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