Application of global hydrological datasets for river basin modelling

Recent advances in remote sensing technologies have made available a large suite of observations of hydro-meteorological variables worldwide. These satellite-based observations can be integrated into hydrological models to improve water resources management not only at a global scale, but also at a regional or river basin scale. To this end, various approaches can be followed depending mainly on basin characteristics and in situ data availability, which can be classified in three groups: (i) meteorological datasets, (ii) model parameters estimation and data assimilation and (iii) model evaluation. Previous scientific efforts have been carried out following these approaches, however, further research is needed to reach the full potential of satellite-derived observations for increasing the accuracy of large- and local-scale hydrological models.
In this current status, this PhD research aims to evaluate the applicability of global water resources datasets (including satellite-derived observations, in situ data and models) for hydrological modelling at the river basin scale. In view of the complexity of this goal, this research focuses on applying and testing techniques that can potentially be implemented in any river basin around the world, depending on its hydro-meteorological characteristics and in situ data richness. Four river basins were selected and different approaches were applied to optimally integrate global datasets into large- and local-scale hydrological models, including the Brahmaputra basin in Bangladesh, the Magdalena-Cauca basin in Colombia, the Oum Er Rbia basin in Morocco and the Murrumbidgee basin in Australia.