GloFAS – global ensemble streamflow forecasting and flood early warning

L Alfieri,E Dutra,J Thielen,D Muraro,B Krzeminski,F Pappenberger,P Burek

doi:10.5194/hess-17-1161-2013

Abstract

Abstract. Anticipation and preparedness for large-scale flood events have a key role in mitigating their impact and optimizing the strategic planning of water resources. Although several developed countries have well-established systems for river monitoring and flood early warning, figures of populations affected every year by floods in developing countries are unsettling. This paper presents the Global Flood Awareness System (GloFAS), which has been set up to provide an overview on upcoming floods in large world river basins. GloFAS is based on distributed hydrological simulation of numerical ensemble weather predictions with global coverage. Streamflow forecasts are compared statistically to climatological simulations to detect probabilistic exceedance of warning thresholds. In this article, the system setup is described, together with an evaluation of its performance over a two-year test period and a qualitative analysis of a case study for the Pakistan flood, in summer 2010. It is shown that hazardous events in large river basins can be skilfully detected with a forecast horizon of up to 1 month. In addition, results suggest that an accurate simulation of initial model conditions and an improved parameterization of the hydrological model are key components to reproduce accurately the streamflow variability in the many different runoff regimes of the earth.

Highlights

Weather-driven natural hazards, including storm surges, floods, flash floods, and subsequent mass movements, are the most prominent natural disasters in worldwide statistics (CRED, 2011)
One should note that, in arid regions, results calculated with the coefficient of variation (CV) as defined above are penalized by rather low average discharges, compared to high flow conditions, which induces a low runoff-to-rainfall ratio
In this article we present a probabilistic flood early warning system running at global scale, aimed at forecasting the threshold exceedance of ensemble streamflow predictions on the basis of a model-consistent discharge climatology

Summary

Introduction

Weather-driven natural hazards, including storm surges, floods, flash floods, and subsequent mass movements, are the most prominent natural disasters in worldwide statistics (CRED, 2011). A total of 57 % of the reported number of victims in 2011 are associated with so-called “hydrological disasters”. These have caused a total economic damage of more than 70 billion US dollars, meaning a 230 % average increase compared to the previous decade (Guha-Sapir et al, 2012). According to the United Nations International Strategy for Disaster Reduction (UN/ISDR, 2002) and statistics from insurance companies, the socioeconomic impact of floods is increasing. Floods can no longer be treated as isolated events, as they are heavily linked with issues such as food insecurity, disease outbreaks and environmental degradation (IFRC, 2011)

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Conclusion