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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2017-152
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model description paper
13 Jul 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
A map of global peatland distribution created using machine learning for use in terrestrial ecosystem and earth system models
Yuanqiao Wu1, Ed Chan2, Joe R. Melton3, and Diana L. Verseghy4,* 1Firmex Inc. 110 Spadina Avenue, Suite 700, Toronto, ON M5V 2K4, Canada
2Climate Research Division, Environment and Climate Change Canada, 4905 Dufferin Street, Toronto, ON, M3H 5T4, Canada
3Climate Research Division, Environment and Climate Change Canada, Victoria, B.C., Canada
4Climate Research Division, Environment and Climate Change Canada, 4905 Dufferin Street, Toronto, ON, M3H 5T4, Canada
*retired
Abstract. Peatlands store large amounts of soil carbon and constitute an important component of the global carbon cycle. Accurate information on the global extent and distribution of peatlands is presently lacking but it important for earth system models (ESMs) to be able to simulate the effects of climate change on the global carbon balance. The most comprehensive peatland map produced to date is a qualitative presence/absence product. Here, we present a spatially continuous global map of peatland fractional coverage using the extremely randomized tree machine learning method suitable for use as a prescribed geophysical field in an ESM. Inputs to our statistical model include spatially distributed climate data, soil data and topographical slopes. Available maps of peatland fractional coverage for Canada and West Siberia were used along with a proxy for non-peatland areas to train and test the statistical model. Regions where the peatland fraction is expected to be zero were estimated from a map of topsoil organic carbon content below a threshold value of 13 kg/m2. The modelled coverage of peatlands yields a root mean square error of 4 % and a coefficient of determination of 0.91 for the 10,978 tested 0.5 degree grid cells. We then generated a complete global peatland fractional coverage map. In comparison with earlier qualitative estimates, our global modelled peatland map is able to reproduce peatland distributions in places remote from the training areas and capture peatland hot spots in both boreal and tropical regions, as well as in the southern hemisphere. Additionally we demonstrate that our machine-learning method has greater skill than solely setting peatland areas based on histosols from a soil database.

Citation: Wu, Y., Chan, E., Melton, J. R., and Verseghy, D. L.: A map of global peatland distribution created using machine learning for use in terrestrial ecosystem and earth system models, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-152, in review, 2017.
Yuanqiao Wu et al.
Yuanqiao Wu et al.
Yuanqiao Wu et al.

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Short summary
Peatlands are an important component of the carbon cycle that is expected to change under climate change, but accurate information on the global distribution of peatlands is presently unavailable. We use a machine-learning method to create a map of global peatland extent suitable for use in an Earth system model. For areas where data exists we find excellent agreement with observations and our method has greater skill than solely using soil datasets to estimate peatland coverage.
Peatlands are an important component of the carbon cycle that is expected to change under...
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