GloFAS v4, the 0.05 degrees quasi-global (-180,180,90,-60) implementation of the LISFLOOD model, was calibrated using in-situ discharge gauge stations with a minimum drainage area of 500 km2 and at least 4-years-long time series of measurements more recent than 01 January 1980.
1996 quality checked time series were used for calibration. More specifically, 212 calibration points were in Europe, 250 in Asia, 61 in Oceania, 420 in Africa, 617 in Centre-North America, and 436 in South America.
Catchments for which in situ discharge data could be used for calibration entailed 47.5 % of the quasi-global domain (Figure 6, blue area). For these catchments, the Distributed Evolutionary Algorithm for Python (DEAP, Fortin et al. 2012) was used to explore the parameter space and identify the parameter set leading to the highest value of the modified Kling Gupta Efficiency (KGE', Gupta et al., 2009).
Parameters of the catchments for which in situ discharge data were not available (Figure 5, yellow area) were estimated by parameter regionalization.
Figure 6- Subdivision GloFAS domain into LISFLOOD calibration inter-catchments.
Figure 5 – Catchments with available discharge data (gauged catchments) in blue.
Parameter estimation for gauged catchments
The Distributed Evolutionary Algorithm for Python (DEAP, Fortin et al. 2012) was used to explore the parameter space and identify the parameter set leading to the highest value of the modified Kling Gupta Efficiency (KGE, Gupta et al., 2009), as implemented by the open-source calibration tool.
When multiple calibration points are available in one basin, the calibration protocol follows a top-down approach from head-catchments to downstream catchments; each segmentation of the area is called inter-catchment. Figure 6 shows the fragmentation of the area with available discharge observations into inter-calibration catchments.
