Results on general hydrological performance and hydrological drought characterization and identification have been summarized at the global scale by using globally weighted mean values, with land area as weighting factor. Moreover, a division was made in results per Giorgi region. Apart from the global and regional-weighted means, we also evaluated at the global and regional scale for how many GRDC stations the model-performance (general and with regards to drought characterization and identification) improved or decreased when using one of the satellite-based precipitation datasets in comparison to the MSWEP dataset.
For a selection of 16 stations we present more in-depth results. For these stations we evaluated in more detail the variation in performance across the precipitation datasets and models by showing their hydrographs, exceedance probability curves, and matrices of missed hits and false alarms.
These results are accompanied by tables summarizing the per station results, both with respect to their general hydrological performance as with regards to the identification and characterization of hydrological droughts. Station-specific results for all other stations can be provided upon request.
Figure 3.1: Division of Giorgi regions and location of GRDC outlet measurement stations (n= 602).
3.3 Results
3.3.1General hydrological performance
Figures 2a-d shows for each of the precipitation forcing datasets and the global models the weighted mean global KGE performance, as well as for its sub-parameters: the KGE bias ratio, the KGE variability ratio, and the correlation coefficient. When looking at the KGE bias ratio we find that, using the globally-weighted means, CMORPH and GSMAP show an underestimation of the long-term mean monthly discharge for all models. TRMM and TRMMT, on the other hand, show overestimations using ecmwf, an underestimations using jrc. For MSWEP the results are most close to zero for ecmwf and jrc. Univk shows, at the global scale, a slight underestimation, though, for the MSWEP forcing, especially in comparison with TRMM and TRMMT. The globally-weighted mean variability ratio results hint at a significant overestimation of the hydrological variability for all models and under all precipitation forcing datasets. Univk shows consistently the lowest variability ratio as compared to the other models. At the same time, GSMAP shows a significantly higher variability ratio compared to the other precipitation forcing datasets. Using the global-means, we find that correlation coefficients are reasonably high, especially for ecmwf and univk. Results are significantly lower for jrc. No significant differences were found when comparing the different precipitation forcing dataset, although TRMMT shows substantial lower results for jrc.
Figures 3.2e-f show for each precipitation forcing dataset the number of stations with the highest KGE or KGE sub-parameter values. For both the overall KGE as well as for the KGE bias ratio and the KGE correlation coefficient we find for univk and jrc the highest number of stations with the highest performance when using the MSWEP precipitation forcing dataset. For ecmwf the highest performer is often TRMM, although differences between TRMM and WFDEI are small. The KGE variability ratio shows mixed results, variability is best represented in most GRDC stations by univk and ecmwf when using the GSMAP precipitation forcing dataset whilst for jrc the MSWEP precipitation forcing dataset yields the highest results.
Figure 3.2: Weighted mean global KGE performance and number of stations per precipitation forcing dataset and global model with the highest KGE performance.
Figure 3.3 shows the weighted mean regional KGE performance. When comparing the different regions we find significant difference in KGE performance. For example with very low performance in SSA and WNA across all models and precipitation datasets and generally high performance in AMZ, ENA, ALA, NEU, SAF, EAS. Regional differences in performance across the different models highlight that univk is the overall best performer in CAM, WNA, ENA, ALA, NEU, SAF while results are mixed in the other regions. Only for a few regions significant differences in performance were found when comparing the precipitation forcing datasets being used in the different models. GSMAP clearly underperforms in all models in WAF and SAF, whilst the TRMMT dataset leads to negative outliers in AMZ, CAM, CNA, ENA, ALA, SAF, and EAS, and especially in combination with the jrc model.
Figure 3.3: Weighted mean regional KGE performance per precipitation forcing dataset and global model using the Giorgi regions.
The hydrographs and KGE performance values for the 16 focus catchments (Figure 3.4, Table 3.1) highlight the differences in results across precipitation forcing dataset and global model further.
When looking at the hydrographs we find, in general, that the largest differences in results stem from the choice of model rather than from the choice of precipitation forcing dataset. Moreover, the results indicate that there is no universally best performing model or precipitation dataset. Different combinations of model and precipitation forcing dataset lead to the highest KGE results for different stations. We find that ecmwf is the best performer for 6 stations, univk for 7 stations, and jrc for 3 stations. On the other hand, when looking at the forcing datasets we find that CMORPH produces the highest results in 5 instances, GSMAP for 7 stations, TRMM and TRMMT for 5 stations, and MSWEP for 6 stations.
Figure 3.4: Hydrographs of the 16 focus catchments showing the LTM monthly discharge per station per precipitation forcing dataset and global model.
Congo 0.23
0.67 precipitation forcing datasets to characterize hydrological droughts. Hydrological droughts have been identified here by using a Q90 variable threshold level method. Figures 3.5, 3.6 and 3.7 show the weighted mean global and regional drought frequency and average duration per precipitation forcing dataset and global model. Moreover, the figures show the weighted mean global and regional drought frequency and average duration as estimated using the GRDC observations.
All models and all precipitation forcing dataset overestimate both the globally-weighted mean frequency and average duration of Q90 drought events, as compared to the observations. When looking at the differences per global model we find an inverse relation between frequency and average duration. Whilst jrc shows – on average- the highest weighted mean average duration across all precipitation forcing dataset it gives the lowest frequency. The opposite holds for ecwmf and univk. While these models indicate a relative lower average duration, they hint at a relative higher global weighted mean frequency of drought events. Globally weighted mean results on average duration and frequency are relatively similar across the different precipitation forcing dataset when looking at univk and ecmwf. Larger differences were found, however, for jrc. Overall, we find that the differences between estimated frequency and average duration are larger when comparing models, as when comparing forcing datasets. At the global scale, only for the jrc model significant variations are found when looking at the frequency and average duration estimates for the different precipitation forcing datasets.