Usefulness of the Reversible Jump Markov Chain Monte Carlo Model in Regional Flood Frequency Analysis

TL;DR

This paper introduces a Bayesian reversible jump MCMC model for regional flood frequency analysis, effectively reducing estimation uncertainty with limited data and outperforming traditional methods.

Contribution

It presents a novel Bayesian reversible jump MCMC approach that allows a non-zero probability for a fixed shape parameter, improving regional flood estimates with scarce data.

Findings

The new estimator performs well with limited data at the target site.

Bayesian estimators are less affected by errors in target site index flood estimation.

Proposed pooling group configurations enhance estimator performance.

Abstract

Regional flood frequency analysis is a convenient way to reduce estimation uncertainty when few data are available at the gauging site. In this work, a model that allows a non-null probability to a regional fixed shape parameter is presented. This methodology is integrated within a Bayesian framework and uses reversible jump techniques. The performance on stochastic data of this new estimator is compared to two other models: a conventional Bayesian analysis and the index flood approach. Results show that the proposed estimator is absolutely suited to regional estimation when only a few data are available at the target site. Moreover, unlike the index flood estimator, target site index flood error estimation seems to have less impact on Bayesian estimators. Some suggestions about configurations of the pooling groups are also presented to increase the performance of each estimator.