Determining the sensitivity of grassland area burned to climate variation in Xilingol, China, with an autoregressive distributed lag approach
Ali Hassan Shabbir A B C , Jiquan Zhang A B C G , Xingpeng Liu A B C , James A. Lutz D , Carlos Valencia E and James D. Johnston F
+ Author Affiliations
- Author Affiliations
A Institute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun, 130024, China.
B State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Northeast Normal University, Changchun, 130024, China.
C Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun, 130024, China.
D Wildland Resources Department, Utah State University, 5230 Old Main Hill, Logan, UT 84322-5230, USA.
E Industrial Engineering Department, University of los Andes, Carrera 1 Este No. 19 A 40, Bogota, Colombia.
F College of Forestry, Oregon State University, 140 Peavy Hall, 3100 SW Jefferson Way, Corvallis, OR 97333, USA.
G Corresponding author. Email: zhangjq022@nenu.edu.cn
International Journal of Wildland Fire 28(8) 628-639 https://doi.org/10.1071/WF18171
Submitted: 2 May 2018 Accepted: 8 May 2019 Published: 1 July 2019
Journal Compilation © IAWF 2019 Open Access CC BY-NC-ND
Abstract
We examined the relationship between climate variables and grassland area burned in Xilingol, China, from 2001 to 2014 using an autoregressive distributed lag (ARDL) model, and describe the application of this econometric method to studies of climate influences on wildland fire. We show that there is a stationary linear combination of non-stationary climate time series (cointegration) that can be used to reliably estimate the influence of different climate signals on area burned. Our model shows a strong relationship between maximum temperature and grassland area burned. Mean monthly wind speed and monthly hours of sunlight were also strongly associated with area burned, whereas minimum temperature and precipitation were not. Some climate variables like wind speed had significant immediate effects on area burned, the strength of which varied over the 2001–14 observation period (in econometrics terms, a ‘short-run’ effect). The relationship between temperature and area burned exhibited a steady-state or ‘long-run’ relationship. We analysed three different periods (2001–05, 2006–10 and 2011–14) to illustrate how the effects of climate on area burned vary over time. These results should be helpful in estimating the potential impact of changing climate on the eastern Eurasian Steppe.
Additional keywords: ARDL model, climate change, climate sensitivity, grassland fire.
References
Abatzoglou JT, Williams AP (2016) Impact of anthropogenic climate change on wildfire across western US forests. Proceedings of the National Academy of Sciences of the United States of America 113, 11770–11775.| Impact of anthropogenic climate change on wildfire across western US forests.Crossref | GoogleScholarGoogle Scholar | 27791053PubMed |
Abdullah Yusof S, Duasa J (2010) Household decision-making and expenditure patterns of married men and women in Malaysia. Journal of Family and Economic Issues 31, 371–381.
| Household decision-making and expenditure patterns of married men and women in Malaysia.Crossref | GoogleScholarGoogle Scholar |
Ahmed K, Shahbaz M, Qasim A, Long W (2015) The linkages between deforestation, energy and growth for environmental degradation in Pakistan. Ecological Indicators 49, 95–103.
| The linkages between deforestation, energy and growth for environmental degradation in Pakistan.Crossref | GoogleScholarGoogle Scholar |
Akaike H (1981) Likelihood of a model and information criteria. Journal of Econometrics 16, 3–14.
| Likelihood of a model and information criteria.Crossref | GoogleScholarGoogle Scholar |
Argañaraz JP, Pizarro GG, Zak M, Landi MA, Bellis LM (2015) Human and biophysical drivers of fires in semiarid Chaco mountains of central Argentina. The Science of the Total Environment 520, 1–12.
| Human and biophysical drivers of fires in semiarid Chaco mountains of central Argentina.Crossref | GoogleScholarGoogle Scholar | 25782079PubMed |
Baum CF (2004) A review of Stata 8.1 and its time series capabilities. International Journal of Forecasting 20, 151–161.
| A review of Stata 8.1 and its time series capabilities.Crossref | GoogleScholarGoogle Scholar |
Bliege Bird R, Bird DW, Codding BF (2016) People, El Niño Southern Oscillation and fire in Australia: fire regimes and climate controls in hummock grasslands. Philosophical Transactions of the Royal Society B 371, 20150343
| People, El Niño Southern Oscillation and fire in Australia: fire regimes and climate controls in hummock grasslands.Crossref | GoogleScholarGoogle Scholar |
Brown RL, Durbin J, Evans JM (1975) Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society: Series B Methodological 37, 149–163.
| Techniques for testing the constancy of regression relationships over time.Crossref | GoogleScholarGoogle Scholar |
Chen J, Hori Y, Yamamura Y, Shiyomi M, Huang DM (2008) Spatial heterogeneity and diversity analysis of macrovegetation in the Xilingol region, Inner Mongolia, China, using the beta distribution. Journal of Arid Environments 72, 1110–1119.
| Spatial heterogeneity and diversity analysis of macrovegetation in the Xilingol region, Inner Mongolia, China, using the beta distribution.Crossref | GoogleScholarGoogle Scholar |
Deser C, Phillips A, Bourdette V, Teng H (2012) Uncertainty in climate change projections: the role of internal variability. Climate Dynamics 38, 527–546.
| Uncertainty in climate change projections: the role of internal variability.Crossref | GoogleScholarGoogle Scholar |
Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74, 427–431.
Eugenio FC, dos Santos AR, Fiedler NC, Ribeiro GA, da Silva AG, Juvanhol RS, Pedra BD (2016) GIS applied to location of fires detection towers in domain area of tropical forest. The Science of the Total Environment 562, 542–549.
| GIS applied to location of fires detection towers in domain area of tropical forest.Crossref | GoogleScholarGoogle Scholar | 27110968PubMed |
Farrar DE, Glauber RR (1967) Multicollinearity in regression analysis: the problem revisited. The Review of Economics and Statistics 49, 92–107.
| Multicollinearity in regression analysis: the problem revisited.Crossref | GoogleScholarGoogle Scholar |
Francos M, Úbeda X, Tort J, Panareda JM, Cerdà A (2016) The role of forest fire severity on vegetation recovery after 18 years. Implications for forest management of Quercus suber L. in Iberian Peninsula. Global and Planetary Change 145, 11–16.
| The role of forest fire severity on vegetation recovery after 18 years. Implications for forest management of Quercus suber L. in Iberian Peninsula.Crossref | GoogleScholarGoogle Scholar |
Gang C, Zhou W, Chen Y, Wang Z, Sun Z, Li J, Qi J, Odeh I (2014) Quantitative assessment of the contributions of climate change and human activities on global grassland degradation. Environmental Earth Sciences 72, 4273–4282.
| Quantitative assessment of the contributions of climate change and human activities on global grassland degradation.Crossref | GoogleScholarGoogle Scholar |
Garbolino E, Sanseverino-Godfrin V, Hinojos-Mendoza G (2016) Describing and predicting of the vegetation development of Corsica due to expected climate change and its impact on forest fire risk evolution. Safety Science 88, 180–186.
| Describing and predicting of the vegetation development of Corsica due to expected climate change and its impact on forest fire risk evolution.Crossref | GoogleScholarGoogle Scholar |
Gaudreau J, Perez L, Drapeau P (2016) BorealFireSim: a GIS-based cellular automata model of wildfires for the boreal forest of Quebec in a climate change paradigm. Ecological Informatics 32, 12–27.
| BorealFireSim: a GIS-based cellular automata model of wildfires for the boreal forest of Quebec in a climate change paradigm.Crossref | GoogleScholarGoogle Scholar |
Ge X, Dong K, Luloff AE, Wang L, Xiao J (2016) Impact of land use intensity on sandy desertification: an evidence from Horqin Sandy Land, China. Ecological Indicators 61, 346–358.
| Impact of land use intensity on sandy desertification: an evidence from Horqin Sandy Land, China.Crossref | GoogleScholarGoogle Scholar |
Goldberger AS (1991) ‘A course in econometrics.’ (Harvard University Press: Cambridge, MA, USA)
Gowlett JAJ (2016) The discovery of fire by humans: a long and convoluted process. Philosophical Transactions of the Royal Society B 371, 20150164
| The discovery of fire by humans: a long and convoluted process.Crossref | GoogleScholarGoogle Scholar |
Granger CWJ, Newbold P (1974) Spurious regressions in econometrics. Journal of Econometrics 2, 111–120.
| Spurious regressions in econometrics.Crossref | GoogleScholarGoogle Scholar |
Guo F, Zhang L, Jin S, Tigabu M, Su Z, Wang W (2016) Modeling anthropogenic fire occurrence in the boreal forest of China using logistic regression and random forests. Forests 7, 250
| Modeling anthropogenic fire occurrence in the boreal forest of China using logistic regression and random forests.Crossref | GoogleScholarGoogle Scholar |
Hannan EJ, Quinn BG (1978) The determination of the lag length of an autoregression. Journal of the Royal Statistical Society. Series A (General) 41, 190–195.
Hardy CC (2005) Wildland fire hazard and risk: problems, definitions, and context. Forest Ecology and Management 211, 73–82.
| Wildland fire hazard and risk: problems, definitions, and context.Crossref | GoogleScholarGoogle Scholar |
Harris R, Remenyi TA, Williamson GJ, Bindoff NL, Bowman DM (2016) Climate vegetation fire interactions and feedbacks: trivial detail or major barrier to projecting the future of the Earth system? Wiley Interdisciplinary Reviews: Climate Change 7, 910–931.
| Climate vegetation fire interactions and feedbacks: trivial detail or major barrier to projecting the future of the Earth system?Crossref | GoogleScholarGoogle Scholar |
Haug AA (2002) Temporal aggregation and the power of cointegration tests: a Monte Carlo study. Oxford Bulletin of Economics and Statistics 64, 399–412.
| Temporal aggregation and the power of cointegration tests: a Monte Carlo study.Crossref | GoogleScholarGoogle Scholar |
Holsinger Lisa, Parks Sean A, Miller Carol (2016) Weather, fuels, and topography impede wildland fire spread in western US landscapes. Forest ecology and management 380, 59–69.
| Weather, fuels, and topography impede wildland fire spread in western US landscapes.Crossref | GoogleScholarGoogle Scholar |
Ikemori F, Honjyo K, Yamagami M, Nakamura T (2015) Influence of contemporary carbon originating from the 2003 Siberian forest fire on organic carbon in PM2.5 in Nagoya, Japan. The Science of the Total Environment 530–531, 403–410.
| Influence of contemporary carbon originating from the 2003 Siberian forest fire on organic carbon in PM2.5 in Nagoya, Japan.Crossref | GoogleScholarGoogle Scholar | 26066482PubMed |
Kaloudis S, Tocatlidou A, Lorentzos NA, Sideridis AB, Karteris M (2005) Assessing wildfire destruction danger: a decision support system incorporating uncertainty. Ecological Modelling 181, 25–38.
| Assessing wildfire destruction danger: a decision support system incorporating uncertainty.Crossref | GoogleScholarGoogle Scholar |
Keeley JE, Syphard AD (2017) Different historical fire–climate patterns in California. International Journal of Wildland Fire 26, 253–268.
| Different historical fire–climate patterns in California.Crossref | GoogleScholarGoogle Scholar |
Kipling RP, Virkajärvi P, Breitsameter L, Curnel Y, De Swaef T, Gustavsson AM, Nendel C (2016) Key challenges and priorities for modelling European grasslands under climate change. The Science of the Total Environment 566–567, 851–864.
| Key challenges and priorities for modelling European grasslands under climate change.Crossref | GoogleScholarGoogle Scholar | 27259038PubMed |
Kobayashi H, Sugiura T, Uto M, Suzuki S (1994) International study on the pasture improvement in the Inner Mongolia desert-steppe, China. In ‘Proceedings of the international symposium on grassland resources’. (Ed. B Li) pp. 1007–1009. (Agricultural Sciencetech Press: Beijing, China).
Kukavskaya EA, Buryak LV, Shvetsov EG, Conard SG, Kalenskaya OP (2016) The impact of increasing fire frequency on forest transformations in southern Siberia. Forest Ecology and Management 382, 225–235.
| The impact of increasing fire frequency on forest transformations in southern Siberia.Crossref | GoogleScholarGoogle Scholar |
Li Y, Zhao J, Guo X, Zhang Z, Tan G, Yang J (2017) The influence of land use on the grassland fire occurrence in the north-eastern Inner Mongolia Autonomous Region, China. Sensors 17, 437
| The influence of land use on the grassland fire occurrence in the north-eastern Inner Mongolia Autonomous Region, China.Crossref | GoogleScholarGoogle Scholar |
Liu Z, Wimberly MC (2016) Direct and indirect effects of climate change on projected future fire regimes in the western United States. The Science of the Total Environment 542, 65–75.
| Direct and indirect effects of climate change on projected future fire regimes in the western United States.Crossref | GoogleScholarGoogle Scholar | 26519568PubMed |
Liu Z, 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 north-east 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 north-east China.Crossref | GoogleScholarGoogle Scholar |
Liu M, Zhao J, Guo X, Zhang Z, Tan G, Yang J (2017) Study on climate and grassland fire in HulunBuir, Inner Mongolia Autonomous Region, China. Sensors 17, 616
| Study on climate and grassland fire in HulunBuir, Inner Mongolia Autonomous Region, China.Crossref | GoogleScholarGoogle Scholar |
Mooney H, Larigauderie A, Cesario M, Elmquist T, Hoegh-Guldberg O, Lavorel S, Mace GM, Palmer M, Scholes R, Yahara T (2009) Biodiversity, climate change, and ecosystem services. Current Opinion in Environmental Sustainability 1, 46–54.
| Biodiversity, climate change, and ecosystem services.Crossref | GoogleScholarGoogle Scholar |
Moritz MA, Moody TJ, Krawchuk MA, Hughes M, Hall A (2010) Spatial variation in extreme winds predicts large wildfire locations in chaparral ecosystems. Geophysical Research Letters 37, L04801
| Spatial variation in extreme winds predicts large wildfire locations in chaparral ecosystems.Crossref | GoogleScholarGoogle Scholar |
Narayan PK (2005) The saving and investment nexus for China: evidence from cointegration tests. Applied Economics 37, 1979–1990.
| The saving and investment nexus for China: evidence from cointegration tests.Crossref | GoogleScholarGoogle Scholar |
Niu R, Zhai P (2012) Study on forest fire danger over northern China during the recent 50 years. Climatic Change 111, 723–736.
| Study on forest fire danger over northern China during the recent 50 years.Crossref | GoogleScholarGoogle Scholar |
Oteng-Abayie EF, Frimpong JM (2006) Bounds testing approach: an examination of foreign direct investment, trade, and growth relationships. American Journal of Applied Sciences 3, 2079–2085.
Pesaran B, Pesaran MH (2010) ‘Time series econometrics using Microfit 5.0: a user’s manual.’ (Oxford University Press, Inc.: Oxford, UK)
Pesaran MH (2015) ‘Time series and panel data econometrics.’ (Oxford University Press: Oxford, UK)
Pesaran MH, Shin Y, Smith RP (1999) Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association 94, 621–634.
| Pooled mean group estimation of dynamic heterogeneous panels.Crossref | GoogleScholarGoogle Scholar |
Pesaran MH, Shin Y, Smith RJ (2001) Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 16, 289–326.
| Bounds testing approaches to the analysis of level relationships.Crossref | GoogleScholarGoogle Scholar |
Phillips PCB (1986) Understanding spurious regressions in econometrics. Journal of Econometrics 33, 311–340.
| Understanding spurious regressions in econometrics.Crossref | GoogleScholarGoogle Scholar |
Phillips PCB (1987) Time series regression with a unit root. Econometrica 55, 277–301.
| Time series regression with a unit root.Crossref | GoogleScholarGoogle Scholar |
Ramsey J (1969) Tests for specification errors in classical linear least-squares regression analysis. Journal of the Royal Statistical Society: Series B (Methodological) 31, 350–371.
| Tests for specification errors in classical linear least-squares regression analysis.Crossref | GoogleScholarGoogle Scholar |
Ravi S, Breshears DD, Huxman TE, D’Odorico P (2010) Land degradation in drylands: interactions among hydrologiceaeolian erosion and vegetation dynamics. Geomorphology 116, 236–245.
| Land degradation in drylands: interactions among hydrologiceaeolian erosion and vegetation dynamics.Crossref | GoogleScholarGoogle Scholar |
Rolinski T, Capps SB, Fovell RG, Cao Y, D’Agostino BJ, Vanderburg S (2016) The Santa Ana wildfire threat index: methodology and operational implementation. Weather and Forecasting 31, 1881–1897.
| The Santa Ana wildfire threat index: methodology and operational implementation.Crossref | GoogleScholarGoogle Scholar |
Schwarz G (1971) A sequential Student test. Annals of Mathematical Statistics 42, 1003–1009.
Semeraro T, Mastroleo G, Aretano R, Facchinetti G, Zurlini G, Petrosillo I (2016) GIS fuzzy expert system for the assessment of ecosystems vulnerability to fire in managing Mediterranean natural protected areas. Journal of Environmental Management 168, 94–103.
| GIS fuzzy expert system for the assessment of ecosystems vulnerability to fire in managing Mediterranean natural protected areas.Crossref | GoogleScholarGoogle Scholar | 26696610PubMed |
Su H, Liu GX (2004) Elementary analyses on the progress of grassland fire disaster information management technique. Grassland of China 26, 69–71.
Tian X, Sohngen B, Kim JB, Ohrel S, Cole J (2016) Global climate change impacts on forests and markets. Environmental Research Letters 11, 035011
| Global climate change impacts on forests and markets.Crossref | GoogleScholarGoogle Scholar |
Verón SR, Paruelo JM, Oesterheld M (2006) Assessing desertification. Journal of Arid Environments 66, 751–763.
| Assessing desertification.Crossref | GoogleScholarGoogle Scholar |
Wessels KJ, Prince SD, Malherbe J, Small J, Frost PE, VanZyl D (2007) Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa. Journal of Arid Environments 68, 271–297.
| Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa.Crossref | GoogleScholarGoogle Scholar |
Wessels KJ, Prince SD, Reshef I (2008) Mapping land degradation by comparison of vegetation production to spatially derived estimates of potential production. Journal of Arid Environments 72, 1940–1949.
| Mapping land degradation by comparison of vegetation production to spatially derived estimates of potential production.Crossref | GoogleScholarGoogle Scholar |
Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and earlier spring increase western US forest wildfire activity. Science 313, 940–943.
| Warming and earlier spring increase western US forest wildfire activity.Crossref | GoogleScholarGoogle Scholar | 16825536PubMed |
Yang H, Zhao C, Liu Y (2008) GIS-based Inner Mongolia grassland fire spread simulation system. In ‘Computer science and software engineering, 2008 international conference on Computer Science and Software Engineering’, Wuhan, China. Vol. 3, pp. 923–925. (IEEE: Piscataway, NJ, USA)
Yang Y, Wang Z, Li J, Gang C, Zhang Y, Zhang Y, Qi J (2016) Comparative assessment of grassland degradation dynamics in response to climate variation and human activities in China, Mongolia, Pakistan and Uzbekistan from 2000 to 2013. Journal of Arid Environments 135, 164–172.
| Comparative assessment of grassland degradation dynamics in response to climate variation and human activities in China, Mongolia, Pakistan and Uzbekistan from 2000 to 2013.Crossref | GoogleScholarGoogle Scholar |
Yang X, Zhang K, Jia B, Ci L (2005) Desertification assessment in China: an overview. Journal of Arid Environments 63, 517–531.
| Desertification assessment in China: an overview.Crossref | GoogleScholarGoogle Scholar |
Yeganeh H, Khajedein SJ, Amiri F, Shariff ARBM (2014) Monitoring rangeland ground cover vegetation using multitemporal MODIS data. Arabian Journal of Geosciences 7, 287–298.
| Monitoring rangeland ground cover vegetation using multitemporal MODIS data.Crossref | GoogleScholarGoogle Scholar |
Zhang T, Zhang Y, Xu M, Xi Y, Zhu J, Zhang X, Sun X (2016) Ecosystem response more than climate variability drives the inter-annual variability of carbon fluxes in three Chinese grasslands. Agricultural and Forest Meteorology 225, 48–56.
| Ecosystem response more than climate variability drives the inter-annual variability of carbon fluxes in three Chinese grasslands.Crossref | GoogleScholarGoogle Scholar |
Zhang ZX, Zhang HY, Zhou DW (2010) Using GIS spatial analysis and logistic regression to predict the probabilities of human-caused grassland fires. Journal of Arid Environments 74, 386–393.
| Using GIS spatial analysis and logistic regression to predict the probabilities of human-caused grassland fires.Crossref | GoogleScholarGoogle Scholar |
Zheng YR, Xie ZX, Robert C, Jiang LH, Shimizu H (2006) Did climate drive ecosystem change and induce desertification in Otindag sandy land, China, over the past 40 years? Journal of Arid Environments 64, 523–541.
| Did climate drive ecosystem change and induce desertification in Otindag sandy land, China, over the past 40 years?Crossref | GoogleScholarGoogle Scholar |
Zhijun T, Jiquan Z, Xingpeng L (2009) GIS-based risk assessment of grassland fire disaster in western Jilin province, China. Stochastic Environmental Research and Risk Assessment 23, 463–471.
| GIS-based risk assessment of grassland fire disaster in western Jilin province, China.Crossref | GoogleScholarGoogle Scholar |
