Addressing Climate Change as an Emerging Risk to Infrastructure Systems

Shortridge, Julie; Camp, Janey

doi:10.1111/risa.13234

Cited by 29 publications

(13 citation statements)

References 59 publications

(61 reference statements)

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“…In particular, one of the primary factors motivating the development of RDM was the observation that in multi-stakeholder climate change contexts, probabilistic characterizations of uncertainty are likely to be subject to contention and bias (Lempert et al, 2006;Lempert, Nakicenovic, Sarewitz, & Schlesinger, 2004). Previous research has also demonstrated that climate change experts may be resistant to probabilistic characterizations of their beliefs (Arnell, Tompkins, & Adger, 2005;Vaughan & Spouge, 2002), and that these beliefs and areas of expertise may not be well matched to adaptation needs (Shortridge & Camp, 2019). Thus, it may be reasonable to base exploratory sampling on distributions that may actually deviate from expert beliefs if they better support the goals of RDFs; namely, to identify robust strategies and characterize vulnerabilities.…”

Section: Uniform Normalmentioning

confidence: 99%

Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty

Reis

Shortridge

2019

Risk Analysis

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Deep uncertainty in future climatic and economic conditions complicates developing infrastructure designed to last several generations, such as water reservoirs. In response, analysts have developed multiple robust decision frameworks to help identify investments and policies that can withstand a wide range of future states. Although these frameworks are adept at supporting decisions where uncertainty cannot be represented probabilistically, analysts necessarily choose probabilistic bounds and distributions for uncertain variables to support exploratory modeling. The implications of these assumptions on the analytical outcomes of robust decision frameworks are rarely evaluated, and little guidance exists in terms of how to select uncertain variable distributions. Here, we evaluate the impact of these choices by following the robust decision‐making procedure, using four different assumptions about the probabilistic distribution of exogenous uncertainties in future climatic and economic states. We take a water reservoir system in Ethiopia as our case study, and sample climatic parameters from uniform, normal, extended uniform, and extended normal distributions; we similarly sample two economic parameters. We compute regret and satisficing robustness decision criteria for two performance measures, agricultural water demand coverage and net present value, and perform scenario discovery on the most robust reservoir alternative. We find lower robustness scores resulting from extended parameter distributions and demonstrate that parameter distributions can impact vulnerabilities identified through scenario discovery. Our results suggest that exploratory modeling within robust decision frameworks should sample from extended, uniform parameters distributions.

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Section: Uniform Normalmentioning

confidence: 99%

Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty

Reis

Shortridge

2019

Risk Analysis

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“…Weather and environment-related variables are not as common as the previously mentioned variables. This is an important shortcoming of the previous studies as it is well established that the management and assessment of infrastructure risk usually rely on climate-related historical data (Shortridge & Camp, 2019). The most common variables in this category are soil type, humidity, temperature, and precipitation.…”

Section: Type Of Explanatory Variables Usedmentioning

confidence: 99%

A Two‐Stage Data‐Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

et al. 2021

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Risk-informed asset management is key to maintaining optimal performance and efficiency of urban sewer systems. Although sewer system failures are spatiotemporal in nature, previous studies analyzed failure risk from a unidimensional aspect (either spatial or temporal), not accounting for bidimensional spatiotemporal complexities. This is owing to the insufficiency of good-quality data, which ultimately leads to under-/overestimation of failure risk. Here, we propose a generalized methodology/framework to facilitate a robust spatiotemporal analysis of urban sewer system failure risk, overcoming the intrinsic challenges of data imperfections-e.g., missing data, outliers, and imbalanced information. The framework includes a two-stage data-driven modeling technique that efficiently models the highly rightskewed sewer system failure data to predict the failure risk, leveraging a bidimensional spacetime approach. We implemented our analysis for Bogotá, the capital city of Colombia. We train, test, and validate a battery of machine learning algorithms-logistic regression, decision trees, random forests, and XGBoost-and select the best model in terms of goodnessof-fit and predictive accuracy. Finally, we illustrate the applicability of the framework in planning/scheduling sewer system maintenance operations using state-of-the-art optimization techniques. Our proposed framework can help stakeholders to analyze the failure-risk models' performance under different discrimination thresholds, and provide managerial insights on the model's adequate spatial resolution and appropriateness of decentralized management for sewer system maintenance.

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“…Effective regional adaptation strategies typically involve extensive planning, large investments, and persuasion of vulnerable residents through outreach and education. Extensive modeling must precede implementation of strategies and, for the models to be trusted, they must be exhaustively tested against data and results must be communicated appropriately [71]. Different regions and agencies use differing modeling capabilities to forecast severe weather and ocean events.…”

Section: Strategies For Adapting To Future Flooding Land Loss and Health Threatsmentioning

confidence: 99%

Anticipating and Adapting to the Future Impacts of Climate Change on the Health, Security and Welfare of Low Elevation Coastal Zone (LECZ) Communities in Southeastern USA

Allen

Behr

Bukvic

et al. 2021

JMSE

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Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.

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Addressing Climate Change as an Emerging Risk to Infrastructure Systems

Cited by 29 publications

References 59 publications

Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty

Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty

A Two‐Stage Data‐Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

Anticipating and Adapting to the Future Impacts of Climate Change on the Health, Security and Welfare of Low Elevation Coastal Zone (LECZ) Communities in Southeastern USA

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