Geoffrey R. Tick

Seawater intrusion (SWI) increasingly threatens coastal aquifers that serve growing communities, emphasizing the need for generalizable, process-based diagnostics. We evaluate SWI in a multilayer coastal aquifer system in southern Alabama, USA, by integrating three groundwater sampling campaigns during spring–early summer 2025 (March, April, and June), hydrogeochemical–isotopic analyses, and variable-density SEAWAT modeling. This study couples HFE–Gibbs–isotope diagnostics with hydraulic-head-constrained SEAWAT modeling to characterize seawater intrusion extent and process state in a multilayer coastal setting. Groundwater major ions and δ¹⁸O/δ²H indicate dominantly meteoric, rock-weathering waters (Ca–HCO₃ inland) with localized Na–Cl near the coast; most samples plot in the rock dominance field, and stable isotope values cluster near the meteoric water line. A 3-D SEAWAT model (seven layers, 150 × 150 m cells), calibrated to available groundwater head observations using PEST, indicates that modeled intrusion is concentrated at depth in confined Miocene units, whereas shallow groundwater sampled from the unconfined zone remains largely fresh based on hydrogeochemical and isotopic evidence. Because chloride observations were used primarily for interpretation rather than direct transport calibration, the modeled deep salinity distribution is treated as a process-based estimate rather than a uniquely calibrated chloride field. To quantify exposure and process state, we introduce two indicators: a Depth-Weighted Intrusion Index (DWII) integrating the extent and intensity of the transition zone across layers, and an Ion-Exchange Departure Index (IEDI) capturing normalized Na–Cl departures from conservative mixing due to cation exchange. Predominantly negative IEDI values indicate reverse exchange with weaker intensity during spring freshening and only localized forward exchange episodes. Beyond the site-specific findings, the DWII and IEDI introduced in this study provide practical tools for detecting and quantifying subtle seawater intrusion in low-salinity coastal aquifers and may be applicable to other coastal aquifer systems with similar hydrogeological settings.

Understanding streamflow dynamics in watersheds affected by human activity and climate variability is important for sustainable water and environmental resource management. This study evaluates the vulnerability of Alabama watersheds to anthropogenic and climatic changes using an integrated framework combining GIS, remote sensing, hydrological modeling, and machine learning (ML). Three Soil and Water Assessment Tool (SWAT) models, differing in spatial resolution and soil inputs, were developed to simulate streamflow under baseline and land-use/land cover (LULC) scenarios from 1990 to 2023. The model, built with consistent 100 × 100 m rasters and fine-resolution SSURGO (Soil Survey Geographic Database) soil data, achieved the best calibration and was selected for detailed analysis. Streamflow trends were assessed over two periods (1993–2009 and 2010–2023) to help isolate climate variability (from LULC effects), while LULC changes were evaluated using 1992, 2011, and 2021 maps. A Long Short-Term Memory (LSTM) model further enhanced simulation accuracy by integrating partially calibrated SWAT outputs. Watershed vulnerability was ranked using a multi-criteria framework. Two watersheds were classified as highly vulnerable, nine as moderately vulnerable, and three as having low vulnerability. Basin-level contrasts revealed moderate climate impacts in the Tombigbee Basin, greater climate sensitivity in the Black Warrior Basin, and LULC-dominated impacts in the Alabama Basin. Overall, LULC change exerted stronger and more spatially variable effects on streamflow than climate variability. This study introduces a transferable SWAT–ML vulnerability ranking framework to guide watershed and environmental management in data-scarce, human-modified regions.

Rising nitrate contamination in water systems poses significant risks to public health and ecosystem stability, necessitating advanced modeling to understand nitrate dynamics more accurately. This study applies the long short-term memory (LSTM) modeling to investigate the hydrologic and environmental factors influencing nitrate concentration dynamics in rivers and aquifers across the state of Alabama in the southeast of the United States. By integrating dynamic data such as streamflow and groundwater levels with static catchment attributes, the machine learning model identifies primary drivers of nitrate fluctuations, offering detailed insights into the complex interactions affecting multi-year nitrate concentrations in natural aquatic systems. In addition, a novel LSTM-based approach utilizes synthetic surface water nitrate data to predict groundwater nitrate levels, helping to address monitoring gaps in aquifers connected to these rivers. This method reveals potential correlations between surface water and groundwater nitrate dynamics, which is particularly meaningful given the lack of water quality observations in many aquifers. Field applications further show that, while the LSTM model effectively captures seasonal trends, limitations in representing extreme nitrate events suggest areas for further refinement. These findings contribute to data-driven water quality management, enhancing understanding of nitrate behavior in interconnected water systems.

A weighting function model was developed to account for the relationship between flow unsteadiness and frictional resistance. The Manning equation cannot fully describe such processes, leading to deficiency in predictive capability under some conditions. Two fundamentally new contributions to our approach are the development of (1) an additional unsteady component using time derivatives of flow-state, and (2) an additional component that utilizes instantaneous and historical information to update the current system. Compared to the results from the Manning equation, the proposed model reduces the overall simulation error and also improves the accuracy in peak simulation. Furthermore, parameter values can be conveniently constrained for model prediction and show strong dependence with the friction coefficient n. However, the proposed model incorporates more parameters and is more complicated than the Manning equation. Further research is needed to explore specific physical meaning of the parameters, for its simplification, practical application, and parameter identification.

It has been observed in literature that for unsteady flow conditions the one-to-one relationships between flow depth, cross-sectional averaged velocity, and frictional resistance as determined from steady uniform flow cases may not be appropriate for these more complex flow systems. Thus, a general friction resistance formula needs to be modified through the addition of new descriptive terms to account for flow unsteadiness, in order to eliminate errors due to uniform and steady-flow assumptions. An extended Chezy formula incorporating both time and space partial derivatives of hydraulic parameters was developed using dimensional analysis to investigate the relationship between flow unsteadiness and friction resistance. Results show that the proposed formula performs better than the traditional Chezy formula for simulating real hydrograph cases whereby both formula coefficients are individually identified for each flood event and coefficients are predetermined using other flood events as calibration cases. Although the extended Chezy formula as well as the original Chezy formula perform worse with the increasing degree of flow unsteadiness, its results are less dramatically affected by unsteadiness intensity, thereby improving estimations of flood routing. As a result, it tends to perform much better than traditional Chezy formula for severe flood events. Under more complex conditions whereby peak flooding events may occur predominantly under unsteady flow, the extended Chezy model may provide as a valuable tool for researchers, practitioners, and water managers for assessing and predicting impacts for flooding and for the development of more appropriate mitigation strategies and more accurate risk assessments.