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Updated: Dec 23, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

A water quality management methodology for optimizing best management practices considering changes in long-term

Guowangchen Liu¹, Lei Chen¹, Wenzhuo Wang¹

¹State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, PR China.

The Science of the Total Environment

|April 18, 2020

Summary

This study introduces a new method for optimizing long-term best management practices (BMPs) to improve water quality. The long-term BMP optimization method (LBMP-OM) reduces costs and enhances BMP configuration for nonpoint source pollution control.

Keywords:

Best management practices (BMPs)Changing BMP effectiveness Long-term simulation Nonpoint source pollution Optimization

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Area of Science:

Environmental Science
Water Resource Management
Ecological Engineering

Background:

Nonpoint source (NPS) pollution significantly impacts water quality, necessitating effective long-term management strategies.
Current best management practices (BMPs) optimization lacks methods to account for changing BMP efficiency over time.
Developing dynamic BMP configuration strategies is crucial for sustainable water quality management.

Purpose of the Study:

To develop and validate a novel long-term BMP optimization method (LBMP-OM).
To recommend optimal maintenance and replacement strategies for BMPs.
To enhance BMP configuration for effective NPS pollution control.

Main Methods:

Development of the long-term BMP optimization method (LBMP-OM).
Incorporation of time-varying BMP effectiveness into the optimization model.
Application and testing of LBMP-OM in the Daning Watershed, China.

Main Results:

Recommended a 1-year maintenance and 3-year replacement period for filter strips based on dynamic effectiveness.
Demonstrated a 57.36% reduction in total watershed management costs compared to conventional methods.
Validated the LBMP-OM's effectiveness in optimizing long-term BMP management and configuration.

Conclusions:

The LBMP-OM provides a robust framework for dynamic BMP management, considering changing efficiencies.
Optimized BMP maintenance and replacement strategies significantly reduce costs while improving water quality.
This method offers a sustainable approach to long-term nonpoint source pollution control at the watershed scale.