Analysis of the uncertainty of fuel model parameters in wildland fire modelling of a boreal forest in north-east China
Longyan Cai A , Hong S. He B C , Yu Liang A D , Zhiwei Wu A and Chao Huang A
+ Author Affiliations
- Author Affiliations
A CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, No.72, Wenhua Road, Shenhe District, Shenyang, Liaoning Province, 110016, PR China.
B School of Natural Resources, University of Missouri, Columbia, MO 65211, USA.
C School of Geographical Sciences, Northeast Normal University, Changchun, 130024, PR China.
D Corresponding author. Email: liangyu@iae.ac.cn
International Journal of Wildland Fire 28(3) 205-215 https://doi.org/10.1071/WF18083
Submitted: 6 June 2018 Accepted: 20 December 2018 Published: 5 March 2019
Abstract
Fire propagation is inevitably affected by fuel-model parameters during wildfire simulations and the uncertainty of the fuel-model parameters makes forecasting accurate fire behaviour very difficult. In this study, three different methods (Morris screening, first-order analysis and the Monte Carlo method) were used to analyse the uncertainty of fuel-model parameters with FARSITE model. The results of the uncertainty analysis showed that only a few fuel-model parameters markedly influenced the uncertainty of the model outputs, and many of the fuel-model parameters had little or no effect. The fire-spread rate is the driving force behind the uncertainty of other fire behaviours. Thus, the highly uncertain fuel-model parameters associated with spread rate should be used cautiously in wildfire simulations. Monte Carlo results indicated that the relationship between model input and output was non-linear and neglecting fuel-model parameter uncertainty of the model would magnify fire behaviours. Additionally, fuel-model parameters have high input uncertainty. Therefore, fuel-model parameters must be calibrated against actual fires. The highly uncertain fuel-model parameters with high spatial-temporal variability consisted of fuel-bed depth, live-shrub loading and 1-h time-lag loading are preferentially chosen as parameters to calibrate several wildfires.
Additional keywords: FARSITE, fire behaviour, fuel model, uncertainty analysis, wildfire modelling.
References
Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-GTR-122. (Ogden, UT, USA)Andrews PL (1986) BEHAVE: fire behavior prediction and fuel modeling system – BURN subsystem, Part 1. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-GTR; 194. (Ogden, UT, USA)
Arabi M, Govindaraju RS, Hantush MM (2007) A probabilistic approach for analysis of uncertainty in the evaluation of watershed management practices. Journal of Hydrology 333, 459–471.
| A probabilistic approach for analysis of uncertainty in the evaluation of watershed management practices.Crossref | GoogleScholarGoogle Scholar |
Arca B, Duce P, Laconi M, Pellizzaro G, Salis M, Spano D (2007a) Evaluation of FARSITE simulator in Mediterranean maquis. International Journal of Wildland Fire 16, 563–572.
| Evaluation of FARSITE simulator in Mediterranean maquis.Crossref | GoogleScholarGoogle Scholar |
Arca B, Duce P, Pellizzaro G, Bacciu V, Salis M, Spano D (2007b) Evaluation of FARSITE simulator in a Mediterranean area. In ‘Proceedings of the 4th International Wildland fire Conference’, 14–17 May 2007, Sevilla, Spain. (Organismo Autónomo de Parques Nacionales, Ministerio de Medio Ambiente: Madrid, Spain)
Beck MB (1987) Water quality modeling: a review of the analysis of uncertainty. Water Resources Research 23, 1393–1442.
| Water quality modeling: a review of the analysis of uncertainty.Crossref | GoogleScholarGoogle Scholar |
Benali A, Ervilha AR, Sá ACL, Fernandes PM, Pinto RMS, Trigo RM, Pereira JMC (2016) Deciphering the impact of uncertainty on the accuracy of large wildfire spread simulations. The Science of the Total Environment 569–570, 73–85.
| Deciphering the impact of uncertainty on the accuracy of large wildfire spread simulations.Crossref | GoogleScholarGoogle Scholar | 27333574PubMed |
Benali A, Sá ACL, Ervilha AR, Trigo RM, Fernandes PM, Pereira JMC (2017) Fire spread predictions: Sweeping uncertainty under the rug. The Science of the Total Environment 592, 187–196.
| Fire spread predictions: Sweeping uncertainty under the rug.Crossref | GoogleScholarGoogle Scholar | 28319706PubMed |
Bossert JE, Linn R, Reisner J, Winterkamp J, Dennison P, Roberts D (2000) Coupled atmosphere–fire behavior model sensitivity to spatial fuels characterization. In ‘Proceedings of the Third Symposium on Fire and Forest Meteorology, Eightieth Annual Meeting of the American Meteorological Society’, 9–14 January 2000, Long Beach, California. pp. 21–26.
Burgan RE, Rothermel RC (1984) BEHAVE: fire behavior prediction and fuel modeling system – FUEL subsystem. USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-167. (Ogden, UT, USA)
Byram GM (1959) Combustion of forest fuels. In ‘Forest Fire: Control and Use’. (Ed. KP Davis) pp. 61–89. (McGraw Hill: New York, NY, USA)
Cai L, He HS, Wu Z, Lewis BL, Liang Y (2014) Development of standard fuel models in boreal forests of Northeast China through calibration and validation. PLoS One 9, e94043
| Development of standard fuel models in boreal forests of Northeast China through calibration and validation.Crossref | GoogleScholarGoogle Scholar | 25542014PubMed |
Carlson J, Burgan R (2003) Review of users’ needs in operational fire danger estimation: the Oklahoma example. International Journal of Remote Sensing 24, 1601–1620.
| Review of users’ needs in operational fire danger estimation: the Oklahoma example.Crossref | GoogleScholarGoogle Scholar |
Chen HW, Chang Y, Hu YM, Liu ZH, Zhou R, Jing GZ, Zhang HX, Hu CH, Zhang CM (2008) Load of forest surface dead fuel in Huzhong area of DaXing’an Mountains and relevant affecting factors. Shengtaixue Zazhi 27, 50–55. , [In Chinese]
Clark JS (1988) Effect of climate change on fire regimes in northwestern Minnesota. Nature 334, 233–235.
| Effect of climate change on fire regimes in northwestern Minnesota.Crossref | GoogleScholarGoogle Scholar |
Cornell CA (1972) First order analysis of model and parameter uncertainty. In ‘Proceedings of the International Symposium on Uncertainties in Hydrologic and Water Resource Systems’, 11–14 December, Tucson, Arizona. (Eds CC Kisiel, L Duckstein.) pp. 1245–1272 (University of Arizona: Tucson, USA)
Cruz MG, Fernandes PM (2008) Development of fuel models for fire behaviour prediction in maritime pine (Pinus pinaster Ait.) stands. International Journal of Wildland Fire 17, 194–204.
| Development of fuel models for fire behaviour prediction in maritime pine (Pinus pinaster Ait.) stands.Crossref | GoogleScholarGoogle Scholar |
Cruz MG, Alexander ME, Wakimoto RH (2004) Modeling the likelihood of crown fire occurrence in conifer forest stands. Forest Science 50, 640–658.
Cruz MG, McCaw WL, Anderson WR, Gould JS (2013) Fire behaviour modelling in semi-arid mallee-heath shrublands of southern Australia. Environmental Modelling & Software 40, 21–34.
| Fire behaviour modelling in semi-arid mallee-heath shrublands of southern Australia.Crossref | GoogleScholarGoogle Scholar |
de Rigo D, Rodriguez-Aseretto D, Bosco C, Di Leo M, San-Miguel-Ayanz J (2013) An architecture for adaptive robust modelling of wildfire behaviour under deep uncertainty. In ‘Proceedings of the 10th IFIP WG 5.11 International Symposium on Environmental Software Systems’, 9–11 October 2013, Neusiedl am See, Austria. (Eds J Hřebíček, G Schimak, M Kubásek, AE Rizzoli.) pp. 367–380. (Springer: Heidelberg, Germany)
De Zorzi P, Belli M, Barbizzi S, Menegon S, Deluisa A (2002) A practical approach to assessment of sampling uncertainty. Accreditation and Quality Assurance, 7, 182–188.
Dodge M (1972) Forest fuel accumulation – a growing problem. Science 177, 139–142.
| Forest fuel accumulation – a growing problem.Crossref | GoogleScholarGoogle Scholar | 17779906PubMed |
Doucet A, De Freitas N, Gordon N (2001) An introduction to sequential Monte Carlo methods. In ‘Sequential Monte Carlo Methods in Practice’. pp. 3–14. (Springer: New York, USA)
Du JH (2004) The study on basal information database of forest fuel and fire behavior of Pinus pumlia in Daxing’anling Mountain of Heilongjiang province. Chinese Academy of Forestry. (Beijing, China, master’s dissertation)
Finney MA, Sapsis DB, Bahro B (1997) Use of FARSITE for simulating fire suppression and analyzing fuel treatment economics. In ‘Symposium on Fire in California Ecosystems: Integrating Ecology, Prevention, and Management.’ 17–20 November 1997, San Diego, California. pp. 180–199. (Association for Fire Ecology: Eugene, OR, USA)
Finney MA (1998) FARSITE, fire area simulator – model development and evaluation. USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS-RP-4. (Ogden, UT, USA)
Francos A, Elorza F, Bouraoui F, Bidoglio G, Galbiati L (2003) Sensitivity analysis of distributed environmental simulation models: understanding the model behaviour in hydrological studies at the catchment scale. Reliability Engineering & System Safety 79, 205–218.
| Sensitivity analysis of distributed environmental simulation models: understanding the model behaviour in hydrological studies at the catchment scale.Crossref | GoogleScholarGoogle Scholar |
Freissinet C, Vauclin M, Erlich M (1999) Comparison of first-order analysis and fuzzy set approach for the evaluation of imprecision in a pesticide groundwater pollution screening model. Journal of Contaminant Hydrology 37, 21–43.
| Comparison of first-order analysis and fuzzy set approach for the evaluation of imprecision in a pesticide groundwater pollution screening model.Crossref | GoogleScholarGoogle Scholar |
Fujioka FM (2002) A new method for the analysis of fire spread modeling errors. International Journal of Wildland Fire 11, 193–203.
| A new method for the analysis of fire spread modeling errors.Crossref | GoogleScholarGoogle Scholar |
Gharun M, Possell M, Vervoort RW, Adams MA, Bell TL (2018) Can a growth model be used to describe forest carbon and water balance after fuel reduction burning in temperate forests? The Science of the Total Environment 615, 1000–1009.
| Can a growth model be used to describe forest carbon and water balance after fuel reduction burning in temperate forests?Crossref | GoogleScholarGoogle Scholar | 29751404PubMed |
Greenland S (2001) Sensitivity analysis, Monte Carlo risk analysis, and Bayesian uncertainty assessment. Risk Analysis 21, 579–584.
| Sensitivity analysis, Monte Carlo risk analysis, and Bayesian uncertainty assessment.Crossref | GoogleScholarGoogle Scholar | 11726013PubMed |
Haan CT, Storm DE, Al-Issa T, Prabhu S, Sabbagh GJ, Edwards DR (1998) Effect of parameter distributions on uncertainty analysis of hydrologic models. Transactions of the ASAE. American Society of Agricultural Engineers 41, 65–70.
| Effect of parameter distributions on uncertainty analysis of hydrologic models.Crossref | GoogleScholarGoogle Scholar |
Hu TY, Zhou GS, Jia BR (2012) Simulating 10-hour time-lag fuel moisture in Daxinganling. Acta Ecologica Sinica 32, 6984–6990.
| Simulating 10-hour time-lag fuel moisture in Daxinganling.Crossref | GoogleScholarGoogle Scholar | , [In Chinese]
Iliopoulos N, Kalabokidis K, Kallos G, Feidas H, Malounis A, Mavromatidis E (2013) Forest fire modeling and the effect of fire-weather in landscape fire behavior for the region of Attica, Greece. In ‘Advances in Meteorology, Climatology and Atmospheric Physics’. pp. 131–136. (Springer: Heidelberg, Germany)
James F (1980) Monte Carlo theory and practice. Reports on Progress in Physics 43, 1145–1189.
Jampani R, Xu F, Wu M, Perez LL, Jermaine C, Haas PJ (2008) MCDB: a Monte Carlo approach to managing uncertain data. In ‘Proceedings of the 2008 ACM SIGMOD international conference on Management of data’, 9–12 June 2008, Vancouver, Canada. (ACM: New York, USA)
Kitanidis PK (1986) Parameter uncertainty in estimation of spatial functions: Bayesian analysis. Water Resources Research 22, 499–507.
Kuczera G, Parent E (1998) Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm. Journal of Hydrology 211, 69–85.
| Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm.Crossref | GoogleScholarGoogle Scholar |
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 Northeast 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 Northeast China.Crossref | GoogleScholarGoogle Scholar |
McCaw W, Neal J, Smith R (2002) Stand characteristics and fuel accumulation in a sequence of even-aged Karri (Eucalyptus diversicolor) stands in south-west Western Australia. Forest Ecology and Management 158, 263–271.
| Stand characteristics and fuel accumulation in a sequence of even-aged Karri (Eucalyptus diversicolor) stands in south-west Western Australia.Crossref | GoogleScholarGoogle Scholar |
Melching CS, Bauwens W (2001) Uncertainty in coupled nonpoint source and stream water-quality models. Journal of Water Resources Planning and Management 127, 403–413.
| Uncertainty in coupled nonpoint source and stream water-quality models.Crossref | GoogleScholarGoogle Scholar |
Melching CS, Yoon CG (1996) Key sources of uncertainty in QUAL2E model of Passaic River. Journal of Water Resources Planning and Management 122, 105–113.
| Key sources of uncertainty in QUAL2E model of Passaic River.Crossref | GoogleScholarGoogle Scholar |
Morris MD (1991) Factorial sampling plans for preliminary computational experiments. Technometrics 33, 161–174.
| Factorial sampling plans for preliminary computational experiments.Crossref | GoogleScholarGoogle Scholar |
Mutlu M, Popescu SC, Zhao K (2008) Sensitivity analysis of fire behavior modeling with LIDAR-derived surface fuel maps. Forest Ecology and Management 256, 289–294.
| Sensitivity analysis of fire behavior modeling with LIDAR-derived surface fuel maps.Crossref | GoogleScholarGoogle Scholar |
Orban G, Lagae L, Verri A, Raiguel S, Xiao D, Maes H, Torre V (1992) First-order analysis of optical flow in monkey brain. Proceedings of the National Academy of Sciences of the United States of America 89, 2595–2599.
| First-order analysis of optical flow in monkey brain.Crossref | GoogleScholarGoogle Scholar | 1557363PubMed |
Palmer MD, Brohan P (2011) Estimating sampling uncertainty in fixed‐depth and fixed‐isotherm estimates of ocean warming. International Journal of Climatology 31, 980–986.
| Estimating sampling uncertainty in fixed‐depth and fixed‐isotherm estimates of ocean warming.Crossref | GoogleScholarGoogle Scholar |
Phillips S, Borchardt B, Estler W, Buttress J (1998) The estimation of measurement uncertainty of small circular features measured by coordinate measuring machines. Precision Engineering 22, 87–97.
| The estimation of measurement uncertainty of small circular features measured by coordinate measuring machines.Crossref | GoogleScholarGoogle Scholar |
Pierce KBPKB, Ohmann JLOJL, Wimberly MCWMC, Gregory MJGMJ, Fried JSFJS (2009) Mapping wildland fuels and forest structure for land management: a comparison of nearest neighbor imputation and other methods. Canadian Journal of Forest Research 39, 1901–1916.
| Mapping wildland fuels and forest structure for land management: a comparison of nearest neighbor imputation and other methods.Crossref | GoogleScholarGoogle Scholar |
Qin X, Wang H, Ye L, Li Y, McConkey B, Lemke R, Li C, Brandt K, Gao Q, Wan Y (2013) A long-term sensitivity analysis of the denitrification and decomposition model. Environmental Modelling & Software 43, 26–36.
| A long-term sensitivity analysis of the denitrification and decomposition model.Crossref | GoogleScholarGoogle Scholar |
Ramsey MH, Argyraki A (1997) Estimation of measurement uncertainty from field sampling: implications for the classification of contaminated land. The Science of the Total Environment 198, 243–257.
| Estimation of measurement uncertainty from field sampling: implications for the classification of contaminated land.Crossref | GoogleScholarGoogle Scholar |
Richardson AD, Hollinger DY (2005) Statistical modeling of ecosystem respiration using eddy covariance data: maximum likelihood parameter estimation, and Monte Carlo simulation of model and parameter uncertainty, applied to three simple models. Agricultural and Forest Meteorology 131, 191–208.
| Statistical modeling of ecosystem respiration using eddy covariance data: maximum likelihood parameter estimation, and Monte Carlo simulation of model and parameter uncertainty, applied to three simple models.Crossref | GoogleScholarGoogle Scholar |
Rodriguez MA, Dabdub D (2003) Monte Carlo uncertainty and sensitivity analysis of the CACM chemical mechanism. Journal of Geophysical Research – D. Atmospheres 108 (D15), 4443
| Monte Carlo uncertainty and sensitivity analysis of the CACM chemical mechanism.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)
Ryu SR, Chen J, Zheng D, Lacroix JJ (2007) Relating surface fire spread to landscape structure: an application of FARSITE in a managed forest landscape. Landscape and Urban Planning 83, 275–283.
Salazar LA (1985) Sensitivity of fire behavior simulations to fuel model variations. Pacific Southwest Forest and Range Experiment Station, USDA Forest Service, Research Paper PSW-178. pp. 11. (Berkeley, CA, USA)
Scott JH, Burgan RE (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-153. (Fort Collins, CO, USA)
Shan YL (2003) Study on forest fuel of Daxing’an Mountains in Northeast China. Northeast Forestry University. PhD dissertation, Northeast Forestry University, Harbin, PR China.
Shen Z, Hong Q, Yu H, Liu R (2008) Parameter uncertainty analysis of the non-point source pollution in the Daning River watershed of the Three Gorges Reservoir Region, China. The Science of the Total Environment 405, 195–205.
| Parameter uncertainty analysis of the non-point source pollution in the Daning River watershed of the Three Gorges Reservoir Region, China.Crossref | GoogleScholarGoogle Scholar | 18639918PubMed |
Sparks JC, Masters RE, Engle DM, Bukenhofer GA (2002) Season of burn influences fire behavior and fuel consumption in restored shortleaf pine–grassland communities. Restoration Ecology 10, 714–722.
| Season of burn influences fire behavior and fuel consumption in restored shortleaf pine–grassland communities.Crossref | GoogleScholarGoogle Scholar |
Stratton RD (2004) Assessing the effectiveness of landscape fuel treatments on fire growth and behavior. Journal of Forestry 102, 32–40.
Sun X, Newham L, Croke B, Norton J (2012) Three complementary methods for sensitivity analysis of a water quality model. Environmental Modelling & Software 37, 19–29.
| Three complementary methods for sensitivity analysis of a water quality model.Crossref | GoogleScholarGoogle Scholar |
van Griensven A, Meixner T, Grunwald S, Bishop T, Diluzio M, Srinivasan R (2006) A global sensitivity analysis tool for the parameters of multi-variable catchment models. Journal of Hydrology 324, 10–23.
| A global sensitivity analysis tool for the parameters of multi-variable catchment models.Crossref | GoogleScholarGoogle Scholar |
van Wagtendonk JW (1996) Use of a deterministic fire growth model to test fuel treatments. In ‘Sierra Nevada Ecosystem Project: Final Report to Congress. Assessment and Scientific Basis for Management Options’. pp. 1155–1165 (University of California: Davis, CA, USA)
Wagner R, Tisdale TS, Zhang J (1996) A framework for phosphorus transport modeling in the lake okeechobee watershed. Journal of the American Water Resources Association 32, 57–73.
| A framework for phosphorus transport modeling in the lake okeechobee watershed.Crossref | GoogleScholarGoogle Scholar |
Wang Z, Liu Y, Fraser K, Liu X (2006) Stochastic stability of uncertain Hopfield neural networks with discrete and distributed delays. Physics Letters. [Part A] 354, 288–297.
| Stochastic stability of uncertain Hopfield neural networks with discrete and distributed delays.Crossref | GoogleScholarGoogle Scholar |
Wang X, He HS, Li X (2007) The long-term effects of fire suppression and reforestation on a forest landscape in Northeastern China after a catastrophic wildfire. Landscape and Urban Planning 79, 84–95.
| The long-term effects of fire suppression and reforestation on a forest landscape in Northeastern China after a catastrophic wildfire.Crossref | GoogleScholarGoogle Scholar |
Weise DR, Chen S, Riggan PJ, Fujioka FM, Jones C (2007) Using high-resolution weather data to predict fire spread using the FARSITE simulator: a case study in California chaparral. In ‘Proceedings of the Seventh Symposium on Fire and Forest Meteorology joint with the Northeast Forest Fire Protection Compact Forest Science Working Team’, 23–25 October 2007, Bar Harbor, ME, USA. (American Meteorological Society) Available at http://ams.confex.com/ams/7firenortheast/techprogram/paper_126873.htm [Verified 17 January 2019]
Wu Z, He HS, Liu Z, Liang Y (2013) Comparing fuel reduction treatments for reducing wildfire size and intensity in a boreal forest landscape of northeastern China. The Science of the Total Environment 454-455, 30–39.
| Comparing fuel reduction treatments for reducing wildfire size and intensity in a boreal forest landscape of northeastern China.Crossref | GoogleScholarGoogle Scholar | 23542479PubMed |
Yegnan A, Williamson D, Graettinger A (2002) Uncertainty analysis in air dispersion modeling. Environmental Modelling & Software 17, 639–649.
| Uncertainty analysis in air dispersion modeling.Crossref | GoogleScholarGoogle Scholar |
Zhang HX (2001) The critical flow-storm approach and uncertainty analysis for the TMDL development process. PhD dissertation, University of Virginia, Charlottesville, VA, USA.
Zhang G, Huang D, Zhu G, Yuan G (2017) Probabilistic model for safe evacuation under the effect of uncertain factors in fire. Safety Science 93, 222–229.
| Probabilistic model for safe evacuation under the effect of uncertain factors in fire.Crossref | GoogleScholarGoogle Scholar |
Zhou Y (1991) ‘Vegetation in Great Xing’an Mountains of China.’ (Science Press: Beijing, PR China)
