Flood Frequency Analysis and Uncertainty Assessment Based on Bayesian MCMC Method

The calculation of design flood value and uncertainty assessment is an important subject of hydraulic engineering planning and water resources management. Taking thegGeneralized extreme value (GEV) distribution as the flood frequency distribution line type, the Bayesian Markov chain Monte Carlo (MCMC) method based on Metropolis-Hastings algorithm was employed to evaluate the GEV distribution parameters and posterior probability distributions of the design flood values, and the point estimations and interval estimations of flood design values under different return periods were deduced. The results show that the effect of parameter fitting with Bayesian MCMC method is similar to that from the maximum likelihood estimation (MLE). However, because the posterior probability distribution contains the uncertainty caused by parameter estimation, Bayesian MCMC methods has more advantages in evaluation of the uncertainty of flood design value. The length between upper confidence limits and estimated values are greater than that of the lower confidence limits and estimated values, this asymmetry is more realistic than that from the traditional method, which further improves the reliability of the flood frequency analysis results.