How do we identify flash droughts? A case study in Central European Croplands [original]

How do we identify ﬂash droughts? A case study in Central European Croplands

Pedro Henrique Lima Alencar a

b,*and Eva Nora Paton a

Ecohydrology and Landscape Evaluation, Institute of Ecology, Technical University of Berlin, Berlin, Germany

Agricultural Engineering, Agricultural Sciences, Federal University of Ceará, Benﬁca, Fortaleza, Brazil

*Corresponding author. E-mail: [email protected]in.de

PHLA, 0000-0001-6221-8580; ENP, 0000-0002-5619-9958

ABSTRACT

Many deﬁnitions and delineation methods exist for identifying ﬂash droughts (FDs), which are events of rapid and unusual large depletion of

root-zone soil moisture, in comparison to average moisture conditions, due to climatic compound conditions over a short period of several

weeks. Six FD identiﬁcation methods were compared to analyse their functioning using data from several experimental cropland sites across

Central Europe. Co- and misidentiﬁcation of the FD time series were assessed using confusion and synchronicity metrics on a local scale.

Even though a large degree of synchronicity of individual FD events was observed, some divergence in drought periods was detected,

which was related to four intrinsic differences in the underlying FD deﬁnitions: (1) type of critical variable; (2) velocity of drought intensiﬁca-

tion; (3) pre-set threshold values for ﬁnal depletion and/or (4) minimum length of the duration of FDs. To balance the strengths and

weaknesses of those methods that are not based on soil moisture, we suggest using an ensemble approach for event identiﬁcation,

which is validated in this study for the temperate central European region. In doing so, the current unclearly deﬁned sub-types of FDs

can be detected, regardless of the different combinations of compound drivers and differences in intensiﬁcation dynamics. All methods

were implemented in an R package and are available as a Shiny app for the public.

Key words: climatic compound events, confusion matrix, event identiﬁcation, ﬂash drought, synchronicity metrics

HIGHLIGHTS

•The multiple methods proposed to identify ﬂash droughts (FDs) show substantial disagreement.

•Soil moisture is the key variable to identify FDs in croplands; however, such data are scarce, and a method based on proxy variables is

necessary.

•A multi-index or multi-method should be favoured in identifying FDs, as a single proxy (to soil moisture) may cause signiﬁcant

misidentiﬁcation.

GRAPHICAL ABSTRACT

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/).

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1. INTRODUCTION

Droughts are among the most extreme climatic weather events that threaten food security (FAO 2021). They have negative

impacts on the global food–energy–water nexus and the sustainable development goals (D’Odorico et al. 2018). Droughts are

generally characterised by unusually high levels of rainfall deﬁcit, runoff deﬁcit or soil moisture deﬁcit (Palmer 1965;Mishra

& Singh 2010) and are expected to increase in many regions of the world in terms of frequency, severity and duration under

current and future climate change conditions (Lesk et al. 2016;Samaniego et al. 2018).

Flash droughts (FDs) are a special form of drought. In contrast to the descriptions of classical droughts (de Araújo &

Bronstert 2015;Oikonomou et al. 2020), FDs are characterised by rapid onset and relatively short durations (Otkin et al.

2018;Lisonbee et al. 2021). They are associated with severe and immediate soil moisture depletions, resulting in plant

water stress and mortality (Ford & Labosier 2017;Liang & Yuan 2021;Osman et al. 2021).

One of the ﬁrst FD studies was by Peters et al. (2002), who were among the ﬁrst to study single short drought events in late

summer, which were characterised by the concurrence of low antecedent moisture and unusually high temperature. Interest

in FDs has increased over the last few years, motivated by extreme FD occurrences in the USA, Russia and China, which

caused extreme impacts on managed vegetation, disruption to the global food supply and increased wildﬁres (Otkin et al.

2013;Mo & Lettenmaier 2016;Christian et al. 2019a,2019b;Liang & Yuan 2021). An FD in Australia during the spring

of 2019 is thought to have played a central role in the massive forest ﬁres that consumed over 1.6 million hectares

(Nguyen et al. 2021).

Over the last two decades, multiple methods have been proposed to identify FD events (Lisonbee et al. 2021), yet there is no

consensus on what an FD entails and how they may be deﬁned in terms of onset, duration, the velocity of intensiﬁcation and

absolute or relative changes (Osman et al. 2021). In their recent literature review, Lisonbee et al. (2021) identiﬁed as many as

20 studies with different deﬁnitions using climate variables or indexes related to soil moisture, air temperature, precipitation,

and actual and potential evapotranspiration (ET). Eleven of these deﬁnitions included an interval of intensiﬁcation or rapid

onset as part of ﬂash ﬂood delineation, whereas nine studies merely considered short-term drought events as FDs. A method

comparison study by Osman et al. (2021) identiﬁed four core types where the deﬁnition of a heatwave FD (Mo & Lettenmaier

2015) was based on temperature anomalies, rapid soil drying (Ford & Labosier 2017;Yuan et al. 2019), actual and/or poten-

tial ET anomalies (Christian et al. 2019a,Pendergrass et al. 2020) and multi-criteria indexes (Chen et al. 2019). For the USA,

they showed that the FD frequency, spatial extent and onset would vary signiﬁcantly depending on which deﬁnition is used.

They also suggested that a root-zone soil moisture-based method effectively captures the FD onset in both humid and semi-

arid regions. Other than the study by Osman et al. (2021), which focused on the identiﬁcation of spatial differences in deli-

neated FDs, there have been no systematic studies on the temporal divergence and synchronicity of delineated FDs. At the

same time, little is known about FD dynamics in Central Europe and it is not known which FD method would apply to this

region, given that the present methods have so far been used for FD identiﬁcation mainly outside Europe. Additionally, little

efforts have been made towards an ensemble method for FD identiﬁcation. In this study, we propose a method of this nature

tested for areas in Central Europe.

Due to the lack of a general deﬁnition, we deﬁne an FD as the process of rapid, accelerated and unusually large depletion of

root-zone soil moisture, in comparison with ‘average’moisture conditions, due to the simultaneous or concurrent occurrence

of two or more atmospheric and/or weather conditions over a short period of several weeks during the main growing season.

The objective of this study was to compare, in a local/point (climatological station) scale, the functioning of six recently

developed FD identiﬁcation methods with data from four well-monitored experimental cropland sites in Central Europe,

by assessing co- and misidentiﬁcation of FD time series using similarity and synchronicity metrics. We selected two soil moist-

ure-based methods (Ford & Labosier 2017;Osman et al. 2021) and four indirect methods that used single or multiple climatic

variables or indices for FD delineation (Christian et al. 2020;Noguera et al. 2020;Pendergrass et al. 2020, and a multi-criteria

method proposed by the authors and validated for Central Europe). The methods were implemented in an R package and a

Shiny app available to the public.

2. MATERIALS AND METHODS

2.1. FD identiﬁcation methods

The following six FD identiﬁcation methods were selected on the basis that they used station data as input and, following our

deﬁnition, included a clear deﬁnition of the rapid onset of water limitation. The ﬁrst two methods are based on soil moisture

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and the other four used indirect proxies of drought conditions, such as anomalies of rainfall, temperature and the ratio of

actual and potential ET. The methods used are described as follows:

M1: Osman et al. (2021) borrowed the concept of volatility from stock market analysis techniques for the analysis of rapid

soil moisture changes. According to them, an FD occurs when the one-pentad (5 d) running average for root-zone soil moist-

ure content falls below the four-pentad (20 d) running average for a period of at least four pentads, with soil moisture at the

end of this period dropping below the 20th percentile for that time of year (Figure 1(a)).

M2: Ford & Labosier (2017) identiﬁed FDs as periods when the pentad-average 0–40 cm volumetric water content declines,

from at least the 40th percentile to below the 20th percentile, in four pentads or less (Figure 1(b)).

M3: The multi-criteria method is a new method that uses a set of 10 anomalies and indexes derived from weekly precipitation,

temperature and potential ET data. It calculates a score for each week equivalent to the proportion of indicators that meet or

surpass the respective pre-set thresholds. Weeks with a score higher than 0.65 and an absolute change of score (Δscore) higher

than 0.25 over up to 3 weeks are classiﬁed as FDs (Figure 1(c)). The event duration is computed as the time from the beginning

of intensiﬁcation until the score is below 0.65. A full description of this method is provided in the Supplementary Material.

M4: Christian et al. (2020) used the standardised evaporative stress ratio (SESR), which is derived as the z-score of the quo-

tient of actual to potential ET rate values for a speciﬁc pentad. They used four criteria which FD events were required to have:

(1) a minimum length of ﬁve SESR changes, equivalent to a length of six pentads (30d minimum length); (2) a ﬁnal SESR

value below the 20th percentile of SESR values; (3) SESR changes must be at or below the 40th percentile between individual

pentads and no more than one SESR change above the 40th percentile following the previous criterion and (4) an overall

mean change in the SESR during the entire length of the FD must be below the 25th percentile in the SESR (Figure 1(d)).

M5: Noguera et al. (2020) used the standard precipitation evapotranspiration index (SPEI) on a short timescale (1 month)

and performed calculations based on a temporal frequency of 1 week (four per month). To identify the rapid onset of a

drought event, the change in the SPEI for each week, in periods of 4 weeks, was calculated and the onset of an FD was

deﬁned as involving a change in the SPEI equal to or less than 2 SPEI units (z-values) over an intensiﬁcation period of

4 weeks. Further, ﬁnal SPEI values had to be equal to or less than 1.28 SPEI units (Figure 1(e)).

M6: Pendergrass et al. (2020) utilised the evaporative demand drought index (EDDI) following Hobbins et al. (2016) and

Lukas et al. (2017), which is calculated based only on the potential ET using the Penman–Monteith equation. The method

identiﬁes an FD when a 50% increase in the EDDI over 2 weeks is sustained for at least another 2 weeks (Figure 1(f)).

Workﬂow and key characteristics are summarised in Figure 1 and Table 1. Readers interested in a more detailed descrip-

tion of the percentiles and thresholds are referred to the original papers.

The six methods used one or more climate variables (rainfall, temperature, soil moisture and actual and potential ET).

Further, they all share an underlying set of characteristics:

1. FDs evolve rapidly, with an intensiﬁcation period lasting between 2 and 4 weeks.

2. The ﬁnal conditions at the end of an FD lean towards extreme values, which are often characterised by the variable reach-

ing values under the 20th percentile or, in some, a z-score value over +1.

3. FDs are considered seasonal phenomena and are identiﬁed based on the expected values of climatic variables for each

speciﬁc time of the year subdivided either in pentads or weeks.

4. FDs depend on crossing certain thresholds and are thought to be correctly identiﬁed if, and only if, environmental con-

ditions meet a set of predeﬁned rules.

The key differences between the methods are how the FD variables and durations are deﬁned. Methods M1 –Osman et al. and

M2 –Ford & Labosier take a direct approach to assess plant water availability using soil water data, whereas all other methods use

proxy variables, which are likely to be less accurate, while simultaneously overcoming severe data limitations. Additionally, differ-

ences exist in the deﬁnition of the onset of the FD periods, the time resolution used (weeks and pentads), the minimum period over

which it should be sustained and the maximum duration beyond which it might be considered a ‘normal’drought (Table 1).

Additionally, the deﬁnition of event duration varies among methods. Most methods (M1 –Osman et al.,M2–Ford & Labo-

sier, M3 –multi-criteria and M6 –Pendergrass et al.) assume that the duration of an FD event comprises the intensiﬁcation

phase (between 1 and 3 weeks), a period of persistence of the dry conditions (between 2 and 4 weeks) ending with the recup-

eration phase when the key variable (climatic or groundwater conditions) surpasses a threshold (Table 1). The methods of M4

–Christian et al. and M5 –Noguera et al. have different assumptions regarding duration. M4 –Christian et al. 2019a assume

that the FD event is limited to the intensiﬁcation period, with the FD event lasting only as long as there is a trend towards dry

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Figure 1 |Flowcharts of the six methods for FD identiﬁcation: (a) M1: Osman et al. (2021); (b) M2: Ford & Labosier (2017); (c) M3: novel multi-

criteria method; (d) M4: Christian et al. (2020); (e) M5: Noguera et al. (2020); and (f) M6: Pendergrass et al. (2020). The implementation of all

methods is available in the supplements of this paper as an R package.

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conditions and not having a threshold for the recuperation phase (Christian et al. 2019a). M5 –Noguera et al., although char-

acterizing all three phases (intensiﬁcation, persistence and recuperation), decided in their work not to consider the

intensiﬁcation phase as part of the FD duration. In this study, the application of the method of Noguera et al. (2020) includes

the intensiﬁcation period in the computation of the total duration of FD events.

All six methods were implemented in R programming language and are organised in an R package named ‘fdClassify’

and

in a Shiny App named ‘FD-Viz’

2.2. Comparison metrics

Two types of metrics were used to compare the six FD methods: synchronicity metrics and the confusion matrix. The method

of Osman et al. (2021) was used as a reference method as it was considered the method that most closely followed our deﬁ-

nition of an FD as given in Section 1 (using the rapid soil moisture decline as a key variable for FD identiﬁcation). This

method was evaluated against measured soil moisture data (see Section 2.3) in the root zone and, therefore, appeared to

be particularly suited to reproduce the FD dynamics of croplands.

2.2.1. Synchronicity metrics

The concept of synchronicity based on the work of Kemter et al. (2020) was employed to compare the rate of identiﬁcation of

FD events and the intervals, which were correctly identiﬁed as intervals with no FD, for a weekly resolution. Synchronicity

metrics were originally developed to analyse whether extreme ﬂoods occur concurrently with the same timing in larger basins

(Kemter et al. 2020). It is based on the two synchronicity metrics sync

and sync

, which are deﬁned as the average proportion

of the successful identiﬁcation of FD events and no FD events, respectively. Here, the Osman reference method was com-

pared separately with the other methods. The metrics can take values between zero and one; therefore, a perfect

Table 1 |Comparison of key variables, statistics and threshold criteria for the detection of FDs in all six methods

Method Variables

Original dataset

type[

] Statistics Onset rate Total duration

M1 Osman et al.

(2021)

Soil moisture Grid

(CONUS)

Soil moisture volatility

index (SMVI)

Running averages (RA)

4 weeks Onset duration þwhile

SM lower than 4-

pentad running

average

M2 Ford & Labosier

(2017)

Soil moisture Station

(Eastern USA)

Percentiles 4 pentads Onset duration þuntil

SM.30th p.

M3 Multi-criteria Precipitation

temperature

potential

evapotranspiration

Station (Central

Europe)

Anomalies

indexes

percentiles

4 weeks Onset duration þuntil 2

consecutive weeks

with score,0.65

M4 Christian et al.

(2019a,2019b,

2020)

Actual

evapotranspiration

potential

evapotranspiration

Grid (Eastern

USA[

] and SW

Russia)

Standardised evaporative

stress ratio (SESR)

6 pentads Onset duration þuntil 2

consecutive weeks

with an increasing

SESR

M5 Noguera et al.

(2020)

Precipitation

potential

evapotranspiration

Station (Spain) Standard precipitation

evapotranspiration index

(SPEI)

4 weeks After onset, until

SPEI.1.28

M6 Pendergrass et al.

(2020)

Potential

evapotranspiration

GRID (CONUS) Evaporative demand

drought index (EDDI)

2 weeks Onset duration þuntil

EDDI increases

towards wet

conditions

The data type used in the original application/publication of the method. Grid indicates methods that were implemented with reanalysis or remote sensing data. Station indicates

methods that were originally implemented with weather station (direct measure) data.

The Christian et al. method studied multiple areas in the continental USA: the Great Plains, Corn Belt, and Great Lake regions, in the states of Georgia, Kansas, Iowa, and Minnesota.

Link to fdClasify git repository: https://github.com/pedroalencar1/fdClassify.

Link to Flash Drought Visualization tool (FD-Viz): https://pedroalencar.shinyapps.io/FD-Viz/.

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