evalhyd::evalp

evalp(q_obs, q_prd, metrics, q_thr=NULL, events=NULL, c_lvl=NULL, t_msk=NULL, bootstrap=NULL, dts=NULL, seed=NULL, diagnostics=NULL)

Function to evaluate probabilistic streamflow predictions.

Parameters

q_obs

A numeric array of streamflow observations. Time steps with missing observations must be assigned NA 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

A numeric array of streamflow predictions. Time steps with missing predictions must be assigned NA 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

A string vector of evaluation metrics to be computed. shape: (metrics,)

See also

Probabilistic metrics

q_thr, optional

A numeric matrix of streamflow thresholds to consider for the metrics assessing the prediction of exceedance events. If the number of thresholds differs across sites, NA can be set as threshold for those sites with fewer thresholds. shape: (sites, thresholds)

events, 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, optional

A numeric vector containing the confidence interval(s) in % to consider for intervals-based metrics.

t_msk, optional

A logical 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, 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)

Warning

The use of this parameter will raise a warning regarding a coerced object from ‘logical’ to ‘double’. This is a known issue (see #2) that does not impact on the numerical validity of the results obtained. However, until this issue is resolved, the user is warned that an unnecessary copy of the provided mask is made to comply with the expected data type.

See also

Temporal masking

bootstrap, optional

A list providing 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, and "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=list(n_samples=100, len_sample=10, summary=0)

See also

Bootstrapping

dts, optional

A string vector of corresponding dates and times for 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, optional

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

diagnostics, optional

A string vector of evaluation diagnostics to be computed. shape: (diagnostics,)

See also

Diagnostics

Returns

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

Examples

> obs <- rbind(
+     c(4.7, 4.3, 5.5, 2.7, 4.1)
+ )
> prd <- array(
+     rbind(
+         c(5.3, 4.2, 5.7, 2.3, 3.1),
+         c(4.3, 4.2, 4.7, 4.3, 3.3),
+         c(5.3, 5.2, 5.7, 2.3, 3.9)
+     ),
+     dim=c(1, 1, 3, 5)
+ )
> thr <- rbind(
+     c(4., 5.)
+ )

> library(evalhyd)
> results <- evalhyd::evalp(obs, prd, c("BS", "BS_LBD"), thr)
> results[[1]][1,1,1,1,]  # BS
[1] 0.2222222 0.1333333
> results[[2]][1,1,1,1,,]  # BS_LBD
           [,1]       [,2]       [,3]
[1,] 0.07222222 0.02777778 0.17777778
[2,] 0.07222222 0.02777778 0.08888889

> evalhyd::evalp(obs, prd, c("CRPS_FROM_QS"))[[1]][1,1,1,]
[1] 0.1875