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Seth P. Howard, Alison P. Boehmer, Kevin M. Simmons, and Kim E. Klockow-Mcclain

Abstract

Tornadoes are nature’s most violent storm and annually cause billions of dollars in damage along with the threat of fatalities and injuries. To improve tornado warnings, the National Weather Service is considering a change from a deterministic to a probabilistic paradigm. While studies have been conducted on how individual behavior may change with the new warnings, no study of which we are aware has considered the effect this change may have on businesses. This project is a response to the Weather Research and Forecasting Innovation Act of 2017, House of Representatives (H.R.) bill 353, which calls for the use of social and behavioral science to study and improve storm warning systems. The goal is to discuss business response to probabilistic tornado warnings through descriptive and regression-based statistics using a survey administered to businesses in north Texas. Prior to release, the survey was vetted by a focus group composed of businesses in Grayson County, Texas, who assisted in the creation of a behavior ranking scale. The scale ranked behaviors from low to high effort. Responses allowed for determining whether the business reacted to the warning in a passive or active manner. Returned surveys came from large and small businesses in north Texas and represent a wide variety of industries. Regression analysis explores which variables have the greatest influence on the behavior of businesses and show that, beyond increases in probability from the probabilistic warnings, trust in the warning provides the most significant change to behavior.

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Eric C. Jones, Corinne Ong, and Jessica Haynes

Abstract

Climate change is an increasingly pressing concern because it generates individual and societal vulnerability in many places in the world and also because it potentially threatens political stability. Aside from sea level rise, climate change is typically manifested in local temperature and precipitation extremes that generate other hazards. In this study, we investigated whether certain kinds of governance strategies were more common in societies whose food supply had been threatened by such natural hazards—specifically, floods, droughts, and locust infestations. We coded and analyzed ethnographic data from the Human Relations Area Files on 26 societies regarding dominant political, economic, and ideological behaviors of leaders in each society for a specified time period. Leaders in societies experiencing food-destroying disasters used different political economic strategies for maintaining power than did leaders in societies that face fewer disasters or that did not face such disasters. In nondisaster settings, leaders were more likely to have inward-focused cosmological and collectivistic strategies; conversely, when a society had experienced food-destroying disasters, leaders were more likely to have exclusionary tribal/family-based and externally focused strategies. This apparent difficulty in maintaining order and coherence of leadership in disaster settings may apply more to politically complex societies than to polities governed solely at the community level. Alternatively, it could be that exclusionary leaders help set up the conditions for disastrous consequences of hazards for the populace. Exceptions to the pattern of exclusionary political economic strategies in disaster settings indicate that workarounds do exist that allow leaders with corporate governance approaches to stay in power.

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David J. Cox, Joy E. Losee, and Gregory D. Webster

Abstract

The human and economic costs of severe weather damage can be mitigated by appropriate preparation. Despite the benefits, researchers have only begun to examine if known decision-making frameworks apply to severe weather–related decisions. Using experiments, we found that a hyperbolic discounting function accurately described participant decisions to prepare for, and respond to, severe weather, although only delays of 1 month or longer significantly changed decisions to evacuate, suggesting that severe weather that is not imminent does not affect evacuation decisions. In contrast, the probability that a storm would impact the participant influenced evacuation and resource allocation decisions. To influence people’s evacuation decisions, weather forecasters and community planners should focus on disseminating probabilistic information when focusing on short-term weather threats (e.g., hurricanes); delay information appears to affect people’s evacuation decision only for longer-term threats, which may hold promise for climate change warnings.

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Michael K. Ndegwa, Apurba Shee, Calum Turvey, and Liangzhi You

Abstract

Weather index insurance (WII) has been a promising innovation that protects smallholder farmers against drought risks and provides resilience against adverse rainfall conditions. However, the uptake of WII has been hampered by high spatial and intraseasonal basis risk. To minimize intraseasonal basis risk, the standard approaches to designing WII based on seasonal cumulative rainfall have been shown to be ineffective in some cases because they do not incorporate different water requirements across each phenological stage of crop growth. One of the challenges in incorporating crop phenology in insurance design is to determine the water requirement in crop growth stages. Borrowing from agronomy, crop science, and agrometeorology, we adopt evapotranspiration methods in determining water requirements for a crop to survive in each stage that can be used as a trigger level for a WII product. Using daily rainfall and evapotranspiration data, we illustrate the use of Monte Carlo risk modeling to price an operational WII and WII-linked credit product. The risk modeling approach that we develop includes incorporation of correlation between rainfall and evapotranspiration indices that can minimize significant intertemporal basis risk in WII.

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Christine D. Miller Hesed, Michael Paolisso, Elizabeth R. Van Dolah, and Katherine J. Johnson

Abstract

Climate adaptation is context specific, and inclusion of diverse forms of knowledge is crucial for developing resilient social–ecological systems. Emphasis on local inclusion is increasing, yet participatory approaches often fall short of facilitating meaningful engagement of diverse forms of knowledge. A central challenge is the lack of a comprehensive and comparative understanding of the social–ecological knowledge that various stakeholders use to inform adaptation decisions. We employed cultural consensus analysis to quantitatively measure and compare social–ecological knowledge within and across three stakeholder groups: government employees, researchers, and local residents in rural coastal Maryland. The results show that 1) local residents placed more emphasis on addressing socioeconomic and cultural changes than researchers and government employees, and 2) that the greatest variation in social–ecological knowledge was found among local residents. These insights yielded by cultural consensus analysis are beneficial for facilitating more inclusive adaptation planning for resilient social–ecological systems.

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Camilla Risvoll, Grete K. Hovelsrud, and Jan Åge Riseth

Abstract

Rapid and interacting change pose increasing threat to livelihoods and food production, and pastoralists in Nordland, northern Norway are at cross-roads both economically and culturally. Some of these changes are localized and pertain to changing weather and grazing conditions caused by climate change and land fragmentation. Others, driven by national management policies and governance specifically related to predators are poorly adjusted for the different and localized contexts. The pastoralists are inherently adaptive and have a long history of responding well to variable changing conditions. This is now changing with the continued increasing pressures from many directions. Central government systematically ignores pastoralists’ traditional knowledge and enforce narrow sector policies to be implemented at regional and local level. We address the effect of how institutional, physical and societal constraints challenge pastoralists’ prospects for sustainable adaptation. Our results show how pastoralists’ livelihoods become compromised and potentially threatened because they are forced to respond in ways that they know are counter-productive in the long run.

Adaptation outcomes are affected by different approaches and epistemologies that are situated across scale and context in terms of regional and national regulations versus local empirical reality among the pastoral communities. This study concludes that radical change is needed towards a more holistic governance where multiple knowledge systems are integrated to ensure sustainable adaptation at all levels. This study is based on extensive and long-term field work among reindeer herders and sheep farmers in Nordland, through a collaborative process of knowledge co-production.

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Ilan Noy and Eric Strobl

Abstract

This study investigates whether extreme heat episodes (heatwaves) have contributed to the development of air conditioning technology in the United States. To this end we use weather data to identify days at which heat and relative humidity were above levels comfortable to the human body, and match these with patent data at the county level for nearly a hundred years. We find that in the two years after a county has experienced extreme heat air-conditioning patents increase. Overall, average extreme heat exposure results in an increase of 7.5% greater innovation. We find no similar increase in the frequency of non-air conditioning related patent filings, and therefore conclude that heatwaves result in innovation targeting their mitigation.

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Brooke Fisher Liu, Anita Atwell Seate, Ji Youn Kim, Daniel Hawblitzel, Saymin Lee, and Xin Ma

Abstract

This study proposes the concept of quiet weather communication and offers the first framework of quiet weather communication strategies tied to specific public outcomes (e.g., build and maintain organization-public relationships). Most of the risk communication literature focuses on severe weather communication. We posit that through defining and examining quiet weather strategic communication we can better understand how the Weather Enterprise can prepare communities for future severe weather. Through four virtual focus groups with 28 NWS and broadcast meteorologists, we operationalize quiet weather communication strategies (humanize the organization, provide weather education, share the love of blue skies, and showcase quiet weather trends). We then report meteorologists’ perceptions of the strengths and weaknesses of each strategy and propose future directions for research on quiet weather communication.

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Ling Tan and David M. Schultz

Abstract

Because many viral respiratory diseases show seasonal cycles, weather conditions could affect the spread of COVID-19. Although many studies pursued this possible link early in the pandemic, their results were inconsistent. Here, we assembled 158 quantitative empirical studies examining the link between weather and COVID-19. A meta-regression analysis was performed on their 4,793 correlation coefficients to explain these inconsistent results. We found four principal findings. First, 80 of the 158 studies did not state the time lag between infection and reporting, rendering these studies ineffective in determining the weather–COVID-19 relationship. Second, the research outcomes depended on the statistical analysis methods employed in each study. Specifically, studies using correlation tests produced outcomes that were functions of the geographical locations of the data from the original studies, whereas studies using linear regression produced outcomes that were functions of the analyzed weather variables. Third, Asian countries had more positive associations for air temperature than other regions, possibly because the air temperature was undergoing its seasonal increase from winter to spring during the rapid outbreak of COVID-19 in these countries. Fourth, higher solar energy was associated with reduced COVID-19 spread, regardless of statistical analysis method and geographical location. These results help interpret the inconsistent results and motivate recommendations for best practices in future research. These recommendations include calculating the effects of a time lag between the weather and COVID-19, using regression analysis models, considering nonlinear effects, increasing the time period considered in the analysis to encompass more variety of weather conditions and to increase sample size, and eliminating multicollinearity between weather variables.

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David A. Call and Guy A. Flynt

Abstract

Snow has numerous effects on traffic, including reduced traffic volumes, greater crash risk, and increased travel times. This research examines how snow affects crash risk, traffic volume, and toll revenue on the New York State Thruway. Daily data from January for a ten-year period (2010-2019) were analyzed for the Thruway from the Pennsylvania state line in western New York to Syracuse.

Anywhere from 35-50 percent of crashes are associated with inclement weather, with smaller impacts, proportionally, in areas with greater traffic volumes. As expected, snow was almost always involved when weather was a factor. “Unsafe speed” was the most common cause of crashes in inclement weather with all other factors (e.g., animals, drowsiness) much less likely to play a role. The percentage of crashes resulting in an injury did not change significantly with inclement conditions when compared to crashes occurring in fair conditions, and there were too few fatal crashes to make any inferences about them.

Daily snowfall rates predicted about 30 percent of the variation in crash numbers, with every 5.1 cm of snowfall resulting in an additional crash, except in Buffalo where 5.1 cm of snow resulted in an additional 2.6 crashes. Confirming earlier results, daily snowfall had a large impact on passenger vehicle counts while commercial vehicle counts were less affected. Revenue data showed a similar pattern, with passenger revenue typically decreasing by 3-5 percent per 2.5 cm of snow, while commercial revenue decreases were 1-4 percent per 2.5 cm of snow.

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