Using ensemble reforecasts to generate flood thresholds for improved global flood forecasting. Issue 4 (27th August 2020)

• Record Type: Journal Article
• Authors: Zsoter, Ervin; Prudhomme, Christel; Stephens, Elisabeth; Pappenberger, Florian; Cloke, Hannah
• Is Part Of: Journal of flood risk management. Volume 13:Issue 4(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-27
• Subjects: ensemble reforecasts -- flood forecasting -- flood thresholds -- forecast lead times -- global predictions -- reanalysis -- river discharge; Flood control -- Periodicals; Floods -- Risk assessment -- Periodicals; Flood damage prevention -- Periodicals; Flood warning systems -- Periodicals; 627.405
• DOI: 10.1111/jfr3.12658
• Languages: English
• ISSNs: 1753-318X

Abstract

Abstract: Global flood forecasting systems rely on predefining flood thresholds to highlight potential upcoming flood events. Existing methods for flood threshold definition are often based on reanalysis datasets using a single threshold across all forecast lead times, such as in the Global Flood Awareness System. This leads to inconsistencies between how the extreme flood events are represented in the flood thresholds and the ensemble forecasts. This paper explores the potential benefits of using river flow ensemble reforecasts to generate flood thresholds that can deliver improved reliability and skill, increasing the confidence in the forecasts for humanitarian and civil protection partners. The choice of dataset and methods used to sample annual maxima in the threshold computation, both for reanalysis and reforecast, is analysed in terms of threshold magnitude, forecast reliability, and skill for different flood severity levels and lead times. The variability of threshold magnitudes, when estimated from the different annual maxima samples, can be extremely large, as can the subsequent impact on forecast skill. Reanalysis‐based thresholds should only be used for the first few days, after which ensemble‐reforecast‐based thresholds, that vary with forecast lead time and can account for the forecast bias trends, provide more reliable and skilful flood forecasts.