目前常用的洪水预警模式是基于确定性预报结果的洪水要素预警，没有考虑洪水预报结果的不确定性，在评估洪水预警可靠性方面存在局限性。因此，如何量化和降低洪水预报不确定性，实现考虑预报不确定性的洪水预警具有重要意义。本项目针对洪水预报不确定性和洪水预警面临的关键科学问题，引入降水集合预报分析定量降水预报不确定性，采用贝叶斯模型平均（BMA）方法对原始降水集合预报进行偏差订正和概率集成，并重建校正后降水集合预报的时空相关性。基于Copula函数建立多模型多变量水文不确定性处理器，量化多模型组合预报条件下多时段洪水联合预报的水文不确定性；将两种不确定性进行耦合得到考虑总不确定性的多时段洪水联合概率预报。在此基础上，推求预见期时段内洪峰流量的超过概率分布函数，构建概率预报条件下的洪水风险预警模式。研究成果可以降低洪水预报不确定性，提高洪水预报精度和洪水预警可靠性，为洪水风险评估和防洪减灾提供科学支撑。

The commonly used flood warning paradigm at present is the flood characteristic (e.g., peak discharge) warning based on the deterministic forecast result. However, there are some limitations in the evaluation of flood warning risk due to not fully considered uncertainty of flood forecasting. Therefore, it is of great significance to quantify and reduce the uncertainty of flood forecasting and produce flood warning considering the forecast uncertainty. In this project proposal, the ensemble precipitation forecast information will be adopted to analyze the uncertainty of quantitative precipitation forecast, and bias correction and multi-member information integration in a probabilistic manner are used to improve the performance of the raw ensemble precipitation forecast. The corrected ensemble precipitation forecast is randomly generated from the aforementioned precipitation probability distribution, and the spatial-temporal correlation will be reconstructed. Then, the multi-model multivariate hydrological uncertainty processor based on Copula function (Copula-MMHUP) will be derived to quantify the hydrological uncertainty of joint flood forecasting for multiple lead times under the condition of multi-hydrological model combination forecasting. On this basis, the integrated joint probabilistic flood forecast for multiple lead times simultaneously considering two uncertainty components will be obtained by coupling the corrected ensemble precipitation forecast and the Copula-MMHUP. In addition, the exceeding probability distribution function of the peak discharge within the concerned time interval will be derived to construct the risk-based flood warning paradigm based on the probabilistic forecast information. Research results can not only reduce the uncertainty of flood forecasting, improve the accuracy of flood forecasting and the reliability of flood warning, but also provide scientific basis for flood risk assessment, flood control and disaster reduction.