Applying copulas to predict the multivariate reduction effect of best management practices

Area of Science:

• Environmental Science
• Hydrology
• Agricultural Engineering

Background:

• Best management practices (BMPs) are crucial for mitigating agricultural non-point source (NPS) pollution in watersheds.
• Existing models often fail to account for the stochastic nature and multivariate effects of BMPs on different pollutants.

Purpose of the Study:

• To develop and demonstrate a novel modeling approach for predicting the multivariate reduction effect of BMPs on NPS pollutants.
• To incorporate the stochastic characteristics and dependence structures of BMP effects into the prediction model.

Main Methods:

• A new approach combining a hydrological model with copula functions was developed.
• The model simulated two levels of reduction effects: single BMPs with multiple indicators and combined effects of multiple BMPs.
• The methodology was applied to the Zhangjiachong watershed in China.

Main Results:

• Copulas effectively simulated the dependence between the univariate effects of individual BMPs.
• The model accurately predicted the probability of achieving reduction targets for multiple pollutants and BMPs.
• The approach offers a stochastic method for evaluating the multivariate impact of BMPs.

Conclusions:

• The proposed modeling approach provides a robust and stochastic framework for predicting the multivariate effectiveness of BMPs.
• This method has significant potential for informing decision-making processes related to BMP implementation in watershed management.
• Accurate prediction of BMP effectiveness is vital for managing NPS pollution in agricultural areas.