From Ignition to SpreadWildland Fire Forecasting and Color Maps

Managing fire on populated forest landscapesOctober 20 - 25, 2013Banff International Research Station For Mathematical Innovation and Discovery

Haiganoush K. PreislerPacific Southwest Research Station

USDA, FS

Overview

•Uncertainties in Fire danger maps1.1-7 day forecasts2.Seasonal (1-month ahead) forecasts3.1-year ahead4.1-2 hours ahead (will not be covered in this talk -requires a statistical model of fire growth. Future research.)

•Maps of risk that incorporate loss to societal or ecological values

One-day Forecast

One-day Forecast

One-day Forecast

It is hard to perform goodness-of-fit analyses of these mapsNeed probability models to perform validation

(8/23/2013)

EROS = Earth Resources Observation System

Pr[Fire size > C | ignition ] = f x,y,t( FPI )

FPI = Fire Potential Index is a

moisture-based vegetation flammability indicator.

= f(living vegetation greenness, 10-h dead fuel

moisture)

(8/23/2013)

By using the alternative color legend we are able to note the amount of uncertaintyin the maps AND at the same time demonstrate the goodness-of-fit of the forecasts

Alternative color legend

7-day forecast

• Fire occurrence data from MTBS: Monitoring Trends in Burn Severity

Satellite imagery of burned area for fires > 500 acres (in East) and > 1000

acres (in west) starting from 1984 – present

• Explanatory variables: 1) location 2) day-in-year 3) Forecasted FPI values for

upcoming 7-days evaluated daily on a 9km2 grid cell surrounding ignition pt.

• Model: spatially and temporally explicit logistic regression at 1kmx1kmxday

grid cells.

A legend that includes some uncertainty. Goodness-of-fit analysis still to be done.

Seasonal Forecast (one-month ahead)Large Fire Forecast Probabilities for the month of August, 2013

based on explanatory variable values up to July 31, 2013

Explanatories used:

•Moisture Deficit

•ENSO, TEMP

•Elevation

•Lightning Scenario

Anthony WesterlingUC Merced

Predicted Probability of a large fire

Obs

erve

d Fr

actio

n of

larg

e fir

es

Goodness-of-fit for the one-month-ahead forecasts based on large fire occurrences (>200ha) in California and Nevada between 1985-2008

Predicted Probability of a large fire (Grouped)

Obs

erve

d Fr

actio

n of

larg

e fir

es

Same as previous slide but with the Predicted values grouped

Alternative legend demonstrating expected amount of uncertainty and degree of goodness-of-fit of the forecasts to historic data

Forecasting one-year-ahead fire risk 1) Use season specific historic averages based on historic large fire occurrences:

Historic large fire occurrence from MTBS data

Data – Corsica & Sardinia (Alan Ager and Michele Salis)

Forecasting one-year-ahead fire risk 2) Use a model that includes a trend over the years

Risk to social, economic and ecological values

Alan Ager (WWETAC)Western Wildland Environmental Threat Assessment Center

•Color maps to help managers with their fuel treatment

decisions

•Maps based on fire risk AND on #people/homes/type of

habitat at risk

•Produce maps by simulating the process from ignition to

spread

The process to be simulated

Spatial-temporal Marked Point Process {x,y,t,u}

Likelihood for discretized process (km×km×day)

Simulated (red) Observed (orange) fire perimeters

(Farsite, FSPro)

Mark Finney

Once an ignition location and fire size is simulated then fire perimeters/scars may be simulated using a fire growth model

Distribution of Fire SizesObserved vs Simulated Quantiles

Although simulated fire sizes seem to be a good approximation of observed fire sizes, goodness-of-fit of fire growth models still needs to be done.

Simulated fire perimeters/scars are then overlapped with other

polygons with high value (e.g., owl habitat; old growth trees;

houses)

The number of houses, owl habitat or people being affected by

each simulated fire are then used, together with the simulated total

area burned in a given region to produce risk maps based on a

measure of loss of interest.

Number of people exposed vs total area burned by simulated fires ignited on FS land

Num

ber o

f peo

ple

expo

sed

(pow

er o

f 10)

95th %

Grouped total area burned (power of 10)

Criteria based on expected burn area and #people affected

There is a large amount of variation in this color map too. Both spatial (between districts) and temporal (between years) variation as seen in the boxplots of the next slide.

2

3

4

Total area burned per district per year (power of 10)

5

Boxplot colors match the colors in the previous map

References

• D.R. Brillinger, H.K. Preisler, and J.W.Benoit. (2003). Risk assessment: a forest fire example. In Science and Statistics: A Festschrift for Terry Speed. D.R. Goldstein [Ed.]. pp: 177- 196.

• 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

• Brillinger, D. R., Preisler, H. K., and Benoit, J. (2006) "Probabilistic risk assessment for wildfires. Environmetrics, 17 623-633.

• 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.,Chen, S.C. Fujioka, F., Benoit, J.W. and Westerling, A.L. (2008). "Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices". International Journal of Wildland Fire17: 305-316.

• Preisler, H.K., Burgan, R.E., Eidenshink, J.C, Klaver, J.M., Klaver, R.W. (2009) ‘Forecasting distributions of large federal-lands fires utilizing satellite and gridded weather information’ International Journal of Wildland Fire 18, 517-526.

• Preisler, H.K., Westerling, A.L. Gebert, K. and Munoz-Arriola, F. and Holmes, T. (2011) ‘Spatially explicit forecasts of large wildland fire probability and suppression costs for California.’ International Journal of Wildland Fire. 20:508-517

• Preisler, H.K. and A.A.Ager. (2012) ‘Forest fire models’ in A. H. El-Shaarawi and W. Piegorsch (eds.) Encyclopedia of Environmetrics Second Edition, John Wiley and Sons Ltd: Chichester, UK.