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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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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · cited 2×
“…between potential drivers of fire in satellite data and fire-enabled DGVMs, we applied the random forest (RF) machine-learning algorithm to predict monthly burned area (response variable) from climate, trees that are built based on the training data set (Breiman, 2001; Cutler et al., 2012 .”
InfluentialGreedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · cited 3×
“We also computed PDPs for each grid cell to produce global maps of partial sensitivities for selected predictor variables. summarize and map the PDP of each grid cell in a single number, we fitted a linear quantile regression to the median between the partial dependence of burned area and the corresponding predictor va…”
InfluentialUpdated high‐resolution grids of monthly climatic observations – the CRU TS3 .10 Dataset
Harris, I. C., Jones, P. D., Osborn, T. J., & Lister, D. (2013) · International Journal of Climatology · doi:10.1002/joc.3711 · Cited by 6,326
Emission of trace gases and aerosols from biomass burning
Andreae, M., & Merlet, P. (2001) · Global Biogeochemical Cycles · doi:10.1029/2000gb001382 · Cited by 3,950 · cited 2×
“…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 structure with subsequent impacts on…”
Bowman, D. M. J. S., Balch, J. K., Artaxo, P., Bond, W. J., Carlson, J. M., Cochrane, M. A., D’Antonio, C. M., DeFries, R. S., Doyle, J. C., Harrison, S. P., et al. (2009) · Science · doi:10.1126/science.1163886 · Cited by 3,351 · cited 2×
“We then compared the data-and model-derived emergent relationships and sensitivities both globally and on a per grid cell basis.”
Self-citationBenchmark map of forest carbon stocks in tropical regions across three continents
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M. A., Mitchard, E. T. A., Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S. C., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1019576108 · Cited by 2,504
Climate-induced variations in global wildfire danger from 1979 to 2013
Jolly, W. M., Cochrane, M. A., Freeborn, P. H., Holden, Z. A., Brown, T. J., Williamson, G. J., & Bowman, D. M. J. S. (2015) · Nature Communications · doi:10.1038/ncomms8537 · Cited by 2,200 · cited 2×
“Climate influences several aspects of the fire regime, including the seasonal timing of lightning ignitions (Veraver-beke et al., 2017), temperature and moisture controls on fuel drying, and wind-driven fire spread (Jolly et al., 2015).”
The global distribution of ecosystems in a world without fire
Bond, W. J., Woodward, F. I., & Midgley, G. F. (2005) · New Phytologist · doi:10.1111/j.1469-8137.2004.01252.x · Cited by 1,942 · cited 2×
“These dependencies represent the emergent relationships of burned area to drivers in the observation-or model-variable space.”
Goldstein, A., Kapelner, A., Bleich, J., & Pitkin, E. (2015) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2014.907095 · Cited by 1,861
Giglio, L., Randerson, J. T., & van der Werf, G. R. (2013) · Journal of Geophysical Research: Biogeosciences · doi:10.1002/jgrg.20042 · Cited by 1,605 · cited 2×
“sensitivities were derived by computing the individual conditional expectation (ICE) and the partial dependencies curves (Goldstein et al., 2013).”
The dominant role of semi-arid ecosystems in the trend and variability of the land CO 2 sink
Ahlström, A., Raupach, M., Schurgers, G., Smith, B., Arneth, A., Jung, M., Reichstein, M., Canadell, J. G., Friedlingstein, P., Jain, A. K., et al. (2015) · Science · doi:10.1126/science.aaa1668 · Cited by 1,532
Self-citationKaiser, J., Heil, A., Andreae, M., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J., Razinger, M., Schultz, M., Suttie, M., et al. (2011) · Biogeosciences · doi:10.5194/bg-9-527-2012 · Cited by 1,444
Self-citationThe Collection 6 MODIS burned area mapping algorithm and product
Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., & Justice, C. O. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.08.005 · Cited by 1,401
Cutler, A., Cutler, D. R., & Stevens, J. R. (2012) · Springer eBooks · doi:10.1007/978-1-4419-9326-7_5 · Cited by 1,313 · cited 2×
“…between potential drivers of fire in satellite data and fire-enabled DGVMs, we applied the random forest (RF) machine-learning algorithm to predict monthly burned area (response variable) from climate, trees that are built based on the training data set (Breiman, 2001; Cutler et al., 2012 .”
InfluentialMapping forest canopy height globally with spaceborne lidar
Simard, M., Pinto, N. S., Fisher, J. B., & Baccini, A. (2011) · Journal of Geophysical Research Atmospheres · doi:10.1029/2011jg001708 · Cited by 1,298 · cited 4×
“The normal method of determining the importance of predictor variables for RFs (increment in the mean-squared error – MSE) was found to be overly sensitive to the burnt area data set that was used in training because of the highly skewed distribution of burned area, and this hampers its interpretability (Figs .”
InfluentialHurtt, G. C., Chini, L. P., Frolking, S., Betts, R. A., Feddema, J., Fischer, G., Fisk, J. P., Hibbard, K., Houghton, R. A., Janetos, A., et al. (2011) · Climatic Change · doi:10.1007/s10584-011-0153-2 · Cited by 1,284 · cited 2×
“…values of human population density from the HYDE 3.1 data set (Goldewijk et al., 2010), annually updated land use and land cover changes from the Hurtt et al. (2011) data set, and annually updated values of global atmospheric CO 2 (Le Quéré et al., 2014) Although forcing data sets are common…”
InfluentialKlein Goldewijk, K., Beusen, A., van Drecht, G., & de Vos, M. (2011) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2010.00587.x · Cited by 1,270 · cited 1×
“…monthly cloud-to-ground lightning strikes (Rabin et al., 2017), annually updated values of human population density from the HYDE 3.1 data set (Goldewijk et al., 2010), annually updated land use and land cover changes from the Hurtt et al. (2011) data set, and annually updated values of…”
InfluentialA human-driven decline in global burned area
Andela, N., Morton, D. C., Giglio, L., Chen, Y., van der Werf, G. R., Kasibhatla, P. S., DeFries, R. S., Collatz, G. J., Hantson, S., Kloster, S., et al. (2017) · Science · doi:10.1126/science.aal4108 · Cited by 1,262
Self-citationThe human dimension of fire regimes on Earth
Bowman, D. M. J. S., Balch, J., Artaxo, P., Bond, W. J., Cochrane, M. A., D’Antonio, C. M., DeFries, R., Johnston, F. H., Keeley, J. E., Krawchuk, M. A., et al. (2011) · Journal of Biogeography · doi:10.1111/j.1365-2699.2011.02595.x · Cited by 1,230
Quéré, C. L., Andrew, R. M., Canadell, J. G., Sitch, S. A., Korsbakken, J. I., Peters, G. P., Manning, A. C., Boden, T. A., Tans, P. P., Houghton, R. A., et al. (2016) · Earth system science data · doi:10.5194/essd-8-605-2016 · Cited by 1,101
Self-citationGlobal Pyrogeography: the Current and Future Distribution of Wildfire
Krawchuk, M. A., Moritz, M. A., Parisien, M.-A., Van Dorn, J., & Hayhoe, K. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005102 · Cited by 1,000 · cited 2×
“We then compared the data-and model-derived emergent relationships and sensitivities both globally and on a per grid cell basis.”
Global burned area and biomass burning emissions from small fires
Randerson, J. T., Chen, Y., van der Werf, G. R., Rogers, B. M., & Morton, D. C. (2012) · Journal of Geophysical Research Atmospheres · doi:10.1029/2012jg002128 · Cited by 989 · cited 2×
“sensitivities were derived by computing the individual conditional expectation (ICE) and the partial dependencies curves (Goldstein et al., 2013).”
Climate change and disruptions to global fire activity
Moritz, M. A., Parisien, M.-A., Batllori, E., Krawchuk, M. A., Van Dorn, J., Ganz, D. J., & Hayhoe, K. (2012) · Ecosphere · doi:10.1890/es11-00345.1 · Cited by 959
Global covariation of carbon turnover times with climate in terrestrial ecosystems
Carvalhais, N., Forkel, M., Khomik, M., Bellarby, J., Jung, M., Migliavacca, M., Mu, M., Saatchi, S. S., Santoro, M., Thurner, M., et al. (2014) · Nature · doi:10.1038/nature13731 · Cited by 953
Self-citation21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions
Aragão, L. E. O. C., Anderson, L. O., Fonseca, M. G., Rosan, T. M., Vedovato, L. B., Wagner, F. H., Silva, C. V. J., Silva Junior, C. H. L., Arai, E., Aguiar, A. P., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02771-y · Cited by 940 · cited 2×
“We then compared the data-and model-derived emergent relationships and sensitivities both globally and on a per grid cell basis.”
The impact of boreal forest fire on climate warming
Randerson, J. T., Liu, H., Flanner, M. G., Chambers, S. D., Jin, Y., Hess, P. G., Pfister, G., Mack, M. C., Treseder, K. K., Welp, L. R., et al. (2006) · Science · doi:10.1126/science.1132075 · Cited by 896 · cited 2×
“We then compared the data-and model-derived emergent relationships and sensitivities both globally and on a per grid cell basis.”
Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps‐Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., et al. (2016) · Biogeosciences · doi:10.5194/bg-13-4291-2016 · Cited by 883 · cited 1×
“Data on gross primary production (GPP) and leaf area index (LAI) were taken to account for the seasonal effects of vegetation productivity and canopy development GPP was taken from the FLUXCOM data set which is up-scaled from GPP estimates at FLUXNET measurement sites (Tramontana et al., 2016 .”
Defining pyromes and global syndromes of fire regimes
Archibald, S., Lehmann, C. E. R., Gómez-Dans, J. L., & Bradstock, R. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1211466110 · Cited by 845 · cited 2×
“Following previous studies, we found that climate is the primary control of global burned area which directly affects fire through weather and fuel moisture conditions, and indirectly through ecosystem productivity, vegetation type, and fuel loads (Archibald et al., 2013; Krawchuk and Moritz, 2011).”
Calibration of process-oriented models
Janssen, P., & Heuberger, P. (1995) · Ecological Modelling · doi:10.1016/0304-3800(95)00084-9 · Cited by 808
What limits fire? An examination of drivers of burnt area in Southern Africa
ARCHIBALD, S., ROY, D. P., Van WILGEN, B. W., & SCHOLES, R. J. (2009) · Global Change Biology · doi:10.1111/j.1365-2486.2008.01754.x · Cited by 773
Thonicke, K., Venevsky, S., Sitch, S., & Cramer, W. (2001) · Global Ecology and Biogeography · doi:10.1046/j.1466-822x.2001.00175.x · Cited by 684
Self-citationAn active-fire based burned area mapping algorithm for the MODIS sensor
Giglio, L., Loboda, T., Roy, D., Quayle, B., & Justice, C. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2008.10.006 · Cited by 671
Klein Goldewijk, K., Beusen, A., & Janssen, P. (2010) · The Holocene · doi:10.1177/0959683609356587 · Cited by 633
Influence of tree species on continental differences in boreal fires and climate feedbacks
Rogers, B. M., Soja, A. J., Goulden, M. L., & Randerson, J. T. (2015) · Nature Geoscience · doi:10.1038/ngeo2352 · Cited by 479
Constraints on global fire activity vary across a resource gradient
Krawchuk, M. A., & Moritz, M. A. (2011) · Ecology · doi:10.1890/09-1843.1 · Cited by 472
Decreasing fire season precipitation increased recent western US forest wildfire activity
Holden, Z. A., Swanson, A., Luce, C. H., Jolly, W. M., Maneta, M., Oyler, J. W., Warren, D. A., Parsons, R., & Affleck, D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1802316115 · Cited by 466
The status and challenge of global fire modelling
Hantson, S., Arneth, A., Harrison, S. P., Kelley, D. I., Prentice, I. C., Rabin, S. S., Archibald, S., Mouillot, F., Arnold, S. R., Artaxo, P., et al. (2016) · Biogeosciences · doi:10.5194/bg-13-3359-2016 · Cited by 460 · cited 2×
“We then compared the data-and model-derived emergent relationships and sensitivities both globally and on a per grid cell basis.”
Self-citationYebra, M., Dennison, P. E., Chuvieco, E., Riaño, D., Zylstra, P., Hunt, E. R., Danson, F. M., Qi, Y., & Jurdao, S. (2013) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.05.029 · Cited by 455 · 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…”
Self-citationLightning as a major driver of recent large fire years in North American boreal forests
Veraverbeke, S., Rogers, B., Goulden, M., Jandt, R., Miller, C., Wiggins, E., & Randerson, J. (2017) · Nature Climate Change · doi:10.1038/nclimate3329 · Cited by 449
(2010) · doi:10.5194/bg-7-1991-2010 · Cited by 448
Self-citationThe global fire-productivity relationship
Pausas, J. G., & Ribeiro, E. (2013) · Global Ecology and Biogeography · doi:10.1111/geb.12043 · Cited by 440 · cited 2×
“…in turn, influ-ences the patterns of available fuel and moisture that directly determine fire spread, severity, and extent (Krawchuk and Moritz, 2011; Pausas and Ribeiro, 2013) People set and suppress fires and use them to manage agricultural and natural ecosystems, for land use change and…”
Bloom, A. A., Exbrayat, J., van der Velde, I. R., Feng, L., & Williams, M. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1515160113 · Cited by 420
Flammability as an ecological and evolutionary driver
Pausas, J. G., Keeley, J. E., & Schwilk, D. W. (2016) · Journal of Ecology · doi:10.1111/1365-2745.12691 · Cited by 365
Terrestrial and Inland Water Systems
(IPCC), I. P. O. C. C. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781107415379.009 · Cited by 355
Self-citationBiological and geophysical feedbacks with fire in the Earth system
Archibald, S., Lehmann, C. E. R., Belcher, C. M., Bond, W. J., Bradstock, R. A., Daniau, A.-L., Dexter, K. G., Forrestel, E. J., Greve, M., He, T., et al. (2018) · Environmental Research Letters · doi:10.1088/1748-9326/aa9ead · Cited by 341
Poulter, B., MacBean, N., Hartley, A. J., Khlystova, I., Arinò, O., Betts, R., Bontemps, S., Boettcher, M., Brockmann, C., Defourny, P., et al. (2015) · Geoscientific model development · doi:10.5194/gmd-8-2315-2015 · Cited by 339
Li, W., MacBean, N., Ciais, P., Defourny, P., Lamarche, C., Bontemps, S., Houghton, R. A., & Peng, S. (2018) · Earth system science data · doi:10.5194/essd-10-219-2018 · Cited by 333
Chuvieco, E., Lizundia-Loiola, J., Pettinari, M. L., Ramo, R., Padilla, M., Tansey, K. J., Mouillot, F., Laurent, P., Storm, T., Heil, A., et al. (2018) · Earth system science data · doi:10.5194/essd-10-2015-2018 · Cited by 321
Self-citationRabin, S. S., Melton, J. R., Lasslop, G., Bachelet, D., Forrest, M., Hantson, S., Kaplan, J. O., Li, F., Mangeon, S., Ward, D. S., et al. (2017) · Geoscientific model development · doi:10.5194/gmd-10-1175-2017 · Cited by 319
Self-citationCarbon stock and density of northern boreal and temperate forests
Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D. G., Shvidenko, А., Kompter, E., Ahrens, B., Levick, S. R., et al. (2013) · Global Ecology and Biogeography · doi:10.1111/geb.12125 · Cited by 315
Global and regional analysis of climate and human drivers of wildfire
Aldersley, A., Murray, S. J., & Cornell, S. E. (2011) · Science of The Total Environment · doi:10.1016/j.scitotenv.2011.05.032 · Cited by 311
Recent trends in African fires driven by cropland expansion and El Niño to La Niña transition
Andela, N., & van der Werf, G. R. (2014) · Nature Climate Change · doi:10.1038/nclimate2313 · Cited by 302 · cited 2×
“We then compared the data-and model-derived emergent relationships and sensitivities both globally and on a per grid cell basis.”
Self-citationThe changing radiative forcing of fires: global model estimates for past, present and future
Ward, D. S., Kloster, S., Mahowald, N. M., Rogers, B. M., Randerson, J. T., & Hess, P. (2012) · Atmospheric chemistry and physics · doi:10.5194/acp-12-10857-2012 · Cited by 298 · cited 2×
“…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 structure with subsequent impacts on the carbon cycle and…”
Fire as an interactive component of dynamic vegetation models
Arora, V. K., & Boer, G. J. (2005) · Journal of Geophysical Research Atmospheres · doi:10.1029/2005jg000042 · Cited by 281
Elvidge, C., Baugh, K., Anderson, S., Sutton, P., & Ghosh, T. (2012) · Social geography · doi:10.5194/sg-7-23-2012 · Cited by 278 · cited 2×
“GDP Gross domestic product per capita (C) W18 (World Bank, 2018) W18 W18 – – NLDI Night-light development index (C) E12 (Elvidge et al., 2012) E12 – – – CattleDens Cattle density (C) WR07 (Wint and Robinson, 2007) WR07 – – –”
Wei, Y., Liu, S., Huntzinger, D. N., Michalak, A. M., Viovy, N., Post, W. M., Schwalm, C. R., Schaefer, K. M., Jacobson, A. R., Lü, C., et al. (2014) · Geoscientific model development · doi:10.5194/gmd-7-2875-2014 · Cited by 275 · cited 2×
“The models were forced using inputs of meteorological variables from the CRUNCEP V5 data set (Wei et al., 2014), monthly cloud-to-ground lightning strikes (Rabin et al., 2017), annually updated values of human population density from the HYDE 3.1 data set (Goldewijk et al., 2010), annually updated…”
InfluentialKorontzi, S., McCarty, J. L., Loboda, T. V., Kumar, S., & Justice, C. (2006) · Global Biogeochemical Cycles · doi:10.1029/2005gb002529 · Cited by 269
Managing the human component of fire regimes: lessons from Africa
Archibald, S. A. (2016) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2015.0346 · Cited by 246
Integrating geospatial information into fire risk assessment
Chuvieco, E., Aguado, I., Jurdao, S., Pettinari, M. L., Yebra, M., Salas, J., Hantson, S., de la Riva, J., Benlloch, P. I., Rodrígues, M., et al. (2012) · International Journal of Wildland Fire · doi:10.1071/wf12052 · Cited by 237 · cited 8×
“4f), with precedent 6-monthly LAI (Fig.”
InfluentialSelf-citationBiomass burning fuel consumption rates: a field measurement database
van Leeuwen, T. T., van der Werf, G. R., Hoffmann, A. A., Detmers, R. G., Rücker, G., French, N. H. F., Archibald, S. A., de Carvalho, J. A., Cook, G. D., de Groot, W. J., et al. (2014) · Biogeosciences · doi:10.5194/bg-11-7305-2014 · Cited by 231
Li, F., Zeng, X., & Levis, S. (2012) · Biogeosciences · doi:10.5194/bg-9-2761-2012 · Cited by 221
Self-citationLi, F., Levis, S., & Ward, D. S. (2013) · Biogeosciences · doi:10.5194/bg-10-2293-2013 · Cited by 209
Self-citationA new global burned area product for climate assessment of fire impacts
Chuvieco, E., Yue, C., Heil, A., Mouillot, F., Alonso‐Canas, I., Padilla, M., Pereira, J. M. C., Oom, D., & Tansey, K. J. (2016) · Global Ecology and Biogeography · doi:10.1111/geb.12440 · Cited by 204
Self-citationPlant functional type mapping for earth system models
Poulter, B., Ciais, P., Hodson, E. L., Lischke, H., Maignan, F., Plummer, S., & Zimmermann, N. E. (2011) · Geoscientific model development · doi:10.5194/gmd-4-993-2011 · Cited by 196 · cited 1×
“For this conversion, we used the cross-walking approach (Poulter et al., 2011, 2015) based on the conversion table in Forkel et al. (2017) .”
Tepley, A. J., Thomann, E., Veblen, T. T., Perry, G. L. W., Holz, A., Paritsis, J., Kitzberger, T., & Anderson‐Teixeira, K. J. (2018) · Journal of Ecology · doi:10.1111/1365-2745.12950 · Cited by 191
Competition between plant functional types in the Canadian Terrestrial Ecosystem Model (CTEM) v. 2.0
Melton, J. R., & Arora, V. K. (2016) · Geoscientific model development · doi:10.5194/gmd-9-323-2016 · Cited by 190
Self-citationImpact of human population density on fire frequency at the global scale
Knorr, W., Kaminski, T., Arneth, A., & Weber, U. (2014) · Biogeosciences · doi:10.5194/bg-11-1085-2014 · Cited by 189
Self-citationBeck, P., Goetz, S., Mack, M., Alexander, H., Jin, Y., Randerson, J., & Loranty, M. (2011) · Global Change Biology · doi:10.1111/j.1365-2486.2011.02412.x · Cited by 186
Causal relationships vs. emergent patterns in the global controls of fire frequency
Bistinas, I., Harrison, S. P., Prentice, I., & Pereira, J. (2014) · doi:10.5194/bgd-11-3865-2014 · Cited by 177
Self-citationA comprehensive benchmarking system for evaluating global vegetation models
Kelley, D. I., Prentice, I. C., Harrison, S. P., Wang, H., Simard, M., Fisher, J. B., & Willis, K. J. (2013) · Biogeosciences · doi:10.5194/bg-10-3313-2013 · Cited by 169 · cited 3×
“State-ofthe-art fire-enabled DGVMs partly capture the spatial patterns of burned area (Andela et al., 2017; Kelley et al., 2013); however, doubt has been cast on the ability of these models to capture the response to extreme events and recent trends in burned area (Andela et al.”
InfluentialSelf-citationThe spatially varying influence of humans on fire probability in North America
Parisien, M.-A., Miller, C., Parks, S. A., DeLancey, E. R., Robinne, F.-N., & Flannigan, M. D. (2016) · Environmental Research Letters · doi:10.1088/1748-9326/11/7/075005 · Cited by 166
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…”
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Knorr, W., Arneth, A., & Jiang, L. (2016) · Nature Climate Change · doi:10.1038/nclimate2999 · Cited by 157
Self-citationA 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 157 · cited 2×
“…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.”
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 147
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
Spatial 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×
“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
FRY, 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 125
Self-citationSPITFIRE within the MPI Earth system model: Model development and evaluation
Lasslop, G., Thonicke, K., & Kloster, S. (2014) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2013ms000284 · Cited by 121
Self-citationScale-dependent controls on the area burned in the boreal forest of Canada, 1980–2005
Parisien, M., Parks, S. A., Krawchuk, M. A., Flannigan, M., Bowman, L. M., & Moritz, M. A. (2010) · Ecological Applications · doi:10.1890/10-0326.1 · Cited by 121
Harrison, S. P., Marlon, J. R., & Bartlein, P. J. (2010) · Springer eBooks · doi:10.1007/978-90-481-8716-4_3 · Cited by 110 · cited 1×
“We then compared the data-and model-derived emergent relationships and sensitivities both globally and on a per grid cell basis.”
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×
“…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-citationForkel, M., Dorigo, W. A., Lasslop, G., Teubner, I., Chuvieco, E., & Thonicke, K. (2017) · Geoscientific model development · doi:10.5194/gmd-10-4443-2017 · Cited by 107 · cited 2×
“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-citationFire and deforestation dynamics in Amazonia (1973–2014)
van Marle, M., Field, R. D., van der Werf, G. R., de Wagt, I. A. E., Houghton, R. A., Rizzo, L. V., Artaxo, P. E., & Tsigaridis, K. (2016) · Global Biogeochemical Cycles · doi:10.1002/2016gb005445 · Cited by 98
Role 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×
“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 90
Self-citationINFERNO: a fire and emissions scheme for the UK Met Office's Unified Model
Mangeon, S., Voulgarakis, A., Gilham, R. J. J., Harper, A., Sitch, S. A., & Folberth, G. (2016) · Geoscientific model development · doi:10.5194/gmd-9-2685-2016 · Cited by 88
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
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
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 58
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×
“…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-citationLarge‐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
Self-citationPausas, J. G., & Dantas, V. D. L. (2016) · Global Ecology and Biogeography · doi:10.1111/geb.12562 · Cited by 46
Rabin, S. S., Ward, D. S., Malyshev, S. L., Magi, B. I., Shevliakova, E., & Pacala, S. W. (2018) · Geoscientific model development · doi:10.5194/gmd-11-815-2018 · Cited by 44
Modelling burned area in Africa
Lehsten, V., Harmand, P., Palumbo, I., & Arneth, A. (2010) · Biogeosciences · doi:10.5194/bg-7-3199-2010 · Cited by 40
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).”
Müller, M. M., Vacik, H., & Valese, E. (2015) · Forests · doi:10.3390/f6040903 · Cited by 33
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
Self-citationFire Behavior Simulation from Global Fuel and Climatic Information
Pettinari, M. L., & Chuvieco, E. (2017) · Forests · doi:10.3390/f8060179 · Cited by 26 · cited 1×
“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
Self-citationHashimoto, S., Nanko, K., Ťupek, B., & Lehtonen, A. (2017) · Geoscientific model development · doi:10.5194/gmd-10-1321-2017 · Cited by 17
Pettinari, M. L. (2015) · doi:10.1594/pangaea.849808 · Cited by 6
Research works printed but not linked: 2
- Le Quéré, C., Peters, G. P., Andres, R. J., Andrew, R. M., Boden, T. A., Ciais, P., Friedlingstein, P., Houghton, R. A., Marland, G., Moriarty, R., Sitch, S., Tans, P., Arneth, A., Arvanitis, A., Bakker, D. C. E., Bopp, L., Canadell, J. G., Chini, L. P., Doney, S. C., Harper, A., Harris, I., House, J. I., Jain, A. K., Jones, S. D., Kato, E., Keeling, R. F., Klein Goldewijk, K., Körtzinger, A., Koven, C., Lefèvre, N., Maignan, F., Omar, A., Ono, T., Park, G.-H., Pfeil, B., Poulter, B., Raupach, M. R., Regnier, P., Rödenbeck, C., Saito, S., Schwinger, J., Segschneider, J., Stocker, B. D., Takahashi, T., Tilbrook, B., van Heuven, S., Viovy, N., Wanninkhof, R., Wiltshire, A., and Zaehle, S.: Global carbon budget 2013, Earth Syst. Sci. Data, 6, 235–263, 10.5194/essd-6-235-2014, 2014. · DOI · Paper page
- Myneni, R., Knyazikhin, Y., and Park, T.: MOD15A2 MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 1km SIN Grid, Boston University and MODAPS SIPS, NASA, NASA LP DAAC, 10.5067/MODIS/MOD15A2.006, 2015. · DOI
Cited by
Sorted by citations
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 945
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
“…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 693
“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. S. A., Camps‐Valls, G., Chirici, G., Mauri, A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21399-7 · Cited by 414
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 367
“…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).”
Background
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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