Efficacy of Fuzzy c-Means Cluster Analysis of Naturally Occurring Radioisotope Datasets for Improved Groundwater Resource Management under the Continued Risk of Climate Change

Global change is recognized as an additional potential stressor on already over-tapped groundwater systems. Mitigation of impacts due to global change requires planning for sustainable use of groundwater systems. Identifying and developing mitigation plans for sustainable use of groundwater resources require detailed knowledge of aquifer dynamics and temporal behavior for a higher level of certainty on which decisions can be made by a knowledgeable group of stakeholders. The principal hypothesis of this study was that a robust set of uranium (238U) and thorium (232Th) decay series data from multiple wellfields representing different confining and geochemical conditions would cluster in a meaningful manner using a fuzzy c-means technique for better understanding of aquifer dynamics for management purposes. Three conceptual models were represented by the wellfields: 1) a well-confined artesian aquifer; 2) an area receiving recharge via a confining layer window; and 3) a regional recharge zone where the aquifer sub-crops near the land surface. These conceptual models were defined as C1, C2, and C3 according to the respective definitions. Eleven samples from the three wellfields were analyzed for ten parameters consisting of 238U and 232Th decay series isotopes. The data clustered successfully into three cluster types providing discrimination of behavior within each wellfield. Clusters C2 and C3 were characterized by the higher values of 222Rn, 226Ra, 228Ra, and 224Ra. Whereas, C1 was characterized by a higher values of 228Th, which was mostly absent from C2 and C3. The data clustered as expected between the well-confined, window, and regional recharge conceptual models with insights into individual well behavior. The data offer a robust conceptualization of aquifer dynamics in the regional area that may benefit decision makers.