Joint Modeling of Precipitation and Temperature Using Copula Theory for Current and Future Prediction under Climate Change Scenarios in Arid Lands (Case Study, Kerman Province, Iran)

TLDR

Precipitation and temperature are key climatic variables whose changes influence life conditions, societal and urban planning, and whose interdependence means that independent analyses can lead to errors. The study aims to project future precipitation and temperature trends in Kerman Province using IPCC RCP scenarios and joint copula modeling to capture their interdependence for risk assessment. The authors applied IPCC RCP2.6, RCP4.5, and RCP8.5 scenarios and fitted joint distributions using a Frank copula for RCP2.6 and Gaussian copulas for RCP4.5 and RCP8.5. The analysis predicts rising precipitation and temperature in Kerman Province, with a Frank copula best fitting RCP2.

Abstract

Precipitation and temperature are very important climatic parameters as their changes may affect life conditions. Therefore, predicting temporal trends of precipitation and temperature is very useful for societal and urban planning. In this research, in order to study the future trends in precipitation and temperature, we have applied scenarios of the fifth assessment report of IPCC. The results suggest that both parameters will be increasing in the studied area (Iran) in future. Since there is interdependence between these two climatic parameters, the independent analysis of the two fields will generate errors in the interpretation of model simulations. Therefore, in this study, copula theory was used for joint modeling of precipitation and temperature under climate change scenarios. By the joint distribution, we can find the structure of interdependence of precipitation and temperature in current and future under climate change conditions, which can assist in the risk assessment of extreme hydrological and meteorological events. Based on the results of goodness of fit test, the Frank copula function was selected for modeling of recorded and constructed data under RCP2.6 scenario and the Gaussian copula function was used for joint modeling of the constructed data under the RCP4.5 and RCP8. Scenario and the Gaussian copula function was used for joint modeling of the constructed data under the RCP4.5 and RCP8.5 scenarios.