In this study, a Copula Monte Carlo (CMC) method was proposed to improve flood risk for confluence flooding control downstream of a reservoir. The effects of reservoir operation and joint relationships between two rivers’ streamflow were both considered in the method. The Copula function modeled the dependence between the main stream and tributary; whereas the Monte Carlo (MC) method estimated the flood risk after the reservoir routing of main-stream flood and the simulated tributary flood from the Copula function. This proposed method was applied to a case study in the confluence flooding control downstream (Jinsha and Min Rivers) of Xiluodu-Xiangjiaba reservoirs, southwest China. After the simulation, the results were compared with the current MC method. Our results indicated that with the CMC method, the downstream flood risk caused by the Min River flood was 4.32, 2.35, and 1.08% for the 0.5, 1, and 2% design floods of the Jinsha River, respectively; whereas the MC method underestimated the flood risk. The CMC method is more robust than the MC method because it can consider both the flood spatial correlations and the inside flood domain stochastic characteristics. Further, this method can provide decision support for joint operation of Xiluodu and Xiangjiaba cascaded reservoirs and flood risk planning and management.

Dynamic visual simulation of flood risk is crucial for scientific and intelligent emergency management of flood disasters, in which data quality, availability, visualization, and interoperability are important. Here, a seamless integration of a spatio-temporal Geographic Information System (GIS) with one-dimensional (1D) and two-dimensional (2D) hydrodynamic models is achieved for data flow, calculation processes, operation flow, and system functions. Oblique photography-based three-dimensional (3D) modeling technology is used to quickly build a 3D model of the study area (including the hydraulic engineering facilities). A multisource spatio-temporal data platform for dynamically simulating flood risk was built based on the digital earth platform. Using the spatio-temporal computation framework, a dynamic visual simulation and decision support system for flood risk management was developed for the Xiashan Reservoir. The integration method proposed here was verified using flood simulation calculations, dynamic visual simulations, and downstream river channel and dam-break flood simulations. The results show that the proposed methods greatly improve the efficiency of flood risk simulation and decision support. The methods and system put forward in this study can be applied to flood risk simulations and practical management.

The coincidence of floods on a mainstem and its tributaries may cause significant flood damage downstream of the confluence. In this study, the coincidence risks of annual maximum (AM) floods on Jinsha River and Min River, China, were analyzed using multivariate copulas based on both systematic records and available historical information. Archimedean and elliptical copulas were selected to establish the joint distributions of flood magnitudes and occurrence dates of the two rivers. The coincidence probabilities of AM floods were investigated and compared with monthly maximum floods. The results show that the coincidence probabilities of AM flood magnitudes and occurrence dates are much less than those of monthly maximum floods. Higher coincidence probabilities of AM flood occurrence dates occur in the period from mid-June to early September with three coincidence peaks, while there is only one coincidence peak in each month for monthly maximum floods. Ignoring historical flood information would underestimate the coincidence probabilities of flood magnitudes. The results can provide decision support for cascade reservoir operation on Jinsha River and flood mitigation downstream.

Streamflow plays a critical role in sediment transport. Traditional frequency analysis of annual maximal (AM) suspended sediment concentrations (SSCs) is usually based on univariate distribution of SSCs and may provide a limited assessment of SSC state, leading to an underestimate or overestimate of the corresponding probability. To address this issue, a multivariate frequency analysis framework is proposed for extreme sediment events using copula functions that account for the joint behavior of SSC and flood flow (i.e., peak discharge and volume). The uncertainties associated with copula modeling are also discussed. The Jinsha River in China is used as a case study. The results show that the copula-based multivariate method provides a more comprehensive assessment of extreme SSCs and their recurrence intervals than univariate frequency analysis. The uncertainty of copula modeling decreases with longer observation time and increases with larger return periods. The uncertainty ranges of the most likely design quantiles vary with different copulas, and the uncertainty range for the best-fit copula is not the smallest. This study is useful for the risk assessment of extreme SSC events under flood conditions.