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Title: | Bivariate Modeling of Hydroclimatic Variables in Humid Tropical Coastal Region Using Archimedean Copulas |
Authors: | Uttarwar S.B. 0000-0003-3619-0021 Barma S.D. 0000-0003-0849-3033 Mahesha A. M.asce |
Issue Date: | 2020 |
Citation: | Journal of Hydrologic Engineering , Vol. 25 , 9 , p. - |
Abstract: | 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. © 2020 American Society of Civil Engineers. |
URI: | https://doi.org/10.1061/(ASCE)HE.1943-5584.0001981 http://idr.nitk.ac.in/jspui/handle/123456789/16010 |
Appears in Collections: | 1. Journal Articles |
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