Models, data, and graphical results

for submitted Groundwater publication titled "Linked data-driven, physics-based modeling of groundwater-induced subsidence with application to Bangkok, Thailand"

Authors: Jenny T. Soonthornrangsan¹, Mark Bakker², Femke C. Vossepoel¹

¹Department of Geoscience & Engineering, Faculty of Civil Engineering and Geoscience, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands

²Department of Water Management, Faculty of Civil Engineering and Geoscience, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands

Current release:

Libraries: Pastas: 1.3.0 Pandas: 2.1.4 Numpy: 1.26.3

Various python scripts are provided that create different graphical results. Jupyter Note book versions are in JupyterNotebooks\.

• Figures.py: Produces the figures shown in the main text of the paper

  • Notebook version: https://githubtocolab.com/jsoontho/BKKSubPastasModels/blob/main/JupyterNotebooks/Figures.ipynb

• SubsidenceModelResults_1978-2020.py: Computes land subsidence at each well nest location. Plots annual subsidence rates (cm) bar graphs during 1978-2020 (Shown in the main text and supplemental information), error maps (subsidence RMSE for each well nest), forecast maps (2020-2060 in cm), sensitivity analysis (LCBKK013 for parameter sets Sske (clay), Sske (sand), Sskv (clay), K (clay), thickness; long run time so only calculating for one well nest at a time) (Shown in Supplemental Information)

  • Notebook version: https://githubtocolab.com/jsoontho/BKKSubPastasModels/blob/main/JupyterNotebooks/SubsidenceModelResults_1978-2020.ipynb

• Pastas_ModelGraphs_1950-2020.py: Creates Pastas models with the option to save and import the model as well as produces graphical results shown in the paper and supplemental information. Models simulate groundwater for each well nest

  • Notebook version: https://githubtocolab.com/jsoontho/BKKSubPastasModels/blob/main/JupyterNotebooks/Pastas_ModelGraphs_1950-2020.ipynb

• Pastas_ResultsMaps_1950-2020.py: Creates spatial maps that show the groundwater RMSE and t₉₀ results for each well in each well nest. Imports previously created Pastas models

  • Notebook version: https://githubtocolab.com/jsoontho/BKKSubPastasModels/blob/main/JupyterNotebooks/Pastas_ResultsMaps_1950-2020.ipynb

figures\ : figures created from the scripts

figures_test\: figures used in publication to be compared to output figures from script. Should match.

models\: Pastas models created from Pastas_ModelGraphs_1950-2020.py script (Model files end with .pas)

inputs\: inputs needed for scripts

• Groundwater observations for each well nest. Note that in the paper, each well nest is referred to without "LC" (LC******.xlsx)

• Subsidence observations for each well nest (SurveyingLevels.xlsx)

• Parameters required for subsidence calcultion for each well nest e.g. Sske, Sskv, K, thickness (SUBParameters.xlsx)

• Initial estimate of steady state heads for each well nest (SS_Head_GWWellLocs.xlsx)

• Land surface elevation for each well nest (LandSurfElev_GWWellLocs.xlsx)

  • Coastal DEM 2.1: https://www.climatecentral.org/coastaldem-v2.1
  • Kulp, S. A., and B. H. Strauss. 2021. CoastalDEM v2. 1: A high-accuracy and high-resolution global coastal elevation model trained on ICESat-2 satellite lidar.

• Location, well depth, screen depth, available observation years for each well for each well nest (GroundwaterWellLocs.xls)

• Basin-wide pumping data 1954-2060 (BasinPumping.xlsx)

  • 500,000 m³/day scenario: EstTotalPump_54-60_Int50 sheet
  • 250,000 m³/day scenario: EstTotalPump_54-60_IntF25 sheet
  • 1,000,000 m³/day scenario: EstTotalPump_54-60_IntF100 sheet
  • 500,000 to 250,000 m³/day in 2040 scenario: EstTotalPump_54-60_IntF50_25 sheet
  • No pumping scenario: EstTotalPump_54-60_IntF0 sheet