Recent approaches to model the risk of storm and fire
Abstract
The aim of this paper is to discuss the different recently developed empirical and mechanistic modelling approaches for assessing the risk of wind and fire damage to forests. Additionally the work will explore possible ways to integrate these approaches, including feedback mechanisms, into growth and yield models and decision support tools used in forestry. The integration of mechanistic and empirical storm risk models, as well as an empirical/mechanistic fire risk model into growth simulators is demonstrated and future challenges and options for risk modelling and for creating complex decision support tools, including growth simulators, meteorological components and risk modules, are discussed.Downloads
References
Agee J.K., Skinner C.N., 2005. Basic principles of forest fuel reduction treatments. Forest Ecology and Management 211, 83-96.
http://dx.doi.org/10.1016/j.foreco.2005.01.034
Albrecht A., 2009. Sturmschadensanalysen langfristiger waldwachstumskundlicher Versuchsflächendaten in Baden- Württemberg. PhD-thesis. Albert-Ludwigs Universität, Freiburg. 170 pp. [In German].
Ancelin P., Courbaud B., Fourcaud T., 2004. Development of an individual tree-based mechanical model to predict wind damage within forest stands. Forest Ecology and Management 203, 101-121.
http://dx.doi.org/10.1016/j.foreco.2004.07.067
Bettinger P., 2010. An overview of methods for incorporating wildfires into forest planning models. International Journal of Mathematical and Computational Forestry and Natural-Resource Sciences 2, 43-52.
Blennow K., Sallnäs O., 2004. WINDA: a system of models for assessing the probability of wind damage to forest stands within a landscape. Ecological Modelling 175, 87-99.
http://dx.doi.org/10.1016/j.ecolmodel.2003.10.009
Blennow K., Andersson M., Sallnäs O., Olofsson E., 2010. Climate change and the probability of wind damage in two Swedish forests. Forest Ecology and Management 259, 818-830.
http://dx.doi.org/10.1016/j.foreco.2009.07.004
Ciais P., Reichstein M., Valentini R., 2005. Europewide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529-533.
http://dx.doi.org/10.1038/nature03972
PMid:16177786
Cruz M.G., Alexander M.E., Wakimoto R.H., 2004. Modeling the likelihood of crown fire occurrence in conifer forest stands. Forest Science 50, 640-658.
Cruz M.G., Alexander M.E., Wakimoto R.H., 2005. Development and testing of models for predicting crown fire rate of spread in conifer forest stands. Canadian Journal of Forest Research 35, 1626-1639.
http://dx.doi.org/10.1139/x05-085
Cruz M.G., Alexander M.E., Fernandes P.A.M., 2008. Development of a model system to predict wildfire behaviour in pine plantations. Australian Forestry 71(1), 113-121.
Dupont S., Brunet Y., 2008. Influence of foliar density profile on canopy flow: a large eddy simulation study. Agricultural and Forest Meteorology 148, 976-990
http://dx.doi.org/10.1016/j.agrformet.2008.01.014
EM-DAT. 2009. Emergency Events Database. The OFDA/CRED International Disaster Database, www.emdat.be.Université Catholique de Louvin, Brussels, Belgium.
Fernandes P.M., Loureiro C.A., Botelho H.S., 2004. Fire behaviour and severity in a maritime pine stand under differing fuel conditions. Annals of Forest Science 61(6), 537-544.
http://dx.doi.org/10.1051/forest:2004048
Fernandes P.M., Vega J.A., Jiménez E., Rigolot E., 2008. Fire resistance of European pines. Forest Ecology and Management 256(3), 246-255.
http://dx.doi.org/10.1016/j.foreco.2008.04.032
Fernandes P.M., 2009. Combining forest structure data and fuel modelling to classify fire hazard in Portugal. Annals of Forest Science 66. doi: 10.1051/forest/2009013
http://dx.doi.org/10.1051/forest/2009013
Foudhil H., Brunet Y., Caltagirone J.P., 2005. A k-ε model for atmospheric flow over heterogeneous landscapes. Environmental Fluid Mechanics 5, 245-267.
http://dx.doi.org/10.1007/s10652-004-2124-x
Fowler J.F., Sieg C.H., 2004. Postfire mortality of ponderosa pine and Douglas-fir: a review of methods to predict tree death. USDA Forest Service General Technical Report RMRSGTR- 132. 25 pp.
Gadow K.V., 2000. Evaluating risk in forest planning models. Silva Fennica 34,181-191.
Gardiner B., Peltola.H., Kellomäki S., 2000. Comparison of two models for predicting the critical wind speeds required to damage coniferous trees. Ecological Modelling 129, 1-23.
http://dx.doi.org/10.1016/S0304-3800(00)00220-9
Gardiner B., Marshall B., Achim A., Belcher R.E., WOOD C.J., 2005. The stability of different silvicultural systems: a wind-tunnel investigation. Forestry 78, 471-484.
http://dx.doi.org/10.1093/forestry/cpi053
Gardiner B., Byrne K., Hale S., Kamimura K., Mitchell S., Peltola H., Ruel J.C., 2008. A review of mechanistic modelling of wind damage risk to forests. Forestry 81(3), 447-461.
http://dx.doi.org/10.1093/forestry/cpn022
González J.R., Palahí M., Pukkala T., 2005. Integrating fire risk considerations in forest management planning in Spain – a landscape level perspective. Landscape Ecology 20(8), 957-970.
http://dx.doi.org/10.1007/s10980-005-5388-8
González J.R., Palahí M., Trasobares A., Pukkala T., 2006. A fire probability model for forest stands in Catalonia (north-east Spain). Annals of Forest Science 63, 169-176.
http://dx.doi.org/10.1051/forest:2005109
González J.R., Trasobares A., Palahí M., Pukkala T., 2007. Predicting tree survival in burned forests in Catalonia (North-East Spain) for strategic forest planning. Annals of Forest Science 64, 733-742
http://dx.doi.org/10.1051/forest:2007053
González J.R., Palahí M., Pukkala T., Trasobares A., 2009. Optimising the management of Pinus nigra Arn. stands under endogenous risk of fire in Catalonia. Invest Agrar: Sist Recur For 17(1), 10-17. HAIMES Y.Y. 2004. Risk modeling, assessment, and management, 2nd ed. John Wiley & Sons, Inc, Hoboken, NJ.
Hanewinkel M., Breidenbach J., Neeff T., Kublin E., 2008. 77 years of natural disturbances in a mountain forest area - the influence of storm, snow and insect damage analysed with a long-term time-series. Canadian Journal of Forest Research 38, 2249-2261.
http://dx.doi.org/10.1139/X08-070
Hanewinkel M., Zhou W., Schill C., 2004. A neural network approach to identify forest stands susceptible to wind damage. Forest Ecology and Management 196, 227-243
http://dx.doi.org/10.1016/j.foreco.2004.02.056
Holecy J., Hanewinkel M., 2006. A forest management risk insurance model and its application to coniferous stands in southwest Germany. Forest Policy and Economics. Forest Policy and Economics 8, 161-174.
http://dx.doi.org/10.1016/j.forpol.2004.05.009
Jylhä K., Ruosteenoja K., Räisänen J., Venäläinen A., Tuomenvirta H., Ruokolainen L., Saku S., Seitola T., 2009. The changing climate in Finland: estimates for adaptation studies. ACCLIM project report 2009. Reports 2009:4. Finnish Meteorological Institute, in Finnish with English abstract and figure and table captions. 102 pp.
Kamimura K., Gardiner B., Kato A., Hiroshima T., Shiraishi N., 2008. Developing a decision support approach to reduce wind damage risk – a case study on sugi [Cryptomeria japonica (L.f.) D.Don] forests in Japan. Forestry 81(3), 429-446.
http://dx.doi.org/10.1093/forestry/cpn029
Kellomäki S., Väisänen H., 1997. Modelling the dynamics of the forest ecosystem for climate change studies in the boreal conditions. Ecological Modelling 97, 121-140.
http://dx.doi.org/10.1016/S0304-3800(96)00081-6
Kellomäki S., Väisänen H., Hänninen H., Kolström T., Lauhanen R., Mattila U., Pajari B., 1992. Sima: a model for forest succession based on the carbon and nitrogen cycles with application to silvicultural management of the forest ecosystem. Silva Carelica 22, 1-91.
Kurz W.A., Dymond C.C., Stinson R.G.J., Neilson E.T., Carroll A.L., Ebata T., Safranyik L., 2008. Mountain pine beetle and forest carbon feedback to climate change. Nature 452, 987-990.
http://dx.doi.org/10.1038/nature06777
PMid:18432244
Lanquaye-Opoku N., Mitchell S.J., 2005. Portability of stand-level empirical windthrow risk models. Forest Ecology and Management 216, 134-148.
http://dx.doi.org/10.1016/j.foreco.2005.05.032
Linn R., Reisner J., Colman J.J., WINTERKAMP J., 2002. Studying wildfire behaviour using FIRETEC. Internacional Journal of Wildland Fire 11, 233-246.
http://dx.doi.org/10.1071/WF02007
Mchugh C.W., Kolb T.E., 2003. Ponderosa pine mortality following fire in northern Arizona. International Journal of Wildland Fire 12, 7-22.
http://dx.doi.org/10.1071/WF02054
Mitsopoulos I.D., Dimitrakopoulos A.P., 2007. Canopy fuel characteristics and potential crown FIRE behaviour in Alepo pine (Pinus halepensis Mill.) forest. Annals of Forest Science 64, 287-299.
http://dx.doi.org/10.1051/forest:2007006
Moreira F., Duarte I., Catry F., Acácio V., 2007. Cork extraction as a key factor determining post-fire cork oak survival in a mountain region of southern Portugal. Forest Ecology and Management 253, 30-37.
http://dx.doi.org/10.1016/j.foreco.2007.07.001
Nagel J., 1997. BWIN Program for Stand analysis and prognosis. User's manual for Version 3.0, Niedersächsische Forstliche Versuchsanstalt Göttingen. 44 pp.
Nothdurft A., Saborowski J., Breidenbach J., 2009. Spatial prediction of forest stand variables. European Journal of Forest Research 128, 241-251.
http://dx.doi.org/10.1007/s10342-009-0260-z
Olofsson E., Blennow K., 2005. Decision support for identifying spruce forest stand edges with high probability of wind damage. Forest Ecology and Management 207, 87-98.
http://dx.doi.org/10.1016/j.foreco.2004.10.019
Palahí M., Tomé M., Pukkala T., Trasobares A., Montero G., 2003. Site index model for Scots pine (Pinus sylvestris L.) in north-east Spain. Forest Ecology and Management 187, 35-47.
http://dx.doi.org/10.1016/S0378-1127(03)00312-8
Peltola H., 2006. Mechanical stability of trees under static loads. American Journal of Botany 93(10), 1501-1511.
http://dx.doi.org/10.3732/ajb.93.10.1501
PMid:21642097
Peltola H., Kellomäki S., Väisänen H., Ikonen V. P., 1999. A mechanistic model for assessing the risk of wind and snow damage to single trees and stands of Scots pine, Norway spruce and birch. Canadian Journal of Forest Research 29, 647-661.
http://dx.doi.org/10.1139/x99-029
Peltola H., Ikonen V-P., Gregow H., Strandman H., Kilpeläinen A., Venäläinen A., Kellomäki S., 2010. Impacts of climate change on timber production and regional risks of wind-induced damage to forests in Finland. Forest Ecology and Management 260, 833-845.
http://dx.doi.org/10.1016/j.foreco.2010.06.001
Pollet J., Omi P.N., 2002. Effect of thinning and prescribed burning on crown fire severity in ponderosa pine forests. International Journal of Wildland Fire 11, 1-10.
http://dx.doi.org/10.1071/WF01045
Pretzsch H., 2001. Modellierung des Waldwachstums. Parey Buchverlag, Berlin. 341 pp.
PMid:11383987
Rauthe M., Kunz M., Kottmeier CH., 2010. Changes in the storm climatology over Central Europe derived from a small ensemble of high resolution regional climate models. Meteorlogische Zeitschrift. [In press].
http://dx.doi.org/10.1127/0941-2948/2010/0350
Rigolot E., 2004. Predicting postfire mortality of Pinus halepensis Mill. and Pinus pinea L. Plant Ecology 171, 139-151.
http://dx.doi.org/10.1023/B:VEGE.0000029382.59284.71
Ritchie M.W., Skinner C.N., Hamilton T.A., 2007. Probability of tree survival after wildfire in an interior pine forest of northern California: effects of thinning and prescribed fire. Forest Ecology and Management 247, 200-208.
http://dx.doi.org/10.1016/j.foreco.2007.04.044
Rothermel R.C., 1983. How to predict the spread and intensity of forest and range fires. Gen Tech Rep INT-143. Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 161 pp.
Rothermel R.C., 1991. Predicting the behavior and size of crown fires in the northern Rocky Mountains. Res Pap INT-RP-438. Intermountain Forest and Range Experiment Station, Ogden, UT. 46 pp.
Schehaas M.J., Nabuurs G.J., Schuck A., 2003. Natural disturbances in the European forests in the 19th and 20th centuries. Global Change Biology 9, 1620-1633.
http://dx.doi.org/10.1046/j.1365-2486.2003.00684.x
Schelhaas M.J., Kramer K., Peltola H., Van Der Werf D.C., Wijdeven S.M.J., 2007. Introducing tree interactions in wind damage simulation. Ecological Modelling 207, 197-209.
http://dx.doi.org/10.1016/j.ecolmodel.2007.04.025
Schmidt M., Hanewinkel M., Kändler G., Kublin E., Kohnle U., 2010. An inventory-based approach for modeling single tree storm damage-experiences with the winter storm 1999 in southwestern Germany. Canadian Journal of Forest Research 40(8), 1636-1652.
http://dx.doi.org/10.1139/X10-099
Scott R.E., Mitchell S.J., 2005. Empirical modelling of windthrow risk in partially harvested stands using tree neighbourhood and stand attributes. Forest Ecology And Management 218(1), 193-209.
http://dx.doi.org/10.1016/j.foreco.2005.07.012
Scott J.H., Reinhardt E.D., 2001. Assessing crown fire potential by linking models of surface and crown fire behavior. USDA Forest Service, Fort Collins. 59 pp.
Seidl R., Baier P., Rammera W., Schopf A., Lexer M.J., 2007. Modelling tree mortality by bark beetle infestation in Norway spruce forests. Ecological Modelling 206, 383-399.
http://dx.doi.org/10.1016/j.ecolmodel.2007.04.002
Sidoroff K., Kuuluvainen T., Tanskanen H., Vanha-Majamaa L., 2007. Tree mortality after lowintensity prescribed fires in managed Pinus sylvestris stands in southern Finland. Scandinavian Journal of Forest Research 22(1), 2-12.
http://dx.doi.org/10.1080/02827580500365935
Suzuki T., 1971. Forest transition as a stochastic process. Mitteilungen der Forstlichen Bundesversuchsanstalt (FBVA) 91, 137-150.
Trasobares A., Pukkala T., Miina J., 2004a. Growth and yield model for uneven-aged mixtures of Pinus sylvestris L. and Pinus nigra Arn. in Catalonia, north-east Spain. Annals of Forest Science 61(1), 9-25.
http://dx.doi.org/10.1051/forest:2003080
Trasobares A., Tomé M., Miina J., 2004b. Growth and yield model for Pinus halepensis in Catalonia, northeast Spain. Forest Ecology and Management 203, 49-62.
http://dx.doi.org/10.1016/j.foreco.2004.07.060
Valinger E., Fridman J., 1999. Models to assess the risk of snow and wind damage in pine, spruce and birch forests in Sweden. Environmental Management 24, 209-217.
http://dx.doi.org/10.1007/s002679900227
PMid:10384030
Wood S.N., 2006. Generalized additive models: an introduction with R. Chapman & Hall/CRC, Boca Raton. 391 pp.
Wood S.N., 2004. Stable and efficient multiple smoothing parameter estimation for generalized additive models. Journal of the American Statistical Association 99 (467), 673-686.
http://dx.doi.org/10.1198/016214504000000980
Yoder J. 2004. Playing with fire: endogenous risk in resource management. American Journal of Agricultural Economics, 86(4), 933-948.
http://dx.doi.org/10.1111/j.0002-9092.2004.00644.x
Yue C., Kohnle U., Hein S., 2008. Combining tree- and stand-level models: a new approach to growth prediction. Forest Science 54(5), 553-566.
Zeng H., García-Gonzalo J., Peltola H., Kellomäki S., 2010. The effects of forest structure on the risk of wind damage at a landscape level in a boreal forest ecosystem. Annals of Forest Science 67(1). [Available online].
http://dx.doi.org/10.1051/forest/2009090
Zeng H., Peltola H., Talkkari A., Venäläinen A., Strandman H., Kellomäki S., Wang K., 2004. Influence of clear-cutting on the risk of wind damage at forest edges. Forest Ecology and Management 203, 77-88.
http://dx.doi.org/10.1016/j.foreco.2004.07.057
Zeng H., Peltola H., Talkkari A., Venäläinen A., Wang K., Kellomäki S., 2006. Simulations of the influence of clear-cutting on the risk of wind damage on a regional scale over a 20-year period. Canadian Journal Forest Research 36, 2247-2258.
http://dx.doi.org/10.1139/x06-123
Zeng H., Pukkala T., Peltola H., 2007. The use of heurestic optimization in risk management of wind damage in forest planning. Forest Ecology and Management 241, 189-199.
http://dx.doi.org/10.1016/j.foreco.2007.01.016
Zeng H., Peltola H., Väisänen H., Kellomäki S., 2009. The effects of fragmentation on the susceptibility of a boreal forest ecosystem to wind damage. Forest Ecology and Management 257, 1165-1173.
http://dx.doi.org/10.1016/j.foreco.2008.12.003
Zimmermann N.E., Yoccoz N.G., Edwards T.C., Meier E.S., Thuiller W., Guisan A., Schmatz D.R., Pearman P.B., 2009. Climatic extremes improve predictions of spatial patterns of tree species. Proceedings of the National Academy of Science 109, 19723-19728.
http://dx.doi.org/10.1073/pnas.0901643106
PMid:19897732 PMCid:2780931
© CSIC. Manuscripts published are the property of Consejo Superior de Investigaciones Científicas, and quoting this source is a requirement for any partial or full reproduction.
Forest Systems is an Open Access Journal. All articles are distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License. You may read here the basic information and the legal text of the license. The indication of the license CC-by must be expressly stated in this way when necessary.
