An official website of the United States government
This data set consists of 3,244 gridded, daily averaged temperature, practical salinity, potential density, and dissolved oxygen profiles. These profiles were collected from October 2014 to May 2025 by the NSF Ocean Observatories Initiative Washington Offshore Profiler Mooring (CE09OSPM) located at 46.8517°N, 124.982°W between approximately 35 and 510 meters water depth using a McLane® Moored Profiler (MMP). The MMP was equipped with a Sea-Bird Scientific 52-MP (SBE 52-MP) CTD instrument and an associated Sea-Bird Scientific (SBE 43F) dissolved oxygen sensor. Raw binary data files [C*.DAT (CTD data); E*.DAT (engineering data plus auxiliary sensor data) and A*.DAT (current meter data)] were converted to ASCII text files using the McLane® Research Laboratories, Inc. Profile Unpacker v3.10 application. Dissolved oxygen calibration files for each of the twenty deployments were downloaded from the Ocean Observatories Initiative asset-management GitHub® repository. The unpacked C*.TXT (CTD data); E*.TXT (engineering data plus auxiliary sensors) and A*.TXT (current meter data) ASCII data files associated with each deployment were processed using a MATLAB® toolbox that was specifically created to process OOI MMP data. The toolbox imports MMP A*.TXT, C*.TXT, and E*.TXT data files, and applies the necessary calibration coefficients and data corrections, including adjusting for thermal-lag, flow, and sensor time constant effects. mmp_toolbox calculates dissolved oxygen concentration using the methods described in Owens and Millard (1985) and Garcia and Gordon (1992). Practical salinity and potential density are derived using the Gibbs-SeaWater Oceanographic Toolbox. After the corrections and calculations for each profile are complete, the data are binned in space to create a final, 0.5-dbar binned data set. The more than 24,000 individual temperature, practical salinity, pressure, potential density, and dissolved oxygen profiles were temporally averaged to form the final, daily averaged data set presented here. Using the methods described in Risien et al. (2023), daily temperature, practical salinity, potential density, and dissolved oxygen climatologies were calculated for each 0.5-dbar depth bin using a three-harmonic fit (1, 2, and 3 cycles per year) based on the 10-year period January 2015 to December 2024.
more »
« less
Data from: Flooding Projections due to Groundwater Emergence Caused by Sea Level Variability
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
more »
« less
- Award ID(s):
- 2113984
- PAR ID:
- 10638587
- Publisher / Repository:
- UC San Diego Library Digital Collections
- Date Published:
- Subject(s) / Keyword(s):
- Empirical groundwater modeling Infrastructure groundwater vulnerability Coastal aquifer dynamics Domain: Earth sciences Groundwater table elevation (GWT) Groundwater-driven flooding Compound flood hazards Transmissive coastal soils Imperial Beach (Calif.) Oceanography FOS: Earth and related environmental sciences
- Format(s):
- Medium: X Other: text/plain; application/zip; application/zip; application/zip; application/zip; application/zip
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
This dataset includes water-column data collected from the Beaufort Shelf during the open-water seasons in 2020, 2021, and 2022. The 2020 data include water-column profiles (salinity, temperature, depth, turbidity, particle size distributions, particle volume concentrations, and uncorrected clorophyll-a) collected with an RBR CTD/Tu (conductivity, temperature, depth, turbidity) sensor and LISST sensor from R/V Sikuliaq and its workboat. Most sites were in the Harrison Bay region (north of the Colville Delta and Simpson Lagoon) and a few were located farther east. The 2021 and 2022 data include the same CTD/Tu and LISST data that were collected in 2020, but are focused in Harrison Bay and also include profiles of light intensity (photosynthetically active radiation) as well as ADCP (acoustic doppler current profile) profiles from a pole-mounted Nortek Signature 500 kilohertz (kHz) sensor. In 2021, additional data include filtration data (total suspended solids, suspended sediment concentrations, and organic fractions) from water samples and hi-resolution echosounder data from the Nortek ADCP. These data are being incorporated into publications about summertime water-column properties and sediment transport dynamics within Harrison Bay (Eidam et al., pending).more » « less
-
Stream water was collected at weekly to monthly intervals at 29 stream sites in New Hampshire (USA). Ten of the stream sites were instrumented with high‐frequency sensors. Twenty-one of the stream sites (including 5 sensor sites) are in the Lamprey River Hydrologic Observatory (LRHO; Wymore et al 2021) and two stream sites were nearby the LRHO. Groundwater was collected from two riparian well fields (JF, 14 wells and WHB, 13 wells). Wells were installed in 2004 and sampled monthly through May 2007, then quarterly until December 2009, after which a subset (JF, 6 and WHB, 5) was generally sampled quarterly. Stream and groundwater samples span a 17-year collection period and were analyzed for sodium, chloride and specific conductance. Methods and findings are described in the associated Limnology and Oceanography Letters manuscript.more » « less
-
This dataset contains a compressed folder of the data and MATLAB scripts used produce relevant figures and candidates for GRITCLEAN: A glitch veto scheme for Gravitational wave data as presented in https://arxiv.org/abs/2401.15237 The codes in this dataset include: A PSO-based matched filtering search pipeline which can be run on either the positive or the negative chirp time space. A standalone MATLAB script called GRITCLEAN.m which can run the GRITCLEAN hierarchical vetoes on a set of positive and negative chirp time space estimated parameters. A plotting script to generate relevant figures. The files in this dataset include: GVSsegPSDtrainidxs.mat, a binary MATLAB file containing training indices for all segments from which the Power Spectral Densities (PSDs) are estimated, this is done via the scripts provided, namely, getsegPSD.m and createPSD.m. A sample HDF5 file used (H-H1_GWOSC_O3a_4KHZ_R1-1243394048-4096.hdf5) JSON files containing information about the data segments and the strain data files from which they originate from. Text files containing the parameters estimated by the PSO-based pipeline across the positive and negative chirp time space runs. Detailed instructions on dependencies, downloading the dataset and running the codes are given in a README.txt file included with this dataset. The user is recommended to go through this file first. The scripts enclosed have dependencies on JSONLAB , the Parallel Computing Toolbox and Signal Processing Toolbox for MATLAB, along with additional scripts provided in GitHub repositories Accelerated-Network-Analysis and SDMBIGDAT19 . Instructions on installing these dependencies are provided in README.txt. All codes have been developed and tested on MATLAB R2022 and R2023.more » « less
-
Abstract Air–water interfacial adsorption represents a major source of retention for many per‐ and poly‐fluoroalkyl substances (PFAS). Therefore, transient hydrological fluxes that dynamically change the amount of air–water interfaces are expected to strongly influence PFAS retention in their source zones in the vadose zone. We employ mathematical modeling to study how seasonal groundwater table (GWT) fluctuations affect PFAS source‐zone leaching. The results suggest that, by periodically collapsing air–water interfaces, seasonal GWT fluctuations can lead to strong temporal variations in groundwater concentration and significantly enhance PFAS leaching in the vadose zone. The enhanced leaching is more pronounced for longer‐chain PFAS, coarser‐textured porous media, drier climates, and greater amplitudes of fluctuations. GWT fluctuations and lateral migration above the GWT introduce a downgradient persistent secondary source zone for longer‐chain PFAS. However, the enhanced leaching and the secondary source zone are greatly reduced when subsurface heterogeneity is present. In highly heterogeneous source zones, GWT fluctuations may even lead to overall slower leaching due to lateral flow (in the GWT fluctuation zone and above the GWT) moving PFAS into local regions with greater retention capacities. Model simplification analyses suggest that the enhanced source‐zone leaching due to GWT fluctuations may be approximated using a static but shallower GWT. Additionally, while vertical 1D models underestimate source‐zone leaching due to not representing lateral migration, they can be revised to account for lateral migration and provide lower‐ and upper‐bound estimates of PFAS source‐zone leaching under GWT fluctuations. Overall, our study suggests that representing GWT fluctuations is critical for quantifying source‐zone leaching of PFAS, especially the more interfacially active longer‐chain compounds.more » « less
