Scholarlink

<p>Socially vulnerable coastal communities in Houston, Texas, lack resources and infrastructure investment for flood hazard mitigation. Further, a scalable, data-driven approach for Green Stormwater Infrastructure (GSI) planning that addresses the unique challenges of urbanized coastal topography has not been developed. We propose a framework wherein vulnerable coastal communities can evaluate GSI performance and identify optimal contributing areas at the neighborhood scale (i.e., areas &lt;2 km<sup>2</sup>) while maximizing the impervious surfaces treated. GSI performance curves are generated using Stormwater Management Model (SWMM) simulations, and the best-performing GSI for different land uses are identified. Bio-retention cells, permeable pavements, and infiltration trenches demonstrate higher efficiency as individual GSI in industrial and commercial spaces. Simulated runoff and pollutant reductions range from 10% to 80% and 20% to 90%, respectively. In residential areas, rain gardens and infiltration trenches are the best performing individual GSI with maximum runoff and pollutant reductions of &gt;30% and &gt;60%, respectively. However, the greatest benefits can also be achieved if a combination of GSI is implemented across a smaller contributing area. Optimal GSI contributing areas estimated with an MCDM (multi-criteria decision-making) tool, TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), highlight that strategic GSI implementation can enhance flood resilience in socially vulnerable coastal communities. Decision-makers, urban planners, and future researchers may use the framework demonstrated to plan GSI strategies in similar urban settings or expand it from the neighborhood scale to the watershed scale for improving environmental and ecosystem health.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-Objective Performance Evaluation and Contributing Area Optimization Framework for Urban Green Stormwater Infrastructure

  • 期刊论文

  • 发表日期：

    2025年12月26日

查看全文
  • Afiya Narzis,
  • Jessica A. Eisma

摘要

Socially vulnerable coastal communities in Houston, Texas, lack resources and infrastructure investment for flood hazard mitigation. Further, a scalable, data-driven approach for Green Stormwater Infrastructure (GSI) planning that addresses the unique challenges of urbanized coastal topography has not been developed. We propose a framework wherein vulnerable coastal communities can evaluate GSI performance and identify optimal contributing areas at the neighborhood scale (i.e., areas <2 km2) while maximizing the impervious surfaces treated. GSI performance curves are generated using Stormwater Management Model (SWMM) simulations, and the best-performing GSI for different land uses are identified. Bio-retention cells, permeable pavements, and infiltration trenches demonstrate higher efficiency as individual GSI in industrial and commercial spaces. Simulated runoff and pollutant reductions range from 10% to 80% and 20% to 90%, respectively. In residential areas, rain gardens and infiltration trenches are the best performing individual GSI with maximum runoff and pollutant reductions of >30% and >60%, respectively. However, the greatest benefits can also be achieved if a combination of GSI is implemented across a smaller contributing area. Optimal GSI contributing areas estimated with an MCDM (multi-criteria decision-making) tool, TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), highlight that strategic GSI implementation can enhance flood resilience in socially vulnerable coastal communities. Decision-makers, urban planners, and future researchers may use the framework demonstrated to plan GSI strategies in similar urban settings or expand it from the neighborhood scale to the watershed scale for improving environmental and ecosystem health.