Assessment of Spatiotemporal Varying Relationships Between Rainfall,Land Cover and Surface Water Area Using Geographically Weighted Regression |
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Authors: | Email author" target="_blank">Stuart?BrownEmail author Vincent?L?Versace Laurie?Laurenson Daniel?Ierodiaconou Jonathon?Fawcett Scott?Salzman |
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Affiliation: | (1) School of Life and Environmental Sciences, Deakin University, PO Box 423, Warrnambool, VIC, 3280, Australia;(2) Sinclair-Knight-Mertz, Bendigo, VIC, Australia;(3) School of Information Systems, Deakin University, Warrnambool, VIC, Australia |
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Abstract: | 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. |
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