Remotely Sensed Soil Moisture Assimilation in the Distributed Hydrological Model Based on the Error Subspace Transform Kalman Filter

The data assimilation of remotely sensed soil moisture observations provides a feasible path improving river flow simulation. In this work, we studied the performance error subspace transform Kalman filter (ESTKF) algorithm on from SMAP, including improvement and in hydrological model. Additionally, discussed advantages added value ESTKF compared to ensemble (EnKF) To achieve objective, solved spatial resolution gap between simulated SMAP was assimilated into first layer distributed model 600 m, while 9 km. There is considerable two resolutions. By employing observation operators localization based geolocation, multiple values for each grid, thereby ensuring consistent updates results show following: (1) terms moisture, found that both EnKF were effective, ubRMSE lower than EnKF. (2) improved most cases where open-loop high simulations too low, but did not improve situation. (3) ESTKF, relative flood volume reduced average 2.52%, peak improve. provide evidence