How Successful Are the Restoration Efforts of China's Lakes And

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Environment International 123 (2019) 96–103

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How successful are the restoration eﬀorts of China's lakes and reservoirs? T ⁎ ⁎ Jiacong Huanga,b, Yinjun Zhangc, George B. Arhonditsisd, Junfeng Gaoa, , Qiuwen Chenb, , Naicheng Wue,f, Feifei Dongd, Wenqing Shib a Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China b Center for Eco-Environment Research, Nanjing Hydraulic Research Institute, Nanjing 210098, China c China National Environmental Monitoring Centre, 8(B) Dayangfang Beiyuan Road, Chaoyang District, Beijing 100012, China d Ecological Modelling Laboratory, Department of Physical & Environmental Sciences, University of Toronto, Toronto, ON M1C 1A4, Canada e Aarhus Institute of Advanced Studies, Aarhus University, Høegh-Guldbergs Gade 6B, 8000 Aarhus C, Denmark f Department of Bioscience, Aarhus University, Ole Worms Allé 1, 8000 Aarhus C, Denmark

ARTICLE INFO ABSTRACT

Handling Editor: Yong-Guan Zhu China has made considerable efforts to mitigate the pollution of lakes over the past decade, but the success rate Keywords: of these restoration actions at a national scale remains unclear. The present study compiled a 13-year Lakes (2005–2017) comprehensive dataset consisting of 24,319 records from China's 142 lakes and reservoirs. We Restoration developed a novel Water Quality Index (WQI-DET), customized to China's water quality classification scheme, to Pollution investigate the spatio-temporal pollution patterns. The likelihood of regime shifts during our study period is Eutrophication examined with a sequential algorithm. Our analysis suggests that China's lake water quality has improved and is Heavy metals also characterized by two WQI-DET abrupt shifts in 2007 and 2010. However, we also found that the eutrophication problems have not been eradicated and heavy metal (HM) pollution displayed an increasing trend. Our study suggests that the control of Cr, Cd and As should receive particular attention in an effort to alleviate the severity of HM pollution. Priority strategies to control HM pollution include the reduction of the contribution from mining activities and implementation of soil remediation in highly polluted areas. The mitigation efforts of lake eutrophication are more complicated due to the increasing importance of internal nutrient loading that can profoundly modulate the magnitude and timing of system response to external nutrient loading reduction strategies. We also contend that the development of a rigorous framework to quantify the socioeconomic benefits from well-functioning lake and reservoir ecosystems is critically important to gain leeway and keep the investments to the environment going, especially if the water quality improvements in many Chinese lakes and reservoirs are not realized in a timely manner.

1. Introduction China's lake water quality is increasingly disconcerting due to the hazardous impacts of water quality deterioration (Tong et al., 2017; Covering a small part (1.8%) of earth's surface (Messager et al., Zhou et al., 2017). Water resources in nearly half of 634 Chinese rivers, 2016), lake ecosystems provide considerable services to human society lakes and reservoirs are polluted, failing to meet drinking water stan- including drinking water supply, transportation, recreation, and ﬁsh- dards for all or parts of the year. According to a 2009 nationwide eries. However, the water quality degradation has become a global survey, one-quarter of 4000 urban water-treatment plants did not problem, following the rapid post-World War II economic development comply with quality controls. These worrisome trends of water pollu- (Conley et al., 2009; Hering et al., 2015; Michalak et al., 2013; Stone, tion are responsible for an annual shortage of 40 billion tons of water in 2011; Ulrich et al., 2016). Water quality deterioration may be mani- China (Tao and Xin, 2014). In May 2007, a severe algal bloom event in fested as toxic algal blooms, prolonged hypoxia, severe contamination, Lake Taihu resulted in a drinking water crisis, aﬀecting the drinking which can collectively undermine the integrity of biotic communities water supply for approximately 2 million people in Wuxi, China (Qin and ultimately lead to loss of biodiversity (Dudgeon et al., 2006; Smith et al., 2010). In order to improve China's lake water quality, substantial and Schindler, 2009). investments from the Chinese Central government have been made to

⁎ Corresponding authors. E-mail addresses: [email protected] (J. Gao), [email protected] (Q. Chen). https://doi.org/10.1016/j.envint.2018.11.048 Received 8 October 2018; Received in revised form 17 November 2018; Accepted 19 November 2018 0160-4120/ © 2018 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). J. Huang et al. Environment International 123 (2019) 96–103 environmental remediation (Zhou et al., 2017). Between the 2005–2017 period, the Central government of China has introduced a wide range of strict laws, plans and guidelines, including the Guidelines on Strengthening Water Environmental Protection for Critical Lakes in 2008 and the Water Pollution Control Action Plan (10-Point Water Plan) in 2015 (Table S1 in the Supporting Information). Given the ominous water quality trends, China's lake water quality has been extensively studied with special emphasis on topics, such as eutrophication (Ni et al., 2016; Tong et al., 2017), harmful algal blooms (Duan et al., 2017; Stone, 2011), and nutrient loading (Cui et al., 2013; Liu et al., 2013; Yi et al., 2017). Three large eutrophic lakes (Lakes Taihu, Chaohu, and Dianchi) have received considerable attention due to their severe pollution and dire ramifications for the urban popula- tions in the surrounding watersheds (Huang et al., 2018; Wu et al., 2017a; Yang et al., 2008). However, little work has been done to evaluate the progress made with respect to the efficiency of water quality mitigation measures at a national scale, likely due to the challenges in obtaining datasets that impartially represent all the geographic regions in China. Based on national records of dissolved oxygen + (DO), chemical oxygen demand (COD), and ammonium (NH4 ), Zhou et al. (2017) reached the conclusion that China's increasing gross domestic product (GDP) during the 2006–2015 period did not occur at the expense of its inland waters, which was highlighted as a promising Fig. 1. Locations of China's 80 monitored lakes and 62 reservoirs during the ff prospect about our ability to e ectively balance between economic 2005–2017 period. growth and environmental sustainability (Zhou et al., 2017). None- theless, the sole use of three indicators may not be adequate to capture have a longer monitoring period (2005–2017) with a total of 16,543 the multifaceted nature of China's water quality problems. Viewed from records (68% of the dataset) (Fig. S1). The list of the studied lakes this standpoint, the question regarding the degree of success of the and reservoirs can be found in Table S2 of the Supporting In- restoration efforts in China's lakes and reservoirs has not been un- formation. equivocally addressed and invites further investigation. • This dataset included 12 variables from water quality samples: To this end, we compiled a 13-year (2005–2017) dataset comprising dissolved oxygen (DO, mg/L), chemical oxygen demand (COD, mg/ 24,319 records from 142 lakes and reservoirs at a national scale to L), ammonium (NH +, mg/L), total phosphorus (TP, mg/L), copper examine the trends of water quality in China's lakes and reservoirs. 4 (Cu, mg/L), zinc (Zn, mg/L), selenium (Se, mg/L), arsenic (As, mg/ Counter to Zhou et al.'s (2017) study, our work offers a more compre- L), mercury (Hg, mg/L), cadmium (Cd, mg/L), chromium (Cr, mg/ hensive assessment of the water quality trends based on twelve (12) L), lead (Pb, mg/L). Eight heavy metals (HMs) were included be- environmental quality indicators established in the literature. Our aim cause the severity of HM pollution is increasingly reported in China's is to delineate the presence of distinct spatio-temporal trends and pin- aquatic ecosystems (Bing et al., 2016). point promising trajectories and striking problems. Our study also provides recommendations that will consolidate the on-going remediation efforts of eutrophication and heavy metal (HM) pollution, 2.2. Methods including the rigorous valuation of ecosystem services and the estab- fi lishment of quality standards that can reliably capture the impact of the 2.2.1. A modi ed index to evaluate China's lake and reservoir water quality multitude of external stressors on China's lakes and reservoirs. The Water Quality Index (WQI) has been widely applied to quantify the degree of water quality degradation in a variety of lakes worldwide 2. Material and methods (Akkoyunlu and Akiner, 2012; Hurley et al., 2012; Wu et al., 2017b). In this study, we used a modification (i.e., linear transformation) of the 2.1. Study area and data Water Quality Index (WQI-DET) customized to China's water quality classification scheme. Specifically, WQI-DET is an adaptation of the fi “ In China, according to a national survey with satellite images during existing WQI to the ve water quality classes of China's Environmental ” the 2004–2007 period, there is a total of 2693 lakes (surface area > Quality Standards for Surface Water (GB3838-2002), i.e., I (excellent), 1km2) covering 81,415 km2 (Ma et al., 2011). There are also 89,700 II (good), III (moderate), IV (poor) and V (bad). Compared with the reservoirs covering 26,870 km2 (Yang and Lu, 2014). To address the original index, ranging from 0 to 100 (Wu et al., 2017b), WQI-DET has ∞ objectives of our study, we obtained a water quality dataset from the a broader range from - (extremely poor water quality) to 100 (ex- ff national sampling program in environmentally or socioeconomically cellent water quality) which allows to clearly di erentiate between bad important lakes and reservoirs conducted by the China National En- and extremely bad water quality conditions. Calculation details of WQI- vironmental Monitoring Centre (http://www.cnemc.cn/). To the best of DET are as follows: our knowledge, the present dataset is by far the most intensive to comprehensively represent China's lake and reservoir water quality: (1) Calculating WQI-DET for individuals water samples

j • The large dataset with 24,319 records covered a 13-year WQI-DET (WQIDET ) for a water sample j can be calculated as: (2005–2017) period with a monthly resolution at 80 lakes and 62 j j j j WQIDET =……min( WQIDET_1,, WQIDET_ i,, WQIDET_ n) (1) reservoirs across 31 Provinces in China (Fig. 1). These monitored I lakes and reservoirs were selected due to the prevailing water CCij − WQI j =−100max ⎛ 0, i × 100⎞ quality conditions and potential socioeconomic impacts, including DET_ i ⎜ V I ⎟ ⎝ CCi − i ⎠ (2) the 5 largest freshwater lakes in China. Moreover, 14 lakes and 1 j reservoir were of particular concern of the government, and thus where WQIDET_i is the WQI-DET value for the variable i of the water

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sample j; Cij is the concentration of the environmental variable i of the primarily impacted by HM pollution, especially Cr, and displays sig- I V water sample j; Ci and Ci are the concentration of the variable i at class nificant spatial variation (Fig. 2). Among the 142 monitored lakes and I and V, respectively. Twelve (12) water quality variables were used to reservoirs, evidence of water quality impairment was recorded in 31 calculate WQI-DET, i.e., n = 12, and their concentrations were eval- lakes and 7 reservoirs. Polluted water caused by Cr, Cd and As was uated against the corresponding water quality classes (Table S3). found in 19, 7 and 7 lakes or reservoirs, respectively, while inferior + water quality caused by TP, NH4 and COD was found in 1, 2 and 2 (2) Calculating monthly and annual WQI-DET values for a lake lakes or reservoirs, respectively (Fig. 2b). Several “hot spots” of poor water quality (WQI-DET < 40) were identified, including the Yunnan, Based on the calculated WQI-DET for individual water samples, the middle reach of Yellow River and Yangtze River (red cycles in monthly WQI-DET was calculated for each lake or reservoir by aver- Fig. 2a). The main variables responsible for the poor water quality aging all the values within a given month. This simple averaging characterization were HMs (Cr, As and Cd) in the middle reach of method allowed us to rule out the WQI-DET bias due to the unequal Yangtze River and Yellow River, and COD, TP, and As in Yunnan number of samples among the various lakes and reservoirs. Annual (Fig. 2b). Several lakes and reservoirs in Eastern China (see the red WQI-DET value for a given lake or reservoir was calculated by aver- cycle in Fig. 2b) were polluted by HMs (Cr, As and Cd), even though aging all the monthly WQI-DET values. their annual WQI-DET values were not low (60 < WQI-DET < 80 in Fig. 2a). Water quality in the lower reach of Pearl River and Yellow 2.2.2. A regime shift detection method to characterize the prevailing water River was relatively good, especially in Southeast China with WQI-DET values higher than 80 (Fig. 2a). Our spatial assessment of China's water quality conditions quality problems differs somewhat from Zhou et al.'s (2017) study, We used a sequential method, originally proposed by Rodionov which confined the water quality problems in Eastern China based on (2004), to tease out any abrupt shifts of the monthly WQI-DET values DO, COD and NH + data. during the 2005–2017 period. The method uses a pre-whitening pro- 4 Both monthly and annual WQI-DET trends provide evidence of an cedure to remove the red noise component from the time-series data improving water quality in 15 systems with long-term records (Fig. 3). (Rodionov, 2006), and subsequently examines the likelihood of regime The annual WQI-DET showed an increasing trend during the shifts using the Student's t-test (Rodionov, 2004). Compared with other 2005–2013 period (p < 0.001), suggestive of a water quality im- regime shift detection methods, this method has the advantage of de- provement. Two regime shifts were detected in June 2007 and July tecting an ecological regime shift earlier and then monitoring how its 2010 with an increase of the average WQI-DET values from −133.0 to magnitude changes over time. This advantage is mainly due to the use −68.1, and from −68.1 to 8.6, respectively. Polluted water (WQI- of exploratory analysis that does not require an a priori hypothesis on DET < 0) in China's lakes accounted for > 10% during the 2005–2009 the timing of regime shifts (Rodionov, 2004; see also the Supporting period, but decreased to < 10% during the 2014–2016 period. WQI- Information for further technical details). DET was relatively stable from 2011 to 2017 without any significant changes detected. Several lakes, especially Lakes Baiyangdian, Dianchi, 3. Results Taihu and Chaohu, showed significantly improved water quality from 2005 to 2010 (see annual WQI-DET trends for each lake and reservoir in 3.1. Spatio-temporal water quality trends in China's lakes and reservoirs Fig. S1). Considering that these 15 lakes and reservoirs constitute a primary concern in China, we herein provide their water quality Our results showed that China's lake and reservoir water quality is

Fig. 2. Spatial patterns of water quality in China's lakes and reservoirs in 2017. (a) Annual WQI-DET values. (b) Critical variables responsible for water quality impairment. Three red cycles (dashed lines) mark the “hot spots” of poor water quality (low WQI-DET). (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)

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Fig. 3. Trends of the monthly and annual WQI-DET values for China's 15 high-risk lakes and reservoirs during the 2005–2017 period. Blue line represents the 10th percentile of monthly WQI-DET values. Red line represents the average WQI-DET for each period derived from the regime shift detection exercise. Blue bars represent the median of the annual WQI-DET values in 15 lakes and reservoirs. The number of water samples for each month can be found in Fig. S2. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) conditions, as well as the exogenous sources of pollutants (Table S4). In 2008, a legislative framework came into effect to protect China's critical lakes and control water pollution (Fig. 4). Since 2008, many 3.2. Critical variables causing water quality deterioration in China's lakes guidelines, plans, and regulations were released to ensure that the ap- and reservoirs propriate remedial measures were taken to improve water quality, especially to control phosphorus and nitrogen loading (Table S1). In- Our investigation results showed that China's lake and reservoir vestments to total environmental restoration for improving China's lake fi water quality impairment was due to both HM pollution (i.e., high Cr, and river water quality signi cantly increased from nearly 0 in 1994 to + 1000 billion RMB yuan in 2014 (Zhou et al., 2017). The most important Cd and As) and eutrophication (i.e., high TP, NH4 and COD). Nonetheless, there is a clear shift from eutrophication to HM pollution strategy to control water pollution was based on the construction of ff in 2012. More than 60% of polluted water was caused by eutrophica- wastewater treatment plants (WWTPs) that can e ectively remove + tion during the 2005–2011 period (green colors in Fig. 4), but both TP pollutants (TP, NH4 and COD) from sewage (Yang et al., 2015; Zhang + et al., 2016). Both the number and daily wastewater treatment capacity and NH4 over-enrichment appear to have been reduced during the – of WWTPs in China increased significantly from 2000 (176 and 2011 2017 period (Fig. 4). HM pollution evidently becomes more 7 8 pronounced, especially Cr pollution (brown color in Fig. 4). Water 1.64 × 10 t/d) to 2010 (2739 and 1.25 × 10 t/d) (Fig. S3). In particular, during the 2008–2011 period, 2006 WWTPs with a total waste- quality impairment caused by Cr pollution increased from 2.0% in 2011 7 to 52.1% in 2017. water treatment capacity of 6.39 × 10 t/d were built that can partly fi + The critical variables responsible for the water quality impairment explain the signi cant decrease of TP, NH4 and COD pollution in in China's 15 lakes and reservoirs with long-term record suggest that 2012. + eutrophication (NH +, TP or COD) has been alleviated but not fully Although TP, NH4 and COD levels have been considerably reduced 4 ffl eliminated, while HM pollution displayed a distinctly increasing trend in e uents from WWTPs, there is still a distinct proportion (16.3%) of due to excessive discharges of Cr, Cd and As from industrial waste- polluted water caused by eutrophication in 2017 (Fig. 4). Eutrophica- water. Eutrophication was the main cause of water quality degradation tion is still a predominant factor of water quality impairment in many in Lakes Baiyangdian, Chaohu and Dianchi, while HM pollution was the large lakes (e.g., Lakes Baiyangdian and Dianchi) that provide im- predominant stressor in Lakes Poyang and Dongting (Fig. 5). Both Lakes portant ecosystem services, thereby posing considerable socioeconomic Taihu and Dalai showed a decreasing eutrophication trend, but in- threats. With the increasing number of WWTPs in place to control creasing HM pollution, whereas no impaired water quality was found in point-source pollution, the major drivers of China's lake eutrophication ff Lakes Jingbo and Qiandao during the 2005–2017 period. are gradually shifting from point to di use/non-point sources and internal nutrient loading (Huang et al., 2016; Wu et al., 2017a).

4. Discussion • Non-point source pollution from agriculture has been widely recognized to be a critically important source of nutrients for lakes ff 4.1. How successful have been the restoration e orts of China's lakes and due to intensive fertilization. Although there are several guidelines reservoirs over the past decade? and rules (e.g., Action plan for water pollution controls in China in Supporting Information) to mitigate non-point source pollution from Our analysis suggests that China has made considerable progress in agriculture, it is still very challenging for local farmers to effectively improving water quality, especially in alleviating eutrophication. Clear apply them in the field. China's crop production maintained an fi evidence about the latter conclusion is provided by the signi cant de- average P fertilization rate of 80 kg/ha in 2012, more than double + cline of both TP and NH4 concentrations. This success was mainly due the input intensity that crops can assimilate (Liu et al., 2016). Even ff to China's ambitious plans and considerable e orts in mitigating lake though the estimation of the magnitude of non-point source pollu- eutrophication, especially after the Lake Taihu's drinking water crisis in tion can be particularly uncertain (Ongley et al., 2010; Shen et al., late May of 2007 (Qin et al., 2010).

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Fig. 4. Contribution of critical variables to water quality impairment in China's lakes and reservoirs during the 2005–2017 period, combined with important laws, i m i i plans, and guidelines established to improve China's water quality. The contribution value for each variable was calculated by nnDET/ ∑i=1 DET , where nDET is the number of water samples with a WQI-DET value lower than 0 for the i variable, and m (=12) is the total number of variables used for our analysis. (For interpretation of the references to color in this ﬁgure, the reader is referred to the web version of this article.)

2012), China's N runoff is similarly projected to have increased by 46% and 31% for rice paddy fields and upland areas since 1990, respectively (Hou et al., 2018). • Despite the substantial external nutrient loading reduction for China's lakes, it is not surprising that eutrophication is not well controlled so far, because internal nutrient loading from lake sediments would delay their recovery (Schindler et al., 2016). A case study in Lake Dianchi revealed an overwhelmingly high (72–77%) contribution of internal phosphorus and nitrogen loading (Wu et al., 2017a). Another case study in Lake Taihu was suggestive of significant internal nutrient fluxes primarily mediated by wind resuspension (Huang et al., 2016). Therefore, eutrophication of these lakes cannot be well-controlled until the internal nutrient sources are permanently buried and/or washed out. • Although our study is based on a comprehensive dataset, it is important to note that there are still data limitations and knowledge gaps. Water samples were collected from 80 lakes and 62 reservoirs that cannot fully represent China's lake and reservoir water quality. For example, there are > 3000 reservoirs in Fujian Province, Southeast China (Zhu and Yang, 2018), but only one reservoir was available in our dataset (Table S2 in the Supporting Information). Moreover, the dataset used includes 12 water quality variables from Fig. 5. Critical variables responsible for the water quality impairment in water samples, but does provide any information for other con- China's 15 lakes with long-term record during the 2005–2017 period. It should taminants, such as pesticides and Persistent Organic Pollutants be noted that Lake Qiandao is a reservoir. (POPs). Consequently, a similar assessment exercise with more environmental variables could paint a somewhat different picture with respect to the spatiotemporal water quality trends in China.

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4.2. Why heavy metal pollution has been increasing in China over the past et al., 2018; Sharpley et al., 2011). Although their implementation has decade? been based on the stipulation that both short- and long-term effectiveness are guaranteed, emerging evidence is suggestive of moderate Although China's lake and reservoir water quality showed an overall water quality improvements in many watersheds and significant improvement from 2005 to 2017, HM pollution showed an increasing variability in BMP performance (Jarvie et al., 2013; Kleinman et al., trend. This problem becomes gradually worrisome for the Chinese 2011). In order to rectify the discrepancy between expected and actual government due to the high toxicity, prevalent existence and persis- BMP effectiveness into watershed management plans, Liu et al. (2018) tence of HMs (He et al., 2013). Our detected trends of elevated Cr, Cd proposed a framework that explicit accounts for the variability in and As concentrations can mainly be attributed to the following in- starting BMP efficiency to reduce the severity of runoff and pollutant tensive anthropogenic activities. concentrations due to local condition differences and installation practices; an intrinsic variability of operational performance due to 4.2.1. Industrial production watershed geophysical conditions, differential response to storm events, China is responsible for a considerable amount of Cr, Cd and As and seasonality; and an expected decline in BMP performance over wastes due to industrial production. Coal and oil combustion are two time, which in turn underscores the need for regular maintenance. main sources of Cr pollution, contributing > 60% of the Cr emissions to For the lakes with substantial internal nutrient loading (e.g, Lakes the atmosphere in China (Cheng et al., 2014). Cd has been widely used Dianchi and Taihu), ecological restoration can be helpful and has been in electroplating due to its corrosion resistance. Both Cr and Cd emis- generally implemented by restoring aquatic vegetation (Qin, 2009). sions to atmosphere can profoundly shape soil and water pollution (Wei The measure can be effective in remedying relatively small areas with and Yang, 2010). As is widely used in glass making, wood preservatives, substantial internal nutrient loading, but so far is scarcely applied to a batteries, semi-conductors and alloys, which can explain the significant larger scale due to its costly implementation and variant degree of increase in As mining activity from about 67,000 t in 1990 to over success. Thus, before outlining the most suitable remedial measures, it 300,000 t in 2010 (Shi et al., 2017). As losses from the anthroposphere is important to improve our mechanistic understanding of sediment to natural environment increased from about 60,000 t in 1990 to > diagenesis in most of China's freshwater lakes, i.e., the characterization 320,000 t in 2010. An exceptionally high fraction (about 98.7% in of organic matter mineralization and redox-controlled nutrient trans- 2010) of these losses have been discharged into soils (Shi et al., 2017), formation processes, or the determination of phosphorus fractionation and can subsequently cause As pollution in lake waters. within different sediment layers. A rich research agenda should be in place in order to effectively predicting the degree and timing of the 4.2.2. Atmospheric deposition sediment response or the likelihood of unexpected feedback loops that Cr, Cd and As emissions into the air are considerable due to coal and could delay the realization of the anticipated outcomes of the on-going oil combustion (Cheng et al., 2014; He et al., 2013; Tang et al., 2016). restoration efforts. Cr emissions to atmosphere amounted to approximately 20,000 t/yr in Eliminating HMs from lake and reservoir water is very challenging, 2009, and demonstrated an increasing trend during the 1990–2009 because they cannot be removed by the natural in-lake decomposition period (Cheng et al., 2014). These high emissions can lead to an ex- processes, which in turn can partly explain the increasing HM pollution cessively high atmospheric deposition of Cr, Cd and As into China's in China's lakes and reservoirs. Therefore, we believe that one of China's lakes and reservoirs. next priorities should be the control of HM sources and soil remediation. 4.2.3. Sludge production Reducing Cd, Cr and As pollution from anthropogenic activities. In The amount of sludge waste from China's WWTPs displayed an China, the most important sources of Cd, Cr and As stem from mining annual increase rate of 13% during the 2007–2013 period (Yang et al., activities with substantial waste discharge (Li et al., 2014). Therefore, 2015; Zhang et al., 2016). In 2013, China's WWTPs produced 6.25 the most effective approach is the strict regulation and meticulous million tons of sludge (dry solids) containing considerable amounts of control of mining discharges, especially small-scale mining, due to the Cr, Cd and As. Although the maximum allowable levels for Cr and Cd application of obsolete technology and inefficient mining operations (Li concentrations in the sludge from China's WWTPs are much lower than et al., 2014). From a long-term perspective, we strongly advocate the those in the European Union and the United States, most of the residues optimization of China's energy-resource sector by opting for the de- after sludge anaerobic digestion still cannot meet the standard of sludge velopment and gradual establishment of renewable energy sources as land application (Yang et al., 2015). The sludge can also potentially an alternative to coal and oil combustion. cause HM pollution of groundwater and rivers, which are ultimately Soil remediation. HM pollution in China's soil is severe (He et al., connected with lakes and reservoirs. 2013). These HM pollutants in agricultural soils are conceivably transported to rivers, lakes and reservoirs, and thus accentuate the 4.3. What are the appropriate next steps to mitigate water pollution in extent and severity of HM pollution. Soil remediation, such as electro- China's lakes and reservoirs? kinetic remediation, chemical elution, stabilisation and solidification, represent a promising suite of techniques to alleviate HM pollution in Our study provides evidence that remedial efforts to control eu- both soils and lake water (Tang et al., 2016). This strategy is particu- trophication and HM pollution should continue to be a priority in larly important to control Cd pollution, because 25.2% of China's China's research and environmental management agenda in order to agricultural soils are characterized by excessively high Cd levels (Teng eliminate issues of water quality impairment. Although eutrophication et al., 2014). Soil remediation is both technically difficult and costly, has been alleviated in recent years, the trends are still disconcerting due and therefore a priority list necessitates to first carry out soil re- to its harmful effects (Sinha et al., 2017; Stone, 2011). Nutrient load mediation in heavily polluted and/or high-risk areas (Li et al., 2014). reduction and ecological restoration are the most widely used mitiga- Viewing lakes and reservoirs as providers of economically valuable tion strategies (Le et al., 2010). benefits to society, the concept of ecosystem services effectively links In lakes and reservoirs, where the external nutrient loading is the their structural and functional integrity with human welfare (Pascual predominant factor modulating the eutrophication severity, the control et al., 2010). Given that environmental policy affects both the eco- of non-point source pollution is widely recognized as the most effective system state and the provision of services that human societies benefit framework (Schindler et al., 2016; Smith and Schindler, 2009). A from, we argue that the efficacy of China's lake and reservoir restora- variety of costly best management practices (BMPs) is available to tion efforts will be significantly enhanced by the development of a control pollution from diffuse agricultural and urban sources (Leitão rigorous framework that quantifies the economic benefits from

101 J. Huang et al. Environment International 123 (2019) 96–103 investing in environmental sustainability. To best communicate the Dudgeon, D., Arthington, A.H., Gessner, M.O., Kawabata, Z.-I., Knowler, D.J., Lévêque, trade-offs among policy choices, ecosystem service valuation must ex- C., Naiman, R.J., Prieur-Richard, A.-H., Soto, D., Stiassny, M.L., 2006. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. 81 amine the marginal improvement in ecosystem services attributable to (2), 163–182. https://doi.org/10.1017/S1464793105006950. a policy change. Economic values of ecosystem services can help policy- Egoh, B., Rouget, M., Reyers, B., Knight, A.T., Cowling, R.M., van Jaarsveld, A.S., Welz, makers determine the optimal degree of investment and action needed A., 2007. Integrating ecosystem services into conservation assessments: a review. fi ff ff Ecol. Econ. 63 (4), 714–721. https://doi.org/10.1016/j.ecolecon.2007.04.007. at each time step by de ning the monetary trade-o s from di erent He, B., Yun, Z., Shi, J., Jiang, G., 2013. Research progress of heavy metal pollution in courses of management action (Egoh et al., 2007). Viewed it from this China: sources, analytical methods, status, and toxicity. Chin. Sci. Bull. 58 (2), perspective, at the beginning of each restoration effort, the total returns 134–140. https://doi.org/10.1007/s11434-012-5541-0. and benefits are typically commensurate with the costs and invest- Hering, D., Carvalho, L., Argillier, C., Beklioglu, M., Borja, A., Cardoso, A.C., Duel, H., Ferreira, T., Globevnik, L., Hanganu, J., Hellsten, S., Jeppesen, E., Kodeš, V., Solheim, ments, but this pattern may not hold true after a certain point, where A.L., Nõges, T., Ormerod, S., Panagopoulos, Y., Schmutz, S., Venohr, M., Birk, S., we get diminishing (and ultimately negative) returns and marginal 2015. Managing aquatic ecosystems and water resources under multiple stress — an – benefits. Given the presence of a wide array of feedback loops, ecolo- introduction to the MARS project. Sci. Total Environ. 503-504, 10 21. https://doi. org/10.1016/j.scitotenv.2014.06.106. gical unknowns, and other external stressors, the rigorous valuation of Hou, X., Zhan, X., Zhou, F., Yan, X., Gu, B., Reis, S., Wu, Y., Liu, H., Piao, S., Tang, Y., the ecosystem services gained by China's lakes and reservoirs will likely 2018. Detection and attribution of nitrogen runoff trend in China's croplands. – provide the much-needed leeway to keep the investments to the en- Environ. Pollut. 234, 270 278. https://doi.org/10.1016/j.envpol.2017.11.052. Huang, L., Fang, H., He, G., Jiang, H., Wang, C., 2016. Effects of internal loading on vironment going. phosphorus distribution in the Taihu Lake driven by wind waves and lake currents. Environ. Pollut. 219, 760–773. https://doi.org/10.1016/j.envpol.2016.07.049. 5. Conclusions Huang, J., Zhang, Y., Huang, Q., Gao, J., 2018. When and where to reduce nutrient for controlling harmful algal blooms in large eutrophic lake Chaohu, China? Ecol. Indic. 89, 808–817. https://doi.org/10.1016/j.ecolind.2018.01.056. Spatio-temporal water quality trends in China's 142 lakes and re- Hurley, T., Sadiq, R., Mazumder, A., 2012. Adaptation and evaluation of the Canadian servoirs were investigated using a 13-year dataset with 24,319 records. Council of Ministers of the Environment Water Quality Index (CCME WQI) for use as an effective tool to characterize drinking source water quality. Water Res. 46 (11), Our results suggest that China has been successful in improving water 3544–3552. https://doi.org/10.1016/j.watres.2012.03.061. quality during the 2005–2017 period. The mitigation of eutrophication Jarvie, H.P., Sharpley, A.N., Spears, B., Buda, A.R., May, L., Kleinman, P.J.A., 2013. has been particularly successful, but there is still significant space for Water quality remediation faces unprecedented challenges from “legacy phos- ” – improvement in many lakes and reservoirs of societal and ecological phorus . Environ. Sci. Technol. 47 (16), 8997 8998. https://doi.org/10.1021/ es403160a. importance. HM (Cr, Cd, and As) pollution showed an increasing trend Kleinman, P.J.A., Sharpley, A.N., McDowell, R.W., Flaten, D.N., Buda, A.R., Tao, L., as a result of the significant contributions from industrial activities, Bergstrom, L., Zhu, Q., 2011. Managing agricultural phosphorus for water quality – atmospheric deposition, and sludge production. Reduction of non-point protection: principles for progress. Plant Soil 349 (1), 169 182. https://doi.org/10. ff 1007/s11104-011-0832-9. source pollution, internal nutrient loading, and cost-e ective soil re- Le, C., Zha, Y., Li, Y., Sun, D., Lu, H., Yin, B., 2010. Eutrophication of lake waters in mediation represent major challenges in our efforts to restore China's China: cost, causes, and control. Environ. Manag. 45 (4), 662–668. https://doi.org/ water resources. 10.1007/s00267-010-9440-3. Leitão, J.P., Carbajal, J.P., Rieckermann, J., Simões, N.E., Sá Marques, A., de Sousa, L.M., 2018. Identifying the best locations to install flow control devices in sewer networks Acknowledgments to enable in-sewer storage. J. Hydrol. 556, 371–383. https://doi.org/10.1016/j. jhydrol.2017.11.020. Li, Z., Ma, Z., van der Kuijp, T.J., Yuan, Z., Huang, L., 2014. A review of soil heavy metal This work was supported by Major Science and Technology Program pollution from mines in China: pollution and health risk assessment. Sci. Total for Water Pollution Control and Treatment of China (2017ZX07301- Environ. 468-469, 843–853. https://doi.org/10.1016/j.scitotenv.2013.08.090. 001-02) and “135” Key Program in Nanjing Institute of Geography and Liu, X., Zhang, Y., Han, W., Tang, A., Shen, J., Cui, Z., Vitousek, P., Erisman, J.W., Goulding, K., Christie, P., Fangmeier, A., Zhang, F., 2013. Enhanced nitrogen de- Limnology, Chinese Academy of Sciences (NIGLAS2018GH06). The position over China. Nature 494 (7438), 459–462. https://doi.org/10.1038/ authors would like to thank the Lake and Watershed Data Center nature11917. (http://lwdc.niglas.cas.cn) in Nanjing Institute of Geography and Liu, X., Sheng, H., Jiang, S., Yuan, Z., Zhang, C., Elser, J.J., 2016. Intensification of Limnology, Chinese Academy of Sciences for providing China's geo- phosphorus cycling in China since the 1600s. Proc. Natl. Acad. Sci. 113 (10), 2609–2614. https://doi.org/10.1073/pnas.1519554113. graphical data. Special thanks to Hongbin Lian (Wuyi University) for Liu, Y., Engel, B.A., Flanagan, D.C., Gitau, M.W., McMillan, S.K., Chaubey, I., Singh, S., his considerable work on data collection and processing. 2018. Modeling framework for representing long-term effectiveness of best management practices in addressing hydrology and water quality problems: framework development and demonstration using a Bayesian method. J. Hydrol. 560, 530–545. Appendix A. Supporting Information https://doi.org/10.1016/j.jhydrol.2018.03.053. Ma, R., Yang, G., Duan, H., Jiang, J., Wang, S., Feng, X., Li, A., Kong, F., Xue, B., Wu, J., Supporting Information to this article can be found online at Li, S., 2011. China's lakes at present: number, area and spatial distribution. Sci. China Earth Sci. 54 (2), 283–289. https://doi.org/10.1007/s11430-010-4052-6. https://doi.org/10.1016/j.envint.2018.11.048. Messager, M.L., Lehner, B., Grill, G., Nedeva, I., Schmitt, O., 2016. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. References Commun. 7 (13603). https://doi.org/10.1038/ncomms13603. Michalak, A.M., Anderson, E.J., Beletsky, D., Boland, S., Bosch, N.S., Bridgeman, T.B., Chaffin, J.D., Cho, K., Confesor, R., Daloğlu, I., Depinto, J.V., Evans, M.A., Akkoyunlu, A., Akiner, M.E., 2012. Pollution evaluation in streams using water quality Fahnenstiel, G.L., He, L., Ho, J.C., Jenkins, L., Johengen, T.H., Kuo, K.C., LaPorte, E., indices: a case study from Turkey's Sapanca Lake Basin. Ecol. Indic. 18, 501–511. Liu, X., McWilliams, M.R., Moore, M.R., Posselt, D.J., Richards, R.P., Scavia, D., https://doi.org/10.1016/j.ecolind.2011.12.018. Steiner, A.L., Verhamme, E., Wright, D.M., Zagorski, M.A., 2013. Record-setting algal Bing, H., Zhou, J., Wu, Y., Wang, X., Sun, H., Li, R., 2016. Current state, sources, and bloom in Lake Erie caused by agricultural and meteorological trends consistent with potential risk of heavy metals in sediments of Three Gorges Reservoir, China. expected future conditions. Proc. Natl. Acad. Sci. 110 (16), 6448–6452. https://doi. Environ. Pollut. 214, 485–496. https://doi.org/10.1016/j.envpol.2016.04.062. org/10.1073/pnas.1216006110. Cheng, H., Zhou, T., Li, Q., Lu, L., Lin, C., 2014. Anthropogenic chromium emissions in Ni, Z., Wang, S., Wang, Y., 2016. Characteristics of bioavailable organic phosphorus in China from 1990 to 2009. PLoS One 9 (2), e87753. https://doi.org/10.1371/journal. sediment and its contribution to lake eutrophication in China. Environ. Pollut. 219, pone.0087753. 537–544. https://doi.org/10.1016/j.envpol.2016.05.087. Conley, D.J., Paerl, H.W., Howarth, R.W., Boesch, D.F., Seitzinger, S.P., Havens, K.E., Ongley, E.D., Zhang, X., Yu, T., 2010. Current status of agricultural and rural non-point Lancelot, C., Likens, G.E., 2009. Controlling eutrophication: nitrogen and phos- source pollution assessment in China. Environ. Pollut. 158 (5), 1159–1168. https:// phorus. Science 323 (5917), 1014–1015. https://doi.org/10.1126/science.1167755. doi.org/10.1016/j.envpol.2009.10.047. Cui, S., Shi, Y., Groffman, P.M., Schlesinger, W.H., Zhu, Y., 2013. Centennial-scale ana- Pascual, U., Muradian, R., Rodríguez, L.C., Duraiappah, A., 2010. Exploring the links lysis of the creation and fate of reactive nitrogen in China (1910–2010). Proc. Natl. between equity and efficiency in payments for environmental services: a conceptual Acad. Sci. https://doi.org/10.1073/pnas.1221638110. approach. Ecol. Econ. 69 (6), 1237–1244. https://doi.org/10.1016/j.ecolecon.2009. Duan, H., Tao, M., Loiselle, S.A., Zhao, W., Cao, Z., Ma, R., Tang, X., 2017. MODIS ob- 11.004. servations of cyanobacterial risks in a eutrophic lake: implications for long-term Qin, B., 2009. Lake eutrophication: control countermeasures and recycling exploitation. safety evaluation in drinking-water source. Water Res. 122, 455–470. https://doi. Ecol. Eng. 35 (11), 1569–1573. https://doi.org/10.1016/j.ecoleng.2009.04.003. org/10.1016/j.watres.2017.06.022. Qin, B., Zhu, G., Gao, G., Zhang, Y., Li, W., Paerl, H.W., Carmichael, W.W., 2010. A

102 J. Huang et al. Environment International 123 (2019) 96–103

drinking water crisis in Lake Taihu, China: linkage to climatic variability and lake concentration accompanied by shift in sources since 2006. Nat. Geosci. 10, 507–511. management. Environ. Manag. 45 (1), 105–112. https://doi.org/10.1007/s00267- https://doi.org/10.1038/ngeo2967. 009-9393-6. Ulrich, A.E., Malley, D.F., Watts, P.D., 2016. Lake Winnipeg Basin: Advocacy, challenges Rodionov, S.N., 2004. A sequential algorithm for testing climate regime shifts. Geophys. and progress for sustainable phosphorus and eutrophication control. Sci. Total Res. Lett. 31 (9). https://doi.org/10.1029/2004GL019448. Environ. 542 (Part B), 1030–1039. https://doi.org/10.1016/j.scitotenv.2015.09.106. Rodionov, S.N., 2006. Use of prewhitening in climate regime shift detection. Geophys. Wei, B., Yang, L., 2010. A review of heavy metal contaminations in urban soils, urban Res. Lett. 33 (12). https://doi.org/10.1029/2006GL025904. road dusts and agricultural soils from China. Microchem. J. 94 (2), 99–107. https:// Schindler, D.W., Carpenter, S.R., Chapra, S.C., Hecky, R.E., Orihel, D.M., 2016. Reducing doi.org/10.1016/j.microc.2009.09.014. phosphorus to curb lake eutrophication is a success. Environ. Sci. Technol. 50 (17), Wu, Z., Liu, Y., Liang, Z., Wu, S., Guo, H., 2017a. Internal cycling, not external loading, 8923–8929. https://doi.org/10.1021/acs.est.6b02204. decides the nutrient limitation in eutrophic lake: a dynamic model with temporal Sharpley, A.N., Kleinman, P.J., Flaten, D.N., Buda, A.R., 2011. Critical source area Bayesian hierarchical inference. Water Res. 116, 231–240. https://doi.org/10.1016/ management of agricultural phosphorus: experiences, challenges and opportunities. j.watres.2017.03.039. Water Sci. Technol. 64 (4), 945–952. https://doi.org/10.2166/wst.2011.712. Wu, Z., Zhang, D., Cai, Y., Wang, X., Zhang, L., Chen, Y., 2017b. Water quality assessment Shen, Z., Liao, Q., Hong, Q., Gong, Y., 2012. An overview of research on agricultural non- based on the water quality index method in Lake Poyang: the largest freshwater lake point source pollution modelling in China. Sep. Purif. Technol. 84, 104–111. https:// in China. Sci. Rep. 7 (1), 17999. https://doi.org/10.1038/s41598-017-18285-y. doi.org/10.1016/j.seppur.2011.01.018. Yang, X., Lu, X., 2014. Drastic change in China's lakes and reservoirs over the past dec- Shi, Y., Chen, W., Wu, S., Zhu, Y., 2017. Anthropogenic cycles of arsenic in mainland ades. Sci. Rep. 4 (6041). https://www.nature.com/articles/srep06041. China: 1990–2010. Environ. Sci. Technol. 51 (3), 1670–1678. https://doi.org/10. Yang, M., Yu, J., Li, Z., Guo, Z., Burch, M., Lin, T., 2008. Taihu Lake not to blame for 1021/acs.est.6b01669. Wuxi's woes. Science 319 (5860), 158. https://doi.org/10.1126/science.319.5860. Sinha, E., Michalak, A.M., Balaji, V., 2017. Eutrophication will increase during the 21st 158a. century as a result of precipitation changes. Science 357 (6349), 405–408. https:// Yang, G., Zhang, G., Wang, H., 2015. Current state of sludge production, management, doi.org/10.1126/science.aan2409. treatment and disposal in China. Water Res. 78, 60–73. https://doi.org/10.1016/j. Smith, V.H., Schindler, D.W., 2009. Eutrophication science: where do we go from here? watres.2015.04.002. Trends Ecol. Evol. 24 (4), 201–207. https://doi.org/10.1016/j.tree.2008.11.009. Yi, Q., Chen, Q., Hu, L., Shi, W., 2017. Tracking nitrogen sources, transformation, and Stone, R., 2011. China aims to turn tide against toxic lake pollution. Science 333 (6047), transport at a basin scale with complex Plain River networks. Environ. Sci. Technol. 1210–1211. https://doi.org/10.1126/science.333.6047.1210. https://doi.org/10.1021/acs.est.6b06278. Tang, X., Li, Q., Wu, M., Lin, L., Scholz, M., 2016. Review of remediation practices re- Zhang, Q.H., Yang, W.N., Ngo, H.H., Guo, W.S., Jin, P.K., Dzakpasu, M., Yang, S.J., Wang, garding cadmium-enriched farmland soil with particular reference to China. J. Q., Wang, X.C., Ao, D., 2016. Current status of urban wastewater treatment plants in Environ. Manag. 181, 646–662. https://doi.org/10.1016/j.jenvman.2016.08.043. China. Environ. Int. 92-93, 11–22. https://doi.org/10.1016/j.envint.2016.03.024. Tao, T., Xin, K., 2014. A sustainable plan for China's drinking water: tackling pollution Zhou, Y., Ma, J., Zhang, Y., Qin, B., Jeppesen, E., Shi, K., Brookes, J.D., Spencer, R.G.M., and using different grades of water for different tasks is more efficient than making Zhu, G., Gao, G., 2017. Improving water quality in China: environmental investment all water potable. Nature 511 (7511), 527–529. https://doi.org/10.1038/511527a. pays dividends. Water Res. 118, 152–159. https://doi.org/10.1016/j.watres.2017. Teng, Y., Wu, J., Lu, S., Wang, Y., Jiao, X., Song, L., 2014. Soil and soil environmental 04.035. quality monitoring in China: a review. Environ. Int. 69, 177–199. https://doi.org/10. Zhu, Z., Yang, J., 2018. The spatial distribution of reservoirs in Fujian Province: higher 1016/j.envint.2014.04.014. density but lower storage in coastal than inland regions. J. Lake Sci. 30 (2), 567–580. Tong, Y., Zhang, W., Wang, X., Couture, R.-M., Larssen, T., Zhao, Y., Li, J., Liang, H., Liu, https://doi.org/10.18307/2018.0227. X., Bu, X., He, W., Zhang, Q., Lin, Y., 2017. Decline in Chinese lake phosphorus

103 HOW SUCCESSFUL ARE THE RESTORATION EFFORTS OF

CHINA’S LAKES AND RESERVOIRS?

[SUPPORTING INFORMATION]

Jiacong Huanga, b, Yinjun Zhangc, George B. Arhonditsisd, Junfeng Gaoa,*, Qiuwen Chenb,*, Naicheng Wue, f, Feifei Dongd, Wenqing Shib

a Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China b Center for Eco-Environment Research, Nanjing Hydraulic Research Institute, Nanjing 210098, China c China National Environmental Monitoring Centre, 8(B) Dayangfang Beiyuan Road, Chaoyang District, Beijing, 100012, China d Ecological Modelling Laboratory, Department of Physical & Environmental Sciences, University of Toronto, Toronto, ON M1C 1A4, Canada e Aarhus Institute of Advanced Studies, Aarhus University, Høegh-Guldbergs Gade 6B, 8000 Aarhus C, Denmark f Department of Bioscience, Aarhus University, Ole Worms Allé 1, 8000 Aarhus C, Denmark

* Corresponding author. E-mail: [email protected] (Junfeng Gao), [email protected] (Qiuwen Chen) 1

1. Sequential algorithm for regime shift detection

The sequential algorithm, proposed by Rodionov (2004), was used to detect any abrupt shifts with the monthly WQI-DET values for China’s lakes during the 2005-2017 period. The software used can be freely downloaded from https://www.beringclimate.noaa.gov/regimes/. The regime shift detection method is based on the following seven steps (Rodionov, 2004): Step 1. Set the cut-off length l (l=12 in our case) of the regimes to be determined for WQI-DET. The parameter l is similar to the cut-off point in low-pass filtering. Step 2. Determine the difference (diff) between mean values of two subsequent regimes that would be statistically significant according to the Student’s t-test:

2 푑푖푓푓 = 푡√2휎푙 /푙 where t is the value of t-distribution with 2l-2 degrees of freedom at the given probability level p (p=0.05 in our case). Here, we assume that the variances for both

2 regimes are the same and equal to the average variance 휎푙 for running l-month intervals in the WQI-DET time series.

Step 3. Calculate the mean 푥푅1 of the initial l values of WQI-DET as an estimate for regime R1 and the levels that should be reached in the subsequent l months to qualify ′ for a shift to regime R2, 푥푅2 = 푥푅1 ± 푑푖푓푓. Step 4. For each new value starting with month i=l+1 check whether it is greater than

푥푅1 + 푑푖푓푓 or less than 푥푅1 − 푑푖푓푓. If it does not exceed the 푥푅1 ± 푑푖푓푓 range, it is assumed that the current regime has not changed. In this case, recalculate the average 푥푅1 to include the new value xi and l-1 previous values of WQI-DET and wait for the next value to come. If the new value xi exceeds the 푥푅1 ± 푑푖푓푓 range, then this month is considered as a possible start point j of the new regime R2.

Step 5. After the shift point is established, each new value of xi , where i>j, is used to confirm or reject the null hypothesis of a regime shift at month j. If the anomaly 푥푖 − ′ 푥푅2 is of the same sign as the one at the time of a regime shift, it would increase the confidence that the shift did occur. The reverse is true if the anomalies have the opposite signs. This change in the confidence of a regime shift at i=j is reflected in the 2 value of the regime shift index (RSI), which represents a cumulative sum of the normalized anomalies: 푗+푚 ∗ 푥푖 푅푆퐼푖푗 = ∑ , 푚 = 0,1, … , 푙 − 1 푙휎푙 푖=푗

∗ ′ ∗ ′ Here 푥푖 = 푥푖 − 푥푅2 if the shift is up, or 푥푖 = 푥푅2 − 푥푖 if the shift is down. If at any time from i=j+1 to i=j+l-1 the RSI value turns negative, proceed to step 6, otherwise proceed to step 7. Step 6. The negative value of RSI means that the test for a regime shift at month j failed. Assign zero to RSI. Recalculate the average value 푥푅1 to include the value of

푥푗 and keep testing the values of 푥푖 starting with i=j+1 for their exceedence of the range 푥푅1 + 푑푖푓푓 as in step 4. Step 7. The positive value of RSI means that the regime shift at month j is significant ′ at the probability level p. Calculate the actual mean value for the new regime 푥푅2. At this point, it becomes the base one, against which the test will continue further. The search for the next shift to regime R3 starts from month i=j+1. This step back is necessary to make sure that the timing of the next regime shift is determined correctly even if the actual duration of regime R2 was less than l month. The calculations continue in a loop from step 4 through step 7 until all the available data for WQI-DET are processed.

Table S1: Recent laws, plans, guidelines and regulations aiming to improve China’s water quality.

Date of issuance Name of laws, plans, guidelines and regulations

2017/11/20 Guidelines on the lake chief system

2016/12/11 Guidelines on the river chief system

2016/3/16 Program of national surface water quality monitoring network in 13th Five-Year (2016-2020)

2015/7/9 Rules of funding management on water pollution control

2015/6/16 Measures for special funds management of substitute subsidies with rewards for urban sewage treatment facilities supporting pipe network construction

2015/4/16 Action plan for water pollution controls in China

2015/2/4 Regulations on pollution control from the large-scale livestock breeding

2014/9/26 Ecological overall planning for lakes with good water quality in China (2013-2020)

2014/6/16 Guidelines on how to improve rural sanitation and living environment

2014/4/25 Environmental Protection Law of the People's Republic

2014/3/26 Guidelines on further strengthening the environmental impact assessment of water conservancy facilities

2013/12/11 Guidelines on the funding for ecological environment protection of rivers and lakes in China

2013/11/11 Guidelines on constructions and investment of rural domestic sewage treatment facilities

2013/11/11 Guidelines for the construction of facilities treating the rural domestic waste

2013/11/11 Guidelines for the construction of facilities to protect the rural drinking water sources

2013/11/11 Guidelines for the construction of facilities to control the pollutions from the scatter-farming in the rural

2013/10/18 Regulation on Urban Drainage and Sewage Treatment

2013/7/17 Guidelines for funding on the ecological environment protection of rivers and lakes

2013/2/17 12th Five-Year plan of national environmental protection standard in China

2013/1/24 Statistics and monitoring on reduction of major water pollutants emissions in 12th Five-Year

2012/5/16 Pollution control plan in the main watersheds in China (2011-2015)

2011/12/22 Accounting rules for reduction of major pollutants in 12th Five-Year

2011/12/21 12th Five-Year plan for environmental protections in China

2011/12/8 Guide on strengthening the special funds management of the rural environmental protection during 12th Five-Year

2011/9/16 Management regulations of Taihu Basin

2011/9/12 Water pollution prevention and control plans of middle and lower reaches of Yangtze River (2011-2015)

2011/4/17 Measures for fund management of medium and small sized rivers in China

2010/12/30 Guidelines for prevention and control technology of pollutions from livestock and poultry breeding

2010/2/8 Guidelines for prevention and control technology of domestic pollutions in the rural

2009/8/26 Special fund management measures of sewage treatment and related facilities in the urban

2009/7/15 Guidelines to manage the funding for environmental protections in rural areas

2008/1/22 Guidelines on strengthening water environmental protection for critical lakes

2008/2/29 Law of the People's Republic of China on the Prevention and Control of Water Pollution

2007/11/26 11th Five-Year-Plan of national environmental protection in China

2007/11/21 Guidelines on strengthening rural environmental protection

2007/7/15 Guidelines to manage funding on the reduction of main pollutants funded by the central government

Table S2: List of China’s 82 lakes and 60 reservoirs monitored during the 2005-2017 period.

Lake Location (Province) Sampling stations Sample number Lake Ebinur Xinjiang 1 17 Reservoir Baiguishan Henan 1 24 Reservoir Bailianhe Hubei 1 23 Lake Baima Jiangsu 1 24 Reservoir Baipenzhu Guangdong 1 19 Lake Baiyangdian Hebei 9 923 Lake Baihua Guizhou 5 108 Lake Bangong Tibet 2 25 Lake Buir Inner Mongolia 1 89 Reservoir Biliuhe Liaolin 3 50 Lake Bosten Xinjiang 17 1109 Lake Caizi Anhui 1 72 Lake Chagan Jilin 3 45 Reservoir Chaersen Inner Mongolia 2 30 Lake Chaohu Anhui 14 1596 Lake Chenghai Yunnan 2 72 Lake Dalai Inner Mongolia 2 252 (Lake Hulun) Reservoir Daguangba Hainan 1 24 Reservoir Dahuofang Liaolin 3 54 Reservoir Dalong Hainan 1 24 Lake Daming Shandong 3 281 Lake Datong Hunan 1 24 Reservoir Danjiangkou Henan 6 144 Reservoir Danghe Gansu 1 23 Lake Dianchi Yunnan 11 1550 Lake Dianshan Shanghai 2 72 Lake Donghu Hubei 5 419 Reservoir Dongjiang Hunan 2 48 Lake Dongping Shandong 4 142 Lake Dongqian Zhejiang 2 45 Reservoir Dongpu Anhui 1 24 Lake Dongting Hunan 23 1984 Reservoir Erwangzhuang Tianjin 1 24 Lake Erhai Yunnan 11 1141 Reservoir Fenhe Shanxi 6 60 Reservoir Fengshuba Guangdong 1 21 Lake Fuxian Yunnan 5 210 Lake Futou Hubei 2 142 Reservoir Fushui Hubei 1 23 Reservoir Gangnan Hebei 1 21

Lake Shuanghai Shandong 1 21 Lake Gaoyou Anhui 3 210 Reservoir Gaozhou Guangdong 2 48 Reservoir Geheyan Hubei 1 24 Reservoir Hedi Guangdong 1 24 Lake Heihe Gansu 3 63 Lake Hengshui Hebei 4 71 Lake Hongfeng Guizhou 7 150 Lake Hongjian Shanxi 1 13 Reservoir Hongjiannao Gansu 1 24 Lake Honghu Hubei 4 284 Lake Hongze Jiangsu 8 934 Reservoir Hunanzhen Zhejiang 1 24 Lake Huating Anhui 1 24 Reservoir Huairou Beijing 1 21 Lake Huandong Hubei 3 56 Reservoir Huangbizhuang Hebei 1 19 Lake Huangda Anhui 5 91 Reservoir Huanglongtan Hubei 2 48 Lake Jiaogang Anhui 1 23 Reservoir Jiefangcun Gansu 1 24 Lake Jingpo Heilongjiang 3 270 Lake Kunming Beijing 1 65 Reservoir Laoshan Shandong 1 24 Reservoir Lishimen Zhejiang 1 24 Reservoir Lianhua Heilongjiang 1 18 Lake Liangzi Hubei 10 572 Lake Longgan Anhui 1 72 Reservoir Longtan Guangxi 3 57 Reservoir Longyangxia Qinghai 3 58 Lake Longnan Gansu 1 21 Lake Lugu Yunnan 2 72 Reservoir Luban Sichuan 1 24 Reservoir Luhun Henan 1 21 Lake Luoma Jiangsu 2 144 Reservoir Meishan Anhui 1 21 Reservoir Miyun Beijing 1 23 Reservoir Mopanshan Heilongjiang 1 23 Lake Namtso Tibet 2 31 Reservoir Nanshui Guangdong 1 21 Lake Nansi Shandong 5 777 Reservoir Nanwan Henan 1 24 Lake Nanyi Anhui 2 144 Reservoir Nierji Heilongjiang 2 30

Reservoir Nianyushan Henan 1 24 Lake Poyang Jiangxi 18 1498 Lake Qilu Yunnan 1 24 Lake Qiandao Zhejiang 3 370 (Reservoir Xinanjiang) Lake Qianhe Henan 1 21 Reservoir Mengjin Xinjiang 1 2 Lake Qiong Sichuan 3 66 Lake Sayram Xinjiang 1 16 Reservoir Sanmenxia Henan 1 23 Lake Selin Tibet 1 17 Lake Shahu Ningxia 1 22 Reservoir Shankouhu Heilongjiang 3 39 Reservoir Shanmei Fujian 3 66 Lake Shengjin Anhui 1 71 Reservoir Shimen Shanxi 1 24 Reservoir Shuangta Gansu 1 24 Reservoir Shuifeng Liaolin 3 49 Reservoir Shuifumiao Hunan 1 21 Lake Songhua Jilin 5 371 Reservoir Songtao Hainan 1 24 Lake Taihu Jiangsu 30 3264 Lake Taiping Anhui 1 72 Reservoir Tongshanyuan Zhejiang 1 24 Lake Wabu Anhui 1 72 Lake Wanfeng Guizhou 1 22 Reservoir Wangkuai Hebei 1 19 Reservoir Wangyao Shanxi 1 24 Lake Wolong Liaolin 3 31 Lake Wuliangsu Inner Mongolia 3 56 Lake Ulungur Xinjiang 3 113 Lake Wuchang Anhui 1 72 Reservoir Xidayang Hebei 1 18 Lake West Zhejiang 3 285 Lake Ximaoli Hunan 1 21 Reservoir Xiashan Shandong 1 21 Lake Xiannv Jiangxi 4 87 Lake Xiangshan Ningxia 1 21 Reservoir Xianghongdian Anhui 1 21 Reservoir Xiaolangdi Henan 3 66 Lake Xiaoxingkai Heilongjiang 3 192 Reservoir Xinfengjiang Guangdong 1 24 Lake Xingyun Yunnan 1 24 Lake Khanka Heilongjiang 6 504

Lake Xuanwu Jiangsu 2 173 Reservoir Yazidang Ningxia 1 24 Lake Yangzhuoyong Tibet 1 20 Lake Yangcheng Jiangsu 1 72 Lake Yangzong Yunnan 1 72 Lake Yilong Yunnan 1 24 Lake Yinghu Shanxi 1 24 Reservoir Yuqiao Tianjin 1 18 Reservoir Yutan Chongqing 3 66 Lake Yunmeng Shandong 3 66 Reservoir Zhanghe Hubei 2 48 Lake Changshou Chongqing 3 63 Reservoir Changtan Zhejiang 1 24 Reservoir Zhaopingtai Henan 1 24 Lake Zhelin Jiangxi 3 66

Table S3: Threshold concentrations (mg/L) of twelve environmental variables to characterize five water quality classes in China’s “Environmental Quality Standards for Surface Water” (GB3838-2002).

Variables I II III IV V DO ≥7.5 [6, 7.5) [5, 6) [3, 5) [2, 3) COD ≤2 (2, 4] (4, 6] (6, 10] (10, 15] + NH4 ≤0.15 (0.15, 0.5] (0.5, 1.0] (1.0, 1.5] (1.5, 2.0] TP ≤0.02 (0.02, 0.1] (0.1, 0.2] (0.2, 0.3] (0.3, 0.4] Cu ≤0.01 (0.01, 1.0] (0.01, 1.0] (0.01, 1.0] (0.01, 1.0] Zn ≤0.05 (0.05, 1.0] (0.05, 1.0] (1.0, 2.0] (1.0, 2.0] Se ≤0.01 ≤0.01 ≤0.01 (0.01, 0.02] (0.01, 0.02] As ≤0.05 ≤0.05 ≤0.05 (0.05, 0.1] (0.05, 0.1] Hg ≤0.00005 ≤0.00005 (0.00005, 0.0001] (0.0001, 0.001] (0.0001, 0.001] Cd ≤0.001 (0.001, 0.005] (0.001, 0.005] (0.001, 0.005] (0.005, 0.01] Cr ≤0.01 (0.01, 0.05] (0.01, 0.05] (0.01, 0.05] (0.05, 0.1] Pb ≤0.01 ≤0.01 (0.01, 0.05] (0.01, 0.05] (0.05, 0.1]

Table S4: Water pollution conditions and sources of pollutants in 15 lakes and reservoirs of particular concern in China..

Primary-concern Lakes Description of pollution sources pollutants

Nutrients mainly originate from agricultural activities, but nutrients Lake Nutrients from wastewater in urban areas have also shown an increasing trend Baiyangdian (Yang et al., 2019). The heavy metals of primary concern in the sediments were Cd and As, Lake Dalai HMs mainly originating from agricultural activities (Sun et al., 2018). Internal nutrient loading has a large contribution of TP (77%) and TN (72%), thereby dominating the total loading throughout the year (Wu et Lake Dianchi Nutrients al., 2017). External nutrient loadings were mainly from wastewater in urban areas, agricultural activities, and phosphorus mining and processing (Li et al., 2019). Both external and internal nutrient loading were the important sources Lake Chaohu Nutrients for Lake Chaohu (Huang et al., 2018). The lake is classified as mesotrophic, but its trophic state has been Lake Bosten Nitrogen deteriorating during the past few years. N sources were mainly from agricultural activities and industrial wastewater (Xie et al., 2011). Sediment resuspension is an important process replenishing the water column with nutrients and heavy metals (Huang et al., 2016; Wang et Nutrients and al., 2013). HM concentrations in surface sediments are relatively high, Lake Taihu HMs suggesting a high potential of heavy metal release (Yin et al., 2011). The external nutrient sources mainly comprise industrial sources, domestic sewage and agricultural activities (Qin et al., 2007). Heavy metal contamination is a problem in Lake Poyang with high Cr, Cu, Cd, Pb, and Zn concentrations registered in the surface sediments Lake Poyang HMs and sediment cores. HM sources for Lake Poyang are mainly the upstream industrial wastewater and sewage discharge (Dai et al., 2018; Niu et al., 2017). The lake is moderately polluted by HMs mainly stemming from metals Lake Nansi HMs mining and smelting, industry/traffic sources, and agricultural activities (Wang et al., 2014). Heavy metal pollution is severe especially in the south and east lake areas. Cd, Hg and As are the priority pollutants in the sediments. Zn, Lake HMs Pb, Cd and As mainly originate from mining wastewater and industrial Dongting wastewater. Cr and Cu are mainly derived from natural erosion and agricultural activities (Li et al., 2013). Water pollution in this lake has been scarcely reported in previous Lake Jingpo publications. The risk of HM pollution demonstrated an increasing trend (Zhao et al., Lake Songhua 2011). Water pollution in this lake has been scarcely reported in previous Lake Khanka publications. Heavy metal pollution, especially Cd pollution, in Lake Hongze is particularly disconcerting (Yu et al., 2011). The main sources of HMs Lake Hongze HMs include the industrial wastewater, agricultural activities, and aquatic breeding (Zhu et al., 2017). As, Cd and Hg are the most concerning heavy metals with respect to environmental monitoring and management. Heavy metals mainly Lake Erhai HMs originate from atmospheric, industrial, and agricultural sources (Lin et al., 2016). This lake is major drinking water supplier in China. Water quality is Lake Qiandao relatively good (Fig. S1).

Note: P stands for phosphorus; N nitrogen; TP, total phosphorus; TN, total nitrogen; HMs, heavy metals. 11

Fig. S1: Trends of the annual WQI-DET values for China’s 15 lakes and reservoirs during the 2005-2017 period: Lake Baiyangdian (a), Lake Dalai (b), Lake Dianchi (c), Lake Chaohu (d), Lake Bosten (e), Lake Taihu (f), Lake Poyang (g), Lake Nansi (h), Lake Dongting (i), Lake Jingpo (j), Lake Songhua (k), Lake Khanka (l), Lake Hongze (m), Lake Erhai (n) and Lake Qiandao (o). Outlier values (red dots) were not used for our quadratic fitting. Outlier values were detected based on traditional

Tukey's fences. The lower and upper fences were calculated by Q1-3×(Q3-Q1) and Q3+3×(Q3-Q1), where Q1 and Q3 are the 25% and 75% percentile values.

Fig. S2: The number of water samples obtained from China’s 15 lakes and reservoirs during the 2005-2017 period: Lake Baiyangdian, Lake Dalai, Lake Dianchi, Lake Chaohu, Lake Bosten, Lake Taihu, Lake Poyang, Lake Nansi, Lake Dongting, Lake Jingpo, Lake Songhua, Lake Khanka, Lake Hongze, Lake Erhai and Lake Qiandao (which is actually a reservoir).

Fig. S3: Daily wastewater treatment capacity and number of wastewater treatment plants (WWTPs) in China. Data obtained from the Ministry of Ecology and Environment, the People’s Republic of China (http://english.mep.gov.cn/#) and past literature (Yang et al., 2011).

References

Dai, L., Wang, L., Li, L., Liang, T., Zhang, Y., Ma, C., Xing, B., 2018. Multivariate geostatistical analysis and source identification of heavy metals in the sediment of Poyang Lake in China. Sci. Total Environ. 621, 1433-1444. https://doi.org/10.1016/j.scitotenv.2017.10.085. Huang, J., Zhang, Y., Huang, Q., Gao, J., 2018. When and where to reduce nutrient for controlling harmful algal blooms in large eutrophic lake Chaohu, China? Ecol. Indic. 89, 808-817. https://doi.org/10.1016/j.ecolind.2018.01.056. Huang, L., Fang, H., He, G., Jiang, H., Wang, C., 2016. Effects of internal loading on phosphorus distribution in the Taihu Lake driven by wind waves and lake currents. Environ. Pollut. 219, 760-773. http://dx.doi.org/10.1016/j.envpol.2016.07.049. Li, F., Huang, J., Zeng, G., Yuan, X., Li, X., Liang, J., Wang, X., Tang, X., Bai, B., 2013. Spatial risk assessment and sources identification of heavy metals in surface sediments from the Dongting Lake, Middle China. Journal of Geochemical Exploration 132, 75-83. https://doi.org/10.1016/j.gexplo.2013.05.007. Li, X., Janssen, A.B.G., de Klein, J.J.M., Kroeze, C., Strokal, M., Ma, L., Zheng, Y., 2019. Modeling nutrients in Lake Dianchi (China) and its watershed. Agric. Water Manag. 212, 48-59. https://doi.org/10.1016/j.agwat.2018.08.023. Lin, Q., Liu, E., Zhang, E., Li, K., Shen, J., 2016. Spatial distribution, contamination and ecological risk assessment of heavy metals in surface sediments of Erhai Lake, a large eutrophic plateau lake in southwest China. CATENA 145, 193-203. https://doi.org/10.1016/j.catena.2016.06.003. Niu, Y., Niu, Y., Yu, H., Jiang, X., Guo, X., Pang, Y., Xu, X., 2017. Concentration distribution and toxicity of heavy metals in surface sediment of Poyang Lake, China. Wetlands 2017, 1-8. https://doi.org/10.1007/s13157-017-0904-6. Qin, B., Xu, P., Wu, Q., Luo, L., Zhang, Y., 2007. Environmental issues of Lake Taihu, China. Hydrobiologia 581(1), 3-14. https://doi.org/10.1007/s10750-006-0521-5. Rodionov, S.N., 2004. A sequential algorithm for testing climate regime shifts. Geophysical Research Letters 31(9). https://doi.org/10.1029/2004GL019448. Sun, D., Zang, S., Sun, H., Zhang, N., Zhang, K., Sun, L., 2018. Pollution history and potential ecological risk assessment of heavy metals in core sediments in Hulun Lake during the past 150 years. Journal of Agro-Environment Science 37(1), 137-147 (In Chinese with English Abstract). Wang, C., Shen, C., Wang, P., Qian, J., Hou, J., Liu, J., 2013. Modeling of sediment and heavy metal transport in Taihu Lake, China. Journal of Hydrodynamics 25(3), 379-387. http://dx.doi.org/10.1016/S1001-6058(11)60376-5. Wang, L., Yang, L., Kong, L., Li, S., Zhu, J., Wang, Y., 2014. Spatial distribution, source identification and pollution assessment of metal content in the surface sediments of Nansi Lake, China. Journal of Geochemical Exploration 140, 87-95. https://doi.org/10.1016/j.gexplo.2014.02.008. Wu, Z., Liu, Y., Liang, Z., Wu, S., Guo, H., 2017. Internal cycling, not external loading, decides the nutrient limitation in eutrophic lake: A dynamic model with temporal Bayesian hierarchical inference. Water Res. 116, 231-240. https://doi.org/10.1016/j.watres.2017.03.039. Xie, G., Zhang, J., Tang, X., Cai, Y., Gao, G., 2011. Spatio-temporal heterogeneity of water quality (2010-2011) and succession patterns in Lake Bosten during the past 50 years. Journal of Lake 15

Sciences 23(6), 837-846 (In Chinese with English Abstract). Yang, J., Strokal, M., Kroeze, C., Wang, M., Wang, J., Wu, Y., Bai, Z., Ma, L., 2019. Nutrient losses to surface waters in Hai He basin: A case study of Guanting reservoir and Baiyangdian lake. Agric. Water Manag. 213, 62-75. https://doi.org/10.1016/j.agwat.2018.09.022. Yang, Y., Wang, Y., Huang, Q., Zheng, X., Yang, Y., Sun, C., Xiao, Q., 2011. Analysis on current municicpal wastewater treatment plants operation in China. Water & Wastewater Engineering 37(8), 35-39 (In Chinese with English Abstract). Yin, H., Gao, Y., Fan, C., 2011. Distribution, sources and ecological risk assessment of heavy metals in surface sediments from Lake Taihu, China. Environ. Res. Lett. 6, 044012. https://doi.org/10.1088/1748-9326/6/4/044012. Yu, h., Zhang, W., Yu, J., 2011. Distribution and potential ecological risk assessment of heavy metals in surface sediments of Hongze Lake. Environmental Science 32(2), 437-444 (In Chinese with English Abstract). https://doi.org/10.13227/j.hjkx.2011.02.035. Zhao, Y., Lu, J., Hao, L., Sun, L., 2011. Grey prediction of Cd, Hg pollution risk in Songhua Lake sediments. Journal of Shenzhen University Science and Engineering 28(4), 368-372 (In Chinese with English Abstract). Zhu, C., Zhu, Y., Han, J., Li, C., 2017. The heavy metal pollution situation and control in Hongze Lake. Journal of Nanjing Forestry University 41(3), 175-181 (In Chinese with English Abstract). https://doi.org/10.3969/j.issn.1000-2006.201604014.