Title: Calibration of the Global Flood Awareness System (GloFAS) using daily streamflow data
Authors: HIRPA FEYERASALAMON PETERBECK HYLKELORINI VALERIOALFIERI LORENZOZSOTER ERVINDADSON SIMON J.
Citation: JOURNAL OF HYDROLOGY vol. 566 p. 595–606
Publisher: ELSEVIER SCIENCE BV
Publication Year: 2018
JRC N°: JRC111490
ISSN: 0022-1694 (online)
URI: https://linkinghub.elsevier.com/retrieve/pii/S0022169418307467
http://publications.jrc.ec.europa.eu/repository/handle/JRC111490
DOI: 10.1016/j.jhydrol.2018.09.052
Type: Articles in periodicals and books
Abstract: This paper presents the calibration and evaluation of the Global Flood Awareness System (GloFAS), an operational system that produces ensemble streamflow forecasts and threshold exceedance probabilities for large rivers worldwide. The system generates daily streamflow forecasts using the H-TESSEL land surface scheme and the LISFLOOD flow routing model forced by ECMWF IFS meteorological forecasts. The hydrology model currently uses a priori parameter estimates with uniform values over a large spatial area, which may limit the streamflow forecast skill. Here, the LISFLOOD routing model parameters are calibrated with ECMWF reforecasts from 1995 to 2015 as forcing using daily streamflow data from 1287 stations worldwide. The model calibration is performed using an evolutionary optimization algorithm with the Kling-Gupta Efficiency (KGE) as objective function. The skill improvements are quantified by computing the skill scores as the change in KGE relative to the baseline simulation using a priori parameters. The results show that simulation skill has improved after calibration (KGE skill score > 0.08) for the large majority of stations during the calibration (67% globally and 77% outside of North America) and validation (60% globally and 69% outside of North America) periods compared to the baseline simulation. However, the skill gain was impacted by the bias in the baseline simulation (the lowest skill score was obtained in basins with negative bias) due to the limitation of the routing model in correcting the bias in streamflow. Hence, further skill improvements could be achieved by reducing the bias in the streamflow by improving the precipitation forecasts and the land surface model. The results of this work will have implications on improving the operational GloFAS flood forecasting (www.globalfloods.eu).
JRC Directorate:Space, Security and Migration

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