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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Global models continue to improve their representation of fire frequency, burned area and seasonality across years to decades.
“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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Self-citationThe spatially varying influence of humans on fire probability in North America
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Yebra, M., Quan, X., Riaño, D., Larraondo, P. R., van Dijk, A., & Cary, G. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.04.053 · Cited by 163 · cited 1×
“…relevant information about the spatial distributions of fuel structure (Pettinari and Chuvieco, 2016; Ri-año et al., 2002), fuel moisture content (Yebra et al., 2013, 2018), fire ignitions and spread (Laurent et al., 2018), fuel consumption (Andela et al., 2016), and fire radiative energy (Kaiser…”
A MODIS-based burned area assessment for Russian croplands: Mapping requirements and challenges
Hall, J., Loboda, T. V., Giglio, L., & McCarty, G. W. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.07.022 · Cited by 155 · cited 2× · see citing works
“…learning approach, we are able to evaluate the response of fire models independent of their underlying DGVMs This allows us to derive (as partial dependencies or individual conditional expectations) and evaluate the relationships between predictors and the response for each fire module separately.”
Demographic controls of future global fire risk
Knorr, W., Arneth, A., & Jiang, L. (2016) · Nature Climate Change · doi:10.1038/nclimate2999 · Cited by 150 · see citing works
Self-citationGeneration of fuel type maps from Landsat TM images and ancillary data in Mediterranean ecosystems
Riaño, D., Chuvieco, E., Salas, J., Palacios-Orueta, A., & Bastarrika, A. (2002) · doi:10.1139/x02-052 · Cited by 144 · cited 1×
“Such efforts should be complemented by calibrating DGVMs against satellite observations that provide relevant information about the spatial distributions of fuel structure (Pettinari and Chuvieco, 2016; Riaño et al., 2002), fuel moisture content (Yebra et al.”
Self-citationUsing multi variate data mining techniques for estimating fire susceptibility of Tyrolean forests
Arpaci, A., Malowerschnig, B., Sass, O., & Vacik, H. (2014) · Applied Geography · doi:10.1016/j.apgeog.2014.05.015 · Cited by 140 · see citing works
Yue, C., Ciais, P., Cadule, P., Thonicke, K., Archibald, S. A., Poulter, B., Hao, W. M., Hantson, S., Mouillot, F., Friedlingstein, P., et al. (2014) · Geoscientific model development · doi:10.5194/gmd-7-2747-2014 · Cited by 139 · see citing works
Self-citationSpatial and temporal intercomparison of four global burned area products
Humber, M., Boschetti, L., Giglio, L., & Justice, C. (2018) · International Journal of Digital Earth · doi:10.1080/17538947.2018.1433727 · Cited by 131 · cited 1× · see citing works
“There are several global burned area data sets; both the spatial patterns and temporal dynamics differ between these models (Hantson et al., 2016; Humber et al., 2018) as they use different satellite sensors and retrieval algorithms, and have different sensitivities to small fires (Chuvieco et al.”
Zylstra, P., Bradstock, R. A., Bedward, M., Penman, T. D., Doherty, M. D., Weber, R. O., Gill, A. M., & Cary, G. J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0160715 · Cited by 129 · see citing works
Scale-dependent controls on the area burned in the boreal forest of Canada, 1980-2005.
Parisien, M., Parks, S., Krawchuk, M., Flannigan, M., Bowman, L. M., & Moritz, M. (2011) · Ecological Applications · doi:10.1890/10-0326.1 · Cited by 125 · see citing works
SPITFIRE within the MPI <scp>E</scp>arth system model: <scp>M</scp>odel development and evaluation
Lasslop, G., Thonicke, K., & Kloster, S. (2014) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2013ms000284 · Cited by 121 · see citing works
Self-citationFRY, a global database of fire patch functional traits derived from space-borne burned area products
Laurent, P., Mouillot, F., Yue, C., Ciais, P., Moreno, M. V., & Nogueira, J. (2018) · Scientific Data · doi:10.1038/sdata.2018.132 · Cited by 118 · see citing works
Self-citationLPJmL4 – a dynamic global vegetation model with managed land – Part 2: Model evaluation
(2018) · doi:10.5194/gmd-11-1377-2018 · Cited by 109 · cited 1× · see citing works
“…uses, for example, maps of burned area, biomass, and tree cover to quantify the model–data mismatch between these variables (Kel-ley et al., 2013; Schaphoff et al., 2018) However, classical model benchmarking does not allow one to disentangle the individual effects of the vegetation or fire…”
Self-citationHarrison, S. P., Marlon, J. R., & Bartlein, P. J. (2010) · Springer eBooks · doi:10.1007/978-90-481-8716-4_3 · Cited by 102 · cited 1× · see citing works
“We then compared the data-and model-derived emergent relationships and sensitivities both globally and on a per grid cell basis.”
Self-citationRole of Fire in the Global Land Water Budget during the Twentieth Century due to Changing Ecosystems
Li, F., & Lawrence, D. M. (2016) · Journal of Climate · doi:10.1175/jcli-d-16-0460.1 · Cited by 91 · cited 1× · see citing works
“We then compared the data-and model-derived emergent relationships and sensitivities both globally and on a per grid cell basis.”
Self-citationYue, C., Ciais, P., Cadule, P., Thonicke, K., & van Leeuwen, T. T. (2015) · Geoscientific model development · doi:10.5194/gmd-8-1321-2015 · Cited by 84 · see citing works
Self-citationFire and deforestation dynamics in Amazonia (1973–2014)
van Marle, M. V., Field, R., van der Werf, G. R., de Wagt, I. A. E., Houghton, R., Rizzo, L., Artaxo, P., & Tsigaridis, K. (2017) · Global Biogeochemical Cycles · doi:10.1002/2016gb005445 · Cited by 82 · see citing works
Forkel, M., Dorigo, W., Lasslop, G., Teubner, I. E., Chuvieco, E., & Thonicke, K. (2017) · Geoscientific model development · doi:10.5194/gmd-10-4443-2017 · Cited by 75 · cited 2× · see citing works
“We also used annually varying gross domestic product per capita (GDP; World Bank, 2018), a static satellite-derived index of socio-economic development based on night-time lights for the year 2006 (Elvidge et al., 2012), and a data set on cattle density for the year 2007 (Wint and Robinson, 2007).”
Self-citationINFERNO: a fire and emissions scheme for the UK Met Office's Unified Model
Mangeon, S., Voulgarakis, A., Gilham, R., Harper, A., Sitch, S., & Folberth, G. (2016) · Geoscientific model development · doi:10.5194/gmd-9-2685-2016 · Cited by 74 · see citing works
Self-citationBiomass consumed in anthropogenic vegetation fires: global patterns and processes
Lauk, C., & Erb, K.-H. (2009) · Ecological Economics · doi:10.1016/j.ecolecon.2009.07.003 · Cited by 70 · see citing works
Thurner, M., Beer, C., Ciais, P., Friend, A., Ito, A., Kleidon, A., Lomas, M., Quegan, S., Rademacher, T., Schaphoff, S., et al. (2017) · Global Change Biology · doi:10.1111/gcb.13660 · Cited by 63 · see citing works
Generation of a global fuel data set using the Fuel Characteristic Classification System
Pettinari, M. L., & Chuvieco, E. (2016) · Biogeosciences · doi:10.5194/bg-13-2061-2016 · Cited by 57 · see citing works
Self-citationBiomass burning fuel consumption dynamics in the tropics and subtropics assessed from satellite
Andela, N., van der Werf, G. R., Kaiser, J. W., van Leeuwen, T. T., Wooster, M. J., & Lehmann, C. E. R. (2016) · Biogeosciences · doi:10.5194/bg-13-3717-2016 · Cited by 53 · cited 1× · see citing works
“…about the spatial distributions of fuel structure (Pettinari and Chuvieco, 2016; Ri-año et al., 2002), fuel moisture content (Yebra et al., 2013, 2018), fire ignitions and spread (Laurent et al., 2018), fuel consumption (Andela et al., 2016), and fire radiative energy (Kaiser et al., 2012).”
Self-citationRabin, S., Ward, D. S., Malyshev, S., Magi, B., Shevliakova, E., & Pacala, S. (2017) · Geoscientific model development · doi:10.5194/gmd-11-815-2018 · Cited by 48 · see citing works
Large‐scale variation in boreal and temperate forest carbon turnover rate related to climate
Thurner, M., Beer, C., Carvalhais, N., Forkel, M., Santoro, M., Tum, M., & Schmullius, C. (2016) · Geophysical Research Letters · doi:10.1002/2016gl068794 · Cited by 47 · see citing works
Self-citationPausas, J. G., & Dantas, V. D. L. (2016) · Global Ecology and Biogeography · doi:10.1111/geb.12562 · Cited by 46 · see citing works
Modelling burned area in Africa
Lehsten, V., Harmand, P., Palumbo, I., & Arneth, A. (2010) · Biogeosciences · doi:10.5194/bg-7-3199-2010 · Cited by 44 · see citing works
Self-citationGlobal Gap-Free MERIS LAI Time Series (2002-2012)
Tum, M., Günther, K., Böttcher, M., Baret, F., Bittner, M., Brockmann, C., & Weiss, M. (2016) · Remote Sensing · doi:10.3390/rs8010069 · Cited by 35 · cited 1×
“Especially MERIS land observations are subject to substantial gaps in raw data acquisitions (Tum et al., 2016).”
Global analysis of radiative forcing from fire-induced shortwave albedo change
López-Saldaña, G., Bistinas, I., & Pereira, J. (2014) · doi:10.5194/bg-12-557-2015 · Cited by 32 · cited 1×
“Fire directly affects global and regional climate through changing surface albedo (López-Saldaña et al., 2015; Randerson et al., 2006), atmospheric trace gas, and aerosol concentrations (Andreae and Merlet, 2001; Ward et al., 2012), and on longer timescales by affecting vegetation composition and…”
The effect of fire on tree–grass coexistence in savannas: a simulation study
Lehsten, V., Arneth, A., Spessa, A., Thonicke, K., & Moustakas, A. (2016) · International Journal of Wildland Fire · doi:10.1071/wf14205 · Cited by 28 · see citing works
Self-citationMüller, M. M., Vacik, H., & Valese, E. (2015) · Forests · doi:10.3390/f6040903 · Cited by 27 · see citing works
Fire Behavior Simulation from Global Fuel and Climatic Information
Pettinari, M. L., & Chuvieco, E. (2017) · Forests · doi:10.3390/f8060179 · Cited by 26 · cited 1× · see citing works
“Other variables have been found important for fire at a regional scale, including total precipitation, tree cover, forest cover type, tree height, biomass and litter fuel loads, and grazing (Archibald et al., 2009; Chuvieco et al., 2014; Parisien et al., 2010; Pettinari and Chuvieco, 2017).”
Self-citationRelations Between Human Factors and Global Fire Activity
Chuvieco, E., & Justice, C. (2010) · Springer eBooks · doi:10.1007/978-90-481-9085-0_14 · Cited by 19 · see citing works
Self-citationHashimoto, S., Nanko, K., Ťupek, B., & Lehtonen, A. (2017) · Geoscientific model development · doi:10.5194/gmd-10-1321-2017 · Cited by 17 · see citing works
Pettinari, M. L. (2015) · doi:10.1594/pangaea.849808 · Cited by 6
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A review of machine learning applications in wildfire science and management
Jain, P., Coogan, S. C. P., Subramanian, S. G., Crowley, M., Taylor, S., & Flannigan, M. (2020) · Environmental Reviews · doi:10.1139/er-2020-0019 · Cited by 850 · see citing works
Fire as a fundamental ecological process: Research advances and frontiers
McLauchlan, K. K., Higuera, P. E., Miesel, J., Rogers, B. M., Schweitzer, J., Shuman, J. K., Tepley, A. J., Varner, J. M., Veblen, T. T., Adalsteinsson, S. A., et al. (2020) · Journal of Ecology · doi:10.1111/1365-2745.13403 · Cited by 729 · see citing works
“…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).”
Chuvieco, E., Mouillot, F., van der Werf, G. R., Miguel, J. F. S., Tanase, M. A., Koutsias, N., Garcı́a, M., Yebra, M., Padilla, M., Gitas, I. Z., et al. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.02.013 · Cited by 659 · see citing works
“Therefore, it is particularly relevant to improve our knowledge of drivers behind fire occurrence and fire impacts to alleviate current and foreseen future effects of biomass burning (Forkel et al., 2019; Hantson et al., 2016).”
BackgroundSelf-citationEmergent vulnerability to climate-driven disturbances in European forests
Forzieri, G., Girardello, M., Ceccherini, G., Spinoni, J., Feyen, L., Hartmann, H., Beck, P., Camps-Valls, G., Chirici, G., Mauri, A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21399-7 · Cited by 329 · see citing works
Global increase in wildfire risk due to climate‐driven declines in fuel moisture
Ellis, T., Bowman, D. M. J. S., Jain, P., Flannigan, M., & Williamson, G. J. (2021) · Global Change Biology · doi:10.1111/gcb.16006 · Cited by 349 · see citing works
“…projections, increasing fuel dryness in ecoregions such as temperate coniferous forests with moisture- limited fire regimes have been posited to increase the total burned in both regional (Mann et al., 2016; Williams et al., 2019) and global studies (Bistinas et al., 2014; Forkel et al., 2019).”
BackgroundGlobal rise in forest fire emissions linked to climate change in the extratropics
Jones, M. W., Veraverbeke, S., Andela, N., Doerr, S. H., Kolden, C. A., Mataveli, G., Pettinari, M. L., Quéré, C. L., Rosan, T. M., van der Werf, G. R., et al. (2024) · Science · doi:10.1126/science.adl5889 · Cited by 216 · see citing works
“In addition, the productivity of vegetation during the growing season influences fuel availability during subsequent dry seasons ( 40 , 53 , 54 ).”
BackgroundSelf-citationLange, S., Volkholz, J., Geiger, T., Zhao, F., del Valle, I. V., Veldkamp, T., Reyer, C., Warszawski, L., Huber, V., Jägermeyr, J., et al. (2020) · Earth s Future · doi:10.1029/2020ef001616 · Cited by 214 · see citing works
“The large structural wildfire modeling uncertainty found here resembles results of earlier model intercomparison studies (Andela et al., 2017; Forkel et al., 2019; Wu et al., 2015).”
BackgroundResultSelf-citationEco-evolutionary optimality as a means to improve vegetation and land-surface models.
Harrison, S., Cramer, W., Franklin, O., Prentice, I., Wang, H., Brännström, Å., de Boer, H. D., Dieckmann, U., Joshi, J., Keenan, T., et al. (2021) · New Phytologist · doi:10.1111/nph.17558 · Cited by 154 · see citing works
“However, the performance of these models is relatively poor beyond the largest-scale geographic patterns (Forkel et al., 2019; Hantson et al., 2020).”
BackgroundSelf-citationMelton, J., Arora, V., Wisernig-Cojoc, E., Seiler, C., Fortier, M., Chan, E., & Teckentrup, L. (2019) · Geoscientific Model Development · doi:10.5194/gmd-2019-329-supplement · Cited by 149
“…13, 2825–2850, 2020 https://doi.org/10.5194/gmd-13-2825-2020 uated as part of the Fire Model Intercomparison Project (FireMIP; Hantson et al., 2016; Forkel et al., 2019) in addition to being used to estimate carbon cycle implications of the reduction in global wildfire since the 1930s (Arora and…”
BackgroundInfluentialSelf-citationGlobal burned area increasingly explained by climate change
Burton, C. A., Lampe, S., Kelley, D., Thiery, W., Hantson, S., Christidis, N., Gudmundsson, L., Forrest, M., Burke, E., Chang, J.-F., et al. (2024) · Nature Climate Change · doi:10.1038/s41558-024-02140-w · Cited by 143 · see citing works
“However, many of these regions also see human-induced savanna fragmentation which other studies have shown probably leads to reductions in burnt area 3,5,7,43 .”
Self-citationInfluence of Fire on the Carbon Cycle and Climate
Lasslop, G., Coppola, A. I., Voulgarakis, A., Yue, C., & Veraverbeke, S. (2019) · Current Climate Change Reports · doi:10.1007/s40641-019-00128-9 · Cited by 167 · see citing works
Self-citationHantson, S., Kelley, D. I., Arneth, A., Harrison, S. P., Archibald, S. A., Bachelet, D., Forrest, M., Hickler, T., Lasslop, G., Li, F., et al. (2020) · Geoscientific model development · doi:10.5194/gmd-13-3299-2020 · Cited by 190 · see citing works
“…https://apps.ecmwf.int/datasets/data/cams-gfas/ (Kaiser et al., 2012) Fire size and number Hantson https://zenodo.org/record/3564818#.Xelidi2ZOcY (Hantson et al., 2019) GPP Luyssaert Upon request to the author (Luyssaert et al., 2007) Jung https://www.bgc-jena.mpg.de/geodb/projects/Home.php…”
BackgroundMethodInfluentialSelf-citationJolly, C. J., Dickman, C. R., Doherty, T. S., van Eeden, L. M., Geary, W. L., Legge, S., Woinarski, J. C. Z., & Nimmo, D. G. (2022) · Global Change Biology · doi:10.1111/gcb.16044 · Cited by 169 · see citing works
“Although fire affects 300– 500 million ha of land globally each year (Forkel et al., 2019), there is surprisingly little knowledge of fire as an agent of direct animal mortality (Nimmo et al., 2021).”
BackgroundChuvieco, E., Yebra, M., Martino, S., Thonicke, K., Gómez-Giménez, M., Miguel, J. F. S., Oom, D., Velea, R., Mouillot, F., Molina, J. R., et al. (2023) · Fire · doi:10.3390/fire6050215 · Cited by 161 · see citing works
“Increases in small-scale agriculture and the abandonment of fire-management practices to manage the landscape are likely to be responsible for the global decline of burned areas (particularly clear in Africa) and fire-related carbon emissions [154,155].”
BackgroundSelf-citationWelcome to the Pyrocene: Animal survival in the age of megafire
Nimmo, D. G., Carthey, A. J. R., Jolly, C. J., & Blumstein, D. T. (2021) · Global Change Biology · doi:10.1111/gcb.15834 · Cited by 126 · see citing works
Observational evidence of wildfire-promoting soil moisture anomalies
O, S., Hou, X., & Orth, R. (2020) · Scientific Reports · doi:10.1038/s41598-020-67530-4 · Cited by 108 · see citing works
Lasslop, G., Hantson, S., Harrison, S. P., Bachelet, D., Burton, C., Forkel, M., Forrest, M., Li, F., Melton, J. R., Yue, C., et al. (2020) · Global Change Biology · doi:10.1111/gcb.15160 · Cited by 153 · see citing works
Self-citationRecent global and regional trends in burned area and their compensating environmental controls
Forkel, M., Dorigo, W., Lasslop, G., Chuvieco, E., Hantson, S., Heil, A., Teubner, I. E., Thonicke, K., & Harrison, S. (2019) · Environmental Research Communications · doi:10.1088/2515-7620/ab25d2 · Cited by 93 · see citing works
“Interpretations based on bivariate relationships only reflect the emergent controls onfire trends; attribution to fundamental underlying controls requires amore sophisticated approach usingmultivariate statistical ormachine learning approaches (Bistinas et al 2014, Forkel et al 2019).”
BackgroundInfluentialSelf-citationHuman and climate drivers of global biomass burning variability
Chuvieco, E., Pettinari, M. L., Koutsias, N., Forkel, M., Hantson, S., & Turco, M. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.146361 · Cited by 118 · see citing works
Self-citationUnderstanding and modelling wildfire regimes: an ecological perspective
Harrison, S. P., Prentice, I. C., Bloomfield, K. J., Dong, N., Forkel, M., Forrest, M., Ningthoujam, R. K., Pellegrini, A. F. A., Shen, Y., Baudena, M., et al. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/ac39be · Cited by 109 · see citing works
“It also shows results from an empirical model that accounts for the effects of multiple climatic and land-use predictors, and for the influence of human population density (Forkel et al 2019).”
BackgroundMethodInfluentialSelf-citationA fiery wake-up call for climate science
Sanderson, B., & Fisher, R. (2020) · Nature Climate Change · doi:10.1038/s41558-020-0707-2 · Cited by 80 · see citing works
Tackling unresolved questions in forest ecology: The past and future role of simulation models
Maréchaux, I., Langerwisch, F., Huth, A., Bugmann, H. K. M., Morin, X., Reyer, C., Seidl, R., Collalti, A., de Paula, M. D., Fischer, R., et al. (2021) · Ecology and Evolution · doi:10.1002/ece3.7391 · Cited by 98 · see citing works
“The development of machine learning techniques offers new possibilities to use the resulting huge datasets for model development and evaluation (Botella et al., 2018; Forkel et al., 2019; Rammer & Seidl, 2019; Reichstein et al., 2019).”
BackgroundMethodCrocetti, L., Forkel, M., Fischer, M., Jurečka, F., Grlj, A., Salentinig, A., Trnka, M., Anderson, M. C., Ng, W.-T., Kokalj, Ž., et al. (2020) · Regional Environmental Change · doi:10.1007/s10113-020-01710-w · Cited by 78
“Hence, observation-based relationships provide a unique opportunity to identify and improve the model processes (Forkel et al. 2019).”
BackgroundSelf-citationYu, Y., Mao, J., Wullschleger, S. D., Chen, A., Shi, X., Wang, Y., Hoffman, F. M., Zhang, Y., & Pierce, E. M. (2022) · Nature Communications · doi:10.1038/s41467-022-28853-0 · Cited by 92 · see citing works
Global Wildfire Susceptibility Mapping Based on Machine Learning Models
Shmuel, A., & Heifetz, E. (2022) · Forests · doi:10.3390/f13071050 · Cited by 74 · see citing works
Teckentrup, L., Harrison, S. P., Hantson, S., Heil, A., Melton, J. R., Forrest, M., Li, F., Yue, C., Arneth, A., Hickler, T., et al. (2019) · Biogeosciences · doi:10.5194/bg-16-3883-2019 · Cited by 109 · see citing works
Self-citationClimate drivers of global wildfire burned area
Grillakis, M., Voulgarakis, A., Rovithakis, A., Seiradakis, K., Koutroulis, A., Field, R., Kasoar, M., Papadopoulos, A., & Lazaridis, M. (2022) · Environmental Research Letters · doi:10.1088/1748-9326/ac5fa1 · Cited by 73 · see citing works
“The region-specific sensitivity of BA to each climatological parameter may serve as a benchmarking tool for fire-enabled vegetationmodels and intercomparison projects such as FireMIP [13, 71] and ISIMIP [72].”
BackgroundMethodRevisiting Global Vegetation Controls Using Multi‐Layer Soil Moisture
Li, W., Migliavacca, M., Forkel, M., Walther, S., Reichstein, M., & Orth, R. (2021) · Geophysical Research Letters · doi:10.1029/2021gl092856 · Cited by 88 · see citing works
Self-citationProgress and challenges in remotely sensed terrestrial carbon fluxes
Wang, T., Zhang, Y., Yue, C., Wang, Y., Wang, X., Lyu, G., Wei, J., Yang, H., & Piao, S. L. (2024) · Geo-spatial Information Science · doi:10.1080/10095020.2024.2336599 · Cited by 74 · see citing works
“…properties such as leaf area index, the fraction of absorbed photosynthetically active radiation, and sun-induced fluorescence (Bacour et al. 2015; Forkel et al. 2019; MacBean et al. 2015; Migliavacca et al. 2009) to constrain parameters associated with photosynthesis, phenology, carbon turnover…”
Self-citationPlant-water sensitivity regulates wildfire vulnerability
Rao, K., Williams, A. P., Diffenbaugh, N., Yebra, M., & Konings, A. (2022) · Nature Ecology & Evolution · doi:10.1038/s41559-021-01654-2 · Cited by 66 · see citing works
Pyrogenic carbon decomposition critical to resolving fire’s role in the Earth system
Bowring, S., Jones, M. W., Ciais, P., Guenet, B., & Abiven, S. (2022) · Nature Geoscience · doi:10.1038/s41561-021-00892-0 · Cited by 71 · see citing works
Forkel, M., Drüke, M., Thurner, M., Dorigo, W., Schaphoff, S., Thonicke, K., von Bloh, W., & Carvalhais, N. (2019) · Scientific Reports · doi:10.1038/s41598-019-55187-7 · Cited by 58 · see citing works
Self-citationIncreasing global human exposure to wildland fires despite declining burned area.
Seydi, S. T., Abatzoglou, J., Jones, M. W., Kolden, C. A., Filippelli, G., Hurteau, M. D., Aghakouchak, A., Luce, C., Miao, C.-Y., & Sadegh, M. (2025) · Science · doi:10.1126/science.adu6408 · Cited by 46 · see citing works
A hydroclimatic model for the distribution of fire on Earth
Boer, M. M., de Dios, V. R., Stefaniak, E. Z., & Bradstock, R. A. (2021) · Environmental Research Communications · doi:10.1088/2515-7620/abec1f · Cited by 46 · see citing works
The importance of antecedent vegetation and drought conditions as global drivers of burnt area
Kuhn-Régnier, A., Voulgarakis, A., Nowack, P. J., Forkel, M., Prentice, I. C., & Harrison, S. P. (2021) · Biogeosciences · doi:10.5194/bg-18-3861-2021 · Cited by 66 · see citing works
Self-citationImproving the LPJmL4-SPITFIRE vegetation–fire model for South America using satellite data
(2019) · doi:10.5194/gmd-12-5029-2019 · Cited by 42 · see citing works
Self-citation2019–2020 Australia Fire and Its Relationship to Hydroclimatological and Vegetation Variabilities
Ehsani, M., Arevalo, J., Risanto, C. B., Javadian, M., Devine, C., Arabzadeh, A., Venegas-Quiñones, H., Dell'Oro, A., & Behrangi, A. (2020) · Water · doi:10.3390/w12113067 · Cited by 40 · see citing works
“NDVI and LAI have also been used directly by models to predict wildfire risk, severity, and extent [42,43].”
MethodSpatial distribution of tree species in mountain national parks depends on geomorphology and climate
Dyderski, M., & Pawlik, Ł. (2020) · doi:10.1016/j.foreco.2020.118366 · Cited by 37
Kganyago, M., & Shikwambana, L. (2019) · Sustainability · doi:10.3390/su11236811 · Cited by 37 · see citing works
“Thus, information on fire frequency, intensity and burned area (BA) is critical for understanding and alleviating the environmental, atmospheric and socio-economic impacts of wildfires [3].”
BackgroundQuantifying the drivers and predictability of seasonal changes in African fire
Yu, Y., Mao, J., THORNTON, P. E., Notaro, M., Wullschleger, S. D., Shi, X., Hoffman, F. M., & Wang, Y. (2020) · Nature Communications · doi:10.1038/s41467-020-16692-w · Cited by 44 · see citing works
Harrison, S. P., Villegas‐Diaz, R., Cruz‐Silva, E., Gallagher, D., Kesner, D., Lincoln, P. C., Shen, Y., Sweeney, L., Colombaroli, D., Ali, A. A., et al. (2021) · Earth System Science Data · doi:10.5194/essd-2021-272-supplement · Cited by 1 · see citing works
“However, wildfires exhibit considerable local to regional variability because of the spatial heterogeneity of the various factors controlling their occurrence and intensity (Bistinas et al., 2014; Andela et al., 2019; Forkel et al., 2019).”
BackgroundSelf-citationGannon, C., & Steinberg, N. (2021) · Environmental Research Communications · doi:10.1088/2515-7620/abd836 · Cited by 33 · see citing works
Modelling Human-Fire Interactions: Combining Alternative Perspectives and Approaches
Ford, A. E. S., Harrison, S. P., Kountouris, Y., Millington, J. D. A., Mistry, J., Perkins, O., Rabin, S. S., Rein, G., Schreckenberg, K., Smith, C., et al. (2021) · Frontiers in Environmental Science · doi:10.3389/fenvs.2021.649835 · Cited by 47 · see citing works
“For example, scaling ABMs to the global scale could involve defining a limited number of Agent Functional Types (AFTs) based on theoretical typologies derived for example from local fire studies. This would be parallel to the use of Plant Functional Types (PFTs) in most of the current generation of dynamic global vege…”
MethodSelf-citationWorden, J. R., Saatchi, S. S., Keller, M., Bloom, A. A., Liu, J., Parazoo, N. C., Fisher, J. B., Bowman, K. W., Reager, J. T., Fahy, K. A., et al. (2021) · Reviews of Geophysics · doi:10.1029/2020rg000711 · Cited by 51 · see citing works
Hantson, S., Kelley, D., Arneth, A., Harrison, S., Archibald, S., Bachelet, D., Forrest, M., Hickler, T., Lasslop, G., Li, F., et al. (2020) · doi:10.5194/gmd-2019-261 · Cited by 30
Self-citationLizundia-Loiola, J., Franquesa, M., Böttcher, M., Kirches, G., Pettinari, M. L., & Chuvieco, E. (2021) · Remote Sensing · doi:10.3390/rs13214295 · Cited by 29 · see citing works
Self-citationToward quantification of the feasible potential of land-based carbon dioxide removal
Perkins, O., Alexander, P. M., Arneth, A., Brown, C., Millington, J. D. A., & Rounsevell, M. (2023) · One Earth · doi:10.1016/j.oneear.2023.11.011 · Cited by 37 · see citing works
Self-citationGlobal Warming Reshapes European Pyroregions
Galizia, L., Barbero, R., Rodrigues, M., Ruffault, J., Pimont, F., & Curt, T. (2023) · Earth's Future · doi:10.1029/2022ef003182 · Cited by 28 · see citing works
“Yet, such models often struggle to represent interannual variations in fire activity and observed trends (Forkel et al., 2019; Jones et al., 2022).”
BackgroundMiezïte, L. E., Améztegui, A., de Cáceres, M., Coll, L., Morán-Ordóñez, A., Vega-García, C., & Rodrigues, M. (2022) · Journal of Environmental Management · doi:10.2139/ssrn.4068658 · Cited by 26
Wang, S., Qian, Y., Leung, L.-Y. R., & Zhang, Y. (2022) · Atmospheric chemistry and physics · doi:10.5194/acp-22-3445-2022 · Cited by 34 · see citing works
Drivers of forest aboveground biomass and its increments in the Tatra Mountains after 15 years
Dyderski, M., & Pawlik, Ł. (2021) · CATENA · doi:10.1016/j.catena.2021.105468 · Cited by 25 · see citing works
Developing novel machine-learning-based fire weather indices
Shmuel, A., & Heifetz, E. (2023) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/acc008 · Cited by 23 · see citing works
“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…”
MethodInfluentialWhen the Land Surface Shifts Gears
Orth, R. (2021) · AGU Advances · doi:10.1029/2021av000414 · Cited by 23 · see citing works
A monthly gridded burned area database of national wildland fire data
Gincheva, A., Pausas, J., Edwards, A., Provenzale, A., Cerdà, A., Hanes, C., Royé, D., Chuvieco, E., Mouillot, F., Vissio, G., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-03141-2 · Cited by 22 · see citing works
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 · see citing works
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 23 · see citing works
“…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 · see citing works
Theoretical uncertainties for global satellite-derived burned area estimates
Brennan, J., Gómez-Dans, J., Disney, M., & Lewis, P. (2019) · Biogeosciences · doi:10.5194/bg-16-3147-2019 · Cited by 20
“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., Zimmermann, K., Jones, C. G., Betts, R., Burton, C. A., Jones, C. D., Mengel, M., Reyer, C., et al. (2024) · Earth's Future · doi:10.1029/2024ef004901 · Cited by 19 · see citing works
“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. R., Harrison, S., & Prentice, I. (2024) · Environmental Research Letters · doi:10.1088/1748-9326/ad21b0 · Cited by 19 · see citing works
“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 · see citing works
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 18 · see citing works
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 20 · see citing works
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 · see citing works
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 27 · see citing works
“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., Jin, M.-Z., Yu, Y., Shi, X.-Y., Wullschleger, S., Tang, R., & Liu, J. (2023) · Science of Remote Sensing · doi:10.1016/j.srs.2023.100088 · Cited by 17 · see citing works
Integration of a Deep‐Learning‐Based Fire Model Into a Global Land Surface Model
Son, R., Stacke, T., Gayler, V., Nabel, J., 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 16 · see citing works
“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 16 · see citing works
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 19 · see citing works
Perkins, O., Matej, S., Erb, K., & Millington, J. D. A. (2022) · Socio-Environmental Systems Modeling · doi:10.18174/sesmo.18130 · Cited by 24 · see citing works
“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. (2023) · Natural Hazards and Earth System Sciences · doi:10.5194/nhess-23-65-2023 · Cited by 12 · see citing works
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 · see citing works
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 · see citing works
“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.-C., Zhang, T., Xu, D., & Chen, Y. (2025) · Geoscientific Model Development · doi:10.5194/gmd-18-4103-2025 · Cited by 11 · see citing works
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 · see citing works
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 10
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 · see citing works
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 · see citing works
“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., & Magi, B. (2019) · Fire · doi:10.3390/fire2040055 · Cited by 9 · see citing works
“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.-Y., Zhang, Y.-G., Zohner, C., Peñuelas, J., & Ge, Q.-S. (2024) · National Science Review · doi:10.1093/nsr/nwae292 · Cited by 8
“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 · see citing works
“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. J., Leotta, M. J., Lipsa, D. R., Li, Z., Xu, B., & Jie, S. (2019) · arXiv.org · doi:10.48550/arxiv.1904.08537 · Cited by 8 · see citing works
“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 · see citing works
“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 · see citing works
“…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 · see citing works
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 · see citing works
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 5 · see citing works
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 9 · see citing works
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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
- 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)
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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