GloFAS - global ensemble streamflow forecasting and flood early warning

被引:376
作者
Alfieri, L. [1 ,2 ]
Burek, P. [2 ]
Dutra, E. [1 ]
Krzeminski, B. [1 ]
Muraro, D. [2 ]
Thielen, J. [2 ]
Pappenberger, F. [1 ,3 ]
机构
[1] European Ctr Medium Range Weather Forecasts, Reading RG2 9AX, Berks, England
[2] Commiss European Communities, Joint Res Ctr, Inst Environm & Sustainabil, I-21020 Ispra, Italy
[3] Hohai Univ, Coll Hydrol & Water Resources, Nanjing, Jiangsu, Peoples R China
关键词
HYDROLOGICAL MODEL; ALERT SYSTEM; RIVER-BASIN; ECMWF MODEL; PREDICTION; PRECIPITATION; SCHEME; UNCERTAINTY; PERFORMANCE; SIMULATION;
D O I
10.5194/hess-17-1161-2013
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
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.
引用
收藏
页码:1161 / 1175
页数:15
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