Using multivariate statistics to link major ion chemistry changes at karst springs to agriculture.

ABSTRACT

Agricultural contamination is a pervasive problem in karst landscapes that negatively impacts groundwater quality, and subsequently harms hydraulically connected springs and associated ecosystems. While most research on agricultural contamination in karst aquifers has focused on nitrate, agricultural operations also alter major ion chemistry in groundwater, which can have significant implications on water rock interactions, geochemical interpretability, and enrichment of harmful contamination. Additionally, recent evidence has questioned the singular role of nutrient enrichment on aquatic ecosystem degradation, thus prompting further investigation into the broader extent of agricultural contamination for improved water quality restoration. In karst aquifers, quantifying the disruption of groundwater quality from agricultural contamination at springs can be challenging as major ions associated with agricultural contamination may be conflated with natural processes and interpretation further compounded by complex flow dynamics. These factors can render traditional statistical methods inadequate for identifying more comprehensive groundwater quality disruptions. To overcome these challenges, we use principal component analysis to classify the major ion covariance signature of agriculturally impaired and unimpaired groundwater and develop a new impairment metric to characterize the relative major ion impairment from agriculture to karst spring water quality. We apply our method to a primarily rural region in northern Florida whereby major ion chemistry has been changing over the last several decades. Our results identify a significant difference in major ion covariance structure between agriculturally impaired and unimpaired groundwater which is exploited to directly link major ion changes at karst springs to agriculture via relating a derived impairment metric to measured nitrate. Our newly developed method is easy to apply and can be used to identify more comprehensive contamination from pollutant sources and prioritize relative water quality restoration.