Dong, Zhaokai
(2023)
Development of Integrative Models to Quantify the Benefits of Green Infrastructure (GI) to Urban Stormwater Management.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Stormwater management is a growing challenge in urban environments. Historically, stormwater has been managed through sewer systems and natural pathways by discharging runoff into urban water bodies or treatment facilities. Many of the oldest cities in the USA, such as the City of Pittsburgh, built combined sewer systems for stormwater conveyance. During wet weather, the combined flow of wastewater and stormwater may exceed the pipe's maximum conveyance capacity and lead to combined sewer overflows (CSOs) at the permitted outfall, degrading water quality in urban waterways. Nation-wide upgrading of existing traditional gray stormwater infrastructure is impractical, so in many developed countries there is a move towards more natural-based stormwater control systems. Green infrastructure (GI) aims to restore and maintain natural hydrological conditions and has emerged as one of the most promising and popular stormwater management strategies.
In this work, we evaluated the impacts of GI at both the site scale and the city-wide scale to enhance understanding of systems-level performance of green and gray infrastructure networks. We deployed sensor networks at the Phipps Conservatory in Pittsburgh to continuously monitor the performance of existing rain gardens and a green roof. We evaluated the hydrological performance of the green roof using multiple years of monitored sensor data. We developed a version of the US EPA’s Stormwater Management Model for our sewershed and machine learning models to simulate stormwater flow. The Stormwater Management Model was used to simulate the systems-level performance of GI networks for stormwater control, considering GI selection, design, and the interaction among different installations across sites, based on Monte Carlo simulations.
Our findings suggest that GI can effectively reduce stormwater runoff volume and peak flow at the site scale. GI implementations at the city-wide scale, considering three different types of GI (rain gardens, green roofs, and rain barrels), could benefit stormwater control. Weather conditions and substrate properties can impact GI water retention. Long-term monitoring based on sensor networks can track GI performance and provide insights into GI design and maintenance. Overall, our findings render critical information regarding GI siting, design, and performance at scale to support stormwater decision-making.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
27 June 2023 |
| Defense Date: |
12 July 2023 |
| Approval Date: |
14 September 2023 |
| Submission Date: |
8 July 2023 |
| Access Restriction: |
1 year -- Restrict access to University of Pittsburgh for a period of 1 year. |
| Number of Pages: |
302 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Civil and Environmental Engineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Green infrastructure, urban stormwater management, sensor network monitoring, modeling |
| Date Deposited: |
14 Sep 2023 13:41 |
| Last Modified: |
14 Sep 2024 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/45070 |
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