Research data related to the article "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)"

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 Water Resources Research Dear reader, 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: files related to the parameter estimation procedure using PEST (Doherty, 2021a,b) (see subfolder "parameter_estimation") 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., 'Model_BC_slice_01.py' etc. (see 'scripts.zip' 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') 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 ('depth_interface_mbgs'), 1, 5, 10 and 20 g TDS L-1, respectively (see subfolders 'output/npy_arrays' within each model variant subfolder). Moreover, sea levels, time slice names and stress period numbers are provided in the 'output/npy_arrays' subfolders as well as final concentrations and heads (3-D) for each time slice model of each model variant (e.g., 'Model_BC_slice_01_final_concentrations.npz' and 'Model_BC_slice_01_final_heads.npz'; see 'output.zip' in the model variant subfolders) iMOD-Python (Visser and Bootsma, 2019) input files, such as digital elevation models, geologic models etc. (see 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, consult the corresponding meta-data files or get in touch with one of the authors for further information bash scripts for the execution of iMOD-Python .py- and iMOD-WQ .run-files in a linux environment (see subfolder 'bash_scripts') figure files as well as the corresponding .py and .m scripts and shape-files, where applicable (see subfolder 'figures'); note that consent for re-distribution for some figure input files doesn't exist, compare corresponding meta-data files videos presenting the concentration evolution of the different model variants (vertically averaged concentrations & cross-sectonal view, see subfolder 'videos') Meta-data files are usually provided with data files in the different subfolders for clarification. 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. Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/ Literature: Doherty, J. E., (2021a). PEST Model-Independent Parameter Estimation User Manual Part I: PEST, SENSAN and Global Optimisers. Watermark Numerical Computing. p.394. Doherty, J. E. (2021b). PEST Model-Independent Parameter Estimation User Manual Part II: PEST Utility Support Software. Watermark Numerical Computing. p.274. 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. Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/ If you have further questions, please, contact one of the following authors: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de) or Gudrun Massmann (gudrun.massmann@uol.de