Why is the effect of live fuel moisture content on fire rate of spread underestimated in field experiments in shrublands?
F. Pimont A , J. Ruffault A , N. K. Martin-StPaul A and J.-L. Dupuy AA URFM, INRA, Domaine Saint Paul Site Agroparc 84000 Avignon, France.
B Corresponding author. Email: francois.pimont@inra.fr
International Journal of Wildland Fire 28(2) 127-137 https://doi.org/10.1071/WF18091
Submitted: 20 June 2018 Accepted: 27 November 2018 Published: 18 January 2019
Abstract
Live fuel moisture content (LFMC) influences fire activity at landscape scale and fire behaviour in laboratory experiments. However, field evidence linking LFMC to fire behaviour are very limited, despite numerous field experiments. In this study, we reanalyse a shrubland fire dataset with a special focus on LFMC to investigate this counterintuitive outcome. We found that this controversy might result from three causes. First, the range of experimental LFMC data was too moist to reveal a significant effect with the widespread exponential or power functions. Indeed, LFMC exhibited a strong effect below 100%, but marginal above this threshold, contrary to these functions. Second, we found that the LFMC significance was unlikely when the number of fire experiments was smaller than 40. Finally, an analysis suggested that 10 to 15% measurement error – arising from the estimation of environmental variables from field measurements – could lead to an underestimation by 30% of the LFMC effect. The LFMC effect in field experiments is thus stronger than previously reported in the range of LFMC occurring during the French fire season and in accordance with observations at different scales. This highlights the need to improve our understanding of the relationship between LFMC and fire behaviour to refine fire-danger predictions.
Additional keywords: GAM, generalised additive model, measurement error, Réseau Hydrique, sample size.
References
Alexander ME, Cruz MG (2013a) Assessing the effect of foliar moisture content on the spread rate of crown fires. International Journal of Wildland Fire 22, 415–427.| Assessing the effect of foliar moisture content on the spread rate of crown fires.Crossref | GoogleScholarGoogle Scholar |
Alexander ME, Cruz MG (2013b) Are the applications of wildland fire behaviour models getting ahead of their evaluation again? Environmental Modelling & Software 41, 65–71.
| Are the applications of wildland fire behaviour models getting ahead of their evaluation again?Crossref | GoogleScholarGoogle Scholar |
Anderson WR, Cruz MG, Fernandes PM, McCaw L, Vega JA, Bradstock RA, Fogarty L, Gould J, McCarthy G, Marsden-Smedley JB, Matthews S, Mattingley G, Pearce HG, van Wilgen BW (2015) A generic, empirical-based model for predicting rate of fire spread in shrublands. International Journal of Wildland Fire 24, 443–460.
| A generic, empirical-based model for predicting rate of fire spread in shrublands.Crossref | GoogleScholarGoogle Scholar |
Catchpole WR, Bradstock RA, Choate J, Fogarty LG, Gellie N, McCarthy G, McCaw WL, Marsden-Smedley JB, Pearce G (1998) Cooperative development of equations for heathland fire behaviour. In ‘Proceedings of 3rd International Conference on Forest Fire Research and 14th Conference on Fire and Forest Meteorology, Volume II’, 16–20 November 1998, Luso–Coimbra, Portugal. (Ed. DX Viegas) pp. 631– 645. (University of Coimbra: Coimbra, Portugal)
Chandler C, Cheney P, Thomas P, Trabaud L, Williams D (1983) ‘Fire in Forestry. Volume 1. Forest Fire Behavior and Effects.’ (Wiley: New York, NY, USA)
Chuvieco E, González I, Verdú F, Aguado I, Yebra M (2009) Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem. International Journal of Wildland Fire 18, 430–441.
| Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem.Crossref | GoogleScholarGoogle Scholar |
Countryman CM, Dean WA (1979) Measuring moisture content in living chaparral: a field user’s manual. USDA Forest Service, Pacific Southwest Research Station, General Technical Report PSW-36. (Berkeley, CA, USA)
Dennison PE, Moritz MA (2009) Critical live fuel moisture in chaparral ecosystems: a threshold for fire activity and its relationship to antecedent precipitation. International Journal of Wildland Fire 18, 1021–1027.
| Critical live fuel moisture in chaparral ecosystems: a threshold for fire activity and its relationship to antecedent precipitation.Crossref | GoogleScholarGoogle Scholar |
Duché Y, Savazzi R, Toutchkov M, Cabanne E (2017) Multisite and multispecies live fuel moisture content (LFMC) series in the French Mediterranean since 1996. (Zenodo) Available at http://doi.org/10.5281/zenodo.162978 [Verified 5 December 2018]
Fernandes PM, Cruz MG (2012) Plant flammability experiments offer limited insight into vegetation–fire dynamics interactions. New Phytologist 194, 606–609.
| Plant flammability experiments offer limited insight into vegetation–fire dynamics interactions.Crossref | GoogleScholarGoogle Scholar | 22288940PubMed |
Finney MA, Cohen JD, McAllister SS, Jolly MW (2013) On the need for a theory of wildland fire spread. International Journal of Wildland Fire 22, 25–36.
| On the need for a theory of wildland fire spread.Crossref | GoogleScholarGoogle Scholar |
Flannigan MD, Wotton BM, Marshall GA, De Groot WJ, Johnston J, Jurko N, Cantin AS (2016) Fuel moisture sensitivity to temperature and precipitation: climate change implications. Climatic Change 134, 59–71.
| Fuel moisture sensitivity to temperature and precipitation: climate change implications.Crossref | GoogleScholarGoogle Scholar |
Fuller WA (1987) ‘Measurement Error Models.’ (Wiley: New York, NY, USA)
Hastie T, Tibshirani R (1990) ‘Generalized Additive Models.’ (CRC Press: Boca Raton, FL, USA)
Jolly WM, Johnson DM (2018) Pyro-ecophysiology: shifting the paradigm of live wildland fuel research. Fire 1, 8
| Pyro-ecophysiology: shifting the paradigm of live wildland fuel research.Crossref | GoogleScholarGoogle Scholar |
Marino E, Dupuy JL, Pimont F, Guijarro M, Hernando C, Linn R (2012) Fuel bulk density and fuel moisture content effect on fire rate of spread: a comparison between FIRETEC model predictions and experimental results in shrub fuels. Journal of Fire Sciences 30, 277–299.
| Fuel bulk density and fuel moisture content effect on fire rate of spread: a comparison between FIRETEC model predictions and experimental results in shrub fuels.Crossref | GoogleScholarGoogle Scholar |
Martin-StPaul N, Ruffault J, Pimont F, Dupuy JL (2018a) Live fuel moisture content: variability, predictability and impact on fire behavior and activity. In ‘Advances in forest fire research 2018’. pp. 246–253. (Imprensa da Universidade de Coimbra: Coimbra, Portugal)
Martin-StPaul N, Pimont F, Dupuy JL, Rigolot E, Ruffault J, Fargeon H, Cabane E, Duché Y, Savazzi R, Toutchkov M (2018b) Live fuel moisture content (LFMC) time series for multiple sites and species in the French Mediterranean area since 1996. Annals of Forest Science 75, 57.
| Live fuel moisture content (LFMC) time series for multiple sites and species in the French Mediterranean area since 1996.Crossref | GoogleScholarGoogle Scholar |
Matthews S (2010) Effect of drying temperature on fuel moisture content measurements. International Journal of Wildland Fire 19, 800–802.
| Effect of drying temperature on fuel moisture content measurements.Crossref | GoogleScholarGoogle Scholar |
Mell W, Jenkins MA, Gould J, Cheney P (2007) A physics-based approach to modelling grassland fires. International Journal of Wildland Fire 16, 1–22.
| A physics-based approach to modelling grassland fires.Crossref | GoogleScholarGoogle Scholar |
Nolan RH, Boer MM, Resco de Dios V, Caccamo G, Bradstock RA (2016) Large-scale, dynamic transformations in fuel moisture drive wildfire activity across southeastern Australia. Geophysical Research Letters 43, 4229–4238.
| Large-scale, dynamic transformations in fuel moisture drive wildfire activity across southeastern Australia.Crossref | GoogleScholarGoogle Scholar |
Pimont F, Dupuy J-L, Linn RR, Parsons R, Martin-StPaul N (2017) Representativeness of wind measurements in fire experiments: lessons learned from large-eddy simulations in a homogeneous forest. Agricultural and Forest Meteorology 232, 479–488.
| Representativeness of wind measurements in fire experiments: lessons learned from large-eddy simulations in a homogeneous forest.Crossref | GoogleScholarGoogle Scholar |
Rossa CG, Fernandes PM (2017a) Fuel-related fire-behaviour relationships for mixed live and dead fuels burned in laboratory. Canadian Journal of Forest Research 47, 883–889.
| Fuel-related fire-behaviour relationships for mixed live and dead fuels burned in laboratory.Crossref | GoogleScholarGoogle Scholar |
Rossa CG, Fernandes PM (2017b) Short communication: on the effect of live fuel moisture content on fire-spread rate. Forest Systems 26, eSC08.
| Short communication: on the effect of live fuel moisture content on fire-spread rate.Crossref | GoogleScholarGoogle Scholar |
Rossa CG, Fernandes PM (2018) Empirical modeling of fire spread rate in no-wind and no-slope conditions. Forest Science 64, 358–370.
| Empirical modeling of fire spread rate in no-wind and no-slope conditions.Crossref | GoogleScholarGoogle Scholar |
Rossa CG, Veloso R, Fernandes PM (2016) A laboratory-based quantification of the effect of live fuel moisture content on fire spread rate. International Journal of Wildland Fire 25, 569–573.
| A laboratory-based quantification of the effect of live fuel moisture content on fire spread rate.Crossref | GoogleScholarGoogle Scholar |
Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service, Intermountain Forest and Range Experiment Station, Research Paper INT-115 (Ogden, UT, USA)
Ruffault J, Curt T, Martin-StPaul NK, Moron V, Trigo RM (2018a) Extreme wildfire events are linked to global change type droughts in the northern Mediterranean. Natural Hazards and Earth System Sciences 18, 846–856.
Ruffault J, Martin-StPaul N, Pimont F, Dupuy J-L (2018b) How well do meteorological drought indices predict live fuel moisture content (LFMC)? An assessment for wildfire research and operations in Mediterranean ecosystems. Agricultural and Forest Meteorology 262, 391–401.
| How well do meteorological drought indices predict live fuel moisture content (LFMC)? An assessment for wildfire research and operations in Mediterranean ecosystems.Crossref | GoogleScholarGoogle Scholar |
Sullivan AL (2009a) Wildland surface fire spread modelling, 1990–2007. 1. Physical and quasi-physical models. International Journal of Wildland Fire 18, 349–368.
| Wildland surface fire spread modelling, 1990–2007. 1. Physical and quasi-physical models.Crossref | GoogleScholarGoogle Scholar |
Sullivan AL (2009b) Wildland surface fire spread modelling, 1990–2007. 2. Empirical and quasi-empirical models. International Journal of Wildland Fire 18, 369–386.
| Wildland surface fire spread modelling, 1990–2007. 2. Empirical and quasi-empirical models.Crossref | GoogleScholarGoogle Scholar |
Sullivan AL, Knight IK (2001) Estimating error in wind speed measurements for experimental fires. Canadian Journal of Forest Research 31, 401–409.
| Estimating error in wind speed measurements for experimental fires.Crossref | GoogleScholarGoogle Scholar |
Van Wagner CE (1989) Prediction of crown fire behavior in conifer stands. In ‘Proceedings of the 10th Conference on Fire and Forest Meteorology’, 17–21 April 1989, Ottawa, ON, Canada. (Eds DC MacIver, H Auld, R Whitewood) pp. 207–212. (Forestry Canada and Environment Canada: Ottawa, ON, Canada)
Viegas DX, Piñol J, Viegas MT, Ogaya R (2001) Estimating live fine fuels moisture content using meteorologically based indices. International Journal of Wildland Fire 10, 223–240.
| Estimating live fine fuels moisture content using meteorologically based indices.Crossref | GoogleScholarGoogle Scholar |