Lake resources support human life and production, and represent some of the most biodiverse ecological landscapes in nature (Heino et al., 2021). In addition, lakes hold most of the planet’s liquid surface freshwater (Carpenter et al., 2011; Vadeboncoeur et al., 2011) and provide a range of key ecosystem services. Numerous organisms depend on lakes either fully or partially for survival (Butchart et al. 2010). For example, the 14 largest lakes in the world support approximately 15% of the global fish diversity and 3% of insect, mollusk, and crustacean diversity (Mammides, 2020). However, lakes are continuously being degraded in terms of water quality and decreasing lake areas (Tao et al., 2020).

Lake eutrophication has long been considered a threat to aquatic ecosystem health globally (Bhagowati and Ahamad, 2019). Particularly, in the wake of rapid agricultural and industrial development, urbanization, and land use change, water quality has been degraded considerably, leading to severe eutrophication in most of lakes (Mammides, 2020). Storm water runoff from agriculture is usually considered non-point pollution or diffuse pollution, and wastewater from various industries, such as tourism and aquaculture industries, is considered point pollution. Over the past few decades, aquatic ecosystems across the globe have been indiscriminately receiving pollutants from several point and non-point sources (Srinivas et al., 2020). The concentrations of pollutants, such as chemical oxygen demand (COD_Mn), total nitrogen (TN), and total phosphorus (TP) (Xu et al., 2017), which are the main cause of eutrophication in lake waters (Bouwman et al., 2013), have increased dramatically in surface waters.

Despite the importance of lakes, and the numerous threats they are facing, it remains largely unclear whether climate change or human activities are to blame for the deterioration of water quality. Lake water quality exhibits significant spatial and temporal variability (Chang et al., 2017; Li et al., 2017a). The seasonality and regionality of water quality at the lake scale have become key aspects of the physical and chemical characterization of aquatic environments (Ouyang et al., 2006; Sundaray et al., 2006). Existing studies on recent and historical changes in lake water quality have shown that climate change and human impacts drive the changes in lake area (Tao et al., 2020). However, it is not clear whether climate change and human activities are the main causes of spatial and temporal variations of water quality. Monitoring water quality and pollution sources could facilitate sustainable water resource use and management strategies (Crosa et al., 2006; Sarkar et al., 2007).

In this study, we investigated the factors driving spatial and temporal variations of water quality in Xiao Xingkai Lake, which is a typical aquatic ecosystem exposed to anthropogenic activities such as agricultural irrigation, fisheries, and tourism in a region with a temperate monsoon climate in China (Li et al., 2015). We monitored and analyzed the water quality parameters in the entire lake from 2012 to 2014. After determining the spatial and temporal characteristics water quality parameters and trophic status, we evaluated the effects of climate and human activities on water quality, and identified the key factors correlated with water quality and pollutants. Understanding the dominant processes shaping eutrophication could facilitate sustainable water resource exploitation and management in lake catchments.

Xingkai Lake (Khanka Lake) is the largest freshwater lake in Northeast Asia, and is a boundary lake between China and Russia (Long and Shen, 2017) (Fig. 1a). Xiao Xingkai Lake (45°16′N–45°24′N, 132°20′E–132°50′E) is part of Xingkai Lake, and has a total area of 176 km² (Fig. 1b). Xiao Xingkai Lake is separated from Xingkai Lake by a 90-km natural sand dam that was formed following a decrease in lake water area (Fig. 1c). Water exchange between the large and small lake takes place via two sluice gates, Sluice 1 and Sluice 2. Xiao Xingkai Lake is 35.0 km long and 4.5 km wide, with average and maximum water depths of 1.8 m and 5.0 m, respectively. The lake freezes in December every year and thaws in late April in the subsequent year. The maximum and minimum monthly mean temperatures are 27℃ and –19℃, respectively, which occur in summer and winter, respectively. Rainfall mainly occurs in summer (from June to August), reaching 750 mm annually (Long and Shen, 2017).

Figure 1.  The location of Xiao Xingkai Lake (a, b) and sampling sites distribution (c) from 2012 to 2014. There were five types of sampling sites. AC represents aquaculture sites with two sampling points near the Dongbeipao and Baiyutan aquaculture centers; S represents sluices with two sampling points near sluice gates connecting Xiao Xingkai Lake and Xingkai Lake to control the water level. T represents tourism sites with two sampling sites near the major ecotourism areas, wetland park and a new flow scenic spot. The new flow scenic spot is located between the large and small lake and offers numerous tourist activities (ecological tours, ancient culture tours, leisure travels, etc.) that attract a lot of visitors. The wetland park is located in the eastern portion of the small lake and represents an ecological tours project. AD represents agricultural sites, with nine sampling points located in the northern part of the lake, which are river outlets used as agricultural irrigation channels and drainages from rice paddies. The rice planting area near of the agricultural site of the lake was 146 215 ha in 2013, accounting for 99% of the total agricultural area in the study region. LC represents the lake center with three sampling points in the lake center were used as controls for comparisons with other sites

The lake water freezes in December and thaws in late April, and water quality monitoring and sampling times were generally carried out between May and October from 2012 to 2014. The seven monitoring and sampling times were September in 2012, May, July, August, and October in 2013, and June and September in 2014. Nineteen sites were monitored and sampled each time (Fig. 1c).

Water temperature (WT), electroconductivity (EC), pH, salinity, total dissolved solids (TDS) and dissolved oxygen (DO) were monitored using a portable YSI Pro Plus multiparameter meter (YSI Incorporated, Yellow Springs, OH, USA). According to the setting method of vertical sampling points for lake monitoring (Wei, 2002), two water samples were collected 0.5 m below the water surface at each site, mixed, and placed in coolers until they were transported to the laboratory. Secchi depth (SD) was determined using a Secchi disk and water depth was recorded using a depth recorder. Water samples were sent to the Public Technology Service Center at the Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, for analysis. The concentrations of TN, TP, nitrate nitrogen (NO₃⁻-N), and ammonium nitrogen (NH₄⁺-N) were determined using flow injection analysis (Skalar, Netherlands) (Bremne, 1998), while chlorophyll a (Chl-a) and total suspended solids (TSS) were measured using the spectrophotometric and gravimetric methods, respectively (Wu et al. 2017a). Air temperature and precipitation data were collected from Xingkai Lake Monitoring Station of China during the growing seasons (from May to October) of 2012–2014.

We used the trophic state index (TLI) (Carlson 1977; Wu et al. 2017a) to assess the trophic status of Xiao Xingkai Lake. The lake was considered eutrophic when the TLI value was greater than 50. Furthermore, we divided eutrophication into three levels: light eutrophic (50 < TLI ≤ 60), medium eutrophic (60 < TLI ≤ 70) and heavy eutrophic (> 70) . The TLI value was determined according to the following equation:

where TLI$\left(\displaystyle\sum\right) $ is the comprehensive nutrition state index, TLI(j) is the nutrition state index of a parameter j. The different parameters in the tropic state index were calculated according to Yu et al. (2013). W_j is the relevant importance of the nutrition state index in parameter j. The normalized weights in the relative importance of parameter j on the nutrition state index were calculated using the following equation:

where R_ij is the correlation coefficient of parameter j for Chl-a, and m is the number of the selected important parameters (3 to 4 generally). If the importance of Chl-a to the state of eutrophication is considered 1, the correlation of parameter j to Chl-a is R_ij (j=1, 2 ,…m), and R_ij=R_ji; therefore, the relative importance of each parameter to eutrophication is proportional to the correlation coefficient R_ij². The correlation index R_ij and R_ij² between Chl-a and other parameters in the lake was calculated according to Jin and Tu (1990).

IBM SPSS Statistics 22 (IBM Corp., Armonk, NY, US), Origin 2018 (OriginLab, Northampton, MA, US), and SigmaPlot 12.0 (Systat Software, Inc., San Jose, California, US) were used to analyze and plot the data. The water quality levels of Xiao Xingkai Lake were evaluated based on the Surface Water Environmental Quality Standard ( (SEP and GAQSIQ, 2002, GB 3838−2002) of China. Seasonal variations in lake water quality were examined from late Spring (May) to mid-Autumn (October) between 2012 and 2014, because of the long freeze-thaw period in Xiao Xingkai Lake. In Xiao Xingkai Lake, Spring is from March to May, summer is from June to August, and autumn is from September to November. Spatial variation of lake water quality in the sampling sites under anthropogenic influence, which included tourism, agriculture, sluice and aquaculture, was analyzed and compared with the water quality at the center of the lake independent sample. Non-parametric tests and Kruskal-Wallis tests were used to compare water quality parameters among different sampling sites and sampling times. Histogram plots and vertical box plots were used to illustrate spatial and temporal variation in water quality, respectively. Spearman’s rank correlation coefficient was used to evaluate correlations among water quality parameters.

The average (ranges) values of physicochemical characteristics are displayed in Table 1. From May to October, the average water temperature in the lake was 18.07℃ (range, 5.00℃ to 26.00℃). The average DO concentrations was 6.45 mg/L, which falls under Grade Ⅱ (6.00–7.50 mg/L). The average NH₄⁺-N concentration was 0.17 mg/L, which also falls under Grade Ⅱ (0.15–0.5 mg/L). The average COD_Mn concentration was 7.68 mg/L, which falls under Grade Ⅳ (6.00–10.00 mg/L). The average TP and TN concentrations were 0.11 (0.10–0.20 mg/L) and 1.63 mg/L (1.50–2.00 mg/L), respectively, which fall under Grade Ⅴ (GB 3838–2002).

Water quality varied seasonally and spatially, as illustrated in Figs. 2 and 3. The concentrations of TN and Chl-a in autumn (September to November) were lower in 2013 than in 2012 and 2014 (Figs. 2a and 2c); however, TP concentrations exhibited opposite trends (Fig. 2b). COD_Mn and DO decreased markedly in autumn, from 2012 to 2014 (Figs. 2d and 2h). In addition, WT, TN, and TSS were higher in summer (June to August) than in spring (March to May) and autumn (September to November); however, DO was lower in summer than in spring and autumn (Figs. 2g, 2a, 2k and 2h). Chl-a and NH₄⁺-N concentrations were higher in spring than in summer and autumn (Figs. 2c, 2f). pH and TDS exhibited no significant seasonal variability (Figs. 2j, 2l).

Figure 2.  Temporal variation in lake water quality from September of 2012 to September of 2014. Some months had no values because of freezing in December and thawing in late April, and lack of monitoring; TN, total nitrogen; TP, total phosphorus; COD_Mn, oxygen demand; WT, Water temperature; DO, dissolved oxygen; EC, electroconductivity; TSS, total suspended solids; TDS, total dissolved solids; Chal-a, chlorophyll a

Figure 3.  Mean (dashes), median (solid line), and ranges of water quality parameters at different sampling sites. Edges in the boxes represent 25% and 75% percentiles; whiskers extend to the minimum and maximum, dots indicate outliers outside the 10th and 90th percentiles. Different letters indicate significant differences at the 0.05 significance level; TN, total nitrogen; TP, total phosphorus; COD_Mn, oxygen demand; WT, Water temperature; DO, dissolved oxygen; EC, electroconductivity; TSS, total suspended solids; TDS, total dissolved solids; Chal-a, chlorophyll a

Water quality in the central sections of the lake were relatively better, and the concentrations of TN, TP, NH₄⁺-N, COD_Mn, and DO were 8.20, 1.52, 0.10, 3.68, and 0.07 mg/L, respectively (Figs. 3a, 3b, 3d, 3e, and 3h). Based on the Surface Water Environmental Quality Standard (GB 3838–2002), the water quality in the lake center was better than grade Ⅱ, with the exception of TN and TP, which were Grade Ⅴ.

There were no significant differences in DO and TDS contents between the selected sampling sites and the lake center (Figs. 3h and 3k). In the tourism sites, the average NH₄⁺-N concentrations were higher than at the lake center, while WT was lower at the sites than at the lake center (Figs. 3d and 3g). In sluice areas, the EC was lower than at the lake center (Fig. 3i). In the aquaculture sites, the NH₄⁺-N and COD_Mn concentrations were higher; however, pH in the aquaculture sites was lower than at the lake center (Figs. 3d, 3e, and 3i). In addition, the pH in the agricultural sites were lower than at the lake center (Fig. 3i). Although the TN and NO₃^–-N contents did not differ significantly among sampling sites, they were higher in agricultural sites than in tourism sites (Figs. 3a and 3c). The sampling points proportion of TN concentrations greater than 2.0 mg/L were 15.69%, 17.86%, and 32.73% at the tourism, aquaculture and agriculture sites, respectively. The sampling points proportion of TP concentrations greater than 0.2 mg/L were 3.92%, 7.14%, and 5.45% at the tourism, aquaculture, and agricultural sites, respectively (Figs. 3a and 3b).

The TLI ranged from 55.16 to 64.41 in 2012 to 2014 (Table 2). When compared with the eutrophication standard, Xiao Xingkai Lake was in an light and medium eutrophic state. In addition, the TLI value in 2012 (64.41) was greater than those in 2013 (58.33) and 2014 (59.59). Our results indicated that the water quality changed from medium eutrophic to light eutrophic. In 2013 and 2014, the TLI values in summer (June, July, and August) were higher than those in autumn (September and October). The TLI values in different sites ranged from 56.70 to 60.44, and were greater than 60 in the tourist and aquaculture sites, indicating medium eutrophication, while they were lower than 60 in the lake center and agricultural and sluice areas, indicating light eutrophication.

The total precipitation in the growing seasons from May to October was 454, 499, and 507 mm in 2012, 2013 and 2014, respectively, and the highest rainfall occurred in July (Fig. 4). The air temperature ranged from 2.79℃ to 27.21℃ during the growing season from May to October in 2012, 2013, and 2014, with the highest air temperatures observed in July and August (Fig. 4). A Spearman’s Rank Correlation Coefficient test did not reveal a significant relationship between water quality and precipitation or air temperature (Table 3), except for a marked positive correlation between air temperature and TSS. However, there were negative correlations between water level and TN, COD_Mn, Chl-a, and NH₄-N (Table 3).

Figure 4.  Monthly precipitation and daily average temperature in Xiao Xingkai Lake in the growing season from 2012 to 2014

The results of nonparametric tests on the effect of sampling times and sites on water quality indicated that all water quality parameters were significantly influenced by sampling time (P < 0.05, Table 3). In addition, all water quality parameters, excluding TP, TSS, and Chl-a, were influenced by sampling site (Table 4).

There were significant correlations among water quality parameters based on Spearman’s rank correlation coefficient (Table 5). TN was significantly negatively correlated with DO and pH, and positively correlated with Chl-a. TP was significantly negatively correlated with EC and positively correlated with DO, NO₃-N, and NH₄-N. Chl-a was negatively correlated with pH, salinity and NO₃-N, and positively correlated with TN and COD_Mn. COD_Mn was negatively correlated with pH, and positively correlated with TN, Chl-a, and NH₄-N (Table 5).

Water quality often exhibits remarkable seasonal variations. For example, Hukou (outlet of Poyang Lake) exhibited the optimal water quality in summer and the worst in winter (Li et al., 2017b), NO₃⁻-N concentrations peaked in late winter and early spring in water bodies with different degrees of agricultural and urban pressures across Guangzhou and Foshan in China (Xu et al., 2017). Our results showed that water quality in Xiao Xingkai Lake also varied seasonally, with the TN, TP, and TSS concentrations, and EC and temperature being relatively high in summer, whereas DO concentrations were relatively low. Such indicators are key indices in evaluating water quality of lakes (Lin et al., 2020), and, according to our results, water quality in Xiao Xingkai Lake was worse in summer.

According to Sinha et al. (2017), global changes in precipitation are likely to exacerbate eutrophication particularly in India, China, and Southeast Asia based on climate models. The Spearman’s Rank Correlation Coefficient test results indicated that large water level fluctuation was the key factor driving shifts in water quality parameters, because high water levels have a dilution effect on nutrients (Liu et al. 2016). Agricultural drainage through the ecological ditches could have facilitated the elevation of the water levels of the lake after September, and as the water level increased, the dilution effect was reinforced, and the distance between the sampling points and the bottom sediment also increased. Consequently, seasonal variability in water quality could be correlated with agricultural activities.

Domestic wastewater and agricultural activities are major sources of nutrient pollutants (Chique et al., 2018, Bhagowati and Ahamad, 2019). Nutrients can promote the extensive growth of harmful algae, which results in eutrophication, which is widespread in highly polluted areas (Tong et al., 2017). Among all the water quality parameters, TN and TP have major implications for the control and management of eutrophication (Wang et al., 2017). According to our results, Chl-a was positively correlated with TN and COD_Mn, whereas COD_Mn was positively correlated with TN, Chl-a, and NH₄-N (Table 5), which indicated that TN could be the key factor driving water eutrophication.

Tourism, aquaculture, and agricultural drainage are the three dominant activities around Xiao Xingkai Lake. Intensive agricultural activities have led to significant environmental pollution (Xu et al., 2017), and agricultural non-point source pollution is currently the primary contributor to surface water eutrophication and ecosystem degradation, exceeding the influence of both residential and industrial inputs, in China (Sun et al., 2012). The northern part of Xiao Xingkai Lake is surrounded by farmland, connected to the lake by some inflow rivers and ecological ditches, which are used for irrigation and drainage. However, according to our results, the TLI value in agricultural areas indicated light eutrophic conditions, which could be attributed to absorption of nutrients by emergent vegetation in the river banks and wetlands in the area. The TLI values (58.52–64.41) from June to September were much higher than that in October (55.16), which indicated that human activities (tourism, aquaculture, agriculture) peaked in June to September. The intensity of the human activities corresponded to the climatic conditions. Overall, the climate was relatively warm from June to September, so that water quality monitoring and management activities in Xiao Xingkai Lake should pay particular attention to the four months. Lake eutrophication could also be attributed to excess nutrient inputs from internal sources of the surficial sediment (Bhattrai et al. 2017). Consequently, in future, more comprehensive analyses should be carried out to estimate the contributions of external and internal pollution resources in Xiao Xingkai Lake.

Eutrophication is a major problem globally that has to be addressed by the definition of water quality targets, and establishment of monitoring systems and intelligent watershed management plans (Abell et al., 2020). Although it is critical to prevent water quality deterioration in lake catchment environments (Li et al., 2017c); conflict between environmental protection and economic development makes the task challenging (Mueller et al., 2019). Nevertheless, to improve lake water quality, and in turn aquatic ecosystem health, numerous strategies could be adopted.

First, water quality and eutrophication monitoring in lakes should be enhanced. According to the results of the present study, water quality in Xiao Xingkai Lake was worse in summer than in early autumn, and was potentially exacerbated by human activities in summer. In addition, water quality models could be developed to explore the causes of water quality deterioration and eutrophication. Watershed modeling is another potential strategy of understanding the causes of eutrophication and formulating solutions to the problem. For example, the impacts of Welsh agri-environment schemes on water quality and freshwater ecosystem conditions have been explored using combined monitoring and modeling frameworks (Jones et al., 2017).

Second, measures should be taken to control external nutrient inputs into aquatic ecosystems. In Xiao Xingkai Lake examined in this study, external pollution inputs mainly originated from tourism, aquaculture, and agricultural activities. External pollution control is a complex process that requires the cooperation of local governments and nature reserves to establish discharge permit systems and buffer-zones before the drainage of agricultural waste into lakes, as well as tourism and fishing control. Furthermore, local governments should monitor and control upstream and non-point nutrient discharge (Kast et al., 2021). Multiple-pollutant discharge permit systems could also be established (Imani et al., 2017) between nature reserves and external pollutant producers and emitters. Such an approach requires a regional institution to coordinate monitoring, dynamic pricing, fair fund reallocation, and dissemination of information to participants, while ensuring sustainable incomes.

Decreasing local aquaculture activities is also one of the most effective strategies of reducing nutrient loads (Yazdi and Moridi, 2017), and agri-environment schemes can deliver improvements in water quality through reductions in diffuse pollution from agricultural sources (Jones et al., 2017). In addition, the construction and preservation of riparian buffer zones in such regions could offer considerable protection from non-point source pollutants and nutrients, for example, in areas within 200 m of riverbanks (Jabłońska et al., 2020). Although the area examined in this study already had a wetland, which could have cleaned the water via nutrient intake by emergent vegetation in the riverbank, the area and width were small, and the wetlands had not been protected. Stakeholders could also expand the areas of buffer zones in catchment areas.

Third, researchers and stakeholders should evaluate the contributions of external and internal pollution sources, to determine the main causes of water quality deterioration. The results of our previous study suggested that the Xiao Xingkai Lake is at high risk of eutrophication due to the release of phosphorus from surficial sediments (Yu, 2014). The results of our study indicated that external pollution, for example, from tourism, aquaculture, and agricultural drainage, were the major contributor to water quality deterioration in Xiao Xingkai Lake. Global changes in precipitation are also likely to exacerbate eutrophication based on climate models (Sinha et al., 2017). To facilitate the control and management of eutrophication, the relative contributions of internal and external pollution sources should be explored using high resolution datasets.

Last, nitrogen and phosphorus pollution should be controlled to manage eutrophication in lakes such as Xiao Xingkai Lake. Our results in this study indicate that TN and TP were the key factors influencing water quality in the lake. In addition to improving the control of agricultural nitrogen and phosphorus input, for example, through not only rational use of agricultural fertilizer but also through the control of endogenous nitrogen and phosphorus release. Consequently, accelerating the circulation and replenishment of lake water is a potential strategy of reducing nutrient concentrations, which has also been demonstrated in a study in Shahu Lake in a semiarid region in northwest China (Wu et al., 2017b). Additionally, to control the emission of internal nitrogen and phosphorus, emergent and submerged vegetation could be planted to purify the water and harvested to transfer some of the nutrients or pollutants (Yu, 2014).

This study revealed clear spatial and temporal patterns with regard to water quality and eutrophication status in Xiao Xingkai Lake, China. The seasonal shifts in water quality were not correlated with seasonal variations in precipitation and air temperature, but were potentially correlated with anthropogenic activities such as agriculture. Water quality was worse in summer, and TN and TP were the key pollutants that affected water quality. In addition, water level was the key environmental factor that influenced the water quality and eutrophication in Xiao Xingkai Lake; consequently, increasing water level could decrease water quality deterioration and the risk of eutrophication.

The lake transitioned to an light eutrophic status in 2013 and 2014 from a medium eutrophic status in 2012. Moreover, eutrophication was more widespread in summer than in autumn, and tourism, aquaculture and agriculture were the major activities that polluted the aquatic environment. The TLI values associated with agricultural activities were lower than those associated with tourism and aquaculture, potentially because the water was purified by inflows from rivers and adjacent wetlands. Consequently, the economic activities could be sustained in Xiao Xingkai Lake while maintaining the functions of the inflows and wetlands.

To control external point and non-point pollution in the lake, stakeholders could establish monitoring and control systems based on water quality models, multiple-pollutant discharge permit systems, agri-environment schemes, and inflow channels and wetlands. Accelerating the circulation and replenishment of lake water and planting large areas of submerged and emergent vegetation could also reduce pollutant concentrations in such lakes.

We thank Dr. Zhang Jitao, Lu Xinrui, Yang Yanli, Dr. Qin Yan and Dr. Song Hongli for help with sampling.