# noqa: D100
from __future__ import annotations
import warnings
from typing import cast
import numpy as np
import xarray
import xclim.indices.run_length as rl
from xclim.core.calendar import parse_offset, select_time
from xclim.core.units import (
amount2lwethickness,
convert_units_to,
declare_units,
rate2amount,
to_agg_units,
)
from xclim.core.utils import DateStr, DayOfYearStr, Quantified
from xclim.indices._conversion import potential_evapotranspiration
from xclim.indices._simple import tn_min
from xclim.indices._threshold import (
first_day_temperature_above,
first_day_temperature_below,
)
from xclim.indices.generic import aggregate_between_dates, get_zones
from xclim.indices.helpers import _gather_lat, day_lengths
from xclim.indices.stats import standardized_index
# Frequencies : YS: year start, QS-DEC: seasons starting in december, MS: month start
# See https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
# -------------------------------------------------- #
# ATTENTION: ASSUME ALL INDICES WRONG UNTIL TESTED ! #
# -------------------------------------------------- #
__all__ = [
"biologically_effective_degree_days",
"cool_night_index",
"corn_heat_units",
"dryness_index",
"effective_growing_degree_days",
"hardiness_zones",
"huglin_index",
"latitude_temperature_index",
"qian_weighted_mean_average",
"rain_season",
"standardized_precipitation_evapotranspiration_index",
"standardized_precipitation_index",
"water_budget",
]
[docs]
@declare_units(
tasmin="[temperature]",
tasmax="[temperature]",
thresh_tasmin="[temperature]",
thresh_tasmax="[temperature]",
)
def corn_heat_units(
tasmin: xarray.DataArray,
tasmax: xarray.DataArray,
thresh_tasmin: Quantified = "4.44 degC",
thresh_tasmax: Quantified = "10 degC",
) -> xarray.DataArray:
r"""Corn heat units.
Temperature-based index used to estimate the development of corn crops.
Formula adapted from :cite:t:`bootsma_risk_1999`.
Parameters
----------
tasmin : xarray.DataArray
Minimum daily temperature.
tasmax : xarray.DataArray
Maximum daily temperature.
thresh_tasmin : Quantified
The minimum temperature threshold needed for corn growth.
thresh_tasmax : Quantified
The maximum temperature threshold needed for corn growth.
Returns
-------
xarray.DataArray, [unitless]
Daily corn heat units.
Notes
-----
Formula used in calculating the Corn Heat Units for the Agroclimatic Atlas of Quebec :cite:p:`audet_atlas_2012`.
The thresholds of 4.44°C for minimum temperatures and 10°C for maximum temperatures were selected following
the assumption that no growth occurs below these values.
Let :math:`TX_{i}` and :math:`TN_{i}` be the daily maximum and minimum temperature at day :math:`i`. Then the daily
corn heat unit is:
.. math::
CHU_i = \frac{YX_{i} + YN_{i}}{2}
with
.. math::
YX_i & = 3.33(TX_i -10) - 0.084(TX_i -10)^2, &\text{if } TX_i > 10°C
YN_i & = 1.8(TN_i -4.44), &\text{if } TN_i > 4.44°C
Where :math:`YX_{i}` and :math:`YN_{i}` is 0 when :math:`TX_i \leq 10°C` and :math:`TN_i \leq 4.44°C`, respectively.
References
----------
:cite:cts:`audet_atlas_2012,bootsma_risk_1999`
"""
tasmin = convert_units_to(tasmin, "degC")
tasmax = convert_units_to(tasmax, "degC")
thresh_tasmin = convert_units_to(thresh_tasmin, "degC")
thresh_tasmax = convert_units_to(thresh_tasmax, "degC")
mask_tasmin = tasmin > thresh_tasmin
mask_tasmax = tasmax > thresh_tasmax
chu: xarray.DataArray = (
xarray.where(mask_tasmin, 1.8 * (tasmin - thresh_tasmin), 0)
+ xarray.where(
mask_tasmax,
(3.33 * (tasmax - thresh_tasmax) - 0.084 * (tasmax - thresh_tasmax) ** 2),
0,
)
) / 2
chu = chu.assign_attrs(units="")
return chu
[docs]
@declare_units(
tas="[temperature]",
tasmax="[temperature]",
lat="[]",
thresh="[temperature]",
)
def huglin_index(
tas: xarray.DataArray,
tasmax: xarray.DataArray,
lat: xarray.DataArray | None = None,
thresh: Quantified = "10 degC",
method: str = "smoothed",
start_date: DayOfYearStr = "04-01",
end_date: DayOfYearStr = "10-01",
freq: str = "YS",
) -> xarray.DataArray:
r"""Huglin Heliothermal Index.
Growing-degree days with a base of 10°C and adjusted for latitudes between 40°N and 50°N for April-September
(Northern Hemisphere; October-March in Southern Hemisphere). Originally proposed in :cite:t:`huglin_nouveau_1978`.
Used as a heat-summation metric in viticulture agroclimatology.
Parameters
----------
tas : xarray.DataArray
Mean daily temperature.
tasmax : xarray.DataArray
Maximum daily temperature.
lat : xarray.DataArray
Latitude coordinate.
If None, a CF-conformant "latitude" field must be available within the passed DataArray.
thresh : Quantified
The temperature threshold.
method : {"smoothed", "icclim", "jones"}
The formula to use for the latitude coefficient calculation.
start_date : DayOfYearStr
The hemisphere-based start date to consider (north = April, south = October).
end_date : DayOfYearStr
The hemisphere-based start date to consider (north = October, south = April). This date is non-inclusive.
freq : str
Resampling frequency (default: "YS"; For Southern Hemisphere, should be "YS-JUL").
Returns
-------
xarray.DataArray, [unitless]
Huglin heliothermal index (HI).
Notes
-----
Let :math:`TX_{i}` and :math:`TG_{i}` be the daily maximum and mean temperature at day :math:`i` and
:math:`T_{thresh}` the base threshold needed for heat summation (typically, 10 degC). A day-length multiplication,
:math:`k`, based on latitude, :math:`lat`, is also considered. Then the Huglin heliothermal index for dates between
1 April and 30 September is:
.. math::
HI = \sum_{i=\text{April 1}}^{\text{September 30}} \left( \frac{TX_i + TG_i)}{2} - T_{thresh} \right) * k
For the `smoothed` method, the day-length multiplication factor, :math:`k`, is calculated as follows:
.. math::
k = f(lat) = \begin{cases}
1, & \text{if } |lat| <= 40 \\
1 + ((abs(lat) - 40) / 10) * 0.06, & \text{if } 40 < |lat| <= 50 \\
NaN, & \text{if } |lat| > 50 \\
\end{cases}
For compatibility with ICCLIM, `end_date` should be set to `11-01`, `method` should be set to `icclim`. The
day-length multiplication factor, :math:`k`, is calculated as follows:
.. math::
k = f(lat) = \begin{cases}
1.0, & \text{if } |lat| <= 40 \\
1.02, & \text{if } 40 < |lat| <= 42 \\
1.03, & \text{if } 42 < |lat| <= 44 \\
1.04, & \text{if } 44 < |lat| <= 46 \\
1.05, & \text{if } 46 < |lat| <= 48 \\
1.06, & \text{if } 48 < |lat| <= 50 \\
NaN, & \text{if } |lat| > 50 \\
\end{cases}
A more robust day-length calculation based on latitude, calendar, day-of-year, and obliquity is available with
`method="jones"`. See: :py:func:`xclim.indices.generic.day_lengths` or :cite:t:`hall_spatial_2010` for more
information.
References
----------
:cite:cts:`huglin_nouveau_1978, hall_spatial_2010`
"""
tas = convert_units_to(tas, "degC")
tasmax = convert_units_to(tasmax, "degC")
thresh = convert_units_to(thresh, "degC")
if lat is None:
lat = _gather_lat(tas)
if method.lower() == "smoothed":
lat_mask = abs(lat) <= 50
lat_coefficient = ((abs(lat) - 40) / 10).clip(min=0) * 0.06
k = 1 + xarray.where(lat_mask, lat_coefficient, np.NaN)
k_aggregated = 1
elif method.lower() == "icclim":
k_f = [0, 0.02, 0.03, 0.04, 0.05, 0.06]
k = 1 + xarray.where(
abs(lat) <= 40,
k_f[0],
xarray.where(
(40 < abs(lat)) & (abs(lat) <= 42),
k_f[1],
xarray.where(
(42 < abs(lat)) & (abs(lat) <= 44),
k_f[2],
xarray.where(
(44 < abs(lat)) & (abs(lat) <= 46),
k_f[3],
xarray.where(
(46 < abs(lat)) & (abs(lat) <= 48),
k_f[4],
xarray.where(
(48 < abs(lat)) & (abs(lat) <= 50), k_f[5], np.NaN
),
),
),
),
),
)
k_aggregated = 1
elif method.lower() == "jones":
day_length = (
select_time(
day_lengths(dates=tas.time, lat=lat, method="simple"),
date_bounds=(start_date, end_date),
include_bounds=(True, False),
)
.resample(time=freq)
.sum()
)
k = 1
k_aggregated = 2.8311e-4 * day_length + 0.30834
else:
raise NotImplementedError(f"'{method}' method is not implemented.")
hi: xarray.DataArray = (((tas + tasmax) / 2) - thresh).clip(min=0) * k
hi = (
select_time(
hi, date_bounds=(start_date, end_date), include_bounds=(True, False)
)
.resample(time=freq)
.sum()
* k_aggregated
)
hi = hi.assign_attrs(units="")
return hi
[docs]
@declare_units(
tasmin="[temperature]",
tasmax="[temperature]",
lat="[]",
thresh_tasmin="[temperature]",
low_dtr="[temperature]",
high_dtr="[temperature]",
max_daily_degree_days="[temperature]",
)
def biologically_effective_degree_days(
tasmin: xarray.DataArray,
tasmax: xarray.DataArray,
lat: xarray.DataArray | None = None,
thresh_tasmin: Quantified = "10 degC",
method: str = "gladstones",
low_dtr: Quantified = "10 degC",
high_dtr: Quantified = "13 degC",
max_daily_degree_days: Quantified = "9 degC",
start_date: DayOfYearStr = "04-01",
end_date: DayOfYearStr = "11-01",
freq: str = "YS",
) -> xarray.DataArray:
r"""Biologically effective growing degree days.
Growing-degree days with a base of 10°C and an upper limit of 19°C and adjusted for latitudes between 40°N and 50°N
for April to October (Northern Hemisphere; October to April in Southern Hemisphere). A temperature range adjustment
also promotes small and large swings in daily temperature range. Used as a heat-summation metric in viticulture
agroclimatology.
Parameters
----------
tasmin : xarray.DataArray
Minimum daily temperature.
tasmax : xarray.DataArray
Maximum daily temperature.
lat : xarray.DataArray, optional
Latitude coordinate.
If None and method in ["gladstones", "icclim"],
a CF-conformant "latitude" field must be available within the passed DataArray.
thresh_tasmin : Quantified
The minimum temperature threshold.
method : {"gladstones", "icclim", "jones"}
The formula to use for the calculation.
The "gladstones" integrates a daily temperature range and latitude coefficient. End_date should be "11-01".
The "icclim" method ignores daily temperature range and latitude coefficient. End date should be "10-01".
The "jones" method integrates axial tilt, latitude, and day-of-year on coefficient. End_date should be "11-01".
low_dtr : Quantified
The lower bound for daily temperature range adjustment (default: 10°C).
high_dtr : Quantified
The higher bound for daily temperature range adjustment (default: 13°C).
max_daily_degree_days : Quantified
The maximum amount of biologically effective degrees days that can be summed daily.
start_date : DayOfYearStr
The hemisphere-based start date to consider (north = April, south = October).
end_date : DayOfYearStr
The hemisphere-based start date to consider (north = October, south = April). This date is non-inclusive.
freq : str
Resampling frequency (default: "YS"; For Southern Hemisphere, should be "YS-JUL").
Returns
-------
xarray.DataArray, [K days]
Biologically effective growing degree days (BEDD)
Warnings
--------
Lat coordinate must be provided if method is "gladstones" or "jones".
Notes
-----
The tasmax ceiling of 19°C is assumed to be the max temperature beyond which no further gains from daily temperature
occur. Indice originally published in :cite:t:`gladstones_viticulture_1992`.
Let :math:`TX_{i}` and :math:`TN_{i}` be the daily maximum and minimum temperature at day :math:`i`, :math:`lat`
the latitude of the point of interest, :math:`degdays_{max}` the maximum amount of degrees that can be summed per
day (typically, 9). Then the sum of daily biologically effective growing degree day (BEDD) units between 1 April and
31 October is:
.. math::
BEDD_i = \sum_{i=\text{April 1}}^{\text{October 31}} min\left( \left( max\left( \frac{TX_i + TN_i)}{2} - 10, 0 \right) * k \right) + TR_{adj}, degdays_{max}\right)
.. math::
TR_{adj} = f(TX_{i}, TN_{i}) = \begin{cases}
0.25(TX_{i} - TN_{i} - 13), & \text{if } (TX_{i} - TN_{i}) > 13 \\
0, & \text{if } 10 < (TX_{i} - TN_{i}) < 13\\
0.25(TX_{i} - TN_{i} - 10), & \text{if } (TX_{i} - TN_{i}) < 10 \\
\end{cases}
.. math::
k = f(lat) = 1 + \left(\frac{\left| lat \right|}{50} * 0.06, \text{if }40 < |lat| <50, \text{else } 0\right)
A second version of the BEDD (`method="icclim"`) does not consider :math:`TR_{adj}` and :math:`k` and employs a
different end date (30 September) :cite:p:`project_team_eca&d_algorithm_2013`.
The simplified formula is as follows:
.. math::
BEDD_i = \sum_{i=\text{April 1}}^{\text{September 30}} min\left( max\left(\frac{TX_i + TN_i)}{2} - 10, 0\right), degdays_{max}\right)
References
----------
:cite:cts:`gladstones_viticulture_1992,project_team_eca&d_algorithm_2013`
"""
tasmin = convert_units_to(tasmin, "degC")
tasmax = convert_units_to(tasmax, "degC")
thresh_tasmin = convert_units_to(thresh_tasmin, "degC")
max_daily_degree_days = convert_units_to(max_daily_degree_days, "degC")
if method.lower() in ["gladstones", "jones"]:
low_dtr = convert_units_to(low_dtr, "degC")
high_dtr = convert_units_to(high_dtr, "degC")
dtr = tasmax - tasmin
tr_adj = 0.25 * xarray.where(
dtr > high_dtr,
dtr - high_dtr,
xarray.where(dtr < low_dtr, dtr - low_dtr, 0),
)
if lat is None:
lat = _gather_lat(tasmin)
if method.lower() == "gladstones":
if isinstance(lat, (int, float)):
lat = xarray.DataArray(lat)
lat_mask = abs(lat) <= 50
k = 1 + xarray.where(
lat_mask, ((abs(lat) - 40) * 0.06 / 10).clip(0, None), 0
)
k_aggregated = 1
else:
day_length = (
select_time(
day_lengths(dates=tasmin.time, lat=lat, method="simple"),
date_bounds=(start_date, end_date),
include_bounds=(True, False),
)
.resample(time=freq)
.sum()
)
k = 1
k_huglin = 2.8311e-4 * day_length + 0.30834
k_aggregated = 1.1135 * k_huglin - 0.1352
elif method.lower() == "icclim":
k = 1
tr_adj = 0
k_aggregated = 1
else:
raise NotImplementedError()
bedd: xarray.DataArray = (
(((tasmin + tasmax) / 2) - thresh_tasmin).clip(min=0) * k + tr_adj
).clip(max=max_daily_degree_days)
bedd = (
select_time(
bedd, date_bounds=(start_date, end_date), include_bounds=(True, False)
)
.resample(time=freq)
.sum()
* k_aggregated
)
bedd = bedd.assign_attrs(units="K days")
return bedd
[docs]
@declare_units(tasmin="[temperature]")
def cool_night_index(
tasmin: xarray.DataArray,
lat: xarray.DataArray | str | None = None,
freq: str = "YS",
) -> xarray.DataArray:
"""Cool Night Index.
Mean minimum temperature for September (northern hemisphere) or March (Southern hemisphere).
Used in calculating the Géoviticulture Multicriteria Classification System (:cite:t:`tonietto_multicriteria_2004`).
Warnings
--------
This indice is calculated using minimum temperature resampled to monthly average, and therefore will accept monthly
averaged data as inputs.
Parameters
----------
tasmin : xarray.DataArray
Minimum daily temperature.
lat : xarray.DataArray or {"north", "south"}, optional
Latitude coordinate as an array, float or string.
If None, a CF-conformant "latitude" field must be available within the passed DataArray.
freq : str
Resampling frequency.
Returns
-------
xarray.DataArray, [degC]
Mean of daily minimum temperature for month of interest.
Notes
-----
Given that this indice only examines September and March months, it is possible to send in DataArrays containing
only these timesteps. Users should be aware that due to the missing values checks in wrapped Indicators, datasets
that are missing several months will be flagged as invalid. This check can be ignored by setting the following
context:
.. code-block:: python
with xclim.set_options(check_missing="skip"):
cni = cool_night_index(tasmin)
Examples
--------
>>> from xclim.indices import cool_night_index
>>> tasmin = xr.open_dataset(path_to_tasmin_file).tasmin
>>> cni = cool_night_index(tasmin)
References
----------
:cite:cts:`tonietto_multicriteria_2004`
"""
tasmin = convert_units_to(tasmin, "degC")
# Use September in northern hemisphere, March in southern hemisphere.
months = tasmin.time.dt.month
if lat is None:
lat = _gather_lat(tasmin)
if isinstance(lat, xarray.DataArray):
month = xarray.where(lat > 0, 9, 3)
elif isinstance(lat, str):
if lat.lower() == "north":
month = 9
elif lat.lower() == "south":
month = 3
else:
raise ValueError(f"Latitude value unsupported: {lat}.")
else:
raise ValueError(f"Latitude not understood {lat}.")
tasmin = tasmin.where(months == month, drop=True)
cni: xarray.DataArray = tasmin.resample(time=freq).mean(keep_attrs=True)
cni = cni.assign_attrs(units="degC")
return cni
[docs]
@declare_units(pr="[precipitation]", evspsblpot="[precipitation]", wo="[length]")
def dryness_index(
pr: xarray.DataArray,
evspsblpot: xarray.DataArray,
lat: xarray.DataArray | str | None = None,
wo: Quantified = "200 mm",
freq: str = "YS",
) -> xarray.DataArray:
r"""Dryness Index.
Approximation of the water balance for the categorizing the winegrowing season. Uses both precipitation and an
adjustment of potential evapotranspiration between April and September (Northern Hemisphere) or October and March
(Southern hemisphere). Used in calculating the Géoviticulture Multicriteria Classification System
(:cite:t:`tonietto_multicriteria_2004`).
Warnings
--------
Dryness Index expects CF-Convention conformant potential evapotranspiration (positive up). This indice is calculated
using evapotranspiration and precipitation resampled and converted to monthly total accumulations, and therefore
will accept monthly fluxes as inputs.
Parameters
----------
pr : xarray.DataArray
Precipitation.
evspsblpot : xarray.DataArray
Potential evapotranspiration.
lat : xarray.DataArray or {"north", "south"}, optional
Latitude coordinate as an array, float or string.
If None, a CF-conformant "latitude" field must be available within the passed DataArray.
wo : Quantified
The initial soil water reserve accessible to root systems [length]. Default: 200 mm.
freq : str
Resampling frequency.
Returns
-------
xarray.DataArray, [mm]
Dryness Index.
Notes
-----
Given that this indice only examines monthly total accumulations for six-month periods depending on the hemisphere,
it is possible to send in DataArrays containing only these timesteps. Users should be aware that due to the missing
values checks in wrapped Indicators, datasets that are missing several months will be flagged as invalid. This check
can be ignored by setting the following context:
.. code-block:: python
with xclim.set_options(check_missing="skip"):
di = dryness_index(pr, evspsblpot)
Let :math:`Wo` be the initial useful soil water reserve (typically "200 mm"), :math:`P` be precipitation,
:math:`T_{v}` be the potential transpiration in the vineyard, and :math:`E_{s}` be the direct evaporation from the
soil. Then the Dryness Index, or the estimate of soil water reserve at the end of a period (1 April to 30 September
in the Northern Hemispherere or 1 October to 31 March in the Southern Hemisphere), can be given by the following
formulae:
.. math::
W = \sum_{\text{April 1}}^{\text{September 30}} \left( Wo + P - T_{v} - E_{s} \right)
or (for the Southern Hemisphere):
.. math::
W = \sum_{\text{October 1}}^{\text{March 31}} \left( Wo + P - T_{v} - E_{s} \right)
Where :math:`T_{v}` and :math:`E_{s}` are given by the following formulae:
.. math::
T_{v} = ETP * k
and
.. math::
E_{s} = \frac{ETP}{N}\left( 1 - k \right) * JPm
Where :math:`ETP` is evapotranspiration, :math:`N` is the number of days in the given month. :math:`k` is the
coefficient for radiative absorption given by the vine plant architecture, and :math:`JPm` is the number of days of
effective evaporation from the soil per month, both provided by the following formulae:
.. math::
k = \begin{cases}
0.1, & \text{if month = April (NH) or October (SH)} \\
0.3, & \text{if month = May (NH) or November (SH)} \\
0.5, & \text{if month = June - September (NH) or December - March (SH)} \\
\end{cases}
.. math::
JPm = \max\left( P / 5, N \right)
Examples
--------
>>> from xclim.indices import dryness_index
>>> dryi = dryness_index(pr_dataset, evspsblpot_dataset, wo="200 mm")
References
----------
:cite:cts:`tonietto_multicriteria_2004,riou_determinisme_1994`
"""
if parse_offset(freq) != (1, "Y", True, "JAN"):
raise ValueError(f"Freq not allowed: {freq}. Must be `YS` or `YS-JAN`")
# Resample all variables to monthly totals in mm units.
evspsblpot = (
amount2lwethickness(rate2amount(evspsblpot), out_units="mm")
.resample(time="MS")
.sum()
)
pr = amount2lwethickness(rate2amount(pr), out_units="mm").resample(time="MS").sum()
wo = convert_units_to(wo, "mm")
# Different potential evapotranspiration rates for northern hemisphere and southern hemisphere.
# wo_adjustment is the initial soil moisture rate at beginning of season.
adjustment_array_north = xarray.DataArray(
[0, 0, 0, 0.1, 0.3, 0.5, 0.5, 0.5, 0.5, 0, 0, 0],
dims="month",
coords=dict(month=np.arange(1, 13)),
)
adjustment_array_south = xarray.DataArray(
[0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0.1, 0.3, 0.5],
dims="month",
coords=dict(month=np.arange(1, 13)),
)
has_north, has_south = False, False
if lat is None:
lat = _gather_lat(pr)
if isinstance(lat, xarray.DataArray):
if (lat >= 0).any():
has_north = True
if (lat < 0).any():
has_south = True
adjustment = xarray.where(
lat >= 0,
adjustment_array_north,
adjustment_array_south,
)
elif isinstance(lat, str):
if lat.lower() == "north":
adjustment = adjustment_array_north
has_north = True
elif lat.lower() == "south":
adjustment = adjustment_array_south
has_south = True
else:
raise ValueError(f"Latitude value unsupported: {lat}.")
else:
raise ValueError(f"Latitude not understood: {lat}.")
# Monthly weights array
k = adjustment.sel(month=evspsblpot.time.dt.month)
# Drop all pr outside seasonal bounds
pr_masked = (k > 0) * pr
# Potential transpiration of the vineyard
t_v = evspsblpot * k
# Direct soil evaporation
e_s = (
(evspsblpot / evspsblpot.time.dt.daysinmonth)
* (1 - k)
* (pr_masked / 5).clip(max=evspsblpot.time.dt.daysinmonth)
)
di_north: xarray.DataArray | None = None
di_south: xarray.DataArray | None = None
# Dryness index
if has_north:
di_north = wo + (pr_masked - t_v - e_s).resample(time="YS-JAN").sum()
if has_south:
di_south = wo + (pr_masked - t_v - e_s).resample(time="YS-JUL").sum()
# Shift time for Southern Hemisphere to allow for concatenation with Northern Hemisphere
di_south = di_south.shift(time=1).isel(time=slice(1, None))
di_south["time"] = di_south.indexes["time"].shift(-6, "MS")
di: xarray.DataArray
if has_north and has_south:
di = di_north.where(lat >= 0, di_south)
elif has_north:
di = di_north # noqa
elif has_south:
di = di_south # noqa
else:
raise ValueError("No hemisphere data found.")
di = di.assign_attrs(units="mm")
return di
[docs]
@declare_units(tas="[temperature]", lat="[]")
def latitude_temperature_index(
tas: xarray.DataArray,
lat: xarray.DataArray | None = None,
lat_factor: float = 75,
freq: str = "YS",
) -> xarray.DataArray:
"""Latitude-Temperature Index.
Mean temperature of the warmest month with a latitude-based scaling factor :cite:p:`jackson_prediction_1988`.
Used for categorizing wine-growing regions.
Parameters
----------
tas : xarray.DataArray
Mean daily temperature.
lat : xarray.DataArray, optional
Latitude coordinate.
If None, a CF-conformant "latitude" field must be available within the passed DataArray.
lat_factor : float
Latitude factor. Maximum poleward latitude. Default: 75.
freq : str
Resampling frequency.
Returns
-------
xarray.DataArray, [unitless]
Latitude Temperature Index.
Notes
-----
The latitude factor of `75` is provided for examining the poleward expansion of wine-growing climates under
scenarios of climate change (modified from :cite:t:`kenny_assessment_1992`). For comparing 20th century/observed
historical records, the original scale factor of `60` is more appropriate.
Let :math:`Tn_{j}` be the average temperature for a given month :math:`j`, :math:`lat_{f}` be the latitude factor,
and :math:`lat` be the latitude of the area of interest. Then the Latitude-Temperature Index (:math:`LTI`) is:
.. math::
LTI = max(TN_{j}: j = 1..12)(lat_f - |lat|)
References
----------
:cite:cts:`jackson_prediction_1988,kenny_assessment_1992`
"""
tas = convert_units_to(tas, "degC")
tas = tas.resample(time="MS").mean(dim="time", keep_attrs=True)
mtwm = tas.resample(time=freq).max(dim="time", keep_attrs=True)
if lat is None:
lat = _gather_lat(tas)
lat_mask = (abs(lat) >= 0) & (abs(lat) <= lat_factor)
lat_coeff = xarray.where(lat_mask, lat_factor - abs(lat), 0)
lti: xarray.DataArray = mtwm * lat_coeff
lti = lti.assign_attrs(units="")
return lti
[docs]
@declare_units(
pr="[precipitation]",
evspsblpot="[precipitation]",
tasmin="[temperature]",
tasmax="[temperature]",
tas="[temperature]",
lat="[]",
hurs="[]",
rsds="[radiation]",
rsus="[radiation]",
rlds="[radiation]",
rlus="[radiation]",
sfcWind="[speed]",
)
def water_budget(
pr: xarray.DataArray,
evspsblpot: xarray.DataArray | None = None,
tasmin: xarray.DataArray | None = None,
tasmax: xarray.DataArray | None = None,
tas: xarray.DataArray | None = None,
lat: xarray.DataArray | None = None,
hurs: xarray.DataArray | None = None,
rsds: xarray.DataArray | None = None,
rsus: xarray.DataArray | None = None,
rlds: xarray.DataArray | None = None,
rlus: xarray.DataArray | None = None,
sfcWind: xarray.DataArray | None = None,
method: str = "BR65",
) -> xarray.DataArray:
r"""Precipitation minus potential evapotranspiration.
Precipitation minus potential evapotranspiration as a measure of an approximated surface water budget,
where the potential evapotranspiration can be calculated with a given method.
Parameters
----------
pr : xarray.DataArray
Daily precipitation.
evspsblpot: xarray.DataArray, optional
Potential evapotranspiration
tasmin : xarray.DataArray, optional
Minimum daily temperature.
tasmax : xarray.DataArray, optional
Maximum daily temperature.
tas : xarray.DataArray, optional
Mean daily temperature.
lat : xarray.DataArray, optional
Latitude coordinate, needed if evspsblpot is not given.
If None, a CF-conformant "latitude" field must be available within the `pr` DataArray.
hurs : xarray.DataArray, optional
Relative humidity.
rsds : xarray.DataArray, optional
Surface Downwelling Shortwave Radiation
rsus : xarray.DataArray, optional
Surface Upwelling Shortwave Radiation
rlds : xarray.DataArray, optional
Surface Downwelling Longwave Radiation
rlus : xarray.DataArray, optional
Surface Upwelling Longwave Radiation
sfcWind : xarray.DataArray, optional
Surface wind velocity (at 10 m)
method : str
Method to use to calculate the potential evapotranspiration.
See Also
--------
xclim.indicators.atmos.potential_evapotranspiration
Returns
-------
xarray.DataArray
Precipitation minus potential evapotranspiration.
"""
pr = convert_units_to(pr, "kg m-2 s-1", context="hydro")
if lat is None and evspsblpot is None:
lat = _gather_lat(pr)
if evspsblpot is None:
pet = potential_evapotranspiration(
tasmin=tasmin,
tasmax=tasmax,
tas=tas,
lat=lat,
hurs=hurs,
rsds=rsds,
rsus=rsus,
rlds=rlds,
rlus=rlus,
sfcWind=sfcWind,
method=method,
)
else:
pet = convert_units_to(evspsblpot, "kg m-2 s-1", context="hydro")
if xarray.infer_freq(pet.time) == "MS":
pr = pr.resample(time="MS").mean(dim="time", keep_attrs=True)
out: xarray.DataArray = pr - pet
out = out.assign_attrs(units=pr.attrs["units"])
return out
[docs]
@declare_units(
pr="[precipitation]",
thresh_wet_start="[length]",
thresh_dry_start="[length]",
thresh_dry_end="[length]",
)
def rain_season(
pr: xarray.DataArray,
thresh_wet_start: Quantified = "25.0 mm",
window_wet_start: int = 3,
window_not_dry_start: int = 30,
thresh_dry_start: Quantified = "1.0 mm",
window_dry_start: int = 7,
method_dry_start: str = "per_day",
date_min_start: DayOfYearStr = "05-01",
date_max_start: DayOfYearStr = "12-31",
thresh_dry_end: Quantified = "0.0 mm",
window_dry_end: int = 20,
method_dry_end: str = "per_day",
date_min_end: DayOfYearStr = "09-01",
date_max_end: DayOfYearStr = "12-31",
freq="YS-JAN",
) -> tuple[xarray.DataArray, xarray.DataArray, xarray.DataArray]:
"""Find the length of the rain season and the day of year of its start and its end.
The rain season begins when two conditions are met: 1) There must be a number of wet days with precipitations above
or equal to a given threshold; 2) There must be another sequence following, where, for a given period in time, there
are no dry sequence (i.e. a certain number of days where precipitations are below or equal to a certain threshold).
The rain season ends when there is a dry sequence.
Parameters
----------
pr : xr.DataArray
Precipitation data.
thresh_wet_start : Quantified
Accumulated precipitation threshold associated with `window_wet_start`.
window_wet_start : int
Number of days when accumulated precipitation is above `thresh_wet_start`.
Defines the first condition to start the rain season
window_not_dry_start : int
Number of days, after `window_wet_start` days, during which no dry period must be found as a second and last
condition to start the rain season.
A dry sequence is defined with `thresh_dry_start`, `window_dry_start` and `method_dry_start`.
thresh_dry_start : Quantified
Threshold length defining a dry day in the sequence related to `window_dry_start`.
window_dry_start : int
Number of days used to define a dry sequence in the start of the season.
Daily precipitations lower than `thresh_dry_start` during `window_dry_start` days are considered a dry sequence.
The precipitations must be lower than `thresh_dry_start` for either every day in the sequence
(`method_dry_start == "per_day"`) or for the total (`method_dry_start == "total"`).
method_dry_start : {"per_day", "total"}
Method used to define a dry sequence associated with `window_dry_start`.
The threshold `thresh_dry_start` is either compared to every daily precipitation
(`method_dry_start == "per_day"`) or to total precipitations (`method_dry_start == "total"`) in the sequence
`window_dry_start` days.
date_min_start : DayOfYearStr
First day of year when season can start ("mm-dd").
date_max_start : DayOfYearStr
Last day of year when season can start ("mm-dd").
thresh_dry_end : str
Threshold length defining a dry day in the sequence related to `window_dry_end`.
window_dry_end : int
Number of days used to define a dry sequence in the end of the season.
Daily precipitations lower than `thresh_dry_end` during `window_dry_end` days are considered a dry sequence.
The precipitations must be lower than `thresh_dry_end` for either every day in the sequence
(`method_dry_end == "per_day"`) or for the total (`method_dry_end == "total"`).
method_dry_end : {"per_day", "total"}
Method used to define a dry sequence associated with `window_dry_end`.
The threshold `thresh_dry_end` is either compared to every daily precipitation (`method_dry_end == "per_day"`)
or to total precipitations (`method_dry_end == "total"`) in the sequence `window_dry` days.
date_min_end : DayOfYearStr
First day of year when season can end ("mm-dd").
date_max_end : DayOfYearStr
Last day of year when season can end ("mm-dd").
freq : str
Resampling frequency.
Returns
-------
rain_season_start: xr.DataArray, [dimensionless]
rain_season_end: xr.DataArray, [dimensionless]
rain_season_length: xr.DataArray, [time]
Notes
-----
The rain season starts at the end of a period of raining (a total precipitation of `thresh_wet_start` over
`window_wet_start` days). This must be directly followed by a period of `window_not_dry_start` days with no dry
sequence. The dry sequence is a period of `window_dry_start` days where precipitations are below `thresh_dry_start`
(either the total precipitations over the period, or the daily precipitations, depending on `method_dry_start`).
The rain season stops when a dry sequence happens (the dry sequence is defined as in the start sequence, but with
parameters `window_dry_end`, `thresh_dry_end` and `method_dry_end`). The dates on which the season can start are
constrained by `date_min_start`and `date_max_start` (and similarly for the end of the season).
References
----------
:cite:cts:`sivakumar_predicting_1998`
"""
# Unit conversion.
pram = rate2amount(pr, out_units="mm")
thresh_wet_start = convert_units_to(thresh_wet_start, pram)
thresh_dry_start = convert_units_to(thresh_dry_start, pram)
thresh_dry_end = convert_units_to(thresh_dry_end, pram)
# should we flag date_min_end < date_max_start?
def _get_first_run(run_positions, start_date, end_date):
run_positions = select_time(run_positions, date_bounds=(start_date, end_date))
first_start = run_positions.argmax("time")
return xarray.where(
first_start != run_positions.argmin("time"), first_start, np.NaN
)
# Find the start of the rain season
def _get_first_run_start(_pram):
last_doy = _pram.indexes["time"][-1].strftime("%m-%d")
_pram = select_time(_pram, date_bounds=(date_min_start, last_doy))
# First condition: Start with enough precipitation
da_start = _pram.rolling({"time": window_wet_start}).sum() >= thresh_wet_start
# Second condition: No dry period after
if method_dry_start == "per_day":
da_stop = _pram <= thresh_dry_start
window_dry = window_dry_start
elif method_dry_start == "total":
da_stop = (
_pram.rolling({"time": window_dry_start}).sum() <= thresh_dry_start
)
# equivalent to rolling forward in time instead, i.e. end date will be at beginning of dry run
da_stop = da_stop.shift({"time": -(window_dry_start - 1)}, fill_value=False)
window_dry = 1
else:
raise ValueError(f"Unknown method_dry_start: {method_dry_start}.")
# First and second condition combined in a run length
events = rl.extract_events(da_start, 1, da_stop, window_dry)
run_positions = rl.rle(events) >= (window_not_dry_start + window_wet_start)
return _get_first_run(run_positions, date_min_start, date_max_start)
# Find the end of the rain season
# FIXME: This function mixes local and parent-level variables. It should be refactored.
def _get_first_run_end(_pram):
if method_dry_end == "per_day":
da_stop = _pram <= thresh_dry_end
run_positions = rl.rle(da_stop) >= window_dry_end
elif method_dry_end == "total":
run_positions = (
_pram.rolling({"time": window_dry_end}).sum() <= thresh_dry_end
)
else:
raise ValueError(f"Unknown method_dry_end: {method_dry_end}.")
return _get_first_run(run_positions, date_min_end, date_max_end)
# Get start, end and length of rain season. Written as a function so it can be resampled
# FIXME: This function mixes local and parent-level variables. It should be refactored.
def _get_rain_season(_pram):
start = _get_first_run_start(_pram)
# masking value before start of the season (end of season should be after)
# Get valid integer indexer of the day after the first run starts.
# `start != NaN` only possible if a condition on next few time steps is respected.
# Thus, `start+1` exists if `start != NaN`
start_ind = (start + 1).fillna(-1).astype(int)
mask = _pram * np.NaN
# Put "True" on the day of run start
mask[{"time": start_ind}] = 1
# Mask back points without runs, propagate the True
mask = mask.where(start.notnull()).ffill("time")
mask = mask.notnull()
end = _get_first_run_end(_pram.where(mask))
length = xarray.where(end.notnull(), end - start, _pram["time"].size - start)
# converting to doy
crd = _pram.time.dt.dayofyear
start = rl.lazy_indexing(crd, start)
end = rl.lazy_indexing(crd, end)
_out = xarray.Dataset(
{
"rain_season_start": start,
"rain_season_end": end,
"rain_season_length": length,
}
)
return _out
# Compute rain season, attribute units
out = cast(xarray.Dataset, pram.resample(time=freq).map(_get_rain_season))
rain_season_start = out.rain_season_start.assign_attrs(
units="", is_dayofyear=np.int32(1)
)
rain_season_end = out.rain_season_end.assign_attrs(
units="", is_dayofyear=np.int32(1)
)
rain_season_length = out.rain_season_length.assign_attrs(units="days")
return rain_season_start, rain_season_end, rain_season_length
[docs]
@declare_units(
pr="[precipitation]",
params="[]",
)
def standardized_precipitation_index(
pr: xarray.DataArray,
freq: str | None = "MS",
window: int = 1,
dist: str = "gamma",
method: str = "ML",
fitkwargs: dict | None = None,
cal_start: DateStr | None = None,
cal_end: DateStr | None = None,
params: Quantified | None = None,
**indexer,
) -> xarray.DataArray:
r"""Standardized Precipitation Index (SPI).
Parameters
----------
pr : xarray.DataArray
Daily precipitation.
freq : str, optional
Resampling frequency. A monthly or daily frequency is expected. Option `None` assumes that desired resampling
has already been applied input dataset and will skip the resampling step.
window : int
Averaging window length relative to the resampling frequency. For example, if `freq="MS"`,
i.e. a monthly resampling, the window is an integer number of months.
dist : {"gamma", "fisk"}
Name of the univariate distribution. (see :py:mod:`scipy.stats`).
method : {'APP', 'ML'}
Name of the fitting method, such as `ML` (maximum likelihood), `APP` (approximate). The approximate method
uses a deterministic function that doesn't involve any optimization.
fitkwargs : dict, optional
Kwargs passed to ``xclim.indices.stats.fit`` used to impose values of certains parameters (`floc`, `fscale`).
cal_start : DateStr, optional
Start date of the calibration period. A `DateStr` is expected, that is a `str` in format `"YYYY-MM-DD"`.
Default option `None` means that the calibration period begins at the start of the input dataset.
cal_end : DateStr, optional
End date of the calibration period. A `DateStr` is expected, that is a `str` in format `"YYYY-MM-DD"`.
Default option `None` means that the calibration period finishes at the end of the input dataset.
params : xarray.DataArray
Fit parameters.
The `params` can be computed using ``xclim.indices.stats.standardized_index_fit_params`` in advance.
The output can be given here as input, and it overrides other options.
\*\*indexer
Indexing parameters to compute the indicator on a temporal subset of the data.
It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
Returns
-------
xarray.DataArray, [unitless]
Standardized Precipitation Index.
Notes
-----
* N-month SPI / N-day SPI is determined by choosing the `window = N` and the appropriate frequency `freq`.
* Supported statistical distributions are: ["gamma", "fisk"], where "fisk" is scipy's implementation of
a log-logistic distribution
* If `params` is given as input, it overrides the `cal_start`, `cal_end`, `freq` and `window`, `dist` and `method` options.
* "APP" method only supports two-parameter distributions. Parameter `loc` needs to be fixed to use method `APP`.
* The standardized index is bounded by ±8.21. 8.21 is the largest standardized index as constrained by the float64 precision in
the inversion to the normal distribution.
* The results from `climate_indices` library can be reproduced with `method = "APP"` and `fitwkargs = {"floc": 0}`
Example
-------
>>> from datetime import datetime
>>> from xclim.indices import standardized_precipitation_index
>>> ds = xr.open_dataset(path_to_pr_file)
>>> pr = ds.pr
>>> cal_start, cal_end = "1990-05-01", "1990-08-31"
>>> spi_3 = standardized_precipitation_index(
... pr,
... freq="MS",
... window=3,
... dist="gamma",
... method="ML",
... cal_start=cal_start,
... cal_end=cal_end,
... ) # Computing SPI-3 months using a gamma distribution for the fit
>>> # Fitting parameters can also be obtained first, then re-used as input.
>>> # To properly reproduce the example, we also need to specify that we use a
>>> # (potentially) zero-inflated distribution. For a monthly SPI, this should rarely
>>> # make a difference.
>>> from xclim.indices.stats import standardized_index_fit_params
>>> params = standardized_index_fit_params(
... pr.sel(time=slice(cal_start, cal_end)),
... freq="MS",
... window=3,
... dist="gamma",
... method="ML",
... zero_inflated=True,
... ) # First getting params
>>> spi_3 = standardized_precipitation_index(pr, params=params)
References
----------
:cite:cts:`mckee_relationship_1993`
"""
fitkwargs = fitkwargs or {}
dist_methods = {"gamma": ["ML", "APP", "PWM"], "fisk": ["ML", "APP"]}
if dist in dist_methods.keys():
if method not in dist_methods[dist]:
raise NotImplementedError(
f"{method} method is not implemented for {dist} distribution"
)
else:
raise NotImplementedError(f"{dist} distribution is not yet implemented.")
# Precipitation is expected to be zero-inflated
zero_inflated = True
spi = standardized_index(
pr,
freq=freq,
window=window,
dist=dist,
method=method,
zero_inflated=zero_inflated,
fitkwargs=fitkwargs,
cal_start=cal_start,
cal_end=cal_end,
params=params,
**indexer,
)
return spi
[docs]
@declare_units(
wb="[precipitation]",
offset="[precipitation]",
params="[]",
)
def standardized_precipitation_evapotranspiration_index(
wb: xarray.DataArray,
freq: str | None = "MS",
window: int = 1,
dist: str = "gamma",
method: str = "ML",
fitkwargs: dict | None = None,
offset: Quantified = "0.000 mm/d",
cal_start: DateStr | None = None,
cal_end: DateStr | None = None,
params: Quantified | None = None,
**indexer,
) -> xarray.DataArray:
r"""Standardized Precipitation Evapotranspiration Index (SPEI).
Precipitation minus potential evapotranspiration data (PET) fitted to a statistical distribution (dist), transformed
to a cdf, and inverted back to a gaussian normal pdf. The potential evapotranspiration is calculated with a given
method (`method`).
Parameters
----------
wb : xarray.DataArray
Daily water budget (pr - pet).
freq : str, optional
Resampling frequency. A monthly or daily frequency is expected. Option `None` assumes that desired resampling
has already been applied input dataset and will skip the resampling step.
window : int
Averaging window length relative to the resampling frequency. For example, if `freq="MS"`, i.e. a monthly
resampling, the window is an integer number of months.
dist : {'gamma', 'fisk'}
Name of the univariate distribution. (see :py:mod:`scipy.stats`).
method : {'APP', 'ML'}
Name of the fitting method, such as `ML` (maximum likelihood), `APP` (approximate), or
`PWM` (probability weighted moments).
The approximate method uses a deterministic function that doesn't involve any optimization. Available methods
vary with the distribution: 'gamma':{'APP', 'ML', 'PWM'}, 'fisk':{'APP', 'ML'}
fitkwargs : dict, optional
Kwargs passed to ``xclim.indices.stats.fit`` used to impose values of certains parameters (`floc`, `fscale`).
offset : Quantified
For distributions bounded by zero (e.g. "gamma", "fisk"), the two-parameters distributions only accept positive
values. An offset can be added to make sure this is the case. This option will be removed in xclim >=0.50.0, ``xclim``
will rely on proper use of three-parameters distributions instead.
cal_start : DateStr, optional
Start date of the calibration period. A `DateStr` is expected, that is a `str` in format `"YYYY-MM-DD"`.
Default option `None` means that the calibration period begins at the start of the input dataset.
cal_end : DateStr, optional
End date of the calibration period. A `DateStr` is expected, that is a `str` in format `"YYYY-MM-DD"`.
Default option `None` means that the calibration period finishes at the end of the input dataset.
params : xarray.DataArray, optional
Fit parameters.
The `params` can be computed using ``xclim.indices.stats.standardized_index_fit_params`` in advance.
The output can be given here as input, and it overrides other options.
\*\*indexer
Indexing parameters to compute the indicator on a temporal subset of the data.
It accepts the same arguments as :py:func:`xclim.indices.generic.select_time`.
Returns
-------
xarray.DataArray
Standardized Precipitation Evapotranspiration Index.
See Also
--------
standardized_precipitation_index
"""
fitkwargs = fitkwargs or {}
uses_default_offset = offset != "0.000 mm/d"
if uses_default_offset is False:
warnings.warn("Inputting an offset will be deprecated in xclim>=0.50.0. ")
if params is not None:
if "offset" in params.attrs:
params_offset = params.attrs["offset"]
# no more offset in params needed after the next step.
# This step will be removed in xclim >=0.50.0 once offset is no longer needed
params.attrs.pop("offset")
else:
params_offset = ""
if uses_default_offset is False and offset != params_offset:
warnings.warn(
"The offset in `params` differs from the input `offset`."
"Proceeding with the value given in `params`."
)
offset = params_offset
offset = 0 if offset == "" else convert_units_to(offset, wb, context="hydro")
if offset != 0:
with xarray.set_options(keep_attrs=True):
wb = wb + offset
dist_methods = {"gamma": ["ML", "APP", "PWM"], "fisk": ["ML", "APP"]}
if dist in dist_methods.keys():
if method not in dist_methods[dist]:
raise NotImplementedError(
f"{method} method is not implemented for {dist} distribution"
)
else:
raise NotImplementedError(f"{dist} distribution is not yet implemented.")
# Water budget is not expected to be zero-inflated
zero_inflated = False
spei = standardized_index(
wb,
freq=freq,
window=window,
dist=dist,
method=method,
zero_inflated=zero_inflated,
fitkwargs=fitkwargs,
cal_start=cal_start,
cal_end=cal_end,
params=params,
**indexer,
)
return spei
[docs]
@declare_units(tas="[temperature]")
def qian_weighted_mean_average(
tas: xarray.DataArray, dim: str = "time"
) -> xarray.DataArray:
r"""Binomial smoothed, five-day weighted mean average temperature.
Calculates a five-day weighted moving average with emphasis on temperatures closer to day of interest.
Parameters
----------
tas : xr.DataArray
Daily mean temperature.
dim : str
Time dimension.
Returns
-------
xr.DataArray, [same as tas]
Binomial smoothed, five-day weighted mean average temperature.
Notes
-----
Qian Modified Weighted Mean Indice originally proposed in :cite:p:`qian_observed_2010`,
based on :cite:p:`bootsma_impacts_2005`.
Let :math:`X_{n}` be the average temperature for day :math:`n` and :math:`X_{t}` be the daily mean temperature
on day :math:`t`. Then the weighted mean average can be calculated as follows:
.. math::
\overline{X}_{n} = \frac{X_{n-2} + 4X_{n-1} + 6X_{n} + 4X_{n+1} + X_{n+2}}{16}
References
----------
:cite:cts:`bootsma_impacts_2005,qian_observed_2010`
"""
units = tas.attrs["units"]
weights = xarray.DataArray([0.0625, 0.25, 0.375, 0.25, 0.0625], dims=["window"])
weighted_mean: xarray.DataArray = (
tas.rolling({dim: 5}, center=True).construct("window").dot(weights)
)
weighted_mean = weighted_mean.assign_attrs(units=units)
return weighted_mean
[docs]
@declare_units(
tasmax="[temperature]",
tasmin="[temperature]",
thresh="[temperature]",
)
def effective_growing_degree_days(
tasmax: xarray.DataArray,
tasmin: xarray.DataArray,
*,
thresh: Quantified = "5 degC",
method: str = "bootsma",
after_date: DayOfYearStr = "07-01",
dim: str = "time",
freq: str = "YS",
) -> xarray.DataArray:
r"""Effective growing degree days.
Growing degree days based on a dynamic start and end of the growing season,
as defined in :cite:p:`bootsma_impacts_2005`.
Parameters
----------
tasmax : xr.DataArray
Daily mean temperature.
tasmin : xr.DataArray
Daily minimum temperature.
thresh : Quantified
The minimum temperature threshold.
method : {"bootsma", "qian"}
The window method used to determine the temperature-based start date.
For "bootsma", the start date is defined as 10 days after the average temperature exceeds a threshold.
For "qian", the start date is based on a weighted 5-day rolling average,
based on :py:func`qian_weighted_mean_average`.
after_date : str
Date of the year after which to look for the first frost event. Should have the format '%m-%d'.
dim : str
Time dimension.
freq : str
Resampling frequency.
Returns
-------
xarray.DataArray, [K days]
Effective growing degree days (EGDD).
Notes
-----
The effective growing degree days for a given year :math:`EGDD_i` can be calculated as follows:
.. math::
EGDD_i = \sum_{i=\text{j_{start}}^{\text{j_{end}}} max\left(TG - Thresh, 0 \right)
Where :math:`TG` is the mean daly temperature, and :math:`j_{start}` and :math:`j_{end}` are the start and end dates
of the growing season. The growing season start date methodology is determined via the `method` flag.
For "bootsma", the start date is defined as 10 days after the average temperature exceeds a threshold (5 degC).
For "qian", the start date is based on a weighted 5-day rolling average, based on :py:func:`qian_weighted_mean_average`.
The end date is determined as the day preceding the first day with minimum temperature below 0 degC.
References
----------
:cite:cts:`bootsma_impacts_2005`
"""
tasmax = convert_units_to(tasmax, "degC")
tasmin = convert_units_to(tasmin, "degC")
thresh = convert_units_to(thresh, "degC")
thresh_with_units = f"{thresh} degC"
tas = (tasmin + tasmax) / 2
tas.attrs["units"] = "degC"
if method.lower() == "bootsma":
fda = first_day_temperature_above(
tas=tas, thresh=thresh_with_units, window=1, freq=freq
)
start = fda + 10
elif method.lower() == "qian":
tas_weighted = qian_weighted_mean_average(tas=tas, dim=dim)
start = first_day_temperature_above(
tas_weighted, thresh=thresh_with_units, window=5, freq=freq
)
else:
raise NotImplementedError(f"Method: {method}.")
# The day before the first day below 0 degC
end = (
first_day_temperature_below(
tasmin,
thresh="0 degC",
after_date=after_date,
window=1,
freq=freq,
)
- 1
)
deg_days = (tas - thresh).clip(min=0)
egdd: xarray.DataArray = aggregate_between_dates(
deg_days, start=start, end=end, freq=freq
)
egdd = to_agg_units(egdd, tas, op="integral")
return egdd
[docs]
@declare_units(tasmin="[temperature]")
def hardiness_zones(
tasmin: xarray.DataArray, window: int = 30, method: str = "usda", freq: str = "YS"
):
"""Hardiness zones.
Hardiness zones are a categorization of the annual extreme temperature minima, averaged over a certain period.
The USDA method defines 14 zones, each divided into two sub-zones, using steps of 5°F, starting at -60°F.
The Australian National Botanic Gardens method defines 7 zones, using steps of 5°C, starting at -15°C.
Parameters
----------
tasmin : xr.DataArray
Minimum temperature.
window : int
The length of the averaging window, in years.
method : {'usda', 'anbg'}
Whether to return the American (`usda`) or the Australian (`anbg`) classification zones.
freq : str
Resampling frequency.
Returns
-------
xr.DataArray, [dimensionless]
{method} hardiness zones.
US sub-zones are denoted by using a half step. For example, Zone 4b is given as 4.5.
Values are given at the end of the averaging window.
References
----------
:cite:cts:`usda_2012,dawson_plant_1991`
"""
if method.lower() == "usda":
zone_min, zone_max, zone_step = "-60 degF", "70 degF", "5 degF"
elif method.lower() == "anbg":
zone_min, zone_max, zone_step = "-15 degC", "20 degC", "5 degC"
else:
raise NotImplementedError(
f"Method must be one of `usda` or `anbg`. Got {method}."
)
tn_min_rolling = tn_min(tasmin, freq=freq).rolling(time=window).mean()
zones: xarray.DataArray = get_zones(
tn_min_rolling, zone_min=zone_min, zone_max=zone_max, zone_step=zone_step
)
zones = zones.assign_attrs(units="")
return zones