imod.mf6.StorageCoefficient#

class imod.mf6.StorageCoefficient(storage_coefficient, specific_yield, transient, convertible)[source]#

Storage Package with a storage coefficient. Be careful, this is not the same as the specific storage.

From wikipedia (https://en.wikipedia.org/wiki/Specific_storage):

“Storativity or the storage coefficient is the volume of water released from storage per unit decline in hydraulic head in the aquifer, per unit area of the aquifer. Storativity is a dimensionless quantity, and is always greater than 0.

Under confined conditions:

S = Ss * b, where S is the storage coefficient, Ss the specific storage, and b the aquifer thickness.

Under unconfined conditions:

S = Sy, where Sy is the specific yield”

If the STO Package is not included for a model, then storage changes will not be calculated, and thus, the model will be steady state. Only one STO Package can be specified for a GWF model.

Parameters
  • storage_coefficient (array of floats (xr.DataArray)) – Is specific storage. Storage coefficient values must be greater than or equal to 0. (ss)

  • specific_yield (array of floats (xr.DataArray)) – Is specific yield. Specific yield values must be greater than or equal to 0. Specific yield does not have to be specified if there are no convertible cells (convertible=0 in every cell). (sy)

  • convertible (array of int (xr.DataArray)) – Is a flag for each cell that specifies whether or not a cell is convertible for the storage calculation. 0 indicates confined storage is used. >0 indicates confined storage is used when head is above cell top and a mixed formulation of unconfined and confined storage is used when head is below cell top. (iconvert)

  • transient (({True, False})) – Boolean to indicate if the model is transient or steady-state.

__init__(storage_coefficient, specific_yield, transient, convertible)[source]#

Methods

__init__(storage_coefficient, ...)

from_file(path, **kwargs)

Loads an imod mf6 package from a file (currently only netcdf and zarr are supported).

isel()

render(directory, pkgname, globaltimes, binary)

sel()

to_sparse(arrdict, layer)

Convert from dense arrays to list based input

write(directory, pkgname, globaltimes, binary)

write_binary_griddata(outpath, da, dtype)

write_blockfile(directory, pkgname, ...)

write_netcdf(directory, pkgname[, ...])

Write to netcdf.

write_text_griddata(outpath, da, dtype)