Frequentist and Bayesian Approaches in Modeling and Prediction of Extreme Rainfall Series: A Case Study from Southern Highlands Region of Tanzania
| dc.contributor.author | Kyojo, Erick | |
| dc.contributor.author | Mirau, Silas | |
| dc.contributor.author | Osima, Sarah | |
| dc.contributor.author | Masanja, Verdiana | |
| dc.date.accessioned | 2024-05-02T06:25:34Z | |
| dc.date.available | 2024-05-02T06:25:34Z | |
| dc.date.issued | 2024-01-30 | |
| dc.description | This Research article was Published in the Advances in Meteorology, 2024 | en_US |
| 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 |
| dc.description.uri | https://doi.org/10.1155/2024/8533930 | |
| dc.identifier.uri | https://dspace.nm-aist.ac.tz/handle/20.500.12479/2538 | |
| dc.identifier.uri | https://doi.org/10.1155/2024/8533930 | |
| dc.language.iso | en | en_US |
| dc.publisher | Hindawi | en_US |
| dc.subject | Moments method | en_US |
| dc.subject | Generalized normal | en_US |
| dc.title | Frequentist and Bayesian Approaches in Modeling and Prediction of Extreme Rainfall Series: A Case Study from Southern Highlands Region of Tanzania | en_US |
| dc.type | Article | en_US |