Abstract: Bayesian model is a powerful tool for reducing model uncertainty in flood frequency analysis, and the key is how to calculate prior probability. To improve the universality of the calculation, first we work out a formula for calculating the comprehensive index and quantifying the effect of fitting used by different models based on model evaluation criteria. Then, this index is used in calculation of the prior probability adopted in our Bayesian model so as to reduce the uncertainty in model selection. Results show that the comprehensive index calculations lead to more reliable values of the prior probability that help improve the estimation of posterior probability, thereby reducing the uncertainty in flood frequency calculations. Compared with Bayesian model without such prior information, the present results are much better. A comprehensive index coupled with multiple evaluation criteria provides a new idea in determining prior probability.

Key words: design flood, flood frequency analysis, model uncertainty, Bayesian model, prior probability, comprehensive index