Spatiotemporal Modelling of Rooftop Rainwater Harvesting with LiDAR Data in the Taita Hills, Kenya

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Title: Spatiotemporal Modelling of Rooftop Rainwater Harvesting with LiDAR Data in the Taita Hills, Kenya
Author: Oyedayo, Oyelowo
Contributor: University of Helsinki, Faculty of Science
Publisher: Helsingin yliopisto
Date: 2019
Thesis level: master's thesis
Abstract: The puzzling thing about water is that, while it is very abundant in our planet – earth, millions of people globally face water scarcity. Some places, however, do not. In other places, it is not really that there is no water at all, but it is not available all year round, in most cases. This underscores the importance of putting into cognizance, the spatial and temporal context of water scarcity, hence, the basis for this project. Developing countries, especially have had worse situations with water scarcity due to population explosion and the lack of the technological advancement to harness, purify, transport, store, deliver and reuse water. One of such countries is Kenya, where many do not have access to potable water. Many solutions have been proffered without adequately addressing the issue itself. Rooftop rainwater harvesting is a potential solution to ameliorate this problem. In this thesis, I took a holistic approach to evaluate the potential of Rooftop Rainwater Harvesting (RRWH) in meeting the domestic water needs of the Taita People, Kenya. Importantly, contrary to other RRWH studies, I attempt to introduce and synergize the temporal aspect with the spatial context, in order to deeply understand the monthly dynamics of RRWH. This is crucial in answering the ‘where’ and ‘when’ questions of RRWH. This aims to provide a decision support for stakeholders, by presenting the results visually and quantifiably. The project is mainly divided into three parts. The first part involves the validation and utilization of a Light and Range Detection (LiDAR) data, for automatically generating the footprints of roofs in Taita. Herein, I compared the accuracies of LiDAR datasets from same area but different years. The second part utilizes the roofs’ polygons generated from the LiDAR data to estimate the Rooftop Rainwater Harvesting Potential in the region, by integrating it with Climatologies at high resolution for the earth’s land surface areas (CHELSA) and a strategically chosen universal roof coefficient. Lastly, household survey was carried out in the study area to understand the social context and integrate the data into my model. The result shows that there is a clear temporal trend to RRWHP in the area, and a single annual RRWHP model might be too generalized to give sufficient insight into understanding how much the system can mitigate water problem in the area. It also logically incorporates the survey data into the model to provide information about measurable monthly and annual values, as to percentage of the households that RRWH can fulfill their needs.
Discipline: Maantiede

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