Bayesian and Non-Bayesian Methodologies to Hydrological Flood Frequency Analysis

Abstract

Climate change (CC) impacts extreme weather events such as weather conditions, temperature changes, droughts, and floods necessitating accurate flood frequency analysis (FFA) for risk assessment. In this chapter, Bayesian and non-Bayesian methods were compared for at-site FFA forecasting particularly in the context of CC impacts on hydrological processes using data from three sites of Pakistan. Generalized Extreme Value Distribution (GEV) was the best fit for these sites. Markov Chain Monte Carlo (MCMC) simulations with the Metropolis-Hastings (M-H) sampling procedure estimated uncertainty quantification. Bayesian method outperformed, with lower standard errors and more accurate parameter estimates. Safety amendments affirmed the robustness of Bayesian method for mitigating CC and flood risks and improving water resource management. Findings support the advantage of Bayesian approach that contribute in flood management strategies amidst climate change challenges. Study underscores the importance of Bayesian methods for uncertainty and enhancing flood management practices in the context of climate change.