Calibration of the Global Flood Awareness System (GloFAS) using daily streamflow data

• Published in: Journal of hydrology (Amsterdam) Vol. 566; pp. 595 - 606
• Main Authors: Hirpa, Feyera A., Salamon, Peter, Beck, Hylke E., Lorini, Valerio, Alfieri, Lorenzo, Zsoter, Ervin, Dadson, Simon J.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.11.2018; Elsevier, etc
• Subjects: Early warning system; Flood forecasting; Global hydrology; GloFAS; Model calibration; GloFAS; Model calibration; Flood forecasting; Global hydrology; Early warning system
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2018.09.052

•GloFAS was calibrated using daily streamflow data from 1287 stations worldwide.•Evolutionary Algorithm with parallel co-evolution of multiple simulations was used.•Parameters will be used in operational flood forecasting (www.globalfloods.eu).•Bias reduction in the forcings is needed to further improve the forecast skill. 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 a coupled H-TESSEL land surface scheme and the LISFLOOD model forced by ECMWF IFS meteorological forecasts. The hydrology model currently uses a priori parameter estimates with uniform values globally, which may limit the streamflow forecast skill. Here, the LISFLOOD routing and groundwater model parameters are calibrated with ECMWF reforecasts from 1995 to 2015 as forcing using daily streamflow data from 1287 stations worldwide. The calibration of LISFLOOD parameters 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 model in correcting the negative 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).