Coupling Bayesian Network and copula theory for water shortage assessment: A case study in source area of the South-to-North Water Division Project (SNWDP)

Updating the appropriate risk measures is vital for management practice in reservoir operation and offers new insights for decision-making. Water diversion may cause water shortage crises both in the source area and in the downstream flow. By focusing on the effect of water demand in the source area and reservoir operation, this study proposed a risk assessment framework with the Bayesian network and copula-based estimation of the parameters. The risk assessment framework comprised four parts: dependencies estimation, specifically established copula, a Bayesian network based on the copula-based estimation of the parameters, and scenario analysis. This framework was applied to the Danjiangkou Reservoir, part of the source area for the Middle Route Project of the South-to-North Water Division Project in China. The study results showed that the framework provided valuable decision-making information. The Clayton and Gumbel functions evidenced superior capabilities for simulating the dependence of hydrological variables using the copula family. Since it revealed the potential correlation of variables, the Bayesian network based on the copula-based estimation of the parameters was more robust and accurate compared with the classic Bayesian network. According to the results yielded in different scenarios, occurrences presenting the highest degree of disadvantage for the Danjiangkou Reservoir will be increased from 8.66% to 23.08% for upstream situations and 59.03% for downstream low-water situations. These results indicate the disadvantages and potential strategies for the successful operation of the reservoir, which will be helpful to both researchers and managers.

Automated summary

Updating risk measures is important for reservoir management practice and decision-making. A risk assessment framework using Bayesian network and copula-based estimation was proposed. The framework was applied to the Danjiangkou Reservoir and provided valuable decision-making information. The Bayesian network based on copula-based estimation was more robust and accurate in simulating the dependence of hydrological variables.