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Title: Collaborative Design of Robust Adaptation Pathways for Urban Flooding Resilience in Baltimore

Abstract

Urban pluvial flooding poses a growing threat to the city of Baltimore, driven by changes in climate and rainfall, increased impervious area, aging infrastructure, and severe social inequity. Adaptations to urban flooding should strengthen community-scale resilience while navigating the uncertainties associated with future actions. However, the highly uncertain future climate and conflicting stakeholder objectives complicate the selection of effective and socially acceptable strategies. This study addresses these challenges through a community-informed Multi Objective Robust Decision Making (MORDM) process, coupled with Dynamic Adaptation Policy Pathways (DAPP). Community stakeholders are engaged on a large scale through community partnerships, including priority identification sessions, focus groups, and metric identification workshops. The combination of community partnership, MORDM, and DAPP will create an original set of adaptation pathways which are aligned with community goals and values, and furthermore are adjustable under changing future constraints and conditions. This framework will enhance the city's capacity to respond to evolving pluvial flood conditions while accounting for diverse stakeholder interests and will provide guidance for addressing both the immediate and long-term challenges of urban flooding in Baltimore.

Authors:
;
  1. Pennsylvania State University
Publication Date:
Research Org.:
Pacific Northwest National Lab (United States)
Sponsoring Org.:
USDOE Office of Science (SC); Biological and Environmental Research (BER); Awarding Entity, Inc.
Subject:
BSEC; Flood; Flood Adaptation; Machine Learning; Optimization; pluvial
OSTI Identifier:
2500249
DOI:
https://doi.org/10.57931/2500249

Citation Formats

Spangler, Ava, and Hadjimichael, Antonia. Collaborative Design of Robust Adaptation Pathways for Urban Flooding Resilience in Baltimore. United States: N. p., 2024. Web. doi:10.57931/2500249.
Spangler, Ava, & Hadjimichael, Antonia. Collaborative Design of Robust Adaptation Pathways for Urban Flooding Resilience in Baltimore. United States. doi:https://doi.org/10.57931/2500249
Spangler, Ava, and Hadjimichael, Antonia. 2024. "Collaborative Design of Robust Adaptation Pathways for Urban Flooding Resilience in Baltimore". United States. doi:https://doi.org/10.57931/2500249. https://www.osti.gov/servlets/purl/2500249. Pub date:Thu Dec 12 23:00:00 EST 2024
@article{osti_2500249,
title = {Collaborative Design of Robust Adaptation Pathways for Urban Flooding Resilience in Baltimore},
author = {Spangler, Ava and Hadjimichael, Antonia},
abstractNote = {Urban pluvial flooding poses a growing threat to the city of Baltimore, driven by changes in climate and rainfall, increased impervious area, aging infrastructure, and severe social inequity. Adaptations to urban flooding should strengthen community-scale resilience while navigating the uncertainties associated with future actions. However, the highly uncertain future climate and conflicting stakeholder objectives complicate the selection of effective and socially acceptable strategies. This study addresses these challenges through a community-informed Multi Objective Robust Decision Making (MORDM) process, coupled with Dynamic Adaptation Policy Pathways (DAPP). Community stakeholders are engaged on a large scale through community partnerships, including priority identification sessions, focus groups, and metric identification workshops. The combination of community partnership, MORDM, and DAPP will create an original set of adaptation pathways which are aligned with community goals and values, and furthermore are adjustable under changing future constraints and conditions. This framework will enhance the city's capacity to respond to evolving pluvial flood conditions while accounting for diverse stakeholder interests and will provide guidance for addressing both the immediate and long-term challenges of urban flooding in Baltimore.},
doi = {10.57931/2500249},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Dec 12 23:00:00 EST 2024},
month = {Thu Dec 12 23:00:00 EST 2024}
}