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Quantitative vulnerability assessment of water quality to extreme drought in a changing climate
Ecological Indicators 103 (2019) 688–697
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Quantitative vulnerability assessment of water quality to extreme drought in a changing climate ⁎
T
⁎
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 China Department of Civil and Environmental Engineering, University of Maine, Orono, ME 04469-5711, USA c Department of Civil Engineering, Joongbu University, KyungGi 10279, South Korea b
A R T I C LE I N FO
A B S T R A C T
Keywords: Meteorological drought Water quality Kernel density estimation Risk assessment
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 droughtrelated effects ranges from humans to environmental systems, the impacts on water quality, in particular, require careful investigation. Such investigation should include the interlinkages across climate and stream quality variables 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 the water quality vulnerability to severe drought, with the goal of informing planning and mitigation as a means to enhance drought resilience. This study was conducted in the Nakdong River basin in South Korea, which is vulnerable to water quality degradation during severe drought. We propose a method to evaluate spatial-temporal droughts based on the water quality risk and to monitor environmental droughts using the probability of exceeding the target water quality to facilitate a resilient proactive response. The results of this study can be used to provide scientific drought monitoring information for assessing real-time water quality monitoring from meteorological drought information. In addition, we expect to categorize vulnerable drought areas and to suggest essential management measures and tailored countermeasures at a local scale.
1. Introduction Freshwater is essential for the sustenance of natural and human systems. Clean water is an important determinant of ecosystem health and, at the same time, is critical to humans from the standpoint of hygiene 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 to warming temperature and droughts tend to increase the impact of pollutant loadings, thereby having compounding deleterious effects on the quality of water and the aquatic ecosystems (Poff et al., 2002; Park et al., 2010; Sjerps et al., 2017). Droughts are one of the most expensive natural disasters with lasting and wide-ranging effects on the environment, society, and the economy (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 projections indicate that future climate changes will probably not be uniform, and regional variations in precipitation and warming temperatures will
exacerbate the potential for drought incidence and severity (Trenberth et al., 2014; Lee et al., 2018). Furthermore, natural climatic variability, for example stemming from El Niño/Southern Oscillation (ENSO), can amplify hydroclimatic extremes (Mosley, 2015; Son et al., 2014; Kim et al., 2016). Consequently, faced with water shortages, the quantification of risks and the development of countermeasures to protect the integrity between human and environmental systems are needed. In what follows, we provide a limited review of recent studies focused on water quality, water resources vulnerability, and land-use effects. Esquivel‐Hernández et al. (2018) developed approaches based on multiple linear regression and cluster analysis to understand the relative role of population size and hydrometeorological events as drivers in water conflicts, specifically linked to floods, droughts, wastewater spills, and water pollution across Costa Rica. Ng et al. (2018) used a multivariable linear model to assess the relative role of variables such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solids toward water quality deterioration based on a water quality index applied to the Kampar River Basin in
⁎ Corresponding authors at: Department of Civil Engineering, Drought Research Center, Joongbu University, Gyeunggi-do 10279, Republic of Korea (J.-H. Lee) and Department of Civil and Environmental Engineering, University of Maine, Orono, ME 04469-5711, USA (S. Jain). E-mail addresses: [email protected] (S. Jain), [email protected] (J.-H. Lee).
https://doi.org/10.1016/j.ecolind.2019.04.052 Received 9 December 2018; Received in revised form 5 March 2019; Accepted 15 April 2019 Available online 25 April 2019 1470-160X/ © 2019 Published by Elsevier Ltd.
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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 used in this study included dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), chlorophyll-a concentration, total nitrogen (TN), and total phosphorus (TP).
Malaysia. Furthermore, their study integrated an El Niño event, which caused an extreme drought and watershed stress. Monica and Choi (2016) applied multivariate statistical methods (discriminant, cluster, and principal component analyses) to identify suspended solids and total phosphorus, which are critical watershed response variables that influence changes in BOD and COD in the Saemangeum watershed, South Korea. Kükrer and Mutlu (2019) used a one-year long record of 28 water quality relevant variables for the Saraydüzü Dam Lake in Turkey to assess the water quality and the relative importance of measured variables for ecosystem health. Their analysis and risk assessment were based on multivariate cluster and factor analysis approaches, as well as cross-correlation. Wang et al. (2012) developed a water resources vulnerability approach, focused on both water quality and quantity issues, based on a parametric-system (PS) method. Their approach involves the Analytical Hierarchy Process to determine the relative weights of the indices considered (including drought and pollution indices). The study contends that the PS method is more general than the existing Fuzzy Optimization and Gray Relational Analysis approaches that only allow for rank analyses and qualitative level classification, respectively. Jun et al. (2011) used a multi-attribute method of decision analysis, coupled with future climate scenarios and watershed modeling, to assess hydrologic vulnerability in four dimensions: floods, droughts, water quality, and watershed risk. Chanat and Yang (2018) applied a seven-parameter spatiotemporal watershed accumulation of net effects model to investigate the changes in nitrogen loadings across four sites in the Chesapeake watershed (USA). Their modeling work indicated that increases in nitrogen loadings stem from urban and suburban development, with changes in temperature and precipitation being the compounding meteorological variables. The current study was conducted in the Nakdong River basin in South Korea, which is vulnerable to water quality degradation during severe droughts. We present a quantitative assessment method for water quality monitoring by applying nonparametric kernel density estimation (KDE). The KDE-based approach is used to quantify the drought impacts on water quality indicators and to diagnose water quality risk (WQR) by target basin to identify hotspots–areas vulnerable to extreme droughts. The WQR information can be provided to water managers and decision-makers for planning and decision-making linked to sustainable water resources management. In addition, this study seeks to inform efforts towards appropriate water quality management measures and facilitate resilient drought adaptation at local and regional scales.
2.1. Meteorological drought index In this study, to investigate drought variability, we applied the Standardized Precipitation Index (SPI), which was developed by McKee et al. (1993), and is used widely as a meteorological drought index. The standardization process of SPI is independent of geographic location and is spatially comparable (Cacciamani et al., 2007). Currently, more than 40 countries, including Korea, use the SPI to make preparations for drought. Compared with the relative water demands used in drought indices, SPI, a supply-side metric, focuses on the fact that droughts entail reduced precipitation, which causes water shortages (Gao et al., 2019). The calculation of the SPI index for each station is based only on historical precipitation records accumulated over a specific time period. These time-series precipitation data are applied to the gamma distribution, which is subsequently transformed into a standard normal distribution through equal probability transformation (Guttman, 1999). Positive SPIs represent wetter conditions than the climatology mean of precipitation and negative SPIs represent drier conditions. The gamma distribution best models the observed precipitation data in most instances because precipitation generally shows a skewed distribution (Bordi and Sutera, 2007). The density probability function of the Gamma distribution is given by Eq. (1).
g(x) =
1 x α − 1e−x β , for x > 0, β α Γ(α )
(1)
where the shape parameter α > 0 and the scale parameter β > 0, and the precipitation x > 0. Γ(α) is the gamma function defined by the integral (Eq. (2)):
Γ(α ) =
∫0
∞
y α − 1e−ydy
(2)
Gamma functions are evaluated numerically or by using the α value of the table. To model precipitation data as a density function of the gamma distribution, α and β should be estimated appropriately. There are many ways to estimate shape and scale parameters. For example, the method introduced by Thom (1958) was adopted by Edwards and McKee (1997).
2. Material and methods The Korean Nakdong River basin (KNRB) is selected for demonstrating the applicability of our proposed approach. Despite the importance of drinking water sources to more than 15 million people in the river basin, the water quality has deteriorated, even after completion of the four-river restoration project, and for many years, the river basin has suffered serious damage to downstream water quality (Yoon et al., 2015). In this study, water quality in 22 sub-basins of the Nakdong River were evaluated to assess the vulnerability to extreme droughts (Fig. 1). The hydrometeorological data used in this study were obtained from the 60 stations in the Automatic Synoptic Observation System, comprising daily rainfall data collected between 1976 and 2016 by the Korean Meteorological Administration. Streamflow and dam water information was provided by the Korean Water Management Information System (WAMIS, 2017). Water quality vulnerability was assessed using the 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 for Water Quality and Aquatic Ecosystem Targets (Ministry of Environment, South Korea) was used as the reference target criteria in our analyses.
â=
1 ⎛ ⎜1 + 4A ⎝
−
1+
4A ⎞ x β = ⎟, 3 ⎠ α̂
(3)
where for n observations −
A = ln(x ) −
∑ ln(x ) n
(4)
Using α and β, the density probability function g (x) for a given amount of a given month can be integrated for x and the cumulative probability G (x) and a specific time scale can be obtained (Eq. (5)):
G(x) =
∫0
x
g (x ) dx =
1 = β Γ(a )̂
∫0
x
x α −̂ 1e−x βdx (5)
The gamma function requires x > 0. As the precipitation could be zero, the cumulative probability is Eq. (6):
H(x) = q + (1 − q) G (x ),
(6)
where q is the probability of no rain and H (x) represents the cumulative probability of the recorded precipitation. The cumulative probability is converted to a standardized normal 689
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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 Nakdong River basin.
1 fh (WQ) = n
distribution to obtain the SPI. However, the method is numerically complex when too many stations are involved. An alternative approach using approximate transformation techniques to convert the cumulative probability to a standard variable Z was developed by Edwards and McKee (1997).
( (
⎧− t − ⎪ SPI = Z = ⎨ ⎪+ t − ⎩
c 0 + c1 t + c 2 t 2 1 + d1 t + d2 t 2 + d3 t 3 c 0 + c1 t + c 2 t 2 1 + d1 t + d2 t 2 + d3 t 3
), ),
n
∑i =1 Kh (WQ − WQi) =
1 nh
n
∑i =1 K ⎛ ⎝
WQ − WQi ⎞, h ⎠
(9)
where K is the kernel function and h is the bandwidth, Kh is the scaled 1 WQ kernel and is defined as Kh (WQ) = h K h . The choice of the bandwidth (h) was evaluated using the following simple optimal formula (Eq. (10)):
( )
for 0 < H (x ) ≤ 0.5,
1 (p + 4)
for 0.5 < H (x) < 1,
4 ⎫ hi = ⎧ ⎨ ⎩ (p + 2) n ⎬ ⎭
(7)
where
⎧ ln ⎡ 1 ⎤, for 0 < H (x) ≤ 0.5, ⎪ ⎣ (H (x ))2 ⎦ t= ⎨ 1 ⎪ ln ⎡ (1 − H (x ))2 ⎤, for 0.5 < H (x ) < 1, ⎣ ⎦ ⎩
σi ̂
(10)
where p denotes the number of dimensions, hi denotes the optimal smoothing 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 mean square error) is expressed as Eq. (11): (8)
1 5
4σ ̂ ⎫ h=⎧ ≈ 1.06σn̂ −1 5, ⎨ ⎩ 3n ⎬ ⎭ 5
2.2. Nonparametric kernel density estimation
(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 KDE approach for each water quality (WQ) indicator and target criterion (Eq. (12)).
The drought classification criteria and the drought stage impact factors proposed by the US Drought Mitigation Center (USDMC, 2017) were applied to differentiate between severe drought and extreme drought. The characteristics of water quality and the aquatic ecosystem were analyzed considering these drought classification criteria. KDE is a representative nonparametric method to estimate the probability density function (PDF) of random variables based on finite data samples (Bowman and Azzalini, 1997). We applied KDE to estimate the exceedance probability of each water quality (WQ) indicator on the target water quality (Eq. (9)).
WQR =
P (WQ ≥ WQcriteria DI ≤DIcriteria ) , P (WQ ≥ WQcriteria )
(12)
where WQ is the water quality index, WQcriteria is the target water quality standard, DI is the drought index, and DIcriteria is extreme drought conditions. This approach follows a past study of the multi690
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3.2. Analysis of water quality characteristics for extreme drought conditions
centennial drought variations in the northeastern United States (Gupta et al., 2011), however no water quality variables were integrated in that study. In this study, WQR was proposed to assess the vulnerability of water to extreme drought conditions (SPI < −1.2 and SPI < −1.5 for different drought durations). The drought index is estimated on a daily basis for each drought duration (30 days, 60 days, and 90 days) using rainfall data from 1976 to 2016. To assess the relationship between drought statistics and episodic water quality events, we applied the rank-based approach. Different drought durations, which are ranked by magnitude of SPI according to dry (SPI < −1.2) and wet (SPI > 1.2) conditions are shown below, followed by the observed water quality events during the period 2007–2016. Through analysis of the rank, it is possible to determine the sensitivity of water quality to wet and dry conditions in a certain river basin.
To analyze the water quality change based on the duration of a drought, we analyzed the risk for deleterious changes in the water quality standards of BOD and TP, related to the severe drought and extreme drought thresholds. Fig. 3 shows an example of applying the target water quality standards for BOD and TP in the Nam River basin. The figure indicates the instances where the water quality exceeded the target criteria for the different SPI periods (30 days [30D], 60 days [60D]). Different symbols indicate the instances where only BOD is exceeded, only TP is exceeded, and both BOD and TP exceed the target water quality (BOD: 2 mg/L, TP: 0.04 mg/L) in the basin. Applying the SPI30D for the period 2007–2016, the water quality data showed that severe drought threshold was crossed 53 times, and extreme drought threshold 23 times on a daily time scale. The water quality target of the Nam River basin is based on second-grade (lb), BOD 2.0 mg/L, and TP 0.04 mg/L, and 94.3% in severe drought and 91.3% in extreme drought, exceeding the BOD and TP standards. This tendency was similar for the 60D and 90D drought periods. Fig. 4 shows the results of the sensitivity of the water quality indices to drought statistics. Rank analysis, shown in Fig. 4a and b, was performed to assess the effects of extreme drought on the water quality. Sensitivity to drought and the achievement of the target water quality were also evaluated. In the figures, the data were arranged by the SPI30D index for dry/wet conditions. The two drought indices (SPI60D, SPI90D) are shown below, followed by the water quality indices (BOD and TP). The data exceeding each reference criteria are shaded in gray. The percentile values noted in the upper panels are the probability of exceeding the water quality (PEWQ) for the target criteria (BOD: 2 mg/ L, TP: 0.04 mg/L) in the Nam River basin (sub-basin ID: 2019). In this basin, the water quality was shown to be deteriorating as the drought progressed. The wet condition showed a noticeable improvement in water quality for BOD compared with the drought condition, and TP improved slightly but still exceeded the target water quality. The evaluation results of the target water quality exceeding the dry/wet conditions for the 22 sub-basins are shown in Fig. 4c and d. The analysis
3. Analysis results 3.1. Historical droughts in the KNRB Drought characteristics such as duration, magnitude, and the severity of drought events that occurred in the past were analyzed using the SPI index, and drought frequency analysis was performed to investigate historical droughts quantitatively. Fig. 2 shows the time history of SPI for the KNRB. The most severe drought year in the KNRB was 1988, with magnitude −15.5, duration of 11 months, average severity of −1.4, and a return period of 100 years. The significant drought years in the KNRB are 1968, 1982, 2014, 1996, 2001, 2009, and 2015, and the return period is 10–50 years. As regards meteorological drought in the 1960s, the years 1967 and 1968 are drought years. In 1968, the drought magnitude was −13.6, the duration was 11 months, and the average severity was −1.2. On the other hand, a drought of more than two years was evaluated as long-term drought from 1967 to 1968, 1976 to 1977, and 1994 to 1996 in the KNRB. In the following section, we discuss analyzing water quality changes attributable to extreme droughts.
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 or dryness compared to normal condition in that region. 691
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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 the seasonality 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 were crossed, 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 start time of January 1, and the calendar dates are displayed clockwise.
Fig. 7 shows an example of environmental drought risk assessment and environmental drought monitoring linked to water quality indicators. This was employed in the Andong Dam, Young River, Geumho River, Hwuang River, and Nam River basin, which are located in the upper, middle, and downstream of the Nakdong River. Scatter plots of BOD and TP data in the middle panel of each figure show the magnitude of the SPI indicator, with the red color indicating negative values and the blue positive values. The contour lines (10th, lower quartile, median, upper quartile, and 90th levels are shown) in the joint probability distribution were plotted using a threshold of SPI below −1.2. The probability distributions of the BOD and TP values are shown at the top and right panels of each figure, and the conditional probability and unconditional probability distribution for the target water quality in each sub-basin are shaded in red and gray. The WQR analysis results and the PEWQ for both BOD and TP water quality data are expressed in numerical 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 analysis results showed that the WQR was 1.074 based on BOD and 0.946 based on TP. The basins with a high risk to environmental drought were analyzed, 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 environmental droughts was found in the Geumho River basin (WQRBOD = 1.306, WQRTP = 1.156), which, compared with the other basins, was subject to extreme drought. In the Geumho River basin, the probability of exceeding the BOD target water quality standard was 73.7% (SPI < −1.2), and the probability of exceeding the TP target water quality was 90.5% (SPI < −1.2). The Nam River basin, located downstream of the Nakdong River, was also indicated as an area relatively vulnerable to drought. As a direct result of the effects of the mainstream of the Nakdong River, changes in the water quality characteristics occurred at the end of the basin. The probability of exceeding the BOD target water quality was 78.2% (SPI < −1.2) and the
results show that BOD had a negative relationship with the SPI drought index, and the water quality in the lower stream of the Nakdong River tended to deteriorate when drought occurred relatively more frequently in the other sub-basins. However, for TP, the relationship with the drought index was unclear.
3.3. Environmental risk assessment for extreme drought conditions Fig. 5 shows an example of WQR analysis for droughts by applying the target water quality standards (BOD: 2 mg/L) of the Nam River basin based on BOD data for the 2007–2016 period. The conditional (unconditional) probability distribution using the KDE for the target water quality in the Nam River basin is shaded in red (gray). The contour lines in the joint probability distribution were plotted for selected quantile levels (0.1, 0.25, 0.5, 0.75, and 0.9). The results of the analysis show that the WQRBOD is 1.24, which is a moderately elevated, yet not a significant risk; however, careful management of the water quality is warranted. Fig. 6 shows the results of the WQR analysis using BOD and TP data for each of the 22 sub-basins. The spatial distribution on the left of each figure shows the WQR result of applying extreme drought conditions (SPI30D < −1.2 and SPI30D < −1.5). The results of using the different 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 regards BOD, WQR was higher than 1 in many basins, but the spatial conherence was not significant. However, regarding TP, WQR varied considerably depending on the basin. As the severity of drought increased, the spatial variation also increased relatively. As regards severe drought, the basins showing WQR of 2.0 or higher (exceptionally high) for BOD were the Imha Dam and Hapcheon Dam basins, with SPI30D and SPI60D showing WQR of 2.21 and 2.25, respectively, relatively higher than those of the other basins. For extreme drought, the Imha Dam and Hapcheon Dam basins showed exceptionally high WQR (> 2.0). The Nakdong, Goryeong, and Hapchon basins showed relatively higher WQR related to drought in terms of TP. 692
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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 River basin (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 using the target water quality grades of each of the 22 sub-basins of the Nakdong River basin are shown spatially in the lower panel.
Kükrer and Mutlu, 2019), (c) decision analysis (Jun et al., 2011; Wang et al., 2012), and (d) interlinked process and statistical models (Chanat and Yang, 2018). As the frequency and severity of droughts because of climate change are likely to increase, understanding the interlinked nature of the effects on human–environmental systems is critical for adaptation to the expected conditions (Mortazavi-Naeini et al., 2015; Svoboda et al., 2015). However, despite many studies on drought and water quality, little attention has been paid to the development of methodologies that quantitatively assess the environmental drought risk related to the significant impact of drought on both humans and the environment. This is related in particular to the existence of thresholds over or below which there is a dramatic escalation of effects, some of which could be irreversible. It is also essential to distinguish between areas that are affected significantly by water quality and aquatic patterns when droughts occur, and areas that can maintain some level of resilience during droughts. To this end, this study investigated drought assessment and the vulnerability of water quality to severe drought, with the goal of informing planning and mitigation as a means to enhance drought resilience. Monica and Choi (2016) argued that TP and suspended solids (SS) are critical variables affecting BOD changes in the Saemangeum watershed, South Korea because the chemical composition of pollutants could be changed due to temporal variations in water pollutants. In this
probability of exceeding the TP target water quality was 89.6% (SPI < −1.2). Therefore, for this basin, the target water quality grade must 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 temperatures and a highly variable hydrologic cycle, portend 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, require careful investigation. Such investigation should include the interlinkages across climate and stream quality variables and how risk translates for various watersheds, given their particularities with respect to land use, watershed characteristics, and infrastructure. Recent studies on water quality have been conducted with a focus on the identification of important watershed variables that modulate stream water quality, as well as the role of weather and climate variables. From a methodological standpoint, four major types of approaches are commonly pursued in the published literature: (a) multiple linear regression (Esquivel‐Hernández et al., 2018; Ng et al., 2018), (b) multivariate statistical methods (Monica and Choi, 2016; 693
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consistent with Monica and Choi (2016). These results are evident in shorter drought periods (SPI30D). The current study proposes a KDE-based approach to evaluate spatial-temporal droughts based on the WQR and to monitor environmental droughts using the probability of exceeding the target water quality to facilitate a resilient proactive response. A WQR of 1 indicates no specific change in the risk of water quality relative to extreme drought, whereas a WQR of greater than 1 indicates that the water quality at the point and the basin is deteriorating. During drought, it would be necessary to separate and manage the sub-basins that show water quality decline compared with those that show smaller changes in risk. The KDE-based approach was applied for the Nakdong River basin in South Korea, which is vulnerable to water quality degradation during severe droughts. The analysis results were shown in Figs. 4–7. Among the five pilot basins located in the upper, middle, and downstream of the Nakdong River (Young River, Andong Dam, Hwuang River, Geumho River, Nam River basins), the highest WQR was at the Geumho River basin (WQRBOD = 1.306, WQRTP = 1.156), which suffered severe drought compared with the other basins. In the Geumho River 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 middle of the NRB, and contaminants are emitted from wastewater treatment plants. High-concentration pollutants in the middle river basins often flow downstream, leading to a decline in water quality. Therefore, these potential water contaminants may also be reflected in the WQR analysis results for mid- and downstream areas. Furthermore, the development of the Geumho River basin (sub-basin ID: 2012), which began in the late 2000s, reflects the result of a large number of apartment complexes being built and the resulting in a gradual increase in the population. The reason for the failure of the management of the water pollution load is because the target water quality standards failed to keep up with the pace of local development. Most of the pollutants loadings in the Geumho River are municipal and industrial effluents from Daegu,
Fig. 5. Example of water quality risk (WQR) analysis for droughts by applying the target water quality standard (BOD: 2 mg/L) to the Nam River basin (subbasin ID: 2019) for the period 2007–2016. a. Empirical probability distribution functions (PDF). b. Joint probability distribution function (PDF) using the kernel approach. Here, the conditional PDF represents the excess probability of BOD data exceeding the target water quality (BOD: 2 mg/L) in an instance of drought (SPI < −1.2).
study, the results of Fig. 3 illustrated in the circular plot showed that both TP and BOD were relatively high, especially during the winter months (January-February) and early summer (June-July) that was
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 spatial distribution on the left of each figure shows the WQR result of applying extreme drought conditions (SPI30D < −1.2 and SPI30D < −1.5). 694
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Fig. 7. Environmental drought risk assessment and environmental drought monitoring linked to water quality. This is similar to Fig. 2, but the joint probability distributions 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. Andong Dam basin, c. Hwuang River basin, d. Nam River basin, e. Geumho River basin.
and appropriates management measures and tailored countermeasures are likely to also be informed by results from the approach presented here. The KDE-based WQR assessment method is very flexible, as it accommodates linear and nonlinear relationships with ease. In addition to BOD and TP standards, the proposed approach can be applied to other water quality standards, such as DO, COD, and chlorophyll-a, and to other drought indices, including meteorological droughts, hydrological droughts, and agricultural droughts, to quantify the droughtrelated changes in water quality parameters to inform planning and decision-making.
making it difficult to meet the target water quality goal. However, with the recent establishment of an advanced sewage treatment plant, the water quality in the Geumho River is expected to improved significantly. The Nam River basin (sub-basin ID: 2019), located downstream of the Nakdong River, was also appeared to be an area relatively vulnerable to drought (WQRBOD = 1.240, WQRTP = 1.085). As a direct result of the effects of the mainstream of the Nakdong River, changes in the water quality characteristics occurred at the end of the basin. The probability of exceeding the BOD target water quality was 78.2% (SPI < −1.2) and the probability of exceeding the TP target water quality was 89.6% (SPI < −1.2). Therefore, it is necessary to adjust the target water quality grade to this basin or establish additional water quality management measures. For this basin, the policy framework proposed by Yoon et al. (2015) can be applied to downstream water quality management by considering three alternatives including upstream water treatment, expanding dam discharge, and developing new water sources, in order to achieve the target of water quality level. The upstream water treatment is to cut off incoming pollutants directly, minimizing the downstream impact. It is an advantageous method when the reliability of water quality is of primary concern. Dam discharge is the most flexible tool and can be used strategically during the low flow season. Instead of considering building a new reservoir at a high cost, utilizing existing reservoirs and developing waterways could also be a useful policy measure in the Nam River basin. The approach proposed in this study can be used to inform the potential risk for water quality declines based on meteorological drought information. Localized estimates of vulnerable drought areas
5. Summary and conclusions This study proposed an environmental drought monitoring method by applying the WQR index and the probability of exceeding the water quality standard based on the non-parametric KDE. Our primary results are summarized as follows: 1. The analysis indicated a statistically significant relationship between 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 River are shown according to the duration and severity of droughts. As regards BOD, the WQR was high for extreme drought, but the spatial variation was not significant. However, regarding TP, the WQR was found to vary significantly depending on the basins, and it was shown that the spatial change increased relatively as the severity of the drought increased. 695
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3. Under severe drought conditions, the WQR of TP was relatively high in the Nakdong River, Goryeong, and Hapcheon basins. Furthermore, the WQR of BOD was exceptionally high in the Imha Dam and Daecheon Dam basins in conditions of extreme drought. The longer the duration of the drought, the higher was the WQR compared with the other watersheds. 4. This diagnostic study and assessment confirmed that the WQR and PEWQ methods, applying the KDE to the Nakdong River basin in Korea, could be an evaluation method to monitor quantitative changes in water quality in relation to drought conditions. We expect to be able to provide useful information to establish appropriate water quality management measures to facilitate resilient drought measurements at local and regional scales.
https://doi.org/10.1111/1752-1688.12617. Fenner, R.A., 2017. Water: an essential resource and a critical hazard. In: Building Sustainable Cities of the Future, pp. 75–97. Gao, Q.G., Kim, J.S., Chen, J., Chen, H., Lee, J.H., 2019. Atmospheric teleconnectionbased extreme drought prediction in the core drought region in China. Water 11 (2), 232. https://doi.org/10.2290/w11020232. Gupta, A.S., Jain, S., Kim, J.S., 2011. Past climate, future perspective: an exploratory analysis using climate proxies and drought risk assessment to inform water resources management and policy in Maine, USA. J. Environ. Manage. 92 (3), 941–947. https://doi.org/10.1016/j.jenvman.2010.10.054. Guttman, N.B., 1999. Accepting the standardized precipitation index: a calculation algorithm 1. J. Am. Water Resour. Assoc. 35 (2), 311–322. https://doi.org/10.1111/j. 1752-1688.1999.tb03592.x. Hellberg, S., 2017. Water for survival, water for pleasure-A biopolitical perspective on the social sustainability of the basic water agenda. Water Altern. 10 (1), 65. Hong, Y.X., Lee, T.S., Kim, J.S., 2019. Serial multiple mediation analysis: how to enhance individual public health emergency preparedness and response to environmental disasters. Int. J. Environ. Res. Public Health 16, 223. https://doi.org/10.3390/ ijerph16020223. Jun, K.S., Chung, E.S., Sung, J.Y., Lee, K.S., 2011. Development of spatial water resources vulnerability index considering climate change impacts. Sci. Total Environ. 409 (24), 5228–5242. https://doi.org/10.1016/j.scitotenv.2011.08.027. Kim, J.S., Seo, G.S., Jang, H.W., Lee, J.H., 2017. Correlation analysis between Korean spring drought and large-scale teleconnection patterns for drought forecasting. KSCE J. Civ. Eng. 21 (1), 458–466. https://doi.org/10.1007/s12205-016-0580-8. Kim, J.S., Son, C.Y., Moon, Y.I., Lee, J.H., 2016. Seasonal rainfall variability in Korea within the context of different evolution patterns of the central pacific El Niño. J. Water Clim. Change 8 (3), 412–422. https://doi.org/10.2166/wcc.2016.020. Kükrer, S., Mutlu, E., 2019. Assessment of surface water quality using water quality index and multivariate statistical analyses in Saraydüzü Dam Lake, Turkey. Environ. Monit. Assess. 191 (2), 71. https://doi.org/10.1007/s10661-019-7197-6. Lee, J.H., Park, S.Y., Kim, J.S., Sur, C., Chen, J., 2018. Extreme drought hotspot analysis for adaptation to a changing climate: assessment of applicability to the five major river basins of the Korean Peninsula. Int. J. Climatol. 38 (1–4), 4025–4032. https:// doi.org/10.1002/joc.5532. McKee, T.B., Doesken, N.J., Kleist, J., 1993. The relationship of drought frequency and duration to time scales. Proc. 8th Conf. Appl. Climatol. 17 (22), 179–183. Meybeck, M., 2003. Global analysis of river systems: from Earth system controls to Anthropocene syndromes. Philos. Trans. Royal Soc. Series B: Biol. Sci. 358 (1440), 1935–1955. https://doi.org/10.1098/rstb.2003.1379. Monica, N., Choi, K., 2016. Temporal and spatial analysis of water quality in Saemangeum watershed using multivariate statistical techniques. Paddy Water Environ. 14 (1), 3–17. https://doi.org/10.1007/s10333-014-0475-6. Mortazavi-Naeini, M., Kuczera, G., Kiem, A.S., Cui, L., Henley, B., Berghout, B., Turner, E., 2015. Robust optimization to secure urban bulk water supply against extreme drought and uncertain climate change. Environ. Modell. Softw. 69, 437–451. https:// doi.org/10.1016/j.envsoft.2015.02.021. Mosley, L.M., 2015. Drought impacts on the water quality of freshwater systems; review and integration. Earth-Sci. Rev. 140, 203–214. https://doi.org/10.1016/j.earscirev. 2014.11.010. Ng, C.K.C., Goh, C.H., Lin, J.C., Tan, M.S., Bong, W., Yong, C.S., Chong, J.Y., Ooi, P.A.C., Wong, W.L., Khoo, G., 2018. Water quality variation during a strong El Niño event in 2016: a case study in Kampar River, Malaysia. Environ. Monit. Assess. 190 (7), 402. https://doi.org/10.1007/s10661-018-6784-2. Park, J.H., Duan, L., Kim, B., Mitchell, M.J., Shibata, H., 2010. Potential effects of climate change and variability on watershed biogeochemical processes and water quality in Northeast Asia. Environ. Int. 36 (2), 212–225. https://doi.org/10.1016/j.envint. 2009.10.008. Park, S.Y., Sur, C., Kim, J.S., Lee, J.H., 2018. Evaluation of multi-sensor satellite data for monitoring different drought impacts. Stochastic Environ. Res. Risk Assess. 32 (9), 2551–2563. https://doi.org/10.1007/s00477-018-1537-x. Poff, N.L., Brinson, M.M., Day, J.W., 2002. Aquatic Ecosystems and Global Climate Change. Pew Center on Global Climate Change, Arlington, VA, pp. 1–36. Sjerps, R.M., ter Laak, T.L., Zwolsman, G.J., 2017. Projected impact of climate change and chemical emissions on the water quality of the European rivers Rhine and Meuse: a drinking water perspective. Sci. Total Environ. 601, 1682–1694. https://doi.org/10. 1016/j.scitotenv.2017.05.250. Son, C.Y., Kim, J.S., Moon, Y.I., Lee, J.H., 2014. Characteristics of tropical cyclone-induced precipitation over the Korean river basins according to three evolution patterns of the central-pacific El Niño. Stochastic Environ. Res. Risk Assess. 28 (5), 1147–1156. https://doi.org/10.1007/s00477-013-0804-0. Svoboda, M.D., Fuchs, B.A., Poulsen, C.C., Nothwehr, J.R., 2015. The drought risk atlas: enhancing decision support for drought risk management in the United States. J. Hydrol. 526, 274–286. https://doi.org/10.1016/j.jhydrol.2015.01.006. Thom, H.C., 1958. A note on the gamma distribution. Mon. Weather Rev. 86 (4), 117–122. https://doi.org/10.1175/1520-0493(1958) 086<0117:ANOTGD>2.0. CO;2. Trenberth, K.E., Dai, A., Van Der Schrier, G., Jones, P.D., Barichivich, J., Briffa, K.R., Sheffield, J., 2014. Global warming and changes in drought. Nat. Clim. Change 4 (1), 17. https://doi.org/10.1038/nclimate2067. USDMC (US Drought Mitigation Center) 2017 http://drought.unl.edu/ accessed 12 April 2017. Vörösmarty, C.J., Chapter 7: Fresh Water in: Millennium Ecosystem Assessment, Volume 1: Conditions and Trends Working Group Report, 2005, pp. 165–207. WAMIS (Water management information system) 2017 http://wamis.go.kr/eng/ accessed 10 August 2017.
In closing, we proposed a probabilistic water quality monitoring method conditioned on the Standardized Precipitation Index, a representative meteorological drought index indicative of seasonal and longer climatic variations. Interlinked variations in climate and water quality offer critically important information watershed-scale drought vulnerability and impacts. The approach presented here affords quantitative assessment of risk for water quality declines that have important implications for human and ecological systems. Finally, in a changing climate, changes in drought frequency and severity remain critical concerns. Our work provides a quantitative approach to link climatic changes to water quality vulnerability. Acknowledgements This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-20171R1D1A1A02018546) and the Korea Environmental Industry & Technology Institute (KEITI) grant funded 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 manuscript significantly. Conflict of interest The authors declare that they have no conflict of interest. References Apurv, T., Sivapalan, M., Cai, X., 2017. Understanding the role of climate characteristics in drought propagation. Water Resour. Res. 53 (11), 9304–9329. https://doi.org/10. 1002/2017WR021445. Bordi, I., Sutera, A., 2007. Drought monitoring and forecasting at large scale. In: Methods and Tools for Drought Analysis and Management, pp. 3–27. Brooks, B.W., Lazorchak, J.M., Howard, M.D., Johnson, M.V.V., Morton, S.L., Perkins, D.A., Reavie, E.D., Scott, G.I., Smith, S.A., Steevens, J.A., 2016. Are harmful algal blooms becoming the greatest inland water quality threat to public health and aquatic ecosystems? Environ. Toxicol. Chem. 35, 6–13. https://doi.org/10.1002/etc. 3220. Bowman, A.W., Azzalini, A., 1997. Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations. 18. OUP Oxford. Cacciamani, C., Morgillo, A., Marchesi, S., Pavan, V., 2007. Monitoring and forecasting drought on a regional scale: Emilia-Romagna region. In: Methods and Tools for Drought Analysis and Management, pp. 29–48. Chanat, J.G., Yang, G., 2018. Exploring drivers of regional water-quality change using differential spatially referenced regression—a pilot study in the chesapeake bay watershed. Water. Resour. Res. 54 (10), 8120–8145. https://doi.org/10.1029/ 2017WR022403. Corvalan, C., Hales, S., McMichael, A.J., Butler, C., Michael, A., 2005. Ecosystems and Human Well-being: Health Synthesis. World Health Organization. Edwards, D.C., McKee, T.B., 1997. Characteristics of 20th Century Drought in the United States at Multiple Time Scales (Climatology Report No. 97–2). Colorado State University, Fort Collins, CO. Eshghi, K., Larson, R.C., 2008. Disasters: lessons from the past 105 years. Disaster Prev. Manage. Int. J. 17 (1), 62–82. https://doi.org/10.1108/09653560810855883. Esquivel-Hernández, G., Sánchez-Murillo, R., Birkel, C., Boll, J., 2018. Climate and water conflicts coevolution from tropical development and hydro-climatic perspectives: a case study of Costa Rica. JAWRA J. Am. Water Resour. Assoc. 54 (2), 451–470.
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World Water Development Report, UNESCO Paris, 2009. Yoon, T., Rhodes, C., Shah, F.A., 2015. Upstream water resource management to address downstream pollution concerns: a policy framework with application to the Nakdong River basin in South Korea. Water Resour. Res. 51, 787–805. https://doi.org/10. 1002/2013WR014201.
Wang, X., Ma, F.B., Li, J.Y., 2012. Water resources vulnerability assessment based on the parametric-system method: a case study of the Zhangjiakou Region of Guanting Reservoir Basin, North China. Procedia Environ. Sci. 13, 1204–1212. https://doi.org/ 10.1016/j.proenv.2012.01.114. WIS (Water information system) 2017 www.water.nier.go.kr accessed 15 January 2017. WWAP (World Water Assessment Programme). Water in a Changing World the Third 6
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Quantitative vulnerability assessment of water quality to extreme drought in a changing climate ⁎
T
⁎
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 China Department of Civil and Environmental Engineering, University of Maine, Orono, ME 04469-5711, USA c Department of Civil Engineering, Joongbu University, KyungGi 10279, South Korea b
A R T I C LE I N FO
A B S T R A C T
Keywords: Meteorological drought Water quality Kernel density estimation Risk assessment
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 droughtrelated effects ranges from humans to environmental systems, the impacts on water quality, in particular, require careful investigation. Such investigation should include the interlinkages across climate and stream quality variables 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 the water quality vulnerability to severe drought, with the goal of informing planning and mitigation as a means to enhance drought resilience. This study was conducted in the Nakdong River basin in South Korea, which is vulnerable to water quality degradation during severe drought. We propose a method to evaluate spatial-temporal droughts based on the water quality risk and to monitor environmental droughts using the probability of exceeding the target water quality to facilitate a resilient proactive response. The results of this study can be used to provide scientific drought monitoring information for assessing real-time water quality monitoring from meteorological drought information. In addition, we expect to categorize vulnerable drought areas and to suggest essential management measures and tailored countermeasures at a local scale.
1. Introduction Freshwater is essential for the sustenance of natural and human systems. Clean water is an important determinant of ecosystem health and, at the same time, is critical to humans from the standpoint of hygiene 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 to warming temperature and droughts tend to increase the impact of pollutant loadings, thereby having compounding deleterious effects on the quality of water and the aquatic ecosystems (Poff et al., 2002; Park et al., 2010; Sjerps et al., 2017). Droughts are one of the most expensive natural disasters with lasting and wide-ranging effects on the environment, society, and the economy (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 projections indicate that future climate changes will probably not be uniform, and regional variations in precipitation and warming temperatures will
exacerbate the potential for drought incidence and severity (Trenberth et al., 2014; Lee et al., 2018). Furthermore, natural climatic variability, for example stemming from El Niño/Southern Oscillation (ENSO), can amplify hydroclimatic extremes (Mosley, 2015; Son et al., 2014; Kim et al., 2016). Consequently, faced with water shortages, the quantification of risks and the development of countermeasures to protect the integrity between human and environmental systems are needed. In what follows, we provide a limited review of recent studies focused on water quality, water resources vulnerability, and land-use effects. Esquivel‐Hernández et al. (2018) developed approaches based on multiple linear regression and cluster analysis to understand the relative role of population size and hydrometeorological events as drivers in water conflicts, specifically linked to floods, droughts, wastewater spills, and water pollution across Costa Rica. Ng et al. (2018) used a multivariable linear model to assess the relative role of variables such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solids toward water quality deterioration based on a water quality index applied to the Kampar River Basin in
⁎ Corresponding authors at: Department of Civil Engineering, Drought Research Center, Joongbu University, Gyeunggi-do 10279, Republic of Korea (J.-H. Lee) and Department of Civil and Environmental Engineering, University of Maine, Orono, ME 04469-5711, USA (S. Jain). E-mail addresses: [email protected] (S. Jain), [email protected] (J.-H. Lee).
https://doi.org/10.1016/j.ecolind.2019.04.052 Received 9 December 2018; Received in revised form 5 March 2019; Accepted 15 April 2019 Available online 25 April 2019 1470-160X/ © 2019 Published by Elsevier Ltd.
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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 used in this study included dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), chlorophyll-a concentration, total nitrogen (TN), and total phosphorus (TP).
Malaysia. Furthermore, their study integrated an El Niño event, which caused an extreme drought and watershed stress. Monica and Choi (2016) applied multivariate statistical methods (discriminant, cluster, and principal component analyses) to identify suspended solids and total phosphorus, which are critical watershed response variables that influence changes in BOD and COD in the Saemangeum watershed, South Korea. Kükrer and Mutlu (2019) used a one-year long record of 28 water quality relevant variables for the Saraydüzü Dam Lake in Turkey to assess the water quality and the relative importance of measured variables for ecosystem health. Their analysis and risk assessment were based on multivariate cluster and factor analysis approaches, as well as cross-correlation. Wang et al. (2012) developed a water resources vulnerability approach, focused on both water quality and quantity issues, based on a parametric-system (PS) method. Their approach involves the Analytical Hierarchy Process to determine the relative weights of the indices considered (including drought and pollution indices). The study contends that the PS method is more general than the existing Fuzzy Optimization and Gray Relational Analysis approaches that only allow for rank analyses and qualitative level classification, respectively. Jun et al. (2011) used a multi-attribute method of decision analysis, coupled with future climate scenarios and watershed modeling, to assess hydrologic vulnerability in four dimensions: floods, droughts, water quality, and watershed risk. Chanat and Yang (2018) applied a seven-parameter spatiotemporal watershed accumulation of net effects model to investigate the changes in nitrogen loadings across four sites in the Chesapeake watershed (USA). Their modeling work indicated that increases in nitrogen loadings stem from urban and suburban development, with changes in temperature and precipitation being the compounding meteorological variables. The current study was conducted in the Nakdong River basin in South Korea, which is vulnerable to water quality degradation during severe droughts. We present a quantitative assessment method for water quality monitoring by applying nonparametric kernel density estimation (KDE). The KDE-based approach is used to quantify the drought impacts on water quality indicators and to diagnose water quality risk (WQR) by target basin to identify hotspots–areas vulnerable to extreme droughts. The WQR information can be provided to water managers and decision-makers for planning and decision-making linked to sustainable water resources management. In addition, this study seeks to inform efforts towards appropriate water quality management measures and facilitate resilient drought adaptation at local and regional scales.
2.1. Meteorological drought index In this study, to investigate drought variability, we applied the Standardized Precipitation Index (SPI), which was developed by McKee et al. (1993), and is used widely as a meteorological drought index. The standardization process of SPI is independent of geographic location and is spatially comparable (Cacciamani et al., 2007). Currently, more than 40 countries, including Korea, use the SPI to make preparations for drought. Compared with the relative water demands used in drought indices, SPI, a supply-side metric, focuses on the fact that droughts entail reduced precipitation, which causes water shortages (Gao et al., 2019). The calculation of the SPI index for each station is based only on historical precipitation records accumulated over a specific time period. These time-series precipitation data are applied to the gamma distribution, which is subsequently transformed into a standard normal distribution through equal probability transformation (Guttman, 1999). Positive SPIs represent wetter conditions than the climatology mean of precipitation and negative SPIs represent drier conditions. The gamma distribution best models the observed precipitation data in most instances because precipitation generally shows a skewed distribution (Bordi and Sutera, 2007). The density probability function of the Gamma distribution is given by Eq. (1).
g(x) =
1 x α − 1e−x β , for x > 0, β α Γ(α )
(1)
where the shape parameter α > 0 and the scale parameter β > 0, and the precipitation x > 0. Γ(α) is the gamma function defined by the integral (Eq. (2)):
Γ(α ) =
∫0
∞
y α − 1e−ydy
(2)
Gamma functions are evaluated numerically or by using the α value of the table. To model precipitation data as a density function of the gamma distribution, α and β should be estimated appropriately. There are many ways to estimate shape and scale parameters. For example, the method introduced by Thom (1958) was adopted by Edwards and McKee (1997).
2. Material and methods The Korean Nakdong River basin (KNRB) is selected for demonstrating the applicability of our proposed approach. Despite the importance of drinking water sources to more than 15 million people in the river basin, the water quality has deteriorated, even after completion of the four-river restoration project, and for many years, the river basin has suffered serious damage to downstream water quality (Yoon et al., 2015). In this study, water quality in 22 sub-basins of the Nakdong River were evaluated to assess the vulnerability to extreme droughts (Fig. 1). The hydrometeorological data used in this study were obtained from the 60 stations in the Automatic Synoptic Observation System, comprising daily rainfall data collected between 1976 and 2016 by the Korean Meteorological Administration. Streamflow and dam water information was provided by the Korean Water Management Information System (WAMIS, 2017). Water quality vulnerability was assessed using the 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 for Water Quality and Aquatic Ecosystem Targets (Ministry of Environment, South Korea) was used as the reference target criteria in our analyses.
â=
1 ⎛ ⎜1 + 4A ⎝
−
1+
4A ⎞ x β = ⎟, 3 ⎠ α̂
(3)
where for n observations −
A = ln(x ) −
∑ ln(x ) n
(4)
Using α and β, the density probability function g (x) for a given amount of a given month can be integrated for x and the cumulative probability G (x) and a specific time scale can be obtained (Eq. (5)):
G(x) =
∫0
x
g (x ) dx =
1 = β Γ(a )̂
∫0
x
x α −̂ 1e−x βdx (5)
The gamma function requires x > 0. As the precipitation could be zero, the cumulative probability is Eq. (6):
H(x) = q + (1 − q) G (x ),
(6)
where q is the probability of no rain and H (x) represents the cumulative probability of the recorded precipitation. The cumulative probability is converted to a standardized normal 689
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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 Nakdong River basin.
1 fh (WQ) = n
distribution to obtain the SPI. However, the method is numerically complex when too many stations are involved. An alternative approach using approximate transformation techniques to convert the cumulative probability to a standard variable Z was developed by Edwards and McKee (1997).
( (
⎧− t − ⎪ SPI = Z = ⎨ ⎪+ t − ⎩
c 0 + c1 t + c 2 t 2 1 + d1 t + d2 t 2 + d3 t 3 c 0 + c1 t + c 2 t 2 1 + d1 t + d2 t 2 + d3 t 3
), ),
n
∑i =1 Kh (WQ − WQi) =
1 nh
n
∑i =1 K ⎛ ⎝
WQ − WQi ⎞, h ⎠
(9)
where K is the kernel function and h is the bandwidth, Kh is the scaled 1 WQ kernel and is defined as Kh (WQ) = h K h . The choice of the bandwidth (h) was evaluated using the following simple optimal formula (Eq. (10)):
( )
for 0 < H (x ) ≤ 0.5,
1 (p + 4)
for 0.5 < H (x) < 1,
4 ⎫ hi = ⎧ ⎨ ⎩ (p + 2) n ⎬ ⎭
(7)
where
⎧ ln ⎡ 1 ⎤, for 0 < H (x) ≤ 0.5, ⎪ ⎣ (H (x ))2 ⎦ t= ⎨ 1 ⎪ ln ⎡ (1 − H (x ))2 ⎤, for 0.5 < H (x ) < 1, ⎣ ⎦ ⎩
σi ̂
(10)
where p denotes the number of dimensions, hi denotes the optimal smoothing 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 mean square error) is expressed as Eq. (11): (8)
1 5
4σ ̂ ⎫ h=⎧ ≈ 1.06σn̂ −1 5, ⎨ ⎩ 3n ⎬ ⎭ 5
2.2. Nonparametric kernel density estimation
(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 KDE approach for each water quality (WQ) indicator and target criterion (Eq. (12)).
The drought classification criteria and the drought stage impact factors proposed by the US Drought Mitigation Center (USDMC, 2017) were applied to differentiate between severe drought and extreme drought. The characteristics of water quality and the aquatic ecosystem were analyzed considering these drought classification criteria. KDE is a representative nonparametric method to estimate the probability density function (PDF) of random variables based on finite data samples (Bowman and Azzalini, 1997). We applied KDE to estimate the exceedance probability of each water quality (WQ) indicator on the target water quality (Eq. (9)).
WQR =
P (WQ ≥ WQcriteria DI ≤DIcriteria ) , P (WQ ≥ WQcriteria )
(12)
where WQ is the water quality index, WQcriteria is the target water quality standard, DI is the drought index, and DIcriteria is extreme drought conditions. This approach follows a past study of the multi690
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3.2. Analysis of water quality characteristics for extreme drought conditions
centennial drought variations in the northeastern United States (Gupta et al., 2011), however no water quality variables were integrated in that study. In this study, WQR was proposed to assess the vulnerability of water to extreme drought conditions (SPI < −1.2 and SPI < −1.5 for different drought durations). The drought index is estimated on a daily basis for each drought duration (30 days, 60 days, and 90 days) using rainfall data from 1976 to 2016. To assess the relationship between drought statistics and episodic water quality events, we applied the rank-based approach. Different drought durations, which are ranked by magnitude of SPI according to dry (SPI < −1.2) and wet (SPI > 1.2) conditions are shown below, followed by the observed water quality events during the period 2007–2016. Through analysis of the rank, it is possible to determine the sensitivity of water quality to wet and dry conditions in a certain river basin.
To analyze the water quality change based on the duration of a drought, we analyzed the risk for deleterious changes in the water quality standards of BOD and TP, related to the severe drought and extreme drought thresholds. Fig. 3 shows an example of applying the target water quality standards for BOD and TP in the Nam River basin. The figure indicates the instances where the water quality exceeded the target criteria for the different SPI periods (30 days [30D], 60 days [60D]). Different symbols indicate the instances where only BOD is exceeded, only TP is exceeded, and both BOD and TP exceed the target water quality (BOD: 2 mg/L, TP: 0.04 mg/L) in the basin. Applying the SPI30D for the period 2007–2016, the water quality data showed that severe drought threshold was crossed 53 times, and extreme drought threshold 23 times on a daily time scale. The water quality target of the Nam River basin is based on second-grade (lb), BOD 2.0 mg/L, and TP 0.04 mg/L, and 94.3% in severe drought and 91.3% in extreme drought, exceeding the BOD and TP standards. This tendency was similar for the 60D and 90D drought periods. Fig. 4 shows the results of the sensitivity of the water quality indices to drought statistics. Rank analysis, shown in Fig. 4a and b, was performed to assess the effects of extreme drought on the water quality. Sensitivity to drought and the achievement of the target water quality were also evaluated. In the figures, the data were arranged by the SPI30D index for dry/wet conditions. The two drought indices (SPI60D, SPI90D) are shown below, followed by the water quality indices (BOD and TP). The data exceeding each reference criteria are shaded in gray. The percentile values noted in the upper panels are the probability of exceeding the water quality (PEWQ) for the target criteria (BOD: 2 mg/ L, TP: 0.04 mg/L) in the Nam River basin (sub-basin ID: 2019). In this basin, the water quality was shown to be deteriorating as the drought progressed. The wet condition showed a noticeable improvement in water quality for BOD compared with the drought condition, and TP improved slightly but still exceeded the target water quality. The evaluation results of the target water quality exceeding the dry/wet conditions for the 22 sub-basins are shown in Fig. 4c and d. The analysis
3. Analysis results 3.1. Historical droughts in the KNRB Drought characteristics such as duration, magnitude, and the severity of drought events that occurred in the past were analyzed using the SPI index, and drought frequency analysis was performed to investigate historical droughts quantitatively. Fig. 2 shows the time history of SPI for the KNRB. The most severe drought year in the KNRB was 1988, with magnitude −15.5, duration of 11 months, average severity of −1.4, and a return period of 100 years. The significant drought years in the KNRB are 1968, 1982, 2014, 1996, 2001, 2009, and 2015, and the return period is 10–50 years. As regards meteorological drought in the 1960s, the years 1967 and 1968 are drought years. In 1968, the drought magnitude was −13.6, the duration was 11 months, and the average severity was −1.2. On the other hand, a drought of more than two years was evaluated as long-term drought from 1967 to 1968, 1976 to 1977, and 1994 to 1996 in the KNRB. In the following section, we discuss analyzing water quality changes attributable to extreme droughts.
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 or dryness compared to normal condition in that region. 691
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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 the seasonality 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 were crossed, 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 start time of January 1, and the calendar dates are displayed clockwise.
Fig. 7 shows an example of environmental drought risk assessment and environmental drought monitoring linked to water quality indicators. This was employed in the Andong Dam, Young River, Geumho River, Hwuang River, and Nam River basin, which are located in the upper, middle, and downstream of the Nakdong River. Scatter plots of BOD and TP data in the middle panel of each figure show the magnitude of the SPI indicator, with the red color indicating negative values and the blue positive values. The contour lines (10th, lower quartile, median, upper quartile, and 90th levels are shown) in the joint probability distribution were plotted using a threshold of SPI below −1.2. The probability distributions of the BOD and TP values are shown at the top and right panels of each figure, and the conditional probability and unconditional probability distribution for the target water quality in each sub-basin are shaded in red and gray. The WQR analysis results and the PEWQ for both BOD and TP water quality data are expressed in numerical 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 analysis results showed that the WQR was 1.074 based on BOD and 0.946 based on TP. The basins with a high risk to environmental drought were analyzed, 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 environmental droughts was found in the Geumho River basin (WQRBOD = 1.306, WQRTP = 1.156), which, compared with the other basins, was subject to extreme drought. In the Geumho River basin, the probability of exceeding the BOD target water quality standard was 73.7% (SPI < −1.2), and the probability of exceeding the TP target water quality was 90.5% (SPI < −1.2). The Nam River basin, located downstream of the Nakdong River, was also indicated as an area relatively vulnerable to drought. As a direct result of the effects of the mainstream of the Nakdong River, changes in the water quality characteristics occurred at the end of the basin. The probability of exceeding the BOD target water quality was 78.2% (SPI < −1.2) and the
results show that BOD had a negative relationship with the SPI drought index, and the water quality in the lower stream of the Nakdong River tended to deteriorate when drought occurred relatively more frequently in the other sub-basins. However, for TP, the relationship with the drought index was unclear.
3.3. Environmental risk assessment for extreme drought conditions Fig. 5 shows an example of WQR analysis for droughts by applying the target water quality standards (BOD: 2 mg/L) of the Nam River basin based on BOD data for the 2007–2016 period. The conditional (unconditional) probability distribution using the KDE for the target water quality in the Nam River basin is shaded in red (gray). The contour lines in the joint probability distribution were plotted for selected quantile levels (0.1, 0.25, 0.5, 0.75, and 0.9). The results of the analysis show that the WQRBOD is 1.24, which is a moderately elevated, yet not a significant risk; however, careful management of the water quality is warranted. Fig. 6 shows the results of the WQR analysis using BOD and TP data for each of the 22 sub-basins. The spatial distribution on the left of each figure shows the WQR result of applying extreme drought conditions (SPI30D < −1.2 and SPI30D < −1.5). The results of using the different 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 regards BOD, WQR was higher than 1 in many basins, but the spatial conherence was not significant. However, regarding TP, WQR varied considerably depending on the basin. As the severity of drought increased, the spatial variation also increased relatively. As regards severe drought, the basins showing WQR of 2.0 or higher (exceptionally high) for BOD were the Imha Dam and Hapcheon Dam basins, with SPI30D and SPI60D showing WQR of 2.21 and 2.25, respectively, relatively higher than those of the other basins. For extreme drought, the Imha Dam and Hapcheon Dam basins showed exceptionally high WQR (> 2.0). The Nakdong, Goryeong, and Hapchon basins showed relatively higher WQR related to drought in terms of TP. 692
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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 River basin (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 using the target water quality grades of each of the 22 sub-basins of the Nakdong River basin are shown spatially in the lower panel.
Kükrer and Mutlu, 2019), (c) decision analysis (Jun et al., 2011; Wang et al., 2012), and (d) interlinked process and statistical models (Chanat and Yang, 2018). As the frequency and severity of droughts because of climate change are likely to increase, understanding the interlinked nature of the effects on human–environmental systems is critical for adaptation to the expected conditions (Mortazavi-Naeini et al., 2015; Svoboda et al., 2015). However, despite many studies on drought and water quality, little attention has been paid to the development of methodologies that quantitatively assess the environmental drought risk related to the significant impact of drought on both humans and the environment. This is related in particular to the existence of thresholds over or below which there is a dramatic escalation of effects, some of which could be irreversible. It is also essential to distinguish between areas that are affected significantly by water quality and aquatic patterns when droughts occur, and areas that can maintain some level of resilience during droughts. To this end, this study investigated drought assessment and the vulnerability of water quality to severe drought, with the goal of informing planning and mitigation as a means to enhance drought resilience. Monica and Choi (2016) argued that TP and suspended solids (SS) are critical variables affecting BOD changes in the Saemangeum watershed, South Korea because the chemical composition of pollutants could be changed due to temporal variations in water pollutants. In this
probability of exceeding the TP target water quality was 89.6% (SPI < −1.2). Therefore, for this basin, the target water quality grade must 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 temperatures and a highly variable hydrologic cycle, portend 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, require careful investigation. Such investigation should include the interlinkages across climate and stream quality variables and how risk translates for various watersheds, given their particularities with respect to land use, watershed characteristics, and infrastructure. Recent studies on water quality have been conducted with a focus on the identification of important watershed variables that modulate stream water quality, as well as the role of weather and climate variables. From a methodological standpoint, four major types of approaches are commonly pursued in the published literature: (a) multiple linear regression (Esquivel‐Hernández et al., 2018; Ng et al., 2018), (b) multivariate statistical methods (Monica and Choi, 2016; 693
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consistent with Monica and Choi (2016). These results are evident in shorter drought periods (SPI30D). The current study proposes a KDE-based approach to evaluate spatial-temporal droughts based on the WQR and to monitor environmental droughts using the probability of exceeding the target water quality to facilitate a resilient proactive response. A WQR of 1 indicates no specific change in the risk of water quality relative to extreme drought, whereas a WQR of greater than 1 indicates that the water quality at the point and the basin is deteriorating. During drought, it would be necessary to separate and manage the sub-basins that show water quality decline compared with those that show smaller changes in risk. The KDE-based approach was applied for the Nakdong River basin in South Korea, which is vulnerable to water quality degradation during severe droughts. The analysis results were shown in Figs. 4–7. Among the five pilot basins located in the upper, middle, and downstream of the Nakdong River (Young River, Andong Dam, Hwuang River, Geumho River, Nam River basins), the highest WQR was at the Geumho River basin (WQRBOD = 1.306, WQRTP = 1.156), which suffered severe drought compared with the other basins. In the Geumho River 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 middle of the NRB, and contaminants are emitted from wastewater treatment plants. High-concentration pollutants in the middle river basins often flow downstream, leading to a decline in water quality. Therefore, these potential water contaminants may also be reflected in the WQR analysis results for mid- and downstream areas. Furthermore, the development of the Geumho River basin (sub-basin ID: 2012), which began in the late 2000s, reflects the result of a large number of apartment complexes being built and the resulting in a gradual increase in the population. The reason for the failure of the management of the water pollution load is because the target water quality standards failed to keep up with the pace of local development. Most of the pollutants loadings in the Geumho River are municipal and industrial effluents from Daegu,
Fig. 5. Example of water quality risk (WQR) analysis for droughts by applying the target water quality standard (BOD: 2 mg/L) to the Nam River basin (subbasin ID: 2019) for the period 2007–2016. a. Empirical probability distribution functions (PDF). b. Joint probability distribution function (PDF) using the kernel approach. Here, the conditional PDF represents the excess probability of BOD data exceeding the target water quality (BOD: 2 mg/L) in an instance of drought (SPI < −1.2).
study, the results of Fig. 3 illustrated in the circular plot showed that both TP and BOD were relatively high, especially during the winter months (January-February) and early summer (June-July) that was
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 spatial distribution on the left of each figure shows the WQR result of applying extreme drought conditions (SPI30D < −1.2 and SPI30D < −1.5). 694
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Fig. 7. Environmental drought risk assessment and environmental drought monitoring linked to water quality. This is similar to Fig. 2, but the joint probability distributions 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. Andong Dam basin, c. Hwuang River basin, d. Nam River basin, e. Geumho River basin.
and appropriates management measures and tailored countermeasures are likely to also be informed by results from the approach presented here. The KDE-based WQR assessment method is very flexible, as it accommodates linear and nonlinear relationships with ease. In addition to BOD and TP standards, the proposed approach can be applied to other water quality standards, such as DO, COD, and chlorophyll-a, and to other drought indices, including meteorological droughts, hydrological droughts, and agricultural droughts, to quantify the droughtrelated changes in water quality parameters to inform planning and decision-making.
making it difficult to meet the target water quality goal. However, with the recent establishment of an advanced sewage treatment plant, the water quality in the Geumho River is expected to improved significantly. The Nam River basin (sub-basin ID: 2019), located downstream of the Nakdong River, was also appeared to be an area relatively vulnerable to drought (WQRBOD = 1.240, WQRTP = 1.085). As a direct result of the effects of the mainstream of the Nakdong River, changes in the water quality characteristics occurred at the end of the basin. The probability of exceeding the BOD target water quality was 78.2% (SPI < −1.2) and the probability of exceeding the TP target water quality was 89.6% (SPI < −1.2). Therefore, it is necessary to adjust the target water quality grade to this basin or establish additional water quality management measures. For this basin, the policy framework proposed by Yoon et al. (2015) can be applied to downstream water quality management by considering three alternatives including upstream water treatment, expanding dam discharge, and developing new water sources, in order to achieve the target of water quality level. The upstream water treatment is to cut off incoming pollutants directly, minimizing the downstream impact. It is an advantageous method when the reliability of water quality is of primary concern. Dam discharge is the most flexible tool and can be used strategically during the low flow season. Instead of considering building a new reservoir at a high cost, utilizing existing reservoirs and developing waterways could also be a useful policy measure in the Nam River basin. The approach proposed in this study can be used to inform the potential risk for water quality declines based on meteorological drought information. Localized estimates of vulnerable drought areas
5. Summary and conclusions This study proposed an environmental drought monitoring method by applying the WQR index and the probability of exceeding the water quality standard based on the non-parametric KDE. Our primary results are summarized as follows: 1. The analysis indicated a statistically significant relationship between 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 River are shown according to the duration and severity of droughts. As regards BOD, the WQR was high for extreme drought, but the spatial variation was not significant. However, regarding TP, the WQR was found to vary significantly depending on the basins, and it was shown that the spatial change increased relatively as the severity of the drought increased. 695
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3. Under severe drought conditions, the WQR of TP was relatively high in the Nakdong River, Goryeong, and Hapcheon basins. Furthermore, the WQR of BOD was exceptionally high in the Imha Dam and Daecheon Dam basins in conditions of extreme drought. The longer the duration of the drought, the higher was the WQR compared with the other watersheds. 4. This diagnostic study and assessment confirmed that the WQR and PEWQ methods, applying the KDE to the Nakdong River basin in Korea, could be an evaluation method to monitor quantitative changes in water quality in relation to drought conditions. We expect to be able to provide useful information to establish appropriate water quality management measures to facilitate resilient drought measurements at local and regional scales.
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In closing, we proposed a probabilistic water quality monitoring method conditioned on the Standardized Precipitation Index, a representative meteorological drought index indicative of seasonal and longer climatic variations. Interlinked variations in climate and water quality offer critically important information watershed-scale drought vulnerability and impacts. The approach presented here affords quantitative assessment of risk for water quality declines that have important implications for human and ecological systems. Finally, in a changing climate, changes in drought frequency and severity remain critical concerns. Our work provides a quantitative approach to link climatic changes to water quality vulnerability. Acknowledgements This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-20171R1D1A1A02018546) and the Korea Environmental Industry & Technology Institute (KEITI) grant funded 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 manuscript significantly. Conflict of interest The authors declare that they have no conflict of interest. References Apurv, T., Sivapalan, M., Cai, X., 2017. Understanding the role of climate characteristics in drought propagation. Water Resour. Res. 53 (11), 9304–9329. https://doi.org/10. 1002/2017WR021445. Bordi, I., Sutera, A., 2007. Drought monitoring and forecasting at large scale. In: Methods and Tools for Drought Analysis and Management, pp. 3–27. Brooks, B.W., Lazorchak, J.M., Howard, M.D., Johnson, M.V.V., Morton, S.L., Perkins, D.A., Reavie, E.D., Scott, G.I., Smith, S.A., Steevens, J.A., 2016. Are harmful algal blooms becoming the greatest inland water quality threat to public health and aquatic ecosystems? Environ. Toxicol. Chem. 35, 6–13. https://doi.org/10.1002/etc. 3220. Bowman, A.W., Azzalini, A., 1997. Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations. 18. OUP Oxford. Cacciamani, C., Morgillo, A., Marchesi, S., Pavan, V., 2007. Monitoring and forecasting drought on a regional scale: Emilia-Romagna region. In: Methods and Tools for Drought Analysis and Management, pp. 29–48. Chanat, J.G., Yang, G., 2018. Exploring drivers of regional water-quality change using differential spatially referenced regression—a pilot study in the chesapeake bay watershed. Water. Resour. Res. 54 (10), 8120–8145. https://doi.org/10.1029/ 2017WR022403. Corvalan, C., Hales, S., McMichael, A.J., Butler, C., Michael, A., 2005. Ecosystems and Human Well-being: Health Synthesis. World Health Organization. Edwards, D.C., McKee, T.B., 1997. Characteristics of 20th Century Drought in the United States at Multiple Time Scales (Climatology Report No. 97–2). Colorado State University, Fort Collins, CO. Eshghi, K., Larson, R.C., 2008. Disasters: lessons from the past 105 years. Disaster Prev. Manage. Int. J. 17 (1), 62–82. https://doi.org/10.1108/09653560810855883. Esquivel-Hernández, G., Sánchez-Murillo, R., Birkel, C., Boll, J., 2018. Climate and water conflicts coevolution from tropical development and hydro-climatic perspectives: a case study of Costa Rica. JAWRA J. Am. Water Resour. Assoc. 54 (2), 451–470.
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