This data set includes the *.csv data and the used scripts to reproduce the plots of the three different scenarios presented in S. Kiemle, K. Heck, E. Coltman, R. Helmig (2022) Stable water isotopologue fractionation during soil-water evaporation: Analysis using a coupled soil-atmosphere model. (Under review) Water Resources Research.

.csv files The isotope distribution has been analyzed in the vertical and in horizontal direction of a soil column for all scenarios. Therefore, we provide .csv files generated using the ParaView Tools "plot over line" or "plot over time". Each .csv file contains information about the saturation, temperature, and component composition for each phase in mole fraction or in the isotopic-specific delta notation. Additionally, information about the evaporation rate is given in a separate file .txt file.

python scripts For each scenario, we provide scripts to reproduce the presented plots.

Scenarios We used different free flow conditions to analyze the fractionation processes inside the porous medium.

Scenario 1. laminar flow, Scenario 2. laminar flow, but with isolation of parameter affecting the fractionation process, Scenario 3. turbulent flow.

Please find below a detailed description of the data labeling and needed scripts to reproduce a certain plot for each scenario.

Scenario:

The spatial distribution of stable water isotopologues in horizontal (-0.01 m depth) and vertical (at 0.05 m width) inside a soil column at selected days (DoE (Day of Experiment)): Use the python scripts plot_concentration_horizontal_all.py (horizontal direction) and plot_concentration_spatial_all.py (vertical direction) to create the specific plots. In the folder IsotopeProfile_Horizontal and IsotopeProfile_Vertical the belonging .csv can be found. The .csv files are named after the selected day (e.g. DoE_80 refers to day 80 of the virtual experiment).

The influence of the evaporation rate on isotopic fractionation processes in various depths (-0.001, -0.005, -0.009, and -0.018 m ) during the whole virtual experiment time: Use the python script plot_evap_isotopes_v2.py to create the plots. The data for the isotopologues distribution and the saturation can be found in the folder PlotOverTime. All data is named as PlotOverTime_xxxxm with xxxx representing the respective depth (e.g. PlotOverTime_0.001m refers to -0.001 m depth). The data for the evaporation rate can be found in the folder EvaporationRate. Note, the evaporation rate data is available as a .txt because we extract the information about the evaporation directly during the simulation and do not derive it through any post-processing.

Scenario: Process behavior of isolated parameters that influences the isotopic fractionation: Use plot_concentration.py to reproduce the plots either represented in the isotopic-specific delta notation or in mole fraction. The corresponding data can be found in the folder IsotopeProfile_Vertical. The data labeling refers to the single cases (1- no fractionation; 2 - only equilibrium fractionation; 3 - only kinetic fractionation; 4 - only liquid diffusion; 5 - Reference).

Scenario:

Evaporation rate during the virtual experiment for different flow cases: With plot_evap.py and the .txt files which can be found in the folder EvaporationRate, the evaporation progression can be plotted. The labeling of the .txt files refers to the different flow cases (1 - 0.1 m/s (laminar); 2 - 0.13 m/s (laminar); 3 - 0.5 m/s (turbulent); 4 - 1 m/s (turbulent); 5 - 3 m/s (turbulent)).

The isotope profiles in the vertical and horizontal direction of the soil column (similar to Scenario 1) for selected days: With plot_cocentration_horizontal_all.py and plot_concentration_spatial_all.py the plots for the horizontal and vertical distribution of isotopologues can be generated. The corresponding data can be found in the folders IsotopeProfile_Horizontal and IsotopeProfile_Vertical. These folders are structured with subfolders containing the data of selected days of the virtual experiments (DoE - Day of Experiments), in this case, day 2, 10, and 35. The data labeling remains similar to Scenario 3a).