Journal article

An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting

Y Li, D Ryu, AW Western, QJ Wang, DE Robertson, WT Crow

Journal of Hydrology | Elsevier | Published : 2014


For operational flood forecasting, discharge observations may be assimilated into a hydrologic model to improve forecasts. However, the performance of conventional filtering schemes can be degraded by ignoring the time lag between soil moisture and discharge responses. This has led to ongoing development of more appropriate ways to implement sequential data assimilation. In this paper, an ensemble Kalman smoother (EnKS) with fixed time window is implemented for the GR4H hydrologic model (modèle du Ge´nie Rural à 4 paramètres Horaire) to update current and antecedent model states. Model and observation error parameters are estimated through the maximum a posteriori method constrained by prior..

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Funding Acknowledgements

This work has been financially supported by a CSIRO research studentship and the Water Information Research and Development Alliance between the Australian Bureau of Meteorology and CSIRO Water for a Healthy Country Flagship. We would like to acknowledge Australian Bureau of Meteorology and Department of Environment and Primary Industries Water Resources Division for providing data. We would like to thank Dr Chun-Hsu Su, four anonymous reviewers, and the associate editor for their constructive comments that significantly improved this paper.