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Understanding subsurface heterogeneity is critical for predicting groundwater flow, pollutant transport, and managing water resources. While traditional methods often rely on sparse borehole or geophysical data, this study explores a spectral analysis approach to infer aquifer structure from groundwater level fluctuations. We use a coupled surface–subsurface flow model to simulate hydraulic head time series in synthetic aquifers with bimodal hydraulic conductivity distributions. The frequency characteristics of these head fluctuations are analyzed to compute the scaling exponent (defined as the slope of the log-power spectral density of head fluctuations versus log-frequency) and its spatial gradient magnitude. Results show that areas with significant heterogeneity, such as transitions between high- and low-permeability zones, exhibit strong spatial gradients in the scaling exponent. These features can be used to delineate unsaturated zones, groundwater flow systems, and aquifer heterogeneity. By testing four scenarios with different hydraulic conductivity contrasts, we demonstrate that this method is sensitive to aquifer configuration. Our findings suggest that the gradient magnitude of the scaling exponent may serve as a diagnostic tool for characterizing heterogeneity in groundwater models and has the potential for future applications in estimating permeability distributions from monitored groundwater level data.