Wildland Fire Predictive Services "Fire Potential" Models: Why I Don't Trust Them, and Why You Shouldn't Either

I'll try to make this simple. It's taken me 28 years to learn this, so I should be able to explain it in simple terms in a few paragraphs. Right? Probably not, it's hard to explain fire science, but I'll do my best. My concerns are for community protection and firefighter safety.

Every fall in Southern California, we get some rain and it slows fire season down a bit. This is nothing unusual nor unpredictable. Some green starts to appear in the form of annual grasses and the days become shorter and cooler. The public and firefighters get lulled into a sense of complacency and then... whammo. Usually, this slowdown hastily disappears after the Santa Ana winds abruptly return.... and the public and firefighters alike say, "We didn't see that coming".

Over the last several years, a dangerous pattern has been developing. It's not a fire weather pattern, but rather it's a pattern of increased reliance on unproven "predictive services" computer models. I have a problem with these models, as I've seen them woefully under-predict the consequences of changing fire weather patterns. These under-predictions cause a steep lapse in the preparedness and communication message for both the emergency response agencies as well as the public.
"The potential for large fire will be low to non-existent across the region through the forecast period". ~ SoOps Predictive Services, 11/09/2010.

Any fire manager or fire officer knows that when the winds blow, all bets are off. I wish the fire managers, fire behavior experts, and fuels specialists would have had more input before an unproven model was allowed to replace competent decision making and sound judgment.

Here is the crux of why these models aren't reliable: Garbage in, garbage out. In order for these models to be relevant and reliable, the developers need to integrate some basic fire behavior and fuels truths into the model. There are some obvious flaws in the model not being addressed.

Fire Behavior 101

First: Brush fires are primarily driven by live fuels, and by the dead 1 hour and 10 hour timelag fuels.

Second: To understand why I don't trust these models, you must first understand what the carriers of fire (or the fuels) are. Specifically, carriers of fire are classified as live fuels and dead fuels. Both live and dead fuels exist within the fuelbed and contribute to fire behavior. Both are very unique in how they react to rainfall, winds, and seasonal changes. In addition, the percentages of live fuels vs. dead fuels (expressed as a live:dead ratio) in a fuelbed is important also.
Live Fuels

Live fuels are just that: Live or living plants, trees, shrubs, etc. In Southern California, the primary live fuels we concentrate on are grasses and chaparral. While the grasses are green, they are considered to be a live fuel, but once they've died and cured, they become a 1 Hr. dead fuel (see below). We measure relative fire behavior potential by looking at live fuel moisture in two species: Chamise and Manzanita. I'm only going to use Chamise in my example.

Chamise - Live Fuel Moisture Variables > 120% - Large fires unlikely 90%-120% - Large fires possible with winds and/or slope
60%-90% - Typical fire season conditions
< 60% - Critical fuel conditions

So, where are we at with the Chamise Fuel Moisture? Chamise is currently bordering on critical in one local site (City Creek, CA) and several others, regardless of what the "General Discussion" says. Last weeks measured moistures were 58% old growth and 64% new growth. Like most chaparral species, regardless of rainfall amounts, they will stay dormant throughout the winter and not begin their "green-up" until spring. During this period, their fuel moisture percentages will only deviate +/- approx. 10%.

Dead Fuels

Dead fuels are classified into four categories based upon their timelag for drying: 1 hr., 10 hr., 100 hr., and 1000 hr. The primary carriers of brush fires are the 1 hour and 10 hour fuels, as well as the live fuels. Timelag references the amount of time needed to gain or lose 63% of the current moisture based upon optimum conditions. For simplicity, we just use 50% as a field wag. Example: We start with a 10 hour fuel moisture of 12. After 10 hours of optimum drying (as in Santa Ana winds), the 10 hour fuel moisture would be roughly 6. It works exactly the same for grasses and other 1 hr. fuels, but much faster based upon 1 hour increments.

Wildland Fire Assessment Definitions of 1 hr. and 10 hr. Dead Fuel Classes:

1-hour time lag fuels - < 1/4" Diameter

1-hour time lag fuels are the most important for carrying surface fires and their moisture content governs fire behavior. One-hour fuels include fallen needle and leaf litter, grassy fuels, lichens, and small twigs. Within this category, response times vary by fuel type. Lichen, grass, and well-cured needles respond to changes faster than freshly fallen needles and hardwood leaves. Due to their high surface area to volume, low moisture content, and location in the combustion zone, they produce little smoke and have low flame residence time. One-hour fuels are consumed by both flaming and smoldering combustion, regularly undergoing complete consumption in most surface fires.

10-hour timelag fuels - 1/4" to 1" Diameter

Common 10-hour fuels include small branches and woody stems. Due to their resistance to drying and greater heat capacity, 10-hour fuels often do not combust in low-intensity surface fires. When moisture is low, however, 10-hour fuels can carry hot fires and help ignite larger (100- and 1000-hour) fuels. Ten-hour fuels are readily consumed when fuel moistures are low.
So, where do we stand with upcoming wind events in Southern California? Right now, the live fuel moistures in chaparral are near critical levels and would easily support large fires. The dead fuel moistures are lowering with the return of dry and windy weather. Based upon the fire behavior that we saw three days ago on the Scott Fire (CA-BDU/RRU) and the Sepulveda Fire (CA-BDF)...

... I think it's best to prepare for the worst, hope for the best.... and give the models a rest. IMHO.

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