evalhyd.evalp

evalhyd.evalp(q_obs, q_prd, metrics, q_thr=None, events=None, c_lvl=None, t_msk=None, m_cdt=None, bootstrap=None, dts=None, seed=None, diagnostics=None)

Function to evaluate probabilistic streamflow predictions.

Warning

Parameters that are numpy.ndarray must not be array views. This may be the case if array indexing is performed. If indexing cannot be avoided, array copies must be made (i.e. using numpy.ndarray.copy).

Parameters

q_obs: numpy.ndarray [dtype('float64')]

2D array of streamflow observations. Time steps with missing observations must be assigned numpy.nan values. Those time steps will be ignored both in the observations and in the predictions before the metrics are computed. shape: (sites, time)

q_prd: numpy.ndarray [dtype('float64')]

4D array of streamflow predictions. Time steps with missing predictions must be assigned numpy.nan values. Those time steps will be ignored both in the observations and in the predictions before the metrics are computed. shape: (sites, lead times, members, time)

metrics: List[str]

The sequence of evaluation metrics to be computed. shape: (metrics,)

See also

Probabilistic metrics

q_thr: numpy.ndarray [dtype('float64')], optional

2D array of streamflow threshold(s) to consider for the metrics assessing the prediction of exceedance events. If the number of thresholds differs across sites, numpy.nan can be set as threshold for those sites with fewer thresholds. shape: (sites, thresholds)

events: str, optional

A string specifying the type of streamflow events to consider for threshold exceedance-based metrics. It can either be set as "high" when flooding conditions/high flow events are evaluated (i.e. event occurring when streamflow goes above threshold) or as "low" when drought conditions/low flow events are evaluated (i.e. event occurring when streamflow goes below threshold). It must be provided if q_thr is provided.

c_lvl: numpy.ndarray [dtype('float64')], optional

1D array of confidence interval(s) in % to consider for intervals-based metrics.

t_msk: numpy.ndarray [dtype('bool')], optional

4D array of masks to generate temporal subsets of the whole streamflow time series (where True/False is used for the time steps to include/discard in a given subset). If not provided and neither is m_cdt, no subset is performed and only one set of metrics is returned corresponding to the whole time series. If provided, as many sets of metrics are returned as they are masks provided. shape: (sites, lead times, subsets, time)

See also

Temporal masking

m_cdt: numpy.ndarray [dtype('|S32')], optional

2D array of conditions to generate temporal subsets. Each condition consists in a string and can be specified on observed/predicted streamflow values (mean, median, quantile), or on time indices. If provided in combination with t_msk, the latter takes precedence. If not provided and neither is t_msk, no subset is performed and only one set of metrics is returned corresponding to the whole time series. If provided, as many sets of metrics are returned as they are conditions provided. shape: (sites, subsets)

See also

Conditional masking

bootstrap: dict, optional

The values for the parameters of the bootstrapping method used to estimate the sampling uncertainty in the evaluation of the predictions. It takes three parameters: "n_samples" the number of random samples, "len_samples" the length of one sample in number of years; "summary" the statistics to return to characterise the sampling distribution. If not provided, no bootstrapping is performed. If provided, dts must also be provided.

Parameter example:

bootstrap={"n_samples": 100, "len_sample": 10, "summary": 0}

See also

Bootstrapping

dts: numpy.ndarray [dtype('|S32')], optional

1D array of dates and times corresponding to the temporal dimension of the streamflow observations and predictions. The date and time must be specified in a string following the ISO 8601-1:2019 standard, i.e. “YYYY-MM-DD hh:mm:ss” (e.g. the 21st of May 2007 at 4 in the afternoon is “2007-05-21 16:00:00”). If provided, it is only used if bootstrap is also provided. shape: (time,)

seed: int, optional

An integer value for the seed used by random generators. This parameter guarantees the reproducibility of the metric values between calls.

diagnostics: List[str], optional

The sequence of evaluation diagnostics to be computed. shape: (diagnostics,)

See also

Diagnostics

Returns

List[numpy.ndarray]

The sequence of evaluation metrics computed in the same order as given in metrics, followed by the sequence of evaluation diagnostics in the same order as given in diagnostics. shape: [(sites, lead times, subsets, samples, {quantiles,} {thresholds,} {components,} {ranks,} {intervals}), …]

Examples

>>> import numpy
>>> import evalhyd
>>> obs = numpy.array(
...     [[4.7, 4.3, 5.5, 2.7, 4.1]]
... )
>>> prd = numpy.array(
...     [[[[5.3, 4.2, 5.7, 2.3, 3.1],
...        [4.3, 4.2, 4.7, 4.3, 3.3],
...        [5.3, 5.2, 5.7, 2.3, 3.9]]]]
... )

>>> bs, bs_lbd = evalhyd.evalp(obs, prd, ['BS', 'BS_LBD'], [[4., 5.]])
>>> print(bs)
[[[[[0.22222222 0.13333333]]]]]
>>> print(bs_lbd)
[[[[[[0.07222222 0.02777778 0.17777778]
     [0.07222222 0.02777778 0.08888889]]]]]]

>>> crps, = evalhyd.evalp(obs, prd, ['CRPS_FROM_QS'])
>>> print(crps)
[[[[0.1875]]]]