Mapping the spatial distribution of subsurface saline material in the Darling River valley

In the Australian landscape larg stores of soluble salt are present naturally. In many cases it is attributable to salts entrapped as marine sediment in earlier geological time. At the district level, the need for information on the presence of saline subsurface material is increasing, particularly for its application to salinity hazard assessment and environmental management. This is the case in irrigated areas, where changes in hydrology can result in secondary salinisation. To reduce the expense, environmental studies use a regression relationship to make use of more readily observed measurements (e.g. electromagnetic (EM) data) which are strongly correlated with the variable of interest. In this investigation a methodology is outlined for mapping the spatial distribution of average subsurface (6–12 m) salinity (EC_e — mS m^? 1) using an environmental correlation with EM34 survey data collected across the Bourke Irrigation District (BID) in the Darling River valley. The EM34 is used in the horizontal dipole mode at coil configurations of 10 (EM34-10), 20 (EM34-20), and 40 (EM34-40). A multiple-linear regression (MLR) relationship is established between average subsurface EC_e and the three EM34 signal data using a forward modeling stepwise linear modeling approach. The spatial distribution of average subsurface salinity generally reflects the known surface expression of point-source salinisation and provides information for future environmental monitoring and natural resource management. The generation of EM34 data on various contrived grids (i.e. 1, 1.5, 2. 2.5 and 3 km) indicates that in terms of accuracy, the data available on the 0.5 (RMSE = 188) and 1 km (RMSE = 283) grid are best, with the least biased predictions achieved using 1 (ME = ? 1) and 2 km (ME = 12) grids. Viewing the spatial distribution of subsurface saline material showed that the 0.5 km spacing is optimal, particularly in order to account for short-range spatial variation between various physiographic units. The Relative Improvement (RI) shows that increasing EM grids from 1, 1.5, 2, 2.5 to 3 km gave RI of ? 53, ? 100%, ? 107%, ? 128% and ? 140%, respectively. We conclude that at a minimum a 1 km grid is needed for reconnaissance EM34 surveying.