ORCID Identifier(s)

0009-0003-3981-0684

Graduation Semester and Year

Fall 2025

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Civil Engineering

Department

Civil Engineering

First Advisor

Dr. Jessica Eisma

Abstract

With a high social vulnerability index (SVI=0.92), urbanized coastal communities of Houston, Texas, disproportionately receive the benefits of flood mitigation initiatives, such as Green Stormwater Infrastructure (GSI). While policymakers pursue strategies to reduce flood risk and inequality, a data-driven, scalable approach for GSI planning has not yet been developed, especially given the unique challenges of coastal low-lying floodplains. Thus, the present study provides a data-driven framework for GSI planning to enhance flood resilience and improve water quality in the low-lying urban coastal communities of Houston, Texas.

The first part of the study demonstrates how optimized GSI area and land use-specific placement can maximize GSI benefits. GSI performances are evaluated, and optimal GSI contributing areas are identified at the neighborhood scale while maximizing the impervious surfaces treated. The findings highlight the need for strategic GSI implementation to enhance flood resilience in socially vulnerable coastal communities.

The next part of the study discusses the differences in GSI modeling techniques and model predictions in the One-dimensional (1D) and two-dimensional (2D) hydrologic and hydraulic (H & H) GSI models using a comprehensive model parameter sensitivity and uncertainty analysis. The findings demonstrate the most influential GSI design parameter and the uncertainty ranges for runoff reduction, which will help engineers and planners to select the most suitable GSI modeling approach for flood hazard mitigation.

Finally, the study provides data-driven evidence showing the potential of different levels of watershed-scale GSI implementation. For this purpose, streamflow and pollutant concentration reductions are simulated using a physics-based two-dimensional (2D) model and a spreadsheet-based water quality analysis tool. The framework developed will assist decision makers to select best GSI design alternatives and decide on investment opportunities to build long-term resilience in the socially vulnerable communities, ensuring a healthy environment.

Keywords

LID, SWMM, StormWise, Flood Resilience, Water Quality, Sensitivity, Uncertainty, Physics-Based Model, Watershed-scale LID, Bayous

Disciplines

Civil Engineering | Computational Engineering | Environmental Engineering | Hydraulic Engineering | Other Civil and Environmental Engineering

Comments

Publication is supported in part by an Institutional Grant (NA18OAR4170088) to the Texas Sea Grant College Program from the National Sea Grant Office, National Oceanic and Atmospheric Administration, U.S. Department of Commerce.

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Available for download on Saturday, December 05, 2026

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