Considering the periodical changes in stream flow, it is essential to use rule curves for the optimal operation of reservoirs. This study aims to investigate the performance of Zarrineh Rud reservoir by implementing strategies for adaptation to climate change. Daily meteorological and hydrometric data were collected from selected stations upstream of the dam over a 26-year period (1990-2016). Using sequent peak algorithm (SPA) and with respect to the drinking and agricultural water demand, the active storage and its rule curve were simulated. Then, the optimal rule curve was procured through GA-SPA, aiming to minimize the downstream water shortage. The future climate data were downscaled using SDSM based on CanEsm2 model and under RCP2.6 and RCP8.5 scenarios for near (2020-2038), middle (2039-2058), and far (2059-2076) future periods. Then, the rainfall-runoff of HBV-light model was employed to calculate reservoir inflow for the mentioned periods. Finally, in view of environmental demand, reservoir performance indices were calculated for both non-adaptive and adaptive (static and dynamic hedging rules) policies. Results showed a significant decrease in the annual reservoir inflow compared to the baseline for all future periods. The least decrease was observed in RCP2.6 (nearly 23%) for the near future, whereas the largest decrease was in RCP8 (39%) for the middle period. Simulation with the static hedging rules managed to significantly reduce the average vulnerability index (by 60%) compared to no hedging, while the dynamic hedging rules outperformed static hedging rules only by 9%. Therefore, considering the insignificant improvement in reservoir performance using dynamic rules and their complexity, static hedging rules are recommended as the better option for adaptation during climate change.
Moghaddasi, M; Anvari, S; Akhondi, N
A trade-off analysis of adaptive and non-adaptive future optimized rule curves based on simulation algorithm and hedging rules
Theoretical And Applied Climatology