Application of forensic hydrology to develop a conceptual model for a stratigraphically and structurally compartmentalized alluvial aquifer in the Lower Umatilla Basin, USA

Abstract Alluvial deposits provide vital freshwater aquifers, necessitating a detailed understanding of sustainable groundwater management. Frequent reassessment of aquifer permeability is crucial to address spatial heterogeneity to refine or revise historical hydrogeologic models. From a geologic perspective, the recognition of aquifers as being inherently heterogeneous is essential for the integration of historical geologic context into studies, can overcome the limitations of traditional homogeneous models, and update existing conceptual models with new data to test alternative hypotheses. Fine-scale spatial and temporal variability can reveal behaviors that homogeneous models cannot predict, leading to more accurate models for contaminant transport and site characterization. This study applied a forensic hydrology framework with multiple working hypotheses to investigate spatial variability and heterogeneity of the alluvial aquifer in the Lower Umatilla Basin Groundwater Management Area (Lower Umatilla Basin GWMA), Oregon, USA. Previously unrecognized channeled filled belts (CFBs) containing low-permeability, fine-grained deposits that act as potentially serviceably impermeable barriers to groundwater flow. Seven geological cross sections were created using well logs from the State of Oregon well database. Findings suggest that CFBs, folds, and faults may have compartmentalized the aquifer, influencing groundwater flow patterns. Steep hydraulic gradients support the presence of potentially serviceably impermeable barriers. The clustering of mean groundwater levels within aquifer compartments led to the development of a new hydrogeologic conceptual model (here termed conceptual model 2), potentially revealing new groundwater flow directions in the GWMA. These findings will aid in further investigation into groundwater management challenges within the GWMA through data collection and numerical modeling.