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

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.