Changes in Water Environment in Erhai Lake and Its Influencing Factors

water

Article Changes in Water Environment in Erhai Lake and Its Inﬂuencing Factors

Liang Zheng 1 , Zeyu An 2, Xiaoling Chen 1,* and Hai Liu 2,*

1 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; [email protected] 2 Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China; [email protected] * Correspondence: [email protected] (X.C.); [email protected] (H.L.)

Abstract: In recent years, the rapid development of the population, agriculture, and tourism around Erhai Lake has caused increasing environmental problems, which have seriously affected the ecological status of the lake. This study analyzed changes in water volume and quality in Erhai Lake, based on statistical data from 2000 to 2019, combined with climate, land-use type, and socioeconomic data, as well as the inﬂuencing factors of water environmental changes in the Erhai Lake basin. The main conclusions include: the water storage of Erhai Lake increased by 3.8 × 106 m3 year−1, from 2000 to 2019. The monthly variation in water volume showed a trend of ﬁrst decreasing and then increasing, in which it increased from August to December and decreased from January to July. The change in water volume was mainly affected by climate factors. From 2000 to 2019, the nitrogen concentration in Erhai Lake showed an increasing trend, and the changes in water quality were closely related to human activities. In the northern part of the basin, agricultural nonpoint source pollution was the main factor affecting water quality, while in the southern part of the basin, economic development, Citation: Zheng, L.; An, Z.; Chen, X.; accelerated urbanization, and tourism were the main factors affecting water quality. Liu, H. Changes in Water Environment in Erhai Lake and Its Influencing Keywords: climate; human activities; water environment; Erhai Lake Factors. Water 2021, 13, 1362. https:// doi.org/10.3390/w13101362

Academic Editors: Jean-Luc Probst, 1. Introduction Richard C. Smardon and As an important part of the natural ecosystem, lakes have multiple functions, such Jianzhong Lu as regulating and storing runoff, water supply and irrigation, regulating the climate and environment, and maintaining the balance of the ecosystem, all of which have important Received: 23 March 2021 impacts on human survival and development [1]. In recent decades, climate warming and Accepted: 11 May 2021 Published: 14 May 2021 increased human activities have caused many problems for the lake water environment, which have led to lake water ecological degradation, and caused serious environmental

Publisher’s Note: MDPI stays neutral problems worldwide [2–5]. The study of the lake water environment is not only related to with regard to jurisdictional claims in the sustainable development of lake basins but also the strategic need to realize the sustain- published maps and institutional affil- able development of the regional ecological economy and national security and stability. iations. Climate change can potentially have a large impact on the global water cycle by modi- fying precipitation and evaporation patterns [6,7]. Precipitation is the main supplement to the water volume of inland lakes, and its fluctuations will affect the water volume of lakes [8]. With the increase in temperature, the evaporation of plants and land will increase, the water consumption of crops will increase, and the water resources will change [9]. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. With the rapid development of China’s economy, the acceleration of industrialization and This article is an open access article urbanization, the trade-off between human and environmental needs has gradually become distributed under the terms and more prominent, and human activities have become a factor that cannot be ignored when conditions of the Creative Commons intervening in lake ecosystems. Studies have shown that human activities such as chang- Attribution (CC BY) license (https:// ing land use [10], economic development [11], building water conservancy projects [8], creativecommons.org/licenses/by/ and combined sewer overflow [12] can change the ecological environments of lakes in a 4.0/). relatively short period. Quantitative assessment of the impact of human activities on the

Water 2021, 13, 1362. https://doi.org/10.3390/w13101362 https://www.mdpi.com/journal/water Water 2021, 13, 1362 2 of 17

water environment has attracted the attention of many scholars. Human activities involve several aspects of the economy and society that, in turn, are modulated by a wide range of factors. However, due to the limitations of traditional statistical survey methods, the acquisition of socioeconomic parameters often has some shortcomings, such as a large error, considerable time requirements, and a lack of spatial information. As an important data source for measuring the spatial and temporal characteristics of human activity intensity, remote sensing and other spatial data have shown great potential in acquiring land-use information and ecological environmental parameter information [13–15]. Erhai Lake is the core of China’s National Nature Reserve, a scenic spot, and the main drinking water source of Dali city. In recent years, with the rapid development of social economy, the shortage of water resources and the decline in water quality in Erhai Lake are prominent [16]. This lake is considered to be in the representative preliminary eutrophic stage [17,18]. Huang et al. showed that changes in precipitation and temperature affected the water volume of Erhai Lake [19]. With the acceleration of urbanization, human activities have gradually become an important factor affecting the water quality of Erhai Lake in recent years. Li et al. studied the impact of human activities on water quality by estimating the net nitrogen input of human activities in the Erhai Lake basin [20]. Wang et al. and Ying et al. showed that the impact of human activities on the Erhai Lake environment is mainly manifested in agricultural nonpoint source pollution caused by unreasonable human farming methods and excessive fertilization [21,22]. Yang et al. showed that topdressing activities would lead to the eutrophication of Erhai Lake [23]. Sun et al. [24] showed that tourism activities and development caused pollution to the seawater environment of Erhai Lake. At present, how to comprehensively use multisource spatiotemporal data and other types of ecological–economic–social development data to quantitatively measure the intensity of human activity and analyze its temporal and spatial distribution law is still scarce. Based on the statistical data, this study analyzed the water volume and water quality changes in Erhai Lake from 2000 to 2019, analyzed the human activities in the basin with the help of land-use type, social and economic data, and discussed the inﬂuencing factors of Erhai Lake environmental changes, the results provide theoretical support for the sustainable development of the Erhai Lake basin.

2. Study Area and Data 2.1. Study Area The Erhai Lake basin (25◦250–26◦160 N, 99◦320–100◦270 E) is located in the Bai Au- tonomous Prefecture of Dali city, Yunnan Province, China (Figure1). The basin includes the urban area of Dali city and Eryuan County. The basin area is approximately 2565 km2, and the lake area is approximately 250 km2. The Erhai Lake basin belongs to the mid- subtropical southwest monsoon climate zone, which is affected by the Indian Ocean climate. The annual average precipitation is 994.2 mm, and the precipitation is unevenly distributed throughout the year. Approximately 85.6% of the precipitation is concentrated from May to October, and the multiyear average temperature is 14.6 ◦C. Water 2021Water, 202113, ,x13 FOR, 1362 PEER REVIEW 3 of 17 3 of 16

FigureFigure 1. Location1. Location of the of Erhai the LakeErhai basin Lake and basin Erhai. and Erhai. 2.2. Data 2.2.The Data data used in this study included remote sensing image data, meteorological stationThe data, data and statistical used in data; this thestudy data listincluded is shown remote in Table 1sensing. image data, meteorological sta-

Tabletion 1.data,Data andand sources statistical used in thedata; study. the data list is shown in Table 1. The gross domestic product (GDP) and population spatial distribution data came Type Time Product Source from the China Resource and Environmental Science and Data Center. The GDP and pop- http://www. ulation distribution data have aGlobal spatial land coverresolution with ofgloballandcover.com/ 1 km, and the, unit is ten thousand GlobeLand30 2000, 2020 yuan/square kilometer and person/square30 m spatial resolution kilometer, respectively.accessed on The temporal period for the selected data was 2000, 2005, 2010, and 2015. 9 October 2020. Monthly precipitation http://data.cma.cn/, Precipitation and temperatureand data average come from weather stations. The land-use type Meteorological data 2000–2019 accessed on temperature at data selected were GlobeLand30, with a resolution of 309 Octoberm donated 2020. by China to the United Nations meteorological stations December water http://www.dali.gov.cn/ The year-end (December) watervolume, volume water use, waterdlrmzf/c102607/common_ use structure, and wastewater dis- Statistical data 2007–2019 charge data of Erhai Lake camestructure, from the wastewater Dali Waterlist.shtml Resources, accessed Bulletin. on The spatial distribution data of water quality candischarge obtain data only the nitrogen9 October concentration, 2020. and nitrogen http://www.cnemc.cn/, Annual and monthly hasTN always concentration been the primary 2000–2019 pollutant affecting the wateraccessed quality on of Erhai Lake [25]; thus, TN concentration the total nitrogen (TN) concentration was used as the 9water October quality 2020. data for Erhai Lake. http://www.resdc.cn/ WeGDP, collected population spatialannual 2000,and 2005,monthly TN concentrations in Erhai Lake, as well as TN concen- 1 km spatial resolution Default.aspx, accessed on trationdistribution data datafrom four2010, water 2015 quality monitoring sections. 9 October 2020.

Table 1. Data and sources used in the study. The gross domestic product (GDP) and population spatial distribution data came from the China Resource and Environmental Science and Data Center. The GDP and population distributionType data have a spatialTime resolution of 1 km,Product and the unit is ten thousand Source yuan/square kilometer and person/square kilometer, respectively. The temporalhttp://www.globalland- period for the selected data was 2000, 2005, 2010, and 2015.Global land cover with 30 GlobeLand30 2000, 2020 cover.com/, accessed on m spatial resolution 9 October 2020. Monthly precipitation http://data.cma.cn/, ac- Meteorological data 2000–2019 and average temperature cessed on 9 October 2020. at meteorological stations December water volume, http://www.dali.gov.cn water use structure, /dlrmzf/c102607/com- Statistical data 2007–2019 wastewater discharge mon_list.shtml, ac- data cessed on 9 October 2020.

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Precipitation and temperature data come from weather stations. The land-use type data selected were GlobeLand30, with a resolution of 30 m donated by China to the United Nations. The year-end (December) water volume, water use structure, and wastewater discharge data of Erhai Lake came from the Dali Water Resources Bulletin. The spatial distribution data of water quality can obtain only the nitrogen concentration, and nitrogen has always been the primary pollutant affecting the water quality of Erhai Lake [25]; thus, the total nitrogen (TN) concentration was used as the water quality data for Erhai Lake. We collected annual and monthly TN concentrations in Erhai Lake, as well as TN concentration data from four water quality monitoring sections.

3. Methods 3.1. Correlation Analysis Correlation analysis is a statistical method to deal with the relationship between variables. We used correlation analysis to study the relationship between various factors, Table2 shows the variables that need to calculate the correlation. The correlation analysis formula is as follows:

− n − ∑i=0 ai − a bi − b rab = s (1) 2 −2 n − n ∑i=0 ai − a ∑i=0 bi − b

where rab is the correlation coefficient between variables a and b, ai and bi are the values of variables a and b in the i-th year, a is the average value of variable a in the study period, b is the average value of variable b in the study period, and n is the study period. We tested the significance of correlation analysis results, p < 0.05 indicated a significant correlation, and p < 0.01 indicated an extremely significant correlation.

Table 2. Correlation analysis factor.

Factor a Factor b Annual precipitation water volume Annual average temperature water volume Monthly precipitation water volume Monthly average temperature water volume Annual precipitation TN concentration Annual average temperature TN concentration Monthly precipitation TN concentration Monthly average temperature TN concentration water consumption water volume

3.2. Land-Use Transfer Matrix GlobeLand30 contains 7 primary types: cultivated land, forest, grassland, shrubland, wetland, water body, and man-made surface. We merged shrubs and forests, wetlands and water, and artificial ground as construction land. Finally, the land-use types are divided into five types: cultivated land, forest, grassland, water, and construction land. The land-use transfer matrix is the main method to quantitatively study the changes in the areas devoted to specific land uses and to highlight mutual conversions among different land uses. We used the land-use transfer matrix to analyze the land-use changes in the Erhai Lake Basin from 2000 to 2020. Water 2021, 13, x FOR PEER REVIEW 5 of 16

Water 2021, 13, 1362 5 of 17 where i and j represent the land-use types of the Erhai Lake basin in 2000 and 2020, respectively; n represents the total number of land-use types; and sij is the total area trans- The data reﬂect the structural characteristics and change direction of land-use types in formedthe study area. from The class speciﬁc i calculationto class formulaj in the is asstudy follows: period.   S11 S12 ...S1n 4. Results  S21 S22 ...S2n  Sij =   (2) 4.1. Dynamic Change in Water...... Volume......  Sn1 Sn2 ...Snn

whereFigure i and j represent 2 shows the land-usethe change types of in the water Erhai Lake volume basin in of 2000 Erhai and 2020, Lake at the end of each year fromrespectively; 2000 n to represents 2019. theThe total figure number shows of land-use that types; the and average sij is the water total area volume of Erhai Lake at the transformed from class i to class j in the study period. end of the 20 years was 2.81 billion m3, the maximum water volume at the end of the year was4. Results 2.94 billion m3 (2010), the minimum water volume at the end of the year was 2.52 4.1. Dynamic Change in Water Volume 3 billionFigure m2 shows (2003), the change and inthe water ratio volume between of Erhai Lake the at maximum the end of each value year from and the minimum value was 1.17.2000 to During 2019. The figurethe study shows that period, the average the water annual volume end of- Erhaiof-year Lake atwater the end volume of of Erhai Lake showed 3 the 20 years was 2.81 billion m , the maximum water volume at the end of the year was 6 3 –1 an2.94 upward billion m3 (2010), fluctuating the minimum trend, water volumewith ata thechange end of thetrend year wasof 2.523.8 billion× 10 m year . The interannual changm3 (2003),e kurtosis and the ratio was between 1.447, the maximum which was value steeper and the minimum than the value normal was 1.17. distribution, indicating that During the study period, the annual end-of-year water volume of Erhai Lake showed an theupward end fluctuating-of-year trend, water with volume a change trend fluctuation of 3.8 × 106 mof3 yearErhai–1. The Lake interannual contained more extreme values, withchange a kurtosis skewness was 1.447, of which −1.013, was steeper indicating than the normalthat the distribution, extreme indicating values that were distributed to the left the end-of-year water volume fluctuation of Erhai Lake contained more extreme values, withof the a skewness mean ofvalue,−1.013, and indicating the thatdata the on extreme the values left side were distributedwere more to the discrete. left of the mean value, and the data on the left side were more discrete.

Figure 2. Changes in the water volume of Erhai Lake in December from 2000 to 2019. Figure 2. Changes in the water volume of Erhai Lake in December from 2000 to 2019. The average monthly water volume of Erhai Lake from 2000 to 2019 showed that the annual cycle of monthly water storage of Erhai Lake (Figure3) oscillated from a minimum value inThe July average (2.62 × 109 monthlym3) to a maximum water value volume in November of Erhai (2.981 Lake× 109 mfrom3), with 2000 to 2019 showed that the annualan average cycle of 2.788 of× monthly109 m3. During water the storage year, the water of Erhai volume Lake ﬁrst decreased (Figure and 3) oscillated from a minimum then increased; speciﬁcally, it decreased9 3 from January to July and increased from August 9 3 valueto December. in July (2.62 × 10 m ) to a maximum value in November (2.981 × 10 m ), with an average of 2.788 × 109 m3. During the year, the water volume first decreased and then increased; specifically, it decreased from January to July and increased from August to De- cember.

Figure 3. Average monthly water volume change in Erhai Lake from 2000 to 2019.

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where i and j represent the land-use types of the Erhai Lake basin in 2000 and 2020, respectively; n represents the total number of land-use types; and sij is the total area transformed from class i to class j in the study period.

4. Results 4.1. Dynamic Change in Water Volume Figure 2 shows the change in water volume of Erhai Lake at the end of each year from 2000 to 2019. The figure shows that the average water volume of Erhai Lake at the end of the 20 years was 2.81 billion m3, the maximum water volume at the end of the year was 2.94 billion m3 (2010), the minimum water volume at the end of the year was 2.52 billion m3 (2003), and the ratio between the maximum value and the minimum value was 1.17. During the study period, the annual end-of-year water volume of Erhai Lake showed an upward fluctuating trend, with a change trend of 3.8 × 106m3 year–1. The interannual change kurtosis was 1.447, which was steeper than the normal distribution, indicating that the end-of-year water volume fluctuation of Erhai Lake contained more extreme values, with a skewness of −1.013, indicating that the extreme values were distributed to the left of the mean value, and the data on the left side were more discrete.

Figure 2. Changes in the water volume of Erhai Lake in December from 2000 to 2019.

The average monthly water volume of Erhai Lake from 2000 to 2019 showed that the annual cycle of monthly water storage of Erhai Lake (Figure 3) oscillated from a minimum value in July (2.62 × 109 m3) to a maximum value in November (2.981 × 109 m3), with an average of 2.788 × 109 m3. During the year, the water volume first decreased and then increased; specifically, it decreased from January to July and increased from August to De-

Water 2021, 13, 1362 cember. 6 of 17

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4.2. Dynamic Change in Water Quality 4.2.1. Time Change From 2000 to 2019, the annual average TN concentration in Erhai Lake showed a fluctuating upward trend (Figure 4a), with a change trend of 0.0075 mg/L year–1. The average value was 0.52 mg/L, the minimum value appeared in 2000 (0.33 mg/L), and the maximum value appeared in 2006 (0.66 mg/L). Regarding the interannual change trend, there was an upward trend from 2000 to 2003, a downward trend from 2004 to 2014, an FigureFigure 3. 3.Average Average monthly monthly water volume water change volume in Erhai Lake change from 2000 in toErhai 2019. Lake from 2000 to 2019. upward trend from 2015 to 2019, and a large fluctuation range from 2004 to 2010. 4.2. Dynamic Change in Water Quality Figure 4b shows the4.2.1. monthly Time Change variation trend of the TN concentration in Erhai Lake from 2000 to 2019. The figureFrom shows 2000 to 2019,that the the annual TN averageconcentration TN concentration was highest in Erhai Lake in August showed a (0.620 mg/L) and lowestﬂuctuating in April upward (0.475 trend mg/L). (Figure The4a), TN with concentration a change trend of showed 0.0075 mg/L an year upward–1. The average value was 0.52 mg/L, the minimum value appeared in 2000 (0.33 mg/L), and the trend from May to Augustmaximum and valuea downward appeared in trend 2006 (0.66 from mg/L). September Regarding theto interannualApril. The change TN con- trend, centration was highest inthere August. was an upward trend from 2000 to 2003, a downward trend from 2004 to 2014, an upward trend from 2015 to 2019, and a large ﬂuctuation range from 2004 to 2010.

(a) (b) Figure 4. (a) AnnualFigure and 4. ( a(b) Annual) monthly and (b )TN monthly concentration TN concentration in Erhai in Erhai Lake, Lake, from from 2000 2000 to 2019. to 2019. Figure4b shows the monthly variation trend of the TN concentration in Erhai Lake 4.2.2. Spatial Change from 2000 to 2019. The figure shows that the TN concentration was highest in August (0.620 mg/L) and lowest in April (0.475 mg/L). The TN concentration showed an upward Figure 5 shows thetrend variation from May in to the August TN andconcentration a downward trend in Erhai from September Lake and to April.its sections The TN in the north–south directionconcentration from was2002 highest to 2019. in August. The figure shows that from 2002 to 2019, the mass concentration 4.2.2.of TN Spatial in the Change four sections of Erhai Lake first increased, then fluctuated and decreased, and Figurethen 5increased, shows the variation which in thewas TN basically concentration consistent in Erhai Lake with and itsthe sections varia- in tion trend of the TN concentrationthe north–south in direction the whole from 2002 lake. to 2019. The The average figure shows annual that from TN 2002 concentra- to 2019, the mass concentration of TN in the four sections of Erhai Lake first increased, then fluctuated tion in the four sectionsand ranged decreased, from and 0.34 then increased,mg/L to which 0.75 wasmg/L, basically and consistent the maximum with the variation and min- trend imum values appeared ofin the the TN Taoyuan concentration-Shuanglang in the whole lake. section. The average Spatially, annual the TN concentrationTN concentra- in the tions of the Taoyuan–Shuanglang section in the north and the Xiaoguanyi–Shifangzi section in the south were higher than the average value for Erhai Lake, while the TN concentrations of the Xizhou–Kanglang section and Longkan-Tacun section in the middle were lower than the average value for Erhai Lake.

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four sections ranged from 0.34 mg/L to 0.75 mg/L, and the maximum and minimum values appeared in the Taoyuan-Shuanglang section. Spatially, the TN concentrations of the Taoyuan–Shuanglang section in the north and the Xiaoguanyi–Shifangzi section in the Water 2021, 13, x FOR PEER REVIEW south were higher than the average value for Erhai Lake, while the TN concentrations7 of 16 of the Xizhou–Kanglang section and Longkan-Tacun section in the middle were lower than the average value for Erhai Lake.

Figure 5. TN concentration of Erhai Lake and its sections in the north-south direction from 2002 to 2019. Figure 5. TN concentration of Erhai Lake and its sections in the north-south direction from 2002 to 2019. 4.3. Influence of Precipitation and Temperature on Water Environment 4.3.1. Precipitation Change 4.3. Influence of PrecipitationFrom and 2000 Temperature to 2019, the average on Water annual Environment precipitation of Erhai Lake presented a fluctuating upward trend (Figure6a), with a change trend of 2.939 mm/year, and the average 4.3.1. Precipitation Changeannual precipitation over 20 years was 949.54 mm, with a minimum annual precipitation of 613.2 mm in 2006 and a maximum annual precipitation of 1282.2 mm in 2008. According From 2000 to 2019,to the the change average in annual annual precipitation precipitation and the of trend Erhai of theLake sixth-order presented principal a fluc- value tuating upward trendfunction (Figure in the6a), Erhai with basin, a change the interannual trend variationof 2.939 in mm/year, precipitation and could the be dividedaverage into annual precipitation overfive periods. 20 years Specifically, was 949.54 2000–2002, mm, 2008–2012, with a minimum and 2016–2019 annual were periodsprecipitation with more precipitation, and the average precipitation in these periods was higher than the average of 613.2 mm in 2006 andprecipitation a maximum for many annual years. precipitation There was less of precipitation 1282.2 mm in 2003–2007in 2008. According and 2013–2015, to the change in annualand precipitation the average precipitation and the in trend these periodsof the wassixth lower-order than principal the average value precipitation func- for tion in the Erhai basin,many the years.interannual variation in precipitation could be divided into five periods. Specifically, 2000–2002, 2008–2012, and 2016–2019 were periods with more precipitation, and the average precipitation in these periods was higher than the average precipitation for many years. There was less precipitation in 2003–2007 and 2013–2015, and the average precipitation in these periods was lower than the average precipitation for many years.

(a) (b) Figure 6. (a) Annual and (b) monthly precipitation in Erhai Lake, from 2000 to 2019.

From 2000 to 2019, the average monthly precipitation reached a maximum (215.1 mm) in August and a minimum (12.0 mm) in December (Figure 6b). Regarding the change trend, the increasing trend of precipitation was not obvious from January to April, but it increased significantly from May to August and decreased significantly from September to December. The seasonal variation in precipitation in the Erhai Lake basin was obvious.

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Figure 5. TN concentration of Erhai Lake and its sections in the north-south direction from 2002 to 2019.

4.3. Influence of Precipitation and Temperature on Water Environment 4.3.1. Precipitation Change From 2000 to 2019, the average annual precipitation of Erhai Lake presented a fluctuating upward trend (Figure 6a), with a change trend of 2.939 mm/year, and the average annual precipitation over 20 years was 949.54 mm, with a minimum annual precipitation of 613.2 mm in 2006 and a maximum annual precipitation of 1282.2 mm in 2008. According to the change in annual precipitation and the trend of the sixth-order principal value function in the Erhai basin, the interannual variation in precipitation could be divided into five periods. Specifically, 2000–2002, 2008–2012, and 2016–2019 were periods with more precipitation, and the average precipitation in these periods was higher than the average precipitation for many years. There was less precipitation in 2003–2007 and 2013–2015, and

Waterthe2021 average, 13, 1362 precipitation in these periods was lower than the average precipitation for8 of 17 many years.

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The precipitation was mainly distributed from May to October (flood season and wet season), accounting for 87% of the annual precipitation. The precipitation from November to (a) (b) April (dry season) accounted for only 13% of the annual precipitation. Figure 6. (a) AnnualFigure and 6. (ab)) Annual monthly and (precipitationb) monthly precipitation in Erhai in ErhaiLake, Lake, from from 2000 2000 to 2019.2019. 4.3.2. Temperature ChangeFrom 2000 to 2019, the average monthly precipitation reached a maximum (215.1 mm) FromFrom 2000 to to 2019, 2019,in Augustthe the annual average and a average minimum monthly temperature (12.0 precipitation mm) in December of Erhai reached (Figure Lake6 b). a sho maximum Regardingwed a fluctuat- the (215.1 change mm) in August and a minimumtrend, the increasing (12.0 mm) trend in of December precipitation (Figure was not obvious 6b). Regarding from January the to April,change but it ing upward trend (Figureincreased 7a), significantly with a change from May trend to August of 0.043 and decreased °C/year. significantly The average from September annual to trend,temperature the increasing for 20 years trendDecember. was of precipitation The15.55 seasonal °C. variationThe was minimum innot precipitation obvious annual infrom the temperature Erhai January Lake basin to April,appeared was obvious. but init The inc2000reased at 14.71 significantly °C, and theprecipitation from maximum May was to annual mainlyAugust distributed average and decreased from temperature May to significantly October appeared (flood seasonfrom in 2010 andSeptember wetat 16.14 season), to December. The seasonalaccounting variation for 87% in of precipitation the annual precipitation. in the TheErhai precipitation Lake basin from was November obvious. to April °C. According to the annual(dry season) average accounted temperature for only 13% change of the annual in the precipitation. Erhai Lake basin and the trend of the sixth-order principal value function, the interannual variation in the average 4.3.2. Temperature Change annual temperature was mainly divided into two periods. During 2000–2007, there was a From 2000 to 2019, the annual average temperature of Erhai Lake showed a fluctuating low average annual temperature,upward trend (Figureand the7a), average with a change annual trend temperature of 0.043 ◦C/year. during The averagethis period annual was lower than the multiyeartemperature average. for 20 years During was 15.55 2008◦C.– The2019, minimum there annualwas a temperature high annual appeared average in 2000 ◦ ◦ temperature, and the averageat 14.71 C, annual and the maximumtemperature annual during average this temperature period appeared was higher in 2010 than at 16.14 theC. According to the annual average temperature change in the Erhai Lake basin and the trend multiyear average. of the sixth-order principal value function, the interannual variation in the average annual The monthly averagetemperature temperature was mainly from divided 2000 into two to periods. 2019 reached During 2000–2007, a maximum there was value a low (20.65 °C) in June andaverage a minimum annual temperature, value (9.04 and °C) the averagein November. annual temperature In terms during of the this periodchange was lower than the multiyear average. During 2008–2019, there was a high annual average trends, the average temperaturetemperature, andshowed the average a clear annual increasing temperature trend during from this January period was to higher June than and the a clear decreasing trendmultiyear from average.July to December (Figure 7b).

(a) (b) Figure 7. (a) AnnualFigure and 7. ((ab)) Annual monthly and ( btemperature) monthly temperature in Erhai in Erhai Lake, Lake, from from 2000 2000 to 2019. 2019.

4.3.3. Relationship between Changes in Climate Factors and Water Environment The interannual fluctuation in water volume in the Erhai Lake basin was greatly affected by precipitation (Table 3). There was a significant positive correlation between water volume and annual precipitation from 2000 to 2019, with a correlation coefficient of 0.577 (p < 0.01). There was no significant correlation between the water volume at the end of the year and the annual average temperature, with a correlation coefficient of −0.113. The correlation coefficients were −0.759 (p < 0.01) and −0.841 (p < 0.01), respectively, indicating that the seasonal fluctuation in water volume was greatly affected by climate factors. On the annual scale, there was no significant correlation between precipitation, temperature and TN concentration. On the monthly scale, there was a significant positive correlation between TN concentration and precipitation, and the correlation coefficient was 0.833 (p < 0.01).

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The monthly average temperature from 2000 to 2019 reached a maximum value (20.65 ◦C) in June and a minimum value (9.04 ◦C) in November. In terms of the change trends, the average temperature showed a clear increasing trend from January to June and a clear decreasing trend from July to December (Figure7b).

4.3.3. Relationship between Changes in Climate Factors and Water Environment The interannual fluctuation in water volume in the Erhai Lake basin was greatly affected by precipitation (Table3). There was a significant positive correlation between water volume and annual precipitation from 2000 to 2019, with a correlation coefficient of 0.577 (p < 0.01). There was no significant correlation between the water volume at the end of the year and the annual average temperature, with a correlation coefficient of −0.113. The correlation coefficients were −0.759 (p < 0.01) and −0.841 (p < 0.01), respectively, indicating that the seasonal fluctuation in water volume was greatly affected by climate factors.

Table 3. Correlation coefﬁcients of climatic factors, water volume, and pollutant concentration of Erhai Lake.

Water Volume TN Concentration Annual precipitation 0.577 (p < 0.01) −0.09 Annual average temperature −0.113 −0.049 Monthly precipitation −0.759 (p < 0.01) 0.644 (p < 0.05) Monthly average temperature −0.841(p < 0.01) 0.372

On the annual scale, there was no significant correlation between precipitation, temperature and TN concentration. On the monthly scale, there was a significant positive correlation between TN concentration and precipitation, and the correlation coefficient was 0.833 (p < 0.01).

4.4. Impact of Human Activities on Water Environment 4.4.1. Changes in Human Activities From 2000 to 2020, the change in land-use types in the Erhai Lake basin was mainly as follows (Table4): cultivated land and construction land increased, and forest land, grassland, and water areas decreased. Cultivated land and construction land increased by 7.21 and 80.26 km2, respectively, while forest, grassland and water areas decreased by 25.22, 58.96, and 3.30 km2, respectively. The increased cultivated land was mainly converted from forest and grassland, and the increased construction land was mainly converted from cultivated land and forest. The increase in cultivated land was concentrated in the north of the basin, and the increase in construction land was mainly concentrated in the area around Erhai Lake and in the south of Erhai Lake.

Table 4. Land-use change in the Erhai Lake basin from 2000 to 2020 (km2).

2020 Land-use type Cultivated land Forests Grassland Water Construction land Sum Cultivated land 49,992.75 2343.33 2576.43 215.01 6822 61,949.52 Forests 3666.33 114,568.65 6472.71 66.06 1921.59 126,695.34 Grassland 6253.29 6825.51 26,571.06 12.51 2061 41,723.37 2000 Water 163.53 300.69 89.82 25,536.06 94.14 26,184.24 Construction land 2595.15 134.91 117.45 24.93 10,180.71 13,053.15 Sum 62,671.05 124,173.09 35,827.47 25,854.57 21,079.44 269,605.62

Figure8 shows the spatial distribution and changes in GDP in the Erhai Lake basin from 2000 to 2015. The ﬁgure shows that the GDP distribution in the basin was low in the north and high in the south. Dali city had the highest GDP, followed by the Erhai Lake area, while the northern part of the basin had a lower GDP. Water 2021, 13, x FOR PEER REVIEW 9 of 16

Table 3. Correlation coefficients of climatic factors, water volume, and pollutant concentration of Erhai Lake.

4.4. Impact of Human Activities on Water Environment 4.4.1. Changes in Human Activities From 2000 to 2020, the change in land-use types in the Erhai Lake basin was mainly as follows (Table 4): cultivated land and construction land increased, and forest land, grassland, and water areas decreased. Cultivated land and construction land increased by 7.21 and 80.26 km2, respectively, while forest, grassland and water areas decreased by 25.22, 58.96, and 3.30 km2, respectively. The increased cultivated land was mainly converted from forest and grassland, and the increased construction land was mainly converted from cultivated land and forest. The increase in cultivated land was concentrated in the north of the basin, and the increase in construction land was mainly concentrated in the area around Erhai Lake and in the south of Erhai Lake.

Table 4. Land-use change in the Erhai Lake basin from 2000 to 2020 (km2).

2020 Cultivated Construction Land-use type Forests Grassland Water Sum land land Cultivated land 49,992.75 2343.33 2576.43 215.01 6822 61,949.52

Forests 3666.33 114,568.65 6472.71 66.06 1921.59 126,695.34 Grassland 6253.29 6825.51 26,571.06 12.51 2061 41,723.37 2000 Water 163.53 300.69 89.82 25,536.06 94.14 26,184.24 Construction 2595.15 134.91 117.45 24.93 10,180.71 13,053.15 land Sum 62,671.05 124,173.09 35,827.47 25,854.57 21,079.44 269,605.62

Figure 8 shows the spatial distribution and changes in GDP in the Erhai Lake basin from 2000 to 2015. The figure shows that the GDP distribution in the basin was low in the Waternorth2021, 13 ,and 1362 high in the south. Dali city had the highest GDP, followed by the Erhai Lake10 of 17 area, while the northern part of the basin had a lower GDP.

Water 2021, 13, x FOR PEER REVIEW 10 of 16

Figure 8. SpatialFigure distribution 8. Spatial distributionof Erhai Lake of Erhai GDP Lake from GDP 2000 from 2000to 2015 to 2015 (unit: (unit: 10 1044 yuan).yuan). around Erhai Lake and the urban area of Dali increased the most. This result is related to During the study period, the GDP growth trend was significant, and the growth rate the growDuringth of the tourist studyremained arrivals period, at and the approximately theGDP economic growth 10%. trenddevelopment Spatially, was the significant, rate of of Dali GDP in growthand recent the in growth theyears. southern rate part remainedFigure at 9 approxi shows matelytheof the distribution Erhai 10%. Lake Spatially, basin of the was populationthe greater rate than of GDP that in the in growth the Erhai northern Lakein the part. basin. southern Specifically, The partdistri- the of GDP butionthe Erhai characteristics Lake basinaround of was the Erhai greater population Lake than and the thatin urbanthe in Erhai area the of northern Lake Dali increased basin part. were the Specifically, most. similar This result to thethose is related GDP of to GDP, both of which werethe growth low ofin touristthe north arrivals and and high the economic in the s developmentouth. The population of Dali in recent distribu- years. Figure9 shows the distribution of the population in the Erhai Lake basin. The distribution on both sides oftion the characteristics river in the of north the population was higher in the Erhaithan Lake that basin in other were similarareas, to and those Dali of GDP, city in the south hadboth the oflargest which werepopulation. low in the During north and the high study in the south.period, The the population populat distributionion in the north of the basinon both showed sides of a the decreasing river in the trend,north was while higher the than population that in other areas, in the and south Dali city in the south had the largest population. During the study period, the population in the showed a significant northincreasing of the basin trend. showed Additionally, a decreasing the trend, increase while the around population Dali in city the south was showedthe most obvious, whicha was significant related increasing to the acceleration trend. Additionally, of economic the increase developmen around Dalit city and was urban- the most ization. obvious, which was related to the acceleration of economic development and urbanization.

Figure 9. SpatialFigure distribution 9. Spatial distribution of the population of the population in Erhai in Erhai Lake Lake from from 20002000 to to 2015 2015 (unit: (unit: 104). 104). There were great differences in the economic development and population scale There were greatbetween differences the northern in the and economic southern regionsdevelopment of the Erhai and Lake population basin, and scale the degree be- of tween the northern andhuman southern activities regions in the south of the was Erhai stronger Lake than basin, that in and the the north. degree The human of human activities activities in the southin thewas northern stronger part than of the that basin in were the mainly north. related The to human agricultural activities farming, in while the the human activities in the southern part of the basin were mainly related to urban expansion, northern part of the economicbasin were development, mainly related and tourism. to agricultural farming, while the human activities in the southern part of the basin were mainly related to urban expansion, economic development, and tourism.

4.4.2. Impact on Water Resources The water consumption structure and trend of the Erhai Lake basin from 2007 to 2019 are shown in Figure 10, which shows that the total water consumption of the Erhai Lake basin fluctuated and decreased slowly, and the change trend was 1.12 billion m3/year. The total water consumption was greatly affected by agricultural water consumption, and the trend of total water consumption was basically consistent with that of agricultural water consumption. The Erhai Lake basin is dominated by agriculture, and the proportion of agricultural water (farmland irrigation, water supplementation for forest and fruit and fish ponds, livestock industry) is relatively high. Agricultural water consumption accounted for approximately 83% of the total water consumption. From 2007 to 2019, agricultural water consumption showed a slow downward trend, with a change trend of 5.1 billion m3/year. Living water consumption (urban domestic water consumption and rural domestic water consumption) was ranked second, accounting for approximately 9% of the total water consumption. From 2007 to 2019, living water consumption showed a fluctuating upward trend, with a change trend of 1.33 billion m3/year. Industrial water consumption (industry and construction industry) accounted for approximately 9%. From 2007 to 2019, living water consumption showed a fluctuating downward trend, with a change trend of 0.01 billion m3/year. The consumption of eco-environmental supplemental water (Urban and rural greening, Prevent the lake from shrinking) was the lowest, accounting for approximately 2% of the total water consumption. From 2007 to 2019, the

Water 2021, 13, 1362 11 of 17

4.4.2. Impact on Water Resources The water consumption structure and trend of the Erhai Lake basin from 2007 to 2019 are shown in Figure 10, which shows that the total water consumption of the Erhai Lake basin fluctuated and decreased slowly, and the change trend was 1.12 billion m3/year. The total water consumption was greatly affected by agricultural water consumption, and the trend of total water consumption was basically consistent with that of agricultural water consumption. The Erhai Lake basin is dominated by agriculture, and the proportion of agricultural water (farmland irrigation, water supplementation for forest and fruit and fish ponds, livestock industry) is relatively high. Agricultural water consumption accounted for approximately 83% of the total water consumption. From 2007 to 2019, agricultural water consumption showed a slow downward trend, with a change trend of 5.1 billion m3/year. Living water consumption (urban domestic water consumption and rural domestic water consumption) was ranked second, accounting for approximately 9% of the total water consumption. From 2007 to 2019, living water consumption showed a fluctuating up- Water 2021, 13, x FOR PEER REVIEWward trend, with a change trend of 1.33 billion m3/year. Industrial water consumption 11 of 16 (industry and construction industry) accounted for approximately 9%. From 2007 to 2019, living water consumption showed a fluctuating downward trend, with a change trend of 0.01 billion m3/year. The consumption of eco-environmental supplemental water (Urban and rural greening, Prevent the lake from shrinking) was the lowest, accounting for ap- consumptionproximately 2% of the of total eco water-environmental consumption. From supplemental 2007 to 2019, the water consumption showed of a significant upward trend,eco-environmental with a change supplemental trend water of showed 2.65 abillion significant m upward3/year. trend, with a change trend of 2.65 billion m3/year.

Figure 10. Water use structure and trend for Erhai Lake from 2007 to 2019. Figure 10. Water use structure and trend for Erhai Lake from 2007 to 2019. There was no significant negative correlation between water consumption and total water consumption from 2007 to 2019, and the correlation coefficient was −0.22. The correlationThere coefficient was no between significant water volume negative and agricultural correlation water consumptionbetween water was consumption and total water0.163, the consumption correlation coefficient from between 2007 water to volume2019, and living the watercorrelation consumption coefficient was was −0.22. The correlation−0.324, the coefficient correlation coefficient between between water water volume and and industrial agricultural water consumption water consumption was 0.163, was −0.126, and the correlation coefficient between water volume and eco-environmental thesupplemental correlation water consumption coefficient was between−0.658 (p < 0.05). water The volume change in Erhaiand Lake living water water consumption was −0.324,was less affected the correlation by the total water coefficient consumption between of the basin water (Table5 ).volume and industrial water consumption was −0.126, and the correlation coefficient between water volume and eco-environmental supplemental water consumption was −0.658 (p < 0.05). The change in Erhai Lake water was less affected by the total water consumption of the basin (Table 5).

Table 5. Correlation coefficient between different water consumption and water volume.

Eco-Environmental Total Agricultural Living Industrial Supplemental Water volume −0.22 0.163 −0.324 −0.126 −0.658 (p < 0.05)

Compared with 2000, the water area decreased in 2019. From the results of land-use changes, we can see that during the study period, the reduced waters were mainly transformed into grassland and construction land. In recent years, tourism in Erhai Lake has developed rapidly. The reclamation of lakes and land and the construction and reconstruction of housing have resulted in more towns around Erhai Lake; thus, the lake area was reduced. However, the trend of vegetation swamps in the shallow water area of Erhai Lake is becoming increasingly serious, decreasing the water area.

4.4.3. Impact on Water Quality The structure and trend of wastewater discharge in the Erhai Lake basin from 2007 to 2019 are shown in Figure 11, which shows that the total wastewater discharge in the Erhai Lake basin presented a fluctuating upward trend, with a change trend of 2.7 billion m3/year. Wastewater discharge was greatly affected by industry, and the total wastewater discharge was basically consistent with the trend of industrial wastewater discharge. The industrial wastewater discharge in the Erhai Lake basin accounted for 64% of the total wastewater discharge. During the period of 2007–2019, the industrial wastewater discharge showed an obvious upward trend, with a change trend of 1.57 billion m3/year. Living wastewater discharge was ranked second, accounting for approximately 24% of the total wastewater discharge. During 2007–2019, living wastewater discharge showed a slow increasing trend, with a change trend of 1.03 billion m3/year. The service industry accounted for 7.2% of the total wastewater discharge. The wastewater discharge of the service industry showed a downward trend from 2007 to 2019, with a change trend of 0.21

Water 2021, 13, x FOR PEER REVIEW 11 of 16

consumption accounted for approximately 83% of the total water consumption. From 2007 to 2019, agricultural water consumption showed a slow downward trend, with a change trend of 5.1 billion cubic meters/year. Living water consumption (urban domestic water consumption and rural domestic water consumption) was ranked second, accounting for approximately 9% of the total water consumption. From 2007 to 2019, living water consumption showed a fluctuating upward trend, with a change trend of 1.33 billion cubic meters/year. Industrial water consumption (industry and construction industry) accounted for approximately 9%. From 2007 to 2019, living water consumption showed a fluctuating downward trend, with a change trend of 0.01 billion cubic meters/year. The consumption of eco-environmental supplemental water (Urban and rural greening, Prevent the lake from shrinking) was the lowest, accounting for approximately 2% of the total water consumption. From 2007 to 2019, the consumption of eco-environmental supplemental water showed a significant upward trend, with a change trend of 2.65 billion cubic meters/year. Water 2021, 13, 1362 There was no significant negative correlation between12 water of 17 consumption and total water consumption from 2007 to 2019, and the correlation coefficient was -0.22. The cor-

Tablerelation 5. Correlation coefficient coefﬁcient betweenbetween different water water consumption volume and and water agricultural volume. water consumption was

0.163, the correlation coefficient between water Eco-Environmentalvolume and living water consumption Total Agricultural Living Industrial was -0.324, the correlation coefficient between waterSupplemental volume and industrial water con- sumptionWater volume was−0.22 -0.126, 0.163 and the−0.324 correlatio−0.126 n coefficient−0.658 (p < 0.05)between water volume and eco-environmental supplemental water consumption was -0.658 (P < 0.05). The change in Compared with 2000, the water area decreased in 2019. From the results of land- useErhai changes, Lake we water can see was that during less affected the study period, by the the total reduced water waters consumption were mainly of the basin (Tab. 5). transformed into grassland and construction land. In recent years, tourism in Erhai Lake hasTable. developed 5 Correlation rapidly. The coefficient reclamation between of lakes differe and landnt andwater the consumption construction and and water volume reconstruction of housing have resulted in more towns around Erhai Lake; thus, the lake area was reduced. However, the trend of vegetation swamps in the shallow water area of Erhai Lake is becoming increasingly serious, decreasing the water area. Eco-environmental Total Agricultural Living Industrial 4.4.3. Impact on Water Quality supplemental WaterThe structurevolume and trend -0.22 of wastewater discharge 0.163 in the Erhai -0.324Lake basin from 2007 -0.126 to -0.658 (P < 0.05) 2019 are shown in Figure 11, which shows that the total wastewater discharge in the Erhai Lake basin presented a ﬂuctuating upward trend, with a change trend of 2.7 billion m3/year. WastewaterCompared discharge waswith greatly 2000, affected the by water industry, area and thedecreased total wastewater in 2019. discharge From the results of land-use was basically consistent with the trend of industrial wastewater discharge. The industrial wastewaterchanges, discharge we can in thesee Erhai that Lake during basin accounted the stud for 64%y period, of the total the wastewater reduced waters were mainly discharge.transformed During into the period grassland of 2007–2019, and the construction industrial wastewater land. discharge In recent showed years, tourism in Erhai Lake an obvious upward trend, with a change trend of 1.57 billion m3/year. Living wastewater dischargehas developed was ranked rapidly. second, accounting The reclamation for approximately of 24%lakes of the and total land wastewater and the construction and re- discharge.construction During of 2007–2019, housing living have wastewater resulted discharge in mo showedre towns a slow around increasing Erhai Lake; thus, the lake trend, with a change trend of 1.03 billion m3/year. The service industry accounted for 7.2%area of was the total reduced. wastewater However, discharge. The the wastewater trend of discharge vegetation of the service swamps industry in the shallow water area of showedErhai aLake downward is becoming trend from 2007 increasingly to 2019, with aserious, change trend decreasing of 0.21 billion the m3/year. water area. Construction wastewater discharge accounted for 1.8% of the total wastewater discharge. From 2007 to 2019, construction wastewater discharge showed a signiﬁcant increasing trend,4.4.3 with Impact a change on trend water of 0.32 quality billion m3 /year.

FigureFigure. 11. The12 structureThe structure and trend ofand wastewater trend discharge of wastewater in the Erhai disch Lake basinarge from in 2007 the to Erhai 2019. Lake basin from 2007 to 2019

Water 2021, 13, 1362 13 of 17

The spatial distribution of the TN concentration in Erhai Lake was as follows: the northern part had the highest concentration, followed by the southern part, and the central part had the lowest concentration. Within the same section, the nearshore point had a higher concentration, and the lake center point had a lower concentration. The northern part of the basin was dominated by agricultural development, which is polluted by agricultural nonpoint sources and has a high TN concentration. The southern part has a large population distribution and has experienced rapid economic development in recent years; thus, the region is affected by living sewage and industrial development, and the TN concentration is relatively high. Compared with the lake center point, the nearshore point had a higher nitrogen concentration because the nearshore point was more affected by living sewage and agricultural nonpoint sources.

5. Discussion 5.1. Influencing Factors of Water Volume Change From 2000 to 2019, the annual fluctuation in Erhai water was large, but the overall change was not obvious. The relationship between water volume and annual precipitation is positive (+0.577), whereas the relationship between water volume and monthly precipitation is negative (−0.759). The water resources of Erhai Lake mainly come from precipitation, so there was a significant positive correlation between the interannual variation trend of water volume and the precipitation variation trend. On the monthly scale, the water volume of Erhai Lake is greatly affected by the water regime of the basin. The precipitation in Erhai Lake is mainly distributed from May to October (Figure7); however, during the flood season (May–October), the amount of water flowing out of Erhai Lake is relatively large, accounting for 64.9% of the total outflow. The dry season (January–April, November-December) accounts for 35.1% of the total outflow [26]. Additionally, as the peak tourist season around Erhai Lake is from May to August, this directly increases the consumption of living and industrial water. Therefore, although summer rainfall is abundant, the volume of Erhai Lake shows a downward trend. With the development of tourism and the economy, the urbanization process of the Erhai Lake basin has accelerated in recent years, the population growth trend is obvious, and living water consumption has shown an increasing trend. Agricultural irrigation is the main way to consume freshwater resources in Erhai Lake. The change in cultivated land area was small from 2007 to 2019. At the same time, agricultural water consumption was affected by crop structure, irrigation methods, irrigation project construction, water-saving measures, water supplemental area of fruit and fish ponds, livestock scale, and other factors; thus, the fluctuation of agricultural water consumption was small. Erhai Lake is the main water source of Dali city and its surrounding towns. In recent years, with the ecological construction and protection of Erhai Lake and the implementation of the strictest water resources management system, high water consumption enterprises (such as cement plants) have developed rapidly. As a result, industrial water consumption has been controlled, showing a significant downward trend. With a deeper emphasis on the ecological environment, the consumption of ecological compensation water has increased each year. This is confirmed in the research of Zhou [27].

5.2. Influencing Factors of Water Quality Change During the study period, the TN concentration in Erhai Lake showed an increasing trend, and the interannual fluctuation of pollutant concentration was less affected by climate factors, while the monthly fluctuation was mainly positively correlated with the monthly precipitation. This result was because although the summer precipitation was higher, the pollutant concentration increased significantly due to the lower amount of water. The concentrations of pollutants are high in summer and low in winter. The average water temperature in summer is approximately 19–22 ◦C, which is conducive to the growth of phytoplankton, while the lower water temperature in winter inhibits the growth of Water 2021, 13, 1362 14 of 17

phytoplankton. Additionally, the exchange capacity of water bodies is stronger in winter, and the concentration of pollutants is significantly reduced. Human activities have a significant impact on water quality in the Erhai Lake basin, and there is a significant correlation between wastewater discharge and pollutant concentration. During the study period, the construction land area, GDP value, and population in the Erhai Lake basin showed significant increasing trends. As an important feature of the urbanization and economic development of Erhai Lake, the population and industrial agglomeration have a serious impact on the water supply environment with rapid economic development [28]. The discharge intensity of living and construction wastewater caused by the increase in the number of urban residents and the expansion of the urban area increased the number of backward facilities that pose great pressure on water quality protection. Industrial agglomerations mean there is an expansion in the production scale, and the increase in production will promote the discharge of a large amount of pollution, increasing the load pressure into the lake [29]. However, Dali’s tourism industry has developed rapidly in recent years. According to official statistics, in 2017, it received 42.22 million tourists from home and abroad, with a total tourism income of 64.775 billion yuan, values that were up 9.4% and 21.2% year over year, respectively [30]. The rapid development of tourism has resulted in the rapid increase in river basin pollution, which makes it difficult to improve the water quality of rivers entering the lake. Additionally, the rapid urbanization brought by the development of tourism facilities has led to an increase in urban building area, an increase in impervious underlying surfaces such as cement, and an acceleration of sewage entering the water body [24]. Spatially, human activities have different ways of interfering with the basin. In Dali city and the area around Erhai Lake in the south of the basin, human activities are mainly manifested in urban expansion, economic development, and tourism activities. In the northern part of the basin, the economic and population growth is slow, the intensity of regional agricultural production is increasing, the use of chemical fertilizers and pesticides is excessive, the utilization of livestock manure resources is low, and the contradiction of the low phosphorus utilization efficiency in the production process leads to the accumulation of surplus phosphorus in the soil or phosphorus that is moved through runoff, leaching, and erosion into the water body, which aggravates the eutrophication of the water body [31].

5.3. Future Work This study comprehensively considers land-use type, water use structure, population, and GDP data to analyze the impact of human activities on the water environment, the results show that human activities have less impact on the volume of Erhai Lake but a greater impact on water quality. Wu et al. conducted an empirical analysis of 22 lakes in China and found that the intensity of human activities in the past 15 years has a signiﬁcant positive correlation with the nutritional status of lakes [32]. This is consistent with the results of this study. Current research on the pollution of Erhai Lake is mostly focused on agricultural nonpoint source pollution. This study believes that economic development and tourism activities are also important factors inﬂuencing water pollution. Yang et al. also showed that the rapid development of population and social economy are the main factors affecting pollution in the Erhai Lake basin [33]. In this study, many factors were considered to evaluate the impact of human activities on the water environment of Erhai Lake, but the evaluation system of human activities was not constructed, and the analysis of the impact of human activities on the water environment was still in a semiquantitative state. On the other hand, the scope of human activities is wide, this paper only selected some socioeconomic factors and statistical data analysis, which may have a certain impact on the results. Water 2021, 13, 1362 15 of 17

6. Conclusions In this study, the evolution of the Erhai Lake basin was analyzed in 2000–2019, through the comparison of water volume and water quality data with information on climatic conditions and human activities, as well as the reasons for the changes in the Erhai water environment. The main conclusions are as follows: (1) From 2000 to 2019, the water volume of Erhai Lake fluctuated greatly, showing an overall upward trend, with a change trend of 3.8 × 106m3 year−1. The annual change in water volume showed a trend of first decreasing and then increasing. It increased from August to November and decreased from December to July. (2) From 2000 to 2019, the TN concentration in Erhai Lake showed an increasing trend. Among the monthly variations, the nitrogen concentration reached the highest value in August, and the value from August to December was higher than that from January to July. Spatially, the concentration of TN was the highest in the north, followed by that in the south, and it was lowest in the middle. In the same section, the concentration of TN was higher at the nearshore point and lower at the lake center point. (3) The interannual variation in Erhai Lake water was mainly affected by annual precipitation, and there was a significant positive correlation between precipitation and water volume (p < 0.05), with a correlation coefficient of 0.577. Monthly water volume was affected by climate factors and river water regimes. During the study period, the water consumption of the Erhai Lake basin showed a downward trend, and the impact of water consumption on water volume was small. (4) From 2000 to 2019, the change in land-use types in the Erhai Lake basin was mainly manifested by the increase in agricultural and construction land, the increase in agricultural land was mainly distributed in the north of the basin, the increase in construction land was distributed around Erhai Lake and Dali city, and the GDP and population increased significantly in the area where the construction land increased. In the north of the basin, human activities were mainly manifested as agricultural development, while in the south, human activities were mainly reflected by the acceleration of economic growth and urbanization. (5) The correlation between water quality change and climate factors was small, and the correlation between water quality change and human activities was large. In the northern part of the basin, agricultural nonpoint source pollution was the main factor affecting water quality, while in the southern part of the basin, economic development, accelerated urbanization, and tourism were the main factors affecting water quality.

Author Contributions: The authors undertook different tasks for this paper. L.Z. wrote the paper. Z.A. analyzed the data. X.C. provided direction to the research work. H.L. designed the research and revised the paper. All authors have read and agreed to the published version of the manuscript. Funding: This research was financially supported by the National Key Research and Development Program of China (2018YFC1506500) and National Natural Science Foundation of China (41971402). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data developed in this study will be made available on request to the corresponding authors. Acknowledgments: This research was financially supported by the National Natural Science Foun- dation of China (41971402) and the National Key Research and Development Program of China (2018YFC1506500). Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Water 2021, 13, 1362 16 of 17

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