Optimizing Best Management Practices (BMPS) For Watershed Plans Considering Temporal Dynamics, Maintenance, and Investment Constraints

Abstract

In an era increasingly challenged by environmental issues in watersheds, understanding and optimizing land-use management practices becomes crucial. This thesis examines the optimization of Best Management Practices (BMPs) for water quality improvement using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to address its multifaceted nature.

The research highlights the most recent studies in BMP optimizations, emphasizing their benefits while drawing attention to certain limitations, particularly the lack of temporal dynamics, maintenance simulations and budget considerations in the optimization process. The core of this study lies in the incorporation of time-related effectiveness changes, maintenance needs, scenario initialization, and investment constraints into the BMP optimization strategies. The nine designed scenarios, labeled Sce1 to Sce9, provided a robust comparative network.

Experiment results from a case study area, the Youwuzhen Watershed, indicate that BMP strategies that account for time-related changes more accurately gauge the environmental benefits, highlighting potential inaccuracy in static configuration models. The inclusion of maintenance reveals its function in enhancing BMP effectiveness over time, emphasizing the necessity of regular maintenance. The study also identifies the advantage of scenario initialization, which helps reach optimal outcomes more efficiently.

Furthermore, when examining financial constraints, BMP scenarios derived from initial inputs display superior effectiveness, both in low- and high-level investment scenarios. Two distinct investment patterns: a front-load strategy focusing on early investments and a uniform strategy distributing funds more evenly, both have their advantages and challenges in BMP configuration. The front-load investment strategy achieves environmental objectives rapidly but lacks long-term sustainability.

In conclusion, this thesis offers a new perspective in BMP optimization considering time, maintenance, and financial constraints. It emphasizes the significance of a dynamic, multidimensional approach for sustainable and effective watershed plans.