Geochemical and statistical evidence of deep carbonate groundwater within overlying volcanic rock aquifers/aquitards of southern Nevada, USA

Stetzenbach, Klaus J.; Hodge, Vernon F.; Guo, Chenguang; Farnham, Irene; Johannesson, Karen H.

doi:10.1016/s0022-1694(00)00418-2

Cited by 87 publications

(42 citation statements)

References 38 publications

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“…Two multivariate statistical methods were applied in this study using the Statistical Package for the Social Sciences (SPSS Version 17.0) program: the hierarchical cluster analysis (HCA) and the principal component analysis (PCA). The description of HCA and PCA techniques and the methodology used for their application in ground water composition have been detailed in previous studies (e.g., Stetzenbach et al 2001;Guler et al 2002;Guler and Thyne 2004;Dragon 2006;Helstrup et al 2007;Papatheodorou et al 2007; Van den Brink et al 2007;Cloutier et al 2008;Yidana 2010;Monjerezi et al 2011;Zghibi et al 2014;Dragon and Gorski 2015). In this study, the parameters involved in the statistics include major constituents Ca 2+ , Mg 2+ , Na + , K + , HCO 3…”

Section: Data Interpretationmentioning

confidence: 99%

Statistical study to identify the key factors governing ground water recharge in the watersheds of the arid Central Asia

Zhu

Wang

2015

Environ Monit Assess

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Understanding the source and recharge of ground waters is of great significance to our knowledge in hydrological cycles in arid environments over the world. Northern Xinjiang in northwestern China is a significant repository of information relating to the hydrological evolution and climatic changes in central Asia. In this study, two multivariate statistical techniques, hierarchical cluster analysis (HCA) and principal component analysis (PCA), were used to assess the ground water recharge and its governing factors, with the principal idea of exploring the above techniques to utilize all available hydrogeochemical variables in the quality assessment, which are not considered in the conventional techniques like Stiff and Piper diagrams. Q-mode HCA and R-mode PCA were combined to partition the water samples into seven major water clusters (C1-C7) and three principal components (PC1-PC3, PC1 salinity, PC2 hydroclimate, PC3 contaminant). The water samples C1 + C4 were classified as recharge area waters (Ca-HCO 3 water), C2 + C3 as transitional zone waters (Ca-Mg-HCO 3 -SO 4 water), and C5 + C6 + C7 as discharge area waters (Na-SO 4 water). Based on the Q-mode PCA scores, three groups of geochemical processes influencing recharge regimes were identified: geogenic (i.e., caused by natural geochemical processes), geomorphoclimatic (caused by topography and climate), and anthropogenic (caused by ground water contamination). It is proposed that differences in recharge mechanism and ground water evolution, and possible bedrock composition difference, are responsible for the chemical genesis of these waters. These will continue to influence the geochemistry of the northern Xinjiang drainage system for a long time due to its steady tectonics and arid climate. This study proved that the chemistry differentiation of ground water can effectively support the identification of ground water recharge and evolution patterns.

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Section: Data Interpretationmentioning

confidence: 99%

Statistical study to identify the key factors governing ground water recharge in the watersheds of the arid Central Asia

Zhu

Wang

2015

Environ Monit Assess

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“…The loadings describe the proportion of each individual sample that is necessary to project the variables onto the factor axis as opposed to describing the proportion of each variable used to project each sample onto the factor axis, as in R-mode analysis [16,17]. The method plays a key role in the assessment of surface water-groundwater interaction [18][19][20][21]. In this study, the factor analysis method is applied by the principal component analysis technique.…”

Section: Factor Analysismentioning

confidence: 99%

Applying Environmental Isotope Theory to Groundwater Recharge in the Jiaozuo Mining Area, China

Huang

Wang

2017

Geofluids

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This study establishes the surface water evaporation line in theory and numerically simulates the D and 18 O value distribution interval of the recharge source of deep groundwater in the Jiaozuo mining area. The recharge elevation is calculated based on hydrogen and oxygen isotope tracer theory. Theoretical calculation and experimental data indicate that the surface water evaporation line in the study area in theory is almost the same as the measured surface data-fitting line. A significant linear relationship is identified between 18 O and the elevation of spring outcrop. The topography increases per 100 m, and the 18 O value reduces by 0.23‰ on average. The 18 O value is converted into formula to calculate the groundwater recharge elevation, which is approximately from 400 to 800 m. The measured tritium values of karst groundwater are greater than 3 TU. The second factor score is a fraction distribution in shallow groundwater and negative fraction distribution in spring and deep groundwater, which indicates that the Northern Taihang Mountain is the main recharge area, where carbonate-exposed areas exist. The research conclusion holds a certain value for the flood evaluation of local coal mines.

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“…The multivariate statistical methods of HCA and PCA do not indicate cause-and-effect relationships; they can only provide information from which such relationships can be inferred. Therefore, in order to better identify the physical and chemical processes controlling the geochemical evolution of groundwater, the results of the multivariate statistical analysis have to be combined with the knowledge of the geological and hydrogeological setting [47,48].…”

Section: Origin Of Formation Water In Buriedmentioning

confidence: 99%

Characterization of Origin and Evolution of Formation Water in Buried Hill of Jizhong Depression, China, Using Multivariate Statistical Analysis of Geochemical Data

Zeng

2017

Geofluids

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Groundwater samples from buried hill of Jizhong Depression were evaluated using two statistical analyses: hierarchical cluster analysis (HCA) and principal component analysis (PCA). The samples were classified into four clusters, C1-C4, in HCA and the hydrochemical types of C1-C4 are HCO 3 -Na, Cl⋅HCO 3 -Na, Cl-Na, and Cl-Na⋅Ca. From C1 to C2, C3, and C4, the water-rock interaction becomes increasingly intensive, and rNa/rCl gets lower while total dissolved solids and r(Cl-Na)/rMg get higher. Three components of PCA explain 86.87% of the variance. Component1 (PC1), characterized by highly positive loadings in Na + and Cl − , is related to evaporation concentration. Component2 (PC2) is defined by highly positive loading in HCO 3 − and is related to influence of atmospheric water. With high positive loadings in Ca 2+ and high negative loadings in Na + and SO 4 2− , component3 (PC3) suggests plagioclase albitization. The combination of HCA and PCA within the hydrogeological contexts allowed the division of study area into five dynamic areas. From recharge area to discharge area, the influence of atmospheric water gets weaker and water-rock interactions such as evaporation concentration and plagioclase albitization become intensive. Therefore groundwater in buried hill showed paths of hydrochemical evolution, from C1, to C2, C3, and C4. Buried hill reservoir in Jizhong Depression is mainly distributed in hydrodynamic blocking and discharge area; therefore the two regions can be the favorable areas for petroleum migration.

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Geochemical and statistical evidence of deep carbonate groundwater within overlying volcanic rock aquifers/aquitards of southern Nevada, USA

Cited by 87 publications

References 38 publications

Statistical study to identify the key factors governing ground water recharge in the watersheds of the arid Central Asia

Statistical study to identify the key factors governing ground water recharge in the watersheds of the arid Central Asia

Applying Environmental Isotope Theory to Groundwater Recharge in the Jiaozuo Mining Area, China

Characterization of Origin and Evolution of Formation Water in Buried Hill of Jizhong Depression, China, Using Multivariate Statistical Analysis of Geochemical Data

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