A top-down approach to understand the effect of vegetation changes on stream salinity

Large scale clearing of native forests for agriculture modifies the patterns of rainfall partitioning into evapotranspiration, runoff and infiltration. As deep rooted trees have higher evapotranspiration rates compared with shallow rooted crops for a given amount of rainfall, removal of native trees results in an increased recharge. This can mobilize salts stored in the unsaturated zone and groundwater. The discharge of salts from catchments causes the salinisation of water and land resources in sub-humid to semi-arid regions. In addition to causing significant loss to economy, the salinity issue is a serious threat to the environmental sustainability. Stream salinity being a function of climate, physical characteristics of catchments and land cover, is highly variable in space and time. This variability can be explained on the basis of catchment scale data on climate, catchment attributes and landuse. However, the limitations of data availability require simpler approaches. The aim of this paper is, therefore, to demonstrate the use of readily available catchment-scale data and an annual evapotranspiration model in explaining the variability in stream salinity due to vegetation changes in a sub-humid environment. We use a Budyko-type, an annual evapotranspiration model (Zhang et al. 2004) to understand the effects of vegetation changes on stream salinity. Catchment-scale observed data on climate (mean annual rainfall, actual evapotranspiration, and potential evapotranspiration) and time series of streamflow and salinity from 78 catchments across Victoria, Australia, were used in the analysis. Physical topographic characteristics of the study catchments were derived with the help of a digital elevation model of the area. Spatial information on vegetation, landuse, geology, and soils were obtained from digital maps of these variables. Groundwater salinity was derived from the interpolated surface of salinity data of groundwater bores in the region. The change in the native forest cover was estimated from the native vegetation maps of pre-European settlement (1788) and post-European settlement (1988). Statistical methods, including correlation analysis and ordinary least square regression were used in the analysis. Based on catchment scale observed data and an annual evapotranspiration model (Zhang et al. 2004), it is shown that low flow salinity in our study catchments is highly associated with the evapotranspiration efficiency, fraction of catchment having flatter slopes, and the index of wetness. The analysis for our study region reveals that the catchments having evapotranspiration efficiencies between 0.5-0.6 and catchment parameter 'w' (Zhang et al. 2004) less than 1.46, can have saline low flow. These catchments are characterised with less forest cover, greater fraction of area having flatter slopes and dryland pasture and fall in the medium rainfall zone (500-850mm per year). Correlation analysis shows that low flow salinity is highly associated with evapotranspiration efficiency, index of wetness, catchment fraction having flatter slopes, area under native forests and dryland pasture. An ordinary least squares regression model for low flow salinity identified catchment parameter 'w' and catchment fraction having 2% to 4% slopes and index of wetness as the explanatory variables. We expect that improvement in the model could be achieved by considering other alternatives, such as robust and spatially weighted regression and principal component analysis.