Ensemble Kalman Filtering and Particle Filtering in a Lag-Time Window for Short-Term Streamflow Forecasting with a Distributed Hydrologic Model
Publication: Journal of Hydrologic Engineering
Volume 18, Issue 12
Abstract
The performance of the ensemble Kalman filter (EnKF) and the particle filter (PF) is assessed for short-term streamflow forecasting with a distributed hydrologic model, namely, the water and energy transfer processes (WEP) model. To mitigate the drawbacks of conventional filters, the ensemble square root filter (EnSRF) and the regularized particle filter (RPF) are implemented. For both the EnSRF and the RPF, sequential data assimilation is performed within a lag-time window to consider the response times of internal hydrologic processes. The proposed methods are applied to two catchments in Japan and Korea to assess their performance. The model ensembles are perturbed by the noise of the soil moisture content and are assimilated with streamflow observations. The forecasting accuracy of both the EnSRF and the RPF is improved when sufficient lag-time windows are provided. The EnSRF is sensitive to the length of the lag-time window and has a limited ability to forecast within short lead times, whereas the RPF has a more stable forecasting capability for the entire range of lead times. Filtering with a limited number of ensembles also yields improved performance using a lag-time window.
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Acknowledgments
This research is supported partly by a Strategic Research Project funded by the Korea Institute of Construction Technology and Collaborative Research Program for Large-Scale Computation of ACCMS and IIMC, Kyoto University, which is gratefully acknowledged. This manuscript has benefited greatly from the comments of anonymous reviewers.
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Published In
Journal of Hydrologic Engineering
Volume 18 • Issue 12 • December 2013
Pages: 1684 - 1696
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Feb 13, 2012
Accepted: Nov 26, 2012
Published online: Nov 28, 2012
Discussion open until: Apr 28, 2013
Published in print: Dec 1, 2013
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