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The new higher resolution v4 GloFAS outperforms the earlier v3 almost everywhere (Figure 7). Exceptions are mainly in eastern USA, Amazonia and western Europe. In other areas, apart form the odd catchments, v4 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 had KGE below -1, in v4 this has decreased to only 7%.
When considering only stations that were used in both v4 and v3 calibrations and here we also exclude the stations with larger reservoir or lake influence (2nd column in Figure 7), the geographical distribution of KGE differences is similar to the full picture in the 1st column of Figure 7, but with this selection of stations the difference looks more modest. Here differences can only come from better calibration methodologies and better general model quality, such as the higher resolution, the better river network and other improved features, such as better soil maps and similar improvements in v4. The KGE median improvement decreases to 0.68 to 0.77, with +0.08 as the median value of the KGE differences, which is still very noticeable.
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 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.
It is clear, the general hydrological improvement is noticeable for the common calibration stations, but much larger for the non-calibration stations, quite possibly highlighting the impact of the regionalisation.
Figure 7. KGE error difference maps between GloFAS v4 and v3 simulations (top row) and cumulative distributions of KGE for both v4 and v3. 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).
Bias ratio
The bias, measured by the absolute value of the 0-centred version of the KGE's bias ratio component (abspbias), is very clearly largely contributing to the improved KGE by drastically reduced bias errors in v4 (Figure 8). This is generally the same with all the stations (Figure 8 1st column), of the calibrated (Figure 8 2nd column) or non-calibrated stations (Figure 8 3rd column).
Figure 8. Abspbias error difference maps between GloFAS v4 and v3 simulations (top row) and cumulative distributions of abspbias for both v4 and v3. 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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