| dc.description.abstract | The Nile River is the major river basin in the northeast of Africa with an ever-growing population
dependent on it for its water needs. These needs are only expected to grow in future. Predicting Nile
discharge is hard as discharge of the river itself can vary a lot from year to year. Furthermore, Global
Hydrological Models, such as PCR-GLOBWB2, also struggle to accurately predict Nile flows and tend
to overestimate discharge, most likely due to poor data availability. The Nile River itself can be
divided into several basins, with the Blue Nile being the most important in terms of discharge, with
60-70 % of discharge originating here, but this comes mostly in the summer months. The rest of the
year the White Nile, a much larger basin with its origins in the region around Lake Victoria, is the
main source of water.
To improve model performance of the PCR-GLOBWB 2 it was chosen to improve input data and
parameterization. By using updated data soil data (SoilGrids), topographic data (MERIT-DEM) and
meteorological data (WFDE5) and using an updated river routing method. The results were validated
by GRDC data, but as these are limited, both spatially and temporally it was chosen to seek an
additional way of validating data. In this case GLEAM was used to validate evaporation of the model.
Combining of both validation methods was used to better understand the discrepancies in model
performance for the Nile Basin.
Results still showed poor results for the run without any improvement as expected. It showed that
the White Nile performance was very poor, owing to an underestimation of evaporation, mainly in
the months of no precipitation. This was observed in some other basin as well. The Blue Nile
performance was better but still has overestimations of discharge, especially in the drier months.
Validation of the results with GLEAM showed good options to close the water balance and get better
temporal and spatial data despite some of its current limitations.
Introducing new datasets and new parameterization did not improve model results as expected and
the poor datasets were not the main cause of the poor performance. The same regions which
performed poorly for the standard run also performed poorly for the other runs, indicating an
underlying problem. This has to do with the evaporation in the model, especially in the Sudd
swamps, where flooding does not occur as expected leading to larger discharge and decreased water
storage. Here it was identified that missing model descriptions is a large problem and that this
process needs to be better described. For future, many improvements can be made to model and by
this, combined with better validation methods, the description of PCR-GLOBWB 2 of the hydrology
of the Nile River could be improved. | |
