smash.metrics#

smash.metrics(model, metric='nse', end_warmup=None)[source]#

Compute the efficiency/error metrics of Model based on observed and simulated discharges for each gauge in a multi-catchment hydrological model evaluation.

Parameters:

modelModel

Primary data structure of the hydrological model smash.

metricstr, default ‘nse’

The efficiency or error criterion. Should be one of

‘nse': Nash-Sutcliffe Efficiency
‘nnse': Normalized Nash-Sutcliffe Efficiency
‘kge': Kling-Gupta Efficiency
‘mae': Mean Absolute Error
‘mape': Mean Absolute Percentage Error
‘mse': Mean Squared Error
‘rmse': Root Mean Squared Error
‘lgrm': Logarithmic

end_warmupstr, pandas.Timestamp or None, default None

The end of the warm-up period. Evaluation metrics will only be calculated between the end of the warm-up period and the end time. The value can be a string that can be interpreted as pandas.Timestamp. The end_warmup date value must be between the start time and the end time defined in Model.setup.

Note

If not given, the metrics will be computed for the entire period.

Returns:

metricsnumpy.ndarray: An array of shape (n,) representing the computed evaluation metric for n catchment(s).

Examples

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

Optimize Model

>>> model.optimize()
</> Optimize
    At iterate      0    nfg =     1    J =      0.695010    ddx = 0.64
    At iterate      1    nfg =    30    J =      0.098411    ddx = 0.64
    At iterate      2    nfg =    59    J =      0.045409    ddx = 0.32
    At iterate      3    nfg =    88    J =      0.038182    ddx = 0.16
    At iterate      4    nfg =   117    J =      0.037362    ddx = 0.08
    At iterate      5    nfg =   150    J =      0.037087    ddx = 0.02
    At iterate      6    nfg =   183    J =      0.036800    ddx = 0.02
    At iterate      7    nfg =   216    J =      0.036763    ddx = 0.01
    CONVERGENCE: DDX < 0.01

Compute the Mean Squared Error for all catchments

>>> smash.metrics(model, metric="mse")
array([44.78328323,  4.38410997,  0.50611502])

The Kling-Gupta Efficiency

>>> smash.metrics(model, metric="kge")
array([0.94752783, 0.84582865, 0.8045246 ])

Add end warm-up date (1 month)

>>> model.setup.start_time, model.setup.end_time
('2014-09-15 00:00', '2014-11-14 00:00')
>>> end_warmup = "2014-10-14 00:00"

Compute the Nash-Sutcliffe Efficiency with and without warm-up

>>> smash.metrics(model, metric="nse")
array([0.96327233, 0.90453297, 0.84956211])
>>> smash.metrics(model, metric="nse", end_warmup=end_warmup)
array([0.97294462, 0.91321117, 0.86573941])