Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.699582
Title: Statistical downscaling of future hourly precipitation extremes in the UK using regional climate models and circulation patterns
Author: Rau, Markus
ISNI:       0000 0004 5990 3386
Awarding Body: University of East Anglia
Current Institution: University of East Anglia
Date of Award: 2016
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Abstract:
Observational trends, physical reasons and modelling results suggest an increase in extreme precipitation with climate warming. In particular, sub-daily precipitation extremes are expected to increase heavily raising concerns about the future impacts of flash floods in urban environments and for small or steep river catchments. In order to quantify the potential risk of flash floods in the future, impact studies often require site-specific sub-daily estimates of precipitation extremes. But in their current stage, most Regional Climate Models (RCMs) are only able to provide areal averaged projections at ca. 12.5km resolution and simulated sub-daily precipitation extremes tend to be heavily biased. As a result, statistical downscaling methods are needed to provide site-specific more reliable projections of sub-daily precipitation extremes. In this thesis, a statistical downscaling method was developed to project site-specific future hourly precipitation extremes over the UK. Circulation patterns (CPs) were classified using a fuzzy rules based approach to categorize extreme hourly precipitation events according to their corresponding atmospheric conditions. In a next step, an analogue day method was applied to find the most similar day in the past by comparing the RCM simulated daily precipitation and temperature with the observations for each CP. The daily maximum hourly precipitation record on the most similar day was extracted and perturbed based on precipitation duration-temperature relationships conditioned on CPs. Within the field of statistical downscaling techniques, the applied method is best described as a hybrid of the analogue and the regression-based method. It was shown that the method is capable of reproducing observed extreme hourly precipitation over different validation periods. Projections based on the applied statistical downscaling method indicate increases in UK hourly extremes but with high variations depending on the twelve different stations, the two future time periods, the two emission scenarios and the four different GCM-driven RCMs.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.699582  DOI: Not available
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