This section considers the techniques used to parameterize the PYROCART model such that it could be tested and validated using the fire described above. The techniques used in the evaluation of both physical (wind vector and terrain) and vegetation data are described. As with any model, predictive accuracy was largely a function of the success of the parameterization process, and so parameterization of the PYROCART model proved to be a major component of this research.
Topography and wind data collection
The topography of the area of the 1995 fire was evaluated using three-dimensional data obtained through a combination of Global Positioning System (GPS) surveying of the area and stereo-aerial photography. Eighteen control points were surveyed across the data site using the Trimble ProXL GPS (Trimble Navigation Ltd 1996) system. These points were differentially corrected through post-processing. The accuracy of this surveyed data was well in excess of the spatial resolution of the fire spread model (50 m). The horizontal accuracy of the control points was estimated to be approximately 1·1 m, with vertical accuracy two to five times worse than horizontal. These data were then imported to Virtuozo (Virtuozo 1996), a digital virtual photogrammetry system that creates a digital terrain model (DTM) and an ortho-photo from a stereo pair of aerial photographs. Virtuozo was used to create a DTM of the fire-scar of the study site, which was subsequently exported to the Arc/Info GIS (ESRI 1996).
The wind field of the study site was analysed (particularly with respect to the influences of topography) under north-westerly conditions similar to those under which the fire took place. Readings of wind speed and direction were taken at 5-min intervals at nine sites across the fire-scar. These sites were chosen such that the range of landscape units found on the fire-scar was represented. From this point wind speed and direction surfaces were interpolated using the quintic interpolation method of Akima (1978) as implemented by Arc/Info. Data layers representing the standard deviation in wind speed and direction across the fire-scar were also created. These were used to simulate random variations in wind speed and direction across the fire-scar.
The Rothermel (1972) fire spread model requires a number of stylized ‘fuel models’ of the vegetation through which fire is spreading. Fuel models are quantitative descriptions of those components of vegetation structure that are important for fire behaviour (Brown 1981). As the Rothermel model had not been previously used in New Zealand, it was necessary to construct fuel models for each of the 11 vegetation types observed.
When fuel models are constructed, any dead vegetation is divided into various ‘time lag’ (TL) classes. Herbaceous and woody fuels are considered separately. Data are collected for each fuel class and type combination and are then aggregated using a complex weighting method that is outlined in Rothermel (1972), Burgan & Rothermel (1984) and Burgan (1987). The TL divisions for the dead fuels are an attempt to simplify the variable rate(s) at which dead fuels dry. Based on their TL periods, four categories of fuel particles and fuel beds were recognized (Table 3). Fuels were committed to one of these categories as a function of their diameter in the case of particles, or their depth in the case of fuel beds. The concept of TL periods is based around that of equilibrium moisture content, the value that the actual moisture content would approach if the fuel particle is exposed to constant atmospheric conditions for an indefinite length of time (Bond & van Wilgen 1995; Pyne, Andrews & Laven 1996).
Table 3. Time lag fuel categories
|Time lag (h)||Fuel particle diameter (mm)|
Burgan & Rothermel (1984) assume three of the nine fuel-related parameters required as input to the Rothermel model to be constant (particle density, and total and effective mineral content). They also assume the surface-area-to-volume ratios of 10-h TL and 100-h TL fuel particles to be constants. However, for the purposes of this study these surface-area-to-volume ratios were assessed.
Fuel modelling was carried out in the following manner. After the creation of the vegetation map it was necessary to assess the species composition of each fuel class. The basal diameter of 30 specimens of each species represented in any fuel class was measured, the mean basal area calculated and termed the ‘standard unit’ for that species. The only exceptions were exotic grasses (e.g. Agrostis capillaris browntop), for which the basal area was based on the average number of upright tillers per 1-m2 plot, based on an average of 30 samples. This approach was taken so that fuel models could be built for each species represented in a fuel class. These could then be aggregated, using weighted averages, to create a fuel model for each vegetation class.
Assessment of each vegetation class consisted of surveying the basal diameter of each plant (except exotic grasses, for which the number of upright tillers was counted) within three randomly located 10 × 10-m plots. For especially abundant species, small subsamples were surveyed; for example, this was how the number of upright tillers for grassy species was assessed. After all plots were surveyed, percentage abundance (by basal area) for each species and the number of standard units per plot was calculated and, where appropriate, scaled to the 1-m2 level. Within each plot the proportions of herbaceous, woody and dead fuel(s) were assessed, and surface litter was collected.
For each species one specimen was ‘dissected’ in the laboratory in order to assess load, surface-area-to-volume ratio and the fuel moisture content (FMC). The specimen was selected to be as close as possible to the standard unit size so that the data obtained were directly comparable to the data from the survey plots. Before dissection each whole (above-ground) plant was weighed to assess the total load for each species ‘standard unit’. The specimen was then divided into functional categories (e.g. leaves, branches, boles) that were classed according to their TL division. The biomass of each TL size class within each functional category was then calculated. FMC was assessed for each TL division by weighing a sample, drying it for 24 h at 80 °C, then reweighing it and using the weight loss to calculate the moisture content. Although antecedent weather conditions are not included in the fuel moisture assessment required by the Rothermel model, a problem with fuel modelling a posteriori was the adequate parameterization of fuel variables such as FMC. The same is obviously true for an a posteriori reconstruction of the wind field. Despite variables such as fuel load and fuel bed depth being able to be estimated on the basis of an ‘average’ fuel unit, FMC is extremely temporally variable. As a result, FMC was estimated from material that was available at the time of fuel modelling. While it is recognized that there are likely to be differences between FMC and wind as modelled and the FMC and wind at the time of the fire, this was unavoidable.
Estimation of the surface-area-to-volume ratio was also carried out on the basis of functional category and TL division. The expressions of Brown (1970), which estimate the surface-area-to-volume ratio according to whether the particle falls into the class of ‘needles, grasses and lichens’ or ‘hardwood leaves’, were used. Having collected data for each TL category, the characteristic surface-area-to-volume ratio of the species could be calculated using the techniques described by Burgan & Rothermel (1984) and Burgan (1987). These techniques use a weighted averaging system based on the surface area within each TL class as a proportion of the total surface area. Because the largest proportion of the surface area usually falls with the finest (1-h and 10-h) TL classes, they receive the highest weighting. This is appropriate as the Rothermel model considers the passage of the flaming front, which is itself carried by the fine fuel particles. For all woody species, multiple 1-h TL classes were used. This technique is employed where there are different types of particles within one TL class (e.g. twigs and leaves).
Fuel bed depth was assessed by calculating the average height of 30 specimens of each species represented in the fuel models. The final fuel bed depth was estimated to be 70% of the maximum depth as described by Burgan & Rothermel (1984).
Fuel modelling for N. solandri var. cliffortioides was problematic as it was not possible to dissect and evaluate a specimen of this species. Thus, the fuel model constructed was estimated from data obtained from a variety of sources, such as data on the structure of individual trees at Cass (B.J. Maister, unpublished data; Burrows 1977), from general data on the species in New Zealand (Wardle 1984) and from published US fuel models for natural forest vegetation. The difficulties associated with the accurate modelling of fire spread through this species are considered more fully below.
After fuel models were compiled for one standard unit of each species represented in the various fuel classes, the standard unit values were aggregated on the basis of the number of standard units present per square metre (load) and by percentage abundance based on the basal area (FMC, depth and surface-area-to-volume ratio). All units were calculated in accordance with Wilson's (1980) metric revision of the fire spread model of Rothermel (1972), although two of these revisions needed to be corrected. The full fuel models for the various fuel classes are presented in Appendix 2.