Examples and Usage

This section shows briefly how to use the pydeltasnow package. Before you can start, please install the package as described in the Installation section.

After installing you can import the model alongside with pandas, which is necessary since pydeltasnow relies on pandas objects as input data:

from pydeltasnow import swe_deltasnow
import pandas as pd

Then you can access and calculate the snow water equivalent (SWE) with the DeltaSNOW model via:

hs_series = pd.read_csv("path/to/some/hs_data.csv",
                      parse_dates=['date'],
                      index_col='date').squeeze()

swe = swe_deltasnow(hs_series)

where hs_data is a pandas.Series with the snow depth (HS) data and a pandas.DatetimeIndex. The variable swe will also be a pandas.Series with the same index as the input data.

If you want to add a new column of the modeled SWE to an existing pandas.DataFrame which also holds the HS data, you can use the pandas.DataFrame.assign() operator to add a new column. Assume we load data from a .csv table in which the column hs holds the snow depth in [cm] and we want add a swe column in [m]:

data = (pd.read_csv("path/to/some/hs_data.csv",
                    parse_dates=['date'],
                    index_col='date')
        .assign(swe_in_m=lambda x: swe_deltasnow(x['hs'],
                                                 hs_input_unit='cm'
                                                 swe_output_unit='m')
        )

By default, the model uses the optimized parameters from the DeltaSNOW paper. You can change these parameters by overwriting the default parameters in the pydeltasnow.main.swe_deltasnow() function:

swe = swe_deltasnow(hs_series,
                    rho_max=450)

Please read the paper on how the parameters influence the behaviour of the model and what potential pitfalls are.

If your input data contains gaps that are irrelavant for you (i.e gaps that are surrounded or followed by gaps), you can ignore these gaps:

swe = swe_deltasnow(hs_series,
                    ignore_zerofollowed_gaps=True)

Otherwise, pydeltasnow.main.swe_deltasnow() will not accept any missing data in your input series. Additionally, you can interpolate gaps that are shorter or equally long as max_gap_length if you set interpolate_small_gaps to True.