Linking Spatial Variations in Water Quality with Water and Land Management using Multivariate Techniques

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 O-2 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.