Influence of wind speed on the global variability of burned fraction: a global fire model’s perspective
Gitta Lasslop A D , Stijn Hantson B C and Silvia Kloster A
+ Author Affiliations
- Author Affiliations
A Max Planck Institute for Meteorology, Land in the Earth System, Bundesstraße 53, Hamburg, Germany.
B Department of Geology, Geography and Environment, University of Alcala, c/ Colegios 2, 28801 Alcala de Henares, Spain.
C Institute of Meteorology and Climate Research/Atmospheric Environmental Research (IMK/IFU), Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany.
D Corresponding author. Email: gitta.lasslop@mpimet.mpg.de
International Journal of Wildland Fire 24(7) 989-1000 https://doi.org/10.1071/WF15052
Submitted: 21 March 2014 Accepted: 13 June 2015 Published: 4 August 2015
Abstract
Understanding of fire behaviour, especially fire spread, is mostly based on local-scale observations but the same equations are applied in global models on a much coarser scale. Most model formulations include the effect of wind speed with a positive influence on fire spread. Availability of global datasets offers new possibilities to evaluate these approaches based on local-scale observations at the global scale. Here, we analyse the relation between wind speed derived from three datasets and remotely sensed burned fraction (burned area divided by grid cell area) on a climate model grid scale. The bivariate relationship between burned fraction and wind speed is characterised by an initial increase in burned fraction and a decrease in burned fraction for wind speeds higher than 2–3 ms–1. In a multivariate analysis we additionally included the effect of tree cover, precipitation or atmospheric moisture, temperature, vegetation net primary productivity and population density on burned fraction. This analysis confirmed the lack of an increase in burned fraction for high wind speeds on annual and daily time scale. From the observation-based analysis we conclude that a positive response of burned fraction for high wind speed should not be applied in coarse-scale global fire models.
Additional keywords: fire spread, generalised additive models, Global Fire Emissions Database (GFED).
References
Andrews PL, Cruz MG, Rothermel RC (2013) Examination of the wind speed limit function in the Rothermel surface fire spread model. International Journal of Wildland Fire 22, 959–969.| Examination of the wind speed limit function in the Rothermel surface fire spread model.Crossref | GoogleScholarGoogle Scholar |
Archibald S, Roy DP (2009) Identifying individual fires from satellite-derived burned area data. Available from http://www.researchgate.net/profile/Sally_Archibald2/publication/224117269_Identifying_individual_fires_from_satellite-derived_burned_area_data/links/0deec529330ce1ce60000000.pdf [Verified 14 July 2015]
Archibald S, Roy DP, van Wilgen BW, Scholes RJ (2009) What limits fire? An examination of drivers of burnt area in Southern Africa. Global Change Biology 15, 613–630.
| What limits fire? An examination of drivers of burnt area in Southern Africa.Crossref | GoogleScholarGoogle Scholar |
Archibald S, Scholes RJ, Roy DP, Roberts G, Boschetti L (2010) Southern African fire regimes as revealed by remote sensing. International Journal of Wildland Fire 19, 861–878.
| Southern African fire regimes as revealed by remote sensing.Crossref | GoogleScholarGoogle Scholar |
Arora VK, Boer GJ (2005) Fire as an interactive component of dynamic vegetation models. Journal of Geophysical Research 110, G02008
| Fire as an interactive component of dynamic vegetation models.Crossref | GoogleScholarGoogle Scholar |
Ashby WC, Kolar CA, Hendricks TR, Phares R (1979) Effects of shaking and shading on growth of three hardwood species. Forest Science 25, 212–216.
Beer T (1991) The interaction of wind and fire. Boundary-Layer Meteorology 54, 287–308.
| The interaction of wind and fire.Crossref | GoogleScholarGoogle Scholar |
Bistinas I, Oom D, Sá ACL, Harrison SP, Prentice IC, Pereira JMC (2013) Relationships between human population density and burned area at continental and global scales. PLoS One 8, e81188
| Relationships between human population density and burned area at continental and global scales.Crossref | GoogleScholarGoogle Scholar | 24358108PubMed |
Boerner REJ, Kooser JG (1989) Leaf litter redistribution among forest patches within an Allegheny Plateau watershed. Landscape Ecology 2, 81–92.
| Leaf litter redistribution among forest patches within an Allegheny Plateau watershed.Crossref | GoogleScholarGoogle Scholar |
Bowman DMJS, Balch JK, Artaxo P, Bond WJ, Carlson JM, Cochrane MA, D’Antonio CM, Defries RS, Doyle JC, Harrison SP, Johnston FH, Keeley JE, Krawchuk MA, Kull CA, Marston JB, Moritz MA, Prentice IC, Roos CI, Scott AC, Swetnam TW, van der Werf GR, Pyne SJ (2009) Fire in the Earth system. Science 324, 481–484.
| Fire in the Earth system.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXkvVGmtb8%3D&md5=acf0f16875fe2576863ea8a4f6d043c7CAS |
Brotak EA, Reifsnyder WE (1977) An investigation of the synoptic situations associated with major wildland fires. Journal of Applied Meteorology 16, 867–870.
| An investigation of the synoptic situations associated with major wildland fires.Crossref | GoogleScholarGoogle Scholar |
Cheney N, Gould J (1997) Fire growth and acceleration. International Journal of Wildland Fire 7, 1
| Fire growth and acceleration.Crossref | GoogleScholarGoogle Scholar |
Coen JL (2005) Simulation of the Big Elk Fire using coupled atmosphere–fire modeling International Journal of Wildland Fire 14, 49–59.
Consul PC (1990) On some properties and applications of quasi-binomial distribution. Communications in Statistics. Theory and Methods 19, 477–504.
| On some properties and applications of quasi-binomial distribution.Crossref | GoogleScholarGoogle Scholar |
Giglio L, Loboda T, Roy DP, Quayle B, Justice CO (2009) An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sensing of Environment 113, 408–420.
| An active-fire based burned area mapping algorithm for the MODIS sensor.Crossref | GoogleScholarGoogle Scholar |
Giglio L, Randerson JT, van der Werf GR, Kasibhatla PS, Collatz GJ, Morton DC, DeFries RS (2010) Assessing variability and long-term trends in burned area by merging multiple satellite fire products. Biogeosciences 7, 1171–1186.
| Assessing variability and long-term trends in burned area by merging multiple satellite fire products.Crossref | GoogleScholarGoogle Scholar |
Giorgetta MA, Jungclaus J, Reick CH, Legutke S, Bader J, Böttinger M, Brovkin V, Crueger T, Esch M, Fieg K, Glushak K, Gayler V, Haak H, Hollweg H-D, Ilyina T, Kinne S, Kornblueh L, Matei D, Mauritsen T, Mikolajewicz U, Mueller W, Notz D, Pithan F, Raddatz T, Rast S, Redler R, Roeckner E, Schmidt H, Schnur R, Segschneider J, Six KD, Stockhause M, Timmreck C, Wegner J, Widmann H, Wieners K-H, Claussen M, Marotzke J, Stevens B (2013) Climate and carbon cycle changes from 1850 to 2100 in MPI–ESM simulations for the Coupled Model Intercomparison Project phase 5. Journal of Advances in Modeling Earth Systems 5, 572–597.
| Climate and carbon cycle changes from 1850 to 2100 in MPI–ESM simulations for the Coupled Model Intercomparison Project phase 5.Crossref | GoogleScholarGoogle Scholar |
Goldewijk KK (2001) Estimating global land use change over the past 300 years. Global Biogeochemical Cycles 15, 417–433.
| Estimating global land use change over the past 300 years.Crossref | GoogleScholarGoogle Scholar |
Grace J (1988) 3. Plant response to wind. Agriculture, Ecosystems & Environment 22–23, 71–88.
| 3. Plant response to wind.Crossref | GoogleScholarGoogle Scholar |
Hansen MC, DeFries RS, Townshend JRG, Carroll M, Dimiceli C, Sohlberg RA (2003) Global percent tree cover at a spatial resolution of 500 meters: first results of the MODIS vegetation continuous fields algorithm. Earth Interactions 7, 1–15.
| Global percent tree cover at a spatial resolution of 500 meters: first results of the MODIS vegetation continuous fields algorithm.Crossref | GoogleScholarGoogle Scholar |
Hantson S, Pueyo S, Chuvieco E (2015) Global fire size distribution is driven by human impact and climate. Global Ecology and Biogeography 24, 77–86.
| Global fire size distribution is driven by human impact and climate.Crossref | GoogleScholarGoogle Scholar |
Hoffmann WA, Jaconis SY, Mckinley KL, Geiger EL, Gotsch SG, Franco AC (2012) Fuels or microclimate? Understanding the drivers of fire feedbacks at savanna–forest boundaries. Austral Ecology 37, 634–643.
| Fuels or microclimate? Understanding the drivers of fire feedbacks at savanna–forest boundaries.Crossref | GoogleScholarGoogle Scholar |
Kanamitsu M, Ebisuzaki W, Woollen J, Yang S-K, Hnilo JJ, Fiorino M, Potter GL (2002) NCEP–DOE AMIP-II Reanalysis (R-2). Bulletin of the American Meteorological Society 83, 1631–1643.
| NCEP–DOE AMIP-II Reanalysis (R-2).Crossref | GoogleScholarGoogle Scholar |
Keywood M, Kanakidou M, Stohl A, Grassi G, Meyer CP, Torseth K, Edwards D, Thompson AM, Lohmann U (2013) Fire in the air: Biomass burning impacts in a changing climate. Critical Reviews in Environmental Science and Technology 43, 40–83.
| Fire in the air: Biomass burning impacts in a changing climate.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhvVOqsrzM&md5=2c52a215fc4f040fc58a32c05f2986c2CAS |
Kloster S, Mahowald NM, Randerson JT, Thornton PE, Hoffman FM, Levis S, Lawrence PJ, Feddema JJ, Oleson KW, Lawrence DM (2010) Fire dynamics during the 20th century simulated by the Community Land Model. Biogeosciences 7, 1877–1902. http://www.biogeosciences.net/7/1877/2010/10.5194/BG-7-1877-2010
Knorr W, Kaminski T, Arneth A, Weber U (2014) Impact of human population density on fire frequency at the global scale. Biogeosciences 11, 1085–1102.
| Impact of human population density on fire frequency at the global scale.Crossref | GoogleScholarGoogle Scholar |
Krawchuk MA, Moritz MA (2011) Constraints on global fire activity vary across a resource gradient. Ecology 92, 121–132.
Lasslop G, Thonicke K, Kloster S (2014) SPITFIRE within the MPI Earth system model: model development and evaluation. Journal of Advances in Modeling Earth Systems 6, 740–755.
| SPITFIRE within the MPI Earth system model: model development and evaluation.Crossref | GoogleScholarGoogle Scholar |
Le Page Y, Oom D, Silva JMN, Jönsson P, Pereira JMC (2010) Seasonality of vegetation fires as modified by human action: observing the deviation from eco-climatic fire regimes. Global Ecology and Biogeography 19, 575–588.
| Seasonality of vegetation fires as modified by human action: observing the deviation from eco-climatic fire regimes.Crossref | GoogleScholarGoogle Scholar |
Magi BI, Rabin S, Shevliakova E, Pacala S (2012) Separating agricultural and non-agricultural fire seasonality at regional scales. Biogeosciences 9, 3003–3012.
| Separating agricultural and non-agricultural fire seasonality at regional scales.Crossref | GoogleScholarGoogle Scholar |
McArthur A (1969) The Tasmanian bushfires of 7th February 1967 and associated fire behaviour characteristics. In ‘Mass Fire Symposium: The Technical Cooperation Programme’, 10–12 February 1969, Canberra, ACT. (Ed. The Technical Cooperation Programme) Vol. 1, Paper A7 (Defence Standards Laboratories, Melbourne, Vic.)
Minnich RA (1983) Fire mosaics in southern California and northern Baja California. Science 219, 1287–1294.
| Fire mosaics in southern California and northern Baja California.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC3cvhvVymsQ%3D%3D&md5=ded9817f4a68522eb92f484caa757678CAS | 17735593PubMed |
Morandini F, Santoni PA, Balbi JH, Ventura JM, Mendes-Lopes JM (2002) A two-dimensional model of fire spread across a fuel bed including wind combined with slope conditions. International Journal of Wildland Fire 11, 53–63.
| A two-dimensional model of fire spread across a fuel bed including wind combined with slope conditions.Crossref | GoogleScholarGoogle Scholar |
Mu M, Randerson JT, van der Werf GR, Giglio L, Kasibhatla P, Morton D, Collatz GJ, DeFries RS, Hyer EJ, Prins EM, Griffith DWT, Wunch D, Toon GC, Sherlock V, Wennberg PO (2011) Daily and 3-hourly variability in global fire emissions and consequences for atmospheric model predictions of carbon monoxide. Journal of Geophysical Research 116, D24303
| Daily and 3-hourly variability in global fire emissions and consequences for atmospheric model predictions of carbon monoxide.Crossref | GoogleScholarGoogle Scholar |
Padilla M, Stehman SV, Chuvieco E (2014) Validation of the 2008 MODIS–MCD45 global burned area product using stratified random sampling. Remote Sensing of Environment 144, 187–196.
| Validation of the 2008 MODIS–MCD45 global burned area product using stratified random sampling.Crossref | GoogleScholarGoogle Scholar |
Padilla M, Stehman SV, Ramo R, Corti D, Hantson S, Oliva P, Alonso-Canas I, Bradley AV, Tansey K, Mota B, Pereira JM, Chuvieco E (2015) Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation. Remote Sensing of Environment
| Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation.Crossref | GoogleScholarGoogle Scholar |
Pfeiffer M, Spessa A, Kaplan JO (2013) A model for global biomass burning in preindustrial time: LPJ–LMfire (v1.0). Geoscientific Model Development 6, 643–685.
| A model for global biomass burning in preindustrial time: LPJ–LMfire (v1.0).Crossref | GoogleScholarGoogle Scholar |
Prentice IC, Kelley DI, Foster PN, Friedlingstein P, Harrison SP, Bartlein PJ (2011) Modeling fire and the terrestrial carbon balance. Global Biogeochemical Cycles 25, 1–13.
| Modeling fire and the terrestrial carbon balance.Crossref | GoogleScholarGoogle Scholar |
Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service, Intermountain Forest and Range Experiment Station, Research Paper RP-INT-115. (Ogden, UT)
Roy DP, Boschetti L, Justice CO, Ju J (2008) The collection 5 MODIS burned area product – global evaluation by comparison with the MODIS active fire product. Remote Sensing of Environment 112, 3690–3707.
| The collection 5 MODIS burned area product – global evaluation by comparison with the MODIS active fire product.Crossref | GoogleScholarGoogle Scholar |
Scott AC, Bowman DMJS, Bond WJ, Pyne SJ, Alexander ME (2014) ‘Fire on Earth: an introduction.’ (Wiley-Blackwell: Hoboken, NJ)
Sullivan AL (2009) Wildland surface fire spread modelling, 1990–2007. 2: empirical and quasi-empirical models. International Journal of Wildland Fire 18, 369–386.
| Wildland surface fire spread modelling, 1990–2007. 2: empirical and quasi-empirical models.Crossref | GoogleScholarGoogle Scholar |
Thonicke K, Venevsky S, Sitch S, Cramer W (2008) The role of fire disturbance for global vegetation dynamics: coupling fire into a Dynamic Global Vegetation Model. Global Ecology and Biogeography 10, 661–677.
| The role of fire disturbance for global vegetation dynamics: coupling fire into a Dynamic Global Vegetation Model.Crossref | GoogleScholarGoogle Scholar |
Thonicke K, Spessa A, Prentice IC, Harrison SP, Dong L, Carmona-Moreno C (2010) The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model. Biogeosciences 7, 1991–2011.
| The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhtl2ksrvE&md5=f63babcbab3afc9c431d99c4b2d510deCAS |
Trauernicht C, Murphy BP, Portner TE, Bowman DMJS (2012) Tree cover–fire interactions promote the persistence of a fire-sensitive conifer in a highly flammable savanna. Journal of Ecology 100, 958–968.
| Tree cover–fire interactions promote the persistence of a fire-sensitive conifer in a highly flammable savanna.Crossref | GoogleScholarGoogle Scholar |
Trollope WSW (1984) Fire in Savanna. In ‘Ecological effects of fire in South African ecosystems.’ (Eds P de Booysen, N Tainton) pp. 149–175. (Springer: Berlin)
van der Werf GR, Randerson JT, Giglio L, Collatz GJ, Mu M, Kasibhatla PS, Morton DC, DeFries RS, Jin Y, van Leeuwen TT (2010) Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmospheric Chemistry and Physics 10, 11707–11735.
| Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009).Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXmvFemsb0%3D&md5=e4141b6bc1ec84d7e5ca7cc2873c78beCAS |
Weedon GP, Gomes S, Viterbo P, Shuttleworth WJ, Blyth E, Österle H, Adam JC, Bellouin N, Boucher O, Best M (2011) Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. Journal of Hydrometeorology 12, 823–848.
| Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century.Crossref | GoogleScholarGoogle Scholar |
Wood SN (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society. Series B. Methodological 73, 3–36.
| Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models.Crossref | GoogleScholarGoogle Scholar |
Yue C, Ciais P, Cadule P, Thonicke K, Archibald S, Poulter B, Hao WM, Hantson S, Mouillot F, Friedlingstein P, Maignan F, Viovy N (2014) Modelling the role of fires in the terrestrial carbon balance by incorporating SPITFIRE into the global vegetation model ORCHIDEE – Part 1: simulating historical global burned area and fire regimes. Geoscientific Model Development 7, 2747–2767.
| Modelling the role of fires in the terrestrial carbon balance by incorporating SPITFIRE into the global vegetation model ORCHIDEE – Part 1: simulating historical global burned area and fire regimes.Crossref | GoogleScholarGoogle Scholar |
