The prevalent life cycle of agricultural flash droughts

doi:10.21203/rs.3.rs-3307900/v1

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The prevalent life cycle of agricultural flash droughts

https://doi.org/10.21203/rs.3.rs-3307900/v1

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Flash droughts negatively impact agriculture and natural ecosystems worldwide. This study examines agricultural flash droughts globally. To this end, we introduce an approach to identify agricultural flash droughts based on root-zone soil moisture and hydraulic soil properties (field capacity and wilting point) to reflect the soil moisture deficit and plant water stress conditions. The highest frequencies of agricultural flash droughts predominantly occur during the critical growth periods of crops, particularly in southern China, southeastern South America, India, central-eastern Europe, southern Russia, and the central-eastern US. Similar atmospheric and surface processes drive the agricultural flash drought life cycle regardless of the location or climatic regime. We assert that the transition from energy-limited to water-limited evapotranspiration regimes is crucial in all agricultural flash drought onsets and intensification. While the precipitation deficit is the main driver for rapid soil moisture depletion, in all cases, high temperatures affect their persistence under water-limited conditions.

Flash droughts, characterized by the rapid drying of soils over durations lasting weeks to a couple of months^1,2, have become more common than slow droughts over much of the world since the 1950s³. The frequency and severity of flash droughts are projected to increase worldwide during the 21st century, especially over croplands under high global warming scenarios^4,5. Given their rapid onset and intensification, flash droughts are often unexpected and challenging to predict⁶. They can negatively impact crop yields in agricultural regions and damage natural ecosystems worldwide^7–9.

Agricultural flash droughts affect vegetation when there is a soil moisture deficit and plant water requirements are not met, especially during the critical growth period of crops. The primary forcings are precipitation deficits and positive temperature anomalies that favor rapid soil moisture depletion rates^2,10,11. Soil moisture can be considered a proxy representing the moisture state of the land-atmosphere system as it dominates dryness stress on vegetation productivity across most of the world’s vegetated land areas¹². Thus, plants with shallow roots, such as crops and pastures, are sensitive to soil moisture deficits in the upper soil layer and can become moisture-stressed faster than plants with deep roots¹¹. Hence, rapid soil moisture depletion is expected to impact crops and grasslands more severely than forests¹³.

Any attempt at predicting flash droughts requires a better understanding of their evolution and life cycle. During the evolution of flash droughts, precipitation rapidly decreases, playing a key role in the rapid soil moisture depletion^10,14. Case studies show that the coevolution of anomalous meteorological conditions, such as high evaporative demand and temperatures, favor the development and propagation of flash droughts^11,15,16,17. Dry land-atmospheric coupling also favors flash drought onsets^18,19.

The main flash drought drivers have been studied at the regional and global scales^14,20,21, employing different indicators²² primarily based on soil moisture variability^23–25, the evaporative stress ratio²⁶, evaporative demand²⁷, or multivariate approaches^14,15,28. The variety of definitions responds to specific needs, as flash drought physical processes are complex and difficult to represent using a single definition. However, different classifications may produce diverse results regarding flash drought characteristics and key drivers^29,30.

This study presents the life cycle of agricultural flash droughts and demonstrates the similarities observed in the world’s agricultural regions. We analyze the processes involving atmospheric and surface drivers during the agricultural flash drought life cycle. As highlighted by [30], a crucial aspect of flash drought development is distinguishing its impacts on vegetation health. To this end, we propose a flash drought indicator that can integrate the dominant physical processes with the corresponding effects on vegetation health, mainly for agricultural regions.

A coupled soil moisture deficit–plant water stress method to detect agricultural flash droughts

Soil moisture ($\theta$) is a crucial variable for monitoring the stress of the land system^10,31. Its conditions reflect the actual water availability for plants while considering the time-integrated impact of preceding meteorological states that affect the soil wetness (e.g., precipitation, solar radiation, and wind speed). A soil moisture deficit limits direct soil evaporation, water uptake by roots for plant transpiration, and groundwater recharge³². As soil moisture decreases, the corresponding increase in evaporative demand induces plant water stress². The soil moisture deficit and plant water stress can be explained through the hydraulic properties of the soil, specifically the field capacity (${\theta }_{FC}$) and the permanent wilting point (${\theta }_{WP}$). Soil cannot retain water above the field capacity because gravitational drainage predominates in the soil matrix. Plants cannot use soil moisture below the wilting point because the soil matrix holds water too tightly around the soil particles^31,33. Consequently, the maximum amount of water that soil can store and provide for plant growth and transpiration is the available water capacity (${\theta }_{AWC}= {\theta }_{FC}- {\theta }_{WP}$) (see, e.g., ref. 34 for further details).

Building from these principles, ref. 35 defined the soil water deficit index (SWDI) as an agricultural drought index formulated as:

$$SWDI= \left(\frac{\theta - {\theta }_{FC}}{{\theta }_{FC} - {\theta }_{WP}}\right)\times 10 \left(1\right)$$

The SWDI equals zero when the soil moisture is at the field capacity, i.e., when plants have full water availability and no soil moisture deficit. Negative SWDI values indicate a soil moisture deficit, which becomes absolute when ${\theta \le \theta }_{WP}$ (SWDI ≤ -10). Below this point, there is no available water for plants, i.e., roots cannot absorb soil water, and plants wilt³⁶.

Figure 1a (adapted from refs. 10,31) is a schematic that represents the transition between water-limited and energy-limited regimes. It presents the scatterplot of $\theta$ and the evaporative fraction ($EF$) for the illustrative flash drought case shown in Fig. 1b. The $\theta$ - $EF$ figure includes the soil property values and their corresponding SWDI values. The $\theta$ - $EF$ relationship for another five flash drought cases is shown in Supplementary Fig. 1. An essential threshold, the critical soil moisture value^31,39${(\theta }_{CRIT})$, lies between${\theta }_{WP}$ and ${\theta }_{FC}$ and differentiates evapotranspiration regimes: When${ \theta >\theta }_{CRIT}$, evapotranspiration is independent of soil moisture (energy-limited regime). When${ \theta <\theta }_{CRIT}$, evapotranspiration is constrained by soil moisture (water-limited regime). Thus, ${\theta }_{CRIT}$ is identified when a slight decrease in soil moisture leads to diminished evapotranspiration³¹.

The system is in a dry regime when no evapotranspiration can occur, that is, when ${\theta <\theta }_{WP}$ (or SWDI < -10). A transitional regime corresponds to the range ${\theta }_{WP} \le \theta \le {\theta }_{CRIT}$, where soil moisture limits evapotranspiration variability and, thus, land-atmosphere feedbacks. Our method establishes the upper threshold (SWDI = -3) at the beginning of the transitional regime and the lower SWDI threshold (SWDI = -5) to ensure that plants begin to experience water stress (see further details on Methods).

Figure 1b presents the SWDI typical evolution for a flash drought case along with the proposed thresholds to identify agricultural flash droughts. The figure illustrates the three fundamental characteristics of a flash drought (e.g., refs. 2,3,11,24): (1) a rapid depletion of the root-zone soil moisture, (2) an intensification period sufficiently long to avoid short synoptic scale events that deplete soil moisture rapidly but recover suddenly, and (3) plant water stress. The rapid decay of the soil moisture is represented by an SWDI decay from more than − 3 to less than − 5 in 20 days (or 4 pentads). The intensification period is met (following ref. 24) by requiring the soil moisture depletion period to last at least 15 days (or three pentads). The plant water stress is intrinsically integrated into the proposed indicator, as it directly addresses the water availability loss for plants by defining the SWDI thresholds. The SWDI thresholds for the third condition are based on the following refs. 10,31,37,38 that have addressed the relationship between soil moisture and evapotranspiration (see also Methods).

With a primary focus on crops, the proposed definition couples the rapid intensification of soil moisture drying and vegetation stress as crucial factors. Therefore, the proposed method is designed to detect flash droughts in agricultural areas rather than in regions with extreme climates where soil moisture flash drought conditions are rare (see Methods for further details).

Representation of well-known historical flash droughts

The proposed method is applied to the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) data. It is first tested by examining the well-known 2012 flash drought in the central-eastern United States^11,40,41 that severely impacted agriculture⁴². Figure 2 presents the SWDI evolution for key dates between April and July 2012. The spatiotemporal evolution of the flash drought aligns closely with findings from several previous studies^11,19,40. The flash drought began to develop rapidly in early May towards the north of the southeastern region (Figs. 2b and 2f). Throughout late May and June, the flash drought expanded in a radial pattern with varying rates of intensification (Figs. 2c and 2g; 2d and 2h). By July, it spreads further into the northern States of the Midwestern region, when it reaches its maximum spatial extent (Fig. 2e). In these States (see the Minnesota example, Fig. 2i), the drought becomes extremely severe between late July and August (-9 < SWDI < -7). Note that soil moisture flash drought conditions do not develop towards the semi-arid regions west of 100 ° W and the desert areas of the Southwest USA, as they show low SWDI values (SWDI < -7, and SWDI < -10, respectively) but constant over time (Figs. 2a-e).

We also assess the robustness of the proposed method by testing its ability to detect several well-documented flash droughts in different regions of the world. The approach successfully identifies (a) a severe flash drought in southwestern Russia and eastern Ukraine that rapidly intensified during late April and early May 2010 (see Supplementary Fig. 2, consistent with findings by refs. 43,44), (b) one of the most severe flash droughts experienced in India, which occurred at the end of the 2001 monsoon season (see Supplementary Fig. 3, in agreement with ref. 45), and (c) the extremely rapid intensification and spatiotemporal evolution of the once-a-century 2013 flash drought in southern China (see Supplementary Fig. 4, in line with the studies by ref. 46,47).

Annual and seasonal frequencies of agricultural flash drought

Our approach is employed to estimate the global spatial distribution of agricultural flash drought frequencies over the 1960–2020 period. Regions with the highest annual frequency of agricultural flash drought events are identified as hotspots, that is, regions where agricultural flash droughts have an area-averaged frequency higher than two events per decade and in which half of the area experiences at least three events per decade. Eight world regions prone to high agricultural flash drought occurrence are found with the proposed method. These regions, highlighted in Fig. 3a, are identified in Table 1 along with the corresponding flash drought frequencies. Southeastern South America (SESA) and southern China (SCh) present the highest frequencies per decade (with a maximum of about four area-averaged events per decade). India (In), central-eastern USA (CEUSA), central-eastern Europe (CEEu), and southern Russia (SRus) have area-averaged frequencies between 3.6 to 2.9 events per decade. Lastly, the transition belt between the Sahel and the tropical forests in central-western Africa (CWAf), northern South America (NSA), and southeastern Asia (SEAs) are also identified as agricultural flash drought hotspots with frequencies of about 2.5 area-averaged events per decade. Figure 3a shows that the most prominent event frequencies occur in croplands (see Supplementary Figs. 5 and 6) of SESA, SCh, CEEu and SRus, central In, and the northern and southeastern portions of the CEUSA.

Table 1

**Regions identified as agricultural flash drought occurrence hotspots**. The columns show the acronyms used to identify the agricultural flash drought hotspots and the area-averaged and maximum decadal frequencies of flash droughts (FD) in each hotspot.
Hotspot	Acronym	Area-averaged FD frequency	Maximum FD frequency
Southern China	SCh	4.0	14.2
Southeastern South America	SESA	4.0	12.6
India	In	3.6	11.5
Central-eastern Europe	CEEu	3.4	8.3
Southern Russia	SRus	3.1	8.4
Central-eastern United States of America	CEUSA	2.9	8.8
Southeastern Asia	SEAs	2.8	11.9
Central-western Africa	CWAf	2.4	8.4
Northern South America	NSA	2.4	9

While the number of area-averaged cases is seemingly low, they encompass smaller regions with frequent agricultural flash droughts (Fig. 3a). SESA, SCh, In, and SAs present small regions with frequencies of more than eight events per decade, where also a few grid points show maximum frequencies of more than one flash drought event per year (Table 1). Large regions of SRus and the eastern CEEu and isolated areas of CEUSA and NSA experience between 5 and 8 events per decade.

Figure 3b presents the seasonal frequency of agricultural flash drought events, while Fig. 3c shows the annual cycles of their area-averaged frequency in each hotspot region. Figures 3b and 3c show that agricultural flash droughts tend to occur during the crops’ critical growth periods. In extratropical areas, agricultural flash droughts are most frequent in springtime. Central-eastern Europe and southern Russia have the most prominent flash drought event frequencies during boreal spring (March-April-May; MAM), thus impacting the planting and pollination periods of diverse rainfed crops, including wheat, barley, corn, soybeans, and rice (see Supplementary Fig. 6b). The main crops grown in the southern USA eastern seaboard and the midwestern USA corn belt (e.g., ref. 48) are impacted during the same season (MAM), with effects extending into June for the latter region. Last, in southeastern South America, agricultural flash droughts are most frequent in austral spring (November and December), thus impacting the planting and pollination periods of corn, soybean, and sunflower (see, e.g., ref. 49).

In subtropical and tropical regions, the highest agricultural flash drought frequency may occur at different times of the year but is always related to the growing season. In southern China, the maximum flash drought frequency occurs during boreal summer (July and August), thus reducing the water availability for rice cultivation in an area that concentrates most of the country’s rice production⁵⁰. Agricultural flash droughts in India, southeastern Asia, and central-western Africa are most frequent during September-October-November (SON), affecting the main crop yields (e.g., refs. 45,51,52). In the croplands of northern South America (Colombia and Venezuela), frequent flash drought events in December-January-February (DJF) affect the critical growth periods of mixed crops, cotton, and coffee (Supplementary Fig. 6b, ref. 53).

Physical evolution of agricultural flash droughts

Analyzing the temporal progression of agricultural flash droughts helps understand the physical processes involved in their life cycle and the associated land-atmosphere feedbacks. Figure 4 presents the temporal evolution (lags − 4 to + 4) of the area-averaged standardized anomalies of relevant variables for all hotspot regions under agricultural flash drought conditions. All hotspots exhibit a similar temporal evolution (smooth color lines in Fig. 4), suggesting worldwide analogous agricultural flash drought development. Figure 5 shows the generic spatial structure of agricultural flash droughts to further study the spatiotemporal flash drought development. To this end, a composite of all flash droughts in the world is created over a 12°x12° degree window centered at the corresponding flash drought location (see Methods for further details). Both Fig. 4 and Fig. 5 show a consistent spatiotemporal development of agricultural flash droughts. Figure 4 shows precipitation and temperature have almost constant values before the flash drought onset (lags − 4 to -1). During this period, sufficient soil moisture (energy-limited regime, see Fig. 1b) allows a slight increase in evapotranspiration. Between the lag − 1 and the onset, precipitation quickly decays while the temperature rises (Figs. 4a and 4b; Fig. 5, second and third columns). At this point, an enhanced evaporative demand favored by the warming rapidly increases evapotranspiration (Fig. 4c and Fig. 5, third column). Together with the precipitation decay, this increased evapotranspiration produces a soil moisture dry-down that accelerates over the following pentads.

At the flash drought onset (lag = 0), when the SWDI is above the upper threshold (SWDI = -3; Fig. 4e and Fig. 5, fifth column), the precipitation deficit deepens, and the soil moisture starts depleting (Fig. 4a and 4d; Fig. 5, first and fourth columns). At this time, temperature and evapotranspiration play a crucial role in flash drought development. After the flash drought onset (lag + 1), the precipitation deficit reaches its maximum intensity and extent (Fig. 5, first column) and soil moisture becomes insufficient to supply further water for evapotranspiration, signaling the start of the water-limited conditions (see Fig. 1b). From lag + 1, evapotranspiration starts to decrease, despite the increased temperature, due to water stress (Fig. 4c and Fig. 5, third column). As the flash drought progresses (lags + 1 to + 3), the mechanism intensifies, that is, the temperature continues to rise, evapotranspiration drops and soil moisture diminishes. By lag + 2 the precipitation deficit begins stabilizing and reverses slightly by lag + 3, although negative precipitation anomalies persist (Fig. 4a and Fig. 5, first column). At this time (lag + 3), the temperature starts to drop (while positive anomalies continue), and the negative anomalies of both evapotranspiration and soil moisture stabilize, indicating the approaching end of the flash drought intensification period.

The characteristics just described are seen in all regions with agricultural flash droughts, regardless of the location or climatic regime. The features are more marked in the main hotspots (CEEu and SRus, SCh, CEUSA, SESA, and In), where the largest negative precipitation anomalies and the highest positive evapotranspiration anomalies occur (see Supplementary Fig. 7).

Flash droughts are difficult to predict. However, we can enhance our understanding of their development by identifying common features across all regions. We have introduced an approach to identify agricultural flash droughts based on physical principles that use the root-zone soil moisture and two soil hydraulic properties - field capacity and wilting point - to assess the vegetation stress that severely impacts agriculture and ecosystems. We suggest using the well-known SWDI along with a threshold range within the transitional regime. The upper SWDI threshold marks the initiation of the soil moisture deficit, while the lower SWDI threshold indicates water stress conditions on crops.

Despite recognized uncertainties in estimating soil hydraulic properties and their weak representativeness in some regions^54,55, the approach yields robust results according to our sensitivity tests, as it correctly identifies the onset and spatiotemporal evolution of several well-documented flash drought events in different world regions. The spatial patterns of agricultural flash drought frequency mostly agree with those obtained using different definitions, whether at global scales^14,21 or regional scales (USA^11,26, China²⁴, India⁵⁶).

The highest frequencies of agricultural flash droughts occur in croplands of southern China, southeastern South America, India, central-eastern Europe, southern Russia, and the central-eastern USA. Agricultural flash droughts are generally more frequent during the crops’ critical growth periods. This finding aligns with the underlying mechanisms, as the transition from an energy-limited regime to a water-limited regime is more likely to occur during the growing season when evaporative demand is at its highest climatological levels^2,41. During the growing season, this transition leads to a rapid increase of vegetation stress and magnifies the impact of flash droughts on agriculture^2,11.

Agricultural flash droughts exhibit similar evolution of relevant atmospheric and surface variables regardless of the geographical location or climatic regime. As expected, a precipitation deficit is the main driver for rapid soil moisture depletion. Before the flash drought onset, there is sufficient soil moisture (energy-limited regime). The favorable conditions of abundant soil moisture allow evapotranspiration to increase considerably, intensifying and accelerating soil moisture depletion. During the flash drought’s intensification stage, soil moisture becomes insufficient to supply more water for evapotranspiration, which in turn decreases. In this water-limited regime with decreased evapotranspiration, energy is transferred to sensible heat flux^11,57, i.e., to an increase in temperature that is crucial in the persistence of flash drought events and which may conduct to subsequent heat waves⁴³.

Data

The primary variable used in this study was the root-zone volumetric soil moisture (θ) in the soil’s top meter. Soil moisture data representing the 1960–2020 period at a 0.5°×0.5° latitude-longitude grid spacing was obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) product^58,59. ERA5 soil moisture data has shown better accuracy than data from other reanalyses compared to in-situ observations at various locations around the world^60,61. The evaporative fraction, EF = LH/(LH + SH), was estimated from ERA5’s latent heat (LH) and sensible heat (SH). Total precipitation, evapotranspiration, and 2-m air temperature were also used. Non-overlapped pentads (five-day means) of these variables were computed from the original hourly data. The pentad values were then used to assess the spatiotemporal evolution of flash droughts’ main variables. Such a method avoids higher-frequency variabilities while capturing short-duration flash droughts^15,23. The pentad anomalies at each grid point were computed as departures from the 1960–2020 mean annual cycle and normalized with their corresponding standard deviations. Such standardized anomalies facilitate worldwide comparisons.

The proposed flash drought definition also employed ERA5 parameters representing soil hydraulic properties. These parameters included the field capacity (${\theta }_{FC}$) and the wilting point ${(\theta }_{WP}$). Both parameters depend on the soil class at each grid point and do not vary temporally. The global soil datasets needed by Earth System Models are often based on limited and heterogeneously distributed soil profiles (e.g., refs. 62,63). The soil properties also present uncertainty due to the pedotransfer functions used in their estimation⁶⁴. However, significant efforts have been made in recent decades to mitigate these limitations. Information on the uncertainty is commonly provided along with the soil datasets (e.g., refs. 65,66), thereby enhancing the accuracy of Earth System Models’ land component.

Vegetation types were employed to identify the dominant land cover in flash drought-prone regions. These vegetation types were defined in ERA5 as a percentage of high and low vegetation coverage for each grid point following the Global Land Cover Characteristics data⁶⁷ of the Biosphere-Atmosphere Transfer Scheme biome classification⁶⁸. Our interest focused on discussing the impacts of agricultural flash droughts on croplands. For this reason, the ERA5 data were complemented with products representing the cropland extent, irrigated vs. rainfed crops, and crop dominance. These products were taken from the Global Food Security-Support Analysis Data (GFSAD) Project: The Global Cropland-extent product at a 30-m resolution⁶⁹ and the Global Cropland-extent 1-km Cropland Dominance product⁷⁰.

Proposed agricultural flash drought definition

The main features of the proposed method for identifying agricultural flash droughts are discussed in the Results section (see “A coupled soil moisture deficit–plant water stress indicator to detect agricultural flash droughts”). Complementary, this section discusses the selection of thresholds and the pros and cons of the proposed method.

Selection of SWDI thresholds

The choice of SWDI = -3 as the upper threshold reflects the fact that this value is less than the ${\theta }_{CRIT}$ value, which determines the beginning of the transitional regime (see Fig. 1a and Supplementary Fig. 1). By setting the upper threshold within the transitional regime, it ensures a discernible decrease in evapotranspiration, thereby capturing a level of moisture deficit in the land system. The ${\theta }_{CRIT}$ value may be difficult to establish because of the vast influence of varying soil textures, climate conditions, and vegetation characteristics. Ref. 71 reported that it is typically 50–80% of ${\theta }_{FC}$.

The fraction of available water content a plant can extract from the root zone without suffering water stress is known as the readily available soil water⁷². An average fraction of available water content close to 0.5, corresponding to SWDI = -5, is a common feature of several crops^72,73. Thus, below the SWDI = -5 threshold, plant roots can no longer transport soil water rapidly enough to respond to the transpiration demand, and plants begin to experience water stress. Although soil water is theoretically available for plants before reaching ${\theta }_{WP}$, the plant water uptake is reduced well before that value. In addition, ref. 35 classify soil moisture droughts below the SWDI = -5 threshold as severe.

Although defined based on physical constraints, the precise thresholds have some arbitrariness. Thus, we test the sensitivity of the threshold range with modified threshold values (see Supplementary Fig. 8). The results show that while the frequency of events varies with the different threshold ranges, the flash drought occurrence patterns remain similar, providing consistent insights for agricultural flash droughts detection. It suggests that the proposed method is robust. In addition, the selected thresholds allow proper recognition of the onset and spatiotemporal evolution of several well-documented flash drought events that are distinguished using other definitions in different world regions (e.g., refs. 16,40,43,45).

Comparison of the SWDI-based method with an $\varvec{\theta }$ percentile-based method

Supplementary Fig. 9 compares the characteristics of flash droughts identified in this article with those obtained with a widely used indicator based on root-zone soil moisture percentiles (e.g., refs. 23,24). Flash drought identification under extreme climates presents interesting differences between the two approaches. To illustrate this, we examine a grid point in a hot desert climate (Case 1, Sahara Desert) where flash droughts would not be feasible under these circumstances. Still, the time series of $\theta$ percentiles from 2015 to 2020 (Supplementary Fig. 9a) suggests repeated flash drought conditions, as $\theta$ frequently decreases from at least 40th to the 20th percentile within four pentads or less. In contrast, the SWDI time series (Supplementary Fig. 9b) indicates that soil moisture fluctuates within a narrow range consistently well below the wilting point throughout the five years. This means that the soil remains under conditions of permanent dryness. An equivalent situation is observed in a very wet climate (Case 2, Indonesia). The $\theta$ percentiles time series (Supplementary Fig. 9c) suggests the existence of flash drought conditions. However, the SWDI time series (Supplementary Fig. 9d) reveals that soil moisture varies in a small range always around the field capacity, never reaching the drought onset threshold (SWDI < -3), recognizing that there is no plant water stress, and therefore, no drought periods. These results suggest that identifying soil moisture flash droughts based on physical thresholds helps avoid misrepresentation in extreme climates.

Supplementary Fig. 9 also presents flash drought cases in agricultural-suitable non-extreme climates, aligning with the indicator’s focus on agricultural droughts. Case 3 shows a flash drought event in southern China during Aug-Sep 2020 (as in Fig. 1), characterized by the evolution of the $\theta$ percentiles (Supplementary Fig. 9e) and by the SWDI and $\theta$ time series (Supplementary Fig. 9f). The SWDI-based approach identifies a flash drought of shorter duration and lower intensity than the one estimated from the $\theta$ percentile thresholds. Case 4 presents a flash drought event in southeastern South America during Nov-Dec 1970. Both definitions (Supplementary Figs. 9g and 9h) identify a flash drought of similar duration, but the proposed method in this study indicates a higher intensity. These examples demonstrate that both the SWDI-based and θ percentile-based methods identify similar characteristics of soil moisture flash droughts with slight differences. The $\theta$ percentile-based indicators’ thresholds may be sensitive to soil moisture variabilities in non-extreme climate regimes. The thresholds of the SWDI-based method are based on the physical conditions of each type of soil.

Evolution of crucial atmospheric and surface variables

Composites of precipitation, temperature, evapotranspiration, and soil moisture standardized anomalies were constructed to examine agricultural flash droughts' characteristic temporal and spatial evolution. First, flash drought events were detected at each grid point within the hotspots. Then, area averages for all hotspots were computed for their onset (lag 0), the four preceding pentads (lags − 4 to -1), and the four subsequent pentads (lags + 1 to + 4). Next, the spatial structure of standardized anomalies across all identified events worldwide was examined by generating a flash drought composite over a 12°x12° window centered on each event location.

Acknowledgments

This research was supported by Argentina’s National Agency for Scientific and Technological Promotion (Projects PICT-2019-2019-03982 and PICT-2019-2019-00481), the Argentine National Council for Scientific and Technical Research (Grant PIP-CONICET-11220200102257CO), and the Universidad Nacional del Litoral (Grant CAI+D-2020-50620190100082LI). E.H.B was supported by NOAA grant NA19NES4320002 (Cooperative Institute for Satellite Earth System Studies (CISESS)) at the University of Maryland/ESSIC. E.H.B thanks the Fulbright Commission for facilitating the last stages of this research.

Author Contributions

O.V.M., M.A.L. and E.H.B. designed the study. M.A.L. and M.J.P. carried out the research. M.A.L., M.J.P., O.V.M., G.V.M., and E.H.B analyzed and discussed the results. M.A.L, G.V.M. and E.H.B. acquired funding and resources. M.A.L. prepared the original manuscript, and all authors contributed to its subsequent versions.

Competing Interests statement

The authors declare no competing interests.

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The prevalent life cycle of agricultural flash droughts

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Abstract

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Introduction

Results

A coupled soil moisture deficit–plant water stress method to detect agricultural flash droughts

Representation of well-known historical flash droughts

Annual and seasonal frequencies of agricultural flash drought

Physical evolution of agricultural flash droughts

Discussion

Methods

Data

Proposed agricultural flash drought definition

Selection of SWDI thresholds

Comparison of the SWDI-based method with an \(\varvec{\theta }\) percentile-based method

Evolution of crucial atmospheric and surface variables

Declarations

References

Additional Declarations

Supplementary Files

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