Data Assimilation of Satellite-Based Soil Moisture into a Distributed Hydrological Model for Streamflow Predictions

The authors examine the impact of assimilating satellite-based soil moisture estimates on real-time streamflow predictions made by distributed hydrologic model HLM. They use SMAP (Soil Moisture Active Passive) and SMOS Ocean Salinity) data in an agricultural region state Iowa central U.S. explore three different strategies for updating states using observations. first is a “hard update” method equivalent to replacing with satellite observed moisture. second Ensemble Kalman Filter (EnKF) update moisture, accounting modeling observational errors. third strategy introduces time-dependent error variance observations perturbation EnKF. study compares 131 USGS gauge four years (2015–2018). results indicate that EnKF reduces predicted peak compared from open-loop hard assimilation. Furthermore, inclusion improves overall prediction performance. Implications are useful application operational forecasting.