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Capturing some convective aspects of wildfire in a empirical model – PHOENIX RapidFire
Kevin Tolhurst & Derek ChongDept Forest and Ecosystem ScienceUniversity of Melbourne / Bushfire CRC
Coupled Atmosphere-Bushfire Modelling WorkshopUniversity of Melbourne, 16 – 18 May 2012
Bunyip Ridge Fire, photo: Derek Chong
13 minutes later
Wind ReductionFactors
INPUTS
Fire History
Topography
Assets & Values
Road Proximity
Weather
Suppression Resources
Fire Grid Creation
DATA MANAGEMENTGeospatial Database
Fire BehaviourBehaviour Models
(McArthur MkV, CSIRO grass)
Spotting / Embers(Indraught, Ember transport, Ignition)
Slope Correction(Wind-Slope interaction)
Wind Field Models(Wind Wizard, Wind Ninja)
Road /River/Break Impact(Fuel-f ree linear features)
Fire Perimeter Propagation
Point Spread Modeling(Huygen’s, Buildup )
Self-Extinction(Moisture, Fuel, Fire Intensity)
Reprojection on Map(Surface to Plan conversion)
Suppression Model(Resources, Fuels, Topo, Fire,Road)
MODELS
OUTPUTS(to Fire Grid)
FIRE:
ORIGINEXTENT
INTENSITYFREQUENCY
FLAME HEIGHTSIZE (AREA)
TIME TO IMPACTSPOTTING
Solar Radiation Model(Fuel Moisture in space & time)
Fuel Types
Fuel Disruptions
FIRE
Fuel Accumulation(Veg Type, Time Since Fire)
FIRE GRID
Fuel Moisture(Time, Date, Cloud, Cover, Latitude)
Convection / Heat Centres(Heat output, Extent)
Spot Fires(Ember density, Ignition probability,
Buildup, Coalescence)
Asset Impact
Asset Type & Location(Value, Number)
Vulnerability(Intensity, Flame Ht, Freq, Embers)
LOSSVALUE
ASSETS
PHOENIX RapidFire
A deterministic, dynamic, continuous, empirical fire characterization model
– Deterministic – combines mechanistic and empirical elements (not stochastic or physical)
– Dynamic – inputs conditional on base fire behaviour conditions (not steady-state).
– Continuous – fire spread is a continuous process calculated as perimeter point vectors (not discrete event or transition model, not CA).
– Empirical – dynamics in model have been tuned to observed fire behaviour patterns over a range of wildfire situations
“Modelling is an abstraction of a complex reality in the simplest way that is adequate for the purpose.” (Mulligan, M. and Wainwright, J. (2004). Modelling and model building. )
Design
Input Data from 30m source to PHOENIX grid
Cann River 1983
Effect of spotting on rate of spread
Ember distribution
Fig. 5. Ground-level distribution of N0 fallen firebrands for a fire intensity of 30 MWm−1 and a wind speed of 11.17 ms−1for (a) νc = 0 and Ntot = 5052, (b) νc = 0.39 and Ntot = 7338, the left-hand scale corresponding to “short-distance” firebrands,the right-hand scale to “long-distance” firebrands, and (c) only “char oxidation process” and Ntot = 8337. Black-filled barscorrespond to firebrands that land at a temperature higher than 500 K.
N. Sardoy et al. / Combustion and Flame 154 (2008) 478–488
Convective Centres
Red – extent of spotting
Pink/blue –ember density
Brown/yellow –flame height
Grey/black –convective centre
Green/grey –fuel loads
Convection
• Heat centres = most intense 25% of segments (10 vertices)
• Coalescence if “pull” strong enough
• Unstable conditions (constant)
• No convective inertia
Comparison of Phoenix convection model (bubbles)with Laverton weather radar data looking side on.
Smoke as reflectance > 33 dBZ (pink rays)12:37 pm
1:06 pm ≈ 550 ha
2:21 pm ≈ 2000 ha
Bunyip Ridge fire (12:20 am start)
Comparison of Phoenix convection model (bubbles)with Laverton weather radar data looking side on.
Smoke as reflectance > 33 dBZ (pink rays)
12:37 pm
1:40 pm
2:21 pm
Kilmore fire (11:45 am ignition)
12:37 pm
1:40 pm
2:21 pm
6:10 pm
6:44 pm
7:36 pm
Kilmore post wind change front on
Study Data (houses)
Principal Component Analysis
House loss and convective strength
“Flame height”
“Convective Density”
50/50 Thresholds
Interactive Factors
Operational Implementation
• Fire shape metrics
– Size, shape, orientation– Performance evaluation– Real-time model reparameterization
Planned Enhancements(Shape metrics)
Raw gridded weather forecast(65,000 vs 41,000 ha)
3.6km Wx grid, 5mins(100,000 ha)
3.6km Wx grid, 5mins (1 hr offset)(86,000 ha)
3.6km Wx grid, 5mins (1 hr offset, +5 degrees)(82,000 ha)
3.6km Wx grid, 5mins (1 hr offset, +10 degrees)(70,000 ha)
Summary
• Some aspects of convection can be caught in a 2D model if a number of assumptions are made.
• Convective processes are driving the spotting process and house loss estimation.
• PHOENIX RapidFire is being used operationally for fire spread prediction in Victoria through an automated process taking 1 to 2 minutes.
• PHOENIX RapidFire is being used for strategic planning and risk assessment (house loss, water, biodiversity).
• Many underpinning assumptions of the convective model need testing.
• Input data quality is the biggest single area for improvement