Articles | Volume 28, issue 13
https://doi.org/10.5194/hess-28-2809-2024
https://doi.org/10.5194/hess-28-2809-2024
Research article
 | 
03 Jul 2024
Research article |  | 03 Jul 2024

Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method

Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu

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Cited articles

Baran, S., Hemri, S., and El Ayari, M.: Statistical post-processing of water level forecasts using Bayesian model averaging with doubly-truncated normal components, Water Resour. Res., 55, 3997–4013, https://doi.org/10.1029/2018WR024028, 2019. 
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Chen, L. and Guo, S.: Copulas and its application in hydrology and water resources, Springer Water, Springer Singapore, https://doi.org/10.1007/978-981-13-0574-0, 2019. 
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Cloke, H. L. and Pappenberger, F.: Ensemble flood forecasting: A review, J. Hydrol., 375, 613–626, https://doi.org/10.1016/j.jhydrol.2009.06.005, 2009. 
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Short summary
Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
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