Modeling and Experiment Driven Stormwater Quality Improvement: Case Studies in Lower Rio Grande Valley, TX

Author

• Saika Nowshin Nowrin, The University of Texas Rio Grande Valley

Date of Award

5-1-2026

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Civil Engineering

First Advisor

Chu-Lin Cheng

Second Advisor

Jungseok Ho

Third Advisor

Fatemeh (Noosheen) Nazari

Abstract

Urbanization and changing climate extremes, i.e., floods and droughts, have increased stormwater runoff and pollutant transport, posing significant risks to surface water quality. This study presents an integrated framework combining regional Water Quality Index (WQI) assessment using machine learning (ML) and Low Impact Development (LID) porous media modeling to evaluate and mitigate stormwater impacts regarding water quality and quantity in urban watersheds. Surface water datasets collected from the Texas Commission on Environmental Quality (TCEQ) were analyzed to compute WQI for Austin (Central Texas) monitoring stations using unsupervised machine learning models, i.e., Principal Component Analysis, K-means, and One Class Support Vector Machine to obtain weight-driven water quality indices. The developed model was further validated with dataset from Port Harlingen (South Texas). Results indicate that water quality at Port Harlingen significantly exceeds surface water standards compared to the selected sites in Austin. The proposed WQI model showed strong correlations with the modified NSF-WQI (R² = 0.91) (National Sanitation Foundation), which further confirms the effectiveness of this model.

On the other hand, Lower Rio Grande Valley (LRGV) is vulnerable to severe and frequent flooding attributing from various natural and anthropogenic factors, e.g., increasing intensity of precipitation, lowland topography, insufficient or clogged drainage systems, limited soil permeability, and changes of land cover from rapid urban development. Incorporating natural and recycled porous media in urban green infrastructure such as LIDs (Low Impact Developments) can enable higher infiltration rates and longer pollutant retention for stormwater, which in return can improve surface water quality and groundwater recharge. Hydraulic properties were investigated using laboratory experiments and literature for zeolite, pumice, manufactured sand, and recycled crushed glass. PCSWMM (Personal Computer Storm Water Management Model), then was applied to compare their performance under different rainfall scenarios. Outcomes demonstrated that natural zeolite significantly reduced runoff (14% total, 52% peak) and enhanced infiltration with high pollutant removal efficiencies (80% total suspended solids, 50% total nitrogen, 40% total phosphorus, 60% lead, and 50% zinc). Results highlight potential of such applications using LIDs with engineered porous media as a sustainable and cost-effective method in improving urban water quality. This integrated approach effectively bridges data-driven water quality assessment with stormwater modeling, providing a practical tool to inform better water resource planning and flood/drought management for the region.

Comments

Copyright 2026 Saika Nowshin Nowrin. All Rights Reserved. https://proquest.com/docview/3371138190

Recommended Citation

Nowrin, S. (2026). Modeling and Experiment Driven Stormwater Quality Improvement: Case Studies in Lower Rio Grande Valley, TX [Master's thesis, The University of Texas Rio Grande Valley]. ScholarWorks @ UTRGV. https://scholarworks.utrgv.edu/etd/1946