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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/3617

Title: Integrating socio-economic and biophysical data to support water allocations within river basins: an example from the Inkomati Water Management Area in South Africa
Authors: De Lange, WJ
Wise, RM
Forsyth, GG
Nahman, A
Keywords: Water allocation decision making
Geographical Information Systems
GIS
Meta-modelling
Data integration
Spatial analysis
River basins
Inkomati water management area
Environmental modelling
Biophysical data
Socio-economic data
Issue Date: Jan-2010
Publisher: Elsevier Science
Citation: De Lange, WJ, Wise, RM, Forsyth, GG and Nahman, A. 2009. Integrating socio-economic and biophysical data to support water allocations within river basins: an example from the Inkomati Water Management Area in South Africa. Environmental Modelling and Software, Vol. 25(1) pp 43-50
Abstract: Sustainable natural resource management requires inputs from both the natural and the social sciences. Since social and natural systems are inter-related and inter-dependent, it is essential that these data can be integrated within a given analysis, which requires that they are temporally and spatially compatible. However, existing environmental and socio-economic monitoring networks tend to observe, collect and report socio-economic and biophysical data separately; with the result that much of these data are spatially and temporally incompatible and therefore add to the complexity of objective and consistent trade-off analyses. This paper presents an approach for overcoming spatial incompatibilities between socio-economic and biophysical data; based on a meta-modelling approach using Geographical Information Systems, the geo-spatial analysis platform, and an application of a water-use simulation model. The method is developed and applied to the irrigation agriculture sector in the Inkomati River Basin, South Africa. Agricultural census data, which is measured on a magisterial district level, is integrated with geo-referenced land-cover data, which is independent of political boundaries. Initial results indicate that the method could enable natural-resource managers and policy makers to develop an understanding of the spatial relationships between socio-economic and biophysical variables over time, and assist them in the allocation of land and water resources across heterogeneous landscapes.
Description: Copyright: 2009 Elsevier. This is the author's version of the work. It is posted here by permission of Elsevier for your personal use. Not for redistribution. The definitive version was published in the Journal, Environmental Modelling and Software, Vol. 25(1) pp 43-50
URI: http://hdl.handle.net/10204/3617
ISSN: 1364-8152
Appears in Collections:Pollution and waste
Environmental management
Environmental and resource economics
General science, engineering & technology

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