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On this page, the model performance is analysed over the final v4.0 reanalysis time series (https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical?tab=overview; which is not expected to be noticeably different to the one used in the calibration evaluation). In addition, all stations are considered here, which have at least 1 year of good enough quality observation data in the 1979-2021 period (while it was at least 4 year for the calibration), supplemented also with a separate station network without larger noticeable impact of reservoirs or lakes. In total, 2293 stations were considered for the general v4.0 verification with all stations, 996 for the v4.0 vs v3.1 model comparison with stations used in calibration for both models and also a third set with 233 stations that were not used in either calibrations. Details on the station selection and other aspects of the verification, including the used metrics, are available on the verification methodology page (place holder GloFAS hydrological performance verification methodology).
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Figure 2. KGE of the GloFAS v4.0 simulation.
Bias, variability and correlation
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Figure 3. Bias ratio error of the GloFAS v4.0 simulation.
Figure 4. Variability ratio error of the GloFAS v4.0 simulation.
Figure 5. Pearson correlation of the GloFAS v4.0 simulation.
Timing
The timing error shows quite a lot of areal variability (Figure 6). Some of this probably comes from the potentially short sample period, which makes the verification scores less robust. Also, some larger errors in large variability areas can come from the type of catchments which have lower quality simulation, combined with less clear signal distribution, i.e. no clear peak and trough structure, which can result in not little correlation change by shifting the simulation.
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Figure 6. Timing error of the GloFAS v4.0 simulation.
General v4.0 vs v3.1 performance comparison
When comparing the v4.0 performance with the previous v3 model.1 one, we provide 3 flavours of the comparison, one which uses all possible stations, regardless of the lake and reservoir impact and two which includes only points that has maximum small reservoir or lake influence. One of these two is for the calibration comparison, i.e. with points used in both v4 and v3 calibrationcalibrations, while the other is with only points that were used in neither of the calibrations.
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The new higher resolution of v4.0 GloFAS outperforms the earlier v3.1 almost everywhere (Figure 7). Exceptions are mainly in eastern USA, Amazonia and western Europe. In other areas, apart form the odd catchments, v4.0 is better, or largely better. In many of the tropical catchments and also in central/southern North America the KGE improvement is larger than 0.5 over a very large area. The cumulative KGE distributions highlight that including all stations, the median improves from about 0.31 to 0.65, with +0.22 as the median of the KGE differences. Moreover, while about 25% of catchments in v3.1 had KGE below -1, in v4.0 this has decreased to only 7%.
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Another aspect of the v4 vs v3 comparison is the non-calibrated catchments, which were used in neither of model calibrations. For these areas, the v4 model had some major improvements by transferring the calibrated parameters to non-calibrated catchments by a regionalisation method. Indeed, v4.0 shows much higher KGE, in general, over these non-calibrated catchments, with only a very few catchment exceptions. The median of the 233 catchments in this category improves from -1.02 to +0.125, with +0.82 as the median of the KGE differences.
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Figure 7. KGE error difference maps between GloFAS v4.0 and v3.1 simulations (top row) and cumulative distributions of KGE for both v4.0 and v3.1. Using all all points (1st column), using only calibration points for both models without larger reservoir or lake influence (2nd column) and non-calibration points for both models without larger reservoir or lake influence (3rd column).
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The bias, measured by the 0-centred version of the KGE's bias ratio component (bias), is very clearly largely contributing to the improved KGE by drastically reduced bias errors in v4.0 (Figure 8). The first row in Figure 8 shows the difference in absvar, the absolute value of bias, as the bias error magnitude difference between v4.0 and v3.1. The large impact of the bias is generally the same with all station versions, the full list (Figure 8, 1st column), the calibrated (Figure 8 2nd column) or non-calibrated station networks (Figure 8 3rd column). The geographical distribution of the errors is very similar to the KGE's picture in Figure 7, with the tropics in general showing very large bias improvement, often more than halving the bias ratio error of v3.1 by v4.0.
The cumulative distributions of the bias highlight that the bias error is generally getting lower in v4.0, seemingly everywhere. In fact, the distribution of the actual bias difference values (not shown here) highlight that about 85% of the catchments indeed has lower bias ratio error in v4.0 than in v3.1. Figure 7 (2nd row) also highlight that the high median value of 0.39 in v3.1 decreased to only 0.05 in v4.0 (see Figure 7, 2nd row, 1st graph), with -0.22 as the median of the absbias difference values (the graph is not shown here). This confirms that the new v4 model delivers an almost optimal bias in global average sense, and that the improvement in the bias error magnitude (measured by absbias) is a very large -0.22 on the basis of all stations that could be verified. The same bias median values are 0.14 to 0.02 for the calibration stations, with -0.09 as the median of the absbias difference, while 1.92 to 0.40, with -0.88 as the median of the absbias differences for the non-calibrated case. This confirms the same picture seen for the KGE, with the calibrated stations showing much smaller improvement in bias than the non-calibrated stations.
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Figure 8. Abspbias error difference maps between GloFAS v4.0 and v3.1 simulations (top row) and cumulative distributions of bias for both v4.0 and v3.1 (bottom row). Using all all points (1st column), using only calibration points for both models without larger reservoir or lake influence (2nd column) and non-calibration points for both models without larger reservoir or lake influence (3rd column).
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The variability, measured by the 0-centred version of the KGE's variability ratio component, shows a quite homogeneous geographical distribution globally (Figure 9, top row). Improvement by v4.0, i.e. negative var difference, is the overwhelming picture, other than for the non-calibrated stations, which seem more mixed. There is not really any emerging area with a clear cluster of better variability in v3.1 (i.e. blue dots). It is also clear, that the variability improvement is smaller than the bias improvement seen in Figure 8, there are much less dark red stations in Figure 9 than we had in Figure 8.
The cumulative distributions of var confirm these conclusions. The purple curve (v4.0) is very clearly more centred on the 0 optimal variability line (centre of the graphs), a little less so with the calibrated stations only, and more with all the stations. However, the non-calibrated stations behave differently, with not too much difference, reflecting the rather mixed picture we saw in the absvar difference map in Figure 9.
The median var value change from -0.10 to -0.03 in v4.0, with -0.07 as the median of the absvar differences for the all-station case. For the calibration stations the improvement is from -0.06 to -0.02, with -0.04 as the median of the absvar differences, while for the non-calibrated stations it is from -0.24 to -0.15, with -0.05 as the median of the absvar differences. These number also confirm that the variability error improved in v4.0, but less than the bias errors improved in Figure 8. Moreover, the difference between calibrated and non-calibrated catchments is again less pronounced than it was for the bias case.
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Figure 9. Absvar error difference maps between GloFAS v4.0 and v3.1 simulations (top row) and cumulative distributions of var for both v4.0 and v3.1 (bottom row). Using all all points (1st column), using only calibration points for both models without larger reservoir or lake influence (2nd column) and non-calibration points for both models without larger reservoir or lake influence (3rd column).
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The cumulative distributions confirms that v4.0 provides only marginal improvement over v3.1 in correlation. For the high correlations v3.1 seems to be even very slightly better, while v4.0 is noticeably better for low to medium correlations. For the calibrated stations this the difference is even less, while for the non-calibrated stations v3.1 actually seems to be better. It seems the up and downs of the simulations could not really be improved very noticeably by the v4 modelv4 model.
Regarding the actual correlation values, the median changes from 0.748 to 0.759 in v4.0, with 0.000 as the median of the correlation differences for the all-station case, i.e. no change on average at all. For the calibration stations, the improvement is from 0.817 to 0.816 (so actually even very slight decrease), with -0.002 as the median of the correlation differences, while for the non-calibrated stations it is from 0.672 to 0.629, with -0.006 as the median of the correlation differences. These number also confirm that the correlation aspect of the river discharge simulation in v4.0 did improve only marginally when measured using all stations, while the calibration station comparison shows no change at all and the non-calibration comparison shows rather some small deterioration.
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Figure 10. Correlation error difference maps between GloFAS v4.0 and v3.1 simulations (top row) and cumulative distributions of correlation for both v4.0 and v3.1 (bottom row). Using all all points (1st column), using only calibration points for both models without larger reservoir or lake influence (2nd column) and non-calibration points for both models without larger reservoir or lake influence (3rd column).