Temperature and rainfall are the two critical climatic parameters influence agricultural productivity and many other extreme hydrological and meteorological phenomena. Temperature and rainfall have significant temporal variation. Rainfall in many cases marginal of these two parameters known, but joint-distribution is unknown. Modelling of joint distribution or possible interdependence between these may be achieved through using Copula. In the present study, monthly rainfall and temperature time series of two stations, one from the humid region (Agartala) and another from the arid region (Bikaner) were used for copula-based analysis. Based on the AIC and BIC selection criterion best copula model was selected. The Normal copula was found to be a suitable model for rainfall and minimum temperature; rainfall and mean temperature and Clayton copula for rainfall and maximum temperature for the humid region. Similarly, for arid region Student or T copula is the best suitable model for rainfall and minimum temperature and rainfall and maximum temperature; and for rainfall and mean temperature, the Normal copula is the best suitable model. Furthermore, copula-rank correlations were obtained for the best-fit copula model to assess the inter-dependence. It was observed that the interdependence between mean temperature and rainfall is more crucial in the context of the global warming and climate change because of the occurrence of the similar types of copulas (mainly normal) in both the humid and arid climatic conditions.
Modeling Earth Systems and Environment – Springer Journals
Published: Apr 2, 2018
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