This is a Python implementation for calculating the Standard Precipitation Index (SPI). This is one of the key indicies in identifying droughts. See [NCAR's Climate Data Guide] (https://climatedataguide.ucar.edu/climate-data/standardized-precipitation-evapotranspiration-index-spei) for a usefull discussion of the relative merits of SPI vs SPEI.

These functions are loosely based on the SPEI package in R by Santiago Beguería and Sergio M. Vicente-Serrano.

There are many papers on SPI and SPEI. I found the paper which most clearly describes the algorithms is: Lloyd‐Hughes, Benjamin, and Mark A. Saunders. "A drought climatology for Europe." International journal of climatology 22.13 (2002): 1571-1592. This paper is included in the docs folder.

There is some consensus in the literature as to which distribution to fit historical data. For precipitation data only (SPI) it is suggested to use a Gamma distribution. This is the default distribution in the SPI function. However, the user can select their own distribution (see Notes).

The current implementation allows for the user to fit precipitation data with using either L-moments or Maximum Likelihood Estimation (MLE). It also allows for the fitting of daily, weekly, monthly or any custom time frame of SPI data.

Currently on compatible with Python3.

The following is a table of distributions used to fit the precipitation data. The table indicates whether the distribution is available for L-moments or MLE.

pip install standard-precip

For more detailed example see the example notebook.

Imports

import pandas as pd
from standard_precip.spi import SPI
from standard_precip.utils import plot_index

The SPI function expects the data to be in a Pandas DataFrame Read example monthly precipitation data (included in data folder).

rainfall_data = pd.read_csv('monthly_data.csv')

For this example we will calculate SPI, therefore initialize the SPI class

spi = SPI()

Calculate the 1-Month SPI using Gamma function and L-moments. You must indicate the date column and the precipitation column of the DataFrame. You can have a list of precipitation columns to process.

df_spi = new_spi.calculate(
    rainfall_data, 
    'date', 
    'precip', 
    freq="M", 
    scale=1, 
    fit_type="lmom", 
    dist_type="gam"
)

Calculate the 3-Month SPI using Gamma function and L-moments. You must indicate the date column and the precipitation column of the DataFrame. You can have a list of precipitation columns to process.

df_spi = new_spi.calculate(
    rainfall_data, 
    'date', 
    'precip', 
    freq="M", 
    scale=3, 
    fit_type="lmom", 
    dist_type="gam"
)

The freq parameter indicates the type of data you are using, daily, weekly, monthly. However, if you have a custom time period you are interested you can over-ride the freq parameter by using creating a column in the DataFrame for grouping the observations and indicating this column in the freq_col parameter. The distributions and indicies will be calculated using the integer grouping in the freq_col.

Plot data

fig = plot_index(df_spi, 'date', 'precip_scale_3_calculated_index')

1. Implement calculations of PET for SPEI
2. Add other drought indicators
3. Create functionality for finding best distribution based on data

1. Although the user is allowed to select the distribution (from scipy stats) that they wish to fit historical data to, one should be aware of the support of each particular distribution. Precipitation data can have zero values and P-PEI can take on negative values. This should be considered when selecting a distribution.

Author - Eric Nussbaumer (ebaumer@gmail.com)

Apache License, Version 2.0

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you may not use this file except in compliance with the License.
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    http://www.apache.org/licenses/LICENSE-2.0

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