Climate Data, Köppen-Geiger Subclasses and Python Scripts for "A Köppen-Geiger Classification Derivative Tailored for Numerical Modeling of Ecosystems within Watershed Dynamics"

CC_files_sorted.tar.gz This archive contains: MODAL2_E_CLD_FR_YR-MONTH-DAY.FLOAT/ – All the TIFF files used to extract cloud cover data, which were provided as input parameters for the sampling plan through the main_weather.py Python script. sampling_plan.tar.gz This archive contains: All the Python scripts used to extract climate data for building the subclasses (with main_weather.py used to initiate the database construction); Two Python scripts (check_if_absent.py followed by check_if_only_partial.py) used to validate the database construction phase. The files (blocks) containing the sampling plan (latitude, longitude) resultat_degre_polynome_10.tar.gz This archive contains: PolyFit_sous_climats_data/ – For each subclass, the data of the polynomial regression, including fitted values and confidence bands; PolyFit_sous_climats_Figures/ – For each subclass, figures of the polynomial regression with the associated confidence bands. Additional material will be made available shortly in a v2. It will include: Subclass-to-group assignment files corresponding to group numbers (transverse classification) ranging from 8 to 20; All scripts and datasets used to perform GLM simulations of Lake Mendota under stationary conditions.