Fuel burning efficiency under various fire severities of a boreal forest landscape in north-east China
Xiaoying Ping A B , Yu Chang A E , Miao Liu A , Yuanman Hu A , Zhelong Yuan C , Sixue Shi A B , Yuchen Jia A B , Dikang Li A B and Lili Yu A D
+ Author Affiliations
- Author Affiliations
A CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China.
B University of Chinese Academy of Sciences, Beijing 100049, China.
C Forest Police Brigade, Jinhe forestry Bureau, Jinhe 022359, China.
D Shenyang Jianzhu University, Shenyang 110168, China.
E Corresponding author. Email: changyu@iae.ac.cn
International Journal of Wildland Fire 30(9) 691-701 https://doi.org/10.1071/WF20143
Submitted: 4 September 2020 Accepted: 19 June 2021 Published: 6 July 2021
Abstract
Forest fires are important natural disturbances that influence accurate estimations of forest carbon budgets, largely owing to the uncertainty of carbon emissions from forest fires. Fuel burning efficiency is an important factor affecting accurate estimations of carbon emissions and is difficult to quantify. Here, we quantified burning efficiencies of fuel strata by fire severity and forest types and investigated influencing factors. Burning efficiencies of fuel strata increased with increasing fire severity. The tree stratum had low values of burning efficiency of 0.76, 0.83, 6.84% under low-, moderate-, high-severity fires respectively. The burning efficiency of the herb stratum was the highest, over 95%, followed by the litter stratum between 49 and 85%. Although the tree stratum accounted for the largest carbon storage of aboveground fuels, most carbon consumed during fires came from the shrub and herb strata. Among forest types, the burning efficiency of aboveground fuels in Pinus pumila–Larix gmelinii forest was much higher than the other two studied. Fire Weather Index (FWI) and temperature exerted a positive effect on the burning efficiency of understorey fuels. Precipitation mainly had a negative influence on the burning efficiency of shrub and duff.
Keywords: burning efficiency, carbon emissions, carbon storage, fire severity, forest fires, forest types, the Great Xing’an Mountains, environmental factors.
References
Andela N, Morton DC, Giglio L, Chen Y, van der Werf GR, Kasibhatla PS, DeFries RS, Collatz GJ, Hantson S, Kloster S, Bachelet D, Forrest M, Lasslop G, Li F, Mangeon S, Melton JR, Yue C, Randerson JT (2017) A human-driven decline in global burned area. Science 356, 1356–1362.| A human-driven decline in global burned area.Crossref | GoogleScholarGoogle Scholar | 28663495PubMed |
Bradshaw L, Bartlette R, McGinely J, Zeller K (2003) Hayman Fire Case Study. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-114. (Ogden, UT).
Campbell J, Alberti G, Martin J, Law BE (2009) Carbon dynamics of a Ponderosa pine plantation following a thinning treatment in the northern Sierra Nevada. Forest Ecology and Management 257, 453–463.
| Carbon dynamics of a Ponderosa pine plantation following a thinning treatment in the northern Sierra Nevada.Crossref | GoogleScholarGoogle Scholar |
Campbell JL, Fontaine JB, Donato DC (2016) Carbon emissions from decomposition of fire‐killed trees following a large wildfire in Oregon, United States. Journal of Geophysical Research. Biogeosciences 121, 718–730.
| Carbon emissions from decomposition of fire‐killed trees following a large wildfire in Oregon, United States.Crossref | GoogleScholarGoogle Scholar |
Chang Y, He HS, Hu Y, Bu R, Li X (2008) Historic and current fire regimes in the Great Xing’an Mountains, northeastern China: Implications for long-term forest management. Forest Ecology and Management 254, 445–453.
| Historic and current fire regimes in the Great Xing’an Mountains, northeastern China: Implications for long-term forest management.Crossref | GoogleScholarGoogle Scholar |
Chang Y, Leng WF, He HS, Liu BF (2010) Using weights of evidence to estimate the probability of forest fire occurrence: A case study in Huzhong area of the Daxing’an Mountains. Scientia Silvae Sinicae 46, 103–109.
| Using weights of evidence to estimate the probability of forest fire occurrence: A case study in Huzhong area of the Daxing’an Mountains.Crossref | GoogleScholarGoogle Scholar |
Chang Y, Chen HW, Hu YM, Feng YT, Li Y (2012) Advances in the assessment of forest fire severity and its spatial heterogeneity. Ziran Zaihai Xuebao 21, 28–34.
Chang Y, Hang WT, Hu YM, Li YH, Bu RC, Liu YY (2015) Contemporary research advances on carbon emissions by forest fires and future prospects. Shengtaixue Zazhi 34, 2922–2929.
Chang Y, Zhu ZL, Feng YT, Li YH, Bu RC, Hu YM (2016) The spatial variation in forest burn severity in Heilongjiang Province, China. Natural Hazards 81, 981–1001.
| The spatial variation in forest burn severity in Heilongjiang Province, China.Crossref | GoogleScholarGoogle Scholar |
Chen CG, Zhu JF (1989) ‘A handbook of biomass of main tree species in northeastern China.’ (China Forestry Press: Beijing)
Chen X, Hao ZH (2018) Sentinel-2A data products’ characteristics and the potential applications. China RS Geo-informatics Company Limited 16, 48–50.
Cheng YX, Li ZX (1989) A study on biomass of three main forest types in Larix gmelinii forest. Inner Mongolia Forestry Investigation and Design 4, 89–100.
Dixon RK, Brown S, Houghton RA, Solomon AM, Trexler MC, Wisniewski J (1994) Carbon pools and flux of global forest ecosystem. Science 263, 185–190.
| Carbon pools and flux of global forest ecosystem.Crossref | GoogleScholarGoogle Scholar | 17839174PubMed |
Drury SA, Veblen TT (2008) Spatial and temporal variability in fire occurrence within the Las Bayas Forestry Reserve, Durango, Mexico. Plant Ecology 197, 299–316.
| Spatial and temporal variability in fire occurrence within the Las Bayas Forestry Reserve, Durango, Mexico.Crossref | GoogleScholarGoogle Scholar |
Dupuy JL, Mare’chal J, Portier D, Valette JC (2011) The effects of slope and fuel bed width on laboratory fire behaviour. International Journal of Wildland Fire 20, 272–288.
| The effects of slope and fuel bed width on laboratory fire behaviour.Crossref | GoogleScholarGoogle Scholar |
Fang JY, Huang Y, Zhu JL, Sun WJ, Hu HF (2015) Carbon budget of forest ecosystems and its driving forces. China Basic Science 3, 20–25.
Flannigan MD, Stocks BJ, Wotton BM (2000) Climate change and forest fires. The Science of the Total Environment 262, 221–229.
| Climate change and forest fires.Crossref | GoogleScholarGoogle Scholar | 11087028PubMed |
Flannigan MD, Krawchuk MA, de Groot WJ, Wotton BM, Gowman LM (2009) Implications of changing climate for global wildland fire. International Journal of Wildland Fire 18, 483–507.
| Implications of changing climate for global wildland fire.Crossref | GoogleScholarGoogle Scholar |
French NHF (2004) Uncertainty in estimating carbon emissions from boreal forest fires. Journal of Geophysical Research 109, D14S08
| Uncertainty in estimating carbon emissions from boreal forest fires.Crossref | GoogleScholarGoogle Scholar |
Gao YG, Gu H, Zhang GY (2010) Integrated index study on forest lightning fire for Daxinganling Mountains. Zhongguo Nongxue Tongbao 26, 87–92.
Graham RT, McCaffrey S, Jain TB (2004) Science basis for changing forest structure to modify wildfire behavior and severity. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-120.
Hu HQ, Sun L, Guo QX, Lü XS (2007) Carbon emissions from forest fires on main Arbor species in Daxing’an Mountains in Heilongjiang province. Scientia Silvae Sinicae 43, 82–88.
| Carbon emissions from forest fires on main Arbor species in Daxing’an Mountains in Heilongjiang province.Crossref | GoogleScholarGoogle Scholar |
Hu HQ, Wei SJ, Sun L (2012) Estimation of carbon emissions due to forest fire in Daxing’an Mountains from 1965 to 2010. Acta Phytoecologica Sinica 36, 629–644.
| Estimation of carbon emissions due to forest fire in Daxing’an Mountains from 1965 to 2010.Crossref | GoogleScholarGoogle Scholar |
Hu YM, Xu CG, Chang Y, Li XZ, Bu RC, He HS, Leng WF (2004) Application of spatially explicit landscape model (LANDIS): A case researches in Huzhong area, Mt. Daxing’anling. Acta Ecologica Sinica 24, 1846–1856.
IPCC (2019) 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. In ‘Agriculture, Forestry and Other Land Use’. Vol. 4 (International Panel on Climate Change: Japan)
Kasischke ES, Johnstone JF (2005) Variation in post-fire organic layer thickness in a black spruce forest complex in interior Alaska and its effects on soil temperature and moisture. Canadian Journal of Forest Research 35, 2164–2177.
| Variation in post-fire organic layer thickness in a black spruce forest complex in interior Alaska and its effects on soil temperature and moisture.Crossref | GoogleScholarGoogle Scholar |
Lentile LB, Smith FW, Shepperd WD (2006) Influence of topography and forest structure on patterns of mixed severity fire in ponderosa pine forests of the South Dakota Black Hills, USA. International Journal of Wildland Fire 15, 557–566.
| Influence of topography and forest structure on patterns of mixed severity fire in ponderosa pine forests of the South Dakota Black Hills, USA.Crossref | GoogleScholarGoogle Scholar |
Lepš J, Šmilauer P (2003) ‘Multivariate analysis of ecological data using CANOCO 5.’ (Cambridge University Press: Cambridge)
Li Z, Nadon S, Cihlar J (2000) Satellite-based detection of Canadian boreal forest fires: development and application of the algorithm. International Journal of Remote Sensing 21, 3057–3069.
| Satellite-based detection of Canadian boreal forest fires: development and application of the algorithm.Crossref | GoogleScholarGoogle Scholar |
Ling K (2012) A study on the decomposition of falled trees of Larix gmelinii and Betula platyphylla forests in Daxinganling Mountains. Master’s Thesis. Inner Mongolia Agricultural University.
Liu ZH, Yang J, Chang Y, Weisberg PJ, He HS (2012) Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of northeast China. Global Change Biology 18, 2041–2056.
| Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of northeast China.Crossref | GoogleScholarGoogle Scholar |
Louis J, Debaecker V, Pflug B, Main-Knorn M, Gascon F (2016) SENTINEL-2 SEN2COR: L2A Processor for Users. In ‘Proceedings of the Living Planet Symposium 2016’, Prague, Czech Republic, 9–13 May 2016 (ESA SP-740, August 2016). Available at https://elib.dlr.de/107381/1/LPS2016_sm10_3louis.pdf
Luo BZ (2020) ‘Effects of forest fire disturbance on carbon pools of subtropical forest ecosystem in Guangdong Province, China.’ (Northeast Forestry University: Harbin).
Luo TX (1996) ‘Patterns of net primary productivity for Chinese major forest types and their mathematical models.’ (Graduate University of Chinese Academy of Sciences: Beijing).
Lutes DC (2020) First Order Fire Effects Model: FOFEM 6.7, User’s guide. (Fire And Aviation Management Rocky Mountain Research Station Fire Modeling Institute) Available at https://www.firelab.org/sites/default/files/images/downloads/FOFEM_6-7_User_Guide.pdf
Lutz JA, Matchett JR, Tarnay LW, Smith DF, Becker KML, Furniss TJ, Brooks ML (2017) Fire and the distribution and uncertainty of carbon sequestered as aboveground tree biomass in Yosemite and Sequoia & Kings Canyon National Parks. Land 6, 10
| Fire and the distribution and uncertainty of carbon sequestered as aboveground tree biomass in Yosemite and Sequoia & Kings Canyon National Parks.Crossref | GoogleScholarGoogle Scholar |
Ma W, Feng Z, Cheng Z, Chen S, Wang F (2020) Identifying forest fire driving factors and related impacts in China using Random Forest algorithm. Forests 11, 507
| Identifying forest fire driving factors and related impacts in China using Random Forest algorithm.Crossref | GoogleScholarGoogle Scholar |
Meigs GW, Donato DC, Campbell JL, Martin JG, Law BE (2009) Forest fire impacts on carbon uptake, storage, and emission: The role of burn severity in the Eastern Cascades, Oregon. Ecosystems 12, 1246–1267.
| Forest fire impacts on carbon uptake, storage, and emission: The role of burn severity in the Eastern Cascades, Oregon.Crossref | GoogleScholarGoogle Scholar |
Meigs GW, Dunn CJ, Parks SA, Krawchuk MA (2020) Influence of topography and fuels on fire refugia probability under varying fire weather conditions in forests of the Pacific Northwest, USA. Canadian Journal of Forest Research 50, 636–647.
| Influence of topography and fuels on fire refugia probability under varying fire weather conditions in forests of the Pacific Northwest, USA.Crossref | GoogleScholarGoogle Scholar |
Merschel AG, Heyerdahl EK, Spies TA, Loehman RA (2018) Influence of landscape structure, topography, and forest type on spatial variation in historical fire regimes, Central Oregon, USA. Landscape Ecology 33, 1195–1209.
| Influence of landscape structure, topography, and forest type on spatial variation in historical fire regimes, Central Oregon, USA.Crossref | GoogleScholarGoogle Scholar |
Musyimi Z, Said MY, Zida D, Rosenstock TS, Udelhoven T, Savadogo P, de Leeuw J, Aynekulu E (2017) Evaluating fire severity in Sudanian ecosystems of Burkina Faso using Landsat 8 satellite images. Journal of Arid Environments 139, 95–109.
| Evaluating fire severity in Sudanian ecosystems of Burkina Faso using Landsat 8 satellite images.Crossref | GoogleScholarGoogle Scholar |
Nyman P, Metzen D, Noske PJ, Lane PNJ, Sheridan GJ (2015) Quantifying the effects of topographic aspect on water content and temperature in fine surface fuel. International Journal of Wildland Fire 24, 1129–1142.
| Quantifying the effects of topographic aspect on water content and temperature in fine surface fuel.Crossref | GoogleScholarGoogle Scholar |
Ottmar RD (2014) Wildland fire emissions, carbon, and climate: Modeling fuel consumption. Forest Ecology and Management 317, 41–50.
| Wildland fire emissions, carbon, and climate: Modeling fuel consumption.Crossref | GoogleScholarGoogle Scholar |
Pan Y, Birdsey RA, Fang J, Houghton R, Kauppi PE, Kurz WA, Phillips OL, Shvidenko A, Lewis SL, Canadell JG, Ciais P, Jackson RB, Pacala SW, McGuire AD, Piao S, Rautiainen A, Sitch S, Hayes D (2011) A large and persistent carbon sink in the world’s forests. Science 333, 988–993.
| A large and persistent carbon sink in the world’s forests.Crossref | GoogleScholarGoogle Scholar | 21764754PubMed |
Perez-Verdin G, Marquez-Linares M, Salmeron-Macias M (2014) Spatial heterogeneity of factors influencing forest fires size in northern Mexico. Journal of Forestry Research 25, 291–300.
| Spatial heterogeneity of factors influencing forest fires size in northern Mexico.Crossref | GoogleScholarGoogle Scholar |
Perry JJ, Cook GD, Graham E, Meyer CP, Murphy HT, VanDerWal J (2020) Regional seasonality of fire size and fire weather conditions across Australia’s northern savanna. International Journal of Wildland Fire 29, 1–10.
| Regional seasonality of fire size and fire weather conditions across Australia’s northern savanna.Crossref | GoogleScholarGoogle Scholar |
Peterson JL (1987) ‘Analysis and reduction of the errors of predicting prescribed burn emission.’ (University of Washington).
Prichard SJ, Kennedy MC, Wright CS, Cronan JB, Ottmar RD (2017) Predicting forest floor and woody fuel consumption from prescribed burns in southern and western pine ecosystems of the United States. Forest Ecology and Management 405, 328–338.
| Predicting forest floor and woody fuel consumption from prescribed burns in southern and western pine ecosystems of the United States.Crossref | GoogleScholarGoogle Scholar |
R Core Team (2019) R: A Language and Environment for Statistical Computing. Version 3.6.1. (R Foundation for Statistical Computing: Vienna, Austria).
Schimel DS (1995) Terrestrial ecosystems and the carbon cycle. Global Change Biology 1, 77–91.
| Terrestrial ecosystems and the carbon cycle.Crossref | GoogleScholarGoogle Scholar |
Seiler W, Crutzen PJ (1980) Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning Climatic Change 2, 207–247.
| Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burningCrossref | GoogleScholarGoogle Scholar |
Sommers WT, Loehman RA, Hardy CC (2014) Wildland fire emissions, carbon, and climate: Science overview and knowledge needs. Forest Ecology and Management 317, 1–8.
| Wildland fire emissions, carbon, and climate: Science overview and knowledge needs.Crossref | GoogleScholarGoogle Scholar |
Spracklen DV, Mickley LJ, Logan JA, Hudman RC, Yevich R, Flannigan MD, Westerling AL (2009) Impacts of climate change from 2000 to 2050 on wildfire activity and carbonaceous aerosol concentrations in the western United States. Journal of Geophysical Research 114, D20301
| Impacts of climate change from 2000 to 2050 on wildfire activity and carbonaceous aerosol concentrations in the western United States.Crossref | GoogleScholarGoogle Scholar |
Stenzel JE, Bartowitz KJ, Hartman MD, Lutz JA, Kolden CA, Smith AMS, Law BE, Swanson ME, Larson AJ, Parton WJ, Hudiburg TW (2019) Fixing a snag in carbon emissions estimates from wildfires. Global Change Biology 25, 3985–3994.
| Fixing a snag in carbon emissions estimates from wildfires.Crossref | GoogleScholarGoogle Scholar | 31148284PubMed |
Sullivan AL, Sharples JJ, Matthews S, Plucinski MP (2014) A downslope fire spread correction factor based on landscape-scale fire behaviour. Environmental Modelling & Software 62, 153–163.
| A downslope fire spread correction factor based on landscape-scale fire behaviour.Crossref | GoogleScholarGoogle Scholar |
Tang X, Zhao X, Bai Y, Tang Z, Wang W, Zhao Y, Wan H, Xie Z, Shi X, Wu B, Wang G, Yan J, Ma K, Du S, Li S, Han S, Ma Y, Hu H, He N, Yang Y, Han W, He H, Yu G, Fang J, Zhou G (2018) Carbon pools in China’s terrestrial ecosystems: New estimates based on an intensive field survey. Proceedings of the National Academy of Sciences of the United States of America 115, 4021–4026.
| Carbon pools in China’s terrestrial ecosystems: New estimates based on an intensive field survey.Crossref | GoogleScholarGoogle Scholar | 29666314PubMed |
Tian X, Douglas JM, Jin J, Shu L, Zhao F, Wang M (2010) Changes of forest fire danger and the evaluation of the FWI System application in the Daxing’anling region. Scientia Silvae Sinicae 46, 127–132.
| Changes of forest fire danger and the evaluation of the FWI System application in the Daxing’anling region.Crossref | GoogleScholarGoogle Scholar |
Turetsky MR, Kane ES, Harden JW, Ottmar RD, Manies KL, Hoy E, Kasischke ES (2011) Recent acceleration of biomass burning and carbon losses in Alaskan forests and peatlands. Nature Geoscience 4, 27–31.
| Recent acceleration of biomass burning and carbon losses in Alaskan forests and peatlands.Crossref | GoogleScholarGoogle Scholar |
van der Werf GR, Randerson JT, Giglio L, van Leeuwen TT, Chen Y, Rogers BM, Mu M, van Marle MJE, Morton DC, Collatz GJ, Yokelson RJ, Kasibhatla PS (2017) Global fire emissions estimates during 1997–2016. Earth System Science Data 9, 697–720.
| Global fire emissions estimates during 1997–2016.Crossref | GoogleScholarGoogle Scholar |
van Leeuwen TT, van der Werf GR, Hoffmann AA, Detmers RG, Rücker G, French NHF, Archibald S, Carvalho JA, Cook GD, de Groot WJ, Hély C, Kasischke ES, Kloster S, McCarty JL, Pettinari ML, Savadogo P, Alvarado EC, Boschetti L, Manuri S, Meyer CP, Siegert F, Trollope LA, Trollope WSW (2014) Biomass burning fuel consumption rates: A field measurement database. Biogeosciences 11, 7305–7329.
| Biomass burning fuel consumption rates: A field measurement database.Crossref | GoogleScholarGoogle Scholar |
Veraverbeke S, Hook SJ (2013) Evaluating spectral indices and spectral mixture analysis for assessing fire severity, combustion completeness and carbon emissions. International Journal of Wildland Fire 22, 707–720.
| Evaluating spectral indices and spectral mixture analysis for assessing fire severity, combustion completeness and carbon emissions.Crossref | GoogleScholarGoogle Scholar |
Waddell KL (2002) Sampling coarse woody debris for multiple attributes in extensive resource inventories. Ecological Indicators 1, 139–153.
| Sampling coarse woody debris for multiple attributes in extensive resource inventories.Crossref | GoogleScholarGoogle Scholar |
Wang CK, Gower ST, Wang YH, Zhao HX, Yan P, Bond‐Lamberty BP (2001) The influence of fire on carbon distribution and net primary production of boreal Larix gmelinii forests in north-eastern China. Global Change Biology 7, 719–730.
| The influence of fire on carbon distribution and net primary production of boreal Larix gmelinii forests in north-eastern China.Crossref | GoogleScholarGoogle Scholar |
Wang XL, Wang WJ, Chang Y, Feng YT, Chen HW, Hu YM, Chi JG (2013) Fire severity of burnt area in Huzhong forest region of Great Xing’an Mountains, northeast China based on normalized burn ratio analysis. Ying Yong Sheng Tai Xue Bao 24, 967–974.
Woodwell GM, Whittaker RH, Reiners WA, Likens GE, Delwiche CC, Botkin DB (1978) The biota and the world carbon budget. Science 199, 141–146.
| The biota and the world carbon budget.Crossref | GoogleScholarGoogle Scholar | 17812932PubMed |
Wright CS (2013) Fuel consumption models for pine flatwoods fuel types in the southeastern United States. Southern Journal of Applied Forestry 37, 148–159.
| Fuel consumption models for pine flatwoods fuel types in the southeastern United States.Crossref | GoogleScholarGoogle Scholar |
Xu WR, He HS, Hawbaker TJ, Zhu ZL, Henne PD (2020) Estimating burn severity and carbon emissions from a historic megafire in boreal forests of China. The Science of the Total Environment 716, 136534
| Estimating burn severity and carbon emissions from a historic megafire in boreal forests of China.Crossref | GoogleScholarGoogle Scholar |
Yu GR, Li XR, Wang QF, Li SG (2010) Carbon storage and its spatial pattern of terrestrial ecosystem in China. Journal of Resources and Ecology 1, 97–109.
Zhao F, Liu Y, Shu L (2020) Change in the fire season pattern from bimodal to unimodal under climate change: The case of Daxing’anling in northeast China. Agricultural and Forest Meteorology 291, 108075
| Change in the fire season pattern from bimodal to unimodal under climate change: The case of Daxing’anling in northeast China.Crossref | GoogleScholarGoogle Scholar |
