Gamma generalized linear model to investigate the effects of climate variables on the area burned by forest fire in northeast China

Abstract

The purpose of this study was to determine a suitable model for investigating the effects of climate factors on the area burned by forest fire in the Tahe forest region, Daxing’an Mountains, in northeast China. The response variables were the area burned by lightning-caused fire, human-caused fire, and total burned area. The predictor variables were nine climate variables collected from the local weather station. Three regression models were utilized, including multiple linear regression, log-linear model (log-transformation on both response and predictor variables), and gamma-generalized linear model. The goodness-of-fit of the models were compared based on model fitting statistics such as R², AIC, and RMSE. The results revealed that the gamma-generalized linear model was generally superior to both multiple linear regression model and log-linear model for fitting the fire data. Further, the best models were selected based on the criteria that the climate variables were statistically significant at α = 0.05. The gamma best models indicated that maximum wind speed, precipitation, and days that rainfall greater than 0.1 mm had significant impacts on the area burned by the lightning-caused fire, while the mean temperature and minimum relative humidity were the main drivers of the burned area caused by human activities. Overall, the total burned area by forest fire was significantly influenced by days that rainfall greater than 0.1 mm and minimum relative humidity, indicating that the moisture condition of forest stands determine the burned area by forest fire.

Keywords

Lightning-caused fire / Human-caused fire / Multiple linear regression / Log-linear model / Daxing’an mountains

References

[1] Anderson RS, Hallett DJ, Berg E, Jass RB, Toney JL, de Fontaine CS, DeVolder A. Holocene development of boreal forests and fire regimes on the Kenai lowlands of Alaska. Holocene, 2006, 16: 791-803.

[2] Balling RC, Meyer G, Wells SG. Relation of surface climate and burned area in Yellowstone National Park. Agric For Meteorol, 1992, 60: 285-293.

[3] Baskerville G. Use of logarithmic regression in the estimation of plant biomass. Can J For Res, 1972, 2(1): 49-53.

[4] Bergeron Y, Gauthier S, Kafka V, Lefort P, Lesieur D. Natural fire frequency for the eastern Canadian boreal forest: consequences for sustainable forestry. Can J For Res, 2001, 31: 384-391.

[5] Carcaillet C, Bergeron Y, Richard PJH, Fréchette B, Gauthier S, Prairie YT. Change of fire frequency in the eastern Canadian boreal forests during the Holocene: does vegetation composition or climate trigger the fire regime?. J Ecol, 2001, 89(6): 930-946.

[6] Collins BM, Omi PN, Chapman PL. Regional relationships between climate and wildfire-burned area in the Interior West, USA. Can J For Res, 2006, 36: 699-709.

[7] Du CY, Yu CL, Liu D. Environmental analysis on lightning forest fires in Daxing’an mountains. Chin J Agrometeorol, 2010, 31(4): 596-599.

[8] Duff PA, Walsh JE, Graham JM, Mann DH, Rupp TS. Impacts of large-scale atmospheric-ocean variability on Alaskan fire season severity. Ecol Appl, 2005, 15: 1317-1330.

[9] Fauria MM, Johnson EA. Climate and wildfires in the North American boreal forest. Philos Trans R Soc B, 2008, 363: 2317-2329.

[10] Flannigan MD, Van Wagner CE. Climate change and wildfire in Canada. Can J For Res, 1991, 21: 66-72.

[11] Flannigan MD, Logan KA, Amiro BD, Skinner WR, Stocks BJ. Future area burned in Canada. Clim Change, 2005, 72: 1-16.

[12] Gillett NP, Weaver AJ, Zwiers FW, Flannigan MD. Detecting the effect of climate change on Canadian forest fires. Geophys Res Lett, 2004, 31 L18211

[13] Hanna CT. Statistical methods in hydrology, 2002 2 Ames: Iowa State University Press

[14] Hess JC, Scott CA, Hufford GL, Fleming MD. EI Niño and its impact on the fire weather in Alaska. Int J Wildland Fire, 2001, 10: 1-13.

[15] Hessl AE, McKenzie D, Schellhaas R. Drought and Pacific decadal oscillation linked to fire occurrence in the inland Pacific Northwest. J Appl Ecol, 2004, 14: 425-442.

[16] Holsten A, Dominic AR, Costa L, Kropp JP. Evaluation of the performance of meteorological forest fire indices for German federal states. For Ecol Manag, 2013, 287: 123-131.

[17] Johnson EA, Wowchuk DR. Wildfires in the southern Canadian Rocky mountains and their relationship to mid-tropospheric anomalies. Can J For Res, 1993, 23: 1213-1222.

[18] Johnstone JF, Hollingsworth TN, Chapin FS III, Mack MC. Changes in fire regime break the legacy lock on successional trajectories in the Alaskan boreal forest. J Glob Change Biol, 2010, 16(4): 1281-1295.

[19] Kasischke ES, Verbyla DL, Rupp TS, McGuire AD, Murphy KA, Jandt R, Barnes JL, Hoy EE, Duffy PA, Calef M, Turetsky MR. Alaska’s changing fire regime implications for the vulnerability of boreal forests. Can J For Res, 2010, 40: 1360-1370.

[20] Larsen CPS. Fire and climate dynamics in the boreal forest of northern Alberta between AD 1850 and 1989. Holocene, 1996, 6: 449-456.

[21] Littell JS, McKenzie D, Peterson DL, Westerling AL. Climate and wildfire area burned in western U.S. ecoprovinces, 1916–2003. Ecol Appl, 2009, 19(4): 1003-1021.

[22] Madgwick H, Satoo T. On estimating the aboveground weights of tree stands. Ecology, 1975, 56: 1446-1450.

[23] McCoy VM, Burn CR. Potential alteration by climate change of the forest-fire regime in the boreal forest of Central Yukon territory. Arctic, 2005, 58: 276-285.

[24] McCullagh P, Nelder JA. Generalized linear models, 1989 2 Boca Raton: Chapman & Hall/CRC

[25] Myers RH, Montgomery DC, Vining GG. Generalized linear models with applications in engineering and the sciences. 2002, New York: John Wiley and Sons, Inc.

[26] Nakagawa S, Schielzeth H. A general and simple method for obtaining R² from generalized linear mixed-effects models. Methods Ecol Evol, 2013, 4(2): 133-142.

[27] Qu ZL, Hu HQ. A prediction model for forest fire-burnt area based on meteorological factors. Chin J Appl Ecol, 2007, 18(12): 2705-2709.

[28] Rawlings JO, Pantula SG, Dickey DA. Applied regression analysis: a research tool, 1998 2 New York: Springer

[29] Schoenberg FP, Peng R, Huang ZJ, Rundel P. Detection of non-linearities in the dependence of burn area on fuel age and climatic variables. Int J Wildland Fire, 2003, 12(1): 1-6.

[30] Skinner WR, Flannigan MD, Stock BJ, Martell DL, Wotton BM, Todd JB, Mason JA, Logan KA, Bosch EM. A 500 hpa synoptic wildland fire climatology for large Canadian forest fires 1959–1996. Theor Appl Climatol, 2002, 71: 157-169.

[31] Skvarenina J, Mindas J, Holecy J, Tucek J. Analysis of the natural and meteorological conditions during two largest forest fire events in the Slovak Paradise National Park. J Meteorol, 2004, 7: 167-171.

[32] Tymstra C, Wang D, Rogeau MP (2005) Alberta wildfire regime analysis. Wildfire Science and Technology Report PFFC-01-5. Alberta Sustainable Resource Development, Forest Protection Division, Edmonton AB

[33] Wang XL, Wang WJ, Chang Y, Feng YT, Chen HW, Hu YM, Chi JG. Fire severity of burnt area in Huzhong forest region of Great Xing’an mountains, northeast China based on normalized burn ration analysis. Chin J Appl Ecol, 2013, 24(4): 967-974.

[34] Wilks DL. Maximum likelihood estimation of the gamma distribution using data containing zeros. J Clim, 1990, 3: 1495-1501.

[35] Xiao X, White EP, Hooten MB, Durham SL. On the use of log-transformation vs. nonlinear regression for analyzing biological power laws. Ecology, 2011, 92: 1887-1894.

[36] Yang G, Di XY, Zeng T, Shu Z, Wang C, Yu HZ. Prediction of area burned under climatic change scenarios: a case study in the Great Xing’an mountains boreal forest. J For Res, 2010, 21(2): 213-218.

[37] Zhang LJ, Gove JH. Spatial assessment of model errors from four regression techniques. For Sci, 2005, 51(4): 334-346.

[38] Zhao FJ, Shu LF, Di XY, Tian XR, Wang MY. Changes in the occurring date of forest fires in the Inner Mongolia Daxing’anling forest region under global warming. Sci Silvae Sin, 2009, 45(6): 166-172.