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Ecological Indicators

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Quantitative vulnerability assessment of water quality to extreme drought ina changing climate

Jong-Suk Kima, Shaleen Jainb,⁎, Joo-Heon Leec,⁎, Hua Chena, Seo-Yeon Parkc

a State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, PR ChinabDepartment of Civil and Environmental Engineering, University of Maine, Orono, ME 04469-5711, USAc Department of Civil Engineering, Joongbu University, KyungGi 10279, South Korea

A R T I C L E I N F O

Keywords:Meteorological droughtWater qualityKernel density estimationRisk assessment

A B S T R A C T

Projected changes in the global climate indicate warmer temperatures and a highly variable hydrologic cycle,portending significant societal effects, particularly those stemming from drought. Whereas the extent of drought-related effects ranges from humans to environmental systems, the impacts on water quality, in particular, requirecareful investigation. Such investigation should include the interlinkages across climate and stream qualityvariables and how risk translates for various watersheds, given their particularities with respect to land use,watershed characteristics, and infrastructure. To this end, this study investigated drought assessment and thewater quality vulnerability to severe drought, with the goal of informing planning and mitigation as a means toenhance drought resilience. This study was conducted in the Nakdong River basin in South Korea, which isvulnerable to water quality degradation during severe drought. We propose a method to evaluate spatial-tem-poral droughts based on the water quality risk and to monitor environmental droughts using the probability ofexceeding the target water quality to facilitate a resilient proactive response. The results of this study can be usedto provide scientific drought monitoring information for assessing real-time water quality monitoring frommeteorological drought information. In addition, we expect to categorize vulnerable drought areas and tosuggest essential management measures and tailored countermeasures at a local scale.

1. Introduction

Freshwater is essential for the sustenance of natural and humansystems. Clean water is an important determinant of ecosystem healthand, at the same time, is critical to humans from the standpoint ofhygiene and health (Meybeck, 2003; Corvalan et al., 2005; Vörösmarty,2005; WWAP, 2009; Brooks et al., 2016; Fenner, 2017; Hellberg, 2017;Hong et al., 2019). However, changes in water resources owing towarming temperature and droughts tend to increase the impact ofpollutant loadings, thereby having compounding deleterious effects onthe quality of water and the aquatic ecosystems (Poff et al., 2002; Parket al., 2010; Sjerps et al., 2017).

Droughts are one of the most expensive natural disasters withlasting and wide-ranging effects on the environment, society, and theeconomy (Eshghi and Larson, 2008; Apurv et al., 2017; Kim et al., 2017;Lee et al., 2018; Park et al., 2018; Gao et al., 2019). Climate projectionsindicate that future climate changes will probably not be uniform, andregional variations in precipitation and warming temperatures will

exacerbate the potential for drought incidence and severity (Trenberthet al., 2014; Lee et al., 2018). Furthermore, natural climatic variability,for example stemming from El Niño/Southern Oscillation (ENSO), canamplify hydroclimatic extremes (Mosley, 2015; Son et al., 2014; Kimet al., 2016). Consequently, faced with water shortages, the quantifi-cation of risks and the development of countermeasures to protect theintegrity between human and environmental systems are needed.

In what follows, we provide a limited review of recent studies fo-cused on water quality, water resources vulnerability, and land-useeffects. Esquivel‐Hernández et al. (2018) developed approaches basedon multiple linear regression and cluster analysis to understand therelative role of population size and hydrometeorological events asdrivers in water conflicts, specifically linked to floods, droughts, was-tewater spills, and water pollution across Costa Rica. Ng et al. (2018)used a multivariable linear model to assess the relative role of variablessuch as biochemical oxygen demand (BOD), chemical oxygen demand(COD), and total suspended solids toward water quality deteriorationbased on a water quality index applied to the Kampar River Basin in

https://doi.org/10.1016/j.ecolind.2019.04.052Received 9 December 2018; Received in revised form 5 March 2019; Accepted 15 April 2019

⁎ Corresponding authors at: Department of Civil Engineering, Drought Research Center, Joongbu University, Gyeunggi-do 10279, Republic of Korea (J.-H. Lee) andDepartment of Civil and Environmental Engineering, University of Maine, Orono, ME 04469-5711, USA (S. Jain).

E-mail addresses: shaleen.jain@maine.edu (S. Jain), leejh@joongbu.ac.kr (J.-H. Lee).

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Malaysia. Furthermore, their study integrated an El Niño event, whichcaused an extreme drought and watershed stress. Monica and Choi(2016) applied multivariate statistical methods (discriminant, cluster,and principal component analyses) to identify suspended solids andtotal phosphorus, which are critical watershed response variables thatinfluence changes in BOD and COD in the Saemangeum watershed,South Korea. Kükrer and Mutlu (2019) used a one-year long record of28 water quality relevant variables for the Saraydüzü Dam Lake inTurkey to assess the water quality and the relative importance ofmeasured variables for ecosystem health. Their analysis and risk as-sessment were based on multivariate cluster and factor analysis ap-proaches, as well as cross-correlation.

Wang et al. (2012) developed a water resources vulnerability ap-proach, focused on both water quality and quantity issues, based on aparametric-system (PS) method. Their approach involves the AnalyticalHierarchy Process to determine the relative weights of the indicesconsidered (including drought and pollution indices). The study con-tends that the PS method is more general than the existing Fuzzy Op-timization and Gray Relational Analysis approaches that only allow forrank analyses and qualitative level classification, respectively. Jun et al.(2011) used a multi-attribute method of decision analysis, coupled withfuture climate scenarios and watershed modeling, to assess hydrologicvulnerability in four dimensions: floods, droughts, water quality, andwatershed risk. Chanat and Yang (2018) applied a seven-parameterspatiotemporal watershed accumulation of net effects model to in-vestigate the changes in nitrogen loadings across four sites in theChesapeake watershed (USA). Their modeling work indicated that in-creases in nitrogen loadings stem from urban and suburban develop-ment, with changes in temperature and precipitation being the com-pounding meteorological variables.

The current study was conducted in the Nakdong River basin inSouth Korea, which is vulnerable to water quality degradation duringsevere droughts. We present a quantitative assessment method forwater quality monitoring by applying nonparametric kernel densityestimation (KDE). The KDE-based approach is used to quantify thedrought impacts on water quality indicators and to diagnose waterquality risk (WQR) by target basin to identify hotspots–areas vulnerableto extreme droughts. The WQR information can be provided to watermanagers and decision-makers for planning and decision-making linkedto sustainable water resources management. In addition, this studyseeks to inform efforts towards appropriate water quality managementmeasures and facilitate resilient drought adaptation at local and re-gional scales.

2. Material and methods

The Korean Nakdong River basin (KNRB) is selected for demon-strating the applicability of our proposed approach. Despite the im-portance of drinking water sources to more than 15 million people inthe river basin, the water quality has deteriorated, even after comple-tion of the four-river restoration project, and for many years, the riverbasin has suffered serious damage to downstream water quality (Yoonet al., 2015). In this study, water quality in 22 sub-basins of the Nak-dong River were evaluated to assess the vulnerability to extremedroughts (Fig. 1).

The hydrometeorological data used in this study were obtained fromthe 60 stations in the Automatic Synoptic Observation System, com-prising daily rainfall data collected between 1976 and 2016 by theKorean Meteorological Administration. Streamflow and dam water in-formation was provided by the Korean Water Management InformationSystem (WAMIS, 2017). Water quality vulnerability was assessed usingthe 10-year record from the water monitoring network (2007–2016),provided by the Water Information System (WIS, 2017). In particular,the water quality framework proposed by the Evaluation Regulations forWater Quality and Aquatic Ecosystem Targets (Ministry of Environment,South Korea) was used as the reference target criteria in our analyses.

Furthermore, we employed the Standardized Precipitation Index(SPI), which is used commonly in South Korea for drought assessment(Kim et al., 2017). The SPI meteorological drought index (McKee et al.,1993) was applied over various periods (30 days [30D], 60 days [60D],90 days [90D], and 180 days [180D]). The water quality indicators usedin this study included dissolved oxygen (DO), biological oxygen de-mand (BOD), chemical oxygen demand (COD), chlorophyll-a con-centration, total nitrogen (TN), and total phosphorus (TP).

2.1. Meteorological drought index

In this study, to investigate drought variability, we applied theStandardized Precipitation Index (SPI), which was developed by McKeeet al. (1993), and is used widely as a meteorological drought index. Thestandardization process of SPI is independent of geographic locationand is spatially comparable (Cacciamani et al., 2007). Currently, morethan 40 countries, including Korea, use the SPI to make preparations fordrought. Compared with the relative water demands used in droughtindices, SPI, a supply-side metric, focuses on the fact that droughtsentail reduced precipitation, which causes water shortages (Gao et al.,2019). The calculation of the SPI index for each station is based only onhistorical precipitation records accumulated over a specific time period.These time-series precipitation data are applied to the gamma dis-tribution, which is subsequently transformed into a standard normaldistribution through equal probability transformation (Guttman, 1999).Positive SPIs represent wetter conditions than the climatology mean ofprecipitation and negative SPIs represent drier conditions.

The gamma distribution best models the observed precipitation datain most instances because precipitation generally shows a skewed dis-tribution (Bordi and Sutera, 2007). The density probability function ofthe Gamma distribution is given by Eq. (1).

= >− −β α

x eg(x) 1Γ( )

, for x 0,αα x β1

(1)

where the shape parameter α > 0 and the scale parameter β > 0, andthe precipitation x > 0. Γ(α) is the gamma function defined by theintegral (Eq. (2)):

∫=∞ − −α y e dyΓ( ) α y

01

(2)

Gamma functions are evaluated numerically or by using the α valueof the table. To model precipitation data as a density function of thegamma distribution, α and β should be estimated appropriately. Thereare many ways to estimate shape and scale parameters. For example,the method introduced by Thom (1958) was adopted by Edwards andMcKee (1997).

⎜ ⎟= ⎛⎝

+ + ⎞⎠

=−

aA

A β xα

14

1 1 43

, (3)

where for n observations

= −∑−

A xx

nln( )

ln( )(4)

Using α and β, the density probability function g (x) for a givenamount of a given month can be integrated for x and the cumulativeprobability G (x) and a specific time scale can be obtained (Eq. (5)):

∫ ∫= = = − −g x dx

β ax e dxG(x) ( ) 1

Γ( )

x x α x β0 0

1 (5)

The gamma function requires x > 0. As the precipitation could bezero, the cumulative probability is Eq. (6):

= + −q q G xH(x) (1 ) ( ), (6)

where q is the probability of no rain and H (x) represents the cumula-tive probability of the recorded precipitation.

The cumulative probability is converted to a standardized normal

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distribution to obtain the SPI. However, the method is numericallycomplex when too many stations are involved. An alternative approachusing approximate transformation techniques to convert the cumulativeprobability to a standard variable Z was developed by Edwards andMcKee (1997).

= =⎧

⎨⎪

⎩⎪

− − < ≤

+ − < <

+ ++ + +

+ ++ + +

( )( )

t H x

t HSPI Z

, for 0 ( ) 0.5,

, for 0.5 (x) 1,

c c t c td t d t d t

c c t c td t d t d t

1

1

0 1 2 2

1 2 2 3 3

0 1 2 2

1 2 2 3 3 (7)

where

=

⎧

⎨⎪

⎩⎪

⎡⎣

⎤⎦

< ≤

⎡⎣

⎤⎦

< <−

H

H xt

ln , for 0 (x) 0.5,

ln , for 0.5 ( ) 1,

H x

H x

1( ( ))

1(1 ( ))

2

2 (8)

2.2. Nonparametric kernel density estimation

The drought classification criteria and the drought stage impactfactors proposed by the US Drought Mitigation Center (USDMC, 2017)were applied to differentiate between severe drought and extremedrought. The characteristics of water quality and the aquatic ecosystemwere analyzed considering these drought classification criteria. KDE is arepresentative nonparametric method to estimate the probability den-sity function (PDF) of random variables based on finite data samples(Bowman and Azzalini, 1997). We applied KDE to estimate the ex-ceedance probability of each water quality (WQ) indicator on the targetwater quality (Eq. (9)).

∑ ∑= − = ⎛⎝

− ⎞⎠= =

fn

K WQnh

K WQh

(WQ) 1 (WQ ) 1 WQ ,h i

nh i i

n i1 1

(9)

where K is the kernel function and h is the bandwidth, Kh is the scaledkernel and is defined as = ( )K WQ K( )h h

WQh

1 . The choice of the band-width (h) was evaluated using the following simple optimal formula(Eq. (10)):

= ⎧⎨⎩ +

⎫⎬⎭

+

hp n

σ4( 2)i

p

i

1 ( 4)

(10)

where p denotes the number of dimensions, hi denotes the optimalsmoothing parameter, and σi is the standard deviation in dimension i.When Gaussian basis functions are used to approximate univariate data,the optimal choice for h (i.e., the bandwidth that minimizes the meansquare error) is expressed as Eq. (11):

= ⎧⎨⎩

⎫⎬⎭

≈ −h σn

σn43

1.06 ,5

1 515

(11)

where σ is the standard deviation of the samples.The WQR can be defined as the ratio of conditional extreme drought

probability (DI) to unconditional exceedance probability, using the KDEapproach for each water quality (WQ) indicator and target criterion(Eq. (12)).

= ≥ ≤≥

WQR P WQ WQ DI DIP WQ WQ

( )( )

,criteria criteria

criteria (12)

where WQ is the water quality index, WQcriteria is the target waterquality standard, DI is the drought index, and DIcriteria is extremedrought conditions. This approach follows a past study of the multi-

Fig. 1. The study area of the Korean Nakdong River basin. In this figure, the numbers represent the basin identification number for 22 sub-basins in the NakdongRiver basin.

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centennial drought variations in the northeastern United States (Guptaet al., 2011), however no water quality variables were integrated in thatstudy.

In this study, WQR was proposed to assess the vulnerability of waterto extreme drought conditions (SPI < −1.2 and SPI < −1.5 for dif-ferent drought durations). The drought index is estimated on a dailybasis for each drought duration (30 days, 60 days, and 90 days) usingrainfall data from 1976 to 2016. To assess the relationship betweendrought statistics and episodic water quality events, we applied therank-based approach. Different drought durations, which are ranked bymagnitude of SPI according to dry (SPI < −1.2) and wet (SPI > 1.2)conditions are shown below, followed by the observed water qualityevents during the period 2007–2016. Through analysis of the rank, it ispossible to determine the sensitivity of water quality to wet and dryconditions in a certain river basin.

3. Analysis results

3.1. Historical droughts in the KNRB

Drought characteristics such as duration, magnitude, and the se-verity of drought events that occurred in the past were analyzed usingthe SPI index, and drought frequency analysis was performed to in-vestigate historical droughts quantitatively. Fig. 2 shows the time his-tory of SPI for the KNRB. The most severe drought year in the KNRBwas 1988, with magnitude −15.5, duration of 11months, average se-verity of −1.4, and a return period of 100 years. The significantdrought years in the KNRB are 1968, 1982, 2014, 1996, 2001, 2009,and 2015, and the return period is 10–50 years. As regards meteor-ological drought in the 1960s, the years 1967 and 1968 are droughtyears. In 1968, the drought magnitude was −13.6, the duration was11months, and the average severity was −1.2. On the other hand, adrought of more than two years was evaluated as long-term droughtfrom 1967 to 1968, 1976 to 1977, and 1994 to 1996 in the KNRB. In thefollowing section, we discuss analyzing water quality changes attribu-table to extreme droughts.

3.2. Analysis of water quality characteristics for extreme drought conditions

To analyze the water quality change based on the duration of adrought, we analyzed the risk for deleterious changes in the waterquality standards of BOD and TP, related to the severe drought andextreme drought thresholds. Fig. 3 shows an example of applying thetarget water quality standards for BOD and TP in the Nam River basin.The figure indicates the instances where the water quality exceeded thetarget criteria for the different SPI periods (30 days [30D], 60 days[60D]). Different symbols indicate the instances where only BOD isexceeded, only TP is exceeded, and both BOD and TP exceed the targetwater quality (BOD: 2mg/L, TP: 0.04mg/L) in the basin. Applying theSPI30D for the period 2007–2016, the water quality data showed thatsevere drought threshold was crossed 53 times, and extreme droughtthreshold 23 times on a daily time scale. The water quality target of theNam River basin is based on second-grade (lb), BOD 2.0mg/L, and TP0.04mg/L, and 94.3% in severe drought and 91.3% in extreme drought,exceeding the BOD and TP standards. This tendency was similar for the60D and 90D drought periods.

Fig. 4 shows the results of the sensitivity of the water quality indicesto drought statistics. Rank analysis, shown in Fig. 4a and b, was per-formed to assess the effects of extreme drought on the water quality.Sensitivity to drought and the achievement of the target water qualitywere also evaluated. In the figures, the data were arranged by theSPI30D index for dry/wet conditions. The two drought indices (SPI60D,SPI90D) are shown below, followed by the water quality indices (BODand TP). The data exceeding each reference criteria are shaded in gray.The percentile values noted in the upper panels are the probability ofexceeding the water quality (PEWQ) for the target criteria (BOD: 2mg/L, TP: 0.04mg/L) in the Nam River basin (sub-basin ID: 2019). In thisbasin, the water quality was shown to be deteriorating as the droughtprogressed. The wet condition showed a noticeable improvement inwater quality for BOD compared with the drought condition, and TPimproved slightly but still exceeded the target water quality. The eva-luation results of the target water quality exceeding the dry/wet con-ditions for the 22 sub-basins are shown in Fig. 4c and d. The analysis

Fig. 2. The Standardized Precipitation Index (SPI) in the Nakdong River Basin over long time-scales. The values (above 2 or below −2) indicate extreme wetness ordryness compared to normal condition in that region.

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results show that BOD had a negative relationship with the SPI droughtindex, and the water quality in the lower stream of the Nakdong Rivertended to deteriorate when drought occurred relatively more frequentlyin the other sub-basins. However, for TP, the relationship with thedrought index was unclear.

3.3. Environmental risk assessment for extreme drought conditions

Fig. 5 shows an example of WQR analysis for droughts by applyingthe target water quality standards (BOD: 2mg/L) of the Nam Riverbasin based on BOD data for the 2007–2016 period. The conditional(unconditional) probability distribution using the KDE for the targetwater quality in the Nam River basin is shaded in red (gray). Thecontour lines in the joint probability distribution were plotted for se-lected quantile levels (0.1, 0.25, 0.5, 0.75, and 0.9). The results of theanalysis show that the WQRBOD is 1.24, which is a moderately elevated,yet not a significant risk; however, careful management of the waterquality is warranted.

Fig. 6 shows the results of the WQR analysis using BOD and TP datafor each of the 22 sub-basins. The spatial distribution on the left of eachfigure shows the WQR result of applying extreme drought conditions(SPI30D < −1.2 and SPI30D < −1.5). The results of using the dif-ferent SPI periods (30 days [30D], 60 days [60D], and 90 days [90D])shown on the right of each figure are summarized in the violin plot,which is a combination of boxplot and kernel density plot. As regardsBOD, WQR was higher than 1 in many basins, but the spatial con-herence was not significant. However, regarding TP, WQR varied con-siderably depending on the basin. As the severity of drought increased,the spatial variation also increased relatively. As regards severedrought, the basins showing WQR of 2.0 or higher (exceptionally high)for BOD were the Imha Dam and Hapcheon Dam basins, with SPI30Dand SPI60D showing WQR of 2.21 and 2.25, respectively, relativelyhigher than those of the other basins. For extreme drought, the ImhaDam and Hapcheon Dam basins showed exceptionally high WQR(> 2.0). The Nakdong, Goryeong, and Hapchon basins showed rela-tively higher WQR related to drought in terms of TP.

Fig. 7 shows an example of environmental drought risk assessmentand environmental drought monitoring linked to water quality in-dicators. This was employed in the Andong Dam, Young River, GeumhoRiver, Hwuang River, and Nam River basin, which are located in theupper, middle, and downstream of the Nakdong River. Scatter plots ofBOD and TP data in the middle panel of each figure show the magni-tude of the SPI indicator, with the red color indicating negative valuesand the blue positive values. The contour lines (10th, lower quartile,median, upper quartile, and 90th levels are shown) in the joint prob-ability distribution were plotted using a threshold of SPI below −1.2.The probability distributions of the BOD and TP values are shown at thetop and right panels of each figure, and the conditional probability andunconditional probability distribution for the target water quality ineach sub-basin are shaded in red and gray. The WQR analysis resultsand the PEWQ for both BOD and TP water quality data are expressed innumerical values in the figures, respectively. The Andong Dam basin,where a first-grade (Ia) target water quality standard was applied,showed the lowest WQR among the five pilot sub-basins. The analysisresults showed that the WQR was 1.074 based on BOD and 0.946 basedon TP. The basins with a high risk to environmental drought wereanalyzed, which are the Hwuang River basin (WQRBOD=1.033,WQRTP= 1.020), Young River basin (WQRBOD=1.154,WQRTP= 1.095), and Nam River basin (WQRBOD=1.240,WQRTP= 1.085).

Among the five pilot basins, the highest risk to environmentaldroughts was found in the Geumho River basin (WQRBOD=1.306,WQRTP= 1.156), which, compared with the other basins, was subjectto extreme drought. In the Geumho River basin, the probability of ex-ceeding the BOD target water quality standard was 73.7%(SPI < −1.2), and the probability of exceeding the TP target waterquality was 90.5% (SPI < −1.2). The Nam River basin, locateddownstream of the Nakdong River, was also indicated as an area rela-tively vulnerable to drought. As a direct result of the effects of themainstream of the Nakdong River, changes in the water quality char-acteristics occurred at the end of the basin. The probability of exceedingthe BOD target water quality was 78.2% (SPI < −1.2) and the

Fig. 3. Drought occurrence and target water quality for the Nam River basin (sub-basin ID: 2019) for the period 2007–2016. For each figure, circular plot shows theseasonality of water quality events that exceed the local target water quality for two drought durations (30 days and 60 days). In times from drought thresholds werecrossed, water quality events exceeding the standards of BOD and TP are represented by different symbols. In each figure, the 3 o’clock direction indicates the starttime of January 1, and the calendar dates are displayed clockwise.

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probability of exceeding the TP target water quality was 89.6%(SPI < −1.2). Therefore, for this basin, the target water quality grademust be adjusted from Level 2 (Ib) to Level 3 (II) or, alternatively,additional water quality management measures should be established.

4. Discussion

Projected changes in the global climate indicate warmer tempera-tures and a highly variable hydrologic cycle, portend significant societaleffects, particularly those stemming from drought. Whereas the extentof drought-related effects ranges from humans to environmental sys-tems, the impacts on water quality, in particular, require careful in-vestigation. Such investigation should include the interlinkages acrossclimate and stream quality variables and how risk translates for variouswatersheds, given their particularities with respect to land use, wa-tershed characteristics, and infrastructure.

Recent studies on water quality have been conducted with a focuson the identification of important watershed variables that modulatestream water quality, as well as the role of weather and climate vari-ables. From a methodological standpoint, four major types of ap-proaches are commonly pursued in the published literature: (a) mul-tiple linear regression (Esquivel‐Hernández et al., 2018; Ng et al.,2018), (b) multivariate statistical methods (Monica and Choi, 2016;

Kükrer and Mutlu, 2019), (c) decision analysis (Jun et al., 2011; Wanget al., 2012), and (d) interlinked process and statistical models (Chanatand Yang, 2018). As the frequency and severity of droughts because ofclimate change are likely to increase, understanding the interlinkednature of the effects on human–environmental systems is critical foradaptation to the expected conditions (Mortazavi-Naeini et al., 2015;Svoboda et al., 2015). However, despite many studies on drought andwater quality, little attention has been paid to the development ofmethodologies that quantitatively assess the environmental droughtrisk related to the significant impact of drought on both humans and theenvironment. This is related in particular to the existence of thresholdsover or below which there is a dramatic escalation of effects, some ofwhich could be irreversible. It is also essential to distinguish betweenareas that are affected significantly by water quality and aquatic pat-terns when droughts occur, and areas that can maintain some level ofresilience during droughts. To this end, this study investigated droughtassessment and the vulnerability of water quality to severe drought,with the goal of informing planning and mitigation as a means to en-hance drought resilience.

Monica and Choi (2016) argued that TP and suspended solids (SS)are critical variables affecting BOD changes in the Saemangeum wa-tershed, South Korea because the chemical composition of pollutantscould be changed due to temporal variations in water pollutants. In this

Fig. 4. Drought statistics and water quality indices during the period 2007–2016. a. Rank analysis according to the dry condition (SPI < −1.2) for the Nam Riverbasin (sub-basin ID: 2019). b. Rank analysis according to the wet condition (SPI > 1.2) for the Nam River basin (sub-basin ID: 2019). The results of the PEWQ usingthe target water quality grades of each of the 22 sub-basins of the Nakdong River basin are shown spatially in the lower panel.

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study, the results of Fig. 3 illustrated in the circular plot showed thatboth TP and BOD were relatively high, especially during the wintermonths (January-February) and early summer (June-July) that was

consistent with Monica and Choi (2016). These results are evident inshorter drought periods (SPI30D).

The current study proposes a KDE-based approach to evaluate spa-tial-temporal droughts based on the WQR and to monitor environ-mental droughts using the probability of exceeding the target waterquality to facilitate a resilient proactive response. A WQR of 1 indicatesno specific change in the risk of water quality relative to extremedrought, whereas a WQR of greater than 1 indicates that the waterquality at the point and the basin is deteriorating. During drought, itwould be necessary to separate and manage the sub-basins that showwater quality decline compared with those that show smaller changesin risk. The KDE-based approach was applied for the Nakdong Riverbasin in South Korea, which is vulnerable to water quality degradationduring severe droughts. The analysis results were shown in Figs. 4–7.Among the five pilot basins located in the upper, middle, and down-stream of the Nakdong River (Young River, Andong Dam, HwuangRiver, Geumho River, Nam River basins), the highest WQR was at theGeumho River basin (WQRBOD=1.306, WQRTP=1.156), which suf-fered severe drought compared with the other basins. In the GeumhoRiver basin, the water quality standards for BOD exceeded 73.7%(SPI < −1.2), and the TP target water quality exceeded 90.5%(SPI < −1.2).

With regard to the potential increase in water pollutants, Kim et al.(2015) noted that large-scale industrial estates are located in the middleof the NRB, and contaminants are emitted from wastewater treatmentplants. High-concentration pollutants in the middle river basins oftenflow downstream, leading to a decline in water quality. Therefore, thesepotential water contaminants may also be reflected in the WQR analysisresults for mid- and downstream areas. Furthermore, the developmentof the Geumho River basin (sub-basin ID: 2012), which began in thelate 2000s, reflects the result of a large number of apartment complexesbeing built and the resulting in a gradual increase in the population.The reason for the failure of the management of the water pollutionload is because the target water quality standards failed to keep up withthe pace of local development. Most of the pollutants loadings in theGeumho River are municipal and industrial effluents from Daegu,

Fig. 5. Example of water quality risk (WQR) analysis for droughts by applyingthe target water quality standard (BOD: 2mg/L) to the Nam River basin (sub-basin ID: 2019) for the period 2007–2016. a. Empirical probability distributionfunctions (PDF). b. Joint probability distribution function (PDF) using thekernel approach. Here, the conditional PDF represents the excess probability ofBOD data exceeding the target water quality (BOD: 2mg/L) in an instance ofdrought (SPI < −1.2).

Fig. 6. Water quality risk (WQR) analysis using BOD and TP data in the Nakdong River basin for the period 2007–2016. a. WQRBOD. b. WQRTP. The spatialdistribution on the left of each figure shows the WQR result of applying extreme drought conditions (SPI30D < −1.2 and SPI30D < −1.5).

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making it difficult to meet the target water quality goal. However, withthe recent establishment of an advanced sewage treatment plant, thewater quality in the Geumho River is expected to improved sig-nificantly.

The Nam River basin (sub-basin ID: 2019), located downstream ofthe Nakdong River, was also appeared to be an area relatively vulner-able to drought (WQRBOD=1.240, WQRTP=1.085). As a direct resultof the effects of the mainstream of the Nakdong River, changes in thewater quality characteristics occurred at the end of the basin. Theprobability of exceeding the BOD target water quality was 78.2%(SPI < −1.2) and the probability of exceeding the TP target waterquality was 89.6% (SPI < −1.2). Therefore, it is necessary to adjustthe target water quality grade to this basin or establish additional waterquality management measures. For this basin, the policy frameworkproposed by Yoon et al. (2015) can be applied to downstream waterquality management by considering three alternatives including up-stream water treatment, expanding dam discharge, and developing newwater sources, in order to achieve the target of water quality level. Theupstream water treatment is to cut off incoming pollutants directly,minimizing the downstream impact. It is an advantageous methodwhen the reliability of water quality is of primary concern. Dam dis-charge is the most flexible tool and can be used strategically during thelow flow season. Instead of considering building a new reservoir at ahigh cost, utilizing existing reservoirs and developing waterways couldalso be a useful policy measure in the Nam River basin.

The approach proposed in this study can be used to inform thepotential risk for water quality declines based on meteorologicaldrought information. Localized estimates of vulnerable drought areas

and appropriates management measures and tailored countermeasuresare likely to also be informed by results from the approach presentedhere. The KDE-based WQR assessment method is very flexible, as itaccommodates linear and nonlinear relationships with ease. In additionto BOD and TP standards, the proposed approach can be applied toother water quality standards, such as DO, COD, and chlorophyll-a, andto other drought indices, including meteorological droughts, hydro-logical droughts, and agricultural droughts, to quantify the drought-related changes in water quality parameters to inform planning anddecision-making.

5. Summary and conclusions

This study proposed an environmental drought monitoring methodby applying the WQR index and the probability of exceeding the waterquality standard based on the non-parametric KDE. Our primary resultsare summarized as follows:

1. The analysis indicated a statistically significant relationship be-tween drought indices and water quality variables, such as BOD,COD, chlorophyll-a, and TP.

2. The results of the WQR analysis of the basins of the Nakdong Riverare shown according to the duration and severity of droughts. Asregards BOD, the WQR was high for extreme drought, but the spatialvariation was not significant. However, regarding TP, the WQR wasfound to vary significantly depending on the basins, and it wasshown that the spatial change increased relatively as the severity ofthe drought increased.

Fig. 7. Environmental drought risk assessment and environmental drought monitoring linked to water quality. This is similar to Fig. 2, but the joint probabilitydistributions of BOD and TP data for different sub-basins are located in the upper, middle, and downstream of the Nakdong River: a. Young River basin, b. AndongDam basin, c. Hwuang River basin, d. Nam River basin, e. Geumho River basin.

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3. Under severe drought conditions, the WQR of TP was relatively highin the Nakdong River, Goryeong, and Hapcheon basins.Furthermore, the WQR of BOD was exceptionally high in the ImhaDam and Daecheon Dam basins in conditions of extreme drought.The longer the duration of the drought, the higher was the WQRcompared with the other watersheds.

4. This diagnostic study and assessment confirmed that the WQR andPEWQ methods, applying the KDE to the Nakdong River basin inKorea, could be an evaluation method to monitor quantitativechanges in water quality in relation to drought conditions. We ex-pect to be able to provide useful information to establish appro-priate water quality management measures to facilitate resilientdrought measurements at local and regional scales.

In closing, we proposed a probabilistic water quality monitoringmethod conditioned on the Standardized Precipitation Index, a re-presentative meteorological drought index indicative of seasonal andlonger climatic variations. Interlinked variations in climate and waterquality offer critically important information watershed-scale droughtvulnerability and impacts. The approach presented here affords quan-titative assessment of risk for water quality declines that have im-portant implications for human and ecological systems.

Finally, in a changing climate, changes in drought frequency andseverity remain critical concerns. Our work provides a quantitativeapproach to link climatic changes to water quality vulnerability.

Acknowledgements

This work was supported by Basic Science Research Programthrough the National Research Foundation of Korea (NRF) funded bythe Ministry of Education (NRF-20171R1D1A1A02018546) and theKorea Environmental Industry & Technology Institute (KEITI) grantfunded by the Ministry of Environment (Grant 18AWMP-B079625-05).The authors are grateful for the editor and two anonymous reviewers’insightful comments, and also allowing us to improve this manuscriptsignificantly.

Conflict of interest

The authors declare that they have no conflict of interest.

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