Peng, T.; Qi, H.X., and Wang, J.C., 2020. Case study on extreme flood forecasting based on ensemble precipitation forecast in Qingjiang Basin of the Yangtze River. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 178–187. Coconut Creek (Florida), ISSN 0749-0208.

The ensemble precipitation forecasting provides a new idea for flood forecasting. The ensemble forecasting takes into account the uncertainty of initial field and model. A set of forecasting results can be obtained by inputting the ensemble precipitation forecasting products into hydrological model, which can avoid the limitation of single deterministic numerical forecasting results. Taking the typical flood process of Qingjiang Basin in June 2016 as an example, the flood forecast experiment was carried out based on the ensemble precipitation forecast products. Firstly, the flood forecast model was built on the Xin'anjiang hydrological Model, and the ensemble precipitation forecast of the European Center for Medium-Range Weather Forecasting (ECMWF) was estimated and used to drive basin flood forecasting model. The results show that the 51 members of ECMWF ensemble forecast can better capture this rainstorm process, the cumulative precipitation and precipitation process for 72 hours is close to the observation. The precipitation ensemble forecast drive the hydrological model, and can provide more precipitation forecasting information than deterministic forecasting. Moreover, it enriches the input information of hydrological model, to estimate the range of the peak flood discharge and the arrival time of flood peak. Therefore, we can get a probability of flood occurrence in different amounts level through the frequency analysis of flood peak, solves the problem of the accuracy of the single forecast result, and transforms the single deterministic accurate forecast into the probabilistic forecast, which can better meet the demand of risk information for flood control and disaster reduction.