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Ensemble Kalman Filtering and Particle Filtering in a Lag-Time Window for Short-Term Streamflow Forecasting with a Distributed Hydrologic Model
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.
Ensemble Kalman Filtering and Particle Filtering in a Lag-Time Window for Short-Term Streamflow Forecasting with a Distributed Hydrologic Model
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.
Ensemble Kalman Filtering and Particle Filtering in a Lag-Time Window for Short-Term Streamflow Forecasting with a Distributed Hydrologic Model
Noh, Seong Jin (author) / Tachikawa, Yasuto (author) / Shiiba, Michiharu (author) / Kim, Sunmin (author)
Journal of Hydrologic Engineering ; 18 ; 1684-1696
2012-11-28
132013-01-01 pages
Article (Journal)
Electronic Resource
English
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