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Estimation and Spatio-temporal Patterns of Carbon Emissions from Grassland Fires in Inner Mongolia, China

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Abstract

Grassland fires results in carbon emissions, which directly affects the carbon cycle of ecosystems and the carbon balance. The grassland area of Inner Mongolia accounts for 22% of the total grassland area in China, and many fires occur in the area every year. However, there are few models for estimation of carbon emissions from grassland fires. Accurate estimation of direct carbon emissions from grassland fires is critical to quantifying the contribution of grassland fires to the regional balance of atmospheric carbon. In this study, the regression equations for aboveground biomass (AGB) of grassland in growing season and MODIS NDVI (Normalized Difference Vegetation Index) were established through field experiments, then AGB during Nov.–Apr. were retrieved based on that in Oct. and decline rate, finally surface fuel load was obtained for whole year. Based on controlled combustion experiments of different grassland types in Inner Mongolia, the carbon emission rate of grassland fires for each grassland type were determined, then carbon emission was estimated using proposed method and carbon emission rate. Results revealed that annual average surface fuel load of grasslands in Inner Mongolia during 2000–2016 was approximately 1.1978 × 1012 kg. The total area of grassland which was burned in the Inner Mongolia region over the 17-year period was 5298.75 km2, with the annual average area of 311.69 km2. The spatial distribution of grassland surface fuel loads is characterized by decreasing from northeast to southwest in Inner Mongolia. The total carbon emissions from grassland fires amounted to 2.24 × 107 kg with an annual average of 1.32 × 106 for the study area. The areas with most carbon emissions were mainly concentrated in Old Barag Banner and New Barag Right Banner and on the right side of the Oroqin Autonomous Banner. The spatial characteristics of carbon emission depend on the location of grassland fire, mainly in the northeast of Inner Mongolia include Hulunbuir City, Hinggan League, Xilin Gol League and Ulanqab City. The area and spatial location of grassland fires can directly affect the total amount and spatial distribution of carbon emissions. This study provides a reference for estimating carbon emissions from steppe fires. The model and framework for estimation of carbon emissions from grassland fires established can provide a reference value for estimation of carbon emissions from grassland fires in other regions.

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References

  • Baes C F Jr, Goeller H E, Olson J S et al., 1976. The Global Carbon Dioxide Problem. Oak Ridge National Laboratory, ORNL-5194, Oak Ridge, Tennessee.

    Google Scholar 

  • Boschetti L, Roy D P, Justice C O et al., 2010. Global assessment of the temporal reporting accuracy and precision of the MODIS burned area product. International Journal of Wildland Fire, 19(6): 705–709. doi: https://doi.org/10.1071/WF09138

    Google Scholar 

  • Chander G, Markham B L, Helder D L, 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5): 893–903. doi: https://doi.org/10.1016/j.rse.2009.01.007

    Google Scholar 

  • Choi S D, Chang Y S, Park B K, 2006. Increase in carbon emissions from forest fires after intensive reforestation and forest management programs. Science of the Total Environment, 372(1): 225–235. doi: https://doi.org/10.1016/j.scitotenv.2006.09.024

    Google Scholar 

  • Conard S G, Solomon A M, 2008. Chapter 5 Effects of wildland fire on regional and global carbon stocks in a changing environment. Developments in Environmental Science, 8: 109–138. doi: https://doi.org/10.1016/S1474-8177(08)00005-3

    Google Scholar 

  • Crowley T J, 2000. Causes of climate change over the past 1000 years. Science, 289(5477): 270–277. doi: https://doi.org/10.1126/science.289.5477.270

    Google Scholar 

  • de Groot W J, 2006. Modeling Canadian wildland fire carbon emissions with the Boreal Fire Effects (BORFIRE) model. Forest Ecology and Management, 234: S224. doi: https://doi.org/10.1016/j.foreco.2006.08.251

    Google Scholar 

  • de Groot W J, Landry R, Kurz W A et al., 2007. Estimating direct carbon emissions from Canadian wildland fires. International Journal of Wildland Fire, 16(5): 593–606. doi: https://doi.org/10.1071/WF06150

    Google Scholar 

  • Doolin D M, Sitar N, 2005. Wireless sensors for wildfire monitoring. In Smart Structures and Materials 2005: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems. International Society for Optics and Photonics, 5765: 477–484. doi: https://doi.org/10.1117/12.605655

    Google Scholar 

  • Fan J W, Wang K, Harris W et al., 2009. Allocation of vegetation biomass across a climate-related gradient in the grasslands of Inner Mongolia. Journal of Arid Environments, 73(4–5): 521–528. doi: https://doi.org/10.1016/j.jaridenv.2008.12.004

    Google Scholar 

  • Feng Q, Cheng G D, Mikami M, 2001. The carbon cycle of sandy lands in China and its global significance. Climatic Change, 48(4): 535–549. doi: https://doi.org/10.1023/A:1005664307625

    Google Scholar 

  • Flannigan M D, van Wagner C E, 1991. Climate change and wildfire in Canada. Canadian Journal of Forest Research, 21(1): 66–72. doi: https://doi.org/10.1139/x91-010

    Google Scholar 

  • French N H F, de Groot W J, Jenkins L K et al., 2011. Model comparisons for estimating carbon emissions from North American wildland fire. Journal of Geophysical Research: Biogeosciences, 116(G4): G00K05. doi: https://doi.org/10.1029/2010JG001469

    Google Scholar 

  • Hall F G, Townshend J R, Engman E T, 1995. Status of remote sensing algorithms for estimation of land surface state parameters. Remote Sensing of Environment, 51(1): 138–156. doi: https://doi.org/10.1016/0034-4257(94)00071-T

    Google Scholar 

  • Hoelzemann J J, Schultz M G, Brasseur G P et al., 2004. Global Wildland Fire Emission Model (GWEM): evaluating the use of global area burnt satellite data. Journal of Geophysical Research: Atmospheres, 109(D14): D14S04. doi: https://doi.org/10.1029/2003JD003666

    Google Scholar 

  • Hu Haiqing, Wang Guangyu, Sun Long, 2009. Analyses of gas emission in ground covers combustion of main forest fuel types in Xiaoxing’an Mountain. Scientia Silvae Sinicae, 45(5): 109–114. (in Chinese)

    Google Scholar 

  • Hu Haiqing, Wei Shujing, Jin Sen et al., 2012. Measurement model of carbon emission from forest fire: a review. Chinese Journal of Applied Ecology, 23(5): 1423–1434. (in Chinese)

    Google Scholar 

  • Hu Haiqing, Wei Shujing, Sun Long et al., 2013. Interaction among climate change, fire disturbance and ecosystem carbon cycle. Arid Land Geography, 36(1): 57–75. (in Chinese)

    Google Scholar 

  • Kanury A M, 1972. Thermal decomposition kinetics of wood pyrolysis. Combustion & Flame, 18(1): 75–83. doi: https://doi.org/10.1016/S0010-2180(72)80228-1

    Google Scholar 

  • Kasischke E S, French N H F, Bourgeau-Chavez L L et al., 1995. Estimating release of carbon from 1990 and 1991 forest fires in Alaska. Journal of Geophysical Research, 100(D2): 2941–2951. doi: https://doi.org/10.1029/94JD02957

    Google Scholar 

  • Lasslop G, Kloster S, 2015. Impact of fuel variability on wildfire emission estimates. Atmospheric Environment, 121: 93–102. doi: https://doi.org/10.1016/j.atmosenv.2015.05.040

    Google Scholar 

  • Lehsten V, Tansey K, Balzter H et al., 2009. Estimating carbon emissions from African wildfires. Biogeosciences, 6(3): 349–360. doi: https://doi.org/10.5194/bg-6-349-2009

    Google Scholar 

  • Li Linghao, Liu Xianhua, Chen Zuozhong, 1998. Study on the carbon cycle of Leymus chinensis steppe in the Xilin River Basin. Acta Botanica Sinica, 40(10): 955–961. (in Chinese)

    Google Scholar 

  • Li Y P, Zhao J J, Guo X Y et al., 2017. The influence of land use on the grassland fire occurrence in the Northeastern Inner Mongolia autonomous region, China. Sensors, 17(3): 437. doi: https://doi.org/10.3390/s17030437

    Google Scholar 

  • Liu Bin, Tian Xiaorui, 2011. Carbon emission from Huzhong forest fire in Daxing’anling. Forest Resources Management, (3): 47–51. (in Chinese)

  • Liu M F, Zhao J J, Guo X Y et al., 2017. Study on climate and grassland fire in HulunBuir, Inner Mongolia autonomous region, China. Sensors, 17(3): 616. doi: https://doi.org/10.3390/s17030616

    Google Scholar 

  • Liu X P, Zhang J Q, Tong Z J, 2010. The dynamic danger assessment for grassland fire disaster in Xilingol, Inner Mongolia. Computational Intelligence: Foundations and Applications, 1110–1116. doi: https://doi.org/10.1142/9789814324700_0171

  • Liu X P, Zhang J Q, Tong Z J, 2015. Modeling the early warning of grassland fire risk based on fuzzy logic in Xilingol, Inner Mongolia. Natural Hazards, 75(3): 2331–2342. doi: https://doi.org/10.1007/s11069-014-1428-5

    Google Scholar 

  • Moreau S, Bosseno R, Gu X F et al., 2003. Assessing the biomass dynamics of Andean bofedal and totora high-protein wetland grasses from NOAA/AVHRR. Remote Sensing of Environment, 85(4): 516–529. doi: https://doi.org/10.1016/S0034-4257(03)00053-1

    Google Scholar 

  • Ni J, 2002. Carbon storage in grasslands of China. Journal of Arid Environments, 50(2): 205–218. doi: https://doi.org/10.1006/jare.2201.0902

    Google Scholar 

  • Peters A, Verhoeven K J F, 1994. Impact of artificial lighting on the seaward orientation of hatchling loggerhead turtles. Journal of Herpetology, 28(1): 112–114. doi: https://doi.org/10.2307/1564691

    Google Scholar 

  • Possell M, Nicholas Hewitt C, Beerling D J, 2005. The effects of glacial atmospheric CO2 concentrations and climate on isoprene emissions by vascular plants. Global Change Biology, 11: 60–69. doi: https://doi.org/10.1111/j.1365-2486.2004.00889.x

    Google Scholar 

  • Prasad V K, Gupta P K, Sharma C et al., 2002. CO and CO2 emissions from biomass burning of tropical dry deciduous and mixed deciduous forests in shifting cultivation areas of India. Pollution Research, 21(2): 143–155. doi: https://doi.org/10.1016/S0140-6701(03)82155-0

    Google Scholar 

  • Reister D B, 1984. The use of a simple model in conjunction with a detailed carbon dioxide emissions model. Energy, 9(8): 637–643. doi: https://doi.org/10.1016/0360-5442(84)90092-6

    Google Scholar 

  • Rodhe H, 1990. A comparison of the contribution of various gases to the greenhouse effect. Science, 248(4960): 12171219. doi: https://doi.org/10.1126/science.248.4960.1217

    Google Scholar 

  • Running S W, 2006. CLIMATE CHANGE: is global warming causing more, larger wildfires?. Science, 313(5789): 927–928. doi: https://doi.org/10.1126/science.1130370

    Google Scholar 

  • Schultz M G, Heil A, Hoelzemann J J et al., 2008. Global wildland fire emissions from 1960 to 2000. Global Biogeochemical Cycles, 22(2): GB2002. doi: https://doi.org/10.1029/2007GB003031

    Google Scholar 

  • Shi Y S, Sasai T, Yamaguchi Y, 2014. Spatio-temporal evaluation of carbon emissions from biomass burning in Southeast Asia during the period 2001–2010. Ecological Modelling, 272: 98–115. doi: https://doi.org/10.1016/j.ecolmodel.2013.09.021

    Google Scholar 

  • Soja A J, Cofer W R, Shugart H H et al., 2004. Estimating fire emissions and disparities in boreal Siberia (1998–2002). Journal of Geophysical Research, 109(D14): D14S06. doi: https://doi.org/10.1029/2004JD004570

    Google Scholar 

  • Tett S F B, Stott P A, Allen M R et al., 1999. Causes of twentieth-century temperature change near the Earth’s surface. Nature, 399(6736): 569–572. doi: https://doi.org/10.1038/21164

    Google Scholar 

  • Tian Xiaorui, Shu Lifu, Wang Mingyu, 2003. Direct carbon emissions from Chinese forest fires, 1991–2000. Fire Safety Science, 12(1): 6–10. (in Chinese)

    Google Scholar 

  • Van der Werf G R, Randerson J T, Giglio L et al., 2010. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmospheric Chemistry and Physics, 10: 11707–11735. doi: https://doi.org/10.5194/acp-10-11707-2010

    Google Scholar 

  • Villars P, Cenzual K, 2011. Space Groups (140) I4/mcm-(136) P42/mnm. Berlin: Springer.

    Google Scholar 

  • Wang Xinyun, Guo Yige, He Jie, 2014. Estimation of above-ground biomass of grassland based on multi-source remote sensing data. Transactions of the Chinese Society of Agricultural Engineering, 30(11): 159–166. (in Chinese)

    Google Scholar 

  • Wen Kegang, Shen Jianguo, 2008. Chinese Meteorological Disasters Ceremony (Inner Mongolia Volume). Beijing: China Meteorological Press. (in Chinese)

    Google Scholar 

  • Yang H Y, Zhao C, Liu Y W, 2008. GIS-based Inner Mongolia grassland fire spread simulation system. In: 2008 International Conference on Computer Science and Software Engineering. Hubei, China: IEEE, 923–925. doi: https://doi.org/10.1109/CSSE.2008.764

    Google Scholar 

  • Yin Li, Tian Xiaorui, Kang Lei et al., 2009. Research development of carbon emissions from forest fires. World Forestry Research, 22(3): 46–51. (in Chinese)

    Google Scholar 

  • Zhang Z X, Feng Z Q, Zhang H Y et al., 2017. Spatial distribution of grassland fires at the regional scale based on the MODIS active fire products. International Journal of Wildland Fire, 26(3): 209–218. doi: https://doi.org/10.1071/WF16026

    Google Scholar 

  • Zhao C, Meng K Q L, Yang H Y, 2010. The design and realization of Inner Mongolia grassland fire spread simulation system based on GIS and CA. In: 2009 1st International Conference on Information Science and Engineering. Nanjing, China: IEEE, 2205–2208. doi: https://doi.org/10.1109/ICISE.2009.1197

    Google Scholar 

  • Zhao Mengli, Xu Zhixin, 2000. Rational use of grassland resources and sustainable development of animal husbandry in Inner Mongolia. Resources Science, 22(1): 73–76. (in Chinese)

    Google Scholar 

  • Zheng Wei, Shao Jiali, Wang Meng et al., 2013. Dynamic monitoring and analysis of grassland fire based on multi-source satellite remote sensing data. Journal of Natural Disasters, 22(3): 54–61. (in Chinese)

    Google Scholar 

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Correspondence to Hongyan Zhang.

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Foundation item: Under the auspices of National Natural Science Foundation of China (No. 41761101, 41771450, 41871330), National Natural Science Foundation of Inner Mongolia (No. 2017MS0409), Fundamental Research Funds for the Central Universities (No. 2412019BJ001)

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Yu, S., Jiang, L., Du, W. et al. Estimation and Spatio-temporal Patterns of Carbon Emissions from Grassland Fires in Inner Mongolia, China. Chin. Geogr. Sci. 30, 572–587 (2020). https://doi.org/10.1007/s11769-020-1134-z

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