Evaluation of the Kagebunshin-Beta Distribution for Modelling Daily Rainfall in Indonesia
Keywords:
Kagebunshin-Beta distribution, daily rainfall modeling, bias correction, extreme precipitation, climate risk assessmentAbstract
Accurately modelling daily rainfall data is essential for climate studies, hydrological modelling, and disaster risk management. Traditional probability distributions often struggle with the inherent complexities of rainfall data, such as skewness, excess zeros, and high variability. This study explores the potential of the Kagebunshin-Beta (KB) distribution as a novel approach to modelling daily precipitation in Indonesia. The KB distribution, derived from a transformation of beta distribution, offers flexibility in capturing diverse rainfall patterns. This research investigates its statistical properties, parameter estimation via the Nelder-Mead optimization algorithm, and performance compared to 17 well-known distributions, including gamma, Weibull, lognormal, and generalized extreme value distributions. Using two datasets (rainfall data from five major cities and a spatial dataset covering Indonesia), the KB distribution consistently outperformed other models based on the Akaike Information Criterion (AIC), Anderson-Darling, and Cramer-von Mises goodness-of-fit tests. The KB distribution was further applied to estimate the probability of dry and heavy rainy days across Indonesia, identifying regions prone to prolonged droughts and extreme rainfall events. These findings demonstrate the KB distribution's effectiveness as an alternative for rainfall modelling and its potential applications in bias correction, risk assessment, and climate adaptation strategies. By providing a more robust statistical representation of precipitation patterns, this study contributes to improved water resource management, disaster preparedness, and climate resilience.References
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