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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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Tyler Fricker and Corey Friesenhahn

Abstract

Tornadoes account for the third highest average annual weather-related fatality rate in the United States. Here, tornado fatalities are examined as rates within the context of multiple physical and social factors using tornado-level information including population and housing units within killer tornado damage paths. Fatality rates are further evaluated across annual, monthly, and diurnal categories as well as between fatality locations and across age and sex categories. The geographic distribution of fatalities is then given by season, time of day, and residential structures. Results can be used by emergency managers, meteorologists, and planners to better prepare for high-impact (i.e., fatality) events and used by researchers as quantitative evidence to further investigate the relationship between tornadoes, climate, and society.

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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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Keegan Fraser and Jennifer M. Fitchett

Abstract

In an era of globalisation, the spread of misinformation is becoming increasingly problematic. The dissemination of inaccurate and conflicting news on events such as tropical cyclones, can result in people being placed at increased risk and negatively influence the amount of aid received by the region. This study scrutinises media articles, and with the use of comparative analysis, uncovers the potential cause of misinformation in disaster journalism. The results of the study found that 59% (n=80) of the articles reported on wind speed values while 80% (n=80) of the articles reported on the number of fatalities. Results indicate that 44% (n=80) of the articles used official sources, uncovering that the potential source of misinformation is not only what is provided to journalists from official sources, but how the various sources used lead to contradicting news articles. The variations in news reports can be attributed to factors such as, the influx of different reports and the changing conditions during a disaster, all of which make consistent reporting on a disaster a challenging process.

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Kumar Bahadur Darjee, Prem Raj Neupane, and Michael Köhl

Abstract

This study explored people’s perceptions of climate change by conducting interviews and focus group discussions with local residents of three ecological regions of Nepal, i.e., Mountain, Mid-hills and Low-land. Climatic measurements from meteorological stations of the regions were acquired for the period of 1988 to 2018. We compared the people’s perception with trends and variabilities of observed temperature and rainfall patterns. The results showed that over the last three decades, temperature and precipitation trends, and variability between regions varied largely corroborating with the local experiences. The temperature increased in Mountain, Mid-hills, and Low-land by 0.061° C yr−1, 0.063° C yr−1 and 0.017° C yr−1 respectively. On the contrary, rainfall reduced by −9.7 mm yr−1, −3.6 mm yr−1, and −0.04 mm yr−1 for the regions respectively. While the amount of rainfall decrease observed in the Mountain was highest, its variability was found relatively low; and vice-versa in Low-land. Approximately 88% interviewees perceived temperature rise, and 74% noticed rainfall decline. Local residents linked these changes with their livelihood activities and exemplified with, for example, crop’s quality and quantity; and birds’ migration. The results indicate that local understandings complement the scarce observational data and provides a reliable and additional foundations to determine changes in climatic variables. Moreover, the result infers that the small changes in climate variables have noticeable implications on human behavior change. Therefore, besides active participation of local communities, integrating local understanding is crucial in developing climate change related policies and strategies at local and national levels.

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William Turner IV and Terrence R. Nathan

Abstract

The relationship between the El Niño-Southern Oscillation (ENSO) and the Transatlantic Slave Trade (TAST) is examined using the Slave Voyages dataset and a reconstructed ENSO index. The ENSO index is used as a proxy for West African rainfall and temperature. In the Sahel, the El Niño (warm) phase of ENSO is associated with less rainfall and warmer temperatures, whereas the La Niña (cold) phase of ENSO is associated with more rainfall and cooler temperatures. The association between ENSO and the TAST is weak but statistically significant at a two-year lag. In this case, El Niño (drier and warmer) years are associated with a decrease in the export of enslaved Africans. The response of the TAST to El Niño is explained in terms of the societal response to agricultural stresses brought on by less rainfall and warmer temperatures. ENSO-induced changes to the TAST are briefly discussed in light of climate-induced movements of peoples in centuries past, and in the drought-induced movement of peoples in the Middle East today.

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Sara E. Harrison, Sally H. Potter, Raj Prasanna, Emma E.H. Doyle, and David Johnston

Abstract

Impact Forecasts and Warnings (IFW) are key to resilience for hydrometeorological hazards. Communicating the potential social, economic, and environmental hazard impacts allows individuals and communities to adjust their plans and better prepare for the consequences of the hazard. IFW systems require additional knowledge about impacts, and underlying vulnerability and exposure. Lack of data or knowledge about impacts, vulnerability, and exposure has been identified as a challenge for IFW implementation. In this study, we begin to address this challenge by developing an understanding of the data needs and uses for IFWs.

Using Grounded Theory Methodology, we conducted a series of interviews with users and creators of hazard, impact, vulnerability, and exposure data (e.g., warning services, forecasters, meteorologists, hydrologists, emergency managers, data specialists, risk modellers) to understand where these data are needed and used in the Warning Value Chain, a concept used to represent and understand the flow of information amongst actors in the warning chain (Golding et al., 2019).

In support of existing research, we found a growing need for creating, gathering, and using impact, vulnerability, and exposure data for IFWs. Furthermore, we identified different approaches for impact forecasting and defining impact thresholds using objective models and subjective impact-oriented discussions depending on the data available. We also provided new insight into a growing need to identify, model, and warn for social and health impacts, which have typically taken a back seat to modelling and forecasting physical and infrastructure impacts.

Our findings on the data needs and uses within IFW systems will help guide their development and provide a pathway for identifying specific relevant data sources.

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