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Table of contents
- Introduction
- Development philosophy of PHOENIX
- The Fire Grid
- Inputs
- Fire Behaviour
- Fire Perimeter Propagation
- Asset Impact
- Outputs
4. Inputs
Input data to the model must be prepared in a GIS such as ESRI's ArcGIS or MapInfo. This base data is then converted into a format read by PHOENIX which is an ASCII grid broken into data tiles. Tiling can be achieved manually, but is better achieved using ancillary support tools provided as part of PHOENIX, installed as part of the ArcGIS Toolbox.
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Fine fuel hazard levels are converted to an equivalent fine fuel load (t/ha). While coarser fuels are consumed during a fire, the combustion of fine fuels is the process that predominantly determines spread rates. Fuels are considered as three separate strata; surface (which includes near-surface fuels), elevated fuel and bark, in accordance with forest fuel measurement standards in Southern Australia (McCarthy et al. 1999; Hines et al. 2010). Fuel classes that have no elevated or bark fuels are considered by PHOENIX as grasslands and are processed using functions derived from the CSIRO grassland fire spread model (Cheney et al. 1998).
Table 3. PHOENIX fuel types currently recognised in southern Australia.
Veg Type | Code | FuelCode | Description | Fuel Characteristics |
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Forest | F01 | 15 | Rainforest | dense vegetation with little dead material, epiphytes, vines, ferns, rarely dry |
| F02 | 32 | Wet Forest with rainforest understory | wet sclerophyll forest with a mesic understorey |
| F03 | 13 | Riparian Forest shrub | dense vegetation but with a small proportion of dead material |
| F04 | 11 | Wet Forest shrub & wiregrass | high biomass forest, but with little dead suspended material unless wiregrass present |
| F05 | 12 | Damp Forest shrub | dense understorey and potentially high bark hazard (karri) |
| F06 | 40 | Semi-mesic Sclerophyll forest | forest with semi-mesic shrubs and flammable grasses, sedge understorey |
| F07 | 33 | Swamp Forest | dense Melaleuca forest with little understorey |
| F08 | 6 | Forest with shrub | potentially high bark hazard, shrubs moderate flammability (mixed jarrah/karri) |
| F09 | 7 | Forest herb-rich | potentially high bark hazard, little elevated fuel |
| F10 | 45 | Dry Forest shrubs | dry forest with continuous understorey, (southern jarrah) |
| F11 | 8 | Dry Open Forest shrub/herbs | dry forest with open understorey (northern jarrah) |
Grass/sedges | G01 | 16 | High Elevation Grassland | dense sward of tussock grasses or herbs, high cover |
| G02 | 4 | Moist Sedgeland / Grassland | dense sward, potentially high dead component, button grass |
| G03 | 29 | Ephemeral grass/sedge/herbs | dense grass and sedges with potentially high levels of dead suspended material |
| G04 | 20 | Temperate Grassland / Sedgeland | grasses and sedges widespread, but varying in biomass |
| G05 | 44 | Hummock grassland | hummock grassland, discontinuous surface fuels |
Herbs | H01 | 30 | Moorland / Feldmarks | low flammability cushion plants |
| H02 | 36 | Alpine herbland | dense, upright, low flammability herbs |
| H03 | 34 | Wet herbland | freshwater herbs on mud flats |
| H03 | 37 | Wet herbland | low herbs in seasonally inundated lakebeds or wetlands |
Mallee | M01 | 27 | Mallee chenopod | low flammability except after exceptional rain bringing grasses |
| M02 | 42 | Mallee grass | mallee woodland with predominantly grass understorey |
| M03 | 25 | Mallee shrub/heath | continuous shrub layer but amount of dead material depending on species present |
| M04 | 26 | Mallee spinifex | discontinuous fuels, very flammable under windy conditions |
Bare | NIL | 0 | Water, sand, no vegetation | fuel absent |
Plantations | P01 | 98 | Softwood Plantation | dense canopy with continuous surface fuels |
| P02 | 99 | Hardwood Plantation | uniform canopy with continuous surface fuels |
Shrubs | S01 | 17 | High Elevation Shrubland/Heath | dense cover of shrubs with surface fuel largely under plants |
| S02 | 14 | Riparian shrubland | dense vegetation with little dead material |
| S03 | 35 | Wet Scrub | flammable shrubland with high level of dead elevated fuels |
| S04 | 1 | Moist Shrubland | dense shrubland, salt affected |
| S05 | 31 | Dry Closed Shrubland | tea-tree or paperbark thickets, little understorey |
| S06 | 21 | Broombush / Shrubland / Tea-tree | dense shrubland, but with relatively low level of dead material |
| S07 | 10 | Sparse shrubland | sparse shrubby vegetation with discontinuous surface fuels |
| S08 | 3 | Low flammable Shrubs | low flammability except after exceptional rain bringing grasses |
| S09 | 38 | Mangroves / Aquatic Herbs | trees, shrubs and herbs in permanent water, unburnable |
Heaths | S10 | 23 | Wet Heath | dense heath possibly with dense sedgy undergrowth |
| S11 | 24 | Dry Heath | dense heath with significant amounts of dead material |
Woodland | W01 | 18 | High Elevation Woodland shrub | wooded area with shrubby understorey |
| W02 | 19 | High Elevation Woodland grass | wooded area with continuous grass tussocks |
| W03 | 97 | Orchard / Vineyard | orchard or vineyard |
| W04 | 2 | Moist Woodland | low trees, shrubby, sedgy understorey, bark hazard |
| W05 | 22 | Woodland bracken/shrubby | wooded area with varying understorey, but not heathy |
| W06 | 9 | Woodland Grass/Herb-rich | surface fuels dominated by grass and herbs |
| W07 | 5 | Woodland Heath | flammable shrubs and high bark hazard |
| W08 | 41 | Gum Woodland heath/shrub | gum woodland with moderate bark hazard, heath/shrub understorey |
| W09 | 43 | Gum Woodland grass/herbs | gum woodland with moderate bark hazard, herbaceous understorey |
| W10 | 39 | Savanna grasslands | tall flammable grasses in an open woodland |
| W11 | 28 | Woodland Callitris/Belah | low flammability except after exceptional rain bringing grasses |
4.2 Wind reduction factors
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4.3.1 PurposeFuel types are used in conjunction with the fire history layer to generate fuel levels at the time of the simulation. Based on the time of ignition specified by the user, fuel levels are calculated through the combination of fuel type and the time since the last fire using fuel accumulation curves defined in the fuel type conversion file. 4.3.2 BasisThis layer is based upon fire history provided by the user. 4.3.3 Assumptions and limitationsIn the case of overlapping fire histories, PHOENIX only uses the most recent fire occurrence. 4.3.4 User interactionsPHOENIX uses the fuel accumulation model (see Section 5.7: Fuel Accumulation) to calculate fine fuel hazard classes which are then converted to an equivalent fuel load (t/ha) for surface, elevated and bark fuels. The accumulation curves are part of the fuel type conversion file. The user can upload a supplementary fire history layer to PHOENIX to capture recent fire events or to explore the effect of hypothetical fires in the landscape. |
4.3.5 Description
Fuel types are used in conjunction with a user-provided fire history layer to create fuel layer information used in PHOENIX simulations and stored in the Fire Grid. The time since the most recent fire is used to estimate fuel levels using negative exponential accumulation curves (discussed in Section 5.7: Fuel Accumulation). As data is retained for only the most recent fires (see Figure 9), where historic fires are being simulated, the fire history layer must be adjusted to be representative of the appropriate conditions.
Figure 9. Diagram of how PHOENIX treats overlapping fire history. On the left, two fires have been mapped, one in 1972 and the other in 1985. On the right, a new fire in 2008 has overlapped these earlier fires and has replaced their fire history in the overlapping areas.
ESRI Shapefiles can be used to supplement the baseline fire history layer for particular simulation runs. This provision is made to account for fires that have occurred since the baseline fire history was processed or to enable hypothetical prescribed burning scenarios to be quickly evaluated. The supplementary fire history is added to any existing fire history layer and processed in the same manner as the fire history stored in the Fire Grid.
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In Victoria and Tasmania, the resolution of the gridded weather forecast data is currently about 3 km, but in Queensland, NSW, WA, and South Australia, the NetCDF weather data has about a 6 km spatial resolution. All NetCDF data used currently has a one-hour temporal resolution.
Figure 10. Victorian gridded Temperature data for 11 am 7 February 2009.
Alternatively, a string of weather data can be specified. Values for the air temperature (oC), relative humidity (%), wind direction (deg), wind speed (km/h), drought factor (0-10), degree of grass curing (%) and cloud cover (%) for specified times must be provided as specified in Table 4. An example is provided in Table 5.
Table 4. Standard weather attributes
Attribute | Comments |
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Date/Time | Date and time of weather condition |
Temperature | 10 minute average in °C measured at 1.5 metres in a screen |
Relative Humidity | 10 minute average as a %, measured at 1.5 metres in a screen |
Wind Direction | 10 minute average in degrees, measured at 10 metres in the open |
Wind Speed | 10 minute average in km/h, measured at 10 metres in the open |
Drought Factor | Fine fuel availability 0-10 |
Curing | Grass curing level as a % (0-100) |
Cloud | Cloud cover as a % (0-100) |
Table 5. An example of user-provided point stream weather inputs that are time-stamped
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4.8.5.1 Upper-level winds
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Spot forecast, gridded forecast and automatic weather station data are often temporally quite coarse. If used in this raw form, weather data would result in instantaneous condition changes at the supplied date and time, which (apart from a frontal system) would not be realistic. To emulate real-world weather behaviour, weather conditions are linearly interpolated between entries.
Figure 11. Raw versus interpolated temperature values (in degrees Celsius).
A simple linear interpolation is used to derive continuous weather conditions for:
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4.9.1 PurposeTo provide the data necessary to run the PHOENIX suppression simulation model. Suppression details only need to be specified when a simulation of suppression is desired. 4.9.2 BasisThe user is required to enter specific resources available during the course of the fire. Resource availability is characterised by the resource type, quantity, the time available at the fire perimeter, and in the case of aircraft, their turnaround time between drops. 4.9.23 Assumptions and limitationsConstruction rate limiting factors have been identified for each of the suppression methods (McCarthy et al. 2003) including fire intensity, terrain, fuel density and turnaround time. In contrast to limiting factors, some elements, such as road proximity, augment construction rates. 4.9.34 User interactionsThe user provides the suppression agent types, quantities, start time and turnaround times (if applicable). The user also provides data on the effect of limiting or augmenting factors on construction rates. Suppression types and productivity rates can also be added or altered in the 'Suppression.xml', but this is an advanced user function and should not be undertaken without careful consideration and knowledge. |
4.9.
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5 Description
Suppression operations are defined by the activities of individual suppression agents (Hu and Sun 2007). The model allows for construction rates to be defined for specific suppression methods. Suppression methods currently supported are:
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For a discussion on how suppression resource inputs are used in simulations see Section 6.4: Suppression Model.
4.9.5.1 Construction rate limiting factors
Construction rate limiting factors have been identified for each of the suppression methods (McCarthy et al. 2003) including fire intensity, terrain, fuel density and in the case of aircraft, turnaround time. The effect of each factor on the suppression method's construction rate is expressed as a series of tabular data points. A sufficient number of points are required to accurately capture the relationship. A simple linear interpolation is then performed to calculate the exact construction rate given a limiting factor value.
This data point-based method for expressing relationships has been chosen over inbuilt functions because of its transparency and ease of modification.
Figure 12. The effect of slope and intensity on fireline construction and holding rates of large tankers (4,000 litres).
The effect of each limiting factor is multiplicative. To calculate the resultant construction rate, the proportion of the maximum construction rate is calculated for each limiting factor then multiplied together. The resulting proportion is then multiplied by the maximum construction rate to calculate the resultant rate.
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Resultant Rate = 0.7 x 0.6 x 2000 = 840 m/h
4.9.5.2 Construction rate augmentation based on road proximity
In contrast to limiting factors, road proximity augments construction rates. The distance from the Fire Grid cell centroid to the closest road is calculated and stored against each cell in the Fire Grid (see Section 4.6: Road Proximity).
PHOENIX incorporates an inbuilt basic road proximity modifier for land-based suppression methods that increases the previously calculated suppression rate based on the proximity of the fire to a road. The effect of road proximity is expressed as an increase in the maximum construction rate. The relationship between road proximity and construction rate increase varies between suppression methods.
Figure 13Figure 13. Increase in construction rate by large tankers (4000 litres) based on road proximity.
To calculate the modified construction rate, the percentage increase is calculated for the suppression type then applied to the previously calculated value.
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