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Decision support for dam release during floods using a distributed biosphere hydrological model driven by quantitative precipitation forecasts
Author(s) -
Saavedra Valeriano Oliver C.,
Koike Toshio,
Yang Kun,
Graf Tobias,
Li Xin,
Wang Lei,
Han Xujun
Publication year - 2010
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2010wr009502
Subject(s) - quantitative precipitation forecast , environmental science , weighting , precipitation , flood control , flood forecasting , hydrology (agriculture) , drainage basin , streamflow , decision support system , mesoscale meteorology , meteorology , flood myth , computer science , geology , data mining , geography , medicine , cartography , geotechnical engineering , archaeology , radiology
This study proposes a decision support system for real‐time dam operation during heavy rainfall. It uses an operational mesoscale quantitative precipitation forecast (QPF) to force a hydrological model and considers the forecast error from the previous time step, which is introduced as a perturbation range applied to the most recent QPF. A weighting module accounts for the location, intensity, and extent of the error. Missing precipitation intensities within contributing areas and information from surrounding areas can both be considered. Forecast error is defined as the ratio of QPF to the observed precipitation within an evaluation zone (sub‐basin, basin, buffer, or total domain). An objective function is established to minimize the flood volume at control points downstream and to maximize reservoir storage. The decision variables are the dam releases, which are constrained to the ensemble streamflow's information. A prototype was applied to one of the most important river basins in Japan, the Tone reservoir system. The efficiency of the approach was evident in reduced flood peaks downstream and increased water storage. The results from three events indicate that the developed decision support system is feasible for real‐life dam operation.