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Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models
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Abstract
Abstract. Recent climate changes increases fire-prone weather conditions and likely affects fire occurrence, which might impact ecosystem functioning, biogeochemical cycles, and society. Prediction of how fire impacts may change in the future is difficult because of the complexity of the controls on fire occurrence and burned area. Here we aim to assess how process-based fire-enabled Dynamic Global Vegetation Models (DGVMs) represent relationships between controlling factors and burned area. We developed a pattern-oriented model evaluation approach using the random forest (RF) algorithm to identify emergent relationships between climate, vegetation, and socioeconomic predictor variables and burned area. We applied this approach to monthly burned area time series for the period 2005–2011 from satellite observations and from DGVMs from the Fire Model Inter-comparison Project (FireMIP) that were run using a common protocol and forcing datasets. The satellite-derived relationships indicate strong sensitivity to climate variables (e.g. maximum temperature, number of wet days), vegetation properties (e.g. vegetation type, previous-season plant productivity and leaf area, woody litter), and to socioeconomic variables (e.g. human population density). DGVMs broadly reproduce the relationships to climate variables and some models to population density. Interestingly, satellite-derived responses show a strong increase of burned area with previous-season leaf area index and plant productivity in most fire-prone ecosystems which was largely underestimated by most DGVMs. Hence our pattern-oriented model evaluation approach allowed to diagnose that current fire-enabled DGVMs represent some controls on fire to a large extent but processes linking vegetation productivity and fire occurrence need to be improved to accurately simulate the role of fire under global environmental change.
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“Further, coupled weather-fire models now capture the unfolding of complex landscape-scale events over a period of days (Coen, Stavros, & Fites-Kaufman, 2018), while global models continue to improve their representation of fire frequency, burned area and seasonality across years to decades (Forkel et al., 2019;Hantson et al., 2016).”
From Fire as a fundamental ecological process: Research advances and frontiers (2020) · cites this paper as Forkel et al., 2019
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“Chuvieco et al (2021) have analyzed the annual variability of biomass burning using ML models in 0.25 ◦ resolution grid; Forkel et al (2019) predicted regional burned areas in a monthly resolution in a 1.89 ◦ × 2.5 ◦ resolution grid; Zhang et al (2021) have developed convolutional neural network…”
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A monthly gridded burned area database of national wildland fire data
Gincheva, A., Pausas, J. G., Edwards, A., Provenzale, A., Cerdà, A., Hanes, C. C., Royé, D., Chuvieco, E., Mouillot, F., Vissio, G., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03141-2 · Cited by 20
Self-citationLand and Atmosphere Precursors to Fuel Loading, Wildfire Ignition and Post‐Fire Recovery
Alizadeh, M., Adamowski, J., & Entekhabi, D. (2024) · Geophysical Research Letters · doi:10.1029/2023gl105324 · Cited by 21
Improving the LPJmL4-SPITFIRE vegetation-fire model for South America using satellite data
Drüke, M., Forkel, M., von Bloh, W., Sakschewski, B., Cardoso, M., Bustamante, M., Kurths, J., & Thonicke, K. (2019) · doi:10.5194/gmd-2019-92 · Cited by 21
Self-citationDahan, K. S., Kasei, R. A., Husseini, R., Said, M. Y., & Rahman, M. M. (2023) · International Journal of Digital Earth · doi:10.1080/17538947.2023.2197263 · Cited by 24
“…weather conditions that are su ffi ciently dry to desiccate the fuel; and a human or natural ignition source (Abram et al. 2021; Bistinas et al. 2014; Forkel et al. 2019; Kelley et al. 2019; Pausas and Ribeiro 2013; Teck-entrup et al. 2019) Therefore, the in fl uences of climatic variables on the…”
BackgroundVitali, R., Belcher, C. M., Kaplan, J. O., & Watson, A. J. (2022) · Nature Communications · doi:10.1038/s41467-022-35081-z · Cited by 25
Theoretical uncertainties for global satellite-derived burned area estimates
Brennan, J. K., Gomez-Dans, J. L., Disney, M. I., & Lewis, P. (2019) · Biogeosciences · doi:10.5194/bg-16-3147-2019 · Cited by 22
“The uncertainties could, for example, be used to drive development and refinement of parameters in DGVMs related to fire processes or improve optimisation routines for parameter selection (Poulter et al., 2015; Forkel et al., 2019).”
BackgroundRegional Impacts Poorly Constrained by Climate Sensitivity
Swaminathan, R., Schewe, J., Walton, J. P. R. B., Zimmermann, K. D., Jones, C., Betts, R., Burton, C., JONES, C. D., Mengel, M., Reyer, C., et al. (2024) · Earth s Future · doi:10.1029/2024ef004901 · Cited by 23
“The difference between the total rainfall amounts of the top 20 such rain events from each period is then calculated as our flood metric.”
Modelling the daily probability of wildfire occurrence in the contiguous United States
Keeping, T., Harrison, S. P., & Prentice, I. C. (2024) · Environmental Research Letters · doi:10.1088/1748-9326/ad21b0 · Cited by 21
“Fuel accumulation is also determined by vegetation characteristics, including primary production and dominant plant type (Harrison et al 2010, Forkel et al 2019).”
BackgroundSelf-citationAtmospheric phosphorus and its geochemical cycling: Fundamentals, progress, and perspectives
Diao, X., Widory, D., Ram, K., Tripathee, L., Bikkina, S., Kawamura, K., Gao, S.-P., Wan, X., Wu, G.-M., Pei, Q.-M., et al. (2023) · Earth-Science Reviews · doi:10.1016/j.earscirev.2023.104492 · Cited by 19
Characterization of land cover-specific fire regimes in the Brazilian Amazon
Cano‐Crespo, A., Traxl, D., Prat-Ortega, G., Rolinski, S., & Thonicke, K. (2022) · Regional Environmental Change · doi:10.1007/s10113-022-02012-z · Cited by 21
Assessing anthropogenic influence on fire history during the Holocene in the Iberian Peninsula
Sweeney, L., Harrison, S. P., & Linden, M. V. (2022) · Quaternary Science Reviews · doi:10.1016/j.quascirev.2022.107562 · Cited by 22
Self-citationEffect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence
Mukunga, T., Forkel, M., Forrest, M., Zotta, R.-M., Pande, N., Schlaffer, S., & Dorigo, W. (2023) · Fire · doi:10.3390/fire6050197 · Cited by 18
Self-citationA global behavioural model of human fire use and management: WHAM! v1.0
Perkins, O., Kasoar, M. R., Voulgarakis, A., Smith, C., Mistry, J., & Millington, J. D. A. (2024) · Geoscientific model development · doi:10.5194/gmd-17-3993-2024 · Cited by 28
“Representation of humans was the biggest cause of disparity between model outputs (Teckentrup et al., 2019) and a major contributor to divergence between models and remotely sensed observations (Forkel et al., 2019).”
InfluentialZhang, Y., Mao, J., Ricciuto, D. M., Jin, M., Yu, Y., Shi, X., Wullschleger, S. D., Tang, R., & Liu, J. (2023) · Science of Remote Sensing · doi:10.1016/j.srs.2023.100088 · Cited by 20
Integration of a Deep‐Learning‐Based Fire Model Into a Global Land Surface Model
Son, R., Stacke, T., Gayler, V., Nabel, J. E. M. S., Schnur, R., Alonso, L., Requena‐Mesa, C., Winkler, A. J., Hantson, S., Zaehle, S., et al. (2024) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2023ms003710 · Cited by 29
“Representing interannual variability in global burnt area is yet a continuous effort for improvement in fire-enable DGVMs.”
BackgroundInfluentialSelf-citationHuang, H.-L., Xue, Y.-K., Li, F., & Liu, Y. (2020) · Geoscientific Model Development · doi:10.5194/gmd-2020-122-supplement · Cited by 16
“In addition, they are widely used to attribute historical variability of burned area to various climate and anthropogenic driving factors (Kloster et al., 2012; Andela et al., 2017; Forkel et al., 2019; Teckentrup et al., 2019).”
BackgroundMethodSelf-citationWildfires on a changing planet
Haas, O., Prentice, I. C., & Harrison, S. P. (2026) · Nature Communications · doi:10.1038/s41467-025-68176-4 · Cited by 23
Self-citationAttribution of extreme events to climate change in the Australian region – A review
Lane, T. P., King, A. D., Perkins-Kirkpatrick, S. E., Pitman, A. J., Alexander, L. V., Arblaster, J. M., Bindoff, N. L., Bishop, C. H., Black, M., Bradstock, R. A., et al. (2023) · Weather and Climate Extremes · doi:10.1016/j.wace.2023.100622 · Cited by 21
Perkins, O., Matej, S., Erb, K., & Millington, J. D. A. (2022) · Socio-Environmental Systems Modeling · doi:10.18174/sesmo.18130 · Cited by 24
“As a result, representations of human activity were found to be both the largest single cause of disagreement between burned area outputs of different DGVMs, and between model outputs and remote sensing observations (Forkel et al., 2019).”
BackgroundTeckentrup, L., Harrison, S., Hantson, S., Heil, A., Melton, J., Forrest, M., Li, F., Yue, C., Arneth, A., Hickler, T., et al. (2019) · doi:10.5194/bg-2019-42 · Cited by 13
Self-citationA data-driven model for Fennoscandian wildfire danger
Bakke, S. J., Wanders, N., van der Wiel, K., & Tallaksen, L. M. (2023) · Natural hazards and earth system sciences · doi:10.5194/nhess-23-65-2023 · Cited by 14
ABWiSE v1.0: toward an agent-based approach to simulating wildfire spread
Katan, J., & Pérez, L. (2021) · Natural hazards and earth system sciences · doi:10.5194/nhess-21-3141-2021 · Cited by 8
Linking fire behaviour and its ecological effects to plant traits, using FRaME in R
Zylstra, P. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13615 · Cited by 12
“This component of climate– vegetation– fire interactions is fundamental to our knowledge of the earth system, yet the broad understanding in the literature is inadequate to meet the current changes (Forkel et al., 2019; Harris et al., 2016).”
BackgroundFire weather indices tailored to regional patterns outperform global models
Shmuel, A., Lazebnik, T., Heifetz, E., Glickman, O., & Price, C. (2025) · npj Natural Hazards · doi:10.1038/s44304-025-00126-y · Cited by 11
“We follow previous studies that have demonstrated the effect of population density on wild fi re probability 53 and add a population density variable 54 .”
Liu, Y., Huang, H., Wang, S., Zhang, T., Xu, D., & Chen, Y. (2025) · Geoscientific model development · doi:10.5194/gmd-18-4103-2025 · Cited by 16
Richardson, D., Ribeiro, A. F. S., Batibeniz, F., Quilcaille, Y., Taschetto, A., Pitman, A., & Zscheischler, J. (2025) · Earth's Future · doi:10.1029/2024ef005030 · Cited by 11
“…with potential changes in other factors that affect wildfires, including compound drought and air dryness (Ribeiro et al., 2022), dry fuel type and load (Forkel et al., 2019) and human controls on fire activity like ignitions, fire supression, and changes in land‐use (Ribeiro et al., 2024).”
Huang, H.-L., Xue, Y.-K., Liu, Y., Li, F., & Okin, G. (2021) · Geoscientific Model Development · doi:10.5194/gmd-2021-116-rc1 · Cited by 11
Self-citationOberhagemann, L., Billing, M., von Bloh, W., Drüke, M., Forrest, M., Bowring, S., Hetzer, J., Batalla, J. R., & Thonicke, K. (2025) · Geoscientific model development · doi:10.5194/gmd-18-2021-2025 · Cited by 16
Self-citationInvestigation of factors that affect post-fire recovery of photosynthetic activity at global scale
Shen, Y., Prentice, I. C., & Harrison, S. P. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.113206 · Cited by 12
Self-citationLi, L.-S., Zhao, L., & Li, Y.-B. (2023) · Agriculture · doi:10.3390/agriculture13020265 · Cited by 9
“Therefore, to evaluate the internal relationships between drought and climatic factors, it is necessary to perform a comprehensive evaluation from different aspects [12,13,33].”
BackgroundA climate database with varying drought‐heat signatures for climate impact modelling
Tschumi, E., Lienert, S., Wiel, K., Joos, F., & Zscheischler, J. (2021) · Geoscience Data Journal · doi:10.1002/gdj3.129 · Cited by 9
“Our scenarios could be used to investigate how wildfire regimes change under different droughtheat regimes and may help pin down reasons behind the large differences in modelled fire characteristics across models (Forkel et al., 2019; Teckentrup et al., 2019).”
BackgroundLand-Cover Dependent Relationships between Fire and Soil Moisture
Schaefer, A. J., & Magi, B. I. (2019) · Fire · doi:10.3390/fire2040055 · Cited by 11
“Overall, our approach of using soil moisture and fire counts as a proxy for evaluating the fire–productivity relationship [22,26] for different land-use and land-cover types is a novel contribution that suggests an incremental approach to overcoming an important challenge in fire modeling [5,23,65] and paleoproxy data…”
MethodEarlier peak photosynthesis timing potentially escalates global wildfires
Lai, G., Li, J., Wang, J., Wu, C., Zhang, Y., Zohner, C. M., Peñuelas, J., & Ge, Q. (2024) · National Science Review · doi:10.1093/nsr/nwae292 · Cited by 11
“The discrepancy may arise from the omission of vegetation feedbacks to the climate system in offline simulations, and the failure to adequately represent pre-season fuel build-up and its subsequent effect on BAs in the models [8 ,38 ].”
The interactive global fire module pyrE (v1.0)
Mezuman, K., Tsigaridis, K., Faluvegi, G., & Bauer, S. E. (2020) · Geoscientific model development · doi:10.5194/gmd-13-3091-2020 · Cited by 11
“This trend is mostly driven by land fragmentation and grazing practices over African savanna, highlighting the challenge of fire models to account for the combined changes in climate, vegetation, and socioeconomic drivers (Forkel et al., 2019).”
BackgroundMaterial Segmentation of Multi-View Satellite ImageryPreprint
Purri, M., Xue, J., Dana, K., Leotta, M. J., Lipsa, D., Li, Z., Xu, B., & Shan, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.08537 · Cited by 9
“Material segmentation for satellite imagery is particularly of interest for applications such as road segmentation [19, 3], land cover albedo analysis [35], and tree-cover for fire risk assessment [17].”
BackgroundPerkins, O., Kasoar, M. R., Voulgarakis, A., Edwards, T., Haas, O., & Millington, J. D. A. (2025) · Earth s Future · doi:10.1029/2024ef004770 · Cited by 8
“This data set addresses a previous barrier to improved representation of anthropogenic fire in DGVMs: the lack of a systematic data set on which to base new parameterizations (Forkel et al., 2019).”
Reconstructing burnt area during the Holocene: an Iberian case studyPreprint
Shen, Y., Sweeney, L., Liu, M., Sáez, J. A. L., Pérez-Díaz, S., Luelmo-Lautenschlaeger, R., Gil-Romera, G., Hoefer, D., Jiménez-Moreno, G., Schneider, H., et al. (2021) · Climate of the Past · doi:10.5194/cp-2021-36 · Cited by 7
“…play a role in determining the occurrence of fire, including climate and fire weather, vegetation properties and human activities (e.g. Harrison et al., 2010; Brotons et al., 2013; Bistinas et al., 2014; Knorr et al., 2014; Andela et al., 2017; Forkel et al., 2019a, b; KuhnRégnier et al., 2020).”
BackgroundResultSelf-citationFire Regimes, Landscape Dynamics, and Landscape Management
Rego, F. C., Morgan, P., Fernandes, P. M., & Hoffman, C. M. (2021) · Springer textbooks in earth sciences, geography and environment · doi:10.1007/978-3-030-69815-7_12 · Cited by 8
Weakening vegetation control on global terrestrial evapotranspiration in a warmer world
Li, H., Wang, W., Chen, Z.-F., Li, X., Mura, M., Teuling, A. J., Cescatti, A., & Forzieri, G. (2026) · Communications Earth & Environment · doi:10.1038/s43247-026-03372-8 · Cited by 6
Barbosa, M. L. F., Kelley, D. I., Burton, C., Ferreira, I. J. M., da Veiga, R. M., Bradley, A., Molin, P. G., & Anderson, L. O. (2025) · Geoscientific model development · doi:10.5194/gmd-18-3533-2025 · Cited by 8
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
Shuman, J. K., Fisher, R. A., Koven, C. D., Knox, R., Kueppers, L. M., & Xu, C. (2024) · Geoscientific model development · doi:10.5194/gmd-17-4643-2024 · Cited by 10
The response of wildfire regimes to Last Glacial Maximum carbon dioxide and climate
Haas, O., Prentice, I. C., & Harrison, S. P. (2023) · Biogeosciences · doi:10.5194/bg-20-3981-2023 · Cited by 14
Self-citationButt, E., Conibear, L., Smith, C., Baker, J. C. A., Rigby, R., Knote, C., & Spracklen, D. V. (2022) · Earth s Future · doi:10.1029/2022ef003048 · Cited by 7
“Other models have also been developed to predict burned area using information on socio-economic features (e.g., population density or gross domestic product) and land cover (Bistinas et al., 2014; Forkel et al., 2019).”
Zhu, Y., Murugesan, S. B., Masara, I., Myint, S. W., & Fisher, J. B. (2024) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.422 · Cited by 8
Tang, R., Jin, M., Mao, J., Ricciuto, D. M., Chen, A., & Zhang, Y. (2024) · Geoscientific model development · doi:10.5194/gmd-17-1525-2024 · Cited by 8
Fuel availability versus flammability: divergent fire controls across Eurasian drylands
Yu, H., Yang, S., Lü, N., Yao, Q., Sun, H., He, B., Zhao, R., & Fu, B. (2026) · Nature Communications · doi:10.1038/s41467-026-71598-3 · Cited by 2
Bezerra, K. F. D. S., Couto, F. T., Gomes, H. B., Nascimento, J., Mendes, P. V. D. A., Herdies, D. L., Baltaci, H., da Silva, M. C. L., Lins, M. C. C., Bresciani, C., et al. (2026) · Atmosphere · doi:10.3390/atmos17020173 · Cited by 3
Metadata
- Authors
- Forkel, M., Andela, N., Harrison, S. P., Lasslop, G., van Marle, M., Chuvieco, E., Dorigo, W., Forrest, M., Hantson, S., Heil, A., Li, F., Melton, J., Sitch, S., Yue, C., & Arneth, A.
- Affiliations
- TU Wien (AT)
- Goddard Space Flight Center (US)
- University of Reading (GB)
- Senckenberg Biodiversity and Climate Research Centre (DE)
- Deltares (NL)
- Universidad de Alcalá (ES)
- University of California, Irvine (US)
- Max Planck Institute for Chemistry (DE)
- Chinese Academy of Sciences (CN)
- Environment and Climate Change Canada (CA)
- University of Exeter (GB)
- Laboratoire des Sciences du Climat et de l'Environnement (FR)
- Karlsruhe Institute of Technology (DE)
- Corresponding author
- Forkel, Matthias
- ORCID
- Venue
- Biogeosciences
- Publisher
- Copernicus GmbH
- Published
- 2019-01-11
- Volume
- 16
- Issue
- 1
- Pages
- 57-76
- ISSN
- 1726-4170 (ISSN-L)
- 1726-4189
- OpenAlex
- W2908971356
- Cited by
- 184
- References
- 117
- Citations by year
- 2026: 18 · 2025: 19 · 2024: 22 · 2023: 17 · 2022: 26 · 2021: 57 · 2020: 14 · 2019: 11
- FWCI
- 13.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Biogeosciences (publishedVersion, cc-by)
- CentAUR (University of Reading) (acceptedVersion, other-oa)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- Repository KITopen (Karlsruhe Institute of Technology) (submittedVersion, cc-by)
- HAL (submittedVersion, cc-by)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion, other-oa)
- reposiTUm (TU Wien) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Open Research Exeter (University of Exeter) (submittedVersion, cc-by)
- Biogeosciences (European Geosciences Union) (submittedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- reposiTUm (TU Wien) (submittedVersion)
- KITopen (cc-by)
- Funders
- Gordon and Betty Moore Foundation (GBMF3269)
- European Space Agency
- Technische Universität Wien
- Technische Universität Wien Bibliothek
- H2020 European Research Council (694481)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Fire effects on ecosystems
- Physical Sciences › Environmental Science › Global and Planetary Change › Plant Water Relations and Carbon Dynamics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Keywords
- burned area, dynamic global vegetation models, model evaluation, random forest, climate drivers of fire, leaf area index, plant productivity, woody litter, human population density, fire-prone ecosystems, global change
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