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 &quot;Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)&quot; by Seibert et al. (2023) published in&nbsp;<em>Water Resources Research</em> </strong></p> <p>Dear reader,</p> <p>research data are provided for the article &quot;Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)&quot; 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&nbsp;research data comprises&nbsp;the following files:</p> <ul> <li>files related to the parameter estimation procedure using PEST (Doherty, 2021a,b) (see subfolder &quot;<em>parameter_estimation</em>&quot;)</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., &#39;<em>Model_BC_slice_01.py&#39;</em> etc. (see &#39;<em>scripts.zip</em>&#39; in the subfolders &#39;Model BC&#39;, &#39;Model CP&#39;, &#39;Model NE-ND-NP&#39;, &#39;Model NE-NP&#39;, &#39;Model NG&#39;, &#39;Model NP&#39;, &#39;Model R1&#39;, &#39;Model R2&#39;, &#39;Model R3&#39;, &#39;Model R4&#39;, &#39;Model R5&#39;, &#39;Model R6&#39;, &#39;Model SS&#39;)</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 (&#39;<em>depth_interface_mbgs</em>&#39;), 1, 5, 10 and 20 g TDS L^-1, respectively (see subfolders &#39;<em>output/npy_arrays&#39;</em>&nbsp;within each model variant subfolder). Moreover, sea levels, time slice names and stress period numbers are provided in the &#39;<em>output/npy_arrays&#39;</em>&nbsp;subfolders as well as final concentrations and heads (3-D) for each time slice model of each model variant (e.g., &#39;<em>Model_BC_slice_01_final_concentrations.npz</em>&#39; and &#39;<em>Model_BC_slice_01_final_heads.npz</em>&#39;; see &#39;<em>output.zip&#39;</em>&nbsp;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 &#39;<em>imod_input&#39;</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&nbsp;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 &#39;<em>bash_scripts&#39;</em>)</li> <li>figure files as well as the corresponding .py and .m scripts and shape-files, where applicable (see subfolder &#39;<em>figures&#39;</em>); note that consent for re-distribution for some figure input files doesn&#39;t exist, compare corresponding meta-data files</li> <li>videos&nbsp;presenting the concentration evolution&nbsp;of the different model variants (vertically averaged concentrations &amp; cross-sectonal view, see subfolder &#39;<em>videos&#39;</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 &#39;Carl&#39;, 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., &amp; Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling &amp; Software, 143, p.105092.</p> <p>Visser, M., &amp; 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>