کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4526106 | 1323814 | 2011 | 12 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Uncertainty assessment for watershed water quality modeling: A Probabilistic Collocation Method based approach Uncertainty assessment for watershed water quality modeling: A Probabilistic Collocation Method based approach](/preview/png/4526106.png)
Watershed water quality models are increasingly used in management. However, simulations by such complex models often involve significant uncertainty, especially those for non-conventional pollutants which are often poorly monitored. This study first proposed an integrated framework for watershed water quality modeling. Within this framework, Probabilistic Collocation Method (PCM) was then applied to a WARMF model of diazinon pollution to assess the modeling uncertainty. Based on PCM, a global sensitivity analysis method named PCM-VD (VD stands for variance decomposition) was also developed, which quantifies variance contribution of all uncertain parameters. The study results validated the applicability of PCM and PCM-VD to the WARMF model. The PCM-based approach is much more efficient, regarding computational time, than conventional Monte Carlo methods. It has also been demonstrated that analysis using the PCM-based approach could provide insights into data collection, model structure improvement and management practices. It was concluded that the PCM-based approach could play an important role in watershed water quality modeling, as an alternative to conventional Monte Carlo methods to account for parametric uncertainty and uncertainty propagation.
► An integrated framework for watershed water quality modeling under uncertainty.
► Efficiency of Probabilistic Collocation Method for complex watershed models.
► A new method of sensitivity analysis based on Probabilistic Collocation Method.
Journal: Advances in Water Resources - Volume 34, Issue 7, July 2011, Pages 887–898