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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 spatiotemporal optimization method for nutrient control in lake watersheds.

Baichuan Zhang¹, Zhenghui Fu², Yijia Ji³

¹School of Earth and Environment, Anhui University of Science and Technology, Huainan, 232001, PR China.

Journal of Environmental Management

|November 22, 2023

Summary

This study presents an improved spatiotemporal optimization method for managing lake watershed nutrients. The dynamic, high-resolution approach enhances environmental and economic strategies for sustainable development.

Keywords:

Agricultural non-point source Nutrient control strategy Spatiotemporal optimization Watershed management

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Area of Science:

Environmental Science
Water Resource Management
Sustainable Development

Background:

Balancing socioeconomic progress with aquatic ecosystem protection in lake watersheds is crucial for sustainable development.
Nutrient management in less-developed lake watersheds requires efficient strategies.
Previous optimization methods for environmental and economic management need improvement.

Purpose of the Study:

To improve an existing environmental and economic management optimization method for lake watersheds.
To develop a dynamic, distributed, and high-resolution nutrient management strategy.
To provide a framework for precise management of agricultural non-point source (ANPS) pollution.

Main Methods:

Applied a spatiotemporal optimization method coupling the Soil and Water Assessment Tool (SWAT), system dynamics model, and objective programming model.
Utilized a case study in the Yilong Lake watershed for agricultural non-point source (ANPS) pollution control and rural sewage treatment programs.
Conducted simulation evaluations to assess the efficiency of the optimized scheme.

Main Results:

The spatiotemporal optimization significantly improved the efficiency of the previous environmental and economic management scheme.
The developed scheme is dynamic, distributed, and provides annual, high-resolution control.
The method offers a basis for precise management of ANPS pollution.

Conclusions:

A framework for coupling simulation and two-step optimization was achieved, despite requiring further improvements.
The dynamic and distributed optimization approach enhances nutrient management strategies in lake watersheds.
This research contributes to achieving sustainable development through improved watershed management.