from copy import deepcopy
from typing import Optional, cast
import numpy as np
import xarray as xr
from imod.logging import init_log_decorator
from imod.mf6.boundary_condition import BoundaryCondition
from imod.mf6.dis import StructuredDiscretization
from imod.mf6.interfaces.iregridpackage import IRegridPackage
from imod.mf6.regrid.regrid_schemes import RechargeRegridMethod
from imod.mf6.utilities.imod5_converter import convert_unit_rch_rate
from imod.mf6.utilities.regrid import RegridderWeightsCache, _regrid_package_data
from imod.mf6.validation import BOUNDARY_DIMS_SCHEMA, CONC_DIMS_SCHEMA
from imod.msw.utilities.imod5_converter import (
get_cell_area_from_imod5_data,
is_msw_active_cell,
)
from imod.prepare.topsystem.allocation import ALLOCATION_OPTION, allocate_rch_cells
from imod.schemata import (
AllInsideNoDataSchema,
AllNoDataSchema,
AllValueSchema,
CoordsSchema,
DimsSchema,
DTypeSchema,
IdentityNoDataSchema,
IndexesSchema,
OtherCoordsSchema,
)
from imod.typing import GridDataArray, GridDataDict, Imod5DataDict
from imod.typing.grid import (
enforce_dim_order,
is_planar_grid,
)
[docs]
class Recharge(BoundaryCondition, IRegridPackage):
"""
Recharge Package.
Any number of RCH Packages can be specified for a single groundwater flow
model.
https://water.usgs.gov/water-resources/software/MODFLOW-6/mf6io_6.0.4.pdf#page=79
Parameters
----------
rate: array of floats (xr.DataArray)
is the recharge flux rate (LT −1). This rate is multiplied inside the
program by the surface area of the cell to calculate the volumetric
recharge rate. A time-series name may be specified.
concentration: array of floats (xr.DataArray, optional)
if this flow package is used in simulations also involving transport, then this array is used
as the concentration for inflow over this boundary.
concentration_boundary_type: ({"AUX", "AUXMIXED"}, optional)
if this flow package is used in simulations also involving transport, then this keyword specifies
how outflow over this boundary is computed.
print_input: ({True, False}, optional)
keyword to indicate that the list of recharge information will be
written to the listing file immediately after it is read.
Default is False.
print_flows: ({True, False}, optional)
Indicates that the list of recharge flow rates will be printed to the
listing file for every stress period time step in which "BUDGET PRINT"is
specified in Output Control. If there is no Output Control option and
PRINT FLOWS is specified, then flow rates are printed for the last time
step of each stress period.
Default is False.
save_flows: ({True, False}, optional)
Indicates that recharge flow terms will be written to the file specified
with "BUDGET FILEOUT" in Output Control.
Default is False.
observations: [Not yet supported.]
Default is None.
validate: {True, False}
Flag to indicate whether the package should be validated upon
initialization. This raises a ValidationError if package input is
provided in the wrong manner. Defaults to True.
repeat_stress: Optional[xr.DataArray] of datetimes
Used to repeat data for e.g. repeating stress periods such as
seasonality without duplicating the values. The DataArray should have
dimensions ``("repeat", "repeat_items")``. The ``repeat_items``
dimension should have size 2: the first value is the "key", the second
value is the "value". For the "key" datetime, the data of the "value"
datetime will be used. Can also be set with a dictionary using the
``set_repeat_stress`` method.
fixed_cell: ({True, False}, optional)
indicates that recharge will not be reassigned to a cell underlying the
cell specified in the list if the specified cell is inactive.
"""
_pkg_id = "rch"
_period_data = ("rate",)
_keyword_map = {}
_init_schemata = {
"rate": [
DTypeSchema(np.floating),
IndexesSchema(),
CoordsSchema(("layer",)),
BOUNDARY_DIMS_SCHEMA,
],
"concentration": [
DTypeSchema(np.floating),
IndexesSchema(),
CoordsSchema(
(
"species",
"layer",
)
),
CONC_DIMS_SCHEMA,
],
"print_flows": [DTypeSchema(np.bool_), DimsSchema()],
"save_flows": [DTypeSchema(np.bool_), DimsSchema()],
}
_write_schemata = {
"rate": [
OtherCoordsSchema("idomain"),
AllNoDataSchema(), # Check for all nan, can occur while clipping
AllInsideNoDataSchema(other="idomain", is_other_notnull=(">", 0)),
],
"concentration": [IdentityNoDataSchema("rate"), AllValueSchema(">=", 0.0)],
}
_template = BoundaryCondition._initialize_template(_pkg_id)
_auxiliary_data = {"concentration": "species"}
_regrid_method = RechargeRegridMethod()
[docs]
@init_log_decorator()
def __init__(
self,
rate,
concentration=None,
concentration_boundary_type="auxmixed",
print_input=False,
print_flows=False,
save_flows=False,
observations=None,
validate: bool = True,
repeat_stress=None,
fixed_cell: bool = False,
):
dict_dataset = {
"rate": rate,
"concentration": concentration,
"concentration_boundary_type": concentration_boundary_type,
"print_input": print_input,
"print_flows": print_flows,
"save_flows": save_flows,
"observations": observations,
"repeat_stress": repeat_stress,
"fixed_cell": fixed_cell,
}
super().__init__(dict_dataset)
self._validate_init_schemata(validate)
def _validate(self, schemata, **kwargs):
# Insert additional kwargs
kwargs["rate"] = self["rate"]
errors = super()._validate(schemata, **kwargs)
return errors
[docs]
@classmethod
def from_imod5_data(
cls,
imod5_data: dict[str, dict[str, GridDataArray]],
target_dis: StructuredDiscretization,
regridder_types: Optional[RechargeRegridMethod] = None,
regrid_cache: RegridderWeightsCache = RegridderWeightsCache(),
) -> "Recharge":
"""
Construct an rch-package from iMOD5 data, loaded with the
:func:`imod.formats.prj.open_projectfile_data` function.
.. note::
The method expects the iMOD5 model to be fully 3D, not quasi-3D.
Parameters
----------
imod5_data: dict
Dictionary with iMOD5 data. This can be constructed from the
:func:`imod.formats.prj.open_projectfile_data` method.
target_dis: GridDataArray
The discretization package for the simulation. Its grid does not
need to be identical to one of the input grids.
regridder_types: RechargeRegridMethod, optional
Optional dataclass with regridder types for a specific variable.
Use this to override default regridding methods.
regrid_cache: RegridderWeightsCache, optional
stores regridder weights for different regridders. Can be used to speed up regridding,
if the same regridders are used several times for regridding different arrays.
Returns
-------
Modflow 6 rch package.
"""
new_idomain = target_dis.dataset["idomain"]
data = {
"rate": convert_unit_rch_rate(imod5_data["rch"]["rate"]),
}
new_package_data = {}
# first regrid the inputs to the target grid.
if regridder_types is None:
regridder_settings = Recharge.get_regrid_methods()
new_package_data = _regrid_package_data(
data, new_idomain, regridder_settings, regrid_cache, {}
)
# if rate has only layer 0, then it is planar.
if is_planar_grid(new_package_data["rate"]):
if "layer" in new_package_data["rate"].dims:
planar_rate_regridded = new_package_data["rate"].isel(
layer=0, drop=True
)
else:
planar_rate_regridded = new_package_data["rate"]
# create an array indicating in which cells rch is active
is_rch_cell = allocate_rch_cells(
ALLOCATION_OPTION.at_first_active,
new_idomain > 0,
planar_rate_regridded,
)
# remove rch from cells where it is not allocated and broadcast over layers.
rch_rate = planar_rate_regridded.where(is_rch_cell)
rch_rate = enforce_dim_order(rch_rate)
new_package_data["rate"] = rch_rate
return cls(**new_package_data, validate=True, fixed_cell=False)
[docs]
@classmethod
def from_imod5_cap_data(
cls,
imod5_data: Imod5DataDict,
target_dis: StructuredDiscretization,
) -> "Recharge":
"""
Construct an rch-package from iMOD5 data in the CAP package, loaded with
the :func:`imod.formats.prj.open_projectfile_data` function. Package is
used to couple MODFLOW6 to MetaSWAP models. Active cells will have a
recharge rate of 0.0.
"""
cap_data = cast(GridDataDict, imod5_data["cap"])
msw_area = get_cell_area_from_imod5_data(cap_data)
msw_active = is_msw_active_cell(target_dis, cap_data, msw_area)
active = msw_active.all
data = {}
layer_da = xr.full_like(
target_dis.dataset.coords["layer"], np.nan, dtype=np.float64
)
layer_da.loc[{"layer": 1}] = 0.0
data["rate"] = layer_da.where(active)
return cls(**data, validate=True, fixed_cell=False)
@classmethod
def get_regrid_methods(cls) -> RechargeRegridMethod:
return deepcopy(cls._regrid_method)
