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

R. O. Imhoff*, C. C. Brauer, K. J. van Heeringen, R. Uijlenhoet, A. H. Weerts

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article numbere2021WR031591
Number of pages29
JournalWater Resources Research
Volume58
Issue number3
DOIs
Publication statusPublished - 2022

Keywords

  • discharge forecasting
  • flood early warning
  • nowcasting
  • radar
  • rainfall

Fingerprint

Dive into the research topics of 'Large-Sample Evaluation of Radar Rainfall Nowcasting for Flood Early Warning'. Together they form a unique fingerprint.

Cite this