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The distributed Grid-Xin'anjiang (Grid-XAJ) model is very sensitive to the spatial and temporal distribution of data when used in humid and semi-humid small and medium catchments. We used the successive correction method to merge the gauged rainfall with rainfall forecasted by the Weather Research and Forecasting (WRF) model to enhance the spatiotemporal accuracy of rainfall distribution. And we used the Penman–Monteith equation to calculate the potential evapotranspiration (PEPM). Then, we designed two forcing scenarios (WRF-driven rainfall (Wr) + PEPM, WRF-merged rainfall (Wm) + PEPM) to drive the Grid-XAJ model for flood forecasting. We found the WRF-driven Grid-XAJ model held significant potential in flood forecasting. The Grid-XAJ model provided only an approximation of flood hygrographs when driven by scenario Wr + PEPM. The results in scenario Wm + PEPM showed a high degree-of-fit with observed floods with mean Nash–Sutcliffe efficiency coefficient (NSE) values of 0.94 and 0.68 in two catchments. Additionally, scenario Wm + PEPM performed better flood hygrographs than scenario Wr + PEPM. The flood volumes and flow peaks in scenario Wm + PEPM had an obvious improvement compare to scenario Wr + PEPM. Finally, we observed that the model exhibited superior performance in forecasting flood hydrographs, flow peaks, and flood volumes in humid catchments compared with semi-humid catchments. [ABSTRACT FROM AUTHOR]

In this paper, a novel ANN flood forecasting model is proposed. The ANN model is combined with traditional hydrological concepts and methods, taking the initial Antecedent Precipitation Index (API), rainfall, upstream inflow and initial flow at the forecast river section as input of model, and flood flow forecast of the next time steps as output of the model. The distributed rainfall is realized as the input of the model. The simulation is processed by dividing the watershed into several rainfall-runoff processing units. Two hidden layers are used in the ANN, and the topology of ANN is optimized by connecting the hidden layer neurons only with the input which has physical conceptual causes. The topological structure of the proposed ANN model and its information transmission process are more consistent with the physical conception of rainfall-runoff, and the weight parameters of the model are reduced. The arithmetic moving-average algorithm is added to the output of the model to simulate the pondage action of the watershed. Satisfactory results have been achieved in the Mozitan and Xianghongdian reservoirs in the upper reaches of Pi river in Huaihe Basin, and the Fengman reservoir in the upper reach of Second Songhua river in Songhua basin in China. [ABSTRACT FROM AUTHOR]