This is the code repository for LRM Stochastic watershed model.

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Machine LearningLRM-Squannacook
Overview

LRM-Squannacook

Input data for generating stochastic streamflows are observed and simulated timeseries of streamflow. their format needs to be CSV with 2 columns for observed and simulated flow named "Qgage" and "Qmodel" respectively.

The "SWM file.py" generates stochastic streamflows with use of simple bootstrap LRM model (for more information on the model see Shabestanipour et.al 2022 (Submitted)). The "SWM_knn.py" generated stochastic streamflows with use of k-NN bootstrap LRM model.

For verifying and validating the stochastic flow generated with SWM or SWM_knn, use the file called "SWM_verify_validate.py".

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