Faculty Publications
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Item Bivariate Modeling of Hydroclimatic Variables in Humid Tropical Coastal Region Using Archimedean Copulas(American Society of Civil Engineers (ASCE) onlinejls@asce.org 1801 Alexander Bell DriveGEO Reston VA 20191 Alabama, 2020) Uttarwar, S.B.; Deb Barma, S.; Mahesha, M.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.Item Bivariate Drought Characterization of Two Contrasting Climatic Regions in India Using Copula(American Society of Civil Engineers (ASCE), 2021) Sajeev, A.; Deb Barma, S.; Mahesha, A.; Shiau, J.-T.This study aims to construct the multiple time-scale joint distributions of drought duration and severity using two-dimensional copulas and compare the drought characteristics in India's two contrasting climate regions: the arid Rajasthan and humid, tropical Kerala. The drought occurrences were defined by the standardized precipitation index (SPI) with a threshold below -0.8 at time scales of 3, 6, 12, and 24 months for 1900-2016. Significant correlations were noted between the drought severity and drought duration in both regions. The Clayton copula gave a better fit than other copulas for modeling the dependence among the observed drought duration and severity. The results indicate that the probability of short-term droughts (SPI-3 and SPI-6) is more significant than those of long-term droughts (SPI-12 and SPI-24) for an identical drought event in both regions. Also, the probability of severe drought events with greater duration and severity for long-term droughts (SPI-12 and SPI-24) is higher in Kerala than that in western Rajasthan. For all the time-scale SPIs, the conditional probability of drought severity for a given duration exceeding a threshold showed an increasing trend in both regions. Furthermore, the conditional probability of the drought duration given the severity for short-term droughts is greater than that of the long-term droughts for the same drought event. For short-term droughts, the conditional return period of an identical drought event is lower in Kerala than in western Rajasthan. In contrast, the conditional return period of long-term droughts is lower in western Rajasthan. Additionally, copula-based nonexceedance conditional distributions for the major crops were established based on rainfall. © 2020 American Society of Civil Engineers.