This repository shares two interactive Jupyter Notebooks to learn about the calibration of a lumped rainfall-runoff model (the GR6J model)

• GR6J_OATAnalysis_Notebook.ipynb uses one year of data (precipitation, potential evapotranspiration and flow) from an example catchment, to begin to learn about the role of the different parameters, and how varying them changes the shape of the simulated hydrograph and a set of performance metrics
• GR6J_AutCal_Notebook.ipynb applies model calibration to the same example catchment using a simple Monte Carlo simulation approach, whereby a prescribed number of possible parameter combinations are randomly generated and run through the model, and the one that maximise the chosen performance metric is selected.

The repository also includes the python code to simulate the GR6J model and to calculate performance metrics. More explanation and scientific references are embedded in the Notebooks and code.