smash.precipitation_indices#

smash.precipitation_indices(model)[source]#

Compute precipitation indices of Model.

5 precipitation indices are calculated for each gauge and each time step:

'std' : the precipitation spatial standard deviation
'd1' : the first scaled moment, [Zoccatelli et al., 2011]
'd2' : the second scaled moment, [Zoccatelli et al., 2011]
'vg' : the vertical gap [Emmanuel et al., 2015]
'hg' : the horizontal gap [Emmanuel et al., 2015]

Parameters:

modelModel: Primary data structure of the hydrological model smash.

Returns:

precipitation_indicesPrecipitationIndices: It returns an object containing the results of the precipitation indices computation.

See also

PrecipitationIndices: Represents precipitation indices computation result.

Examples

>>> from smash.factory import load_dataset
>>> setup, mesh = load_dataset("cance")
>>> model = smash.Model(setup, mesh)

Compute precipitation indices

>>> prcp_ind = smash.precipitation_indices(model)
>>> prcp_ind
d1: <class 'numpy.ndarray'>
d2: <class 'numpy.ndarray'>
hg: <class 'numpy.ndarray'>
std: <class 'numpy.ndarray'>
vg: <class 'numpy.ndarray'>

Each attribute is a numpy.ndarray of shape (ng, ntime_step) (i.e. number of gauges, number of time steps)

Access a specific precipitation indice

>>> prcp_ind.d1
array([[nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan],
       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)

Note

NaN value means that there is no precipitation at this specific gauge and time step.

Access a specific precipitation indice for a single gauge

>>> ind = np.argwhere(model.mesh.code == "V3524010").item()
>>> ind
0

>>> d1g = prcp_ind.d1[ind, :]
>>> d1g
array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)

Access the time steps where rainfall occured for a specific precipitation indice and a single gauge

>>> ind = np.argwhere(~np.isnan(d1g))
>>> ind
array([[  11],
       [  12],
       ...
       [1410],
       [1411]])

>>> d1gts = d1g[ind]
>>> d1gts
array([[1.2091751 ],
       [0.83805513],
       ...
       [0.89658403],
       [0.48382276]], dtype=float32)
>>> d1gts[0].item(), d1gts[1].item()
(1.2091751098632812, 0.8380551338195801)