A flash drought (FD) event is a relatively new form of severe occurrence, described by the quick onset and intensification of drought situations with serious consequences. This paper aims to understand the wide variety of studies and serve as a basis for future research potentials of FDs. An effort has been made to understand the advantages and limitations of the existing indices used for FD identification. A study in India was carried out for the identification of FDs using daily gridded data of soil moisture for eight days (octad). The results reveal that the Indus basin faced around 82% and the Cauvery basin faced about 88% of severe FDs during the monsoon and non-monsoon seasons, respectively. Additionally, the results show that the Indus basin faces FD in the monsoon season since the basin has mostly barren areas and wasteland. This review also highlights the role of humidity, wind and radiation, soil characteristics, climate oscillations, and the relation between FDs and heavy-rainfall-induced flash floods. Furthermore, the paper has depicted some specific research-needs to monitor, forecast, plan, and respond with crucial points.

  • This study discusses the most extensive indices used to identify FDs.

  • A study is carried out in India to identify FDs using daily gridded soil moisture data for eight days (octad).

  • The study briefly explains the factors that need to be included in identifying and monitoring FDs.

  • The study also depicts the crucial points for future research-needs to monitor, forecast, plan, and respond to FDs.

Droughts are frequently seen as slow-moving natural hazards. However, some severe droughts have such a rapid onset or intensification that it appears as though they occur in a ‘flash’, making them and their consequences challenging to forecast and plan for (NIDIS 2021). The term ‘flash droughts (FDs)’ is commonly used to describe droughts that develop quickly over a short time (Osman et al. 2021). FDs have been a research topic since 2002 (Svoboda et al. 2002), and scientific research on this concern is currently increasing, with a significant increase in publications beginning from 2013 until the present (Lisonbee et al. 2021). The publications increased considerably after the FD of 2012 in the central USA, and the research is continually rising every year (Liang & Yuan 2021). The research on FD has been getting a lot of attention in recent years (Hu et al. 2021; Jiao et al. 2021; Liang & Yuan 2021; Lisonbee et al. 2021; Mishra et al. 2021; Osman et al. 2021) in scientific fields. A recent study indicates that the intensification rates of drought have accelerated over sub-seasonal timescales (Pendergrass et al. 2020). Furthermore, the study highlights a shift toward a greater prevalence of flash droughts in 74% of the global regions that were identified in the Intergovernmental Panel on Climate Change (IPCC) Special Report on extreme events during the past 64 years (IPCC 2021; Yuan et al. 2023). Areas with a high incidence of flash droughts include Brazil, the Sahel, the Great Rift Valley, and India. Additionally, there are noteworthy local hotspots in the central United States, southwestern Russia, and northeastern China (Christian et al. 2021). Another study results indicate that regions experiencing flash droughts are typically those with humid or semi-humid climates. These regions include Southeast Asia, East Asia, the Amazon Basin, eastern North America, and southern South America (Qing et al. 2022). However, Sreeparvathy & Srinivas (2022) identified that areas with the highest incidence of meteorological flash droughts are primarily located in arid and semi-arid regions. FDs occur over sub-seasonal-to-seasonal timelines (‘weeks’ or ‘weeks to months’), posing a novel challenge to the growing interest in enhancing their identification and forecast climate change (Pendergrass et al. 2020). FDs are caused by prolonged periods of soil moisture depletion (Ford & Labosier 2017; Yuan et al. 2019; Liu et al. 2020; Osman et al. 2021), less precipitation (Mo & Lettenmaier 2016), exceptionally high temperature (e.g., heatwaves), and strong wind (Chen et al. 2019), all of which result in abnormally high evaporation and evapotranspiration rates (Christian et al. 2019a; Li et al. 2020a). FD events appear to occur more frequently than is commonly believed and can inflict significant agricultural, water, and economic losses that intensify in the absence of timely forecast and identification (NIDIS 2021). For instance, some of the impacts of past FD events in different countries are presented:

  • India: A recent study shows that FDs affected around 10%–15% of rice crop and maize crop cultivation lands in the country between 1951 and 2018, and about 40% of India faced severe FDs in 1979 (Mahto & Mishra 2020).

  • China: During the 2013 summer, an FD hit 13 provinces in southern China, destroying almost 2 million hectares of crops in Guizhou and Hunan alone (Yuan et al. 2015).

  • Russia: FD that hit western parts of Russia in the summer of 2010 was more rapid and intensified, which affected the wheat yields that reduced by 70% and led to a decline in wheat production in Russia (Christian et al. 2020). The 2010 FD across western Russia had at least these two immediate impacts: (1) the severe loss of life (about 11,000 deaths) caused by the heatwave along with FD and (2) the loss of approximately 20 million MT of wheat (34% decline) compared with Russia's production in the preceding two years (Hunt et al. 2021).

  • Central US: The 2012 FD had a significant and extensive impact on vegetation, agriculture, and the economy of the United States, with more than $30 billion in agricultural losses (Otkin et al. 2016; Liang & Yuan 2021; Lesinger & Tian 2022).

  • US Northern Plains: The resultant impact of the 2017 Northern Plains FD in the United States was about $2.6 billion in agricultural losses (NIDIS 2021). He et al. (2019) found that the FD led to a 25% decline in cropland ET, a 6% reduction in crop yield over the area between April and September 2017.

Presently, researchers mainly focus on (1) the rapid onset and intensification of drought situations and (2) short-time intense extreme drought events (Lisonbee et al. 2021; Noguera et al. 2022). Despite wide-ranging and expanding studies on the FD process, prediction, and trends, no standard quantifiable definition covers all FD features and pathways (Figure 1; please refer to Supplementary Text S1 for the description). The variations in definitions and methodologies for FD among research communities, as well as the use of different data sources, highlight the need to address uncertainties related to the characteristics of global FD and their underlying causes (Mukherjee & Mishra 2022b). Several definitions are given, facilitating broad-ranging research of FD, potentially causing uncertainty about what the term implies and how to define them (Osman et al. 2021). The objective of the drought indices is to integrate various meteorological variables of FD and scientifically modify the indicators for better identification and forecasting of FD events. Technically, the indices are also used as indicators, but there is not a specific index or indicator used to identify all categories of droughts, climatic conditions, and drought-affected areas (Svoboda & Fuchs 2016). The FD events can have direct impacts on agricultural productivity, which leads to economic losses at the national level and then impacts water resources due to the deficiency of rainfall and rapid depletion of the soil moisture of that region. The extending knowledge that FD events include specific progressions and critical effects, and perhaps a climate change aspect, makes them a driving frontline for research to monitor, forecast, plan, and respond to (Pendergrass et al. 2020). In addition, a study in India (river-basin scale) is discussed to illustrate the frequency and duration of FDs in Section 3.
Figure 1

Overview of different indicators which are used to define FDs.

Figure 1

Overview of different indicators which are used to define FDs.

Close modal

The objective of this paper is to combine the concepts related to FD, bring attention to the aspects of FD that are already understood and those that are not, recognize the inadequacies in the present indices used to identify and define FD, and depict some specific points for research needs. To achieve this, the paper will address the following questions:

  • What are the advantages and limitations of the existing indices used for identifying or defining FDs?

  • What are the things that the researchers miss and how to include them in improving identification and prediction?

  • What are the research-needs to monitor, forecast, plan, and respond to FDs?

Soil moisture indices

  • Soil Moisture Index (SMI): The SMI was developed by the High Plains Regional Climate Center to detect the quick start of a drought event by analyzing the reported aridness of the soil compared with the capability of vegetation to obtain water. The SMI ranges from 5 to −5, where 5 denotes field capacity and −5 denotes the wilting point as it is a soil–moisture-based drought index (Sridhar et al. 2008; Hunt et al. 2009). The SMI is based on a range of ±5, with negative numbers signifying times of water shortage. A drought event is defined as a period with negative SMI values, and the average value for the 0–0.5 m layer soil profile is used to determine the daily SMI. The SMI has been used to identify the onset of FDs based on the rapid decline in soil moisture in a three-week period (Mozny et al. 2012; Hunt et al. 2021).

Advantages: The advantage of using SMI is that it is feasible to acquire data from the remote sensing system and to evaluate the frequent measurements of satellite observations as well as to validate the accuracy. Also, SMI has a benefit over soil reflectance as it reflects soil–water conditions better.

Limitations: SMI is calculated as the average value for the 0–0.5 m soil layer. However, it does not include SMI values for multiple soil layers. In fact, during the spring season, the upper layer of soil (sandy soils) may dry out and obstruct the growth of crops. The zero value of SMI indicates no drought. However, it can lead to drought conditions or maybe recovery from drought.

  • Soil Moisture Volatility Index (SMVI):Osman et al. (2021) proposed a definition using SMVI, which is constructed based on average root zone soil moisture (RZSM). In this definition, an FD is said to occur when (1) the one-pentad (five-day) running RZSM falls below the four-pentad (20-day) running average for a period of at least four pentads; and (2) by the end of the period, RZSM drops below the 20th percentile for that time of the year. This index is sensitive to disruptions during the beginning of drought, though rain episodes can change it (Osman et al. 2021). This index demonstrates an ability to identify the start of FDs irrespective of the vegetation or wetness conditions of a particular area similar to the detected impacts on vegetation (Osman et al. 2022). The SMVI is used to estimate the incidence rate, spatial distribution, and seasonality of FD across the contiguous United States (Osman et al. 2021).

Advantages: The advantage of the SMVI approach lies in its capability to detect rapid changes regarding a slower drying trend.

Limitations: SMVI shows an ability to describe the onset of FD events. However, it is restricted to reporting the severity and intensity of FD events. The index is only applicable for intervals in drought onset, and the index must be reset with rainfall events.

Evapotranspiration indices

  • Evaporative Demand Drought Index (EDDI): The EDDI is an experimental tool that uses the anomalies of evaporative demand to indicate the spatial coverage and severity of FDs (Hobbins et al. 2016). The potential evapotranspiration (PET) detects and monitors FD onset and captures FDs triggered by climate variables (Won & Kim 2020; Noguera et al. 2021). The EDDI has significant strength in capturing the early indications of water stress from timescales of one to 12 weeks since 1979, making it a powerful system like USDM for FD preparedness (McEvoy et al. 2016). The EDDI can verify the occurrences of historical FD events to provide an early-warning system. Therefore, this index is used in many studies (Yao et al. 2018; Christian et al. 2019a; Nguyen et al. 2019; Li et al. 2020b; Parker et al. 2021).

Advantages: The main strength of EDDI is its early warning abilities and its efficiency in the attribution of drought changing aspects. The EDDI is a powerful tool for drought preparedness as it acquires the signals of water stress at weekly to monthly timescales. The EDDI can also be deconstructed to discern the impact of individual evaporative drivers on the onset and continuity of drought, including factors like radiation and advection components. The EDDI can report a diversity of frequent droughts without precipitation data.

Limitations: EDDI performs only with evaporative demand and with no other variables (e.g., precipitation). Therefore, it may not be reasonable to interpret FDs using only evaporative demand. EDDI is limited to only the contiguous United States.

  • Evaporative Stress Index (ESI): The ESI represents the ratio of actual evapotranspiration to PET, i.e., ESI = ET/PET (Anderson et al. 2016; McEvoy et al. 2016). The ESI represents these anomalies of the actual-to-potential ET ratio taken through the energy equilibrium using remote sensing parameters such as land surface temperature (LST) and leaf area index (LAI) as inputs (AghaKouchak et al. 2015; Anderson et al. 2016; Lee et al. 2021; Ahmad et al. 2022). The negative ESI value indicates that standardized ET is less than the average for given time intervals, usually, two, four, eight, or 12 weeks, progressing at a seven-day interval, which shows the decreased soil moisture in addition to vegetative stress (Hunt et al. 2021). In many studies, the ESI has precisely identified the FD events and given an early warning in a couple of weeks (Nguyen et al. 2021). If the ESI reduces rapidly and this is sustained for at least two weeks, it indicates the onset of FD (Nguyen et al. 2019).

Advantages: The ESI generates very high-resolution data, and it does not need precipitation data. The ESI has a special significance in regions with minimum in situ precipitation observation.

Limitations: ESI is derived from remote sensing assessment, which is limited to snow-free periods. ESI cannot competently capture the soil moisture from the adjacent fields due to its spatial constraint.

  • Standardized Evapotranspiration Deficit Index (SEDI): The SEDI is an FD index that integrates climate and ecosystem systems by measuring the difference between actual and PET (Zhang et al. 2019; Li et al. 2020b). The SEDI attempts to identify both heat-wave-driven and water-deficit-driven FDs. It is mostly suitable for monitoring and evaluating FD hazards globally (Kim et al. 2019). Li et al. (2020b) used SEDI to identify FD based on these criteria: (i) the extent of the drought is more than five pentads but less than 12 pentads, (ii) the cumulative SEDI instantaneous intensification rate is at or lower than 25% of the cumulative incidence rate of the variation in the cumulative SEDI value throughout FD growth, and (iii) during the FD growth phase, the average rapid intensification rate is at or less than 40% of the cumulative distribution rate of the change in the cumulative SEDI (Li et al. 2020b).

Advantages: The SEDI has an exceptional application in describing the biological transformations of ecosystems in reaction to the subtleties of drought intensity as compared with other indices related to precipitation and temperature.

Limitations: SEDI can be used for drought assessment, which is related to crops and vegetation, but its use is limited to drought impacts such as river discharge, reservoir storage, or groundwater. SEDI has a tendency for large uncertainty in humid and semi-humid regions. Thus, it is more suitable for arid and semi-arid regions.

Precipitation and evaporation indices

  • Standardized Precipitation Index (SPI): The SPI has been developed to measure precipitation deficiency over various timescales. It is used as a drought index that considers the relevance of temporal scales in the study of water availability and usage (Guttman 1998). It is used to show the efficiency of detecting FD vs field observations (Hunt et al. 2014). It is significant to know that SPI alone is not likely to lead to the occurrence of FDs since precipitation deficiency is only a single variable of numerous factors that will lead to quick intensification (Otkin et al. 2013). Precipitation is an essential component and a driver of FDs which gives multiple forms of information for calibration and validation of drought conditions (Goyal et al. 2022). In addition, precipitation is not related to evaporative demand, which is specifically significant for FDs (Ford 2022). However, SPI can be used with other indices or parameters, for instance, the SPI and SPEI (Standardized Precipitation Evaporation Index) were utilized in the study of ‘rapid onset drought’, which proved that both SPI and SPEI for one month are relatively sensitive to the onset of FD events and also closely tracked the decrease in soil water (Hunt et al. 2014; Noguera et al. 2021).

Advantages: The SPI has an advantage in computation and usage for any geographical location and for any number of intervals as the SPI values are in units of standard deviation from the long-term mean. The SPI is easy to compute compared with other drought indices as it is based on only precipitation (single input parameter).

Limitations: SPI uses only precipitation, which makes it less linked with the ground conditions. Long-term precipitation data along with climate conditions are required for the accurate identification of FDs.

  • Standardized Precipitation Evaporation Index (SPEI): The SPEI is a drought index based on SPI, measured by precipitation and PET (Vicente-Serrano et al. 2010), but the only change is that the temperature parameter is included in this index to capture climatic water stability (Vicente-Serrano et al. 2010; Faiz et al. 2020). It is used to detect FD events based on a short time scale of one month and weekly high-frequency data and anomalous decline in SPEI index values in four weeks of a short time period focusing on the development stage of an event (Noguera et al. 2020). The SPEI can simultaneously detect variations in rainfall and temperature, which are constructed using weekly rainfall and PET (Hu et al. 2021). A previous study stated that drought indices such as SPI and SPEI probably cannot identify/monitor an FD as they are precipitation-based (Kim et al. 2019). However, the SPEI comprises an indicator of heat-waves, indicating the signs of heat wave-driven droughts (Hari et al. 2020).

Advantages: The SPEI links multi-timescale properties of the SPI with evapotranspiration data, which makes it more suitable for climate change analyses. The main advantage of the SPEI over the SPI is that it counts the role of temperature through its effect on PET.

Limitations: SPEI is sensitive to the method of computing PET and it requires more data compared with the precipitation SPI. As the SPEI is a monthly index, rapidly developing drought conditions may not be identified quickly.

Flash-drought-specific indices

  • Flash Drought Intensity Index (FDII): The FDII describes both the quick intensification rate and subsequent severity of FD events (Otkin et al. 2021). It is computed using two components: extreme rate of intensification (FD_INT) and average drought severity (DRO_SEV). Otkin et al. (2021) used the least intensification rate equal to a 15-percentile reduction in soil moisture through a four-pentad period to identify FD. A study of the 2012 FD in the United States found that the FDII description of extreme drought situations corresponded more directly to locations having bad crop situations and high production losses than the intensification rate component (FD_INT) individually.

Advantages: The FDII offers a more extensive measure of FD severity than other existing identification approaches that emphasize only on the rate of intensification. The FDII is also able to depict severe drought situations associated with crop conditions and yield damages based on the intensification rate alone.

Limitations: FDII is derived using the soil moisture output from the Noah land surface model, which is limited to the 0–40 cm layer. However, various challenges in using soil moisture from remotely sensed data limit their use for FD events. FDII is limited to only the contiguous United States.

  • Flash Drought Stress Index (FDSI): The FDSI is a composite index for monitoring FDs using thresholds of soil-hydrologic and land–atmospheric regimes (Sehgal et al. 2021). The Soil Moisture Stress (SMS; shows drought severity) and Relative Rate of Drydown (RRD; shows drought intensification rate) are combined to provide FDSI. The FDSI has been extensively proved globally across daily, weekly, and monthly timescales using a suitable vegetation and meteorology drought index. The assessment of FDs using FDSI is done at the global level (e.g., the regions of South Africa, Australia, and Northern Great Plains).

Advantages: The FDSI is a composite index used for global FD monitoring, and it can be extensively used across daily, weekly, and monthly timescales. The FDSI is generated on a daily basis and the data are available for the most recent years from 2015 to the present.

Limitations: FDSI is sensitive to changing land-surface heterogeneity, land–atmospheric interactions, and evolving meteorological anomalies. FDSI is generated using SMAP observations from 2015 to the present, which shows the period of record for data is short.

  • Rapid Change Index (RCI): The RCI has been developed to capture moisture stress changes over multiple weeks. It shows the areas experiencing rapid changes in moisture stress as being conditional on weekly fluctuations in the ESI. Then the onset of a drought event is likely when the RCI value is negative (Otkin et al. 2014, 2015). When variations in the ESI during a two-week interval are less than the 20th percentile, the RCI at each time step is used to determine the intensification phase of the FD event (Nguyen et al. 2021). The RCI provides significant drought early-detection abilities and real-time predictions (Chen et al. 2020), which might be applied to report an elevated risk of drought onset in sub-seasonal time frames (Otkin et al. 2018).

Advantages: The RCI provides an early warning capability to notify stakeholders about the increase in the risk for rapidly developing drought events. The potential advantage of the RCI is that it can be used for both rapidly increasing and rapidly decreasing moisture stress. The RCI may be used to predict future increases in drought severity as depicted by USDM.

Limitations: Drought is likely to develop when RCI is negative. Since RCI changes with time, it is difficult to capture FD development by monitoring RCI maps. RCI is limited to only the contiguous United States.

  • Quick Drought Response Index (QuickDRI): The QuickDRI was developed to identify quickly and for early detection of developing drought events. The multiple input parameters, namely, precipitation (as SPI), soil moisture, evapotranspiration (as ESI), and vegetation health (as Standardized Vegetation Index; SVI), are used to show anomalous drought conditions over the previous four weeks for a one-standard deviation threshold (Chen et al. 2019; Jiao et al. 2021; Osman et al. 2021).

Advantages: The purpose of QuickDRI is to provide an alert to identify the regions of developing drought and the regions of intensifying drought situations indicating a wet signal. The QuickDRI can employ SPEI to integrate evaporation calculation using both temperature and precipitation, instead of only precipitation.

Limitations: QuickDRI may not be meaningful for dormant vegetation, snow cover, and frozen soils, since it could lead to a false indication that is not related to a change in the drought condition. QuickDRI is limited to only the continental United States.

Study area and data used

For the present study, 24 major river basins of India (as per India-WRIS (2014) classification) were selected as the study area as shown in Figure 2. As we know India has high spatial and temporal variability in terms of temperature and precipitation, there are 24 river basins that are important for investigating FD.
Figure 2

Spatial distribution of river basins in India.

Figure 2

Spatial distribution of river basins in India.

Close modal

The daily precipitation and temperature datasets for 24 river basins were collected from the India Meteorological Department (IMD), and the soil moisture datasets were obtained from five soil moisture products (ESA-CCI, ERA-Interim, MERRA-2, GLDAS-2-Noah, and IMDAA). All the datasets were re-gridded to a resolution of 0.25° × 0.25° using the IDW (inverse distance weighing) method. Please refer to Table 1 for all datasets and their sources.

Table 1

Datasets used and their sources

DataPeriodSourceOriginal resolutionFinal resolution
Precipitation 1981–2014 Indian Meteorological Department (IMD) 0.25°×0.25° 0.25°×0.25° 
Temperature 1981–2014 Indian Meteorological Department (IMD) 1°×1° 0.25°×0.25° 
Soil moisture 1981–2014 ESA-CCI SM COMBINED datasets (https://www.esa-soilmoisture-cci.org/node/1450.25°×0.25° 0.25°×0.25° 
Global Land Data Assimilation System version 2 Noah (https://ldas.gsfc.nasa.gov/gldas/0.25°×0.25° 
ERA-Interim soil moisture reanalysis product (https://apps.ecmwf.int/datasets/data/interimfulldaily/levtype=sfc/0.75°×0.75° 
Modern-Era Retrospective analysis for Research and Applications, Version 2 (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/0.5°×0.67° 
IMDAA soil moisture data (https://rds.ncmrwf.gov.in/0.12°×0.12° 
DataPeriodSourceOriginal resolutionFinal resolution
Precipitation 1981–2014 Indian Meteorological Department (IMD) 0.25°×0.25° 0.25°×0.25° 
Temperature 1981–2014 Indian Meteorological Department (IMD) 1°×1° 0.25°×0.25° 
Soil moisture 1981–2014 ESA-CCI SM COMBINED datasets (https://www.esa-soilmoisture-cci.org/node/1450.25°×0.25° 0.25°×0.25° 
Global Land Data Assimilation System version 2 Noah (https://ldas.gsfc.nasa.gov/gldas/0.25°×0.25° 
ERA-Interim soil moisture reanalysis product (https://apps.ecmwf.int/datasets/data/interimfulldaily/levtype=sfc/0.75°×0.75° 
Modern-Era Retrospective analysis for Research and Applications, Version 2 (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/0.5°×0.67° 
IMDAA soil moisture data (https://rds.ncmrwf.gov.in/0.12°×0.12° 

Methodology

Due to the unavailability of observed/in situ soil moisture over the study area, several soil moisture products are obtained for the study period. Considering the large ambiguity allied with soil moisture products, such products were assessed against unknown truth using the triple collocation approach. In this approach, triplets were obtained by picking three random soil moisture products from all derived five soil moisture products (MERRA-2 SM, GLDAS-2Noah SM, ESA-CCI SM, ERA-Interim SM, and IMDAA SM). Initially, the above available soil moisture datasets were resampled into 0.25° grid spatial resolution and eight-day temporal resolution (to remove the impact of missing datasets). Further, several triplets were obtained from all the derived five soil moisture products. In this approach, no datasets were treated as observed soil moisture datasets, however, all datasets were assessed against unknown truth for all possible triplets to obtain RMSE (root mean square error) and correlation values. This approach was initially developed to find the error variance of a wind dataset, and later on applied to precipitation and soil moisture datasets (Stoffelen 1998; Gruber et al. 2016). Readers are suggested to refer to McColl et al. (2014) to derive RMSE and correlation values from the triple collocation approach. Reanalysis data (GLDAS) provide better soil moisture datasets despite satellite-based soil moisture datasets (Chen et al. 2013). In the present study, the rapid intensification approach is used to monitor FD over India. FD is computed at the octad scale, i.e., at eight-day temporal scale using soil moisture percentiles. The methodology of the study is presented in Figure 3.
Figure 3

Methodology flowchart of the study.

Figure 3

Methodology flowchart of the study.

Close modal

In this study, soil moisture (SM) is used to detect FD events in India between 1981 and 2014. The daily gridded data of SM were converted to an octad (SM averaged for eight days). As a result, each year got a total of 46 octads, with 16 octads for the rainy season (i.e., the 19th–34th octads) and the residual octads for the non-rainy (non-monsoon) season (i.e., from first to 18th and 35th to 46th octads). The definition used for the FD event is as ‘when the mean SM percentile for eight decreases to 20th percentile or less from above 40th percentile and an average rate of drop not <5%iles for every eight days’, the drop from the 40th to the 20th percentile indicating the beginning of FD (Zhang & Yuan 2020). The retrieval time of the FD event was recognized after the SM began to either fall slowly or rise, and when the SM percentile increases to 20 (or higher), the drought is over. Furthermore, for FDs, a minimum period of 24 days (three time-steps) was measured to eliminate short dry spells that significantly less affect the environment. The FD threshold was set at the 20th percentile (Yuan et al. 2019) to eliminate the conventional droughts from the study.

Results and discussion

In the monsoon season, there is no significant variation in onset duration between climatic regions, nevertheless, in some areas, the duration differed dramatically among grids (i.e., ranging from 0 to above 35 days), as shown in Figure 4. Then, in some parts of the country, the retrieval period is somewhat longer as shown in Figure 4(c). Remarkably, it was found that at least two-thirds of the Ganga basin area has a negligible onset period; nevertheless, the retrieval period is extended to greater than 30 days due to decreased soil moisture levels. The mean interval of the beginning and retrieval stages is 19.9 and 27.7 days, respectively. In the non-rainy season, the northeastern part, the southern part of India, and certain parts of the Ganga basin saw more prolonged beginning and retrieval periods between 1981 and 2014, as shown in Figure 4(b) and 4(d), due to the deficiency of rainfall over a long period. The average lengths of the onset and retrieval periods are 24.7 and 29.2 days, respectively. The results reveal that the Indus basin area experienced about 82% of severe FDs during the rainy season, while the Cauvery basin area faced about 88% of severe FDs during the non-rainy season. The Indus basin faces FD in the monsoon season since this basin has mostly barren areas and wasteland, whereas the Cauvery basin observes high moisture variability in the post-monsoon season which triggers FD.
Figure 4

Frequency (a) monsoon season and (b) non-monsoon season, and mean duration (c) monsoon season and (d) non-monsoon season of FD events during 1981–2014 over India. The frequency refers to how often FD events occur and the duration refers to the number of days that an FD event persists.

Figure 4

Frequency (a) monsoon season and (b) non-monsoon season, and mean duration (c) monsoon season and (d) non-monsoon season of FD events during 1981–2014 over India. The frequency refers to how often FD events occur and the duration refers to the number of days that an FD event persists.

Close modal

Role of humidity

The relationship between humidity and FD is such that increased humidity can mitigate drought conditions by reducing transpiration, the process by which plants lose water through their leaves (Georgii et al. 2017). However, high humidity can also make the outdoor environment feel more unpleasant, even if the relative humidity is lower than that of a cooler day with higher relative humidity. In general, while humidity can have a certain effect on drought conditions, it is only one of several factors that contribute to FD and its level of severity (Means 2021). The measure of the amount of moisture present in the atmosphere is referred to as humidity. The onset of FDs occurs suddenly when drought conditions arise during high-temperature periods accompanied by low humidity levels (Gamelin et al. 2022; Gong et al. 2022). Humidity plays a critical role in determining whether a region is at risk from FDs. When there is low atmospheric moisture available, it becomes increasingly hard for vegetation and soil surfaces to retain enough water, resulting in aridness which leads to a quick depletion of soil moisture content – elevating susceptibility to wildfires especially within habitats vulnerable to frequent drought incidents. Understanding how varying meteorological factors such as climate change exacerbate existing risks needs to be better researched by experts, policymakers, and drought management agencies, thus to come up with more effective strategies in preventing the consequences of these extreme events.

Role of wind and radiation

A study indicated that sudden rises in evapotranspiration rates, induced by variations in heat, wind, and radiation, preceded all the FDs analyzed (Sreeparvathy & Srinivas 2022). FD occurs when there is a decline in the amount of precipitation, which is further intensified by unusual high temperatures, winds, and radiation. These joint climate conditions can cause swift and substantial changes to the local climate (NIDIS 2022). The soil moisture was abundant before the FD, but it declined as the evapotranspiration rates increased. Therefore, the researchers inferred that variations in evapotranspiration rates are a reliable predictor of FDs (Chen et al. 2020). The strong winds can hasten the process of evapotranspiration and cause soil moisture to deplete rapidly, which is a key factor contributing to the occurrence of FDs. FDs are characterized by a sudden onset of drought conditions caused by a combination of low soil moisture, high temperatures, and dry soil, and they are frequently associated with atmospheric anomalies such as low humidity, strong winds, extreme temperatures, and scant precipitation. The reason for this is that winds carry thermal energy to a region and take away the evaporated moisture, which can accelerate the drying of the soil.

Role of soil characteristics

Soil characteristics, including type, depth, and water-holding capacity factors, can affect the onset and severity of FDs and play an important role in soil moisture and the rate at which water is absorbed and released (Chen et al. 2019; Christian et al. 2019b; Qing et al. 2022). Understanding these factors and their interaction is crucial in forecasting and mitigating the consequences of FDs. Efficient handling of water resources, such as soil preservation, land utilization practices, and irrigation, can help in reducing the effects of FDs (Subramoniam et al. 2022). The existence of different vegetation and soil types in various regions can also play a role in the emergence of FDs (Chen et al. 2019). Crops can experience moisture stress at a faster rate by extracting water from deeper layers of soil, which can contribute to the rapid onset of FDs (Qing et al. 2022). Soils that are shallow, which have limited water retention capacity and are vulnerable to rapid evaporation, can increase the possibility and intensity of FDs. This comprises soil types like sandy soils, loamy sands, and those with a high proportion of gravel or rock fragments. Additionally, low organic matter content in soils can also play a role in triggering FDs. This is because soils with less organic matter are less capable of holding moisture and supporting healthy plant growth, leading to quicker soil moisture reduction during times of high temperature or low precipitation (Qing et al. 2022).

Influences of climate oscillations

Climate oscillations are variations in oceanic and atmospheric conditions that happen over a span of several years to decades. These oscillations can have an influence on weather patterns and moisture availability, which can impact the occurrence and severity of FDs. The intensity and severity of FDs can be strongly influenced by climate oscillations, which can occur over large distances through teleconnections (Lesinger & Tian 2022). An intensification of climate warming and an increase in the frequency of El Niño events can intensify the occurrence of FDs in multiple major croplands at the same time, leading to significant consequences for food production (Mahto & Mishra 2023). Climate oscillations are large-scale variations in oceanic and atmospheric conditions that can affect weather patterns and moisture availability, thus influencing the development of FDs (Yuan et al. 2023). El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Indian Ocean Dipole (IOD) are examples of climate oscillations that can impact FDs. ENSO is a pattern of cyclical warming and cooling in the Pacific Ocean that can disturb global weather patterns. NAO refers to the variation of atmospheric pressure patterns over the North Atlantic Ocean, and IOD refers to the variability of sea surface temperatures in the Indian Ocean. During positive IOD events, there tends to be a decrease in rainfall over parts of Southeast Asia and Australia, increasing the risk of FDs. These climate oscillations can have a significant impact on the occurrence of FDs in different regions globally. Thus, understanding the mechanisms by which climate oscillations affect FDs is vital in addressing the problems related to the rapid intensification of drought and its consequences for agriculture, water resources, ecosystems, and the human environment (Christian et al. 2021).

Connection between FDs and heavy-rainfall-induced flash floods

Flash droughts and flash floods induced by heavy rainfall are connected through their impact on the ground. Flash floods can be more destructive in arid regions due to the limited ability of dry soils to absorb water, leading to a higher likelihood of surface runoff and flooding, whereas in wetter regions, the soil can more readily absorb water, resulting in less severe flooding (Gupta et al. 2023; Yin et al. 2023). FDs are more common in humid regions with lower aridity compared with slow droughts, with a frequency that is two to three times higher (Yuan et al. 2023). When an FD occurs, the soil can become dry and compacted, which can decrease its permeability and ability to absorb rainwater. This can lead to rapid runoff of water from the surface of the ground and dead vegetation rather than water being absorbed into the soil during heavy rainfall, resulting in flash flooding. The IPCC Report emphasized with high confidence that the process of aridification and the occurrence of heavy rainfall will lead to severe flooding (IPCC 2022). Consequently, flash floods can become more likely in certain situations due to the occurrence of FDs. In addition, the safety of dams becomes a serious concern when there is a possibility of larger floods. This problem is compounded by the occurrence of flash floods during drought intervals, which creates a misleading sense of safety (Mediero et al. 2007).

The study describes an understanding of the level to which FD characteristics and indicators are susceptible to the use of drought indices, as these are the basis for evaluating the areas most vulnerable to FDs. The study in India reveals that the Indus basin area faced around 82% of severe FD events during the rainy season and the Cauvery basin area faced about 88% of severe FD events during the non-rainy season. In this study, FD is identified using a rapid intensification approach and soil moisture is used as an input variable/driver to define the octad-scale (eight-day) temporal scale using soil moisture percentiles. Reanalysis data (GLDAS) used in the study provide better soil moisture datasets despite satellite-based soil moisture datasets (Chen et al. 2013). Also, adopting a consistent definition and index for what characterizes an FD provides helpful information to administrators, researchers, scientists, and farmers to understand these extreme events of the climate system. However, the standard guidelines should be decided to differentiate FD episodes from conventional droughts. Given the worldwide climatology of the occurrence of FD events and the quick land-surface dryness associated with fast onset, drought occurrences have the potential to have major consequences beyond agricultural production loss and economic loss. Furthermore, it is well acknowledged that current monitoring and forecasting tools do not give enough early warning for FDs (NIDIS 2021; Mukherjee & Mishra 2022a); therefore, certain research needs mentioned below are required in future work.

Research needs: The main aim of FD research is to reduce and prevent harmful consequences to the community and ecosystem. The researchers need to decide on a model framework for differentiating and identifying FD events. The research needs are depicted with crucial points for monitoring, forecast, planning, and response phases (Figure 5).
  • The monitoring should consist of the suitability of indicators like soil moisture, evapotranspiration, and climatic variables. Furthermore, systematic approaches are required to monitor the development of FD accurately beyond the rapid onset and intensification period. Future research is needed to advance the data sources from in situ observations (soil moisture, vegetation, crop health, etc.), satellite data (remote sensing), and climate models' output data along with meteorological and drought impact datasets, which have a significant role in monitoring FDs accurately.

  • Forecasting methods are needed for identifying novel machine-learning algorithms and data sources. Also, researchers should have a better understanding of the land–atmosphere connections and physical systems triggering FD. Also, their validation is required to know their functioning and suitability to forecast FDs using existing and upcoming models. Likewise, to develop a forecast method or model, researchers should have weekly predictions at a higher resolution and use better data integration.

  • For planning, the research needs to be carried out on FDs during every season of the year. Planning should be done of given impacts on water resources, ecosystem, well-being of agriculture and economy for decision-making, and impact evaluation. The research plan is essential to study how the event impacts are effectively reported by the organizational programs, decision-making systems, and policies to expand relief for FDs. Furthermore, a detailed social science study is also necessary for public awareness and participation in effective interaction.

  • During the response, government bodies at different levels (regional, state, local, specific groups, etc.) must evaluate the technical drought data and share them with scientific and non-scientific persons for monitoring and early warning of FDs. The response approaches must be efficient to communicate with the end-users of FD impacts by developing dashboards, interactive maps, apps, etc. Communication supports understanding of practical monitoring and forecast information. The research should also develop appropriate indicators and factors for drought response actions and decision-making.

Figure 5

Future research-needs to monitor, forecast, plan, and respond to FD events.

Figure 5

Future research-needs to monitor, forecast, plan, and respond to FD events.

Close modal

S.R. would like to acknowledge the support of the Department of Science and Technology (DST), Government of India, New Delhi, for his INSPIRE Fellowship. We present this work as a tribute to Prof. Manish Kumar Goyal on their birthday, acknowledging their steadfast support, invaluable guidance, and constant encouragement.

The authors declare that they are aware of and consent to their participation on this paper.

The authors declare that they consent to the publication of this paper.

S.R.: Conceptualization, Software, Visualization, Writing – original draft, and Writing – review and editing; V.P.: Methodology, Formal analysis, Visualization; M.K.G.: Supervision, Validation.

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

AghaKouchak
A.
,
Farahmand
A.
,
Melton
F. S.
,
Teixeira
J.
,
Anderson
M. C.
,
Wardlow
B. D.
&
Hain
C. R.
2015
Remote sensing of drought: progress, challenges and opportunities
.
Rev. Geophys.
53
,
452
480
.
https://doi.org/10.1002/2014RG000456
.
Ahmad
S. K.
,
Kumar S
V.
,
Lahmers
T. M.
,
Wang
S.
,
Liu
P.-W.
,
Wrzesien
M. L.
,
Bindlish
R.
,
Getirana
A.
,
Locke
K. A.
,
Holmes
T. R.
&
Otkin
J. A.
2022
Flash drought onset and development mechanisms captured with soil moisture and vegetation data assimilation
.
Water Resour. Res.
58
,
e2022WR032894
.
https://doi.org/10.1029/2022WR032894
.
Anderson
M. C.
,
Zolin
C. A.
,
Sentelhas
P. C.
,
Hain
C. R.
,
Semmens
K.
,
Yilmaz
M. T.
,
Gao
F.
,
Otkin
J. A.
&
Tetrault
R.
2016
The Evaporative Stress Index as an indicator of agricultural drought in Brazil: an assessment based on crop yield impacts
.
Remote Sens. Environ.
174
,
82
99
.
https://doi.org/10.1016/j.rse.2015.11.034
.
Chen
Y.
,
Yang
K.
,
Qin
J.
,
Zhao
L.
,
Tang
W.
&
Han
M.
2013
Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau
.
J. Geophys. Res. Atmos.
118
,
4466
4475
.
https://doi.org/10.1002/jgrd.50301
.
Chen
L. G.
,
Gottschalck
J.
,
Hartman
A.
,
Miskus
D.
,
Tinker
R.
&
Artusa
A.
2019
Flash drought characteristics based on US drought monitor
.
Atmosphere
10
,
498
.
https://doi.org/10.3390/atmos10090498
.
Chen
L. G.
,
Hartman
A.
,
Pugh
B.
,
Gottschalck
J.
&
Miskus
D.
2020
Real-time prediction of areas susceptible to flash drought development
.
Atmosphere
11
,
1114
.
https://doi.org/10.3390/atmos11101114
.
Christian
J. I.
,
Basara
J. B.
,
Otkin
J. A.
,
Hunt
E. D.
,
Wakefield
R. A.
,
Flanagan
P. X.
&
Xiao
X.
2019a
A methodology for flash drought identification: application of flash drought frequency across the United States
.
J. Hydrometeorol.
20
,
833
846
.
https://doi.org/10.1175/JHM-D-18-0198.1
.
Christian
J. I.
,
Basara
J. B.
,
Otkin
J. A.
&
Hunt
E. D.
2019b
Regional characteristics of flash droughts across the United States
.
Environ. Res. Commun.
1
,
125004
.
https://doi.org/10.1088/2515-7620/ab50ca
.
Christian
J. I.
,
Basara
J. B.
,
Hunt
E. D.
,
Otkin
J. A.
&
Xiao
X.
2020
Flash drought development and cascading impacts associated with the 2010 Russian heatwave
.
Environ. Res. Lett.
15
,
094078
.
https://doi.org/10.1088/1748-9326/ab9faf
.
Christian
J. I.
,
Basara
J. B.
,
Hunt
E. D.
,
Otkin
J. A.
,
Furtado
J. C.
,
Mishra
V.
,
Xiao
X.
&
Randall
R. M.
2021
Global distribution, trends, and drivers of flash drought occurrence
.
Nat. Commun.
12
,
6330
.
https://doi.org/10.1038/s41467-021-26692-z
.
Faiz
M. A.
,
Liu
D.
,
Fu
Q.
,
Naz
F.
,
Hristova
N.
,
Li
T.
,
Niaz
M. A.
&
Khan
Y. N.
2020
Assessment of dryness conditions according to transitional ecosystem patterns in an extremely cold region of China
.
J. Clean. Prod.
255
,
120348
.
https://doi.org/10.1016/j.jclepro.2020.120348
.
Ford
T.
2022
Flash Drought Tools: Advantages and Disadvantages by Indicator Type
.
Ford
T. W.
&
Labosier
C. F.
2017
Meteorological conditions associated with the onset of flash drought in the Eastern United States
.
Agric. For. Meteorol.
247
,
414
423
.
https://doi.org/10.1016/j.agrformet.2017.08.031
.
Gamelin
B. L.
,
Feinstein
J.
,
Wang
J.
,
Bessac
J.
,
Yan
E.
&
Kotamarthi
V. R.
2022
Projected US drought extremes through the twenty-first century with vapor pressure deficit
.
Sci. Rep.
12
,
8615
.
https://doi.org/10.1038/s41598-022-12516-7
.
Georgii
E.
,
Jin
M.
,
Zhao
J.
,
Kanawati
B.
,
Schmitt-Kopplin
P.
,
Albert
A.
,
Winkler
J. B.
&
Schäffner
A. R.
2017
Relationships between drought, heat and air humidity responses revealed by transcriptome-metabolome co-analysis
.
BMC Plant Biol.
17
,
120
.
https://doi.org/10.1186/s12870-017-1062-y
.
Gong
Z.
,
Zhu
J.
,
Li
T.
,
Huang
D.
,
Chen
X.
&
Zhang
Q.
2022
The features of regional flash droughts in four typical areas over China and the possible mechanisms
.
Sci. Total Environ.
827
,
154217
.
https://doi.org/10.1016/j.scitotenv.2022.154217
.
Goyal
M. K.
,
Gupta
A. K.
,
Jha
S.
,
Rakkasagi
S.
&
Jain
V.
2022
Climate change impact on precipitation extremes over Indian cities: non-stationary analysis
.
Technol. Forecast. Soc. Change
180
,
121685
.
https://doi.org/10.1016/j.techfore.2022.121685
.
Gruber
A.
,
Su
C.-H.
,
Zwieback
S.
,
Crow
W.
,
Dorigo
W.
&
Wagner
W.
2016
Recent advances in (soil moisture) triple collocation analysis
.
Int. J. Appl. Earth Obs. Geoinf.
45
,
200
211
.
https://doi.org/10.1016/j.jag.2015.09.002
.
Gupta
V.
,
Rakkasagi
S.
,
Rajpoot
S.
,
Saad El Imanni
H.
&
Singh
S.
2023
Spatiotemporal analysis of Imja Lake to estimate the downstream flood hazard using the SHIVEK approach
.
Acta Geophys.
71
,
2233
2244
.
https://doi.org/10.1007/s11600-023-01124-2
.
Guttman
N. B.
1998
Comparing the Palmer Drought Index and the Standardized Precipitation Index
.
J. Am. Water Resour. Assoc.
34
,
113
121
.
https://doi.org/10.1111/j.1752-1688.1998.tb05964.x
.
Hari
V.
,
Rakovec
O.
,
Markonis
Y.
,
Hanel
M.
&
Kumar
R.
2020
Increased future occurrences of the exceptional 2018–2019 Central European drought under global warming
.
Sci. Rep.
10
,
12207
.
https://doi.org/10.1038/s41598-020-68872-9
.
He
M.
,
Kimball
J. S.
,
Yi
Y.
,
Running
S.
,
Guan
K.
,
Jensco
K.
,
Maxwell
B.
&
Maneta
M.
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
.
Hobbins
M. T.
,
Wood
A.
,
McEvoy
D. J.
,
Huntington
J. L.
,
Morton
C.
,
Anderson
M.
&
Hain
C.
2016
The Evaporative Demand Drought Index. Part I: Linking drought evolution to variations in evaporative demand
.
J. Hydrometeorol.
17
,
1745
1761
.
https://doi.org/10.1175/JHM-D-15-0121.1
.
Hu
C.
,
Xia
J.
,
She
D.
,
Li
L.
,
Song
Z.
&
Hong
S.
2021
A new framework for the identification of flash drought: multivariable and probabilistic statistic perspectives
.
Int. J. Climatol.
41
,
5862
5878
.
https://doi.org/10.1002/joc.7157
.
Hunt
E. D.
,
Hubbard
K. G.
,
Wilhite
D. A.
,
Arkebauer
T. J.
&
Dutcher
A. L.
2009
The development and evaluation of a soil moisture index
.
Int. J. Climatol.
29
,
747
759
.
https://doi.org/10.1002/joc.1749
.
Hunt
E. D.
,
Svoboda
M.
,
Wardlow
B.
,
Hubbard
K.
,
Hayes
M.
&
Arkebauer
T.
2014
Monitoring the effects of rapid onset of drought on non-irrigated maize with agronomic data and climate-based drought indices
.
Agric. For. Meteorol.
191
,
1
11
.
https://doi.org/10.1016/j.agrformet.2014.02.001
.
Hunt
E.
,
Femia
F.
,
Werrell
C.
,
Christian
J. I.
,
Otkin
J. A.
,
Besara
J.
,
Anderson
M.
,
White
T.
,
Hain
C.
,
Randall
R.
&
McGaughey
K.
2021
Agricultural and food security impacts from the 2010 Russia flash drought
.
Weather Clim. Extrem.
34
,
100383
.
https://doi.org/10.1016/j.wace.2021.100383
.
IPCC
2021
Weather and climate extreme events in a changing climate
. In:
Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change
Cambridge University Press, Cambridge, UK and New York, USA, pp. 1513–1766. https://doi.org/10.1017/9781009157896.013
.
IPCC
2022
Climate Change 2022: Impacts, Adaptation, and Vulnerability
.
Cambridge University Press
,
Cambridge, UK and New York, USA
.
Jiao
W.
,
Wang
L.
&
McCabe
M. F.
2021
Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future
.
Remote Sens. Environ.
256
,
112313
.
https://doi.org/10.1016/j.rse.2021.112313
.
Kim
D.
,
Lee
W.
,
Kim
S. T.
&
Chun
J. A.
2019
Historical drought assessment over the contiguous United States using the generalized complementary principle of evapotranspiration
.
Water Resour. Res.
55
,
6244
6267
.
https://doi.org/10.1029/2019WR024991
.
Lesinger
K.
&
Tian
D.
2022
Trends, variability, and drivers of flash droughts in the contiguous United States
.
Water Resour. Res.
58
,
e2022WR032186
.
https://doi.org/10.1029/2022WR032186
.
Lee
H.-J.
,
Nam.
W.-H.
,
Yoon
D.-H.
,
Svoboda
M. D.
&
Wardlow
B. D.
2021
Detection of flash drought using Evaporative Stress Index in South Korea
.
J. Korea Water Resour. Assoc.
54
,
577
587
.
https://doi.org/10.3741/JKWRA.2021.54.8.577
.
Li
J.
,
Wang
Z.
,
Wu
X.
,
Xu
C.
,
Guo
S.
&
Chen
X.
2020a
Toward monitoring short-term droughts using a novel daily scale, Standardized Antecedent Precipitation Evapotranspiration Index
.
J. Hydrometeorol.
21
,
891
908
.
https://doi.org/10.1175/JHM-D-19-0298.1
.
Li
J.
,
Wang
Z.
,
Wu
X.
,
Chen
J.
,
Guo
S.
&
Zhang
Z.
2020b
A new framework for tracking flash drought events in space and time
.
CATENA
194
,
104763
.
https://doi.org/10.1016/j.catena.2020.104763
.
Liang
M.
&
Yuan
X.
2021
Critical role of soil moisture memory in predicting the 2012 central United States flash drought
.
Front. Earth Sci.
9
,
615969
.
https://doi.org/10.3389/feart.2021.615969
.
Lisonbee
J.
,
Woloszyn
M.
&
Skumanich
M.
2021
Making sense of flash drought: definitions, indicators, and where we go from here
.
J. Appl. Serv. Climatol.
2021
,
1
19
.
https://doi.org/10.46275/JOASC.2021.02.001
.
Liu
Y.
,
Zhu
Y.
,
Ren
L.
,
Otkin
J.
,
Hunt
E. D.
,
Yang
X.
,
Yuan
F.
&
Jiang
S.
2020
Two different methods for flash drought identification: comparison of their strengths and limitations
.
J. Hydrometeorol.
21
,
691
704
.
https://doi.org/10.1175/JHM-D-19-0088.1
.
Mahto
S. S.
&
Mishra
V.
2020
Dominance of summer monsoon flash droughts in India
.
Environ. Res. Lett.
15
,
104061
.
https://doi.org/10.1088/1748-9326/abaf1d
.
Mahto
S. S.
&
Mishra
V.
2023
Increasing risk of simultaneous occurrence of flash drought in major global croplands
.
Environ. Res. Lett.
18
,
044044
.
https://doi.org/10.1088/1748-9326/acc8ed
.
McColl
K. A.
,
Vogelzang
J.
,
Konings
A. G.
,
Entekhabi
D.
,
Piles
M.
&
Stoffelen
A.
2014
Extended triple collocation: estimating errors and correlation coefficients with respect to an unknown target
.
Geophys. Res. Lett.
41
,
6229
6236
.
McEvoy
D. J.
,
Huntington
J. L.
,
Hobbins
M. T.
,
Wood
A.
,
Morton
C.
,
Anderson
M.
&
Hain
C.
2016
The Evaporative Demand Drought Index. Part II: CONUS-wide assessment against common drought indicators
.
J. Hydrometeorol.
17
,
1763
1779
.
https://doi.org/10.1175/JHM-D-15-0122.1
.
Means, T
.
2021
Climate change and droughts: what's the connection?
Yale Climate Connections (18 August)
.
Mediero
L.
,
Garrote
L.
&
Martín-Carrasco
F.
2007
A probabilistic model to support reservoir operation decisions during flash floods
.
Hydrol. Sci. J.
52
,
523
537
.
https://doi.org/10.1623/hysj.52.3.523
.
Mishra
V.
,
Aadhar
S.
&
Mahto
S. S.
2021
Anthropogenic warming and intraseasonal summer monsoon variability amplify the risk of future flash droughts in India
.
npj Clim. Atmos. Sci.
4
,
1
.
https://doi.org/10.1038/s41612-020-00158-3
.
Mo
K. C.
&
Lettenmaier
D. P.
2016
Precipitation deficit flash droughts over the United States
.
J. Hydrometeorol.
17
,
1169
1184
.
https://doi.org/10.1175/JHM-D-15-0158.1
.
Mozny
M.
,
Trnka
M.
,
Zalud
Z.
,
Hlavinka
P.
,
Nekovar
J.
,
Potop
V.
&
Virag
M.
2012
Use of a soil moisture network for drought monitoring in the Czech Republic
.
Theor. Appl. Climatol.
107
,
99
111
.
https://doi.org/10.1007/s00704-011-0460-6
.
Mukherjee
S.
&
Mishra
A. K.
2022a
A multivariate flash drought indicator for identifying global hotspots and associated climate controls
.
Geophys. Res. Lett.
49
,
e2021GL096804
.
https://doi.org/10.1029/2021GL096804
.
Mukherjee
S.
&
Mishra
A. K.
2022b
Global flash drought analysis: uncertainties from indicators and datasets
.
Earth's Future
10
,
e2022EF002660
.
https://doi.org/10.1029/2022EF002660
.
Nguyen
H.
,
Wheeler
M. C.
,
Otkin
J. A.
,
Cowan
T.
,
Frost
A.
&
Stone
R.
2019
Using the evaporative stress index to monitor flash drought in Australia
.
Environ. Res. Lett.
14
,
064016
.
https://doi.org/10.1088/1748-9326/ab2103
.
Nguyen
H.
,
Wheeler
M. C.
,
Hendon
H. H.
,
Lim
E.-P.
&
Otkin
J. A.
2021
The 2019 flash droughts in subtropical eastern Australia and their association with large-scale climate drivers
.
Weather Clim. Extrem.
32
,
100321
.
https://doi.org/10.1016/j.wace.2021.100321
.
NIDIS
2021
Flash Drought: Current Understanding and Future Priorities
.
NOAA National Integrated Drought Information System
,
Boulder, CO, USA
.
NIDIS
2022
Flash Drought
.
The National Integrated Drought Information System
.
Noguera
I.
,
Domínguez-Castro
F.
&
Vicente-Serrano
S. M.
2020
Characteristics and trends of flash droughts in Spain, 1961–2018
.
Ann. N. Y. Acad. Sci.
1472
,
155
172
.
https://doi.org/10.1111/nyas.14365
.
Noguera
I.
,
Domínguez-Castro
F.
&
Vicente-Serrano
S. M.
2021
Flash drought response to precipitation and atmospheric evaporative demand in Spain
.
Atmosphere
12
,
165
.
https://doi.org/10.3390/atmos12020165
.
Noguera
I.
,
Vicente-Serrano
S. M.
&
Domínguez-Castro
F.
2022
The rise of atmospheric evaporative demand is increasing flash droughts in Spain during the warm season
.
Geophys. Res. Lett.
49
,
e2021GL097703
.
https://doi.org/10.1029/2021GL097703
.
Osman
M.
,
Zaitchik
B. F.
,
Badr
H. S.
,
Christian
J. I.
,
Tadesse
T.
,
Otkin
J. A.
&
Anderson
M. C.
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
.
Osman
M.
,
Zaitchik
B.
,
Badr
H. S.
,
Otkin
J.
,
Zhong
Y.
,
Lorenz
D.
,
Anderson
M.
,
Keenan
T. F.
,
Miller
D. L.
,
Hain
C.
&
Holmes
T.
2022
Diagnostic classification of flash drought events reveals distinct classes of forcings and impacts
.
J. Hydrometeorol.
23
,
275
289
.
https://doi.org/10.1175/JHM-D-21-0134.1
.
Otkin
J. A.
,
Anderson
M. C.
,
Hain
C.
,
Mladenova
I. E.
,
Basara
J. B.
&
Svoboda
M.
2013
Examining rapid onset drought development using the thermal infrared-based Evaporative Stress Index
.
J. Hydrometeorol.
14
,
1057
1074
.
https://doi.org/10.1175/JHM-D-12-0144.1
.
Otkin
J. A.
,
Anderson
M. C.
,
Hain
C.
&
Svoboda
M.
2014
Examining the relationship between drought development and rapid changes in the Evaporative Stress Index
.
J. Hydrometeorol.
15
,
938
956
.
https://doi.org/10.1175/JHM-D-13-0110.1
.
Otkin
J. A.
,
Shafer
M.
,
Svoboda
M.
,
Wardlow
B.
,
Anderson
M. C.
,
Hain
C.
&
Basara
J.
2015
Facilitating the use of drought early warning information through interactions with agricultural stakeholders
.
Bull. Am. Meteorol. Soc.
96
,
1073
1078
.
https://doi.org/10.1175/BAMS-D-14-00219.1
.
Otkin
J. A.
,
Anderson
M. C.
,
Hain
C.
,
Svoboda
M.
,
Johnson
D.
,
Mueller
R.
,
Tadesse
T.
,
Wardlow
B.
&
Brown
J.
2016
Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought
.
Agric. For. Meteorol.
218–219
,
230
242
.
https://doi.org/10.1016/j.agrformet.2015.12.065
.
Otkin
J. A.
,
Svoboda
M.
,
Hunt
E. D.
,
Ford
T. W.
,
Anderson
M. C.
,
Hain
C.
&
Basara
J. B.
2018
Flash droughts: a review and assessment of the challenges imposed by rapid-onset droughts in the United States
.
Bull. Am. Meteorol. Soc.
99
,
911
919
.
https://doi.org/10.1175/BAMS-D-17-0149.1
.
Otkin
J. A.
,
Zhong
Y.
,
Hunt
E. D.
,
Christian
J. I.
,
Basara
J. B.
,
Nguyen
H.
,
Wheeler
M. C.
,
Ford
T. W.
,
Hoell
A.
,
Svoboda
M.
&
Anderson
M. C.
2021
Development of a flash drought intensity index
.
Atmosphere
12
,
741
.
https://doi.org/10.3390/atmos12060741
.
Parker
T.
,
Gallant
A.
,
Hobbins
M.
&
Hoffmann
D.
2021
Flash drought in Australia and its relationship to evaporative demand
.
Environ. Res. Lett.
16
,
064033
.
https://doi.org/10.1088/1748-9326/abfe2c
.
Pendergrass
A. G.
,
Meehl
G. A.
,
Pulwarty
R.
,
Hobbins
M.
,
Hoell
A.
,
AghaKouchak
A.
,
Bonfils
C. J. W.
,
Gallant
A. J. E.
,
Hoerling
M.
,
Hoffmann
D.
,
Kaatz
L.
,
Lehner
F.
,
Llewellyn
D.
,
Mote
P.
,
Neale
R. B.
,
Overpeck
J. T.
,
Sheffield
A.
,
Stahl
K.
,
Svoboda
M.
,
Wheeler
M. C.
,
Wood
A. W.
&
Woodhouse
C. A.
2020
Flash droughts present a new challenge for subseasonal-to-seasonal prediction
.
Nat. Clim. Change
10
,
191
199
.
https://doi.org/10.1038/s41558-020-0709-0
.
Qing
Y.
,
Wang
S.
,
Ancell
B. C.
&
Yang
Z.-L.
2022
Accelerating flash droughts induced by the joint influence of soil moisture depletion and atmospheric aridity
.
Nat. Commun.
13
,
1139
.
https://doi.org/10.1038/s41467-022-28752-4
.
Sehgal
V.
,
Gaur
N.
&
Mohanty
B. P.
2021
Global flash drought monitoring using surface soil moisture
.
Water Resour. Res.
57
,
e2021WR029901
.
https://doi.org/10.1029/2021WR029901
.
Sreeparvathy
V.
&
Srinivas
V. V.
2022
Meteorological flash droughts risk projections based on CMIP6 climate change scenarios
.
npj Clim. Atmos. Sci.
5
,
77
.
https://doi.org/10.1038/s41612-022-00302-1
.
Sridhar
V.
,
Hubbard
K. G.
,
You
J.
&
Hunt
E. D.
2008
Development of the soil moisture index to quantify agricultural drought and its ‘user friendliness’ in severity-area-duration assessment
.
J. Hydrometeorol.
9
,
660
676
.
https://doi.org/10.1175/2007JHM892.1
.
Subramoniam
S. R.
,
Ravindranath
S.
,
Rakkasagi
S.
&
Hebbar
R.
2022
Water resource management studies at micro level using geospatial technologies
. In:
Geospatial Technologies for Resources Planning and Management
(Jha, C. S., Pandey, A., Chowdary, V. M. & Singh, V., eds), Springer, Cham, Switzerland, pp. 49–74.
Svoboda
M.
&
Fuchs
B. A.
2016
Handbook of Drought Indicators and Indices
.
Integrated Drought Management Programme, World Meteorological Organization
,
Geneva, Switzerland
.
Svoboda
M.
,
LeComte
D.
,
Hayes
M.
,
Heim
R.
,
Gleason
K.
,
Angel
J.
,
Rippey
B.
,
Tinker
R.
,
Palecki
M.
,
Stooksbury
D.
,
Miskus
D.
&
Stephens
S.
2002
The Drought Monitor
.
Bull. Am. Meteorol. Soc.
83
,
1181
1190
.
https://doi.org/10.1175/1520-0477-83.8.1181
.
Vicente-Serrano
S. M.
,
Beguería
S.
&
López-Moreno
J. I.
2010
A multiscalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index
.
J. Clim.
23
,
1696
1718
.
https://doi.org/10.1175/2009JCLI2909.1
.
Won
J.
&
Kim
S.
2020
Future drought analysis using SPI and EDDI to consider climate change in South Korea
.
Water Supply
20
,
3266
3280
.
https://doi.org/10.2166/ws.2020.209
.
Yao
N.
,
Li
Y.
,
Lei
T.
&
Peng
L.
2018
Drought evolution, severity and trends in mainland China over 1961–2013
.
Sci. Total Environ.
616–617
,
73
89
.
https://doi.org/10.1016/j.scitotenv.2017.10.327
.
Yin
J.
,
Gao
Y.
,
Chen
R.
,
Yu
D.
,
Wilby
R.
,
Wright
N.
,
Ge
Y.
,
Bricker
J.
,
Gong
H.
&
Guan
M.
2023
Flash floods: why are more of them devastating the world's driest regions?
Nature
615
,
212
215
.
https://doi.org/10.1038/d41586-023-00626-9
.
Yuan
X.
,
Ma
Z.
,
Pan
M.
&
Shi
C.
2015
Microwave remote sensing of short-term droughts during crop growing seasons
.
Geophys. Res. Lett.
42
,
4394
4401
.
https://doi.org/10.1002/2015GL064125
.
Yuan
X.
,
Wang
L.
,
Wu
P.
,
Ji
P.
,
Sheffield
J.
&
Zhang
M.
2019
Anthropogenic shift towards higher risk of flash drought over China
.
Nat. Commun.
10
,
4661
.
https://doi.org/10.1038/s41467-019-12692-7
.
Yuan
X.
,
Wang
Y.
,
Ji
P.
,
Wu
P.
,
Sheffield
J.
&
Otkin
J. A.
2023
A global transition to flash droughts under climate change
.
Science
380
,
187
191
.
https://doi.org/10.1126/science.abn6301
.
Zhang
M.
&
Yuan
X.
2020
Rapid reduction in ecosystem productivity caused by flash droughts based on decade-long FLUXNET observations
.
Hydrol. Earth Syst. Sci.
24
,
5579
5593
.
https://doi.org/10.5194/hess-24-5579-2020
.
Zhang
X.
,
Li
M.
,
Ma
Z.
,
Yang
Q.
,
Lv
M.
&
Clark
R.
2019
Assessment of an Evapotranspiration Deficit Drought Index in relation to impacts on ecosystems
.
Adv. Atmos. Sci.
36
,
1273
1287
.
https://doi.org/10.1007/s00376-019-9061-6
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).

Supplementary data