Interdependency analysis between precipitation and temperature in Bangladesh: a copula-based approach

Climate change presents a significant challenge to global ecosystems and human well-being, primarily through alterations in temperature and precipitation patterns. Bangladesh is a low-lying deltaic country that experiences climate change impacts every year. So, understanding the interdependence between these crucial climatic parameters is essential 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 marginal distributions. The findings revealed weak but significant (p < 0.01) correlation, 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). Precipitation and minimum temperature 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 emerged as the most suitable, exhibiting minimal AIC, BIC, RMSE, and maximal log-likelihood (LL) for all temperature (mean, maximum, and minimum) types. The practical and standard joint return period (T_OR) at selected years calculated using Clayton copula indicated that the temperature and precipitation would increase with the increase in return periods. Overall, this study emphasized the utility of copula-based approaches in analyzing the complex interdependence of two vital climatic variables, and the results might help understand and plan for mitigating extreme events in Bangladesh.