A multimodel assessment of the climate change effect on the drought severity–duration–frequency relationship

HYDROLOGICAL PROCESSESHydrol. Process. 27, 2800–2813 (2013)Published online 15 June 2012 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/hyp.9390

A multimodel assessment of the climate change effect on thedrought severity–duration–frequency relationship

Joo Heon Lee* and Chang Joo KimDepartment of Civil Engineering, Joongbu University, Geumsan, Chungnam-Do, Korea

*CJooE-m

Co

Abstract:

In this study, the patterns of past and future drought occurrences in the Seoul region were analysed using observed historical data fromthe Seoul weather station located in the Korean Peninsula and four different types of general circulation models (GCMs), namely,GFDL:CM2_1, CONS:ECHO-G, MRI:CGCM2_3_2 and UKMO:HADGEM1. To analyse statistical properties such as droughtfrequency duration and return period, the Standardized Precipitation Index was used to derive the severity–duration–frequency (SDF)curve from the drought frequency analysis. In addition, a drought spell analysis was conducted to estimate the frequency and change ofdrought duration for each drought classification. The results of the analysis suggested a decrease in the frequency of mild droughts and anincrease in the frequency of severe and extreme droughts in the future. Furthermore, the average duration of droughts is expected toincrease. A comparison of the SDF relationship derived from the observed data with that derived via the GCMs indicated that the droughtseverity for each return periodwas reduced as drought duration increased and that the drought severity derived from theGCMswas severerthan the severity obtained using the observed data for the same duration and return period. Furthermore, among the four types of GCMsused in this study, theMRImodel predicted themost severe future drought for the Seoul region, and the SDF curve derived using theMRImodel also resulted in the highest degree of drought severity compared with the other GCMs. Copyright © 2012 JohnWiley& Sons, Ltd.

KEY WORDS SPI; drought frequency analysis; drought spell; SDF curve; climate change

Received 16 November 2011; Accepted 20 April 2012

INTRODUCTION

In recent years, the frequency of floods and droughts thatare due to global warming-related climate change hasincreased and is accompanied by a rise in the severity ofthese phenomena [Intergovernmental Panel on ClimateChange (IPCC), 2007]. It is difficult to recognize theprogression of droughts because, unlike floods, droughtprogression is slow; however, the magnitude of drought-related damage as well as the extent of affected areas andother detrimental effects is more severe than those of floods.Drought characteristics make drought monitoring andforecast difficult, especially from a hydrologic or disasterprevention perspective, and defining the beginning and theend of a drought can be challenging.Thus, many statistical analyses and approaches have

been used in an attempt to predict and monitor droughtsusing sources such as hydrologic data, weather data anddrought indices. In addition, various countries are currentlyconducting studies to monitor droughts in real time. Leeet al. (2006a, b) verified the applicability of certain droughtindices, including the Palmer Drought Severity Index, theStandardized Precipitation Index (SPI) and the SurfaceWater Supply Index, for establishing a drought monitoringsystem and monitoring drought phenomena.However, as climate changes have had an important

impact on meteorological and hydrologic environment in

orrespondence to: Joo Heon Lee, Department of Civil Engineering,ngbu University, Geumsan, Chungnam-Do, Korea.ail: [email protected]

pyright © 2012 John Wiley & Sons, Ltd.

recent years, real-time drought monitoring and short-termdrought predictions alone cannot prevent the extent ofdamage caused by unexpected severe droughts. Therefore,studies that attempt to forecast droughts on a long-term scaleby analyzing the effects of climate change on droughtphenomena are necessary.Hydrological and weather data are often used to

study droughts after they are converted into droughtindices, and statistical analyses performed using thesedrought indices include analyzing drought trends andperiodicities or estimating the drought return periods usinga drought frequency analysis Kim et al. (2011) analyseddrought trends and periodicities using the 6-month SPItimescale, which was calculated using the generalcirculation model (GCM) data, the Mann–Kendall testand a wavelet transform to estimate changes in droughtfrequency using a drought spell analysis. Khadr (2009)conducted a trend analysis on droughts in the Ruhr Riverbasin, and Torrence and Compo (1998) introduced thewavelet transform analysis and identified a correlation withdroughts using the El Nino-Southern Oscillation timeseries. Grinsted et al. (2004) demonstrated the applicabilityof the cross-wavelet transform and wavelet transformcoherence analysis in investigating drought trends andperiodicities.Climate data produced by the GCM and regional

climate model, which apply climate change scenarios,are being used to calculate drought indices in an attemptto forecast droughts. Ghosh and Mujumdar (2007)calculated the 12-month SPI timescale from 19 separateGCMs to forecast future droughts for every 10-year

Figure 1. Weather stations in Korea and information of the Seoul AutomatedWeather Station

2801CLIMATE CHANGE EFFECT ON DROUGHT SEVERITY–DURATION–FREQUENCY RELATION

period, whereas Lehner et al. (2008) forecasted ‘2020s’(mid-term future) and ‘2070s’ (long-term future) droughtsfor the continent of Europe. Vidal and Wade (2007) usedsix GCMs, which applied A2 and B2 scenarios, tocalculate the 3-, 6-, 12- and 24-month timescale SPIs andthen studied the changes in future drought severity bycomparing the SPI with the standard normal distributioncurve of the standardized SPI.In addition, recent drought forecast studies using a

drought frequency analysis have been conducted throughthe development of analytic methods such as the severity–area–duration (SAD) and the severity–area–frequency(SAF) methods.The SAD method analyses drought by replacing the

rainfall depth (depth) of the depth–area–duration that isapplied to characterize extreme rainfall events and replacesthe depth with an appropriate index to represent the droughtseverity (Kim et al., 2006). Kim et al. (2010) suggested thatit is important to assess climate change vulnerability for thecurrent water supply system through a comparison ofcurrent and historical SAD curves.The SAF method is a tool that can be used to effectively

analyse droughts by considering the spatial area of impact,where the recurrent characteristics of the drought andthe area of impact can be calculated through derivation ofthe SAF curve (Henriques and Santos, 1999; Chang et al.,2006). Loukas et al. (2008) calculated the 1-, 3-, 6-, 9- and12-month SPI timescales using the CGCM2, created theSAF curve and assessed the changes in drought severity asa function of climate change, whereas Mishra and Singh(2009) used 12 GCMs to calculate the SPI and predictedthe results for each scenario through a comparison withthe SAF curve. As a result, Mishra and Singh’s (2009)forecast from the 3- and 12-month SPI timescales predictedthat extreme droughts were likely to occur in several areas.Akhtari et al. (2008) attempted to analyse droughtcharacteristics in the Razavi and South Khorasan regionsof Iran using the 12-month SPI timescale to derive theSAF curve.Alternatively, it is possible to derive the severity–

duration–frequency (SDF) curve using a frequencyanalysis of past drought events and to use the derivedSDF curve to estimate the return period of those events(Yoo andKim, 2006). Kim andYoo (2006) applied an SDFcurve derived from the SPI and a rectangular pulse modelto conduct a drought frequency analysis of the KoreanPeninsula and used the results of that analysis tocharacterize the spatial distribution of drought severity inSouth Korea.In this study, SPI for multiple timescales (3, 6 and 12

months) were calculated for Seoul weather station inKorea based on the four different GCMs and previouslyobserved precipitation data. The SDF curve was alsoderived through a drought frequency analysis. The SDFcurve was compared with the past SDF curve to analysethe effect of climate changes on the SDF curve. Variousdrought-spell analyses were used to estimate currentand future changes in frequency and duration for eachdrought classification.

Copyright © 2012 John Wiley & Sons, Ltd.

MATERIALS AND METHODS

Study area

The Seoul weather station at the Han River basin wasselected from 57 weather stations under the KoreaMeteorological Administration (KMA) within the KoreanPeninsula to conduct the drought frequency analysis inthis study. The Seoul weather station represents the watersystem at the Han River basin, and observed historicalweather data were collected from 1970 to 2010 (Figure 1).

GCMs for multimodel assessment

The IPCC Data Distribution Center (http://www.ipcc-data.org) provides 23 GCMs that are produced based on theSpecial Report on Emissions Scenarios. The NationalInstitute of Meteorological Research of Korea collects the23 GCM results into a 2.5� � 2.5� grid.In this study, the four GCMswere used for the analysis as

shown in Table I. The four GCMs that provided a commonresult for the A2 scenario (CO2 830 ppm at 2100) applied inIPCCAR4were selected: GFDL:CM2_1, CONS:ECHO-G,MRI:CGCM2_3_2 and UKMO:HADGEM1.The downscaling of coarse scale GCM simulations

(>200 km) to regional scale (<50 km) is required forreasonable climate impact studies and for providing bias-corrected information on local scale climate change (e.g.Giorgi and Mearns, 1991; Wood et al., 2004; Christensen

Hydrol. Process. 27, 2800–2813 (2013)

http://www.ipcc-data.org

http://www.ipcc-data.org

Table I. Detailed information on the four GCMs used in this study

No.Model

(agency: version) Country

Resolution

Atmosphere Ocean

1 GFDL: CM2_1 USA 144X90 360X2002 CONS: ECHO-G Germany/Korea 96X48 128X1173 MRI: CGCM2_3_2 Japan 128X64 144X1114 UKMO: HADGEM1 UK 192X144 360X216

2802 J. H. LEE AND C. J. KIM

et al., 2007; Im et al., 2010). This study used the statisticaldownscaling approach based on a combination of theCSEOF (CycloStationary Empirical Orthogonal Fuction)analysis (Kim and North, 1997) and the WXGEN (WeatherGenerator) (Sharpley andWilliams, 1990), whichwas used inthe study of Jung et al. (2011). The CSEOF generateslocalized climate scenarioswith a resolution of approximately20 km from the coarse scale GCM simulations, which have aresolution of approximately 100–350km, and the WXGENdisaggregates monthly data to daily simulations. A similarapproach is used by Bae et al. (2011a, b).CSEOF analysis is one of many eigenvalue techniques

applied to decompose the climate data into a set ofindependent modes and their principal component (PC)time series (Lim et al., 2007). The Korean National Instituteof Meteorological Research in KMA used the CSEOFto downscale the 13 GCM simulations to a fine spatialresolution on the basis of 57 climate stations. They firstcomputed CSEOFmodes and their PC time series from bothobservations and GCM simulations for the reference period.The PC time series represents the long-term amplitudefluctuation of climate signals, such as seasonal cycles,El Nino-Southern Oscillation and low frequency variations(Kim, 2002). The multiple regression method is then used todetermine the statistical relation between the climate signalsof the climate station data and the GCM simulations for thereference period. On the basis of this statistical relationship,the PC time series of GCMs are generated for the futureperiod of 2001–2100. Finally, future downscaled data areconstructed using the generated PC time series of GCMs andthe CSEOF modes of the observation of 57 climate stations(Jung et al., 2011).WXGEN(Sharpley andWilliams, 1990),which is based on

theweather generatormodel developed byRichardson (1981),generates daily precipitation, maximum and minimum

Figure 2. Observed and projected monthly precipitation by the four GCMs


temperature, solar radiation, relative humidity and wind speedbased on the statistics of observed climate variables (Junget al., 2011).

Validation of GCMs

To review the uncertainty of precipitation, which is usedin this research, observed and projected monthly precipita-tion (gray shading in the Figure 2) through the four GCMswere depicted. The annual maximum of monthly precipi-tation (red dots), the annual average ofmonthly precipitation(white dots) and the annual minimum of monthly precipita-tions (blue dots) were also presented in Figure 2.In the four GCMs, the annual maximum of monthly

precipitation was approximately 300mm during the years1970–2000 and approximately 400mm during the years2000–2010, whereas higher annual maximum monthlyprecipitation was observed as compared with those by the fourGCMs. This result might be the consequence of not simulatingthe extreme events like typhoons in case of the four GCMs.The GCMs are useful tools that can provide changes

patterns in atmosphere–ocean–ground surface according togreenhouse gas emission scenario in an objective way basedon the dynamical and physical process, and generally GCMssimulate climate changes under the assumption of futuregreenhouse gas emission. However, because of differentdynamical systems, grid size, parameterization processesand physical processes, GCMs generate large differences indifferent models (Im et al., 2010).Although some differences were demonstrated with each

model, these models showed approximately 100mm of theannual average of monthly precipitation in both historicallyobserved data as well as projected data by the four GCMs. Itshowed that average values well captured the observed dataas compared with that of annual maximum precipitation.

(GFDL, CONS, MRI and UKMO) for the baseline period (1970–2010)

Hydrol. Process. 27, 2800–2813 (2013)

Table II. Moisture conditions and drought classificationsusing SPI

Range Dry condition

More than +2.00 Extremely Wet+1.50 to +1.99 Very wet+1.00 to +1.49 Moderately wet+0.99 to �0.99 Near normal�1.00 to �1.49 Moderately dry�1.50 to �1.99 Severely dryLess than �2.00 Extremely dry


The annual minimum of monthly precipitation also showedsimilar trends of around 10-mm level in observed data andapproximately 25–30mm in the four GCMs, which isconsidered that projected precipitations by the fourGCMs inthis research can be used in drought analysis studies.

Choice of drought index

The SPI was developed by McKee et al. (1993) fordefining and monitoring local droughts. It was intended toidentify drought periods as well as the severity of droughts,at multiple timescales, such as at 1, 3, 6, 12 or 24months. Aregional SPI was developed and tested for the Alentejoregion (Paulo et al., 2005) using several timescales.Different timescales reflect lags in the response of variouswater resources to precipitation anomalies. For example, soilmoisture conditions respond to precipitation anomalies on arelatively short scale, whereas groundwater, streamflow andreservoir storage reflect longer-term precipitation anomalies.SPI values range from -2.0 (extremely dry) to +2.0(extremely wet). A drought event occurs when the SPI iscontinuously reaches an severity of –1.0 or less (Table II).The SPI is based on the probability distribution of

the long-term precipitation record for a desired period. Thetimescale, embedded in SPI computing, is the length inmonths of the movingwindow for which precipitation totalsare obtained. The long-term record of precipitation totals foreach ending month is fitted to a probability distribution,which is then transformed into a normal distribution so thatthe mean SPI for the location and desired period is zero(Edwards and Mckee, 1997).The SPI is computed by fitting a probability density

function to the frequency distribution of precipitationsummed over the timescale of interest. This computationis performed separately for each month (or for any temporalbasis of the raw precipitation time series) and for eachlocation in space. Each probability density function is thentransformed into the standardized normal distribution.The gamma distribution is defined by its frequency

or probability density function and is calculated as follows:

g xð Þ ¼ 1baΓ að Þ x

a�1e�x=b; for x > 0 (1)

where a> 0 is a shape factor, b> 0 is a scale factor andx> 0 is the amount of precipitation. Γ(a) is the gammafunction, which is defined as


Γ að Þ ¼Z 1

0ya�1e�ydy (2)

Fitting the distribution to the data requires estimatinga and b. Edwards and McKee (1997) suggestedestimating these parameters using the approximation ofThom (1958) for maximum likelihood as follows:

a ¼ 14A

1þffiffiffiffiffiffiffiffiffiffiffiffiffiffi1þ 4A

3

r !;

b ¼ �x

a(3)

where for n observations,

A ¼ ln �xð Þ �P

ln xð Þn

(4)

The estimated parameters are then used to find thecumulative probability of an observed precipitation eventfor the given month and timescale:

G xð Þ ¼Z x

0g xð Þdx ¼ 1

ba Γ að Þ ¼

Z x

0xa �1e�x=bdx (5)

Substituting t for x=b reduces the equation to anincomplete gamma function. McKee et al. (1995) used ananalytical method along with recommended software fromPress et al. (1986). Because the gamma function isundefined for x= 0 and a precipitation distribution maycontain zero, the cumulative probability becomes

H xð Þ ¼ qþ 1� qð ÞG xð Þ (6)

where q is the probability of zero precipitation.The cumulative probability, H(x), is then transformed to

the standard normal random variable Z with mean zero andvariance one, which is the value of the SPI. FollowingEdwards and McKee (1997) and Hughes and Saunders(2002), we used the approximate conversion provided byAbramowitz and Stegun (1965) as an alternative:

Z ¼ SPI ¼ � t � c0 þ c1t þ c2t2

1þ d1t þ d2t2 þ d3t3

� �; for 0 < H xð Þ≤0:5

(7a)

Z ¼ SPI ¼ þ t � c0 þ c1t þ c2t2

1þ d1t þ d2t2 þ d3t3

� �; for 0:5 < H xð Þ < 1

(7b)

where

t ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiln

1

H xð Þð Þ2" #vuut ; for 0 < H xð Þ≤0:5 (8a)

t ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiln

1

1� H xð Þð Þ2" #vuut ; for 0:5 < H xð Þ < 1 (8b)

Hydrol. Process. 27, 2800–2813 (2013)


Monthly SPI were calculated for multiple timescales 3, 6and 12 months. Figures 3 and 4 show the time series of the3-month SPI timescale, which was calculated using thehistorical and GCM data, in which the dotted line representsthe severely dry stage and the solid line represents theextremely dry stage.On the basis of the Historical Drought Event Survey

Report (MLTM, 1995; MLTM, 2002), extreme droughtevents were confirmed from Seoul area during the years1988–1989 and 2000–2001, and severe drought events also

Figure 3. The 3-month SPI time series calculated using the historicallyobserved data (1970–2010)

a) GFDL:CM2_1

c) MRI:CGCM2_3_2

Figure 4. The 3-month SPI time series ca


occurred during 1981–1982 and year 2007 (Lee and Kim,2011). Estimated SPI during this period appropriatelymonitored those drought events as extremely dry stage(with �2.0≥SPI) and severely dry stage (with �1.5≥SPI�2.0), indicating that SPI is a suitable indicator for droughtmonitoring in this study. In the SPI time series by the fourGCMs, extremely dry stage would be occurred morefrequently during the years 2071–2099 as compared withthose during the years 2010–2071.

Methods for assessing the climate change effect ondrought patterns

To predict the trends of future projected droughts, droughtspell analyses were performed for each drought classifica-tion and drought duration using the 3-, 6- and 12-month SPItimescales based on the historical and GCM data. Inaddition, a drought frequency analysis was conducted forthe SDF curve derivation.

Drought spell

A drought spell analysis was performed to assess thecharacteristics of both historical and projected droughts atthe Seoul region. The monthly SPI that had been calculatedusing the observed and GCM data were divided into each ofthe drought classification stages (mild, moderate, severe andextreme), and the frequency at each stage was estimated bythe drought spell analysis. The theoretical value of the

b) CONS:ECHO-G

d) UKMO:HADGEM1

lculated using the GCMs (2011–2099)

Hydrol. Process. 27, 2800–2813 (2013)


probability density function for each drought classificationwas as follows: mild drought, 34.1%; moderate drought,9.2%; severe drought; 4.4%; and extreme drought, 2.3%(as shown in Table III). The period for historical andprojected droughts consisted of four different time framesdivided into 30-year periods, including the period of theobserved data (S0), years 2011–2040 (S1), years 2041–2070(S2) and years 2071–2099 (S3), as shown in Table IV.The SPIs that were calculated using the historical and

GCM data were classified according to the different droughtdurations, and the changes in occurrence patterns wereanalysed.To analyze the monthly 3-, 6- and 12-month SPI

timescales for each drought duration, the drought eventswith values that were classified as severer than a moderate

Table III. Drought classification by the SPI and correspondingclass probabilities

Drought classification Range Probability (%)

Mild 0≥ x>�1.0 34.1Moderate �1.0≥ x>�1.5 9.2Severe �1.5≥ x>�2.0 4.4Extreme �2.0≥ x 2.3

Table IV. Four different time frames divided into 30-year periodsincluding historically observed data used in this study

ID Period (years) Data type

S0 1970–2010 Observed dataS1 2011–2040 GCMsS2 2041–2070 GCMsS3 2071–2099 GCMs

Figure 5. Flow chart of the drought frequency anal


drought (with �1.0≥SPI) were selected, and the droughtspell analysis was then performed according to the differentdrought durations. The results were used to calculate thenumber of drought occurrences, accumulated magnitude,average magnitude and maximum magnitude according tothe duration to analyse the differences between historicaland projected future drought durations.

Drought frequency analysis

The second method that was used to analyse droughtcharacteristics involved determining the statistical charac-teristics of the droughts that occurred at Seoul by derivingthe SDF curve through a drought frequency analysis. Theprocedure used for the drought frequency analysis used inthe derivation of the SDF curve is shown in Figure 5. Theanalysis periods in this study were between 1970 and 2010for the past and 2011 and 2099 for the future.First, the monthly 3-, 6- and 12-month SPI timescales that

were calculated from the historical and the GCM data werearranged by maximum drought severity for each of thecontinuous durations from 1 to 12months. The observeddata were arranged into a series containing the entire period,whereas the GCM data were arranged into three, 30-yeartime slice from 2011 to 2040, from 2041 to 2070 and from2071 to 2099.To select the best fit probability distribution for the

calculated SPI, goodness-of-fit tests were conducted ata significance level of 5% for 15 different probabilitydistribution, including the gamma, general extreme value(GEV), Gumbel, log-Gumbel, log-normal, log-Pearson typeIII, normal, Pearson type III, Weibull and Wakebydistributions, which are commonly used in frequencyanalysis of hydrologic data.TheKolmogorov–Smirnov testwas used in the goodness-

of-fit test, and as a result, the log-Gumbel, gamma, GEV,Gumbel, log-normal and Weibull distribution types were

ysis used to derive the SDF curve using the SPI

Hydrol. Process. 27, 2800–2813 (2013)


determined to be the appropriate probability distributions forthe frequency analysis of the drought index.As a result of the Kolmogorov–Smirnov test, GEV

distribution, which showed the best fit probability distribu-tion among the candidate probability, was selected as theoptimal probability distribution for the frequency analyses.After conducting the drought frequency analysis for eachdrought duration for the observed and GCM data, the SDFcurve was derived.

Table V. Frequency of the 3-month SPI timescale for each drought c(S1, S2 a

Period Frequency of mild drought (%)

Observed KMAS0 33.54GCMs GFDL CONS MRI UKMO AveraS1 35.83 32.50 34.72 29.44 32.2S2 28.33 30.83 28.06 38.06 32.3S3 27.59 25.57 30.75 26.72 27.6

Period Frequency of severe drought (%)

Observed KMAS0 4.67GCMs GFDL CONS MRI UKMO AveraS1 2.50 4.17 1.67 4.17 3.34S2 5.00 5.28 5.28 3.33 4.63S3 4.60 3.74 5.75 4.89 4.79

a) Mild drought

c) Severe drought

Figure 6. Frequency of the 3-month SPI timescale by drought spell for each d(S1, S2 a


RESULTS AND ANALYSIS

Changes in drought severity and frequency

Table V and Figure 6 show the changes in frequency foreach drought classification using the drought spell, wherethe x-axis represents the analysis period and the y-axisrepresents the frequency (%) of each drought classification.In the case of mild and moderate droughts, SPI for the

observed historical period revealed a frequency of 33.54%

lassification as calculated from the observed data (S0) and GCMsnd S3)

Frequency of moderate drought (%)

KMA11.38

ge GFDL CONS MRI UKMO Average2 9.17 8.06 5.28 7.50 6.952 9.17 5.00 5.56 7.50 6.028 10.92 7.47 12.07 8.33 9.29

Frequency of extreme drought (%)

KMA0.81

ge GFDL CONS MRI UKMO Average1.94 1.94 0.83 3.33 2.031.94 3.33 1.94 2.78 2.684.89 4.60 9.77 6.90 7.09

b) Moderate drought

d) Extreme drought

rought classification as calculated from the historical data (S0) and GCMsnd S3)

Hydrol. Process. 27, 2800–2813 (2013)

Figure 7. SDF curve derived from the observed historical data (1970–2010)and the 3-month SPI timescale


and 11.38%, respectively, whereas the GCM showed adecreased drought frequency over time. At the S3 period, theaverage frequency of mild and moderate drought decreasedto 27.68% and 9.29%, respectively.For severe droughts, the S1 period showed a lower

drought frequency relative to the historical data, but theprojected frequency gradually increased over time.For extreme droughts, the SPI of historical data indicated a

frequency of 0.81%, which was lower than the theoreticalprobability density function value, but the frequencyincreased significantly with all GCMs based on the projectedfuture SPI.At the S3period, the average frequency of extremedrought increased to 7.09%. In particular, the MRI showed afrequency of 1.94% in the S2 period, which is similar to thatgiven by the other GCMs, but it showed a higher frequency(9.77%) in the S3 period relative to the other GCMs.Overall, theMRI predicted the highest drought frequency

at all stages, and the CONS showed the lowest frequency.When frequency were separated for each drought classifi-cation, the frequency of mild droughts decreased over time,the frequency of moderate and severe droughts increasedslightly over time and the frequency of extreme droughtshad the greatest and most pronounced increase.

Changes in drought duration

Drought events that had consistent values below (�1) in themonthly SPI (3-month SPI time series was selected andclassified by each duration of 1–12months. TableVI shows thechanges in drought duration of past and future drought events.

Table VI. Changes in drought occurrence for each drought duratio(with �1.0

Duration(months)

No. drought events (%)

Observed GFDL CONS MRI UKM

1 26 (55.3) 45 (47.9) 14 (24.1) 38 (51.4) 27 (32 10 (21.3) 31 (33.0) 15 (25.9) 13 (17.6) 23 (33 9 (19.1) 11 (11.7) 16 (27.6) 13 (17.6) 9 (14 – 3 (3.2) 6 (10.3) 1 (1.4) 5 (65 2 (4.3) – 3 (5.2) 2 (2.7) 4 (56 – 3 (3.2) 4 (6.9) 2 (2.7) 2 (27 – – – 1 (1.4) 3 (48 – 1 (1.1) – 3 (4.1) –9 – – – – –10 – – – 1 (1.4) –

Duration(months)

Average severity (magnitude/duration)

Observed GFDL CONS MRI UKM

1 �1.28 �1.37 �1.58 �1.30 �1.2 �1.45 �1.52 �1.42 �1.51 �1.3 �1.41 �1.57 �1.80 �1.76 �1.4 �1.77 �1.61 �1.36 �1.5 �1.51 �1.62 �1.50 �1.6 – �1.67 �1.91 �1.46 �2.7 – – – �2.33 �1.8 – �2.47 – �2.56 –9 – – – – –10 – – – �1.76 –


The analysis showed that, historically, droughtswith a shortdurationof 1–3month occurred frequently in the past,whereasdrought eventswith longer durations (i.e.more than 6months)showed an increase in the future.Drought eventswith durationof 8months were found in the case of the GFDL, whereasdrought events with 8- and 10-month durations were alsofound in the MRI. Even in terms of the accumulatedmagnitude, according to the duration, the GCMs yieldedlower (severer) values than the historical data; the same resultswere found for the average and maximum severity.

Changes in the drought SDF relationship

A drought frequency analysis was performed using the3-, 6- and 12-month SPI timescales that were derived fromthe historical andGCMoutput for the Seoul weather station.

n using the 3-month SPI timescale and a drought spell analysis≥SPI)

Magnitude of drought event (negative sum ofspi values during the event)

O Observed GFDL CONS MRI UKMO

7.0) �1.28 �1.37 �1.58 �1.30 �1.511.5) �2.90 �3.03 �2.84 �3.02 �3.022.3) �4.24 �4.72 �5.41 �5.28 �5.23.8) – �7.09 �6.45 �5.44 �7.12.5) �7.55 – �8.08 �7.48 �7.08.7) – �10.00 �11.45 �8.79 �14.41.1) – – – �16.34 �13.34

– �19.78 – �20.48 –– – – – –– – – �17.62 –

Maximum severity

O Observed GFDL CONS MRI UKMO

51 �1.99 �2.65 �2.61 �3.01 �2.9251 �2.17 �2.39 �2.43 �2.26 �2.4174 �1.58 �2.09 �2.48 �2.49 �2.3778 – �1.97 �1.86 �1.36 �2.2142 �1.69 – �1.77 �1.50 �1.6140 – �1.85 �2.10 �1.66 �2.7191 – – – �2.33 �2.01

– �2.47 – �3.06 –– – – – –– – – �1.76 –

Hydrol. Process. 27, 2800–2813 (2013)


The SDF curve was derived for each return period (30, 50,100, 200, 300 and 500 years) and drought duration(1–12months).In Figure 7–10, the x-axis represents the duration of

drought, and the y-axis represents the drought severity,which refers to the value obtained by dividing the droughtmagnitude by the drought duration.Table VII shows the results of the drought frequency

analysis for the 3-month SPI timescale using the observeddata (1970–2010) for each duration.

a) GFDL:CM2_1

c) MRI:CGCM2_3_2

Figure 8. Projected SDF curves derived from the G

a) GFDL:CM2_1

c) MRI:CGCM2_3_2

Figure 9. Projected SDF curves derived from the G


Figure 7 shows the SDF curve that was derivedusing the observed historical data, and Figures 8–10 showthe projected SDF curves that were derived from theGCMs.The SDF curve derivation showed that the projected

drought severity for each duration in the GCMs was lower(severer) than the drought severity for each duration inthe observed data. Furthermore, as the duration(1–12months) increased, the severity of the drought foreach return period increased as well. The same pattern of

b) CONS:ECHO-G

d) UKMO:HADGEM1

CMs (2011–2040) and the 3-month SPI timescale

b) CONS:ECHO-G

d) UKMO:HADGEM1

CMs (2041–2070) and the 3-month SPI timescale

Hydrol. Process. 27, 2800–2813 (2013)

a) GFDL:CM2_1 b) CONS:ECHO-G

c) MRI:CGCM2_3_2 d) UKMO:HADGEM1

Figure 10. Projected SDF curves derived from the GCMs (2071–2099) and the 3-month SPI timescale

Table VII. Result of drought frequency analysis for the 3-month SPI timescale using the observed data (1970–2010) for each duration

Observeddata

Duration(months)

Frequency

30 years 50 years 100 years 200 years 300 years 500 years

1970–2010

1 �2.2840 �2.3902 �2.5197 �2.6362 �2.6994 �2.77472 �1.9433 �2.0313 �2.1363 �2.2286 �2.2780 �2.33603 �1.7288 �1.8119 �1.9103 �1.9962 �2.0419 �2.09554 �1.5573 �1.6453 �1.7501 �1.8421 �1.8912 �1.94905 �1.4181 �1.5090 �1.6178 �1.7139 �1.7654 �1.82616 �1.2637 �1.3513 �1.4563 �1.5491 �1.5988 �1.65757 �1.1618 �1.2511 �1.3583 �1.4533 �1.5041 �1.56448 �1.0587 �1.1452 �1.2490 �1.3410 �1.3903 �1.44869 �0.9832 �1.0659 �1.1649 �1.2523 �1.2991 �1.3543

10 �0.9294 �1.0105 �1.1075 �1.1931 �1.2389 �1.292811 �0.8644 �0.9433 �1.0377 �1.1209 �1.1655 �1.218012 �0.7488 �0.8165 �0.8968 �0.9671 �1.0044 �1.0483


results was found in the frequency analysis using the6- and 12-month SPI timescales.Using the SDF curve derived from the drought frequency

analysis, we compared the severity and the frequency of thedrought for multiple timescales SPI as a function of droughtduration (1–12months).Table VIII and Figure 11 show the comparison results

of the drought severity corresponds to the 200-year returnperiod for each drought duration (1, 6 and 12months) andeach 3-, 6- and 12-month SPI timescales using the SDFcurve derived from observed data (S0) and GCM output(S1, S2 and S3). Figure 11 shows these results in graphicalform, where the x-axis represents the analysis period (S0 toS3) and the y-axis represents drought severity; the analysisperiod is the same as that shown in Table III.An analysis of the differences in the drought severity

according to the different SPI timescales revealed a higherdegree of decreasing trend in drought severity over time


(S0–S3) in the 3-month SPI timescale than that in the 6- and12-month SPI timescales for the identical duration andreturn period (200 year). This result may be because theincreased summer precipitation was included in thecalculations process for the 6- and 12-month SPI timescales.An examination of the differences in the drought severity

according to duration (1month to 12months) revealed ahigher drought severity (milder drought) for the 12- and6-month durations relative to the drought severity for the1-month duration.An examination of the differences in the drought severity

according to the GCMs showed that in all the four GCMs,drought severity increased over time, although the differ-ence in drought severity between the GCMs was largerin the 3-month SPI timescale than that in the 12-monthSPI timescale. In particular, the MRI showed the largestdecrease in drought severity over time for the 3- and6-month SPI timescales compared with the other GCMs.

Hydrol. Process. 27, 2800–2813 (2013)

Table VIII. Comparison of the drought severity during a 200-year return period for each drought duration (1, 6 and 12months) and each3-, 6- and 12-month SPI timescales using the SDF curve derived from observed data (S0) and GCM data (S1, S2 and S3)

3-month SPI, 1-month duration 3-month SPI, 6-month duration 3-month SPI, 12-month duration

Observed KMA KMA KMAS0 �2.64 �1.55 �0.97GCMs GFDL CONS MRI UKMO GFDL CONS MRI UKMO GFDL CONS MRI UKMOS1 �3.04 �3.94 �2.58 �4.41 �1.27 �2.41 �1.44 �1.86 �0.87 �1.11 �0.70 �1.16S2 �3.15 �4.19 �3.47 �3.08 �2.04 �2.18 �1.68 �1.64 �1.21 �1.01 �0.90 �0.90S3 �4.20 �4.39 �5.43 �4.11 �2.62 �2.33 �3.69 �3.14 �1.36 �0.91 �1.87 �1.40






Return periods for the drought events in historicaldrought years

The return periods for major drought events that occurredpreviously in the Seoul regionwere examined using the SDFcurve that was derived from the drought frequency analysison the 6-month SPI timescale. To select historical droughtevents, the past major drought years were surveyed based onreports of historical drought records of Korea. The surveyedreports include theHistorical Drought Event SurveyReportsby the Ministry of Land, Transport and Maritime Affairs ofKorea (MLTM, 1995; MLTM, 2002), and these reportsconfirmed that a severe drought occurred in Seoul duringthe years between 1988 and 1989 and between 2000and 2001. The 6-month SPI timescale that was derivedfrom the observed data (1970–2010) accurately replicatedthe droughts in 1988 to 1989 and 2000; the duration, thedrought magnitude and the drought severity for the previousdrought events are shown in Table IX.The drought that occurred throughout the central region

of the Korean Peninsula in 1988, as investigated by the6-month SPI timescale from the observed data, was found tohave duration of 7months and a return period of 289 years.In 2000, a severe drought occurred throughout the southernregion of Korea, and a drought with a duration of 4monthswas observed from the 6-month SPI timescale of Seoulweather station, with a return period of 24 years.In addition, the return periods that were estimated by the

GCM-derived SDF curves for the historical major droughtevents are shown in Table X. To compare the return periodof the historical drought events, we applied the four differentSDF curves derived from observed data (1970–2010) as


well as GCMs (2011–2040, 2041–2070 and 2071–2099)through the drought frequency analysis using the 6-monthSPI timescale.In case of SDF curve for the years 2011 to 2040, the

historical drought events for the years 1988 to 1989 showedsignificant differences in return periods, with a 189-yearreturn period for the CONS and a 46-year return period forthe UKMO. In contrast, the GFDL and the MRI showedreturn period of 500 and 308 years, respectively, indicatingthe longer return periods based on the GCM data comparedwith those based on the observed data. This result may bedue to themilder drought severity in the S1 period comparedwith the S0 period, as can be seen in Figure 11. For thedrought event that occurred in the year 2000, theMRImodelshowed a 25-year return period similar to the observed data,whereas the GFDL, CONS and UKMO models showedapproximately a 10-year return period or approximately halfof the return period derived from the MRI model andobserved data.In case of the SDF curve for the years 2041 to 2070, the

drought events for the years 1988 to 1989 each showedshorter return periods for the GFDL, MRI and UKMOmodels of 47, 168 and 95 years, respectively, whereas theCONSmodel showed a longer return period with 500 years.For the drought event in the year 2000, the four GCMsshowed shorter return periods than the corresponding resultsof the SDF curves for the years 2011 to 2040.In case of the SDF curve for the years 2071 to 2099,

the drought events for the years 1988 to 1989 and 2000 allshowed significantly shorter return periods than theobserved data.

Hydrol. Process. 27, 2800–2813 (2013)

Table IX. Drought magnitude and severity of major historical drought events at the Seoul based on the 6-month SPI timescale

Year Drought month Duration (months) Magnitude Severity

1988–1989 August to February 7 �12.14 �1.732000 April to July 4 �6.41 �1.60

a) SPI (3) - Duration(1) b) SPI (3) - Duration(6) c) SPI (3) - Duration(12)

d) SPI (6) - Duration(1) e) SPI (6) - Duration(6) f) SPI (6) - Duration(12)

g) SPI (12) - Duration(1) h) SPI (12) - Duration(6) i) SPI (12) - Duration(12)

Figure 11. Comparison of the drought severity during a 200-year return period for each duration (1, 6 and 12months) and each 3-, 6- and 12-month SPItimescales using the SDF curve derived from observed data (S0) and GCM data (S1, S2 and S3)


The results of the analyses indicated that the fourGCMs showed shorter return periods for historicaldroughts over time.

CONCLUSION

Projected future drought occurrence patterns wereanalysed using historically observed data from the Seoulweather station on the Korean Peninsula and the fourGCMs that reflect the climatic change scenarios.

1. Estimating the changes in the frequency for each droughtclassification using the drought spell analyses showed


that the projected SPIs that were calculated using theGCMs were found to decrease in frequency for mild andmoderate drought cases over time. However, in cases ofsevere droughts, the frequency was found to increaseslightly over time, and the frequency was found toincrease sharply in the case of extreme droughts. Inparticular, the MRI model generally showed a longerreturn period of droughts relative to the frequencyprojected by the other GCMs, and the extreme droughtfrequency was found to be higher in the S3 period (2071–2099) compared with the S1 and S2 periods. In otherwords, we predicted that severe and extreme droughts,rather than mild droughts, would occur more frequentlyin the future.

Hydrol. Process. 27, 2800–2813 (2013)

Table X. Comparison of the return periods for major historical drought events that occurred at the Seoul using the SDF curve derivedfrom the observed and GCM data

Year

Return period of major historical droughtsbased on the SDF (1970–2010) (years)

Return period of major historical droughtsbased on the projected SDF (2011–2040) (years)

KMA GFDL CONS MRI UKMO

1988–1989 289 500 189 308 462000 24 11 10 25 9

Year



GFDL CONS MRI UKMO GFDL CONS MRI UKMO

1988–1989 47 500 168 95 21 50 9 242000 10 8 11 13 8 8 7 8


2. Changes in the drought duration for the droughtevent (with �1.0≥SPI) indicated that the number ofoccurrences of droughts with longer durations increasedaccording to the projected data obtained from the GCMsrelative to the observed data. Specifically, long-termdroughts of more than 6 or 10 months in duration, whichhave never occurred in the historical data, were found inthe projected data from the GCMs, including the GFDLand the MRI.

3. In the SDF curve derived from the drought frequencyanalysis for the Seoul weather station, the droughtseverity for each return period decreased as droughtduration increased, whereas the droughts identified fromthe GCMswere found to bemore severe than those foundin the historically observed data. The drought severitywas compared for each SPI and GCM by using the SDFcurve derived from drought frequency analysis using the3-, 6- and 12-month SPI timescales. The differences in thedrought severity for each SPI showed that the droughtseverity for the 3-month SPI timescale was most severerelative to the 6- and 12-month SPI timescales during thesame duration and the same return period and that thedrought severity tends to be more pronounced over time.Examining the drought severity for each GCM indicatedthat all the four GCMs predicted more intensifieddroughts over time, especially based on the SDF curveusing the MRI model, which was found to show the mostextreme drought severity comparedwith the other GCMs.

4. The return periods for historical major drought eventswere examined by using the SDF curve derived from thedrought frequency analysis of the 6-month SPI timescale.Severe droughts were determined to have occurred in theyears 1988 and 2000. Results of the drought investigationwith the 6-month SPI timescale confirmed that severedroughts occurred in the years 1988 to 1989 (7-monthduration) and 2000 (4-month duration). The resultsobtained by estimating the return period using the SDFcurve revealed a 289-year return period for the droughtevent between 1988 and 1989 and a 24-year return periodfor the drought of 2000. Furthermore, when estimatingthe return periods of historical droughts using the SDF


curve derived from the GCMs, some GCMs showedlonger return periods because of differences in thedrought severity according to the analysis period.However, in the four GCMs, the return periods wereshorter than the SDF curve derived from the observeddata, and the return periods of historical droughts wereestimated to decrease more over time.

Although uncertainty regarding the ability of GCMs toaccurately reflect climatic change scenarios cannot be ruledout, we conclude that the multimodal GCMs predictintensified drought severity and drought duration in theSeoul region of Korea, relative to previous droughtcharacteristics, and that the magnitude of these factors willincrease over time.

ACKNOWLEDGEMENTS

This research was supported by a grant from the CCAPH-KResearch Center funded by the Ministry of Land, Transportand Maritime Affairs of the Korean government.

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A multimodel assessment of the climate change effect on the drought severity–duration–frequency relationship