Python implementation of a synthetic streamflow generator based on Cholesky decomposition. Method developed by Kirsch et al. (2013)

At the moment the implementation is not so efficient 😞 could be many things to correct. However, it can generate 10000s of samples for different locations in a couple of seconds.

This class is inspired by the Matlab implementation found in:

• https://github.com/jdherman/Qsynth, and
• https://github.com/julianneq/Kirsch-Nowak_Streamflow_Generator

dataframe: DataFrame A pandas DataFrame containing the historical inflows for the different locations in the basing. Each location must to be in a separately column in the DataFrame. Note: be sure you remove the index column and any str column name.

monthly: bool, default False If True your historical inflows are in a monthly timestep.

weekly: bool, default False If True your historical inflows are in a weekly timestep.

daily: bool, default False If True your historical inflows are in a daily timestep.

sample_size: integer, default None Number of sample to generate by each inflows locations.

• Kirsch, B. R., G. W. Characklis, and H. B. Zeff (2013), Evaluating the impact of alternative hydro-climate scenarios on transfer agreements: Practical improvement for generating synthetic streamflows, Journal of Water Resources Planning and Management, 139(4), 396–406.

• Herman, J.D, H.B. Zeff, J.R. Lamontagne, P.M. Reed, and G.W. Characklis, Synthetic drought scenario generation to support bottom-up water supply vulnerability assessments, Journal of Water Resources Planning and Management, 142(11), 04016050, 2016.