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

Viewed

Total article views: 952 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
723 174 55 952 30 29
  • HTML: 723
  • PDF: 174
  • XML: 55
  • Total: 952
  • BibTeX: 30
  • EndNote: 29
Views and downloads (calculated since 28 Jun 2023)
Cumulative views and downloads (calculated since 28 Jun 2023)

Viewed (geographical distribution)

Total article views: 952 (including HTML, PDF, and XML) Thereof 923 with geography defined and 29 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 05 Jul 2024
Download
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.