Extreme dry and wet spells face changes in their duration and timing

Abstract

Dry spells are sequences of days without precipitation. They can have negative implications for societies, including water security and agriculture. For example, changes in their duration and within-year timing can pose a threat to food production and wildfire risk. Conversely, wet spells are sequences of days with precipitation above a certain threshold, and changes in their duration and within-year timing can impact agriculture, flooding or the prevalence of water-related vector-borne diseases. Here we assess changes in the duration and within-year timing of extreme dry and wet spells over 60 years (1958-2017) using a consistent global land surface precipitation dataset of 5,093 rain gauge locations. The dataset allowed for detailed spatial analyses of the United States, Europe and Australia. While many locations exhibit statistically significant changes in the duration of extreme dry and wet spells, the changes in the within-year timing are less often significant. Our results show consistencies with observations and projections from state-of-the-art climate and water resources research. In addition, we provide new insights regarding trends in the timing of extreme dry and wet spells, an aspect being equally important for possible future implications of extremes in a changing climate, which has not yet received the same level of attention and is characterized by larger uncertainty.

Full text

Environ. Res. Lett. 15 (2020) 074040 https://doi.org/10.1088/1748-9326/ab7d05
Environmental Research Letters
Extreme dry and wet spells face changes in their duration
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and timing
Korbinian Breinl1,2,5, Giuliano Di Baldassarre2,3, Maurizio Mazzoleni2,3, David Lun1and Giulia Vico4
1Institute of Hydraulic Engineering and Water Resources Management, Vienna University of Technology, Karlsplatz 13/222, 1040 Wien,
Austria
2Centre of Natural Hazards and Disaster Science, CNDS, Villav¨
agen 16, 75236 Uppsala, Sweden
3Department of Earth Sciences, Uppsala University, Villav¨
agen 16, 75236 Uppsala, Sweden
4Department of Crop Production Ecology, Swedish University of Agricultural Sciences, Ulls v¨
ag 16, 75007 Uppsala, Sweden
5Author to whom any correspondence should be addressed
E-mail: breinl@hydro.tuwien.ac.at
Keywords: extreme dry spells, extreme wet spells, change in duration, change in timing, climate change, global analysis, global trends
Abstract
Dry spells are sequences of days without precipitation. They can have negative implications for
societies, including water security and agriculture. For example, changes in their duration and
within-year timing can pose a threat to food production and wildfire risk. Conversely, wet spells
are sequences of days with precipitation above a certain threshold, and changes in their duration
and within-year timing can impact agriculture, flooding or the prevalence of water-related
vector-borne diseases. Here we assess changes in the duration and within-year timing of extreme
dry and wet spells over 60 years (1958–2017) using a consistent global land surface precipitation
dataset of 5093 rain gauge locations. The dataset allowed for detailed spatial analyses of the United
States, Europe and Australia. While many locations exhibit statistically significant changes in the
duration of extreme dry and wet spells, the changes in the within-year timing are less often
significant. Our results show consistencies with observations and projections from state-of-the-art
climate and water resources research. In addition, we provide new insights regarding trends in the
timing of extreme dry and wet spells, an aspect being equally important for possible future
implications of extremes in a changing climate, which has not yet received the same level of
attention and is characterized by larger uncertainty.
1. Introduction
Dry spells are sequences of days without precipitation
[1]. They can affect societies in many ways, including
negative effects on water security and food produc-
tion [2–5]. The intensity and duration of droughts are
directly proportional to the number of days without
precipitation [6]. Extreme dry spells refer to meteor-
ological droughts [4]. Long dry spells can increase,
for example, the risk of agricultural losses or wild-
fires. Wet spells, on the other hand, are sequences of
days with precipitation above a certain (minimum)
threshold. Extremely long wet spells can lead to sat-
urated soils and thus influence the flood hazard and,
depending on the climatic conditions, can have direct
impacts on agriculture and the prevalence of water-
related vector-borne diseases [7]. Additionally, dry
and wet spells can affect water quality [8–13]. For
example, dry spells can lead to lower river flows,
reduced velocities and thus higher water residence
times enhancing the potential for toxic algal blooms
and low dissolved oxygen levels [14,15]. Other effects
include releases of water quality pollutants during a
wet spell after a prolonged dry spell, such as sulph-
ate [16,17] or nitrate [18]. Dry and wet spell prop-
erties are also important for urban water manage-
ment, for example in the design of water storage
components [19]. Hence, although the underlying
mechanisms can be complex, extreme dry and wet
spells are an essential contribution to precipitation
extremes related to droughts and floods and their
various impacts.
While research has mainly focused on the changes
of the duration or frequency of dry and wet spells
[1,20–25], changes in their timing have been poorly
explored. Yet, trends in the timing of natural haz-
ards can lead to considerable economic and envir-
onmental consequences, as societies and ecosystems
have adapted to their average within-year timing [26].
Changes in the timing of dry spells can for instance
© 2020 The Author(s). Published by IOP Publishing Ltd

Environ. Res. Lett. 15 (2020) 074040 K Breinl et al
pose a threat to food production. Specific mechan-
isms make systems more sensitive to dry and wet
spells in specific seasons. Crops are not only sens-
itive to water deficit and hence the duration of dry
spells, but also to their timing with respect to sow-
ing and developmental stages [27]. Provided the crop
establishes, the highest sensitivity to excessive rain
and water logging occurs during the vegetative phases
[28] and to drought during the reproductive stages
[27,29]. Also, excessive rain can enhance the risk
of lodging or pathogen development [30]. Further,
in croplands, near soil saturation during sowing and
harvesting make field operations impossible [31].
Similar, forests are most sensitive to drought when
it occurs early in the growing season, when radial
growth peaks [32]. The timing of extreme dry and
wet spells has the potential to enhance their con-
sequences, with dry spells being more impactful if
occurring during warmer periods, when evapora-
tion and transpiration rates are high; and wet spells
potentially causing more intense runoff during colder
months, in particular over frozen soils, except where
precipitation occurs as snow.
Here, we estimate changes in the duration and
the within-year timing (hereafter simply referred to
as ‘timing’) of extreme dry spells (EDS) and extreme
wet spells (EWS) over a period of 60 years using
observations from 5093 rain gauges across the globe
(figure 1). The dataset allowed for detailed spatial
analyses of the United States, Europe and Australia
which thus are in the main focus of our work. Asia
had little missing data but a low spatial coverage of
suitable rain gauges. Results for Asia are reported in
the appendix (figure A1). Other regions (e.g. Africa
and South America) are not considered in our ana-
lyses because of the lack of available rain gauges or
extensive missing data. The three regions in focus
have a population of about 1.1 billion people, accoun-
ted for a share of about 47% of the global GDP in 2018
according to the World Bank [33], and are important
agricultural areas with a contribution of about 40% to
the global production of important staple crops (bar-
ley, maize, oat, rye, sorghum, wheat) in 2018 [34]. We
compare changes of the duration and timing of EDS
and EWS between 1958 and 2017.
2. Methods
2.1. Study area and data
We took a global perspective by using the GHCN-
Daily dataset [35] providing global rain gauge records
of daily precipitation (figure 1). Dry days were
defined as days with precipitation < 0.1 mm. Wet days
were defined as days with precipitation ⩾0.1 mm. In
a first step, rain gauges covering the period 1958–2018
(61 years) were extracted from the global GHCN-
Daily dataset and all flagged records with potential
errors were removed. From these 9519 available rain
gauges, 5093 had 10% missing records or less, which is
the highest acceptable threshold for dry spell analyses
as identified by Zolina et al [21]. The 10% restric-
tion was applied separately to two periods, 1958–
1987 and 1988–2018 to avoid pronounced imbalances
of missing records in a subset of the dataset. The
duration of EDS/EWS at each site was based on the
annual maximum in the 60-year period 1956–2017
(2018 was added to the analysis as an EDS/EWS can
start in 2017 but last until 2018), i.e. we focused on
low-frequency events potentially causing the highest
damage. Accordingly, the timing refers to the mean
date of the annual maximum dry (wet) spell between
1958 and 2017. Analyses of dry spells put particu-
larly strict requirements on data completeness [21],
as missing values in longer dry spells can bias the res-
ults. To address this challenge, we applied a weather
generation technique to fill the gaps of records. Data
gaps were filled stochastically with a first-order two-
state Markov chain rainfall model [36,37]. The model
was calibrated to calendar months to account for
seasonality; the gap filling procedure was conducted
separately in the periods 1958–1987 and 1988–2018
to maintain climatic properties of the two different
(30-year, 31-year) periods. The gap filling was applied
50 times over the entire dataset, the median of these
analyses was taken for our analyses. The network
density and data availability allowed for detailed ana-
lyses of EDS/EWS for the United States, Europe and
Australia. These three regions comprise 4645 (91.2%)
of all rain gauges considered.
2.2. Linear trend model and mean dates
We applied a linear trend model to identify trends.
The linear trend was estimated using the adjusted
Theil–Sen slope estimator [26,38], which is robust
to outliers. The linear regression was applied for the
duration of EDS/EWS (duration of annual maxima
over 60 years) and the timing of EDS/EWS (starting
date of annual maxima of EDS/EWS over 60 years,
i.e. day 1 to 365/366). The generic Theil–Sen slope
estimator is not directly applicable to the timing as
the starting dates belong to the class of circular quant-
ities. Thus, the Theil–Sen slope estimator was adap-
ted for the timing [26]. The trend estimator ß is the
median of the difference of dates over all pairs of years
(i and j) in the time series, where the dates are centred
around the average starting date of occurrence
D(equation 1):
D∗
i=




Di−D if |D−Di|<¯
m− |D−Di|
¯
m−(D−Di)if |D−Di|>¯
m− |D−Di|andD >Di
(Di−D)−¯
m if |D−Di|>¯
m− |D−Di|andD <Di
β=median (D∗
j−D∗
i
j−i)(1)
These adjustments consider the circular nature of
the data and compare the relative delay and advance
in the arrival date with respect to the mean date of
occurrence; βhas units of days per year.
2

Environ. Res. Lett. 15 (2020) 074040 K Breinl et al
Figure 1. Exploring changes in the duration and timing of extreme dry and wet spells. The map shows the location of the rain
gauges considered in this study derived from a consistent dataset: the Global Historical Climatology Network Daily
(GHCN-Daily) [35]. The entire network plotted covers the period 1958–2018, gauges in red denote data completeness of 90%
(maximum 10% missing data) and were used in the trend analyses.
We analysed the mean duration (i.e. average
length) and timing (i.e. average starting date) of
EDS/EWS. While an arithmetic mean is suitable for
the duration, the mean of the timing requires dir-
ectional statistics [26,39,40]. More specifically, the
starting dates of the EDS/EWS were calculated as
(equation 2):
θi=Di
2π
mi
0⩽θi⩽2π(2)
where January 1 corresponds to Di=1 and Decem-
ber 31 to Di=mi, i.e. the total number of days in that
given year. The average starting date of occurrence D
of the maximum dry (wet) spell at a station was cal-
culated as (equation 3):
D=¯
m
2π
·







π
2
−atan2(¯
y,¯
x)¯
x⩾0,¯
y⩾0
5π
2
−atan2(¯
y,¯
x)¯
x<0,¯
y⩾0
π
2
−atan2(¯
y,¯
x)¯
x⩽0,¯
y<0
π
2
−atan2(¯
y,¯
x)¯
x>0,¯
y⩽0
(3)
where ¯
xand ¯
yare the sine and cosine of the compon-
ent of the average date, considering nthe total number
years (equation 4):
¯
x=1
n
n
∑
i=1
sin(θi)
¯
y=1
n
n
∑
i=1
cos(θi)
(4)
These formulas differ from those found for
example in Burn [41], because January first is taken
to be (0,1) (most Northern point on the unit circle)
and the succession of dates follows a clockwise rota-
tion on the circle, instead of counter-clockwise.
2.3. Trend tests
At each location, a Mann–Kendall trend test was
applied at the α=0.05 level, both for the trends
in the duration and the trends in the timing. For
the latter, the differences of the (centred) occurrence
dates were employed. Details of the Mann–Kendall
test can be found for example in Yue et al [42].
However, although ignored most of the time [43], a
problem can arise when applying multiple statistical
tests across atmospheric field data, which is the prob-
lem of multiplicity or multiple hypothesis testing.
Field significance addresses the question if a num-
ber of locally significant statistical tests occurred by
chance [43–46]. We employ the procedure for con-
trolling the false discovery rate (FDR) by Benjamini
and Hochberg [45] in order to assess field significance
of the trend tests. This approach controls the pro-
portion of falsely rejected local null hypotheses and
is conducted at a global significance level (which is
set to αglobal =0.05). Global (field) significance can
be declared if—after applying the FDR approach—
at least one local null hypothesis is rejected [44]. A
remarkable advantage of the FDR procedure is its
robustness to spatial correlation [43,47], as typically
found in atmospheric data [43].
3. Results and discussion
We present the results split into the three main
regions of the United States, Europe and Australia.
The GHCN dataset also provided suitable rain gauges
(i.e. 90% complete) in other regions (mainly in Asia,
see figure 1), but the density was too low for detailed
regional spatial analyses and thus robust conclusions.
3

Environ. Res. Lett. 15 (2020) 074040 K Breinl et al
For the sake of completeness, results for Asia (less
densely gauged) are enclosed in the appendix (figure
A1). For both EDS and EWS, we present fourmetrics:
(i) the duration (ii) changes in the duration, (iii) the
timing, and (iv) changes in the timing. The changes
of the duration and timing are presented as the mean
of the individual locations across hexagons with a cell
size of 1.5◦. By this we avoid presenting misleading
trends across large regions without any rain gauge
(which would result from generic interpolation tech-
niques), such as in central Australia. For validation,
we additionally generated means over hexagons in
the US (due to its high gauge density) from ordin-
ary kriging over a continuous grid of 0.1◦. The sim-
ilar patterns from ordinary kriging (see figure A2)
and hexagon means (figure 2(b)) justify the selec-
ted approach. Field significance of the entire data-
set was detected for trends in the duration of EDS
and EWS. For the trends in the timing, field signific-
ance was detected for the EDS but not for the EWS.
When ignoring the FDR, for the duration and timing
of EDS, 995 and 313 gauges were locally significant,
respectively. When considering the FDR, the numbers
reduced to 283 and 4. For EWS and the duration and
the timing, 1323 and 282 gauges were locally signific-
ant, respectively. The numbers reduced to 619 and 0
when considering the FDR. The detailed analysis of
the FDR can be obtained from figure A3.
3.1. United States
In the US, a strong West-East gradient of the dura-
tion of EDS exists (figure 2(a)). EDS are longest in
California, Nevada, Arizona and in the West of Texas.
Durations are smaller in mountainous regions. The
West-East gradient is the opposite for EWS, with the
exception of the North of California and the North-
West (Western parts of the states of Oregon and
Washington) (figure 2(b)). EDS have become longer
in the West and South-West of the US, and have
become shorter in the East (figure 2(c)). The state of
Florida has experienced an increase in the duration
of EDS. The rest of the country is more scattered but
is generally characterized by trends towards shorter
durations. Our results of EDS are consistent with
trends in annual precipitation in the period 1951–
2010, as presented by the Intergovernmental Panel
on Climate Change (IPCC) [48], where a decreasing
trend has been identified for the West and an increas-
ing trend for the East. Similar trends in the annual
precipitation have been described by Wuebbles et al
[49]. Analyses in the changes of EWS indicate regions
of increasing extremes (figure 2(d)). For example,
the North-West, large parts of the South and Flor-
ida in the South-East have experienced increasing
durations of EWS (where also the duration of EDS
has increased). Some regions such as California have
been facing increasing duration of EDS and decreas-
ing duration of EWS, indicating a trend towards a
drier climate, which is consistent with the observation
that drought conditions have become more and more
devastating in this area [50]. As already indicated by
the increase in the duration of EDS, the mean dry-
ing in the South-West throughout the 20th century is
mainly related to an increase in dry days and not to
significant trends in seasonal precipitation amounts
[51], as the precipitation intensity has increased dur-
ing the same period in most of the region [51]. That
is, the (not significantly changed) seasonal precip-
itation tends to fall today on fewer days with pro-
found consequences for wildfires, agriculture, and
ecosystems and new challenges for water resource
management.
There are pronounced differences in the timing of
EDS (figure 2(e)). In the West, EDS start in the sum-
mer and fall. In the mountain regions starting dates
have a high variability (Rocky Mountains and north-
ern parts of the Appalachian Mountains). EDS start
in the late fall across the Interior Plains, where win-
ters are generally cold and dry. In most of the East,
EDS start in the late summer and early fall. An excep-
tion in the South-East are the EDS in Florida, which
start in winter and spring. There are three prominent
regions where EDS starting dates have become earlier:
the mountainous West, parts of the South, and large
parts of the Midwest (figure 2(g)). In the East, there
are trends towards earlier and later timing. The later
timing in California is in line with projections sug-
gesting a later wildfire season towards fall and winter
in the future [52]. The timing of EWS is in many
regions mirroring the timing of the EDS (figure 2(f)).
Extreme wet spells occur in winter in the West, in
spring across the Interior Plains, in summer in the
South, and in spring and summer in the East. As in
the case of EDS, starting dates of EWS are variable
in the mountainous regions. Interestingly, spatial pat-
terns of the changes in the timing of EWS (figure 2(h))
show large similarities to the changes in timing of
the EDS (figure 2(g)). Exceptions are for example the
North and South-East, where starting dates of EDS
and EWS have become later and earlier, respectively.
3.2. Europe
In Europe, there is a pronounced North-South gradi-
ent of the duration of EDS (figure 3(a)), with EDS
becoming longer towards the South. The opposite
applies to EWS, which tend to become shorter from
the North towards the South (figure 3(b)).
The duration of EDS has become shorter
throughout Scandinavia (figure 3(c)), which is in line
with trends described by Kovats et al [53]. There are
local exceptions such as the South of Sweden. Obser-
vations and climate simulations for Sweden suggest
a trend towards drier summers in the South [54]. A
shortening of EDS over southern Scandinavia during
the warm season has also been observed by Zolina
et al [55]. In Western Europe, there is a North-South
gradient in regard to trends towards shorter (Den-
mark, most of Germany) and longer (Netherlands,
4

Environ. Res. Lett. 15 (2020) 074040 K Breinl et al
Figure 2. Extreme dry and wet spell analysis across the United States. (a), (b) Duration of extreme dry (wet) spells. (c), (d)
Changes in the duration of extreme dry (wet) spells as percentage change per decade with reference to the duration in 1958. (e),
(f) Average within-year timing of extreme dry (wet) spells in the period 1958–2017. (g), (h) Changes in the within-year timing of
extreme dry (wet) spells as days per decade with reference to the timing in 1958. Results of (c), (d), (g) and (h) are shown as
means across hexagons. Black dots represent the rain gauges examined with locally significant (green) and field significant
(magenta) sites.
France without the Eastern parts) EDS. The East of
Germany has been facing a small trend towards longer
EDS, which is line with increased drought stress in the
East of Germany as described by Zebisch et al [56].
Central and Southern France have experienced longer
EDS, whereas EDS have become shorter in the West.
Central Spain has experienced longer EDS, while the
duration has decreased in the Southern Spain. East-
ern Europe has been facing a general trend towards
longer EDS (e.g. Slovakia, Hungary, and Romania).
Not only is there a general tendency of EDS becom-
ing longer in Southern Europe (a trend also projec-
ted for the future by the IPCC [53]), but also EWS
show similar patterns, i.e. EWS become longer in
Northern Europe and shorter in Southern Europe
(figure 3(d)). Similarly, Zolina et al [55] showed that
the duration of EWS in the Mediterranean region
decreases. The analysis of the durations gives some
indication of a wetter climate in Northern Europe
and a drier climate in Southern Europe throughout
the year, although an extreme dry or wet spell does
not necessarily provide certain information about
trends in total rainfall amounts of a season, which
may be different. However, an increasing tendency of
the mean annual precipitation over northern Europe
and a decreasing tendency over Southern Europe
have been reported for the 20th century [57]. The
increasing duration of EDS in Southern Europe may
affect the wildfire seasons duration, which has been
reported by Jolly et al [58], who reported a length-
ening of the fire weather season in Southern Europe.
As in the case of the US and its South-West, the dry-
ing trend in the South of Europe is mainly related
to an increase in dry days, while the precipitation
5

Environ. Res. Lett. 15 (2020) 074040 K Breinl et al
Figure 3. Extreme dry and wet spell analysis across Europe. (a), (b) Duration of extreme dry (wet) spells. (c), (d) Changes in the
duration of extreme dry (wet) spells as percentage change per decade with reference to the duration in 1958. (e), (f) Average
within-year timing of extreme dry (wet) spells in the period 1958–2017. (g), (h) Changes in the within-year timing of extreme dry
(wet) spells as days per decade with reference to the timing in 1958. Results of (c), (d), (g) and (h) are shown as means across
hexagons. Black dots represent the rain gauges examined with locally significant (green) and field significant (magenta) sites.
intensity from an annual perspective has faced little
changes in the 20th century (Kovats et al [53] reports
more frequent short-time extreme precipitation for
the winter season throughout most of Europe) but
is projected to further increase by the end of the
21st century [51]. These different trends in various
precipitation extremes demonstrate the complexity of
the link between EDS or EWS and potential impacts.
Figures 3(e) and (g) show the timing and changes
in timing of EDS, respectively. In the Central and
Northern parts of Scandinavia, EDS on average start
in spring, while they start in summer in the South.
In the South of Scandinavia, EDS have been shift-
ing towards earlier timing (i.e. for most sites towards
earlier in spring), in the North of Scandinavia EDS
have shifted towards later starting dates (i.e. towards
6

Environ. Res. Lett. 15 (2020) 074040 K Breinl et al
Figure 4. Extreme dry and wet spell analysis across Australia. (a), (b) Duration of extreme dry (wet) spells. (c), (d) Changes in the
duration of extreme dry (wet) spells as percentage change per decade with reference to the duration in 1958. (e), (f) Average
within-year timing of extreme dry (wet) spells in the period 1958–2017. (g), (h) Changes in the within-year timing of extreme dry
(wet) spells as days per decade with reference to the timing in 1958. Results of (c), (d), (g) and (h) are shown as means across
hexagons. Black dots represent the rain gauges examined with locally significant (green) and field significant (magenta) sites. To
facilitate the interpretation of results, colors in in (e) and (f) match those in figures 2(e), 2(f), 3(e) and 3(f ) season-wise.
later in spring). In Denmark, the North of Germany,
and the Netherlands, EDS usually start in summer
and have shifted towards earlier timing (also reported
by Demuth et al [59]), potentially negatively affecting
spring crop establishment but also improving sow-
ing possibilities in spring—now the most commonly
used window [60]. EDS start later in Central Germany
(mainly fall) and the Mountainous Alpine parts of
7

Environ. Res. Lett. 15 (2020) 074040 K Breinl et al
Germany (late fall, winter). There is a trend towards
later timing in the South (Alps). In France, EDS start
in summer in the North and have shifted towards
earlier timing. In the South of France and the Pyren-
ees, similar to the Alps, EDS set in in winter and show
a trend towards later timing. Throughout Spain, EDS
are a summer phenomenon. For most of Spain, there
is a trend towards earlier starting dates, thus poten-
tially shifting more towards the reproductive phase of
winter crops. In Eastern Europe, EDS predominately
start in the fall, and have shifted towards later timing
in many regions. There are exceptions such as in Ser-
bia (earlier timing). Our findings on the EDS are in
agreement with a detailed study by Serra et al (2014),
who used daily records from more than 250 rain
gauges between the years 1951 and 2000 to improve
the knowledge of drought over Europe. Also, Lehner
et al [61] report that the timing of EDS may lead to a
change in drought risk in Europe. Gudmundsson and
Seneviratne [62] attributed the change in drought risk
in northern Europe and the Mediterranean areas to
anthropogenic climate change. EWS start in the fall
in most of Scandinavia, and in summer in the central
parts (figure 3(f)). In Western Europe, EWS start in
the fall and winter. In Southern and Eastern Europe
EWS are a spring phenomenon at most locations.
Spatial patterns of the changes in the timing of EWS
(figure 3(h)) shows similarities to the changes in tim-
ing of the EDS (figure 3(g)). Major differences are the
South-East of Norway (later timing of EWS), South
of Sweden (later timing of EWS) and Spain (primar-
ily later timing of EWS).
3.3. Australia
In Australia, EDS are generally long at locations dis-
tant from the coasts (figure 4(a)). EDS tend to exceed
six weeks except for on the continental edges in the
Southwest, Southeast and East. The opposite applies
to EWS, which become longer along the continental
edges (figure 4(b)). Trends in the duration of EDS
are small across the country (especially along the con-
tinental edges) (figure 4(c)). There is a general small
trend towards shorter EDS, with local exceptions. The
small change is in line with the IPCC that describes
no significant change in drought risk (analysis solely
based on rainfall) between 1900–2007 [63]. In addi-
tion, Jolly, Cochrane, Freeborn, Holden et al [58]
state that Australia has not been affected by a pro-
nounced trend in wildfire season length. However
they identified regional increments in the frequency
of anomalously long wildfire seasons. As for the tim-
ing, in the North, EDS start in the fall and winter
(figure 4(e)) and there is no consistent spatial pat-
tern in the North in regard to the changes in timing
(figure 4(g)). In the East, EDS on average start in fall
and winter and there is a trend towards later timing
(also reported by Murphy and Timbal [64]). In the
South-East, EDS typically start in summer and there
is a trend towards earlier timing. The earlier timing
in the South-East is consistent with observed trends
[65,66], indicating an earlier start of the bushfire sea-
son in the region. In the South-West, EDS are a sum-
mer phenomenon and there is a shifts towards later
timing. In Western Australia, Power et al [67] recog-
nized that the change in onset and duration of EDS
could be characterized more as a regime shift of dry
spells rather than a downwards trend. For most of
Australia, EWS have become longer, except for a small
region in the East near the coastline and a larger area
in the South-West where the duration has decreased
(figure 4(d)). Cook and Heerdegen [68] stated that
in Northern Australia the duration of EWS decreases
with increasing latitude also because of the effect of
the El Niño Southern Oscillation. EWS are a summer
phenomenon in the North, and typically start in the
fall and winter in the South-East and South-West (fig-
ure 4(f)). In general, EWS have shifted towards earlier
timing in the North and towards later timing in the
South-East and South-West (figure 4(h)).
4. Conclusions
We presented a global analysis of changes in the dur-
ation and timing of EDS and EWS across three major
continents over a period of 60 years (1958–2017).
There is a larger number of statistically significant
sites for trends in the duration. There is weaker evid-
ence for significant changes in the timing. Specifically,
field significance could be detected for the duration of
EDS and EWS. For the timing, field significance could
only be detected for EDS. However, for both, trends
in duration and timing, we could not only demon-
strate links between our study and other precipita-
tion related research (such as in the case of precip-
itation trends across the US), but also find poten-
tial links between the observed trends in timing and
trends in other environmental hazards. Hence, our
analyses add to the evidence that the duration and
timing of environmental hazards such as droughts
are changing (for example in case of increasing dur-
ation of EDS and drought in the South-West of the
US or shifts in the timing of EDS in Australia and the
bushfire season). Yet, other changes in precipitation-
related extremes such as changes in the precipita-
tion intensity can also be important to come up with
more detailed conclusions about EDS and EWS, and
their possible implications for water resources man-
agement. It remains uncertain whether the uncovered
trends of EDS and EWS will continue in the future,
but the results demonstrate that a comprehensive
disaster risk reduction approach may consider both
changes in the magnitude and the timing of envir-
onmental hazards. For example, resilience in agricul-
ture could be increased by developing crops or vari-
eties with phenology and most sensitive periods that
are best suited to the most likely periods of EDS.
In addition, the feasibility of supplementary irriga-
tion should be evaluated. However, as many other
8

Environ. Res. Lett. 15 (2020) 074040 K Breinl et al
Figure A1. Extreme dry and wet spell analysis across Asia. (a), (b) Duration of extreme dry (wet) spells. (c), (d) Changes in the
duration of extreme dry (wet) spells as percentage change per decade with reference to the duration in 1958. (e), (f) Average
within-year timing of extreme dry (wet) spells in the period 1958–2017. (g), (h) Changes in the within-year timing of extreme dry
(wet) spells as days per decade with reference to the timing in 1958. Results of (c), (d), (g) and (h) are shown as means across
hexagons. Black dots represent the rain gauges examined with locally significant (green) and field significant (magenta) sites.
types of infrastructure, irrigation can create harmful
social and environmental impacts [69,70]. The meth-
ods presented here are universally applicable to any
land surface precipitation dataset and can thus sup-
port future research in changes of the duration and
within-year timing of extremes.
Appendix
Acknowledgments
KB gratefully received funding from the European
Union’s Horizon 2020 research and innovation
programme under the Marie Sklodowska-Curie
grant agreement STARFLOOD No. 793558 (www.
starflood.at). GDB acknowledges the funding from
the European Research Council (ERC), ‘HydroSo-
cialExtremes: Uncovering the Mutual Shaping of
Hydrological Extremes and Society’, Consolidator
Grant No. 771678. DL gratefully acknowledges fin-
ancial support from the DFG project “SPATE“ (FOR
2416), the FWF project “SPATE“ (I 3174) and the
FWF Vienna Doctoral Program on Water Resource
Systems (DK W1219-N28). GV was partially suppor-
ted by the Swedish Research Council Vetenskapsrådet
under grant 2016–04910 and the Swedish Research
9

Environ. Res. Lett. 15 (2020) 074040 K Breinl et al
Figure A2. Changes in the duration of extreme dry spells as percentage change per decade with reference to the duration in 1958.
The figure shows means per hexagons, estimated from an interpolated grid of 0.1◦from ordinary kriging. The interpolation
provided values across the entire country, but hexagons that do not contain any rain gauge are grayed out to allow for better
comparison with the hexagon means applied in the main part of the article (see figure 2(b)). Black dots represent the rain gauges
examined with locally (green) and field significant (magenta) sites.
Figure A3. Illustration of the FDR approach for duration of EDS (a) and EWS (b) and the timing of EDS (c) and EWS (d), as
suggested by, using αFDR =0.05 (dashed diagonal line) as control level, and the test level without considering FDR α=0.05
(dashed horizontal line) indicating rejections at the local level. Plotted points in each subplot represent a part of the (sorted)
smallest p-values of 5309 local tests. Points below the diagonal lines represent significant results according to the FDR control
level. As can be seen, field significance (i.e. rejection of the global hypothesis H0,global=”no trend at all stations”) can be declared
for (a)–(c), although for the latter only four local tests can be declared significant under the FDR approach (cross symbols). In
general, when ignoring the FDR, a considerably larger number of local tests would be declared significant.
10

Environ. Res. Lett. 15 (2020) 074040 K Breinl et al
Council FORMAS under grant 2018-02872. The
authors would like to thank the Swedish Research
Council FORMAS for research funding, in the frame
of the collaborative international consortium STEEP-
STREAMS financed under the ERA-NET Cofund
WaterWorks2014 Call. This ERA-NET is an integ-
ral part of the 2015 Joint Activities developed by the
Water Challenges for a Changing World Joint Pro-
gramme Initiative (Water JPI). Comments from the
anonymous reviewers and the editor are gratefully
acknowledged.
Data availability
The data that support the findings of this study
are openly available at https://www.ncdc.noaa.gov/
ghcnd-data-access.
ORCID iDs
Korbinian Breinl https://orcid.org/0000-0003-
0489-4526
Giuliano Di Baldassarre https://orcid.org/0000-
0002-8180-4996
David Lun https://orcid.org/0000-0003-0382-
3371
Giulia Vico https://orcid.org/0000-0002-7849-
2653
References
[1] Singh D, Tsiang M, Rajaratnam B and Diffenbaugh N S 2014
Observed changes in extreme wet and dry spells during the
South Asian summer monsoon season Nat. Clim. Change
4456–61
[2] Lesk C, Rowhani P and Ramankutty N 2016 Influence of
extreme weather disasters on global crop production Nature
529 84
[3] Vicente-Serrano S M and Begueria-Portugues S 2003
Estimating extreme dry-spell risk in the middle Ebro valley
(Northeastern Spain): a comparative analysis of partial
duration series with a general Pareto distribution and annual
maxima series with a Gumbel distribution Int. J. Climatol.
23 1103–18
[4] Van Loon A F et al 2016 Drought in the Anthropocene Nat.
Geosci. 989–91
[5] Ye H C 2018 Changes in duration of dry and wet spells
associated with air temperatures in Russia Environ. Res.
Lett. 13 034036
[6] Dracup J A, Lee K S and Paulson E G 1980 On the Definition
of Droughts Water Resour. Res. 16 297–302
[7] Breinl K, Di Baldassarre G, Lopez M G, Hagenlocher M, Vico
G and Rutgersson A 2017 Can weather generation capture
precipitation patterns across different climates, spatial scales
and under data scarcity? Sci. Rep. 75449
[8] Goh S G, Bayen S, Burger D, Kelly B C, Han P, Babovic V and
Gin K Y H 2017 Occurrence and distribution of bacteria
indicators, chemical tracers and pathogenic vibrios in
Singapore coastal waters Mar. Pollut. Bull. 114 627–34
[9] Wang X, Zhang J, Babovic V and Gin K Y H 2019 A
comprehensive integrated catchment-scale monitoring and
modelling approach for facilitating management of water
quality Environ. Modell. Softw. 120 104489
[10] van Vliet M T H and Zwolsman J J G 2008 Impact of
summer droughts on the water quality of the Meuse river J.
Hydrol. 353 1–17
[11] Whitworth K L, Baldwin D S and Kerr J L 2012 Drought,
floods and water quality: drivers of a severe hypoxic
blackwater event in a major river system (the southern
Murray-Darling Basin, Australia) J. Hydrol. 450 190–8
[12] Benotti M J, Stanford B D and Snyder S A 2010 Impact of
drought on wastewater contaminants in an urban water
supply J. Environ. Qual. 39 1196–200
[13] Sprague L A 2005 Drought effects on water quality in the
South Platte River Basin, Colorado J. Am. Water Resour.
Assoc. 41 11–24
[14] Whitehead P G, Wilby R L, Battarbee R W, Kernan M and
Wade A J 2009 A review of the potential impacts of climate
change on surface water quality Hydrol. Sci. J.
54 101–23
[15] Mosley L M 2015 Drought impacts on the water quality of
freshwater systems; review and integration Earth Sci. Rev.
140 203–14
[16] Girija T R, Mahanta C and Chandramouli V 2007 Water
quality assessment of an untreated effluent impacted urban
stream: the Bharalu tributary of the Brahmaputra River,
India Environ. Monit. Assess. 130 221–36
[17] Eng C T, Paw J N and Guarin F Y 1989 The
environmental-impact of aquaculture and the effects of
pollution on coastal aquaculture development in
Southeast-Asia Mar. Pollut. Bull. 20 335–43
[18] Van Metre P C, Frey J W, Musgrove M, Nakagaki N, Qi S,
Mahler B J, Wieczorek M E and Button D T 2016 High
nitrate concentrations in some midwest United States
streams in 2013 after the 2012 drought J. Environ. Qual.
45 1696–704
[19] Chowdhury R K and Beecham S 2013 Characterization of
rainfall spells for urban water management Int. J. Climatol.
33 959–67
[20] Donat M G et al 2013 Updated analyses of temperature and
precipitation extreme indices since the beginning of the
twentieth century: the HadEX2 dataset J. Geophys. Res.
Atmos. 118 2098–118
[21] Zolina O, Simmer C, Belyaev K, Gulev S K and Koltermann
P 2013 Changes in the duration of European wet and dry
spells during the last 60 years J. Clim. 26 2022–47
[22] Ratan R and Venugopal V 2013 Wet and dry spell
characteristics of global tropical rainfall Water Resour. Res.
49 3830–41
[23] Marengo J A, Jones R, Alves L M and Valverde M C 2009
Future change of temperature and precipitation extremes in
South America as derived from the PRECIS regional climate
modeling system Int. J. Climatol. 29 2241–55
[24] Pachauri R K, Allen M R, Barros V R, Broome J, Cramer W,
Christ R, Church J A, Clarke L, Dahe Q and Dasgupta P 2014
Climate Change 2014: Synthesis Report. Contribution of
Working Groups I, II and III to the Fifth Assessment Report of
the Intergovernmental Panel on Climate Change ed R
Pachauri and L Meyer (Geneva: IPCC)
[25] Li X, Meshgi A and Babovic V 2016 Spatio-temporal
variation of wet and dry spell characteristics of tropical
precipitation in Singapore and its association with ENSO Int.
J. Climatol. 36 4831–46
[26] Blöschl G, Hall J, Parajka J, Perdig˜
ao R A P, Merz B,
Arheimer B, Aronica G T, Bilibashi A, Bonacci O and Borga
M 2017 Changing climate shifts timing of European floods
Science 357 588–90
[27] Troy T J, Kipgen C and Pal I 2015 The impact of climate
extremes and irrigation on US crop yields Environ. Res.
Lett. 10 054013
[28] Malik A I, Colmer T D, Lambers H, Setter T L and
Schortemeyer M 2002 Short-term waterlogging has
long-term effects on the growth and physiology of wheat
New Phytol. 153 225–36
[29] Daryanto S, Wang L X and Jacinthe P A 2017 Global
synthesis of drought effects on cereal, legume, tuber and root
11

Environ. Res. Lett. 15 (2020) 074040 K Breinl et al
crops production: a review Agric. Water Manage.
179 18–33
[30] Berry P M, Sterling M, Spink J H, Baker C J,
Sylvester-Bradley R, Mooney S J, Tams A R and Ennos A R
2004 Understanding and reducing lodging in cereals Adv.
Agron. 84 217–71
[31] Trnka M et al 2011 Agroclimatic conditions in Europe under
climate change Glob. Change Biol. 17 2298–318
[32] D’Orangeville L et al 2018 Drought timing and local climate
determine the sensitivity of eastern temperate forests to
drought Glob. Change Biol. 24 2339–51
[33] World Bank 2019 World Bank Open Data
(https://data.worldbank.org/)
[34] USDA 2018 United States Department of Agriculture:
Foreign Agricultural Service (https://www.fas.usda.gov/data)
[35] Menne M J, Durre I, Vose R S, Gleason B E and Houston T G
2012 An overview of the global historical climatology
network-daily database J. Atmos. Ocean. Technol. 29 897–910
[36] Richardson C W 1981 Stochastic simulation of daily
precipitation, temperature, and solar-radiation Water Resour.
Res. 17 182–90
[37] Okoli K, Mazzolenni M, Breinl K and Di Baldassarre G 2019
A systematic comparison of statistical and hydrological
methods for design flood estimation Hydrol. Res. 50 1665–78
[38] Theil H 1992 Henri Theil’s Contributions to Economics and
Econometrics (Dordrecht: Kluwer Academic) pp 345–81
[39] Mardia K V and Jupp P E 2009 Directional Statistics
vol 494 (London: Wiley)
[40] Bayliss A C and Jones R C 1993 Peaks-over-Threshold Flood
Database (Wallingford: Institute of Hydrology) IH Report
No 121
[41] Burn D H 1997 Catchment similarity for regional flood
frequency analysis using seasonality measures J. Hydrol.
202 212–30
[42] Yue S, Pilon P and Cavadias G 2002 Power of the
Mann-Kendall and Spearman’s rho tests for detecting
monotonic trends in hydrological series J. Hydrol.
259 254–71
[43] Wilks D S 2016 “The stippling shows statistically significant
grid points” how research results are routinely overstated
and overinterpreted, and what to do about it Bull. Am.
Meteorol. Soc. 97 2263
[44] Wilks D S 2006 On “field significance” and the false
discovery rate J. Appl. Meteorol. Clim. 45 1181–9
[45] Benjamini Y and Hochberg Y 1995 Controlling the false
discovery rate—a practical and powerful approach to
multiple testing J. R. Stat. Soc. B57 289–300
[46] Murawski A, Zimmer J and Merz B 2016 High spatial and
temporal organization of changes in precipitation over
Germany for 1951–2006 Int. J. Climatol. 36 2582–97
[47] Mehrotra R, Srikanthan R and Sharma A 2006 A comparison
of three stochastic multi-site precipitation occurrence
generators J. Hydrol. 331 280–92
[48] Romero-Lankao P, Smith J B, Davidson D J, Diffenbaugh N
S, Kinney P L, Kirshen P, Kovacs P and Villers-Ruiz L 2014
Climate Change 2014: Impacts, Adaptation, and Vulnerability.
Part B: Regional Aspects. Contribution of Working Group II to
the Fifth Assessment Report of the Intergovernmental Panel of
Climate Change ed V R Barros et al (Cambridge, New York:
Cambridge University Press) pp 1439–98
[49] Wuebbles D J, Fahey D W, Hibbard K A, Dokken D J, Stewart
B C and Maycock T K (ed) 2017 Climate Science Special
Report: Fourth National Climate Assessment vol I
(Washington, DC: US Global Change Research Program)
(https://doi.org/10.7930/J0J964J6)
[50] AghaKouchak A, Feldman D, Hoerling M, Huxman T and
Lund J 2015 Water and climate: recognize anthropogenic
drought Nature 524 409–11
[51] Tebaldi C, Hayhoe K, Arblaster J M and Meehl G A 2007
Going to the extremes—an intercomparison of
model-simulated historical and future changes in extreme
events Clim. Change 82 233–4
[52] Guzman-Morales J and Gershunov A 2019 Climate change
suppresses santa ana winds of Southern California and
sharpens their seasonality Geophys. Res. Lett.
46 2772–80
[53] Kovats R S, Valentini R, Bouwer L M, Georgopoulou E, Jacob
D, Martin E, Rounsevell M and Soussana J F 2014 Climate
Change 2014: Impacts, Adaptation, And Vulnerability. Part B:
Regional Aspects. Contribution of Working Group II to the
Fifth Assessment Report of the Intergovernmental Panel of
Climate Change ed V R Barros et al. (Cambridge: Cambridge
University Press) pp 1267–326
[54] Ministry of the Environment 2007 Sweden Facing Climate
Change–Threats and Opportunities (Stockholm: Swedish
Government Official Reports) Report No. SOU 2007:60
https://www.government.se/legal-documents/2007/12/sou-
200760/
[55] Zolina O, Simmer C, Gulev S K and Kollet S 2010 Changing
structure of European precipitation: longer wet periods
leading to more abundant rainfalls Geophys. Res. Lett.
37 L06704
[56] Zebisch M, Grothmann T, Schröter D, Hasse C, Fritsch U
and Cramer W 2005 Climate change in Germany.
vulnerability and adaption of climate sensitive sectors;
Klimawandel in Deutschland. Vulnerabilit¨
at und
Anpassungsstrategien klimasensitiver Systeme (Dessau:
German Environment Agency (Umweltbundesamt))
[57] New M, Hulme M and Jones P 1999 Representing
twentieth-century space-time climate variability. Part I:
development of a 1961-90 mean monthly terrestrial
climatology J. Clim. 12 829–56
[58] Jolly W M, Cochrane M A, Freeborn P H, Holden Z A,
Brown T J, Williamson G J and Bowman D M J S 2015
Climate-induced variations in global wildfire danger from
1979 to 2013 Nat. Commun. 67537
[59] Demuth S, Lehner B and Stahl K 2000 Assessment of the
vulnerability of a river system to drought Drought and
Drought Mitigation in Europe (Advances in Natural and
Technological Hazards Research vol 14) ed J V Vogt and F
Somma (Berlin: Springer) pp 209–19
[60] Trnka M, Rotter R P, Ruiz-Ramos M, Kersebaum K C,
Olesen J E, Zalud Z and Semenov M A 2014 Adverse weather
conditions for European wheat production will become
more frequent with climate change Nat. Clim. Change
4637–43
[61] Lehner B, Doll P, Alcamo J, Henrichs T and Kaspar F 2006
Estimating the impact of global change on flood and
drought risks in Europe: a continental, integrated analysis
Clim. Change. 75 273–99
[62] Gudmundsson L and Seneviratne S I 2016 Anthropogenic
climate change affects meteorological drought risk in Europe
Environ. Res. Lett. 11 044005
[63] Reisinger A, Kitching R L, Chiew F, Hughes L, Newton P C
D, Schuster S S, Tait A and Whetton P 2014 Climate Change
2014: Impacts, Adaptation, and Vulnerability. Part B: Regional
Aspects. Contribution of Working Group II to the Fifth
Assessment Report of the Intergovernmental Panel of Climate
Change ed V R Barros et al (Cambridge: Cambridge
University Press) pp 1371–438
[64] Murphy B F and Timbal B 2008 A review of recent climate
variability and climate change in southeastern Australia Int.
J. Climatol. 28 859–79
[65] Clarke H G, Smith P L and Pitman A J 2011 Regional
signatures of future fire weather over eastern Australia from
global climate models Int. J. Wildland Fire 20 550–62
[66] Lucas C, Hennessy K, Mills G and Bathols J 2007 Bushfire
weather in southeast Australia: recent trends and projected
climate change impacts (Melbourne: Bushfire Cooperative
Research Centre, Australian Bureau of Meteorology and
CSIRO Marine and Atmospheric Research)
https://doi.org/10.25919/5e31c82ee0a4c
[67] Power S, Sadler B and Nicholls N 2005 The influence of
climate science on water management in Western
12

Environ. Res. Lett. 15 (2020) 074040 K Breinl et al
Australia—lessons for climate scientists Bull. Am. Meteorol.
Soc. 86 839
[68] Cook G D and Heerdegen R G 2001 Spatial variation in the
duration of the rainy season in monsoonal Australia Int. J.
Climatol. 21 1723–32
[69] Di Baldassarre G, Wanders N, AghaKouchak A, Kuil L,
Rangecroft S, Veldkamp T I E, Garcia M, van Oel P R,
Breinl K and Van Loon A F 2018 Water shortages worsened
by reservoir effects Nat. Sustain.
1617–22
[70] Thacker S, Adshead D, Fay M, Hallegatte S, Harvey M,
Meller H, O’Regan N, Rozenberg J, Watkins G and Hall J W
2019 Infrastructure for sustainable development Nat.
Sustain. 2324–31
13