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The KGE's component scores (Figure 3.) 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.
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The timing error shows quite a lot of areal variability. 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 negativepositive, 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, or 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.
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