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. The best set of operating rule curves that we got from the second optimization were then used with the local LAS curves to see how the system would perform with these operating rule curves. This gave us an idea of the feasibility of using remotely sensed data to come up with water management decisions and also to assess the benefits of using remotely sensed time-series data. Though the performance over the three objectives was not as good as the results we got by optimizing the system with local LAS curves, it was better than the system performance across the three objectives with the existing set of operating rules and local LAS curves. Thus, it can be used when there is a dearth of proper LAS curves.
Besides, optimizing operating rule curves, the remotely sensed time-series data of reservoir surface area was used to assess the effects of sedimentation in the reservoir storage. It was observed that for larger reservoirs the percentage change is not much as compared to the smaller reservoirs. Apart from the size of the reservoir, more study is required to make a detailed analysis of how factors like topography and soil texture influence the rate of sedimentation. Despite its limitations, the remotely sensed time-series data of reservoir surface area can be used to perform a qualitative analysis of the rate of sedimentation and can give reservoir authorities an idea of the need for bathymetry. This can help in avoiding unnecessary bathymetries which are infeasible both economically and physically.