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1. Social Vulnerability across the Great Lakes Basin: A County-Level Comparative and Spatial Analysis

   https://doi.org/10.3390/su13137274  

   Fergen, Joshua T.; Bergstrom, Ryan D. (July 2021, Sustainability)

   Social vulnerability refers to how social positions affect the ability to access resources during a disaster or disturbance, but there is limited empirical examination of its spatial patterns in the Great Lakes Basin (GLB) region of North America. In this study, we map four themes of social vulnerability for the GLB by using the Center for Disease Control’s Social Vulnerability Index (CDC SVI) for every county in the basin and compare mean scores for each sub-basin to assess inter-basin differences. Additionally, we map LISA results to identify clusters of high and low social vulnerability along with the outliers across the region. Results show the spatial patterns depend on the social vulnerability theme selected, with some overlapping clusters of high vulnerability existing in Northern and Central Michigan, and clusters of low vulnerability in Eastern Wisconsin along with outliers across the basins. Differences in these patterns also indicate the existence of an urban–rural dimension to the variance in social vulnerabilities measured in this study. Understanding regional patterns of social vulnerability help identify the most vulnerable people, and this paper presents a framework for policymakers and researchers to address the unique social vulnerabilities across heterogeneous regions. 

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2. Community vulnerability perspective on robust protection planning in interdependent infrastructure networks

   https://doi.org/10.1177/1748006X21991038  

   Lobban, Hannah; Almoghathawi, Yasser; Morshedlou, Nazanin; Barker, Kash (October 2021, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability)

   Critical infrastructure networks, including water, power, communication, and transportation, among others, are necessary to society’s functionality. In recent years, the threat of different types of disruptions to such infrastructure networks has become an increasingly important problem to address. Due to existing interdependencies, damage to a small area of one of the networks could have far-reaching effects on the ability to meet demand across the entire system. Common disruption scenarios include, among others, intentional malevolent attacks, natural disasters, and random failures. Similar works have focused on only one type of scenario, but combining a variety of disruptions may lead to more realistic results. Additionally, the concept of social vulnerability, which describes an area’s ability to prepare for and respond to a disruption, must be included. This should promote not only the protection of the most at-risk components but also ensure that socially vulnerable communities are given adequate resources. This work provides a decision making framework to determine the allocation of defensive resources that accounts for all these factors. Accordingly, we propose a multi-objective mathematical model with the objectives of: (i) minimizing the vulnerability of a system of interdependent infrastructure networks, and (ii) minimizing the total cost of the resource allocation strategy. Moreover, to account for uncertainty in the proposed model, this paper incorporates a means to address robustness in finding the most adaptable network protection plan to reduce the vulnerability of the system of interdependent networks to a variety of disruption scenarios. The proposed work is illustrated with an application to social vulnerability and interdependent power, gas, and water networks in Shelby County, Tennessee. 

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3. An Internal Validation Assessment of Scale Across Social Vulnerability Index Model Structures (Uncertainty and Global Sensitivity Analysis)

   https://doi.org/10.4211/hs.719a8d719907435dade9102aa9a0fa8d  

   Hinojos, Selena; Grady, Caitlin (May 2025, Hydroshare)

   Proactive and equitable planning for natural hazards is vital, as these events can cause mass destruction and severely impact livelihoods. To aid hazard preparedness decision-making, organizations can utilize tools like a social vulnerability index (SVI), developed to identify vulnerable populations to ensure that those with inherent social inequities are considered in planning. However, SVI construction involves various approaches that introduce epistemic uncertainty, potentially affecting resulting decisions. While progress has been made in understanding how construction processes affect index results, the spatial elements of SVI models are underexplored, with conflicting views on the influence of scale selection. This study addresses this gap by evaluating how changes in the selection of scalar properties (areal units and geographic boundaries) and indicator selection impact SVI ranks for two indices, the Centers for Disease Control SVI and the Hazards Vulnerability and Resilience Institute SVI. We examine these changes across three model structures: hierarchical with z-score standardization, hierarchical with percentile ranking normalization, and inductive with z-score standardization, employing an uncertainty and sensitivity analysis. When altering scalar and indicator properties, we found the inductive model less robust than hierarchical models. We also observed indicator selection as the primary driver of variability in SVI ranks across all model structures. However, we found significant yet mixed effects of scale selection and interaction effects on variability in SVI ranks. Our findings highlight the role of scale selection in shaping index outcomes and underscore the need for critical evaluation in SVI creation to advance equitable hazard management. 

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4. Multi-decadal historical regional hydroclimate simulation with two mid 21st century Pseudo-Global Warming futures over Alaska and the Yukon at 4 km resolution

   https://doi.org/10.5065/zpsb-ps82  

   Cheng, Yifan; Craig, Anthony; Musselman, Keith; Newman, Andrew (January 2024, NSF National Center for Atmospheric Research)

   Hydroclimate and terrestrial hydrology greatly influence the local communities, ecosystems, and economies across Alaska and Yukon River Basin. Therefore, we utilized the Regional Arctic Systems Model (RASM) to model the coupled land-atmosphere, and generated a climate and hydrology dataset at 4-km grid spacing to improve our understanding of the regional hydroclimate and terrestrial hydrology. Our model domain encompasses all of the U.S. State of Alaska, the entire Yukon River Basin, part of Western Canada, and the eastern coastal region of Russia. This dataset includes 1) one simulation of the historical climate (Water Years 1991-2021), which serves as a benchmark for climate change studies, and 2) two future simulations (Equivalent Water Years 2035-2065) using the Pseudo-Global Warming method under future greenhouse gas emission scenario SSP2-4.5. The two future scenarios represent median and high changes derived from ensemble means across different Global Climate Models in the Coupled Model Intercomparison Project Phase 6 within SSP2-4.5 respectively. The microphysics schemes in the Weather Research and Forecast (WRF) atmospheric model were manually tuned for optimal model performance. The land component in RASM was replaced using the Community Terrestrial Systems Model (CTSM) given its comprehensive process representations for cold regions. We conducted optimization for uncoupled CTSM to improve its performance in terrestrial hydrologic simulations, especially streamflow and snow (Cheng et al., 2023). In order to maintain the quality for both hydroclimate and terrestrial hydrologic simulation, we implemented a strategy of iterative testing and re-optimization of CTSM. This dataset was then generated using RASM with optimized CTSM parameters and manually tuned WRF microphysics. The historical simulation was evaluated against multiple observational datasets for five key weather variables and hydrologic fluxes, including precipitation, air temperature, snow fraction, evaporation-to-precipitation ratios, and streamflow. The evaluation details can be found in Cheng et al. (2024). 

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5. Sensitivity of tropical cyclone risk across the US to changes in storm climatology and socioeconomic growth

   https://doi.org/10.1088/1748-9326/add60d  

   Gori, Avantika; Lin, Ning; Chavas, Daniel; Oppenheimer, Michael; Xian, Siyuan (May 2025, Environmental Research Letters)

   Tropical cyclone (TC) hazards coupled with dense urban development along the coastline have resulted in trillions in US damages over the past several decades, with an increasing trend in losses in recent years. So far, this trend has been driven by increasing coastal development. However, as the climate continues to warm, changing TC climatology may also cause large changes in coastal damages in the future. Approaches to quantifying regional TC risk typically focus on total storm damage. However, it is crucial to understand the spatial footprint of TC damage and ultimately the spatial distribution of TC risk. Here, we quantify the magnitude and spatial pattern of TC risk (in expected annual damage (EAD)) across the US from wind, storm surge, and rainfall using synthetic TCs, physics-based hazard models, and a county-level statistical damage model trained on historical TC data. We then combine end-of-century TC hazard simulations with US population growth and wealth increase scenarios (under the SSP2 4.5 emission scenario) to investigate the sensitivity of changes in TC risk across the US Atlantic and Gulf coasts. We find that not directly accounting for the effects of rainfall and storm surge results in much lower risk estimates and smaller future increases in risk. TC climatology change and socioeconomic change drive similar magnitude increases in total EAD across the US (roughly 160%), and that their combined effect (633% increase) is much higher. 

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