Using ecological forecasting of future vegetation transition
and fire frequency change in the Sierra Nevada to assess fire
management strategies
James H. Thorne1*, Mark W. Schwartz1, Andrew J. Holguin1,
Max Moritz2, Enric Barllori2, Karen Folger3, Koren Nydeck3
1. UC Davis
2. UC Berkeley
3. Sequoia & Kings Canyon National Parks
NPS, pho
Climate change research in Ecological Systems has focused on
Range Shifts
Direct, Indirect mechanisms, and Interactions.
But…
Resource management is imperfectly aligned with these studies because,
Tied to location
Management obligations require priority setting
Actions are constrained by budgets & regulations
This work integrates projections of climate effects on
vegetation and fire
with spatial priority-setting for resource management
We then provide a gaming environment in which spatial decisions of fire treatments
can be evaluated against various future fire scenarios.
Climate change
adaptation
planning region
(A. Flint)
Two models of future climate: GFDL, PCM
Four time periods (1970-2000, 2010-2040; 2040-2070; 2070-2100)
82 vegetation types with > 50 occurrences.
Vegetation Exposure
The Calveg types
HG
BA
QD
MF
MP
RF
PP
SA
BS
QW
PJ
CQ
LP
QC
CX
JP
TV
BQ
PD
WB
CA
EP
QK
BB
AC
HJ
MB
NX
FP
CL
SB
Annual Grasses and Forbs
Barren
Blue Oak
Mixed Conifer - Fir
Mixed Conifer - Pine
Red Fir
Ponderosa Pine
Subalpine Conifers
Basin Sagebrush
Interior Live Oak
Singleleaf Pinyon Pine
Lower Montane Mixed Chaparral
Lodgepole Pine
Canyon Live Oak
Upper Montane Mixed Chaparral
Jeffrey Pine
Mountain Sagebrush
Great Basin Mixed Scrub
Gray Pine
Whitebark Pine
Chamise
Eastside Pine
Black Oak
Bitterbrush
Alpine Grasses and Forbs
Wet Meadows
Mixed Conifer - Giant Sequoia
Interior Mixed Hardwood
Foxtail Pine
Wedgeleaf Ceanothus
Buckwheat
77296
54249
42356
39677
34997
30150
27853
24566
23865
23159
22712
17685
16943
15340
13852
11001
7452
7031
6955
6835
6555
6088
5455
5146
4478
4211
3905
3758
3318
3275
3094
BM
WJ
BZ
WF
WW
TB
BX
HM
BL
TT
QQ
WL
NQ
DP
BR
DX
NA
CS
CH
JT
TN
DA
MH
HS
CM
JC
TR
AX
AD
NR
CY
CG
Curlleaf Mountain Mahogany
Western Juniper
Great Basin - Desert Mixed Scrub
White Fir
Western White Pine
Bitterbrush - Sagebrush
Great Basin - Mixed Chaparral
Transition
Perennial Grasses and Forbs
Low Sagebrush
Big Basin Sagebrush
Quaking Aspen
Willow (Shrub)
High Desert Mixed Scrub
Douglas-Fir - Ponderosa Pine
Rabbitbrush
Desert Mixed Shrub
Alkaline Mixed Scrub
Scrub Oak
Huckleberry Oak
California Juniper (tree)
Black Sagebrush
Blackbush
Mountain Hemlock
Cheesebush
Upper Montane Mixed Shrub
California Juniper (shrub)
Rothrock Sagebrush
Alpine Mixed Scrub
White Bursage
Riparian Mixed Hardwood
Mountain Whitethorn
Greenleaf Manzanita
3005
2861
2827
2806
2610
2375
2304
2074
1980
1876
1667
1624
1528
1334
1290
1215
1208
1108
1062
1059
992
745
714
519
518
408
378
347
335
308
272
249
QI
FM
QL
BC
TX
KP
TS
WM
PL
CV
CP
DF
CJ
SQ
CW
QF
CI
KQ
BT
JU
PW
HA
QX
CE
TM
QO
CN
MI
SE
UJ
NM
CC
California Buckeye
Curlleaf Mountain Mahogany (tree)
Valley Oak
Saltbush
Montane Mixed Hardwood
Knobcone Pine
Snowberry
Birchleaf Mountain Mahogany
Limber Pine
Snowbrush
Bush Chinquapin
Pacific Douglas-Fir
Brewer Oak
Soft Scrub Mixed Chaparral
Whiteleaf Manzanita
Fremont Cottonwood
Deerbrush
Aspen (shrub)
Big Tree (Giant Sequoia)
Utah Juniper
Ponderosa Pine - White Fir
Alkaline Mixed Grasses
Black Cottonwood
Mountain Misery
Horsebrush
Willow
Pinemat Manzanita
Piute Cypress
Encelia Scrub
Joshua Tree
Riparian Mixed Shrub
Ceanothus Mixed Chaparral
Summary: 82 types with > 50 occurrences (98.1% of landscape); 19 irrelevant
types make up 1.8 % of landscape. 28 really minor types make up 0.07% of
landscape; most of these were lumped in with others.
222
216
188
176
174
170
169
147
142
140
123
118
104
93
87
80
64
54
51
37
32
30
30
27
26
24
23
23
22
19
18
16
Vegetation Exposure
1.
Multivariate analysis to capture bioclimatic space.
•
•
•
2.
GFDL, PCM climate models;
A2 - high emission scenario;
climate downscale: 270 m.
Contour scatter plots of points in current climate space. Can
use SD units or % units.
–
–
–
3.
Low (<90%);
Marginal 90-99,
Outside (>99th%) the bioclimatic distribution of the type.
Plot PCA scores of projected future climate onto contours of
current climate for all vegetation types.
4. Map onto the landscape
Both data sets are well resolved with two
PCA axes. I.E., first two axes explained
almost all the variation (>(98%).
Ponderosa Pine
A. Multivariate analysis
Vegetation Exposure to Climate Change
B. Evaluate fit
Central <90%
Marginal 90-99%
Outside >99%
D. Assess exposure under future climate
C. Identify vegetation
type
Vegetation exposure | Climate Change
Current
2079
2039
2099
Central <90%
Marginal 90-99%
Outside >99%
Fire Exposure
Delta Fire | Climate Change
GFDL
PCM
GFDL futures
The same type of analysis that can be done for climate exposure
can also be done for projected changes in fire frequency
Combining Vegetation and
Fire Exposure
+
=
Ponderosa Pine
Exposure
90 to
<=90%
99
> 99
1971 - 2000
2010 - 2039
2040 - 2069
2070 - 2099
1834.2
1802.7
1634.9
627.8
154.2
188.4
299.6
524.7
Delta Fire Probability
33.33 0 - 33.33%
66.67%
66.67 - 100%
-0.071 to
0.017 to
0.042 to
0.017
0.042
0.227
25.5 NA
NA
NA
22.1
1274.5
555.6
0.4
77.7
47.2
517.6
1265.7
116.0
860.4
266.2
1448.4
Combined
Exposure
Category:
Fire Prob
Category:
2010 - 2039
2040 - 2069
2070 - 2099
90 to
<=90 <=90
<=90 99
90 to 99
0033.33 33.33 - 66.67 - 33.33 33.33 %
66.67% 100% %
66.67%
1133.4
11.7
0.1
490.6
347.4
10.1
0.0
1124.5
587.8
108.8
12.5
5.6
57.4
136.9
52.1
90 to
99
> 99 > 99
> 99
066.67 - 33.33 33.33 - 66.67 100% %
66.67% 100%
0.4
113.3
425.0
11.5
15.5
93.9
7.6
20.8
201.2
0.0
26.2
433.2
2039
2079
Combined GFDL A2 futures – Vegetation Exposure & Fire
2099
Decision Support for
Resource Management
Let’s Play Games!
Two key features
Fire Management: Managing
Research by Committee:
fire is the most influential, and
costly, land management practice
in the Sierra Nevada
NPS, USFS, USGS, University
collaboration
Where to Next: The gaming
environment- situational planning
• The Hume Landscape
Early exposure / end-of-century refugia
Sensitivity Analysis
Model agreement
Green-secure
Red - exposed
Brown-uncertain
Blue- uncertain
Model disagreement
Fire Management Game
1. Data Layers available for consideration
of where to deploy fire management
1.
2.
3.
4.
Fire Return Interval Departure (FRID)
Cultural Resources
End of Century Vegetation Sensitivity
Sequoia grove boundaries
2. Prioritization options
1.
2.
3.
4.
Firewall the WUI
Defend the groves
Enhance resilience of vulnerability sites
Protect refugia
Expected magnitude of change
Near term Vegetation exposure (orange and red: major type conversion)
(red); 2010-2040; GFDL
End of century refugia
(green; 2070-2100; GFDL)
Hume, Veg map
Fisher habitat
Cultural Values
Fire likelihood
Ignitions (#/time)
Flame length
FRID
Different decisions; different support
• Strategic Decisions:
– Deciding that slow change is desirable,
prioritizing fire management to reduce future risk
of a catastrophic fire that drives type change.
– Deciding that change is inevitable and prioritizing
management to ease transition.
• Operational Decisions:
– Identify old growth conifer regions or stands that
are more or less resilient to change and more or
less vulnerable to fire.
Fire Burn Out
Gradual Change
Fuel Build -up
Mega Mosaic
45 years of fire simulations with no fuels management
Fire
Frequency
Fire
Severity
Fire
Return
Interval
Departure
Vegetation
Risk of
Type
Conversion
Magnitude Decision Support
of
projected
change
Low
Low
Low
Low
Low
Low risk to resource
value/No Intervention
Needed
Low
Low
High
Medium
Medium
Intervention
required/current
mgmt OK
High
Medium
Medium
High
High
Intervention
required/new
management regime
High
High
High
High
High
High risk to resource
value no matter what
we do
Geospatial vulnerability assessment: Roll
Up into decision support tool
Rim Fire Boundary.
Green – places that remain within bioclimatic envelope at end of century.
Red: places that fall outside of bioclimatic envelope by 2040
Where we are going next:
Planning for fire management
• Current criteria checklist
–
–
–
–
Human Safety
Cultural resource safety
Fire Return Interval Departure (FRID)
Ecological priorities (e.g., groves).
• Possible climate change adaptation attributes
– Fire intensity model (how likely is a fire in dry conditions to
be stand clearing crown fires based on current fuels and
physical features?)
– Future fire Likelihood (ignition model, frequency model)
– Future vegetation change sensitivity / resilience
– Future magnitude of likely change
Thank you for
your attention
jhthorne@ucdavis.edu