Articles | Volume 21, issue 8
Hydrol. Earth Syst. Sci., 21, 4103–4114, 2017

Special issue: Sub-seasonal to seasonal hydrological forecasting

Hydrol. Earth Syst. Sci., 21, 4103–4114, 2017

Research article 14 Aug 2017

Research article | 14 Aug 2017

Skill of a global forecasting system in seasonal ensemble streamflow prediction

Naze Candogan Yossef1,2, Rens van Beek1, Albrecht Weerts2,3, Hessel Winsemius2, and Marc F. P. Bierkens1 Naze Candogan Yossef et al.
  • 1Faculty of Geosciences, Utrecht University, Utrecht, 3508 TC, the Netherlands
  • 2Deltares, Delft, 2600 MH, the Netherlands
  • 3Department of Environmental Sciences, Wageningen University, 6708 PB, the Netherlands

Abstract. In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system Flood Early Warning System (FEWS)-World, which has been set up within the European Commission 7th Framework Programme Project Global Water Scarcity Information Service (GLOWASIS). FEWS-World incorporates the distributed global hydrological model PCR-GLOBWB (PCRaster Global Water Balance). We produce ensemble forecasts of monthly discharges for 20 large rivers of the world, with lead times of up to 6 months, forcing the system with bias-corrected seasonal meteorological forecast ensembles from the European Centre for Medium-range Weather Forecasts (ECMWF) and with probabilistic meteorological ensembles obtained following the ESP procedure. Here, the ESP ensembles, which contain no actual information on weather, serve as a benchmark to assess the additional skill that may be obtained using ECMWF seasonal forecasts. We use the Brier skill score (BSS) to quantify the skill of the system in forecasting high and low flows, defined as discharges higher than the 75th and lower than the 25th percentiles for a given month, respectively. We determine the theoretical skill by comparing the results against model simulations and the actual skill in comparison to discharge observations. We calculate the ratios of actual-to-theoretical skill in order to quantify the percentage of the potential skill that is achieved. The results suggest that the performance of ECMWF S3 forecasts is close to that of the ESP forecasts. While better meteorological forecasts could potentially lead to an improvement in hydrological forecasts, this cannot be achieved yet using the ECMWF S3 dataset.

Short summary
This paper presents a skill assessment of the global seasonal streamflow forecasting system FEWS-World. For 20 large basins of the world, forecasts using the ESP procedure are compared to forecasts using actual S3 seasonal meteorological forecast ensembles by ECMWF. The results are discussed in the context of prevailing hydroclimatic conditions per basin. The study concludes that in general, the skill of ECMWF S3 forecasts is close to that of the ESP forecasts.