Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System

Abstract

Abstract. Operational global-scale hydrological forecasting systems are widely used to help manage hydrological extremes such as floods and droughts. The vast amounts of raw data that underpin forecast systems and the ability to generate information on forecast skill have, until now, not been publicly available. As part of the Global Flood Awareness System (GloFAS; https://www.globalfloods.eu/) service evolution, in this paper daily ensemble river discharge reforecasts and real-time forecast datasets are made free and openly available through the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). They include real-time forecast data starting on 1 January 2020 updated operationally every day and a 20-year set of reforecasts and associated metadata, available through the dedicated GloFAS FTP service. This paper describes the model components and configuration used to generate the real-time river discharge forecasts and the reforecasts. An evaluation of ensemble forecast skill using the Continuous Ranked Probability Skill Score (CRPSS) was also undertaken for river points around the globe. Results show that GloFAS is skilful in over 93thinsp;% of catchments in the short- (1- to 3-days) and medium-range (5- to 15-days) against a persistence benchmark forecast, and skilful in over 80 % of catchments out to the extended-range (16- to 30-days) against a climatological benchmark forecast. However, the strength of skill varies considerably by location with GloFAS found to have no or negative skill at longer lead times in broad hydroclimatic regions in tropical Africa, western coast of South America, and catchments dominated by snow and ice in high northern latitudes. Forecast skill is summarised as a new headline skill score to be added as a layer on the GloFAS forecast Web Map Viewer at the next GloFAS cycle release, expected Autumn 2020, to aid user’s interpretation and understanding of forecast quality.

Uncertainties and their interaction in flood risk assessment with climate change

“…The Boyne catchment has been subject to significant arterial drainage works which have resulted in a faster rainfall runoff response and elevated high flows (Harrigan et al, 2020;Berghuijs et al, 2019). Our findings suggest that bias correction and flood frequency distribution are the dominant sources of uncertainty in the projected 100 year flood by the 2080s in this catchment.…”

Key factors influencing the severity of fluvial flood hazard from tropical cyclones

“…Coughlan de Perez et al, 2016) as part of the Copernicus Emergency Management Service for floods. In GloFAS, ensemble meteorological forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) are processed by the revised land surface hydrology scheme (HTESSEL) to create land surface runoff fields, with additional hydrological processes such as flow routing provided by the LISFLOOD hydrological model in order to forecast river discharge (Harrigan et al, 2020a;Alfieri et al, 2013;Balsamo et al, 2009 and2011;Van Der Knijff et al, 2010;Hirpa et al, 2018).…”

“…For each day within this case period the GloFAS-ERA5 discharge data in the land footprint area were compared to return period flood thresholds to calculate the return period exceeded at each point. The return period thresholds were taken directly from GloFAS, where they are determined from the GloFAS-ERA5 river discharge reanalysis, by fitting a Gumbel extreme value distribution on the annual maxima time series over the 1979-2018 period (Alfieri et al¸2019;Harrigan et al, 2020a;Zsoter et al, 2020).…”

“…The magnitude of the flood is defined by first calculating the maximum return period that is exceeded at each of these river points during this TC event, and then ranking the points from highest to lowest and calculating the average return period exceeded over the top 100 points. The return periods are capped at 200 years as a sensible upper limit, given that the return period thresholds are generated on a 40-year sample (Harrigan et al, 2020a). In this way the magnitudebased definition of flood severity compares the most severely affected river points in each TC case, in terms of the extremeness of the discharge levels experienced.…”

Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System

“…In this study, reforecasts were used to evaluate the post-processing method. Reforecasts are forecasts for past dates created using a forecasting system as close to the operational system as possible (Hamill et al, 2006;Harrigan et al, 2020). As the EFAS system has recently been updated, using reforecasts allowed a larger number of forecasts to be evaluated.…”