Evaluation of flood prediction capability of the distributed Grid-Xinanjiang model driven by weather research and forecasting precipitation

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 gauge-measured 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.