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Delft University of Technology (TU Delft) 2020

Benefits of using remotely sensed time series data in optimizing water management decisions (Oum Er Rbia basin, Morocco)

Keshav, S.

Titre : Benefits of using remotely sensed time series data in optimizing water management decisions (Oum Er Rbia basin, Morocco)

Auteur : Keshav, S.

Université de soutenance : Delft University of Technology (TU Delft)

Grade : Master of Science (MS) 2020

Résumé partiel
Big data sources can play an important role in revolutionizing the field of water resources research. Time series data with high temporal and spatial dimensions encapsulates with itself numerous factors essential for coming up with robust decisions. In this thesis, we assess one such big data source for efficient water management in the Oum Er Rbia basin, Morocco. The surface water detection technique furnished used in this thesis is found to be accurate in detecting the surface water sources and its temporal and spatial dynamics.The remotely sensed time-series data of reservoir area was used to come up with Level-Area-Storage(LAS) relationships for the five main reservoirs in the Oum Er Rbia basin. These curves were able to approximate the present set of LAS curves well. Hence, were used in place of the local LAS curves in a water allocation decision model called RIBASIM. Thus, we had two scenarios one where the local LAS curves were used to optimize reservoir operations and the other where remotely sensed LAS curves were used instead of the local LAS curves.The operating rule curves in the water allocation decision model were then optimized for the two scenarios. The optimization was done to maximize the performance of the system across three objectives : a)public water supply, b)irrigation and c)hydroelectricity generation. A trade-off between the three objective functions was then shown using parallel and scatter plots. It was observed that for the same set of LAS curves the performance across all three objectives improved post-optimization of the operating rule curves. This showed that there were rooms for improvement in the existing reservoir operating rule curves. The operating rule curves for the water allocation decision model with remotely sensed LAS curves were then optimized

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