气象水文集合预报的多源不确定性影响评估研究  

Evaluation of the Impact of Multi-Source Uncertainties on Meteorological and Hydrological Ensemble Forecasting

作　　者：Zhangkang Shu Jianyun Zhang Lin Wang Junliang Jin Ningbo Cui Guoqing Wang Zhouliang Sun Yanli Liu Zhenxin Bao Cuishan Liu 

机构地区：[1]State Key Laboratory of Hydraulics and Mountain River Engineering,College of Water Resource and Hydropower,Sichuan University,Chengdu 610065,China [2]State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering,Nanjing Hydraulic Research Institute,Nanjing 210029,China [3]Yangtze River Protection and Green Development Research Institute,Nanjing 210098,China [4]Research Center for Climate Change of Ministry of Water Resources,Nanjing 210029,China [5]School of Water Resources and Hydropower Engineering,Wuhan University,Wuhan 430072,China

出　　处：《Engineering》2023年第5期212-228,I0006,共18页工程（英文）

基　　金：the funding provided by the National Key Research and Development Program of China(2021YFC3200201);the National Natural Science Foundation of China(52121006,U2240203,and 51779144);the Second Tibetan Plateau Scientific Expedition and Research(2019QZKK0203);the Fundamental Research Funds for the Central Universities of China(B210204015 and B210204014);the Consulting Research Project of Chinese Academy of Engineering(2020-ZD-20 and 2021-ZD-CQ-2)。

摘　　要：评估复杂水文预报的来源不确定性对于深刻理解和改进水文预报精度至关重要,以往研究较少关注多源不确定性对气象水文预报复杂过程的影响。本研究提出了一种通用的基于贝叶斯模型平均(BMA)的集合框架,用于评估多源不确定性对气象水文预报全过程的影响。采用TIGGE中心的八种数值天气预报产品,四种完全不同结构的水文模型和1000组参数分别考虑来自输入、结构和参数的不确定性。在中国金溪池潭流域的实际应用表明:气象水文预报中数值预报输入的不确定性比水文模型的不确定性更大,水文模型结构的不确定性则明显大于模型参数的不确定性。洪峰流量预报的精度与数值天气预报的精度紧密相关,水文模型结构和参数及其交互作用则是枯水期流量预报的主要不确定性来源。当同时考虑三种不确定性来源时,径流过程预报精度更高。通过考虑复杂预报过程的主要不确定性源,基于BMA集合预报的预测精度更高,并可降低其他因素带来的不确定性。本文提出的多源不确定性评估框架可以较好地提升对气象水文预报过程的理解,在提高复杂水文预报精度方面具有广阔的应用前景。Evaluating the impact of multi-source uncertainties in complex forecasting systems is essential to understanding and improving the systems.Previous studies have paid little attention to the influence of multisource uncertainties in complex meteorological and hydrological forecasting systems.In this study,we developed a general ensemble framework based on Bayesian model averaging(BMA)for evaluating the impact of multi-source uncertainties in complex forecast systems.Based on this framework,we used eight numerical weather prediction products from the International Grand Global Ensemble(TIGGE)dataset,four hydrological models with different structures,and 1000 sets of parameters to comprehensively account for the input,structure,and parameter uncertainties.The framework’s application to the Chitan Basin in China revealed that the numerical weather prediction input uncertainty in the forecasting system was more significant than the hydrological model uncertainty.The hydrological model structure uncertainty was more prominent than the parameter uncertainty.The accuracy of the numerical weather prediction dominates the accuracy of the forecast of high flows.In addition,the structures and parameters of the hydrological model and their interactions contributed to the main uncertainty of the low flow forecasts.The streamflow was more realistically represented when the three uncertainty sources were considered jointly.By accounting for the significant uncertainty sources in complex forecast systems,the BMA ensemble forecasting produces more realistic and reliable predictions and reduces the influences of other incomplete considerations.The developed multi-source uncertainty assessment framework improves our understanding of the complex meteorological and hydrological forecasting system.Therefore,the framework is promising for improving the accuracy and reliability of complex forecasting systems.

关 键 词：数值天气预报 水文模型 集合预报 水文预报 数值预报 影响评估 径流过程 气象水文 

分 类 号：P456.7[天文地球—大气科学及气象学] P338[天文地球—水文科学]