Research papersAssimilation of soil moisture and streamflow observations to improve flood forecasting with considering runoff routing lags

- An effective data assimilation scheme was developed for short-term flood forecasting.
- The effect of runoff routing lag is considered in the data assimilation.
- This assimilation scheme can relax the impact of initial soil moisture conditions on flood forecasting.
- The scheme has the advantage of assimilating multi-source data including ground measurement and remote sensing retrievals.

Assimilation of either soil moisture or streamflow has been well demonstrated to improve flood forecasting. However, it is difficult to assimilate two different types of observations into a rainfall-runoff model simultaneously because there is a time lag between soil moisture and streamflow owing to the runoff routing process. In this study, we developed an effective data assimilation scheme based on the ensemble Kalman filter and smoother (named as EnKF-S) to exploit the benefits of the two observation types while accounting for the runoff routing lag. To prove the importance of accounting for the time lag, a scheme named Dual-EnKF was used to compare. To demonstrate the schemes, we designed synthetic cases regarding two typical flood patterns, i.e., flash flood and gradual flood. The results show that EnKF-S can effectively improve flood forecasting compared with Dual-EnKF, particularly when the runoff routing has distinct time lags. For the synthetic cases, EnKF-S reduced root-mean-square error (RMSE) by more than 70% relative to the data assimilation scheme without considering runoff routing lags. Therefore, this effective data assimilation scheme holds great potential for short-term flood forecasting by merging observations from ground measurement and remote sensing retrievals.