hydrosignatures.hydrosignatures#

Function for computing hydrologic signature.

Module Contents#

class hydrosignatures.hydrosignatures.HydroSignatures#

Hydrological signatures.

Parameters:

  • q_mmpt (pandas.Series) – Discharge in mm per unit time (the same timescale as precipitation).

  • p_mmpt (pandas.Series) – Precipitation in mm per unit time (the same timescale as discharge).

  • si_method (str, optional) – Seasonality index method. Either walsh or markham. Default is walsh.

  • fdc_slope_bins (tuple of int, optional) – The percentage bins between 1-100 to compute the slope of FDC within it, defaults to (33, 67).

  • bfi_alpha (float, optional) – Alpha parameter for baseflow separation filter using Lyne and Hollick method. Default is 0.925.

property signature_names: dict[str, str]#

Return a dictionary with the hydrological signatures.

property values: SignaturesFloat#

Return a dictionary with the hydrological signatures.

bfi()#

Compute Baseflow Index.

diff(other)#

Compute absolute difference between two hydrological signatures.

fdc()#

Compute exceedance probability (for flow duration curve).

fdc_slope()#

Compute FDC slopes between a list of lower and upper percentiles.

isclose(other)#

Check if the signatures are close between with a tolerance of 1e-3.

mean_annual_flood()#

Compute mean annual flood.

mean_monthly()#

Compute mean monthly flow (for regime curve).

runoff_ratio()#

Compute total runoff ratio.

seasonality_index()#

Compute seasonality index.

streamflow_elasticity()#

Compute streamflow elasticity.

to_dict()#

Return a dictionary with the hydrological signatures.

to_json()#

Return a JSON string with the hydrological signatures.

hydrosignatures.hydrosignatures.compute_ai(pet, prcp)#
hydrosignatures.hydrosignatures.compute_ai(pet: pandas.DataFrame, prcp: pandas.DataFrame) → pandas.Series
hydrosignatures.hydrosignatures.compute_ai(pet: xarray.DataArray, prcp: xarray.DataArray) → xarray.DataArray

Compute (Budyko) aridity index (PET/Prcp).

Parameters:
Returns:

float or pandas.Series or xarray.DataArray – The aridity index.

hydrosignatures.hydrosignatures.compute_baseflow(discharge, alpha=0.925, n_passes=3, pad_width=10)#

Extract baseflow using the Lyne and Hollick filter (Ladson et al., 2013).

Parameters:

  • discharge (numpy.ndarray or pandas.DataFrame or pandas.Series or xarray.DataArray) – Discharge time series that must not have any missing values. It can also be a 2D array where each row is a time series.

  • n_passes (int, optional) – Number of filter passes, defaults to 3. It must be an odd number greater than 3.

  • alpha (float, optional) – Filter parameter that must be between 0 and 1, defaults to 0.925.

  • pad_width (int, optional) – Padding width for extending the data from both ends to address the warm up issue.

Returns:

numpy.ndarray or pandas.DataFrame or pandas.Series or xarray.DataArray – Same discharge input array-like but values replaced with computed baseflow values.

hydrosignatures.hydrosignatures.compute_bfi(discharge, alpha=0.925, n_passes=3, pad_width=10)#

Compute the baseflow index using the Lyne and Hollick filter (Ladson et al., 2013).

Parameters:

  • discharge (numpy.ndarray or pandas.DataFrame or pandas.Series or xarray.DataArray) – Discharge time series that must not have any missing values. It can also be a 2D array where each row is a time series.

  • n_passes (int, optional) – Number of filter passes, defaults to 3. It must be an odd number greater than 3.

  • alpha (float, optional) – Filter parameter that must be between 0 and 1, defaults to 0.925.

  • pad_width (int, optional) – Padding width for extending the data from both ends to address the warm up issue.

Returns:

numpy.float64 – The baseflow index.

hydrosignatures.hydrosignatures.compute_exceedance(daily, threshold=0.001)#

Compute exceedance probability from daily data.

Parameters:

  • daily (pandas.Series or pandas.DataFrame) – The data to be processed

  • threshold (float, optional) – The threshold to compute exceedance probability, defaults to 1e-3.

Returns:

pandas.Series or pandas.DataFrame – Exceedance probability.

hydrosignatures.hydrosignatures.compute_fdc_slope(discharge, bins, log)#

Compute FDC slopes between the given lower and upper percentiles.

Parameters:
Returns:

numpy.ndarray – The slopes between the given percentiles.

hydrosignatures.hydrosignatures.compute_flood_moments(streamflow)#

Compute flood moments (MAF, CV, CS) from streamflow.

Parameters:

streamflow (pandas.DataFrame) – The streamflow data to be processed

Returns:

pandas.DataFrame – Flood moments; mean annual flood (MAF), coefficient of variation (CV), and coefficient of skewness (CS).

hydrosignatures.hydrosignatures.compute_mean_monthly(daily, index_abbr=False)#

Compute mean monthly summary from daily data.

Parameters:

  • daily (pandas.Series or pandas.DataFrame) – The data to be processed

  • index_abbr (bool, optional) – Whether to use abbreviated month names as index instead of numbers, defaults to False.

Returns:

pandas.Series or pandas.DataFrame – Mean monthly summary.

hydrosignatures.hydrosignatures.compute_rolling_mean_monthly(daily)#

Compute rolling mean monthly.

hydrosignatures.hydrosignatures.compute_si_markham(data)#

Compute seasonality index based on Markham, 1970.

hydrosignatures.hydrosignatures.compute_si_walsh(data)#

Compute seasonality index based on Walsh and Lawler, 1981 method.

hydrosignatures.hydrosignatures.extract_extrema(ts, var_name, n_pts)#

Get local extrema in a time series.

Parameters:

  • ts (pandas.Series) – Variable time series.

  • var_name (str) – Variable name.

  • n_pts (int) – Number of points to consider for detecting local extrema on both sides of each point.

Returns:

pandas.DataFrame – A dataframe with three columns: var_name, peak (bool) and trough (bool).