Assessment of Spatiotemporal Varying Relationships Between Rainfall,Land Cover and Surface Water Area Using Geographically Weighted Regression

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 (AIC_C). 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 AIC_C. 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.     

The consequences of land use change on nutrient exports: a regional scale assessment in south-west Victoria, Australia

Estimation of nutrient load production based on multi-temporal remotely sensed land use data for the Glenelg-Hopkins region in south-west Victoria, Australia, is discussed. Changes in land use were mapped using archived Landsat data and computerised classification techniques. Land use change has been rapid in recent history with 16% of the region transformed in the last 22 years. Total nitrogen and phosphorus loads were estimated using an export coefficient model. The analysis demonstrates an increase in modelled nitrogen and phosphorus loadings from 1980 to 2002. Whilst such increases were suspected from past anecdotal and ad-hoc evidence, our modelling estimated the magnitude of such increases and thus demonstrated the enormous potential of using remote sensing and GIS for monitoring regional scale environmental processes.     

Regional-scale models for relating land cover to basin surface-water quality using remotely sensed data in a GIS

Plant-based management systems implementing deep-rooted, perennial vegetation have been identified as important in mitigating the spread of secondary dryland salinity due to its capacity to influence water table depth. The Glenelg Hopkins catchment is a highly modified watershed in the southwest region of Victoria, where dryland salinity management has been identified as a priority. Empirical relationships between the proportion of native vegetation and in-stream salinity were examined in the Glenelg Hopkins catchment using a linear regression approach. Whilst investigations of these relationships are not unique, this is the first comprehensive attempt to establish a link between land use and in-stream salinity in the study area. The results indicate that higher percentage land cover with native vegetation was negatively correlated with elevated in-stream salinity. This inverse correlation was consistent across the 3 years examined (1980, 1995, and 2002). Recognising the potential for erroneously inferring causal relationships, the methodology outlined here was both a time and cost-effective tool to inform management strategies at a regional scale, particularly in areas where processes may be operating at scales not easily addressed with on-site studies.