Model initialization#

Setup#

In this section all the setup options that can be passed to the setup dictionary in order to initialize the Model object will be presented.

Structure options#

structure: str, default “gr-a”

Model structure. There are 4 different structures available:

  • “gr-a”

    4 parameters and 3 states structure.

  • “gr-b”

    4 parameters and 4 states structure.

  • “gr-c”

    5 parameters and 5 states structure.

  • “gr-d”

    3 parameters and 3 states structure.

Note

See the User Guide section: Model structure for more.

Time options#

dt: int, default 3600

Simulation time step in seconds.

start_time: str

Simulation start time. Required format is either %Y%m%d%H%M or %Y-%m-%d %H%M.

end_time: str

Simulation end time. Required format is either %Y%m%d%H%M or %Y-%m-%d %H%M.

Input data options#

read_qobs: bool, default False

Enables the reading of observed dicharge files.

qobs_directory: str

Path to the directory with the observed discharges files.

read_prcp: bool, default False

Enables the reading of precipitation files.

prcp_format: str, default “tif”

Precipitation files format selection. Possible formats are:

  • “tif”

    Tag Image File Format

  • “nc”

    Warning

    NetCDF. Section in development.

prcp_conversion_factor: float, default 1.0

Precipitaton conversion factor. Precipitation will be multiplied by the conversion factor.

prcp_directory: str

Path to the directory with precipitaton files.

read_pet: bool, default False

Enables the reading of evapotranspiration (PET) files.

pet_format: str, default “tif”

PET files format selection. Possible formats are:

  • “tif”

    Tag Image File Format

  • “nc”

    Warning

    NetCDF. Section in development.

pet_conversion_factor: float, default 1.0

PET conversion factor. PET will be multiplied by the conversion factor.

daily_interannual_pet: bool, default False

Enables the reading of PET in the form of interannual PET.

pet_directory: str

Path to the directory with PET files.

sparse_storage: bool, default False

Enables the sparse storage of atmospheric data (i.e. precipitation and PET) and simulated discharge.

mean_forcing: bool, default True

Enables the calculation of average atmospheric data (i.e. precipitation and PET) by catchment.

read_descriptor: bool, default False

Enables the reading of physiographic descriptor files.

descriptor_directory: str

Path to the directory with physiographic descriptor files.

descriptor_name: list[str]

List of physiographic descriptor names (the size of the list will be used to allocate the descriptor array and used to read the corresponding files).

Note

See the User Guide section: Model input data convention for more.

Output options#

save_qsim_domain: bool, default False

Enables the save of simulated discharge on the entire domain.

save_net_prcp_domain: bool, default False

Enables the save of simulated net precipitation on the entire domain.

Mesh#

In this section all the mesh options that can be passed to the mesh dictionary needed to initialize the Model object will be presented.

Spatial options#

dx: float, default 1000

Simulation spatial step in meters.

nrow: int

Number of rows in the grid.

ncol: int

Number of columns in the grid.

xmin: float

Lower left corner x value. This value depends on the projection system used.

ymax: float

Upper left corner y value. This value depends on the projection system used.

Gauge options#

ng: int

Number of gauges in the grid.

gauge_pos: numpy.ndarray, shape=(2, ng), dtype=np.int32

Position of gauges in the grid.

code: numpy.ndarray, shape=(20, ng), dtype=U

Code of gauges.

area: numpy.ndarray, shape=(ng), dtype=np.float32

Area of gauges in square meters.

Grid options#

flwdir: numpy.ndarray, shape=(nrow, ncol), dtype=np.int32

Grid flow directions. smash is using a D8 flow directions with the following convention.

../../_images/flwdir_convention.png
flwacc: numpy.ndarray, shape=(nrow, ncol), dtype=np.int32

Grid flow accumulation in number of cells.

flwdst: numpy.ndarray, shape=(nrow, ncol), dtype=np.float32

Grid flow distances from the most downstream outlet for each group of nested catchments.

path: numpy.ndarray, shape=(2, nrow * ncol), dtype=np.int32

Grid calculation path. Sorting grid cells in ascending order of flow accumulation.

Active cell options#

nac: int

Number of active cells.

active_cell: numpy.ndarray, shape=(nrow, ncol), dtype=np.int32

Grid active cells. Cells that contribute to the discharge of any gauge on the grid.