Purpose of this page

This page describes how the hydrological performance of GloFAS is verified, from GloFAS v5 onwards. The method is used for the hydrological model performance layer in the GloFAS map viewer.

From GloFAS v5, the performance is evaluated for the first time on the GloFAS historical river discharge reanalysis (the long-term run), at all the river gauging stations used in the model calibration. Parts of the method therefore differ from earlier GloFAS versions.

Selection of stations

The verification uses the river gauging stations from the GloFAS model calibration, which is carried out by the Joint Research Centre (JRC) of the European Commission (see GloFAS v5 calibration methodology and parameters). Each station lies on the GloFAS river network and has daily river discharge observations available to the GloFAS team. For GloFAS v5, these are 5,378 stations in 119 countries (there was one extra station for calibration that was assigned as duplicate based on updated observational metadata). The same set of stations is used for the hydrological model performance assessment and for the hydrological model performance layer in the map viewer.

• Length of observation record: The verification applies no record-length criterion of its own. The record length is already controlled by the calibration station selection, which requires at least 4 equivalent years of daily observations (365 × 4 days with data) within 1980–2023 (see GloFAS v5 calibration data). A few stations with shorter records were kept in the calibration, as exceptions, to improve the spatial coverage in data-scarce areas. In the verification, every station is assessed over all days on which observations and the reanalysis are both available, so a station's record can differ from the one used in the calibration. Scores at stations with short records are less robust and should be interpreted with care.
• Lake, reservoir and human influence: The verification does not exclude any stations because of lake, reservoir or other human influence. Unlike earlier GloFAS versions, there are no separate station sets without such influence. The calibration station selection already excluded stations that are highly affected by reservoir operations or by processes the model does not represent, such as river diversions (see GloFAS v5 calibration data). The remaining stations can still be influenced by lakes, reservoirs or human activity. These catchments are harder to model, so their scores should be interpreted with care.

Verification period and reference simulation

The verification is carried out on the GloFAS historical river discharge reanalysis, which is the long-term simulation of the GloFAS hydrological model forced with meteorological reanalysis. For GloFAS v5, the reanalysis runs from January 1979 to December 2025 and can be accessed via EWDS.

The whole reanalysis period is verified against all available river discharge observations. There is no separate verification period. For each station, the scores are computed over all days on which both the reanalysis and the observations are available, and gaps in the observations are not filled. The observations are the most recent ones in the GloFAS hydrological observation database at the time of the verification. The model spin-up years before the start of the reanalysis are not part of the verification.

The reanalysis is the simulation from which the GloFAS flood thresholds (return periods) are derived. The verification therefore shows how well the simulation that underpins the forecast flood thresholds reproduces the observed river discharge.

The verification scores differ from those reported for the model calibration (see GloFAS v5 calibration hydrological model performance). The calibration and the verification cover different periods, and the verification uses a more recent observation record. Moreover, at some stations, reservoirs upstream were built during the period covered by the observations. In the calibration, these reservoirs were switched off where the observations used describe the natural flow regime before construction. In the reanalysis, all reservoirs are switched on, so that it represents present-day regulated conditions for the flood thresholds. At these stations the reanalysis is partly compared with a flow regime that no longer exists, and its scores can be lower than in the calibration. This is expected and does not mean the reanalysis or the flood thresholds are less reliable for present-day conditions. These stations, their scores and an interactive map are described on GloFAS v5 verification of stations with reservoirs built during the observation record.

Performance scores

GloFAS hydrological performance verification is done against the river discharge observations available to the GloFAS team. The hydrological model performance analysis is based on the modified Kling–Gupta efficiency (KGE′; Gupta et al., 2009; Kling et al., 2012; ideal value is 1):

KGE' = 1 - \sqrt{(r - 1)^2 + (\beta - 1)^2 + (\gamma - 1)^2}, \qquad
\beta = \frac{\mu_s}{\mu_o}, \qquad
\gamma = \frac{\sigma_s / \mu_s}{\sigma_o / \mu_o}

where s and o are the simulated and observed river discharge, μ is the mean and σ the standard deviation.

The three components of KGE′ are also shown:

• Pearson correlation (r) highlights timing errors through the strength of the linear relationship between the simulated and observed time series. It ranges from −1 to 1, with 1 as the ideal value.
• Bias ratio (β) represents the volume errors, as the ratio of the mean simulated to the mean observed discharge. It ranges from 0 to +∞, with 1 as the ideal value. Values above 1 mean the simulated volume is too large, values below 1 that it is too small.
• Variability ratio (γ) shows the errors in the variability of the flow, as the ratio of the coefficients of variation of the simulated and observed discharge. Using the coefficient of variation makes it independent of the bias. It ranges from 0 to +∞, with 1 as the ideal value.

In addition, three further scores are used in GloFAS v5:

• Nash–Sutcliffe efficiency (NSE; Nash and Sutcliffe, 1970) measures how much better the simulation is than simply using the mean observed discharge. It ranges from −∞ to 1, with 1 as the ideal value; 0 means the simulation is no better than the observed mean. It is very sensitive to errors in high flows.

NSE = 1 - \frac{\sum_{i=1}^{n}(s_i-o_i)^2}{\sum_{i=1}^{n}(o_i-\mu_o)^2}

• Jensen–Shannon divergence (JSD; Lin, 1991) measures how different the distributions of the simulated and observed discharge are, regardless of their timing. Following Ficchì et al. (2026), it compares the histograms of the logarithm of the discharge, so it gives weight to low flows as well as to medium and high flows. It ranges from 0 to 1, with 0 as the ideal value (identical distributions). Values below 0.1 generally indicate an adequate representation of low flows (Ficchì et al., 2026).
• Joint Divergence Kling–Gupta efficiency (JDKGE; Ficchì et al., 2026) extends KGE′ with the JSD as a fourth, equally weighted component, adding a penalty for differences in the flow distribution. It ranges from −∞ to 1, with 1 as the ideal value.  

JDKGE = 1 - \sqrt{(r - 1)^2 + (\beta - 1)^2 + (\gamma - 1)^2 + JSD^2}

 References

• Ficchì, A., Bavera, D., Grimaldi, S., Moschini, F., Pistocchi, A., Russo, C., Salamon, P. and Toreti, A. (2026). 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
• Gupta, H. V., Kling, H., Yilmaz, K. K. and 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://doi.org/10.1016/j.jhydrol.2009.08.003
• Kling, H., Fuchs, M. and 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
• Lin, J. (1991). Divergence measures based on the Shannon entropy. *IEEE Transactions on Information Theory*, 37(1), 145–151. https://doi.org/10.1109/18.61115
• Nash, J. E. and Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I — A discussion of principles. *Journal of Hydrology*, 10(3), 282–290. https://doi.org/10.1016/0022-1694(70)90255-6