Comparing the selection and placement of best management practices in improving water quality using a multiobjective...

Area of Science:

• Environmental Science
• Agricultural Engineering
• Water Resource Management

Background:

• Nonpoint source (NPS) pollution from agricultural areas is a significant environmental challenge.
• Best Management Practices (BMPs) are crucial for mitigating NPS pollutants efficiently.
• Selecting and placing BMPs requires balancing economic and environmental factors within a watershed.

Purpose of the Study:

• To compare multiobjective optimization and targeting methods for selecting and placing BMPs in a pasture-dominated watershed.
• To evaluate the effectiveness of different BMP combinations, including grazing management, vegetated filter strips (VFS), and poultry litter applications.
• To analyze the trade-offs between pollutant reduction, implementation area, and computational efficiency.

Main Methods:

• Integrated a multi-objective genetic algorithm (GA) with the Soil and Water Assessment Tool (SWAT) watershed model for optimization.
• Employed a targeting method to place optimal BMPs in critical pollutant-contributing areas.
• Assessed 171 combinations of BMPs, focusing on grazing management, VFS, and poultry litter.

Main Results:

• Optimization methods were less effective and required longer computation times when vegetated filter strips (VFS) were excluded.
• The targeting method efficiently identified optimal BMPs and their placement.
• Achieving equivalent pollutant reductions using the targeting method necessitated larger implementation areas compared to optimization.

Conclusions:

• Multiobjective optimization, particularly when including VFS, offers a more effective approach to pollutant reduction in watersheds.
• Targeting methods provide a computationally efficient alternative for BMP selection and placement, though potentially requiring larger areas.
• The choice between optimization and targeting depends on balancing pollutant reduction goals, area constraints, and computational resources.