Published June 23, 2014

Journal of Environmental Quality

TECHNICAL REPORTS Surface Water Quality

Linking Spatial Variations in Water Quality with Water and Land Management using Multivariate Techniques Yongshan Wan,* Yun Qian, Kati White Migliaccio, Yuncong Li, and Cecilia Conrad

S

urface water quality, which is greatly influenced

Most studies using multivariate techniques for pollution source evaluation are conducted in free-flowing rivers with distinct point and nonpoint sources. This study expanded on previous research to a managed “canal” system discharging into the Indian River Lagoon, Florida, where water and land management is the single most important anthropogenic factor influencing water quality. Hydrometric and land use data of four drainage basins were uniquely integrated into the analysis of 25 yr of monthly water quality data collected at seven stations to determine the impact of water and land management on the spatial variability of water quality. Cluster analysis (CA) classified seven monitoring stations into four groups (CA groups). All water quality parameters identified by discriminant analysis showed distinct spatial patterns among the four CA groups. Two-step principal component analysis/factor analysis (PCA/FA) was conducted with (i) water quality data alone and (ii) water quality data in conjunction with rainfall, flow, and land use data. The results indicated that PCA/FA of water quality data alone was unable to identify factors associated with management activities. The addition of hydrometric and land use data into PCA/FA revealed close associations of nutrients and color with land management and storm-water retention in pasture and citrus lands; total suspended solids, turbidity, and NO3 + NO2 with flow and Lake Okeechobee releases; specific conductivity with supplemental irrigation supply; and dissolved O2 with wetland preservation. The practical implication emphasizes the importance of basin-specific land and water management for ongoing pollutant loading reduction and ecosystem restoration programs.

by anthropogenic activities, has gained national and international interest due to its correlation with human health and the structure and function of aquatic ecosystems (Osborne and Wiley, 1988; Ravichandrana et al., 1996; Ferrier et al., 2001; Simeonov et al., 2003; Shrestha and Kazama, 2007; Samsudin et al., 2011). The assessment of water quality requires the measurement of multiple parameters at varying spatial and temporal scales. Such monitoring programs generally produce complex multidimensional data that require multivariate statistical techniques as analytical tools for the characterization and evaluation of water quality (e.g., Vega et al., 1998; Wunderlin et al., 2001; Singh et al., 2004; Papatheodorou et al., 2006). A review of the literature indicated that numerous studies on water quality applications of multivariate techniques have been published. Table 1 summarizes a subset of these studies with a focus on the water quality of surface water bodies. Most studies used data collected for several years, with biweekly or monthly sampling intervals at multiple sampling locations. Multivariate statistical techniques were applied for evaluation of spatial and temporal variations, data reduction, and identification and apportionment of pollution sources, with only a few exceptions such as identification of the biologic and physical processes that affect water quality (Petersen et al., 2001) and evaluation of a monitoring network (Ouyang, 2005). The most commonly used multivariate statistical techniques are cluster analysis (CA), discriminant analysis (DA), and principal component analysis/factor analysis (PCA/FA), although other statistical methods such as analysis of variance, multiple linear regression, and receptor modeling have also been used to supplement the evaluation (Table 1). Details of the mathematics of CA, DA, and PCA/FA can be found elsewhere (e.g., Johnson and Wichern, 1992; Fraley and Raftery, 2002). Briefly, CA identifies groups (clusters) in the data based on the similarities within a cluster and dissimilarities among clusters so that objects can be grouped by variability (Fraley and Raftery,

Y. Wan and C. Conrad, South Florida Water Management District, 3301 Gun Club Road, West Palm Beach, FL 33406; Y. Qian, K.W. Migliaccio, and Y. Li, Soil and Water Science Dep. and Agricultural and Biological Engineering Dep. at Tropical Research and Education Center, IFAS, Univ. of Florida, 18905 SW 280th Street, Homestead, FL 33031. Assigned to Associate Editor Ying Ouyang.

Copyright © American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. 5585 Guilford Rd., Madison, WI 53711 USA. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.

Abbreviations: CA, cluster analysis; CPC, common principal component; DA, discriminant analysis; FA, factor analysis; IRL, Indian River Lagoon; PCA, principal component analysis; SLE, St. Lucie Estuary; SFWMD, South Florida Water Management District; STA, storm-water treatment area; TFE, total iron; TKN, total Kjeldahl nitrogen; TP, total phosphorus; TSS, total suspended solids.

J. Environ. Qual. 43:599–610 (2014) doi:10.2134/jeq2013.09.0355 Received 6 Sep. 2013. *Corresponding author ([email protected]).

599

2002; Kaufman and Rousseeuw, 1990; Wunderlin et al., 2001). Thus, the results of CA typically exhibit significant homogeneity within clusters and heterogeneity between clusters. Cluster analysis is a convenient way to explore water quality patterns associated mostly with spatial variability (by sampling sites) (e.g., Singh et al., 2004; Shrestha and Kazama, 2007; Zhang et al., 2009). Some researchers have also used CA for seasonal grouping (by months) to evaluation temporal variations (e.g., Zhou et al., 2007; Yang et al., 2010). Because it is not necessary to know the class characteristics of the data in advance, CA is called unsupervised pattern recognition (Fraley and Raftery, 2002; Kowalkowski et al., 2006; Wunderlin et al., 2001). In contrast, DA is a statistical supervised pattern recognition method used to discriminate a priori known groups or clusters (e.g., data from different seasons or regions) by determining the variables with significant mean differences among them (Statsoft Inc., 1984; Fraley and Raftery, 2002; Insightful Corporation, 2005). Discriminant analysis has been applied to identify the most significant water quality parameters discriminating between groups, and these parameters are considered to account for the spatial or temporal variations in water quality (Singh et al., 2004; Shrestha and Kazama, 2007). Thus, DA can render considerable reduction of the dimensionality of the original data matrix.

Discriminant analysis is also useful for determining temporal and spatial variations caused by natural and anthropogenic factors linked to seasonality. Santos-Roman et al. (2003) related water quality groups to physical characteristics of watersheds using DA and thus predicted water quality for unmonitored watersheds. Both PCA and FA are exploratory methods concerned with explaining the variance–covariance structure of the data. Principal component analysis is generally applied for data reduction and interpretation of water quality data (e.g., Petersen et al., 2001; Ouyang, 2005). Factor analysis, as an extension of PCA, aims to identify the underlying but unobservable quantities, called factors, by analyzing the covariance relationships among multiple variables. This is commonly achieved through varimax rotation (orthogonal), which redistributes the variance of each variable to allow a high loading on a single factor and low loadings on the other factors ( Johnson and Wichern, 1992). Thus, PCA is a linear combination of observable water quality variables, whereas FA identifies unobservable, hypothetical, latent variables (Vega et al., 1998). Principal component analysis and FA are commonly applied together in water quality data analysis to identify pollution sources, i.e., naturally occurring (weathering or geological processes) or anthropogenic (agricultural, industrial, or domestic origins) (e.g., Wunderlin et al., 2001; Singh et al.,

Table 1. Selected surface water quality studies using multivariate techniques published since 2001. Study

Multivariate techniques†

Wunderlin et al. (2001) Petersen et al. (2001) Simeonov et al. (2003)

CA, FA, DA

Singh et al. (2004) Singh et al. (2005)

CA, PCA/FA, DA CA, PCA/FA, DA

PCA CA, PCA

Ouyang (2005)

CA, FA

Zhou et al. (2007) Shrestha and Kazama (2007) Pejman et al. (2009) Zhang et al. (2009) Varol and Şen (2009) Yang et al. (2010) Koklu et al. (2010) Li et al. (2011)

CA, DA

Mustapha and Abdu (2012)

CA, PCA/FA, DA CA, PCA/FA CA, PCA/FA, CA, PCA/FA CA, PCA/FA, DA PCA/FA,DA CA, PCA/FA PCA/FA

Auxiliary analysis‡

Study area (Country)

Data analysis period and Sampling Water quality Study objectives sampling interval sites parameters yr ——— no. ——— Suquia River 2 (1998–2000, monthly) 9 22 spatial and temporal analysis, data (Argentina) reduction, source identification bootstrap Elbe River 5 (1994–1997, once every 14 7 identification of processes procedure (Germany) 14 d) affecting water quality RM River systems 3 (1997–2000, monthly) 25 27 spatial analysis, data reduction, (Greece) source identification and apportionment Gomti River 5 (1994–1998, monthly) 8 24 spatial and temporal analysis, data (India) reduction, source identification RM Gomti River 3 (1999–2001, monthly) 8 34 spatial and temporal analysis, data (India) reduction, source identification and apportionment St. Johns River 3 (1999–2001, daily or 22 42 evaluation of monitoring network, (USA) monthly) identification of essential parameters rivers (Hong 5 (2000–2004, monthly) 23 23 spatial and temporal analysis, data Kong) reduction. source identification Fuji River (Japan) 8 (1995–2002, monthly) 13 25 spatial and temporal analysis, data reduction, source identification Haraz River (Iran) 2 (2007–2008, seasonal) 8 10 spatial and temporal analysis, source identification Xiangjiang River 7 (1994– 2000) 34 12 spatial analysis, data reduction, (China) source identification Behrimaz Stream 1 (2003, monthly) 4 20 spatial and temporal analysis, data (Turkey) reduction, source identification IDW Lake Dianchi 5 (2003–2007, monthly) 8 12 spatial and temporal analysis, data (China) reduction, source identification MLR Melen River 11 (1995–2006, once 2–3 5 26 data reduction, source (Turkey) mo) identification RM, ANOVA 19 rivers (China) 2 (4 sampling trips in 2006 19 21 spatial and temporal analysis, and 2007) source identification, source apportionment MLR Jakara River 0.17 (July 31 to Sept. 30, 4 15 source identification (Nigeria) 2011, daily)

† CA, cluster analysis; PCA, principal components analysis; FA, factor analysis; DA, discriminant analysis. ‡ RM, receptor modeling; MLR, multiple linear regression; IDW, inverse distance weighting; ANOVA, analysis of variance. 600

Journal of Environmental Quality

2004; Shrestha and Kazama, 2007; Li et al., 2011). Receptor modeling is also used in conjunction with PCA/FA to quantify source contributions associated with each factor (e.g., Singh et al., 2005; Li et al., 2011). Several observations are worth noting with respect to applications of multivariate techniques in water quality evaluation. First, most studies have dealt with water quality issues in rivers that are free flowing, with distinct nonpoint and domestic and/or industrial pollution sources in the drainage basins (e.g., Singh et al., 2005; Zhou et al., 2007; Mustapha and Abdu, 2012). For example, Zhou et al. (2007) found that the highly polluted zones of the river system in Hong Kong were associated with untreated sewage and domestic sources, whereas the relatively low-pollution zones were free of major point sources. Li et al. (2011) indicated that the highly polluted areas in rivers along the water conveyance system of China were associated with industrial and domestic wastewaters. Few studies have examined data collected in highly managed “canal” systems where water and land management are the single most important anthropogenic factor influencing water quality. Second, when spatial variations in water quality were evaluated, source identification using PCA/FA was exercised by simply contemplating the nature of water quality constituents within a factor (e.g., Koklu et al., 2010; Mustapha and Abdu, 2012). It can be difficult to relate all water quality parameters within a factor to pollution sources, especially when several factors and multiple parameters are involved. Few studies have validated the results of multivariate techniques with data pertaining to pollution sources or management activities that cause the spatial variations. This study attempted to expand on previous research on water quality applications using multivariate statistical techniques to a managed “canal” system where land use and water management were uniquely integrated into the analysis to determine their impacts on water quality. Twenty-five years of monthly water quality data collected at seven stations in a dense canal network discharging into the Indian River Lagoon (IRL), Florida, were used. Specifically, the objective of the study was to demonstrate the utility of the multivariate techniques in (i) delineating the spatial variability of water quality in the canal network where water and land resources are highly managed for agricultural production and flood control, and (ii) linking the spatial variations in water quality with specific water and land management practices. To aid in the analysis, land use coverage and hydrometric data (canal flow and rainfall depth) were integrated into a PCA/FA of the water quality data.

Materials and Methods Study Area The IRL, located on the southeast coast of Florida (Fig. 1), is regarded as one of the most biologically diverse ecosystems in North America, with approximately 2200 identified plant and animal species (Swain et al., 1995). The St. Lucie Estuary (SLE) is the largest tributary to the IRL. Before development, the IRL/SLE watershed was characterized by nearly level terrain and poorly drained forested wetlands and wet prairie. During the last century, land use and drainage patterns have undergone substantial changes after construction of a network of primary, secondary, and tertiary canals (Fig. 1) draining a total area of www.agronomy.org • www.crops.org • www.soils.org

2694 km2. The large primary canals managed by the South Florida Water Management District (SFWMD) and the U.S. Army Corps of Engineers (USACE), including C-44 (completed in 1924 and enlarged to its current size in 1949), C-23, C-24, and C-25 (completed about 1961), were constructed to allow widespread agricultural and urban development in the watershed. In particular, the C-44 canal connects Lake Okeechobee, the second largest lake in the contiguous United States, with the SLE, allowing large volumes of lake water released for flood control into the estuary. Because there are two distinct seasons (wet and dry) in South Florida, the canals act as a riverine system during the wet season ( June–October), providing flood protection through rapid transport of storm water into the estuary. During the dry season (November–May), the canal system stores water for irrigation and maintains water table elevations at coastal water control structures to prevent saltwater intrusion. Discharges occur only when the canal stage exceeds a critical level during a storm event. Due to the high water table and flat terrain in the region, runoff is delivered to the primary canals mostly through water control structure such as culverts or pumps. Table 2 summarizes the basin characteristics of the study area. The altered drainage pattern and increased hydraulic and pollutant loading to the estuary, especially from Lake Okeechobee releases, have become a growing concern for the IRL/SLE system (Chamberlain and Hayward, 1996; Sigua et al., 2000; Wan et al., 2006, 2012). A recent water quality assessment conducted by the Florida Department of Environmental Protection (FDEP) indicated that dissolved O2 (DO) and nutrients are common parameters causing water quality impairment throughout the watershed (Florida Department of Environmental Protection, 2008). To address these issues, the SFWMD and the USACE developed a comprehensive restoration plan with a budget of US$1 billion to construct reservoirs and treatment wetlands (i.e., storm-water treatment areas, STAs) to restore historic flow patterns and to reduce nutrient loads (U.S. Army Corps of Engineers and South Florida Water Management District, 2004). Meanwhile, the FDEP developed total maximum daily loads for the SLE using total N and total P (TP) concentrations to set targets for load reduction allocations (Florida Department of Environmental Protection, 2008). The Florida legislature also mandated that the SFWMD, in coordination with the FDEP and the Florida Department of Agriculture and Consumer Services, develop and implement a watershed protection plan to reduce nutrient loading to the SLE (South Florida Water Management District, 2009). Essential to these restoration and load reduction programs is identifying water quality characteristics of surface water delivered into the IRL/SLE system.

Data Freshwater discharges through the primary canals (C-44, C-23, C-24, and C-25) are controlled through seven water control structures: S-308, S-80, S-97, S-48, S-49, S-99, and S-50 (Fig. 1). The naming convention for each station is the combination of the primary canal and the structure; for example, the station at S-308 in the C-44 canal is C44S308. Water quality and flow data measured at these stations were downloaded from the SFWMD database. Rainfall data collected at 11 rainfall stations in the region were used to derive the average rainfall 601

Fig. 1. Location of water monitoring stations (red circles) and their respective drainage basins in the southern Indian River Lagoon watershed. The drainage canal network and surface-water retention reservoirs are shown in blue. Note that the large reservoir in the C-44 basin belongs to a power plant (not for water retention in citrus land).

depth for each basin using the Thiessen Polygon method. The period of record for the analysis was from 1981 through 2004. The 12 water quality parameters selected for analysis in this study were: DO (mg/L), specific conductivity (mS/cm), pH, turbidity (nephelometric turbidity units, NTU), color (Pt–Co units, PCU), total suspended solids (TSS, mg/L), NO3–N + NO2–N (NOX–N, mg/L), NH4–N (mg/L), total Kjeldahl N (TKN, mg/L), PO4–P (mg/L), TP (mg/L), and total Fe (TFe, mg/L). Analytical methods are summarized in Table 3. The SFWMD developed land use/land cover GIS data layers for 1988, 1995, 1999, and 2004 by photointerpretation of aerial photography and digital orthophotographic quarter quadrangles. Each layer was processed to derive the percentage of major land use types for each basin. Land use types were aggregated into seven categories: pasture, citrus, other agriculture, urban (including transportation), wetland, forest, and water (Table 2). These categories are reflective of basin-specific land and water management practices and are consistent with the land use types used for hydrologic simulations of the basins (Wan et al., 2006). Because flows are routed into the primary canals mainly 602

through secondary and tertiary canals, land use of the “buffer zone” along the primary canal was considered an insignificant factor influencing spatial variation in water quality (Carey et al., 2011), and thus the “buffer zone” concept was not examined in this study.

Data Treatment and Statistical Methods Before performing statistical analyses, the data were logarithmically transformed to normalize the distribution of each water quality parameter and minimize the effects of outliers. All statistical analyses were performed using S-Plus (Version 7.0, Insightful Corporation, 2005).

Cluster Analysis For each station, the mean values of the transformed data were normalized to minimize the effects of the scale of the units on the clustering, so that each variable was considered equally important (Kaufman and Rousseeuw, 1990): x if - m f [1] zif = sf Journal of Environmental Quality

where mf and sf are the mean and the mean absolute deviation, respectively, of the logarithmically transformed variable f across all stations; and xif and zif are the original and normalized mean values, respectively, of the transformed variable f at the ith station. The agglomerative hierarchical clustering method, which merges each group until all observations are in a single group, was adopted to cluster the seven monitoring stations. In this method, each object is initially considered as a separate group (cluster); the two groups with the smallest dissimilarity are then merged. The dissimilarity (distance) between two objects, d(i,j), was computed using Euclidean distance:

d (i , j ) =

2

å f =1( zif - z jf ) p

[2]

Table 2. Characteristics of the C-23, C-24, C-25, and C-44 basins. Characteristic

C-23

Basin area, km2 Land use, %†  Pasture  Citrus   Other agriculture  Urban  Wetland  Forest  Water Rainfall (depth)‡   Annual mean, mm/yr

454.9

C-24 355.1

36.3(2.1) 28.5(1.8) 6.5(4.9) 3.9(1.0) 16.4(4.8) 6.7(3.8) 1.6(0.9)

  Dry-season monthly mean, mm/mo   Wet-season monthly mean, mm/mo Flow (depth)‡   Annual mean, mm/yr   Dry-season monthly mean, mm/mo   Wet-season monthly mean, mm/mo

46.0(3.1) 18.7(3.1) 6.3(4.4) 5.1(1.2) 14.9(1.9) 7.4(6.6) 1.7(0.7)

C-25

C-44

437.2

471.8

23.3(5.9) 47.4(9.2) 5.5(0.5) 3.1(0.8) 10.2(2.0) 6.6(6.4) 4.0(3.6)

20.3(1.9) 32.5(1.1) 8.6(2.6) 7.3(2.6) 14.4(5.5) 13.0(4.1) 3.9(2.3)

1285

1303

1348

1341

66 165

67 167

69 173

70 170

427 23 52

482 19 68

390 15 56

993 74 92

† The mean percentages of different land use types were calculated from the South Florida Water Management District land use GIS layers for 1988, 1995, 1999, and 2004. Standard deviations are included in the parentheses.

where i and j represent different stations and p is the number of variables (Kaufman ‡ Based on long-term hydrologic data (1965–2005) collected by the South Florida Water Management District. The C-44 flow was measured at C44S80 and included releases from Lake Okeechobee. and Rousseeuw, 1990). Distances between clusters were determined by linkages. seasonality. The second data set included annual means of water Ward’s and group-average linkage algorithms were used for crossquality constituents plus land use coverage and hydrometric data validation of the results. (canal flow and rainfall depth) for each basin. The percentage of each land use category for each survey year was assigned for the Discriminant Analysis years in between (four or five preceding survey years) assuming Discriminant analysis was applied to identify individual there was no significant land use change during that period. The variables that discriminate between groups identified by CA. Several number of principal components in PCA with eigenvalues >1 models have been developed for discriminating between k groups was selected as the number of factors to use in FA to extract the based on the relationships among the groups’ covariance matrices, loading scores (e.g., Singh et al., 2004; Zeng and Rasmussen, ranging from homoscedastic to heteroscedastic models (Insightful 2005; Shrestha and Kazama, 2007). This two-step PCA/FA Corporation, 2005). The common principal component (CPC) exercise allowed better detection of links between water quality model was selected to identify the discriminating constituents, i.e., and basin-specific water and land management practices by station means calculated from logarithmically transformed data, comparing the loading values in rotated components. To verify with CA groups as the dependent variable. The CPC is a robust nonlinear model developed from PCA. In the CPC model, the Table 3. Analytical methods for selected water-quality parameters. covariance matrices of different k groups are assumed to have Water-quality Analytical Detection some common features (i.e., the k groups’ covariance structures are parameter method† limit‡ simultaneously diagonalizable) (Flury, 1984). To verify the results Dissolved O2 EPA 360.1 0.1 mg/L from the CPC model, DA was also performed using a canonical Specific conductivity EPA 120.1 1 mS/cm discriminant function based on the homoscedastic covariance pH EPA 150.1 – structure, as well as a classical heteroscedastic model. Discriminant Turbidity SM 2130B 0.1 NTU§ analysis was performed using all data (year around) and seasonal Color SM 2120B 1.0 PCU¶ data representing the dry (November–May) or wet (June– Total suspended solids EPA 160.2 1 mg/L October) seasons (Qian et al., 2007). The Kruskal–Wallis test NOX–N SM 4500NO3F 0.004 mg/L (significance level a = 0.01) was performed to test the significance NH4–N SM 4500-NH3H 0.008 mg/L of differences among the CA groups. Total Kjeldahl N EPA 351.2 0.05 mg/L

Principal Component Analysis/Factor Analysis To further elucidate the relationship between spatial variations in water quality and basin management characteristics, PCA/FA with varimax rotation (orthogonal solution) was conducted with two sets of data. The first data set was the annual means of water quality data for each basin. Station C44S308 was excluded from this analysis because basin runoff was primarily discharged via C44S80. Annual means were used to remove the influence of www.agronomy.org • www.crops.org • www.soils.org

PO4–P Total P Total Fe

SM 4500PF SM 4500PF SM 3120 B

0.002 mg/L 0.002 mg/L 1 mg/L

† EPA methods are from the USEPA; SM methods are from the American Public Health Association. ‡ Detection limits could vary due to the improvement of laboratory techniques during the study period (1981–2004). § Nephelometric turbidity units. ¶ Platinum–cobalt units. 603

the PCA/FA result, Pearson correlation analysis of the annual means of water quality constituents against land use percentage, rainfall, and flow rate was conducted. These data were also ranked for Spearman’s rank correlation analysis for cross-validation. The significance criterion of the correlation analyses was p ≤ 0.01.

Results

Groups of Monitoring Stations The classification patterns obtained by both group-average linkage and Ward’s method using Euclidean distance were similar, and only the dendrogram obtained from the groupaverage method is presented (Fig. 2). The seven monitoring stations were clustered into four groups. Group 1 (G1) included C23S48, C23S97, and C24S49; Group 2 (G2) included C25S50 and C25S99; Group 3 (G3) consisted of C44S80; and Group 4 (G4) consisted of C44S308. This grouping is consistent with the basin delineation and water/land management. Group 4 (C44S308) had the greatest separation from the other stations. Group 3 (C44S80) also had distinctly different features than the other groups. These two stations control the discharge from Lake Okeechobee and C-44 basin runoff to the SLE (Fig. 1). Group 1 stations receive flows from the C-23 and C-24 basins, which have similar land use and development history, with pasture as the predominant land use (36 and 43%, respectively) (Table 2). In contrast, G2 stations receive storm water from the C-25 basin, with citrus being by far the dominant land use (47%).

Spatial Variation of Discriminating Constituents The DA results obtained from the CPC model, the canonical discriminant function, and the classical heteroscedastic model provided the same result (Table 4). Dissolved O2, specific conductivity, pH, color, TSS, and turbidity were selected as discriminating constituents using all data or seasonal data. Among the nutrients, PO4–P, TP, and NH4–N were also selected as discriminating constituents using all data or seasonal subsets; NOX–N was a discriminating constituent only for the dry-season data, while TKN and TFe were discriminating constituents using all data and dry-season data. During the dry season, all CA groups exhibited greater spatial heterogeneity than in the wet season, reflecting the influence of seasons on water quality (Qian et al., 2007). All water quality constituents showed distinct spatial patterns (Table 5). The p values derived from the Kruskal–Wallis test for water quality constituents were all 1, 604

Fig. 2. Dendrogram of the cluster analysis of seven water quality monitoring stations on the C-23, C-24, C-25, and C-44 canals.

explaining 75% of the total variance of the water quality data set (Table 6). A high loading of a variable on a factor shows a strong relationship between the factor and the respective variable. Liu et al. (2003) classified the significant loadings as strong (absolute loading value >0.75), moderate (0.50–0.75), and weak (0.30– 0.50). This classification was commonly adopted in later studies (e.g., Ouyang, 2005; Singh et al., 2005). In the present study, factor loadings >0.5 were considered significant. As shown in Table 6, the first factor, explaining 26.88% of the total variance, had significant positive loadings on TP, PO4–P, NH4–N, TKN, and color. The second factor, explaining 18.47% of the total variance, had positive loadings on TSS, turbidity, and NOx–N. The third factor, explaining 16.48% of the total variance, had positive loadings on pH and DO and a negative loading on color. The fourth factor, explaining 13.44% of the total variance, had a negative loading on specific conductivity and a positive loading on TFe. The PCA/FA of the annual means of the water quality data as well as rainfall depth, flow, and land use percentage identified six factors with eigenvalues >1, explaining 76% of the total variance of the data set (Table 7). The first factor explained 23.73% of the total variance, with significant positive loadings on TP, PO4–P, NH4–N, TKN, color, and pasture and negative loadings on citrus and water. The second factor, explaining 13.97% of the total variance, had positive loadings on TSS, turbidity, NOx–N, and flow. The third factor, explaining 10.94% of the total variance, had significant positive loadings on rainfall, flow, and TFe and a Table 4. Water quality discriminating constituents identified by discriminant analysis. Water-quality parameters Dissolved O2 Specific conductivity pH Turbidity Color Total suspended solids NOX–N NH4–N Total Kjeldahl N PO4–P Total P Total Fe

All data

Dry-season data

Wet-season data

yes yes yes yes yes yes

yes yes yes yes yes yes

yes yes yes yes yes yes

no yes yes yes yes yes

yes yes yes yes yes yes

no yes no yes yes no

Journal of Environmental Quality

Table 5. Descriptive statistics of water quality data on dissolved O2 (DO), specific conductivity, pH, turbidity (in nephelometric turbidity units, NTU), color (in platinum–cobalt units, PCU), total suspended solids (TSS), NOx–N, NH4–N, total Kjeldahl N (TKN), PO4–P, total P (TP), and total Fe (TFe) for the four groups identified by cluster analysis. DO

Specific conductivity

mg/L

mS/cm

Min. Mean Median Max. Skewness

0.1 5.0 4.9 a‡ 12.9 1.91

249 945 857 c 2140 0.76

5.13 7.23 7.20 a 8.45 0.01

Min. Mean Median Max. Skewness

0.3 4.8 4.6 a 12.8 0.46

286 1072 1029 d 2920 1.06

5.94 7.23 7.19 a 8.63 0.39

Min. Mean Median Max. Skewness

0.8 6.1 6.3 b 12.4 −0.01

125 666 619 b 1937 1.48

5.45 7.51 7.51 b 8.91 −0.34

Min. Mean Median Max. Skewness

0.3 6.9 7.2 c 12.6 −0.39

260 545 531 a 3500 8.24

6.30 7.79 7.82 c 9.09 −0.30

Statistic

pH

Turbidity

Color

TSS

NOX–N

NH4–N

TKN

PO4–P

TP

NTU PCU ———————————— mg/L ———————————— Group 1 (C24S49, C23S48, and C23S97) 0.7 10 –† – – – – 0.025 3.9 129 3 0.11 0.10 1.33 0.183 0.290 3.0 b 110 d 2a 0.07 a 0.06 c 1.30 c 0.161 c 0.262 c 52.0 400 92 1.64 0.87 10.48 0.879 1.400 6.10 0.70 5.39 3.46 1.77 3.72 1.29 1.25 Group 2 (C25S50 and C25S99) 0.4 20 – – – – – – 3.3 90 4 0.12 0.08 1.24 0.085 0.162 2.2 a 80 c 2a 0.09 a 0.07 d 1.22 b 0.062 b 0.127 a 170.0 272 356 1.29 1.42 6.19 0.710 1.046 17.02 0.99 12.89 3.39 5.42 0.88 2.30 2.17 Group 3 (C44S80) 1.1 5 – – – – 0.005 0.045 9.4 70 8 0.23 0.05 1.21 0.085 0.156 4.5 c 55 b 5b 0.22 b 0.04 b 1.13 a 0.064 b 0.134 a 90.3 221 110 0.89 0.44 5.87 0.368 0.446 3.32 1.28 3.28 0.96 2.21 0.10 1.65 1.16 Group 4 (C44S308) 1.5 2 – – – – – 0.050 37.1 57 37 0.25 0.05 1.64 0.065 0.175 24.1 d 43 a 20 c 0.22 b 0.02 a 1.49 d 0.04 7a 0.146 b 321.0 416 541 1.72 0.70 7.96 0.977 1.088 2.79 3.10 4.97 1.71 2.64 3.41 5.47 3.68

TFe mg/L – 428 342 a 2300 1.78 – 325 277 a 2100 2.54 40 381 286 a 2420 3.54 28 939 595 b 4673 1.95

† Value below detection limits. ‡ Medians followed by the same letter are not significantly different at a = 0.01 with pairwise least significance difference contrast of the Kruskal–Wallis test.

significant negative loading on specific conductivity. The fourth factor, explaining 10.48% of the total variance, had significant positive loadings on pH, DO, urban, and other agriculture land. The fifth factor, explaining 8.86% of the total variance, had a significant negative loading on forest and a significant positive loading on TKN. The sixth factor, explaining 7.83% of the total variance, had significant negative loadings on DO and wetland. The Pearson and Spearman’s rank correlation procedures resulted in similar correlations between water quality constituents and land use percentage, rainfall, and flow, with Pearson giving a stronger diagnosis in some cases, and thus only the Pearson correlation matrix is reported (Table 8). Pasture and citrus are perhaps the most dominant land use types influencing water quality. For example, TP, PO4–P, TKN, and NH4–N were all significantly correlated with pastures (positive) and citrus (negative). Associated with this correlation pattern was significant correlation (negative) between these nutrients and the water land use type. Significant correlations (negative) between forest and TP, TKN, and NH4–N were detected. Urban land also showed significant correlations with some of these constituents. Color was significantly correlated with pasture (positive), citrus (negative), and water (negative), while DO was significantly correlated with pasture (negative), other agriculture (positive), urban (positive), and wetland (positive). Rainfall was significantly correlated with DO (negative), specific conductivity (negative), and TFe (positive). Flow was significantly correlated www.agronomy.org • www.crops.org • www.soils.org

with specific conductivity (negative), TSS (positive), turbidity (positive), NOx–N (positive), and TFe (positive).

Discussion While spatial variations in water quality were logically linked to contributing sources associated with domestic or industrial origins in the literature (e.g., Simeonov et al., 2003; Zhou et Table 6. Rotated loading matrix (varimax) using the annual mean concentrations of water quality constituents. Significant factor loadings are in bold type. Water-quality parameter Total P PO4–P NH4–N Total Kjeldahl N Color Total suspended solids Turbidity NOx–N pH Dissolved O2 Specific conductivity Total Fe Eigenvalues Variance explained, %

Factor 1

Factor 2

Factor 3

Factor 4

0.92 0.80 0.79 0.69 0.64 0.00 −0.02 −0.23 0.13 −0.18 0.08 0.37 3.23 26.88

−0.14 −0.26 0.13 0.00 −0.29 0.86 0.78 0.73 0.11 0.08 −0.28 0.25 2.22 18.47

−0.05 −0.20 −0.16 0.12 -0.59 0.04 0.26 0.07 0.88 0.80 −0.26 −0.34 1.98 16.48

0.14 0.25 −0.40 0.00 0.16 0.02 0.39 0.18 0.12 0.00 −0.80 0.72 1.61 13.44 605

Table 7. Rotated loading matrix (varimax) using the annual mean concentrations of water quality constituents and percentage of land uses, rainfall and flow data. Significant factor loadings are in bold type. Water-quality parameter

Factor 1

Factor 2

Factor 3

Factor 4

Factor 5

Factor 6

Pasture PO4–P Total P Citrus Color Water NH4–N Turbidity Total suspended solids NOx–N Flow Rainfall Total Fe Specific conductivity Other agriculture pH Urban Forest Total Kjeldahl N Dissolved O2 Wetland Eigenvalues Variance explained, %

0.87 0.84 0.83 −0.81 0.78 −0.69 0.61 −0.05 −0.11 −0.19 −0.05 −0.13 0.41 0.17 0.09 −0.14 0.17 −0.05 0.50 −0.30 0.27 4.98 23.73

−0.19 −0.24 −0.14 −0.05 −0.24 −0.09 0.11 0.83 0.78 0.71 0.68 0.01 0.32 −0.28 0.09 0.10 0.40 0.32 0.09 0.08 0.02 2.93 13.97

−0.21 0.20 0.11 0.16 0.27 0.31 −0.26 0.23 −0.03 0.08 0.51 0.76 0.70 -0.70 0.07 −0.07 0.04 −0.03 0.09 −0.20 −0.08 2.30 10.94

−0.26 0.07 0.17 0.04 −0.36 −0.21 −0.12 0.33 0.07 0.07 0.10 −0.22 −0.02 −0.36 0.88 0.86 0.74 −0.15 0.09 0.53 −0.17 2.20 10.48

0.08 0.05 0.26 0.32 0.09 −0.14 0.50 −0.12 0.10 −0.22 −0.11 0.04 0.04 −0.01 0.14 0.25 −0.30 −0.85 0.60 −0.09 0.09 1.81 8.60

−0.13 0.06 0.08 0.35 0.11 0.19 0.15 −0.06 0.17 −0.25 −0.05 0.26 0.14 0.24 0.20 −0.13 0.08 0.00 −0.14 −0.54 −0.90 1.65 7.83

al., 2007; Li et al., 2011), source identification in this study was not readily apparent with PCF/FA of the water quality data alone (Table 6). While the first factor in Table 6 (TP, PO4–P, NH4–N, TKN, and color) can be considered nutrient related, it is difficult to identify specific pollution sources or anthropogenic activities in association with these factors. The PCA/FA of both water quality data and land use and hydrometric data (Table 7) revealed clear associations of specific water quality parameters with rainfall depth, flow, and land use, allowing identification of basin-specific water and land management practices that cause spatial variations in water quality.

transport mechanisms such as leaching, dilution, and wash-off phenomena (Helsel and Hirsch, 1992; Ravichandrana et al., 1996; Swanson et al., 2000). Our two-step PCA/FA suggested that there existed a latent factor for TSS, turbidity, and NOx–N (Factor 2 in Table 6), and the factor was related to flow (Factor 2 in Table 7). The most significant water resources management practice affecting flow is flood control releases of freshwater from Lake Okeechobee to the SLE via C-44 (Fig. 1). The separation of C44S308 and C44S80 from the remaining stations in the cluster analysis (Fig. 2) reflects the influence of lake releases. A further distinction between C44S308 and C44S80 shown in Fig. 2 was probably related to the dry-season water supply releases from Lake Okeechobee via C44S308, which were not readily captured by water quality samples collected at C44S80. High flows at C44S80 and C44S308 associated with lake releases were

Water Resources Management Spatial patterns of water quality have been linked to hydrologic processes and water resources management through pollutant

Table 8. Pearson correlation matrix between water quality constituents and rainfall, flow, and land use. Water-quality parameter

Rainfall

Flow

Pasture

Citrus

Other agriculture

Urban

Wetland

Forest

Water

Dissolved O2 Specific conductivity pH Turbidity Color Total suspended solids NOx

−0.31** −0.35** −0.19 0.04 0.22 0.06

0.07 −0.48** 0.11 0.78 −0.16 0.37**

−0.32** 0.44** −0.30** −0.32** 0.76** −0.24

0.06 −0.19 0.17 0.01 −0.54** 0.11

0.26 −0.34** 0.69** 0.30** −0.21 0.16

0.30** −0.32** 0.57** 0.57** −0.22 0.30**

0.29** −0.04 −0.03 −0.05 0.17 −0.11

0.05 −0.07 −0.26 0.30** −0.17 0.10

−0.05 −0.14 −0.20 −0.04 −0.34** 0.07

−0.02 −0.13 0.02 −0.01 −0.05 0.41**

0.46** −0.18 0.09 −0.15 −0.13 0.50**

−0.37** 0.54** 0.43** 0.67** 0.63** 0.13

−0.01 −0.30** −0.27** −0.55** −0.48** −0.14

0.11 0.03 0.18 0.10 0.22 0.14

0.36** −0.06 0.07 0.05 0.09 0.16

0.15 0.12 0.23 0.16 0.14 −0.08

0.38** −0.33** −0.39** −0.21 −0.32** 0.00

0.13 −0.49** −0.49** −0.40** −0.48** −0.13

NH4–N Total Kjeldahl N PO4–P Total P Total Fe ** Significant at a = 0.01. 606

Journal of Environmental Quality

probably the most significant factor contributing to their higher DO, turbidity, TSS, NOx–N, and lower specific conductivity, color, and NH4–N than at G1 and G2 stations (Table 5). Specifically, the higher DO, turbidity, and TSS were probably related to enhanced aeration of water and erosion of sediment under high-flow conditions. Figure 3 shows that increases in TSS and turbidity with flows at C44S80 can be well defined by linear relationships at monthly or annual time scales. The lower color, lower NH4–N, and higher NOx–N were possibly because Lake Okeechobee water has a longer residence time than stormwater runoff originating from the local basins, thereby allowing photolysis and oxidation of these constituents in lake water (Doering and Chamberlain, 1999). Another latent factor identified by PCA/FA was specific conductivity and TFe (Factor 4 in Table 6), and this factor was linked to rainfall and flow (Factor 3 in Table 7). Total Fe is mostly in particulate form, and positive correlation with rainfall and flow is probably transport related. Correlation of specific conductivity with flow and rainfall depth is, however, due to the irrigation practice on citrus lands. Since large-scale expansion of citrus in the 1960s, storage capacity in the drainage network has been inadequate to meet irrigation demands. In the C-23, C-24, and C-25 basins, a common practice is to use artesian well water from the Floridan Aquifer, a confined aquifer with high mineral content, as an irrigation supplement when surface water becomes limited. In contrast, supplemental irrigation water is supplied by Lake Okeechobee in the C-44 basin (South Florida Water Management District, 2004). The different supplemental irrigation sources lead to the spatial variation in specific conductivity across these basins. The higher value at G2 than at G1 corresponds with the greater citrus area in the C-25 basin than in the C-23 or C-24 basins (Table 2). The positive

correlation of specific conductivity with rainfall (Table 8) also suggests that lower specific conductivity in high-rainfall years is associated with less supplemental irrigation supplies from the Floridan Aquifer.

Land Use and Storm-Water Retention Land use and development have long been shown to influence surface water quality, with Osborne and Wiley (1988) linking urbanization to water quality changes in the St. Fork River in Illinois, Long and Plummer (2004) attributing high levels of specific conductance to dense residential development in the Wachusett Reservoir watershed in Massachusetts, and Yin et al. (2005) relating urban development patterns to shifts in pollution sources in Shanghai, China. Water quality has also been evaluated with respect to the amount of arable land and grassland (Ferrier et al., 2001), urban land and upland agriculture (Santos-Roman et al., 2003; Zampella et al., 2007), and land development intensity and impervious percentage (Carey et al., 2011). The two-step PCA/FA indicated that the nutrient-related factor (TP, PO4–P, NH4–N, TKN, and color) was associated with land management of pasture and citrus and the area of open water in a basin (Factor 1 in Tables 6 and 7). The C-23 and C-24 basins had the greatest percentage of pasture (Table 2). Mean concentrations of TP, PO4–P, NH4–N, and color in G1 were the highest (Table 5). Graves et al. (2004) reported that color in runoff from pasture was significantly higher than from citrus and urban land, and they attributed the high color to the leaching of organic materials (humic and tannic acids from vegetative decay) into surface water. Cattle wastes in pastures are subject to storm-water runoff, and that is probably the reason why TP, PO4–P, TKN, and NH4–N were positively related to pasture. In addition, the C-23 and C-24 basins receive applications of

Fig. 3. Linear regressions of total suspended solids (TSS) and turbidity (in nephelometric turbidity units, NTU) with flow at C44S80 on (A,B) a monthly time scale and (C,D) an annual time scale. All regressions are significant at a = 0.01. www.agronomy.org • www.crops.org • www.soils.org

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sewage sludge and biosolids to supplement fertilization, leaving a large amount of legacy P in the soil. This practice is construed to be partly responsible for the high color and P concentrations observed at G1. Negative correlations between TP, PO4–P, NH4–N, TKN, color, and the percentages of both citrus and water were because most open water, representing storm-water retention reservoirs, are embedded in citrus land. Storm-water management permits in large areas of pasture in the C-23 and C-24 basins allow direct discharge of storm water into the C-23 and C-24 canals without water detention. In contrast, the C-25 basin has a basin rule requiring on-site water detention for storm-water management in citrus land. This is evidenced by the highest areal percentage of open water as a land use type for the C-25 basin (Table 2, Fig. 1). Although these reservoirs are shallow (about 2 m in depth), they allow a large portion of storm water to be retained on site before discharging into the C-25 canal. The water quality benefit is obvious, as shown in Table 5, for its lower color, turbidity, TSS, TP, and PO4–P than for G1. The practical implication for this spatial pattern is that the existing swale, block, and canal systems for citrus in the C-44, C-23, and C-24 basins could be modified to increase retention while still being effective for drainage during heavy storms.

Wetland Preservation and Dissolved Oxygen

vegetation and periphyton contributed to high DO in Florida’s pristine wetlands. Frequent low DO concentrations during runoff events from the watershed are a major stressor to the SLE (Chamberlain and Hayward, 1996; Florida Department of Environmental Protection, 2008). At all stations, DO values were periodically below the Florida State Class III water quality standard (5 mg/L). The median DO was 4.9 mg/L for G1, 4.6 mg/L for G2, 6.3 mg/L for G3, and 7.2 mg/L for G4 (Table 5). Low DO occurred mostly in the wet season when the temperature and nutrient concentrations in runoff were high. Figure 4 indicated that DO was negatively correlated with temperature (only C24S49 data are shown because the pattern was similar at other stations), supporting the argument by Graves et al. (2004) in that low DO in South Florida is partly caused by high temperature in the summer (lower DO saturation point) and inhibited re-aeration in canal environments. Interestingly, DO also correlated significantly with PO4–P, NH4–N, and TP in a similar fashion as with temperature (negative), but not with NOx–N (Fig. 4). While low DO can be due to inputs of nutrientrich detritus in secondary and tertiary canals from agricultural lands, which increased O2 demand, TP, PO4–P, NH4–N, DO, and temperature could simply be covariants associated with seasonality (Qian et al., 2007). The complexity of enhanced primary productivity under elevated nutrient concentrations and DO dynamics needs to be studied further to elucidate how reducing nutrient loading can effectively improve DO conditions in South Florida.

Although wetlands, the third largest land use type in the watershed, are known for their nutrient retention capability (McCormick and Laing, 2003; Graves et al., 2004), their association with nutrients was not significant (Tables 7 and 8). Management Implications Water quality measured at these stations represents a blend of runoff originating from all types of land use in the basins. The Issues associated with the quantity, quality, timing, and net effect of wetlands on nutrient concentrations was probably distribution of freshwater discharged into the IRL/SLE system overshadowed by that of pasture and citrus. The only water quality parameter that was significantly associated with wetlands was DO. While the higher DO in C-44 was partly attributable to Lake Okeechobee releases, the percentage of wetland was found to be significantly correlated with DO across the watershed (Tables 7 and 8). This is consistent with the observation by Graves et al. (2004) about higher DO in storm water from wetland than from other major land uses in the watershed. SantosRoman et al. (2003) also reported that watersheds with protected forest areas had higher DO in Puerto Rico. Dissolved O2 dynamics in wetland is complex due to the influence of aquatic vegetation on the rate of O2 exchange and metabolic O2 production and consumption. Although low DO has been observed in temperate wetlands (e.g., Rose and Crumpton, 1996), relatively high DO is common in wetlands in South Florida’s subtropical setting (McCormick and Laing, 2003; Graves et al., 2004). Higher DO in wetlands can be attributable to lower temperature due to the shading effect of forest and/ or enhanced aeration in an open-water environment in contrast to narrow canals in agricultural lands. Fig. 4. Correlation of dissolved O (DO) with (A) temperature, (B) PO4–P, (C) NH4–N, and McCormick and Laing (2003) also indicated (D) NOX–N measured at C24S49 (n2 = 310). All correlations are significant at a = 0.01 that the metabolic activities of submerged aquatic except for NOX–N. 608

Journal of Environmental Quality

have instigated federal and state restoration efforts at both regional and local scales (U.S. Army Corps of Engineers and South Florida Water Management District, 2004; Florida Department of Environmental Protection, 2008; South Florida Water Management District, 2009). These restoration programs address both water quantity management through regulation of flow discharge patterns and water quality treatment to reduce nutrient concentrations in runoff. The water and material loads associated with controlled freshwater releases from Lake Okeechobee represent an artificial stressor, causing adverse impacts on water quality and ecological conditions in the IRL/SLE (Chamberlain and Hayward, 1996; Wan et al., 2012). With the restoration of the Greater Everglades under the Comprehensive Everglades Restoration Plan (CERP) at the regional level, it is anticipated that releases from Lake Okeechobee into the SLE will be substantially reduced from present levels (U.S. Army Corps of Engineers and South Florida Water Management District, 2004). Accompanying this regional restoration effort will be significant decreases in TSS, turbidity, and N concentrations in the C-44 canal. These parameters were among the major discriminating constituents associated with Lake Okeechobee releases. Restoration efforts at the local scale under CERP aim to restore the inflow pattern to the hydrologic characteristics of the predrainage or natural condition through the construction of large storage reservoirs and STAs in the C-44, C-23, C-24, and C-25 basins (U.S. Army Corps of Engineers and South Florida Water Management District, 2004; Wan et al., 2006). Our analyses of spatial variations in watershed water quality suggest that emphasis should be placed on the C-23 and C-24 basins, where nutrient concentrations and color were the highest. This supports the current design of the project, which includes the C-23/C24 reservoirs and STAs as the largest facilities in the watershed. Our observation of better water quality (P, color, and TSS) at G2 than at G1 also suggests that the proposed storage reservoirs will have water quality benefits in addition to their capacity to regulate flow patterns. Furthermore, these storage reservoirs will provide a supplemental irrigation supply during the dry season (Wan at el., 2006); thus, specific conductivity in G1 and G2 will become lower when reliance on the Floridan Aquifer is reduced. In conjunction with constructing CERP reservoirs and STAs is an effort to rehydrate about 36,422 ha of drained wetlands throughout the watershed and an ongoing regulatory source control program to implement best management practices (BMPs) (South Florida Water Management District, 2009). Storm-water retention and filtering capabilities of forests and wetlands have been shown to improve water quality (SantosRoman et al., 2003; Graves et al., 2004). Ongoing BMPs specific to citrus and pastures will be especially effective due to their substantial spatial extent and correlation with water quality constituents (N, P, and color) in the watershed.

Conclusions Multivariate statistical techniques including CA, DA, and PCA/FA are standard tools for analyzing multidimensional water quality data (e.g., Simeonov et al., 2003; Shrestha and Kazama, 2007). This study demonstrated the use of multivariate techniques to link spatial variations in water quality with land www.agronomy.org • www.crops.org • www.soils.org

and water resources management in a dense drainage/irrigation canal network where storm-water runoff serves as the single most important nonpoint source of water pollution. While CA and DA helped to delineate the spatial patterns of water quality, two-step PCA/FA of water quality data in conjunction with hydrometric and land use data allowed detection of associations between selected water quality parameters and specific land and water management practices. Such analyses can be invaluable on many occasions when intentions are to identify anthropogenic activities that influence water quality, to explore possible interactions between external drivers and water quality processes that may take place in the field, or to develop technically sound pollution abatement programs.

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