Robust data-driven approach evolution for multi-factor driving effect understanding of nutrient loading variations at...

Area of Science:

• Environmental Science
• Water Resource Management
• Ecosystem Dynamics

Background:

• Reservoir water quality management is vital for human consumption and ecosystem health.
• Understanding nutrient loading drivers at the basin scale is essential for effective pollution control.
• The Danjiangkou Reservoir (DJKR) in China, a major water source, faces challenges in maintaining water quality.

Purpose of the Study:

• To develop and validate a comprehensive framework for identifying nutrient loading drivers in reservoirs.
• To investigate the synergistic effects of meteorological, economic, and water quality parameters on nitrogen and phosphorus loading.
• To provide insights for targeted water quality management strategies in large-scale water source areas.

Main Methods:

• A novel framework integrating self-organizing map algorithm, Mantel's test, and partial least squares structural equation modeling.
• Monthly data collection from 16 tributaries of the Danjiangkou Reservoir from 2017 to 2022.
• Analysis of meteorological, economic, and water quality parameters influencing nitrogen and phosphorus loading.

Main Results:

• The Hanjiang River is the primary source of nitrogen (67.90%) and phosphorus (80.20%) loading in DJKR.
• Shending and Si Rivers exhibit high nitrogen and phosphorus concentrations, requiring specific attention.
• Meteorological and economic factors significantly influence nitrogen and phosphorus loading, with meteorological factors having both direct and indirect effects.

Conclusions:

• Multi-factor coupling is critical for effective mid-scale basin management.
• Targeted upstream management and stricter controls are necessary to mitigate pollution in DJKR.
• The proposed framework offers a valuable approach for pollution prevention and water quality management in similar ecosystems.