w ildland f ire d ecision s upport s ystem overview april, 2008
TRANSCRIPT
Wildland
Fire
Decision
Support
System
Overview
April, 2008
Why WFDSS?
An alternative selection decision and documentation process has been used for nearly 30 years – Wildland Fire Situation Analysis Process (WFSA).
Additional processes are used for other wildland fires:• Wildland Fire Implementation Plan (WFIP),• Long-Term Implementation Plan (LTIP)
Why WFDSS? (con’t)
National Fire and Aviation Executive Board chartered WFDSS in June 2005 to re-engineer the wildland fire decision process (replace WFSA) and develop support application software to• provide a scaleable decision support system,• utilize appropriate fire behavior modeling,
economic principles, and information technology,• support effective wildland fire decisions
consistent with Resource and Fire Management Plans.
Key Sections• Incident - location• Situation Assessment• Fire Behavior Assessment• Impacts• Objectives• Course of Action – strategic decision not
alternatives• Complexity Analysis• BAER• Reports
WFDSS MilestonesJune 2007 –
• Situation Assessment, • Rapid Assessment of Values At Risk (RAVAR), • Stratified Cost Index (SCI) and,• Fire Spread Probability (FSPro) available.
RAVAR Results
2007 Utilization
•500 users•170 fires•630 FSPro runs•70 RAVAR assessments
Planned WFDSS MilestonesJune 2008 – improvements to working prototype,
• Additional fire behavior tools,• New situational assessment features,• Automate impact tools,• Online help and Help Center,• Limited replacement for WFSA, WFIP, LTIP.
February 2009 - Delivery of WFDSS and terminate WFSA supported processes.
Beyond 2009 – Post-fire rehabilitation and fire planning components.
Fuel Moisture Calculations
N
10hr @ 2230
10hr Dead Fuel Moisture
5.2 - 6.2%6.2 - 7.3%7.3 - 8.3%8.3 - 9.4%9.4 - 10.4%10.4 - 11.5%11.5 - 12.5%12.5 - 13.6%13.6 - 14.6%
CFD Spatial Wind Grids
WFDSS Goals• Documents strategic decisions for individual fires,• Provides decision support, • Allows for operational plan preparation,• Is linear, scalable, progressive, and responsive to fire
complexity,• Is map oriented, graphically displayed, with no reliance on
large text input requirements,• Is Internet-based to provide risk and decision sharing
simply and efficiently,• Is applicable to all wildland fires as a single process,• Replaces the multiple processes of WFSA, WFIP, and LTIP,
What does this mean for you?
• DATA, Data, data! • The instant availability of WFDSS products to the entire
wildfire community will have a profound impact.• Use of fire behavior tools will be faster and easier as
WFDSS removes the drudgery of gathering up data.• Continuing education will become important to improve
skills to meet new demands.
RMRS
Forecast Data Needs for Ensemble Fire Simulations
April 15th, 2008Boise, Idaho
Mark A. Finney
Rocky Mountain Research StationFire Sciences Laboratory
Missoula, Montana
RMRS
Objectives for FSPro
• Risk-based strategic decisions for operations. Assess:– Probable Impacts– Expected Impact (loss) with and without
suppression– Point protection vs. Perimeter Control
• Estimate probabilities of fire impact from a known perimeter or point over a fixed time period (e.g. 7, 14 days)
RMRS
Forecasts In FSPro• Weather data for fire simulations are obtained
for a specific station for three periods: – Historic observations through previous day
• Want 10-20 years of daily observations• RAWS data for winds
– Forecast (several days)• Currently NDFD • Desired: Ensemble forecast members for arbitrary
latitude-longitude: obtained by computer query, 24/7• Temp, Humid, Precip, Winds
– Synthetic data from Time-Series (to ~ 21 days)• Time-series analysis of ERC• Wind rose
Obs
erva
tions
For
ecas
t
Obs
erva
tions
For
ecas
t
Obs
erva
tions
Autocorrelation + trend + Random Normal
Syn
thet
ic
For
ecas
t
Obs
erva
tions
Autocorrelation + trend + Random Normal
Syn
thet
ic
Winds, hourly afternoon
Used to
Initialize
Wind Ninja
RMRSFuture of FSPro & WFDSS Fire
Simulations
• Increase use of ensemble forecasts:– Improve fire simulations– Improve coordination with IMETs– Longer-range forecasts– Improve consistency in methods
• Will be adding spatial wind & time-series modeling– Rely on access to NOAA data & research
• Concurrent validation – 2008
Predicted Probability of Burning
Ob
serv
ed P
rob
abil
ity
of
Bu
rnin
g
Preliminary Comparison of Observed Burn Probability with FSPro Predicted Burn Probabilities for 9 Wildfires of 2007
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Perfect A
greement
CorpralBridgeAhorn
Brush CreekCalbickChippy
Conger CreekFoolcreek
Jocko Lakes
RMRS
Forecast Data Needs for Ensemble Fire Simulations
April 15th, 2008Boise, Idaho
Mark A. Finney
Rocky Mountain Research StationFire Sciences Laboratory
Missoula, Montana
RMRS