The 0.05 degrees quasi-global (-180,180,90,-60) implementation of the LISFLOOD model was calibrated using 5379 in-situ discharge gauge stations with at least 4-years-long time series of measurements more recent than 01 January 1980, and information (inflow, outflow, volume in-situ time series, as well as climate, geometry, and use attributes) from 360 reservoirs.
Firstly, reservoir parameter were calibrated using SCE-UA1(where in-situ observed time series were available) and estimated using a random forest regressor estimator (for the remainder of the reservoirs included in GloFASv5 set-up).
Second, the Distributed Evolutionary Algorithm for Python (DEAP, Fortin et al. 20122) was used to optimize the parameters of catchments for which discharge data were available (gauged catchments), covering 51.6% of the semi-global domain.
Third, a parameter learning regionalization approach was implemented to estimate parameters regulating snow melt, water infiltration into the soil, surface water flow, groundwater flow, lakes dynamic in 48.4% of the semi-global domain.
JDKGE, a revised version of the widely used the modified Kling Gupta Efficiency (KGE') proposed by Ficchì et al. (2026)3 was used as objective function. JDKGE has the purpose to optimize both low and high flows. A minimum drainage area of 500 km2 was used for the above explained steps of the calibration.
The combined calibration approach delivered 12 parameter maps with quasi-global extent and 2 tables indicating reservoir parameters.
This page summarises GloFAS v5 calibration hydrological skill [NOT THE LONG RUN!!]. IN situ discharge time series have different length and different temporal coverage in the semi-global domain. For this reason, hydrological modelling performance is evaluated using all available discharge data for each calibrated station.
Overview
The hydrological performance of GloFAS v5 is expressed by the modified Kling-Gupta Efficiency (KGE') (Knoben et al., 20193). A detailed explanation of the modified Kling-Gupta Efficiency (KGE') is available from this page. -→ ADD JDKGE IN THE PAGE?
Albeit JDKGE was used as objective function for model calibration (as explained here), this page presents the outcomes in terms of KGE’ to allow a straightforward comparison with other models and with GloFASv4 evaluation pages. Evaluation of low flow performance is presented by showing the outcomes in terms of JSD, an estimate of the Jensen-Shannon Divergence, as indicated by Ficchì et al. (2026).
Figure 1 shows the cumulative distribution function of KGE' values, as well as the KGE' distribution for the 5379 calibration stations. The median KGE' is 0.716, with the calibrated parameters leading to higher accuracy than the mean flow benchmark (i.e. KGE’ > -0.41, Knoben et al., 20193) for 99.61% of the gauged catchments.
Figure 1 – KGE' histogram (blue bars) distribution and empirical cumulative distribution function(red line) for all the 5379 calibration stations of GloFAS v5 and all the available data.The green line shows the optimal performance.
Figure 2 presents the results of GloFAS v5 for the 5379 calibration points in terms of KGE' components: linear correlation between observations and simulations, bias, and a measure of the flow variability error (Knoben et al., 20193).
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Figure 2 – KGE' components (correlation, bias, variability) histogram distribution (light blue bars) and empirical cumulative distribution function red line) for all the 5379 calibration stations of GloFAS v5 and all the available data. The green lines show the optimal performance.
Figure 3 shows the results of GloFAS v5 for the 5379 calibration points in terms of JSD: while the ideal value is 0, Ficchì et al. (2026) explains that values lower than 0.1 generally indicate adequate representation of low flows. In GloFASv5 calibration, approximately 80% of the 5379 calibration stations achieved JSD smaller than 0.1.
Figure 3 – JSD histogram distribution pink bars) and empirical cumulative distribution function magenta line) for all the 5379 calibration stations of GloFAS v5 and all the available data.
The green lines show the optimal value of JSD metric, blue dashed line indicate the upper boundary of value for adequate low flow representation.
Spatial analysis
Figure 4 shows the spatial distribution of KGE' values for the calibration stations. KGE' values > 0.7 are shown in light blue and blue. KGE’ values < -0.41 are shown in black. KGE' is generally uniformly distributed across the domain, with higher performance (light blue and blue) in large parts of North and South America, Central Europe, and Asia. Calibrated catchments with high performances are also found in Africa and Oceania. The lowest performances (black) are often concentrated in catchments with strongly regulated rivers.
Figure 4 – Spatial distribution of the hydrological performance (KGE') of GloFAS v5 across the domain for the 5379 calibration stations and all the available data.