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Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Civil Engineering

Year Degree Awarded

Spring 2014

First Advisor

Casey Brown

Subject Categories

Civil Engineering

Abstract

Sustainable water resources planning and management under climate change requires a proper treatment of uncertainties that emerge in an impacts analysis. A primary source of this uncertainty originates from the difficulties in projecting how anthropogenic greenhouse gas emissions will evolve over time and influence the climate system at regional and local scales. However, other sources of uncertainty, such as errors in modeling hydrologic response to climate and the influences of internal climate variability, compound the effects of climate change uncertainty and further obscure our understanding of water resources performance under future climate conditions. This work presents an approach to quantify the interactions, propagation, and relative contributions of different sources of uncertainty in a water resources impacts assessment under climate change. Hydrologic modeling uncertainty is addressed using Bayesian methods that can quantify both parametric and structural errors. Hydrologic uncertainties are propagated through an ensemble of climate projections to explore their joint uncertainty. A new stochastic weather generator is presented to develop a wide ensemble of climate projections that can extend beyond the limited range of change often afforded by global climate models and better explore climate risks. The weather generator also enables the development of multiple realizations of the same mean climate conditions, allowing an exploration of the effects of internal climate variability. The uncertainties from mean climate changes, internal climate variability, and hydrologic modeling errors are then integrated in two climate change analyses of a flood control facility and a multi-purpose surface reservoir system, respectively, to explore their separate and combined effect on future system performance. The primary goal of this work is to present methods that can better estimate the precision associated with future projections of water resource system performance under climate change, and through this provide information that can guide the development of adaptation strategies that are robust to these uncertainties.

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