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