Robust Stormwater Management in the Pittsburgh Region: A Pilot Study

Fischbach, Jordan R.; Siler-Evans, Kyle; Tierney, Devin; Wilson, Michael T.; Cook, Lauren M.; May, Linnea Warren

doi:10.7249/rr1673

Cited by 17 publications

(28 citation statements)

References 29 publications

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“…These methodological choices can be difficult to communicate to non‐expert stakeholders. A climate change factor, one of the more common representations used in the literature (Wasko et al., 2021), is a relatively simple way to represent future scenarios (Casal‐Campos et al., 2015; Cook et al., 2017; Fischbach et al., 2017; Kapetas & Fenner, 2020). This factor representing the ratio between some statistic in the future and the same statistic in the past or present may be also easier to explain to policy makers and non‐experts (Ruiter, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…The relatively large number of publications using descriptive statistics rather than satisficing or regret highlights the computational complexity of DMDU approaches. Formal models and techniques can be expensive and time consuming as well as data intensive, requiring hundreds of thousands of dollars and thousands of CPU‐hours for simulation (Fischbach et al., 2017; Hallegatte et al., 2012). Pursuing green infrastructure studies using DMDU approaches that are anchored in the criterion of regret and satisficing requires significant processing and/or calculations that may be outside of some organizations' capabilities or expertise (Fischbach et al., 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Cells on the off‐diagonals represent the number of publications in which these two strategies were used together. The strongest cluster of green infrastructure strategies included green roofs, permeable pavements and bioretention cells, with a second cluster among detention tanks or ponds and rainwater or storm water harvesting as shown in the value of the non‐diagonal cells in the heat map in Figure 3 (Fischbach et al., 2017; Kirshen et al., 2015; Mei et al., 2018; M. Wang et al., 2017; Xie et al., 2017; Xu et al., 2019). Clusters observed among the 24 green infrastructure strategies for all 64 case studies can be found in the Supplemental Figures (see Figure S3 in Supporting Information S1).…”

Section: Green Infrastructure Adaptation Strategies Within Dmdu Appro...mentioning

confidence: 99%

“…Satisficing approaches were also found in the literature (8 of 64 case studies), most of which used scenario discovery or similar models (e.g., Babovic & Mijic, 2019;Fischbach et al, 2017Fischbach et al, , 2020Radhakrishnan et al, 2019;Ramm et al, 2018a;Tariq et al, 2017). More complex regret-based approaches (Casal-Campos et al, 2015;Kim & Chung, 2014;van der Pol et al, 2021) were less popular.…”

Section: Dmdu Metricsmentioning

confidence: 99%

Section: Modeling and Computational Complexitymentioning

confidence: 99%

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A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

Webber

Samaras

2022

Earth's Future

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Decision making under deep uncertainty (DMDU) approaches are well‐suited for making decisions about infrastructure to manage flooding exacerbated by climate change. One important system for climate resilience and flood management is green infrastructure, which refers to a network of natural and semi‐engineered areas that provides ecosystem functions. Green infrastructure is often characterized as a low‐regret strategy with multiple co‐benefits under uncertainty. These attributes enable green infrastructure to be an important adaptation strategy under DMDU frameworks for flood management. However, DMDU analyses that include green infrastructure are still relatively limited, perhaps due to computational or modeling complexity and other barriers. This paper identifies and reviews publications in the flood management literature that use DMDU frameworks and refer to green infrastructure adaptation strategies, in order to identify trends and inform future research. The reviewed publications are categorized according to a variety of performance metrics, climate change scenarios, DMDU metrics, and hydrologic modeling techniques, and represent several adaptation strategies applied to case studies on five continents using a range of data sources and assumptions. This paper highlights a number of solutions that can be employed to facilitate additional research at the intersection of these fields. Primary among these is the transparent documentation and use of open source models, methods, and data. Future research should also focus on communication among different stakeholders, particularly in ensuring definitions, assumptions, and data requirements are clear. These partnerships can facilitate effective application of robust strategies such as green infrastructure for urban adaptation to the effects of climate change.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Green Infrastructure Adaptation Strategies Within Dmdu Appro...mentioning

confidence: 99%

Section: Dmdu Metricsmentioning

confidence: 99%

Section: Modeling and Computational Complexitymentioning

confidence: 99%

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A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

Webber

Samaras

2022

Earth's Future

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Nature‐based solutions for urban pluvial flood risk management

Huang

Tian

et al. 2020

WIREs Water

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Urban pluvial flooding now occurs more frequently than it has in past decades, mainly due to an increasing number of extreme precipitation events occurring in the context of a changing climate. To limit the evolving risks of urban pluvial flooding in a more environmentally friendly manner, the research community has recently paid increasing attention to Nature-Based Solutions (NBS), which are based on new green technologies. To meet the urgent demand for a comprehensive review of the most recent literature, this review conducts a systematic survey of the literature to characterize various NBS adopted in different regions of the world and to elaborate on the benefits and limitations of such NBS. The review highlights the role of NBS in urban flood risk management under ongoing climate change and rapid urbanization. It shows that NBS could effectively mitigate urban flooding caused by high-frequency precipitation events, with additional economic, ecological, and social benefits. However, NBS are less effective at helping cope with pluvial flooding caused by extreme precipitation events over a short period of time, while gray infrastructures also have limitations as a mitigation measure against extreme pluvial flooding. We thus recommend identifying flood risk management strategies by evaluating the performance of alternative combinations of NBS with gray infrastructures in preventing pluvial flooding in the cities. Finally, recent advances made in the applications of NBS are presented with suggestions (e.g., long-term monitoring) to improve urban flood adaptive management. This article is categorized under: Engineering Water > Planning Water Engineering Water > Sustainable Engineering of Water Science of Water > Water Extremes K E Y W O R D S extreme precipitation, nature-based solution, risk management, urban pluvial flood Yijing Huang and Zhan Tian equally contributed to this study.[Correction added on 27 April 2020, after first online publication: the unit of second affiliation has been updated and funding information has been reordered.]

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Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System Under Deep Climate Uncertainty

Sundar,

Lehner,

Voisin

et al. 2024

Earth's Future

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Climate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate‐related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling.

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Robust Stormwater Management in the Pittsburgh Region: A Pilot Study

Cited by 17 publications

References 29 publications

A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

Nature‐based solutions for urban pluvial flood risk management

Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System Under Deep Climate Uncertainty

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