The lead time of operational flood forecasting is critical for the effectiveness of flood alert and flood risk reduction. It is impossible to extend the lead time of flood forecasting by solely using rain gauge observations. However, the weather research and forecasting (WRF) model has the potential to produce quantitative precipitation forecasts that can facilitate the flood risk management by increasing the flood forecasting lead time. This study investigates the flood prediction capabilities of the well-tested Grid-Xinanjiang model (GXM) in a flood-prone area, located in the upper region of the Huaihe River Basin, when driven by gauge observations and WRF precipitation forecasts, respectively. The results indicate that GXM is capable of producing improved flood predictions by using the WRF precipitation forecasts. The incoming floods are difficult to be predicted in advance by using the gaugemeasured precipitation, especially when the lead time is larger than the flow concentration time. However, with the WRF forecasts, the occurrence of flood events can be predicted for longer lead times. This study also demonstrates that the temporal and spatial patterns of precipitation forecasts have an important impact on the prediction of both timing and magnitude of incoming floods. K E Y W O R D Sflood defence measures, forecasting and warning, hydrological modelling, precipitation

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Based on a short-time heavy rainfall in Anhui and the weather research and forecasting (WRF) model, the water vapor in the initial field of the model is retrieved using the statistical relationships of the reflectivity factor from the Doppler weather radar with the relative humidity and hydrometeor. Three-dimensional variational (3DVAR) assimilation method is used to assimilate the radar reflectivity factor and radial velocity, and then the impact of assimilating retrieved water vapor on the analysis and forecast of the torrential rain is assessed. The results show that, after assimilating the retrieved water vapor, the water vapor field in the model is significantly improved. The water vapor content in the middle layer of the model in the analyzed field is increased, corresponding well with the convective region. Meanwhile, the precipitation distribution during this weather process is successfully simulated. The mesoscale characteristics are better presented by the imageries of radar reflectivity factor, and false echoes are partially reduced. Besides, the prediction of short-time heavy rainfall regions is closer to the actual observations. After assimilating the retrieved water vapor, the simulated one-hour accumulated rainfall is closer to the actual observation, and the fraction skill score (FSS) is higher.

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