GloGAS v5 was calibrated using 5379 in-situ discharge gauge stations with at least 365x4 daily measurements from 01/01/1980 to 31/12/2023. Furthermore, reservoir information including inflow, outflow, volume in-situ time series for 360 reserviors as well as climate, geometry, and use attributes for all the 1486 reservoirs included in the modelling domain were used to tune the parameters regulating reservoir release. 

Model calibration required to estimate up to14 parameters for each catchment: this objective was achieved in four sequential steps. First, 2 reservoir parameters were calibrated using SCE-UA (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, NSGA-II within DEAP framework was used to optimize up to12 parameters regulating snow melt, water infiltration into the soil, surface water flow, groundwater flow, lakes dynamic of 5379 inter-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 up to 12 parameters for 10,916 ungauged catchments, covering XX of the semi-global domain. A minimum drainage area of 500 km² was used for the above explained steps of the calibration. Lastly, the parameters of the remainder XX area (catchments with upstream area smaller than 500 km²) were estimated using nearest neighbour interpolation.

JDKGE, a revised version of KGE' proposed by Ficchì et al. (2026)¹ was used as objective function. JDKGE has the purpose to optimize both low and high flows. 

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. Specifically, calibration performance is evaluated using all the available in-situ discharge data for each calibrated station: duration and coverage 

Overview

The hydrological performance of GloFAS v5 is expressed by the modified Kling-Gupta Efficiency (KGE', Gupta et al., 2009², Kling et al, 2012³). 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)¹.

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). 

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.

A low score during evaluation of LISFLOOD OS model calibration is not necessarily an indicator for decreased forecast performance of the global flood awareness system. GloFAS forecasts are compared to model derived thresholds (Thielen et al., 2009⁴; Bartholmes et al., 2009⁵), this comparison eliminates systematic bias. In some calibration stations, the systematic bias leads to an overall lower score in hydrological performance. Nevertheless, correlation is a desired quality in hydrological performance as it represents the timing of flood peaks. Given the mathematical structure of KGE', all stations where KGE'>=0.7 have correlation >=0.7 (Gupta et al., 2009²). Conversely, some of the stations with KGE'< 0.7 can have correlation>= 0.7; but associated to a large mean bias and/or variability bias. Calibration points with low KGE' but correlation >=0.7 won't decrease the forecast performance of the Global Flood Awareness System, even if forecast discharge will exhibit large bias. Figure 4 shows a combination of the spatial distribution of GloFAS KGE' and correlation. Stations with KGE'<0.7 and Correlation>=0.7 are highlighted in white. Compared to Figure 3, 336 calibration stations with KGE<0.7 show a Correlation>0.7: these stations are represented in white in Figure 5.

Figure 5 –  Spatial distribution of the hydrological performance (KGE') of GloFAS v4 across the domain combined with correlation: stations with KGE'<0.7  and correlation>=0.7 are highlighted in white.

Figures 6, 7, 8 present the spatial distribution of GloFAS v5 hydrological performance across the quasi-global domain in terms of KGE' components: correlation, mean bias and variability bias.

Figure 6 –  Spatial distribution of correlation of GloFAS v5 at all 5379 calibration stations (evaluated using all the available data).

Figure 7 –  Spatial distribution of bias of GloFAS v5 at all 5379 calibration stations (evaluated using all the available data).

Figure 8 –  Spatial distribution of bias of GloFAS v5 at all 5379 calibration stations (evaluated using all the available data).

ADD JSD!!!!!!! MAP!!!!!

Comparison of GloFASv5 against GloFASv4: overview

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References

¹ Ficchì, A., Bavera, D., Grimaldi, S., Moschini, F., Pistocchi, A., Russo, C., Salamon, P., and Toreti, A.: Improving low and high flow simulations at once: An enhanced metric for hydrological model calibration, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-43, 2026. 

² Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of hydrology, 377(1-2), 80-91. https://www.sciencedirect.com/science/article/pii/S0022169409004843?via%3Dihub

³ Kling, H., Fuchs, M., Paulin, M. (2012). Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. Journal of hydrology, 424-425, 264-277. https://doi.org/10.1016/j.jhydrol.2012.01.011