Preprints
https://doi.org/10.5194/hess-2023-106
https://doi.org/10.5194/hess-2023-106
28 Jun 2023
 | 28 Jun 2023
Status: a revised version of this preprint is currently under review for the journal HESS.

Quantify and reduce flood forecast uncertainty by the CHUP-BMA method

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

Abstract. The Bayesian model averaging (BMA), hydrological uncertainty processor (HUP), and HUP-BMA methods have been widely used to quantify flood forecast uncertainty. This study, for the first time, introduced a copula-based HUP in the framework of BMA and proposed the CHUP-BMA method to bypass the need for normal quantile transformation of the HUP-BMA method. The proposed ensemble forecast scheme consists of 8 members (two forecast precipitation inputs, two advanced long short-term memory (LSTM) models, and two objective functions used to calibrate parameters) and is applied to the interval basin between Xiangjiaba and Three Gorges Reservoir (TGR) dam-site. The ensemble forecast performance of the HUP-BMA and CHUP-BMA methods is explored in the 6–168h forecast horizons. The TGR inflow forecasting results show that the two methods can improve the forecast accuracy over the selected member with the best forecast accuracy, and the CHUP-BMA performs much better than the HUP-BMA. Compared with the HUP-BMA method, the forecast interval width with the 90 % confidence level and continuous ranked probability score metrics of the CHUP-BMA method are highest reduced by 28.42 % and 17.86 %, respectively. The probability forecast of the CHUP-BMA method has better reliability and sharpness and is more suitable for flood ensemble forecasts, providing reliable risk information for flood control decision-making.

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

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-106', Anonymous Referee #1, 21 Jul 2023
    • AC1: 'Reply on RC1', Shenglian Guo, 26 Jul 2023
  • CC1: 'Comment on hess-2023-106', Shaokun He, 21 Oct 2023
    • AC2: 'Reply on CC1', Shenglian Guo, 01 Nov 2023
  • CC2: 'Comment on hess-2023-106', Guang Yang, 23 Oct 2023
    • AC3: 'Reply on CC2', Shenglian Guo, 01 Nov 2023
  • RC2: 'Comment on hess-2023-106', Anonymous Referee #2, 15 Feb 2024
    • AC4: 'Reply on RC2', Shenglian Guo, 26 Feb 2024
  • RC3: 'Comment on hess-2023-106', Anonymous Referee #1, 28 Feb 2024
    • AC5: 'Reply on RC3', Shenglian Guo, 08 Mar 2024
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu

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Short summary
Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (HUP) with the Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method to reduce 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.