|Title||Optimal drought management using sampling stochastic dynamic programming with a hedging rule|
|Publication Type||Journal Article|
|Year of Publication||2011|
|Authors||Eum H-I, Kim Y-O, Palmer RN|
|Journal||Journal of Water Resources Planning and Management|
|Keywords||Droughts, Dynamic programming, Korea, Reservoir operation|
This study develops procedures that calculate optimal water release curtailments during droughts using a future value function derived with a sampling stochastic dynamic programming model. Triggers that switch between a normal operating policy and an emergency operating policy (EOP) are based on initial reservoir storage values representing a 95% water supply reliability and an aggregate drought index that employs 6-month cumulative rainfall and 4-month cumulative streamflow. To verify the effectiveness of the method, a cross-validation scheme (using 2,100 combination sets) is employed to simulate the Geum River basin system in Korea. The simulation results demonstrate that the EOP approach: (1) reduces the maximum water shortage; (2) is most valuable when the initial storages of the drawdown period are low; and (3) is superior to other approaches when explicitly considering forecast uncertainty.