Flood forecasting based on radar precipitation nowcasting using U-net and its improved models

Accurate and timely short-term precipitation nowcasting is important for achieving reliable flood forecasting. The data-driven approaches have performed well in radar echo extrapolation to nowcast precipitation. In this paper, U-Net and its improved models, including SmaAt-Unet, Nested-Unet, and U-Net 3Plus were applied to perform radar echo extrapolation and precipitation nowcasting for 0.5 h, 1 h, and 2 h lead times of typical rainfall processes. The nowcasted precipitation was used as input to the HEC-HMS hydrological model for flood forecasting to compare the effect of different structural improvements to U-Net on the accuracy of flood forecasting. The results demonstrated that Nested-Unet and U-Net 3Plus aided in enhancing the accuracy of the extrapolation of moderate intensity radar echoes. With fewer discrepancies and better correlation with measured rainfall, the U-Net and U-Net 3Plus precipitation nowcasting results also produced improved flood forecasting outcome. The precipitation nowcasting and flood forecasting for SmaAt-Unet were slightly worse than other models; the relative errors of both flood peak and depth for Nested-Unet at 0.5 h lead time were less than 20 %, showing a good performance. Moreover, in a separate control experiment, the accuracy of the echo extrapolation was significantly decreased when convolutional block attention module (CBAM) was added to each model. However, all models have better extrapolation accuracy than the basic ConvLSTM. In general, Nested-Unet and U-Net 3Plus were helpful to improve the accuracy of precipitation nowcasting and flood forecasting, and the forecasted flood with 0.5 h and 1 h lead times could match the actual flood processes, but the peak discharge from nowcasting with 2 h lead time were severely underestimated, while the peak occurrence time could be forecasted correctly. These conclusions and attempts can provide effective guidelines for regional precipitation nowcasting and flood forecasting.