New framework for managing the water environmental capacity integrating the watershed model and stochastic algorithm

Area of Science:

• Environmental Science
• Water Resource Management
• Hydrology

Background:

• Effective water quality management requires integrated calculations of pollution load and water environmental capacity (WEC).
• Limited research exists on dynamic WEC and pollution loads in nonpoint source pollution (NPS)-dominant watersheds experiencing changing rainfall patterns.

Purpose of the Study:

• To propose a novel framework (SR-WEC) for calculating dynamic WEC and NPS pollution load under stochastic rainfall conditions.
• To assess the framework's performance against traditional methods in a temperate monsoon watershed.

Main Methods:

• Generated stochastic rainfall series using a first-order Markov chain and gamma distribution.
• Input generated rainfall into the Soil and Water Assessment Tool (SWAT) to simulate water quality responses.
• Applied the framework to the Daning River watershed, Three Gorges Reservoir Region, China.

Main Results:

• The SR-WEC framework revealed significant underestimations of ideal WEC and NPS pollution load by traditional methods (23-51%).
• Key periods (April-June) and areas were identified for substantial nutrient reduction (46% TN, 51% TP).
• SR-WEC demonstrated superior regression relationships (R² 0.718-0.989) between rainfall and WEC/pollution load compared to traditional methods (R² 0.005-0.797).
• A rainfall threshold (120 mm/month) was identified affecting organic nitrogen WEC dynamics.

Conclusions:

• The proposed SR-WEC framework accurately captures the dynamic nature of WEC and NPS pollution under rainfall uncertainty.
• It provides a more reliable basis for NPS pollution management by identifying critical temporal and spatial factors.
• The study highlights the limitations of traditional methods relying on limited rainfall data.