气候变化领域集成服务门户(CCPortal): Bayesian Model Averaging With Fixed and Flexible Priors: Theory, Concepts, and Calibration Experiments for Rainfall-Runoff Modeling

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DOI	10.1029/2019MS001924
	Bayesian Model Averaging With Fixed and Flexible Priors: Theory, Concepts, and Calibration Experiments for Rainfall-Runoff Modeling
	Samadi S.; Pourreza-Bilondi M.; Wilson C.A.M.E.; Hitchcock D.B.
发表日期	2020
ISSN	19422466
卷号	12 期号:7
英文摘要	This paper introduces for the first time the concept of Bayesian model averaging (BMA) with multiple prior structures, for rainfall-runoff modeling applications. The original BMA model proposed by Raftery et al. (2005, https://doi.org.10.1175/MWR2906.1) assumes that the prior probability density function (pdf) is adequately described by a mixture of Gamma and Gaussian distributions. Here we discuss the advantages of using BMA with fixed and flexible prior distributions. Uniform, Binomial, Binomial-Beta, Benchmark, and Global Empirical Bayes priors along with Informative Prior Inclusion and Combined Prior Probabilities were applied to calibrate daily streamflow records of a coastal plain watershed in the southeast United States. Various specifications for Zellner's g prior including Hyper, Fixed, and Empirical Bayes Local (EBL) g priors were also employed to account for the sensitivity of BMA and derive the conditional pdf of each constituent ensemble member. These priors were examined using the simulation results of conceptual and semidistributed rainfall-runoff models. The hydrologic simulations were first coupled with a new sensitivity analysis model and a parameter uncertainty algorithm to assess the sensitivity and uncertainty associated with each model. BMA was then used to subsequently combine the simulations of the posterior pdf of each constituent hydrological model. Analysis suggests that a BMA based on combined fixed and flexible priors provides a coherent mechanism and promising results for calculating a weighted posterior probability compared to individual model calibration. Furthermore, the probability of Uniform and Informative Prior Inclusion priors received significantly lower predictive error, whereas more uncertainty resulted from a fixed g prior (i.e., EBL). ©2020. The Authors.
英文关键词	Bayesian model averaging; coastal plain watershed; fixed and flexible priors; rainfall-runoff simulation
语种	英语
scopus关键词	Bayesian networks; Probability density function; Rain; Runoff; Sensitivity analysis; Uncertainty analysis; Bayesian model averaging; Calibration experiments; Coastal plain watersheds; Hydrologic simulations; Parameter uncertainty; Rainfall-runoff modeling; Rainfall-runoff models; Southeast United States; Probability distributions; algorithm; Bayesian analysis; calibration; coastal plain; experimental study; Gaussian method; probability density function; rainfall-runoff modeling; sensitivity analysis; theoretical study; watershed; United States
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156689
作者单位	Agricultural Sciences Department, Clemson University, Clemson, SC, United States; Department of Water Engineering, University of Birjand, Birjand, Iran; Hydro-environmental Research Centre, School of Engineering, Cardiff University, Cardiff, United Kingdom; Department of Statistics, University of South Carolina, Columbia, SC, United States
推荐引用方式 GB/T 7714	Samadi S.,Pourreza-Bilondi M.,Wilson C.A.M.E.,et al. Bayesian Model Averaging With Fixed and Flexible Priors: Theory, Concepts, and Calibration Experiments for Rainfall-Runoff Modeling[J],2020,12(7).
APA	Samadi S.,Pourreza-Bilondi M.,Wilson C.A.M.E.,&Hitchcock D.B..(2020).Bayesian Model Averaging With Fixed and Flexible Priors: Theory, Concepts, and Calibration Experiments for Rainfall-Runoff Modeling.Journal of Advances in Modeling Earth Systems,12(7).
MLA	Samadi S.,et al."Bayesian Model Averaging With Fixed and Flexible Priors: Theory, Concepts, and Calibration Experiments for Rainfall-Runoff Modeling".Journal of Advances in Modeling Earth Systems 12.7(2020).