Research papersReservoir adaptive operating rules based on both of historical streamflow and future projections

- Historical streamflow and future projections are simultaneously used for the derivation of adaptive operating rules.
- Robustness index is developed by comparing benefits of HAFOR with those of COR.
- HAFOR can not only balance benefits but also obtain higher robustness between historical scenarios and future projections.

Climate change is affecting hydrological variables and consequently is impacting water resources management. Historical strategies are no longer applicable under climate change. Therefore, adaptive management, especially adaptive operating rules for reservoirs, has been developed to mitigate the possible adverse effects of climate change. However, to date, adaptive operating rules are generally based on future projections involving uncertainties under climate change, yet ignoring historical information. To address this, we propose an approach for deriving adaptive operating rules considering both historical information and future projections, namely historical and future operating rules (HAFOR). A robustness index was developed by comparing benefits from HAFOR with benefits from conventional operating rules (COR). For both historical and future streamflow series, maximizations of both average benefits and the robustness index were employed as objectives, and four trade-offs were implemented to solve the multi-objective problem. Based on the integrated objective, the simulation-based optimization method was used to optimize the parameters of HAFOR. Using the Dongwushi Reservoir in China as a case study, HAFOR was demonstrated to be an effective and robust method for developing adaptive operating rules under the uncertain changing environment. Compared with historical or projected future operating rules (HOR or FPOR), HAFOR can reduce the uncertainty and increase the robustness for future projections, especially regarding results of reservoir releases and volumes. HAFOR, therefore, facilitates adaptive management in the context that climate change is difficult to predict accurately.