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13 results for “Groundwater salinity”
Continuous groundwater well temperature, salinity and water level measurements at the GCE-LTER Seawater Addition Long-Term Experiment (SALTEx) site from May 2014 to February 2018
The Georgia Coastal Ecosystems LTER Seawater Addition Long-Term Experiment (SALTEx) is a large-scale field experiment designed to simulate saltwater intrusion in a tidal freshwater wetland to predict how chronic (Press) and acute (Pulse) salinization will affect this and other tidal freshwater ecosystems. In order to characterize groundwater salinity, temperature, and plot flooding following experimental manipulation, unvented water pressure, temperature and conductivity were continuously measured in a PVC groundwater well installed at the SALTEx site. Measurements were made at the bottom of the well using a submerged Schlumberger CTD-Diver logger every 15 minutes from 30-May-2014 to 14-Feb-2018. In February 2016 a second CTD-Diver was deployed near the top of the well. Data were downloaded from the loggers using Diver Office communication software, then imported into MATLAB for post-processing, quality control and documentation. Raw, unvented pressure readings were corrected for atmospheric pressure and sensor height from the bottom of the well to generate corrected pressure readings, then water level, salinity and density were calculated from the measured variables using UNESCO algorithms. These data were collected as part of the Georgia Coastal Ecosystems LTER SALTEx project (http://gce-lter.marsci.uga.edu/public/app/send_project_eml.asp?id=73), and will be updated annually.
Groundwater well pressure, temperature, conductance, salinity and oxygen measurements for GCE-LTER study hammock HN_i_1 from 30-Oct-2008 to 03-Dec-2013
Groundwater pressure, temperature and other parameters (e.g. conductance, salinity, oxygen and pH) were measured using various instruments installed in PVC wells at the GCE-LTER HN_i_1 holocene study hammock from 30-Oct-2008 to 03-Dec-2013. Observations were logged continuously at 15 minute intervals, and accumulated data were downloaded from loggers using manufacturer-provided communications software semi-monthly, then imported into MATLAB for post-processing, quality control and documention. Raw, unvented pressure readings were corrected for atmospheric pressure and sensor height from the bottom of the well to generate corrected pressure readings. Separate data tables are provided for each combination of instrument and well due to differences in parameters measured and post-processing steps, but all tables include detailed information on location and well characteristics to support integration and analysis. These data were collected as part of the Georgia Coastal Ecosystems LTER Intensive Hammock Characterization and Hammock Groundwater Modeling projects, and will be used for groundwater modeling studies at two marsh hammocks on Sapelo Island, Georgia.
Groundwater well pressure, temperature, conductance, salinity and oxygen measurements for GCE-LTER study hammock PC_i_29 from 15-Aug-2008 to 13-Dec-2013
Groundwater pressure, temperature and other parameters (e.g. conductance, salinity, oxygen and pH) were measured using various instruments installed in PVC wells at the GCE-LTER PC_i_29 pleistocene study hammock from 15-Aug-2008 to 13-Dec-2013. Observations were logged continuously at 15 minute intervals, and accumulated data were downloaded from loggers using manufacturer-provided communications software semi-monthly, then imported into MATLAB for post-processing, quality control and documention. Raw, unvented pressure readings were corrected for atmospheric pressure and sensor height from the bottom of the well to generate corrected pressure readings. Separate data tables are provided for each combination of instrument and well due to differences in parameters measured and post-processing steps, but all tables include detailed information on location and well characteristics to support integration and analysis. These data were collected as part of the Georgia Coastal Ecosystems LTER Intensive Hammock Characterization and Hammock Groundwater Modeling projects, and will be used for groundwater modeling studies at two marsh hammocks on Sapelo Island, Georgia.
Research data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems"
<p><strong>Research Data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em></strong></p> <p>Dear reader,</p> <p>reasearch data are provided for the research article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em>. The authors hope that the research data allows for a better understanding of the modeling workflow. Questions regarding the modeling approach etc. can be directed to the authors, see contact details below.</p> <p>The research data covers the following files:</p> <ul> <li>iMOD-Python (Visser and Bootsma, 2019) scripts to create the iMOD-WQ (Verkaik et al., 2021) input files for the n=566 model variants. Subfolders for each model variant and corresponding files are stored in the subfolder 'model_variants'. An overview regarding the set-up of the model variants is presented in the .xlsx spreadsheet 'model_variants_overview.xlsx' in the folder 'model_variants'.</li> <li>Base data files, used as input files to iMOD-WQ (Verkaik et al., 2021), stored in the subfolder 'imod_input'. However, in most cases no consent for re-distribution of these data sets exists, and they cannot be made freely available through this publication. Please, consider the corresponding meta-data files and/or get in touch with one of the authors for further information.</li> <li>Post-processed model output data, which was further used for model evaluation, stored in the subfolder 'model_output'.</li> <li>Figure files and the corresponding .py scripts, stored in the subfolder 'figures'.</li> </ul> <p>Meta-data files are provided with data files in the different subfolders for clarification.</p> <p>iMOD-WQ (Verkaik et al., 2021) input and .run-files were executed on the University Oldenburg High-Performance Cluster 'Rosa', funded by DFG through its Major Research Instrumentation Program, INST 184/225-1 FUGG, and the Ministry of Science and Culture (MWK) of the Lower Saxony State.</p> <p>Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/</p> <p>The DFG is thanked for SALTSA project funding (DFG project number MA 3274/9-1) within the Priority Programme ‘Regional Sea Level Change and Society (SeaLevel)’. Research related to this article further benefited from funding of the projects WAKOS (BMBF; support code 01LR2003E) and the DFG research unit FOR 5094: The dynamic deep subsurface of high-energy beaches (DynaDeep).</p> <p>Literature:</p> <p>Verkaik, J., Hughes, J. D., van Walsum, P. E. V., Oude Essink, G. H. P., Lin, H. X., & Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling & Software, 143, p.105092.</p> <p>Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/</p> <p>Seibert, S. L., Greskowiak, J., Oude Essink, G. H. P., & Massmann, G. (2024). Understanding climate change and anthropogenic impacts on the salinization of low‐lying coastal groundwater systems. Earth's Future, 12, e2024EF004737. https://doi.org/10.1029/2024EF004737<br><br><strong>Contact one of the authors if you have further questions</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Gualbert H.P. Oude Essink (Gualbert.OudeEssink@deltares.nl) or Gudrun Massmann (gudrun.massmann@uol.de)</p>
Video supplement: A three-dimensional palaeo-reconstruction of the groundwater salinity distribution in the Nile Delta Aquifer
<p>Videos of validated models. The filenames indicate simulation code, for explanation see accompanying paper, submitted to HESS.</p> <p><strong>Abstract</strong></p> <p>The Nile Delta is an important agricultural area with a fast-growing population. Though traditionally irrigated with surface water, the delta increasingly relies on groundwater. However, saline groundwater extends far land inward, rendering groundwater close to the coastal zone useless for consumption or agriculture. To aid groundwater management decisions, hydrogeologists reconstructed this saline and brackish groundwater zone using variable-density groundwater models with very large dispersivities. However, this approach cannot explain the observed freshening of this zone as observed by hydrogeochemists, who hypothesize that the coastal saline zone is the effect of the Holocene transgression. Here, we investigated physical plausibility of this hypothesis by conducting a palaeo-reconstruction of groundwater salinity for the last 32 ka with a complex 3D variable-density groundwater flow model, using state-of-the-art model code that allows for parallel computation. Several scenarios with different lithologies and hypersaline groundwater provenances were simulated, of which five were selected that showed the best match with the observations. Amongst these selections, total fresh water volumes varied strongly, ranging from 1526 to 2659 km3, mainly due to uncertainties in the lithology offshore and at larger depths. This range is smaller (1511-1989 km3) when we consider the volumes of onshore fresh groundwater within 300 m depth. Regardless of the variance, in all cases the total volume of hypersaline groundwater exceeded that of sea water. We also show that during the last 32 ka, the total fresh groundwater volumes significantly declined, with a factor ranging from 1.9 to 5.4, due to the rising sea-level. Compared to a steady-state solution with present-day boundary conditions, the palaeo-reconstruction improved our validation for the saline zone (5 g/L – 35 g/L TDS). Also, under highly permeable conditions the marine transgression simulated with the palaeo-reconstruction led to a steeper fresh-salt interface compared to its steady-state equivalent, while low permeable clay layers allowed for the preservation of volumes of fresh groundwater. This shows that long-term transient simulations are needed when estimating present-day fresh-salt groundwater distribution in large deltas. The insights of this study are also applicable to other major deltaic areas, given the wide-range of lithological model scenarios used in this study and since many deltas also experienced a Holocene marine transgression.</p>
Saline groundwater irrigation affects the date palm bulk soil associated fungal guild community and enhances the pathotroph abundance
<p><strong><em> Exploring the Influence of Saline Groundwater Irrigation on Soil Fungal Biodiversity in Date Palm (Phoenix dactylifera) Bulk Soil</em></strong></p>
Irrigation water source matters: Saline groundwater irrigation lowers date palm root-associated fungal richness and alters their community patterns
<p>Saline groundwater irrigation is predominantly used for date palm (<em>Phoenix dactylifera</em>) cultivation in the arid regions of the Middle East and North Africa without knowing its impact on root-associated fungal (RAF) communities. We investigated the impact of irrigation water sources (freshwater <em>vs.</em> saline groundwater) on date palm RAF diversity, communities, and their assembly processes. Summarily, we show that water chemistry (pH and EC) mainly structures RAF communities, whereas water pH shapes saprotrophic and pathotrophic communities, and the higher abundance of saprotrophic RAF (i.e. <em>Acrocalymma vagum</em>) under saline groundwater irrigation, despite the higher importance of the drift process, indicates their potential role in nutrient cycling and plant growth promotion under saline conditions in arid agroecosystems.</p>
Dominant controls on coastal saline groundwater across North America: groundwater head and density interface at the stable state
<p>Resulting datasets described and evaluated in "Dominant controls on coastal saline groundwater across North America". </p> <p>The CSV's data is organized in columns.</p> <p>They contain the columns "spatID", "lat", "lon" and "head 492" / "zeta1 492", respectively. </p> <p>The columns "spatID" may be used to merge the two datasets.</p>
Study of Groundwater Salinity Distribution in Coastal Areas of Mirit District, Kebumen Regency Central Java
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Spatial data for precipitation, groundwater aesenic and salinity.
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Research data related to the article "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)"
<p><strong>Research Data related to the publication "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)" by Seibert et al. (2023) published in <em>Water Resources Research</em> </strong></p> <p>Dear reader,</p> <p>research data are provided for the article "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)" by Seibert et al. (2023). The authors hope that the research data allows for a better understanding of the paleo-modeling workflow. Feedback on the model files or questions regarding the modeling approach etc. can be addressed to the authors of the article, see contact details below. The research data comprises the following files:</p> <ul> <li>files related to the parameter estimation procedure using PEST (Doherty, 2021a,b) (see subfolder "<em>parameter_estimation</em>")</li> <li>iMOD-Python (Visser and Bootsma, 2019) scripts to create the iMOD-WQ (Verkaik et al., 2021) input files for each model variant. Note that model variants consist of several time slice models, indicated by the corresponding file names, e.g., '<em>Model_BC_slice_01.py'</em> etc. (see '<em>scripts.zip</em>' in the subfolders 'Model BC', 'Model CP', 'Model NE-ND-NP', 'Model NE-NP', 'Model NG', 'Model NP', 'Model R1', 'Model R2', 'Model R3', 'Model R4', 'Model R5', 'Model R6', 'Model SS')</li> <li>simulation output files, including concentration and head data for each model stress period (3-D), mean/max. concentration and head data for each model stress period (2-D), as well as depth [mbgs] of different salinity interfaces (2-D), i.e., marking the transitions from fresher to more saline groundwater using thresholds of 0.45 ('<em>depth_interface_mbgs</em>'), 1, 5, 10 and 20 g TDS L<sup>-1</sup>, respectively (see subfolders '<em>output/npy_arrays'</em> within each model variant subfolder). Moreover, sea levels, time slice names and stress period numbers are provided in the '<em>output/npy_arrays'</em> subfolders as well as final concentrations and heads (3-D) for each time slice model of each model variant (e.g., '<em>Model_BC_slice_01_final_concentrations.npz</em>' and '<em>Model_BC_slice_01_final_heads.npz</em>'; see '<em>output.zip'</em> in the model variant subfolders)</li> <li>iMOD-Python (Visser and Bootsma, 2019) input files, such as digital elevation models, geologic models etc. (see subfolder '<em>imod_input'</em>). However, in most cases no consent for re-distribution of these data sets exists, and they cannot be made freely available through this publication. Please, consult the corresponding meta-data files or get in touch with one of the authors for further information</li> <li>bash scripts for the execution of iMOD-Python .py- and iMOD-WQ .run-files in a linux environment (see subfolder '<em>bash_scripts'</em>)</li> <li>figure files as well as the corresponding .py and .m scripts and shape-files, where applicable (see subfolder '<em>figures'</em>); note that consent for re-distribution for some figure input files doesn't exist, compare corresponding meta-data files</li> <li>videos presenting the concentration evolution of the different model variants (vertically averaged concentrations & cross-sectonal view, see subfolder '<em>videos'</em>)</li> </ul> <p>Meta-data files are usually provided with data files in the different subfolders for clarification.</p> <p>iMOD-WQ (Verkaik et al., 2021) input data and .run-files were executed on the University Oldenburg High-Performance Cluster 'Carl', running simulations in parallel with 32 computational cores.</p> <p>Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/</p> <p>Literature:</p> <p>Doherty, J. E., (2021a). PEST Model-Independent Parameter Estimation User Manual Part I: PEST, SENSAN and Global Optimisers. Watermark Numerical Computing. p.394.</p> <p>Doherty, J. E. (2021b). PEST Model-Independent Parameter Estimation User Manual Part II: PEST Utility Support Software. Watermark Numerical Computing. p.274.</p> <p>Verkaik, J., Hughes, J. D., van Walsum, P. E. V., Oude Essink, G. H. P., Lin, H. X., & Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling & Software, 143, p.105092.</p> <p>Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/</p> <p><strong>If you have further questions, please, contact one of the following authors</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de) or Gudrun Massmann (gudrun.massmann@uol.de)</p>
Grand Bahama Groundwater and Soil Salinity Dataset
<p>This file contains groundwater salinity and water levels and soil salinity data collected post hurricane Dorian during the period of February 2020 - November 2020. </p>
Data from: Anthropogenic Basin Closure and Groundwater Salinization (ABCSAL)
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