Research papersBayesian forecasting and uncertainty quantifying of stream flows using Metropolis-Hastings Markov Chain Monte Carlo algorithm

- A Bayesian approach for river flow rate forecast and uncertainty analysis is proposed.
- Information from regional gage stations is used to identify the priori distribution.
- Metropolis-Hastings MCMC algorithm is applied for posterior distribution sampling.
- The Bayesian approach performs similarly with MLE in parameter estimation.
- The Bayesian approach performs over the MLE in uncertainty quantification.

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. The Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.