2013 asprs track, modeling the time until a wildfire arrives by mark mclean

Time Until Fire Arrival (TUFA) modeling

Mark McLean, Ph.D. October 9, 2013

Anchor Point Company Profile

• Wildfire hazard and risk mitigation – solutions from pre-planning through mitigation management

• Company formed by firefighters • Clients both private and government

– Other consulting firms – Home owner associations – Fire departments – Local, State and Federal Government

• Hazard and risk assessments • Community Wildfire Protection Plans (CWPPs) • Fuel treatment project management • National Hazard and Risk Model (No-HARM)

Background

• Important for many reasons to know when a potential fire will arrive – Suppression trigger points – Evacuation concerns – Production shutdown (oil and gas)

• Excellent fire behavior modeling tools available (FlamMap, FARSITE, BehavePlus) but none appropriate for this particular task

• Fire rate of spread is available – why not turn this into time?

http://www.isciencetimes.com/articles/5392/20130612/colorado-fires-force-2-000-homes-4.htm

http://news.yahoo.com/thousands-flee-colo-wildfire-92-homes-destroyed-084723709.html

TUFA – What is it?

• Modeling technique that makes lines of equal time (isochrones) for a fire to arrive at a given location

• Works for fires located anywhere on the map

• Worst case scenario – Wind blowing toward focus

area – 97th percentile weather

scenario (adjustable) – Shortest time until fire

arrives

Methodology Overview

• Prepare input data – Fuel (LANDFIRE) – Weather – Topography

• Run FlamMap • Export Rate of Spread • Convert to time • Adjust for vectoring • Run cost distance • Visualize outputs

Fire Behavior Modeling

Fuel Weather Topography

Flame Length Rate of Spread Crown Fire

Model Builder

• Great for non-programmers!

• Visual nature makes it easy to follow

• Readily available in basic ArcMap

• Once built, the model can be run quickly

• NOT magic (see next slide)

Challenges

• Vectoring – Reconciling wind

and slope direction – Cyclic nature of

directional data

• Input data – Projection is crucial – Input data are

sometimes cranky (focus as point vs. polygon)

Limitations

• Only takes flaming front into account (not spotting/embering)

• Worst case scenario might be too extreme

• Difficult to relate model conditions to those experienced on the particular day of a fire

jalcornphoto.photoshelter.com

Case Study 1 – Future Housing Development

• Access route as focus

• Mostly one way in and out

• Negotiations with county for mitigation requirements – Second access

road? – Landscape fuel

treatment? – Treat fuel along

road?

Case Study 2

• Theme park with a large number of visitors

• Aerial tram as main access

• Shelter-in-place vs. full scale evacuation (depends on time)

Future Directions/Applications

• Other disasters? • Better handling of

slope • Address multiple

source problem • Refine fire behavior

modeling

http://www.listal.com/list/pets-steal-show

Questions or Comments?

? http://www.jimmyfungus.com/2012/11/epic-facepalm-compilation-most-epic.html