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FDOP guide

Fire Danger Operating Plan information & material

What is a FDOP?

Fire Danger Operating Plan

A fire danger operating plan is a fire danger rating applications guide for agency users at the local level. A fire danger operating plan documents the establishment and management of the local unit fire weather system and incorporates fire danger modeling into local unit fire management decisions. Fire danger operating plans include but are not limited to responsible parties (e.g. station maintenance, data entry); fire danger rating areas (e.g. location, development criteria); NFDRS thresholds and breakpoints (e.g. staffing levels, adjective ratings, preparedness levels, and indexes used for each); operational procedures; and Fire Danger PocketCards.


Example FDOP's

CAL FIRE SLU/San Luis Obispo County Fire FDOP_2013_FinalProduct.pdf

Creating an FDOP

Getting Started

1. Download FireFamilyPlus from:

2. Create a Database: File> New> Name your Database and save it to an appropriate location> Give it a description.

Station Catalog

1. Download station catalog information from:

Select Weather> Station Catalog> Station Information> Select ‘Single Station’> Enter in Station ID> Select Output Destination to “Send file to FTP site”> Save .txt file to appropriate folder

2. Importing the Station Catalog:


1. Download weather data from:

2. Importing the Weather Data:

3. Missing Records:

In my case, there were so many non-consecutive missing dates that I downloaded data for the entire 10 years rather than many multiple single date files. The directions that follow will explain how to edit and filter out only the records you need.

4. Editing Weather data

5. Importing Edited Weather data

6. Searching for Anomalies in the data


1. Download fire data from:

Select Fire> Standard Extract> Enter in Region/Forest and date range

2. Importing Fire Data

3. Obtain LE-66 Data

4. Preparing LE-66 Data

5. Additional Editing for LE-66 Data

If you do not need your lat and long data to be in DD, then disregard the following instructions..

Once all the data has been converted to DD I made sure that all the Longitude values were (-). I then created final columns for both lat and long and copied and pasted only the values from my lat and long ‘working data’ columns into my lat and long ‘final data’.

6. Using Fire Reports

7. Populating the ‘SubUnit’ Field

Stats Graphs

1. Fire Occurrence graphs

2. Determining Thresholds
For each FDRA the thresholds need to be determined. The program will do this for you however; you need to confirm that the default percentage values for the thresholds are indeed accurate after all climatology statistics graphs have been run. The given percentages should coincide with the data output of the fire analysis.

3. Three indice graphs for each FDRA

4. Decision Points for Dispatch Level

5. When updating numbers and percent values in FDOP

Staffing Level

Staffing Level is an important component of the Adjective Fire Danger Rating and is calculated in WIMS. It is a way for us to break up the BI continuum based on percentile to make it more useful. Staffing level, along with Ignition Component, is a way for describe relative fire risk.

Staffing Level will be determined using fuel model G for the San Luis Obispo Unit. This is determined by running different fuel models against fire and weather history data. When running the different fuel models with your data you should choose the fuel model that yields the ideal statistical output. The ideal statistical output includes a BI of at least 100, all graphs should be a bell curve, the data should have a linear distribution across the spectrum, and no graphs should be inverted. If you find a fuel model that yields these results, it means that fuel model is the best for fit for your data set and should be the fuel model you should used in the analysis of staffing level. In this analysis, fuel model G (which also happens to be the standard fuel model) was determined to be the best fit for the data set.

Preparedness Level

The point at which large fires start to take off based on ERC.

Pocket Cards

A pocket card must be created for each FDRA, which will display the three largest fires.

For the Inland FDRA, instead of using the largest fires by acreage we used the top three most well known fires in San Luis County. This is because these fires are the events that resonate with the SLU.


Fire Name Date
. 7/1/1985
. 8/18/1994
. 8/15/1996

Fire Name is left Blank or replaced with '.' because the symbology is cluttered and not legible on the graph. Once the graph is complete, Photoshop/edit the names in. (I did so using paint)

Past Experience Text:

This bit of text can be typed into the Pocket card window. All other descriptive text and the logo was edited using paint. Unless a fire occurs that exceeds one of these three major fires, these are the fires that are to be displayed on the Inland Pocket Card. The Coastal FDRA pocket card displayed the top three largest fires in that FDRA.

The bit map for each FDRA is already created: X://projectdata>master_data>Coastal.bmp

Under the ‘Fire Danger Area’ enter in the region (i.e. coastal or inland), the RAWS located in the region,

Updating the FDOP Annually

Cause Code Conversion Chart

CDF Cause Code Description Federal Cause Code
0 Unknown 9
1 Undetermined 9
2 Lightning 1
3 Campfire 4
4 Smoking 3
5 Debris Burning 5
6 Arson 7
7 Equiptment Use 2
8 Playing w/ Fire 8
9 Miscellaneous 9
10 Vehicle 2
11 Railroad 6
12 Powerline 2

(Obtained from CAL FIRE intranet)

In preparation for statistical analysis, the CAL FIRE cause code must be translated to the federal cause code for use in Fire Family Plus software. The chart above outlines the cause code conversion from CDF to Federal cause codes.

Fuels Info

Q: How are live and woody Fuel Moistures Calculated?

A: "Measured Woody Fuel Moisture: The modeled fuel moisture of live woody material does not always track with the measured woody fuel moistures from sampling sites. This is because live fuel moisture values in the NFDRS are modeled values designed for the broad scale of fire danger, rather than site-specific measured values. In these instances, fire managers have a couple of options. Physical measurements of the moisture content of the small branch wood and foliage of live woody plants can be collected monthly. Preferably the user will already be monitoring these measured live fuel moistures in parallel to the outputs of the NFDRS and using experience to include the measured fuel moisture values as another tool in their decision-making toolbox. This allows the NFDRS model to work for the user as it was intended.

Alternatively, the user can regularly enter measured live woody fuel moisture into the NFDRS processor to calibrate the woody fuel moisture model. The calibration based on measured woody fuel moisture is valid for 30 days. If no new measured value is entered within 30 days, the model returns to using only weather data. The woody moisture computed solely from weather data may be quite different from that computed from both weather data and the measured woody moisture. This may result in sudden or unacceptable changes to NFDRS outputs. This approach requires consistent care and feeding of the processor (i.e., a regular live fuel monitoring program) and has been discouraged by some agencies."

"X-1000 Hr Fuel Moisture Value – The X-1000 value is not truly a dead fuel moisture value. It is the live fuel moisture recovery value. It is discussed here since it is derived from the 1000-hr fuel moisture value. It is an independent variable used in the calculation of the herbaceous fuel moisture. The X-1000 is a function of the daily change in the 1000-hour timelag fuel moisture, and the average temperature. Its purpose is to better relate the response of the live herbaceous fuel moisture model to the 1000-hour timelag fuel moisture value. The X-1000 value is designed to decrease at the same rate as the 1000-hour timelag fuel moisture, but to have a slower rate of increase than the 1000-hour timelag fuel moisture during periods of precipitation, hence limiting excessive herbaceous fuel moisture recovery. "

"Live woody and herbaceous moistures fluctuate in response to drying and wetting cycles. Greenness factor values fluctuate up and down within the 20 and 1 range during this period. Annual herbaceous vegetation most likely will cure sometime during this period. In the NFDRS model, the live fuel moisture is initially calculated using the same formulas as are used after the completion of greening in the 1978 models, but is adjusted by a factor equal to the greenness factor divided by 20. The woody fuel moisture is calculated using the same formulas as are used in the 1978 models, and it too is adjusted by the greenness factor."

Q: What is the difference between Annuals and Perennials?

A: "Grass Type (live fuel type): The National Fire Danger Rating System recognizes that there are seasonal differences in fire danger related to the type of grass vegetation present. Annual vegetation produces a different dynamic situation within the fuel complex than does perennial vegetation. Annuals sprout from a seed each year, grow, reach maturity and die usually all in one season. This process is not affected significantly by seasonal weather factors such as temperature or precipitation. Perennial grasses on the other hand, generally start in a dormant condition, grow, reach maturity, then go back into dormancy. Their cycle is greatly affected by temperature and precipitation. Because of these differences, the mathematical formulas or algorithms associated with the drying of herbaceous vegetation are different for the two types of grasses. The loading of fine fuels associated with annual grasses shifts from live to dead and stays there for the duration of the season. For perennial grasses the shift from live to dead is much slower and may even stop or reverse if the right combinations of temperature and precipitation occur during the season. Where both annual and perennial grasses occur together select the type that predominates the site."

Q: When should we start seeing changes in our fuel moistures after Green Up?

A: "The equations that predict 1000-hr and live fuel moisture contents require at least four weeks to stabilize and predict accurate fuel moisture content values."