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The value of updating ensemble streamflow prediction in reservoir operations
Author(s) -
Eum HyungIl,
Kim YoungOh
Publication year - 2010
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.7702
Subject(s) - streamflow , computer science , sampling (signal processing) , hydrology (agriculture) , flood forecasting , structural basin , environmental science , measure (data warehouse) , drainage basin , meteorology , data mining , geology , cartography , paleontology , physics , geotechnical engineering , filter (signal processing) , computer vision , geography
This study proposes a new monthly ensemble streamflow prediction (ESP) forecasting system that can update the ESP in the middle of a month to reflect the meteorological and hydrological variations during that month. The reservoir operating policies derived from a sampling stochastic dynamic programming model using ESP scenarios updated three times a month were applied to the Geum River basin to measure the value of updated ESP for 21 years with 100 initial storage combinations. The results clearly demonstrate that updating the ESP scenario improves the accuracy of the forecasts and consequently their operational benefit. This study also proves that the accuracy of the ESP scenario, particularly when high flows occur, has a considerable effect on the reservoir operations. Copyright © 2010 John Wiley & Sons, Ltd.