Interdependence between temperature and precipitation: modeling using copula method toward climate protection

Interdependence between temperature and precipitation: modeling using copula method toward climate protection

In this study, copula analysis was used to eventually fill the void of the negligence of interdependence of two climate variables (temperature and precipitation). Copulas are powerful tools to model the joint distribution of two or more variables simultaneously by preserving their dependence structu...

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Bibliographic Details
Published in:Modeling earth systems and environment Vol. 8; no. 2; pp. 2753 - 2766
Main Authors: Hussain, Bushra, Qureshi, Naeem Ahmed, Buriro, Riaz Ali, Qureshi, Sundus Saeed, Pirzado, Ali Akbar, Saleh, Tawfik A.
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01-06-2022
Springer Nature B.V
Subjects:
Bivariate analysis
Chemistry and Earth Sciences
Climate
Climate change
Climate models
Computer Science
Correlation
Drought
Earth and Environmental Science
Earth Sciences
Earth System Sciences
Ecosystems
Environment
Environmental risk
Math. Appl. in Environmental Science
Mathematical Applications in the Physical Sciences
Modelling
Negligence
Original Article
Physics
Precipitation
Statistics for Engineering
Temperature
Protection: climate
Precipitation
Green environment
Marginal distribution
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Summary:In this study, copula analysis was used to eventually fill the void of the negligence of interdependence of two climate variables (temperature and precipitation). Copulas are powerful tools to model the joint distribution of two or more variables simultaneously by preserving their dependence structure. The data for monthly average temperature °C and precipitation (mm) were collected. The analyzed results indicated that a weak positive 0.233 but highly significant (0.000) correlation existed between the variables. It was also found that every 1 °C increased in temperature was associated with a 0.684 mm increase in precipitation. A scattered plot indicates a non-linear dependence structure between the variables. Generalized Pareto (GP) and Exponential distributions were selected as variables distributions for temperature and precipitation, respectively. Two Elliptical (Gaussian, Student’s t ) and three Archimedean (Gumbel, Clayton, and Frank) copulas were applied to evaluate the nature of interdependence between the variables. Based on different information criteria, the Gumbel copula was identified as the best-fitted copula. The selected copula showed that extreme positive temperature and precipitation had a higher correlation (right tail dependence) as compared to their lower values. Conclusively, copula was found to be a very effective tool for bivariate modeling of drought and flood risks i.e., extreme temperature and precipitation.
ISSN:2363-6203
2363-6211
DOI:10.1007/s40808-021-01256-8