Forecasting global climatic change impacts on Mediterranean agricultural land use in the 21st Century

• Main Authors: Stan Openshaw, Andy Turner
• Format: Article
• Language: deu
• Published: Unité Mixte de Recherche 8504 Géographie-cités 2000-02-01
• Series: Cybergeo
• Subjects: integrated modeling/modelling; fuzzy logic; land degradation; neural networks; risk; Synoptic Prediction System (SPS)
• Online Access: http://journals.openedition.org/cybergeo/2255

The paper describes the development of a prototype Synoptic Prediction System (SPS) for the European Union (EU) funded Mediterranean desertification and land use project (MEDALUS III). The prototype SPS was designed to forecast the possible impacts of global climatic change on agricultural land use patterns across the Mediterranean region of the EU. Designing such a system was a challenging task because many of the theoretically desirable data sets were either unavailable or did not exist, whilst significant uncertainties were apparent in the available data. Additionally, process knowledge was woefully deficient as virtually all the principal mechanisms for linking the dynamics of the climate and physical environment with the associated socio-economic systems were poorly understood. In order to make the best predictions of the geographical impacts of climatic change for around 75 years hence we developed an integrated, synoptic, GIS, scenario based modelling approach as we believed it was the best available option. So, the paper describes the development of a prototype Synoptic Prediction System (SPS) which employs a mix of GIS, neurocomputing, and fuzzy logic technologies to attempt the almost impossible yet important task of forecasting agricultural land degradation risk under various climate change scenarios. The paper focuses on methods developed to enrich the available data, the quantitative approach to modelling and forecasting land use using neural networks, and the fuzzy logic based translation of the modelling results into land degradation terms.