Determining and Distributing Real-Time
Strategic-Scale Fire Danger Assessments
Chris Woodall and Greg Liknes
USDA North Central Research Station, Forest Inventory and
Analysis Program, St. Paul, MN
Jay Charney and Brian Potter
USDA North Central Research Station, AtmosphereEcosystem Interactions, East Lansing, MI
Outline
• Introduction
– Fire Danger
– Mapping Fuels
– Simulating Weather
• Objectives
• Results
– Simulations & Index
– Wed-Based Dissemination
• Conclusions
Fire Danger
+
Index Incorporating both Amount of
Fuel and Condition of Fuel
High Fire Hazard
+
Low Fuel Moisture
High Fuel Loading
Down Woody Materials
Forest Inventory and Analysis Program
Down Woody Materials
Forest Inventory and Analysis Program
Strategic-Scale, Field Inventory-Based
Estimate of Woody Fuels
Mesoscale Weather Data
The Eastern Area Modeling Consortium
(EAMC) produces 48-hour fire-related weather
predictions for all of Region 9:
Precipitation
Surface winds
Surface moisture
Temperature
Simulated Fuel Moisture
Daily simulated precipitation
totals from March 19th - April 1st,
2004 (cm and inches).
Daily simulated fine fuel
moisture from March 19th April 1st 2004 (in % / 100).
Study Question???
Can index of fuel loadings and moisture be
determined and distributed in real-time ?
Objectives
• Link fuel loading and moisture
estimates
– Interpolate fuel inventory to
meteorological grid
– Estimate fuel moisture over
series of time-steps
• Create meaningful hazard index
• Develop/suggest real-time
distribution techniques
Down Woody Materials Data
• Forest inventory
plots of North
Central States
• XXX forested
plots
• 2001-2004
• Bailey’s (1995)
Ecological
Provinces 212,
222, 251
• 1-hr and 10-hr
fuels
Down Woody Materials Interpolation
Meteorological Grid
Results
Burnable Fuels Index
BFI = L (30-fm)/100
Where…
L = 1- and 10-hr fuel loadings/acre
fm = fine fuel moisture (%)
Burnable Fuels Index
The BFI was a first attempt to
combine weather models data,
fuel moisture, and fuel loading
information into a single index.
The BFI has some utility in
representing when dangerous fuel
conditions are developing, but
quantitative interpretation of the
BFI is difficult at best, and
misleading at worst.
Before Event
During Event
After Event
Atmospheric Stability Index
•
We have developed a new way of coupling fuel information with a fire severity
diagnostic.
•
We have used an atmospheric stability index that is used to predict thunderstorm
severity. This index also indicates how fire plumes and, by association, the fires
themselves, might behave under given atmospheric conditions.
•
Atmospheric stability indices are highly sensitive to moisture (water vapor) and
temperature.
•
Fires produce large quantities of heat and water vapor. The amount of water vapor, in
particular, depends on the fuel loading and fuel moisture.
•
We have combined an atmospheric stability index called the Convective Available
Potential Energy (CAPE) with fuel moisture and FIA fuel loading information to
produce a new fire severity index called the CFIA index.
The CFIA Index
The CFIA index has the following formulation:
CFIA =
Where θe = Equivalent Potential Temperature (a variable that accounts for temperature (T) and
moisture (q) effects on atmospheric stability)
The CFIA accounts for the changes that a fire can cause in temperature and moisture (ΔT and
Δq) above a fire and computes the impact of those changes on plume dynamics.
The CFIA Index represents a step forward in that the impact of the fuel loading and fuel
moisture is based on the physics of the atmosphere, rather than on an “engineered”
equations (like the BFI).
Using the CFIA
To use the CFIA index, fire
managers or fire weather
meteorologists would start with a
map of the CAPE (unmodified).
In this example, the CAPE is
high across southern Minnesota
and near Madison, WI.
Using the CFIA (continued)
We can produce maps of the
CAPE modified simply by the
effects of fuel moisture over
the region, without concern for
fuel loading.
This map shows higher CAPE
(suggesting higher potential
for extreme fire behavior) over
broad areas in Minnesota,
Wisconsin, and Iowa.
Using the CFIA (continued)
CFIA Index
By adding in information
about the fuel loading,
however, we can state where
the potential for extreme fire
behavior exists based on where
there are fuels to burn.
This technique produces a map
that indicates geographic
regions where fire risk is
elevated for that time period.
These maps can up updated
hourly through the day, and
predicted for 48-72 hours into
the future.
Real-Time Distribution
• Fuel Maps Static –
Annually updated
• Fuel Moisture Maps
Dynamic – Updated
Daily with maps that
predict hourly changes.
• Fire Hazard Maps
Dynamically Updated on
Web, every day.
Dissemination Hurdles
• Performance of the CFIA index
has still not been tested for a
variety of weather and fuel
loading.
• The CFIA has not been validated
against field observations of fire
behavior
• Producing hourly maps of the
CFIA is practical, but timeconsuming, and would be
simplified with a dedicated web
server
Texas Example
Fuel Map for Texas
Texas Example
The Southern High
Resolution Modeling
Consortium (SHRMC) in
Athens, GA produces the
same weather data as the
EAMC for the southern
United States.
SHRMC weather data could
be combined with FIA
DWM data from the south
to produce fire risk maps for
that region.
Simulated
accumulated
precipitation
in mm
Conclusions
• Large-scale fuel inventory
connected to meteorological data
• Dynamic assessment of fire
hazards
• Incident management
• Fire hazard dynamics
cwoodall@fs.fed.us