Rural poverty driven soil degradation under climate change: The sensitivity of the disposition towards the Sahel Syndrome with respect to climate

Starting from the basic assumption of the syndrome concept that essentially all of the present problematic civilization-nature interactions on the global scale can be subdivided into a limited number of typical patterns, the analysis of the response of these patterns (syndromes) to climate change can make a major contribution to climate impact research, surmounting the difficulties of more common sectoral "ceteris paribus" impact studies with respect to their systemic integration. In this paper we investigate in particular the influence of climate on the regional proneness or "disposition" towards one of the most important syndromes with respect to famines and malnutrition, the "SAHEL SYNDROME". It describes the closely interlinked natural and socioeconomic aspects of rural poverty driven degradation of soil and vegetation on marginal sites. Two strategies of global climate impact assessment on a spatial 0.5° × 0.5° grid were pursued: (a) As a measure for the climate sensitivity of the regional proneness, the absolute value of the gradient of the disposition with respect to the global field of 3 × 12 monthy normals of temperature, irradiation and precipitation is calculated. (b) The disposition was evaluated for two different climate forecasts under doubled atmospheric CO2 concentration. For both strategies two new quantitative global models were incorporated in a fuzzy-logic-based algorithm for determining the disposition towards the SAHEL SYNDROME: a neural-net-based model for plant productivity and a waterbalance model which calculates surface runoff considering vertical and lateral fluxes, both driven by the set of 36 monthly climatological normals and designed to allow very fast global numerical evaluation. Calculation (b) shows that the change in disposition towards the SAHEL SYNDROME crucially depends on the chosen climate forecast, indicating that the disagreement of climate forecasts is propagated to the impact assessment of the investigated socio-economic pattern. On the other hand the regions with a significant increase in disposition in at least one of the climate scenario-based model runs form a subset of the regions which are indicated by the local climate sensitivity study (a) as highly sensitive - illustrating that the gradient measure applied here provides a resonable way to calculate an "upper limit" or "worst case" of negative climate impact. This method is particularly valuable in the case of uncertain climate predictions as, e.g., for the change in precipitation patterns.