STATISTICAL MODELING OF RAINFALL VARIABILITY USING DISTRIBUTION FITTING AND TEMPORAL DEPENDENCE ANALYSIS
DOI:
https://doi.org/10.69980/t8ymgr83Keywords:
Rainfall Variability, Probability Distribution Fitting, Temporal Autocorrelation, Precipitation Persistence, Statistical HydrologyAbstract
In monsoon-dominated regions, rainfall variability is a crucial factor affecting hydrological processes, agricultural productivity and water-resource management. This study examined the characteristics of rainfall distribution and temporal dependence using 543,839 rainfall daily meteorological observations from 35 weather stations in Bangladesh. Precipitation variability and persistence were characterized using descriptive statistical analysis, fitting of probability distributions, goodness of fit and temporal autocorrelation techniques. Positive rainfall was fitted to the gamma, Weibull and Lognormal distribution, by applying maximum likelihood estimation. The results showed that the daily precipitation varied significantly across the region, with a mean daily precipitation of 6.73 mm and 67.64% of zero rainfall events. The mean rainfall for July was the highest (17.34 mm) while that of January was the lowest (0.24 mm). The Lognormal distribution had the smallest Akaike Information Criterion (AIC) of 1,390,345.7 and the smallest Kolmogorov–Smirnov statistic of 0.07313 with respect to the other distributions. The positive lag-one autocorrelation (0.3937) and significant persistence of wet days and dry days were found from the temporal analysis. The results highlight the importance of incorporating distributional and temporal analyses of rainfall data for characterizing rainfall for use in statistical applications relevant to hydrological assessment and sustainable water resource planning.
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