| dc.description.abstract | This study focuses on modeling and predicting extreme rainfall based on data from the Southern Highlands region, the critical for
rain-fed agriculture in Tanzania. Analyzing 31 years of annual maximum rainfall data spanning from 1990 to 2020, the Gen-
eralized Extreme Value (GEV) model proved to be the best for modeling extreme rainfall in all stations. Tree estimation
methods–L-moments, maximum likelihood estimation (MLE), and Bayesian Markov chain Monte Carlo (MCMC)–were
employed to estimate GEV parameters and future return levels. Te Bayesian MCMC approach demonstrated superior per-
formance by incorporating noninformative priors to ensure that the prior information had minimal infuence on the analysis,
allowing the observed data to play a dominant role in shaping the posterior distribution. Furthermore, return levels for various
future periods were estimated, providing guidance for food protection measures and infrastructure design. Trend analysis using p
value, Kendall’s tau, and Sen’s slope indicated no statistically signifcant trends in rainfall patterns, although a weak positive trend
in extreme rainfall events was observed, suggesting a gradual and modest increase over time. Overall, the study contributes
valuable insights into extreme rainfall patterns and underscores the importance of L-moments in identifying the best ft dis-
tribution and Bayesian MCMC methodology for accurate parameter estimation and prediction, enabling effective measures and
infrastructure planning in the region. | en_US |
