hydromt.stats.extremes.eva#

hydromt.stats.extremes.eva(da: DataArray, ev_type: str = 'BM', min_dist: int = 0, qthresh: float = 0.9, period: str = '365.25D', min_sample_size: int = 0, distribution: str | None = None, rps: ndarray = array([2, 5, 10, 25, 50, 100, 250, 500]), criterium: str = 'AIC') → Dataset[source]#

Return Extreme Value Analysis.

Extreme value analysis based on block maxima (BM) or Peaks Over Threshold (POT). The method selects the peaks, fits a distribution and calculates return values for provided return periods.

Parameters:

da (xr.DataArray) – Timeseries data, must have a regular spaced ‘time’ dimension.
ev_type ({"POT", "BM"}) – Peaks over threshold (POT) or block maxima (BM) peaks, by default “BM”
period (str, optional) – Period string, by default “365.25D”. See pandas.Timedelta for options.
min_dist (int, optional) – Minimum distance between peaks measured in time steps, by default 0
qthresh (float, optional) – Quantile threshold used with peaks over threshold method, by default 0.9
min_sample_size (int, optional) – Minimum number of finite values in a valid block, by default 0. Peaks of invalid blocks are set to NaN.
distribution (str, optional) – Short name of distribution. If None (default) the optimal block maxima distribution (“gumb” or “gev” for BM and “exp” or “gpd” for POT) is selected based on criterium.
rps (np.ndarray, optional) – Array of return periods, by default [2, 5, 10, 25, 50, 100, 250, 500]
criterium ({'AIC', 'AICc', 'BIC'}) – Selection criterium, by default “AIC”

Returns:

Dataset with peaks timeseries, distribution name and parameters and return values.

Return type:

xr.Dataset