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FEBRUARY 2022 OSM A N E T A L . 275 Diagnostic Classiﬁcation of Flash Drought Events Reveals Distinct Classes of Forcings and Impacts a,b a c d d MAHMOUD OSMAN, BENJAMIN F. ZAITCHIK, HAMADA S. BADR, JASON OTKIN, YAFANG ZHONG, d e f,g f h DAVID LORENZ, MARTHA ANDERSON, TREVOR F. KEENAN, DAVID L. MILLER, CHRISTOPHER HAIN, AND THOMAS HOLMES Department of Earth and Planetary Sciences, The Johns Hopkins University, Baltimore, Maryland Irrigation and Hydraulics Department, Cairo University, Cairo, Egypt Department of Civil and Systems Engineering, The Johns Hopkins University, Baltimore, Maryland Space Science and Engineering Center, Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, Maryland Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, California Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, California Earth Science Ofﬁce, NASA Marshall Space Flight Center, Huntsville, Alabama Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland (Manuscript received 14 July 2021, in ﬁnal form 15 December 2021) ABSTRACT: Recent years have seen growing appreciation that rapidly intensifying ﬂash droughts are signiﬁcant climate hazards with major economic and ecological impacts. This has motivated efforts to inventory, monitor, and forecast ﬂash drought events. Here we consider the question of whether the term “ﬂash drought” comprises multiple distinct classes of event, which would imply that understanding and forecasting ﬂash droughts might require more than one framework. To do this, we ﬁrst extend and evaluate a soil moisture volatility–based ﬂash drought deﬁnition that we introduced in previous work and use it to inventory the onset dates and severity of ﬂash droughts across the contiguous United States (CONUS) for the period 1979–2018. Using this inventory, we examine meteorological and land surface conditions associated with ﬂash drought onset and recovery. These same meteorological and land surface conditions are then used to classify the ﬂash droughts based on precursor conditions that may represent predictable drivers of the event. We ﬁnd that distinct classes of ﬂash drought can be diagnosed in the event inventory. Speciﬁcally, we describe three classes of ﬂash drought: “dry and demanding” events for which antecedent evaporative demand is high and soil moisture is low, “evaporative” events with more modest antecedent evaporative demand and soil moisture anomalies, but positive antecedent evaporative anomalies, and “stealth” ﬂash droughts, which are different from the other two classes in that precursor meteorological anomalies are modest relative to the other classes. The three classes exhibit somewhat different geographic and seasonal distributions. We conclude that soil moisture ﬂash droughts are indeed a composite of distinct types of rapidly intensifying droughts, and that ﬂash drought analyses and forecasts would beneﬁt from approaches that recognize the existence of multiple phenome- nological pathways. KEYWORDS: Drought; Extreme events; Hydrometeorology; Soil moisture; Climate classiﬁcation/regimes 1. Introduction losses, wildﬁres, and economic damages in the tens of billions of dollars. These droughts occurred at different times of the In recent years, a number of rapid-onset drought events year in different climate zones with different ecological char- have struck the contiguous United States (CONUS), with acteristics, yet they have all been described as ﬂash droughts, severe consequences for ecological and agricultural systems. a term ﬁrst coined by Peters et al. (2002) and Svoboda et al. For example, droughts in the Southern Plains in 2011, the cen- (2002) to reﬂect the fact that some droughts emerge rapidly tral United States in 2012, the Southeast in 2016, the Northern and quickly develop into high-impact extreme events. Plains in 2017, and Texas in 2019 led to widespread crop A challenging characteristic of ﬂash droughts is that they appear suddenly}seemingly without warning}and therefore leave farmers, ranchers, and other vulnerable stakeholders lit- Denotes content that is immediately available upon publica- tle time to prepare mitigation responses (Otkin et al. 2015b, tion as open access. 2018a; Haigh et al. 2019). The 2012 ﬂash drought, for exam- ple, received tremendous attention because of its impact on the nation’s corn crop. Yet there was virtually no sign of an Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/10.1175/JHM- impending rapid intensiﬁcation prior to the event in standard D-21-0134.s1. drought monitoring products at that time or in dynamically based seasonal forecasting systems (Hoerling et al. 2014). Postevent analyses concluded that the event was largely Corresponding author: Mahmoud Osman, mahmoud.osman@ jhu.com driven by random atmospheric variability, and perhaps was DOI: 10.1175/JHM-D-21-0134.1 Ó 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). 276 J OUR N A L O F H Y D R O M E T E OR O L OGY VOLUME 23 inherently unpredictable using conventional methods (Kumar severity when determining the intensity of a ﬂash drought. et al. 2013). Poor model performance both in forecasting and Their study showed that there are important regional differ- reproducing these events presents an additional challenge in ences in ﬂash drought severity when both of these components efforts to project ﬂash drought impacts and feedbacks under are considered. nonstationary climate conditions (Wolf et al. 2016). Notwith- Most proposed deﬁnitions and intensity metrics for ﬂash standing these challenges, there is evidence that ﬂash droughts droughts have focused exclusively on capturing the phenome- are amenable to seasonal-to-subseasonal scale prediction on non rather than assessing whether it represents a coherent account of their sensitivity to initial conditions (Lorenz et al. class from the perspective of drought process. An exception is 2017a,b), the perceived importance of forecastable drivers of the work of Mo and Lettenmaier (2015, 2016), which explic- evaporative demand during ﬂash drought intensiﬁcation (Hob- itly distinguished between precipitation deﬁcit ﬂash droughts bins et al. 2016), and the potentially predictable role of vegeta- and heat wave ﬂash droughts. The method used to deﬁne tion in ﬂash drought processes (Wolf et al. 2016). these droughts has been debated, in large part because Mo Any such generalized statements on the predictability of ﬂash and Lettenmaier consider duration of the heatwave event droughts, however, implicitly assume that the occurrence and rather than intensiﬁcation rate, which is more typically under- severity of ﬂash droughts can be diagnosed in a consistent and stood to be the deﬁning characteristic of ﬂash drought (Otkin process-relevant manner, and that the term “ﬂash drought” et al. 2018b; Lisonbee et al. 2021), but their concept that ﬂash refers to a single class of event. In recent years, many studies droughts might be the product of multiple different pathways have sought to describe and diagnose the occurrence of ﬂash with distinct meteorological drivers is highly relevant to droughts by proposing a variety of deﬁnitions that can be used to understanding and prediction. While Mo and Lettenmaier inventory and map ﬂash droughts. Otkin et al. (2013, 2014, made this distinction a priori by incorporating different varia- 2015a) identiﬁed ﬂash droughts based on rapid changes in the bles and thresholds in their deﬁnitions, we are not aware of ratio between actual evapotranspiration (EVP) and potential any study that empirically classiﬁes different ﬂash drought evapotranspiration (PEVP). Other studies (Hunt et al. 2014; Mo types within an inventory generated using a common ﬂash and Lettenmaier 2015)deﬁned ﬂash droughts as a function of drought deﬁnition. That is: if an inventory of ﬂash drought the rapid drop in soil moisture with time. Chen et al. (2019) sug- events is generated using a deﬁnition based on ﬂash drought gested the degradation of two categories in the U.S. Drought phenomenology alone, are there distinct classes within that Monitor (USDM) in a period of four weeks as a deﬁnition for inventory that can be identiﬁed due to different precursors in the onset of ﬂash droughts. Christian et al. (2019) introduced the meteorology or surface conditions? If so, that implies that deﬁnition for ﬂash droughts based on the rate of change in stan- understanding and predicting ﬂash droughts may require that dardized ratio between EVP and PEVP over a six-pentad (6 3 5 we adopt different perspectives for each class. days) period. Another quantitative deﬁnition (Ford and Labosier Here, we apply our recently introduced SMVI ﬂash drought 2017) identiﬁed ﬂash droughts as the drop of the one pentad deﬁnition (Osman et al. 2021) to address this question. First, averaged soil moisture (SM) from the 40th to 20th percentiles in we extend the SMVI presented in Osman et al. (2021) to a period of four pentads or less. A subsequent study by Hoff- include estimates of drought severity, and we compare the mann et al. (2021) followed a similar methodology with adjust- SMVI to independent vegetation and crop datasets for semi- ments to reduce the number of identiﬁed events. In a recent nal ﬂash drought events. Next, we apply a retrospective inven- study, (Osman et al. 2021) introduced a deﬁnition based on a soil tory of ﬂash droughts, generated using SMVI, to derive moisture volatility index (SMVI), and also compared the SMVI composites of meteorological and surface conditions in the with six other deﬁnitions to highlight the fact that there are dif- predrought, onset, and recovery phases of all ﬂash droughts. ferent pathways to identify ﬂash drought onset. All of the listed Finally, we perform objective classiﬁcation of the ﬂash studies focused on CONUS, but the ﬂash drought phenomenon drought inventory on the basis of meteorological and surface has been observed in many regions across the globe (Nguyen condition precursors to identify ﬂash drought classes relevant et al. 2019; Zhang and Yuan 2020), with a number of studies to process understanding and prediction. focusing on China and India (Wang et al. 2016; Yuan et al. 2019; Mahto and Mishra 2020). These studies have yielded additional 2. Data and methods deﬁnitions. Indeed, the need to understand the implications of different deﬁnitions has become a research question in its own We generate an inventory of soil moisture ﬂash droughts right (Lisonbee et al. 2021). for all of CONUS over the period 1979–2018 for spring Fewer studies have attempted to quantify the severity of the through fall (March–November). SMVI is calculated using ﬂash droughts, but informative efforts do exist. Chen et al. root zone soil moisture (RZSM) from the SMERGE dataset. (2019) and Otkin et al. (2015a) both used USDM categories to SMERGE is a hybrid daily product at 0.1258 spatial resolution diagnose and assess severity of ﬂash droughts. Christian et al. that combines satellite-derived soil moisture estimates from (2019) used standardized evaporative stress ratio (SESR) for the European Space Agency Climate Change Initiative and both purposes, Yuan et al. (2019) used soil moisture deﬁcit, and NLDAS-2 Noah model output for RZSM averaged from 0- to Li et al. (2020) used evapotranspiration deﬁcit. Basedonmod- 40-cm depth (Tobin et al. 2019). The SMERGE dataset has eled soil moisture, Otkin et al. (2021) developed a ﬂash drought been evaluated against normalized difference vegetation intensity index (FDII) that explicitly accounts both for the mag- index (NDVI) products (Rouse et al. 1974) as well as in situ nitude of the rapid intensiﬁcation and the resultant drought soil moisture observations, and it has been found to be a FEBRUARY 2022 OSM A N E T A L . 277 reliable dataset for agricultural and ecological applications method’s skill to capture changes in satellite-observed vegetation (Tobin et al. 2019). greenness due to ﬂash drought. The cloud-free NDVI data were The SMVI is motivated by the fact that ﬂash drought diagno- obtained from the 16-day MODIS composite product sis is concerned with capturing change that is more rapid than (MOD13C1) at 0.058 spatial resolution (Didan 2021)for the usual, so that it could be used to identify both rapid onset and years 2000 to present. NDVI grid points with anomalies below rapid intensiﬁcation drought events. For SMVI, rapid changes 20.5 standard deviation from the mean are deﬁned as are identiﬁed by the crossover of simple moving averages “negatively impacted” in comparisons with SMVI. This approxi- (SMAs) combined with duration and dryness thresholds. Onset mately corresponds to a probability of occurrence less than 30% is recorded when 1) the 5-day (1-pentad) RZSM SMA falls and for normally distributed conditions. Further, we evaluate the per- stays below the 20-day (4-pentad) SMA for at least a 20-day formance of the SMVI deﬁnition for the 2012 central United period or 2) both SMAs are below the 20th percentile of the States and 2017 Northern Plains ﬂash droughts versus in situ 1979–2018 time-of-year RZSM climatology (Osman et al. reports of soil and crop conditions collected by the USDA 2021). If two sequential ﬂash droughts are identiﬁed with a National Agricultural Statistics Service (NASS) observers. Data period of three pentads or less between them, then they are showing poor conditions are marked as negatively impacted. combined into a single event. We do this because a short rain- These data are collected at county scale, then spatially smoothed fall event may result in a temporary reduction in the severity of to reduce noise, and protect conﬁdentiality (access to data at a ﬂash drought but is often not sufﬁcient to restore predrought county level was provided to the coauthors after signing a conﬁ- conditions and end the drought event. dentiality agreement with the USDA NASS). The performance Severity is quantiﬁed basedonRZSMdeﬁcit during the iden- analyses are carried out for the spring and summer seasonal aver- ages due to data availability and temporal resolution. tiﬁed ﬂash drought event according to Eqs. (1) and (2) as illus- The performance of the SMVI is assessed with hit–miss confu- trated in the example in Fig. S1 in the online supplemental sion matrices that use NDVI and NASS data as observational material. This scale is based on the standardized distribution of reference datasets. True positive values represent grid points and the integrated RZSM deﬁcit below the 20th percentile (and pentads depicted by SMVI as being in ﬂash drought and also over the 5-day running average) during the drought event: marked as negatively impacted by the NASS or NDVI validation t5t datasets, while false positives are the events classiﬁed as ﬂash SV 5() RZSM 2 RZSM (1) 20th 5d drought by SMVI where NASS or NDVI do not meet drought t5t impact criteria. True negative values represent grid points not marked as negatively impacted by the NASS or NDVI validation datasets and not identiﬁed as ﬂash drought grid points. False neg- SV SV 5 , (2) CAT atives represent grid points identiﬁed by SMVI as having no ﬂash STD() SV drought while marked as negatively impacted by the NASS or where SV is the computed severity, and RZSM and RZSM NDVI validation datasets. Hit–miss statistics are calculated 20th 5d are the 20th percentile and 5-day moving average RZSM, according to Eqs. (3)–(10): respectively. Parameters t and t represent the times at which o f TP identiﬁed ﬂash drought onset occurs and ends, respectively. The sensitivity() TPR 5 , (3) TP 1 FN standardized severity category is represented by SV with a CAT range from zero (no ﬂash drought) up to 5 (maximum severity), and STD(SV ) is the severity standard deviation calcu- TN 1979–2018 () specificity TNR 5 , (4) lated from the ﬂash drought inventory for all grid points, mea- TN 1 FP sured against the severity of all other identiﬁed ﬂash drought events within the inventory. The use of categories to indicate FP drought severity is a common approach, as used in systems such () false discovery rate FDR 5 , (5) FP 1 TP as the USDM. In contrast to the USDM, the SMVI-based severity is intended to capture the severity of the rapid onset ﬂash drought process. FN () false negative rate FNR 5 , (6) The end of the ﬂash drought period (recovery period) date FN 1 TP is identiﬁed when the rate of drop in RZSM during an identi- ﬁed ﬂash drought event begins to recover (i.e., the 1-pentad FP () running average is no longer below 4-pentad running average) false positive rate FPR 5 , (7) FP 1 TN or the 1-pentad RZSM is no longer below the 20th percentile of the 1979–2018 time-of-year RZSM. TP SMVI performance was previously evaluated based on precision() PPV 5 , (8) TP 1 FP descriptions of reported major ﬂash drought events (Osman et al. 2021). Inﬂuenced by the methodology followed by Peters et al. (2002) to detect drought using standardized NDVI, in this study TP 1 TN we use MODISNDVItime-of-yearanomaliestoassess the accuracy() ACC 5 , (9) TP 1 TN 1 FP 1 FN 278 J OUR N A L O F H Y D R O M E T E OR O L OGY VOLUME 23 FIG. 1. Flash drought maps as captured by SMVI deﬁnition during the active growing season (March–November): (left) 2012 and (right) 2017. (a),(b) Onset maps, where each color represents the month of ﬂash drought onset. (c),(d) Estimated severity category maps. classes subjectively, but there are recommended diagnostics TP () critical success index CSI 5 , (10) for use in choosing the optimal number of classes. Here we TP 1 FN 1 FP apply the commonly used elbow method (Thorndike 1953) for this purpose. where TP, TN, FP, and FN represent true positive, true nega- tive, false positive, and false negative grid points, respectively. Values of Eqs. (3)–(10) range from 0 to 1, with 1 being the 3. Results and discussion perfect score for the TP or TN numerator-based ratios and a. The SMVI flash drought intensity metric the opposite for the FP and FN numerator-based ratios. Drawing on previous studies that have described meteoro- The United States was hit by several major ﬂash drought logical and surface conditions associated with ﬂash drought events over the past decade, resulting in excessive agricultural onset (Mo and Lettenmaier 2015, 2016; Ford and Labosier losses and livestock destruction. In 2012, the country experi- 2017; He et al. 2019; Osman et al. 2021), we select multiple enced one of the largest and most destructive ﬂash droughts variables from the NLDAS-2 datasets (temperature, precipi- recorded to date, with more than $30 billion estimated dam- tation, RZSM, PEVP, EVP, and surface pressure) along with ages (Hoerling et al. 2013, 2014; Basara et al. 2019; Mallya the computed vapor pressure deﬁcit (VPD) and total cloud et al. 2013; Fuchs et al. 2012; Otkin et al. 2016). A warm spring cover (TCC) from NCEP–NCAR reanalysis products (Kalnay followed by early summer heatwaves set the stage for a rap- et al. 1996), and analyze their progression through the pre- idly intensifying drought that struck much of the middle part drought, onset and end of the ﬂash drought periods for all of the country in late spring and early summer and extended events included in the 40-yr (1979–2018) SMVI-derived ﬂash to the north later in summer and in early fall (Fig. 1a). Nota- drought inventory. To focus on events with meaningful bly, though the occurrence of ﬂash drought was very wide- impact, we analyze only SMVI-derived ﬂash drought events spread (according to both SMVI and other deﬁnitions) with severity greater than 2. Unsupervised multivariate classi- (Osman et al. 2021), the central United States had the greatest ﬁcation is then performed as a function of these meteorologi- severity, as diagnosed by the SMVI (Fig. 1c). cal variables, using principal components transformation to Five years after the 2012 ﬂash drought, the Northern Plains control for collinearity between variables. This classiﬁcation is region was hit by another major ﬂash drought, causing more used to characterize different types of ﬂash droughts driven than $2.6 billion in agricultural losses and sparking wildﬁres. by different processes. The classes are determined using the The 2017 Northern Plains ﬂash drought was focused on Mon- k-means partitioning unsupervised classiﬁcation algorithm tana, North Dakota, South Dakota, and parts of Alberta and (Hartigan and Wong 1979; Lloyd 1982) as a heuristic cluster- Saskatchewan (Jencso et al. 2019). The event started in May ing method. We apply a sensitivity analysis to determine the over western Montana and swiftly intensiﬁed through high statistically optimal number of clusters. The anomalies are evaporative demand and precipitation deﬁcits (Hoell et al. calculated as the in-time (predrought, onset, or recovery) pen- 2019a; Osman et al. 2021). The drought eventually spread tad anomaly relative to the 1979–2018 time-of-year average. over much of the Northern Plains region (Fig. 1b) causing The k-means algorithm allows the user to set the number of enormous economic losses (Gerken et al. 2018; Jencso et al. FEBRUARY 2022 OSM A N E T A L . 279 FIG.2.Maps of hit–miss analysis for the 2012 and the 2017 ﬂash droughts during the actively growing season (March–November): (left) 2012 and (right) 2017. (a),(b) SMVI vs negative NDVI anomaly hit–miss map, in which lavender represents false positive (FP), orange rep- resents true positive (TP), white represents true negative (TN), green represents false negative (FN), and gray represents missing/unavail- able data. (c),(d) As in (a) and (b), but for NASS reported negative average crop conditions. (e),(f) As in (a) and (b), but for the observed topsoil moisture. 2019; He et al. 2019). Montana was the most impacted state With this caveat in mind, we compare the SMVI ﬂash (Jencso et al. 2019), and this is evident in the SMVI-based drought index to MODIS NDVI anomalies and NASS crop severity analysis (Fig. 1d). The severity analysis is also consis- and topsoil condition anomalies. Using a simple hit/miss met- tent with the USDM reports that showed an exceptional (D4 ric in which negative anomalies in MODIS NDVI (more than category) drought over Montana (Jencso et al. 2019). It is 0.5 standard deviation below the mean) or the NASS condi- important to highlight that estimation of ﬂash droughts’ sever- tion maps are interpreted as evidence of drought conditions, ity in this study is a method to relatively quantify soil moisture we ﬁnd that there is broad agreement between the SMVI and deﬁcit with a methodology similar to Yuan et al. (2019) study observed drought conditions for both the 2012 and 2017 ﬂash given the different ﬂash drought identiﬁcation method. drought events (Figs. 1 and 2). We do see considerable false Independent, quantitative validation of drought indices is negatives on the margins of the drought-affected area, particu- notoriously difﬁcult, since impacts of drought vary with cli- larly in 2012, but this is consistent with our liberal deﬁnition of mate context, land cover, and economic system. Since ﬂash agricultural drought in the NDVI and NASS ﬁelds (i.e., ﬂash drought is a subset of all droughts which is typically consid- drought identiﬁed area is smaller than NDVI and NASS nega- ered in agricultural and ecological contexts (Wang et al. 2016; tive anomalies). We also note a concentration of false positives Mo and Lettenmaier 2015; Christian et al. 2019; Otkin et al. along edge of drought regions, particularly in 2017, indicate 2018b), we consider vegetation health and crop status to be that the SMVI approach overestimated the extent of drought- two relevant indicators of drought impact that can verify the affected area relative to NASS estimates. utility of SMVI as a useful drought metric. In doing this, we Focusing on the central and northern High Plains regions recognize that the independent comparisons do not necessar- [as deﬁned by Bukovsky (2011)] for the years 2012 and 2017, ily conﬁrm the presence of ﬂash drought; rather, they are respectively, we ﬁnd that for ﬂash droughts based on negative interpreted as indicators of whether an agricultural drought NDVI anomalies the accuracy was 0.68 in 2012 and 0.56 in may have occurred. 2017. Precision was higher in 2012 (0.74) than 2017 (0.50), 280 J OUR N A L O F H Y D R O M E T E OR O L OGY VOLUME 23 TABLE 1. SMVI–NASS and SMVI–NDVI summary hit–miss statistics for the 2012 central region ﬂash drought showing the geographically dominant crops and observed soil moisture conditions. Corn Range Soybean Subsoil Topsoil Avg crop condition NDVI ACC 0.74 0.74 0.78 0.84 0.77 0.75 0.68 CSI 0.71 0.73 0.76 0.84 0.76 0.73 0.63 FDR 0.21 0.07 0.16 0.05 0.05 0.11 0.26 FNR 0.12 0.22 0.10 0.13 0.21 0.20 0.19 FPR 0.65 0.55 0.64 0.50 0.38 0.51 0.60 PPV 0.79 0.93 0.84 0.95 0.95 0.89 0.74 TNR 0.35 0.45 0.36 0.50 0.62 0.49 0.40 TPR 0.88 0.78 0.90 0.87 0.79 0.80 0.81 while the probability of detection (sensitivity) was higher in they are 0.84 and 0.95 for the 2017 event. We also note that 2017: 0.93, versus 0.81 in 2012 (Tables 1 and 2). The critical irrigation is a complicating factor that may affect comparison success index was signiﬁcantly higher for the 2012 event (0.63) between datasets. While SMVI does include partial consider- compared to that observed in 2017 (0.48). These values of ation of irrigation, insomuch as SMERGE captures irrigation hit–miss statistics are consistent with moderate to strong per- signals, this representation is imperfect and might not align formance in event identiﬁcation (Hoerling et al. 2013, 2014; with observed vegetation response to irrigation. Basara et al. 2019; Mallya et al. 2013; Fuchs et al. 2012; Otkin b. Proposed drivers of flash drought et al. 2016; Gerken et al. 2018; Jencso et al. 2019; He et al. 2019). It is important to note that this is an imperfect compari- Figure 3 presents composites of predrought (onset minus son. The SMVI approach is one pathway of identifying ﬂash three pentads), onset, and recovery period conditions, using droughts, and a comparison with a vegetation index metric, composites of standardized anomalies of meteorological ﬁelds such as NDVI anomalies, is not exactly indicative of perfor- for all ﬂash droughts of severity greater than 2 in the SMVI- mance in capturing a soil moisture ﬂash drought. derived 1979–2018 inventory. Composites are calculated sepa- NASS-based evaluation, based on NASS identiﬁcation of rately for each grid cell, such that the anomalies represent poor crop and soil conditions, led to comparable statistics for conditions when a ﬂash drought occurred in that exact loca- each impacted region’s dominant crop (Figs. 2c–f). Tables 1 tion. Precipitation (PRCP) anomalies in the predrought and and 2 summarize SMVI–NASS statistics for both the 2012 onset periods are mostly negative, as one would expect, which and 2017 ﬂash droughts. In the 2012 central U.S. ﬂash is also associated with suppression of the convective available drought, SMVI shows an accuracy of 0.79, 0.75, and 0.74 for potential energy (CAPE) over most of CONUS (we include negatively impacted soybean, range, and corn, with a preci- CAPE in addition to precipitation in order to isolate local sion of 0.84, 0.79, and 0.89, respectively. The 2017 Northern convective potential as distinct from total realized rainfall). Plains ﬂash drought captured by SMVI is similarly evaluated This is similar to the observed scenario before and during the and statistical evaluation was slightly higher than that seen for 2017 northern High Plains ﬂash drought (Gerken et al. 2018). the NDVI analysis. Accuracy for detecting grids of ﬂash The magnitude of these standardized anomalies, however, is drought in the Northern Plains compared to negatively generally small relative to the anomalies in RZSM and poten- impacted dominant crops (barley and spring wheat) are 0.8 tial evaporation (PEVP), particularly during the pentad of and 0.76, respectively, with precision values of 0.91 and 0.88, drought onset. and probability of detection greater than 0.84. Comparing These ﬁndings are consistent with previous studies (Otkin SMVI to the reported NASS topsoil moisture conditions et al. 2018b, 2013; Anderson et al. 2013), which have empha- shows a very similar pattern for the negatively reported condi- sized the importance of precursor soil moisture conditions and tions. The accuracy and precision of SMVI detection of the PEVP in the onset of a ﬂash drought. Low RZSM, high PEVP reported negative NASS topsoil moisture conditions for the and high VPD conditions force the rapid transition from an 2012 ﬂash drought event are 0.77 and 0.95, respectively, and energy limited environment to a water limited environment, TABLE 2. As in Table 1, but for the 2017 northern High Plains region ﬂash drought. Barley Oats Spring wheat Winter wheat Subsoil Topsoil Avg crop condition NDVI ACC 0.80 0.73 0.76 0.78 0.84 0.84 0.72 0.56 CSI 0.79 0.72 0.75 0.77 0.83 0.83 0.70 0.48 FDR 0.09 0.15 0.12 0.08 0.03 0.02 0.18 0.50 FNR 0.15 0.18 0.16 0.17 0.15 0.16 0.17 0.07 FPR 0.55 0.73 0.65 0.61 0.29 0.25 0.72 0.72 PPV 0.91 0.85 0.88 0.92 0.97 0.98 0.82 0.50 TNR 0.45 0.27 0.35 0.39 0.71 0.75 0.28 0.28 TPR 0.85 0.82 0.84 0.83 0.85 0.84 0.83 0.93 FEBRUARY 2022 OSM A N E T A L . 281 FIG. 3. Composite maps of standardized anomalies of climate conditions for selected atmospheric variables (TEMP: 2-m above ground temperature, PRCP: precipitation, RZSM: root-zone soil moisture, EVP: actual evapotranspiration, PEVP: potential evapotranspiration, SPRESS: surface pressure, TCC: total cloud cover, WS: 10-m above ground wind speed, CAPE: convective available potential energy, VPD: vapor pressure deﬁcit) based on the full SMVI ﬂash droughts inventory from 1979 to 2018 for severity higher than 2, during onset, recovery, and onset minus 3 pentads. leading to rapid drought onset and loss of green cover (Otkin more water limited environments the EVP anomalies are neg- et al. 2018b). This elevated PEVP only leads to an increase in ative in the predrought and onset periods, as elevated PEVP actual evapotranspiration (EVP) in regions with greater water cannot translate into an increase in EVP. As described later, variability}e.g., the Midwest and Great Lakes regions. In this distinction is important when considering process-based 282 J OUR N A L O F H Y D R O M E T E OR O L OGY VOLUME 23 FIG. 4. Boxplot of the standardized anomalies of atmospheric variables and root zone soil moisture averaged for the three pentads before drought onset for each class for the full SMVI inventory from 1979 to 2018. A separate ﬁgure for each of the ﬁelds’ variability across years is shown in Fig. S4. Maps of the anomalies averaged over the three pentads prior to onset are shown in Fig. S5. ﬂash drought classiﬁcation: the concept that elevated PEVP ﬂash drought deﬁnition; analyses that use different deﬁnitions leads to elevated EVP, drying the soil column, is an important might lead to different conclusions. That said, Ford and Lab- aspect of some theories of vegetation-mediated ﬂash drought osier (2017) examine some of the same variables and found intensiﬁcation (Otkin et al. 2018b), but it is not a feature of all broadly similar patterns using a different ﬂash drought deﬁni- events in our inventory. tion formulation based on the drop in RZSM from the 40th to Other potential predictor variables show regionally variable the 20th percentile in a period that does not exceed four signals. Temperature (TEMP), often identiﬁed as a driver of pentads. ﬂash drought, is generally elevated in the predrought period, c. Flash drought classification but the anomalies are weak, and the sign of anomaly is not entirely consistent. It is only during the onset pentad that ele- The composite analysis of conditions at different stages of vated temperatures are observed over most regions (though ﬂash droughts shown in the previous section provides a useful even then the southeast is not particularly anomalously perspective on the ﬂash drought development process; how- warm). Surface pressure (SPRES) might be expected to be ever, it does not consider the possibility that the inventoried anomalously high in the lead-up to a drought, but the anoma- ﬂash droughts consist of distinct forms of drought develop- lies are weak and mixed over much of the country, as is the ment. It is therefore possible that the weak or mixed anoma- average near-surface wind speed (WS). TCC tends toward lies found for certain proposed drivers are simply an artifact negative anomalies in predrought and onset periods, matching of averaging across different types of events, blurring the expectation, but again there are weak or mixed anomalies for inﬂuence of hydrometeorological drivers in different drying a number of regions. scenarios. Considering the recovery pentad, which is deﬁned as the To test this hypothesis, we perform K-means classiﬁcation ﬁrst pentad in which any of the onset conditions is violated, it on our SMVI-based ﬂash drought inventory. We use onset is evident that the role of rainfall is signiﬁcant in ending the pentad standardized anomalies for the nine variables applied ﬂash drought. Both PRCP and TCC show strong positive in composite analysis (TMP, PRCP, RZSM, EVP, PEVP, anomalies in recovery, which stands in contrast to the modest SPRES, TCC, WS, CAPE, and VPD) as the basis for classiﬁ- anomalies seen during the predrought and onset periods. cation, and ﬁrst mask out unvegetated classes (bare soil and Rain breaks the ﬂash drought cycle, quickly switching envi- urban classes) and potentially deep-rooted vegetation classes ronmental conditions to a non-water-limited status, provided (forest and woodland classes) according to the University of that the volume of rain is sufﬁcient. TEMP, PEVP, EVP, Maryland (UMD) Land Cover Classiﬁcation (Fig. S2). Only VPD, and SPRES anomalies are mixed during the recovery events with severity greater than 2 are included in the classiﬁ- period. RZSM anomalies are still strongly negative, reﬂecting cation, and we perform principal component analysis on the fact that we have deﬁned the recovery (end of ﬂash meteorological variables prior to classiﬁcation. Using the drought period) based on the change in rate of declination or elbow method (Thorndike 1953), we ﬁnd that three classes if RZSM higher than the 20th percentile, which are still below normal conditions but no longer a ﬂash drought. It is worth are optimal (Fig. S3). We emphasize that our classiﬁcation is emphasizing that these composites are based on our SMVI intended to draw out indicative patterns and is not meant to FEBRUARY 2022 OSM A N E T A L . 283 FIG. 5. Daily time series plots of selected atmospheric variables and RZSM from four pentads prior to drought onset to one pentad after onset for (a) class 1, (b) class 2, and (c) class 3 events. For each class, the time series of each variable represents an average of 20 grid cells, each selected from the core area of a separate ﬂash drought event. The y axis shows the standard devi- ation for the normalized variables’ values. imply that the three classes are entirely separable or indepen- its negative precipitation (PRCP) anomalies are modest relative dent phenomena. The use of a different dataset of meteoro- to the other two classes. logical variables, study region, or ﬂash droughts identiﬁcation These systematic differences between classes suggests that method may lead to a different number of classes. ﬂash droughts can be triggered by a diversity of meteorologi- The character of each class with respect to precursor soil cal conditions. Class 2 bears the most classic signatures of moisture conditions and meteorology in the pentads leading up drought, with its dry antecedent conditions, high temperature to event onset is shown in Fig. 4. Notably, classes 2 and 3 are and evaporative demand conditions, low cloud cover, and characterized by elevated air temperature (TMP) and vapor reduced total evapotranspiration. From a ﬂash drought per- pressure deﬁcit (VPD) prior to ﬂash drought onset, while class spective, these can be thought of as “dry and demanding” 1 is not. And while classes 2 and 3 have similar TMP anomalies, events, in which atmospheric evaporative demand combines class 2 exhibits substantially more severe antecedent VPD than with low rainfall and dry predrought conditions to allow for class 3, as well as stronger positive potential evapotranspiration rapid intensiﬁcation of already dry conditions. Notably, (PEVP) anomalies and stronger negative root zone soil mois- PEVP anomalies for these events tend to be quite high, but ture (RZSM) and total cloud cover (TCC) anomalies. Class 3, EVP anomalies are strongly negative on account of the pre- meanwhile, is the only class that shows positive anomalies in vailing dry conditions prior to drought onset. It is important antecedent actual evapotranspiration (EVP) and in CAPE, and to emphasize that our interpretation of the different classes is 284 J OUR N A L O F H Y D R O M E T E OR O L OGY VOLUME 23 based on the mean value, which adds a margin of uncertainty in classifying an identiﬁed ﬂash drought event. Figure 5b shows composite time series of key variables for 20 grid cells picked from the core of different class 2 drought events. As indicated in these time series, TMP, VPD, and PEVP are all elevated in the four pentads before ﬂash drought onset while EVP anomalies are consistently negative over this period. PRCP anomalies are generally negative, with some noise evi- dent in this 20 grid cell sample, while NDVI and RZSM anomalies are strongly negative even four pentads before onset date. In contrast to the classic drought character of class 2, class 3 bears some surprising features. The fact that the events inten- sify rapidly even though, on average, the antecedent PRCP anomalies are modest and CAPE is enhanced, suggest that for these events rapid drying is largely driven by evaporative demand (positive VPD and PEVP anomalies) combined with sufﬁcient moisture access to support elevated EVP. This com- bination makes class 3 the only class to exhibit anomalies con- sistent with the hypothesis that vegetation can contribute to ﬂash drought onset by responding to elevated temperature and evaporative demand with increased evapotranspiration, accelerating depletion of root zone soil moisture. Based on these characteristics, we term class 3 events “evaporative” ﬂash droughts. As shown in Fig. 5c for a random sample of points from different class 3 events, PRCP anomalies are mixed, with a negative signal only evident in the 2 pentads before onset, and positive anomalies seen at longer leads and even after ﬂash drought onset. EVP is consistently elevated before and during onset, while strongly positive TMP, VPD, and PEVP anomalies emerge only in the two pentads before onset. Interestingly, RZSM and NDVI anomalies are, on average for this sample, positive until two pentads before onset, such that the rapid decline observed just before onset leads to negative anomalies that are substantially smaller than those observed for class 2 events at date of onset. Class 1, for its part, is noteworthy for the fact that air tem- perature and evaporative demand preceding ﬂash drought FIG. 6. Frequency (% of years with an event) for each ﬂash onset are unremarkable compared to average conditions. Pre- drought class at each grid point for the period 1979 to 2018, based cipitation is below average in the predrought period, skies are on the SMVI ﬂash droughts deﬁnition. relatively clear (low TCC), and convective potential is low (negative CAPE anomaly). But anomalies in all other varia- bles commonly invoked to explain the rapidity of ﬂash to predict with precision more than a few days in advance drought intensiﬁcation are modest, i.e., there is a near-zero (Tian et al. 2017). The sample time series shown in Fig. 5a temperature, PEVP and VPD predrought anomalies. In this indicates that positive anomalies in VPD and PEVP are mod- sense, class 1 ﬂash droughts appear to be dominated by pre- est and emerge only within two pentads of onset, and TMP cipitation deﬁcit forcing rather than evaporative demand forc- ing, placing them at a far end of the PEVP versus PRCP anomalies are essentially neutral. Interestingly, the decline in balance of ﬂash drought forcings described by Christian et al. NDVI is dramatic for this class, suggesting that these events (2021). As described later, class 1 events are, on average, strike vegetation that is particularly sensitive to drought stress slightly less severe than other classes, but they are not always on account of vegetation type or timing. The fact that NDVI low severity events. We will refer to these events as “stealth” anomalies are strongly positive at three and four pentad leads, ﬂash droughts in that they have characteristics that would and that negative EVP signals are not evident at longer leads, make them difﬁcult to forecast: where classes 2 and 3 show suggests that these events might be associated with favorable meteorological drivers that might be forecasted with skill at early season growing conditions leading to structural over- extended weather to subseasonal time scales, class 1 appears shoot in vegetation (Zhang et al. 2021). to be the product almost solely of moderately dry antecedent At the national scale, 45% of all ﬂash drought events in our soil moisture and below average rainfall, which can be difﬁcult inventory are class 1, 31% are class 2, and 22% are class 3. FEBRUARY 2022 OSM A N E T A L . 285 FIG.7.Classiﬁcation maps of the 2011, 2012, and 2017 ﬂash drought events. But there are distinct geographic patterns for each (Fig. 6). conditions are simply too dry. The prevalence of class 1 events Class 1 events are most common in the western High Plains, in the western High Plains is less easily explained, but it is class 2 are dominant in the southern Great Plains and Texas, consistent with experience in that the iconic 2017 ﬂash and class 3 are the most common type in the upper Midwest. drought that affected Montana and North Dakota was a nota- This is not a deterministic split}all three classes are found in bly poorly predicted event (Jencso et al. 2019; Hoell et al. all regions}but the geographic distribution aligns with expec- 2019b). tation. In the relatively humid and cool upper Midwest, one Indeed, if we map the class associations of the 2017 ﬂash might expect that high TMP and VPD can trigger elevated drought event, along with the seminal ﬂash drought events of EVP even when soils are somewhat dry relative to their aver- 2011 and 2012 (Fig. 7), we see that 2017 was almost entirely age state, while in the warmer and drier southern Great Plains class 1. The 2011 event, focused on Texas and Oklahoma, is those conditions are less likely to be met with increased EVP: predominantly class 2. The widespread event of 2012 is a mix 286 J OUR N A L O F H Y D R O M E T E OR O L OGY VOLUME 23 FIG. 8. Average area in each ﬂash drought class in each month included in this study. Average is calculated for the 1979–2018 period. of class 2 and class 3, consistent with the fact that this was a meanwhile, is the most widespread drought class in all months hot event affecting a broad swath of the Great Plains and except for June, when it is brieﬂyexceeded byclass 2. Thefact Midwest, including a diversity of climate zones and land cover that class 1 events continue to be relatively common in late types. summer is in part a reﬂection of geography, since these events Seasonally, all three ﬂash drought classes can be observed in dominate in some of the coolest portions of the analysis any month included in our analysis (March–November; Fig. 8). domain. The drivers of ﬂash drought risk, then, appear to vary Class 2 shows a dramatic peak in June, coincident with the by both region and season, a fact that is relevant for the devel- onset of summer heat and dryness over much of the drought- opment of ﬂash drought risk monitoring and forecasting sys- susceptible United States. Class 3 shows a similar, albeit more tems. We note that these seasonal patterns are sensitive to our muted June peak. This is the least common ﬂash drought class inventory method, which is subject to the previously discussed on average, but in the spring it does show slightly greater total assumptions, and clustering may vary accordingly. We note that area than class 2, and the drop in area after June is dramatic. our inventory method, which includes only the ﬁrst instance of This is consistent with a drought process that includes sufﬁcient ﬂash drought in each grid cell in each year, may slightly under- available soil moisture to support elevated EVP. Class 1, represent late season ﬂash droughts in general, since in cases of FIG. 9. Boxplots of the ﬂash droughts average severity categories in the three classes after ﬁl- tering out events of severity category less than 2 (box widths are proportional to the square root of the total number of grid points in each class). FEBRUARY 2022 OSM A N E T A L . 287 two ﬂash droughts in the same location in the same year (which We emphasize that the classes deﬁned here are representative are rare) the second event would not be captured by our of a continuum of processes associated with ﬂash drought devel- method. opment. We choose to work with three classes because it proved Finally, we ﬁnd that all three diagnosed classes of ﬂash to be a stable, separable, and interpretable number of classes in drought include cases of severe drought [according to our cre- our analysis, but the result does not imply that there are only ated inventory of ﬂash droughts severity from Eqs. (1) and three pathways that can lead to ﬂash drought, or that an event (2)], but that there are statistical differences in severity cannot exhibit a mix of properties from two or three classes. The between classes, as estimated using the SMVI severity classes contrasting meteorological and surface process signatures of the deﬁned in this study (Fig. 9). There is a slight tendency for three classes do, however, indicate that events identiﬁed as ﬂash greater severity in class 2, the dry and demanding droughts, drought using a reasonable deﬁnition, including events that have and the most severe events in the record are dominated by been widely reported as seminal ﬂash droughts, represent a class 2, followed by slightly decreased average severity for diversity of onset and intensiﬁcation processes. Our results sug- class 3 and class 1. The differences in severity between classes gest that recognizing this diversity is critical to advance our are statistically signiﬁcant, as evaluated using a Welch’s t test, understanding and ability to predict these events. for both raw and log transformed data, and conﬁrmed with a nonparametric Wilcoxon signed-rank test. This result empha- Acknowledgments. We sincerely thank the journal editor sizes the potential severity of ﬂash droughts that develop and the anonymous reviewers for their constructive comments. under the combined conditions of high evaporative demand, This work was funded by the National Science Foundation low precipitation, and dry antecedent conditions. Neverthe- (NSF) Grant 1854902. less, the distribution of event severities shown in Fig. 9 makes it clear that all three classes contain severe events. This is also REFERENCES clear from our analysis of seminal ﬂash droughts (Fig. 1). We note that Fig. 9 shows results for events ﬁltered for severity Anderson, M. 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Journal of Hydrometeorology – American Meteorological Society
Published: Feb 25, 2022
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