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

Abstract

Abstract 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.