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  • 1
    ISSN: 1573-2967
    Keywords: global change ; environmental policy ; rural poverty ; soil degradation ; qualitative modelling
    Source: Springer Online Journal Archives 1860-2000
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Abstract A novel transdisciplinary approach to investigate Global Change (GC) is presented. The approach rests on the decomposition of the intrigue dynamics of GC into patterns of civilization–nature interactions (“syndromes”) by an iterative scientific process of observations, data and system theoretical analyses, and modelling attempts. We illustrate the approach by a detailed analysis of the Sahel Syndrome, which describes the rural poverty driven overuse of natural resources. The investigation is performed by (i) identifying relevant “symptoms” and interlinkages which are characteristics for this pattern, and (ii) a qualitative model representing the internal dynamics of the essential flywheel. The geographical patchwork of the regions affected by the syndrome which is obtained by global data analysis, proves the high global relevance of this pattern. The qualitative model is employed for an evaluation of basic policy strategies debated in the context of rural poverty driven environmental degradation. It turns out that a mixed policy of combating poverty and introducing soil preserving agricultural techniques and practices is most promising to tackle the syndrome dynamics.
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 1573-2967
    Keywords: integrated modeling ; climate change impact ; rural poverty ; soil degradation ; syndrome concept ; vulnerability
    Source: Springer Online Journal Archives 1860-2000
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Abstract 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.
    Type of Medium: Electronic Resource
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  • 3
    Publication Date: 1989-12-01
    Print ISSN: 0304-3800
    Electronic ISSN: 1872-7026
    Topics: Biology
    Published by Elsevier
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