The aim of these web pages is to document the research performed for MEDALUS III in the Centre for Computational Geography at the University of Leeds over the period 1996-1999. The research involved the development of a prototype Synoptic Prediction System (SPS) to link physical, climatic and socio-economic data in order to make predictions of land use change and land degradation for the Mediterranean region of the EU for around 50 years hence at a spatial resolution of approximately 1 km2. This was a long and arduous task involving the integration and linkage of the widest range of available datasets. These web pages describe both the methodolgy and technology used to approach this task, presents some results from the various modelling tasks involved, and offers some suggestions for further research.
If you want to view a powerpoint presentation containing some of the
preliminary results then click
here. If you want to read a copy of the final report then click
here.
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We hope to construct a web interface to the modelling system and subsequently
revise and extend it as better physical, climatic and socio-economic data
becomes available and as a clearer understanding about how to link these
data is gained.
The effects of climatic change and socio-economic development on land use patterns which relate to land degradation are massively complex, scale sensitive and not obvious. Intricate relationships exist between the climate and the distribution and motivation of people who make agricultural land use decisions which affect land degradation. Decisions to change crop patterns, abandon cultivation or abandon agriculture altogether are made in highly specific contexts. Forecasting when these decisions will affect land degradation is very difficult and was probably impossible at any reasonable level of uncertainty with contemporary data (even for a single farm). As a consequence, all that can could be expected to be done in any reasonable way was to produce broad brush results which were generally right and which could serve as the foundations for more detailed analysis to be undertaken as better and more improved data became available and as greater understanding of the process of land use change and land degradation is gained.
For the integration of physical, climatic and socio-economics to be of any value for regional scale decision making and awareness raising in the near future we believed that it had to be undertaken at a spatial resolution of about 1km and produce forecasts for about 25-50 years time. To attempt this, some way to identify the principal relationships between key physical, climatic and socio-economic variables mainly responsible for contemporary land use patterns was needed. Using a model of these relationships it was felt that (by assuming that many varying factors stay constant) the first bold educated scientific estimates of land use for about 50 years hence using forecasts of the contemporary predictor variables could be produced. By translating the expected land use changes into land degradation risk surfaces and combining these with other measures of environmental risk it was further expected that reasonable estimates of relative land degradation could be made.
To explore the nature of geoenvironmental interations relating to land degradation we have argued that a model of land use change would be extremely useful. In the early stages of developing this model it became clear that it would have to be synoptic given the nature of the available data. Despite this imposed generality we believed such a model could at least be used in gaining a greater appreciation and understanding of the complexity and sensitivity of the processes invlolved. The prototype Synoptic Prediction System (SPS) that has been developed is designed to model and investigate the nature of land use patterns and forecast the likely impact of land degradation driven by global climate change. The prototype SPS modelled geoenvironmental interactions in a relatively objective manner by using neural networks to represent and interpolate spatial data patterns making a minimum of apriori assumptions about the nature of these patterns. Fuzzy logic based inference methods were employed to incorporate geographical understanding relating to environmental risk and translate differences in the neural network based land use classifications to produce synoptic land degradation indicators. A major assumption has been that different types of land use and land use change represent varying levels of land degradation risk. The hope is that the SPS will develop into a practical educational tool which raises awareness of environmental sensitivity, improves land use planning and helps devise strategies to mitigate land degradation problems. The SPS provides a basic framework for a scenario based forecasting system that can be easily updated and extended as new and improved input data becomes available. Although the modelling involved is geared specifically to produce a set of agricultural land degradation forecasts for the Mediterranean region of the EU, there is no reason why it could not be adapted to make similar forecasts for other areas at more detailed scales provided sufficient data is available.
At an appropriate stage in the development of the SPS, measures of subsidy and information about set-a-side rates and information about policy could be incorporated into the modelling to develop some form of decision support capability. Land use change and land degradation risk forecasts under different agricultural subsidy and set-a-side policy scenarios might then be produced and variations in the forecasts may reveal the sensitivities of the process of land use change and land degradation to these agricultural control mechanisms. However, it is likely to be some time before the SPS progresses from a scenario based forecasting tool to anything like a full blown decision support tool for evaluating strategies aimed at alleviating land degradation. We expect the prototype SPS will primarily function as a way to raise awareness of land degradation issues and promote the use of an integrated geocomputational approach to the study of complex geographical phenomenon. In order to appropriately allocate EU funds to combat agricultural land degradation at even national and regional scales much better data than were available for this research are required.
In summary, the synoptic strategy implemented was not designed to directly attempt to model geoenvironmental interactions with future socio-economic systems because this was believed to be almost impossible at the time in any non-trivial manner. Instead it was an attempt to classify the likely relative broad scale effects of climate change on land use patterns and translate the expected changes in land use patterns into land degradation terms. The choice of the word synoptic reflects the generality of the approach both in terms of the assumptions that are made, the variables that are used as inputs and the spatial and temporal frame of its operation. The modelling basically involved classifying contemporary agricultural land use, forecasting future agricultural land use, creating land use change related land degradation risk indicators and related scenario based land degradation forecasts. Please understand that the SPS is a prototype system, it is a first bold attempt at modelling land degradation at this scale with a geocomputational modelling approach and although we believe that the framework offers a useful approach to assessing the possible impacts of climatic change on land use, the quality of the results so far really only reflect the quality of the data inputs that have been used. We hope at least that the SPS and these web pages will provide a useful platform and reference for further research.
Click here or the right arrow icon button below to move to the next page which introduces neural networks and describes the development of a socio-economic database at a 1 decimal-minute resolution for the Mediterranean climate region of the EU.