from imod.msw.fixed_format import VariableMetaData
from imod.msw.pkgbase import MetaSwapPackage
[docs]
class LanduseOptions(MetaSwapPackage):
"""
Land use options. This object is responsible for luse_svat.inp
Parameters
----------
landuse_name: array of strings (xr.DataArray)
Names of land use
vegetation_index: array of integers (xr.DataArray)
Vegetation indices
jarvis_o2_stress: array of floats (xr.DataArray)
Jarvis parameter for oxygen stress
jarvis_drought_stress: array of floats (xr.DataArray)
Jarvis parameter for drought stress
feddes_p1: array of floats (xr.DataArray)
p1 (m) in Feddes function for transpiration reduction
feddes_p2: array of floats (xr.DataArray)
p2 (m) in Feddes function for transpiration reduction
feddes_p3h: array of floats (xr.DataArray)
p3h (m) in Feddes function for transpiration reduction
feddes_p3l: array of floats (xr.DataArray)
p3l (m) in Feddes function for transpiration reduction
feddes_p4: array of floats (xr.DataArray)
p4 (m) in Feddes function for transpiration reduction
feddes_t3h: array of floats (xr.DataArray)
t3h (mm/d) in Feddes function for transpiration reduction
feddes_t3l: array of floats (xr.DataArray)
t3l (mm/d) in Feddes function for transpiration reduction
threshold_sprinkling: array of floats (xr.DataArray)
If <0, pressure head (m) at which sprinkling begins. If >0 drought
stress at which sprinkling begins.
fraction_evaporated_sprinkling: array of floats (xr.DataArray)
Fraction evaporated sprinkling water
gift: array of floats (xr.DataArray)
Gift (mm) during rotational period
gift_duration: array of floats (xr.DataArray)
Gift duration (d)
rotational_period: array of floats (xr.DataArray)
Rotational period (d)
start_sprinkling_season: array of floats (xr.DataArray)
Day of year at which sprinkling season starts (d)
end_sprinkling_season: array of floats (xr.DataArray)
Day of year at which sprinkling season ends (d)
interception_option: array of integers (xr.DataAray)
Choose interception model. 0=Rutter, 1=Von Hoyningen. NOTE: option
2=GASH, but this is not supported by MetaSWAP v8.1.0.3 and lower
interception_capacity_per_LAI: array of floats (xr.DataArray)
Interception capacity (mm/LAI) will be set for both Rutter and Von
Hoyningen.
interception_intercept: array of floats (xr.DataArray)
Intercept of the interception evaporation curve. Pun unintended.
Notes
-----
No Penman-Monteith is supported in iMOD Python, so albedo, rsc, rsw, rsoil,
kdif, and kdir cannot be specified. (We might create a seperate object for
this if there is a demand for it.)
The GASH model (interception_option = 2) and salt stress parameters Maas &
Hoffman are not supported by MetaSWAP at the time of writing this class. So
these are not supported.
"""
_metadata_dict = {
"landuse_index": VariableMetaData(6, 1, 999, int),
"landuse_name": VariableMetaData(20, None, None, str),
"vegetation_index": VariableMetaData(6, 0.0, 1e6, int),
# Jarvis stress
# Columns 33-35 and 36-38, but both F6?
"jarvis_o2_stress": VariableMetaData(3, 0.0, 1e6, float),
"jarvis_drought_stress": VariableMetaData(3, 0.0, 1e6, float),
# Feddes transpiration function
"feddes_p1": VariableMetaData(8, -160.0, 100.0, float),
"feddes_p2": VariableMetaData(8, -160.0, 100.0, float),
"feddes_p3h": VariableMetaData(8, -160.0, 0.0, float),
"feddes_p3l": VariableMetaData(8, -160.0, 0.0, float),
"feddes_p4": VariableMetaData(8, -160.0, 0.0, float),
"feddes_t3h": VariableMetaData(8, 0.1, 10.0, float),
"feddes_t3l": VariableMetaData(8, 0.1, 10.0, float),
# Sprinkling
"threshold_sprinkling": VariableMetaData(8, -160.0, 1.0, float),
"fraction_evaporated_sprinkling": VariableMetaData(8, 0.0, 1.0, float),
"gift": VariableMetaData(8, 1.0, 1000.0, float),
"gift_duration": VariableMetaData(8, 0.01, 1000.0, float),
"rotational_period": VariableMetaData(6, 1.0, 366.0, float),
"start_sprinkling_season": VariableMetaData(6, 0.0, 366.0, float),
"end_sprinkling_season": VariableMetaData(6, 0.0, 366.0, float),
# Penman-Monteith: not supported
"albedo": VariableMetaData(8, None, None, str),
"rsc": VariableMetaData(8, None, None, str),
"rsw": VariableMetaData(8, None, None, str),
"rsoil": VariableMetaData(8, None, None, str),
"kdif": VariableMetaData(8, None, None, str),
"kdir": VariableMetaData(8, None, None, str),
# Interception
"interception_option": VariableMetaData(6, 0, 2, int),
"interception_capacity_per_LAI_Rutter": VariableMetaData(8, 0.0, 10.0, float),
"interception_intercept": VariableMetaData(8, 0.0, 1.0, float),
"interception_capacity_per_LAI_VonHoyningen": VariableMetaData(
8, 0.0, 10.0, float
),
# Gash interception: not supported
"pfree": VariableMetaData(8, None, None, str),
"pstem": VariableMetaData(8, None, None, str),
"scanopy": VariableMetaData(8, None, None, str),
"avprec": VariableMetaData(8, None, None, str),
"avevap": VariableMetaData(8, None, None, str),
# Maas-Hoffman: not supported
"saltmax": VariableMetaData(8, None, None, str),
"saltslope": VariableMetaData(8, None, None, str),
}
_file_name = "luse_svat.inp"
[docs]
def __init__(
self,
landuse_name,
vegetation_index,
jarvis_o2_stress,
jarvis_drought_stress,
feddes_p1,
feddes_p2,
feddes_p3h,
feddes_p3l,
feddes_p4,
feddes_t3h,
feddes_t3l,
threshold_sprinkling,
fraction_evaporated_sprinkling,
gift,
gift_duration,
rotational_period,
start_sprinkling_season,
end_sprinkling_season,
interception_option,
interception_capacity_per_LAI,
interception_intercept,
):
super().__init__()
self.dataset["landuse_name"] = landuse_name
self.dataset["vegetation_index"] = vegetation_index
self.dataset["jarvis_o2_stress"] = jarvis_o2_stress
self.dataset["jarvis_drought_stress"] = jarvis_drought_stress
self.dataset["feddes_p1"] = feddes_p1
self.dataset["feddes_p2"] = feddes_p2
self.dataset["feddes_p3h"] = feddes_p3h
self.dataset["feddes_p3l"] = feddes_p3l
self.dataset["feddes_p4"] = feddes_p4
self.dataset["feddes_t3h"] = feddes_t3h
self.dataset["feddes_t3l"] = feddes_t3l
self.dataset["threshold_sprinkling"] = threshold_sprinkling
self.dataset["fraction_evaporated_sprinkling"] = fraction_evaporated_sprinkling
self.dataset["gift"] = gift
self.dataset["gift_duration"] = gift_duration
self.dataset["rotational_period"] = rotational_period
self.dataset["start_sprinkling_season"] = start_sprinkling_season
self.dataset["end_sprinkling_season"] = end_sprinkling_season
self.dataset["interception_option"] = interception_option
self.dataset["interception_capacity_per_LAI_Rutter"] = (
interception_capacity_per_LAI
)
self.dataset["interception_capacity_per_LAI_VonHoyningen"] = (
interception_capacity_per_LAI
)
self.dataset["interception_intercept"] = interception_intercept
self._pkgcheck()
def _render(self, file, *args):
dataframe = self.dataset.to_dataframe(
dim_order=("landuse_index",)
).reset_index()
self._check_range(dataframe)
# Find missing columns
missing_keys = set(self._metadata_dict.keys()) ^ set(dataframe.columns)
# Add missing columns
for key in missing_keys:
dataframe[key] = ""
# Reorder columns to _metadata_dict order
dataframe = dataframe[list(self._metadata_dict.keys())]
return self.write_dataframe_fixed_width(file, dataframe)
def _pkgcheck(self):
dims = self.dataset.dims
dims_expected = ("landuse_index",)
if len(set(dims) - set(dims_expected)) > 0:
raise ValueError(
f"Please provide DataArrays with dimensions {dims_expected}"
)
