def _mask_valid(obs: DataArray, sim: DataArray) -> Tuple[DataArray, DataArray]:
# mask of invalid entries. NaNs in simulations can happen during validation/testing
idx = (~sim.isnull()) & (~obs.isnull())
obs = obs[idx]
sim = sim[idx]
return obs, sim
def _is_all_nan(obs: DataArray, sim: DataArray) -> bool:
"""Check if all observations or simulations are NaN and log a warning if this is the case. """
all_nan = False
if all(obs.isnull()):
LOGGER.warning(
"All observed values are NaN, thus metrics will be NaN, too.")
all_nan = True
if all(sim.isnull()):
LOGGER.warning(
"All simulated values are NaN, thus metrics will be NaN, too.")
all_nan = True
return all_nan
def _check_all_nan(obs: DataArray, sim: DataArray):
"""Check if all observations or simulations are NaN and raise an exception if this is the case.
Raises
------
AllNaNError
If all observations or all simulations are NaN.
"""
if all(obs.isnull()):
raise AllNaNError(
"All observed values are NaN, thus metrics will be NaN, too.")
if all(sim.isnull()):
raise AllNaNError(
"All simulated values are NaN, thus metrics will be NaN, too.")
def _mask_valid(obs: DataArray, sim: DataArray) -> (DataArray, DataArray):
# mask of invalid entries
idx = (obs >= 0) & (~obs.isnull())
obs = obs[idx]
sim = sim[idx]
return obs, sim
def stream_elas(da: DataArray, prcp: DataArray, coord: str = 'date') -> float:
# rename precip coordinate name (to avoid problems with 'index' or 'date')
prcp = prcp.rename({list(prcp.coords.keys())[0]: coord})
# slice prcp to the same time window as the discharge
prcp = prcp.sel({coord: slice(da.coords[coord][0], da.coords[coord][-1])})
# determine the date of the first October 1st in the data period
first_date = da.coords[coord][0].values.astype('datetime64[s]').astype(
datetime)
last_date = da.coords[coord][-1].values.astype('datetime64[s]').astype(
datetime)
if first_date > datetime.strptime(f'{first_date.year}-10-01', '%Y-%m-%d'):
start_date = datetime.strptime(f'{first_date.year + 1}-10-01',
'%Y-%m-%d')
else:
start_date = datetime.strptime(f'{first_date.year}-10-01', '%Y-%m-%d')
end_date = start_date + relativedelta(years=1) - relativedelta(days=1)
# mask only valid time steps (only discharge has missing values)
idx = (da >= 0) & (~da.isnull())
da = da[idx]
prcp = prcp[idx]
# calculate long-term means
q_mean_total = da.mean()
p_mean_total = prcp.mean()
values = []
while end_date < last_date:
q = da.sel({coord: slice(start_date, end_date)})
p = prcp.sel({coord: slice(start_date, end_date)})
val = (q.mean() - q_mean_total) / (p.mean() - p_mean_total) * (
p_mean_total / q_mean_total)
values.append(val)
start_date += relativedelta(years=1)
end_date += relativedelta(years=1)
return np.median([float(v) for v in values])
def stream_elas(da: DataArray,
prcp: DataArray,
datetime_coord: str = None) -> float:
"""Calculate stream elasticity.
Streamflow precipitation elasticity (sensitivity of streamflow to changes in precipitation at
the annual time scale) [#]_.
Parameters
----------
da : DataArray
Array of flow values.
prcp : DataArray
Array of precipitation values.
datetime_coord : str, optional
Datetime coordinate in the passed DataArray. Tried to infer automatically if not specified.
Returns
-------
float
Stream elasticity.
References
----------
.. [#] Sankarasubramanian, A., Vogel, R. M., and Limbrunner, J. F.: Climate elasticity of streamflow in the
United States. Water Resources Research, 2001, 37, 1771--1781, doi:10.1029/2000WR900330
"""
if datetime_coord is None:
datetime_coord = utils.infer_datetime_coord(da)
# rename precip coordinate name (to avoid problems with 'index' or 'date')
prcp = prcp.rename({list(prcp.coords.keys())[0]: datetime_coord})
# slice prcp to the same time window as the discharge
prcp = prcp.sel({
datetime_coord:
slice(da.coords[datetime_coord][0], da.coords[datetime_coord][-1])
})
# determine the date of the first October 1st in the data period
first_date = da.coords[datetime_coord][0].values.astype(
'datetime64[s]').astype(datetime)
last_date = da.coords[datetime_coord][-1].values.astype(
'datetime64[s]').astype(datetime)
if first_date > datetime.strptime(f'{first_date.year}-10-01', '%Y-%m-%d'):
start_date = datetime.strptime(f'{first_date.year + 1}-10-01',
'%Y-%m-%d')
else:
start_date = datetime.strptime(f'{first_date.year}-10-01', '%Y-%m-%d')
end_date = start_date + relativedelta(years=1) - relativedelta(seconds=1)
# mask only valid time steps (only discharge has missing values)
idx = (da >= 0) & (~da.isnull())
da = da[idx]
prcp = prcp[idx]
# calculate long-term means
q_mean_total = da.mean()
p_mean_total = prcp.mean()
values = []
while end_date < last_date:
q = da.sel({datetime_coord: slice(start_date, end_date)})
p = prcp.sel({datetime_coord: slice(start_date, end_date)})
val = (q.mean() - q_mean_total) / (p.mean() - p_mean_total) * (
p_mean_total / q_mean_total)
values.append(val)
start_date += relativedelta(years=1)
end_date += relativedelta(years=1)
return np.median([float(v) for v in values])
