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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.
The generic GloFAS v4.0 model performance is measured by the modified Kling Gupta efficiency 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.
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 and variability ratio and 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.
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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 4. Timing error of the GloFAS v4 simulation.