Improving Curve Number Based Storm Runoff Estimates Using Soil Moisture Proxies

Advances in data dissemination and the availability of new remote sensing datasets present both opportunities and challenges for hydrologists in improving flood forecasting systems. The current study investigates the improvement in SCS curve number (CN)-based storm runoff estimates obtained after inclusion of various soil moisture proxies based on additional data on precipitation, baseflow, and soil moisture. A dataset (1980-2007) comprising 186 Australian catchments (ranging from 51 to 1979 km ² in size) was used. In order to investigate the value of a particular proxy, the observed S (potential maximum retention) was compared to values obtained with different soil moisture proxies using linear regression. An antecedent precipitation index (API) based on gauged precipitation using a decay parameter proved most valuable in improving storm runoff estimates, stressing the importance of high quality precipitation data. An antecedent baseflow index (ABFI) also performed well. Proxies based on remote sensing (TRMM and AMSR-E) gave promising results, particularly when considering the expected arrival of higher accuracy data from upcoming satellites. The five-day API performed poorly. The inclusion of soil moisture proxies resulted in mean modeled versus observed correlation coefficients around 0.75 for almost all proxies. The greatest improvement in runoff estimates was observed in drier catchments with low Enhanced Vegetation Index (EVI) and topographical slope (all intercorrelated parameters). The present results suggest the usefulness of incorporating remotely sensed proxies for soil moisture and catchment wetness in flood forecasting systems.