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Author ORCID Identifier

https://orcid.org/0000-0002-8236-6182

AccessType

Open Access Dissertation

Document Type

dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Civil and Environmental Engineering

Year Degree Awarded

2022

Month Degree Awarded

September

First Advisor

Casey Brown

Subject Categories

Environmental Engineering

Abstract

Water quality deterioration is a global and pervasive issue due to pollution caused by industrialization, urbanization, agriculturalization, and human population growth in the modern era. This issue is even more challenging in the context of climate change due to warming temperatures and the intensification of precipitation. Therefore, assessing the potential impacts of climate change on water quality is a concern. Assessment is necessary so that planners can prepare for and reduce the negative impacts on water quality. At present, climate change impact assessment frameworks are relatively adolescent. Most studies rely on climate projections from General Circulation Models for simulations of future climate conditions under increased anthropogenic forcings. Due to pervasive biases, it remains unclear whether these projections can diagnose the true potential climate change impacts on water quality or provide sufficient information for adaptation decisionmaking, especially considering the deep uncertainties of climate change. This dissertation aims to tackle the impacts of the deep uncertainties of climate change on water quality. Three studies in this dissertation combine the decision scaling framework using stochastic climate scenarios with a machine-learning water quality model to estimate vulnerabilities of multiple critical water quality indicators at small and large spatial scales for built and natural environments. Water quality models are core components in climate change assessment frameworks. Machine-learning water quality ix models are promising tools for studies requiring many simulations and taking advantage of large environmental datasets. In Chapter 1, a Composite Quantile Regression Neural Network (CQRNN) is developed to improve the prediction of mean and extremes of turbidity and total organic carbon (TOC) in the Hetch Hetchy Regional Water System (HHRWS). In Chapter 2, the Decision Scaling framework based on a stochastic weather generator, a water demand model, a hydrologic model, a water system model, and a water quality model (CQRNN) is built to investigate responses of turbidity and TOC under climate and water demand changes in HHRWS. In Chapter 3, a largescale simulation of turbidity, phosphorus, and water temperature at the Central and Eastern US streams under climate and land cover scenarios is implemented by using the Decision Scaling framework, including climate and land cover scenario generators, a simplified and regression-based stream flow generator, and a water quality model (CQRNN). The results of this dissertation demonstrate that water quality is vulnerable to changes in climate, water demand, and land cover at small and large spatial scales for built and natural environments.

DOI

https://doi.org/10.7275/31053719

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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