Integrating socio-economic and biophysical data to support water allocations within river basins: an example from the Inkomati Water Management Area in South Africa

De Lange, Willem JWise, RMForsyth, GGNahman, Anton2009-09-282009-09-282010-01De 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 43-501364-8152http://hdl.handle.net/10204/3617Copyright: 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 43-50Sustainable 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.enWater allocation decision makingGeographical Information SystemsGISMeta-modellingData integrationSpatial analysisRiver basinsInkomati water management areaEnvironmental modellingBiophysical dataSocio-economic dataIntegrating socio-economic and biophysical data to support water allocations within river basins: an example from the Inkomati Water Management Area in South AfricaArticleDe Lange, W. J., Wise, R., Forsyth, G., & Nahman, A. (2010). Integrating socio-economic and biophysical data to support water allocations within river basins: an example from the Inkomati Water Management Area in South Africa. http://hdl.handle.net/10204/3617De Lange, Willem J, RM Wise, GG Forsyth, and Anton Nahman "Integrating socio-economic and biophysical data to support water allocations within river basins: an example from the Inkomati Water Management Area in South Africa." (2010) http://hdl.handle.net/10204/3617De Lange WJ, Wise R, Forsyth G, Nahman A. Integrating socio-economic and biophysical data to support water allocations within river basins: an example from the Inkomati Water Management Area in South Africa. 2010; http://hdl.handle.net/10204/3617.TY - Article AU - De Lange, Willem J AU - Wise, RM AU - Forsyth, GG AU - Nahman, Anton AB - 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. DA - 2010-01 DB - ResearchSpace DP - CSIR KW - Water allocation decision making KW - Geographical Information Systems KW - GIS KW - Meta-modelling KW - Data integration KW - Spatial analysis KW - River basins KW - Inkomati water management area KW - Environmental modelling KW - Biophysical data KW - Socio-economic data LK - https://researchspace.csir.co.za PY - 2010 SM - 1364-8152 T1 - Integrating socio-economic and biophysical data to support water allocations within river basins: an example from the Inkomati Water Management Area in South Africa TI - Integrating socio-economic and biophysical data to support water allocations within river basins: an example from the Inkomati Water Management Area in South Africa UR - http://hdl.handle.net/10204/3617 ER -