Predictive simulation and optimal allocation of surface water resources in reservoir basins under climate change

Abstract

Predicting and allocating surface water resources are becoming increasingly important tasks for addressing the risk of water shortages and challenges of climate change, especially in reservoir basins. However, surface water resource management includes many systematic uncertainties and complexities that are difficult to address. Thus, advanced models must be developed to support predictive simulations and optimal allocations of surface water resources, which are urgently required to ensure regional water supply security and sustainable socioeconomic development. In this study, a soil and water assessment tool-based interval linear multi-objective programming (SWAT-ILMP) model was developed and integrated with climate change scenarios, SWAT, interval parameter programming, and multi-objective programming. The developed model was applied to the Xinfengjiang Reservoir basin in South China and was able to identify optimal allocation schemes for water resources under different climate change scenarios. In the forecast year 2025, the optimal water quantity for power generation would be the highest and account for ∼60% of all water resources, the optimal water quantity for water supply would account for ∼35%, while the optimal surplus water released from the reservoir would be the lowest at ≤5%. In addition, climate change and reservoir initial storage would greatly affect the surplus water quantity but not the power generation or water supply quantity. In general, the SWAT-ILMP model is applicable and effective for water resource prediction and management systems. The results from different scenarios can provide multiple alternatives to support rational water resource allocation in the study area.