Flow forecasting is an essential topic for flood prevention and mitigation. This study utilizes a data-driven approach, the Long Short-Term Memory neural network (LSTM), to simulate rainfall–runoff relationships for catchments with different climate conditions. The LSTM method presented was tested in three catchments with distinct climate zones in China. The recurrent neural network (RNN) was adopted for comparison to verify the superiority of the LSTM model in terms of time series prediction problems. The results of LSTM were also compared with a widely used process-based model, the Xinanjiang model (XAJ), as a benchmark to test the applicability of this novel method. The results suggest that LSTM could provide comparable quality predictions as the XAJ model and can be considered an efficient hydrology modeling approach. A real-time forecasting approach coupled with the k-nearest neighbor (KNN) algorithm as an updating method was proposed in this study to generalize the plausibility of the LSTM method for flood forecasting in a decision support system. We compared the simulation results of the LSTM and the LSTM-KNN model, which demonstrated the effectiveness of the LSTM-KNN model in the study areas and underscored the potential of the proposed model for real-time flood forecasting.

The Weather Research and Forecasting (WRF)-Hydro model as a physical-based, fully-distributed, multi-parameterization modeling system easy to couple with numerical weather prediction model, has potential for operational flood forecasting in the small and medium catchments (SMCs). However, this model requires many input forcings, which makes it difficult to use it for the SMCs without adequate observed forcings. The Global Land Data Assimilation System (GLDAS), the WRF outputs and the ideal forcings generated by the WRF-Hydro model can provide all forcings required in the model for these SMCs. In this study, seven forcing scenarios were designed based on the products of GLDAS, WRF and ideal forcings, as well as the observed and merged rainfalls to assess the performance of the WRF-Hydro model for flood simulation. The model was applied to the Chenhe catchment, a typical SMC located in the Midwestern China. The flood prediction capability of the WRF-Hydro model was also compared to that of widely used Xinanjiang model. The results show that the three forcing scenarios, including the GLDAS forcings with observed rainfall, the WRF forcings with observed rainfall and GLDAS forcings with GLDAS-merged rainfall, are optimal input forcings for the WRF-Hydro model. Their mean root mean square errors (RMSE) are 0.18, 0.18 and 0.17 mm/h, respectively. The performance of the WRF-Hydro model driven by these three scenarios is generally comparable to that of the Xinanjiang model (RMSE = 0.17 mm/h).

Utilizing floodwater resources is important in relieving water shortages, and dynamic control of the flood limited water level (FLWL) for reservoir operation in a flood season is an effective method to achieve this objective. Based on the capacity-constrained pre-release method, this study proposed an improved dynamic control method that considered the duration of dry periods and the lead time of flood forecasts. The pre-release process was divided into two periods: water use and flood control. Taking Xianghongdian Reservoir in the Huai River Basin of China as an example, this study analyzed the statistical laws of continuous dry periods and effective flood forecast lead times and compared the effects of the negative exponential and asymptotic regression models in fitting the dry period distribution. We also calculated the floodwater volume over the FLWL in different situations and evaluated the flood control risks in a worst-case scenario. Statistical law of the dry period duration showed obvious negative index distribution characteristics; the relationship between increased water storage, dry period, and lead time can provide support for the operation decisions of the reservoir. The method did not increase the flood control risk under worst-case scenarios, and it can be used to effectively utilize reservoir floodwater resources.