Assimilation of an L-Band Microwave Soil Moisture Proxy to Compensate for Uncertainties in Precipitation Data

Abstract

The accuracy of hydrological model simulations is dependent on the reliability of model input data like, for example, meteorological information or land cover and soil information. Uncertainties of simulations of soil water fluxes are hereby directly related to the accuracy of available precipitation data. As precipitation is characterized by small temporal and spatial correlation lengths, the uncertainties in precipitation data increase with decreasing density of available precipitation gauges. As soil moisture directly depends on precipitation dynamics, its variation can be used as a proxy for precipitation variability. Remote sensing techniques allow for monitoring of surface soil moisture dynamics at different spatiotemporal scales. In particular, low-frequency microwave data are most sensitive to soil moisture dynamics. This paper investigates the potential of integrating L-band (1-2 GHz) microwave radiometer data into a simple model for soil wetness to compensate for uncertainties in a priori information of precipitation. The study is based on a short-term ground-based L-band radiometer data set over grassland. A high correlation between the microwave signature and surface soil moisture was found, which is consistent with previous findings. An analytical data assimilation scheme for the integration of that information into a soil wetness model, based on an antecedent precipitation index (API), was established. The results revealed that the data assimilation filter adds or removes an amount of water partially compensating for the actual precipitation error. The correlation coefficient between the filter update and the actual precipitation error was found to be 0.6 les r les 0.8, and the model simulations did show a better coincidence with in situ soil moisture records when integrating the microwave data. The results indicate high potential for use of L-band microwave data to compensate for uncertainties in precipitation data.