کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8883290 1625595 2018 72 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach
ترجمه فارسی عنوان
تجزیه و تحلیل مشترک ورودی و عدم قطعیت پارامتری در مدل سازی کیفیت آب رودخانه: رویکرد رسمی
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات فرآیندهای سطح زمین
چکیده انگلیسی
Significant Input uncertainty is a major source of error in watershed water quality (WWQ) modeling. It remains challenging to address the input uncertainty in a rigorous Bayesian framework. This study develops the Bayesian Analysis of Input and Parametric Uncertainties (BAIPU), an approach for the joint analysis of input and parametric uncertainties through a tight coupling of Markov Chain Monte Carlo (MCMC) analysis and Bayesian Model Averaging (BMA). The formal likelihood function for this approach is derived considering a lag-1 autocorrelated, heteroscedastic, and Skew Exponential Power (SEP) distributed error model. A series of numerical experiments were performed based on a synthetic nitrate pollution case and on a real study case in the Newport Bay Watershed, California. The Soil and Water Assessment Tool (SWAT) and Differential Evolution Adaptive Metropolis (DREAM(ZS)) were used as the representative WWQ model and MCMC algorithm, respectively. The major findings include the following: (1) the BAIPU can be implemented and used to appropriately identify the uncertain parameters and characterize the predictive uncertainty; (2) the compensation effect between the input and parametric uncertainties can seriously mislead the modeling based management decisions, if the input uncertainty is not explicitly accounted for; (3) the BAIPU accounts for the interaction between the input and parametric uncertainties and therefore provides more accurate calibration and uncertainty results than a sequential analysis of the uncertainties; and (4) the BAIPU quantifies the credibility of different input assumptions on a statistical basis and can be implemented as an effective inverse modeling approach to the joint inference of parameters and inputs.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Advances in Water Resources - Volume 116, June 2018, Pages 77-94
نویسندگان
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