The effect of reference climatology on global flood forecasting

Journal article

The Global Flood Awareness System (GloFAS) is a preoperational suite performing daily streamflow simulations to detect severe floods in large river basins. GloFAS defines the severity of a flood event with respect to thresholds estimated based on model-simulated streamflow climatology. Hence, determining accurate and consistent critical thresholds is important for its skillful flood forecasting. In this work, streamflow climatologies derived from two global meteorological inputs were compared, and their impacts on global flood forecasting were assessed. The first climatology is based on precipitation-corrected reanalysis data (ERA-Interim), which is currently used in the operational GloFAS forecast, while the second is derived from reforecasts that are routinely produced using the latest weather model. The results of the comparison indicate that 1) flood thresholds derived from the two datasets have substantial dissimilarities with varying characteristics across different regions of the globe; 2) the differences in the thresholds have a spatially variable impact on the severity classification of a flood; and 3) ERA-Interim produced lower flood threshold exceedance probabilities (and flood detection rates) than the reforecast for several large rivers at short forecast lead times, where the uncertainty in the meteorological forecast is smaller. Overall, it was found that the use of reforecasts, instead of ERA-Interim, marginally improved the flood detection skill of GloFAS forecasts.

Authors: Hirpa, F; Salamon, P; Alfieri, L; Pozo, J; Zsoter, E

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: American Meteorological Society
• Journal: Journal of Hydrometeorology
• Volume: 17
• Issue: 4
• Pages: 1131-1145
• Publication date: 2016-04-04
• Acceptance date: 2016-01-07
• DOI: 10.1175/JHM-D-15-0044.1
• EISSN: 1525-7541
• ISSN: 1525-755X
• Keywords: operational forecasting;; forecasting; hydrologic models; ensembles; probability forecasts/models/distribution; models and modeling; SBTMR; land surface model