Prediction of Physico-Chemical Variables and Chlorophyll a Criteria for Ecoregion Lakes Using the Ratios of Land Use to Lake Depth

Environ Earth Sci (2015) 74:3709–3719 DOI 10.1007/s12665-015-4020-8

THEMATIC ISSUE

Prediction of physico-chemical variables and chlorophyll a criteria for ecoregion lakes using the ratios of land use to lake depth

Shouliang Huo • Chunzi Ma • Zhuoshi He • Beidou Xi • Jing Su • Li Zhang • Ji Wang

Received: 24 July 2014 / Accepted: 3 January 2015 / Published online: 17 January 2015 Ó Springer-Verlag Berlin Heidelberg 2015

Abstract Establishing nutrient criteria for regional lakes Keywords Nutrient criteria Á Physico-chemical is necessary to assess human impact on lake aquatic eco- variables Á Chlorophyll a Á Land use systems and protect water quality and biotic integrity. Multiple linear regression models, in which the ratios of land use to mean lake depth (DEP) are the predictor vari- Introduction ables, and the logarithms of physico-chemical variables and Chl a concentrations are the dependent variables, were Establishment of lake nutrient criteria has been recently developed to predict physico-chemical variables and identified as a crucial issue for regulators to assess the chlorophyll a criteria for Yungui Plateau Ecoregion lakes. influence of anthropogenic activities on aquatic ecosys- The contemporary land use data of 22 lake watersheds tems, to control cultural eutrophication, and to protect were analyzed and employed to develop the spatial rela- water quality and biotic integrity (Dodds and Welch 2000; tionship with the regression models. The data of five lake Hawkins et al. 2010). It provides the maximum obtainable watersheds in four periods were used to verify the accuracy level of water quality if human impacts are entirely con- of the regression models, and to test their applicability in trolled (Dodds and Oakes 2004). However, due to intensive time scale. The intercept of these models (i.e., expected human activities leading to the change of land use, lake physico-chemical variables and Chl a concentrations in the water quality conditions are affected by the whole water- absence of human activities) represents the criterion con- shed, and few watersheds are minimally affected by centrations. Results suggested that the percentages of other humans (Lewis 2002). Hence, the identification of nutrient construction land/DEP had strong positive influences on criterion is a difficult issue, especially in China. the concentrations of all variables (except electrical con- Intensification of land use is one of the most significant ductivity). The multiple linear regression models offered a forms of land cover modification, and has been identified potential method for regions with heavy anthropogenic as the major sources of non-point source pollutants (Or- disturbances to develop the physico-chemical variables and tolani 2014; Hadibarata et al. 2012). The development of Chl a criteria. land use can alter and impact the quality of the receiving water bodies (Carney 2009; Nielsen et al. 2012), which directly affect human and ecosystem health, and has become an increasing concern for resource managers and policy makers (Zhang et al. 2012; Fraterrigo and Downing S. Huo (&) Á C. Ma Á Z. He Á B. Xi (&) Á J. Su Á L. Zhang Á 2008). Cultivation which requires more consumption of J. Wang nutrients than natural vegetative cover, would damage the State Key Laboratory of Environmental Criteria and Risk soil structure and the nutrient rich topsoil, causing the loss Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, China of chemicals and soil particles during rainfall (Gandhi e-mail: [email protected] et al. 2008). Forest land may reduce concentrations of B. Xi inorganic ion and play a key role in alleviating the deg- e-mail: [email protected] radation of water quality (Bahar et al. 2008; Sliva and 123 3710 Environ Earth Sci (2015) 74:3709–3719

Williams 2001). In addition, an increase of urban and Materials and methods rural lands has been found to have a negative correlation with stream health, typically increasing flash runoff and Study area nutrient loads. This is because that the increased impervious surface caused by urbanization decreases infiltration The Yungui Plateau, which has complicated topography of precipitation and increases variability in streamflow and various climates, lies within subtropical zone in the (Gandhi et al. 2008; Aichele 2005). During precipitation, southwest of China. The area of the region is more than nutrients and particles from agricultural, urban and rural 7.31 9 105 km2. Mountains, rivers, and canyons are den- land would be carried by runoff, increasing nutrient levels sely distributed, with an altitude between 1,280 and and turbidity of the receiving waters. Thus, increasing use 3,270 m (above sea level). The majorities of lakes are deep of nutrients in agricultural lands coupled with increasing with relatively small surface areas, and are scattered on the urbanization results in significant challenges for the pro- plateau from high to low latitude (Huo et al. 2012). More tection and improvement of water quality, and eutrophi- than 30 lakes are found in this area with depths ranging cation control. from 10 to 200 m, and most of them are structural lakes Some studies have applied the influence of anthropo- formed by crustal movement. Some lakes have deteriorated genic land uses on nutrient concentrations to extrapolate considerably in recent decades and show strong spatial nutrient criteria (Dodds and Oakes 2004; Dodds 2006). The variations in this region (Li et al. 2007). The location and statistical relationships between land uses and nutrient land use of the Yungui Plateau Ecoregion lakes are illus- concentrations were quantified in rivers and streams to trated in Fig. 1. The actual lake water quality and mor- estimate nutrient concentrations occurring in the absence of phometric (i.e., depth) characteristics of the lakes are measurable anthropogenic impact on the landscape (Dodds provided in Table 1. and Oakes 2004). A hydrogeomorphic land use model was used to predict expected lake TP value by setting the Data analysis human land use/cover coefficients to zero (Soranno et al. 2008). We collected four basic types of data such as physico- However, the relationship between land use change and chemical variables, chlorophyll a, mean lake depth and alteration of nutrient concentrations can be complex and land use for each studied lake watershed. Physico-chemical influenced by many factors such as lake mean depth (DEP), parameters and biological variables were studied in this drainage area and slope (OECD 1982; Rawson 1952; study: total nitrogen (TN) and total phosphorus (TP) con- Dodds and Oakes 2004). For example, lake waters will lose centrations as indicators of nutrient conditions; perman- a greater proportion of nutrients with the increase of depth ganate index (CODMn), chemical oxygen demand (CODCr), through the process of sedimentation (Cardoso et al. 2007). and BOD5 as indicators of organic matter; electrical con- Some researches suggested that lower chlorophyll a (Chl a) ductivity (EC) as an indicator of salinity, Chl a as an concentrations would be expected with the increase of lake indicator for phytoplankton abundance. Data for physico- depth (Carvalho et al. 2008). That is to say, DEP would chemical variables, Chl a, and mean lake depth (DEP) in weaken the influence of land use on water quality to some the Yungui ecoregion lakes were obtained from the ambi- extent. Hence, the heterogeneity of lake water depth would ent lake monitoring network, which supported by the impact the relationship of land use and nutrient concen- Department of Environmental Protection of the Yunnan trations in the lakes. and Guizhou Provinces of China. A total of 22 water bodies Previous studies have suggested that land use and lake were selected for this analysis from 1988 to 2008. Data depth are both related to surface water quality (Sliva and were included from lakes that had at least three surveys (in Williams 2001; Tu et al. 2007; Cardoso et al. 2007; Beaver three water periods) in separate years over this time et al. 2014). The ratio of land use intensification to mean interval. The physico-chemical indexes and Chl a were lake depth might play a key role in explaining the spatial analyzed in laboratory using standard testing procedures as variation of water quality in the plateau lakes at a regional recommended by the Ministry of Environmental Protec- scale. The objectives of this study are: (1) to examine the tion, China (PRC EPA 2002). Each integrated water sam- relationships between physico-chemical variables, chloro- ple was a mixture of two sub-samples from 0.5 m below phyll a and land use or land use/DEP in the Yungui Plateau the surface and from 0.5 m above the bottom, respectively. Ecoregion lakes; (2) to establish the predicting models of All water samples were analyzed for different physico- physico-chemical variables and chlorophyll a criteria; (3) chemical parameters within 48 h. The detection limits for to determine appropriate criteria values for Yungui Plateau TN, TP, and CODMn were 0.05, 0.01, and 0.3 mg/L, Ecoregion lakes and (4) to evaluate consistency of the respectively. Observations in the database below detection results. limits were assigned with values equal to one-half the 123 Environ Earth Sci (2015) 74:3709–3719 3711

Fig. 1 The location (a) and land use (b) of the Yungui Plateau Ecoregion in 2008 detection limits since these observations were encountered sufﬁciently accurate for determining descriptive statistics infrequently (less than 15 % of the total dataset). Such a like the mean and standard deviation (Suplee et al. 2007; method to ﬁx the limits of detection has been reported to be US EPA 2006). Annual mean concentrations of the

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Fig. 1 continued aforementioned variables for all samples at each lake were multiple linear regression models. The determined regres- used as the dependent variable. sion models would be used to prove that there were sig- Geographic information system (GIS) was used to nificant spatial relationships between physico-chemical interpret, classify, analyze, and integrate the land use data variables, Chl a, and land use. The data of five lake of lake watersheds (Liu et al. 2013). Four periods of land watersheds in four periods served as the test dataset, were use (1988, 1995, 2000 and 2008) are obtained from Insti- used to verify the accuracy of the models, and to test their tute of Geographic Sciences and Natural Resources applicability in time scale. Research (IGSNRR, Chinese Academy of Sciences). The original land use classes were further grouped into six main Statistical analyses categories: (1) Crop land, including paddy field and dry land; (2) forest land; (3) grass land; (4) water bodies, Both statistical (such as Pearson correlation analysis and including rivers, wetland and sandy beach; (5) urban and regression analysis) and GIS analyses were adopted in this rural land, including urban land, rural residential areas, and study. They were used to examine the general association other construction lands; and (6) unused land (Fig. 1). The of physico-chemical variables, Chl a, and land use to ArcGIS 9.2 Desktop GIS software was used to calculate the derive the criterion concentrations. area of each land-use type within this ecoregion. Percent- Relationships between the aforementioned variables and ages of crop land (including paddy field and dry land), DEP, land use of 22 lakes in 2008 were initially described forest land, grass land, water bodies, urban and rural land by Pearson correlations to account for the effects of DEP (including urban land, rural residential areas, and other and land use variables on water column variables. The construction lands), and unused land in watershed were ratios of land use to DEP as the predictor variables were abbreviated as PC (PC-P and PC-D), PF, PG, PW, PUR used to establish the best multiple linear regression models. (PU, PR and POC), and PUN, respectively. All possible subsets regression was used to determine the The obtained data were assigned into a training dataset best-fit model with p as an index to control the effect of for model establishment, and a test dataset for model adding variables into the model. The data of five lake evaluation. The physico-chemical variables, Chl a, and watersheds in four periods were used to verify the accuracy land use data of 22 lake watersheds in 2008 defined as the of the models. Intercepts of the best regression models training dataset, were analyzed and applied to establish were employed to deduce the concentrations of physico-

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Table 1 The actual water quality and morphometric characteristics of the lakes

Lake Lake name Lake depth TN TP Chl a CODCr CODMn BOD5 EC number (m) (mg/L) (mg/L) (lg/L) (mg/L) (mg/L) (mg/L) (lS/cm)

1 Shudu Lake 4.00 0.993 0.032 – 4.400 4.54 1.294 455 2 Bitahai Lake 20.00 0.648 0.023 – 8.600 3.15 1.680 766 3 Lugu Lake 40.30 0.242 0.018 – 0.880 – 0.560 – 4 Chenghai Lake 25.70 0.519 0.028 2.000 23.234 3.65 2.886 118 5 Jianhu Lake 4.00 0.343 0.026 3.578 – 2.72 2.641 – 6 Haixihai Lake 10.00 0.453 0.015 5.650 5.117 1.29 1.828 212 7 Cibi Lake 11.00 0.452 0.013 3.667 7.250 1.59 1.987 328 8 Xihu Lake 20.00 0.921 0.040 20.650 16.033 4.74 3.295 356 9 Erhai Lake 10.17 0.420 0.020 13.463 15.283 2.58 2.023 265 10 Qinghai Lake 3.00 0.820 0.032 – 7.457 – 1.488 – 11 Yangzonghai Lake 19.50 0.424 0.035 5.185 14.574 2.43 2.167 337 12 Dianchi Lake 2.93 5.023 0.349 63.308 54.575 9.91 5.077 471 13 Fuxian Lake 89.60 0.171 0.009 2.243 8.914 1.13 2.100 534 14 Xingyun Lake 5.30 2.084 0.127 26.731 27.082 7.49 4.639 881 15 Qilu Lake 4.03 2.986 0.066 35.692 26.086 7.51 4.404 2,010 16 Yuxian Lake 7.00 0.425 0.029 – 10.967 3.35 0.848 292 17 Yilong Lake 2.40 2.065 0.047 28.389 42.152 9.38 3.543 405 18 Datunhai Lake 3.70 1.551 0.389 55.389 22.140 7.91 6.500 817 19 Changqiaohaia Lake 3.74 1.903 0.052 38.408 36.010 8.65 4.258 427 20 Gejiu Lake 2.50 1.510 0.027 9.253 20.105 2.47 3.500 549 21 Nanhu Lake 1.50 5.542 0.120 81.605 66.858 16.42 6.413 467 22 Puzhehei Lake 4.00 0.529 0.028 2.833 4.967 3.02 2.982 239 chemical variables and Chl a in the absence of human wastewater from surrounding residential area is the main activities. The variability in this extrapolation could be source of pollution influencing water quality in the Yungui characterized by the 95 % confidence interval around the Plateau Ecoregion lakes. A poor negative correlation estimate for the intercepts. All statistical analyses were (p [ 0.05) between physico-chemical variables, Chl a and performed by using SPSS 16. PF, PG was found, which suggests that forest land, and Prior to the determination of statistical relationship, the grass land would not observably influence water quality normality of the physico-chemical variables, Chl a and change. This may be because that grassy area filtered land use data were tested using the Shapiro–Wilk test. runoff pollutants of river or stream (Gyawali et al. 2013). Values for all the studied physico-chemical variables and It is interesting to observe that poor correlations between Chl a were log10 transformed to satisfy normality physico-chemical, Chl a and PC (including PC-P and PC- assumptions, and ensure that data distributions did not D) were found in these ecoregion lakes, indicating that yield intercept estimates that were less than zero (Dodds crop land would not observably influence the variations of and Oakes 2004). the physico-chemical variables, such as nitrogen and phosphorus. This was in conflict with many previous studied results that the percentage of crop land in the lake Results and discussion watershed is a primary predictor for nitrogen and phosphorus concentrations in the lakes (Ferrier et al. 2001; Correlation of physico-chemical variables, Chl Ahearn et al. 2005; Jones et al. 2004; 2008). As shown in a and land use Table 1, there are significant differences in lake depths for lakes in this ecoregion. Hence, we assumed that the water Data in Table 2 provide the correlation matrix of physico- depth is the reason leading to differences from other chemical variables, Chl a and land use obtained from studies, and this would be verified in the following content. Pearson correlations. Significant positive correlations The percentages of land use of 22 lake watersheds between PUR (containing PU and PR) and TN, Chl a, indicated that the significantly spatially heterogeneity of

CODCr, CODMn, BOD5 were found, which suggest that land use were found in different lake watersheds during the 123 3714 Environ Earth Sci (2015) 74:3709–3719

Table 2 Pearson correlations between physico-chemical variables, Chl a and land use Variable DEP PC PF PG PW PUR PUN PC-P PC-D PU PR POC

TN -0.379 0.317 -0.365 -0.287 -0.035 0.784** -0.206 0.203 0.332 0.742** 0.568** 0.417 TP -0.265 0.114 -0.17 0.015 -0.045 0.254 -0.149 0.006 0.189 0.208 0.166 0.638** Chl a -0.378 0.451 -0.486* -0.305 -0.171 0.727** -0.245 0.226 0.429 0.683** 0.540* 0.381

CODCr -0.337 0.267 -0.383 -0.212 0.076 0.726** -0.273 0.167 0.296 0.706** 0.478* 0.325

CODMn -0.397 0.421 -0.412 -0.336 -0.035 0.759** -0.167 0.282 0.43 0.724** 0.589** 0.231

BOD5 -0.345 0.309 -0.402 -0.064 -0.07 0.592** -0.085 0.165 0.357 0.538** 0.465* 0.441* EC -0.095 0.246 -0.035 -0.279 -0.007 0.098 -0.213 0.383 0.106 -0.013 0.327 0.41 * Correlation is signiﬁcant at the 0.05 level (2-tailed) ** Correlation is signiﬁcant at the 0.01 level (2-tailed)

Fig. 2 The composition of land use of 22 lake watersheds of the Yungui Plateau Ecoregion in 2008

same period (Fig. 2). The main land use patterns in the chemical variables, Chl a and PUR/DEP were also found, watersheds of Shudu Lake and Bitahai Lake were forest but the correlations and significance level of TN, Chl a, and grass, demonstrating that no contamination caused by CODMn, CODCr, BOD5, and PUR/DEP were not significant human activities was identified in these two watersheds. alterations (Friedman test, p [ 0.05) after PUR was divi- Whereas the watersheds of the Qinghai Lake, Qilu Lake, ded by DEP. The results indicated that lake water depth Yuxian Lake, Nanhu Lake, and Puzhehei Lake had the would be the other factor that influences the relationship higher percentages of crop land, implying that these between physico-chemical variables, Chl a and land use. watersheds would be seriously influenced by intensive Note that physico-chemical variables and Chl a were not agricultural lands (Ahearn et al. 2005). However, it can be correlated to PC and to DEP (Table 2), but significantly seen that the lake watersheds with the high percentages of correlated to the ratio of the two parameters (Table 3), crop land did not always lead to the poor water quality in mainly because that the lake depth would weaken the the lakes, such as the Qinghai Lake, Yuxian Lake, and influence of crop land on water quality to some extent. The Puzhehei Lake (Table 1). lakes of the Yungui Ecoregion have significant differences Data in Table 3 provide the correlation of the physico- in mean depth, and the fertilizer and soil particles from chemical variables, Chl a and land use/DEP obtained from crop land flowing into water body would deposit rapidly to the Pearson correlations. Significant positive correlations the sediment with the increase of depth, decreasing nutrient between physico-chemical variables, Chl a and PC/DEP concentrations of surface water (Huo et al. 2012; Cardoso were found. Very strong correlations between physico- et al. 2007). Hence, DEP should be considered when

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Table 3 Pearson correlations between physico-chemical variables, Chl a and land use/lake mean depth Variable PC/DEP PF/DEP PG/DEP PW/DEP PUR/DEP PUN/DEP PC-P/DEP PC-D/DEP PU/DEP PR/DEP POC/DEP

TN 0.692** 0.128 0.158 0.804** 0.739** -0.150 0.595** 0.672** 0.699** 0.760** 0.607** TP 0.238 0.153 0.364 0.407 0.199 -0.107 0.194 0.24 0.162 0.224 0.793** Chl a 0.732** 0.032 0.233 0.686** 0.695** -0.235 0.738** 0.667** 0.655** 0.742** 0.581*

CODCr 0.638** 0.125 0.206 0.849** 0.700** -0.214 0.537* 0.629** 0.672** 0.698** 0.485*

CODMn 0.813** 0.025 0.223 0.782** 0.753** -0.15 0.838** 0.737** 0.711** 0.826** 0.428

BOD5 0.555** 0.135 0.277 0.587** 0.537** -0.018 0.423* 0.571** 0.504* 0.554** 0.548** EC 0.179 0.069 0.027 0.245 0.025 -0.182 0.369 0.078 -0.024 0.161 0.422 The correlation of the physico-chemical variables, Chl a and land use/DEP is significant * Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed)

Table 4 Best regression model for physico-chemical variables and Chl a Variable Best regression model NR2 F Sig.

TN lgTN =-0.444 ? 0.015 9 PF/DEP ? 0.201 9 PR/DEP ? 1.344 9 POC/DEP 21 0.786 22.033 0.000 TP lgTP =-1.705 ? 0.03 9 PC-D/DEP ? 1.977 9 POC/DEP 21 0.712 23.541 0.000 Chl a lgChl a = 0.493 - 0.034 9 PC/DEP ? 0.083 9 PG/DEP ? 0.454 9 PR/DEP ? 1.247 9 POC/DEP 16 0.731 8.159 0.002

CODCr lgCODCr = 0.898 ? 0.072 9 PC-P/DEP ? 0.095 9 PU/DEP - 0.33 9 PR/DEP ? 1.326 9 POC/ 20 0.449 3.256 0.039 DEP

CODMn lgCODMn = 0.368 ? 0.064 9 PC-P/DEP ? 0.01 9 PU/DEP ? 0.603 9 POC/DEP 19 0.633 9.200 0.009

BOD5 lgBOD5 = 0.26 - 0.018 9 PC-P/DEP ? 0.025 9 PC-D/DEP ? 0.018 9 PR/DEP ? 0.938 9 POC/ 21 0.465 3.699 0.024 DEP EC lgEC = 2.518 ? 0.019 9 PC/DEP ? 0.004 9 PF/DEP - 0.017 9 PUR/DEP 18 0.108 0.608 0.620 establishing the reliable relationship between physico- wastewater from surrounding other construction area is chemical variables, Chl a and land use. main source of pollution influencing water quality. This is mainly because that most areas of POC are factories and Establishment of models roads with a large number of the impervious surface. The vast presence of impervious surface would change the The physico-chemical variables, Chl a and the ratios of hydrology and nutrient distribution in the basin, leading to land use to DEP of 22 lake watersheds in 2008 were the significant influence on water quality (Tu et al. 2007; applied to establish the models. All possible subsets Tu 2009). The percentage of PC/DEP may account for the regression with p as an index for model selection of variability of Chl a and most of the physico-chemical anthropogenic land use was used to predict the values of variables (except TN). In addition, there were also signif- physico-chemical variables and Chl a. Backward and icant heterogeneities in the influences of land use on the stepwise regression method was employed to the elimina- different variables. For example, regression analysis for tion of non-significant land use during regression analysis. TN, Chl a, CODCr and BOD5 suggested that PR/DEP was Through the comparison of correlation coefficients (R2), also a strong predictor for these variables. the best regression models for the physico-chemical variables and Chl a are illustrated in Table 3. The analysis Validation of models revealed that the regression method generally failed for EC prediction because few significant relationships existed In addition, the land use data of five lake watersheds in four between EC and land use/DEP for the lakes sampled in this periods (1988, 1995, 2000 and 2008) were applied to verify study (p [ 0.05). This illustrated that land use/DEP could the best regression models, respectively. The comparisons not be used as the predictor variables to deduce the values of observed data with predicted data of TN, TP, Chl a, of EC. CODCr, CODMn, and BOD5 are illustrated in Fig. 3.As As shown in Table 4, it can be seen that except EC, all shown in Fig. 3, the best models established by the spatial the studied variables were strongly related with POC/DEP distribution data of 22 lake watersheds had good prediction in the Yungui Plateau Ecoregion lakes, indicating that abilities. As indicated by coefficient of determination (R2),

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Fig. 3 The comparison of observed values with predicted values of TN, TP, Chl a, CODCr, CODMn, and BOD5 in 5 lake watersheds in the original data

the predictor variables of the best models can explain more best regressions (Table 5). The intercepts represent the than 67 % of the variance in all of the studied water quality points where percentages of land use are zero. A 95 % indicators except BOD5. Their correlation coefficients were confidence interval could be calculated around this pre- 0.975, 0.678, 0.970, 0.820, 0.814, and 0.565, respectively. dicted value based on the regression models. The criterion It could be deduced that the predicted results were in good value, the lower confidence interval, and the upper confi- agreement with the measured ones in the regression dence interval of TN were 0.360, 0.258, and 0.501 mg/L, models. respectively. The criterion levels, the lower and upper Although these regression models had good predictive confidence interval of the other variables are listed in abilities, from the comparison of predicted results with Table 5. The regression models method was compared observed ones, it can be seen that there were certain system with several other statistical methods as reference lake errors for all the models, especially for CODCr and BOD5 method, lake population distribution method and trisection models. This may be because that except land use and method (Gibson et al. 2000; Huo et al. 2012, 2013a, b; see DEP, other factors (such as soil types, land management in Table 5). Friedman test suggested that there were sig- practices, etc.) were not considered in these models. In nificant differences in the results obtained from the addition, the spatial distribution of land use would produce regression method and the other statistical methods the influence on the relationships between physico-chemi- (p \ 0.05). cal variables, Chl a and land use (Tu and Xia 2008; Vanni There were divergences among results from various et al. 2010). For example, in the same land use, the crop methods to some degree, however, with each approach land directly adjacent to lakes had a greater impact on having its own limitations and advantages. The one limi- water quality than lakes with woodland buffer (Miseren- tation causing this result is that the regression model would dino et al. 2011). not quantify all sources of anthropogenic influences because such data were not readily available. For example, Determination of physico-chemical variables atmospheric deposition of nitrogen would have a great and chlorophyll a criteria influence on water quality, resulting in the variability of biomass and physico-chemical variables. There were sig- The intercepts were used to extrapolate the criterion con- nificant differences in release forms and deposition amount centrations of physico-chemical variables and Chl a in the of nitrogen for various land use. High NH3 and NO2

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Table 5 Mean and 95 % conﬁdence intervals for estimated criterion concentrations Variable Regression models Frequency distribution method Concentrations Low 95 % High 95 % Reference Lake population Trisection lake method distribution method method

TN (mg/L) 0.360 0.258 0.501 0.175 0.37 0.21 TP (mg/L) 0.020 0.014 0.027 0.010 0.010 0.010 Chl a (lg/L) 3.112 1.412 6.855 2.20 2.00 1.59

CODCr (mg/L) 7.907 4.529 13.804 4.56 8.00 6.00

CODMn (mg/L) 2.333 1.671 3.251 0.96 2.14 1.52

BOD5 (mg/L) 1.820 1.279 2.588 0.59 1.15 0.95 EC (lS/cm) 329.610 195.884 554.626 216.00 244.00 217.00 The results of the frequency distribution method for TN, TP, and Chl a were from Huo et al. 2012 concentrations in the air were, respectively, observed at the include more detailed land use and soil distribution in the tea and paddy fields, and at the forest and tea field; and the study area using recent satellite images to depict the order for the annual total N deposition of different land use detailed cropping. This will help to evaluate the impact of types was: forest [ tea field [ paddy field (Shen et al. lake buffer strips and alternative best management prac- 2013). In addition, animal farms and point discharges from tices, such as minimum or no-tillage practices and reduced sewage treatment would lead to the increase of nitrogen use of fertilizers and pesticides. and phosphorus nutrients and organic matter. As a result, consideration for quantification factors such as atmospheric deposition of nitrogen, intensive animal farms, and point Conclusions discharges would enhance the accuracy of models (Dodds and Oakes 2004). The interpretation for other human This study found that there are no correlations between nutrient input sources would further decrease the deduced physico-chemical, Chl a and crop land (PC), but there are criterion concentrations of physico-chemical variables and significant correlations between physico-chemical, Chl Chl a. a and the ratios of crop land to mean lake depth (DEP), the Comparing with the frequency distribution methods, the ratios of PC/DEP, urban and rural land (PUR)/DEP had regression models do not need to collect a large amount of strong positive influences on the physico-chemical vari- data from reference or minimally impacted lakes. If there ables and chlorophyll a in the Yungui Plateau Ecoregion are no minimally impacted lakes, this method requires the lakes. This indicated that lake water depth would be the predicted data far from the data points that depict the key factor that influences the relationship between water regression model. Thus, the accuracy of this method should quality and land use. The regression relationships between be greatly improved when the studied lake watersheds land use/DEP and water quality variables were used to include a relative continuum of land use intensity (Dodds infer lake physico-chemical variables and Chl a criteria. and Oakes 2004). The continuum of land use intensity The data of five lake watersheds in four periods (1988, could better reflect the trend of water quality change with 1995, 2000 and 2008) were used to verify the accuracy of land use, establish a more accurate model, and thus reduce the regression models, and to test their applicability in time the uncertainty of the extrapolating result. scale. The intercept of these models (i.e., expected physico- The regression models presented in this article provide a chemical variables and Chl a concentrations in the absence base for the understanding of land use change impacts on of human activities assuming linear extrapolation to the water quality and extrapolate the criterion concentrations origin) represents the criterion concentrations. This method for the protection of water quality. It is preferable to pro- would be very useful in regions that have few or no min- vide a statistically defensible method for other ecoregions imally affected lakes, such as heavily agricultural or urban when degraded conditions prevail (caused by intensive areas. The best regression models successfully linked the land use intensity) and appropriate data exist to adequately land use to water quality parameters and provided a sta- quantify relationships between variables. These results tistically defensible approach to deduce physico-chemical would be beneficial for the regional planners and policy variables and Chl a criterion in the region where lakes can makers to estimate the changes of water quality when land reflect a wide range of anthropogenic influences and use changes over time. The models can be extended to appropriate data exist to adequately quantify those

123 3718 Environ Earth Sci (2015) 74:3709–3719 influences. This study offers a potential method for regions Hadibarata T, Abdullah F, Rahim a, Yusoff M, Ismail R, Azman S, with intensification of land use to develop the physico- Adnan N (2012) Correlation study between land use, water quality, and heavy metals (Cd, Pb, and Zn) content in water and chemical variables and Chl a criteria for effective lake green lipped MusselsPerna viridis(Linnaeus.) at the Johor Strait. basin management. Water Air Soil Pollut 223:3125–3136 Hawkins PC, Olson JR, Hill RA (2010) The reference condition: Acknowledgments The Mega-projects of Science Research for predicting benchmarks for ecological and water-quality assess- Water Environment Improvement (Program No. 2009ZX07106-001; ments. J N Am Benthol Soc 29:312–343 2012ZX07101-002) and the National Natural Science Foundation of Huo SL, Zan FY, Chen Q, Xi BD, Su J, Ji DF, Xu QG (2012) China (No. 40901248) supported this study. 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