dc.contributor.author |
De Lange, Willem J
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dc.contributor.author |
Wise, RM
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dc.contributor.author |
Forsyth, GG
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dc.contributor.author |
Nahman, Anton
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dc.date.accessioned |
2009-09-28T10:11:15Z |
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dc.date.available |
2009-09-28T10:11:15Z |
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dc.date.issued |
2010-01 |
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dc.identifier.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 |
en |
dc.identifier.issn |
1364-8152 |
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dc.identifier.uri |
http://hdl.handle.net/10204/3617
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dc.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 |
en |
dc.description.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. |
en |
dc.language.iso |
en |
en |
dc.publisher |
Elsevier Science |
en |
dc.subject |
Water allocation decision making |
en |
dc.subject |
Geographical Information Systems |
en |
dc.subject |
GIS |
en |
dc.subject |
Meta-modelling |
en |
dc.subject |
Data integration |
en |
dc.subject |
Spatial analysis |
en |
dc.subject |
River basins |
en |
dc.subject |
Inkomati water management area |
en |
dc.subject |
Environmental modelling |
en |
dc.subject |
Biophysical data |
en |
dc.subject |
Socio-economic data |
en |
dc.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 |
en |
dc.type |
Article |
en |
dc.identifier.apacitation |
De 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/10204/3617 |
en_ZA |
dc.identifier.chicagocitation |
De 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/10204/3617 |
en_ZA |
dc.identifier.vancouvercitation |
De 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. |
en_ZA |
dc.identifier.ris |
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 -
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en_ZA |