Title: Optimizing green infrastructure placement under precipitation uncertainty

Abstract

Increased urbanization, infrastructure degradation, and climate change threaten to overwhelm stormwater systems across the nation, rendering them ineffective. Green Infrastructure (GI) practices are low cost, low regret strategies that can contribute to urban runoff management. However, questions remain as to how to best distribute GI practices through urban watersheds given precipitation uncertainty and the variable hydrological responses to them. We develop stochastic programming models to determine the optimal placement of GI practices across a set of candidate locations in a watershed to minimize the total expected runoff under medium-term precipitation uncertainties. Specifically, we first develop a two-stage stochastic programming model. Next, we reformulate this model using perturbed parameters to reduce the requisite computational time and extend it to multi-stage. In addition, we introduce constraints that allow for incorporating sub-catchment-level runoff reduction considerations. Furthermore, we account for hydrological connectivity in the watershed using an underlying acyclic connectivity graph of sub-catchments and incorporate various practical considerations into the models. In addition, we develop a systemic approach to downscale the existing daily precipitation projections into hourly units and efficiently estimate the corresponding hydrological responses. These advancements are brought together in a case study for an urban watershed in a mid-sized city in themore » U.S., where we perform sensitivity analyses, evaluate the importance of the considered constraints, and provide insights.« less

Authors:

Barah, Masoud  ^[1];

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• Search DOE PAGES for ORCID "0000-0003-0953-3789"

Khojandi, Anahita ^[2]; Li, Xueping  ^[2];

• Search DOE PAGES for author "Li, Xueping"
• Search DOE PAGES for ORCID "0000-0003-1990-0159"

Hathaway, Jon ^[2]; Omitaomu, OluFemi  ^[3]

• Search DOE PAGES for author "Omitaomu, OluFemi"
• Search DOE PAGES for ORCID "0000-0002-3078-7196"

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+ Show Author Affiliations

1. Northwestern Univ., Evanston, IL (United States)
2. Univ. of Tennessee, Knoxville, TN (United States)
3. Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Publication Date:

Wed Jan 13 00:00:00 EST 2021

Research Org.:

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:

USDOE; National Science Foundation (NSF)

OSTI Identifier:

1785182

Grant/Contract Number:  

AC05-00OR22725; CMMI-1634975

Resource Type:

Accepted Manuscript

Journal Name:

Omega

Additional Journal Information:

Journal Volume: 100; Journal ID: ISSN 0305-0483

Publisher:

Elsevier

Country of Publication:

United States

Language:

English

Subject:

54 ENVIRONMENTAL SCIENCES; green infrastructure; urban resilience; stochastic programming; chance constraint; climate change