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ISSN : 2288-1115(Print)
ISSN : 2288-1123(Online)
Korean Journal of Ecology and Environment Vol.50 No.1 pp.169-185

Long-term Changes of Physicochemical Water Quality in Lake Youngrang, Korea

Bal Dev Bhattrai, Sungjin Kwak, Kwansoon Choi1, Woomyung Heo*
Department of Earth and Environmental Engineering, Kangwon National University, Samchok 25913, Republic of Korea
1K-Water Institute, Korea Water Resources Corporation, Daejeon 34045, Republic of Korea
Corresponding author : +82-33-570-6573, +82-33-574-7262,
February 28, 2017 March 26, 2017 April 4, 2017


Physicochemical properties of water quality were analyzed to understand the long-term variations in Lake Youngrang from 1998 to 2015. Nonparametric statistical methods were applied to deduct correlation among water quality parameters and water quality trend. In total observations (N=64), the Secchi depth (SD) transparency showed significant positive correlation with salinity (r=0.458) and highly significant negative correlation with chlorophyll-a (r= - 0.649) for p<0.0001 in two-tailed test of Spearman’s rank correlation. Significant negative correlations of SD were observed with chemical oxygen demand (COD), total phosphorus (TP) and total nitrogen (TN). These correlation patterns were very similar in rainy (N=25) and non-rainy (N= 39) periods too. Chlorophyll-a (Chl-a) had significant correlation with COD. Sen’s slope test was performed along with Mann-Kendall trend test (significance α=0.05, two-tailed) to find water quality trend. Positive trends were observed for SD and salinity with Sen’s slopes 0.012 and 0.385, respectively (p<0.0001). Negative significant trends were observed for total nitrogen (TN) and Chl-a with Sen’s slopes - 0.02 (p<0.0001) and - 0.346 (p=0.0010), respectively. Temperature, COD and phosphorus components had no trends. Carlson’s trophic state index (TSI) for SD, TP and Chl-a were obtained in the ranges of 46~80, 37~82 and 39~82, respectively. Trophic index values suggest that Lake Youngrang was mesoeutrophic to eutrophic and there could be possibility of anoxia during the summer and dominance of blue-green algae. Excess nutrient inputs from external and internal sources were the causes of eutrophication in this lake. The findings of this study would be helpful to recognize water quality variables to manage the water body.


    Kangwon National University


    Coastal lagoons are water bodies along with estuaries which are separated by barrier islands, coral reefs and sand bars from the adjacent ocean. And they are distinguished by shallow and slow water flow which is not the characteristics of estuaries. Tidal currents transport water into and out of the lagoons by inlets which are cut through barriers, either by natural forces, winds, waves and tidal currents or manmade. Lagoons can be divided in three major categories as leaky lagoons, choked lagoons and restricted lagoons, considering the tidal channels and water exchange with the ocean (Kjerfve, 1986). Leaky lagoons are with wide tidal channels and water exchange with ocean without disturbance and fast currents. But choked lagoons are in contrast with leaky lagoons, with restricted water exchange through one or more narrow channels with ocean in the coastline of high energy. And wind pattern play the major role for circulation within the lagoons. Restricted lagoons possess the mixed characteristics of leaky and choked lagoons. In the eastern coastal area of South Korea, there are 48 marine-origin natural lakes out of 57 natural lakes which were formed by sand dunes (Lee et al., 2006) and Lake Youngrang is one of the main lagoons in this region. Since it has one inlet channel to exchange water with East Sea, it can be the type of Choked lagoon. Water salinity of poor flushed lagoons; slow inflowing fresh water and frequent intrusion of seawater, in the eastern coastal region is over 0.5%, and are most of them are brackish in nature (Horne and Goldman, 1994; Heo et al., 2004).

    Lagoons occupy the boundary between the ocean and land; and have unique habitats from ocean to inland. Along with the habitats of lagoons, there may be barrier islands, spits, beaches, sand dunes, salt marshes and seagrasses. The diverse ecosystem within the periphery of lagoons, gives rise not only to high species of diversity but also to the creation of ecotones-areas of transition between three habitats: freshwater, terrestrial and marine. In the recent years, these ecosystems were disturbed due to changes in climate and landscape of the region (Ouillon et al., 2005; Limoges et al. 2015; Das et al., 2016). Water quality of the lagoons have been deteriorating due to nutrient enrich from point and non-point sources. Eutrophication is the major threat to ecology and biological communities. Anthropogenic activities such as agriculture, urbanization, industrialization, aquaculture and recreational activities are major cause of the nutrients. Non-point source pollution from agricultural runoff and treated and untreated domestic water discharges are linked to the episodic eutrophic conditions and algal blooms which can be harmful too (Newton et al., 2003; Roselli et al., 2009; Apau et al., 2012). Both lagoons and estuaries receive sediments from inland: estuaries from the catchment of rivers that flow into them and lagoons from surface runoff. Hence, coastal lagoons have a tendency to become filled in with sediments and thus disappear, in time, from the coastal landscape. Excess use of land and rapid industrialization are responsible to decrease the area of lagoons (Yoon et al., 2008; Kim et al., 2016). Human activities are major threat to the existence and good health of the lagoons.

    Coastal lagoons water quality is very important for the marine ecosystems as well as human health. Deterioration of water quality has negative impact on its surround, that’s why it is necessary to monitor coastal water bodies. The impact of natural and anthropogenic processes in the lagoon ecosystem identification requires continuous environmental monitoring. Changes in water quality, temporal and spatial, observed through monitoring help us to understand and analyze the long term trends. Any negative effect on the valuable natural environments could be predicted and mitigated with proper actions (Bearman et al., 2011; Harris and Vinobaba, 2013). Urban wastewater treatment, dredging, artificial inlets, limits on urban development and changes in agricultural practices are best options for lagoons management (Newton and Mudge, 2005).

    Previous literature of Lake Youngrang demonstrated short term water quality monitoring and analysis. Lake Youngrang recorded the highest values of TN/TP ratio (14) and TSI range (56~79) among the nine major eastern coastal lagoons in Korea (Heo et al., 1999). The shore length and area of lagoons were severely reduced due to urbanization and tourism purposes (Lee et al., 2006; Yoon et al., 2008). The variation in stable C and N isotope ratios of pond smelt was low due to brackish nature of Lake Youngrang and salinity was observed high in July. The higher pond smelt δ15N values in Lake Youngrang related to N sources from the watersheds, such as waste water and agricultural runoff (Lee et al., 2013). Lake Yeongrang could be described as eutrophic lagoon and suitable for phytoplankton growth (Moon et al., 2015).

    In this study, we analyzed the state of physicochemical water quality and temporal variations in Lake Youngrang. The data monitored through 17 years from 1998 to 2015 is subjected for statistical analysis. To understand the seasonal variations of water quality parameters, rainy and non-rainy period were analyzed and comparison was done with the total data. The non-parametric tests were selected to detect correlation (Spearman’s rank correlation) and linear trends of water quality. The Mann-Kendall test and Sen’s T test were applied to detect trend (or no trend). Through the research we would be able to obtain the knowledge of water quality variations and trophic status of Lake Youngrang and causes of water quality pollution.

    Materials and Methods

    Lake Youngrang, located in the northern region of Sokcho city, is a shallow brackish lagoon (Fig. 1). The surface area of lagoon is 1.05 km2 with an average depth of 4.1 m (range; 1~8 m) and 10.28 km2 watershed area. Lagoon is connected with the east sea and Jangcheon stream is the main stream of freshwater inflow in this lake, which has watershed area of 4.74 km2 (Socho city, 2013). There are resorts and golf courses in the watershed of this lagoon.

    Sokcho city meteorological data recorded average yearly precipitation of 1,400 mm and the yearly average air temperature of 12.9°C. From June to September, about 60~70 % of yearly precipitation was recorded. Monthly precipitation values were higher (about 600 mm) in 1998 and 2004 (Fig. 2). N and P generation in the drainage area was 167 kgN day-1 and 39 kgP day-1 respectively (Heo et al., 1999).

    Water quality was monitored from 1998 to 2015 in a monthly basis in 64 episodes, non-rainy (March~May and Sept.~Nov.) and rainy period; monsoon season of Korea (June~Aug.), except severe winter (Dec.~Feb.). Data measurements of some parameters were done on the spot while collecting water sample and then collected samples were transported to laboratory within 24 hrs. As pretreatment, water samples were filtered with CF/C filter paper in the lab. The filter papers were kept frozen, and homogenized at the time of chlorophyll-a analysis. Lorenzen’s (1967) method was used to calculate chlorophyll-a concentration. Filtered water was then used to calculate dissolved nitrogen and phosphorus. TP was determined according to standard methods (APHA, 1992), employing persulfate digestion and ascorbic acid method. TN was determined by the cadmium reduction method after persulfate digestion, using a flow injection auto-analyzer (BRAN-LUEBBE, Auto Analyzer3). Potassium permanganate alkaline method is used to determine COD (APHA, 1992). Temperature, salinity, pH and DO were measured with a multi-probe meter (YSI, 6000).

    The non-parametric correlation and trend test methods were performed for statistical analysis in SPSS software (IBM Corporation, Armonk, NY, USA).

    1.Spearman rank-order correlation

    Spearman rank-order correlation is a nonparametric measure of association based on the ranks of the data values. The formula is

    ρ= i ( R i - R ¯ ) ( S i - S ¯ ) i ( R i - R ¯ ) 2 i ( S i - S ¯ ) 2

    Where Ri is the rank of xi, Si is the rank of yi, R is the mean of the Ri values, and S is the mean of the Si values.

    Probability values for the Spearman correlation are computed by treating

    t= ( n-2 ) 1/2 ( r 2 1-r 2 ) 1/2

    as coming from a t distribution with n- 2 degrees of freedom, where r is the sample Spearman correlation.

    2.Sen’s slope estimator

    Sen (1968) slope method is given below,

    Linear model: f(t)=Q(t)+B

    where, Q is slope and B is constant.

    Estimate of the slope Q, the slopes of all data pairs are calculated

    Q i = x j -x k j-k , i=1, 2, L N, j>k

    If there are n values xj in the time series we get as many as N=n(n- 1)/2 slope estimates Qi.

    The Sen’s slope estimator of the slope is the median of these N values of Qi. The N values of Qi are ranked from the smallest to the largest and the Sen’s estimator is

    Q= { Q N+1 2 Q N 2 +Q N+2 2 IF N is odd IF N is even

    Confidence level,

    C α =Z 1- α 2 VAR ( S ) ,


    VAR(S)= 1 18 [ N ( N-1 ) ( 2N+2 ) - p=1 q t p ( t p -1 ) ( 2t p +5 ) ]

    Where q is the number of tied groups, tp is the number of data values in the pth group.

    Next,  M 1 = ( N-C α ) /2 and  M 2 = ( N+C a ) /2.

    The lower and upper limits of the confidence interval, Qmin and Qmax, are the M1th largest and the (M2+1)th largest of the N ordered slope estimates Qi.

    3.Mann-Kendall test

    The M-K test (Mann, 1945; Kendall, 1975) statistic S is calculated using the formula:

    S= k=1 n-1 j=k+1 n sign ( x j -x k ) sign ( x j -x k ) = { +1 if  x j -x k >0 0 if  x j -x k =0 -1 if  x j -x k <0

    Where xj and xk are the annual values in years and j and k,j j>k, respectively.

    The standard test statistic Z is computed as follows

    Z= { S-1 VAR ( S ) if S>0 0 if S=0 S+1 VAR ( S ) if S<0

    Var(S) is given in Sen’s slope test.

    The presence of a statistically significant trend is evaluated using the Z values. A positive (negative) value of Z indicates an upward (downward) trend.

    Trophic State Index (TSI) was calculated according to Carlson (1977). Below are the formulas:

    TSI(P)=14.42*lnTP+4.15 TSI(Chl.a)=9.81*lnChl.a+30.6 TSI(SD)=60- 14.41*lnSD

    Where, TP and Chl.a are in μg L-1 and SD is in meters

    TSI=Carlson’s trophic state index and ln, natural logarithm.Table 1Table 2Table 3Table 4Table 5


    Physicochemical surface water quality variations were presented as total (Table 1), rainy period (Table 3) and nonrainy period (Table 5) and time series data along with slope was presented in Fig. 3. The mean water temperature in the lagoon of the total data was 18.3°C (range; 4.6~30.4°C). Rainy period was of typical of Korean summer with mean 24.8°C (range; 20.6~30.4°C) and non-rainy period was of cool to mild-hot with mean 14.2°C (range; 4.6~23.7°C). The mean value of SD was 0.9 m of total and ranges of SD in rainy and non-rainy were 0.3~2.7 m and 0.4~1.8 m, respectively. Salinity in Lake Youngrang varied from 0.4~32.3 psu with mean value 10.3 psu. As shown in time series plot (Fig. 3), there was significant rise in salinity onward year 2007, while before that it was similar about 2 psu. Salinity of non-rainy period was noted higher than the rainy period with averages 9 psu and 11.1 psu, respectively. DO was recorded as average of 10.3 mg L-1 with maximum 20.1 mg L-1 and minimum 4.8 mg L-1. The average value of DO in rainy was 9.6 mg L-1 and non-rainy was 10.8 mg L-1. The values of DO were observed higher in the non-rainy period than the rainy period. COD average concentrations in rainy period and non-rainy were observed as 4.9 mg L-1 and 5.4 mg L-1, respectively. The SS mean concentration of the total data was 22.5 mg L-1 (range; 3.5~134.0 mg L-1). The mean SS value of non-rainy period (26.6 mg L-1) was higher than the mean SS value of rainy period (16.2 mg L-1).

    Nutrients factors, phosphorus and nitrogen components during the non-rainy period were observed higher than the rainy period. Among the phosphorus components average TP mean values were very similar in both periods. The mean TP values of rainy and non-rainy were 0.07 mg L-1 and 0.08 mg L-1, respectively. Nitrogen components of lagoon TN, NO3-N and NH3-N mean values were detected as 1.44, 0.37 and 0.28 in rainy and 1.14, 0.28 and 0.10 in non-rainy of mg L-1. The higher values of nitrogen components measurements were seen during the non-rainy season than the rainy season. The lowest Chl-a concentration was detected toward 2014~2015. The mean values of Chl-a in rainy and non-rainy were 30 mg m-3 and 36.5 m-3 in the ranges of 4.7~ 131.5 mg m-3 and 1.8~184.0 mg m-3, respectively. Overall Chl-a mean value was 34 mg m-3.

    Box and whiskers plots of water quality parameters showing temporal variations were presented in Fig. 4a, b. The median values of temperature, DIP and Chl-a in rainy period were higher than the median of non-rainy period. But the reverse trend was observed for other parameters (Fig. 4. a, b). Non-parametric statistical tests were performed since there were several outliers.

    Spearman’s rank correlation coefficients (p<0.05, twotailed) were calculated for three data sets, total, rainy and non-rainy and were presented in Tables 2, 4 and 6. The result showed that there was significant increase in SD along with salinity, with correlation coefficient 0.0458. SD is negatively correlated with COD, DO, TP, TN and Chl-a. SD and Chl-a had significantly negative correlations in each data set. SD correlations values with salinity in rainy and nonrainy were positive, as 0.566 and 0.421, respectively. SD showed highly significant negative correlations with Chl-a of rainy and TP of non-rainy with correlation coefficients as - 0.712 and - 0.702, respectively. Nutrient components and Chl-a were positively correlated with COD. TSI indexes were presented in Table 7, and were in the range of the lowest 37 to the highest 82.

    Depth profiles for four different periods, 1998~1999, 2004~2005, 2008~2009 and 2014~2015 were chosen for interpretation with water parameters Temperature, Salinity, DO and pH (Fig. 5a~d). Chemocline; well mixed layer, could be observed in all periods at 2~4 meter in the months of June to October when the warmer water of epilimnion sits on top of cooler water in hypolimnion causing oxygen deficiency at the bottom layer of lake. Hypoxia in 2008~ 2009 period was very severe than the other three periods. The hypoxia in 1998 and 1999 were next larger than the rest two. Period 2014~2015 observed the least.

    Mann-Kendall trend test and Sen’s slope were obtained using SPSS (IBM Corporation, Armonk, NY, USA) and were presented in Table 8. Kendall’s tau values for SD and Salinity were significantly positive with 0.399 and 0.553, respectively (p<0.0001, α=0.05, two-tailed). Notable negative values of Kendall’s tau were observed for TN, NO3-N and Chl-a. Overall, temperature, COD, TP and DIP showed no trend. Decreasing trends were observed for DO, nitrogen components and Chl-a and increasing trends were observed for SD, Salinity and SS.


    Lake Youngrang surface water temperature showed slight increment throughout the monitoring period. Water temperature is taken as an important factor to run physicochemical and biological functions of water body (Jayakumar et al., 2013). The SD of this lagoon showed slight increment as the year progresses. The highest value of SD was detected in 2014~2015. SD of the lagoon was highly influenced by salinity and nutrient components and Chl-a which were shown in correlation charts (Tables 2, 4 and 6). Water inflow and outflow in lagoon plays important role to control suspended solids, as river inflow, inflow form watershed, tides and winds are the key players (Roselli et al., 2009). Salinity of Lake Youngrang was highly increased after year 2006. This increase was related to the restoration activity as mentioned in Sokcho city report in year 2013. The higher salinity fluctuation indicates an increase in water flux between the lagoon and the ocean with internal circulation throughout the lagoon. Sand removal from channel, connecting lagoon to eastern sea, and construction of dyke eased the water movement from sea and reduced sand accumula- tion at the channel. Salinity is a very influencing factor in lagoon (Jayaraman et al., 2007; Kim et al., 2016). Sea level rises could play a role in salinity as well (Rizzeteo and Tosi, 2012). Natural and anthropogenic factors have significant influence to control salinity by controlling the flow from stream and ocean (Dailidienė and Davulienė, 2008; Giskes et al., 2013; Tulipani et al., 2014). The average DO was low during summer monsoon period and lowest value was recorded in rainy period too. Phytoplankton activities during spring and fall could be the reason of higher DO during nonrainy period and also added by wind and tidal circulation. SS loading is mainly related to run-off with high load of solids and wastes from non-point sources of pollution like agricultural fields, open lands, and domestic areas (Gazzaz et al., 2012; Trabelsi et al., 2013). Suspended solid increased significantly during the rainy period which is a concern for management. Chemical oxygen demand and nitrate nitrogen are indicators of organic pollution and relate essentially to anthropogenic point sources like domestic sewage, wastewater treatment plants, and industrial facilities. The other sources of nitrates are agricultural areas where inorganic nitrogen fertilizers are usually applied. The human activities in the lake surrounding have a great localized effect on water chemistry and nutrient loads and land uses had a significant impact on the hydrology system of water body and water quality (Lapido et al., 2011; Magyar et al., 2013; Limoges et al., 2015; Marthe et al., 2015). Sedimentation of nutrients and their release due to suspension and re-suspension also plays important role (Conley et al., 1997; Schallenberg et al., 2010; Zaaboub et al., 2014; Zirono et al., 2016). The decreasing trend of nitrogen nutrients was observed in the study. In the case of Chl-a, the trend was downward with sen’s slope - 0.346 but still eutrophication is noticeable. Trophic equilibrium of the ecosystem could be affected by the accumulation of organic matter in the bottom of lake (Frascari et al., 2002; Christia et al., 2014). Because of these nutrients, excess oxygen is required and releases the amount of carbon dioxide to cause serious dystrophy. The chemocline zone divides the hypoxia zones from the upper zone where photosynthesis occurs. Several cases of fish kill were recorded by Sokcho city which could be related to the nutrient pollution (Sokcho city, unpublished data). In 2006, 246 individuals of fish kill were observed from September to December, most probably related to nutrient pollution. Winter fish kill of 25 individuals of carp fish were reported in January of 2007. In 2008, more than ten thousand fish kill were reported in the months of July (8,000 individuals) and September (6,000 individuals) due to blockage of sea water. Tens of thousands individual fish kill was occurred in October of 2011. This particular episode was related to proliferation of ammonia from benthic zone sediment due to gales which created hypoxia. Despite the conservation efforts in Lake Youngrang, trophic state of lagoon is not decreasing. Internal and external loading of nutrients are the big factors to be considered to achieve the sustainable water environment of the lagoon. Reclamation of water body through dredging and enhancing water circulation could be the long term goals (Markou et al., 2007; Perilla et al., 2012; Acquavita et al., 2015).

    The changes in water quality of Lake Youngrang in 17 years were demonstrated. The results give us a better understanding of temporal lagoon water quality variations through physicochemical properties analyses. Though reduction in nutrient variations and increase in salinity were observed, eutrophication of lake is still a serious concern. Watershed management of Lake Youngrang considering sustainable development could be the step toward healthy environment of lagoon.


    Salinity had increased significantly due to better water circulation with sea by sand removal from the channel and construction of dyke in the east sea. Chl-a had significant reduction during the monitoring period. Very low reductions were noted on the nutrient variables. Though high values of SS and COD were noted in time series but no significant trend was found. The formation of chemocline reduced the oxygen level at the bottom level of lagoon and anoxia was visible. At the surface layer photosynthesis release oxygen and DO was high. Trophic index showed that the lagoon is eutrophic. Despite low reduction in nutrients, TSI values were not decreased which could be the reason of suspension and re-suspension of nutrients and organic matter from the lake bottom.

    To achieve the better water quality in Lake Youngrang and protect limnological value, chemocline need to be removed. That will enhance the turnover frequency and supply the oxygen to the benthic zone to decompose organic carbon in the sediment. Also, sustainable watershed management could play important role to control the inflow of nutrient and suspended solids as well as sediment.


    This study was supported by 2015 Research Grant from Kangwon National University (No. 201510016).



    Map of Lake Youngrang and monitoring sites. The geographic coordinates of the study area are Latitude 38°12ʹ 55ʺN Longitude 128°35ʹ03ʺE.


    Time series of monthly rainfall in Sokcho city from 1995~ 2015.


    Plot of time series data of Lake Youngrang with slope b of linear regression, y=a+bx.


    Box plots of water quality parameters. A line is drawn across the box at the median. The bottom of each box is at the first quartile (Q1), and the top is at the third quartile (Q3) value. The whiskers are the lines that extend from the top and bottom of the box to the adjacent values. The adjacent values are the lowest and highest observations that are still inside the region defined by the following limits: Lower Limit: Q1 - 1.5(Q3 -Q1). Upper Limit: Q3+1.5(Q3 -Q1). Outliers are points outside of the lower and upper limits and are plotted with dots (●).


    Depth profile of water quality parameters in Lake Youngrang for four different periods (a: 1998~2000; b: 2004~2005, c: 2008~2009; d: 2014~2015).


    Descriptive statistics of water quality parameters in Lake Youngrang (1998~2015).

    Temp, water temperature (°C); SD, Sechhi depth (m); Sal, Salinity (psu); DO, dissolved oxygen (mg L-1); COD, chemical oxygen demand (mg L-1); SS, suspended solids (mg L-1); TP, total phosphorus (mg L-1); DIP, dissolved inorganic phosphorus (mg L-1); TN, total nitrogen (mg L-1); NO3-N, nitrate nitrogen (mg L-1); NH3-N, ammonia nitrogen (mg L-1) and Chl-a, chlorophyll-a (mg m-3).

    Spearman’s rank correlation coefficients and p-values of water quality variables in Lake Youngrang.

    Singnificance level (α)=0.05, two-tailed.
    ***p<0.0001, **p<0.01 and *p<0.05, bold, significantly correlated.

    Descriptive statistics of water quality variables in Lake Youngrang of rainy period (June~August of 1998~2015).

    2. Spearman’s rank correlation coefficients and p-values of water quality variables in Lake Youngrang.

    Singnificance level (α)=0.05, two-tailed.
    ***p<0.0001, **p<0.01 and *p<0.05, bold, significantly correlated.

    Descriptive statistics of water quality variables in Lake Youngrang of non-rainy data (March~May and Sept.~Nov. of 1998~2015).

    2. Spearman’s rank correlation coefficients and p-values of water quality variables in Lake Youngrang.

    Singnificance level (α)=0.05, two-tailed.
    ***p<0.0001, **p<0.01 and *p<0.05, bold, significantly correlated.

    Carlson’s trophic state indexes in Lake Youngrang.

    Results of Mann-Kendall tests and Sen’s slope of water quality variables in Lake Youngrang (1994~2015).

    Significance level alpha=0.05; two-tailed.
    Trend: O, No trend; P, Positive trend and N, negative trend.


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