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.