Abstract

Extreme precipitation events pose significant challenges to water resource management, infrastructure planning, and disaster preparedness in Lesotho. Understanding the probabilistic behavior of these extreme events is crucial for effective risk assessment and adaptation strategies. In this study, we present a comprehensive framework for modeling extreme precipitation in Lesotho using Bayesian inference and Extreme Value Theory (EVT).
Lesotho, a landlocked country in Southern Africa, is particularly vulnerable to extreme precipitation events due to its topography and socio-economic factors. Despite its importance, the statistical modeling of extreme precipitation in Lesotho remains relatively understudied. Our research addresses this gap by developing a novel approach that integrates Bayesian inference with EVT to characterize the distribution of extreme precipitation.
Bayesian inference provides a flexible and robust framework for parameter estimation, uncertainty quantification, and model selection. By incorporating prior knowledge, expert opinions, and observational data, Bayesian methods enable us to infer the underlying parameters governing extreme precipitation behavior in Lesotho. We utilize Markov Chain Monte Carlo (MCMC) algorithms to sample from the posterior distribution of model parameters and quantify uncertainties.
Furthermore, we employ Extreme Value Theory to model the tail behavior of precipitation extremes. EVT offers a principled statistical framework for analyzing rare events beyond the range of empirical data. By focusing on the extreme upper tails of the precipitation distribution, EVT allows us to estimate return levels, return periods, and design values for different exceedance probabilities.
We consider various parametric distributions commonly used in climate studies, such as Generalized Extreme Value (GEV) and Generalized Pareto Distribution (GPD). Through Bayesian model comparison, we evaluate the performance of different distributional assumptions and identify the most suitable model for extreme precipitation analysis in Lesotho.
Our results will provide valuable insights into the characteristics and trends of extreme precipitation in Lesotho. We assess the changing frequency and intensity of extreme events over time, which is essential for climate change impact assessment and adaptation planning. Additionally, our study will contribute to a better understanding of extreme precipitation dynamics in Lesotho and support evidence-based decision-making for climate resilience and disaster risk reduction efforts. The integration of Bayesian inference and EVT offers a robust methodology for modeling extreme events in data-scarce regions, with implications for hydrological modeling, infrastructure design, and policy formulation.