Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags

Yuan Li, Dongryeol Ryu, Andrew William Western, Q. J. Wang

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37 Citations (Scopus)


General filtering approaches in hydrologic data assimilation, such as the ensemble Kalman filter (EnKF), are based on the assumption that uncertainty of the current background prediction can be reduced by correcting errors in the state variables at the same time step. However, this assumption may not be valid when assimilating stream discharge into hydrological models to correct soil moisture storage due to the time lag between the soil moisture and the discharge. In this paper, we explore the utility of an ensemble Kalman smoother (EnKS) for addressing this time-lag issue. The EnKF and the EnKS are compared for two different updating schemes with the probability distributed model (PDM) via synthetic experiments: (i) updating soil moisture only and (ii) updating soil moisture and routing states simultaneously. The results show that the EnKS is superior to the EnKF when only soil moisture is updated, while the EnKS and the EnKF exhibit similar results when both soil moisture and routing storages are updated. This suggests that the EnKS can better improve the streamflow forecasting for models that do not adopt storage-based routing schemes (e.g., unit-hydrograph-based routing). For models with dynamic routing stores, errors in soil moisture are transferred to the routing stores, which can be corrected effectively by real-time filters. The EnKS-based soil moisture updating scheme is also tested with the GR4H model, for which unit-hydrograph-based routing is used. The result confirms that the EnKS is superior to the EnKF in improving both soil moisture and streamflow forecasting.

Original languageEnglish
Pages (from-to)1887-1900
Number of pages14
JournalWater Resources Research
Issue number4
Publication statusPublished - 2013
Externally publishedYes


  • discharge assimilation
  • ensemble Kalman filter
  • ensemble Kalman smoother
  • flood forecasting

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