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Abstract Submarine groundwater discharge (SGD), comprising both nearshore and offshore components, plays a vital role in water cycling and solute transport in coastal areas, and affects coastal marine ecosystems. Previous estimations of SGD based on seepage meters, geochemical tracers, water balances, analytical, and numerical approaches frequently overlooked offshore contributions driven by oceanic currents, waves, and tides, resulting in an incomplete understanding of SGD dynamics and its ecological consequences. Therefore, this study quantified the total SGD by integrating offshore (current‐, wave‐, and tide‐driven SGD) and nearshore (fresh SGD and tide‐driven SGD) components in Florida coasts. The calculated total SGD was approximately 15.08% of annual precipitation volume in Florida, with 14.09% offshore SGD (0.7%, 8.2%, and 5.2% from currents, waves, and tides, respectively) and ∼0.986% nearshore SGD (0.44% and 0.55% from fresh and recirculated SGD), underscoring offshore SGD as a major driver of groundwater discharge extending across the continental shelf. Moreover, nearshore SGD‐derived dissolved inorganic nutrient fluxes were estimated as kg/yr for nitrogen and kg/yr for phosphorus, whereas offshore SGD‐derived nutrients were kg/yr for nitrogen and kg/yr for phosphorus. On average, these nutrient inputs were approximately 6 and 4 times greater than those from surface water nutrient fluxes from coastal river discharge for dissolved inorganic nitrogen and dissolved inorganic phosphorus, respectively, highlighting the significant role of SGD in nutrient cycling in Florida. Additionally, we identified 54 SGD hotspots, which are generally aligned spatially with the distribution of coastal springs. Therefore, future research should evaluate the impact on nutrient loads to enhance coastal water management and sustainability. 

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.