Browse
Abstract
The winter season in many U.S. states includes snowfall, and with it comes comments about how drivers always seem to “forget” how to drive in snow when the first snowfall of the season occurs. This study assesses the accuracy of this popular sentiment during Indiana winters from 2007 to 2020. The number of motor vehicle crashes, injuries, and fatalities during the first snowfall of the season was compared with those during subsequent snow events. A grid of 46 cells was constructed to subdivide the state, and instances of snowfall and crashes were aggregated within each cell each day during the study period. Daily crash, injury, and fatality totals in each cell were normalized by their respective means and standard deviations, allowing for data from all cells to be combined into a single dataset. Four snow accumulation thresholds were examined: 1, 13, 25, and 51 mm. Distributions at each threshold show that more crashes occur on average on days with the first snowfall of the winter season than on other days with snowfall, regardless of the accumulation threshold used. Statistical tests support this result, showing significant differences between the mean numbers of crashes at each of the four snowfall thresholds. There were also significantly more injuries on the first snowfall day and more fatalities, although fatalities were only significant for the 13-mm snowfall threshold.
Significance Statement
The purpose of my research is to answer the question: are there more motor vehicle crashes on the first day with snow each winter when compared with the number of crashes on other days with snowfall in the state of Indiana? Using four snowfall thresholds of increasing amounts, statistical tests comparing daily crashes on first snowfall and other snowfall days showed that there were significantly more crashes on average on the first day with snowfall each winter, regardless of the amount of snow accumulation. This supports the popular notion that crashes occur more frequently the first time it snows each year, although it is more likely attributed to drivers reacclimating to snowy road conditions than to forgetfulness.
Abstract
The winter season in many U.S. states includes snowfall, and with it comes comments about how drivers always seem to “forget” how to drive in snow when the first snowfall of the season occurs. This study assesses the accuracy of this popular sentiment during Indiana winters from 2007 to 2020. The number of motor vehicle crashes, injuries, and fatalities during the first snowfall of the season was compared with those during subsequent snow events. A grid of 46 cells was constructed to subdivide the state, and instances of snowfall and crashes were aggregated within each cell each day during the study period. Daily crash, injury, and fatality totals in each cell were normalized by their respective means and standard deviations, allowing for data from all cells to be combined into a single dataset. Four snow accumulation thresholds were examined: 1, 13, 25, and 51 mm. Distributions at each threshold show that more crashes occur on average on days with the first snowfall of the winter season than on other days with snowfall, regardless of the accumulation threshold used. Statistical tests support this result, showing significant differences between the mean numbers of crashes at each of the four snowfall thresholds. There were also significantly more injuries on the first snowfall day and more fatalities, although fatalities were only significant for the 13-mm snowfall threshold.
Significance Statement
The purpose of my research is to answer the question: are there more motor vehicle crashes on the first day with snow each winter when compared with the number of crashes on other days with snowfall in the state of Indiana? Using four snowfall thresholds of increasing amounts, statistical tests comparing daily crashes on first snowfall and other snowfall days showed that there were significantly more crashes on average on the first day with snowfall each winter, regardless of the amount of snow accumulation. This supports the popular notion that crashes occur more frequently the first time it snows each year, although it is more likely attributed to drivers reacclimating to snowy road conditions than to forgetfulness.
Abstract
Climate change is threatening forest ecosystem services, but people who manage their own forestland are in a unique position to observe these threats and take steps to reduce their impacts, especially if they believe that climate change is a contributing factor. We investigate the nature of the relationship between small woodland owner experiences of drought and severe storms and climate change belief in the upper midwestern United States using survey data and structural equation modeling. We find for both events that experience has a modest, positive effect on climate change belief, but only indirectly through perceptions of changing trends in these types of events. In addition, we find that trend perception and climate change belief have an important reciprocal relationship. Our findings suggest that experience as well as cognitive biases are related to believing in climate change, and that greater attention should be given to the potential of bidirectional relationships between key concepts related to climate change belief.
Significance Statement
Belief in climate change increases the likelihood of supporting and participating in climate change mitigation actions. We wanted to better understand the relationships between experiencing severe weather events, believing in global climate change, and noticing changes in the local patterns of severe weather events. Using data from a survey of individual and family forestland owners, also known as small woodland owners, in the upper Midwest, we found that severe weather experience increases climate change belief by increasing the perception that severe weather event trends are changing. The nature of this relationship is also important for informing how future analyses are constructed to avoid misleading findings that overestimate the influence that severe weather experience has on climate change belief.
Abstract
Climate change is threatening forest ecosystem services, but people who manage their own forestland are in a unique position to observe these threats and take steps to reduce their impacts, especially if they believe that climate change is a contributing factor. We investigate the nature of the relationship between small woodland owner experiences of drought and severe storms and climate change belief in the upper midwestern United States using survey data and structural equation modeling. We find for both events that experience has a modest, positive effect on climate change belief, but only indirectly through perceptions of changing trends in these types of events. In addition, we find that trend perception and climate change belief have an important reciprocal relationship. Our findings suggest that experience as well as cognitive biases are related to believing in climate change, and that greater attention should be given to the potential of bidirectional relationships between key concepts related to climate change belief.
Significance Statement
Belief in climate change increases the likelihood of supporting and participating in climate change mitigation actions. We wanted to better understand the relationships between experiencing severe weather events, believing in global climate change, and noticing changes in the local patterns of severe weather events. Using data from a survey of individual and family forestland owners, also known as small woodland owners, in the upper Midwest, we found that severe weather experience increases climate change belief by increasing the perception that severe weather event trends are changing. The nature of this relationship is also important for informing how future analyses are constructed to avoid misleading findings that overestimate the influence that severe weather experience has on climate change belief.
Abstract
Climate change (CC) is a topical issue of profound social interest. This paper aims to analyze the sentiments expressed in Twitter interactions in relation to CC. The study is performed considering the geographical and gender perspectives as well as different user typologies (individual users or companies). A total of 92 474 Twitter messages were utilized for the study. These are characterized by analyzing sentiment polarity and identifying the underlying topics related to climate change. Polarity is examined utilizing different commercial algorithms such as Valence Aware Dictionary and Sentiment Reasoner (VADER) and TextBlob, in conjunction with a procedure that uses word embedding and clustering techniques in an unsupervised machine learning approach. In addition, hypothesis testing is applied to inspect whether a gender independence exists or not. The topics are identified using latent Dirichlet allocation (LDA) and the usage of n-grams is explored. The topics identified are (in descending order of importance) CC activism, biodiversity, CC evidence, sustainability, CC awareness, pandemic, net zero, CC policies and finances, government action, and climate emergency. Moreover, globally speaking, it is found that the interactions on all topics are predominantly negative, and they are maintained as such for both men and women. If the polarity by topic and country is considered, it is also negative in most countries, although there are several notable exceptions. Finally, the presence of organizations and their perspective is studied, and results suggest that organizations post with more frequency when addressing topics such as sustainability, CC awareness, and net zero topics.
Significance Statement
The purpose of this research is to gain a better understanding of the perception of Twitter users in relation to climate change. To do so, Twitter interactions are characterized by analyzing polarity (positive or negative sentiment) and identifying underlying topics that, with greater or lesser intensity, were discussed during the period analyzed. Then, to contextualize the information retrieved, several classifications are performed: by gender, location, and account typology (individual users and companies). Interesting differences and commonalities are found both by geographic dimension and by gender. Similarly, some dissimilarities exist between interactions from individuals and companies. The findings of this work are significant because they can help institutions and governments to properly target public awareness efforts on climate change.
Abstract
Climate change (CC) is a topical issue of profound social interest. This paper aims to analyze the sentiments expressed in Twitter interactions in relation to CC. The study is performed considering the geographical and gender perspectives as well as different user typologies (individual users or companies). A total of 92 474 Twitter messages were utilized for the study. These are characterized by analyzing sentiment polarity and identifying the underlying topics related to climate change. Polarity is examined utilizing different commercial algorithms such as Valence Aware Dictionary and Sentiment Reasoner (VADER) and TextBlob, in conjunction with a procedure that uses word embedding and clustering techniques in an unsupervised machine learning approach. In addition, hypothesis testing is applied to inspect whether a gender independence exists or not. The topics are identified using latent Dirichlet allocation (LDA) and the usage of n-grams is explored. The topics identified are (in descending order of importance) CC activism, biodiversity, CC evidence, sustainability, CC awareness, pandemic, net zero, CC policies and finances, government action, and climate emergency. Moreover, globally speaking, it is found that the interactions on all topics are predominantly negative, and they are maintained as such for both men and women. If the polarity by topic and country is considered, it is also negative in most countries, although there are several notable exceptions. Finally, the presence of organizations and their perspective is studied, and results suggest that organizations post with more frequency when addressing topics such as sustainability, CC awareness, and net zero topics.
Significance Statement
The purpose of this research is to gain a better understanding of the perception of Twitter users in relation to climate change. To do so, Twitter interactions are characterized by analyzing polarity (positive or negative sentiment) and identifying underlying topics that, with greater or lesser intensity, were discussed during the period analyzed. Then, to contextualize the information retrieved, several classifications are performed: by gender, location, and account typology (individual users and companies). Interesting differences and commonalities are found both by geographic dimension and by gender. Similarly, some dissimilarities exist between interactions from individuals and companies. The findings of this work are significant because they can help institutions and governments to properly target public awareness efforts on climate change.
Abstract
The link between climate change and human conflict has received substantial attention in academic research using different measures of “conflict”; however, it is yet to interpret interpersonal violence in terms of homicide. This study takes a global perspective to investigate how climate change, typically represented by temperature and precipitation, directly and indirectly affects national homicide rates across countries. From longitudinal archival data from 171 countries from 2000 to 2018, we detect a direct and positive relationship between higher temperatures and homicide, whereas an indirect pathway between wetter climate and homicide through the occurrence of more natural hazards has also been shown in our empirical results. The relationship between climate change and homicide can be moderated by the level of information and communication technologies (ICT). We conclude that the development of ICT contributes to building the countries’ resilience to climate change with better information and communication technologies to help alleviate the negative impacts of climate change on homicide.
Abstract
The link between climate change and human conflict has received substantial attention in academic research using different measures of “conflict”; however, it is yet to interpret interpersonal violence in terms of homicide. This study takes a global perspective to investigate how climate change, typically represented by temperature and precipitation, directly and indirectly affects national homicide rates across countries. From longitudinal archival data from 171 countries from 2000 to 2018, we detect a direct and positive relationship between higher temperatures and homicide, whereas an indirect pathway between wetter climate and homicide through the occurrence of more natural hazards has also been shown in our empirical results. The relationship between climate change and homicide can be moderated by the level of information and communication technologies (ICT). We conclude that the development of ICT contributes to building the countries’ resilience to climate change with better information and communication technologies to help alleviate the negative impacts of climate change on homicide.
Abstract
The analysis of historical climate change events can deepen the understanding of climate impacts and provide historical examples of coping with extreme events like drought. The data from historical records on droughts and famines were collected during the Chenghua drought (AD 1483–85), Jiajing drought (AD 1527–29), and Wanli drought (AD 1584–89) in Henan Province in the middle Ming Dynasty. Based on this, the average drought index (ADI), average famine index (AFI) and the average social regulation index (ASRI) were defined to quantitatively explore the differences in the social impacts of extreme droughts. The results were as follows: 1) As for ADI, the Wanli drought was the most severe (1.59), followed by the Jiajing drought (1.21) and the Chenghua drought (1.02). In terms of AFI, the famine conditions were the most severe during the Jiajing drought (0.43), followed by Chenghua drought (0.30) and the Wanli drought (0.15). 2) The ASRI values in the Chenghua drought, Jiajing drought, and Wanli drought were 3.90, 3.90, and 4.54, respectively. It could be concluded society showed the highest social regulation ability during the Wanli drought and showed the same level of the two other droughts. However, for the key years, the social regulation ability of the Jiajing drought was higher than that of Chenghua drought, especially in the alleviation of low-grade drought. 3) From historical documents, the progress of agricultural technology, the progress of famine relief policy, and the change in relief supplies greatly improved the social ability to cope with the extreme drought events.
Significance Statement
The analysis of extreme drought events in the past is important for understanding the interactions between human activities and natural variability, and its impact on society, economy, and even politics. Our goal is to explore the changes of ability to cope with extreme droughts through the statistical relationship of drought and famine in the three extreme drought events in Henan during the middle Ming Dynasty. The results showed that the social regulation ability of Henan to cope with extreme drought was significantly strengthened. Progress in agriculture and famine policy, and so on, had an important role in promoting the development of social regulation ability. How to improve the quantitative method for the social regulation by social impacts requires further research.
Abstract
The analysis of historical climate change events can deepen the understanding of climate impacts and provide historical examples of coping with extreme events like drought. The data from historical records on droughts and famines were collected during the Chenghua drought (AD 1483–85), Jiajing drought (AD 1527–29), and Wanli drought (AD 1584–89) in Henan Province in the middle Ming Dynasty. Based on this, the average drought index (ADI), average famine index (AFI) and the average social regulation index (ASRI) were defined to quantitatively explore the differences in the social impacts of extreme droughts. The results were as follows: 1) As for ADI, the Wanli drought was the most severe (1.59), followed by the Jiajing drought (1.21) and the Chenghua drought (1.02). In terms of AFI, the famine conditions were the most severe during the Jiajing drought (0.43), followed by Chenghua drought (0.30) and the Wanli drought (0.15). 2) The ASRI values in the Chenghua drought, Jiajing drought, and Wanli drought were 3.90, 3.90, and 4.54, respectively. It could be concluded society showed the highest social regulation ability during the Wanli drought and showed the same level of the two other droughts. However, for the key years, the social regulation ability of the Jiajing drought was higher than that of Chenghua drought, especially in the alleviation of low-grade drought. 3) From historical documents, the progress of agricultural technology, the progress of famine relief policy, and the change in relief supplies greatly improved the social ability to cope with the extreme drought events.
Significance Statement
The analysis of extreme drought events in the past is important for understanding the interactions between human activities and natural variability, and its impact on society, economy, and even politics. Our goal is to explore the changes of ability to cope with extreme droughts through the statistical relationship of drought and famine in the three extreme drought events in Henan during the middle Ming Dynasty. The results showed that the social regulation ability of Henan to cope with extreme drought was significantly strengthened. Progress in agriculture and famine policy, and so on, had an important role in promoting the development of social regulation ability. How to improve the quantitative method for the social regulation by social impacts requires further research.
Abstract
Historical instrumental weather observations are vital to understanding past, present, and future climate variability and change. However, the quantity of historical weather observations to be rescued globally far exceeds the resources available to do the rescuing. Which observations should be prioritized? Here we formalize guidelines help make decisions on rescuing historical data. Rather than wait until resource-intensive digitization is done to assess the data’s value, insights can be gleaned from the context in which the observations were made and the history of the observers. Further insights can be gained from the transcription platforms used and the transcribers involved in the data rescue process, without which even the best historical observations can be mishandled. We use the concept of trust to help integrate and formalize the guidelines across the life cycle of data rescue, from the original observation source to the transcribed data element. Five cases of citizen science-based historical data rescue, two from Canada and three from Australia, guide us in constructing a trust checklist. The checklist assembles information from the original observers and their observations to the current transcribers and transcription approaches they use. Nineteen elements are generated to help future data rescue projects answer the question of whether resources should be devoted to rescuing historical meteorological material under consideration.
Significance Statement
Historical weather observations, such as ships’ logs and weather diaries, help us to understand our past, present, and future climate. More observations are waiting to be rescued than there are resources. Only after they have been rescued—transcribed—can the records be indexed, searched, and analyzed. Given the vast task, citizen scientists are often recruited to transcribe past weather records. Various tools, including software platforms, help volunteers transcribe these handwritten records. We provide guidance on choosing observations to rescue. This guidance is novel because it emphasizes trust throughout the data rescue process: trust in who the observers were and how the observations were made, trust in who the current transcribers are, and trust in the software tools that are used for transcription.
Abstract
Historical instrumental weather observations are vital to understanding past, present, and future climate variability and change. However, the quantity of historical weather observations to be rescued globally far exceeds the resources available to do the rescuing. Which observations should be prioritized? Here we formalize guidelines help make decisions on rescuing historical data. Rather than wait until resource-intensive digitization is done to assess the data’s value, insights can be gleaned from the context in which the observations were made and the history of the observers. Further insights can be gained from the transcription platforms used and the transcribers involved in the data rescue process, without which even the best historical observations can be mishandled. We use the concept of trust to help integrate and formalize the guidelines across the life cycle of data rescue, from the original observation source to the transcribed data element. Five cases of citizen science-based historical data rescue, two from Canada and three from Australia, guide us in constructing a trust checklist. The checklist assembles information from the original observers and their observations to the current transcribers and transcription approaches they use. Nineteen elements are generated to help future data rescue projects answer the question of whether resources should be devoted to rescuing historical meteorological material under consideration.
Significance Statement
Historical weather observations, such as ships’ logs and weather diaries, help us to understand our past, present, and future climate. More observations are waiting to be rescued than there are resources. Only after they have been rescued—transcribed—can the records be indexed, searched, and analyzed. Given the vast task, citizen scientists are often recruited to transcribe past weather records. Various tools, including software platforms, help volunteers transcribe these handwritten records. We provide guidance on choosing observations to rescue. This guidance is novel because it emphasizes trust throughout the data rescue process: trust in who the observers were and how the observations were made, trust in who the current transcribers are, and trust in the software tools that are used for transcription.
Abstract
Driven by both climate change and urbanization, extreme rainfall events are becoming more frequent and having an increasing impact on urban commuting. Using hourly rainfall data and “metro” origin–destination (OD) flow data in Shanghai, China, this study uses the Prophet time series model to calculate the predicted commuting flows during rainfall events and then quantifies the spatiotemporal variations of commuting flows due to rainfall at station and OD levels. Our results show the following: 1) In general, inbound commuting flows at metro stations tend to decrease with hourly rainfall intensity, varying across station types. The departure time of commuters is usually delayed by rainfall, resulting in a significant stacking effect of inbound flows at metro stations, with a pattern of falling followed by rising. The sensitivity of inbound flows to rainfall varies at different times, high at 0700 and 1700 LT and low at 0800, 0900, 1800, and 1900 LT because of the different levels of flexibility of departure time. 2) Short commuting OD flows (≤15 min) are more affected by rainfall, with an average increase of 7.3% and a maximum increase of nearly 35%, whereas long OD flows (>15 min) decrease slightly. OD flows between residential and industrial areas are more affected by rainfall than those between residential and commercial (service) areas, exhibiting a greater fluctuation of falling followed by rising. The sensitivity of OD flows to rainfall varies across metro lines. The departure stations of rainfall-sensitive lines are mostly distributed in large residential areas that rely heavily on the metro in the morning peak hours and in large industrial parks and commercial centers in the evening peak hours. Our findings reveal the spatiotemporal patterns of commuting flows resulting from rainfall at a finer scale, which provides a sound basis for spatial and temporal response strategies. This study also suggests that attention should be paid to the surges and stacking effects of commuting flows at certain times and areas during rainfall events.
Abstract
Driven by both climate change and urbanization, extreme rainfall events are becoming more frequent and having an increasing impact on urban commuting. Using hourly rainfall data and “metro” origin–destination (OD) flow data in Shanghai, China, this study uses the Prophet time series model to calculate the predicted commuting flows during rainfall events and then quantifies the spatiotemporal variations of commuting flows due to rainfall at station and OD levels. Our results show the following: 1) In general, inbound commuting flows at metro stations tend to decrease with hourly rainfall intensity, varying across station types. The departure time of commuters is usually delayed by rainfall, resulting in a significant stacking effect of inbound flows at metro stations, with a pattern of falling followed by rising. The sensitivity of inbound flows to rainfall varies at different times, high at 0700 and 1700 LT and low at 0800, 0900, 1800, and 1900 LT because of the different levels of flexibility of departure time. 2) Short commuting OD flows (≤15 min) are more affected by rainfall, with an average increase of 7.3% and a maximum increase of nearly 35%, whereas long OD flows (>15 min) decrease slightly. OD flows between residential and industrial areas are more affected by rainfall than those between residential and commercial (service) areas, exhibiting a greater fluctuation of falling followed by rising. The sensitivity of OD flows to rainfall varies across metro lines. The departure stations of rainfall-sensitive lines are mostly distributed in large residential areas that rely heavily on the metro in the morning peak hours and in large industrial parks and commercial centers in the evening peak hours. Our findings reveal the spatiotemporal patterns of commuting flows resulting from rainfall at a finer scale, which provides a sound basis for spatial and temporal response strategies. This study also suggests that attention should be paid to the surges and stacking effects of commuting flows at certain times and areas during rainfall events.
Abstract
As a significant detriment to physical and mental health, millions of motor vehicle crashes occur in the United States each year, with approximately 23% of these crashes linked to adverse weather conditions. This study builds upon a strong knowledge base to provide a deeper understanding of how rainfall intensity influences relative crash risk. Gridded precipitation and temperature data were aggregated to the county level and analyzed alongside motor vehicle crash data for all 146 counties in the Carolinas (North Carolina and South Carolina) for the period 2003–19. A matched-pair analysis routine linked unique time steps of rainfall (daily, 6-h, and hourly) to corresponding dry periods to evaluate relative crash risk across each state. Risk estimates were calculated on the basis of precipitation thresholds (light, moderate, heavy, and very heavy). Results indicate a statistically significant increase in crash risk during periods of rainfall in the Carolinas. As a baseline, the relative risk of experiencing a crash increases by 11.6% during days with accumulating rainfall and as much as 81.0% during heavy rainfall events over a 6-h period. In general, estimates of risk increase relative to the intensity of the rainfall event and the temporal delineation of the matched-pair routine. However, these relationships have unique spatiotemporal patterns indicating that, although hourly risk estimates may be beneficial for urban counties, daily relative risk estimates may be the only way to accurately capture risk in rural areas.
Significance Statement
Each year, more than 1 000 000 motor vehicle crashes in the United States are linked to adverse weather conditions in police reports, with rainfall events being among the largest contributors to increased crash risk. In this study, crash frequencies are evaluated to better understand how the intensity of rainfall events (light vs heavy) influences the risk of experiencing a collision on roadways in North Carolina and South Carolina. The results of statistical analyses revealed that risk increases significantly during rainfall events in both states and that the risk of experiencing a crash is highest during the heaviest rainfall events. However, even during light precipitation events, the risk of experiencing a crash is significantly higher than when driving during dry conditions. These results are helpful to transportation stakeholders and emergency responders in the hope of reducing crash risk in our changing climate.
Abstract
As a significant detriment to physical and mental health, millions of motor vehicle crashes occur in the United States each year, with approximately 23% of these crashes linked to adverse weather conditions. This study builds upon a strong knowledge base to provide a deeper understanding of how rainfall intensity influences relative crash risk. Gridded precipitation and temperature data were aggregated to the county level and analyzed alongside motor vehicle crash data for all 146 counties in the Carolinas (North Carolina and South Carolina) for the period 2003–19. A matched-pair analysis routine linked unique time steps of rainfall (daily, 6-h, and hourly) to corresponding dry periods to evaluate relative crash risk across each state. Risk estimates were calculated on the basis of precipitation thresholds (light, moderate, heavy, and very heavy). Results indicate a statistically significant increase in crash risk during periods of rainfall in the Carolinas. As a baseline, the relative risk of experiencing a crash increases by 11.6% during days with accumulating rainfall and as much as 81.0% during heavy rainfall events over a 6-h period. In general, estimates of risk increase relative to the intensity of the rainfall event and the temporal delineation of the matched-pair routine. However, these relationships have unique spatiotemporal patterns indicating that, although hourly risk estimates may be beneficial for urban counties, daily relative risk estimates may be the only way to accurately capture risk in rural areas.
Significance Statement
Each year, more than 1 000 000 motor vehicle crashes in the United States are linked to adverse weather conditions in police reports, with rainfall events being among the largest contributors to increased crash risk. In this study, crash frequencies are evaluated to better understand how the intensity of rainfall events (light vs heavy) influences the risk of experiencing a collision on roadways in North Carolina and South Carolina. The results of statistical analyses revealed that risk increases significantly during rainfall events in both states and that the risk of experiencing a crash is highest during the heaviest rainfall events. However, even during light precipitation events, the risk of experiencing a crash is significantly higher than when driving during dry conditions. These results are helpful to transportation stakeholders and emergency responders in the hope of reducing crash risk in our changing climate.
Abstract
The changes in climatic conditions and their associated impacts are contributing to a worsening of existing gender inequalities and a heightening of women’s socioeconomic vulnerabilities in South Africa. Using data collected by research methods inspired by the tradition of participatory appraisals, we systematically discuss the impacts of climate change on marginalized women and the ways in which they are actively responding to climate challenges and building their adaptive capacity and resilience in the urban areas of KwaZulu-Natal, South Africa. We argue that changes in climate have both direct and indirect negative impacts on women’s livelihoods and well-being. Less than one-half (37%) of the women reported implementing locally developed coping mechanisms to minimize the impacts of climate-related events, whereas 63% reported lacking any form of formal safety nets to deploy and reduce the impacts of climate-induced shocks and stresses. The lack of proactive and gender-sensitive local climate change policies and strategies creates socioeconomic and political barriers that limit the meaningful participation of women in issues that affect them and marginalize them in the climate change discourses and decision-making processes, thereby hampering their efforts to adapt and reduce existing vulnerabilities. Thus, we advocate for the creation of an enabling environment to develop and adopt progendered, cost-effective, transformative, and sustainable climate change policies and adaptation strategies that are responsive to the needs of vulnerable groups (women) of people in society. This will serve to build their adaptive capacity and resilience to climate variability and climate change–related risks and hazards.
Abstract
The changes in climatic conditions and their associated impacts are contributing to a worsening of existing gender inequalities and a heightening of women’s socioeconomic vulnerabilities in South Africa. Using data collected by research methods inspired by the tradition of participatory appraisals, we systematically discuss the impacts of climate change on marginalized women and the ways in which they are actively responding to climate challenges and building their adaptive capacity and resilience in the urban areas of KwaZulu-Natal, South Africa. We argue that changes in climate have both direct and indirect negative impacts on women’s livelihoods and well-being. Less than one-half (37%) of the women reported implementing locally developed coping mechanisms to minimize the impacts of climate-related events, whereas 63% reported lacking any form of formal safety nets to deploy and reduce the impacts of climate-induced shocks and stresses. The lack of proactive and gender-sensitive local climate change policies and strategies creates socioeconomic and political barriers that limit the meaningful participation of women in issues that affect them and marginalize them in the climate change discourses and decision-making processes, thereby hampering their efforts to adapt and reduce existing vulnerabilities. Thus, we advocate for the creation of an enabling environment to develop and adopt progendered, cost-effective, transformative, and sustainable climate change policies and adaptation strategies that are responsive to the needs of vulnerable groups (women) of people in society. This will serve to build their adaptive capacity and resilience to climate variability and climate change–related risks and hazards.
Abstract
Broadcast meteorologists play an essential role in communicating severe weather information from the National Weather Service to the public. Because of their importance, researchers incorporated broadcast meteorologists in the development of probabilistic hazard information (PHI) in NOAA’s Hazardous Weather Testbed. As part of Forecasting a Continuum of Environmental Threats (FACETs), PHI is meant to bring additional context to severe weather warnings through the inclusion of probability information. Since this information represents a shift in the current paradigm of solely deterministic NWS warnings, understanding end user needs is paramount to create usable and accessible products that result in their intended outcome to serve the public. This paper outlines the establishment of “K-Probabilistic Hazard Information Television” (KPHI-TV), a research infrastructure under the Hazardous Weather Testbed created to study broadcast meteorologists and PHI. A description of the design of KPHI-TV and methods used by researchers are presented, including displaced real-time cases and semistructured interviews. Researchers completed an analysis of the 2018 experiment, using a quantitative analysis of television coverage decisions with PHI, and a thematic analysis of semistructured interviews. Results indicate that no clear probabilistic decision thresholds for PHI emerged among the participants. Other themes arose, including the relationship between PHI and the warning polygon, and communication challenges. Overall, broadcast participants preferred a system that includes PHI over the warning polygon alone, but raised other concerns, suggesting iterative research in the design and implementation of PHI should continue.
Significance Statement
Broadcast meteorologists are the primary source of severe weather warning information for the U.S. public. As a result, researchers at NOAA’s National Severe Storms Laboratory and the Cooperative Institute for Mesoscale Meteorological Studies developed a mock television studio to allow broadcast meteorologists to use and communicate experimental products “on air” as part of the research-and-development process. Feedback provided by broadcasters is incorporated into products through an iterative process. Since 2016, 18 broadcasters have tested probabilistic hazard information at the warning time scale (0–1 h) for severe wind and hail, tornadoes, and lightning.
Abstract
Broadcast meteorologists play an essential role in communicating severe weather information from the National Weather Service to the public. Because of their importance, researchers incorporated broadcast meteorologists in the development of probabilistic hazard information (PHI) in NOAA’s Hazardous Weather Testbed. As part of Forecasting a Continuum of Environmental Threats (FACETs), PHI is meant to bring additional context to severe weather warnings through the inclusion of probability information. Since this information represents a shift in the current paradigm of solely deterministic NWS warnings, understanding end user needs is paramount to create usable and accessible products that result in their intended outcome to serve the public. This paper outlines the establishment of “K-Probabilistic Hazard Information Television” (KPHI-TV), a research infrastructure under the Hazardous Weather Testbed created to study broadcast meteorologists and PHI. A description of the design of KPHI-TV and methods used by researchers are presented, including displaced real-time cases and semistructured interviews. Researchers completed an analysis of the 2018 experiment, using a quantitative analysis of television coverage decisions with PHI, and a thematic analysis of semistructured interviews. Results indicate that no clear probabilistic decision thresholds for PHI emerged among the participants. Other themes arose, including the relationship between PHI and the warning polygon, and communication challenges. Overall, broadcast participants preferred a system that includes PHI over the warning polygon alone, but raised other concerns, suggesting iterative research in the design and implementation of PHI should continue.
Significance Statement
Broadcast meteorologists are the primary source of severe weather warning information for the U.S. public. As a result, researchers at NOAA’s National Severe Storms Laboratory and the Cooperative Institute for Mesoscale Meteorological Studies developed a mock television studio to allow broadcast meteorologists to use and communicate experimental products “on air” as part of the research-and-development process. Feedback provided by broadcasters is incorporated into products through an iterative process. Since 2016, 18 broadcasters have tested probabilistic hazard information at the warning time scale (0–1 h) for severe wind and hail, tornadoes, and lightning.
