Hydrological verification of two rainfall short-term forecasting methods with floods anticipation perspective

Flood forecast remains a significant challenge, particularly when dealing with basins characterized by small drainage areas (i.e. 103 km2 or lower with response time in the range 0.5-10 h) especially because of the rainfall prediction uncertainties (Buzzi et al., 2014) . This study aims to investigate the performances of streamflow predictions using two short-term rainfall forecast methods. These methods utilize a combination of nowcasting extrapolation algorithm and numerical weather predictions by employing three-dimensional variational assimilation system and nudging assimilation techniques, meteorological radar and lightning data are frequently updated, allowing new forecasts with high temporal frequency (i.e. 1-3 hours). A distributed hydrological model is used to convert rainfall forecasts in streamflow prediction. The potential of assimilating radar and lightning data or radar data alone, is also discussed. A hindcast experiment on two rainy periods in the north-west region of Italy was designed. The selected skill scores were analyzed to assess their degradation with increasing lead time, and the results were further aggregated based on basin dimensions to investigate the catchment integration effect. The findings indicate that both rainfall forecast methods yield good performance, with neither definitively outperforming the other. Furthermore, the results demonstrate that, on average, assimilating both radar and lightning data enhances the performance.