Quantitative analysis framework for the benefit-risk feedback system of watershed reservoir groups

Abstract

The dynamic nature of environmental changes poses significant challenges to watershed management, particularly when there is a lack of objective methodologies for evaluating multiple scenarios within simulations. This deficiency often leads to an inadequate understanding of the benefits and risks associated with reservoir operations, thereby hindering the formulation of scientific decisions. To address the above issues, we have improved the conditional value at risk (ICVaR) and proposed a novel quantitative framework for assessing the complex interplay between benefits and risks. This framework is further enhanced by integrating with the panel vector auto-regression (PVAR), providing a more comprehensive approach to decision-making. Taking the Wudongde, Baihetan, Xiluodu, and Xiangjiaba reservoirs in the lower Jinsha River—collectively known as the Jinxia Reservoir Group—as case studies, a multi-objective optimization operational model is designed to effectively integrate flood control with power generation objectives. The analysis reveals that the flood control and economic operation of the Jinxia Reservoir Group exhibit consistency when encountering floods of design frequency P ≥ 1%. Their competition for the flood-carrying capacity exceeds that for water resources. It is recommended that the focus of joint operation should shift from optimizing water resource allocation to reservoir storage capacity. In terms of universally applicable methodologies, the ICVaR is capable of retaining data fluctuations, effectively leveraging both tail risk and front benefit data. This approach significantly diminishes the evaluation error of operational schemes from 50.16% to below 5%. The quantitative analysis framework adeptly addresses the issue of spurious regression in evaluation indicators, clarifies the feedback response relationship between reservoirs and operational demands, empowers managers to identify key operational nodes and vulnerable links, and facilitates the development of adaptive regulation mechanisms. The findings of this study contribute to evaluating and managing the operational benefits and risks of reservoir groups.