Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using geographically weighted regression

Brown, Stuart, Versace, Vincent L., Laurenson, Laurie, Ierodiaconou, Daniel, Fawcett, Jonathon and Salzman, Scott 2012, Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using geographically weighted regression, Environmental modeling and assessment, vol. 17, no. 3, pp. 241-254.

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Title Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using geographically weighted regression
Author(s) Brown, Stuart
Versace, Vincent L.ORCID iD for Versace, Vincent L. orcid.org/0000-0002-8514-1763
Laurenson, LaurieORCID iD for Laurenson, Laurie orcid.org/0000-0003-2321-7512
Ierodiaconou, DanielORCID iD for Ierodiaconou, Daniel orcid.org/0000-0002-7832-4801
Fawcett, Jonathon
Salzman, ScottORCID iD for Salzman, Scott orcid.org/0000-0003-1512-7445
Journal name Environmental modeling and assessment
Volume number 17
Issue number 3
Start page 241
End page 254
Total pages 14
Publisher Springer Netherlands
Place of publication Dordrecht, Netherlands
Publication date 2012-06
ISSN 1420-2026
1573-2967
Keyword(s) climate change
geographically weighted regression
land use
rainfall
water resources
Summary Traditional regression techniques such as ordinary least squares (OLS) are often unable to accurately model spatially varying data and may ignore or hide local variations in model coefficients. A relatively new technique, geographically weighted regression (GWR) has been shown to greatly improve model performance compared to OLS in terms of higher R 2 and lower corrected Akaike information criterion (AICC). GWR models have the potential to improve reliabilities of the identified relationships by reducing spatial autocorrelations and by accounting for local variations and spatial non-stationarity between dependent and independent variables. In this study, GWR was used to examine the relationship between land cover, rainfall and surface water habitat in 149 sub-catchments in a predominately agricultural region covering 2.6 million ha in southeast Australia. The application of the GWR models revealed that the relationships between land cover, rainfall and surface water habitat display significant spatial non-stationarity. GWR showed improvements over analogous OLS models in terms of higher R 2 and lower AICC. The increased explanatory power of GWR was confirmed by the results of an approximate likelihood ratio test, which showed statistically significant improvements over analogous OLS models. The models suggest that the amount of surface water area in the landscape is related to anthropogenic drainage practices enhancing runoff to facilitate intensive agriculture and increased plantation forestry. However, with some key variables not present in our analysis, the strength of this relationship could not be qualified. GWR techniques have the potential to serve as a useful tool for environmental research and management across a broad range of scales for the investigation of spatially varying relationships.
Language eng
Field of Research 050101 Ecological Impacts of Climate Change
Socio Economic Objective 960399 Climate and Climate Change not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2011, Springer Science+Business Media B.V.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30040427

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