the soil erosion risk. When different tillage systems were compared, ploughing at 40 cm depth followed by harrowing resulted in the smallest amount of soil loss. Similar results were obtained after ploughing at 20 cm depth followed by harrowing. In addition, these two tillage systems produced the highest yields and better weed control was obtained. With regard to the protection against erosion afforded by the most common crops of the area, it emerged that medium- and long-term fodder crops give higher soil protection than annual crops, but among annual crops durum wheat is better than horsebean. Taking all the trials together, the amount of runoff water under different tillage systems made up around 6% of the rainfall on average. Soil loss in these hilly areas of southern Italy was limited (generally, slightly more than 1 t ha−1 year−1 , within a range of 0.40–4.15 t ha−1 year−1 ) even if some soil loss occurred every year, as a continuous process. Much more serious are the locally common phenomena of destabilization and landslides, which occur periodically and are the main cause of land degradation. The data obtained in the experimental plots during the period 1990–1995 show that the most significant soil losses corresponded to a small number of rainfall events. These events were characterized by an intensity of between 13.2 and 52 mm h−1 in summer or between 2 and 4.6 mm h−1 in winter when the soil surface was exposed, before the crops had grown to provide an effective cover. The typical Mediterranean climate in the internal hilly areas keeps the soil cold in winter and limits the growth of these crops to the spring months. Superficial runoff was similar under durum wheat and chickpea, but soil erosion was greater under chickpea after all rainfall events. This was due to the poor vegetation cover of the crop in some phases, i.e. at harvest, but also due to other characteristics of the plant that influence the soil surface conditions. In fact, the chickpea crop is small in stature with a poor root system, as well as being sown in rows with a larger space left between crops compared with durum wheat, which fully covers the land on which it is sown. This space allows for the direct erosive action of runoff, leading to a greater superficial runoff.
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REFERENCES Aru A (1991) Il suolo, parte fondamentale dell’ecosistema per l’agricoltura. Agricoltura e Ambiente I, 34–49. Barbieri R and Basso F (1973) Problemi agronomici della conservazione del suolo. Il campo Sperimentale nel Bacino dell’Agri. Annali Facolta Scienze Agrarie dell’ Universita di Napoli-Portici , Serie IV, VII, 1–35. Basso F and Linsalata D (1983) Influenza delle sistemazioni superficiali e delle colture sull’erosione del terreni declivi del bacino dell’Agri. Quaderno Consiglio National Recerca 129, pp. 75–95. Basso F and Postiglione L (1994) Aspetti agronomici della conservazione dei terreni in pendio: sistemazioni e lavorazioni. Rivista di Agronomia XXVIII(4), 273–296. Basso F, Postiglione L and Carone F (1986) Lavorazione del terreno in un ambiente collinare dell’Italia meridionale. Un triennio di prove sull’erosione del suolo e sulla produzione del fumento. Rivista di Agronomia 20(2–3), 218–225. Basso F, Postiglione L and Carone F (1987) Influenza delle modalita di lavorazione di un terreno declive sottoposto a rotazione: favino da seme-frumento duro. Erosione e risultati produttivi. Rivista di Agronomia 21(4), 237–243. Boschi V and Chisci G (1978) Influenza delle colture e delle sistemazioni superficiali sui deflussi della erosione in terreni argillosi di collina. Genio Rurale 41, 7–16. Cavazza L, Linsalata D and De Franchi AS (1983) Nuovi modelli di misuratori per la determinazione della erosione idrica. Quaderno 129, Problemi agronomici per la difesa dai fenomeni erosivi, Consiglio National Recerca, Rome, pp. 45–57. Chisci G and Tellini M (1973) Indagini sperimentali sugli aspetti della conservazione del suolo in piccoli bacini. Ann Ist Sper Studio e Difesa del Suolo, Firenze IV, 39–52. De Falco E, De Franchi AS, Basso F and Postiglione L (1994) Effetti delle modalit`a di lavorazione di un terreno declive a rotazione favino (Vicia faba minor Beck.) frumento (Triticum durum Desf.) sull’erosione e sulla qualit`a dei deflussi. Rivista di Agronomia 28(4), 348–355. Dudal R (1982) Land degradation in word perspective. Journal of Soil and Water Conservation 37, 245–249. Landi R (1984) Regimazione idraulico-agraria e conservazione del suolo. Rivistia di Agronomia XVII, 147–174. Linsalata D, De Franchi AS, Marchione V and Basso F (1983) Un decennio di osservazioni sull’erosivit`a della pioggia in Basilicata. CNR Quaderno 129, 113–124. Marzi V, Linsalata D and De Franchi AS (1983) Primi risultati sull’impiego dei misuratori di erosione del terreno. Quaderno 129, Problemi agronomici per la difesa dai fenomi erosivi, Consiglio National Recerca, Rome, pp. 58–74. Panicucci G (1972) La difesa del suolo. Conv.: La difesa del suolo: le sistemazioni montane e fluviali. CNR, Milan. Pisante M, De Falco E and Basso B (1994) Losses of mineral nitrogen in surface and deep runoff from a durum wheat crop (Triticum durum Desf.) on a sloping soil with different tillage methods. Proceedings of 13th International Conference, 24–29 July, Aalborg, Denmark. International Soil Tillage Research Organization, pp. 341–345. Poesen JWA and Bryan RB (1990) Influence de la longueur de pente sur le russellement: role de la formation de rigoles et de croutes de sedimentation orstoty. Ses Pedol XXV(12), 71–80. Postiglione L (1988) Esperienze di sistemazione nella collina meridionale – Sistemare la collina per difendere il suolo e tutelare I’ambiente. Associazione Nazionale delle Bonifiche. Soceit´a Editore, 11 Mulino, pp. 281–285. Postiglione L (1993) Agriculture and environmental problems in the Mediterranean area (with particular reference to Italy). Medit IV, 35–42. Postiglione L and Marzi V (1983) Prefazione. Quaderno 129, Problemi agronomici per la difesa dai fenomi erosivi, CNR, Rome, pp. 7–9. Postiglione L, Basso F and Linsalata D (1983) Influenza delle sistemazioni superficiali e delle modalita di lavorazione su terreno decisive a rotazione biennale: favino da semefrumento duro. Erosione e risultati produttivi. Quaderno 129, Problemi agronomici per la difesa dai fenomi erosivi , CNR, Rome, pp. 146–161. Postiglione L, Basso F, Amato M and Carone F (1990) Effect of soil tillage methods on soil losses, on soil characteristics and on crop production in a hilly area of Southern Italy. Agricoltura Mediterranea 120, 148–158. Raglione M, Sfalanga M and Torri D (1980) Misura dell’erosione in un ambiente argilloso della Calabria. Annuali Istitute Spermentale Studio e Difesa del Suolo XI, 159–191. Ruhe RV (1969) Quaternary Landscapes in Iowa. Iowa State University Press, Ames, Iowa. Schertz DL (1983) The basics for soil loss tolerances. Journal of Soil and Water Conservation 44, 10–14. Smith RM and Stamey WL (1965) Determining the range of tolerable erosion. Soil Science 100, 414–424.
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Toderi G (1991) Problemi della conservazione del suolo in Italia. In Agricoltura e Ambiente, Edizione Bologna, 50–59. Verity GE and Anderson DW (1990) Soil erosion effects on soil quality and yield. Canadian Journal of Soil Science 70(3), 471–484. Walter H and Leigh H (1960) Klimadiagramm. G. Fisher, Jena, Germany. Wischmeier WH and Smith DD (1965) Predicting Rainfall Erosion Losses from Cropland East of the Rocky Mountains. Agriculture Handbook 282, USDA, Washington, DC.
26
Social and Economic Conditions of Development in the Agri Valley
E. BOVE AND G. QUARANTA
University of Basilicata, Potenza, Italy
1 INTRODUCTION The Agri Valley may be divided geographically and socio-economically into three distinct parts: Upper, Middle and Lower (Basso et al. 1998). Each of the three can be considered roughly homogeneous in terms of physical environment, natural resources, social conditions and economic development (Quaranta 1997). However, the influences on socio-economic development may come from a much wider area, from the mountains to the alluvial lands of the coastal plain.
2 DISTRIBUTION OF POPULATION 2.1 Contemporary Distribution of Population
The Agri Valley is part of Basilicata, which covers an area of 10 000 km2 in southern Italy, with slightly more than 600 000 inhabitants (ISTAT 1997). The area is divided in two provinces, Potenza (the chief town) and Matera, and 131 municipalities of which about 40 are partially or totally within the Agri Valley. This study concentrates on 29 of the municipalities (Table 26.1). Basilicata is predominantly mountainous or hilly. The woodland and pasturelands were once the summer location of transhumant flocks of sheep and goats that spent the winter near the coast. During the summer, people would live in small farms scattered throughout the woodlands and pasturelands. What distinguishes the hilly areas today is the widespread presence of badlands, shrubby areas, and arable lands cultivated with durum wheat (Bove and Quaranta 1996). The summer drought is extreme, the population is very small and the desolate countryside is liable to frequent landslides. In contrast, the coastal plain has recently been characterized by a rapid and continuous population growth and a big increase in the cultivation of intensive crops such as vegetables, strawberries and citrus fruits. 2.2 Historical Features
Historical settlements in the Agri Basin have been described by Boenzi and Giura Longo (1994). Recently, a Neolithic settlement has been discovered in the Upper Agri Valley (Bianco and Cataldo 1994) a short distance from the source of the Agri River, known in ancient times by the name of Akiris (Adamesteanu 1995). There is also evidence of extensive Roman civilization (Soprintendenza Archeologica della Basilicata 1981). In the Middle Agri Valley archaeologists have uncovered a remarkable monastic settlement (Fonseca 1995) indicating ancient contact between the local population and Greek colonization on the coastal margin of the Lower Agri (Ministero per i Beni Culturali e Ambientali 1996). Later the coasts became infested with malaria, driving the population out (Rossi Doria 1963). Mediterranean Desertification: A Mosaic of Processes and Responses. Edited by N.A. Geeson, C.J. Brandt and J.B. Thornes 2002 John Wiley & Sons, Ltd
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Table 26.1 Total surface, resident population and density in 1995
Communes
Total area km2
Population %
Total
%
Density (inhabitants km−2 )
Upper Agri Valley Marsico Nuovo Paterno Marsicovetere Viggiano Tramutola Grumento Nova Moliterno Sarconi Spinoso Montemurro
592.25 101.03 39.25 37.82 89.03 36.48 66.17 97.65 30.46 37.82 56.54
28.80 4.91 1.91 1.84 4.33 1.77 3.22 4.75 1.48 1.84 2.75
32 245 5468 4246 4443 3181 3253 1919 4966 1389 1831 1549
32.55 5.52 4.29 4.49 3.21 3.28 1.94 5.01 1.40 1.85 1.56
54.44 54.12 108.18 117.48 35.73 89.17 29.00 50.86 45.60 48.41 27.40
Middle Agri Valley Castelsaraceno San Chirico Raparo San Martino d’Agri Armento Corleto Perticara Guardia Perticara Gallicchio Missanello Gorgoglione Cirigliano Rocccanova Aliano Sant’Arcangelo Stigliano
960.18 74.18 83.00 50.25 58.50 88.98 52.95 23.48 22.30 34.23 14.93 61.63 96.32 89.47 209.96
46.66 3.61 4.03 2.44 2.84 4.32 2.57 1.14 1.08 1.66 0.73 3.00 4.68 4.35 10.21
29 766 1932 1537 1085 889 3243 789 1071 685 1326 502 1998 1425 7082 6202
30.06 1.95 1.55 1.10 0.90 3.27 0.80 1.08 0.69 1.34 0.51 2.02 1.44 7.15 6.26
31.00 26.04 18.52 21.59 15.20 36.45 14.90 45.61 30.72 38.74 33.62 32.42 14.79 79.16 29.54
504.94 76.28 156.93 132.94 71.50 67.29 2 057.37 9 992.27 0.21
24.55 3.71 7.63 6.46 3.48 3.27 100.00
37 017 894 5812 8594 6578 15 139 99 028 6 09 238 0.16
37.38 0.90 5.87 8.68 6.64 15.29 100.00
73.31 11.72 37.04 64.65 92.00 224.98 48.13 60.97
Lower Agri Valley Craco Tursi Montalbano Jonico Scanzano Jonico Policoro Total Agri Valley Basilicata Total Agri Valley/Basilicata
Source: elaboration on ISTAT data.
3 3.1
DEVELOPMENTAL ASPECTS OF THE POPULATION
Demographic Dynamics The Agri Valley has experienced social and economic problems in common with the rest of southern Italy, mainly concerned with isolation. Mountains with only limited areas worth cultivating, arid hilly areas and malaria-infested lowlands have discouraged investment so that there are few roads and inadequate services. These factors, together with the absence of secure employment opportunities, have been a barrier to socio-economic progress until recently. A hundred years ago the standard of living was particularly low, soil erosion was widespread following deforestation, and there was the
363
Social and Economic Conditions in the Agri Valley
added threat of natural disasters such as earthquakes, so many southern Italians emigrated overseas (Villani and Massafra 1968). The exodus so worried the mayor of Moliterno that he welcomed the Italian president of that time with the words: “I greet you in the name of my eight thousand citizens, of which three thousand have emigrated to America, and five thousand are preparing to follow them” (Sereni 1968). Between the First and Second World Wars the politics of the Fascist regime in Italy largely prevented further migration, but after that more Italians emigrated, particularly to South America and Australia. From the mid-1950s to the end of the 1970s, migration patterns shifted more to northern Italy and other European countries. Since then emigration has become insignificant but within the Agri Valley there have been changes in the distribution of the population, with the population from isolated rural areas tending to move to new centres on the coast. The total population of the Agri Basin rose from 80 000 in 1861 to approximately 100 000 in the mid-1990s. However, the population of the isolated Upper Agri Valley declined by about 20% over the same period. The population of the historic centre of Marsicovetere, at an altitude of 1000 m a.s.l., has declined to only a few hundred, while down in the valley bottom the new centre of Villa d’Agri has a population of more than 4000. Likewise, in Montemurro, the population has halved since 1861 to around 1550 today. The same trends are occurring in the Middle Agri Valley. While the centre of San Brancato, in the territory of Sant’Arcangelo, continues to expand along the principal road, the population continues to decrease in the old historic centres. Between 1861 and 1995 the population declined by over 70% in Armento and Cirigliano. In contrast, the population throughout the Lower Agri Valley has been expanding. With the exception of Craco, where the population halved over the period 1861–1995, the other municipalities have demonstrated exceptional expansion. In this relatively densely populated sub-area the small town of Policoro has emerged with more than 15 000 inhabitants. 3.2 Migration Consequences
The high numbers of people who have felt forced to migrate from the Agri Basin have greatly modified the demographic structure. The elderly persons index is calculated as a percentage ratio between the resident population over 65 years old and the population under 6 years old (in 1991). In the Agri (Figure 26.1) this index shows clearly the demographic fragility and impoverishment. It is clear that many municipalities are inhabited only by elderly retired people. In Figure 26.1, note
Cirigliano Marsico Nuovo
Stigliano
Craco Corleto Perticara Gorgoglione Guardia Perticara Marsicovetere Paterno Montalbano Jonico Viggiano Gallicchio Aliano Montemurro Scanzano Jonico Tramutola Armento Missanello Grumento Nova Tursi Sant'Arcangelo Spinoso SarconiSan Martino D'Agri Roccanova Moliterno
Policoro
San Chirico Raparo Elderly persons index (%)
Castelsaraceno 93−249 249−341 341−457 457−1138
Figure 26.1
Distribution of the elderly persons index for the Agri Basin in 1991
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Table 26.2 Major socio-economic indicators, 1991 (percentages are shown in parentheses)
Demography
Under 6 years Over 6 years 6–14 years 15–65 years More than 65 Elderness index (65/5 × 100) People with university degree/total population Illiteracy in population >6 years old Retired/total population Total migration value Total population variation Active population Employed Unemployed Agriculture Industry Other activity Agricultural Sector Farms Total surface (ha) Farms 50 ha Industry No. of firms Employed
Upper Agri Valley
Middle Agri Valley
Lower Agri Valley
Total
1.903 30.176 4.223 21.218 4.735 249 2
1.595 29.605 3.711 20.006 5.888 369 2
2.495 33.928 5.577 24.756 3.595 144 2
5.993 93.709 13.511 65.980 14.218 237 2
7
10
6
8
16 −123 1.824
23 −209 1.554
12 −34 2.447
17 −366 5.825
12.286 8.788 (72) 3.498 (28) 2.042 (20) 3.578 (35) 4.650 (45)
12.387 8.373 (68) 4.014 (32) 3.039 (30) 2.978 (29) 4.106 (41)
15.101 10.885 (72) 4.216 (28) 3.752 (30) 3.427 (27) 5.310 (43)
39.774 28.046 (70) 11.728 (30) 8.833 (27) 9.983 (30) 14.066 (43)
4.792 50.851 2.246 (47) 1.108 (23) 662 (14) 401 (8) 260 (5) 111 2
6.178 80.863 2.487 (40) 1.474 (24) 783 (13) 642 (10) 483 (8) 307 5
4.550 41.590 1.311 (29) 1.334 (29) 1.216 (27) 334 (7) 237 (5) 116 3
15.520 173.304 6.044 (39) 3.916 (25) 2.661 (17) 1.377 (9) 980 (6) 534 3
2236 4698
2032 3753
Source: elaboration on ISTAT data. Percentage values are in brackets.
2222 5408
6490 13 859
Social and Economic Conditions in the Agri Valley
365
how the index decreases through the three sub-areas, passing from an extreme of more than 1100 at Cirigliano (Middle Agri Basin), to 93 at Policoro (Lower Agri Basin). One of the consequences of having a predominantly elderly population is the difficulty in arranging schooling for the remaining children, who may be dispersed over a wide area. The situation appears particularly alarming in the marginal centres of the Middle Agri Valley where poverty is rife. In these municipalities, the unemployment rate is very high. For the entire basin it was 30% in 1991, which was almost three times the national figure. Employment in agriculture accounted for nearly 30% of the basin’s workforce in 1991 (Table 26.2).
4 ECONOMIC ACTIVITIES Farming has always played a very important role in the economic system of the Agri Basin. In the high part of the basin there is fertile land in the valley bottoms (about 10 000 ha) and an abundant water supply. The availability of water has favoured agriculture, particularly dairy farming, and recently horticulture and fruit growing, aided by irrigation. In the 1920s rice was introduced but the results were not encouraging (Azimonti 1929). It was in the early 1950s that a programme of expansion and reorganization of irrigation was begun. This enterprise has produced excellent results in terms of productivity and revenue, not only for the big farms but also for the numerous small part-time family farms on the right bank of the Agri River. These farms are noted for high productivity of the cropping system and especially for high quality beans. Recently this rather labourintensive crop (Bove 1993) has been recognized by the European Union as a product with Protected Geographical Indication (PGI). Since this prestigious recognition, acreage of beans has increased by 300% in the last seven years. Beginning with this typical agricultural product, marketed with the name “Fagioli di Sarconi ” (Sarconi’s beans) the possibility of introducing a collection of typical, specialist products of the Upper Agri Valley gradually emerged. A number of factors helped to make this idea a success. There was increasing unemployment as industrial ventures and subsidies failed but here was an opportunity to make money out of tourism (Caneva 1996). This area is rich in archaeological treasures and farm holidays are popular. The number of winter tourists is also increasing. People are interested in rediscovering old traditions (Larotonda 1996), and traditional local foods, such as ham, apples, wine and cheeses from goat and sheep milk. In addition, the National Park of Val d’Agri and Lagonegrese was created. In the Middle Agri Valley one can pass with amazing rapidity from the most desolate uninhabited stretches to tracts that are prodigiously fertile. In fact, only in the beds of Agri River and its tributary the Sauro are there the environmental conditions that permit flourishing forms of agriculture, such as the horticulture and fruit growing on the small-holdings known as “Sant’Arcangelo’s Gardens”. Small-holdings are widespread around the populated centre, where on the steeper slopes generations of hardworking peasants have tended olive trees. Another important characteristic of this area lies in the widespread cultivation of durum wheat on land that is far from naturally suitable. Subsidies have encouraged production, even when the return has proved uneconomic. In the area as a whole, uneven annual incomes and hidden unemployment are common. All the new plans and ventures for specialist products were going well until a multinational consortium recently discovered large oil deposits in the Middle and Upper Agri Valley (Figure 26.2). Mining of the crude oil deposits has begun and could completely change the economy of the region, but it is not clear how much the resident population will benefit. The exploitation of the oil will involve the creation of at least 50 wells, which will surely have a negative impact on the image of the quality of the agricultural products and on the environment (Figure 26.3). In the Lower Agri Valley land reclamation and land reform since 1950 have created systems of production that are second to none in the Mediterranean Basin. Here advanced structures and farming practices produce commodities highly competitive in both the national food processing industry and in the international markets for fresh products. However, these farms are under pressure because the demand for land for urban use and recreational activities has increased dramatically.
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Figure 26.2 The valley bottom in the Upper Agri Valley
Figure 26.3 Oil well in a vineyard
Social and Economic Conditions in the Agri Valley
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In conclusion, the Agri Basin is a region of widely divergent extremes. Farming will always play an important role in the rural areas, but it must be adapted to prevalent social and environmental conditions.
REFERENCES Adamesteanu D (1995) Fiumi e torrenti nella Lucania antica. In Le vie dell’acqua in Calabria e Basilicata. Carical, Cosenza. Azimonti E (1929) La colonizzazione dell’Alta Val d’Agri. In La colonizzazione in Basilicata. Tipografia del Senato, Roma. Basso F, Bove E, Del Prete M and Pisante M (1998) The Agri Basin, Basilicata, Italy. In P Mairota, JB Thornes and N Geeson (eds) Atlas of Mediterranean Environments in Europe. John Wiley, Chichester, pp. 144–151. Bianco S and Cataldo L (1994) L’insediamento “appenninico” Civita di Paterno (Potenza). Galatina. Boenzi F and Giura Longo R (1994) La Basilicata: i tempi, gli uomini, l’ambiente. Edipuglia, Bari. Bove E (1993) La Montagna lucana. In Indagine sui lavoratori agricoli dipendenti nelle zone interne del Mezzogiorno. Edizioni Scientifiche Italiane, Napoli, pp. 99–125. Bove E and Quaranta G (1996) Desertification in Southern Italy: The Case of Clay-Hill Areas in Basilicata Region. ICALPE, Corte, Corse. Caneva G (1996) Le risorse naturali. In Omaggio alla Val d’Agri . Ars Grafica, Villa d’Agri. Fonseca CD (1995) ‘Et habitavit secus flumen. . .’: i percorsi fluviali di Basilicata in et`a medioevale. In Le vie dell’acqua in Calabria e Basilicata. Carical, Cosenza, pp. 239–276. ISTAT (1997) Popolazione e movimento anagrafico dei comuni . ISTAT, Roma. Larotonda A (1996) Le tradizioni popolari. In Omaggio alla Val d’Agri . Ars Grafica, Villa d’Agri. Ministero per i Beni Culturali e Ambientali (1996) I Greci in Occidente. Electa Napoli. Quaranta G (1997) Interazioni tra strumenti di politica agraria e politica economica: un’ipotesi interpretativa del loro impatto su famiglie – aziende dell’Alta Val d’Agri. In A Cioffi and A Sorrentino (eds) Le piccole aziende e la nuova politica agricola dell’Unione Europea: problemi economici e strutturali . Franco Angeli, Milano. Rossi Doria M (1963) Memoria illustrativa della carta dell’utilizzazione del suolo della Basilicata. Consiglio Nazionale delle Ricerche, Roma. Sereni E (1968) Storia del paesaggio agrario italiano. Editori Laterza, Bari. Soprintendenza Archeologica della Basilicata (1981) Grumentum: la ricerca archeologica in un centro antico. Congedo Editore, Galatina. Villani P Massafra A (1968) Scritti sulla questione meridionale. Laterza, Bari.
27
Characterization of Soil Hydraulic Properties in a Desertification Context
ALESSANDRO SANTINI AND NUNZIO ROMANO
Department of Agricultural Engineering, University of Naples ‘‘Federico II’’, Portici, Naples, Italy
1 INTRODUCTION Evaluating the impact that practical land management applications can exert on hydrological processes is important when solving hydrological, environmental and soil conservation problems. Mathematical models that describe the basic hydrological processes and interactions over time can represent valuable tools to help agencies and private firms to identify problems, for decision-making, or to give guidance to farmers. Such models have become widely available with the increase of cheap, powerful computers. In the past, several approaches based on empirical or semi-empirical concepts have been proposed. The major aims were to sort the large amounts of measured data, and identify the principal variables of the problem and new relationships. Some scientists endeavoured to give the coefficients of empirical or conceptual models, which basically have the character of fitting parameters, also a physical meaning. However, the effectiveness of the related results depends on the input data sets as well as on the mathematical and statistical techniques employed. Classic examples are the Kostiakov (1932) or the Horton (1940) equation for infiltration, or the Universal Soil Loss Equation (USLE) of Wischmeier and Smith (1978), or simple water budget models such as that proposed by Chopart and Vauclin (1990). Physically based, distributed-parameter hydrological models have recently been developed to overcome the intrinsic limitations in current empirical models. Models of this kind focus their attention mainly on the mathematical description of the most significant processes taking place and structural characteristics. Many hydrological models include descriptions of processes such as saturated–unsaturated flow, evaporation, overland flow and channel flow. These physical processes are expressed as non-linear, partial differential equations which for practical interest have to be solved by employing numerical methods and adopting properly designed algorithms to reduce errors due to the discretization of the flow domain. In some cases, however, not all the equations describing such processes are clearly known or it is not always possible to express mathematically the laws that govern the behaviour of a physical process. Therefore, even sophisticated models often employ empirical or semi-empirical relationships. Furthermore, these models are “distributed”, in the sense that they allow for the spatial description of the system characteristics. As the scale increases, for example to embrace a whole catchment, the natural spatial variability of the soil characteristics becomes an important factor that can influence the assessment of the overall system’s response to specific conditions (Wood et al. 1988). The efficiency of the distributed approach is dependent on spatial variability being properly addressed and factors causing spatial variations being correctly modelled. The problem of spatially integrating at a large scale the processes operating at a small scale can represent a typical problem. When faced with land degradation and sustainability problems, understanding the mechanisms with which water moves from the land surface to the groundwater table through the unsaturated zone is of primary importance in predicting catchment hydrological responses, rainfall erosivity, and Mediterranean Desertification: A Mosaic of Processes and Responses. Edited by N.A. Geeson, C.J. Brandt and J.B. Thornes 2002 John Wiley & Sons, Ltd
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sediment deposition. Because of its boundary position between atmosphere and groundwater, soil represents a crucial element within the hydrological cycle as it determines the partitioning of incident water, either rainfall or irrigation water, into runoff and infiltration. The infiltrated water is in turn subject to evaporation at the land surface, transpiration from plants, and percolation processes, which are all affected by changes in the regime of the unsaturated zone, by the status of vegetation, and by climatic conditions (Santini 1992). Whichever mathematical model is used to solve a particular problem, for example the common parametrization of unsaturated flow processes offered by the Richards equation (Richards 1931), or a detailed stochastic approach (Yevjevich 1987), or the comprehensive and sophisticated SHE model (Abbott et al. 1986), there is the recognition that one of the major limitations to applying a model is tied to the availability of information to correctly assess the soil hydraulic behaviour. Furthermore, soil hydraulic characteristics are highly non-linear functions of the moisture regime in soil and in most cases the existence of a complex structure of spatial variations is shown. Therefore, the reliability of model predictions is extremely sensitive to the accuracy with which the hydraulic properties characterizing the soil are determined. Laboratory and field investigations to determine the soil hydraulic properties at larger scales can result in laborious and very expensive investigations since the inherent spatial variability of such properties should also be accounted for. It can thus be better to compromise between accuracy in experimental evaluations and cost-effectiveness of the investigations by applying simplified methodologies. However, this brings out two important questions: the accuracy in estimates from simplified methods, and the sensitivity of the model to the soil hydraulic parameter data. Greater accuracy can be gained only through specific calibration of the simplified method employed with respect to the soil types and local conditions. Once the soil hydraulic properties have been determined, the influence of variability of these properties on model predictions should be assessed. Effort should thus be devoted to developing accurate and cost-effective methods to measure typical variables affecting flow and transport processes in the unsaturated zone. The main objective of this chapter is to review briefly some methods for determining the soil characteristics related to soil hydrological processes and to summarize some recent results obtained to characterize the soil hydraulic behaviour at different scales in the landscape. Special emphasis is devoted to those methods that have proved a tendency towards successful simplifications, so that a trade-off between efficiency at the scale of interest and accuracy in calculated values can be attained. The first example will deal with a parameter estimation method that was specifically developed to reduce laboratory experimental efforts without sacrificing the accuracy of the estimated soil hydraulic parameters. This method is applied to hydraulically characterize differently tilled field plots. Attention is given to the reliability of the results with respect to the type of soil being investigated, which shows typical features of the soils more easily subject to degradation phenomena. A second example will concern the evaluation of different pedo-transfer rules that permit, in a relatively simple way, prediction of the soil hydraulic properties for which there are no measured data, from available information on basic physical properties of soils. This study has been undertaken as part of a larger, collaborative project, the MEDALUS project, which is improving our understanding of the processes that are responsible for land degradation and developing related sustainability issues in Mediterranean environments. One of the project target areas is the Agri River Basin, which is located in southern Italy and has a total drainage area of approximately 1700 km2 . There is a tendency towards land degradation in this basin, especially in its central part. This is mainly due to the fragile lithological structures of the hilly relief, which are susceptible to erosion by rainfall and uncontrolled human activities. The experiments discussed in this chapter were conducted in the catchment of the Sauro River, the Sauro River being the main tributary flowing into the Agri Basin. The Sauro catchment is located within the Middle Agri Valley and represents an interesting area for site-specific studies of surface erosion and land degradation. The soils of the Sauro catchment develop mainly in a xeric moisture and mesic thermic regime, with parent materials mainly consisting of clayey components. The environment has a very dynamic geomorphology. Slides, slide terraces and accumulation glacis dominate the landscape, and such processes thus affect the soils. Where the landscape is quite stable, layering occurs with
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horizon formation; these soils are well developed and occur only on few areas of the Sauro. As slope gradient increases, chaotic young soils occur with minimum horizon formation. These soils are widely distributed on the slopes of the valley. The hydrology of this catchment is affected by the seasonality of the precipitation. The streamflow regime thus depends strongly on seasonal variations, with low or no flow during most of the year and high discharge peaks of short duration around autumn or early winter.
2 HYDRAULIC CHARACTERIZATION OF SOIL Water flow in soil is typically described by the Richards equation (Richards 1931) whose model parameters are the water retention, θ (h), and hydraulic conductivity, K(θ), functions. These functions are usually referred to as soil hydraulic properties and describe the relationships between the volumetric soil water content, θ , the pressure potential head, h, and the hydraulic conductivity, K, for unsaturated porous media. Comprehensive reviews of existing techniques for measuring soil hydraulic properties are available, but no suitable single method has been developed which performs well in a wide range of circumstances and for all soil types. Laboratory methods entail measurements being taken under controlled conditions by employing complex measuring devices, thus yielding accurate results. They can also turn out to be relatively rapid as it is possible to gather many samples, even originating from different locations, and then run the tests on several of them simultaneously. However, they require extraction of undisturbed samples from soil and this can pose limitations to the validity of laboratory methods in some cases (e.g. structured or cracked soils). Field methods avoid compression of the soil inside the cylinder used to collect the sample and do not lead to changes in soil structure due to sampling and test preparation procedures, but require skilled operators and intensive measuring campaigns, especially if a large number of points need to be characterized. The water retention function θ (h) is usually determined directly in the laboratory on undisturbed soil samples by inducing a series of wetting and drainage events and taking measurements at equilibrium conditions, or in the field by measuring simultaneously water contents and pressure potentials during a transient flow (Bruce and Luxmoore 1986; Klute 1986). Implementation of direct methods to determine the conductivity function is far more difficult, as unsaturated hydraulic conductivity varies over many orders of magnitude not only among different soils, but also for the same soil as the water content varies from saturation to very dry conditions. However, the numerous direct methods for assessing the K(θ) function usually involve measurements of state variables, such as water content and pressure potential, which are well-documented technologies commonly used by different types of users (Dirksen 1991). In general, direct methods are cumbersome and the associated costs of measurement are thus relatively high. Experiments based on these methods often need several stages of steady-state or equilibrium conditions to be reached or require rather restrictive initial and boundary conditions to be imposed in performing the transient flow. Even though they provide very reliable results, their use is limited to specific situations or types of investigations. Some authors have therefore proposed to estimate simultaneously the water retention and hydraulic conductivity functions from a transient flow experiment by using the inverse problem methodology in the form of the parameter optimization technique (Kool et al. 1987). Following this approach experimental operations can be simplified, the employment of sophisticated measuring devices can be avoided, and in most cases the total duration of the experiment can be significantly reduced compared to conventional techniques. Moreover, parameter estimation methods can allow a detailed error analysis of the estimated parameters to be incorporated in the numerical procedure. Both direct and indirect inversion methods perform well when facing water flow problems at a small scale, whereas their use may become inefficient or practically impossible if unsaturated soil hydraulic characterization should involve large land areas or even whole catchments. A valuable attempt to overcome such difficulties was made by introducing predictive methods that estimate the soil hydraulic properties from more easily measured soil attributes. Within these predictive methods, regression equations enabling the unsaturated hydraulic properties to be estimated from
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physico-chemical soil properties such as texture, bulk density, clay mineralogy and organic matter are referred to as pedo-transfer functions, PTFs (Bouma 1989), and are becoming very popular among researchers, soil physicists and field practitioners. 2.1
Parameter Estimation Method
Solving the inverse problem of determining soil hydraulic properties by the parameter optimization approach basically entails minimizing a suitable objective function which expresses the discrepancy between measured values of certain variables during a transient flow experiment and the simulated system response. In this study we present a laboratory inverse method developed to determine simultaneously the water retention and hydraulic conductivity functions of undisturbed soil samples. The laboratory test entails subjecting an initially saturated soil sample of length L to an evaporation process and starts from a hydrostatic equilibrium, with the pressure potential head at the bottom of the soil sample, hL , nearly equal to zero. The evaporation flow is then performed by draining the sample with a small fan placed near the top and with the lower end completely sealed. At the specific time t during the transient flow event, the following variables are measured: total weight of the soil sample, W , and pressure head, h, at different soil depths, assuming z = 0 at the top of the soil sample. The evaporation process is simulated by numerically solving the Richards equation (Richards 1931), which is written here in its pressure head based form: ∂ ∂h ∂h = −1 (1) k(h) C(h) ∂t ∂z ∂z and where C(h) = dθ/dh is the soil water capacity. Equation (1) is subjected to the initial condition: h(z, t) = hL + z − L
t = 0, 0 ≤ z ≤ L
and the following boundary conditions: ∂h K(h) − 1 = E(t) ∂z ∂h −1=0 ∂z
t > 0, z = 0
(2)
(3)
t > 0, z = L
which prescribe at the upper boundary the evaporation rate E(t) derived from sample weight measurements, and the zero-flux condition at the lower boundary. Because of the non-linearity of the partial differential equation (1), due to the strong dependence of K and θ on h, the solution to problem (1) and (2)–(3) is sought numerically using a Crank–Nicolson-type finite-difference scheme (Romano et al. 1998). Unsaturated soil hydraulic properties are usually described by relatively simple, closed-form analytical expressions for the water retention and hydraulic conductivity functions. For the case study reported in the next section, we decided to adopt the following monotonic hydraulic model: −m
θ (h) = θr + (θs − θr )[1 + |αh|1/(1−m) ]
(4a)
k(θ ) = k0 exp[β(θ − θs )]
(4b)
which couples van Genuchten’s θ (h)-function (van Genuchten 1980) with the conductivity function K(θ) as described by an exponential relation (Ciollaro and Romano 1995). Parameters θs and θr represent the saturated and residual values of soil water content, respectively, K0 is the hydraulic conductivity when h = 0, and α(α > 0), m(0 < m < 1) and β are empirical parameters. Parameter β chiefly depends on soil pore size distribution. The unsaturated hydraulic model (4) has been shown to provide reasonable descriptions of the unsaturated behaviour of different types of soils, especially those showing higher percentages of clay contents (Romano and Santini 1999). In this study, the
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saturated water content θs was measured independently in the laboratory, whereas θr was set at zero. Soil hydraulic characteristics are thus defined through the following four-element parameter vector b = {α, m, log(K0 ), β}. The decimal logarithm of the parameter K0 is optimized in this study. The unknown parameter vector b is determined by minimizing the following objective function: O(b) = [h − h∗ (b)]T W[h − h∗ (b)]
(5)
where h is the observation vector, whose elements are pressure heads hij measured at time ti and soil depth zj , and h∗ is the simulated response vector, whose elements are the simulated pressure heads hij (b) at the same space–time co-ordinate as computed by the numerical model for a given parameter vector b. The matrix W is a weighing matrix. Details about specification of the objective function and solution of the optimization problem can be found in the paper by Romano and Santini (1999).
Application of the Laboratory Inverse Method at Plot Scale The laboratory inverse method presented in the previous section was applied to determine the unsaturated soil hydraulic properties at an experimental farm operated by the University of Basilicata and University of Naples “Federico II”. The site is located in the Sauro catchment, near the village of Corleto Perticara, at about 700 m a.s.l., and has an annual average temperature of 12 ◦ C and an annual average precipitation of about 790 mm. The soil at the study area was classified as Vertic Ustorthent, with an Ap horizon of 0.3 m in thickness, overlying a Cca horizon that extends to a depth of approximately 1 m below soil surface. The present investigation was conducted on the four field plots depicted in Figure 27.1 and hereafter identified as Plots P1, P2, P3 and P4. All experimental plots consisted of an area 40 m long by 15 m wide, with an average longitudinal slope of 14% and a nearly zero lateral slope, and were used for two-year rotations of winter wheat (Triticum durum, Desf.) and horsebean for seed (Vicia faba minor, Back.) for seven years before soil sampling. The plots received the following treatments: zero tillage for Plot P1; conventional ploughing (ploughing to a depth of about 20 cm + harrowing) for Plot P2; deep ploughing (ploughing to a depth of about N
735
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P1
P2
Contour lines (m)
g ch uar an d n sa el po mp int lin
P3 P 4
g
dr
ain
g rin su ea e m um fl
720
sto tan rag k e
715
710
Figure 27.1 Plan of the ‘‘Corleto’’ experimental farm, illustrating the plots under investigation. Solid circles identify core sampling locations within each plot
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40 cm + harrowing) for Plot P3; and minimum tillage (scarifying + ploughing to a depth of about 20 cm + harrowing) for Plot P4. Tillage was usually performed in the autumn. The data used in the present investigation were collected in March of the seventh year of tillage treatments when all plots were under winter wheat. Undisturbed soil cores were taken from each of the four plots with a sampling design consisting of 10 sampling points systematically distributed throughout each plot, as shown in Figure 27.1. Two soil cores were taken from the uppermost layer at each sampling point, giving a total of 80 samples. Each soil core was analysed to determine the particle-size distribution, bulk density, saturated water content and hydraulic conductivity values. The laboratory inverse method was employed to estimate simultaneously the water retention and hydraulic conductivity functions of soil cores of a group of 40 samples. Each soil core pertaining to this group had a length L of 10 cm and was 8.5 cm in diameter. Transient evaporation experiments were performed in a constant-temperature room using an apparatus that can test up to eight soil cores simultaneously, as shown schematically in Figure 27.2. Input data for the inverse optimization procedure, such as soil water pressure heads at the two depths of 3 cm and 6 cm below the top of the core and total soil weights, were monitored using a data-logger and a computer. To evaluate the accuracy of the predictions of the proposed inverse method, measured water retention characteristics up to pressure potential heads of −2.5 m were obtained from the other 40 soil cores by a suction table apparatus (Romano et al. 2002). Each soil core from this group was 8 cm in diameter and 5 cm in height. As an example, Figure 27.3 compares the results for Plot P3. In this figure the circles refer to the mean water retention characteristics measured under equilibrium conditions by the sandbox apparatus, whereas the squares represent the mean values of the optimized water retention curves according to equation (4a). It is apparent that while the two methods are virtually indistinguishable in almost all the investigated range of measured pressure heads, they only lead to some discrepancy close to saturation conditions. The different sizes of the cores from the two groups, as well as various levels of disturbance during sampling and some small fan
base pressures
soil sample tensiometer
load cell
control system data-logger
stepper motor
pressure transducer
Figure 27.2 Schematic representation of the system used for the evaporation experiments
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Soil Hydraulic Properties in a Desertification Context 0.5 equilibrium
0.4
Water content
transient
0.3 P3
−4
−3
−2 Pressure head (m)
−1
0
0.2
Figure 27.3 Mean water retention characteristics for Plot P3. Circles are the means of the independently measured retention data points, whereas squares are the means of the inverse estimated water retention curves. Vertical bars are ±1 standard deviation 0.5
0.4
Water content
P1 P2 P3 P4
0.3
−4
Figure 27.4
−3
−2 Pressure head (m)
−1
0
0.2
Mean inverse estimated soil water characteristics for the four investigated plots
different degrees of complete saturation, can account for the relatively small differences observed for h greater than about −0.5 m. Notice that the width of the vertical error bars illustrates that both methods have reproduced the same range of variability, thus indicating a very good efficiency of the proposed inverse method. Figure 27.4 shows the mean water retention curve calculated by averaging the 10 optimized θ (h)-curves for each of the four plots. With the exception of the mean retention curve of Plot P4,
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the shape of the mean θ (h)-curves are similar for Plots P1, P2 and P3, and the three tillage treatments do not appear to affect the retention characteristics significantly. It is interesting that the mean values of water content at saturation are nearly identical among the plots, suggesting that effects of longterm tillage on the structure of the soil being investigated are negligible. Even though the four mean saturated water contents are very close together, the mean θ (h)-curve associated with Plot P4 clearly follows a different pattern with respect to the other three plots. The expectation would be that tillage would exert a greater influence for Plot P3, since this plot was subjected to deeper ploughing, but an explanation of the behaviour of the retention characteristics of Plot P4 can be found by looking at the particle-size distributions of the collected soil cores. Inspection of Figure 27.5, which depicts the mean particle-size distribution (PSD) curves derived by arithmetically averaging all the observed PSD points for each plot, reveals that the mean PSD curves of Plots P1, P2 and P3 do not differ significantly, although some differences are noticeable within the fine sand fraction (according to the ISSS classification). On average, the soil at Plot P4 shows a higher percentage of particles greater than 0.02 mm and a smaller percentage of particles less than 0.002 mm. Thus, the occurrence of remarkable differences between the soil water retention curve at P4 and those of the remaining plots chiefly can be attributed to the observed differences in the particle-size distributions. The rapid decrease in water content as pressure potential increases shown by the θ (h)-curve of Plot P4 (Figure 27.4) can be explained by the fact that on average the soil of this plot has the smallest percentage of clay (Figure 27.5). To summarize the results and further show the effectiveness of the proposed inverse method, in the following all calculations were referred to the data sets from Plots P1, P2 and P3, as they can be regarded as a statistically homogeneous sample. Figure 27.6 shows the mean θ (h) and K(θ) hydraulic properties of the soil under investigation. The mean water retention characteristics (Figure 27.6(a)) obtained with the two methods follow the same pattern: averaging the data from Plots P1, P2 and P3 has made discrepancies between measured saturated water contents of the two different groups of soil cores almost negligible. Figure 27.6(b), which depicts the mean optimized hydraulic conductivity curve, also shows the average values of measured Ks or log(Ks ). These values are both higher than the estimated hydraulic conductivity at saturation obtained by extrapolation. This tendency is consistent with the findings of Ciollaro and Romano (1995), who pointed out the presence of a possible bias between the optimized and independently measured saturated hydraulic conductivity.
Coarse sand
% Particle < d
100
Fine sand
Silt
Clay P1 P2 P3 P4
50
0
2
0.2
0.02
0.002
Particle diameter, d (mm)
Figure 27.5 Mean particle-size distributions for the four investigated plots
0.0002
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(b) 102
0.5
(a) equilibrium
0.4
0.3
−4
−3
−1 −2 Pressure head (m)
0
Water content
transient
Hydraulic conductivity (cm h−1)
mean value of Ks
0.2
mean value of log Ks 100
10−2
10−4
10−6 0.3
0.35 0.40 Water content
0.45
Figure 27.6 Mean soil hydraulic properties for the ‘‘Corleto’’ experimental farm: (a) soil water retention function and (b) hydraulic conductivity function. Dots represent the mean of 30 values using data from plots P1, P2 and P3
Predictive Methods Based on Pedo-transfer Functions A pedo-transfer function is essentially a regression equation which relates (or transfers) some available information of soil physical and chemical properties, i.e. texture, bulk density and organic matter, to soil water retention and hydraulic conductivity characteristics. Results from evaluating two different PTFs proposed in the literature to predict the soil water retention functions are presented here. Romano and Santini (1997) gave more comprehensive validations of widely used PTFs. The PTFs selected here were defined as continuous pedo-transfer functions and developed following two different approaches. The first PTF proposed by Gupta–Larson (Gupta and Larson 1979) θ (hi ) = (ai × Sa) + (bi × Si) + (ci × Cl) + (di × OM) + (ei × ρb )
(6)
which predicts values of water content at specific pressure potential heads, follows the so-called “point regression approach” (Tietje and Tapkenhinrichs 1993). The symbols Sa, Si and Cl represent, respectively, percentages of sand, silt and clay according to the FAO definitions (FAO/UNESCO 1994), OM is organic matter expressed as a percentage of the public Spring control, grazing regime, depopulation Management plans, recovery, re-naturalization, conversion from coppices to high stands
Grazing, fire Coppice rationalization: lengthening rotations, grazing control, fire control
Fire, environmental problems Re-naturalization, recovery, fire control, thinning
REFERENCES Basso F, Bellotti A and De Natale F, Ferrara A and Pisante M (1997) Analisi del rischio di degradazione del suolo in aree agricole della Basilicata: una proposta metodologica. Rivista di Agronomia XXXI(3), 864–871. Basso F, Bove E, Dumontet S, Ferrara A, Pisante M, Quaranta G and Taberner M (2000) Evaluating environmental sensitivity at the basin scale through the use of geographic information systems and remote sensed data: an example covering the Agri basin (southern Italy). Catena 40, 19–35. Bonin G (1978) Contribution a la connaissance de la vegetation des montagnes de l’Apennin centro-meridional. These de Docteur-des-sciences, Univ. de Droit, d’Economie et des Sciences Aix-Marseille III. Cantore V, Iovino F and Pontecorvo G (1987) Aspetti climatici e zone fitoclimatiche della Basilicata. Annali dell’Istituto di Ecol, e Idrologia Forestale, Public N 2, Cosenza. Famiglietti A and Schmidt E (1969) Fitocenosi forestali e fasce di vegetazione dell’Appennino Lucano centrale (gruppo del Volturino e zone contermini). Annali del Centro di Economia Montana delle Venezie, vol. VII, CEDAM, Padova.
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Ferrara A, Bellotti A, Faretta S, Mancino G and Taberner M (1996) The Agri Basin Environment. Website: http//:www.unibas.it/agrimed/ ISTAT (1991) Annuario Statistico Italiano. ISTAT, Rome. Kosmas C, Ferrara A, Briasouli H and Imeson A (1999) Methodology for mapping Environmentally Sensitive Areas (ESAs) to desertification. In C Kosmas, M Kirkby and N Geeson (eds) The Medalus Project: Mediterranean Desertification and Land Use. European Union, pp. 3147. Mainguet M (1994) Desertification: Natural Background and Human Mismanagement . Springer-Verlag, Berlin.
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Modelling Large Basin Hydrology and Sediment Yield with Sparse Data: The Agri Basin, Southern Italy
J.C. BATHURST,1 J. SHEFFIELD,2 C. VICENTE,3 S.M. WHITE4 AND N. ROMANO5 1
Water Resource Systems Research Laboratory, School of Civil Engineering and Geosciences, University of Newcastle upon Tyne, Newcastle upon Tyne, UK 2 Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA 3 C/Cafetos #4, Col. Campestre, Cordoba, Veracruz, Mexico 4 Institute of Water and Environment, Cranfield University, Silsoe, Bedfordshire, MK45 4DT, UK 5 Istituto di Idraulica Agraria, Universita` di Napoli ‘‘Federico II’’, Portici, Italy
1 INTRODUCTION Through its programmes of research into desertification in the Mediterranean Region, the European Commission has sought to develop a thorough understanding of the desertification phenomenon and to provide guidelines for protection, management and rehabilitation. The MEDALUS project has taken on these tasks in part through an emphasis on large river basin scale studies and mathematical modelling. Models provide an important means of integrating the knowledge obtained from experimental studies, of developing an understanding of basin response mechanisms and of supporting the decision-making process for land and water management. In particular, physically based models provide the means for predicting the impacts of possible future changes in land use and climate, and thence for adopting appropriate measures for protection, management and rehabilitation. The large basin scale approaches the regional scale for which management strategies and planning decisions are relevant, while still forming a well-defined hydrological unit. As its contribution to MEDALUS, the Water Resource Systems Research Laboratory (WRSRL), at the University of Newcastle upon Tyne in the UK, applied its SHETRAN hydrological and erosion modelling system to the upper 1532 km2 of the 1700-km2 Agri Basin in southern Italy, one of the MEDALUS focus basins. There have been few tests of physically based catchment models at such scales (Refsgaard et al. (1992) report an example) and the study therefore had the following aims: 1. Extension of the tested SHETRAN scale of application beyond the 700 km2 already modelled in the MEDALUS project (Bathurst et al. 1996). 2. Exploration of SHETRAN applicability and limitations, including model parameter evaluation, at the scale of 1000–2000 km2 and with a simulation grid scale (2 km × 2 km) at the limit of the model’s physical basis. 3. Exploration of the contribution that modelling can make to planning at the large basin scale. The Agri Basin was chosen by the MEDALUS project as a focus for study because of its state of incipient desertification, which could degenerate into serious land degradation without the appropriate control measures. However, as is usually the case except in small intensively monitored research Mediterranean Desertification: A Mosaic of Processes and Responses. Edited by N.A. Geeson, C.J. Brandt and J.B. Thornes 2002 John Wiley & Sons, Ltd
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basins, many of the data that would ideally be used for model validation were not available. In particular, there was no measured river discharge record. A further aim of SHETRAN’s application to the Agri Basin therefore became a demonstration of model use with sparse data, especially quantification of the associated uncertainty in model output. Aspects of the application described here include data assembly, definition of uncertainty limits, and the hydrological and sediment yield simulations.
2 2.1
SHETRAN SHETRAN Description
SHETRAN is a physically based, spatially distributed modelling system for water flow, sediment transport and contaminant migration, applicable at the scale of the river basin (Ewen 1995; Ewen et al. 2000). It incorporates the major elements of the land phase of the hydrological cycle (interception, evapotranspiration, snowmelt, overland and channel flow, unsaturated and saturated zone flow) and the sediment transport component accounts for soil erosion by raindrop impact and overland flow, and transport by overland flow and channel flow (Bathurst et al. 1995; Wicks and Bathurst 1996). Each of the processes is modelled either by finite-difference representations of the partial differential equations of mass and energy conservation or by empirical equations derived from independent experimental research. The spatial distribution of catchment properties, rainfall input and hydrological response is achieved in the horizontal direction through the representation of the catchment by an orthogonal grid network and in the vertical direction by a column of horizontal layers at each grid square. SHETRAN is continually evolving as new process descriptions and solution schemes are introduced. The version used here (v3.4.2) is distinguished by improved numerical stability in the surface flow calculations relative to earlier versions and by the representation of the subsurface as a one-dimensional (vertical flow) unsaturated zone overlying a two-dimensional (lateral flow) saturated zone. 2.2
SHETRAN Parameters
Within each model grid square, each physical characteristic is represented by one parameter value. As long as the grid square is small compared with the distances over which there is significant spatial variability in basin properties and hydrological response, this does not compromise the model’s ability to represent local variations in response. However, as grid scales increase, the local spatial variability in properties and response becomes subgrid. There are then difficulties in applying the equations of small-scale physics which make up SHETRAN and evaluating their parameters, at the grid scale (e.g. Beven 1989). In particular, the field measurements that form the basis of parameter evaluation are most easily carried out at the point or plot scale, which may not be representative of the large grid scales (of the order of 1 km) used in modelling river basins. The solution has been to use “effective” parameter values, which represent the subgrid spatial variability, to give a grid scale response. However, this is a pragmatic approach and it is recognized that the concept may not allow an accurate reproduction of the observed response in all circumstances (as shown for example by Binley et al. 1989). The principal soil parameters and functions in SHETRAN are the soil depth, the saturated zone conductivity (for lateral flow), the saturated values of conductivity and moisture content for the unsaturated zone (for vertical flow) and the water retention (moisture content–tension) and moisture content–conductivity relationships for the unsaturated zone. These characteristics do not vary through a simulation. The proportion of ground covered by vegetation at the grid scale (i.e. the proportion of the grid square that is not bare soil) is accounted for by a proportional index on a scale from 0 to 1 and can be varied in a predetermined manner through the simulation. The vegetation parameters are canopy drainage and storage terms used in calculating interception, properties affecting evapotranspiration, and root distribution, and are mostly time invariant. Overland flow resistance is quantified by the Strickler resistance coefficient (the reciprocal of the Manning coefficient). The coefficient is specified by the modeller, usually according to land use, and does not vary through the simulation.
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The ease with which the soil can be eroded is quantified by two coefficients, representing raindrop impact erodibility and overland flow erodibility respectively. A coefficient is also used to represent channel bank erodibility. These coefficients cannot yet be determined directly from measurable soil properties and therefore require calibration. Topographic elevations are determined from appropriate maps or Digital Elevation Models. Channel characteristics are quantified in terms of the channel cross-sectional shape, elevation and Strickler resistance coefficient. All the above parameters and functions are spatially variable between grid squares (or channel links as appropriate), as are also the specified time-varying rainfall, the specified time-varying meteorological variables determining potential evapotranspiration, and the simulated time-varying hydrological responses. 2.3 Uncertainty in Model Output
An important constraint on the accuracy of physically based, distributed modelling is the uncertainty surrounding the evaluation of the model parameters, which arises from scale effects, sparse field data and the scope for multiple calibrations of apparently equal validity (Beven and Binley 1992). Consideration of this uncertainty is therefore increasingly acknowledged to be an important feature of the modelling process (e.g. Beven and Binley 1992; Ewen and Parkin 1996; Quinton 1997). In particular, uncertainty in model parameter evaluation needs quantification as a basis for evaluating uncertainty in model output. For the Agri simulations, the following method, based on Ewen and Parkin (1996), was applied. Using hydrological judgement, upper and lower bounds are set on the values of the model parameters, reflecting uncertainty in the values. A series of simulations is carried out so that each parameter takes the different values assigned to it. The number of simulations depends on the number of parameters involved, the number of values assigned to each parameter and the number of combinations of different parameter values investigated. The various simulation outputs are then superimposed on each other and the overall time series of maximum and minimum output bounds extracted. These bounds may be composed of contributions from several of the simulation outputs. The bounds on the model parameters thus translate into bounds on the model output and conclusions on model performance are drawn according to the width of the resulting output envelope and the degree to which it contains the measured data.
3 FOCUS BASIN STUDY 3.1 Data Requirements and Data Assembly
The data required for validation of SHETRAN are the property data which describe the catchment (topography, soils and vegetation), the time series of input data which drive the simulation (rainfall and potential evapotranspiration), and the time series of hydrological variables which provide the basis for comparing observed and simulated basin response (e.g. channel discharges and groundwater levels). As far as possible the information should be available on a spatially distributed basis while rainfall and channel discharge should also be available as continuous records (or at least as hourly averages). Assembling and processing such a demanding data set is a time-consuming and intensive activity. Initially contacts have to be developed with the relevant data collection agencies and with local hydrologists familiar with the response of the basin in question. An iterative procedure then unfolds in which data are assembled from the various agencies, the overall availability of data is examined (including coincidence of the meteorological input and hydrological variable time series) and eventually a simulation period is selected. Often this period represents a compromise between availability of data and other requirements such as a particularly interesting sequence of hydrological conditions. Although data are now typically logged in electronic form, many past data records are still provided as hard copy (e.g. raingauge charts and topographic maps) and the next stage is therefore to digitize them for use on a computer. This can take several weeks, even months. Following this the data are checked for quality (e.g. discrepancies in time series data from different sources), and gaps in the time series records are filled (usually by a correlation procedure). Many of the data then need
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processing to put them in a form that has meaning for the model application. For example, rainfall data are often provided as daily values whereas simulation of the dynamic processes responsible for runoff requires the data to be input at hourly or even smaller intervals. Temporal disaggregation of the daily values, usually by comparison with the continuous record from the nearest autographic gauge, is therefore carried out. Finally the data are converted to the format specified in the model software for computer processing. Undertaking this task for the Agri Basin was a major operation which involved several MEDALUS groups (see the Acknowledgements). 3.2
Agri Basin Data Assembly and Processing
The simulation area consists of the 1532-km2 basin upstream of the Gannano barrage (an irrigation offtake structure) (Figure 29.1). It was represented by 383 grid squares of dimensions 2 km × 2 km, this being the finest possible resolution bearing in mind SHETRAN’s current computation requirements (Figure 29.2). The simulation period is 1985–1988 inclusive, which offers the maximum combined availability of autographic rainfall data and reservoir water balance data (used to generate a channel discharge record).
Topographic and Channel Data Topographic elevations in the basin range from 104 m to 1976 m. Contour data for the basin (at 25-m intervals on a 1:25 000 scale map) were obtained from the Istituto Geografico Militare (Italiano) and converted into a 50-m resolution Digital Elevation Model (DEM) by the MEDALUS project. This was then used to derive the model grid network and the elevations characterizing each square (Figure 29.2). The DEM was also used to generate a basin wetness index map from which the channel system was derived. Channel dimensions were obtained by interpolation between the headwaters and outlet (based on the number of upstream channel links at each link) and checked against a number of field measurements. Strickler resistance coefficients for channel and overland flow were evaluated on the basis of past experience with SHETRAN simulations and data in the literature (e.g. Engman 1986; Wicks et al. 1992).
Soil Property Data SHETRAN requires a soil distribution map and values for the soil parameters (section 2.2). However, there is no national soils map of Italy. On the basis of a 1:150 000 scale lithology map, soil type N ITALY
Pertusillo
Gannano
10 km
Figure 29.1 Location map of the Agri Basin
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Elevation (m)
1 2
Channel network
Outlets: 1 Gannano, 2 Pertusillo
100− 300 300− 500 500− 700 700−900 900−1100 1100−1300 1300−1500 1500−1700
Grid resolution = 2000 m x 2000 m
Figure 29.2 SHETRAN grid network, channel system and elevation distribution for the Agri Basin. Grid spacing is 2 km
was therefore determined according to the distribution of the three principal rock types in the basin: flysch, sandstone and limestone. Geostatistical analysis of soil physical and hydraulic property data collected on a hillslope in the basin indicate a correlation scale of 0.4–1.3 km (Santini et al. 1996; Romano and Santini 1997). While ideally this scale would define the maximum distance between evaluations of the model soil parameters, it is reasonably similar to the 2-km grid scale actually used. At each model grid square, the soil column was divided into a topsoil layer and an underlying (fractured) rock layer. A representative, albeit approximate, size distribution was then determined for the area of each lithology by the MEDALUS project, from which hydraulic property data in the topsoil (including retention curves and porosities) were derived using the Brooks and Corey (1964) formulation. The topsoil saturated conductivity was set according to the underlying rock type: 0.0005 m day−1 for soil over flysch, and 2 m day−1 for soil over sandstone and limestone. For the fractured rock layer, the required hydraulic property data were taken from the literature (e.g. Dunne and Leopold 1978; Rawls and Brakensiek 1989; Bras 1990). The overall soil column thickness was set at 10 m throughout the basin, on the basis of a survey of well depths (made with the assistance of the local MEDALUS groups) which indicated a range of thicknesses between 2 and 20 m. Topsoil thicknesses were distributed according to approximate data in the guidebook attached to a 1:100 000 scale land systems map, with magnitudes of 0.05, 0.1, 0.25, 0.5, 0.75 and 1.25 m. The approximate nature of the evaluations was a major source of uncertainty in the simulations.
Vegetation Property Data SHETRAN requires a vegetation distribution map and values for the vegetation parameters (section 2.2). Through the MEDALUS project, a 50-m resolution land-use map was digitized from analysis of multitemporal Landsat Thematic Mapper images. Nine land uses were specified: open water, bare soil, rock, urban, field crops (without distinguishing between different crops), pasture, macchia, deciduous woodland and coniferous woodland. The map was aggregated to a 2-km resolution for use with SHETRAN and for this scale the open water and urban classifications were removed. The main crops are durum wheat (with some maize grown with irrigation) and, in the valleys, fruit and vegetables. Overall, 60% of the basin is arable (30% seedbed, 10% tree crops, including poplar, and 20% permanent pasture), 20% is woodland and 20% is under other uses. Parameter values
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for each vegetation type (relevant to the evapotranspiration and soil erosion calculations) were based on previous applications of SHETRAN in Mediterranean Basins, data in the literature and the plant parameter values from an agricultural research station managed jointly by the Universities of Basilicata, Naples and Bari at Corleto Perticara, inside the basin (e.g. Lukey et al. 1995; Bathurst et al. 1996).
Rainfall Fifteen daily raingauge records were used to represent the basin above the Gannano barrage. In addition, autographic raingauge charts were obtained and digitized for five stations (Castelsaraceno, Nova Siri Scalo, Guardia Perticara, Tramutola and Senise). The autographic records were then used to disaggregate the totals for the daily gauges to hourly amounts, matching each daily gauge to the nearest autographic gauge. In this way a basin record of 11 years (1978–1988) of hourly rainfall was generated for the 15 gauges, whose areal coverages were determined using Thiessen polygons. (Only the period August 1983–December 1988 was actually used in the simulations.) An example of the time series is shown in Figure 29.3. An excellent spatial and temporal distribution was therefore obtained for the principal model input. Generally, rainfall records for the Agri Basin extend over about 70 years. Annual rainfall varies from 530 mm at the coast to 1100 mm in the mountains. However, the distribution of rainfall through time is erratic and uneven, with daily rainfalls in excess of 200 mm occasionally recorded in some areas. Snow may fall on the higher mountains in winter. Further details of the rainfall regime are given in Mazzanti et al. (1998).
Evaporation Daily pan evaporation data and automatic weather station data for calculating evaporation by the Penman formula were available for the simulation period for an experimental site at Policoro, downstream of the Gannano barrage. In addition, daily temperature data were available for the simulation period for this site, the agricultural research station at Corleto Perticara (within the basin) and at the Pertusillo dam. Daily potential evapotranspiration was therefore generated from the daily mean temperature measurements at the three sites using the Blaney–Criddle formula: PE = p (0.46 T + 8.13)
(1)
where PE is daily potential evapotranspiration (mm day−1 ); p is the percentage of the annual hours of daylight each day, expressed as a mean daily value for each month (%); and T is mean daily temperature (◦ C). As the formula tends to overestimate winter evapotranspiration and underestimate summer evapotranspiration, the calculated data were corrected according to the relationship between the ratio of the Blaney–Criddle and Penman derived monthly evapotranspirations and the mean monthly temperature, for the Policoro station. The resulting potential evapotranspiration data were distributed spatially according to the elevations of the three temperature measurement sites (Policoro, 31 m; Pertusillo, 533 m; Perticara, 750 m). Building on Denmead and Shaw (1962) and Feddes et al. (1976) and with the advice of Professor Ian Calder (University of Newcastle upon Tyne), actual evapotranspiration was calculated from a relationship between the ratio of actual to potential evapotranspiration and soil moisture potential (Figure 29.4). Different relationships were proposed for crops (without distinguishing between crop types), native vegetation and bare soil conditions.
Discharge For the simulation period there are no river gauging data. Discharge could therefore be determined only from daily water balance data for the Pertusillo reservoir (Figure 29.1), giving daily inflow to the reservoir from its catchment area of 585 km2 . However, the reservoir outflow to the river downstream (which forms an essential input to the model channel network) could be calculated with only monthly resolution, too coarse to permit satisfactory representation of typical storm events. It
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403
1988 Rainfall (mm/hr)
20
10
0 Jan
Feb Mar
Apr May Jun
Jul
Aug Sep
Oct
Nov Dec
Aug Sep
Oct
Nov Dec
Aug Sep
Oct
Nov Dec
Aug Sep
Oct
Nov Dec
1987 Rainfall (mm/hr)
20
10
0 Jan
Feb Mar
Apr May Jun
Jul 1986
Rainfall (mm/hr)
20
10
0 Jan
Feb Mar
Apr May Jun
Jul 1985
Rainfall(mm/hr)
20
10
0 Jan
Feb Mar
Apr May Jun
Jul
Time (hours)
Figure 29.3 Example of hourly rainfall time series for the simulation period, January 1985–December 1988
was therefore simulated as a small percentage of the calculated inflow (5% as an initial estimate). The remainder of the inflow was removed from the simulation since (in reality) it is transferred out of the basin by pipeline. It had been expected that water balance data could similarly be used to determine a discharge time series at the Gannano barrage (hence the choice of this site for defining the simulation area). However, the data were found to be incomplete and the Pertusillo record therefore provides the only means of checking the simulation results. Essentially it allows an internal validation of the Agri model, for the upper part of the basin. Water from springs and groundwater sources is used for irrigation in the upper Agri Basin and the coastal zone. In addition there are large water transfers out of the Agri Basin to supply irrigation schemes in neighbouring basins.
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Actual/potential evapotranspiration
1.0 Sparse vegetation/bare ground Native vegetation Crops
0.8
Upper Bounds
Lower Bounds 0.6
0.4
0.2
0.0 −150.0
−100.0
−50.0
0.0
−150.0
−100.0
−50.0
0.0
Soil moisture potential (m)
Figure 29.4 Uncertainty bounds for the relationship between the ratio of actual to potential evapotranspiration and soil moisture potential applied in SHETRAN, for three land covers
4 4.1
SHETRAN SIMULATIONS Simulation Constraints
The limited availability of the discharge data and the approximate nature of the soil parameter evaluations meant that a comprehensive, fully validated application of SHETRAN to the Agri Basin was not possible. The applications were therefore carried out with the more limited aim of demonstrating that it is feasible to apply SHETRAN to a basin the size of the Agri and achieve physically reasonable results. Consequently the simulation results should be viewed as illustrations of modelling potential rather than definitive descriptions of the Agri Basin response. Reflecting the uncertainty in the model parameter values, the results are presented in the form of uncertainty envelopes as described in section 2.3. 4.2
Hydrological Simulations
The first stage in applying the model was establishment of parameter baseline values. These are not necessarily the most accurate or most representative values. Instead, they are best-estimate values, based on available data and the modeller’s own hydrological judgement, although there may also be a degree of calibration in their evaluation. They form the basis for selecting the parameter bound values, which in turn enable the output uncertainty envelope to be determined. In the case of the Agri, a number of preliminary simulations assisted in the evaluation of those model parameters and functions for which there was the greatest uncertainty and to which the model output was most sensitive. These were soil depth, the saturated hydraulic conductivity for the fractured rock layer, the Strickler overland flow resistance coefficient and the variation of the ratio of actual to potential evapotranspiration with soil moisture potential. All other model parameters and functions were set directly from measured data or data in the literature as described in section 3.2. Through the preliminary simulations, empirical adjustment of the fractured rock conductivities was found necessary to represent the winter baseflows. The adjusted conductivities are rather higher than might be expected for the local soil types and lithology and are an example of the “effective” values referred to in section 2.2.
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Bound values for the four critical parameters and functions were set by defining maximum and minimum values to bracket the baseline values, based on past experience, hydrological judgement and information in the literature. In the case of the soil thickness, bounds were set only for the total column length; the topsoil thicknesses were not varied. Similarly, bounds for the saturated conductivity were set only for the fractured rock layer which, occupying most of the soil column, exercised more influence on the groundwater simulation results than did the topsoil. For the Strickler coefficient, the baseline and bound values were set on the assumption that land use controls the surface roughness. Similarly, for the evapotranspiration relationship, the bound values varied with land cover (Figure 29.4). Table 29.1 shows the baseline and bound values. To determine the output uncertainty bounds, simulations were carried out for each combination of the four sets of maximum and minimum parameter or function values, giving 24 or 16 runs. Each simulation was started in August 1983 so that the results for the period of interest, 1985–1988, did not show a dependence on the initial conditions. (The length of the “run-in” period was selected from preliminary tests.) Output bounds for the discharge into the Pertusillo reservoir were obtained by superimposing the 16 simulated time series of daily discharge to create an uncertainty envelope. Model application was then completed by determining the proportion of time for which the observed daily discharge record was contained within the envelope. For the period 1 January 1985–31 December 1988, the containment was 79% when calculated for daily flows and 74% when calculated for monthly runoff. (There are 39 days of record in 1986 that are missing from the measured discharge time series, so these results refer to 1422 days out of the full simulation period of 1461 days.) Figure 29.5 compares the simulation bounds with the observed monthly discharge record for the basin to the Pertusillo reservoir for the full simulation period. Figures 29.6 and 29.7 make the same comparison for daily discharge for 1985 and 1987 respectively. (The 1988 discharge time series is similar to 1985 in having several high flow events at the end of the year, while the 1986 time series Table 29.1 Values for the model parameters and functions used in determining the simulation bounds
Parameter/function Soil column thickness (m) Saturated hydraulic conductivity for fractured rock (m day−1 ) Flysch Sandstone Limestone
Minimum value 5
0.1 10 10
Baseline value
Maximum value
10
20
0.2 20 20
0.4 40 40
15 20 10 5 5 2 2
Strickler overland flow resistance coefficient Bare soil Bare rock Field crops Pasture Macchia Deciduous forest Coniferous forest
3.75 5 2.5 1.25 1.25 0.5 0.5
7.5 10 5 2.5 2.5 1 1
Evapotranspiration function: maximum value of actual/potential evapotranspiration ratio (see Figure 29.4) Crops Native vegetation Bare soil
0.6 0.4 0.2
– – –
1.0 0.6 0.2
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observed simulation bounds simulated
Monthly discharge volume (m∗∗3)
1.0e + 08
8.0e + 07
6.0e + 07
4.0e + 07
2.0e + 07
0.0e + 00 0
12
24 Time (months)
36
48
Figure 29.5 Comparison of observed monthly discharge into the Pertusillo reservoir with the simulated uncertainty envelope for 1 January 1985–31 December 1988
2.0e + 07
Daily discharge (m∗∗3)
observed simulation bounds simulated 1.5e + 07
1.0e + 07
5.0e + 06
0.0e + 00
0
50
100
150
200
250
300
350
Time (days)
Figure 29.6 Comparison of observed daily discharge into the Pertusillo reservoir with the simulated uncertainty envelope for 1985
follows 1987 in having no such events.) Table 29.2 shows the measured annual water balance for the basin to the Pertusillo reservoir and an annual summary of the uncertainty bound simulations for the basin to the Pertusillo reservoir and the full 1532-km2 basin to the Gannano barrage. At the annual scale, as also for the full four-year period, the simulation bounds contain the measured runoff totals for the basin to the Pertusillo reservoir. In Table 29.2 simulation results are shown both for the 1422-day period with measured reservoir water balance data (to allow comparison of measured and simulated runoffs) and for the full 1461-day simulation period (for completeness of summary).
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Daily discharge (m∗∗3)
observed simulation bounds simulated 1.0e + 07
5.0e + 06
0.0e + 00 731
781
831
881
931
981
1031
1081
Time (days)
Figure 29.7 Comparison of observed daily discharge into the Pertusillo reservoir with the simulated uncertainty envelope for 1987 Table 29.2 Hydrological mass balance and flow simulation results for the Agri Basin above the Pertusillo reservoir and the full basin above the Gannano barrage
Year
Measured rainfall (mm)
Measureda Total runoff Best-r 2 potential runoff/rainfall Best-r 2 Upper Lower ratio evapoMeasuredb transpiration (mm) simulation simulation simulation (mm) (mm) bound bound (mm) (mm)
Pertusillo reservoir 1985 1257 1986 1038 1987 957 1988 929 Average 1045
1348 1302 1332 1353 1334
536 320 313 321 372
Gannano barrage 1985 1016 1986 832 1987 853 1988 811 Average 878
1363 1327 1348 1364 1350
– – – – –
a
655 467 (366)c 283 238 411 (386)c
851 613 (477)c 429 383 569 (535)c
436 302 (223)c 162 150 263 (243)c
0.52 0.45 (0.35)c 0.30 0.25 0.39 (0.37)c
262 99 79 83 131
335 149 139 140 191
198 63 42 52 89
0.26 0.12 0.09 0.10 0.15
Calculated from measured temperature record; see section 3.2. Calculated from measured reservoir water balance data; see section 3.2. c Calculated only for days with measured reservoir water balance data; all other results refer to all simulated days. b
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Daily discharge (m∗∗3)
observed simulated
5.0e + 06
0.0e + 00
0
50
100
150
200
250
300
350
Time (days)
Figure 29.8 Comparison of observed daily discharge into the Pertusillo reservoir with the best-r 2 simulation for 1985
In the presence of uncertainty, any single simulation is only one out of the range of possible basin representations accounted for by the uncertainty envelope. Single simulations should therefore be presented, if at all, with care. However, a single flow simulation was required as input to the sediment transport simulations. Out of the 16 simulations described above, the one with the closest agreement with the observed hydrograph (as indicated by the r 2 value) was therefore selected. At r 2 = 0.564, that agreement is by no means perfect: visual comparison of observation and simulation shows both overestimation and underestimation of flow peaks (e.g. Figure 29.8). However, the general pattern is well represented. An annual summary of the best-r 2 simulation is shown in Table 29.2. 4.3 Soil Erosion and Sediment Yield Simulations Simulations were carried out to demonstrate the capability of SHETRAN for representing soil erosion and sediment yield at the large basin scale. There are no relevant sediment yield data for the Agri Basin and the simulation results were therefore examined by comparison with measured sediment yields in other Mediterranean basins. The values of the additional model parameters for the sediment simulations were estimated from the soil and vegetation data used in the hydrology simulations, from the literature and from experience with previous applications. In particular, the three soil erodibility coefficients were based on the values used in simulating catchments in Mediterranean France and Portugal (Lukey et al. 1995; Bathurst et al. 1996). Information on the sediment size distribution was obtained from a survey along the Agri channel carried out during the spring of 1994. Five representative sizes (for the hillslope and channel) were selected, ranging from 0.1 mm (fine sediment) to 256 mm (channel bed boulders). The Engelund–Hansen equation was used to determine sediment transport capacity for overland flow. To represent uncertainty, upper and lower bound values were set for the erodibility coefficients (10 and 0.1 J−1 for the raindrop impact erodibility coefficient and 20 and 1 mg m−2 s−1 for the overland flow and the bank material erodibility coefficients). An envelope of sediment yield results was then produced, its upper bound based on the combined use of the upper bounds for the erodibility coefficients, its lower bound corresponding to the lower coefficient values. The sediment simulations were driven by flow data from the best-r 2 hydrology model. In this way, the uncertainty, as
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represented by the bounds, is due only to the uncertainty in evaluating the erodibility coefficients. However, inaccuracies in the flow simulation have a direct impact on the accuracy of the sediment yield simulation. The errors apparent in the best-r 2 simulation (Table 29.2) mean that the sediment yield simulations are similarly in error compared with how they would appear if based on a completely accurate hydrological simulation. The simulation results are given in Table 29.3 and the simulated variation in daily sediment yield for 1985 for the basin to the Pertusillo reservoir is shown in Figure 29.9. In Table 29.3 the sediment yields for the Gannano barrage refer to the full 1532-km2 basin. Table 29.3 Sediment yield simulation results for the Agri Basin above the Pertusillo reservoir and the full basin above the Gannano barrage
Best-r 2 simulated runoff (mm)
Upper bound (t ha−1 year−1 )
Lower bound (t ha−1 year−1 )
Pertusillo reservoir 1985 1257 1986 1038 1987 957 1988 929 Average 1045
655 467 283 238 411
12.2 4.0 3.3 2.9 5.6
11.1 3.4 2.9 2.6 5.0
Gannano barrage 1985 1016 1986 832 1987 853 1988 811 Average 878
262 99 79 83 131
7.0 9.1 6.0 5.1 6.8
4.0 5.3 3.3 2.8 3.9
Year
Rainfall (mm)
Simulated sediment yield
1.0e + 07
8.0e + 07 simulated water discharge 6.0e + 07
5.0e + 06
4.0e + 07
2.0e + 07
0.0e + 00
0
50
100
150 200 Time (days)
250
300
350
Daily sediment yield (kg)
Daily discharge (m∗∗3)
simulated sediment yield bounds
0.0e + 00
Figure 29.9 Simulated sediment yield bounds and best-r 2 simulation of daily water discharge for inputs to the Pertusillo reservoir for 1985. In this case the sediment bounds are almost indistinguishable
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DISCUSSION OF RESULTS
5.1 Hydrological Simulations The estimated nature of important model parameters and the lack of good quality validation data limit the conclusions that can be drawn regarding the performance of SHETRAN in the Agri simulations. However, the simulations, as far as they go, are encouraging concerning the feasibility of applying SHETRAN to basins of the size and nature of the Agri. The 79% containment of the measured Pertusillo discharges by the simulation envelope is considered to be a good result, comparable with figures achieved elsewhere, e.g. around 80% for the Rimbaud catchment in southern France (Parkin et al. 1996; J.C. Bathurst and J. Sheffield unpublished data) and for the Reynolds Creek catchment in Idaho, USA (J.C. Bathurst unpublished data). At the annual scale the containment is 100%, suggesting an ability to represent, within the limits of uncertainty, the annual water balance. It is particularly satisfactory that this ability is demonstrated for a range of annual rainfall totals: from 1985 to 1988 the annual rainfall for the Pertusillo basin fell by 31% relative to the average annual value for the four-year period (Table 29.2). An important indicator of simulation success is the extent to which the simulation bounds represent the pattern of observed hydrograph variability. The uncertainty envelope should be wide enough to contain most of the observed hydrograph but not so wide that its representation of the various features of the hydrograph (individual peaks, recessions, baseflow) is meaningless. In this case, for the basin to the Pertusillo reservoir, the bounds for the daily discharge hydrograph are not unreasonable (Figures 29.6 and 29.7). Some of the winter peaks are overestimated but most are within the bounds and their general shape and timing are well represented. The bounds describe particularly well the late part of the recessions and emphasize the difference between the winter and summer baseflow magnitudes. Envelope width is therefore appropriate. In Figure 29.5, the fluctuations associated with individual events are absorbed into a smoother monthly variation. In this case the bounds represent well the month-to-month variation, with an excellent description of the summer low-flow periods. The timing of the peak monthly runoff each year is well represented but the simulated magnitudes change in accuracy through the simulation. Reflecting the decrease in annual rainfall from 1985 to 1988, both simulated and measured peak discharges decline from year to year. Good containment of the measured peaks within the uncertainty envelope is achieved in 1987 and 1988 but in 1985 and 1986 the simulations are too high. A number of simulation approaches were attempted but it did not prove possible to find one that could represent all four annual peaks with similar accuracy. The reasons for this are unknown but could include the following:
1.
errors in the rainfall data or in the reservoir water balance data from which the observed discharge hydrograph is calculated; 2. some catchment characteristic that acts to reduce the impact of an annual rainfall change on discharge and which is not represented in SHETRAN; 3. unknown human intervention in the Pertusillo basin, including water diversion or transfer. Figure 29.5 shows SHETRAN to be capable of predicting monthly discharge with an uncertainty envelope that is relatively narrow and describes well the observed variation. Such a capability is of particular use to the water resources planner, providing, in the absence of measured runoff data, a basis for setting upper and lower limits on water allocation. Table 29.2 shows that the simulated annual runoff in the catchment to the Gannano barrage is less than that for the basin to the Pertusillo reservoir. The two principal reasons for this are that, in the simulation, 95% of the inflow to the Pertusillo reservoir is removed from the system (to represent transfers from the reservoir by pipeline to a neighbouring basin) and that the mean annual rainfall for the basin to the Gannano barrage is lower than that for the basin to the Pertusillo reservoir (Table 29.2). The simulations thus illustrate an ability to model spatial variability in internal basin response as a function of annual rainfall. This is an important requirement for simulating the impacts of land-use changes (which are typically spatially distributed) and climate changes.
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Because of the extraction of the Pertusillo water, it is not possible to comment on the simulated discharge hydrograph at Gannano as the representation of a natural system. 5.2 Scale Effects
One of the aims of the SHETRAN application was to investigate the appearance of any scale effects in parameter evaluation associated with the use of the 2-km model grid. Because the parameters are evaluated as “effective” values, representative at the scale of the grid square, they must account for different integrations of subgrid processes depending on the grid scale. Previous studies with SHETRAN and other models suggest that the same model parameter values can be applied at the plot (1–100 m2 ) and microbasin (order 1 ha) scales, using small model grid spacings (20 m or less) and with a good availability of field data (Wicks et al. 1988; Connolly and Silburn 1995; Figueiredo 1998). With a 2-km grid, though, there is likely to be significant subgrid variability and it would not be unexpected for the effective parameter value to differ from the measured value, typically obtained at the point scale. For example, the saturated zone conductivity may increase to compensate for a reduction in simulated groundwater gradients caused by the use of large grid squares. Similarly the overland flow resistance may decrease to account for the inclusion of subgrid channel flow within a large grid square. Previous experience has suggested that scale effects in evaluating saturated zone conductivity are not significant as long as basin topography is subdued and there is a general homogeneity of land use, soil characteristics and hydrological response within the basin. For example, applications of the SHE modelling system (SHETRAN’s precursor) to large basins in India (area 800–5000 km2 ) (Jain et al. 1992; Refsgaard et al. 1992) and to the Cobres Basin in Portugal (area 701 km2 ) (Bathurst et al. 1996) suggest that conductivities evaluated at the point or small scale can be successfully applied with a model grid spacing of 2000 m. Figueiredo (1998) similarly found no evidence of a scale effect when modelling a 137-km2 basin in north-east Brazil, although in this case the basin did not typically have a saturated zone in the soil column. However, an application to a more hilly basin in Idaho (area 234 km2 ) shows an increase in the calibrated value of saturated zone conductivity as the grid spacing increases from 50 m to 1000 m (J.C. Bathurst, unpublished data). The results for the Agri (a hilly basin) agree with the latter finding, since the conductivities required for a satisfactory baseflow simulation are large compared with the expected measured values (Table 29.1). For example, a typical conductivity for flysch is 0.0001 m day−1 , much lower than the model baseline value of 0.2 m day−1 . For overland flow resistance, the picture is less clear. In previous applications the Strickler coefficient has been evaluated as 1 (a relatively high resistance) for small basins but also for the Idaho Basin. For the India basins it was in the range 3–7 while for the Cobres it was set at 6. Figueiredo (1998) applied values of 15 and 25 at the basin scale as a function of the amount of bare ground. The Agri baseline values of 1–10 are largely consistent with the previously applied range but vary as a function of land use. It remains possible, therefore, that the Strickler coefficient increases slightly (i.e. resistance decreases) as grid scale increases but the effect does not appear to be large. Other factors such as the type of ground roughness may have a greater effect. 5.3 Soil Erosion and Sediment Yield Simulations
Table 29.3 shows the difference between the upper and lower bounds on the simulated annual sediment yields to be small. This suggests, for this particular case, a relative insensitivity to the soil erodibility coefficients (used to derive the uncertainty bounds), which in turn suggests that the simulated sediment yield is dominated more by limitations in the ability of overland flow or channel flow to transport eroded soil rather than by the hillslope or channel erosion itself. Such limitations could themselves be related to a relative infrequency of simulated overland flow and to the form of the overland flow and channel flow capacity transport equations. The erodibility coefficients provide a clearly defined basis for setting uncertainty bounds, using available experimental results and previous experience (e.g. Wicks et al. 1992). However, given that
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uncertainties in sediment yield simulation are usually larger than uncertainties in water flow simulation, their use in this case produces an unrealistically narrow envelope. Other areas of uncertainty that are known to affect the simulations and which might form an alternative basis for setting uncertainty bounds are the soil size distribution and the choice of transport capacity equation (Norouzi Banis 1998). However, with current data availability and process understanding, such a basis would be more subjective than one based on the erodibility coefficients. It should also be remembered that the sediment simulations are based on the best-r 2 hydrology simulation, with its associated errors. If the flow uncertainty were to be incorporated, the sediment bounds would be rather wider. The wettest year (1985) produces the highest simulated sediment yield. This is consistent with greater raindrop impact and overland flow erosion and a greater amount of runoff to transport the eroded material. Higher river flows also cause more bank erosion. For the other three years (1986–1988) there is rather less inter-annual variability, despite variations in the rainfall and best-r 2 runoff. This may be consistent with the general observation that annual sediment yield is affected more by a few events than by the annual rainfall and runoff totals. However, whether the true sediment yield followed this pattern is of course unknown. Because of the extraction of the Pertusillo water, it is not possible to comment on the simulated sediment yield at Gannano as the representation of a natural system. As the true sediment yield for the Agri Basin is unknown, the simulated sediment yields (in the range 2–13 t ha−1 year−1 ) can be assessed only in the context of measurements made elsewhere in the Mediterranean or other semi-arid regions. The yields are low relative to the long-term yield of 93 t ha−1 year−1 measured for a 804-km2 subcatchment of the neighbouring Sinni River (Blasi et al. 1991). However, they are comparable with the long-term yields of 2–11 t ha−1 year−1 measured for 13 basins of area 150–2400 km2 in a high erosion area of south-east Spain (Romero D´ıaz et al. 1992), and with an approximate range of 1–10 t ha−1 year−1 for catchments of area 1000 km2 in the south-east USA, derived from Walling (1983, Figure 3). The simulations are probably therefore of the generally correct order of magnitude but perhaps err on the low side. It may be noted also that 1985 was relatively wet compared with the 1951–1971 mean annual rainfall and the largest of the simulated yields may therefore also be relatively high for the Agri Basin. In 1985, the simulated sediment yield is higher for the basin to the Pertusillo reservoir than for the basin to the Gannano barrage. This agrees with the general observation that sediment yields tend to decrease as basin area increases (e.g. Walling 1983). However, the reverse is true for the simulated yields of 1986–1988. This is in spite of the lower runoff at the Gannano scale and the removal of transported sediment in the water transfer at the Pertusillo reservoir. (The simulation assumes the same sediment concentration in the water discharged from the reservoir to the Agri as in the water flowing into the reservoir.) One explanation for this pattern may lie in the balance between the sediment yield generated on the hillslopes and the yield derived from in-channel sources (bed and banks). From test simulations it was found that the simulated yield from the in-channel sources tended to mask the contribution from the hillslopes. Even for the hypothetical case where the whole of the Agri Basin was covered with the same vegetation (to ensure a uniform level of protection against soil erosion) and the Pertusillo reservoir was eliminated, the simulations still showed a downstream increase in sediment yield. Only by eliminating the in-channel sources and deriving sediment entirely from the hillslopes did the simulations show a downstream decrease in yields. Thus for the relatively dry period of 1986–1988 it is likely that little hillslope erosion was simulated and that yields were derived mainly from in-channel sources. By contrast, the greater rainfall of 1985 enabled more hillslope erosion to be simulated, with a consequent effect on the downstream variation in sediment yield. Analysis of the simulation data shows that the Gannano yield is higher than the Pertusillo yield during the period May–December; during January–April they are similar. The difference follows closely the different summer growths of vegetation simulated at the two scales. Some 31.5% of the basin to the Pertusillo reservoir is covered by deciduous trees but at the scale of the full basin to the Gannano barrage this cover is only 21.4%. In the Gannano Basin excluding the Pertusillo Basin the cover is 15.2%. In the simulation, as the trees increase their leaf area during the summer, they
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provide increased protection against soil erosion by raindrop impact. When the leaf area decreases at the end of the year, the erosion rates converge at the two basin scales. An additional explanation for the simulated downstream increase in yields may therefore be that the relatively greater extent of summer vegetation growth in the basin above the Pertusillo reservoir provides greater protection against erosion, thus reducing the sediment yield relative to that at the Gannano scale. Without validation data it is not known if the true variation of sediment yield along the Agri system is as simulated, nor if the above explanations are generally correct. However, the analysis shows how SHETRAN output can be examined to explain in physical terms an apparently unexpected result. The simulations indicate that careful account should be taken of in-channel and hillslope contributions when modelling sediment yield. They also raise the possibility that basins with large in-channel sediment supplies (from bed and bank erosion) may not show the conventional downstream decrease in sediment yield.
6 CONCLUSIONS A number of positive conclusions can be drawn concerning the application of SHETRAN to basins of the size of the Agri (1000–2000 km2 ) and use of the model in the management of basins threatened with desertification. However, because of the poor quality of the soil and flow data, it was not possible to validate the model comprehensively and the simulation results should be viewed as an illustration of potential rather than a definitive description of the Agri Basin response. 1. From comparison with the generated Pertusillo flow data, SHETRAN reproduces the overall water balance well, at the monthly and annual scales and within the limits of uncertainty. The daily discharge time series is also reproduced within reasonable uncertainty limits. The simulated sediment yield is similar to yields in high erosion areas; however, the simulation bounds could be revised by incorporating uncertainty from the flow calculations and changing the basis for representing uncertainty in the sediment calculations. Evaluation of the saturated hydraulic conductivity is consistent with previous suggestions that this parameter may show some dependency on model grid scale in hilly basins. This effect needs to be quantified but in general there does not appear to be any fundamental obstacle to applying SHETRAN to basins of the scale of the Agri. The simulation thus doubles SHETRAN’s tested scale of application, from 700 km2 in the MEDALUS Phase I project, increasing its relevance to the larger scales at which planning decisions are typically made. 2. The results show how SHETRAN can be applied to problem solving, even with sparse data, by defining uncertainty envelopes. Lack of data does not stop decisions from being made. The value of SHETRAN in such cases lies in its ability to quantify the potential consequences (i.e. the associated uncertainty) of making decisions in the absence of data. The uncertainty envelope quantifies the range of possible basin responses as determined from the available data. Decision makers can then design their projects to accommodate this range. Alternatively it may be more cost-effective to fund a data collection programme that enables the uncertainty to be reduced. 3. The Agri simulation demonstrates the superiority of physically based models for applications to catchments with poor or non-existent records of output data. Their parameters have a physical meaning and can therefore be specified from field measurements or information in the literature. More traditional models could have been calibrated for the basin to the Pertusillo reservoir using the observed discharge record but would have had no basis for extension to the scale of the basin to the Gannano barrage. 4. In a certain respect, mathematical models have the ability to form the concluding output and the principal practical deliverable of an interdisciplinary project such as MEDALUS. This is because they are the means by which the results of other components of the project can be drawn together to provide an overall view of the central problem. SHETRAN, for example, can incorporate knowledge gained from the small spatial scale experimental studies of soil physics, vegetation growth patterns and the effect of land use on hydrological response. It takes as input the results of meteorological surveys and climate scenario generation. It can be applied to
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examine the basin-scale impacts of trends identified by socio-economic studies and its output can provide information relevant to the mapping of desertification indicators. SHETRAN can be used to develop an understanding of basin response mechanisms and to highlight areas of poor understanding. Specifically, it provides a means of predicting the impacts of possible future changes in land use and climate, and thence for adopting appropriate measures for protection, management and rehabilitation of river basins. Further application of SHETRAN to the Agri Basin will seek to improve the soil property database and to predict the impacts of possible future changes in land use and climate on water and sediment yields.
ACKNOWLEDGEMENTS The authors are most grateful to the MEDALUS group of Professor F. Basso (University of Basilicata) for help in assembling the Agri Basin data set. They also thank the MEDALUS teams of Professor M.J. Kirkby (University of Leeds), Professor J. Thornes (King’s College London) and Dr J. Palutikof (University of East Anglia) for their help with data processing. Dr O. Hamad, Dr J. Sherwood and Dr J. Stunell (formerly postgraduate students at the University of Newcastle upon Tyne) are similarly thanked for digitizing and processing map and chart data. Professor Ian Calder (University of Newcastle upon Tyne) provided valuable advice on evapotranspiration relationships. The work described here was funded by the European Commission through the MEDALUS II (contract number EV5V-CT92-0164) and MEDALUS III (contract numbers ENV4-CT95-0115 and 0119) projects, and this support is gratefully acknowledged. The participation of Ms Vicente (at Departamento de Hidr´aulica y Medio Ambiente, Universidad Polit´ecnica de Valencia, Spain, at the time of the study) was funded by the European Commission’s ERASMUS international exchange programme for students.
REFERENCES Bathurst JC, Wicks JM and O’Connell PE (1995) The SHE/SHESED basin scale water flow and sediment transport modelling system. In Singh VP (ed) Computer Models of Watershed Hydrology. Water Resources Publications, Highlands Ranch, Colorado, pp. 563–594. Bathurst JC, Kilsby C and White S (1996) Modelling the impacts of climate and land-use change on basin hydrology and soil erosion in Mediterranean Europe. In CJ Brandt and JB Thornes (eds) Mediterranean Desertification and Land Use. John Wiley, Chichester, pp. 355–387. Beven K (1989) Changing ideas in hydrology – the case of physically-based models. Journal of Hydrology 105, 157–172. Beven K and Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes 6, 279–298. Binley A, Beven K and Elgy J (1989) A physically based model of heterogeneous hillslopes 2. Effective hydraulic conductivities. Water Resources Research 25, 1227–1233. Blasi L, Cassano G and Grauso S (1991) Valutazione dell’entit`a della sedimentazione nel bacino artificiale di M te Cotugno (media valle del fiume Sinni, Basilicata). Geologia Applicata e Idrogeologia XXVI, 111–139 (in Italian with English abstract). Bras RL (1990) Hydrology: An Introduction to Hydrologic Science. Addison-Wesley, Reading, Massachusetts. Brooks RH and Corey AT (1964) Hydraulic Properties of Porous Media. Hydrology Paper No. 3, Colorado State University, Fort Collins, Colorado. Connolly RD and Silburn DM (1995) Distributed parameter hydrology model (ANSWERS) applied to a range of catchment scales using rainfall simulator data II: application to spatially uniform catchments. Journal of Hydrology 172, 105–125. Denmead OT and Shaw RH (1962) Availability of soil water to plants as affected by soil moisture content and meteorological conditions. Agronomy Journal 54, 385–390. Dunne T and Leopold LB (1978) Water in Environmental Planning. Freeman, San Francisco. Engman ET (1986) Roughness coefficients for routing surface runoff. Proceedings of the American Society of Civil Engineers, Journal of Irrigation and Drainage Engineering 112, 39–53.
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Ewen J (1995) Contaminant transport component of the catchment modelling system SHETRAN. In Trudgill ST (ed.) Solute Modelling in Catchment Systems. John Wiley, Chichester, pp. 417–441. Ewen J and Parkin G (1996) Validation of catchment models for predicting land-use and climate change impacts: 1. Method. Journal of Hydrology 175, 583–594. Ewen J, Parkin G and O’Connell PE (2000) SHETRAN: distributed river basin flow and transport modeling system. Proceedings of the American Society of Civil Engineers, Journal of Hydrologic Engineering 5, 250–258. Feddes RA, Kowalik P, Neuman SP and Bresler E (1976) Finite difference and finite element simulation of field water uptake by plants. Hydrological Sciences Bulletin 21, 81–98. Figueiredo EE (1998) Scale effects and land use change impacts in sediment yield modelling in a semi-arid region of Brazil. PhD thesis, University of Newcastle upon Tyne, UK. Jain SK, Storm B, Bathurst JC, Refsgaard JC and Singh RD (1992) Application of the SHE to catchments in India. Part 2. Field experiments and simulation studies with the SHE on the Kolar subcatchment of the Narmada River. Journal of Hydrology 140, 25–47. Lukey BT, Sheffield J, Bathurst JC, Lavabre J, Mathys N and Martin C (1995) Simulating the effect of vegetation cover on the sediment yield of Mediterranean catchments using SHETRAN. Physics and Chemistry of the Earth 20(3/4), 427–432. Mazzanti B, Preti F, Romano N and Santini A (1998) Characterization of climatic evaluation by analysis of rainfall time series: the Agri Basin case study. Proceedings of the XXVI Congress of Hydraulics and Hydraulic Constructions, vol. II, Catania, Italy, 9–12 September CUECM, pp. 259–271 (in Italian with English abstracts). Norouzi Banis Y (1998) Data provision and parameter evaluation for erosion modelling. PhD thesis, University of Newcastle upon Tyne, UK. Parkin G, O’Donnell G, Ewen J, Bathurst JC, O’Connell PE and Lavabre J (1996) Validation of catchment models for predicting land-use and climate change impacts: 2. Case study for a Mediterranean catchment. Journal of Hydrology 175, 595–613. Quinton JN (1997) Reducing predictive uncertainty in model simulations: a comparison of two methods using the European Soil Erosion Model (EUROSEM). Catena 30, 101–117. Rawls WJ and Brakensiek DL (1989) Estimation of soil water retention and hydraulic properties. In MorelSeytoux HJ (ed.) Unsaturated Flow in Hydrologic Modeling Theory and Practice. Kluwer Academic, Dordrecht, The Netherlands, pp. 275–300. Refsgaard JC, Seth SM, Bathurst JC, Erlich M, Storm B, Jørgensen GH and Chandra S (1992) Application of the SHE to catchments in India. Part 1. General results. Journal of Hydrology 140, 1–23. Romano N and Santini A (1997) Effectiveness of using pedo-transfer functions to quantify the spatial variability of soil water retention characteristics. Journal of Hydrology 202, 137–157. Romero D´ıaz MA, Cabezas F and L´opez-Berm´udez F (1992) Erosion and fluvial sedimentation in the River Segura basin (Spain). Catena 19, 379–392. Santini A, Romano N and Coppola A (1996) Geostatistical analysis of soil spatial variability in a hillslope of the Agri river basin. In Problems with Large Irrigation Districts. Proceedings of the East-Sesia Farmers’ Union Conference, Novara, Italy, 6–7 June, pp. 281–293 (in Italian with English abstracts). Walling DE (1983) The sediment delivery problem. Journal of Hydrology 65, 209–237. Wicks JM and Bathurst JC (1996) SHESED: a physically-based, distributed erosion and sediment yield component for the SHE hydrological modelling system. Journal of Hydrology 175, 213–238. Wicks JM, Bathurst JC, Johnson CW and Ward TJ (1988) Application of two physically-based sediment yield models at plot and field scales. In Bordas P and Walling DE (eds) Sediment Budgets. International Association of Hydrological Sciences Publication No. 174, Centre for Ecology and Hydrology, Wallingford, UK, pp. 583–591. Wicks JM, Bathurst JC and Johnson CW (1992) Calibrating SHE soil-erosion model for different land covers. Proceedings of the American Society of Civil Engineers, Journal of Irrigation and Drainage Engineering 118, 708–723.
Section VIII
Conclusions
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J.B. THORNES
Department of Geography, King’s College London, UK
1 INTRODUCTION “Any policy oriented measures directed at the monitoring and control of land degradation and desertification in the Mediterranean need to recognize that there is no simple panacea for the achievement of sustainable land management.” (Ghazi 1999)
From reading the previous 29 chapters in this book, what emerges most clearly is that there is no magical underlying truth that pulls it all together for the reader. The essential diversity of the landscape arising from physique and culture, and the palimpsest character of this mosaic that arises from its history, are such that the search for universal truths about causes and remedies for desertification and the appropriate actions to be taken are as diverse as the mosaic of landscape itself. As a result, the wheel turns on another axis. It is to identify elements of the mosaic that are homogeneous enough to justify common approaches to management. These can then be supported at national and transnational levels, and policies developed that are robust enough to satisfy the enormous diversity of the Mediterranean environment, thus avoiding the problem that local anomalies undermine the policy by producing unacceptable outcomes. Such outcomes may lead not only to ridicule, but to social injustice among the recipients of the policy and to ultra-conservatism among the policy makers. Unfortunately the anomalies and injustices emerge only in the implementation and then only after 30 or 40 years of struggling on both sides. It would be better to design policies or implementation mechanisms that are flexible enough to cope with the diversity that arises from the mosaic and the palimpsest. To some extent this has been achieved by the sharing of power and costs between the central authority (the European Commission) and the national governments or their autonomous regions. The devolution of power in this form encourages recognition of the spatial mosaic, even at the very local level, and calls for a clearer identification and resolution of the mosaic. This was attempted in MEDALUS I through the concept of desertification response units and in MEDALUS II by examining the desertification of environmentally sensitive areas. More recently, efforts have involved key indicators of desertification, as described by Imeson and Cammeraat in Chapter 14.
2 DIFFERENT PROBLEMS AND CAUSES IN THE TARGET AREAS In MEDALUS II, in an attempt to stimulate interdisciplinary approaches, it was decided to focus on several target areas and this approach served the project well. The target areas were chosen because they were known to be significantly affected by desertification, but at the same time offered marked between-area differences to capture the main contrasts within the Mediterranean region as a whole. They include the Guadalent´ın Basin in south-east Spain, the Agri Valley in southern Italy, north-west Sardinia and the island of Lesvos. All are “dry” Mediterranean areas and come within the ICCD (United Nations 1994) definition of desertified regions. By focusing thematic and modelling efforts, it was possible to draw on the extensive existing knowledge of these areas and Mediterranean Desertification: A Mosaic of Processes and Responses. Edited by N.A. Geeson, C.J. Brandt and J.B. Thornes 2002 John Wiley & Sons, Ltd
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on the earlier monitoring efforts of the MEDALUS Programme, as indicated by Brandt and Thornes (1996). Descriptions of these target areas and the sample thematic and modelling efforts carried out within them are given in Chapters 17 to 29. In this section I take the opportunity to reinforce the point that, although desertification is the recurrent theme, the main thematic problems are different in character in each area: water in the Guadalent´ın, grazing in Sardinia and Lesvos, and tillage methods in the Agri Valley, Basilicata. In the Guadalent´ın Valley water resources, both surface and groundwater, dominate the desertification problem through intensive irrigated agriculture and its associated problems. An increase in irrigated agriculture has been the main form of change in the Guadalent´ın over the last 50 years, using mainly groundwater and water from the inter-basin transfer which brings water some 300 km from the Tajo River in central Spain. In 1973, 24 Hm3 year−1 were extracted locally, rising to 56 Hm3 year−1 in 1990, but reducing to 30 Hm3 by 1996 because, with the water table at a depth of 290 m, water extraction had become very expensive. Irrigated lands that depend on local groundwater are being abandoned, especially where water extracted from wells is becoming saline. These combined effects have led to a wave of land abandonment. The salinity and the effects of intensive cultivation have left the soils impoverished and this is the first step towards desertification. In Chapter 21, the authors track longer-term demographic and economic changes since the end of the 18th century. In developing the Plan to Combat Desertification in the Guadalentin Basin (Chapter 22), Rojo Serrano et al. review the early demands for reafforestation of large areas to avoid damage and loss of life from flooding and debris flows. Afforestation, as the universal palliative to land degradation, is not a new concept, but there was renewed clamour after the disastrous floods in the basin in 1973 which led to great damage of property and loss of life. Chapter 22 gives a careful account of the development of the Management Plan and reveals that 62 watershed restoration projects distributed across the basin have been completed since 1885. The survey of past restoration efforts indicates that the mechanized afforestation techniques, such as terracing subsoiling, have been more effective than manual ones (holes, bench terraces and strips) in cutting hillslope runoff, and retaining and storing as much water and moisture as possible. This leaves some doubt as to the dogma that asserts that traditional knowledge leads to best practice. The authors come implicitly to the conclusion that the ideal element to initiate recovery of the natural vegetation is Pinus halepensis which can act as a nursery species for Quercus rotundifolia. They use an ecologically based classification of the mosaic of land-use patches to propose the required action in all parts of the basin. In most of the basin, but depending on the area to be reforested, the species must be Quercus rotundifolia or Pinus halipensis. However, in some areas it will be possible to use Pinus pinaster or Pinus nigra mixed with Quercus rotundifolia. Elsewhere it has been proposed that the use of non-tree species constitutes a valuable alternative (Francis and Thornes 1990) and that the success or otherwise of afforestation in reducing runoff and soil loss depends on the sequence of climatic conditions in the early years of regeneration and the pattern adopted for planting the new trees (Obando 1997). In a nutshell, very varied rainfalls in the years succeeding planting should lead to little or no reductions, and planting in the lower part of the basin at a rate that is linear with time is most effective in reducing sediment yield. A case study of the Sardinian target area is given in Chapter 6, where the emphasis is on livestock agriculture and the impacts of the reduction of the traditional rotation of cereal–grazing–fallow and the associated abandonment of arable lands and reduction in forage availability. The authors engage in the important debate about overgrazing as a major cause of land degradation. As late as 1996, Seligman claimed that “Amongst all the factors that contribute to land degradation in the Mediterranean Basin, high stocking rates must be placed low on the list.” In Chapter 6, it is shown that this problem is a very complicated one, involving the effects of changing structure and composition of the pastures and their impact on soil porosity. Significant changes in structural porosity of the soil were revealed over a single year and high stocking rates were shown to be important in this particular case. The impact of the combination of fire and overgrazing occurring on the Greek island of Lesvos (Chapter 7) is limited to the survival of phrygana (maquis) through its adaptation and through
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management practices. It is shown that attempted complete exclusion of fire only brings extra problems of biomass accumulation, higher temperature burns and excessive damage to vegetation and seed banks. The embedded study of the island of Chios shows how fires reduce the capacity to produce livestock feed, leading to rises in fodder imports in the decade 1981–1991 and this has led to a feedback spiral: to minimize import costs, natural ecosystems have been “completely” overgrazed. The combination of grazing and fire and its impact on community structure have also been studied in detail as a thematic topic by Dalaka et al. (Chapter 9), where the hypothesis of convergent evolution in Mediterranean environments is also discussed at length. They conclude that, while grazing mainly affects individual activities, it is fire or “corrective activities” (such as reforestation) that change species composition and alter the community status. They also conclude that the duration of that action, rather than specific environmental parameters, seems to be the cause of divergence between communities, indicating how important the planning of land use is when acting to reverse desertification. In the Agri Valley of Basilicata, Italy (Chapters 24–26) the contrast between the upper, middle and lower sections reveals, within the local-scale mosaic, contrasting behaviour and contrasting approaches to desertification mitigation. Unlike the other two target areas, the problems here arise largely from the physiographic conditions, with clay soils, mountainous topography and a history of deforestation giving rise to severe degradation. Here long-term field experiments at Guardia Perticara on appropriate cropping systems for sustainable agriculture have examined how soil tillage practices change the chemical, organic and physical structures of the clay soils under the dominant crops, durum wheat and horsebean for seed. Here, too, the traditional practice is crop rotation with fallow, durum wheat/chickpea, durum wheat, vetch/oats. The experimental results confirm that fallow improves the conservation of water, but the ecological and technical importance of this response varies in the different environments that occur within the Basin. They also conclude that minimum tillage does not produce great differences from traditional tillage practices in terms of economic and energy costs. In the Agri Valley, human activity in the past allowed widespread deforestation as a consequence of the need to enlarge the area of arable land. Subsequently, much of the cleared land situated on slopes has become difficult to farm and has consequently been abandoned. This is a recurrent theme in all target areas and in other areas studied. The onset of abandonment for a variety of reasons leads to a downward spiral of less intensive and poorer labour inputs in the form of conservation measures, leading inexorably to a reduction in the quality of rural life. In the Guadalent´ın, it was the increase in available irrigation resource that led to population increase and extension onto unsuitable territories which subsequently led to abandonment and neglect of land, which in turn produced salinized and eroded soils and unacceptable standards of living. As in other parts of Europe, the push for productionist agriculture, with its associated system of national and European Community subsidies, has sustained agriculture beyond the limit. Today in the Guadalent´ın, “much of the agriculture is now dependent on subsidy” (Chapter 21). To this must be added the over-extension resulting from new irrigation and the associated massive crop changes (the area of “forest” that includes matorral and other scrub forms decreased by 35% between 1947 and 1989). In the Guadalent´ın, the biggest change was in the area devoted to cereals and almonds on dry lands and the area used for citrus on irrigated land. Scrub land has been ploughed for cultivation and afforestation and some of the ploughed land has since been abandoned. It is the view of the authors of Chapter 21 that the current degradation in the Guadalent´ın Basin is closely related to the socio-economic changes they observed and the decline in the rural population. The main thrust of the revised Common Agriculture Policy (CAP), in the form of Agenda 2000, is towards sustaining rural populations by non-productionist methods. This can only be interpreted as implicitly positively beneficial from a desertification point of view. Nevertheless, because the agriculture of the Guadalent´ın Basin, and most of the agriculture of the Mediterranean, is dependent on subsidies, the removal of those subsidies (as proposed in the McSharry reforms to the 1992 GATT round and the restraints of World Trade Organization agreements) could precipitate an economic collapse of marginal incomes that might do far more than climate change in exacerbating the existing land degradation.
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THEMATIC STUDIES
Vegetation as a control on erosion pervades almost all the chapters in this book. Quinton et al. (Chapter 8) provide an analysis of the bio-engineering properties of different species for mitigation, based on rainfall simulations accompanied by measures of soil loss. Their contribution culminates in a table of semi-natural vegetation treatments that will provide an important source for future mitigation activities, whilst recognizing that more work is needed on developing ecological successions and re-vegetation methods that promote a sustainable, high value canopy or ground cover. The decline in the rate of erosion with cover is greatest for shrub and bush covers than for grasses. Improvements in the capacity to identify vegetation covers from remotely sensed imagery in sparse vegetation, where the soil transmits an important part of the received signal (Chapter 10), will make it possible to plan mitigation actions more effectively in future than at present, through the ease of survey of the cover type that this development brings. The tight coupling of vegetation and climate (Chapter 20) means that the normalized difference vegetation index (NDVI) can be inverted to provide indicators of climate and climate change at a regional scale (INDVI). Increases in the INDVI based on a composite monthly NDVI for selected months correspond to decreases in the aridity levels, though there is a high spatial variability of the index, mainly due to seasonal contrasts. The authors conclude that their method “should work in all areas where the thermal factor is limiting in summer”. The recognition that most Mediterranean soils involve a higher percentage of rock fragments on, and in, the soil surface, necessitates a re-thinking of conventional runoff hydrology and hydraulics and its related implications for soil erosion. The extensive experimental work on the impact of rock fragments on soil degradation and water conservation (Chapter 11) indicates the importance of rock fragments to productivity. The relative biomass production of rainfed wheat can be related to the percentage of rock fragments as well as the evapotranspiration conditions. When all rock fragments were removed from the surfaces of 32 plots, the biomass production of rainfed cereals decreased by 2–30%. Another scourge of Mediterranean soils in the context of desertification is the problem of salinization caused by the salt content of the underlying parent material, excessive evaporation, sea-water infiltration, irrigation and other anthropogenic factors (Chapter 12). Again the first-order impact is abandonment of land engendering the downward spiral of rural economies. In Chapter 13, Postiglione also outlines the management option and techniques for this blight.
4
MODELLING
Much of the work in this phase of the project has involved direct observation in the form of field trials, plot experiments and archival sources. These have provided new information and empirical results (in the form of statistical models) that have both added to the armoury of management tools and thereby expanded the range of alternative management options. This volume provide a synthesis of these empirical results, but other products too are disseminating the results to the wider public, such as the Atlas of Mediterranean Environments: The Desertification Context (Mairota et al. 1998) and more than 1000 articles and chapters in books and scientific journals. The limitations of direct observations make them rather inadequate for longer-term decision making. They are but snapshots in time and space. As samples, there are the added problems of representativeness and transportability. Plot experiments are tiny fractions of the landscape and rather artificial at that. Their use is mainly to expand and confirm our best guesses about what is happening and to inform better bases for prediction. To try to overcome these uncertainties, the project has adopted a deterministic model building and realization for hillslope processes at the local and regional scale (as described in Chapter 16) and for catchment and local runoff and sediment yield in the specific context of the Agri target area (Chapter 29). Here, as is almost invariably the case in model applications, the main constraint is data for both calibration and validation. Indeed, the co-operation and complementarity between field observers and laboratory modellers of different disciplines, cultures and philosophies has been one of the most heartening successes of the project,
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which will have a lasting impact as the local ICCD plans develop in the context of this co-operation. It is through this channel that the complex physics of soil hydraulics can enter the decision-making process (Chapter 27). The bridging of science and society, a prerequisite for successful management, is better achieved through mutual co-operation in projects than by decree from paymasters, just as satisfying the needs of end users can only meaningfully occur by equal sharing of knowledge provided by involvement of local scientists who have the necessary insights to local problems and these need not only be the tillers of the soil. Optimally they will be broadly based scientists with wisdom and political influence at the many different decision-making levels. A primary requirement of the planning process is to have a clear knowledge of what has gone before (historical depth) and the capacity to recognize the likelihood of different responses of the different elements of the mosaic (geographical intuition), again stressing the importance of local experience as revealed by the development of mitigation needs in the Guadalent´ın and the Agri (Chapters 22, 24 and 25). The current format for this is to develop indicators that identify the degree of desertification and its spatial distribution as a shortcut to bypassing the modelling approach. Planners are always seeking the “single number” that will tell them what to do and where. The work that has been presented here on the basis of mainly empirical evidence, shows that the desertification problem is simply too complicated to be represented in the desired indicator fashion. This problem is recognized and discussed by Cammeraat et al. (Chapter 15). They follow Rapport and others in defining key indicators as follows: “An environmental attribute that, when measured, quantifies the magnitude of stress, habitat characteristics, degree of exposure to the stressor or degree of ecological response to the exposure.” Cammeraat et al. believe that a key indicator should also reflect linkages to other biotic and abiotic processes both at the same and higher scale levels. In this way, key indicators can be used for upscaling. The literature on indicators is almost as large as the list of indicators that are, or could be, used to identify desertification propensity or changes in desertification tendency.
5 EMERGENCE AND STABILITY Prompted by the chapters on indicators (Chapters 14 and 15), the final section of this concluding chapter attempts to think through this problem to identify a basis for the choice of indicators. The usual approach is to define the core requirement in such a way that it can be identified. Thus the ICCD defines desertification in terms of the climatic parameters of dry sub-humid, arid and semi-arid environments, by the ratio of rainfall to evapotranspiration. This is a kind of “legal” definition. If the mosaic component of interest does not fall into one of these classes, it is technically not covered by the Convention. Alternatively, in the Boolean type of definition, several indicators are used to classify the mosaic elements in a linear programming-type approach. These approaches invite the use of a hotchpotch of misunderstood, unknown, unmeasured or very subjective, often qualitative indicators that fail to capture either the dynamic or complicated character of the phenomenon. The substantive conceptual underpinning of the search for indicators must involve the following questions: • Is there any substantive value at which the rate of change of the process of degradation increases dramatically? • Is there any specific value of a variable at which there is a change from a negative feedback (constraining) to a positive feedback (unconstrained) behaviour in land degradation? The first is a step or catastrophe. The second is a bifurcation. Figure 30.1 shows schematic representations of these two cases and explicitly implies that vegetation cover is absolutely the key indicator that is required, probably in conjunction with measures of rainfall (both population and intensity) and soil water receptivity capacity. We can be sure that, in the mosaic of land uses, these variables do control the thresholds of erosion and the bifurcation of feedback type. They are easy, if tedious, to measure and can be captured at, and related to, different spatial scales. The first can be measured by remote sensing, the second by relating soil properties to geological properties, for which good maps are usually readily available.
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(a)
0% er
v
Soil erosion rate
co
Ve
t ge
at
io
n
30%
60% Rainfall /
rain day
(b)
Biomass (or plant cover)
Critical bifurcation
Trees Critical bifurcation Critical erosion thresholds 30% plant cover Matorral High erosion rates
Critical bifurcation
Degraded matorral Bare soil
Figure 30.1 (a) An example of a catastrophic representation of key indicators. The horizontal plane is the ‘‘control space’’ involving two key indicators for soil erosion (vertical axis). Note that the vegetation axis is decreasing away from the origin and that the rainfall intensity is increasing in the conventional manner. The surface is a three-dimensional representation of the equilibrium values of soil erosion with these key indicators. The whole surface tips down from right to left and is split in two surfaces, separated by a step (catastrophe) running from left to right. In the shaded area there are two possible rates of erosion, very high or very low. Throughout history, variations in the two indicator values move us through trajectories on the ‘‘control space’’ providing different values of erosion. These trajectories are called the ‘‘slow-dynamic’’ of the system. The response, in terms of soil erosion, is the ‘‘fast-dynamic’’. (b) Representation of the critical indicator actual evapotranspiration over rainfall and the vegetation response by biomass (cover) and type. As Eat /R increases to the right, the total biomass falls and the different types separate out at critical key values of the indicators
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One issue that concerns us greatly is that the landscape mosaic we see today has emerged from some primitive initial condition that itself has been evolving over the millennia. The complicated network of political boundaries most readily illustrates the point. These emerged as the interfaces of political or tribal competitive forces from the “spatial soup of prehistory”. This emergence is stimulated and controlled by the pre-existing surface, rather in the way that specialized cell functions are mapped out in the developing foetus (Wolpert 1998). This attracts the question, were the mosaics mapped out by some precursors (chemical in developmental biology, geological in drainage basin, physical features in political boundaries) or does the emergence reflect some underlying forces that give rise to spatial patterning, as with the development of crystals? The 1930s model of desertification as the “advancing Sahara” has been abandoned in derision and is being replaced by a contagion model. In this theory, desertification is a process more akin to measles: an otherwise unblemished surface is broken into patches identified as degradation hotspots which then expand to produce widespread desertification, moving out from many centres, representing a coupled erosion–deposition patchwork (Pickup and Chewing 1986). The key question then becomes, given a relatively uniform and healthy ecological surface, how will new patches susceptible to degradation emerge, and what are the key indicators for this emergence? If the desertification hotspots do emerge as a mosaic, we would like to know how stable they are, when subjected to perturbations, natural or anthropogenic, such as fire, climate change or intensive grazing and are there indicators of this stability? Brunsden and Thornes (1979) argued that this could be developed through a Transient Form Ratio, an idea that has been further developed by Phillips (1999) for desertification. Brunsden and Thornes (1979) suggested that the state of the system could be defined by a Transient Form Ratio (TFR), TFR = ta /tf , analogous to the safety factor in engineering. Here ta and tf are the mean relaxation and recurrence times respectively for the perturbations: ta is the recovery time and tf the average time between the events. If recovery takes a long time and the events are quite frequent, TFR is greater than 1 and transient forms will prevail, but if TFR is less than 1, stable forms prevail. This ratio has the advantage of incorporating the effects of lagged response to the perturbation. In the Mediterranean, the inter-annual fluctuations of rainfall are very great. In the Guadalent´ın, a year with 160 mm of rainfall may be followed by one with 500 mm of rain. In an unstable system, the cover will “track” the rainfall. In a stable ecology, the mean vegetation adjusts to the mean long-term rainfall even though there are violent inter-annual variations. This concept is particularly important for the study of change. In the natural state of the Mediterranean environment, patches are continually subjected to perturbations and those that can resist change are said to be resilient. Systems that are not resilient not only respond to change but may do so in a non-linear fashion. A small pulse can lead to a large change, perhaps even to the destruction of an entire ecosystem. Or the production of many new stable states may occur and this may further complicate the mosaic of land uses. There are many possible pathways and many possible destinations for the trajectories of change. The management skill is to know which trajectory will be followed and where it will lead. Destabilizing an otherwise stable condition is the most serious outcome, for the ripple effect so produced could engulf an entire nation. Once the downward spiral of rural depopulation starts it is progressively more difficult to arrest. The identification of the thresholds between stable and unstable systems and of the trajectories that will be followed after the threshold is crossed should form the basis of indicators that are relevant to the management. There is little to be gained from forcing the system back into an unstable state. The application of these principles has been demonstrated in relation to land degradation, the impacts of grazing, the effects of climate change on dry Mediterranean plant communities and most recently to the restructuring of vegetation cover and erosion along climatic gradients.
6 IMPERATIVES AND PRIORITIES Toulmin (2001) has issued a clear call asking if the ICCD should be substantially reformed. The main shortfalls she outlines are that the International Convention model is inappropriate; that desertification is still a poorly understood problem; that no clear link has been established between desertification
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and climate change or proof found that desertification leads to global climate change; and, above all, that there is no serious funding specifically linked to the Convention. The unwillingness of the World Bank to engage in the problem has limited its attraction for all audiences. Add to these the serious criticism of the Convention made by Thomas and Middleton (1995) that it was promoted as a device to bolster UNEP’s political clout on the world stage, and we must clearly question its relevance to the southern European Annex IV countries. In this book, we have shown that the problem in Europe is quite different from that in Africa, even if the essential causes and indicators are the same. The Convention has, then, a different context and status. Because of the complicated spatial, cultural and historical mosaic, serious cases of land degradation with their associated vicious downward spiral of land abandonment, desertification and unsustainable agriculture are embedded in economically advanced economies with alternative buffering capacities. Nevertheless, unemployment, rural impoverishment and land abandonment can still be linked to land degradation. Following its recognition of the problem at the Mytilene Conference in 1985 (Fantechi and Margaris 1986), through a further conference in Crete (Balabanis et al. 1999) and through debate in the European Parliament as well as its research projects in Research Frameworks IV and V, the European Commission has recognized and supported the Convention, both indirectly and directly. Indeed it could be argued that without the Convention, the interests of the European Commission might not have emerged to push the governments of the Mediterranean European states into action. Here, too, the prospect of financial return appears to be a powerful incentive to recognize the regional problems in the states that are directly bound to desertification, as understood by the Convention. Moreover, as reflected by the European Parliament’s action, the people of Europe understand the problem’s complex and complicated dimensions and are prepared to support direct action through Agenda 2000, the revised CAP. There remains the need for a flexible adaptation of the policy to the mosaic, but the role of the Convention in promulgating change is not in question. If “the lights are dimmed” (Toulmin’s phrase), progress in this direction in Europe will be more difficult to achieve.
7
CONCLUSION
Desertification is a major issue in the Mediterranean environment. Intensification of production has caused agriculture to extend well beyond degradationally stable patches and, as the post-productionist ethos (reduction of subsidies, internalizing of environmental costs) takes hold under a new EU Common Agricultural Policy, these areas will become economically even more marginal, abandonment will occur and desertion will lead to desertification. The imperative is for nations and the EU to recognize this and the priority is to establish machinery to deal with it. Desertification is a serious issue even in the advanced economies of southern Europe, but its special characteristics have to be recognized. Action will be needed up and down the decision chain on an information basis that varies with the level. Farmers’ priorities and national priorities differ, though the common goals of intergenerational equity, stable land-use mosaics and political empowerment need to be addressed at all levels. Throughout, the strong spatial differentiation that is characteristic of the Mediterranean and the conditional stability of its elements will determine how the mosaic can and will evolve.
REFERENCES Balabanis P, Peter D, Ghazi A and Tsogas M (eds) (1999) Mediterranean Desertification. Research Results and Policy Implications. Proceedings of the International Conference, 29 October–1 November 1996, Crete, Greece. Volume 1, EUR 19303, Directorate General for Research, Luxembourg, pp. 5–16. Brandt CJ and Thornes JB (eds) (1996) Mediterranean Desertification and Land Use. John Wiley, Chichester. Brunsden D and Thornes JB (1979) Landscape sensitivity and change. Transactions of the Institute of British Geographers 4, 463–484. Fantechi R and Margaris NS (eds) (1986) Desertification in Europe. Reidel, Dordrecht. Francis CF and Thornes JB (1990) Matorral: erosion and reclamation. In J Albaladejo, MA Stocking and E Diaz (eds) Soil Degradation and Rehabilitation Under Mediterranean Conditions. CSIC, Madrid, pp. 87–117.
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Ghazi A (1999) Biodiversity and global change. In P Balabanis, D Peter, A Ghazi and M Tsogas (eds) Mediterranean Desertification. Research Results and Policy Implications. Proceedings of the International Conference, 29 October–1 November 1996, Crete, Greece. Volume 1, EUR 19303, Directorate General for Research, Luxembourg, pp. 5–16. Mairota P, Thornes JB and Geeson N (eds) (1998) Atlas of Mediterranean Environments in Europe: The Desertification Context. John Wiley, Chichester. Obando JA (1997) Modelling the impact of land abandonment on runoff and soil erosion in a semi-arid catchment. PhD thesis, King’s College London. Phillips JD (1999) Earth Surface Systems: Complexity, Order and Scale. Blackwell, Oxford. Pickup G and Chewing VH (1986) Random field modelling of spatial variation in erosion and deposition in flat alluvial landscapes in arid central Australia. Ecological Modelling 33, 269–296. Seligman NG (1996) Management of Mediterranean grasslands. In J Hodgson and AW Illius (eds) The Ecology and Management of Grazing Systems. CAB International, Wallingford, UK, pp. 359–391. Thomas DSG and Middleton NJ (1995) Desertification: Exploding the Myth. John Wiley, Chichester. Toulmin C (2001) Lessons from the theatre. Should this be the final call for the Convention to Combat Desertification? International Institute for Environment and Development, for the World Summit on Sustainable Development (Johannesburg 2002), London. United Nations (1994) International Convention on Combating Desertification. United Nations, Paris. Wolpert L (1998) Principles of Development. Oxford University Press, Oxford.
Glossary
Ablation rate: quantity of soil eroded from slopes and transported by running water. Generally expressed in t ha−1 year−1 . Absorption feature: a range of wavelengths (or frequencies) in the electromagnetic spectrum within which radiant energy is absorbed by a substance. Aggregate distribution: distribution of classes of soil aggregates (particles adhering to one another) according to their size. Albedo: proportion of incident solar radiation reflected by the clouds surrounding the Earth. Anchor station: an observation site at which quantities are measured that are needed to calibrate measurements made from satellites and to validate the information inferred from these measurements. Aqueduct: an artificial surface channel for conveying water. Aquifer: a geological formation of water-bearing rock with sufficient porosity and permeability to yield economic supplies of groundwater. Badland: an area where gullies adjoin each other and cover all, or nearly all, of the surface. Barrilla: halophytic plants (mainly Halogeton sativus). The burned ashes were used as raw material for soap production. It was an industrial crop of great importance in the 17th century right across the Guadalent´ın Basin. Biancane: a form of erosion with a typical dome-shaped configuration and a radial drainage network (Italy). Blown sand dunes: a hill or ridge of blown sand piled up by the wind. Boqueras: a traditional system of south-east Spain to take sporadic flow from ramblas (ephemeral flow channels) to crops. Bradiseysm: slow movement, either raising or lowering of the soil in localized areas of the Earth’s crust. Calanchi (badlands): erosion form that occurs in blue clay without vegetation (Italy). Calcixeroll: mollisol (i.e. with a dark organic-rich surface horizon) in a xeric soil moisture regime with a calcic or gypsic horizon within 150 cm below the soil surface (Soil Survey Staff 1975). Canestrato: cheese made from the milk of goats or sheep. Catena: a repeated sequence of soil profiles that is geographically related to and associated with relief features. Climate scenarios: internally consistent pictures of a plausible future climate; not predictions of future climate. Coltura mista: different crops (annual and perennial) cultivated on the same plot. Compaction: the development of a dense, compact surface soil layer (e.g. due to cultivation with heavy machinery, or overgrazing), characterized by a much lower permeability so impeding the movement of water and air, and the growth of plant roots. Confined aquifer: groundwater reservoir overlain and underlain by impervious or almost impervious rock formations. Coppice (ceduo): forestry stand originating primarily from sprouts (Italy). Coppice with standards (ceduo matricinato): method of reproduction in which selected trees arising from either seedlings or sprouts are maintained as standards above a simple coppice stand (Italy). Cortijo: farmhouse in southern Spain. Crusting: development of a surface layer on soils ranging in thickness from a few millimetres to a few centimetres, which is more compact, hard and brittle when dry than the material immediately beneath it (see also compaction).
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Desamortizaci´on: laws promoted in Spain by liberals in order to sell municipal, communal and church lands to private owners. Differing-aged stand (soprassuolo disetaneo): stand where trees of at least three age classes are intimately intermingled in the same area (Italy). Digital elevation model: an ordered collection of topographic elevation data for a particular area. Drill sowing (semina a righe): sowing of seeds in parallel, uniform bands that run the length of the seedbed (Italy). Esparto: the common name of the perennial grass Stipa tenacissima, an indigenous plant used for fibre production since at least the Bronze Age. Eustatic movements: raising or lowering, on a global scale, of the average sea level, mainly due to the melting or freezing of the polar ice-caps. Evapotranspiration: the combined loss of water from a given area and during a specified period of time, by evaporation from the soil surface and by transpiration from plants. Even-aged stand (soprassuolo coetaneo): stand where all trees are the same age or at least in the same age class (Italy). Field capacity: the percentage of water remaining in the soil following saturation and free drainage. Fine earth bulk density (Bdfe): the mass of dry soil per unit of volume excluding the volume of rock fragments. Forest index (indice di boscosit`a ): the percentage of the total surface covered by forest (Italy). General circulation models (GCMs): complex, three-dimensional computer-based models of the atmospheric circulation developed from numerical forecasting models and used to investigate future climate change. Geographical information system (GIS): system with digitized computer maps; these maps can be combined with other maps, or can be processed. Greenhouse effect: greenhouse gases in the atmosphere, such as carbon dioxide, are largely transparent to short-wave solar radiation, but absorb long-wave radiation from the Earth and so maintain the Earth at a temperature higher than it would be in their absence. This natural effect is enhanced by the release of greenhouse gases from human activities such as the burning of fossil fuel. Groundwater: subsurface water that occurs beneath the water table, occupying the pores of the soils and geological formations that are fully saturated. Gullies: deep water-worn channels, cutting through soil into weathered material and/or rock. Heat wave: thermal event during which the air temperature increases to several degrees above the normal value. High forest (fustaia): stand originating from seed (Italy). Infrared region: portion of the electromagnetic spectrum just beyond the red end of the visible spectrum, such as radiation emitted by a hot body. Interception: fraction of rainfall lost to evapotranspiration, due to retention of the canopy. Interception is equal to rainfall minus throughfall. LAI: leaf area index. The area of leaves above a given area of ground (usually one square metre). Land abandonment: land that has been converted from any form of agricultural production or from areas that have been heavily grazed, and then left and allowed to revegetate naturally. Complete abandonment implies that the land has been left to return to its natural state without any human influence, directly or from livestock. LANDSAT: acronym indicating a series of Earth resources scanning satellites; the data recorded by the sensors are widely used for land resources assessment. Latent heat flux: the heat of evaporation that is carried with the flow of moist air. Leaf water potential (): the energy status of the water contained in leaves. Loess: fine-grained, permeable, unstratified Pleistocene aeolian deposit. Macchia mediterranea: bushy vegetation made up of shrubs and low trees (Italy). See also matorral, maquis, Mediterranean scrubland. Maquis: a vegetation type of the Mediterranean area, mainly composed of evergreen broadleaved shrubs, less than about 5 m high (France).
Glossary
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Matorral : type of shrubby vegetation cover found in south-east Spain, comparable to the French maquis and garrigue and macchia mediterranea. Matorral arbusto is thicket and matorral matas is brushwood. Mediterranean scrubland: Mediterranean scrublands have resulted from the interaction between natural factors and very ancient human disturbance. The main control on the ecosystem is the annual summer drought. This intense hydric stress imposes a set of adaptations (such as sclerophyllous leaves) and characteristic structures of the plants. Meteorological bomb: depression with a rate of pressure fall of 1 hPa h−1 or more, lasting at least 24 hours at the latitude of 60 ◦ N (17 hPa in 24 hours at the average Mediterranean latitude of 38 ◦ N). Montado: savanna in Portugal, typically with cork oaks (Quercus suber), Quercus rotundifolia and some cultivated fields. Phreatic aquifer: porous water-bearing formation in which the groundwater table forms the upper boundary. Phrygana: undershrubs or dwarf shrubs, e.g. thyme, sage, the many species of broom, and species of Cistus and Phlomis. Undershrubs are not potential trees; they are short-lived and reproduce by seed. Rambla: ephemeral water course (Spain). Recharge: infiltration of the rain, first into the soil, then deeper towards aquifers. Rock fragments: mineral particles larger than 2 mm in diameter including all sizes that have horizontal dimensions less than the size of a pedon (Poesen and Lavee 1994). Rotation (turno): the period of years required to grow a crop of timber to its specific condition of economic or natural maturity (Italy). Saladeres: ecosystems typical of arid and semi-arid environments that can be termed cryptowetlands, as water is very rare over the soil surface but is the driving force responsible for their origin and the accumulation of salts in the soil which is an essential characteristic. Halophytic plants are characteristic (Spain). Salt water intrusion: penetration of salt water into freshwater aquifers under the influence of groundwater development. Savanna: grassland and/or shrubs with scattered trees that do not form a complete canopy. Scenario: internally consistent picture of a plausible future state (e.g. a model simulation of a possible future state, a projection of future climates). A scenario is not a prediction of a future state. Seed tree (riserva): tree left standing singly or in groups for the purpose of furnishing seed to restock the cleared area naturally (Italy). Silvicultural system (forma di trattamento): a planned programme of silvicultural treatment during the whole life of a stand (Italy). Spacing (sesto d’impianto): number and distribution of individual trees in artificial reproduction (Italy). Strip (group) shelterwood method (tagli successivi a strisce o a gruppi ): application of the shelterwood method in strips and groups or patches (Italy). Tending fellings (cure colturali, tagli colturali ): various cuttings with the object of the improvement of the existing stand (Italy). Terrace: narrow surface plane or with shallow slope build-up following the contour lines, with the aim of increasing the water-holding capacity and accumulating fertile soil. Thinnings (diradamenti ): intermediate cuttings aimed primarily at controlling the growth of stands through adjustments in stand density; cuttings in immature stands in order to stimulate growth of the trees that remain (Italy). Total bulk density (BDt): the mass of dry soil per unit of volume including the volume of rock fragments. Uniform shelterwood method (trattamento a tagli successivi uniformi ): cuttings uniformly applied over the entire stand (Italy). Wildings (selvaggione): natural seedling (Italy). Woodlands: portion of farm area devoted to tall forest, coppices and maquis (ISTAT 1982).
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REFERENCES ISTAT (1982) XIX Censimento Nazionale dell’ Agricoltura Italiana. Fascicoli provinciali Matera e Potenza. Poesen J and Lavee H (1994) Rock fragments in topsoils: significance and processes. Catena 23: 1–28. Soil Survey Staff (1975) Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys. USDA-SCS Agricultural Handbook 436, US Government Print Office, Washington, DC.
Index
Abandoned fields, vulnerability to erosion 11 Abbott, M.B. 370 Abies alba 385, 391 Abruzzo, remote-sensing characterization of climate 47–54 Acer lobelii 391 Adams, R.M. 164 Adaptation of Mediterranean ecosystem to survive fire 83–86 Aegean islands 87 Afforestation in Guadalent´ın Valley 393–394, 420 Agenda 21, 10 Agenda 2000 6, 7 Agri Valley 321–330, 419, 420, 422 data assembling and processing for SHE model 400–404 forestry in 385–395 Ganno Barrage 402 geology of 322–323 modelling basin hydrology 397–422 modelling hydrology and sediment yield 397–416 physical environment of 321–325 population 361–363 socio-economic aspects of 325–330, 361–367 soil erosion and land degradation 347–359 sustainable agriculture 331–346 Agriculture agricultural income, Guadalent´ın basin 294 Common Agricultural Policy 6, 7 MEDRUSH model application 203, 215 Alcantarilla climate station 38 Alentejo, Portugal 178–179 Algeria, forest fires 85 Almeria 112 Alqueria functions in 4–6 indicators and response units 178, 180–183 soil erosion 7–9 water supply 6 yield of major crops 5 Anderson, G.W. 347 Animal husbandry 181 Anthyllis cytisoides 99, 114 Apulia salinization 167, 171 Araus, J.L. 38 Arbutus unedo 34, 45 Archaeomedes Project 5
Archibold, O.W. 33 Arianatsu, M. 85 Aridification in Hungary 152–162 Aridity, increasing 38 in Val d’Agri National Park 365 Aridity indices, De Martonne 49 Aru, A. 347 Ashcroft, G.L. 137 Asphodel deserts 86 Asphodelus microcarpus 86 Aswan Dam, and irrigation 161 Atmospheric CO2 change, impacts on vegetation 33–46 Ayers, R.S. 168 Azimonti, D. 365 Balabanis, P. 10 Barbera, G.G. 295 Barbieri, R. 168, 348 Barilleras 295 Basilicata 361 Batha 83 Baudry, J. 7, 10, 270 Bautista, Martin, J. 296 Beerling, D.J. 44 Begon, M. 119 Bell Adell, M.C. 291 Below ground impacts of plants on bioengineering properties 98–99 Betts, R.A. 33 Bevan, K. 381, 399 Bianco, S. 361 Bifurcation theory 423, 424 Binley, A. 399 Biodiversity 168, 393 Bioengineering principles and desertification mitigation 93–105 Biomass 35 above ground in asphodel deserts 86 effects of rock fragments 141 simulated 38, 42 Birdlife International 6 Biswell, H. 84 Blaney Criddle formula 402 Blevins, R.L. 132 Boer, M. 11 Bond, J.J. 137 Bonin, G. 386 Bouma, J. 372
434
Index
Bray–Curtis ordination method Bretherton, F.P. 206 Brown, L.C. 96 Bruce, R.R. 371 Brunsden, D. 425 Bultot, F. 164 Burke, S. 5, 177
110, 112
C++ used in MEDRUSH 203 Calanchi 386 Calder, I. 414 Caliandro, A. 171 Caliandro, F. 167 California 109 sites for landscape characteristics 110 Cameraat, L.H. 423 Ca˜nada de Cazorla, response units 188–190 Ca˜nada Hermosa, response units 188 Caneva, G. 367 Cantore, V. 386 Carbon dioxide (CO2 ), simulated effects of elevated on monthly plant parameters 41 Caroti, L. 49 Carpathian Basin, soil salinization 169 Cataldo, L. 361 Catastrophe theory 423, 424 Cavazza, L. 169, 349 Celicio, P. 166, 167 Cenological surveys, in Hungary 158 Chanduvi, F. 169 Channel routing component in MEDRUSH model 219–221 Chaparral 274 Chewing, V.H. 425 Childs, S.W. 132, 137 Chile 109 Fundo Santa Laura 110 Chios, fires 88 Choresh 83 Chortal, rambla de 269 Ciollaro, G. 372, 376 Cistus species 85 Clark, S.C. 87 Climate change potential effects of rising CO2 on Mediterranean vegetation 33–44 CO2 -I impacts on vegetation 33–46 inter-annual seasonal variability on NPP 43 Coker, P. 110 Colino, J. 291 Commonwealth Association of Universities 275 Conese, C. 53 Convallario-Quercetum 157 Coppices 390, 393 Corleto Perticara 373, 402 Crop yield in Alqueria area (Guadalent´ın) 181 Crop yield reduction due to salinity 168
Crops in Guadalent´ın Basin Cyprus, saline soils 167
298
Dargie, T. 112 De Cillis, E. 169 De Falco, E. 357 De Martonne Aridity Index 324 De Miguel, J.M. 8 De Pascale, S. 168 De Ploey, J. 132 Deforestation 269, 275 and salinization 165 Val d’Agri 325, 362, 390 Degradation paths, indicated by plant species 114 Dehesa 7 Del Prete, M. 323 Denmead, O.T. 402 Desertification, definition of 6 Desertification response units 184 Diamond, S. 8 Digital elevation component in MEDRUSH model 204 Dimase, A.C. 323 Dimitrakopoulos, A. 89 Dirksen, C. 371 Dombois, E. 111 Doorenbos, J. 142 Droughts 8 and soil salinity 164 drought, deciduous shrubs 33, 42 in Greece 8 in Guadalent´ın 296 in Hungary 152 partially offset by rising CO2 38 Dudal, R. 347 Durum wheat 326, 333, 354, 365, 373 Earthquakes 363 Economic development in south-west Spain 269 Egypt, Nile delta and salinity 169 Ehleringer, J.R. 33 Ellenberg, S. 111 Elwell, H.A. 96 Emigration 363 Val d’Agri 326 Environmental risk of erosion 12–13 Environmentally sensitive areas, 177–185, 394, 395 comparing different target areas 178–180 sensitivity to land degradation 178–180 Erosion rates, see Soil; Soil erosion rates Escadafel, R. 169 ESP exchangeable sodium percentage 164, 170 Esparto grass 295 see also Stipa tenacissima Euphorbia acanthothamus 85 European Commission xv
Index Evapotranspiration 424 in field with rock fragments 138–141 in laboratory with rock fragments 137 Ewan, J. 399 Experimental field plots and sites Athens, and rock fragment studies 135–137 Guadalent´ın, for bioengineering experiments 93–105 on impact of increased CO2 33 Sele plain, soil salinity 170 Val d’Agri 333, 350, 370, 373 Fagus sylvatica 385, 391, 393 Feddes, R. 381, 402 Ferrara, A. 323, 324 Fire disturbance regimes, fire and grazing 113–116 grazing and fires in Greece 109–118 landscape protection from 83–92 in Val d’Agri 348, 391, 393 on Lesbos 420 plant species along fire/grazing intensity gradient 115–116 recovery time 8 Flint, D.L. 132, 137 Fonseca, C.D. 361 Forest Borbonic law of 1826 (Italy) 388 canopy density 387 forest index change 294 Guadalent´ın Basin 299 phytoclimatic classification 387 Francis, C.F. 93, 96 Franzluebbers, A.C. 332 Frere, M. 142 Functional performance indicators 181 Functional trends, of nitrogen and phosphorous 113 Functions resisting desertification 181 Galillee, saline soils 167 Galligani, U. 323 Gannano Barrage 403–409 Gatt, Uruguayan round 6 General Circulation model, and Hungary 152 Geographical information system 304 for environmental sensitivity 394 for land capability in Hungary 156–157, 159 Ghazi, A. 419 Gil-Olcina, A. 295 Glenn-Lewin, D.C. 270, 274 Godron, M. 274 Gonzalo Rebollar, J.L. 306 Goodess, C. 8, 44 GRASS, integrated with MEDRUSH model 203 Grayson, R.B. 381 Grazing and fire, Greece 86–92, 118–119 effects on shrub morphology 114
435
grazing level, responses to 114 intensity gradient 114 intensity-plant species 115–116 Larissa, field study 112–113 overgrazing 88 trampling 85 Greece Chios, fires in 88–90, 421 grazing and fires 88, 109–118 Larissa, study of grazing on composition 112–113 Lesbos 8, 87 saline soils 167 Greenhouse effect 161 Griesbach, A. 84 Groundwater Guadalent´ın 296–298 Hungary 153–155 Guadalent´ın Basin 179, 419, 420, 421 agriculture 292 changing social and economic conditions 289–301 changes in income 294 economic conditions 292–299 indicators of desertification 177–187 land-use changes 295, 296 plan to combat desertification 303–317, 420 population 289–292 compared to Murcia region 289 history 289–290 recent (20th century) changes 296 sensitivity analysis 178 water resources 298 Guardia Perticara 328, 332, 349 Gupta–Larson method 379–380 Hamad, O. 414 Hamdy, A. 269 Hanks, R.J. 137 Hanson, C.T. 132 H¨attenschwiler, S. 34, 35, 40 Herbaceous plants, post-fire 85 Hillel, D. 137 Horsebean crop 333, 354 Horton equation for infiltration 369 Hudson, N. 93 Hungary aridification of 152–162 climate change 152–153 Great Hungarian Plain Danube–Tisza interfluve 156, 158 Kiskunag National Park 158 land use changes 159–161 groundwater changes in 153–156 impact on land capability 156–158 index of continentality 158 index of relative heat demand 158 soil dynamics 158–159
436
Index
Hunting 181 Hutchinson, J. 323 Hydraulic conductivity, and rock fragments 132 Hydraulic roughness, and rock fragments 132 Hydrology characterization of soil hydraulic properties 369–383 hillslope scale variability 377–381 in the Agri Valley, hydrological data for different tillage 336, 343 runoff 356, 357 I.C.C.D., see UNEP, International Convention on Combatting Desertification ICONA, Wildland Vegetation Map of Spain 304, 306 Imeson, A. 99 Indicators, desertification environmental characteristics underpinning selection of 177–187 infiltration rate in MEDRUSH model 206 Ingelmo-Sanchez, F. 132 IPPC, International Panel on Climate Change 38 Irrigation drip171 Egypt 9 Greece 9 Guadalent´ın Basin 181, 296, 298, 300 Italy 9 Puglia 326, 327 soil salinization 165, 166, 169, 171 Val d’Agri 329 Isotope 18 O 166 Israel, saline soil areas 167 Johnson, M.G. 332 Jones, M.B. 38 Kabat, P. 381 Kamar, M.J. 137 Kemper, W.D. 132 Kent, M. 110 Kern, J.S. 332 Key indicators 176 and stability 423 Kirkby, M.J. 179 Klute, A. 371 Kool, J.B. 371 Kosmas, C. 8, 178 Kostiakov equation 369 Land abandonment 7 different degrees of 269 Guadalent´ın Basin 269–276, 300 impact on regeneration of semi-natural vegetation 269–276 Land capability change under aridification, Hungary 156–167 Land care 6
Landi, R. 347 Landscape characteristics for MEDALUS II sites and sites in Chile and California 110 Landscape functions 179–182 Landslides 323, 357, 386 Lang, R.D. 96 Larotonda, A. 365 Lavee, H. 136, 137 Le Houerou, H.N. 85, 86 Leaching requirement 170 Leaf area index 35, 37, 345 Leggett, J. 38 Lesbos, 180, 419, 420 Libya, soil salinization 169 Linsalata, D. 348, 355 Litter 42 Lukey, B.T. 402, 408 Luxmore, R.J. 371 Machia 385 MacRae, R.J. 332 Magier, J. 132 Mairota, P. 422 Malaria 361, 362 Management actions, map 306 Management actions, typology of in Guadalent´ın 307 Mancini, F. 323 Maquis 83 and soil salinity 165, 168 Margaris, N.S. 85, 88 Martinez-Carrion, J.M. 295 Marzi, V. 348 Mass movement 332 Massafra, A. 363 Mazzanti, B. 402 McCaffrey, L.A. H. 96 McSharry, approach to reform of CAP 6 MEDALUS II xv MEDRUSH model 203–227 conceptual basis of 203 microtopography in 204, 207, 208 Mehuys, G.Q. 332 Mesopotamia, salt accumulation 161 Mezzogiorno (Italy) 391 Middleton, N.J. 426 Miglietta, F. 34 Migration, consequences in Val d’Agri 363 Miles, J. 274 Mitchell, J.F. B. 38 Modelling large basin hydrology and sediment yield with sparse data: the Agri Basin 397–416 MEDRUSH basin-scale physically based model for forecasting runoff and sediment yield 210–213, 227 changes in surface roughness over time 213
Index construction of sub-basins and representation of flow strips 214, 223 grain-size effects in 209 implementation 224 sediment transport by wash processes 211 sediment transport in general 210 sub-basin component 205–206 mountain grassland 390 SHE model 397–416 uncertainty in 398–399 vegetation simulation models 35–40 water balance model of Doorenbos and Pruit 142 Montero de Burgos, J.L. 306 Mooney, H.A. 33, 39 Moustakas, N. 136 Mulching with gravel experimentally 142–143 Muller, M.J. 35 Munoz Bravo, J. 296 Murcia, climate change 35, 36 Naveh, Z. 8, 85 Negev, central saline soils 167–168 Neolithic soil salinization 163 Net primary productivity 35
437
Pinus spp. 85, 110, 117 Pinus halepensis 389, 391, 420 used in restoration 306 Pinus nigra 388, 391, 392, 420 Pinus pinaster 420 Pinus pinea 389 Pinus radiata 389 Pinus sylvestris 391 Pistacea lentiscus 34 Plantago sp. 99 Plough layer, simulated 133, 137 Policy, desertification control 308 Population by economic sectors 292 demographic projections 291 Guadalent´ın Basin 289–290 Postiglione, L. 163–175, 348, 349, 422 Pre-dawn water potential 34 Pressures responsible for desertification 181 Prez, C. 289 Primary productivity, elevated CO2 39 Pruit, W.J. 142 Puigdefabrigas, J. 114
Oechel, W.C. 85 Oil, recent discovery of in Val d’Agri 365 O’Riordan, T. 5 Ortin, J. 289 Overgrazing and fire 83 Guadalent´ın 269, 272, 275 salinization 165 Overland flow 206
Quaternary, eustatic movement and saline springs 166–167 Quercetum, Hungary 157 Quercus cerris 385, 388, 390, 393 Quercus coccifera 112, 113 Quercus ilex 34, 45, 389 Quercus pubescens 388, 389, 390 Quercus rubra 391 Quercus suber, elevated CO2 33 Quercus sylvatica 393 Quinton, J. 399
Palimpsest 10 Palutikof, J. 8, 38 Pannonian (endemic) species 158 Papadopoulos, I. 167 Parkin, G. 399 Parry, M. 131 Peet, R.K. 274 Penman, H.L. 142 Perniola, M. 168 Perpignan, climate change 35 Pertusillo Dam, Val d’Agri 321, 323, 325, 403–409 Perz-Picazo, M.T. 295a Peter, D. 10, 131 Phlomis 85 Phlomis fructicosa 85 Photosynthesis 34, 40, 42, 85 Phrygana 83, 85, 110, 420 Phyllirea angustifolia 34 Phyllite, vegetation regeneration 272 Phyloxera 296 Pickup, G. 425
Ragab, R. 171 Rain-fed crops 181 Rain-splash 206 Rainfall and rising CO2 38 characteristics of Mediterranean 8 in Val d’Agri 323 inter-annual variability and N.P.P. 42 simulator in bioengineering studies 96 Rainflow 206 Ravina, I. 132 Rawls and Brakensiek method 379 Rebeiro 6 Reforestation and changes in plant species composition census in Greece 88, 116 Refsgaard, J.C. 397 Regeneration of plant cover 269–276 Remote sensing 394, 422 application to Guadalent´ın Target Area 127–128
116
438
Index
Remote sensing (continued) aridity maps from 53 Lambertian reflectance model 123 LANDSAT-5 TM 124 LANDSAT image of Agri Basin 389 linear spectral mixture modelling 119–122, 124 modelling physical scenes 122 NDVI 47, 119 NDVI, new index and De Martonne and Thornthwaite indices 52 NOAA-AVHRR 47–54 use of NOAA-HVRR NDVI data for climatic characterization 47–54 vegetation cover assessment in Mediterranean semi-arid landscapes 47–55 vegetation in relation to NDVI 51 Renard, K.G. 98 Representative flow strips 215 in MEDRUSH model 204 Reproductive tillers, and grass establishment 112 Resilience 177 Respiration in plants 42 Response unit methodology 178 Restoration technical design 306, 309 Retama sp. 114 Reynolds Creek catchment, Idaho 410 Rice 365 Richards, I.D. 93 Richards equation 370, 372, 381 Rill-wash 206 Rimbaud catchment, France 410 Rio Conference 5 River Agri 321, 322, 365, 370 Ebro 9 Guadalent´ın 420 Nile 161 Rhˆone 9 Sauro 327, 365, 370, 373 Segura 9, 296 Sele 166 Tajo 9, 296, 420 Rock fragments 131–145, 422 effects on cereals 142 effects on evapotranspiration 134 in soils on conglomerates 142 in soils on shales and sandstones 142 in soils on marls 142 laboratory tests on soils 138 Rodrigues, V. 6 Rojo-Serrano, L. 420 Roman settlement, Val d’Agri 361 Romero Diaz, A. 412 Romkens, M.J. 133 Root density 333 Root studies in relation to infiltration 99 Rossi Doria, M. 361
Routing of water and sediment in MEDRUSH model 204, 209 distribution of overland and subsurface flow 206 Ruggiero, C. 168 Ruhe, R.V. 348 Runoff 356 from plots 136 Sacropoterium spinosum 85 Salinization causes 165–167 definition, of sodic and saline and sodic-saline soils 164 defloculation of soils 167 extent of problem and impact 167–168 historical perspective 163–164 in Hungary 159 in the Mediterranean 163–175 management of sodicity and salinity 168–171 properties of sodic and saline soils (table) 164 Salsola kali 295 Salsola longifolia 295 primary 165 Sanchez, P. 289 Sardinia 419, 420 Sarno (southern Italy) 1998 disaster 165 Scafati (Camagna region, Italy) evapotranspiration and rainfall 166 Scale effects in SHETRAN applications 411 Scarascia-Mugnozza, G.E. 34 Schertz, D.L. 165, 347 Schimel, D. 38 Sclerophyll tissue 109 Sclerophyllous shrubs and CO2 33–46 Sea water evaporation 164 Sea water infiltration 165 Seasonal variations, response of vegetation to higher CO2 36, 40 Segal, M. 38 Sele river plain (southern Italy) salinization 166–167, 170 Set-aside 331, 332 Seville, climate change 35, 36 Shales, vegetation regeneration 272 Shalhevet, J. 171 Shantz, H.L. 84 Shaw, R.T. 402 SHE/SHETRAN 397–416 Sheep 361 Sherwood, J. 414 SHETRAN model 397–416 data requirements and assembly 399 description 398 parameters of 398 Simanton, R.J. 97 Similarity (vegetational) index 110 Simulated rainfall in laboratory 133
439
Index Skourtos, M. 89 Smith, D.D. 349, 369 Smith, R.M. 347 Smith, T.R. 206 Socio-economic functions and indicators 178 Soil aggregate stability 132, 178 bulk density of fine earth 133 characteristics of in Val d’Agri 333–336 compaction, with rock fragments 132 cracking 170 dynamics under aridification 158 effects of tillage systems 353–355 erosion and land degradation in Val d’Agri 347–359 erosion and rock fragments 131, 132–137 erosion and sediment yield modelled in SHETRAN 408–409 erosion in Alqueria area 186–188 hydraulic properties in Val d’Agri 369–383 loss of fertility due to erosion 347 moisture regime with rock fragments 140, 141 organic matter in field plots 355 pedo-transfer functions 377 physical degradation with rock fragments 132–135 properties in MEDRUSH model 204 quality, indicators of 178 roughness with rock fragments 133 salinization 167–168 vertic soils 349 water potential 34, 37, 42 Soil erosion rates 178 effects of land use on, under Mediterranean conditions 57–71 effects of plant properties 96 Italy 348 plots under durum wheat 357 simulated with SHETRAN 408 socio-economic risk of erosion of the landscape unit 188 with rainfall simulation experiments under different vegetation types by season 97 Solonchak 167 Somez, B. 167 Sørensen index 110, 111, 114 values of 116 Spain Aragon 9 Extremadura 7 Spanish National Hydrological Plan 9 Stability criterion, Smith and Bretherton 206 Stamey, W.L. 347 Stewart, D. 110, 111 Stipa bromoides 112 Stipa tenacissima 95–99, 295 Stocking, M.A. 96
Stocking rate 86 Stomatal closure 34 Stomatal conductance 34 Strickler overland flow resistance coefficient 404, 411 Stunell, J. 414 Sub-basins in MEDRUSH model 204 Subsidy of fodder costs 91 Surface sealing prevented 136 Sustainability 5 sustainable land management 419 Szabolcs, I. 165 Target areas Agri Basin, southern Italy 319–417 Guadalent´ın Basin, Spain 231–303 Tedescchi, P. 168 Terraces 7 abandoned 93–94 accumulation of eroded sediments 9 agriculture 67 reforestation and erosion 12 small bench terraces 93, 391 Thomas, D. 426 Thornes, J.B. 93, 96, 177 Thornthwaite index of humidity 49, 50 Thymus capitata 85 Tillage methods impacts on hydrology, erosion and soil chemistry 333–345 impacts on erosion 352–355 Tilman, D. 274 Toderi, G. 347 Topographic wetness index 206 Toulmin, C. 425 Tourism 327 Trabaud, L. 92 Tractor fuel costs 332 Transfer functions for time of water flow through a reach in MEDRUSH model 221 Transient form ratio 425 Transpiration 34, 36 Trunk volume 34 Tunisia, salinity 169 Turkes, M. 9 Turkey anticyclonic activity 9 forest fires 85 saline soils 167 UNCED 5 UNEP 5 International Convention on Biodiversity International Convention on Combatting Desertification 423 Universal soil loss equation 369 Van der Leeuw, S. 5 Van der Maarel, E. 270, 274
6
440
Index
Van Genuchten, M.Th. 372, 379 Van Wesemael, B. 133 Vegetation adaptive strategies to help post-fire recovery 85 and erosion 422 at bioengineering study sites in Guadalent´ın 95 bioengineering principles 93–105 changes, Guadalent´ın 269 changes on Danube–Tisza interfluve, Hungary 157–159 data collection 270 equilibrium after abandonment 272–274 general scheme for vegetation in relation to climate regime 51 growth component in MEDRUSH model 204, 216–219 in SHE model 42, 401–402, 412 regeneration after abandonment 272–275 root density and infiltration rates 99 sclerophyllous shrubs 34, 83 selection of 100 species for revegetation (table) 100–104 senescence 274 soil and water conservation, indicators of ecosystem function and structure 184 structure 33–46 surface cover characteristics, Guadalent´ın 96, 271–273 used for restoration 306 wildland vegetation map of Spain 304 Verity, G.E. 347
Villani, P. 363 Voisey, H. 5 Volatile oils in phrygana
84
Walling, D.E. 412 Water availability and elevated CO2 39, 42 Water conservation strategies, and rock fragments 131 Water potential of Stipa bromoides and Quercus coccifera in August 113 Water resources Guadalent´ın 296, 298, 300 in Val d’Agri 326 planning with SHE model 410 water budget parameters 152 water deficit in Hungary 152 water retention curves, modelled 375 watershed restoration projects database 306 Water use efficiency of plants 34, 35, 38 Water vapour adsorption with rock fragments 141 Webber, D.J. 110, 111 Weed control 354 Westcot, D.W. 168 Whittaker, R.H. 8, 274 Wicks, J.M. 411 Willis, W.O. 137 Wischmeir, W.H. 349, 369 Wolpert, L. 425 Wood, E.F. 369 Woodward, I. 8 Yevyevich, V. 370 Zarzilla de Ramos 179
Plate 1MFalse-colour composition derived from the LSMM corresponding to 7 April 1993. It depicts the fractional coverage in the Guadalentín Basin of soil (red), crops (green) and natural vegetation (blue) (see Chapter 10)
Plate 2MLocation of the Guadalentín Basin in the south-east of the Iberian Peninsula. This satellite image in false colour clearly emphasizes the aridity of the area (see Chapter 17)
Plate 3M(A) Vegetation and (B) lithology classifications of the Guadalentín Basin as interpreted from spring and autumn Landsat TM imagery (see Chapter 20)
Plate 4MVegetation and land-use map, part of the management plan to combat desertification for the Guadalentín Basin (DGCONA 1995). Reproduced by permission of John Wiley & Sons Ltd, from Mairota, P. et al. (1998) (see Chapter 22)
Plate 5MManagement actions map, part of the management plan to combat desertification for the Guadalentín Basin (DGCONA 1995). Reproduced by permission of John Wiley & Sons Ltd, from Mairota, P. et al. (1998) (see Chapter 22)