Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 5
Articles | Volume 15, issue 5
https://doi.org/10.5194/gmd-15-1899-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1899-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 5
Model description paper
 | 
08 Mar 2022
Model description paper |  | 08 Mar 2022

Building a machine learning surrogate model for wildfire activities within a global Earth system model

Qing Zhu, Fa Li, William J. Riley, Li Xu, Lei Zhao, Kunxiaojia Yuan, Huayi Wu, Jianya Gong, and James Randerson

Supplement

https://doi.org/10.5194/gmd-15-1899-2022-supplement

Data sets

Global Fire Emissions Database, Version 4: https://daac.ornl.gov/VEGETATION/guides/fire_emissions_v4.html J. T. Randerson, G. R. van der Werf, L. Giglio, G. J. Collatz, and P. S. Kasibhatla https://doi.org/10.3334/ORNLDAAC/1293

Fire_cci Burned Area dataset, Fire_CCI51 ESA https://geogra.uah.es/fire_cci/firecci51.php

Fire_cci long-term Burned Area dataset, Fire_CCILT11 ESA https://geogra.uah.es/fire_cci/fireccilt11.php

MODIS Collection 6 Active Fire Product User’s Guide Revision C L. Giglio, W. Schroeder, J. V. Hall, and C. O. Justice https://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_C.pdf

Global Fire Atlas FireAtlas https://www.globalfiredata.org/fireatlas.html

Model outputs: Quantitative assessment of fire and vegetation properties in historical simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project Stijn Hantson, Sam Rabin, Douglas I. Kelley, Almut Arneth, Sandy P. Harrison, Sally Archibald, Dominique Bachelet, Matthew Forrest, Thomas Hickler, Silvia Kloster, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Lars Nieradzik, I. Colin Prentice, Tim Sheehan, Stephen Sitch, Lena Teckentrup, Apostolos Voulgarakis, and Chao Yue https://doi.org/10.5281/zenodo.3555562

Model code and software

Building a machine learning surrogate model for wildfire activities within a global earth system model Q. Zhu https://doi.org/10.5281/zenodo.5508795

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Short summary
Wildfire is a devastating Earth system process that burns about 500 million hectares of land each year. It wipes out vegetation including trees, shrubs, and grasses and causes large losses of economic assets. However, modeling the spatial distribution and temporal changes of wildfire activities at a global scale is challenging. This study built a machine-learning-based wildfire surrogate model within an existing Earth system model and achieved high accuracy.
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