Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis–Hastings Markov Chain Monte Carlo algorithm

Journal Article · Wed Apr 05 04:00:00 UTC 2017 · Journal of Hydrology

DOI: https://doi.org/10.1016/j.jhydrol.2017.03.073 · OSTI ID:1371944

Wang, Hongrui ^[1]; Wang, Cheng ^[2]; Wang, Ying ^[3]; Gao, Xiong ^[1]; Yu, Chen ^[1]

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1. Beijing Normal Univ., Beijing (China)
2. Argonne National Lab. (ANL), Lemont, IL (United States)
3. Chinese Research Academy of Environmental Sciences, Beijing (China)

This paper presents a Bayesian approach using Metropolis-Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLE confidence interval and thus more precise estimation by using the related information from regional gage stations. As a result, the Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.

• Research Organization: Argonne National Lab. (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC); National Natural Science Foundation of China (NSFC)
• Grant/Contract Number: AC02-06CH11357
• OSTI ID: 1371944
• Journal Information: Journal of Hydrology, Journal Name: Journal of Hydrology Journal Issue: C Vol. 549; ISSN 0022-1694
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English