1. Large Fire Simulator (FSIM)
Model Name/Host Institution/URL
FSIM, Rocky Mountain Research Station, Fire, Fuel, Smoke Science Program
User’s Group with Dropbox but no website, contact
Domain/Objective
The Large Fire Simulator is a computer modeling system that estimates burn probabilities and fire size distributions using fire ignitions and 10,000 to 50,000 “years” of artificial weather.
Key Assumptions
Simulated fire is parameterized with daily inputs (constant burn period, wind and weather) which generalizes the fine scale variability of fire spread. Spatial surface & canopy fuels are homogenous for each pixel which generalizes fine scale heterogeneity of fuels. Surface & crown fire models are based on empirical observations and represent wildfire in a simplistic way, resulting in the over- or under-prediction of fire spread.
Temporal/Spatial Scale
Daily timestep/ 2-d; Outputs represent conditions in one point in time representing burn probability for a “fire season”. FSIM lacks a temporal component. Spatially available on a project to national-level, contingent on spatial data availability.
Presentation by Erin Noonan-Wright, , NRSM 532

2. Input Drivers Key Outputs
From Short, K., Riley, K. and M. Finney FSIM Conceptual Framework, Powerpoint presentation.
Input Drivers
Spatial cellular Landscape file (.lcp): topography, surface and canopy fuels – LANDFIRE data
Daily Weather (Fire Danger as Energy Release Component - Daily fuel moistures for dead and live fuels), Wind speed, Direction
Logistic regression of the probability of a large fire: P = 1/1 + EXP (-1 * coeff1 + (-coeff2) * ERC))
Tabular fire records obtained from fire occurrences in the defined area of interest – Fire Family Plus or FOD database (Short, K. 2013).
Ignition locations (random) or based on historical occurrences (gridded).
Fire Suppression (statistical algorithm based on periods of quiescence (fire danger falls below 80th percentile, unburnable fuels, previous and current number of burn periods with mild burning conditions).
Key Outputs
Burn probability by fire intensity level (usually binned by flame length classes 0-1m, 2-4m, etc.)
Conditional flame length probability
Fire Size distributions

3. Testing/Validation, Examples of a result
From “Interpreting and Validating Fsim Outputs” by Karin Riley, Karen Short & Mark FInney

4. Model Validation Fire size distribution
Another metric for validation is evaluate fire size distributions which follow a power-law distribution, as has been documented in the literature. The power-law distribution is linear when plotted on log-log axes, and descends from left to right. Fire size is plotted on the x-axis, and the number of fires of that size on the y-axis. Here, the historic fire size distribution (in black) is plotted against the modeled distribution for each individual FPU. There were not enough historic records to plot the historic distribution for each FPU. Power-law distributions have two parameters: a slope parameter and an intercept parameter. The intercept parameter is not of interest—as you can see, the distribution for FPUs plot with a higher intercept than the historic data because there are more observations in the modeled data (since the simulations have 10,000 years of data versus 18 in the historic).
The slope of the distribution quantifies the relative number of smaller fires versus larger fires, and is the parameter we are interested in for model validation. As you can see, the slopes of the distributions of each FPU are quite similar to those of the historic distribution. The plot on the right shows the 95% confidence interval of the slope parameters of each distribution. Most modeled distributions overlap with the historic data for the region, however, we do expect some variability among FPUs, since even within a region, different areas may have very different weather and vegetation conditions. The strong correspondence in slope parameters indicates the model is simulating the correct number of small versus large fires.
This result suggests that the model captured the major factors that drive fire size.
From “Interpreting and Validating Fsim Outputs” by Karin Riley, Karen Short & Mark FInney

5. Setting up a Run
Command Line interface; parameters determined from a command text file.
The program is run via command line with a command (.cmdx) file that calls the various input files needed to run FSIM.

6. Un-treated vs a Treated Landscape
If the proposed polygons were successfully implemented would burn probability be different for areas of concern?
Same simulation parameters except the “treated” lcp now has the above fuel polygons coded to a lower Rate of Spread brush mode (FM 141) with no canopy cover (so no spotting, crown fire)

7. The Values Resistance/Resilience/Sagebrush Cover Polygons
1 = more resilient, less annual grasses
3 = not very resilient, more annual grasses
A = low sagebrush cover
C = high sagebrush cover
The 1 & 2’s we’d like to keep fire out.

8. Outputs
Un-Treated Landscape
Treated Landscape

9. When fuel polygons were treated, how different were flame length and burn probability?
BUT, large changes in CFL for the treated polygons

10. Problems with Analysis
Landscape size needs to be large enough to model the largest fires without inhibiting them by the edge of the project area.
Canod fire occurred about 20 km north of the project area in 2012.
Huge fire spread not seen in this area before (315,000 acres)
Should burn probability be calibrated to this type of growth which could represent future fire growth trends?...I think YES.
Project areas need to be large enough to contain large fire spread without edge effects (seen in lower image, highlighted blue line is one fire perimeter).
This presents some computational challenges

11. Challenges
FSIM is not “operational” or really supported outside of Forest Service Research personnel
It’s computationally intensive, so you need a fairly high powered computer to run it.
These FSIM runs were done on Amazon Web Services (AWS) and took 8-9 hrs.

12. Un-calibrated Simulation
What is the burn probability for an untreated versus treated (NEPA proposed treated polygons) in a sagebrush/sage grouse habitat area?
If the proposed polygons were successfully implemented would burn probability be different for areas of concern?
Figure 1. Priority Areas for Conservation (PACs) within the range of sage-grouse (USFWS 2013). Colored polygons within Management Zones delineate Priority Areas for Conservation (USFWS 2013), from Chambers et al Red box highlights the general vicinity of the project area in relation to the PAC locations.

13. ArcFuels to Summarize Outputs in Excel

14. Calibration
Was simulated burn probability comparable to what has occurred historically?
No. Simulated fires largely over predicted burn probability ( obs vs simulated, untreated)
The distribution of fires > 100,000 ac suggests the model is over predicting fires.

15. Large Fire Occurrence is Increasing
Dennison, P. E., S. C. Brewer, J. D. Arnold, and M. A. Moritz (2014), Large wildfire trends in the western United States, 1984–2011, Geophys. Res. Lett., 41, 2928–2933, doi: /2014GL

16. (Minimum Travel Time Algorithm)
Fire Growth Model
(Minimum Travel Time Algorithm)
Spatial Fuels Data
(Static Condition:
LandFire)
Fire Weather:
Time Series Analysis
Large Fire Occurrence
(Historical Records)
Fire Behavior
(Spread Rate,
Fireline Intensity)
Fire Suppression
OUTPUTS
Burn probs
Fire sizes
INPUTS
MODEL
COMPONENTS