Large‐Sample Evaluation of Radar Rainfall Nowcasting for Flood Early Warning

To assess the potential of radar rainfall nowcasting for early warning, nowcasts for 659 events were used to construct discharge forecasts for 12 Dutch catchments. Four open‐source nowcasting algorithms were tested: Rainymotion Sparse (RM‐S), Rainymotion DenseRotation (RM‐DR), Pysteps deterministic (PS‐D), and probabilistic (PS‐P) with 20 ensemble members. As benchmark, Eulerian Persistence (EP) and zero precipitation input (ZP) were used. For every 5‐min step in the available nowcasts, a discharge forecast with a 12‐hr forecast horizon was constructed. Simulations using the observed radar rainfall were used as reference. Rainfall and discharge forecast errors were found to increase with both increasing rainfall intensity and spatial variability. For the discharge forecasts, this relationship depends on the initial conditions, as the forecast error increases more quickly with rainfall intensity when the groundwater table is shallow. Overall, discharge forecasts using RM‐DR, PS‐D, and PS‐P outperform the other methods. Threshold exceedance forecasts were assessed by using the maximum event discharge as threshold. Compared to benchmark ZP, an exceedance is, on average, forecast 223 (EP), 196 (RM‐S), 213 (RM‐DR), 119 (PS‐D), and 143 min (PS‐P) in advance. The EP results are counterbalanced by both a high false alarm ratio (FAR) and inconsistent forecasts. Contrarily, PS‐D and PS‐P produce lower FAR and inconsistency index values than all other methods. All methods advance short‐term discharge forecasting compared to no rainfall forecasts at all, though all have shortcomings. As forecast rainfall volumes are a crucial factor in discharge forecasts, a future focus on improving this aspect in nowcasting is recommended. Plain Language Summary Flood warnings in quickly responding catchments are still challenging, as the timing and location of the rainfall forecast should be as accurate as possible. Radar rainfall nowcasting, a technique to statistically extrapolate the most recent rainfall observations, can potentially improve the rainfall forecasts up to several hours ahead. To evaluate the benefits and possible pitfalls of radar rainfall nowcasting for hydrological forecasting, we tested four different nowcasting algorithms for 659 rainfall events in the Netherlands. We used these nowcasts for the construction of discharge forecasts for 12 Dutch lowland catchments. Based on the evaluation of the large sample of discharge forecasts for these catchments, we conclu