1. Van R. Kane University of Washington
Biophysical controls on forest structure and fire severity in Yosemite National Park
OOS 7-10
Contact:
Bob McGaughey
USDA Forest Service – Pacific Northwest Research Station
University of Washington
PO Box
Seattle, WA
(206)
Van R. Kane
University of Washington
Alina Cansler1, Nicholas Povak2, Derek Churchill1, Malcolm North2, Douglas Smith3, James Lutz4
1Univ. Washington, 2Pacific Southwest Research Station, 3Yosemite NP, 4Utah State U.

2. Study Areas

3. Different Ranges of Structure
Illilouette more open and shorter for all forest types and fire severities

4. Predictors and Responses
AET/Deficit
Slope position (100 m & 2000 m scales)
Slope (270 m scale)
Solar radiation
Random
Forest models
Canopy cover
>2 m & 2-8 m
P95 height
Fire location
Number of fires
Fire severity
Tested 12 biophysical predictors, 4 at multiple scales

5. Water Balance Differences
Thornthwaite/Dingman model (Lutz et al. 2010)
(same results with California Basin Model (Priestley-Taylor))

6. Caveat
Results are for a study area with a low- and mixed-severity fire regime
Substantially different results expected for fires burning under extreme fire weather
Results are for all fires 1984 to 2010
Conditions in any given year could override these general trends

7. A Big Data Problem

8. Results – Fire Location & Number

9. Results – Fire Severity (RdNBR)

10. Results – Canopy Cover >2 m

11. Predictor Importance
No Fire – Predict structure and RdNBR

12. Predictor Importance
One Fire – Predict structure and RdNBR

13. Predictor Importance
One fire – Predict structure using RdNBR

14. Other Studies Miller & Urban et al. (1999a,b,c 2000 a,b,c)
Modeled fire, biomass using forest-gap & climate model
Similar predictors to ours
Sequoia National Park
Their results broadly similar to ours
Provide mechanistic explanations for our results
Holden et al. (2009) found similar results for fire severity in New Mexico
Studies with smaller elevation gradients found that topography best explained fire severity ((Beaty and Taylor, 2007; Heyerdahl et al., 2001; Taylor and Skinner, 2003)

15. Conclusions
Biophysical template explains much of the variation in forest structure and fire patterns
Forest structure continues to relate to biophysical mosaic following fire
Managers can use biophysical template
Set goals for forest structure & Prioritize areas for treatment
Add AET & Deficit to local topography (GTR-220)
Predict effects of climate change using biophysical mosaic

16. In review, Forest Ecology and Management
Questions?
In review, Forest Ecology and Management

17. Backup

18. General Trends Biophysical Fire and forest structure > AET
> Fuel accumulation, biomass
> Deficit
> Fuel drying
Slope position
> Fire severity towards ridges
< Biomass towards ridges
> Slope
> Fire severity
< Biomass
> Solar radiation
Results depend on interaction with other biophysical conditions

19. Predictors and Responses

20. Modeling

21. Responses to Predictors