capturing some convective aspects of wildfire in a empirical ......capturing some convective aspects...
TRANSCRIPT
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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
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Bunyip Ridge Fire, photo: Derek Chong
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13 minutes later
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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
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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
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Input Data from 30m source to PHOENIX grid
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Cann River 1983
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Effect of spotting on rate of spread
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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
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Convective Centres
Red – extent of spotting
Pink/blue –ember density
Brown/yellow –flame height
Grey/black –convective centre
Green/grey –fuel loads
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Convection
• Heat centres = most intense 25% of segments (10 vertices)
• Coalescence if “pull” strong enough
• Unstable conditions (constant)
• No convective inertia
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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)
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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)
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12:37 pm
1:40 pm
2:21 pm
6:10 pm
6:44 pm
7:36 pm
Kilmore post wind change front on
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Study Data (houses)
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Principal Component Analysis
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House loss and convective strength
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“Flame height”
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“Convective Density”
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50/50 Thresholds
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Interactive Factors
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Operational Implementation
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• Fire shape metrics
– Size, shape, orientation– Performance evaluation– Real-time model reparameterization
Planned Enhancements(Shape metrics)
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Raw gridded weather forecast(65,000 vs 41,000 ha)
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3.6km Wx grid, 5mins(100,000 ha)
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3.6km Wx grid, 5mins (1 hr offset)(86,000 ha)
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3.6km Wx grid, 5mins (1 hr offset, +5 degrees)(82,000 ha)
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3.6km Wx grid, 5mins (1 hr offset, +10 degrees)(70,000 ha)
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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