OSeMOSYS / CLEWs_GAEZ

The GAEZ v4 land and water data processing for CLEWs modelling
MIT License
2 stars 4 forks source link

Introduction

GeoCLEWs offers an extensive set of beneficial features for developers and users to process high-resolution land and water data for Climate, Land, Energy, and Water systems (CLEWs) modelling. This tool provides:

GeoCLEWs performs processing and calibration of agro-climatic potential yield, crop water deficit, crop evapotranspiration, precipitation, and land cover. Outputs can be efficiently combined with additional data for CLEWs modelling, such as electricity information, to create a detailed CLEWs model without implementing complicated and time-consuming spatial processing. Jupiter Notebook code provides a comprehensive and detailed explanation of the processing steps involved.

Note: GeoCLEWs has been successfully tested and verified on Windows machines. However, there may be incompatibility issues with other operating systems due to differences in Python packages or their versions.

Contributors:

Yalda Saedi - Developer
Taco Niet - Supervisor

Release Notes

Version 2.0.0 (July 2024):

Version 1.0.0 (December 2023):

Creating and Activating the Environment

Please ensure that you install all the necessary Python packages and their dependencies for the smooth functioning of the script. An environment YAML file, 'environment.yml', is provided to facilitate reproducing of the Python environment and avoid any potential version conflicts. To set up the environment, run the following command:

conda env create -f environment.yml
conda activate GeoCLEWs

Download Files and Data

GeoCLEWs with its adaptable design allows for running with any arbitrary shape and incorporating the geospatial characteristics of any geographical region. Additionally, users have the option to utilize the open-source GADM dataset , which provides various levels of administrative boundaries for all countries. With this flexibility, users can seamlessly tailor the analysis to their specific needs and explore a wide range of geospatial data. It is essential to make sure to follow the same naming format as provided in the examples.

Please note: Ensure that all necessary files, such as .shx, .shp, etc., are included. For example, for Kenya, you’ll need to input the following files: KEN_adm0.gpkg, KEN_adm0.prj, KEN_adm0.shp, KEN_adm0.shx, and so on.

Please make sure that all required files, such as .shx, .shp, and others, are included. For instance, for Kenya, you'll need to input files like KEN_data.gpkg, KEN_data.prj, KEN_data.shp, KEN_data.shx, and so forth.

Note: Two datasets (two collections of shapefiles including "..._ adm0" and “…_data”) corresponding to the selected country need to be downloaded and placed inside the 'Data/input' folder. If processing at administrative level 0, the same dataset with different naming formats should be used and placed together in the same directory.

Outputs

GeoCLEWs code produces tabular results in a CSV format which is compatible with clewsy for CLEWs modelling, along with interactive graphs. The GAEZ portal provides continuous raster data representing crop yields in kg DW/ha and crop water deficit, precipitation, and crop evapotranspiration in millimeter. Units presented in this analysis are recalculated based on the CLEWs framework, and therefore, the million tonnes per 1000 km² unit of measurement is used to quantify agro-climatic potential yield. Crop water deficit, crop evapotranspiration, and precipitation are calculated in BCM (billion cubic meters) per 1000 km². These units have been chosen to ensure consistency with the CLEWs methodology and facilitate comparability with other studies. The output of categorical land cover raster data is summarized in units of square kilometers.

Contact

For any inquiries, please contact Yalda Saedi.