Bivariate Modeling of Hydroclimatic Variables in Humid Tropical Coastal Region Using Archimedean Copulas

The present study focuses on the dependence modeling of hydroclimatic variables such as the El Niño-Southern Oscillation (ENSO) index, precipitation, tidal height, and groundwater level (GWL) in humid tropical coastal region of India. The rank-based correlation coefficient was used to determine the dependence between the pairs of cumulative monsoon precipitation of June-July-August-September (P_JJAS) and the postmonsoon groundwater level (PMGWL), ENSO-P_JJAS, ENSO-PMGWL, and GWL-tidal height. The results indicated that P_JJAS-PMGWL, ENSO-PMGWL, and GWL-tidal height had significant dependence, whereas P_JJAS-ENSO had no significant dependence. The best fit distributions for P_JJAS, PMGWL, and tidal height were found to be lognormal, extreme value, and generalized extreme value distributions, respectively, whereas for the ENSO index, it was the normal kernel-density function. The Archimedean families of copulas were used for dependence modeling, and it was observed that the ENSO-PMGWL was best modeled by the Frank copula, the P_JJAS-PMGWL by the Gumbel-Hougaard copula, and the GWL-tidal height by the Frank copula. The copula-based conditional probability for the Gumbel-Hougaard and Frank copulas for GWL were obtained to understand the risk associated with other hydroclimatic variables. Thus, copula-based dependence modeling could be useful for understanding the risk among hydroclimatic variables including groundwater.