hydromt_wflow.WflowModel.setup_other_demand#

WflowModel.setup_other_demand(demand_fn: str | dict | Dataset, variables: list = ['ind_gross', 'ind_net', 'lsk_gross', 'lsk_net'], resampling_method: str = 'average')[source]#

Create water demand maps from other sources (e.g. industry, livestock).

These maps are created from a supplied dataset that either contains one or all of the following variables: - Industrial water demand - Livestock water demand - Domestic water demand (without population downsampling)

For each of these datasets/ variables a gross and a netto water demand should be provided. They can either be provided cyclic or non-cyclic. The maps are then resampled to the model resolution using the provided resampling_method.

Adds model layer:

  • {var}_gross: gross water demand [mm/day]

  • {var}_net: net water demand [mm/day]

Parameters:

  • demand_fn (Union[str, dict, xr.Dataset]) –

    The water demand dataset. This can either be the dataset directly (xr.Dataset), a string referring to an entry in the data catalog or a dictionary containing the name of the dataset (keyword: source) and any optional keyword arguments (e.g. version). The data can be cyclic (with a time dimension) or non-cyclic. Allowed cyclic data can be monthly (12) or dayofyear (365 or 366).

    • Required variables: variables listed in variables in [mm/day].

  • variables (list, optional) – The variables to be processed. Supported variables are [‘dom_gross’, ‘dom_net’, ‘ind_gross’, ‘ind_net’, ‘lsk_gross’, ‘lsk_net’] where ‘dom’ is domestic, ‘ind’ is industrial and ‘lsk’ is livestock. By default gross and net demand for industry and livestock are processed.

  • resampling_method (str, optional) – Resampling method for the demand maps, by default “average”