Assessing 32-Day Hydrological Ensemble Forecasts in the Lake Champlain–Richelieu River Watershed
Publication: Journal of Hydrologic Engineering
Volume 25, Issue 10
Abstract
This paper explored various configurations of the ensemble Kalman filter, the GR4J hydrological model, and the Global Environmental Multiscale (GEM) atmospheric model in order to maximize the skill of ensemble hydrological forecasts for the Lake Champlain–Richelieu River watershed. In open-loop mode, the hydrological model represented very well the observed streamflow (Nash–Sutcliffe value above 90%). It sufficed to assimilate hydrological data to obtain a reliable and skillful analysis of streamflow; assimilation of snow water equivalent (SWE) information did not bring additional benefits. In forecast mode, the opposite was true: hydrological assimilation alone did not improve forecast performance, but assimilating SWE data improved reliability and skill of forecasts with lead times of 15 days to 1 month. The impact of SWE assimilation also depended on the quality of the precipitation analysis. It therefore is recommended to use SWE assimilation for monthly forecasting, especially if the precipitation data used to drive the hydrological model are biased.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors acknowledge the financial support from the Canadian Space Agency. The authors also thank the three anonymous reviewers for their constructive comments and suggestions.
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Journal of Hydrologic Engineering
Volume 25 • Issue 10 • October 2020
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© 2020 American Society of Civil Engineers.
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Received: Oct 29, 2019
Accepted: Apr 28, 2020
Published online: Jul 16, 2020
Published in print: Oct 1, 2020
Discussion open until: Dec 16, 2020
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