Drought episodes in the Czech Republic and their potential effects in agriculture

16 Pages·2009·0.71 MB·English

TheorApplClimatol(2010)99:373–388
DOI10.1007/s00704-009-0148-3
ORIGINAL PAPER
Drought episodes in the Czech Republic and their potential
effects in agriculture
Vera Potop&Luboš Türkott&Věra Kožnarová&
Martin Možný
Received:4July2008/Accepted:23April2009/Publishedonline:15May2009
#TheAuthor(s)2009.ThisarticleispublishedwithopenaccessatSpringerlink.com
Abstract This paper presents the methodology for 1 Introduction
assessment of drought episodes and their potential effects
on winter and spring cereal crops in the Czech Republic Theoccurrenceofextremedroughteventshasalwaysledto
(in the text referred to as Czechia). Historical climate and a number of subsequent studies. The recent European
crop yields data for the period of 47 years (1961–2007) severe droughts (e.g., 2003 in central Europe, 2005 in the
have been integrated into an agrometeorological data- Iberian Peninsula and in 2007 almost throughout Europe)
base. The drought episodes were determined by three have emphasized that the impact on European economies
methods: according to the values of the standardized can be significant. Various studies concluded that in recent
precipitation index (SPI), percentage of long-term pre- decades the drought situation in many European regions
cipitations (r), and on the basis of the Ped drought index and throughout the world had become more severe due to
(S). Consequently, the combined SPI, S, and r indices climate change (e.g., van Lanen and Peters 2000; Lloyd-
i i
have been used as tools in identification of the severity, Hughes and Saunders 2002; Rossi et al. 2003). This
frequency, and extent of drought episodes. Additionally, tendency has been recorded in the twentieth century,
the paper also presents the S drought index and its particularly in its last decade (1990–2000), which was the
i
potential use for real-time monitoring of spatial extension warmest of the century. Climate change predictions for
and severity of droughts in Czechia. The drought risk to Europe indicate considerable changes in the water balance
crops was analyzed by identifying the relationships throughout Europe, with an increased probability for
between the fluctuation of crop yields and drought index summer droughts in the Mediterranean, South Eastern,
(S) based on the multiple regression analysis with and also Central Europe, including the territory of the
i
stepwise selection. In general, models explain that a high CzechRepublic(IPCC2007).Agrowingnumberofstudies
percentage of the variability of the yield is due to drought isfocusingonthedroughtriskandvulnerabilityassessment
(more than 45% of yield variance). (e.g., Lana and Burgueño 1998; Wilhelmi et al. 2002;
Brunetti et al. 2002; Heim 2002; Wilhite 2000), drought
monitoringandearly warming(Wilhiteand Svoboda2000;
Svoboda et al. 2002), and drought policy and mitigation
: :
V.Potop(*) L.Türkott V.Kožnarová activities(LiuandKogan1996;Kogan2000;Sonnettetal.
FacultyofAgrobiology,FoodandNaturalResources, 2006; Brown et al. 2008).
DepartmentofAgroecologyandBiometeorology, Effects of drought are dependent not only on the
CzechUniversityofLifeSciencesPrague,
duration, intensity, and areas affected by a drought episode
Kamýcká129,Suchdol,
and water supplies but also on the level of economic
1650021Prague,CzechRepublic
e-mail:[email protected] development in a given country (Wilhite 1990). Therefore,
the consequences of droughts of identical intensities and
M.Možný
durationswillhavedifferenteffectsindifferent regions.As
AgrometeorologicalObservatoryinDoksany,
anexample,in2007,aseveredroughtoccurred,whichwas
CzechHydrometeorologicalInstitute,
41182Doksany,CzechRepublic much more pronounced in the South-East Europe, in
374 V.Potopetal.
countries such as Romania, Bulgaria, and Moldova, than variables are a primary source responsible for the assess-
elsewhere. As a result, the drought in Moldova, which mentofthedroughteffectandareconsideredakeyelement
considerably reduced yields of winter crops (mostly wheat indefiningadroughtanddecidingonthetechniquesforits
and barley, which were down by 40% and 55%, respec- analysis. The determinant variable for the meteorological
tively) and summer crops (sunflower, maize, grapes, etc.), drought is rainfall, whereas for agricultural drought, it is
affected the overall agricultural production and drastically soilmoisture.Thus,droughtindicesdescribetheseverityof
reduced returnson leased land and on labor to themajority drought as compared to the long-term average or normal
of small holders, who usually receive in-kind payments of condition and are usually calculated from one or more of
wheat,corn,andoil(FAO2007).Atthesametime,asevere the following variables: rainfall, temperature, soil water
springdroughtwasregisteredacrossCzechia,whichstarted holding capacity, and other water supply indicators (Hayes
as a consequence of poor winter snowfalls and little spring 2003; Keyantash and Dracup 2002).
rain. Then, during April of 2007, the drought affected 96% This study of drought brings important theoretical
of the territory of Czechia (Potop et al. 2008). Firstly, due contributions since it allows a more detailed and causal
to the fact that the drought in the Czech Republic had not knowledge of this event and of its role in the characteriza-
occurred during the reproductive phase of the crops, the tionoftheclimateoftheterritoryoftheCzechRepublic.At
yields were not drastically affected. Secondly, different present, agriculture and water management sectors in
effects of the drought in Moldova and Czechia are Czechiaarehighlyvulnerabletodroughtevents.Increasing
associated with different levels of development of agricul- severity of droughts may bring on undesirable effects such
ture and climate conditions in the two countries. as decreased water resource quantity and quality and
In respect to the studies of the drought episodes pattern decreased crop yields. While the drought events have been
in the Czech Republic, over the past decade, many studiedextensively,thereislimiteddiscussionoftheeffects
climatologists have studied the frequency and intensity of of drought on the variation in individual crop’s yields.
this event (Dubrovsky et al. 2000, 2008; Trnka et al. 2008; Yields of crops in the long term are a result of the
Brazdil et al. 2003) and the relationships between agricul- interaction of a farmer’s skill and technical equipment with
ture and crops (Trnka et al. 2007). A group of Czech usual environmental conditions of sites. The actual yield
climatologists (Brazdil et al. 2008) basing their study on and quality are affected by the occurrence of biotic and
both Z index and PDSI indices have concluded that abiotic stresses within a given year governed by the course
droughts in Czechia have an increasing tendency toward of the weather (Craufurd and Oeacock 1993; Calderini and
longer and more intensive dry episodes in which, for Slafer 1998; Chloupek et al. 2004; Claassen and Shaw
example, droughts that occurred in the mid-1930s, late 1970). The second part of this study shows a complex
1940s to early 1950s, late 1980s to early 1990s, and early analysis of droughts and their impact on the fluctuations in
2000s were the most severe. the yields of cereal crops. Drought risk is considered to
The predominant drought indices usually used to have potentially adverse effects on the regional production
establish drought conditions in the Czech Republic are level of cereal crops yields. The major aspects of drought
SPI, PDSI, Palmer's Z index, and Lang's rain factor. The that increase or decrease its adverse effects are the
last mentioned index, i.e., Lang’s rain factor, is one of the frequency, severity, and the spatial extent. A risk assess-
oldest and most frequently used indices for the identifica- ment framework utilizes the historical climate and crop
tionofdryand/orwetareas,whichisalsothemostpopular yield data to characterize and quantify the impact of
intheCzech Republic(Tolasz etal.2007).Its popularityis drought (Unganai and Kogan 1998; Wilhelmi et al. 2002).
mainly due to its simplicity, which is based on the ratio of Themethodshavecertainadvantagessuchashavingsimple
the average annual precipitation total to the average annual processes and easily available data and providing quantita-
airtemperature.Atthesametime,bothindices,PDSIandZ tive and comparative analysis results. The methodology
index, have become two of the most widely used tools for employed in this paper can be applied to thestudy of other
the drought assessment in Czechia. However, the main agrometeorological risks. Information from this study
obstacle in implementing these indices is a lack of input of provides a potentially useful reference in the decision
meteorologicalinformationabouttheamountofmoisturein making concerning the drought disaster prevention and
the soil from the majority of the weather stations in the agricultural sustainable development planning.
country’s network. Since the calculations of the indices The first and main aim of this paper is to propose the
values are based on data from a very small number of usage of more indicators and characteristics for the
stations, their indices have not been used in this paper. detection of a drought episode: the number of drought
Instead, input data from about 600 ombrometric stations months in a warm period, percentage of weather stations,
within the territory of Czechia can be used for the which(cid:1)had(cid:2)detected drought, departures of the regional
calculation of the SPI value. As a result, meteorological yield yðTÞ ofindividualcerealcrops,andtheS,SPI,and
i i
Droughtepisodesandtheirpotentialeffectsinagriculture 375
-950000
26
801 - 1323 m a.s.l.
31 601 - 800 m a.s.l.
301 - 600 m a.s.l.
-1000000 1013 1720 25 100 - 300 m a.s.l.
4 7 27
1
-1050000 8 1112 2212 28 3367 42
35 52
24
3 50
2 54
-1100000 5 41
6 15 23 239034 38 43
48
32
14 40 44 49 55 56
-1150000 9 47
33 51
1189 45 53
39 46
-1200000
16
-900000 -850000 -800000 -750000 -700000 -650000 -600000 -550000 -500000 -450000
Fig.1 Mapoflocationofthe56stationsusedforthecalculationofdroughtindicesinCzechia.ThecoordinateS-JTSKKrovakEastNorthsystem
wasappliedforcreatingthemaps
rindices.ThesecondaimistopresenttheS droughtindex grasscoverinthe0–40-cmlayerfrom1961to2007(April–
i
and its potential use for real time monitoring of spatial September) have been used. Values are expressed in
extension and severity of droughts in Czechia. percentage of available water capacity of soil. The agro-
databases contain yearly region-level logs of winter and
spring cultures of cereal yields as reported by the Czech
2 Materials and methods Statistical Office. The regions are displayed in Fig. 1. The
originalyieldsdatasetavailablefor46years(1961–2006)is
2.1 Station climate database and regions crop yields included: spring wheat, winter wheat, spring barley, winter
barley, winter rye, oats, and maize (Table 1).
This paper has integrated historical climate and yield data
obtainedfromtheagrometeorologicaldatabase.Theweath- 2.2 Methods
er data basis has included the monthly precipitation
amounts (mm) and monthly average air temperatures (°C). Drought effect on cereal crops Comparing the regional
For the 56 climatological stations, which are uniformly yields of crops and the national yields of crops, one can
distributed across the country, the statistical sequence statethattheyields werecomparable orhigher,particularly
covers the interval from 1961 to the present (Fig. 1). The in maize and winter wheat. As Fig. 2 indicates, the fastest
data series was homogenized and checked at the climatol- yield growth was found in maize and wheat (+4.6
ogy section at the Czech Hydrometeorological Institute. In and+2.3t/ha)andspringbarleyandwinterbarley(+2.2t/ha),
addition, the monthly soil moisture measurements under whileslowergrowthwasfoundforoatsand winter rye(+0.7
Table 1 Departures of annual
yield(σ)calculatedforindivid- Crops Threelevelsofdepartures,yi(T)
ualcrops(1961–2006)
(cid:1)0:5s(cid:2)yðTÞ>(cid:1)s (cid:1)s(cid:2)yðTÞ>(cid:1)1:5s yðTÞ(cid:3)(cid:1)1:5s
i i i
TriticumaestivumL. Winterwheat −0.06to−0.11 −0.12to−0.17 ≤−0.18
TriticumaestivumL. Springwheat −0.06to−0.11 −0.12to−0.17 ≤−0.18
HordeumvulgareL. Winterbarley −0.09to−0.17 −0.18to−0.26 ≤−0.27
HordeumvulgareL. Springbarley −0.09to−0.17 −0.18to−0.26 ≤−0.27
SecalecerealeL. Winterrye −0.06to−0.11 −0.12to−0.17 ≤−0.18
AvenasativaL. Oats −0.08to−0.15 −0.16to−0.23 ≤−0.24
ZeamaysL. Maize −0.09to−0.17 −0.18to−0.26 ≤−0.27
376 V.Potopetal.
Fig.2 Quadratictrendsof
8
annualaverageyields(t/ha)for Spring wheat = -5553.59 + 5.56 t - 0.001 t2
springandwintercropsinthe 7 Winter wheat = -5130.85 + 5.13 t - 0.001 t2
CzechRepublic(1961–2006) Spring barley = -12259.3 + 12.32 t - 0.003 t2
6 Winter barley = -12259.3 + 12.32 t - 0.001 t2
Winter rye = -5553.59 + 5.56 t - 0.001 t2
a) 5 Oats = -8613.71 + 8.67 t - 0.001 t2
s (t/h 4 Maize = 4338.51 – 4.47 t + 0.001 t2
d
el
yi 3
2
1 Spring wheat Winter wheat Spring barley Winter barley
Winter rye Oats Maize
0
1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5
6 6 6 6 6 7 7 7 7 7 8 8 8 8 8 9 9 9 9 9 0 0 0
9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
and +1.8 t/ha). The indicator of agricultural drought risk Technological progress and improvement of societal
can be represented by the residuals of the yield. In this conditions are responsible for the generally increasing
study, the fluctuations in crop yields over time were trendofthecropyield.Usingtheweather-yieldmodelasa
calculated on the basis of two components. The first one measure of the fluctuations in crop yields, it is possible to
is determined by the agricultural technology level and/or reflect the changes in the favorable and unfavorable
the climatic conditions, and the second one is based on agrometeorological conditions and their impacts on the
the agrometeorological conditions during the growing crop production every year (Wu and Wilhite 2004). Thus,
season from 1 year to the next (Fig. 3): the interannual departures of the regional yield yðTÞ of
i
individual cereal plants can be expressed as follows:
Cm¼yðtÞþyðTÞ ð1Þ
i i yðTÞ ¼yO(cid:1)yðtÞ ð2Þ
i i i
ðtÞ
where y yield is presentedby a dynamically mean value
i ðtÞ
(influenced by long-term factors such as cultivation where yO is the observed crop yield, and y is the value
i i
technique and standard management), and yðTÞ anomaly of the trend in a separate year. The significant negative
i
of yield was represented by the residuals of the detrended departures were assumed to be primarily an effect of a
yieldbecausetheresidualvariationreflectsthebesteffects drought event. The assumption that in years when the real
ðtÞ
of the weather on the yield. Thus, the response of yield is yield yO was bigger than the mean dynamical value y ,
i i
dependent on the meteorological conditions during the agrometeorological condition was favorable during the
growing season as well as during the preceding periods. growing season yO >yðtÞ. The years in which the real
i i
Fig. 3 The evident annual var- Spring wheat Winter wheat Spring barley Winter barley
iabilityofCmindexforspring Winter rye Oats Maize
9
wheat,winterwheat,spring
barley,winterbarley,winterrye,
8
oats,andmaize
7
6
a)
h 5
m (t/ 4
C
3
2
1
0
0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6
6 6 6 6 6 7 7 7 7 7 8 8 8 8 8 9 9 9 9 9 0 0 0 0
9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2
Droughtepisodesandtheirpotentialeffectsinagriculture 377
yield yO was smaller than the mean dynamical value whenthetimescaleswerelessthan6months.Basedonour
yO < yðitÞ, agrometeorological condition was considered researchresults,werecommendtheagriculturisttousetheSPI
i i
unfavorable. Thus, the response of yield is dependent on of 6 months or less. For the SPI, the categories go from
the meteorological conditions during the growing season extreme drought (SPI≤−2) to extreme wet (SPI≥2), with
as well as during the preceding periods. normal conditions being (−1, +1). In our analysis, the SPI
In agreement with the developed model, the drought definedadroughtepisode,whentheSPIwasalwaysbelow0.
effectassociatedwiththeyieldissmallerthanyðTÞ (cid:3)(cid:1)0:5s.
i
The year with a drought risk was identified by the cereals The r index This represents a percentage of long-term
of detrended yield: low-drought risk (cid:1)0:5s (cid:2)yðTÞ >(cid:1)s, precipitation.Therispermittingquiteaneffectiveestimate
i
a middle-drought risk is (cid:1)s (cid:2) yðTÞ >(cid:1)1:5s, and of a drought episode when used for a single region or a
i
yðTÞ (cid:3)(cid:1)1:5s is a high-drought risk. single season. It is calculated by dividing the actual
i
precipitation by the long-tem precipitation amount and
Drought indices The effect of drought on cereal crops multiplying it by 100%. This index, based solely upon
production depends on the duration, time of occurrence, rainfall, has a wide application in many regions around the
andseverity(asexpressedbydroughtindicesS,SPI,andr). world.Ithasbeenusedwithgreatsuccessinregions,where
i
TheS combinestheeffectsoftemperatureandprecipitation rainfall is normally received at sufficiently frequent
i
indroughtmonitoring,whiletheSPIandrarebasedsolely intervals,anddroughtsordryspellsappearinshortperiods.
ontheprecipitationdata.Also,theSPIisusedfordetecting For the r, the categories range from extreme drought (r≤
the early inception and end of drought, although it is being 20%) to extreme wet (r≥220%) with a normal season (or a
widely used to detect the early emergence of drought in month) falling within the range of 60% to 140%.
Czechia and many other countries (e.g., Agnew 2000;
Vicente-Serrano and Cuadrat-Prats 2007; Livada and The S drought index It was developed by Ped (1975) and
i
Assimakopoulos 2007; Loukas and Vasiliades 2004). makes it possible to determine agricultural drought. The S
i
Therefore, the combination of the SPI, S, and r indices as index has been widely used for drought monitoring in the
i
tools in the identification of severity, frequency, and extent Commonwealth of Independent States, and there are some
of drought episodes has been used. attempts to use it in Central Europe as well. The S has a
i
number of important advantages over the PDSI-index: It is
Standardized precipitation index The standardized precip- simplertocalculate,itdoesnothaveaconstantorcoefficient,
itation index (SPI) calculation for any station is based on a and it can be calculated for any period and site of interest.
series ofaccumulatedprecipitationfor afixed time scaleof The disadvantage of S index is its dependence on the
i
interest (i.e., 1, 3, 6, 9, 12, … months). Such a series is availabilityandqualityoflongdataseriesofsoilmoisture
fitted to a probability distribution, which is then trans- and an insufficient number of observation stations. This
formed into a normal distribution so that the mean SPI for drought index was developed to detect drought and wet
the location and desired period is zero (Edwards and periods and distinguish “atmospheric drought” or “pedo-
McKee1997).Eachdroughtepisodehasadurationdefined logical drought”. It represents a sum of extreme weather
by its beginning and end and the intensity for each month conditions. S is expressed by the following equation:
i
during which the episode continues. Therefore, a drought
episode occurs any time the SPI is continuously negative S ðtÞ¼ ΔT (cid:1)ΔR(cid:1)ΔE ð3Þ
andreachesanintensityof−1.0orless.Thepositivesumof i sT sR sE
the SPI for all the months within a drought event can be
termed drought. In this research, the self-calibrated SPI where ΔT ¼tt (cid:1)tn; ΔR¼rt (cid:1)rn, and ΔE ¼et (cid:1)en
(scSPI) was used for the Czech drought conditions. Being i and τ climatological station and time,
inspired by Dubrovsky et al. (2008), we applied a similar respectively
original algorithm to calculate SPI for a single reference tt monthly mean air temperatures in τ—a
station or an aggregated SPI for all stations (in our case specific year
Zatecand56aggregatestations).Formoredetailsregarding t long-term monthly mean air temperatures
n
the methodology, see Dubrovsky et al. (2008). The present rt monthly precipitation amounts in τ—a
versionofSPIusesagammadistributionwhoseparameters specific year
are estimated separately for each of the 6 months of the r long-term monthly precipitation amounts
n
warmperiodand/orvaluesford(cid:3)iffer(cid:4)enttimescales:1,2,3, et monthly amount of moisture in a
and6months.ThePearsontest 72 wasusedtocheckthe 0–100-cm soil layer in τ—a specific year
e
goodness of fit for each data set. The 72 statistical results e long-term amount of moisture in a
e n
showed that the data fitted the gamma probability density 0–100-cm soil layer
378 V.Potopetal.
σT,σR,andσE root-mean-square deviation in monthly allows the use of this index for comparing purposes in
temperature, precipitation, and moisture various situations since it describes a specific meteorolog-
of soil, respectively. ical situation regarding some mean level. In our analysis,
weincludedinthisindexaprecipitation–temperatureseries
It should be noted that the soil moisture measurements either from a set of weather stations and/or the single test-
and calculated values are not always available. Therefore, station at Žatec (which is situated in the rain shadow).
when calculating the index, it is possible to use the Simultaneously, Doksany observatory (see Fig. 1, number
simplified form without soil moisture. Then, the S may 20 in the map) has the longest statistical series of soil
i
also be calculated by three methods (S-m—meteorological moisture from the Czech Republic. It was selected as a
i
drought, S-p—pedological drought, and S-a—agricultural reference station to test the degree of information of S-a
i i i
drought). Firstly, in case of the identification of meteoro- index in the identification of agricultural drought (1961–
logicaldroughtor“atmosphericdrought”asdefinedbyPed 2007). Furthermore, S-adeterminesdroughtconditions—if
i
(1975),onlythefirstandsecondparametersoftheequation the values of parameters are as follows: ΔT>0 or ΔT/σT>
can be calculated: 0; ΔR<0 or ΔR/σR<0; ΔE<0 or ΔE/σE<0, then S-a>0.
i
Therefore, for the S-a, the categories interval ranges from
i
S (cid:1)mðtÞ¼ ΔT (cid:1)ΔR or extreme drought (Si-a≥3) to extreme wet (Si-a≤−3), with
i (cid:1)sT s(cid:2)R (cid:1) (cid:2) ð4Þ thenormalrangefallingbetween−1and+1.Thecriteriafor
S (cid:1)mðtÞ¼ tt (cid:1)tn (cid:1) rt (cid:1)rn meteorological and agricultural droughts proposed in our
i sT sR analysis are included in Table 2.
The criteria of the index S-m are useful for the
i
Secondly, “pedological drought” (drought in the soil) estimation of monthly droughts. However, droughts can
can be formulated as: occur,forexample,onlyattheendof1monthandcontinue
(cid:5) (cid:6) into the following month. In such a case, the first month
ΔE et(cid:1)en may not be assessed as dry on the basis of the S index,
S (cid:1)pðtÞ¼ or S (cid:1)pðtÞ¼ ð5Þ i
i sE i sE despite the drought having occurred. It has, therefore, been
proposedtodeterminedroughtbymeansoftheS-mandto
i
Thirdly, agricultural drought as a complex event may use an alternative approach. This approach, if we consider
also be identified by Eq. 6. the data of the adjacent months to be independent, requires
that we take S (cid:1)m(cid:2)p2ffiffiffi, where N is the number of
S (cid:1)aðtÞ¼(cid:1)tt (cid:1)tn(cid:2)(cid:1)(cid:1)rt (cid:1)rn(cid:2)(cid:1)(cid:1)et (cid:1)en(cid:2) ð6Þ combined montihs. ThisNhas been put to test in the
i sT sR sE assessment of droughts in Czechia, both for the entire
warmperiodandforindividualseasons.Inthiscase,forthe
The S index as well as PDSI indicate how the soil warm period, the computation was carried out with the
i
moisture compares with long-term climate conditions and following data: S (cid:1)m(cid:2)p1ffiffi; S (cid:1)m(cid:2)p2ffiffi; S (cid:1)m(cid:2)p3ffiffi;
i i i
are calculated based on parameters such as precipitation, S (cid:1)m(cid:2)p4ffiffi, that provided th6e thresholds g6iven in Table 26.
i
6
temperature, and soil moisture conditions. This index In the evaluation of drought during the spring and autumn
provides a measure of the long-term intensity of drought seasons, the period of vegetation was taken into account,
conditions derived from the precipitation and temperature and hence, the calculation was carried out for only
anomalies and their combined effects on the soil water 2 months. The criteria obtained for these seasons were
availability to plants. The application of normalized values S (cid:1)m(cid:2)p1ffiffi; S (cid:1)m(cid:2)p2ffiffi; S (cid:1)m(cid:2)p3ffiffi; S (cid:1)m(cid:2)p4ffiffi. For
i i i i
2 2 2 2
Table2 Thenewdroughtcrite-
riaproposedfordifferentindices Indices Droughtseverityclassification
andtestedontheterritoryofthe
CzechRepublic Mild Moderate Severe Extreme
S-mattimescale6 months 0.41≤S-m<0.81 0.81≤S-m<1.22 1.22≤S-m<1.63 S-m≥1.63
i i i i i
S-mattimescale3 months 0.58≤S-m<1.15 1.15≤S-m<1.73 1.73≤S-m<2.31 S-m≥2.31
i i i i i
S-mattimescale2 months 0.71≤S-m<1.41 1.41≤S-m<2.12 2.12≤S-m<2.86 S-m≥2.86
i i i i i
S-mattimescale1 month 0≤S-m<1 1≤S-m<2 2≤S-m<3 S-m≥3
i i i i i
S-aattimescale1 month 1≤S-a<2 2≤S-a<3 3≤S-a<4 S-a≥4
i i i i i
S-pattimescale1 month 0≥S-p>−1 −1≥S-p>−2 −2≥S-p>−3 S-p≤−3
i i i i i
SPI 0to−0.99 −1.00to−1.49 −1.50to−1.99 ≤−2.00
Droughtepisodesandtheirpotentialeffectsinagriculture 379
thesummer,whendroughtwascalculatedfor3months,the data for the interval under consideration, which indicate
following data were obtained: S (cid:1)m(cid:2)p1ffiffi; S (cid:1)m(cid:2)p2ffiffi; that droughts were observed in 34 years (a total of
i i
S (cid:1)m(cid:2)p3ffiffi and S (cid:1)m(cid:2)p4ffiffi. In this paper,3the use of thi3s 53months)inwarmperiods,20yearsinspring(23months),
i i
3 3
approach allows the identification of drought events in 19 years in summer (22 months), and 8 years (8 months)
Czechia on the basis of the following time scale: 1 month experienced autumn droughts (Table 3). The most frequent
(S-m-1), 2 months (S-m-2), 3 months (S-m-3), and drought events occurred in April (12 cases) and May (11).
i i i
6 months (S-m-6). These time scales reflect the impact of The less frequent drought years occurred in August (five
i
droughtsontheavailabilityofthedifferentwaterresources. cases). Using only S-m calculations, there were 3 months
i
with extreme drought, 17 months with severe drought, and
Area extent of drought The extent of drought expressed in 44 months that suffered from moderate or mild drought
the percentage of the affected area is associated with a (Fig. 4a–b). The most extreme drought months for the
specifieddroughtseverity(ornegativeSPIvalues,positiveS entire reference period were recorded in the following
i
values, and r≤60%), which considered the total number of cases: April (2007, 2000), May (2002), June (2003, 2000),
climatologicalstationsas100%.Itwascalculatedonthebasis July (2006, 1994), August (2003, 1992), September (2006,
of territories affected during individual months of the warm 1982), October (2001, 1967), November (2006, 1994, and
period (April–September) of each drought year. The GIS 2003), December (2006, 1971). The longest drought spells
softwaresuchasSurfer(byGoldenSoftware)andArcGis(by were recorded during 4 months in 1964 and 2003. As can
ESRI) has been used with the inverse distance weighting be seen in Fig. 4a–b, the S-m index has registered the
i
(IDW) and kriging interpolation method. These methods highestpositivevalueinAugust2003(S-m=5.0)andApril
i
produce visually appealing contours and surface plots from 2007(S-m=4.3).Bycontrast,thehighestnegativevalueof
i
irregularly spaced data. Interpolation schemes estimate the the S-m index was reached in March 2000 (S-m=−5.3).
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valueofthesurfaceatlocationswherenoobserveddataexist, During this month, there was a heavy precipitation event;
based on the known data values. The resulting r and SPI hence, the same high values of both indices r≥120% and
valuesincorrespondingdroughtcategoriesaremappedusing S-mi≥2 were registered for the majority the weather
i
inverse distance weighting for the r values and kriging for stations. Thus, S-m index has a high capacity to identify
i
theS index.AS-JTSKKrovakEastNorthcoordinatesystem both drought and wet months on the territory of Czechia.
i
was applied for AcrGIS and the Surfer. The drought Table3showsthatofthethreedroughtindices,theSPIisthe
observed on the surface of up to 10% of the territory of less informative for describing droughts, especially in the
Czechia is classified as a local one. The droughts that cover months of May and August (only three to six cases). At
11–30% of the territory indicate widespread droughts. The the same time, in July, the SPI had almost the same share
droughts that cover a territory of 31–50% are considered as randS-mindices(nine,ten,and11cases,respectively).
i
very widespread, and over 50% are classified as most Table 3 describes only the data that relate to the Žatec
extensive (Potop 2003). weather. Data for all other parts of the country have been
processed in the same way. For each of the 56 weather
stations,thenumericalvaluesofthedroughtindiceshavebeen
calculated, which then allowed us to evaluate the years
3 Results according to drought intensity and affected area. We found
thatmoderateandsevereintensitydroughtsaremostfrequent
3.1 Combining the SPI, S, and r indices as a tool inAprilandJuly,whileinJune(35cases),droughtsaremostly
i
for the identification of drought severity, frequency, middle and moderate. From the total number of drought
and area extent months(168)inthewarmperiod,65%ofthemonthsareJune,
July,andApril.Havinganalyzedthecharacteristicfeaturesof
ThissectiondescribesthedroughtepisodesinCzechiafrom thedroughtinthewholeterritoryofthecountry,wecanstate
56 weather stations, including monthly rainfalls and that approximately every fifth year suffers from severe
temperature measurements obtained during the periods of droughtduringthespringand/orsummer.
47yearseach.Threemeteorologicalindices(SPI,S,andr) Fortheexistingsoilmoisturedata,Eqs.4,5,and6were
i
havebeenappliedtodefine1monthasastandardtimeunit applied to calculate their statistical parameters and to
of drought period. identify thecontribution of S-m andS-p ontheS-a. Thus,
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Since the Žatec weather station is situated in the rain the data analysis in Table 4 makes it possible to reach the
shadow of the Krušné hory (Ore Mountains) chain and is following results:
therefore in the driest region of the country, it was adopted
as a reference station for the evaluations of the drought ■ The high positive values of intensity S-a≥2.0
i
years. The results based on all three indices provided the agricultural drought index are due to the positive
380 V.Potopetal.
Table3 Monitoringtheevolutionofthedrought/wetconditionsforthereferencestation(Žatec)
Years April May June July August September
r SPI S-m r SPI S-m r SPI S-m r SPI S-m r SPI S-m r SPI S-m
i i i i i i
1961 m/e S w w m/e m/e m/e
1962 m/e w w w m/e
1963 w w w w
1964 S m/e S S S E S S S S m/e m/e
1965 w w w w w w w m/e m/e w w w
1966 S m/e w
1967 m/e w w w w w w w w w
1968 w S w m/e m/e
1969 w w m/e w w S m/e m/e
1970 w w m/e m/e w w w m/e
1971 m/e m/e w w w w S S m/e
1972 m/e
1973 w m/e E m/e m/e S m/e S
1974 S m/e m/e w w m/e m/e w w w
1975 S m/e m/e
1976 m/e m/e m/e
1977 w w w w w w w
1978 w w w w
1979 w w w S m/e m/e m/e w m/e w w w
1980 w w w w m/e w
1981 S S S w w w
1982 S m/e S m/e S
1983 w w w S m/e S m/e E w w w
1984 w w w w w w
1985 m/e m/e S m/e S w w w w S m/e m/e
1986 w w w m/e m/e m/e m/e w w w
1987 w w w w w
1988 S m/e m/e S m/e S w w w
1989 w w w m/e m/e
1990 S m/e w E S m/e m/e
1991 d w w w m/e m/e S m/e
1992 w S m/e S w w w S S
1993 E S E w m/e w w w
1994 w w w w w m/e S S S w
1995 w w w w w m/e
1996 w w w w w w
1997 w m/e m/e w w w m/e m/e m/e m/e
1998 m/e S E m/e S w w w
1999 m/e m/e m/e m/e
2000 m/e E m/e m/e S m/e
2001 m/e m/e S m/e w w
2002 m/e m/e w w w w w w w
2003 m/e m/e m/e S w E m/e S S S m/e
2004 S m/e m/e w w
2005 w w w m/e m/e
2006 w w w m/e m/e m/e m/e
2007 E S E m/e
Droughtepisodesandtheirpotentialeffectsinagriculture 381
Table 3 (continued)
Years April May June July August September
r SPI S-m r SPI S-m r SPI S-m r SPI S-m r SPI S-m r SPI S-m
i i i i i i
Cases 12 8 11 13 6 16 10 7 11 10 9 11 10 3 7 9 7 10
Thetableincludestheassessmentofdrought(datainitalics),normal(blanktablecell),andwet(w)monthforeachindex.Themarksindicatethe
typeofdrought:Mmild,m/emoderate,Ssevere,andEextreme
values of S-m≥1.5 meteorological index and the a=6.0) and August (S-m=3.3 and S-a=5.0) of 2003,
i i i
negative values of S-p<−1.0 pedological drought the highest value of the S-a index was recorded.
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index. ■ In June, the greatest contribution to increased
■TheyearswhenthevaluesofS-aindexhadincreased intensity agricultural drought S-a is soil moisture
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due to high values of S-m index have been observed. parameter (S-p). As an example, we can use the year
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For example, the severe July drought years are related 2007,whenS-pindexhadalargersharethanS-m(S-
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to1983(S-m=3.1andS-a=4.60)and1994(S-m=3.3 m=1.8,S-p=−2.0andS-a=3.8).Bycontrast,in1981,
i i i i i
andS-a=4.6).InthemonthsofJune(S-m=3.6andS- the S-p index had a positive value (it indicates normal
i i i i
Fig.4 a–bEvolutionoftheseverityS-mvaluesrepresentativeofthe Anotherinterpretationmaybemadeasfollows:positivevaluesofS-
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Zatecweatherstationforthe47-yeartimeseries.PositivevaluesofS- m correspond to a warmer thermal regime during a given period,
i
mcorrespondtodroughtmonths;negativevaluestothehumidones. whereasthenegativeonesreflectacolderthermalregime
382 V.Potopetal.
Table 4 Example of specifying
meteorologicaldrought June July August
(S-m≥0),pedologicaldrought
i
(Si-p≤0),andagricultural Year Si-m Si-p Si-a Year Si-m Si-p Si-a Year Si-m Si-p Si-a
drought(S-a≥1)forDoksany
i
Observatory(1961–2007) 1964 2.8 −0.8 3.6 1964 2.1 −1.6 3.7 1975 1.4 −0.3 1.7
1976 1.7 −0.9 2.6 1971 1.5 0.1 1.4 1992 2.7 −1.5 4.3
1981 1.3 0.2 1.2 1976 1.9 −2.2 4.2 1997 2.2 0.2 2.0
1992 1.5 −0.6 2.1 1983 3.1 −1.5 4.6 2000 2.2 −1.1 3.2
2000 2.5 −1.6 4.1 1990 1.3 0.1 1.2 2003 3.3 −1.7 5.0
2003 3.6 −2.3 6.0 1992 1.2 0.0 1.2
2005 1.4 −2.5 4.0 1994 3.3 −1.3 4.6
2006 1.5 −2.1 3.6 1995 2.0 0.4 1.6
2007 1.8 −2.0 3.8 1999 1.6 −0.4 1.9
2004 1.1 −2.1 3.2
conditions of soil moisture), which contributed to the have been noticed. The most extensive droughts cover the
decreasing intensity of drought (S-m=1.3, S-p=0.2, whole territory except the mountain sites. For example, the
i i
and S-a=1.2). most extensive drought was registered in 2007 (Fig. 5a–b).
i
It is not the purpose of this paper to introduce the
Droughts also differ in terms of their spatial character- procedures of the Poisson model for the assessment of the
istics. The areas affected by severe drought evolve time distribution of the drought episode. For more details
gradually, and the regions of maximum drought intensity regarding the methodology, see Potop and Soukup (2008).
shift from one season to the next. The local droughts are a (cid:3)We(cid:4)employed the same model tested by Pearson(cid:3)'s c(cid:4)riteria
characteristic of the north-west and southern areas, while #2 for Czechia. According to Pearson's test #2 , the
e e
the widespread ones have been observed in the southern (cid:3)phe(cid:4)nomenon obeys the same rules wh(cid:3)en t(cid:4)he empirical data
andsouth-easternpartsofthecountry.Theverywidespread #2 are smaller than the theoretical #2 ones. Since the
e t
droughtscoveralargeterritoryofupto50%oftheareaand smallest p value among the tests performed is less than
are frequent in the southern, south-eastern, and north- 0.05,wecanrejecttheideathatlocaldroughtsoccurringin
western parts of Czechia. In the above-mentioned territory, June, July, and August come from a Poisson distribution
the droughts of severe intensity occurred in dry regions of with 95% confidence. For the other months such as April,
the country, i.e., South Moravia and the rain shadow of the May,andSeptember,therewereinsufficientdatatoconduct
Krušné hory mountain chain. However, in the rest of the the χ2 test. The χ2 test was not run because the number of
area, droughts of a moderate and mild degree of intensity observations was toosmall.As the results inTable 5 show,
a b
0 35 70 140 Kilometers
extreme severe absent
Fig.5 a–bDelimitationofregionaffectedbydroughtaccordingtor(a)andS-m(b)indicesinApril2007(rbytheIDWmethodandS-mby
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thekriginginterpolationmethods)