Introduction

The GloFAS v4.0 hydrological model performance was evaluated in the model calibration context in GloFAS v4 calibration hydrological model performance, using only the 1995 stations involved in the calibration, verified using the full longterm run (produced within the calibration excercise) and the KGE and the three component scores.

On this page, the model performance is analysed over the final v4.0 reanalysis time series (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, without larger noticeable impact of reservoirs or lakes. In total, 1987 stations were considered for the general v4.0 verification and 1949 for the v4.0 vs v3.1 model comparison. 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).

General v4.0 performance

OBS availability

For this comparison, we used all stations with good quality river discharge observations and minimal human or lake influence that could be mapped (find the corresponding model river network location) onto the higher resolution v4.0 river network. In total 1987 stations could be considered as shown below with the available observation length (gaps are removed to compute the length).

Figure 1. Number of years of available river discharge observations in the 1979-2021 reanalysis period.

KGE

The generic GloFAS v4.0 model performance is measured by the modified Kling Gupta efficiency (KGE) in Figure 2. High skill (above 0.7) is shown over much of the higher latitude areas and also some southest Asian and central south American areas. The lowest KGE, including even some catchments with no skill at all (below -0.41), are spread across some tropical areas, often in central southern USA and Mexico and some areas in Africa, often in the drier climate.

Figure 2. KGE of the GloFAS v4 simulation.

Bias, variability and correlation

The KGE's component scores (Figure 3-4-5.) highlight that much of the lower KGE skill comes from the often high and mainly positive bias, and also larger variability errors. The bias ratio is over 1 for a lot of catchments in the tropical belt, which means the simulation average is more than double the observation average value (i.e. twice as high as it should be). On the other hand, the variability error tend to be negatively oriented and many tropical catchment sees too low variability in the simulations, often 1/3 less than in the observations (-0.33 to -0.5) or even at least 50% less than it should be according to the observations (darkest red).

The correlation is more homogeneous, even though many of the low KGE areas also show low correlation, with exceptions, such as the upstream part of the Niger river basin, or some catchments in the Nile basin, which show high correlation but at the same time really high positive bias and some larger variability errors.

Figure 3. Bias ratio error of the GloFAS v4 simulation.

Figure 4. Variability ratio error of the GloFAS v4 simulation.

Figure 5. Pearson correlation of the GloFAS v4 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.

Still, some pattern emerges and generally the errors are more negative than positive, i.e. the GloFAS v4.0 river discharge simulation is too early in the signal, so peaks happen earlier than in the observations. This is the case in many of the catchments in the higher latitudes, in Amazonia or in Australia. In terms of magnitude, the larger errors mean 5-10 days or even over 10 days timing problem.

Figure 6. Timing error of the GloFAS v4 simulation.

General v4.0 vs v3.1 performance comparison

When comparing the v4 performance with the previous v3 model, 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 calibration, while the other is with only points that were used in neither of the calibrations.

KGE

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

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 (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 and v3. 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 by v4.

The cumulative distributions of the bias highlight that the bias error is generally getting lower in v4, 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 than in v3. Figure 7 (2nd row) also highlight that the high median value of 0.39 in v3 decreased to only 0.05 in v4 (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.

Figure 8. Abspbias error difference maps between GloFAS v4 and v3 simulations (top row) and cumulative distributions of bias for both v4 and v3 (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).

Variability

The variability, measured by the 0-centred version of the KGE's variability ratio component, shows a quite homogeneous geographical distribution globally (Figure 8, top row). Improvement by v4, 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 (i.e. blue dots). It is also clear, that the variability improvement is smaller than the bias improvement seen in Figure 7, there are much less dark red stations in Figure 8 than we had in Figure 7.

The cumulative distributions of var confirm these conclusions. The purple curve (v4) 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 8.

The median var value change from -0.10 to -0.03 in v4, 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, but less than the bias errors improved in Figure 7. Moreover, the difference between calibrated and non-calibrated catchments is again less pronounced than it was for the bias case.

Figure 9. Absvar error difference maps between GloFAS v4 and v3 simulations (top row) and cumulative distributions of var for both v4 and v3 (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).

Correlation

The variabi