State Key Laboratory of Water Environment, School of Environment, Beijing Normal University, Beijing, 100875, PR China; College of Energy and Environmental Engineering, Hebei University of Engineering, Handan, 056038, PR China.
State Key Laboratory of Water Environment, School of Environment, Beijing Normal University, Beijing, 100875, PR China.
J Environ Manage. 2020 Aug 1;267:110641. doi: 10.1016/j.jenvman.2020.110641. Epub 2020 May 4.
Best management practices (BMPs) have been widely applied to mitigate non-point source (NPS) pollution in agricultural watersheds. However, a prediction of the multivariate reduction effect of NPS pollutants by BMPs considering its stochastic nature has not been conducted. A new modeling approach combining a hydrological model and copulas was proposed to predict the multivariate effect of BMPs fully considering the stochastic characteristics of BMPs effects and the dependence structure between them. Two levels of reduction effect, i.e., the multi-indicator effect of a single BMP and the combined effect of multiple BMPs, were simulated. The approach was demonstrated in Zhangjiachong watershed, a typical small watershed in the Three Gorges Reservoir area, China. Results show that copulas can effectively simulate the dependence between the univariate effects of BMPs. The approach can accurately predict the probability to achieve the reduction objective for multiple pollutants and multiple BMPs in a watershed. It provides a stochastic way to predict the multivariate effect of BMPs and has great potential to be widely applied in BMPs related decision making.
最佳管理措施（BMPs）已被广泛应用于减轻农业流域的非点源（NPS）污染。然而，考虑到其随机性，尚未对 BMP 对 NPS 污染物的多元减排效果进行预测。本研究提出了一种结合水文模型和 Copulas 的新模型方法，以充分考虑 BMP 效果的随机特性及其之间的依赖结构，对 BMP 的多元效果进行预测。模拟了两个水平的减排效果，即单个 BMP 的多指标效果和多个 BMP 的综合效果。该方法在三峡库区典型小流域——张家冲流域进行了验证。结果表明，Copulas 可以有效地模拟 BMP 单因素效果之间的相关性。该方法可以准确预测流域内多种污染物和多种 BMP 实现减排目标的概率。它为预测 BMP 的多元效果提供了一种随机方法，在 BMP 相关决策中具有广泛的应用潜力。
