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David W. Walker
,
Noemi Vergopolan
,
Louise Cavalcante
,
Kelly Helm Smith
,
Sehouevi Mawuton David Agoungbome
,
André Almagro
,
Tushar Apurv
,
Nirmal Mani Dahal
,
David Hoffmann
,
Vishal Singh
, and
Zhang Xiang

Abstract

Flash droughts, characterized by rapid onset and intensification, are increasingly occurring as a consequence of climate change and rising temperatures. However, existing hydrometeorological definitions fail to encompass the full range of flash droughts, many of which have distinct local physical attributes. Consequently, these events often go undetected or unforecast in generic global flash drought assessments and are underrepresented in research. To address this gap, we conducted a comprehensive survey to gather information on local nomenclature, characteristics, and impacts of flash droughts worldwide. The survey revealed the widespread occurrence of these phenomena, highlighting their underresearched nature. By analyzing case studies, through literature review often in local languages to unearth elusive studies, we identified five different types of flash droughts based on their specific characteristics. Our study aims to increase awareness about the complexity and diverse impacts of flash droughts, emphasizing the importance of considering regional contexts and the vulnerability of affected populations. The reported impacts underscore the need for better integration of all flash drought types in drought research, monitoring, and management. Monitoring a combination of indicators is crucial for timely detection and response to this emerging and escalating threat.

Significance Statement

This study aims to better understand flash droughts worldwide and their varying characteristics and impacts. We surveyed the experiences of people affected by flash droughts and then examined a wide range of literature, including non-English and nonacademic sources. This helped us understand how flash droughts can differ from those commonly studied in the United States and China. We identified and described five types of flash droughts, some of which may not be detected by current drought measurement methods. It is crucial to include all types of flash droughts in drought monitoring systems and management plans, as they are expected to become more common due to global warming. We can then better prepare for and reduce the impacts of this growing threat.

Open access
Anna Wanless
,
Sam Stormer
,
Joseph T. Ripberger
,
Makenzie J. Krocak
,
Andrew Fox
,
David Hogg
,
Hank Jenkins-Smith
,
Carol Silva
,
Scott E. Robinson
, and
Warren S. Eller

Abstract

National Weather Service (NWS) forecasters have many roles and responsibilities, including communication with core partners throughout the forecast and warning process to ensure that the information they are providing is relevant, understandable, and actionable. Although the NWS communicates to many groups, members of the emergency management community are among the most critical partners. However, little is known about the diverse population of emergency managers (EMs) and how they receive, process, and use forecast information. The Extreme Weather and Emergency Management Survey (WxEM) aims to fill this knowledge gap by 1) building a nationwide panel of EMs and 2) fielding routine surveys that include questions of relevance to NWS operations. The panel was built by creating a database with contact information from more than 4000 EMs across the country. An enrollment survey was sent to the list, and over 700 EMs agreed to participate in the project. Following enrollment, WxEM panelists receive surveys three–four times per year that address how EMs use NWS forecast information. These surveys cover a variety of subjects, with the goal of working with other researchers to develop surveys that address their research needs. By collaborating with other research groups to design short, focused surveys, the WxEM project will reduce the research burden on EMs and, at the same time, increase the quality and comparability of research data in the weather enterprise. The results will be shared with the NWS and the research community, and all data gathered from these surveys will be publicly available.

Significance Statement

The Extreme Weather and Emergency Management Survey aims to better understand how emergency managers use National Weather Service (NWS) forecast information via a series of surveys regularly distributed to a panel of emergency managers across the country. By collaborating with other researchers, these surveys will cover broad topics and should limit the number of participation requests sent to emergency managers. Results will be distributed to participants, researchers, and NWS forecasters. All data will be publicly available.

Restricted access
Mateus P. Lavorato
and
Marcelo J. Braga

Abstract

Weather index insurance (WII) has long been advertised as a viable alternative to crop yield insurance. WII products were first developed to assist climate-vulnerable farmers from developing countries where establishing a well-structured crop insurance market is expressively difficult due to the poor transport infrastructure and the prevalence of sparsely distributed small-scale farms. In Brazil, the semiarid region stands out as the one that concentrates the ideal conditions for the implementation of a WII product since it houses thousands of climate-vulnerable farmers. With this in mind, we designed and priced a WII product for farmers from the semiarid region of Brazil and posteriorly investigated its risk efficiency. To do so, we first investigated crop yield responses to aridity, enabling the selection of locations for which the WII product was posteriorly assessed. Second, we grouped selected locations into specific contracts according to geographical proximity and evaluated each of these contracts to attest the risk efficiency of the proposed WII product using the method of stochastic efficiency with respect to a function (SERF), which identifies utility efficient alternatives for a range of risk attitudes. Our results show that the WII product may be effective in protecting farmers from adverse variations in production revenue, possibly being attractive for utility-maximizer farmers that are sufficiently risk averse.

Restricted access
Angelina Dumlao
and
Neil Debbage

Abstract

A cool environment is critical for protecting vulnerable populations from the adverse health effects associated with exposure to extreme heat. Although cooling centers are commonly established to provide temporary heat relief to the public, there is limited research exploring the spatial distributions and accessibility of cooling centers across cities in Texas. The intent of this study was to examine the spatial characteristics of cooling center locations throughout the Texas Triangle megaregion and evaluate the proximity of cooling centers to vulnerable populations. Specifically, spatial clustering analysis was used to quantitatively characterize the spatial distributions of cooling centers in San Antonio, Houston, and Dallas, while spatial lag regression was conducted to evaluate the relationships between indicators of socioeconomic vulnerability and proximity to cooling centers. The findings indicated that cooling centers exhibited clustering at short distances, which suggested there were potential spatial redundancies. The distributions of the cooling centers also illustrated possible accessibility issues due to the concentration of the locations in urban cores. The spatial lag regression models highlighted several problematic relationships, as elderly and disabled populations were located at significantly greater distances from cooling centers in San Antonio and Dallas, respectively. However, numerous insignificant relationships were also observed, which suggested that cooling center locations did not consistently marginalize or favor vulnerable populations. Therefore, a higher degree of intentionality that explicitly considers cooling center proximity to the vulnerable populations they aim to serve might be beneficial as planners and emergency managers determine cooling center locations in response to extreme heat.

Open access
Hyuk-Jae Roh

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.

Restricted access
Robert Prestley
and
Rebecca E. Morss

Abstract

Common disaster-phase models provide a useful heuristic for understanding how disasters evolve, but they do not adequately characterize the transitions between phases, such as the forecast and warning phase of predictable disasters. In this study, we use tweets posted by professional sources of meteorological information in Florida during Hurricane Irma (2017) to understand how visual risk communication evolves during this transition. We identify four subphases of the forecast and warning phase: the hypothetical threat, actualized threat, looming threat, and impact subphases. Each subphase is denoted by changes in the kinds of visual risk information disseminated by professional sources and retransmitted by the public, which are often driven by new information provided by the U.S. National Weather Service. In addition, we use regression analysis to understand the impact of tweet timing, content, risk visualization and other factors on tweet retransmission across Irma’s forecast and warning phase. We find that cone, satellite, and spaghetti-plot image types are retweeted more, while watch/warning imagery is retweeted less. In addition, manually generated tweets are retweeted more than automated tweets. These results highlight several information needs to incorporate into the current NWS hurricane forecast visualization suite, such as uncertainty and hazard-specific information at longer lead times, and the importance of investigating the effectiveness of different social media posting strategies. Our results also demonstrate the roles and responsibilities that professional sources engage in during these subphases, which builds understanding of disasters by contextualizing the subphases along the transition from long-term preparedness to postevent response and recovery.

Significance Statement

Visual information is an important tool for communicating about evolving tropical cyclone threats. In this study, we investigate the kinds of visualizations posted by professional weather communicators on Twitter during Hurricane Irma (2017) to understand how visual information shifts over time and whether different visuals are more retweeted. We find that visual information shifts substantially in the days before Irma’s impacts, and these shifts are often driven by changes in Irma’s strength or forecast track. Our results show that cone, satellite, and spaghetti-plot visualizations are retweeted more frequently, while watch/warning imagery is retweeted less. These results help us to understand how visual information evolves during predictable disasters, and they suggest ways that visual communication can be improved.

Restricted access
O. V. Wilhelmi
,
B. C. Chamberlain
,
R. E. Morss
,
J. L. Demuth
,
H. D. Walpole
,
J. Boehnert
,
J. M. Gambill
,
H. Lazrus
, and
J. G. Dobson

Abstract

Geovisualizations play a central role in communicating hurricane storm surge risks to the public by connecting information about the hazard to a place. Meanwhile, people connect to places through meaning, functions, and emotional bond, known as a sense of place. The mixed-method approach presented in this paper focuses on the intersection of sense of place, geovisualization, and risk communication. We explored place meaning, scale of place, and place attachment in the coastal communities in Georgia and South Carolina. We conducted cognitive mapping focus groups and developed a series of geovisualizations of storm surge risk with varying representations of place. We then investigated people’s ability to connect visual storm surge information to a place and understand their risk by testing these geovisualizations in a large population survey (n = 1442). We found that a 2D regional-scale map displayed together with a 3D abstract representation of a neighborhood was the most helpful in enabling people to relate to a place, quickly make sense of the information, and understand the risk. Our results showed that while the geovisualizations of storm surge risk can be effective generally, they were less effective in several important and vulnerable groups. We found substantial impacts of race, income, map-reading ability, place attachment, and scale of place on how people connected the storm surge risk shown in the visual to a place. These findings have implications for future research and for considering the way weather forecasters and emergency managers communicate storm surge information with diverse audiences using geovisualizations.

Significance Statement

Weather forecasters and emergency managers often use geovisualizations to communicate hurricane storm surge risks and threats to the public. Despite the important role that geovisualizations play, few studies have empirically investigated their effectiveness in hazardous weather risk communication. With the overarching goal of understanding how geovisualizations enable coastal residents to understand and respond to risk, we use an interdisciplinary approach to create new knowledge about the effectiveness of geovisualizations in storm surge risk communication. Our results show substantial impacts of sociodemographic factors across many of the measures that enable people to connect to a place through visualizations. These findings have implications for communicating geospatially varying risk to diverse audiences.

Open access
Rakesh Gomaji Nannewar
,
Tejal Kanitkar
, and
R. Srikanth

Abstract

Providing knowledge inputs to farmers is critical to reduce their vulnerability and enhance resilience against climate change. In developing countries such as India, where small holdings and rain-fed agriculture are predominant, knowledge inputs become even more critical. The India Meteorological Department has provided integrated agrometeorological advisory services (AAS) to farmers since 2008. In this paper, we estimate the scale of access to AAS and its impact on crop yields in 1000 households across 10 villages in two agroclimatic zones in India. We find evidence suggesting that access to AAS can have a significant impact on crop yields in the kharif (June–September) season, whereas other inputs are more important in the case of rabi (winter) crops. Specifically, the yields of pigeon pea, soybean, and pearl millet are higher by 233, 98, and 318 kg ha−1, respectively, for AAS beneficiaries. For the entire study area, this translates to a value addition of $9.66 million for these three crops in one season. Our results show that AAS can be an important contributor to meeting the developmental goals of enhancing food security in dry-land agriculture and building resilience against climate change.

Significance Statement

In the era of climate change, with rapidly increasing weather and climatic variability, protecting the incomes of small farmers and ensuring they have the capacity to adapt and build resilience to the growing impacts of climate change is an urgent necessity. We have studied the impact of knowledge services such as the agrometeorological advisory services of the India Meteorological Department on crop yields for major crops in dry agroclimatic zones in India. The study shows that large public programs like the agrometeorological advisory services that bring science to people in meaningful ways can contribute significantly to meeting developmental goals and building resilience against climate change.

Restricted access
Taylor A. DeWinter-Maciag
and
Renee A. McPherson

Abstract

Although decision-making in response to tornado warnings is well researched, most studies do not examine whether individual responses to these warnings vary across different geographical locations and demographic groups. This gap is addressed by using data from a decision experiment that places participants virtually in a simulated tornado warning and asks them to minimize the costs of their decisions. The authors examine the following: 1) what demographic attributes may contribute to choices to minimize costs to protect assets at a specific location in a tornado warning, 2) whether there is a spatial component to how these attributes influence decision-making, and 3) if there are specific U.S. regions where individuals do not make protective decisions that minimize their overall cost. Multilevel regression analysis and poststratification are applied to data from the simulated decision experiment to estimate which demographic attributes and National Weather Service County Warning Areas are most associated with the costliest protective decisions. The results are then analyzed using spatial autocorrelation to identify spatial patterns. Results indicate that sex, race, and ethnicity are important factors that influence protection decisions. Findings also show that people across the southern portions of the United States tend to make more costly protective decisions, as defined in this work.

Significance Statement

Tornadoes, although rare, threaten both life and property. Studies have shown that certain demographic groups are more negatively impacted by disasters than others and are at higher risk of severe weather hazards. We ask if there are demographic characteristics or geographic locations in common among people who are more prone to making protection decisions during tornado warnings to minimize economic costs. Results can help warning providers, such as the National Weather Service, direct resources and education to specific types of decision-makers or locations to improve sheltering decisions.

Open access
Mary Anne T. Clive
,
Emma E. H. Doyle
,
Sally H. Potter
,
Chris Noble
, and
David M. Johnston

Abstract

Multiday severe weather outlooks can inform planning beyond the hour-to-day windows of warnings and watches. Outlooks can be complex to visualize, as they represent large-scale weather phenomena overlapping across several days at varying levels of uncertainty. Here, we present the results of a survey (n = 417) that explores how visual variables affect comprehension, inferences, and intended decision-making in a hypothetical scenario with the New Zealand MetService Severe Weather Outlook. We propose that visualization of the time window, forecast area, icons, and uncertainty can influence perceptions and decision-making based on four key findings. First, composite-style outlooks that depict multiple days of weather on one map can lead to biased perceptions of the forecast. When responding to questions about a day for which participants accurately reported there was no severe weather forecast, those who viewed a composite outlook reported higher likelihoods of severe weather occurring, higher levels of concern about travel, and higher likelihoods of changing plans compared to those who viewed outlooks that showed weather for each day on a separate map, suggesting that they perceived the forecast to underrepresent the likelihood of severe weather on that day. Second, presenting uncertainty in an extrinsic way (e.g., “low”) can lead to more accurate estimates of likelihood than intrinsic formats (e.g., hue variation). Third, shaded forecast areas may lead to higher levels of confidence in the forecast than outlined forecast areas. Fourth, inclusion of weather icons can improve comprehension in some conditions. The results demonstrate how visualization can affect decision-making about severe weather and support several evidence-based considerations for effective design of long-term forecasts.

Significance Statement

Severe weather outlook forecasts can be hard to clearly communicate because they show multiple weather patterns across multiple days and regions with varying uncertainty. The purpose of this study is to explore how visual elements of outlook design affect the way that people understand this complex content. We had three separate groups respond to the same series of questions while viewing different modified versions of the MetService Severe Weather Outlook in Aotearoa New Zealand and compared their responses. We find that the way the outlooks’ time window, forecast area, icons, and uncertainty are visualized can influence how people understand outlooks and make inferences and decisions about severe weather. We discuss how these influences may impact communication and action and present several evidence-based considerations for effective outlook design.

Open access