Improved understanding of the water balance in the Blue Nile is of critical importance because of increasingly frequent hydroclimatic extremes under a changing climate. A water budget analysis has been performed over the Eldiem basin from 2002 to 2012 in monthly scale (we consider the hydrological year October to September). The main objectives of this analysis were to understand how different LSMs represent the annual variations in water budget components (Precipitation, ET, Streamflow and Terrestrial Water Storage (TWS)) and the overall hydrologic regime of the basin. To achieve this, the terrestrial water storage estimates were calculated using the equation adapted for watersheds:
𝑇𝑊𝑆(𝑡) = ∫(𝑃(𝑡) − 𝐸𝑇(𝑡) − 𝑄(𝑡))𝑑𝑡
and were evaluated against the NASA Gravity Recovery and Climate Experiment (GRACE) product.
The GRACE data are observations of surface mass changes with a spatial sampling of 1/2 degrees in both latitude and longitude (approx. 56 km at the equator).
4.3. Results
The spatial and temporal distribution of precipitation field have an important role in the water budget. Figure 3.1a and 3.1d show the distribution of the relative errors of the basin-averaged, monthly precipitation form Multi-Source Weighted-Ensemble Precipitation (MSWEP), Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), Tropical Rainfall Measuring Mission (TRMM) and Climate Precipitation Center Morphing Technique (CMORPH) datasets for the two basins. All products obtained lower relative errors over the large basin. This is because of the complex terrain of the Kessie basin. In situ data to characterize precipitation are usually scarce in complex terrain and remote sensing retrieved precipitation estimates are generally characterized by significant biases. Moreover, as shown in the figures mentioned above, MSWEP obtained the closest agreement with the observations over the two basins. MSWEP merge gauge-, satellite-, and reanalysis-based precipitation data. On the contrary, MERRA-2 and CMORPH achieved the highest relative errors over Eldiem and Kessie respectively.
Figure 4.3b and 4.3e show the relative errors of the basin-averaged, monthly ET from the WRR under consideration. The relative errors of ET seem to be slightly higher than the ones of precipitation.
Here, some models represent better the ET over the large basin and some others perfume better over the small basin, but all of them (except one) underestimate the magnitude of ET. The LSMs that have been used to obtain these data have different schemes to estimate ET, which seem to affect the accuracy of the WRR data more than the precipitation forcing. For the datasets produced with the same LSM but with different precipitation forcing the accuracy of precipitation seems to effect the accuracy of ET estimates. VIC and HTESSEL-CaMa achieved the lower relative errors of ET (in terms of the median values) over Eldiem and Kessie basin respectively.
Figure 4.3c and 4.3f show the distribution of the relative errors of the basin-averaged, monthly streamflow from all the products. The relative errors of the streamflows are much higher than of ET and precipitation which implies that the schemes which the LSMs use to estimate streamflow affect the performance of the products more than the precipitation forcing. Here, in contrast with ET, there is no correlation between the accuracy of the precipitation forcing and the streamflow estimates for the datasets produced with the same LSM but with different precipitation forcing. All datasets are in
Figure 4.3: Boxplot of relative errors of a, d) precipitation, b, e) ET and c, f) streamflow in monthly scale for products with reanalysis (a, b and d) and satellite derived (d, e and f) precipitation forcing.
Figure 4.4 demonstrates the relative skill with which each WRR product represents the precipitation, ET and streamflow. For precipitation, as mentioned above, all WRR products are in close agreement with the observations both in terms of correlation and magnitude. Notable in this Figure is the low correlation of ET for some models (WaterGAP3, ORCHIDEE and Jules) mainly over the small basin.
WaterGAP3 and VIC showed the poorest performance in terms of RMSE and SD for both basins. Jules and HTESSEL-CaMa and HTESSEL-CaMa exhibited the best performance statistics (RMSE and SD) for the small and the big basin respectively. The products with satellite derived precipitation forcing seems to be in closer agreement with the observations than the ones with the reanalysis precipitation forcing. In contrast with the ET, the correlation of streamflow estimates from all the WRR products is higher than 0.75 except from ORCHIDEE. Although in ET WaterGAP3 exhibited the poorest performance, the opposite is the case for streamflow. WaterGAP3 with all the three different precipitation forcing data showed the best performance among the other models for both basins. For the Eldiem basin, VIC seems to be in close agreement with the observation.
e) f) d)
Figure 4.4: Taylor diagrams of a, d) precipitation, b, e) ET and c, f) streamflow in monthly scale for products with reanalysis (a, b and d) and satellite derived (d, e and f) precipitation forcing.
Figure 4.5 shows the anomalies of precipitation, ET, streamflow and TWS in centimeters. These anomalies were calculated by subtracting the average value of the twelve years under consideration
a)
f) e)
d)
c) b)
monthly values of ET and streamflow where slightly shifted. There is strong variability between the TWS estimates of the WRR products but an overall agreement in the sign. The TWS values derived from WRR products does not perform well in terms of magnitude during the first years. However, from 2006 most of the models are in close agreement with the GRACE data. VIC and WaterGAP3 achieved the best representation of the observed TWS anomalies. TWS anomalies seem to be slightly shifted in time related to the observations. This might be because TWS estimates derived from the models seem to be driven primarily by precipitation. Moreover, this time shift of TWS might be affected by the fact that during the winter season, the models underestimate ET and produce high values of streamflow.
Figure 4.5: Water budget analysis plot. Annual values of precipitation, ET and streamflow and anomalies of TWS estimates from products with a) reanalysis and b) satellite derived precipitation forcing.
a)
b) TWS (cm) Q (cm) ET (cm) P (cm) TWS (cm) Q (cm) ET (cm) P (cm)
However, a global analysis of the simulated TWS has been carried out to highlight principal performances or bias of models. It has been shown that the spatial and seasonal climatologies are correctly captured (Fig. 2.2 a) and 2.3). The simulated seasonal cycle tends to be ahead of the output: METFR, ECMWF, NERC and CNRS. A multimodel approach is difficult with so few models and has not been considered here. Biases are spatially evenly distributed. The annual mean snow depth is quite well simulated by all models. The seasonal cycle is in phase with the observed one, but longer for ECMWF and NERC, and smaller for METFR and CNRS. The interannual variability is generally well captured by the models, although underestimated by CNRS.
The final study focused on the evaluation of the hydrological cycle components over the Upper Blue Nile basin from a WRR perspective. We evaluated eighteen different reanalysis products related with different LSM and precipitation forcing datasets. We estimated the TWS derived from the fluxes (P, ET and streamflow) of the reanalysis products and we compared four of the most important water budget variables with observations in two spatial scales. The intercomparison and evaluation of these WRR datasets offered improved understanding of the basin scale water budget variables in the Upper Blue Nile basin.
Among the four different precipitation datasets that have been evaluated MSWEP provided the best representation of precipitation over the two basins (in terms of relative errors). In terms of ET, HTESSEL-CaMa (forced with the three different precipitation datasets) achieved the best representation in monthly scale for the two basins. WaterGAP3 exhibited the best representation of streamflow over the large and the small basin with all the three precipitation datasets that it have been forced. There is not one WRR product that is in close agreement with the observations of both ET and streamflow. This might be attributed to the different schemes that the LSM models use to estimate the water budget variables.
Water budget analysis showed that for some products, ET anomalies are slightly shifted in time.
Variations in magnitude of ET and streamflow values seem to be affected mainly from the differences in the schemes that the various LSMs use and not from variations of precipitation forcing. For the TWS there is strong variability of TWS derived from the different WRR products, but an overall
agreement in terms of the sign (between the products). This analysis showed that changes in TWS seem to be driven primarily by precipitation. High annual precipitation values most of the times resulted into higher positive changes of TWS.
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