Skill of a global forecasting system in seasonal ensemble streamflow prediction

Naze Candogan Yossef,Marc F P Bierkens,Hessel Winsemius,Albrecht Weerts,Rens Van Beek

doi:10.5194/hess-21-4103-2017

Abstract

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.

Highlights

Reliable seasonal streamflow forecasts potentially have many benefits including disaster relief, management of hydropower reservoirs, water supply, agriculture and navigation
This paper investigates the skill of seasonal streamflow forecasts for 20 of the largest rivers in the world 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)
The present study shows, that by using European Centre for Medium-range Weather Forecasts (ECMWF) S3 seasonal forecasts the biggest skill improvement over the ESP procedure can be attained at lead times of 1–2 months, but less at longer lead times when meteorological forcing plays a more important role

Summary

Introduction

Reliable seasonal streamflow forecasts potentially have many benefits including disaster relief, management of hydropower reservoirs, water supply, agriculture and navigation. Seasonal hydrological forecasting on a global scale could be especially valuable for developing regions, where effective hydrological forecasting systems are scarce. Global seasonal forecasts provide spatially consistent predictions of streamflow anomalies. These may supply information to disaster management organizations operating at global scale to prepare for response as well as to the international water and energy markets about the regional availability of water and hydropower in the coming months. Approaches to seasonal streamflow forecasting can be divided into two categories, empirical/statistical methods and numerical/dynamical methods. While statistical forecasts are quite successful in some regions of the world and in some seasons, in many cases the available records are too short to accurately capture climatic variability. Statistical methods are the more widely developed and reliable methods that are used for most current operational sea-

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