3.4.1 Fire Frequency


Goal: D. Maintain and restore natural disturbance processes that balance benefits for natural and human communities

Objective: 1. Reduce high severity fire frequency to more natural levels; encourage natural fire regimes that support native communities

WAF Attribute: Natural Disturbance

What is it?

This indicator is a comparison of observed fire frequency to expected fire frequency over a series of decades spanning the last 100 years. Fire return interval, a measure of fire frequency, is a measurable property of terrestrial vegetation that tells us about how natural a fire regime is in an area. This measure is used to score the subwatersheds based on expected fire frequency derived from CalVeg vegetation data.

Why is it Important?

How many fires occur each year in a particular place is an indicator of the state of the landscape regarding the health of plant communities. Specifically, disease pressure, drought, no-burn management practices and timber harvest can directly impact the health of a natural landscape which can be observed in wildfire activity. Forests in a region damaged by increases in pest activity, dry from drought, laden with excessive fuel can burn more frequently and in greater extent. Other factors are also important such as fire intensity, and must be considered along with this information.

What is the target or desired condition?

Fire is a natural part of the Sierra Nevada ecosystem. Historically, fires have naturally ranged from slow-burning under-story fires to raging stand-replacing fires (SNEP, 1996). The target condition for this indicator is for fire patterns and frequencies to oscillate around the central tendency of historical conditions. This is reflected in the vegetation-specific fire return intervals used for this indicator, where return intervals and the corollary frequencies, vary with vegetation type. The undesired condition set for this indicator is both zero fires and greater than twice the natural frequency. Therefore, a score of zero is attained under either of these conditions. A desired trend is for actual fire frequency to return to natural frequencies, depending on the vegetation present and danger to human communities.

What can influence or stress condition?

Fire is affected by climatic variables such as preceeding year’s moisture, El Niño cycles, and the Pacific Decadal Oscillations (Taylor and Beaty, 2005, Norman and Taylor, 2003, Morgan et al., 2008, Skinner et al., 2008). In fact, fire is so strongly determined by these climatic factors that land management seems to play only a minor role in regional fire patterns, except in changing vegetation patterns (through logging), locations of fire suppression (urban development), and soil moisture (thinning and clear-cutting).

What did we find out/How are we doing?

Fire frequency condition scores for the last decade were low for all subwatersheds, reflecting departure of the current fire patterns from historical conditions (Table 1, Figure 1). Only the Lower Feather subwatershed had anything approaching the historical fire frequencies. In this case, 7 of 19 planning watersheds (smaller creek drainages) had fires in the last decade. Throughout the watershed over the last century, there have been large areas of every subwatershed that has not had a mapped fire.

Table 1. Report Card scores for fire frequency for subwatersheds

Goal Measurable Objective Subwatershed Score (2000-2007)
D. Maintain and restore natural disturbance processes that balance benefits for natural and human communities 1. Reduce high severity fire frequency to more natural levels; encourage natural fire regimes that support native communities NFF 9
MFF 14
LF 39
NY 2
MY 3
SY 4
DC 12
LY 15
UB 0
LB 4

Trend Analysis

In order to find out if there was any change in fire frequencies over the century of record for the watershed, number of fires per decade were summed per subwatershed and the Mann-Kendall and Regional-Kendall trends analyses conducted (section 4.3). There were no significant trends for any subwatershed in numbers of fires per decade, where significance was measured as p < 0.05. this means that the number of fires per decade is neither increasing or decreasing over the last 100 years in any subwatershed.

Figure 1 — Fire frequency condition scores for subwatersheds

Temporal and spatial resolution

Fire data are updated on an annual basis and are available as an online GIS dataset. The fire perimeter resolution is high and fires less than one acre are recorded in the dataset. The CalVeg vegetation data were created in 1979, and were recently updated (2000). The spatial resolution is based on the LandSat Multispectral Scanner (MSS) images with a resolution of 80 meters. The smallest CalVeg areas reported are less than one hectare.

How sure are we about our findings (Things to keep in mind)

The method used here of evaluating the last decade for condition (2000 – 2007) is prone to one systematic error — if fire frequencies are low or high in that decade for climatic reasons, the score will reflect that. The scores were in fact lower than the equivalent scores for the last century for 9 of the 11 subwatersheds (Table 2), suggesting that the fire frequencies have recently been low. Measuring fire frequency at the decade time scale is also challenged by the presence of vegetation types that would naturally not burn at greater than once every 20-30 years (Table 3). Thus decadal scores calculated in the way we did would tend to be variable.

Table 2 — Comparison of last decade condition score with the scores for previous century

Subwatershed Name # PW in
Subwatershed (count)
Minimum 100-year Score for any PW Maximum 100-year Score for any PW Average 100-year Score for all PW Average Score
Deer Creek 8 0 32 11 12
East Branch North Fork Feather 75 0 96 23 2
Lower Bear 10 0 66 38 4
Lower Feather 19 0 92 13 39
Lower Yuba 14 0 96 31 15
Middle Fork Feather 98 0 98 28 14
Middle Yuba 20 0 95 25 3
North Fork Feather 86 0 97 20 9
North Yuba 34 0 71 15 2
South Yuba 25 0 95 20 4
Upper Bear 21 0 92 30 0

Technical Information

Data sources:

Fire data were sourced from the CalFire FRAP as a GIS layer that logged each known fire occurrence since 1900. The location and extent are stored as polygons with attributes such as date, cause and cost of fighting the fire (if available). Fire return intervals were obtained from Nagel et al. (2005) and Stephens et al., (2007) and are presented in Table 3. Vegetation classes were acquired from the USFS CalVeg statewide natural vegetation database.

Data Transformations:

The fire return intervals from the literature were converted to expected 10-year and 100-year fire frequency. These values were assigned to the CalVeg vegetation polygons.

Table 3. Expected fire frequency by vegetation class

Vegetation Class Fire Return Interval (years) Vegetation Class Fire Return Interval (years)
Annual Grass - Forb 3 Mixed Conifer - Pine 8
Barren 3 Montane Mixed Shrub 28
Black Oak 8 Mountain Hemlock 20
Blue Oak 8 Mule Ears 30
Canyon Live Oak 13 Perennial Grass 3
Deerbrush 28 Red Fir 15
Desert Ironwood 30 Sagebrush 30
Greenleaf Manzanita 28 Tobacco Brush 28
Interior Live Oak 8 Urban - Agriculture 3
Jeffrey Pine 16 Water 28
Lodgepole Pine 25 Western Juniper 30
Mariposa Manzanita 30 Whiteleaf Manzanita 30
Mixed Conifer - Fir 8    


Fire boundary data were located for the watershed. The number of fires was calculated per year for each of the planning watersheds (referred to as observed fire frequency). These values were combined into 10-year and 100-year summaries to compare decadal trends in fire activity over the century. Ten-year and 100-year fire frequency was calculated based on CalVeg vegetation classes using an area weighted average of the vegetation polygons for each planning watershed. Actual frequencies were compared to expected frequencies at the planning watershed resolution.

The comparison is standardized by generating a value (score) ranging from zero to 100 depending on how close the observed fire rate was to the expected fire frequency. A linear relationship between observed fire frequency and the score was established from zero to the expected fire rate (Figure 2). This relationship was mirrored for values greater than the expected frequency until the score reached zero (and remained zero for all greater values). The same scoring scenario was used for the 100-year analysis. The set of equations for the scoring follows:

Table 4. Scoring scenario for 100-year analysis

a. when observed frequency < 10yr expected frequency Score = observed 10yr frequency / expected 10yr frequency times 100
b. when 10yr expected frequency < observed < 2 x 10yr FR prob Score =  ((2 x 10yr FR frequency) - observed) / 10yr FR frequency times 100
c. when observed > 2 x 10yr Expected Frequency Score = 0

The scores were calculated for all planning watersheds, then aggregated to the subwatershed scale using an area weighted average score of the planning watershed scores. This was completed for each decade and for the entire century of records.

Figure 2. Relationship between score and fire frequency. Two example observed frequencies and corresponding scores are shown.


McKelvey, K. S., C. N. Skinner, et al. 1996. An overview of fire in the Sierra Nevada. Sierra Nevada Ecosystem Project, Final Report to Congress, Vol. II, Assessments and Scientific Basis for Management Options. Davis, Ca. 2: 1033-1040.

Morgan, P., Heyerdahl, E.K., and C.E. Gibson, 2008. Multi-season climate synchronized forest fires throughout the 20th century, northern Rockies, USA. Ecology, 89 (3): 717-728.

Nagel, T. A. and A. H. Taylor. 2005. “Fire and Persistence of Montane Chaparral in Mixed Conifer Forest Landscapes in the Northern Sierra Nevada, Lake Tahoe Basin, California, USA.” Journal of the Torrey Botanical Society 132(3): 442-457.

Norman, S.P. and A.H. Taylor. 2003. Tropical and north Pacific teleconnections influence fire regimes in pine-dominated forests of north-eastern California, USA. Journal of Biogeography, 30(7): 1081-1092.

Skinner, C.N., J.H Burk, M.G. Barbour, E. Franco-Vizcaino, S.L. Stephens. 2008. Influences of climate on fire regimes in montane forests of north-western Mexico. Journal of Biogeography, 35 (8): 1436-1451.

Stephens, S. L., R. E. Martin, et al. 2007. “Prehistoric fire area and emissions from California’s forests, woodlands, shrublands, and grasslands.” Forest Ecology and Management 251(3): 205-216.

Taylor, A.H. and R.M. Beaty. 2005. Climatic influences on fire regimes in the northern Sierra Nevada mountains, Lake Tahoe Basin, Nevada, USA.