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.
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.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
08 Mar 2022
Model description paper |
| 08 Mar 2022
| 08 Mar 2022
Building a machine learning surrogate model for wildfire activities within a global Earth system model
Qing Zhu
CORRESPONDING AUTHOR
Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Fa Li
Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
William J. Riley
Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Li Xu
Department of Earth System Science, University of California Irvine, Irvine, CA, USA
Department of Civil and Environmental Engineering, University of
Illinois Urbana-Champaign, Champaign, IL, USA
Kunxiaojia Yuan
Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Huayi Wu
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Jianya Gong
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan, China
James Randerson
Department of Earth System Science, University of California Irvine, Irvine, CA, USA
Viewed
Total article views: 6,281 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 23 Apr 2021)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 4,281 | 1,823 | 177 | 6,281 | 266 | 184 | 228 |
- HTML: 4,281
- PDF: 1,823
- XML: 177
- Total: 6,281
- Supplement: 266
- BibTeX: 184
- EndNote: 228
Total article views: 4,568 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Mar 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 3,371 | 1,049 | 148 | 4,568 | 266 | 166 | 216 |
- HTML: 3,371
- PDF: 1,049
- XML: 148
- Total: 4,568
- Supplement: 266
- BibTeX: 166
- EndNote: 216
Total article views: 1,713 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 23 Apr 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 910 | 774 | 29 | 1,713 | 18 | 12 |
- HTML: 910
- PDF: 774
- XML: 29
- Total: 1,713
- BibTeX: 18
- EndNote: 12
Viewed (geographical distribution)
Total article views: 6,281 (including HTML, PDF, and XML)
Thereof 5,913 with geography defined
and 368 with unknown origin.
Total article views: 4,568 (including HTML, PDF, and XML)
Thereof 4,329 with geography defined
and 239 with unknown origin.
Total article views: 1,713 (including HTML, PDF, and XML)
Thereof 1,584 with geography defined
and 129 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
31 citations as recorded by crossref.
- Enhancing Alaskan wildfire prediction and carbon flux estimation: a two-stage deep learning approach within a process-based model H. Seo & Y. Kim https://doi.org/10.1088/1748-9326/ad8bdc
- Tree-ring based forest model calibrations with a deep learning algorithm X. Yu et al. https://doi.org/10.1016/j.foreco.2024.122154
- Advancements in Artificial Intelligence Applications for Forest Fire Prediction H. Liu et al. https://doi.org/10.3390/f16040704
- Reconstructed global monthly burned area maps from 1901 to 2020 Z. Guo et al. https://doi.org/10.5194/essd-17-3599-2025
- Projection of Future Fire Emissions Over the Contiguous US Using Explainable Artificial Intelligence and CMIP6 Models S. Wang et al. https://doi.org/10.1029/2023JD039154
- From Ecological Monitoring to Prevention Decision Support: A Critical Review of Artificial Intelligence for Forest Fire Prevention S. Feng et al. https://doi.org/10.3390/f17070817
- Relevance of earth observations of essential climate variables in wildfire adaptation S. Seitzinger et al. https://doi.org/10.1016/j.rse.2025.115082
- Benchmarking ozone stress parameterizations in CLM5: a global mechanistic assessment of thresholds and memory effects P. Zhou et al. https://doi.org/10.5194/gmd-19-5491-2026
- Forecasting Crop Residue Fires in Northeastern China Using Machine Learning B. Bai et al. https://doi.org/10.3390/atmos13101616
- AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics F. Li et al. https://doi.org/10.5194/gmd-16-869-2023
- Applications of Artificial Intelligence in Restoration of Degraded Ecosystems H. Negi & S. Tewari https://doi.org/10.36808/if/2026/v152i6A/171304
- Reimagine fire science for the anthropocene J. Shuman et al. https://doi.org/10.1093/pnasnexus/pgac115
- Deep Learning Surrogate Models of JULES-INFERNO for Wildfire Prediction on a Global Scale S. Cheng et al. https://doi.org/10.1109/TETCI.2024.3445450
- Global fire modelling and control attributions based on the ensemble machine learning and satellite observations Y. Zhang et al. https://doi.org/10.1016/j.srs.2023.100088
- ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model Y. Liu et al. https://doi.org/10.5194/gmd-18-4103-2025
- Machine learning-based surrogate modelling of a robust, sustainable development goal (SDG)-compliant land-use future for Australia at high spatial resolution M. Khan et al. https://doi.org/10.1016/j.jenvman.2024.121296
- Global warming amplifies wildfire health burden and reshapes inequality J. Zhao et al. https://doi.org/10.1038/s41586-025-09612-9
- Methods and technologies for impact-based early warning of weather and geo-hazards: Overview and perspectives for multi-risk integration A. Zollo et al. https://doi.org/10.1016/j.jemets.2026.100038
- A Generative Model for Surrogates of Spatial-Temporal Wildfire Nowcasting S. Cheng et al. https://doi.org/10.1109/TETCI.2023.3298535
- RTM Surrogate Modeling in Optical Remote Sensing: A Review of Emulation for Vegetation and Atmosphere Applications J. Verrelst et al. https://doi.org/10.3390/rs17213618
- A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0) L. Li et al. https://doi.org/10.5194/gmd-16-4017-2023
- Comparison of Individual Sensors in the Electronic Nose for Stress Detection in Forest Stands T. Hüttnerová et al. https://doi.org/10.3390/s23042001
- Approaches for simulating alternative futures of complex forested landscapes: A review P. Bettinger et al. https://doi.org/10.1016/j.envdev.2025.101285
- Causal hybrid modeling with double machine learning—applications in carbon flux modeling K. Cohrs et al. https://doi.org/10.1088/2632-2153/ad5a60
- Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability F. Ji et al. https://doi.org/10.1016/j.rse.2025.114818
- A Review on Fire Research of Electric Power Grids of China: State-Of-The-Art and New Insights Z. Jiaqing et al. https://doi.org/10.1007/s10694-022-01343-x
- Integration of a Deep‐Learning‐Based Fire Model Into a Global Land Surface Model R. Son et al. https://doi.org/10.1029/2023MS003710
- Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application C. Natel et al. https://doi.org/10.5194/gmd-18-4317-2025
- Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. https://doi.org/10.1088/1748-9326/adad02
- Current model capabilities and future predictive pathways of wildfires under a changing climate C. Whaley et al. https://doi.org/10.1038/s43247-026-04013-w
- Regional Prediction of Fire Characteristics Using Machine Learning in Australia Z. Abohaia et al. https://doi.org/10.3390/fire8080330
31 citations as recorded by crossref.
- Enhancing Alaskan wildfire prediction and carbon flux estimation: a two-stage deep learning approach within a process-based model H. Seo & Y. Kim https://doi.org/10.1088/1748-9326/ad8bdc
- Tree-ring based forest model calibrations with a deep learning algorithm X. Yu et al. https://doi.org/10.1016/j.foreco.2024.122154
- Advancements in Artificial Intelligence Applications for Forest Fire Prediction H. Liu et al. https://doi.org/10.3390/f16040704
- Reconstructed global monthly burned area maps from 1901 to 2020 Z. Guo et al. https://doi.org/10.5194/essd-17-3599-2025
- Projection of Future Fire Emissions Over the Contiguous US Using Explainable Artificial Intelligence and CMIP6 Models S. Wang et al. https://doi.org/10.1029/2023JD039154
- From Ecological Monitoring to Prevention Decision Support: A Critical Review of Artificial Intelligence for Forest Fire Prevention S. Feng et al. https://doi.org/10.3390/f17070817
- Relevance of earth observations of essential climate variables in wildfire adaptation S. Seitzinger et al. https://doi.org/10.1016/j.rse.2025.115082
- Benchmarking ozone stress parameterizations in CLM5: a global mechanistic assessment of thresholds and memory effects P. Zhou et al. https://doi.org/10.5194/gmd-19-5491-2026
- Forecasting Crop Residue Fires in Northeastern China Using Machine Learning B. Bai et al. https://doi.org/10.3390/atmos13101616
- AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics F. Li et al. https://doi.org/10.5194/gmd-16-869-2023
- Applications of Artificial Intelligence in Restoration of Degraded Ecosystems H. Negi & S. Tewari https://doi.org/10.36808/if/2026/v152i6A/171304
- Reimagine fire science for the anthropocene J. Shuman et al. https://doi.org/10.1093/pnasnexus/pgac115
- Deep Learning Surrogate Models of JULES-INFERNO for Wildfire Prediction on a Global Scale S. Cheng et al. https://doi.org/10.1109/TETCI.2024.3445450
- Global fire modelling and control attributions based on the ensemble machine learning and satellite observations Y. Zhang et al. https://doi.org/10.1016/j.srs.2023.100088
- ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model Y. Liu et al. https://doi.org/10.5194/gmd-18-4103-2025
- Machine learning-based surrogate modelling of a robust, sustainable development goal (SDG)-compliant land-use future for Australia at high spatial resolution M. Khan et al. https://doi.org/10.1016/j.jenvman.2024.121296
- Global warming amplifies wildfire health burden and reshapes inequality J. Zhao et al. https://doi.org/10.1038/s41586-025-09612-9
- Methods and technologies for impact-based early warning of weather and geo-hazards: Overview and perspectives for multi-risk integration A. Zollo et al. https://doi.org/10.1016/j.jemets.2026.100038
- A Generative Model for Surrogates of Spatial-Temporal Wildfire Nowcasting S. Cheng et al. https://doi.org/10.1109/TETCI.2023.3298535
- RTM Surrogate Modeling in Optical Remote Sensing: A Review of Emulation for Vegetation and Atmosphere Applications J. Verrelst et al. https://doi.org/10.3390/rs17213618
- A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0) L. Li et al. https://doi.org/10.5194/gmd-16-4017-2023
- Comparison of Individual Sensors in the Electronic Nose for Stress Detection in Forest Stands T. Hüttnerová et al. https://doi.org/10.3390/s23042001
- Approaches for simulating alternative futures of complex forested landscapes: A review P. Bettinger et al. https://doi.org/10.1016/j.envdev.2025.101285
- Causal hybrid modeling with double machine learning—applications in carbon flux modeling K. Cohrs et al. https://doi.org/10.1088/2632-2153/ad5a60
- Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability F. Ji et al. https://doi.org/10.1016/j.rse.2025.114818
- A Review on Fire Research of Electric Power Grids of China: State-Of-The-Art and New Insights Z. Jiaqing et al. https://doi.org/10.1007/s10694-022-01343-x
- Integration of a Deep‐Learning‐Based Fire Model Into a Global Land Surface Model R. Son et al. https://doi.org/10.1029/2023MS003710
- Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application C. Natel et al. https://doi.org/10.5194/gmd-18-4317-2025
- Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. https://doi.org/10.1088/1748-9326/adad02
- Current model capabilities and future predictive pathways of wildfires under a changing climate C. Whaley et al. https://doi.org/10.1038/s43247-026-04013-w
- Regional Prediction of Fire Characteristics Using Machine Learning in Australia Z. Abohaia et al. https://doi.org/10.3390/fire8080330
Saved (final revised paper)
Latest update: 19 Sep 2026
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.
Wildfire is a devastating Earth system process that burns about 500 million hectares of land...
