The ability of the different global models and precipitation forcing datasets to correctly identify hydrological droughts was evaluated by means of the Critical Success Index. This index relates the number of missed hits, false alarms, with the number of correct hits. Figure 3.9 shows the weighted mean global CIS performance as well as the number of stations per precipitation forcing dataset and global model with the highest CSI performance. What can be seen from these figures is that, in general, TRMMT results in the globally weighted lowest average CSI performance across the different models. Although differences for univk are small between TRMM and TRMMT. On the other hand, GSMAP and MSWEP result in the highest weighted mean globally average CSI performance for univk and jrc, and ecmwf respectively.
If we not take into account the relative size of the catchment areas the different stations represent but evaluate the number of stations with the highest CSI per precipitation forcing dataset we find that for both jrc and univk, MSWEP gives the highest amount of stations. Hence, CSI values are highest in these stations when using MSWEP as a forcing dataset. For ecmwf we find TRMM to lead to the best CSI results in most stations globally.
Figure 3.9: Weighted mean global CSI performance and number of stations per precipitation forcing dataset and global hydrological model with the highest performance.
When looking at the regional variation in weighted mean CSI performance across the different models and forcing datasets we find that for most regions the variation in results due to model-spread is larger than the variation in result due to choice of precipitation forcing data. Figure 10 shows that this is the case in, for example: AMZ, CAM, ALA, GRL, NEU, WAF, EAS, TIB, NAS, although for almost all regions at least one model shows significant spread across the precipitation forcing datasets. Spread across the different precipitation forcing datasets is for all models relatively large in AUS, ENA, MED, SAF
Figure 3.10: Weighted mean regional CSI performance per precipitation forcing dataset and global model using the Giorgi regions.
Finally we evaluated for the 15 focus stations the number of missed hits and false alarms when using the observational drought threshold in the different model-forcing dataset combinations (Figure 3.11). In doing so, we see for most stations a significant overestimation of drought occurrences although quite some differences exist between the different models. For example, for the Assiniboine river both univk and jrc show a clear overestimation of the historical drought events, resulting in a relatively high number of false alarms but with correct hits. Ecmwf, on the other hand, indicates a relative underestimation of drought events for this particular station. This results in relatively minor number of false alarms but a number of missed hits in times droughts occurred in the observational dataset. For only a few stations we see a clear distinction in results when comparing the different precipitation forcing datasets. This is the case for example when looking at the use of GSMAP in comparison to other precipitation forcing datasets in the ecmwf or jrc models in the Congo basin; the use of CMORPH in combination with univk in the Labe, Neva, Rhine, Volga, or St. Lawrence catchments; or the use of MSWEP in combination with jrc in the Congo, Don, Fraser, Labe, Mackenzie, Neva, Ob, Rhine of Volga catchments.
Figure 3.11: Missed hits and false alarms per precipitation forcing dataset and global model for the 16 focus catchments. Per catchment the panel shows the number of missed hits and false alarms with respect to the identification of a Q90 hydrological drought as estimated with the variable threshold level method based on the observational data.
other sources has provided a unique opportunity to advance understanding of terrestrial hydrologic processes at regions where in situ information is sparse or nonexistent. Africa is a continent where this aspect is particularly emphasized because it is generally characterized by sparse hydrologic observations while at the same time there is need for efficiently managing water resources to enhance water and food security. Ethiopia’s hydrology plays a significant international role, being the headwaters of the Blue Nile Basin, where it contributes about 86% of the total annual flow of the Nile (Sutcliffe, 1999). Managing water resources in a sustainable manner requires at the very least an adequate characterization of hydrological fluxes (precipitation, streamflow, evapotranspiration, and sub-surface flow) and states (soil and vegetation moisture, groundwater) at monthly, seasonal and annual scales.
Comparing models and datasets is an effective way to identify strengths and weaknesses of LSMs and meteorological forcing in different regions in the globe and at different scales. The main objective of this study is to identify the most suitable combination of Land Surface Model (LSM) and precipitation forcing to represent the hydrological cycle components for the Upper Blue Nile. In order to achieve this, we present a comprehensive evaluation of water cycle components for the Upper Blue Nile basin in Ethiopia estimated from four state-of-the-art water resources reanalysis (WRR) products associated with different LSMs, meteorological forcing and precipitation datasets.
Specifically, evaluation is carried out for a dataset produced through NASA’s Land Data Assimilation System (LDAS) regional (FLDAS) scale and the latest version (tier 2) of the global WRR product of the EU Earth2observe project. Each product includes a multi-model ensemble output. The final ensemble output, considering all products, incorporates differences in forcing, model space/time resolutions and assimilation procedures used in each WRR product. Results from this analysis highlight the current strengths and limitations of available WRR datasets for analyzing the hydrological cycle and dynamics of East Africa region and provide unprecedented information for both developers and end users in similar hydroclimatic regimes.
4.2. Methodology 4.2.1 Study Area
The Blue Nile River is the largest tributary of the main Nile River, and its upper part is fully located in Ethiopia (Figure 2.1). The upper Blue Nile River basin contributes about 60% of the annual streamflow to the main Nile River (Conway, 2005). Elevations range from ~4000m in the Ethiopian highlands to ~500 m at the Ethiopia-Sudan border. The precipitation in the upper Blue Nile basin is highly seasonal and subject to a tropical highland monsoon: its main rainy season taking place from
June to September, a short rainy season from March to May, and a dry season from October to May (Taye et al., 2015; Mellander et al., 2013).
Figure 4.1: Study Area
4.2.2 Evaluation
To identify the most accurate representation of the hydrological cycle components over the Upper Blue Nile basin we conducted a comprehensive intercomparison and evaluation of eighteen different reanalysis products. Two datasets produced through NASA’s Land Data Assimilation System (LDAS) regional (FLDAS) scale and sixteen datasets form the latest version (tier 2) of the global WRR product of the EU Earth2observe project. For each dataset have been used different LSM/Hydrological Models, different precipitation forcing and different spatial resolution (Table 2.1). There are two groups of reanalysis data, the one includes the products with reanalysis precipitation forcing and the other the ones satellite derived precipitation (TRMM and CMORPH) forcing. More specific, precipitation, evapotranspiration (ET) and streamflow were evaluated against daily precipitation from rain gauges measurements, remote sensing derived actual ET (Zhang et al 2016) and in-situ streamflow observations respectively. The evaluation have been conducted over two different spatial scales, the Eldiem basin (177642.9 km2) and the Kessie basin (50418.08 km2) (Figure 4.1) on monthly basis. Figure 2.2 shows a schematic representation of the temporal extent of the evaluation for every variable over the two basins. The performance of the WRR products is assessed using relative error (RE) calculated as:
𝑅𝐸 =𝑆𝐼𝑀 − 𝑂𝐵𝑆 𝑂𝐵𝑆
Table 4.1: WRR products under consideration
Figure 4.2: Schematic representation of the temporal extent for the evaluation