hydromt_wflow.workflows.soilgrids#

hydromt_wflow.workflows.soilgrids(ds: ~xarray.core.dataset.Dataset, ds_like: ~xarray.core.dataset.Dataset, ptfKsatVer: str = 'brakensiek', soil_fn: str = 'soilgrids', wflow_layers: ~typing.List[int] = [100, 300, 800], logger=<Logger hydromt_wflow.workflows.soilgrids (WARNING)>)[source]#

Return soil parameter maps at model resolution.

Based on soil properties from SoilGrids datasets. Both soilgrids 2017 and 2020 are supported. Soilgrids 2017 provides soil properties at 7 specific depths, while soilgrids_2020 provides soil properties averaged over 6 depth intervals. Ref: Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotic, A., et al.: SoilGrids250m: Global gridded soil information based on machine learning, PLoS ONE, 12, https://doi.org/10.1371/journal.pone.0169748, 2017. Ref: de Sousa, L.M., Poggio, L., Batjes, N.H., Heuvelink, G., Kempen, B., Riberio, E. and Rossiter, D., 2020. SoilGrids 2.0: producing quality-assessed soil information for the globe. SOIL Discussions, pp.1-37. https://doi.org/10.5194/soil-2020-65.

The following soil parameter maps are calculated:

  • thetaS : average saturated soil water content [m3/m3]

  • thetaR : average residual water content [m3/m3]

  • KsatVer : vertical saturated hydraulic conductivity at soil surface [mm/day]

  • SoilThickness : soil thickness [mm]

  • SoilMinThickness : minimum soil thickness [mm] (equal to SoilThickness)

  • M : model parameter [mm] that controls exponential decline of KsatVer with soil depth (fitted with curve_fit (scipy.optimize)), bounds of M are checked

  • M_ : model parameter [mm] that controls exponential decline of KsatVer with soil depth (fitted with numpy linalg regression), bounds of M_ are checked

  • M_original : M without checking bounds

  • M_original_ : M_ without checking bounds

  • f : scaling parameter controlling the decline of KsatVer [mm-1] (fitted with curve_fit (scipy.optimize)), bounds are checked

  • f_ : scaling parameter controlling the decline of KsatVer [mm-1] (fitted with numpy linalg regression), bounds are checked

  • c_ map: Brooks Corey coefficients [-] based on pore size distribution index for the wflow_sbm soil layers.

  • KsatVer_[z]cm : KsatVer [mm/day] at soil depths [z] of SoilGrids data [0.0, 5.0, 15.0, 30.0, 60.0, 100.0, 200.0]

  • wflow_soil : USDA Soil texture based on percentage clay, silt, sand mapping: [1:Clay, 2:Silty Clay, 3:Silty Clay-Loam, 4:Sandy Clay, 5:Sandy Clay-Loam, 6:Clay-Loam, 7:Silt, 8:Silt-Loam, 9:Loam, 10:Sand, 11: Loamy Sand, 12:Sandy Loam]

Parameters:

  • ds (xarray.Dataset) – Dataset containing soil properties.

  • ds_like (xarray.DataArray) – Dataset at model resolution.

  • ptfKsatVer (str) – PTF to use for calculcation KsatVer.

  • soil_fn (str) – soilgrids version {‘soilgrids’, ‘soilgrids_2020’}

  • wflow_layers (list) – List of soil layer depths [cm] for which c is calculated.

Returns:

ds_out – Dataset containing gridded soil parameters.

Return type:

xarray.Dataset