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
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.) 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 Pearson correlation of the GloFAS v4 simulation.
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 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.