BhutanBioClims: High-resolution (250 m) historical and projected (CMIP6) bioclimatic variables for Bhutan

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This collection provides 121 sets of 19 bioclimatic variables (see Booth et al. 2014) describing the historical (1986–2015) and projected future (CMIP6) climates of Bhutan with a spatial resolution of 250 m. The future 19 bioclimatic variables include four shared socio-economic pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) (O'Neill et al., 2016; Riahi et al., 2017) and three periods (2021–2050, 2051–2080, and 2071–2100) using 10 global climate models (GCMs). These data can be used for many applications in environmental and agricultural science.
Lineage: Each of the 19 bioclimatic variables (see Booth et al., 2014) were generated in R using the dismo package (Hijmans et al., 2017). CMIP6 GCM outputs were acquired from the Copernicus Climate Change Service (C3S) (https://cds.climate.copernicus.eu/). The CMIP6 GCM outputs are downscaled against historical data, developed with the national weather station network (Stewart et al. 2017, Stewart et al. 2021), using the delta change method applied to anomalies interpolated using bivariate thin plate splines (i.e., a function of easting and northing). Further details regarding this collection are provided in the attached README document.

Coordinate reference system: EPSG:5266 - DRUKREF 03 / Bhutan National Grid.

References:

Booth, T. H., Nix, H. A., Busby, J. R., & Hutchinson, M. F. (2014). bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Diversity and Distributions, 20(1), 1-9. doi:10.1111/ddi.12144

Dorji S, Stewart S, Shabbir A, Bajwa A, Aziz A, & Adkins S. (2025). Comparative Analysis of Mechanistic and Correlative Models for Global and Bhutan-Specific Suitability of Parthenium Weed and Vulnerability of Agriculture in Bhutan. Plants, 14(1). doi:10.3390/plants14010083

Hijmans, R. J., Phillips, S., Leathwick, J. R., & Elith, J. (2017). dismo: Species Distribution Modeling. R package version 1.1-4. Retrieved from https://CRAN.R-project.org/package=dismo

O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., . . . Sanderson, B. M. (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev., 9(9), 3461-3482. doi:10.5194/gmd-9-3461-2016

Riahi K, van Vuuren DP, Kriegler E, Edmonds J, O’Neill BC, Fujimori S, . . . Tavoni M (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42:153-168.

Stewart, S. B., Choden, K., Fedrigo, M., Roxburgh, S. H., Keenan, R. J., & Nitschke, C. R. (2017). The role of topography and the north Indian monsoon on mean monthly climate interpolation within the Himalayan Kingdom of Bhutan. International Journal of Climatology, 37(S1), 897-909. doi:10.1002/joc.5045

Stewart, S. B., Fedrigo, M., Kasel, S., Roxburgh, S. H., Choden, K., Tenzin, K., . . . Nitschke, C. R. (2021). Interpolated climate variables for the Himalayan Kindom of Bhutan [Raster]. Retrieved from: https://doi.org/10.25919/m8yh-gt42