Assessment of parameter uncertainty for non-point source pollution mechanism modeling: A Bayesian-based approach

Area of Science:

• Environmental Science
• Water Resource Management
• Computational Hydrology

Background:

• Effective non-point source pollution control relies on accurate parameter uncertainty assessment in environmental models.
• Existing methods may not fully capture the complex interactions of multiple parameters influencing model outputs.
• Understanding parameter uncertainties is crucial for reliable predictions of water quality and quantity.

Purpose of the Study:

• To develop and apply an integrated approach combining Bayesian inference, Markov chain Monte Carlo, and multilevel factorial analysis for parameter uncertainty assessment.
• To quantitatively investigate the main and interactive effects of sensitive parameters on model response variables.
• To enhance the simulating and predicting capabilities of non-point source pollution models.

Main Methods:

• Utilized Bayesian inference coupled with Markov chain Monte Carlo (MCMC) for parameter uncertainty analysis.
• Employed multilevel factorial analysis to determine the main and interactive effects of parameters on model outputs.
• Applied the Soil and Water Assessment Tool (SWAT) to the Shitoukoumen Reservoir Catchment in China.

Main Results:

• Identified key parameters influencing average annual runoff (AAR), average annual sediment (AAS), and average annual total nitrogen (AAN).
• Soil conservation service runoff curve number (CN2) positively affected all response variables; available water capacity (SOL_AWC) negatively affected all.
• Parameter interactions, such as between CN2 and SOL_AWC, significantly impacted AAR, demonstrating interdependent effects on model responses.

Conclusions:

• The developed Bayesian inference approach effectively assesses parameter uncertainties and quantifies parameter effects in non-point source pollution modeling.
• The findings provide crucial insights into parameter influences and interactions, enhancing model predictive power for pollution management.
• This integrated methodology offers a promising solution for robust uncertainty assessment in environmental modeling.