Use this URL to cite or link to this record in EThOS:
Title: An assessment of the impact of climate change on the efficiency and feasibility of four run-of-the-river hydropower schemes in UK catchments
Author: Pasten Zapata, Ernesto
ISNI:       0000 0004 6494 0544
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2017
Availability of Full Text:
Access from EThOS:
Access from Institution:
Globally, climate change is projected to affect the hydrological cycle. Nevertheless, the projected changes vary per region. Global Climate Models (GCMs) are the main tool used to project the changes in the future climate. Nevertheless, their spatial scale is large compared to the scale required for the assessment of local impacts. Therefore, Regional Climate Models (RCMs) have been developed to project climate change in specific regions. Often, the simulation biases of the RCMs are large and require bias correction before being used in the assessment of climate change impacts. From the water users, run-of-the-river (ROR) hydropower is expected to be altered by the future changes in water availability as it relies on the available flow for the generation of energy. This research assesses the impacts of climate change on four ROR hydropower schemes within the UK using the climate projections from the state-of-the-art Euro-CORDEX RCMs driven by RCP 2.6, 4.5 and 8.5. The study evaluates the simulation skill of the RCMs by comparing their outputs with present observations of temperature, precipitation and river flow (generated using the RCM outputs to drive a HEC-HMS hydrological model). Furthermore, two quantile mapping bias correction approaches are employed to evaluate possible differences in their outputs. One approach corrects the RCM biases whereas the other corrects the GCM and RCM biases. The results project an increase in future hydropower generation for two of the sites, little change for another and an uncertain projection for the last one. Furthermore, results show that the selection of the bias correction approaches is an important source of uncertainty as it can lead to opposite direction of change when considering the multi-model ensemble mean. The study highlights the importance of developing site-specific analyses, as the large-scale projections cannot be generalized, and provides a methodology to develop such analyses.
Supervisor: Moggridge, Helen ; Jones, Julie Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available