Unraveling aquifer dynamics: Time series evaluation for informed groundwater management

Abstract

Optimizing groundwater monitoring networks is essential for the sustainable management of this critical resource, which is vulnerable to both anthropogenic and natural changes. Focusing on the Hashtgerd aquifer in Iran, this research aims to enhance the efficiency of groundwater level monitoring systems by integrating statistical methods, artificial intelligence (AI), and hydrogeological studies. The study's core objective is to pinpoint and comprehend the factors that cause certain observation wells to become isolated from the main aquifer body, leading to parts of the aquifer being cut off from the central hydrogeological system. Employing clustering techniques on data from 29 observation wells, based on parameters like groundwater level (GWL), groundwater level change (GWLC), groundwater depth (GWD), air temperature (AT), distance (D), ground level (GL), and temperature (T) through Hierarchical Cluster Analysis (HCA), the research categorizes the groundwater information into four distinct groups. Significantly, Cluster 4 encompasses wells with similar characteristics. The analysis reveals that two wells, categorized in Clusters 1 and 2, exhibit unique behaviors, likely due to their connection to local karstic aquifers, as corroborated by geological evidence. Cluster 3's separation is determined through geophysical investigations, identifying confined conditions near specific wells, notably Wells 4 and 8. AI techniques further distinguish differences in meteorological influences and water discharges among the clusters, noting minimal weather impact on Clusters 1 and 2, considerable shallow groundwater influence on Cluster 3, and a pronounced effect of groundwater discharge on Cluster 4. This nuanced identification of aquifer segments disconnected from the main system underscores the need to refine monitoring networks for accurate assessment of groundwater resources. By leveraging a detailed analysis of aquifer complexities through state-of-the-art methodologies, the study guides towards informed, sustainable groundwater management strategies.