S HORT COMMUN I CAT I ON

Extreme drought hotspot analysis for adaptation to a changingclimate: Assessment of applicability to the five major river basinsof the Korean Peninsula

Joo-Heon Lee1 | Seo-Yeon Park1 | Jong-Suk Kim2 | Chanyang Sur1 | Jie Chen2

1Department of Civil Engineering, JoongbuUniversity, Goyang, South Korea2State Key Laboratory of Water Resources andHydropower Engineering Science, WuhanUniversity, Wuhan, P.R. China

CorrespondenceJong-Suk Kim, Department of Hydrology andWater Resources, School of Water Resources andHydropower Engineering, Wuhan University,Wuhan 430072, P.R. China.Email: jongsuk@whu.edu.cn

Funding informationNational Research Foundation of Korea (NRF)funded by the Ministry of Education (SouthKorea), Grant/Award Number: NRF-2017R1D1A1A02018546; Korea Agency forInfrastructure Technology Advancement (KAIA)funded by Ministry of Land, Infrastructure andTransport (South Korea), Grant/Award Number:18AWMP-B083066-05

This study proposes a quantitative approach for extreme drought hotspot assess-ment related to climate change, the hotspot drought risk index (HDRI), and evalu-ates the response of extreme drought to climate change in the five major riverbasins of the Korean Peninsula. According to an analysis of seasonal and regionaldrought characteristics on the Korean Peninsula, drought occurs most frequentlyin spring, and the central and southern areas are generally more vulnerable todrought. Drought risk analysis under various climate change scenarios indicatessignificant drought risk over the entire Korean Peninsula excepting some parts ofthe Han and Nakdong river basins from 2011 to 2040. Extreme drought may beparticularly severe in the mid-west area as these basins lack available waterresources. Drought risk decreases in the southern Korean Peninsula in the2011–2040 and 2071–2099 periods, during which the area vulnerable to droughtalso decreases. However, some parts of central Korea and the western and easterncoastlines remain susceptible to drought. The drought risk evaluation method sug-gested herein may be useful for predicting drought and establishing realistic cop-ing strategies for extreme drought in a changing climate.

KEYWORDS

climate change, extreme drought, hotspot drought risk index (HDRI), KoreanPeninsula

1 | INTRODUCTION

Climate change is regarded as one of the most significantrisks facing current and future generations and may have del-eterious effects on both civilizations and ecosystems (Ranjanet al., 2006; Pelt and Swart, 2011; Hao et al., 2017; Kanget al., 2017; Kim et al., 2017; Choi et al., 2018). About 9.5million people experienced severe food shortages due todroughts in eastern Africa from 2010 to 2011; this led tonumerous deaths from malnutrition (Hillier and Dempsey,2012). In the Unites States, enormous economic losses wereincurred by the severe Dust Bowl drought in the 1930s;another severe drought, among the worst in history, impactedCalifornia from 2012 to 2015 (Dutra et al., 2013; Smith and

Katz, 2013; Hoerling et al., 2014). In Korea, chronic droughthas occurred regionally between winter and spring since the1990s. One recent drought, which started in summer 2014and lasted until 2015, is regarded as the worst in history interms of severity and duration; during that time, precipitationand water storage in multipurpose dams were the lowest everrecorded (Hong et al., 2016). While rainfall is expected toincrease in general due to climate change, rainfall fluctuationsare also likely to increase, as are drought frequency andstrength (Dai et al., 2004; Sheffield and Wood, 2008).

Various drought indices have been developed to quanti-tatively analyse the occurrence characteristics of differentdrought types at multiple timescales (Palmer, 1965; McKeeet al., 1993; Richard, 2002; Tallaksen et al., 2009). Different

Received: 13 November 2017 Revised: 19 February 2018 Accepted: 26 February 2018 Published on: 16 April 2018

DOI: 10.1002/joc.5532

Int J Climatol. 2018;38:4025–4032. wileyonlinelibrary.com/journal/joc © 2018 Royal Meteorological Society 4025

drought indices based on precipitation, soil moisture, andrunoff data are used to measure various drought patterns(Mo, 2008). Typical drought indices include the Palmerdrought severity index (PDSI; Palmer, 1965), standard pre-cipitation index (SPI), soil moisture index (SMI), standard-ized runoff index (SRI), and surface water supply index(SWSI). It is necessary to select the appropriate index for theinterpretation of an actual drought, considering the strengthsand weaknesses of each drought index.

In past decades, multilateral research has also focusedon reducing damage from drought. For example, droughtfrequency, risk evaluation, and prediction have been ana-lysed for various climate change scenarios (Dai et al., 2004;Sheffield and Wood, 2008; Park et al., 2012; Ficklin et al.,2015; Carrao et al., 2016; Modarres et al., 2016). Ficklinet al. (2015) used the PDSI, potential evapotranspiration(PET), and Mann–Kendall (MK) analysis to assess hydro-logical cycle fluctuations from 1979 to 2013 in order tounderstand drought tendencies and climate effects in neigh-bouring countries. Modarres et al. (2016) applied MK andpre-whitening trend (PWT) tests to evaluate the influence ofrainfall on flood and drought severity in Iran, which islocated in an arid and semi-arid area. Park et al. (2012)developed the drought hazard index (DHI) and drought vul-nerability index (DVI) to evaluate drought risk on theKorean Peninsula via the drought risk index (DRI) and thecorrelation between DHI and DVI. Hugo et al. (2016) eval-uated drought risk from 2000 to 2014 based on three inde-pendent variables (hazard, exposure, and vulnerability),

finding that drought risk is more correlated with regionalexposure than with hazard or vulnerability.

Numerous studies have focused on drought evaluation,but more research is needed to quantitatively assess regionaldrought characteristics considering rainfall patterns and run-off responses from watersheds. Therefore, this study pro-poses a drought evaluation method that quantitativelyconsiders watershed characteristics; drought risk and climatechange adaptation strategies are addressed by applying cli-mate change scenarios for extreme drought.

2 | DATA AND METHODS

In this study, spatiotemporal distribution characteristics wereanalysed for 109 sub-basins in the five major Korean riverbasins (Figure 1) based on the hydrological risk approach,which considers extreme drought under various climatechange scenarios; the representative concentration pathway(RCP)-based national standard climate change scenario wasused. The RCP scenario was input to the HadGEM2-AOglobal atmospheric-ocean coupling model to calculate globalclimate change scenarios with a resolution of approximately135 km (NIMR, 2011). These results were input toHadGEM3-RA, the global atmospheric-ocean model for theCoupled Model Intercomparison Project Phase 5 (CMIP5) inorder to generate regional climate change scenarios with aresolution of 12.5 km over the Korean peninsula (Jin et al.,2016). In order to assess the drought risk level, the SPIdeveloped by McKee et al. (1993) and McKee (1995) was

Russia

Korea

China

Japan

Mongolia

Han River

NakdongRiver

GeumRiver

SumjinRiver

YoungsanRiver

20N

30N

40N

50N

60N

100E 110E 120E 130E 140E 150E

FIGURE 1 The five major river basins (and109 sub-basins) of the Korean peninsula

4026 LEE ET AL.

applied, focusing on the fact that drought begins due to adecrease in rainfall that causes a water deficit when com-pared to relatively greater water demand.

To analyse basin runoff characteristics according to vari-ous climate change scenarios, long-term runoff analysis wasconducted using the Soil and Water Assessment Tool(SWAT) model, which was developed to predict the effectsof various soil types, land use situations, and managementconditions over long periods of time in large, complex basins(Arnold and Allen, 1996). A temporal range of 30 years (S0:1976–2005) was used for runoff simulations. For the variousclimate change scenarios, hydrologic risk was consideredover three 30-year periods (S1: 2011–2040, S2: 2041–2070,and S3: 2071–2099).

To identify seasonal and regional drought characteris-tics, this study applied the widely used MK test, a represen-tative nonparametric analysis method that is used to analysemonotonic trends in time series data (Mann, 1945; Kendall,1975). The MK test is applicable to data sets containingmissing or below-detection-limit values and is widely usedin trend analysis for hydrologic variables. In addition, con-ditional hydrologic risk analysis was performed by estimat-ing non-exceedance probabilities for all observations andclimate change scenarios, in order to analyse the spatiotem-poral characteristics of extreme drought and identify theregional distribution of hydrologic runoff. Significance anal-ysis was used to statistically validate drought risk resultsthrough the application of bootstrap resampling (Beckeret al., 1988; Ripley, 2009).

The hotspot drought risk index (HDRI) proposed inthis study is defined as the ratio of conditional non-exceedance probability (CNP) to unconditional non-exceedance probability (UCNP) for the flow informationof each watershed. Here, the latter means the probabilityof not exceeding the nth percentile streamflow over theentire data period for a given watershed under a particulardrought condition. In this study, the CNP and UCNP werecalculated by applying Kernel density estimation, a repre-sentative nonparametric method that does not estimate theparameters in the probability density function(Bowman and Azzalini, 1997), to reflect the distribution ofgiven streamflow data considering the particular droughtconditions with a relatively small number of occurrences.The HDRI is calculated as:

HDRI i, jð Þ=Prob QSi

j ≤QS0n, j

� �jDISij ≤DS

h i

Prob QSij ≤Q

S0n, j

� �h i ð1Þ

where, Si represents the applied climate change scenarioperiods, j indicates the 109 sub-watersheds of the five majorriver basins in the Korean Peninsula, Q is the streamflow,Qn is the nth percentile based on the historical record, DIjindicates the drought index for each sub-basin, and DS isthe drought severity criterion.

An HDRI value equal to 1 indicates that there is no spe-cific change in the runoff under the given drought indexconditions. If the HDRI is greater than 1, the hydrologic riskis shown to be significant; that is, the basin is subject tolower runoff availability and is thus vulnerable to extremedrought. Figure 2 shows an example of HDRI analysis forextreme drought in the Daecheong Dam basin located in theGeum River basin; in this case, the HDRI is 1.89, indicatingthat the change in regional runoff is significant under SPI3≤−1.5 conditions.

Currently, 40 countries worldwide (including Korea)use the SPI to prepare drought countermeasures. However,it is difficult to judge drought in a basin through the appli-cation of the SPI alone, as this is based solely on waterinputs (such as rainfall) that do not encompass all of thewater management aspects in a given basin. Therefore, theHDRI, coupled with various drought severity scenarios, canbe used to determine drought management priorities thatconsider regional differences in changing characteristicssuch as regional runoff, antecedent precipitation, land use,soil condition, and evaporation. This study proposes a quan-titative approach for hotspot assessment in cases of extremedrought due to climate change using SPI3 (short-termdrought) and SPI6 (mid-term drought), which are commonlyused in drought analysis.

FIGURE 2 Example of risk analysis for extreme drought in Daecheongdam basin (within the Geum River basin) [Colour figure can be viewed atwileyonlinelibrary.com]

LEE ET AL. 4027

3 | ANALYSIS RESULTS

3.1 | Runoff characteristics and spatiotemporaldistribution of drought

Figure 3 shows the percent change in annual mean runofffor each of the 109 sub-basins for each climate change sce-nario in relation to period S0. For the RCP 4.5 scenario, thedata (calculated as S1/S0) show an increase in runoff volumeover the entire region with the exception of the upper regionof the Sumjin River. For the RCP 8.5 scenario, the flowvolume increases with the exception of the mid-Sumjin andNakdong River basins and the downstream section of theHan River basin during period S1 and a middle section ofthe Nakdong River basin during period S3.

Figure 4 shows a seasonal trend analysis using SPI3 forthe five major river basins on the Korean Peninsula. Duringthe spring season, decreasing patterns are dominant; increas-ing trends in drought are shown in some sub-basins of theNakdong River basin, but are not found to be statisticallysignificant. In summer, increases in flow are shown through-out the Korean Peninsula, particularly in the Han River basinand the upper Nakdong River basin. In autumn, the increas-ing trends shown are not statistically significant with theexception of certain Nakdong River sub-basins. In winter,increasing trends are apparent in the Han River basin andsouthern coastal basin of the Nakdong River; other basins(the Geum, Youngsan, and Sumjin Rivers) are dominated bydecreasing trends. Areas showing decreasing SPI3 trends foreach season may be subjected to worsening drought; con-versely, increasing SPI3 trends indicate increased runoff anda slight decrease in drought risk. However, these seasonalchanges may have been caused by episodic extreme events.

Therefore, the following section examines regional droughtcharacteristics and extreme drought frequency.

3.2 | Analysis of changes in extreme droughtfrequency due to climate change

Figure 5 shows the spatial distribution of the annual meanfrequency of extreme drought (SPI < −1.5) during the

FIGURE 3 Changes in annual meanflow for the five major Korean riverbasins according to the RCP 4.5 and RCP8.5 scenarios [Colour figure can beviewed at wileyonlinelibrary.com]

Increasing trend Decreasing trend

March–May June–August

September–November December–February

FIGURE 4 Seasonal drought trend analysis for the five major Koreanriver basins and their sub-basins. Hatched polygons indicate significantchanges at a 95% confidence level [Colour figure can be viewed atwileyonlinelibrary.com]

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reference period (S0: 1974–2005). Using both SPI3 andSPI6, the frequency of extreme droughts was found to behigher in the centre of the country. Using SPI3, the droughtoccurrence frequency is higher in the Geum and Nakdong

River basins and lowest in the Han River basin. The GeumRiver basin has a value of 0.97 using SPI6, which is 0.08higher than when using SPI3; the frequency of extremedroughts was higher than for other basins. The Han Riverbasin, which has the lowest drought occurrence, has a valueof 0.69 using SPI6, similar to SPI3.

Various climate change scenarios and resulting extremedrought frequencies were also analysed. Under the RCP 4.5scenario, the comparisons of SPI3 values in Figure 6a showthat the drought occurrence frequency is similar betweenthe periods S1 and S2. Figure 6b shows that similar SPI6values are also found between the periods S1 and S2; how-ever, the range of drought frequency fluctuation is more sig-nificant in period S1. Period S3 shows lower droughtoccurrence frequencies than does period S0 under both SPI3and SPI6. Results for the RCP 8.5 scenario differ fromthose for the RCP 4.5 scenario; period S1 has a higherextreme drought frequency and fluctuation range than doesperiod S0 using both SPI3 and SPI6. Period S0 has a higherextreme drought occurrence than periods S2 and S3 usingboth SPI3 and SPI6.

(a) SPI3 (b) SPI6

RCP4.5

RCP8.5

1974–2005 2011–2040 2041–2070 2071–2099

1974–2005 2011–2040 2041–2070 2071–2099 1974–2005 2011–2040 2041–2070 2071–2099

1974–2005 2011–2040 2041–2070 2071–2099

2.0

1.5

1.0

0.5

0.0

2.0

1.5

1.0

0.5

0.0

2.0

1.5

1.0

0.5

0.0

2.0

1.5

1.0

0.5

0.0

Ann

ual m

ean

freq

uenc

y of

sev

ere

drou

ght

Ann

ual m

ean

freq

uenc

y of

sev

ere

drou

ght

Ann

ual m

ean

freq

uenc

y of

sev

ere

drou

ght

Ann

ual m

ean

freq

uenc

y of

sev

ere

drou

ght

FIGURE 6 Annual mean frequency of extreme drought (SPI < −1.5) in Korea for climate change scenarios. (a) Short-term drought index (SPI3). (b) Mid-term drought index (SPI6)

0 – 0.20.2 – 0.40.4 – 0.60.6 – 0.80.8 – 1.01.0 – 1.2> 1.2

SPI3 SPI6

(a) (b)

FIGURE 5 Spatial distribution of annual mean frequency of extremedrought (SPI < −1.5) during the reference period (S0: 1974–2005).(a) Short-term drought index (SPI3). (b) Mid-term drought index (SPI6)[Colour figure can be viewed at wileyonlinelibrary.com]

LEE ET AL. 4029

3.3 | Hydrologic risk assessment according to droughtconditions

Figures 7 and 8 show spatiotemporal HDRI results for vari-ous climate change scenarios and drought indexes forperiods S1 (2011–2040), S2 (2041–2070), and S3 (2071–2099). According to the HDRI results for climate changescenario RCP 4.5 (Figure 7), the HDRI is high using bothSPI3 and SPI6 during period S1 throughout the Geum,

Yeongsan, and Sumjin River basins as well as some sec-tions of the Han and Nakdong River basins; the HDRI inthe central Korean Peninsula is particularly significant. Dur-ing period S2, the HDRI is high through the Geum, Nak-dong, Sumjin, and Yeongsan River basins, as well as somesections of the Han River basin. The HDRI during period S3is not significant compared to S1 and S2, indicating that theHDRI decreases over time.

S1 (2011–2040) S2 (2041–2070) S3 (2071–2099)

SPI3

SPI6

Very high (>2.0) High (1.5 – 2.0) Medium (1.0 – 1.5) Low (0.5 – 1.0) Very low (0 – 0.5)

(a) (b) (c)

FIGURE 7 HDRI variability for climatechange scenario RCP 4.5. Hatched areasindicate regions with statistically significanthigh risk at a 95% confidence level [Colourfigure can be viewed at wileyonlinelibrary.com]

S1 (2011–2040) S2 (2041–2070) S3 (2071–2099)

SPI3

SPI6

Very high (>2.0) High (1.5 – 2.0) Medium (1.0 – 1.5) Low (0.5 – 1.0) Very low (0 – 0.5)

(a) (b) (c)

FIGURE 8 HDRI variability for climatechange scenario RCP 8.5. Hatched areasindicate regions with statistically significanthigh risk at a 95% confidence level [Colourfigure can be viewed at wileyonlinelibrary.com]

4030 LEE ET AL.

Figure 8 shows the equivalent results for the RCP 8.5climate scenario. During period S1, the HDRI values aregreater than those for the RCP 4.5 scenario, and thespatial distribution of high values is more extensive. Dur-ing periods S2 and S3, the HDRI values are generallylower than those found for the RCP 4.5 scenario, but thespatial patterns are similar, with reductions in droughtover time.

4 | SUMMARY AND CONCLUSIONS

This study evaluated the use of the HDRI for assessingextreme drought under various climate change scenarios.The results can be summarized as follows:

1. According to comparisons of future and past droughtspatiotemporal characteristics, drought will tend toworsen in spring throughout the Korean Peninsula,except in certain Nakdong River sub-basins. The SPItends to increase over all areas of the Korean Peninsulain summer and fall, and drought risk is reduced withincreases in streamflow. In winter, droughts areexpected to worsen (with the exception of some Hanand Nakdong River sub-basins), but these changes arenot statistically significant.

2. According to extreme drought analysis, the extremedrought risk is high over all areas of the Korean Peninsulaexcept for certain Han River sub-basins. Most of thebasins in the Korean Peninsula show vulnerability toextreme drought due to a lack of available water resources;coastal regions are particularly vulnerable to drought.

3. According to drought analysis during periods S1(2011–2040), S2 (2041–2070), and S3 (2071–2099)under different climate change scenarios and the short-term (SPI3) and mid-term (SPI6) drought indexes,drought risk progressively decreases between periods S1and S3; during the former, the drought risk is found tobe most significant over the mid-west Geum River.

4. Using the short-term drought index (SPI3), the HDRI isrelatively high in the central and southern areas duringperiod S1; in period S2, the HDRI is significant aroundthe Nakdong River basin. However, the HDRI duringperiod S3 lower than those found for periods S1 and S2.The patterns for mid-term drought (SPI6) are similar tothose for short-term drought (SPI3), and drought patternsunder the RCP 8.5 climate scenario are similar to thoseunder RCP 4.5. However, the SPI3 during period S3shows an increase in the area vulnerable to drought underRCP 8.5.

This study proposes a quantitative drought risk assess-ment method considering watershed characteristics by apply-ing climate change scenarios. However, to better understandthe characteristics of accurate drought evaluation and regional

variation of extreme droughts, it is necessary to further studyfurther drought transitions and mechanisms. In addition, thecurrent analysis (which relates flow characteristics to droughtconditions) relies on runoff analyses under climate changescenarios that feature much uncertainty and relatively littleobservational data. Future basin-based drought risk evalua-tions and temporal and spatial analyses of extreme droughtare expected to provide basin-customized drought informa-tion. Lastly, the development of climate change scenariosusing ensemble models with high spatial and temporal resolu-tion may enable better drought evaluation, which can be usedto predict droughts and establish realistic extreme droughtcoping strategies in a changing climate.

ACKNOWLEDGEMENTS

This research was supported by grant (18AWMP-B083066-05) from the Water Management Research Program andBasic Science Research Program through the NationalResearch Foundation of Korea (NRF) funded by the Minis-try of Education (NRF-2017R1D1A1A02018546).

ORCID

Jong-Suk Kim http://orcid.org/0000-0002-5274-5085

Jie Chen http://orcid.org/0000-0001-8260-3160

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How to cite this article: Lee J-H, Park S-Y,Kim J-S, Sur C, Chen J. Extreme drought hotspotanalysis for adaptation to a changing climate: Assess-ment of applicability to the five major river basins ofthe Korean Peninsula. Int J Climatol. 2018;38:4025–4032. https://doi.org/10.1002/joc.5532

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