Application of Archimedean copulas to the impact assessment of hydro-climatic variables in semi-arid aquifers of western India | Application des copules archimédiennes pour l’évaluation de l’impact des variables hydro-climatiques dans les aquifères semi-arides dans l’Ouest de l’Inde Aplicación de cópulas arquimedianas en la evaluación del impacto de las variables hidro-climáticas en los acuíferos semiáridos de la India occidental 阿基米德连接函数在印度西部半干旱含水层水力气候变量影响评价中的应用 Aplicação de copulas Arquimedianas na avaliação do impacto de variáveis hidroclimáticas em aquíferos semiáridos da Índia ocidental
2018
The effects of rainfall and the El Niño Southern Oscillation (ENSO) on groundwater in a semi-arid basin of India were analyzed using Archimedean copulas considering 17 years of data for monsoon rainfall, post-monsoon groundwater level (PMGL) and ENSO Index. The evaluated dependence among these hydro-climatic variables revealed that PMGL-Rainfall and PMGL-ENSO Index pairs have significant dependence. Hence, these pairs were used for modeling dependence by employing four types of Archimedean copulas: Ali-Mikhail-Haq, Clayton, Gumbel-Hougaard, and Frank. For the copula modeling, the results of probability distributions fitting to these hydro-climatic variables indicated that the PMGL and rainfall time series are best represented by Weibull and lognormal distributions, respectively, while the non-parametric kernel-based normal distribution is the most suitable for the ENSO Index. Further, the PMGL-Rainfall pair is best modeled by the Clayton copula, and the PMGL-ENSO Index pair is best modeled by the Frank copula. The Clayton copula-based conditional probability of PMGL being less than or equal to its average value at a given mean rainfall is above 70% for 33% of the study area. In contrast, the spatial variation of the Frank copula-based probability of PMGL being less than or equal to its average value is 35–40% in 23% of the study area during El Niño phase, while it is below 15% in 35% of the area during the La Niña phase. This copula-based methodology can be applied under data-scarce conditions for exploring the impacts of rainfall and ENSO on groundwater at basin scales.
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