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Ecological Modelling Fuel Modelling in Terrestrial Ecological Modelling 220 (2009) 2915–2926 Contents lists available at ScienceDirect Ecological Modelling journal homepage: www.elsevier.com/locate/ecolmodel Fuel modelling in terrestrial ecosystems: An overview in the context of the development of an object-orientated database for wild fire analysis V. Krivtsov a,∗, O. Vigy b, C. Legg a, T. Curt c, E. Rigolot b, I. Lecomte b, M. Jappiot c, C. Lampin-Maillet c, P. Fernandes d, G.B. Pezzatti e a CECS, The University of Edinburgh, School of GeoSciences, King’s Buildings, Edinburgh EH9 3JN, Scotland, UK b INRA, National Institute for Agronomic Research (INRA), Mediterranean Forest Ecology Research Unit (UR629), Fire Physics and Ecology Team Site Agroparc, Domaine Saint Paul, 84914 Avignon Cedex 9, France c Cemagref, UR EMAX, 13182 Aix-en-Provence Cedex 5, France d UTAD, Centro de Investigac¸ ão e de Tecnologias Agro-Ambientais e Biológicas (CITAB), Apt. 1013, 5001-801 Vila Real, Portugal e WSL Swiss Federal Research Institute, Ecosystem Boundaries Research Unit, Insubric Ecosystems Group, via Belsoggiorno 22, CH-6500 Bellinzona, TI, Switzerland article info abstract Article history: Wildfires are a serious problem affecting many terrestrial ecosystems and causing substantial economic Availableonline 24 September 2009 damage. Understanding the variation in structure of fuels (which are predominantly represented by plant litter and live vegetation) is key to understanding the behaviour of wildland fires. An understanding Keywords: of changes to fuels as vegetation develops is also central to the management of both wildfire and the Fire ecology planning of prescribed burning. A description of fuel structure is required for all models of fire behaviour. Terrestrial ecosystem It is therefore important that we have an appropriate system for describing fuel structure and predicting Fuel structure how fuel structure will develop through time (i.e. fuel succession). In this paper we review the range of Vegetation Plant litter published models used for fuel description and fuel succession. We propose an object-orientated database Object oriented database as an appropriate method for storing the complex data structures that are needed to process and analyse Fuel modeling data on fuels. The potential advantages of an object-orientated database as a tool for modelling fuel Fuel succession succession are discussed. Fuel manager © 2009 Elsevier B.V. All rights reserved. 1. Introduction ciently exploited; both result in the increased accumulation of fuel (de la Cueva et al., 2006; Duguy et al., 2007; Lampin-Maillet et al., Wildfires are a serious problem affecting many terrestrial 2009; Lampin et al., 2007; Pinol et al., 2005). Forest fire hazard is ecosystems and causing substantial economic damage (Butry et increasing both within forests and also in wildland–urban inter- al., 2001; NIFC, 2003). Wildfires particularly create damage in the faces where blocks of woodland and shrubland are connected to wildland–urban interfaces that total hundreds of millions of dollars urban systems (Lampin et al., 2007). annually in the United States (Mercer and Prestemon, 2005) and The occurrence and intensity of wildfire are the main elements can also affect human life severely: around 100 people were killed of fire hazard (Blanchi et al., 2002) which depend (in part) on the by wildfire in Europe in 2007, with Greece and Italy most affected availability and characteristics of the fuel (Fernandes et al., 2004; (Lampin-Maillet et al., 2009). Fire is currently a major disturbance Jappiot et al., 2001). In rural landscapes, fuel is predominantly rep- on the Canadian boreal forest (Amiro et al., 2001). In Europe, fire resented by plant litter and live vegetation. Fuels can be classified is the most important natural threat to forests and wooded areas as either ‘fine fuels’ (grass, needles, leaves and thin twigs) or ‘coarse of the Mediterranean Basin, with an average area burned annu- fuels’ (large branches, snags and logs). Fuel types can also be classi- ally of approximately 600,000 ha (Goldammer and Mutch, 2001). fied in relation to their organisation into vertical layers (Sandberg Many large fires are linked with the dramatic land transformation et al., 2001) or to their horizontal structure (Riano et al., 2002). that has been taking place in the Mediterranean region for some Therefore, a comprehensive collection of the information decades and which is increasing the risk of forest fire. On the one relevant to the description of plant/litter fuel complexes, fire occur- hand, agricultural fallows and abandoned orchards are slowly being rences, and the relevant environmental variables is needed to colonised by vegetation whilst, on the other hand, forest is not suffi- enhance the understanding of this phenomenon (and therefore our ability to design effective preventive and mitigating mea- sures). This paper gives an overview of modelling approaches used ∗ for the analysis of wildfire fuels and the understanding of their Corresponding author. Present address: School of Built Environment, Heriot Watt University, Edinburgh EH14 4AS, United Kingdom. dynamics, and argues the case for an object-orientated database E-mail address: [email protected] (V. Krivtsov). of fuel complexes. In particular, we report on the development 0304-3800/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2009.08.019 2916 V. Krivtsov et al. / Ecological Modelling 220 (2009) 2915–2926 of such a database within the ‘Fire Paradox’ project funded by the last fire or other disturbance events (but rarely to the real the European Union under the Framework 6 Programme (see measured age of plants). This generally leads to the construction http://www.fireparadox.org/project summary.php). In this paper of fuel accumulation curves (Abdelmoula et al., 2004; Fernandes we describe the database structure, and give a brief description and Rego, 1998). The temporal description of fuel load is abun- of the underlying models, user interfaces, capabilities to incorpo- dantly portrayed in Australian literature (Birk and Simpson, 1980; rate allometric relationships, and the links with relevant process Burrows and Mccaw, 1990; Fensham, 1992; MarsdenSmedley and models. Catchpole, 1995; McCaw et al., 2002; O’Connell, 1987) in relation to the widespread practice of prescribed burning. Fuel accumula- 2. Modelling of vegetation and fuel succession in terrestrial tion modelling usually involves estimation of a steady-state fuel ecosystems—an overview load and of a decomposition constant, after Olson (Olson, 1963). Dynamic fuel models (Agee, 1998; Fernandes and Rigolot, 2007; Vegetation flammability and fire behaviour are strongly related Hough and Albini, 1978; Rothermel and Philpot, 1973; Schimmel to phytomass structural characteristics and depend on the nature and Granstrom, 1997) offer the possibility of examining how and the organisation of the different vegetation layers. Fires burn flammability and fire behaviour change with the structural modi- with highly variable intensities according to fuel structure (Agee, fications that time induces on the fuel complex. 1998). Forest fire behaviour prediction systems, e.g. the semi- Most allometric models are empirical equations for calculat- empirical model BEHAVE (Andrews and Bevins, 2003; Andrews et ing the values of an important dependent variable (e.g. total stand al., 2005) based on the model of Rothermel (1972), require the biomass, biovolume of particles of certain diameter) from the val- forest fuels to be described in a particular way in which certain ues of other biometrics (e.g. average plant height, diameter at breast wildland fuel characteristics are represented by average values, height) and (sometimes) environmental (e.g. soil type, altitude, her- according to vegetation types. Fuel models are thus the main inputs bivore pressure) variables that are known or relatively simple to to the various models for calculating the physical parameters of estimate. The major relationships have been established between a fire. Whether one is using complex physical models of the fire, biomass and plant height, diameter (basal diameter or diameter at or simpler combinations, it is necessary to identify homogeneous breast height), and crown dimensions, including crown width and vegetation types, according to the composition and the structure of height (Brown, 1976; Halpern et al., 1996). For instance, Halpern their combustible biomass. The set of these representative values is et al. have derived 152 regression equations for predicting above- called a “fuel model”, but here we will use the term “fuel model” in ground biomass of plant species in an early successional forest a broader sense in order to accommodate any quantitative descrip- (Halpern et al., 1996). Brown (1976) gives linear regression equa- tion of fuel. tions for estimating shrub biomass and leaf weight from basal stem The first simple fuel models (qualitative descriptors such as diameter. herbaceous, bushes, forest) date back more than a century (Dubois, Allometric growth models used for fuel succession modelling 1914). Then, with the development of mathematical models of are based on field measurements of different fuel types, often fire behaviour such as that of Rothermel (1972), more elaborate organised along gradients of post-fire succession. There are a num- fuel models were developed. These models, initially devised for ber of techniques used to collect the data necessary for deriving the North American vegetation, were then adopted to Spanish
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