{"title":"A Climate Extremes Resilience Index for the Conterminous United States","authors":"Anuska Narayanan, Brad G. Peter, David Keellings","doi":"10.1175/wcas-d-23-0008.1","DOIUrl":"https://doi.org/10.1175/wcas-d-23-0008.1","url":null,"abstract":"In recent decades, changes in precipitation, temperature, and air circulation patterns have led to increases in the occurrences of extreme weather events. These events can have devastating effects on communities causing destruction to property and croplands, as well as negative impacts on public health. As changes in the climate are projected to continue throughout the remainder of the 21st century, the ability for a community to plan for extreme weather events is essential to its survival. In this paper, we introduce a new index for examining the potential impacts of climate extremes on community resilience throughout the Conterminous United States at the county level. We use an established disaster resilience index (Baseline Resilience Indicators for Communities) together with a revised version of the U.S. Climate Extremes Index to create a combined measure of climate resilience–the Climate Extremes Resilience Index (CERI). To demonstrate the index, we test it on the 2021 Pacific Northwest Heatwave, a 1000-year weather event made 150 times more likely due to climate change. Finally, to promote the use of the index, we also introduce a Google Earth Engine web app to calculate and map the CERI for the CONUS. By developing a web application for calculating the CERI, we expand the use of climate-resilience indices beyond theoretical applications. We anticipate that this tool and the CERI could be useful for policymakers to plan for climate related disasters, as well as help the public with understanding and visualizing the impacts of extreme climatic events.","PeriodicalId":507492,"journal":{"name":"Weather, Climate, and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139167319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniela de Oliveira Maionchi, Adriano Carvalho Nunes e Araújo, Walter Aguiar Martins Junior, Junior Gonçalves da Silva, Danilo Ferreira de Souza
{"title":"A Machine Learning Model for Lightning-Related Deaths in Brazil","authors":"Daniela de Oliveira Maionchi, Adriano Carvalho Nunes e Araújo, Walter Aguiar Martins Junior, Junior Gonçalves da Silva, Danilo Ferreira de Souza","doi":"10.1175/wcas-d-23-0084.1","DOIUrl":"https://doi.org/10.1175/wcas-d-23-0084.1","url":null,"abstract":"Brazil presents the highest number of lightning-related deaths in the world. This study aimed to identify the key victims’ characteristics associated with such fatalities in Brazil and to develop a model that predicts the number of deaths as function of the victims’ data. The dataset provided by the Department of Informatics of the Unified Health System in Brazil- DATASUS was analyzed and machine learning regression techniques were applied. The Gradient Boosting Regressor (GBR) model was found to be the most effective, achieving a prediction accuracy of 97%. Through the analysis of 34 initial variables, 10 variables were identified as having the greatest influence on the model’s outcomes. These variables included race, gender, age group, occupational accidents, education, and location of death. Understanding these characteristics is crucial for implementing targeted prevention and safety strategies in various regions, helping to mitigate the risk of lightning-related deaths worldwide. Additionally, the methodology used in this study can serve as a framework for similar research in different locations, allowing for the identification of important factors specific to each region. By adapting the machine learning regression techniques and incorporating local datasets, researchers can gain valuable insights into the determinants of lightning-related fatalities, enabling the development of effective prevention and safety measures tailored to specific geographical areas.","PeriodicalId":507492,"journal":{"name":"Weather, Climate, and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139169156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Moreira, Ranyére Silva Nóbrega, L. S. Wanderley, A. Matzarakis
{"title":"Urban Heat Island vulnerability in the city of Recife – PE, Brazil","authors":"A. Moreira, Ranyére Silva Nóbrega, L. S. Wanderley, A. Matzarakis","doi":"10.1175/wcas-d-23-0082.1","DOIUrl":"https://doi.org/10.1175/wcas-d-23-0082.1","url":null,"abstract":"This study introduces the Urban Heat Island Vulnerability Index (UHIVI) for Recife, Brazil, the center of the most populated metropolitan area in the Northeast Region. The index, encompassing sensitivity, adaptive capacity, and exposure, integrates demographic data through factor analysis to derive a Social Vulnerability Index (SVI). Urban Heat Island (UHI) intensity data addresses exposure, enabling a comprehensive analysis of both the physical and social dimensions of the city. Results reveal heightened UHI exposure in the city center and coastal areas, correlating with higher urbanization density. However, populations in most areas of these regions demonstrated higher adaptive capacities, translating to lower UHI vulnerability. Conversely, less-discussed areas in traditional UHI approaches, with limited adaptive capacity and heightened sensitivity, emerge, shedding light on previously overlooked urban vulnerabilities. Regions near the city center featuring irregular settlements prove most susceptible to UHI. Illiteracy, aging demographics, and local environmental conditions emerge as the three main factors contributing to UHIVI. The index’s application unveils spatial complexities and inequalities, offering urban planners a nuanced understanding of the city. This comprehensive insight aids in policy development and decision-making, empowering planners to address urban disparities effectively. The UHIVI thus emerges as a valuable tool for understanding the challenges of urban planning, fostering more resilient and equitable urban development.","PeriodicalId":507492,"journal":{"name":"Weather, Climate, and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139211181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Collins, Elizabeth A. Dunn, Rashida K. Jones, A. Polen, Nagashree R. Rao, Stephen Murphy, Mark Welford
{"title":"Hurricane Risk Perceptions and Evacuation Decision-Making in the Post-Vaccine Era of COVID-19 in U.S. Coastal States Impacted by North Atlantic Hurricanes","authors":"J. Collins, Elizabeth A. Dunn, Rashida K. Jones, A. Polen, Nagashree R. Rao, Stephen Murphy, Mark Welford","doi":"10.1175/wcas-d-23-0003.1","DOIUrl":"https://doi.org/10.1175/wcas-d-23-0003.1","url":null,"abstract":"During peak disease transmission in 2021, the compounding threat posed by the pandemic and hurricane season requires coastal states to understand evacuation behaviors during a major hurricane to inform the planning process. While research relating to hurricane evacuation behavior and perceptions of risk has increased since the start of the pandemic, there is minimal understanding of how perceptions have changed now the COVID-19 vaccine is available. A total of 1,075 individuals across seven U.S. coastal states participated in a study on evacuation intentions post-vaccine availability. Findings revealed that most survey participants (50.9%) preferred to stay home if a major hurricane threatened their area, and only 3.9% would evacuate to a public shelter. Approximately half (56.2%) of individuals viewed the risk of being in a shelter as more dangerous than enduring hurricane hazards. When considering shelter use, nearly half of respondents (49.4%) stated they would evacuate to a shelter before the pandemic, now only a third (34.3%) would consider evacuating to a shelter during the pandemic. Statistically significant findings include the relationship between those who lived in Evacuation Zones A or B (25.5%) and the choice to shelter in place at home (40.5%) or evacuate to a hotel (36.9%). There was a statistically significant relationship between the level of education and choosing to evacuate to a hotel. Additionally, the influence of pet ownership on evacuation decision-making was found to be statistically significant. Officials can use the results of this study to strengthen community preparedness and planning strategies across diverse populations.","PeriodicalId":507492,"journal":{"name":"Weather, Climate, and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139270470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"American perceptions and attitudes about domestic climate migrants and migration","authors":"Brittany S. Harris, Mark Brunson, Peter D. Howe","doi":"10.1175/wcas-d-23-0020.1","DOIUrl":"https://doi.org/10.1175/wcas-d-23-0020.1","url":null,"abstract":"Domestic climate migration is likely to increase in the future, but we know little about public perceptions and attitudes about climate migrants and migration. Understanding how perceptions and attitudes are formed is a critical task in assessing public support for assistance policies and developing effective messaging campaigns. In this paper, we aim to better understand how the U.S. public perceives domestic climate migrants. We use novel survey data to identify the relationship between climate change risk perceptions and awareness of ‘climate migrants’, belief that domestic climate migration is currently happening in the U.S., perceived voluntariness of domestic climate migrant relocation, and support for the development of assistance programs for domestic climate migrants. We utilize a large, nationally representative panel of U.S. adults (N = 4,074) collected over three waves in 2022. We find that climate change risk perceptions and perceptions of whether migration is voluntary are key drivers of perceptions and attitudes toward domestic climate migrants. We provide key suggestions to policy and decision-makers to improve outcomes for host and migrant communities.","PeriodicalId":507492,"journal":{"name":"Weather, Climate, and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139272972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Model Development and Simulation for Predicting Proportional Changes in Traffic Volume as a Consequence of Natural Weather Hazards","authors":"Hyuk-Jae Roh","doi":"10.1175/wcas-d-22-0119.1","DOIUrl":"https://doi.org/10.1175/wcas-d-22-0119.1","url":null,"abstract":"This study used a model to calculate the proportional drop for every vehicle class based on 266 climate patterns consisting of seven temperature groups and varied snowfalls. The winter traffic models use weigh-in-motion (WIM) traffic collected on the commuter roadway for 5 years. The marginal impact and combined effect of meteorological conditions on the proportional decrease in winter traffic volume are evaluated. The predicted percentage decrease in traffic for all three vehicle classes increases as temperature decreases and snowfall increases. Mathematical functions are fitted for the decreased patterns for the considered vehicle type. Roadway authorities may utilize traffic percentage decrease to identify weather-related traffic changes when planning winter highway operation and maintenance.","PeriodicalId":507492,"journal":{"name":"Weather, Climate, and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139328842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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