Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management
Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management
复制标题
用于流域水质建模和管理的马尔可夫链蒙特卡罗 (MCMC) 不确定性分析
DOI：
10.1007/s00477-015-1091-8
复制
发表时间：
2015
影响因子：
4.2
通讯作者：
Feng Han
中科院分区：
文献类型：
--
作者：
Yi Zheng;Feng Han
流域尺度水质（WWQ）模型目前广泛用于支持管理决策。然而，模型产出中的重大不确定性仍然是一个基本上未解决的问题。近年来，马尔可夫链蒙特卡罗方法（MCMC）作为一种形式贝叶斯不确定性分析（UA）方法在水文建模领域得到了广泛的应用，但其在水流量建模中的应用却很少。本文以DREAM（ZS）和SWAT为代表的MCMC算法和WWQ模型，系统地评价了MCMC在WWQ建模不确定性评估中的适用性。以新港湾流域硝酸盐污染为例，进行了数值实验研究。结论是，MCMC算法的效率和有效性取决于UA的一些关键设计，包括：(i)在MCMC分析中有多少模型参数和哪些模型参数被认为是随机的；（ii）固定非随机模型参数的位置；（iii）停止马尔可夫链的准则。研究结果还表明，MCMC UA必须是面向管理的，即管理目标应该被考虑到UA的设计中，而不是在UA之后考虑。
Watershed-scale water quality (WWQ) models are now widely used to support management decision-making. However, significant uncertainty in the model outputs remains a largely unaddressed issue. In recent years, Markov Chain Monte Carlo (MCMC), a category of formal Bayesian approaches for uncertainty analysis (UA), has become popular in the field of hydrological modeling, but its applications to WWQ modeling have been rare. This study systematically evaluated the applicability of MCMC in assessing the uncertainty of WWQ modeling, using Differential Evolution Adaptive Metropolis (DREAM(ZS)) and SWAT as the representative MCMC algorithm and WWQ model, respectively. The nitrate pollution in Newport Bay watershed was the case study for numerical experiments. It has been concluded that the efficiency and effectiveness of a MCMC algorithm would depend on some critical designs of the UA, including: (i) how many and which model parameters to be considered as random in the MCMC analysis; (ii) where to fix the non-random model parameters; and (iii) which criteria to stop the Markov Chain. The study results also indicate that the MCMC UA has to be management-oriented, that is, management objectives should be factored into the designs of the UA, rather than be considered after the UA.
登录
查看更多内容
影响因子：
3.7
作者：
MASSEY, FJ
通讯作者：
MASSEY, FJ
DOI：
10.4324/9780203491287
发表时间：
1995
期刊：
--
影响因子：
--
作者：
Andrew Gelman;John B. Carlin;H. Stern;David B. Dunson;A. Vehtari;Donald B. Rubin
通讯作者：
Andrew Gelman;John B. Carlin;H. Stern;David B. Dunson;A. Vehtari;Donald B. Rubin
影响因子：
3.7
作者：
O. Stramer
通讯作者：
O. Stramer
影响因子：
6.4
作者：
Jing Yang;P. Reichert;K. Abbaspour;Hong Yang
通讯作者：
Jing Yang;P. Reichert;K. Abbaspour;Hong Yang
影响因子：
2.5
作者：
C. Robert;G. Casella
通讯作者：
C. Robert;G. Casella