bijanf/k-ppen-climate-classification-analysis

Köppen Climate Classification Analysis

Overview

This repository contains Python code for analyzing global and Asia-specific Köppen-Geiger climate classification data. The analysis is based on the 1 km global Köppen–Geiger climate classification maps for present (1980-2016) and future scenarios. This work leverages the methodology outlined by Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007) in their seminal paper "Updated world map of the Köppen-Geiger climate classification" published in Hydrology and Earth System Sciences Discussions.

Files and Modules

The repository is structured into four primary Python files, each serving a unique role in the data analysis process:

• main.py: The entry point of the analysis, orchestrating the workflow including data loading, processing, and visualization based on the Köppen-Geiger classification.

• koeppen_colors.py: Defines the color mappings for the different Köppen-Geiger climate types, facilitating visual representations of climate classifications.

• koppen_mappings.py: Contains mappings of climate codes to their respective Köppen-Geiger climate categories, enabling classification and analysis of climate data.

• utilities.py: Provides utility functions that support data manipulation and processing tasks, such as reading data files, interpolating missing values, and more.

How It Works

The code analyzes climate data by first categorizing geographical areas according to the Köppen-Geiger climate classification system. It then uses these classifications to perform various analyses, including comparing present and future climate scenarios. Visual representations of the data are generated using color codes defined for each climate type, aiding in the intuitive understanding of climate shifts and variations.

Usage

To run the analysis, ensure that Python 3.6 or later is installed on your system. Follow these steps:

1. Clone the repository to your local machine.
2. Install the required Python dependencies listed in requirements.txt (if provided).
3. Execute the main.py script from the terminal: python main.py.

Dependencies

• Matplotlib (for data visualization)
• NumPy (for numerical computations)
• (Additional dependencies may be listed in a requirements.txt file.)

How to cite

You can cite all versions by using the DOI 10.5281/zenodo.10635816. This DOI represents all versions, and will always resolve to the latest one. url = https://zenodo.org/records/10635817

Citation

If you use this code or the Köppen-Geiger climate classification data in your research, please cite the following paper:

Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences Discussions, 4(2), 439-473.

Brun, Philipp; Zimmermann, Niklaus E.; Hari, Chantal; Pellissier, Loïc; Karger, Dirk Nikolaus (2022). A novel set of global climate-related predictors at kilometreresolution. EnviDat. https://doi.org/10.5194/essd-2022-212

Brun, Philipp; Zimmermann, Niklaus E.; Hari, Chantal; Pellissier, Loïc; Karger, Dirk Nikolaus (2022). CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution. EnviDat. https://doi.org/10.16904/envidat.332