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Superwell Inputs

DOI

The inputs for superwell primarily comprises of aquifer properties on a 0.5° scale, including depth to groundwater, aquifer thickness, WHYMap aquifer classes, recharge rates, porosity, and permeability. These parameters have been digitized and geo-processed from sources like Fan et al. (2013), de Graaf et al. (2015), Richts et al. (2011), Döll and Fiedler (2008), Gleeson et al., (2016), Messager et al., (2016), and Gleeson et al. (2014).

Gridded aquifer properties can be utilized independently to estimate global groundwater availability or as inputs for the superwell model. superwell simulates groundwater extraction, and estimates global extractable volumes and unit costs ($/km³) under various user-defined scenarios.

The inputs/ folder is structured as follows:

File Description
shapefiles/ Spatial preprocessing for aquifer properties
GCAM_Electricity_Rates.csv Electricity cost assumptions in 172 countries
GW_cost_model_comparison_inputs.csv Input aquifer properties for model diagnostics
Theis_well_function_table.csv Lookup table for well function relation
basin_to_country_mapping.csv Mapping between water basins and countries
continent_county_mapping.csv Mapping between continents and countries
inputs_recharge_lakes.csv Gridded aquifer properties
params.csv Model settings and scenario assumptions
prep_inputs.R Script to generate inputs file from geo-processed shapefiles/
prep_inputs_recharge_lakes.R Script to generate inputs_recharge_lakes file (with recharge and lakes) from geo-processed shapefiles/
sample_inputs.py Script to sample from inputs dataset
sampled_data_100.csv Sampled set of aquifer properties for diagnostics
sampled_data_100.png Distribution comparison of sampled data

Model Settings and Scenario Assumptions

params.csv defines model settings and scenario assumptions and contains values of the following parameters:

Parameter Description Units
Country_filter specify country filter if running for one country N/A
Basin_filter specify basin name to run all cells within one basin. See Basin_long_name column in inputs_recharge_lakes.csv N/A
Gridcell_filter specify grid cell number to run one grid cell. See GridCellID column in inputs_recharge_lakes.csv N/A
Total_Simulation_Years constrain total pumping lifetime years
Pumping_Days change pumping days per year days/year
Depletion_Limit Aquifer volume depletion limit -
Recharge_Flag Turn on/off recharge effects (default: 1 = on) N/A
Shallow_Recharge_Ratio part of recharge reducing pumping requirement (default: 0.2) N/A
Shallow_Recharge_Threshold part of ponded depth after which shallow recharge starts contributing to deep aquifer recharge (default: 0.75) N/A
Specific_weight Specific weight of water kg/(m²·s²)
Static_head Static head meters
Max_Initial_Sat_Thickness Maximum initial saturated thickness meters
Max_Lifetime_in_Years Maximum lifetime in years years
Well_Diameter Well diameter meters
Well_Yield Well yield m³/s
Pump_Efficiency Pump efficiency unitless
Energy_cost_rate Energy cost rate $/KWh
Interest_Rate Interest rate unitless
Maintenance_factor Maintenance factor unitless
Well_Install_10 Installation cost for well depth of 10 m $/m
Well_Install_20 Installation cost for well depth of 20 m $/m
Well_Install_30 Installation cost for well depth of 30 m $/m

Create Scenarios

  • Depletion_Limit and Ponded_Depth could be altered to create a scenario for a pumping regime.
  • Country_filter and Gridcell_filter could be changed to run for specific spatial delineation.
  • Total_Simulation_Years and Pumping_Days could be changed to alter temporal extent or yearly pumping duration of groundwater extraction.
  • Change Recharge_Flag to 0 to turn off recharge effects or alter Shallow_Recharge_Ratio and Shallow_Recharge_Threshold to change the recharge effects.
  • All other variables in params.csv could also be varied to create alternative scenario assumptions.

Aquifer Properties

File inputs_recharge_lakes.csv contains data on permeability, porosity, depth to groundwater, total aquifer thickness, grid area, lake area, recharge rages, and the WHY class of 235 water basins DOI.

Variable Description
GridCellID Unique identifier for each (roughly 0.5°) grid cell
Continent Continent name
Country Country name
GCAM_basin_ID Identifier for GCAM hydrologic basin
Basin_long_name Full name of the basin
WHYClass Hydrogeologic classification based on WHYMap aquifer classes (Richts et al., 2011)
Porosity Soil porosity (%) (Gleeson et al., 2014)
Permeability Soil permeability (in square meters; Gleeson et al., 2014)
Aquifer_thickness Thickness of the aquifer (in meters; de Graaf et al., 2015)
Depth_to_water Depth to groundwater (in meters; Fan et al., 2013)
Grid_area Area of the grid cell (in square meters)
Lake_area Area of the grid cell (in square meters)

Cite Inputs

Cite input data as:
Niazi, H., Watson, D., Hejazi, M., Yonkofski, C., Ferencz, S., Vernon, C., Graham, N., Wild, T., & Yoon, J. (2024). Global Geo-processed Data of Aquifer Properties by 0.5° Grid, Country and Water Basins. MultiSector Dynamics-Living, Intuitive, Value-adding, Environment. https://doi.org/10.57931/2484226
DOI

Model documentation

References

  • Fan, Y., Li, H., & Miguez-Macho, G. (2013). Global Patterns of Groundwater Table Depth. Science, 339(6122), 940-943. https://doi.org/10.1126/science.1229881
  • de Graaf, I. E. M., Sutanudjaja, E. H., van Beek, L. P. H., & Bierkens, M. F. P. (2015). A high-resolution global-scale groundwater model. Hydrol. Earth Syst. Sci., 19(2), 823-837. https://doi.org/10.5194/hess-19-823-2015
  • Gleeson, T., Moosdorf, N., Hartmann, J., & van Beek, L. P. H. (2014). A glimpse beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity. Geophysical Research Letters, 41(11), 3891-3898. https://doi.org/10.1002/2014GL059856
  • Richts, A., Struckmeier, W. F., & Zaepke, M. (2011). WHYMAP and the Groundwater Resources Map of the World 1:25,000,000. In J. A. A. Jones (Ed.), Sustaining Groundwater Resources: A Critical Element in the Global Water Crisis (pp. 159-173). Springer Netherlands. https://doi.org/10.1007/978-90-481-3426-7_10
  • Messager, M. L., Lehner, B., Grill, G., Nedeva, I., & Schmitt, O. (2016). Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications, 7(1), 13603. https://doi.org/10.1038/ncomms13603
  • Döll, P., & Fiedler, K. (2008). Global-scale modeling of groundwater recharge. Hydrol. Earth Syst. Sci., 12(3), 863-885. https://doi.org/10.5194/hess-12-863-2008
  • Gleeson, T., Befus, K. M., Jasechko, S., Luijendijk, E., & Cardenas, M. B. (2016). The global volume and distribution of modern groundwater. Nature Geoscience, 9(2), 161-167. https://doi.org/10.1038/ngeo2590