Integrated real-time flood risk identification, analysis, and diagnosis model framework for a multireservoir system considering temporally and spatially dependent forecast uncertainties.

• Proposed integrated flood risk identification, analysis, and diagnosis framework. • Characterized spatial-temporal dependent forecast uncertainties by Copula function. • Discovered the underestimation of risk when neglecting spatial-temporal dependences. • Constructed scenario-based risk diagnosis model to examine causes of risk. Uncertainties in flood forecasts that result from error propagation in meteorological–hydrological simulation processes present spatial and temporal dependences that are the principal sources of risk to real-time flood control in a multireservoir system. Risk analysis models that neglect such dependences potentially yield biased results regarding flood control operations. To enhance the accuracy of flood risk analysis and inform decision making regarding real-time flood control for a multireservoir system, this study proposed an integrated flood risk identification, analysis, and diagnosis model framework that incorporates the multidimensional dependences of forecast uncertainties. The framework includes an uncertainty identification model that uses the copula function to characterize the joint probability function of the mixed dependent uncertainties. It also incorporates a real-time flood risk analysis module that couples simulation and optimization to convert the complex multiobjective operation under uncertainty into a tractable model capable of flexibly evaluating individual and joint flood risks. The third component is a risk diagnosis module that calculates risk contributions using conditional probability, and identifies stable and vulnerable reservoirs within the system to alter operational decisions toward risk reduction. Methodologies were verified through application to a mixed four-reservoir system in the Pi River Basin, China. The principal findings were as follows. (1) Positive-dominated dependences of forecast uncertainties were identified because of error propagation through the meteorological and rainfall–runoff forecasts within the spatial and temporal continuity connection, and the copula function accurately described and quantified the multidimensional temporal and spatial dependences of the uncertainties. (2) In neglecting the spatial dependences of forecast uncertainties, the joint flood risk of the reservoir system was underestimated; in ignoring high-order temporal dependences, individual flood risk was underestimated owing to biased estimation in error variance. (3) Systematic reorganization of the operational strategies of diagnosed stable and vulnerable reservoirs could provide guidance for reduction and homogenization of flood risks. The proposed framework could be used to improve the accuracy of flood risk analysis and support reliable real-time flood control operation of a multireservoir system. [ABSTRACT FROM AUTHOR]