State updating in the Xin'anjiang Model: Joint assimilating streamflow and multi-source soil moisture data via Asynchronous Ensemble Kalman Filter with enhanced Error Models

Abstract. Assimilating either soil moisture or streamflow individually has been well demonstrated to enhance the simulation performance of hydrological models. However, the runoff routing process may introduce a lag between soil moisture and outlet discharge, presenting challenges in simultaneously assimilating the two types of observations into a hydrological model. The Asynchronous Ensemble Kalman Filter (AEnKF), an adaptation of the Ensemble Kalman Filter (EnKF), is capable of utilizing observations from both the assimilation moment and preceding periods, thus holding potential to address this challenge. Our study first merges soil moisture data collected from field soil moisture monitoring sites with China Meteorological Administration Land Data Assimilation System (CLDAS) soil moisture data. We then employ the AEnKF, equipped with improved error models, to assimilate both observed outlet discharge and the merged soil moisture data into the Xin'anjiang model. This process updates the state variables of the model, aiming to enhance real-time flood forecasting performance. The testing on both synthetic and real-world cases demonstrates that assimilation of these two types of observations simultaneously substantially reduces the accumulation of past errors in the initial conditions at the start of the forecast, thereby aiding in elevating the accuracy of flood forecasting. Moreover, the AEnKF with the enhanced error model consistently yields greater forecasting accuracy across various lead times compared to the standard EnKF.