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1. Flooding Projections Due To Groundwater Emergence Caused by Sea Level Variability

   https://doi.org/10.1029/2025EF006270  

   Barnes, Austin T; Merrifield, Mark A; Bagheri, Kian; Levy, Morgan C; Davani, Hassan (July 2025, Earth's Future)

   Abstract Rising groundwater tables due to sea level rise (SLR) pose a critical but understudied threat to low‐lying coastal regions. This study uses field observations and dynamic modeling to investigate drivers of groundwater variability and to project flooding risks from emergent groundwater in Imperial Beach, California. Hourly groundwater table data from four monitoring wells (2021–2024) reveal distinct aquifer behaviors across soil types. In transmissive coastal sandy soils, groundwater levels are dominated by ocean tides, with secondary contributions from non‐tidal sea level variability and seasonal recharge. In this setting, we calibrated an empirical groundwater model to observations, and forced the model with regional SLR scenarios. We project that groundwater emergence along the low‐lying coastal road will begin by the 2060s under intermediate SLR trajectories, and escalate to near‐daily flooding by 2100. Over 20% of San Diego County's coastline shares similar transmissive sandy geology and thus similar flooding risk. Results underscore the urgency of integrating groundwater hazards into coastal resilience planning, as current adaptation strategies in Imperial Beach—focused on surface flooding—are insufficient to address infrastructure vulnerabilities from below. This study provides a transferable framework for assessing groundwater‐driven flooding in transmissive coastal aquifers, where SLR‐induced groundwater rise threatens critical infrastructure decades before permanent inundation. 

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2. Forecasting Groundwater Table in a Flood Prone Coastal City with Long Short-term Memory and Recurrent Neural Networks

   https://doi.org/10.3390/w11051098  

   Bowes, Benjamin D.; Sadler, Jeffrey M.; Morsy, Mohamed M.; Behl, Madhur; Goodall, Jonathan L. (May 2019, Water)

   Many coastal cities are facing frequent flooding from storm events that are made worse by sea level rise and climate change. The groundwater table level in these low relief coastal cities is an important, but often overlooked, factor in the recurrent flooding these locations face. Infiltration of stormwater and water intrusion due to tidal forcing can cause already shallow groundwater tables to quickly rise toward the land surface. This decreases available storage which increases runoff, stormwater system loads, and flooding. Groundwater table forecasts, which could help inform the modeling and management of coastal flooding, are generally unavailable. This study explores two machine learning models, Long Short-term Memory (LSTM) networks and Recurrent Neural Networks (RNN), to model and forecast groundwater table response to storm events in the flood prone coastal city of Norfolk, Virginia. To determine the effect of training data type on model accuracy, two types of datasets (i) the continuous time series and (ii) a dataset of only storm events, created from observed groundwater table, rainfall, and sea level data from 2010–2018 are used to train and test the models. Additionally, a real-time groundwater table forecasting scenario was carried out to compare the models’ abilities to predict groundwater table levels given forecast rainfall and sea level as input data. When modeling the groundwater table with observed data, LSTM networks were found to have more predictive skill than RNNs (root mean squared error (RMSE) of 0.09 m versus 0.14 m, respectively). The real-time forecast scenario showed that models trained only on storm event data outperformed models trained on the continuous time series data (RMSE of 0.07 m versus 0.66 m, respectively) and that LSTM outperformed RNN models. Because models trained with the continuous time series data had much higher RMSE values, they were not suitable for predicting the groundwater table in the real-time scenario when using forecast input data. These results demonstrate the first use of LSTM networks to create hourly forecasts of groundwater table in a coastal city and show they are well suited for creating operational forecasts in real-time. As groundwater table levels increase due to sea level rise, forecasts of groundwater table will become an increasingly valuable part of coastal flood modeling and management. 

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3. Effects of Future Increases in Tidal Flooding on Salinity and Groundwater Dynamics in Coastal Aquifers

   https://doi.org/10.1029/2022WR033195  

   Heiss, James W.; Mase, Bryce; Shen, Chengji (December 2022, Water Resources Research)

   Abstract Future increases in the frequency of tidal flooding due to sea level rise (SLR) are likely to affect pore water salinities in coastal aquifers. In this study, we investigate the impact of increased tidal flooding frequency on salinity and flow dynamics in coastal aquifers using numerical variable‐density variably‐saturated groundwater flow and salt transport models. Short (sub‐daily) and long (decadal) period tides are combined with SLR projections to drive continuous 80‐year models of flow and salt transport. Results show that encroaching intertidal zones lead to both periodic and long‐term vertical salinization of the upper aquifer. Salinization of the upper aquifer due to tidal flooding forces the lower interface seaward, even under SLR. System dynamics are controlled by the interplay between SLR and long period tidal forcing associated with perigean spring tides and the 18.6‐year lunar nodal cycle. Periodic tidal flooding substantially enhances intertidal saltwater‐freshwater mixing, resulting in a 6‐ to 10‐fold expansion of the intertidal saltwater‐freshwater mixing area across SLR scenarios. The onset of the expansion coincides with extreme high water levels resulting from lunar nodal cycling of tidal constituent amplitudes. The findings are the first to demonstrate the combined effects of gradual SLR and short and long period tides on aquifer salinity distributions, and reveal competing influences of SLR on saltwater intrusion. The results are likely to have important implications for coastal ocean chemical fluxes and groundwater resources as tidal flooding intensifies worldwide. 

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4. The Influence of Future Changes in Tidal Range, Storm Surge, and Mean Sea Level on the Emergence of Chronic Flooding

   https://doi.org/10.1029/2023EF003993  

   Hague, Ben S; Talke, Stefan A (February 2024, Earth's Future)

   Abstract Sea‐level rise is leading to increasingly frequent coastal floods globally. Recent research shows that changes in tidal properties and storm surge magnitudes can further exacerbate sea‐level rise‐related increases in flood frequencies. However, such non‐stationarity in tide and storm surge statistics are largely neglected in existing coastal flood projection methodologies. Here we develop a framework to explore the effect that different realizations of various sources of uncertainty have on projections of coastal flood frequencies, including changes in tidal range and storminess. Our projection methodology captures how observed flood rates depend on how storm surges coincide with tidal extremes. We show that higher flood rates and earlier emergence of chronic flooding are associated with larger sea‐level rise rates, lower flood thresholds, and increases in tidal range and skew surge magnitudes. Smaller sea‐level rise rates, higher flood thresholds and decreases in sea level variability lead to commensurately lower flood rates. Percentagewise, changes in tidal amplitudes generally have a much larger impact on flood frequencies than equivalent percentagewise changes in storm surge magnitudes. We explore several implications of these findings. Firstly, understanding future local changes in storm surges and tides is required to fully quantify future flood hazards. Secondly, existing hazard assessments may underestimate future flood rates as changes in tides are not considered. Finally, identifying the flood frequencies and severities relevant to local coastal managers is imperative to develop useable and policy‐relevant projections for decisionmakers. 

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5. Marine Ice Sheet Instability Amplifies and Skews Uncertainty in Projections of Future Sea Level Rise

   https://doi.org/doi:10.1073/pnas.1904822116  

   Robel, A.A.. H. (July 2019, Proceedings of the National Academy of Sciences of the United States of America)

   Sea-level rise may accelerate significantly if marine ice sheets become unstable. If such instability occurs, there would be considerable uncertainty in future sea-level rise projections due to imperfectly modeled ice sheet processes and unpredictable climate variability. In this study, we use mathematical and computational approaches to identify the ice sheet processes that drive uncertainty in sea-level projections. Using stochastic perturbation theory from statistical physics as a tool, we show mathematically that the marine ice sheet instability greatly amplifies and skews uncertainty in sea-level projections with worst-case scenarios of rapid sea-level rise being more likely than best-case scenarios of slower sea-level rise. We also perform large ensemble simulations with a state-of-the- art ice sheet model of Thwaites Glacier, a marine-terminating glacier in West Antarctica that is thought to be unstable. These ensemble simulations indicate that the uncertainty solely related to internal climate variability can be a large fraction of the total ice loss expected from Thwaites Glacier.We conclude that internal climate variability alone can be responsible for significant uncertainty in projections of sea-level rise and that large ensembles are a necessary tool for quantifying the upper bounds of this uncertainty. 

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