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Title: Impacts of climate change on water resources of global dams
Author: Van Soesbergen, Arnout
Awarding Body: King's College London (University of London)
Current Institution: King's College London (University of London)
Date of Award: 2013
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This thesis aims to assess the effects of climate change mediated through the watersheds of global dams on water resources delivered to those dams. Dams and reservoirs play an important role in social and economic development contributing water for 12-16% of global food production and providing around 20% of the world's energy supply through hydropower. The first part of this research has been dedicated to the further development of the first global geo-referenced database of dams (KCL GOOD2) that allows for modelling the impacts of land use and climate changes on water supplies. More than 36,000 dams were identified in a collaborative effort using an open source database (GEOWIKI) and Google Earth. This database was then used to extract all individual dam watersheds. These watersheds combined make up around 18% of global land mass which means that impacts of climate change can have profound impacts on the water resources delivered to dams. By combining the calculated watersheds of dams with climate model projections from the IPCC AR4, changes in the water balance in the catchments of these dams were calculated and changes in reservoir water level were estimated for a range of large dams. The AguaAndes/WaterWorld spatial hydrological model using a multi-GCM scenario was then applied to three case study dams in different climate regions around the world to evaluate directional changes in water and sediment supply. Sensitivity to climate and land cover changes of the basins containing the dams was assessed by running the model for a range of scenarios. The final part of this thesis describes the application of the AguaAndes/WaterWorld model to the Santa basin in Peru to assess the impacts of climate change on a small hydroelectric plant using several multi-GCM scenarios to address uncertainty in the projections.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available