Fuel Moisture: Dead Fuel Moisture Content

1. Nelson Model 1 and 10-hr Fuel Moisture Estimation Methods
2. Fosberg Model 1-hr Fuel Moisture Estimation Methods
   • Table A. Reference Fuel Moisture
   • Table B. 1-hr Fuel Moisture Corrections-May-June-July
   • Table C. 1-hr Fuel Moisture Corrections-Feb-Mar-Apr and Aug-Sep-Oct
   • Table D. 1-hr Fuel Moisture Corrections-Nov-Dec-Jan
3. 10-hr, 100-hr and 1000-hr Fuel Moisture Content
4. Fuel Moisture Conditioning in U.S. Spatial Fire Growth Models

Nelson Model 1 and 10-hr Fuel Moisture Estimation Methods

Ralph M. Nelson (2000) developed a fuel moisture model for estimating the diurnal fuel moisture changes in a 10-hr NFDRS fuelstick. Requiring hourly observations, it produces a more dynamic estimate that better reflects changes in precipitation, humidity, and sunshine. The National Fire Danger Rating System (NFDRS) uses this methodology.

SimpleFFMC 1-hr Fuel Moisture Estimation Tables based on the Nelson Model, have been calibrated for the southeastern U.S. by W. Matt Jolly (2016) and are available as a web-app for online users.

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Fosberg Model 1-hr Fuel Moisture Estimation Methods

Michael A. Fosberg and John E. Deeming (1971) documented procedures for estimating 1 and 10-hour Timelag Fuel Moistures. The methodology, along with seasonal adjustment tables, were integrated into Richard Rothermel’s (1983) tools and methods for surface fire behavior predictions.

Daytime Estimation Procedure

1. Using Table A, determine Reference Fuel Moisture (RFM). Percentage from intersection of temperature and relative humidity. Record this RFM percentage.
2. Select Table B, C, or D to adjust RFM for local conditions by finding current month in table title.
3. Is the fine fuel more than 50% shaded by canopies and clouds? If yes, use bottom shade portion of table. If no, use top exposed portion of table.
4. Determine the appropriate row based on aspect and slope.

5. Determine the appropriate column based on time of day and elevation of area of concern when compared to the wx site elevation. Use (A)bove if the fire is 1-2000’ above your location, (B)elow if the fire is 1-2000’ below you, and (L)evel if the fire is within 1,000’ above or below you. 

   
6. Obtain the 1-hr Moisture Content Correction (%) from the intersection of row and column.
7. Add the resulting 1-hr Moisture Content Correction (%) to the RFM (%).

Nighttime Estimates of 1-hr Fuel Moisture

Published RFM and Correction Tables for Nighttime Conditions are not included here based on recommendation from Pat Andrews at the Missoula Fire Lab. She recommends:

• Estimate Dry Bulb Temperature and relative humidity (RH) for the location of interest.
  Use Table A to estimate the RFM.
  Use the appropriate 1-hr Moisture Content Correction Table based on the time of the year.
  Obtain the correction for 0800, shaded conditions, and appropriate aspect from that table and add it to the RFM to estimate 1-hr moisture content for nighttime conditions.

Table A. Reference Fuel Moisture

Table B. 1-hr Fuel Moisture Corrections-May-June-July

Table C. 1-hr Fuel Moisture Corrections-Feb-Mar-Apr and Aug-Sep-Oct

Table D. 1-hr Fuel Moisture Corrections-Nov-Dec-Jan

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10-hr, 100-hr, and 1000-hr Fuel Moisture Content

10-hr and 100-hr fuel moisture may be estimated in the following ways and applied along with the Fosberg fuel moistures in surface fire behavior predictions. 1000-hr fuel moisture is not usually needed for fire behavior calculations.

• After estimating 1-hr moisture content, 10-hr and 100-hr fuel moisture content can be estimated by adding incremental amounts (e.g. adding 1-2% for 10-hr and 2-4% for 100-hr).
• Using a local Remote Automatic Weather Station (RAWS) or the Geographic Area’s Predictive Service summaries.
• The National Fuel Moisture Database may have sampling locations near your setting that have estimates for these fuel moistures.

In NFDRS, if danger rating calculations are suspended in the dormant season, default dormant fuel moistures are provided for 100-hr (10%-25%) and 1000-hr (15%-30%) fuel moistures when calculations are restarted in the spring. Default values are established with climate class designation for the location.

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Fuel Moisture Conditioning in US Spatial Fire Growth Models

Deterministic spatial analyses in Wildland Fire Decision Support System (WFDSS) (Basic, Short Term Fire Behavior [TFB], and Near Term Fire Behavior [NTFB]) use estimates from historic weather data in the Wildland Fire Applications Information Portal (WIMS) implementation of NFDRS as default initial fuel moistures inputs. Forecast and/or observed weather (for retrospective periods) from the selected weather stations are used to estimate hourly adjustments to dead fuel moistures for those analyses. Initial dead fuel moistures in deterministic analyses default to estimates using the Fosberg dead fuel moisture model while conditioning weather uses the Nelson Model to adjust 1-hr, 10-hr, and 100-hr fuel moisture content over one to several days. In most cases, one or two days of conditioning is sufficient. 

Take care to review the conditioning weather inputs for both observed and forecast days. Precipitation amounts, high overnight humidity recovery, and/or significant cloud cover can raise fine fuel moisture significantly. Use the Basic Outputs from Flammap or Short Term Fire Behavior analyses to review resulting 1-hr and 10-hr fuel moistures and edit inputs as necessary.

Desktop software (FLAMMAP and FARSITE) can use any initial fuel moisture and weather stream that the user supplies to apply these conditioning adjustments.

WFDSS FSPro draws its dead fuel moistures (1-hr, 10-hr, and 100-hr) in the Energy Release Component (ERC) table from the WIMS implementation of NFDRS. It ranks and groups ERC values from the selected weather station climatology and provides average fuel moisture values from the underlying data for each of those groups, or percentile classes. It uses the Fosberg model for all dead fuel moisture defaults. They are held static during the simulation and are not conditioned or changed during any simulation for the period that they are drawn from and used.

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