Modelling extreme rainfall events in Kigali city using generalized Pareto distribution

Singirankabo, Edouard; Iyamuremye, Emmanuel

doi:10.1002/met.2076

Cited by 11 publications

(5 citation statements)

References 42 publications

(51 reference statements)

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“…The generalized Pareto distribution was found to provide the best marginal match for precipitation data, with the greatest loglikelihood value (-5316.3) and minimum AIC (10538.6) and BIC (10652.8) values (Table 4). This result was supported by Martins et al (2020) and Singirankabo & Iyamuremye (2022), who also found generalized Pareto to model extreme rainfall events. On the other hand, the generalized extreme value distribution performed better as the best marginal t for mean temperature data (Fig.…”

Section: Copula Resultssupporting

confidence: 62%

Interdependency analysis between Precipitation and Temperature in Bangladesh: A copula-based approach

Prodhan,

Islam

2024

Preprint

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Climate change presents a significant challenge to global ecosystems and human well-being, primarily through alterations in temperature and precipitation patterns. Understanding the interdependence between these crucial climatic parameters is crucial for assessing the impacts of climate change, particularly on extreme events like floods and droughts. This study employed copula functions to model the joint distribution of temperature and precipitation, surpassing their individual marginal distributions. The findings revealed strong correlations, as indicated by Kendall's tau coefficients and Spearman's rank correlation coefficients, between precipitation and mean temperature (τ = 0.524, ρ = 0.7), precipitation and maximum temperature (τ = 0.306, ρ = 0.456), and precipitation and minimum temperature (τ = 0.645, ρ = 0.795) at the 1% level of significance. Precipitation and minimum temperature both showed a marginal distribution of generalized Pareto, whereas mean temperature and maximum temperature showed marginal distributions of generalized extreme value and Weibull, respectively. Despite differing marginal distributions, copula modeling enables the establishment of a joint distribution. Among the copula functions tested, the Clayton copula emerges as the most suitable, exhibiting minimal AIC, BIC, RMSE, and maximal log-likelihood (LL) for all temperature (mean, maximum, and minimum) types. Overall, this study emphasizes the utility of copula-based approaches in analyzing the complex interdependence of climatic variables and their implications for climate change assessment and extreme event analysis.

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Section: Copula Resultssupporting

confidence: 62%

Interdependency analysis between Precipitation and Temperature in Bangladesh: A copula-based approach

Prodhan,

Islam

2024

Preprint

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“…In the Pra River catchment in Ghana, (Osei et al 2021) found almost similar results to our study, with return periods of the CWD index ranging from 6 to 20 days over a 10-year period. In Kigali, Rwanda, the return periods of extreme maximum rainfall are increasing and vary between 5 and 50 years (Singirankabo and Iyamuremye, 2022). In Senegal, on the other hand, return periods of extreme rainfall tend to be between 100 and 150 years over the period 1951-2005.…”

Section: Extremementioning

confidence: 99%

Trends and variability of extreme climate indices in the Boucle du Mouhoun (Burkina Faso)

Rouamba,

Yameogo,

Sanou

et al. 2023

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“…. , X n 􏼈 􏼉 is a random sample from the GPD with the extreme value X (n) above a threshold u, the log-likelihood function of the GPD can be obtained using the following equation provided by [33,45]:…”

Section: Computing Maximum Likelihood Parameter Estimatesmentioning

confidence: 99%

Frequentist and Bayesian Approaches in Modeling and Prediction of Extreme Rainfall Series: A Case Study from Southern Highlands Region of Tanzania

Kyojo,

Mirau,

Osima

et al. 2024

Advances in Meteorology

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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 Generalized Extreme Value (GEV) model proved to be the best for modeling extreme rainfall in all stations. Three 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. The Bayesian MCMC approach demonstrated superior performance by incorporating noninformative priors to ensure that the prior information had minimal influence 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 flood protection measures and infrastructure design. Trend analysis using p value, Kendall’s tau, and Sen’s slope indicated no statistically significant 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 fit distribution and Bayesian MCMC methodology for accurate parameter estimation and prediction, enabling effective measures and infrastructure planning in the region.

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Modelling extreme rainfall events in Kigali city using generalized Pareto distribution

Cited by 11 publications

References 42 publications

Interdependency analysis between Precipitation and Temperature in Bangladesh: A copula-based approach

Interdependency analysis between Precipitation and Temperature in Bangladesh: A copula-based approach

Trends and variability of extreme climate indices in the Boucle du Mouhoun (Burkina Faso)

Frequentist and Bayesian Approaches in Modeling and Prediction of Extreme Rainfall Series: A Case Study from Southern Highlands Region of Tanzania

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