1. W ildland F ire D ecision S upport S ystem Overview April, 2008

2. 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)

3. 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.

4. Key Sections Incident - location Situation Assessment Fire Behavior Assessment Impacts Objectives Course of Action – strategic decision not alternatives Complexity Analysis BAER Reports

5. WFDSS Milestones June 2007 – Situation Assessment, Rapid Assessment of Values At Risk (RAVAR), Stratified Cost Index (SCI) and, Fire Spread Probability (FSPro) available.

10. RAVAR Results

11. 2007 Utilization 500 users 170 fires 630 FSPro runs 70 RAVAR assessments

12. Planned WFDSS Milestones June 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.

15. 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%

16. CFD Spatial Wind Grids

17. 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,

18. 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.

19. RMRS Forecast Data Needs for Ensemble Fire Simulations April 15 th, 2008 Boise, Idaho Mark A. Finney Rocky Mountain Research Station Fire Sciences Laboratory Missoula, Montana

20. 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)

21. 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

22. Observations

23. Forecast Observations

24. Forecast Observations Autocorrelation + trend + Random Normal Synthetic

25. Forecast Observations Autocorrelation + trend + Random Normal Synthetic

26. Winds, hourly afternoon Used to Initialize Wind Ninja

27. RMRS Future 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

28. Predicted Probability of Burning Observed Probability of Burning 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 00.10.20.30.40.50.60.70.80.91 Perfect Agreement Corpral Bridge Ahorn Brush Creek Calbick Chippy Conger Creek Foolcreek Jocko Lakes

29. RMRS Forecast Data Needs for Ensemble Fire Simulations April 15 th, 2008 Boise, Idaho Mark A. Finney Rocky Mountain Research Station Fire Sciences Laboratory Missoula, Montana

30. RMRS