Groundwater Chemistry Analysis by Multivariate Statistical Methods

The multivariate statistical methods of Principal Component Factor Analysis (PCFA) and K- Means Cluster Analysis (KMCA) are sequentially used on groundwater chemistry form the Amargosa Desert region to estimate possible hydrochemical processes and facies along with probable groundwater flow paths and evolution in the region. PCFA is applied to major ion data and results are rotated, thus reducing the number of variables describing the system and allowing for better interpretation of the system's variation in terms of hydrochemical processes. KMCA is applied to factor scores derived from the rotated PCFA to allow the grouping of sampling locations with similar water chemistries into hydrochemical facies. The resulting rotated factor loadings and scores are presented as biplots, demonstrating relationships between and among variables and sampling locations. Derived factor scores and hydrochemical facies are overlain on a digital elevation map of the region providing a visual picture of hydrochemical evolutions, localized recharge centers and potential groundwater interactions with geologic and topographic features in the region. In particular, a distinct ground- water-chemical signature is observed on the extended flowpath of Fortymile Wash, which presents some contradiction to contemporary water levels, along with potential interaction with a fault line. The signature surrounding the ephemeral Fortymile Wash is believed to represent the relic of water that infiltrated during past-pluvial periods when the amount of runoff in the wash was significantly larger than during the current-drier period. This hypothesis and aforementioned analyses are supported by the examination of chloride, oxygen- 18, hydrogen-2 and carbon-14 data from the region.