"""Functions for individual soil parameters."""
import numpy as np
import xarray as xr
from hydromt import raster
__all__ = [
"ksat_horizontal_ratio",
"ksatver_vegetation",
]
def ksat_horizontal_ratio(
da: xr.DataArray,
ds_like: xr.Dataset,
resampling_method: str,
) -> xr.DataArray:
"""Create KsatHorfrac map.
Based on the data properties of the WflowModel.
Parameters
----------
da : xr.DataArray
KsathorFrac values, i.e. an xarray.DataArray containing the values from \
the predefined KsatHorFrac map.
ds_like : xr.Dataset
Dataset at model resolution.
resampling_method : str
Scale method when up- or downscaling.
Returns
-------
xr.DataArray
A xarray DataArray containing the scaled KsatHorFrac values.
"""
# Transfer data to a logarithmic scale
da = np.log10(da)
# Reproject the data
da = da.raster.reproject_like(
ds_like,
method=resampling_method,
)
# Scale the data back to normal values
da = np.power(10, da)
# Fill all nodata holes in the map
da = da.interpolate_na(dim=da.raster.x_dim, method="linear")
da = da.interpolate_na(
dim=da.raster.x_dim, method="nearest", fill_value="extrapolate"
)
# Set the default no fill value for doubles
da = da.fillna(-9999.0)
da.raster.set_nodata(-9999.0)
# Return as a dataset to be used for 'set_grid'
return da
def calc_kv_at_depth(depth, kv_0, f):
"""
Calculate the kv value at a certain depth.
Value is based on the kv at the surface, the f parameter that describes the
exponential decline and the depth.
Parameters
----------
depth
Depth at which kv needs to be calculated
kv_0
The vertical conductivity at the surface
f
The value describing the exponential decline
Returns
-------
kv_z
The kv value at the requested depth
"""
kv_z = kv_0 * np.exp(-f * depth)
return kv_z
def calc_kvfrac(kv_depth, target):
"""Calculate the kvfrac.
Based on the kv value at a certain depth and the target value.
Parameters
----------
kv_depth:
Value of kv at a certain depth
target:
Target kv value
Returns
-------
kvfrac:
The value which kv_depths needs to be multiplied with to reach the target value
"""
kvfrac = target / kv_depth
return kvfrac
def update_kvfrac(
ds_model, kv0_mask, f_mask, wflow_thicknesslayers, target_conductivity
):
"""
Calculate kvfrac values for each layer.
Done such that the bottom of the layer equals to the target_conductivity.
Calculation assumes exponentially declining vertical conductivities, based on the f
parameter. If no target_conductivity is specified, kvfrac is set to be equal to 1.
Parameters
----------
ds_model
Dataset of the wflow model
kv0_mask
Values of vertical conductivity at the surface, masked to paddy locations
f_mask
Values of the f parameter, masked to paddy locations
wflow_thicknesslayers
List of requested layers in the wflow model
target_conductivity
List of target conductivities for each layer (None if no target value is
requested)
Returns
-------
da_kvfrac
Maps for each layer with the required kvfrac value
"""
# Convert to np.array
wflow_thicknesslayers = np.array(wflow_thicknesslayers)
target_conductivity = np.array(target_conductivity)
# Prepare empty dataarray
da_kvfrac = raster.full_like(ds_model["soil_brooks_corey_c"])
# Set all values to 1
da_kvfrac = da_kvfrac.where(ds_model["elevtn"].raster.mask_nodata().isnull(), 1.0)
# Get the actual depths
wflow_depths = np.cumsum(wflow_thicknesslayers)
# Find the index of the layers where a kvfrac should be set
idx = target_conductivity.nonzero()[0]
# Loop through the target_conductivity values
for idx, target in enumerate(target_conductivity):
if target is not None:
depth = wflow_depths[idx]
# Calculate the kv at that depth (only for the pixels that have paddy
# fields)
kv_depth = calc_kv_at_depth(depth=depth, kv_0=kv0_mask, f=f_mask)
paddy_values = calc_kvfrac(kv_depth=kv_depth, target=target)
# Set the values in the correct places
kvfrac = xr.where(
paddy_values.raster.mask_nodata().isnull(),
da_kvfrac.loc[dict(layer=idx)],
paddy_values,
)
kvfrac = kvfrac.fillna(-9999)
kvfrac.raster.set_nodata(-9999)
# Update layer in dataarray
da_kvfrac.loc[dict(layer=idx)] = kvfrac
return da_kvfrac
def get_ks_veg(ksat_vertical, sndppt, LAI, alfa=4.5, beta=5):
"""
Based on Bonetti et al. (2021) https://www.nature.com/articles/s43247-021-00180-0.
Parameters
----------
ksat_vertical : [xr.DataSet, float]
saturated hydraulic conductivity from PTF based on soil properties [cm/d].
sndppt : [xr.DataSet, float]
percentage sand [%].
LAI : [xr.DataSet, float]
Mean annual leaf area index [-].
alfa : float, optional
Shape parameter. The default is 4.5 when using LAI.
beta : float, optional
Shape parameter. The default is 5 when using LAI.
Returns
-------
ks : [xr.DataSet, float]
saturated hydraulic conductivity based on soil and vegetation [cm/d].
"""
# get the saturated hydraulic conductivity with fully developed vegetation.
ksmax = 10 ** (
3.5 - 1.5 * sndppt**0.13 + np.log10(ksat_vertical.where(ksat_vertical > 0))
)
# get the saturated hydraulic conductivity based on soil and
# vegetation mean LAI
ks = ksmax - (ksmax - ksat_vertical) / (1 + (LAI / alfa) ** beta)
return ks
[docs]
def ksatver_vegetation(
ds_like: xr.Dataset,
sndppt: xr.Dataset,
alfa: float = 4.5,
beta: float = 5,
) -> xr.DataArray:
"""
Calculate saturated hydraulic conductivity based on soil and vegetation [mm/d].
based on Bonetti et al. (2021) https://www.nature.com/articles/s43247-021-00180-0.
Parameters
----------
ds_like : xr.Dataset
Dataset at model resolution.
The required variables in ds_like are LAI [-],
ksat_vertical [mm/d] and subcatchment
sndppt : [xr.DataSet, float]
percentage sand [%].
alfa : float, optional
Shape parameter. The default is 4.5 when using LAI.
beta : float, optional
Shape parameter. The default is 5 when using LAI.
Returns
-------
ks : [xr.DataSet, float]
saturated hydraulic conductivity based on soil and vegetation [mm/d].
"""
sndppt = sndppt.where(sndppt != sndppt._FillValue, np.nan)
# reproject to model resolution
sndppt = sndppt.raster.reproject_like(ds_like, method="average")
sndppt.raster.set_nodata(np.nan)
# interpolate to fill missing values
sndppt = sndppt.raster.interpolate_na("rio_idw")
# mask outside basin
sndppt = sndppt.where(ds_like["basins"] > 0)
# mean annual lai is required (see fig 1 in Bonetti et al. 2021)
LAI_mean = ds_like["LAI"].mean("time")
LAI_mean.raster.set_nodata(255.0)
# in this function, Ksatver should be provided in cm/d
ksatver_vegetation = get_ks_veg(
ds_like["ksat_vertical"] / 10, sndppt, LAI_mean, alfa, beta
)
# convert back from cm/d to mm/d
ksatver_vegetation = ksatver_vegetation * 10
return ksatver_vegetation
