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

Area of Science:

• Environmental Science
• Water Quality Management
• Ecosystem Restoration

Background:

• Traditional pollution source evaluation often focuses on free-flowing rivers.
• Managed canal systems, like those discharging into the Indian River Lagoon, Florida, present unique challenges due to intensive water and land management.
• Anthropogenic factors, particularly water and land management, are primary drivers of water quality in these systems.

Purpose of the Study:

• To assess the impact of water and land management on the spatial variability of water quality in a managed canal system.
• To identify specific management activities influencing key water quality parameters.
• To integrate hydrometric and land use data with water quality data for comprehensive analysis.

Main Methods:

• Analysis of 25 years of monthly water quality data from seven monitoring stations across four drainage basins.
• Application of Cluster Analysis (CA) to classify monitoring stations into distinct groups.
• Utilized Discriminant Analysis to identify spatial patterns in water quality parameters among CA groups.
• Employed a two-step Principal Component Analysis/Factor Analysis (PCA/FA) with and without hydrometric and land use data.

Main Results:

• PCA/FA of water quality data alone failed to identify management-related factors.
• Integration of hydrometric and land use data revealed significant associations: nutrients/color with land management (pasture/citrus), TSS/turbidity/NOx with flow/Lake Okeechobee releases, conductivity with irrigation, and dissolved oxygen with wetland preservation.
• Distinct spatial patterns in water quality were observed across the classified monitoring station groups.

Conclusions:

• Effective water and land management strategies are crucial for reducing pollutant loading in canal systems.
• Basin-specific management approaches are essential for successful ecosystem restoration programs.
• The integration of diverse datasets (water quality, hydrometric, land use) is vital for understanding complex water quality dynamics.