Calibration of the Global Flood Awareness System (GloFAS) using daily streamflow data - Hirpa, F; Salamon, P; Beck, H; Lorini, V; Alfieri, L; Zsoter, E; Dadson, S (Elsevier, 2018)
This paper presents the calibration and evaluation of the Global Flood Awareness System 33 (GloFAS), an operational system that produces ensemble streamflow forecasts and threshold 34 exceedance probabilities for large rivers worldwide. The system generates daily streamflow 35 forecasts using a coupled H-TESSEL land surface scheme and the LISFLOOD model forced by 36 ECMWF IFS meteorological forecasts. The hydrology model currently uses a priori parameter 37 estimates with uniform values globally, which may limit the streamflow forecast skill. Here, the 38 LISFLOOD routing and groundwater model parameters are calibrated with ECMWF reforecasts 39 from 1995 to 2015 as forcing using daily streamflow data from 1287 stations worldwide. The 40 calibration of LISFLOOD parameters is performed using an evolutionary optimization algorithm 41 with the Kling-Gupta Efficiency (KGE) as objective function. The skill improvements are 42 quantified by computing the skill scores as the change in KGE relative to the baseline simulation 43 using a priori parameters. The results show that simulation skill has improved after calibration 44 (KGE skill score > 0.08) for the large majority of stations during the calibration (67% globally 45 and 77% outside of North America) and validation (60% globally and 69% outside of North 46 America) periods compared to the baseline simulation. However, the skill gain was impacted by 47 the bias in the baseline simulation (the lowest skill score was obtained in basins with negative 48 bias) due to the limitation of the model in correcting the negative bias in streamflow. Hence, 49 further skill improvements could be achieved by reducing the bias in the streamflow by 50 improving the precipitation forecasts and the land surface model. The results of this work will 51 have implications on improving the operational GloFAS flood forecasting 52 (www.globalfloods.eu).