2023, Volume 24, Issue 5
Engineering >> 2023, Volume 24, Issue 5 doi: 10.1016/j.eng.2022.06.007
Evaluation of the impact of multi-source uncertainties on meteorological and hydrological ensemble forecasting
a State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China
b State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
c Yangtze River Protection and Green Development Research Institute, Nanjing 210098, China
d Research Center for Climate Change of Ministry of Water Resources, Nanjing 210029, China
e School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan 430072, China
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Abstract
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 multi-source 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.
Keywords
Meteorological and hydrological forecasting ; Uncertainty estimation ; Bayesian model averaging ; Ensemble prediction ; Multi-model
SupplementaryMaterials
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