Journal article

Assimilation of stream discharge for flood forecasting: Updating a semidistributed model with an integrated data assimilation scheme

Yuan Li, Dongryeol Ryu, Andrew W Western, QJ Wang

Water Resources Research | AMER GEOPHYSICAL UNION | Published : 2015


Real-time discharge observations can be assimilated into flood models to improve forecast accuracy; however, the presence of time lags in the routing process and a lack of methods to quantitatively represent different sources of uncertainties challenge the implementation of data assimilation techniques for operational flood forecasting. To address these issues, an integrated error parameter estimation and lag-aware data assimilation (IEELA) scheme was recently developed for a lumped model. The scheme combines an ensemble-based maximum a posteriori (MAP) error estimation approach with a lag-aware ensemble Kalman smoother (EnKS). In this study, the IEELA scheme is extended to a semidistributed..

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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 betweenthe Australian Bureau of Meteorology and CSIRO. We acknowledge CSIRO Land and Water Flagship, Australian Bureau of Meteorology, and Department of Environment, Land, Water, and Planning, Victoria Government for providing data. We would like to thank Robert Pipunic, Aiswarya Poovakka, three reviewers, and the associate editor for their valuable comments which improve the quality of this paper. Precipitation and potential evapotranspiration data used in this paper can be provided by obtaining permission from CSIRO Land and Water Flagship ( Streamflow and rating information can be obtained from Department of Environment, Land, Water, and Planning, Victoria Government ( ACCESS precipitation forecasts can be obtained from Australian Bureau of Meteorology (