This repository provides a set of notebooks that demonstrates various aspects of PCSE models.
The notebooks include introductory examples:
- 01 Getting Started with PCSE provides an impression of how PCSE works and what you can do with it
- 02 Running with custom input data shows how you can run a model using your own input data instead of the demonstration data.
- 03 Running LINTUL3 a similar example, but instead using the LINTUL3 model instead of WOFOST.
- 04 Running PCSE in batch mode demonstrates how to run PCSE simulation in batch for a series of crops and year
- 13 Simulating grassland productivity with LINGRA demonstrates the LINGRA model for simulating productivity of grasslands
Some more advanced features of PCSE are demonstrated in:
- 05 Using PCSE WOFOST with a CGMS8 database this shows how to retrieve data from a CGMS database and run crop model simulations with WOFOST using that data.
- 06 Advanced agromanagement with PCSE demonstrates advanced aspects of the agromanagement definitions including scheduling events based on date and state variables.
- 07 Running crop rotations provides insight on how to run crop rotations with PCSE models.
Finally, highly advanced subjects are treated that require quite some background knowledge and python programming skills:
- 08a Data assimilation with the EnKF provides an introduction to data assimilation with the ensemble Kalman filter.
- 08b Data assimilation with the EnKF multistate demonstrates how to effectively load multiple states into the EnKF state vector.
- 09 Optimizing parameters in a PCSE model demonstrates how to do parameter optimizations in PCSE.
- 10 Sensitivity analysis of WOFOST demonstrates how to use SAlib for sensitivity analysis
Using these notebooks generally require a python environment that includes the following packages:
- PCSE and its dependencies
- matplotlib
- The NLOPT optimization library (notebooks 09, 11)
- The SAlib library (notebook 10)