Global Climatic Moisture Index Zones Derived via the FAO Penman-Monteith Reference Evapotranspiration (ET0) and the Modified Hargreaves-Thornton Equations

In July 2025, a critical update (as version 3.1) was made available of the Global Aridity Index and Potential Evapotranspiration (ET0) Database (here: GAIPED). Methods for developing version 3.0 of the GAIPED database are detailed by Zomer et al. (2022).

In this archive, a comparison is made between Climatic Moisture Index zones calculated via (i) the Potential Evapotranspiration (ET₀) obtained from GAIPED with (ii) the Potential Evapotranspiration (annualPET) obtained via the envirem packages (function envirem::generateEnvirem). Envirem applies the modified Hargreaves-Thornton equations that were used to generate version 1.0 of GAIPED. Annual precipitation data (bioclimatic variable BIO12) that are also required to calculate the Climatic Moisture Index were obtained via WorldClim 2.1 (Fick & Hijmans 2017 ; https://www.worldclim.org/data/worldclim21.html), where the same geospatial data were used to calculate the aridity index in GAIPED. The Climatic Moisture Index was calculated via envirem::climaticMoistureIndex.  

Zonations follow the same system that was implemented when developing the Tree Globally Observed Environmental Ranges (TreeGOER) database (Kindt 2023) and the TreeGOER Global Zones atlas (https://zenodo.org/records/8252756), where the dryland classification system of hyper-arid, arid, semi-arid and dry subhumid zones was complemented by three humid zones.

Change maps were calculated by subtracting the code of the CMI zones calculated via envirem from the CMI zones calculated via the GAIPED database.

Maps included in this archive were created via the terra package (Hijmans 2025, version 1.8-70) in the R 4.5.1 environment. Added country boundaries were obtained from Natural Earth as Admin 0 – countries vector layers (version 5.1.1).

References

• Zomer, R.J.; Xu, J.; Trabucco, A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. Scientific Data 9, 409. https://www.nature.com/articles/s41597-022-01493-1
• Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 41(2), 291–307. https://doi.org/10.1111/ecog.02880
• Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086
• Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914 .
• Kindt, R. (2024). TreeGOER: Tree Globally Observed Environmental Ranges (2024.07) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13132613
• Hijmans, R. (2025). terra: Spatial Data Analysis. R package version 1.8-70, https://CRAN.R-project.org/package=terra .

The development of this archive was supported by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia, by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation project and through the Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso, by the Bezos Earth Fund to the Bezos Quality Tree Seed for Africa in Kenya and Rwanda project and by the German International Climate Initiative (IKI) to the regional tree seed programme on The Right Tree for the Right Place for the Right Purpose in Africa.