Short communication: On the effect of live fuel moisture content on fire-spread rate
Abstract
Aim of study: To reconcile the effects of live fuel moisture content (FMC) on fire rate of spread (ROS) derived from laboratory and field fires.
Methods: The analysis builds on evidence from previous fire-spread experimental studies and on a comparison between two functions for the FMC damping effect: one derived from field burns, based on dead FMC, and another derived from laboratory trials, based on a weighted FMC (dead and live fuels).
Main results: In a typical Mediterranean shrubland, laboratory and field-derived FMC damping functions are linearly related, which is explained by the correlation between monthly average live and dead FMC variation throughout the year. This clarifies why the effect of live FMC on real-world fires ROS has remained elusive.
Research highlights: By providing evidence that the most significant effect of FMC on ROS is independent of vegetation phenology (dead or live condition), and explaining why in specific situations dead FMC is sufficient to provide satisfactory ROS predictions, our results can assist future modelling efforts.Downloads
References
Agee JK, Wright CS, Williamson N, Huff MH, 2002. Foliar moisture content of Pacific Northwest vegetation and its relation to wildland fire behavior. Forest Ecol Manag 167 (1-3): 57-66. https://doi.org/10.1016/S0378-1127(01)00690-9
Alexander ME, Cruz MG, 2013. Assessing the effect of foliar moisture content on the spread rate of crown fires. Int J Wildland Fire 22 (4): 415-427. https://doi.org/10.1071/WF12008
Anderson WR, Cruz MG, Fernandes PM, McCaw L, Vega JA, Bradstock R, Fogarty L, Gould J, McCarthy G, Marsden-Smedley JB, et al., 2015. A generic, empirical-based model for predicting rate of fire spread in shrublands. Int J Wildland Fire 24 (4): 443-460. https://doi.org/10.1071/WF14130
Beck JA, Trevitt AC, 1989. Forecasting diurnal variations in meteorological parameters for predicting fire behaviour. Can J For Res 19 (6): 791-797. https://doi.org/10.1139/x89-120
Cruz MG, Gould JS, Alexander ME, Sullivan AL, McCaw WL, Matthews S, 2015. Empirical-based models for predicting head-fire rate of spread in Australian fuel types. Aust Forest 78 (3): 118-158. https://doi.org/10.1080/00049158.2015.1055063
Fernandes PM, Botelho HS, Rego FC, Loureiro C, 2009. Empirical modelling of surface fire behaviour in maritime pine stands. Int J Wildland Fire 18 (6): 698-710. https://doi.org/10.1071/WF08023
Finney MA, Cohen JD, McAllister SS, Jolly WM, 2013. On the need for a theory of wildland fire spread. Int J Wildland Fire 22 (1): 25-36. https://doi.org/10.1071/WF11117
Fletcher TH, Pickett BM, Smith SG, Spittle GS, Woodhouse MM, Haake E, 2007. Effect of moisture on ignition behavior of moist California chaparral and Utah leaves. Combust Sci Technol 179 (6): 1183-1203. https://doi.org/10.1080/00102200601015574
Lopes S, 2013. Modelos de previsão do teor de humidade de combustíveis florestais. PhD thesis, University of Coimbra, Portugal.
Marino E, Dupuy JL, Pimont F, Guijarro M, Hernando C, Linn R, 2012. Fuel bulk density and fuel moisture content effects on fire rate of spread: A comparison between FIRETEC model predictions and experimental results in shrub fuels. J Fire Sci 30 (4): 277-299. https://doi.org/10.1177/0734904111434286
Pellizzaro G, Cesaraccio C, Duce P, Ventura A, Zara P, 2007. Relationships between seasonal patterns of live fuel moisture and meteorological drought indices for Mediterranean shrubland species. Int J Wildland Fire 16 (2): 232-241. https://doi.org/10.1071/WF06081
Pickett BM, Isackson C, Wunder R, Fletcher TH, Butler BW, Weise DR, 2010. Experimental measurements during combustion of moist individual foliage samples. Int J Wildland Fire 19 (2): 153-162. https://doi.org/10.1071/WF07121
Piñol J, Filella I, Ogaya R, Peñuelas J, 1998. Ground-based spectroradiometric estimation of live fine fuel moisture of Mediterranean plants. Agri For Meteo 90 (3): 173-186. https://doi.org/10.1016/S0168-1923(98)00053-7
Pook EW, Gill AM, 1993. Variation of live and dead fine fuel moisture in Pinus radiata plantations of the Australian Capital Territory. Int J Wildland Fire 3 (3): 155-168. https://doi.org/10.1071/WF9930155
Qi Y, Dennison PE, Spencer J, Riaño D, 2012. Monitoring live fuel moisture using soil moisture and remote sensing proxies. Fire Ecol 8 (3): 71-87. https://doi.org/10.4996/fireecology.0803071
Rossa CG, 2017. The effect of fuel moisture content on the spread rate of forest fires in the absence of wind or slope. Int J Wildland Fire 26 (1): 24-31. https://doi.org/10.1071/WF16049
Rossa CG, Fernandes PM, 2017. Fuel-related fire behaviour relationships for mixed live and dead fuels burned in the laboratory. Can J For Res 47 (7): 883-889. https://doi.org/10.1139/cjfr-2016-0457
Rossa CG, Davim DA, Viegas DX, 2015. Behaviour of slope and wind backing fires. Int J Wildland Fire 24 (8): 1085-1097. https://doi.org/10.1071/WF14215
Rossa CG, Veloso R, Fernandes PM, 2016. A laboratory-based quantification of the effect of live fuel moisture content on fire spread rate. Int J Wildland Fire 25 (5): 569-573. https://doi.org/10.1071/WF15114
Show SB, 1919. Climate and forest fires in northern California. J Forest 17 (8): 965-979.
Van Wagner CE, 1987. Development and structure of the Canadian Forest Fire Weather Index System. Canadian Forest Service, Petawawa Forest Experiment Station. Chalk River, Ontario. Forest Technical Report 35.
Viegas DX, Soares J, Almeida M, 2013. Combustibility of a mixture of live and dead fuel components. Int J Wildland Fire 22 (7): 992-1002. https://doi.org/10.1071/WF12031
Weise DR, Koo E, Zhou X, Mahalingam S, Morandini F, Balbi J, 2016. Fire spread in chaparral - A comparison of laboratory data and model predictions in burning live fuels. Int J Wildland Fire 25 (9): 980-994. https://doi.org/10.1071/WF15177
Yebra M, Chuvieco E, Riaño D, 2008. Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agr For Meteo 148 (4): 523-536. https://doi.org/10.1016/j.agrformet.2007.12.005
Yebra M, Chuvieco E, 2009. Linking ecological information and radiative transfer models to estimate fuel moisture content in the Mediterranean region of Spain: Solving the ill-posed inverse problem. Remote Sens Environ 113 (11): 2403-2411. https://doi.org/10.1016/j.rse.2009.07.001
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