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An assessment of uncertainties in the analysis of the impact of climate change on flooding

This thesis aims to address the role of uncertainty in climate change impact studies, with particular focus on the impacts of climate change on UK flooding. Methods are developed to quantify the uncertainty associated with climate variability, hydrological model parameters and flood frequency estimation. Each is evaluated independently, before being combined to assess the relative importance of the different sources of uncertainty in the 'top down' impact study framework over multiple time horizons. The uncertainty from climate variability is addressed through the creation of a resampling methodology to be applied to global climate model outputs. Through resampling model precipitation, the direction of change for both mean monthly flows and flood quantiles are found to be uncertain with large possible ranges. Hydrological model parameter uncertainty is quantified using Monte Carlo methods to sample the model parameter space. Through sensitivity experiments, individual hydrological model parameters are shown to influence the magnitude of simulated flood quantile changes. If a larger number of climate scenarios are used, hydrological model parameter uncertainty is small only contributing up to 5% to the total range of impacts. The uncertainty in estimating design standard flood quantiles is quantified for the Generalised Pareto distribution. Flood frequency uncertainty is found to be most important for nearer time horizons, contributing up to 50% to the total range of climate change impacts. In catchments where flood estimation uncertainty is less important, global climate models are found to contribute the largest uncertainty in the nearer term, between 40% and 80% of the total range, with emissions scenarios becoming increasingly important from the 2050s onwards.

Identiferoai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:646016
Date January 2012
CreatorsLedbetter, Ralph James
PublisherUniversity of Reading
Source SetsEthos UK
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation

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