Desertification and land degradation: origins, processes and solutions

Table of Contents

1. Authors and acknowledgements
2. Introduction
   1. Aims
   2. Definitions and key concepts
   3. Synthesis of previous and ongoing projects
   4. Key issues and outline
3. Evolution of desertification in the Mediterranean
   1. Climatic fluctuation
   2. Human influence
4. Primary drivers of desertification
   1. Biophysical and socio-economic causes
   2. Pathways of desertification
   3. Driving forces in the Mediterranean
   4. Synthesis of causes for DESIRE hotspots
   5. Conclusions
5. Processes and consequences of desertification in the Mediterranean
   1. Socio-economic and political factors
   2. Biophysical processes
   3. Conclusions
6. Indicators: monitoring desertification
   1. History of monitoring
   2. Indicators
   3. Monitoring and mapping techniques
   4. Concluding remarks
7. Modelling desertification
   1. Definition of modelling
   2. Model types
   3. Biophysical modelling
   4. Socio-economic and participatory modelling
   5. Gaps and progression in modelling
8. Solutions
   1. Biophysical solutions
   2. Political and socio-economic solutions
9. References

Model types

A wide range of model types exist that are appropriate to model the processes that contribute to desertification (Mulligan, 2004); from simple GIS-based desertification indices to complex, physically based multi-process simulation models and decision support systems. In between are models such as GIS-overlay models and empirical models based on field data. Empirical models are based on experience or experimentation and limited to conditions for which they have been developed (Aksoy and Kavvas, 2005). A model is called physically based when it has a physical representation of a (complex) system. For example, in erosion and sediment transport, when it is constructed by using the mass conservation equation of sediment (Aksoy and Kavvas, 2005). The smaller models can mostly be classified as empirical or physically based (Mulligan, 2004). The larger models are mostly a highly mixed and complex combination of empiricism and physical basis with a number of mathematical approaches adopted for different parts of the model.

The trend of an increase in the scales at which models are applied, as well as an increase in complexity of models, is reflecting the increase in computer power. Scales range from one-dimensional models, to two-dimensional hillslope models, to a current emphasis on three-dimensional distributed or GIS-based large scale models applied to catchment hydrology or atmospheric circulation (Mulligan, 2004). Although more complex models are more useful in understanding the system, they often fail in practical applications because of heavy data requirements (Mulligan, 2004). Simpler models are much more readily parameterised and are useful to understanding the reasons for past or present changes in an environmental system, but are less related to real-life situations (Van Lynden, pers. comm.) and not powerful enough to provide estimates or scenarios for future change (Mulligan, 2004).