Supplementary Material for "Higher odds of produced-water-like chemistry in groundwater vulnerable to shale gas spills"

SUPPLEMENTARY MATERIAL FOR Title: "Higher odds of produced-water-like chemistry in groundwater vulnerable to shale gas spills"Authors: Mario A. Soriano Jr., Joshua L. Warren, Yingcan Cathy Wang, David van Velden, Shuqi Lin, Reed M. Maxwell, and James E. SaiersJournal: Nature SustainabilityPublication year: 2026 CONTENTS [1] 01_ReadRun_MODFLOW6_models.html - Jupyter Notebook illustrating workflow for reading and running MODFLOW6 groundwater flow models [2] 02_Run_PESTPP-IES.html - Jupyter Notebook illustrating workflow for setting up and running PESTPP-IES calibration [3] 03_Particle_Tracking.html - Jupyter Notebook illustrating workflow for running MODPATH particle tracking after generating the calibrated parameter ensemble [4] 04_Convert_MODPATH_output_to_raster.html - Jupyter Notebook illustrating workflow for converting MODPATH output to 0/1 rasters [5] 05_Vulnerability_calculation.html - Jupyter Notebook illustrating workflow for calculating Vulnerability from ensemble of MODPATH output rasters [6] 06_Sample_MODFLOW_MODPATH_Simulation.zip - example inputs and scripts to demonstrate running [1] to [3] above [7] 07_V_pop_hexpoly.zip - Zip file containing shapefile of groundwater dependent populations served by domestic wells in locations highly vulnerable (V ≥ 0.5) to groundwater contamination from unconventional oil and gas development. Shown as Figure 2 in journal manuscript. Attributes: Population - Total vulnerable population within hexagon (number of individuals); Pct_TotPop - Total vulnerable population divided by Total population within hexagon (%); V_mean - Mean vulnerability to all UOGD, aggregated from original 250-m x 250-m grid computations; n_UOG - Number of UOGD wells within hexagon; n_spill - Number of UOGD spills within hexagon [8] 08_Table_1_and_Supplementary_Table_1_Regression_and_sensitivity_analysis.nb.html - R Markdown Notebook documenting code for statistical analysis (Table 1 and Supplementary Table 1 in journal manuscript) [9] 09_Extended_Data_Figure_1_Pathline_histogram.nb.html - R Markdown Notebook documenting code for pathline histogram visualization (Extended Data Figure 1 in journal manuscript) [10] 10_Extended_Data_Table_1_Individual_ions.nb.html - R Markdown Notebook documenting code for analysis of individual ions (Extended Data Table 1 in journal manuscript) Notes: - All included Notebooks were run on a Windows laptop (Intel Core i7-8565U CPU @ 1.80 GHz with 16 GB RAM).- Standard installation of R (4.2.1) and Python (3.11) modules required.