1. Introduction
Droughts have a large economic impact and can affect ecological and social processes due to the disruption of normal water availability. Between the years 1980–2022, an average of $7 billion was lost to drought each year through direct (crop or animal loss) or indirect impacts (increase in price of goods; NCEI 2022; Smith and Katz 2013; Smith and Matthews 2015). Droughts lack discrete boundaries and are difficult to define. Conventional drought is thought of as a creeping phenomenon that originates from a precipitation deficit, but recent studies have shown that drought can intensify quickly due to various physical and atmospheric factors (Chen et al. 2019; Christian et al. 2019; Hunt et al. 2014; Otkin et al. 2013). Understanding that all droughts are unique to the land, water use, and impacted sectors allows for context specific characterizations of drought (Heathcote 1969). Multiple types of droughts normally develop over a period of months or years. Definitions for these longer-term droughts are conceptual and include meteorological drought (lack of rainfall), agricultural drought (impacted crops), hydrological drought (water supply is impacted), or ecological (ecosystem services are disrupted) (Crausbay et al. 2017; Wilhite and Glantz 1985).
Our understanding of drought has recently expanded to include flash drought and is particularly relevant given the increasing frequency of rapidly developing droughts (Iglesias et al. 2022; Ford et al. 2021). The term “flash drought” was developed to describe various negative impacts due to an intense heatwave and a short period of dryness characterized by the rapid onset of drought conditions (Svoboda et al. 2002). Flash drought differs from more conventional droughts (meteorological, agricultural, hydrological, ecological) due to the rapid onset and/or rapid intensification. The current operational definition of flash drought, agreed upon by drought experts, relies on present biophysical conditions (e.g., lack of soil moisture, temperature, lack of precipitation) and the speed at which these conditions develop and intensify, but does not include impacts. However, some studies have identified impacts as a key part of defining drought in different community applications (Aldunce et al. 2017; Dagel 1997; Urquijo and De Stefano 2016). For example, flash drought can impact agricultural, hydrological, and ecological processes (Otkin et al. 2018b). One concern regarding flash drought is the potential impact on agricultural crops, including crop yield and livestock forage production. A rapid intensification of drought during critical parts of a growing season can substantially impact agricultural livelihoods and production (Christian et al. 2021). Therefore, it is important to define and communicate the risk of flash drought to communities, both agricultural and municipal, in an accurate and timely manner. A more robust definition for flash drought that considers impacts may influence how people perceive the phenomenon in terms of usability, relevance, accuracy, and legitimacy (Wilhite and Glantz 1985; Crausbay et al. 2017; Cash et al. 2002).
The main objectives of this study are to compare how six definitions of flash drought, which use the U.S. Drought Monitor (USDM) to quantitatively define flash drought, characterize literature- and survey-identified flash drought events and second, to understand the challenges and needs faced by stakeholders when managing flash drought. The current flash drought literature focuses on what physical indicators best depict flash drought and the characteristics of flash droughts (Chen et al. 2019; Christian et al. 2019; Ford and Labosier 2017). However, the literature has yet to address how the definition of flash drought might affect user perceptions of the concept’s usability for their decision-making needs. The definition used to portray flash drought can influence how end users perceive the risk. This study assesses the extent to which existing quantitative definitions of flash drought accurately recognize recent events identified as flash droughts by experts as well as nonexperts and discusses implications for the usability of the concept of flash drought for management and decision-making. This information will help drought-related stakeholders refine their understanding of flash drought and improve the communication of flash drought risk. Moreover, understanding the challenges stakeholders face can improve communication strategies and direct future research.
a. The goals of early warning
Timely and effective communication is key to successful drought decision-making processes. Providing early warning of forthcoming drought for potentially affected systems and people can decrease risk of impacts by offering an increased opportunity to prepare (United Nations Technology Centre & Network 2022). With conventional drought, there is usually a larger lead time between the first indication of drought and when severe impacts are experienced (Noel et al. 2020). In these cases, stakeholders have more time to implement drought plans and mitigation strategies. Flash drought conditions, however, do not afford individuals or communities the opportunity to react in time. For example, flash drought can lead to the rapid deterioration of plant health if appropriate risk management strategies are not in place, particularly those that address unprecedented events such as flash drought (Iglesias et al. 2022; Ford et al. 2021; Kreibich et al. 2022; Tellman and Eakin 2022). The rapid onset of flash droughts reduces the window to implement appropriate mitigation strategies and further supports the necessity of drought early warning information (Otkin et al. 2015).
b. Usable science and the perceptions of drought and early warning information
Developing science that can go from useful to usable has been a challenge across all disciplines, including climate science (Briley et al. 2015). Perceptions of different aspects of drought events have been studied, such as perceptions of risk, vulnerability, and response. Behavior in response to drought is influenced by perceptions of risk; people must perceive risk (possible negative impacts) from drought before behavior change occurs. The following literature exemplifies the importance of developing usable science for receiving and using drought early warning information to reduce risk and vulnerability.
Individual decisions and actions stem, in part, from the perception of risk. One aspect of mitigation response is how people perceive early warning information. If information is unclear or contradictory, additional sources are sought for clarification, which can delay response actions (Lindell and Perry 2012). Furthermore, some sources are perceived as more trustworthy than others (Zommers and Singh 2014), which can also hamper response behaviors of individuals if the first warning comes from a source deemed untrustworthy. Only extremely credible, powerful sources have the means to make people act with urgency (Lindell and Perry 2012). Understanding the relationship between humans and the information from early warning systems can allow for consistent messaging from reliable sources to prompt action. People look for reliable information that clears any confusion before deciding to act. In the case of flash drought, unclear defining characteristics or warnings may lead to a delayed response, further shortening the time to prepare before negative impacts occur. Thus, it is important to critically investigate current flash drought definitions and what differences, if any, may exist between drought experts’ definitions and stakeholders’ perceptions.
During the 2017 flash drought in the High Plains of the United States, adaptation strategies were implemented locally before many drought indicators suggested a drought (Hoell et al. 2020). In this case, farmers experienced warning signs on the ground and acted before early warning communication was triggered. A scenario such as this could lead to distrust and encourage farmers to rely on their own risk assessment. A similar situation occurred the year prior where some indicators did not reflect producer experiences of drying conditions. Conversely, it has also been found that some indicators can lead to false alarms of flash drought (Otkin et al. 2018a). When the risk of flash drought is communicated incorrectly or too late, decision-makers may deem the information as less relevant or untrustworthy Understanding what prompts action is important for decision-making strategies and early warnings. How decision-makers understand drought risk and impacts determines response, highlighting the need to consider social factors and physical elements of drought definitions.
To create usable science, research should be credible, salient, and reliable (Cash et al. 2002). Trust in science is complicated and dependent on many factors (Krause et al. 2019; Pechar et al. 2018), thus emphasizing the need for easily understood definitions (Cash and Belloy 2020).
2. Objectives
The main objective of this study is to assess the differences in characteristics of six flash drought definitions across multiple flash drought events. To accomplish this, we examine the date on which the first instance of flash drought is identified for each event, as well as the percent of area in flash drought based on each definition. This study also investigates the challenges and needs reported by stakeholders including monitoring, managing, and responding to flash drought.
This study has four main research questions:
- 1)Do flash drought definitions differ in their identification of the onset of flash drought events?
- 2)Do flash drought definitions differ in their identification of the geographic area experiencing flash drought?
- 3)What are the needs of the drought community in terms of identifying onset and geographic extent of flash drought?
- 4)Do the current definitions appear to meet the drought community’s needs?
These research questions allow for comparisons between definitions and comparisons between literature-identified and survey-identified flash drought events. Scientists, drought experts, and the public at large are actively engaged in understanding and managing flash drought. Examining the differences in how definitions identify the onset and percent area in drought will enable us to test the hypothesis that definitions are integral to drought decision-making and action. Additionally, determining challenges and needs decision-makers face during flash droughts will highlight gaps in information. The findings can be incorporated into determining what information is necessary for decision-makers and what is missing in order to communicate flash drought to stakeholders.
3. Flash drought definitions
Flash drought has been characterized by the rapid onset and intensification of drought conditions or defined as short duration, lasting a few weeks or months; examples of each type of definition can be found in the review paper by Lisonbee et al. (2021). Most frequently, evaporative demand, soil moisture, temperature, and precipitation are used to monitor flash drought development (Lisonbee et al. 2021). Commonly, evaporative demand drought index (EDDI; Hobbins et al. 2016; McEvoy et al. 2016), evaporative stress index (ESI; Anderson et al. 2007), standardized precipitation index (SPI; McKee et al. 1993), quick drought response index (QuickDRI; National Drought Mitigation Center 2022), and the USDM (Svoboda et al. 2002) have been assessed for their ability to accurately identify flash droughts (Chen et al. 2019). EDDI and ESI both account for evaporative demand that influences the rate of evaporation of moisture from plants and soil (Anderson et al. 2007; Hobbins et al. 2016; McEvoy et al. 2016). The Climate Hazards Infrared Precipitation with Stations (CHIRPS) SPI uses a combination of station and satellite data to measure precipitation anomalies (Funk et al. 2015). QuickDRI is a short-term drought index that combines multiple variables, including precipitation, soil moisture, evapotranspiration, vegetation health, and landscape characteristics on a short-term (less than 6 months) time scale. The USDM is a weekly product that combines multiple data streams to produce maps of areas in drought (Svoboda et al. 2002). Flash drought has been characterized by a precipitation deficit combined with high evaporative demand that rapidly depletes soil moisture (Chen et al. 2019; Ford and Labosier 2017; Otkin et al. 2013) commonly assessed by identifying the change in the indicator over a given period of time (Christian et al. 2019; Ford and Labosier 2017; Koster et al. 2019; Liu et al. 2020; Noguera et al. 2021; Pendergrass et al. 2020).
Studying known flash droughts has led to an increase in information about onset, preparedness, impacts, and resources (Hoell et al. 2020). Flash drought experts have identified multiple occurrences across the United States. In 2012, a flash drought occurred in the central United States and its evolution and characterization have since been studied extensively, leading to the development of new tools to detect and monitor flash drought (AghaKouchak 2014; Basara et al. 2019; Otkin et al. 2016; Pendergrass et al. 2020; DeAngelis et al. 2020; Otkin et al. 2013; Huang et al. 2014; Lisonbee et al. 2021). Similarly, the widespread study of the 2017 flash drought that impacted the northern Great Plains has contributed to the understanding of climatic conditions that can precede a flash drought (Hoell et al. 2020; Gerken et al. 2018; He et al. 2019; Kimball et al. 2019). Other flash droughts, including the 2016 northern Great Plains drought (Otkin et al. 2018a), the 2016 Southeast drought (Osman et al. 2021; Park Williams et al. 2017), and 2019 Southeast drought (Schubert et al. 2021; Liberto 2019) have warranted investigation and expanded our collective understanding of flash drought. Using prior knowledge of past flash drought events, literature-identified events and survey-identified events are presently investigated.
Recent research has also identified the need for a generalized quantitative definition to help forecast subseasonal flash droughts (Pendergrass et al. 2020). However, there are differing opinions as to what conditions indicate a flash drought. Flash droughts can be defined by onset period, intensification, duration, or a combination of all three. One argument suggests flash droughts should be defined by their quick onset rather than a short duration (Otkin et al. 2018b). A quick onset is challenging for stakeholders because it gives them less time to prepare, while a short duration with minimal impacts does not represent the normal definition of drought (Otkin et al. 2018b). This paper looks to understand the quantitative definitions that represent the onset and progression of flash droughts that may or may not persist to longer-term drought conditions.
Pendergrass et al. (2020) identified two definitions from two key indices, the USDM and EDDI. An onset period, and sustained progression of a flash drought were defined using the USDM to quantify the change in drought status over a 4-week period. Similarly, other studies have used the USDM to define flash drought conditions (Ford et al. 2015; Chen et al. 2019; Leeper et al. 2022; Otkin et al. 2018b). Two definitions, one for moderate flash droughts and the other for intense flash droughts have also been developed (Otkin et al. 2018b). Each definition uses a different measure of weeks (i.e., an increase of X number of USDM categories over Y number of weeks).
The USDM has been used frequently in the development of quantitative flash drought definitions and is easily recognized by the public in the United States. While other quantitative definitions of flash drought exist in the literature, this study only assesses the definitions that use the USDM, which are defined in further detail in the methods section and in Table 1. Ford et al. (2015) and Leeper et al. (2022) both quantify flash drought as a 3-category change in the USDM over a specific number of weeks, while the definition that quantifies intense flash drought events in Otkin et al. (2018a) (Otkin et al. 2018b) looks for a 4-category degradation. The three remaining definitions look at a 2-category degradation in the USDM. Different validation methods were used in the development of these definitions that may lead to discrepancies between definitions in determining which flash droughts are captured. It is difficult to validate what is or is not a drought due to the nature of the phenomenon and the lack of “truth” data.
Notation used for each definition in study, the source, and criteria that need to be met for each definition. The method or events used by each study for validating the definitions are also included.
A working quantitative definition of flash drought allows for enhanced forecasting and early warning systems. Quantitative definitions are investigated using both literature-identified and survey-identified flash droughts. Using multiple definitions or unclear terminology to explain the same concept is a barrier to producing knowledge (Grainger et al. 2021).
4. Methods and data
This study draws on three sources of data: a literature search, a survey, and the USDM. The literature search compiles several expert definitions of flash drought and known flash drought events while the survey data offer insight into flash drought events identified by non–flash drought experts. Data from the USDM are first used to determine the start date of a drought event. Each definition was calculated weekly between the years 2010 and 2021. A comparative analysis of the definitions followed. Subsequently, a thematic analysis of multiple survey questions was completed. In the following section, each data source and methodology are described in further detail.
a. Data
1) Survey-identified flash drought events and management challenges
Preexisting survey data were included in this analysis to account for nonexpert perceptions of flash droughts. The survey was administered in preparation for a flash drought workshop (National Integrated Drought Information System 2020) held by the National Integrated Drought Information System (NIDIS). The purpose of the survey was to 1) gain a better understanding of how end users of drought monitoring information understand flash drought and 2) help tailor the workshop to meet the users’ needs and facilitate discussion around how research tools can be adapted to meet these needs based on survey responses. In this study, we focus on those questions pertaining to respondents’ understanding of flash drought.
The survey was administered online via Qualtrics in October 2020. Recipients include NIDIS’s Drought Early Warning Systems (DEWS) email and newsletter lists, the NIDIS Flash Drought Workshop invitee list, a listserv of state climatologists, and the U.S. Drought Monitor listserv. The list of contacts held by NIDIS includes individuals who have expressed an interest in or participated in DEWS activities. Providing regional information to stakeholders to respond to changes in hydrologic conditions, NIDIS DEWS are established in the following U.S. regions: California–Nevada, Intermountain West, Midwest, Missouri River basin, Northeast, Pacific Northwest, Southeast, and southern Great Plains. From these mailing lists, approximately 6000 individuals were invited to participate in the survey. The total response rate was approximately 6.2% (n = 371). Respondents primarily worked in the Southwest (24%), the Southeast (16%), and the Northwest (14%) and in the following sectors: water supply/quality (44%), agriculture (farming and livestock) (24%), and emergency/multihazard planning/disaster response (21%). For this study, only responses from “drought related stakeholders” were analyzed (n = 318). “Drought related stakeholders” refer to respondents that self-reported as nonexperts in flash drought. Some respondents work across various sectors and their concerns show up in multiple places.
Two questions used in this study asked respondents to consider past flash drought events and identify when and where they occurred. The first question asked, “Have you experienced the following flash droughts?” regarding a list of literature-identified flash droughts. Response choices included 1 = yes, 2 = no, and 3 = not sure. These flash drought events are listed in Table 2. The second question was open-ended and asked respondents to describe any other flash drought events they had experienced, including the location and time at which the events occurred. Responses to question 2, events with more than one response, are seen in Table 2.
Flash drought events included in the analysis. The first five are flash drought events that were included in the survey and are in the literature. The last 10 are flash drought events from survey respondents. The states included in the analysis for each event are listed as well as the start date for analysis.
Other questions that are discussed in this study include the following:
- 1)Is the term “flash drought” very, somewhat, or not confusing to you when communicating about a drought event (n = 145)?
- 2)How useful is it to you to make a distinction between “drought” and “flash drought”? You may define “useful” based on the roles you take during drought, as a decision-maker, policy maker, communicator, educator, researcher, or other (n = 145).
- 3)If you checked or listed any flash drought event(s) above, what were the biggest monitoring or management/response challenges you faced with the flash drought event(s)? Were the challenges similar or different to other droughts (n = 84)?
- 4)Going forward, what information, tools, or support would most help you improve your management of a flash drought event? We are interested in your needs related to monitoring, prediction, and response (n = 104).
Questions 1 and 2 are scalar, whereas 3 and 4 are open ended.
While this survey is not representative of the general population because of its low response rate and use of convenience sampling (Wardropper et al. 2021; Battaglia et al. 2008), it is useful in the context of the present study. Using a sample of individuals who are interested in and familiar with flash drought permits a comparison with the scientific literature. A similar understanding of flash drought by respondents would provide support that the current working definitions resonate and are understood by stakeholders. If the opposite is true, more consideration of definition and communication strategies may be necessary. Using this sample survey respondents will allow experts to understand to what extent additional, targeted information may be needed for the general agricultural community or larger public.
2) Literature-identified flash droughts
A list of peer-reviewed studies was obtained from a search of the flash drought literature. The search was completed to identify flash droughts that have been studied in the literature and to find definitions that use the USDM to identify flash droughts. The survey also listed flash droughts that were identified by experts in the field and asked respondents if they had experienced any of the following flash droughts. The final selected flash droughts that represent literature-based flash droughts for this study were included in the survey and have been identified by at least one peer-reviewed study (Table 2 and Fig. 1).
Map of the 15 flash drought events.
Citation: Weather, Climate, and Society 15, 4; 10.1175/WCAS-D-22-0142.1
3) USDM
The USDM is widely known and understood in the drought community and has been useful in developing an understanding of flash drought. For the purpose of determining how well definitions capture onset and progression of conditions, this study assesses flash drought definitions that define flash drought through the USDM. The USDM is a joint initiative between the National Drought Mitigation Center (NDMC) at the University of Nebraska–Lincoln, the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Department of Agriculture (USDA). Released weekly, the USDM identifies areas of drought in the United States using climatic and impact data, near-real-time ground truthing, and local observations (Svoboda et al. 2002).
The USDM categorizes dryness/drought conditions in 5 categories from least intense to most intense: D0 (abnormally dry), D1 (moderate drought), D2 (severe drought), D3 (extreme drought), and D4 (exceptional drought). These categories are based on percentiles where D0 is 21–30, D1 is 11–20, D2 is 6–10, D3 is 3–5, and D4 is 0–2. Percentiles rank the data of the USDM from largest to smallest, showing the state of current conditions based on historical records. The USDM is commonly used as reference data (Brown et al. 2008; Anderson et al. 2011; Otkin et al. 2013) when assessing remotely sensed and combined drought indicators and models, which are often gridded.
The USDM is a hybrid product that combines multiple streams of data to produce maps of areas of drought using a convergence of evidence approach (Svoboda et al. 2002), making it difficult to be compared with individual indicators (National Drought Mitigation Center 2023). However, research shows that the USDM lagged in the identification of flash drought when compared with the standardized evaporative stress ratio (SESR) during the 2012 Midwest flash drought (Christian et al. 2019) and in an assessment of five Oklahoma flash droughts that occurred between 2000 and 2013 when compared with in situ soil moisture (Ford and Labosier 2017). These studies compared the SESR and in situ soil moisture with the USDM for one flash drought event and one region, respectively. Further, at the time of these flash droughts (2012 Midwest and between 2000 and 2013 in Oklahoma), the current data published in recent years (EDDI, ESI, SESR, etc.) were not available to the USDM authors. Therefore, a lag between a new product and the USDM is expected when retroactively studying past flash drought events.
Other definitions of flash drought use evaporative demand and soil moisture most commonly across all types of definitions (onset and duration), whereas the USDM generally has been used to define onset (Lisonbee et al. 2021).
b. Methods
1) Comparison of definitions
For this research, the USDM has been used to identify the states included for each event. Weekly USDM maps were used for the period January 2010–May 2022. One numerical value of 0, 1, 2, 3, 4, or 5 was assigned to each grid cell. These values correspond to no drought, D0, D1, D2, D3, and D4 categories, respectively. Each grid cell has a resolution of 0.042°. Table 2 illustrates the total number of flash droughts that will be assessed, whether they were literature-based or from survey respondents, and what states will be included in each analysis.
Six different definitions of flash drought that use the USDM to quantitatively define flash drought were examined. These definitions are listed in Table 1. Each definition was displayed for the continental United States on a rolling weekly time scale for the period January 2010–May 2022, extracting the areas that met the criteria of a flash drought by definition. The output date corresponds to the last date within the time period of each definition. As an example, Pendergrass et al. (2020) uses 4 weeks of data. Thus, a date of 13 June 2017 includes the 4 weeks prior in order to identify flash drought.
Each flash drought event was assessed across 12 weeks, beginning from the start date listed in Table 2 and focused on the onset and progression of conditions. This study does not assess the duration of the events or if the event transitioned into longer term drought. The year and season of each event was determined by literature or survey responses. If not found in the literature, the specific start date listed in Table 2 was derived from the USDM and subsequently documenting the first week of the season that no definition showed the study area in flash drought. The weekly products for each definition were compared for each flash drought, including the first instance and percent area in drought. The first instance was determined by when the first area of drought was identified for the specific week. The extent of each event qualifying as flash drought for each definition was calculated as percent area on a weekly time scale. The states included for each event are found in Table 2. The calculated area in flash drought for each week was divided by the total area of the states included in the analysis of each event and multiplied by 100 to calculate a percentage. Comparisons between definitions and literature-identified and survey-identified flash droughts were made.
2) Thematic analysis of survey questions
Two open-ended questions from the flash drought survey described above are also included to further examine perceptions of flash drought. These questions asked respondents to consider the following questions in relation to the most recent flash drought they could remember, including “What were the biggest monitoring or management/response challenges you faced?” and “What information, tools, or support would most help you improve your management?” in regard to monitoring, prediction, and response to flash droughts. The full questions are listed in the prior data section, survey-identified flash drought events, and management challenges.
Inductive and deductive coding was used to identify themes in the open-ended responses. First, the primary coder reviewed the responses using an inductive coding process, creating a list of themes. Two trained coders, the primary and secondary coder, each reviewed and coded the responses using an iterative coding process. Using the themes identified by the primary coder, the secondary coder deductively coded the responses. To ensure intercoder reliability, disagreements between coders were discussed and resolved to ensure an agreement rate of at least 80% for each theme.
5. Results and discussion
a. Comparison of definitions and events
The first instance (onset) (Fig. 2) and percent area (Fig. 3) of flash drought was calculated for all events. The date that flash drought is first seen is dependent on the event and the definition. This can be influential for early warning systems and disseminating clear information to the community. Some definitions identified flash drought more quickly than others. In times of flash drought, providing warning information as quickly as possible helps maximize the time to prepare or act.
Scatterplot showing the first week that each definition identified flash drought, for each event. Week 0 indicates the start date from Table 2 and did not show flash drought for any event and definition. The earliest week for a definition to identify flash drought is week 1.
Citation: Weather, Climate, and Society 15, 4; 10.1175/WCAS-D-22-0142.1
Bar graph of percent area in flash drought, averaged over 12 weeks, by definition for each event.
Citation: Weather, Climate, and Society 15, 4; 10.1175/WCAS-D-22-0142.1
First instance captures the first week that each definition identifies flash drought. This means that when flash drought is identified, the definition captured the intensification that occurred in the weeks leading up to “first instance.”
First, we note that some definitions detected all of the events as flash drought, while other definitions failed to detect some events as flash droughts. The more exclusive definitions, based on the number of times flash drought was not detected, include Ford, Leeper, and Otkin Intense. These definitions look for a 3-category change (Leeper and Ford) or a 4-category change (Otkin Intense) over different lengths of onset time. A notable difference between 2- or 3-category change definitions is the length of analysis. While Leeper and Ford both used a 3-category change definition, Ford allowed 3 more weeks for flash drought to develop than Leeper, using 8 weeks or less as compared with 5 weeks. Otkin Intense was the strictest definition, using a 4-category change over 6 weeks. This definition identified more intense flash droughts; 90% of the survey-based events were not intense enough for this definition to identify them as flash droughts. Comparatively, all of the literature-based events were identified using Otkin Intense (Fig. 2). This may suggest that experts use more intense flash drought events in their studies. Examining less-intense flash drought events perceived by stakeholders may help flash drought researchers consider the relevance of their proposed definitions to end users.
Most commonly, Chen and Otkin Moderate identified the earliest first instance date (Table 3). In the literature-identified events, Chen and Otkin Moderate were first to identify flash drought on all of the events. Ford, Leeper, and Otkin Intense did not indicate the earliest first instance in any of the events. This information can be used to assess the level of stringency each definition possesses. For early warning information, communicating risks to the public quickly is ideal, but a level of trust and consistency has to be developed and maintained.
Analysis of the percent of events, literature based and survey based, that identified the earliest week of first instance of flash drought.
First instance provides a snapshot of when each definition first identified flash drought. Examining the average percent area in flash drought allows for a deeper spatial analysis over the 12 weeks. Looking at the average percent area allows decision-makers to assess the usability of each definition for early warning. A definition that captures each event but at a smaller scale may be useful if the end user does not want to alert people who may not be impacted. A definition that consistently captures a larger area might be useful if the end user wants to know of any possibility of flash drought impacting them. A small area of drought does not diminish the impacts felt in those areas. Understanding the different characteristics of the definitions used is necessary in determining how to relay information to end users.
The 2016 northern Great Plains drought included a low average percent area relative to other literature-based events (Fig. 3). While all definitions were met for 2016 northern Great Plains, the average percent area covered was small. Further, this event may be a good example of known moderate or localized flash droughts. Within the survey-identified flash droughts, 2020 Southeast, 2020 Midwest and 2011 Texas also have small average areas of flash drought across all definitions.
The literature-based flash drought events showed a higher average percent area in drought consistently across all six definitions (Fig. 4). Otkin Moderate and Chen showed the highest average percent area in drought for both the literature- and survey-identified events, with highest and second-highest average percent area respectively. Otkin Intense (lowest) and Pendergrass (second lowest) showed the smallest percent area in flash drought. This indicates that Otkin moderate and Chen are the most inclusive when defining flash drought while the criteria of Otkin Intense and Pendergrass is more difficult to meet.
Bar graph of average percent area by definition for literature- and survey-identified flash drought events.
Citation: Weather, Climate, and Society 15, 4; 10.1175/WCAS-D-22-0142.1
Examples of events that show a small average percent area through the 12 weeks assessed are 2020 Pacific Northwest and 2020 Rocky Mountains, where some definitions showed a larger area in flash drought than others. From these figures, the literature-based events were more widespread, showing a higher percent of area in drought through the 12-week analysis (Fig. 5). The survey-based events were smaller in area and potentially more localized. This could be a result of respondents experiencing drought where they live. The smaller area does not diminish the impacts of a flash drought in the areas it occurred, but it shows how the definition may vary between stakeholders and experts. Experts tend toward larger and more intense flash droughts while the community has identified smaller events that may not reach the same magnitude as the expert-identified events. From these results, flash drought researchers should consider how their proposed definitions affect stakeholder perceptions of the geographic area experiencing flash drought. While there is no precedent for drought having to be a specific size to be considered drought, respondents identified more localized flash droughts that were relevant to them. Moving forward, flash drought monitoring should consider these smaller, localized events. If people are experiencing flash droughts but the tools are not validating their experiences, the perceptions of the end users may change to view monitoring as less accurate and legitimate.
Time series analysis of percent area in flash drought for all six definitions for each event over 12 weeks from start date.
Citation: Weather, Climate, and Society 15, 4; 10.1175/WCAS-D-22-0142.1
b. Management, monitoring and response challenges and needs
This study looked at the responses from the survey participants to better understand challenges faced regarding flash drought. This is an exploratory analysis that begins to show what stakeholders are identifying as barriers to managing, monitoring, and responding to flash drought.
1) Quantitative results
Respondents were asked 1) if they found the definition of flash drought to be confusing, and 2) if defining flash drought is useful within their sector/job. First, we found that many respondents find the term “flash drought” to be confusing. In all 12 sectors, 47% or more of the respondents (n = 145) responded that the term “flash drought” was either “somewhat” or “very” confusing (Fig. 6). The percentage of respondents answering “somewhat” or “very” confusing ranged from a low of 47% in livestock to a high of 82% in fisheries. Energy (73%), transportation/navigation (62%) and water supply/quality (61%) were the following three sectors that find the term flash drought to be “somewhat” or “very” confusing. Stakeholders’ current level of confusion may be due to the lack of clear messaging around the phenomenon and there being multiple qualitative and quantitative definitions.
(a) Bar chart of percentage of participants, by sector, on usefulness of the term “flash drought.” (b) Bar chart of percentage of participants, by sector, on confusion of the term “flash drought.” The numbers in parentheses correspond to the number of respondents for each sector.
Citation: Weather, Climate, and Society 15, 4; 10.1175/WCAS-D-22-0142.1
Despite many respondents finding the term confusing, they also found the distinction of “flash drought” to be useful in their positions. The lowest percentage of respondents to find the term “extremely” or “very” useful worked in the energy (33%) and disaster response (37%) sectors. The two sectors that were identified with the highest percentage of respondents finding the term flash drought to be “extremely” or “very” useful were recreation and tourism (80%) and fisheries (64%).
When comparing these results, many people (81%) who work within the fisheries sector found flash drought to be a “somewhat” or “very” confusing term but also over 60% of these respondents identified that they find the term to be “extremely” or “very” useful. In the energy sector, 73% of the respondents found the term confusing but only 33% of these respondents identified the term to be useful. These differing perceptions are worth exploring to improve the usability of “flash drought” as a concept and definition. From this exploratory analysis, people who work within fisheries may benefit from a clearer understanding of the definition of flash drought because they think that the term is useful to their operations. Respondents who are involved with the energy field are finding the term confusing but not useful. This may be due to the lack of understanding the definition of flash drought or it may be due to flash drought having limited impacts on the energy sector. Understanding stakeholders’ perceptions of usability can help flash drought researchers refine definitions to be most useful to audiences who need them.
2) Qualitative results
Analysis of the open-ended responses from the survey was conducted to understand challenges and needs faced when monitoring, managing, or responding to flash drought (Fig. 7). Thematic analysis revealed five challenges and six needs identified by respondents (Table 4). In relation to results from the comparison of definitions, challenges include rapid onset and communication while the needs include a clear definition, more data, and enhanced and efficient communication.
(a) Bar chart of percentage of respondents, by sector, of challenges identified to better manage and respond to flash drought. (b) Bar chart of percentage of respondents, by sector, of needs identified to better manage and respond to flash drought.
Citation: Weather, Climate, and Society 15, 4; 10.1175/WCAS-D-22-0142.1
Table of qualitative themes of the challenges and needs of participants for responding to, managing, and monitoring flash drought.
The top challenges, based on number of respondents identifying each theme, across all sectors were impacts (29%; n = 27), communication (24%; n = 23), and data availability and processing (21%; n = 20). The top needs identified across all sectors were more data (38%; n = 62), enhanced and efficient communication (15%; n = 25), expansion of current products (15%; n = 25), and clear definition (14%; n = 24). Understanding what stakeholders need to better manage, respond to, and prepare for flash droughts is important for researchers to address in the future.
Seasonal timing of flash drought was not identified as a challenge for public health and fisheries respondents. It is likely that the timing of drought would not impact either sector in the same way it would for disaster response or fire/wildfire sectors. In these cases, if the timing of flash drought comes on earlier or later in the season than expected, the tools and resources necessary may not be able to be allocated in a timely manner. All sectors had respondents who identified each of the seven needs. Addressing what is needed by stakeholders to better manage and respond to flash droughts can benefit all sectors.
Respondents from all sectors expressed the need for a clear definition of flash drought. This need was expressed in two ways. First, respondents described confusion with current definitions, particularly in terms of what makes flash drought different than other types of drought. Respondents also identified the lack of education and awareness of the term among stakeholders and the public. Addressing the overall need for a clear definition would allow for experts and stakeholders to be on the same page when monitoring and communicating the risk of flash drought occurring. Defining the characteristics of flash drought in multiple quantitative ways may not currently be helpful to end users if there is a need for better clarification regarding the conceptual definitions produced.
For the respondents who work in agriculture, the most frequently cited challenge was the lack of understanding regarding what impacts should be expected and how they can be managed during flash drought events. Agricultural needs described by respondents included more knowledge of adaptation responses followed by enhanced and efficient communication. By providing more education and knowledge around what impacts should be expected when discussing flash drought and what can be done to decrease loss, many people in the agricultural sector would benefit. To address the need of enhanced communication, using flash drought definitions that consistently capture first instance would allow for faster communication to stakeholders.
Respondents whose work focuses on water supply and quality most frequently identified two primary challenges, including the rapid onset of flash drought and lack of data availability and processing capabilities. The primary two needs included more knowledge of impacts and the unclear definition of flash drought. As in the agricultural sector, education about impacts to expect and the definition of flash drought and how it differs from a normal drought would be beneficial. Identifying the need for a clear definition highlights the gap between useful and usable definitions. While there is a use for developing multiple quantitative flash drought definitions, for some sectors, the usability may be low due to needing more education around the term “flash drought.”
6. Findings
The results from this analysis show that there is a difference between how each definition identifies flash drought, in terms of when flash drought is first identified (onset) and by the percent area (extent) of flash drought. These definitions point to the level of intensity that conditions need to reach as well as the length of onset of conditions. The stricter definitions used a higher change in drought categories (3- or 4-category change) as compared with a two-category change. The results also show that some events are more intense than others, possibly creating the need for different definitions in determining moderate to intense flash droughts. These findings have implications for determining what a flash drought is and, further, communicating this to the community.
The survey-identified events were more localized and less intense than the literature-identified events, only 20% of survey-identified events met the criteria for intense flash drought, supporting this finding. The experts have developed quantitative definitions that meet the criteria of “textbook” events of flash drought while managers work in the gray area. For managers and stakeholders, flash drought events can be more ambiguous and do not always fit into a neat definition. While there may not be a large difference in perception of flash drought, managers have to understand what is occurring on the ground and cannot ignore these smaller flash droughts. Using different quantitative definitions for assessing flash drought for different sectors may be useful to reduce some of the challenges and needs faced by stakeholders. Further, as rapid onset is a challenge for many, definitions that capture first instance at the earliest date can provide early warning of a possible flash drought. Information is needed to determine whether these definitions captured any “false positives.” Capturing the onset of flash drought as quickly as possible without creating unnecessary warnings will improve flash drought risk communication and embolden trust between end users and risk communicators. It is also important for early warning sources to be consistent in defining a flash drought and publishing warnings, to avoid delaying response times or actions to mitigate the impacts of drought.
When comparing flash drought definitions, different characteristics stand out in terms of capturing the first instance of the event and how much area is captured. Specifically, respondents in all sectors found the rapid onset of flash droughts challenging to respond to and manage, and identified a need for more data and at a faster rate. Communication was also identified as a challenge and a need. Responding to flash drought requires up-to-date data as quickly as possible because the drought intensifies rapidly. To address these challenges and needs, definitions that consistently capture the first instance of flash drought can be used. Efficient communication of this information would allow for stakeholders to have early warning of a possible flash drought occurring.
When looking at definitions of flash drought by the percentage of area identified, what definition used for each sector may matter. On a smaller scale, a farmer may experience impacts during a localized event whereas a water-basin manager would not be impacted. By addressing the challenge and the need of improved data availability, definitions tailored to different sectors could provide a better understanding of an event by including possible impacts and response strategies. These definitions could allow for the quantitative, high-level, flash drought definitions to be translated to usable information for end users by providing “metrics relevant for various societal sectors” (National Integrated Drought Information System 2016), in the form of triggers, extension guides or drought plans. Having more specific definitions, alongside the overall definitions of flash drought we currently use, could enhance the challenge and need for efficient and effective communication.
A deeper understanding of these definitions provides information regarding how these definitions show flash drought events that can enhance communication strategies. Developing flash drought definitions are important to creating effective early warning tools, but it is also necessary to understand how useful the information is to the desired end users. Definitions characterize flash drought and can be used in different situations. Credible, salient, and reliable information is necessary (Cash et al. 2002) in proceeding with flash drought communication. These characteristics will allow users to perceive them as useful and are more likely to engage and make decisions on it (Cash and Belloy 2020). Further, the perceived value of the information will determine if users effectively act (Bruno Soares et al. 2018). Understanding what information is deemed useful is essential in developing tools that accurately communicate flash drought risk. This study has demonstrated that the definition affects how flash drought is monitored and perceived by stakeholders and can influence information usability.
7. Limitations and future work
Drought can be challenging to identify because of the lack of discrete boundaries and uncertain timing. In this analysis, regions of states were used to compare definitions. The results may differ based on what states were included in the analysis for each event. For this study, the USDM, literature-identified and survey-identified responses were used together to gather information regarding the general timing of start dates to begin the analysis. Each definition criterion was used to identify the date of first instance for each event. But, because there is no definite start or end date, flash drought could be apparent in other weeks of the season for each event. Another limitation of this study is that the survey data showing community-identified flash drought occurred mostly in 2020. This is likely due to these droughts being most recent and fresh in participants’ memories. Insight about less recent flash droughts is thereby not captured in this study. Such insight could have changed the results.
More research and education are needed to understand the onset and characteristics of flash drought in different regions of the United States. Questions and key takeaways for future flash drought management are as follows:
-
Do experts consider small-scale flash droughts as “flash drought?”
-
Is there a difference in flash droughts in different geographical areas?
-
When developing new definitions, how is the definition useful to the end user? Does it fit the needs?
-
In response to the need for expansion of current products, a larger network of in situ sensors (e.g., soil moisture) would help stakeholders have access to more localized data.
-
Investigate the individual relationships between in situ data products and satellite data products for improved flash drought monitoring.
-
Provide education about impacts to expect and how to respond, a clearer definition and current data products.
When evaluating flash drought on a national scale, data should be evaluated with more than one flash drought event, a weaker flash drought and a more intense flash drought. This would offer an understanding of the differences between events to which stakeholders and managers have to respond to. Further, more education is needed to address some of the needs presented in this study across multiple sectors.
Acknowledgments.
The authors thank C. Poulsen and J. Wisner for their assistance in gathering data; D. Bathke, D. Wood, B. Kemp, and G. Campbell for their editorial support; and the reviewers of the paper for their helpful suggestions. This research was funded by the National Drought Mitigation Center in the School of Natural Resources at the University of Nebraska–Lincoln.
Data availability statement.
Data analyzed in this study were a reanalysis of existing data, which are openly available online (https://droughtmonitor.unl.edu/). Because of confidentiality agreements, survey data cannot be made publicly available. Details of the data and how to request access are available from author Tonya Haigh with the National Drought Mitigation Center at the University of Nebraska–Lincoln.
REFERENCES
AghaKouchak, A., 2014: A baseline probabilistic drought forecasting framework using standardized soil moisture index: Application to the 2012 United States drought. Hydrol. Earth Syst. Sci., 18, 2485–2492, https://doi.org/10.5194/hess-18-2485-2014.
Aldunce, P., D. Araya, R. Sapiain, I. Ramos, G. Lillo, A. Urquiza, and R. Garreaud, 2017: Local perception of drought impacts in a changing climate: The mega-drought in central Chile. Sustainability, 9, 2053, https://doi.org/10.3390/su9112053.
Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. A. Otkin, and W. P. Kustas, 2007: A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation. J. Geophys. Res., 112, D10117, https://doi.org/10.1029/2006JD007506.
Anderson, M. C., C. Hain, B. Wardlow, A. Pimstein, J. R. Mecikalski, and W. P. Kustas, 2011: Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States. J. Climate, 24, 2025–2044, https://doi.org/10.1175/2010JCLI3812.1.
Basara, J. B., J. I. Christian, R. A. Wakefield, J. A. Otkin, E. H. Hunt, and D. P. Brown, 2019: The evolution, propagation, and spread of flash drought in the central United States during 2012. Environ. Res. Lett., 14, 084025, https://doi.org/10.1088/1748-9326/ab2cc0.
Battaglia, M., N. Sampling, and P. Lavrakas, 2008: Encyclopedia of Survey Research Methods. Sage, 1072 pp.
Briley, L., D. Brown, and S. E. Kalafatis, 2015: Overcoming barriers during the co-production of climate information for decision-making. Climate Risk Manage., 9, 41–49, https://doi.org/10.1016/j.crm.2015.04.004.
Brown, J. F., B. D. Wardlow, T. Tadesse, M. J. Hayes, and B. C. Reed, 2008: The Vegetation Drought Response Index (VegDRI): A new integrated approach for monitoring drought stress in vegetation. GISci. Remote Sens., 45, 16–46, https://doi.org/10.2747/1548-1603.45.1.16.
Bruno Soares, M., M. Daly, and S. Dessai, 2018: Assessing the value of seasonal climate forecasts for decision-making. Wiley Interdiscip. Rev.: Climate Change, 9, e523, https://doi.org/10.1002/wcc.523.
Cash, D., and P. G. Belloy, 2020: Salience, credibility and legitimacy in a rapidly shifting world of knowledge and action. Sustainability, 12, 7376, https://doi.org/10.3390/su12187376.
Cash, D., W. C. Clark, F. Alcock, N. M. Dickson, N. Eckley, and J. Jäger, 2002: Salience, credibility, legitimacy and boundaries: Linking research, assessment and decision making. SSRN, 372280, 25 pp., https://doi.org/10.2139/ssrn.372280.
Chen, L. G., J. Gottschalck, A. Hartman, D. Miskus, R. Tinker, and A. Artusa, 2019: Flash drought characteristics based on U.S. Drought Monitor. Atmosphere, 10, 498, https://doi.org/10.3390/atmos10090498.
Christian, J. I., J. B. Basara, J. A. Otkin, E. D. Hunt, R. A. Wakefield, P. X. Flanagan, and X. Xiao, 2019: A methodology for flash drought identification: Application of flash drought frequency across the United States. J. Hydrometeor., 20, 833–846, https://doi.org/10.1175/JHM-D-18-0198.1.
Christian, J. I., J. B. Basara, E. D. Hunt, J. A. Otkin, J. C. Furtado, V. Mishra, X. Xiao, and R. M. Randall, 2021: Global distribution, trends, and drivers of flash drought occurrence. Nat. Commun., 12, 6330, https://doi.org/10.1038/s41467-021-26692-z.
Crausbay, S. D., and Coauthors, 2017: Defining ecological drought for the twenty-first century. Bull. Amer. Meteor. Soc., 98, 2543–2550, https://doi.org/10.1175/BAMS-D-16-0292.1.
Dagel, K. C., 1997: Defining drought in marginal areas: The role of perception. Prof. Geogr., 49, 192–202, https://doi.org/10.1111/0033-0124.00069.
DeAngelis, A. M., H. Wang, R. D. Koster, S. D. Schubert, Y. Chang, and J. Marshak, 2020: Prediction skill of the 2012 U.S. Great Plains flash drought in subseasonal experiment (SubX) models. J. Climate, 33, 6229–6253, https://doi.org/10.1175/JCLI-D-19-0863.1.
Ford, T. W., and C. F. Labosier, 2017: Meteorological conditions associated with the onset of flash drought in the eastern United States. Agric. For. Meteor., 247, 414–423, https://doi.org/10.1016/j.agrformet.2017.08.031.
Ford, T. W., D. B. McRoberts, S. M. Quiring, and R. E. Hall, 2015: On the utility of in situ soil moisture observations for flash drought early warning in Oklahoma, USA. Geophys. Res. Lett., 42, 9790–9798, https://doi.org/10.1002/2015GL066600.
Ford, T. W., L. Chen, and J. T. Schoof, 2021: Variability and transitions in precipitation extremes in the Midwest United States. J. Hydrometeor., 22, 533–545, https://doi.org/10.1175/JHM-D-20-0216.1.
Funk, C., and Coauthors, 2015: The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Sci. Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66.
Gerken, T., G. T. Bromley, B. L. Ruddell, S. Williams, and P. C. Stoy, 2018: Convective suppression before and during the United States northern Great Plains flash drought of 2017. Hydrol. Earth Syst. Sci., 22, 4155–4163, https://doi.org/10.5194/hess-22-4155-2018.
Grainger, S., C. Murphy, and S. M. Vicente-Serrano, 2021: Barriers and opportunities for actionable knowledge production in drought risk management: Embracing the frontiers of co-production. Front. Environ. Sci., 9, 602128, https://doi.org/10.3389/fenvs.2021.602128.
He, M., J. S. Kimball, Y. Yi, S. Running, K. Guan, K. Jensco, B. Maxwell, and M. Maneta, 2019: Impacts of the 2017 flash drought in the US northern plains informed by satellite-based evapotranspiration and solar-induced fluorescence. Environ. Res. Lett., 14, 074019, https://doi.org/10.1088/1748-9326/ab22c3.
Heathcote, R. L., 1969: Drought in Australia: A problem of perception. Geogr. Rev., 59, 175–194, https://doi.org/10.2307/213453.
Hobbins, M. T., A. Wood, D. J. McEvoy, J. L. Huntington, C. Morton, M. Anderson, and C. Hain, 2016: The evaporative demand drought index. Part I: Linking drought evolution to variations in evaporative demand. J. Hydrometeor., 17, 1745–1761, https://doi.org/10.1175/JHM-D-15-0121.1.
Hoell, A., and Coauthors, 2020: Lessons learned from the 2017 flash drought across the U.S. northern Great Plains and Canadian prairies. Bull. Amer. Meteor. Soc., 101, E2171–E2185, https://doi.org/10.1175/BAMS-D-19-0272.1.
Huang, J., and Coauthors, 2014: NOAA Drought Task Force 2016: Research to advance national drought monitoring and prediction capabilities. NOAA, 28 pp., https://doi.org/10.7289/V5V122S3.
Hunt, E. D., M. Svoboda, B. Wardlow, K. Hubbard, M. Hayes, and T. Arkebauer, 2014: Monitoring the effects of rapid onset of drought on non-irrigated maize with agronomic data and climate-based drought indices. Agric. For. Meteor., 191, 1–11, https://doi.org/10.1016/j.agrformet.2014.02.001.
Iglesias, V., W. R. Travis, and J. K. Balch, 2022: Recent droughts in the United States are among the fastest-developing of the last seven decades. Wea. Climate Extremes, 37, 100491, https://doi.org/10.1016/j.wace.2022.100491.
Kimball, J. S., L. Jones, K. Jensco, M. He, M. Maneta, and R. Reichle, 2019: Smap L4 assessment of the US northern plains 2017 flash drought. IGARSS 2019–2019 IEEE Int. Geoscience and Remote Sensing Symp., Yokohama, Japan, IEEE, 5366–5369, https://doi.org/10.1109/IGARSS.2019.8898354.
Koster, R., S. Schubert, H. Wang, S. Mahanama, and A. M. DeAngelis, 2019: Flash drought as captured by reanalysis data: Disentangling the contributions of precipitation deficit and excess evapotranspiration. J. Hydrometeor., 20, 1241–1258, https://doi.org/10.1175/JHM-D-18-0242.1.
Krause, N. M., D. Brossard, D. A. Scheufele, M. A. Xenos, and K. Franke, 2019: Trends—Americans’ trust in science and scientists. Public Opin. Quart., 83, 817–836, https://doi.org/10.1093/poq/nfz041.
Kreibich, H., and Coauthors, 2022: The challenge of unprecedented floods and droughts in risk management. Nature, 608, 80–86, https://doi.org/10.1038/s41586-022-04917-5.
Leeper, R. D., and Coauthors, 2022: Characterizing U.S. drought over the past 20 years using the U.S. Drought Monitor. Int. J. Climatol., 42, 6616–6630, https://doi.org/10.1002/joc.7653.
Liberto, T. D., 2019: Early warning systems for droughts. NOAA, https://www.climate.gov/news-features/event-tracker/flash-drought-engulfs-us-southeast-september-2019.
Lindell, M. K., and R. W. Perry, 2012: The protective action decision model: Theoretical modifications and additional evidence. Risk Anal., 32, 616–632, https://doi.org/10.1111/j.1539-6924.2011.01647.x.
Lisonbee, J., M. Woloszyn, and M. Skumanich, 2021: Making sense of flash drought: Definitions, indicators, and where we go from here. J. Appl. Serv. Climatol., 2021 (1), 1–19, https://doi.org/10.46275/JOASC.2021.02.001.
Liu, Y., Y. Zhu, L. Ren, J. Otkin, E. D. Hunt, X. Yang, F. Yuan, and S. Jiang, 2020: Two different methods for flash drought identification: Comparison of their strengths and limitations. J. Hydrometeor., 21, 691–704, https://doi.org/10.1175/JHM-D-19-0088.1.
McEvoy, D. J., J. L. Huntington, M. T. Hobbins, A. Wood, C. Morton, M. Anderson, and C. Hain, 2016: The evaporative demand drought index. Part II: Conus-wide assessment against common drought indicators. J. Hydrometeor., 17, 1763–1779, https://doi.org/10.1175/JHM-D-15-0122.1.
McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration to time scales. Proc. Eighth Conf. on Applied Climatology, Anaheim, CA, Amer. Meteor. Soc., 179–183.
National Drought Mitigation Center, 2022: Quick drought response index (QuickDRI). National Drought Mitigation Center, https://quickdri.unl.edu/.
National Drought Mitigation Center, 2023: Types of drought. National Drought Mitigation Center, https://drought.unl.edu/Education/DroughtIn-depth/TypesofDrought.aspx.
National Integrated Drought Information System, 2016: National integrated drought information system implementation plan: December 2016 update. NOAA, 17 pp., https://www.drought.gov/sites/default/files/2020-06/Implementation-Plan-December-2016-Update.pdf.
National Integrated Drought Information System, 2020: Flash drought virtual workshop. NIDIS, https://www.drought.gov/events/flash-drought-virtual-workshop.
NCEI, 2022: billion-dollar weather and climate disasters. NOAA, accessed 8 November 2022, https://www.ncei.noaa.gov/access/billions/summary-stats.
Noel, M., and Coauthors, 2020: Linking drought impacts to drought severity at the state level. Bull. Amer. Meteor. Soc., 101, E1312–E1321, https://doi.org/10.1175/BAMS-D-19-0067.1.
Noguera, I., F. Domínguez-Castro, and S. M. Vicente-Serrano, 2021: Flash drought response to precipitation and atmospheric evaporative demand in Spain. Atmosphere, 12, 165, https://doi.org/10.3390/atmos12020165.
Osman, M., B. F. Zaitchik, H. S. Badr, J. I. Christian, T. Tadesse, J. A. Otkin, and M. C. Anderson, 2021: Flash drought onset over the contiguous United States: Sensitivity of inventories and trends to quantitative definitions. Hydrol. Earth Syst. Sci., 25, 565–581, https://doi.org/10.5194/hess-25-565-2021.
Otkin, J. A., M. C. Anderson, C. Hain, I. E. Mladenova, J. B. Basara, and M. Svoboda, 2013: Examining rapid onset drought development using the thermal infrared–based evaporative stress index. J. Hydrometeor., 14, 1057–1074, https://doi.org/10.1175/JHM-D-12-0144.1.
Otkin, J. A., M. Shafer, M. Svoboda, B. Wardlow, M. C. Anderson, C. Hain, and J. Basara, 2015: Facilitating the use of drought early warning information through interactions with agricultural stakeholders. Bull. Amer. Meteor. Soc., 96, 1073–1078, https://doi.org/10.1175/BAMS-D-14-00219.1.
Otkin, J. A., and Coauthors, 2016: Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought. Agric. For. Meteor., 218–219, 230–242, https://doi.org/10.1016/j.agrformet.2015.12.065.
Otkin, J. A., T. Haigh, A. Mucia, M. C. Anderson, and C. Hain, 2018a: Comparison of agricultural stakeholder survey results and drought monitoring datasets during the 2016 U.S. northern plains flash drought. Wea. Climate Soc., 10, 867–883, https://doi.org/10.1175/WCAS-D-18-0051.1.
Otkin, J. A., M. Svoboda, E. D. Hunt, T. W. Ford, M. C. Anderson, C. Hain, and J. B. Basara, 2018b: Flash droughts: A review and assessment of the challenges imposed by rapid-onset droughts in the United States. Bull. Amer. Meteor. Soc., 99, 911–919, https://doi.org/10.1175/BAMS-D-17-0149.1.
Park Williams, A., B. I. Cook, J. E. Smerdon, D. A. Bishop, R. Seager, and J. S. Mankin, 2017: The 2016 southeastern US drought: An extreme departure from centennial wetting and cooling. J. Geophys. Res. Atmos., 122, 10 888–10 905, https://doi.org/10.1002/2017JD027523.
Pechar, E., T. Bernauer, and F. Mayer, 2018: Beyond political ideology: The impact of attitudes towards government and corporations on trust in science. Sci. Commun., 40, 291–313, https://doi.org/10.1177/1075547018763970.
Pendergrass, A. G., and Coauthors, 2020: Flash droughts present a new challenge for subseasonal-to-seasonal prediction. Nat. Climate Change, 10, 191–199, https://doi.org/10.1038/s41558-020-0709-0.
Schubert, S. D., Y. Chang, A. M. DeAngelis, H. Wang, and R. D. Koster, 2021: On the development and demise of the fall 2019 southeast U.S. flash drought: Links to an extreme positive IOD. J. Climate, 34, 1701–1723, https://doi.org/10.1175/JCLI-D-20-0428.1.
Smith, A. B., and R. W. Katz, 2013: US billion-dollar weather and climate disasters: Data sources, trends, accuracy and biases. Nat. Hazards, 67, 387–410, https://doi.org/10.1007/s11069-013-0566-5.
Smith, A. B., and J. L. Matthews, 2015: Quantifying uncertainty and variable sensitivity within the US billion-dollar weather and climate disaster cost estimates. Nat. Hazards, 77, 1829–1851, https://doi.org/10.1007/s11069-015-1678-x.
Svoboda, M., and Coauthors, 2002: The Drought Monitor. Bull. Amer. Meteor. Soc., 83, 1181–1190, https://doi.org/10.1175/1520-0477-83.8.1181.
Tellman, B., and H. Eakin, 2022: Risk management alone fails to limit the impact of extreme climate events. Nature, 608, 41–43, https://doi.org/10.1038/d41586-022-02031-0.
United Nations Technology Centre & Network, 2022: Early warning systems for droughts. CTCN, https://www.ctc-n.org/technologies/early-warning-systems-droughts.
Urquijo, J., and L. De Stefano, 2016: Perception of drought and local responses by farmers: A perspective from the Jucar River basin, Spain. Water Resour. Manage., 30, 577–591, https://doi.org/10.1007/s11269-015-1178-5.
Wardropper, C. B., A. A. Dayer, M. S. Goebel, and V. Y. Martin, 2021: Conducting conservation social science surveys online. Conserv. Biol., 35, 1650–1658, https://doi.org/10.1111/cobi.13747.
Wilhite, D. A., and M. H. Glantz, 1985: Understanding: The drought phenomenon: The role of definitions. Water Int., 10, 111–120, https://doi.org/10.1080/02508068508686328.
Zommers, Z., and A. Singh, 2014: Reducing Disaster: Early Warning Systems for Climate Change. Springer, 387 pp.