Author ORCID Identifier

https://orcid.org/0009-0008-7138-7608

Semester

Spring

Date of Graduation

2026

Document Type

Dissertation

Degree Type

PhD

College

Davis College of Agriculture, Natural Resources and Design

Department

Division of Resource Economics & Management

Committee Chair

Michael J Dougherty

Committee Member

Michael P. Strager

Committee Member

Aaron E. Maxwell

Committee Member

Peter Butler

Abstract

Flooding is one of the most frequent and damaging natural hazards in the United States, with impacts expected to intensify under changing climate conditions. In regions such as West Virginia, complex terrain, development patterns along river corridors, and socioeconomic disparities contribute to elevated flood risk. Traditional approaches to flood risk assessment often focus on single dimensions of vulnerability or rely on binary floodplain maps, which do not fully capture the spatial variability and multidimensional nature of flood resilience.

This dissertation develops an integrated, community-scale framework for assessing flood resilience in West Virginia by combining physical, environmental, institutional, and socioeconomic dimensions of vulnerability. The research is organized into three complementary studies. The first study evaluates physical and institutional vulnerability using building-level data and infrastructure indicators, revealing significant spatial disparities in exposure and resilience across communities. The second study applies a Random Forest machine learning model to map flood susceptibility based on environmental variables and high-water-mark observations, creating a continuous representation of flood risk that extends beyond FEMA-designated flood zones. The third study develops a Socioeconomic Vulnerability Index (SEVI) using American Community Survey data and combines it with physical exposure indicators to identify communities facing compounded risks.

The findings show that interactions among multiple factors influence flood vulnerability and vary across the state, with higher vulnerability primarily in southern and southwestern West Virginia. This research emphasizes the need to consider environmental processes, built environment features, and socioeconomic factors in resilience planning. Overall, this dissertation improves community-scale flood resilience assessment by combining indicator-based and machine-learning methods and offers practical insights to support more targeted and fairer flood risk management strategies.

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