Documentation

Weather Forecasts

Weather forecast data are supplied by the National Weather Service (NWS) National Digital Forecast Database (NDFD).

What this tool does

• Predicts a daily minimum moisture content of dead, standing, over-wintered grass based on forecasted weather.
• Predicts a daily maximum probability of ignition in dead, standing, over-wintered grass. Out of 100 matches dropped on the fuel bed, how many would take?
• Considers dead grass only.

What this tool does not do

• Consider live, green fuels.
• Predict fire spread potential, flame length, spread rate, or any fire behavior parameter other than ignition.
• Predict moisture content or ignition probability in fuel beds other than standing dead grass (e.g., conifer or hardwood leaf litter, twigs, feathermoss, mulch).
• Predict moisture content in matted grass or horizontally-oriented thatch.
• Consider rainfall. There is no routine to handle wetting by precipitation other than indirectly through a rise in humidity.
• Produce a fire weather index or code. This model produces actual estimates of moisture content.

Moisture Content and Probability of Ignition

Dead grass fuel moisture content is estimated from a model based on six years of prescribed burning on military ranges near Fairbanks, Anchorage, and Delta Junction, Alaska. Observations were collected from 17 sites over 74 days between 2009-2014, mostly during prescribed burning between 08:00 and 20:00. Two-hundred eighty five pairs of weather and moisture content measurements were used to develop models of moisture content. Moisture contents ranged between 4 and 29%. Moisture content is generally very close to its equilibrium moisture content (EMC) and can be adequately modelled using an EMC equation. This website predicts moisture content using Anderson's (1990) EMC equation for recently-cast cheatgrass based on temperature and relative humidity. (Anderson, H. (1990). Predicting equilibrium moisture content of some fo- liar forest litter in the northern Rocky Mountains, volume INT-429 of Research Paper. U.S.Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT, USA.)

Probability of Ignition is also empirically calculated from numerous dead grass ignition trials in a laboratory. Dead grass samples were brought to zero percent moisture content in a drying oven. Moisture was re-introduced into the samples in known quantities. Ignition probability curves were developed based on the moisture content using logistic regression.

This tool vs. Fire Danger Indices

Unlike the Canadian or U.S. National Fire Danger Rating Systems (CFFDRS or NFDRS) which produce indices (abstractions of reality), this tool predicts actual fuel moisture content and actual probability of ignition estimates.

Assumptions

• Early spring following snow-melt and prior to green-up
• Between 61° and 65° north latitude
• Peak burn period conditions. It assumes that maximum temperature coincides with minimum relative humidity at the peak of the burn period.
• The fuel bed is 100% cured and continuous dead grass. There is no live fuel to dampen ignition potential.
• No precipitation, frost, or dew that could bring dead grass moisture content higher than the fiber saturation point.<\li>
• No snow on the ground.
• A valid weather forecast.

Warnings

• This tool is limited by the accuracy of the weather forecast. The National Weather Service's National Digital Forecast Database produces an automated response. Do not assume the weather forecast has been reviewed by a human meteorologist. The accuracy of the NDFD in Alaska diminishes with distance from the nearest weather station which can be considerable.
• A spot fire weather forecast will nearly always provide a better weather forecast than the NWS NDFD. If you have access to a meteorologist or a spot weather forecast you may get better results by using the manual DGFM and DGPIG calculator.
• DGFM and PIG are statistical models. A certain amount of error is always present. Errors compound each other. That is, error in weather forecasting compounds error in the DGFM model which compounds error in the PIG model. Errors in one direction often cancel errors in the other.

Change Log

In the 2010 version of this standing dead grass calculator moisture content was based on dew point temperature and wind speed. Subsequent sampling and analysis demonstrates that relative humidity is a better predictor than dew point and that moisture content is not dependent on wind speed in standing dead grass. New fireline tables have been issued (pdf).