Date of Award

Spring 5-7-2021

Level of Access Assigned by Author

Open-Access Thesis

Degree Name

Doctor of Philosophy (PhD)

Department

Civil Engineering

Advisor

Shaleen Jain

Second Committee Member

Willem Brutsaert

Third Committee Member

Huijie Xue

Additional Committee Members

Kim Jong Suk

Sean Birkel

Abstract

Nonstationarity in the means and extremes of water resources (e.g., streamflow) due to climate change presents challenges to water resources management and planning. Changes in hydrologic variables such as streamflow critically impact infrastructure, water use planning and adaptation strategies, and can lead to increased societal vulnerability. As such, existing water resources infrastructure designed based on limited records of historical flow may deteriorate under future hydro-climatic changes. Thus, important scientific tasks are: a) examining and redefining the risk, reliability, and return periods under nonstationary conditions, b) understanding the changes in extreme events statistics (i.e. frequency, and magnitude), and c) linking these changes in atmospheric moisture pathways and large-scale climatic processes. Doing so will help to make the decision of management and to mitigate the risk of extreme events, inform science-based decision tools for early-warning, and ascertaining the incidence of extreme events in a changing climate. This work seeks to inform decision management and risk mitigation of extreme events through two contributions. First, nonstationarity in streamflow regime with linkages to climatic indices (i.e., ENSO and PDO) are analyzed using a case study from the Feather River in California, USA. This includes application of a simple storage-yield-reliability model to quantify the stationary-based risk in the system’s design (i.e. reservoir’s storage requirement) and performance (i.e. reliability, resilience, vulnerability). Second, a comprehensive statistical framework for quantifying the nature of variability in the U.S. floods under the impacts of atmospheric rivers (AR) is presented. The approach includes utilizing a combined database of streamflow, atmospheric rivers, precipitation rate, surface air temperature, and water equivalent of accumulated snow depth data to: a) delineate the generating mechanism of a flood event, b) identify the track and source of a moisture-controlled flood event, and c) quantify the nature of variability in floods conditioned on the atmospheric moisture pathways and their oceanic sources. The complementary research presented in this dissertation seeks to provide improved understanding of hydrologic risk in a variable and changing climate. As such, knowledge gained from this work has implications for engineering design, climate adaptation and decision-making at the local and national levels.

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