4.7 Article

A drought index based on groundwater quantity and quality: Application of multivariate copula analysis

Journal

JOURNAL OF CLEANER PRODUCTION
Volume 417, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2023.137959

Keywords

Drought index; Groundwater; Water quantity and quality; Copula analysis; Lake Urmia; Joint probability distribution

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In this paper, a new Groundwater Quantity-Quality-based Drought Index (GQQI) is developed using multivariate Copula analysis. The developed index is compared to other indices and applied for the evaluation of groundwater quantity and quality in the Lake Urmia basin. The results show that the developed GQQI correlates with other drought indices and identifies more severe droughts by considering the combined effects of groundwater quantity and quality.
In this paper, a new Groundwater Quantity-Quality-based Drought Index (GQQI) is developed based on multi-variate Copula analysis of groundwater quantity and quality indicators. For evaluating the developed index, its temporal and spatial distribution is studied and compared to those of some other indices, such as Standardized Salinity Index (SSI), Standardized Groundwater Index (SGI), and Standardized Water level Index (SWI). The proposed index is applied for the temporal and spatial evaluation of the quantity and quality of groundwater in theLake Urmia basin, having 1084 piezometric and 935 groundwater quality monitoring wells. For a more comprehensive analysis of drought, 24 marginal and 26 joint probability distribution functions are driven. Based on the results, the developed GQQI is correlated with drought indices of SSI, SGI, and SWI by 88%, 86%, and 61%, respectively. Moreover, the values of the developed drought index indicates the occurrence of more severe droughts, compared to those detected by other drought indices. This can be resulted from considering the combined effects of groundwater quantity and quality using the proposed index. In addition, the multivariate GQQI can spatially and temporally represent the drought severity over the basin, and identify severe and extreme drought conditions better than other univariate drought indices.

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