Haiganoush K. Preisler
PSW, Albany
A collaborative project with:
Alan Ager – Threats Assessment Center, PNW, Oregon
Steve Taylor - Pacific Forestry Centre, Victoria, Canada
Barbara Bentz - RMRS, Logan UT
Jeff Hicke - University of Idaho
Anthony Westerling - UC Merced
PSW Workshop on Climate Change
IFG, Placerville, CA
April 30 – May 1 2008
Motivation
• Greenhouse gas balance of forests appear to be
strongly affected by naturally occurring fires and by
cyclical insect outbreaks1.
• Fires cause direct emissions.
• Insect outbreaks reduce growth rates; kill trees,
resulting in transfer of carbon from biomass to dead
organic matter.
• Methods are needed for forecasting these stochastic
disturbances in the presence of changing climate and
unpredictable weather systems.
1
‘Mountain pine beetle and forest carbon feedback to climate change’ Kurz et
al. Nature April 2008
Biologically based statistical models
are needed to
1. Quantify relationships and uncertainties.
2. Assessing goodness-of-fit.
3. Appraise skill of forecasting model.
4. Forecast fire and pest outbreak intensities with
known accuracies and confidence bounds.
5. Appraise skill of process-based simulation
models.
Estimates are needed for two probabilities
1. Probability{ Ai,j(t) =1 | Hj(t-1), Xj(t-1), Vj }
2. Probability{ Bi,j(t) =1 | Hj (t-1), Xj(t-1), Vj }
where
Ai,j(t) = fire of size class i at location j and time t.
Bi,j(t) = insect infestation of size i at location j and
time t.
Hj(t) = history of fire and insect at location j.
Xj(t) = history of climate and weather at location j.
Vj = topography and vegetation at location j.
Historic Data (1980-present)
• Weather stations ÎPrism Îgridded weather
• Gridded products ÎVIC1 Îgridded hydrological data
(e.g. moisture deficit)
• Fire, insect, vegetation, topography
• Weather data ÎBioSIM2 (adaptive seasonality)
Îsuitable habitats for insect outbreaks
• LANSAT fire data + Arial insect survey maps
• Ozone data
1
Script Institute of Oceanorgaphy
2 Service
Canadien des Forêts
Proposed Plan of Work
Historic fire and
insect data
Statistical
Model
VIC
Forecasts/scenarios
for weather, climate
Estimated
probabilities
Forecasts of
fire and insect
damage
BioSIM
Assess fit
Weather, climate,
vegetation, topography,
ozone and nitrogen data
Insect life cycle and
development info
Forecasts of
Greenhouse
Gas emissions
In the following slides we present work from
two pilot studies demonstrating some of the
proposed techniques
Study 1:
Data on three insect species from Oregon
and Washington are used to estimate effects
of climate and other variables on probabilities
of infestation.
Study 2:
Data on large federal fires in California are
used to forecast one-month-ahead fire
probabilities with the help of some fire
weather indices.
Western Pine Beetle - Forest Service lands
Study 1: Data
Yearly sum (sqrt-scale)
30
25
Number of 1km2 pixels with
more than 10 trees killed by
the beetle
20
15
10
5
1985
1990
1995
Year
Spatial distribution of
infestation estimated by
historic (1981 -2005)
probability of a pixel having
> 10 trees killed by beetle.
2000
2005
Moutain Pine Beetle - Forest Service lands
Study 1: Data
Yearly sum (sqrt-scale)
80
70
Number of 1km2 pixels with
more than 10 trees killed by
the beetle
60
50
40
30
1985
1990
1995
Year
Spatial distribution of
infestation estimated by
historic (1981 -2005)
probability of a pixel having
> 10 trees killed by beetle.
2000
2005
Spruce Beetle - Forest Service lands
Study 1: Data
Yearly sum (sqrt-scale)
20
Number of 1km2 pixels with
more than 10 trees killed by
the beetle
15
10
5
1985
1990
1995
Year
Spatial distribution of
infestation estimated by
historic (1981 -2005)
probability of a pixel having
> 10 trees killed by beetle.
2000
2005
Two climate variables examples
Average tem perature July 1972
Study 1: Data
M oisture Deficit July 1972
Climate Research Division
Scripps Institution of Oceanography
La Jolla, California
Study 1: Data
Western North American
Large Fire History
(fires greater than 200 hectares)
Study 1: Model
Statistical Model
Yij = 1 if more than 10 trees killed by insects at
location i (grid size 1x1km) year j
= 0 otherwise
Yij ~ Binomial{1, π(θij )}
θ ij = linear predictor
= α + ∑ g m ( X mij ) + g ( year j ) + h (lon ij , lat ij )
m
Xmij = value of mth explanatory variable at location i year j
g( ) and h( ) ~ smoothing functions
e.g. basis splines and thin plate splines
Study 1: Results
Preliminary Results
Beetle
MPB
WPB
Spruce B
Infestation
(last year)
2.3 – 2.8 times
Increase in odds
1.9 – 2.6 times
Increase in odds
2.9 – 4.9 times
Increase in odds
Drought history
Previous 2 years
moisture deficit
Previous 2 years
moisture deficit
Previous year
moisture deficit
Temperature
Winter temperature
variability
Not significant
Average temperature
Fire history
Not significant
Not significant
2.0 – 4.5 times
increase in odds if
fire 2 years ago
Explanatory
Study 2
•
A statistical model that estimates
relationships between observed climate;
simulated hydrology and observed large
wildfire incidence.
•
the hydrology is simulated with the VIC
hydrologic model using observed climate.
Study 2: Data
•
Location and size of fires in California Federal
lands
•
Moisture Deficit* observed prior to the date the
forecast is made
•
Surface Temperature observed prior to the date
the forecast is made
•
Percent Forest Cover in a grid cell
•
Location and Month of year
*Potential - Actual Evapotranspiration simulated with the VIC hydrologic model
Study 2: Model
Statistical Model
Yij = k
if fire of size class k at location i year j
Yij ~ Multinomial {1, πk(θij )}
k=1, 2, 3, 4, 5
with π1 + … + π5 = 1
π k = exp(θ k )/ ∑ exp(θ k )
k
θ ijk = linear predictor
= α k + ∑ g m ( X mij ) + g ( yearj ) + h(lonij , latij )
m
Xmij = value of mth explanatory variable at location i year j
g( ) and h( ) ~ smoothing functions
e.g. basis splines and thin plate splines
Study 2: Model Skill
One month ahead forecasts
5
Observed
Forecasted
4
Precentage
Large fires (200ha) on
California Federal lands
1985-2003
~95% CL
3
2
1
0
-8
-7
-6
-5
Linear predictor
-4
-3
Study 2: Model Skill
In the following slides
•
We map the forecasted probability of a large fire
(>200ha) on a 1/8 degree grid over California.
•
Superimposed on this we first show simulated
fires: we use the forecasted probabilities to
generate random fires. We show 10 simulations
for selected dates/examples.
•
For each date we show a final map of forecasted
probabilities and the observed large fire locations
•
The selected examples are August 1994 (high fire
year), August 1995 (low fire year) and August
1987 (high fire year with clustered lightning
ignitions)
August 1994 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
August 1994 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
Red dots=model forecasted pixels with >200ha burn area
August 1994 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
Red dots=model forecasted pixels with >200ha burn area
August 1994 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
Red dots=model forecasted pixels with >200ha burn area
August 1994 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
Red dots=model forecasted pixels with >200ha burn area
August 1994 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
Red dots=model forecasted pixels with >200ha burn area
August 1994 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
Red dots=model forecasted pixels with >200ha burn area
August 1994 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
Red dots=model forecasted pixels with >200ha burn area
August 1994 observed
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burn areas
Red dots are observed pixels with >200ha burns
August 1995 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
Red dots=model forecasted pixels with >200ha burn area
August 1995 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
Red dots=model forecasted pixels with >200ha burn area
August 1995 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
Red dots=model forecasted pixels with >200ha burn area
August 1995 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
Red dots=model forecasted pixels with >200ha burn area
August 1995 observed
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burn areas
Red dots are observed pixels with >200ha burns
August 1987 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
Red dots=model forecasted pixels with >200ha burn area
August 1987 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
Red dots=model forecasted pixels with >200ha burn area
August 1987 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
Red dots=model forecasted pixels with >200ha burn area
August 1987 forecasts
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burns
Red dots=model forecasted pixels with >200ha burn area
August 1987 observed
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Map of Probabilities (%) of > 200ha burn areas
Red dots are observed pixels with >200ha burns
Study 2: Conclusions
•
The model with the climate variables appears to have
useful forecast skill
•
The pattern of large wildfire forecasts as shown in the
simulations appear similar to the observed pattern of
historic wildfires
•
This particular model cannot predict ‘clusters’ of
ignitions like those observed in 1987
•
We showed forecasts for one fire size class, but we
are modeling probabilities for multiple size classes
•
Future work will revise this model to generate
scenarios based on historical patterns of lighting
ignitions
Some relevant references
•
•
•
•
•
•
•
•
•
Bentz, B.J., Logan, J.A., and Amman, G.D. 1991. Temperature dependent development of mountain
pine beetle and simulation of its phenology. Canadian Entomologist 123: 1083-1094.
Hicke, J.A., Logan, J.A., Powell, J., and Ojima, D.S. 2006. Changing temperatures influence
suitability for modeled mountain pine beetle (Dendroctonus ponderosae) outbreaks in the western
United States. Journal of Geophysical Research-Biogeosciences 111: G02019,
doi:02010.01029/02005JG000101.
Kruz, W.A., Dymond, C.C., Stinson, G., Rampley, G.J., Neilson, E.T., Carroll, A.L., Ebata, T. and
Safranyik,L. 2008. ‘Mountain pine beetle and forest carbon feedback to climate change’ Nature, Vol
452124, April 2008. doi:10.1038/nature06777.
Preisler,H.K., Westerling, A.L. 2007. "Statistical model for forecasting monthly large wildfire events
in western United States". Journal of Applied Meteorology and Climatology 46, 1020-1030.
Preisler,H.K., Ager, A.A. and Hayes, J.L. (In press). "Probabilistic risk models for multiple
disturbances: An example of forest insects and wildfires". Encyclopedia of Forest Threats.
Preisler,H.K.,Chen, S.C. Fujioka, F., Benoit, J.W. and Westerling, A.L. (In press). "Wildland fire
probabilities estimated from weather model-deduced monthly mean fire danger indices". International
Journal of Wildland Fire.
Preisler, H.K., D.R. Brillinger, R.E. Burgan, and J.W. Benoit. (2004) Probability based models for
estimation of wildfire risk. Journal of Wildland Fire, 13, 133-142. PDF.
Régnière, J., Cooke, B., and Bergeron, V. 1996. BioSIM: a computer-based decision support tool for
seasonal planning of pest management activities; User's Manual. Canadian Forest Service.
Information Report LAU-X-155.
Westerling, A.L., H.G. Hidalgo, D.R. Cayan, T.W. Swetnam 2006: "Warming and Earlier Spring
Increases Western U.S. Forest Wildfire Activity" Science, 313: 940-943.
DOI:10.1126/science.1128834