Articles | Volume 11, issue 10
Geosci. Model Dev., 11, 4139–4153, 2018
Geosci. Model Dev., 11, 4139–4153, 2018
Model evaluation paper
12 Oct 2018
Model evaluation paper | 12 Oct 2018

Global hydro-climatic biomes identified via multitask learning

Christina Papagiannopoulou et al.

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Cited articles

Ando, R. K. and Zhang, T.: A framework for learning predictive structures from multiple tasks and unlabeled data, J. Mach. Learn. Res., 6, 1817–1853, 2005.
Baker, B., Diaz, H., Hargrove, W., and Hoffman, F.: Use of the Köppen–Trewartha climate classification to evaluate climatic refugia in statistically derived ecoregions for the People's Republic of China, Climatic Change, 98, 113,, 2009.
Bartholomé, E. and Belward, A. S.: GLC2000: a new approach to global land cover mapping from Earth observation data, Int. J. Remote Sens., 26, 1959–1977, 2005.
Barzilai, A. and Crammer, K.: Convex multi-task learning by clustering, in: Artificial Intelligence and Statistics, San Diego, California, USA, 9–12 May 2015, 65–73, 2015.
Baxter, J.: A Bayesian/information theoretic model of learning to learn via multiple task sampling, Mach. Learn., 28, 7–39, 1997.
Short summary
Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global hydro-climatic biomes correspond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.