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In December 2021, we installed four groundwater monitoring wells in Imperial Beach, California, to study the effects of sea level variability and implications for flood risks. We collected time series of groundwater table elevation (GWT) relative to a fixed vertical datum and local land surface elevation from 8 December 2021 through 14 May 2024. In each groundwater monitoring well, a single unvented pressure sensor (RBR Solo) was attached to Kevlar line and submerged ~1 m below the GWT. From 8 December 2021 through 21 November 2023, total pressure was recorded at 1 Hz; from 22 November 2023 through 14 May 2024, sampling occurred at 0.1 Hz. Gaps in sampling are a result of battery failures leading to data loss. To estimate hydrostatic pressure from total pressure measurements we subtracted atmospheric pressure measurements that were collected every 6 min from NOAA's National Data Buoy Center (NDBC) station SDBC1-9410170 at the San Diego airport and linearly interpolated to match sensor samples. Hydrostatic pressure is converted to sensor depth below the water table. We determined an average well water density, ρ, using intermittent vertical profiles of conductivity-temperature-depth (CTD) and the TEOS-10 conversion (Roquet et al. 2015). This object includes MATLAB (.mat) and HDF5 (.h5) files that contain the raw total pressure measurements from unvented RBR solos. The original Ruskin files (.rsk) are not included and have been converted to MATLAB files without loss of fidelity. Intermittent CTD profiles used to estimate well water density structure are included as CSV files. GWT that have been processed using atmospheric pressure and vertical datum measurements are included as HDF5 files. The object has five main directories, one for each of the four groundwater wells and one for data downloaded from other sources for archival and reproducibility purposes. Code for generating these files may be found on the GitHub repository (https://github.com/aubarnes/ImperialBeachGroundwater) or on Zenodo (https://doi.org/10.5281/zenodo.14969632). Code run with Python v3.12.7 Pastas v1.5.0 UTide v0.3.0 GSW v3.6.19 NumPy v1.26.4 Pandas v2.1.4 MatPlotLib v3.9.2 SciPy v 1.13.1 requests v2.32.3 intake v0.7.0 datetime pickle os 

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. 

Abstract Rainfall in southern California is highly variable, with some fluctuations explainable by climate patterns. Resulting runoff and heightened streamflow from rain events introduces freshwater plumes into the coastal ocean. Here we use a 105-year daily sea surface salinity record collected at Scripps Pier in La Jolla, California to show that El Niño Southern Oscillation and Pacific Decadal Oscillation both have signatures in coastal sea surface salinity. Averaging the freshest quantile of sea surface salinity over each year’s winter season provides a useful metric for connecting the coastal ocean to interannual winter rainfall variability, through the influence of freshwater plumes originating, at closest, 7.5 km north of Scripps Pier. This salinity metric has a clear relationship with dominant climate phases: negative Pacific Decadal Oscillation and La Niña conditions correspond consistently with lack of salinity anomaly/ dry winters. Fresh salinity anomalies (i.e., wet winters) occur during positive phase Pacific Decadal Oscillation and El Niño winters, although not consistently. This analysis emphasizes the strong influence that precipitation and consequent streamflow has on the coastal ocean, even in a region of overall low freshwater input, and provides an ocean-based metric for assessing decadal rainfall variability.