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Technical University of Dortmund (2022)

Streamflow simulation in data-scarce regions using remote sensing data in combination with ground-based measurements

Baez Villanueva, Oscar Manuel

Titre : Streamflow simulation in data-scarce regions using remote sensing data in combination with ground-based measurements

Auteur : Baez Villanueva, Oscar Manuel

Université de soutenance : Technical University of Dortmund

Grade : Doctor of Engineering (Dr.-Ing.) 2022

Résumé partiel
Global water resources are currently under unprecedented stress, which is projected to increase due to the influence of multiple factors. Therefore, changes in governance are urgently required to improve water management and water use efficiency while maintaining the health of river systems and their water quantity and quality. Data is crucial in this process ; however, most rivers in the world remain ungauged, and in data-scarce regions, the hydrometric and hydrometeorological networks of stations have been decreasing during the last decades. This hinders the implementation of proactive water management approaches that strive towards informed-based decision-making. This cumulative thesis shows how open access global precipitation products can be evaluated, corrected, and used to predict streamflow at the daily temporal scale in data-scarce regions in combination with ground-based measurements by following a three-step approach : i) performance evaluation of different precipitation products over regions with different climates and at multiple temporal scales ; ii) development of a novel merging method to improve the representation of precipitation at the daily scale ; and iii) assessment of the ability of the novel merged product altogether with state-of-the-art precipitation products to predict daily streamflow over ungauged catchments through the implementation of regionalisation approaches. This thesis showed that the precipitation products perform differently depending on the temporal scale, elevation, and climate ; and that these products still have errors in detecting particular precipitation events. These insights served as a basis to develop a novel merging procedure named RF-MEP, which combines data from precipitation products, ground-based measurements, and topographical features to improve the characterisation of precipitation. RF-MEP proved to be a powerful method as the precipitation errors at different temporal scales were substantially reduced, outperforming state-of-the-art precipitation products and merging procedures. The precipitation product derived with RF-MEP has been included in a Chilean precipitation monitor platform from the Center for Climate and Resilience Research (Mawün) and users can apply this method in a friendly manner using the R package RFmerge

Mots clés  : Data scarcity Hydrological modelling Machine learning Merging procedures PUB Precipitation Precipitation products Random Forest Regionalisation RF-MEP

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Page publiée le 31 octobre 2022