Empirical approaches to modeling wildland fire in the Pacific Northwest, United States: methods and applications to landscape simulations1

Donald McKenzie2, Susan Prichard3, Amy E. Hessl4, and David L. Peterson2

2USDA Forest Service, Seattle Forestry Sciences Lab, 400 North 34th Street, Suite 201, Seattle, Washington, 98103, United States. Tel.: 206-732-7824, fax: 206-732-7801 , e-mail: donaldmckenzie@fs.fed.us, wild@u.washington.edu.

3Box 352100, College of Forest Resources, University of Washington, Seattle, WA 98195-2100 Tel. 206-543-9138. sprich@u.washington.edu

4Department of Geology and Geography, Box 6300, West Virginia University, Morgantown, WV 26506. Tel: 304-293-5603. Fax: 304-293-6522. ahessl@geo.wvu.edu

INTRODUCTION

Vegetation dynamics, disturbance (especially fire), and climatic variability are key ingredients in simulations of the future condition of heterogeneous landscapes (Lenihan et al. 1998, Keane et al. 1999, Schmoldt et al. 1999, Dale et al. 2001, He et al. 2002). Spatially explicit models in particular require large amounts of empirical data as inputs, but existing data are rarely adequate. The extent and resolution of the available empirical data are important considerations because different types of data within a database are often collected at different spatial and temporal scales (McKenzie et al. 1996a, McKenzie 1998). In addition, the spatial pattern of sample data may not reflect the spatial pattern of variability in the landscape being modeled. For example, when viewed at broad spatial scales, data points will often be clustered as a result of the local nature (i.e., the relatively small spatial extent) of most data collection. In such cases it is easy to underestimate the intrinsic variability of the data and difficult to discern autocorrelation within the structure of the data (Rossi et al. 1992), and it therefore becomes difficult to aggregate these data to create the continuous coverages that are necessary for landscape-scale simulations.

Empirical models (Keane et al. 2003) are an important source of both input data and model parameters, particularly if careful attention is paid to questions of scaling and aggregation in their development. The empirical data that form the basis for models of fire and vegetation are of three types: climatic data; reconstructions of an area’s fire history; and data on vegetation, fuels, and topography. Each type of data raises key questions regarding extent, resolution, and spatial pattern, and presents key problems that affect the quality of the data and its usefulness for modeling (Table 1). For example, empirical (instrument-collected) climatic data come from weather stations that are generally not optimally distributed to support spatial

1 Chapter 7 in A.H. Perera L. Buse, and M.G. Weber, editors, Emulating natural forest landscape disturbances: concepts and applications. Columbia University Press, New York, NY. In press.

modeling (e.g., they are too widely spaced in steep, complex topography). To model landscapes, gridded datasets must be created via interpolation and extrapolation from these weather station records (Daly et al. 1994, Thornton et al. 1997). In addition, these data typically extend back to only around 1900. Thus, climate reconstructions are necessary before we can investigate associations between climate and historic fire regimes (Swetnam and Betancourt 1990, Swetnam 1993, Grissino-Meyer and Swetnam 2000, Veblen et al. 2000).

Table 1. Key limitations of raw empirical data (scale and pattern) that affect the success of large-scale fire modeling.

Climatic data Fire history data Data on vegetation, fuels, and topography

Instrumental data have limited temporal extent and resolution.

Fire history sites do not span the range of environmental variability of the modeled sites.

There may be unequal taxonomic resolution, the data are often qualitative, and biophysical factors are not incorporated.

Interpolation and extrapolation are difficult in complex terrain.

Data points are clustered, thus interpolation is difficult.

The heterogeneity is not captured at scales critical for fire modeling.

In studies of local fire regimes, most fire history data have been collected with spatially intensive rather than spatially extensive sampling. Continuous, extensive spatial coverage is generally needed for landscape modeling, but at regional scales, fire history sites are often clustered, with large gaps between sites (Heyerdahl et al. 1995). Models to transform fire history records into data structures that are suitable for spatially explicit simulation models.

Vegetation data are available in a variety of classification schemes, including historic and current cover types (Quigley et al. 1996, Sierra Nevada Ecosystem Project 1996), potential vegetation (Küchler 1964), and structural stage (Schmidt et al. 2002). These data are available in a form suitable for landscape modeling, but the links between vegetation and fire regimes are not always spatially well defined. In addition, most vegetation coverage information could no longer be considered raw data, having passed through considerable qualitative and quantitative transformation and aggregation before attaining their final form.

Finally, perhaps the most useful inputs to landscape fire models – and the most difficult to acquire – are models of the relationships among climate, fire, and vegetation that are robust at the spatial and temporal scale of the application. We suggest that the ideal input data or empirical model would have the following attributes:

• The data are spatially explicit, capture heterogeneity at a range of scales, and represent multiple environmental gradients.

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• The time series data have sufficient extent to capture the historical range of variability, but sufficient resolution to capture short-term variability.

• The empirical models are applicable at the temporal and spatial scales (resolution) of the landscape simulation models.

• The model’s output is robust over the historical range of variability being simulated.

We have illustrated methods for linking empirical research to landscape modeling, using as examples four existing studies conducted at different spatial and temporal scales (McKenzie et al. 1996b, 2000; Hessl et al. 2003; Prichard 2003). These studies have produced data layers or time series that capture the spatial and temporal variation in the parameters that define the relationships among climate, fire regimes, and fuels and vegetation. We have categorized the empirical studies based on the types of landscape simulation models for which they provide suitable input, and based on the spatial and temporal scales at which they are appropriate (Table 2). We have given only a brief description of each study; details on their methods and results may be found in the citations provided for each study. We have focused on key characteristics of each study that determine its value for the appropriate class of landscape simulation model. The first two studies were initiated and completed specifically for the purpose of providing inputs and parameters for broad-scale landscape-level modeling of fires, whereas the last two have these and other objectives, and research is ongoing.

Table 2: Scales, model types, and target applications for four empirical studies.

Study Spatial scale Temporal scale

Model categories

Target application

McKenzie et al. (1996b)

Sub-continental Century Qualitative Equilibrium

Rule-based models

McKenzie et al. (2000)

Regional Annual to century

Semi-qualitative Quantitative Statistical Equilibrium

Process-based models

Hessl et al. (2003) Sub-regional, multiple scales

Annual to century, multiple scales

Quantitative Statistical Time-series Dynamic

Process-based or stochastic models

Prichard (2003) Local Millennial Semi-qualitative Time-series Dynamic

Process-based models with broad temporal scale

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MODEL CATEGORIES AND EXAMPLES

Qualitative or rule-based models

Qualitative or rule-based models are based on expert opinions gathered during discussions or workshops, on decision structures that rely on either verbal rules or numerical thresholds, on specific algorithms designed for qualitative modeling (Puccia and Levins 1985), or on knowledge-based systems (Schmoldt and Rauscher 1995, Reynolds et al. 1996). They are appropriate not only when quantitative data are insufficient, but also when logical inference is believed to be superior to statistical inference (Reynolds et al. 1996).

Sub-continental-scale qualitative model of vegetation transitions (McKenzie et al. 1996b)

Alder/ash

Spruce/hemlock

Hemlock/Douglas-fir

Silver fir/Douglas-fir Douglas-fir Ponderosa pine

Cedar/hemlock/pine

Redwood

Mixed conifer Oakwoods

Pine/cypress Pinyon/juniper

Aspen/birch

Aspen parkland

Shortgrass

prairieFir/spruce Lodgepole pine

Alpine tundra GrBasin pine GrBasin shrub

Mixedgrass

prairie

Desert shrub Mesquite

Desert grassChaparral

Alder/ash

Spruce/hemlock

Hemlock/Douglas-fir

Silver fir/Douglas-fir Douglas-fir Ponderosa pine

Cedar/hemlock/pine

Redwood

Mixed conifer Oakwoods

Pine/cypress Pinyon/juniper

Aspen/birch

Aspen parkland

Shortgrass

prairieFir/spruce Lodgepole pine

Alpine tundra GrBasin pine GrBasin shrub

Mixedgrass

prairie

Desert shrub Mesquite

Desert grassChaparral

Figure 1: Summary of the one-step transition rules for vegetation types in the western United States as a function of increased fire frequency. Adapted from McKenzie et al. (1996b). The vegetation type names represent the “aggregated types” of McKenzie et al. 1996b (see also chapter text), not always species names.

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Fire is the principal form of natural disturbance in western North America. As such, it is expected to constrain vegetation development, even though this development is principally controlled by climate (Woodward 1987, Woodward and McKee 1991). In general, fire maintains earlier successional stages than would exist in an undisturbed landscape, and changes in fire frequency would eventually be reflected in a quasi-equilibrium represented by successional stages. “Potential vegetation”, which is assumed to represent the vegetation that would exist in an undisturbed landscape, was aggregated for the study region based on Küchler’s (1964) system into 44 vegetation types based on a qualitative assessment of the observed or inferred similarities in fire regimes. A set of one-step vegetation transition rules was developed based on an exhaustive literature review of the effects of fire on dominant vegetation. The transition rules are summarized for the western United States by means of a flowchart (Figure 1). When applied to the landscape in the region being modeled, the transition rules suggest shifts in the proportional cover and spatial patterns of the dominant vegetation (Figure 2).

Pre-transition Post-transition

Desert

Desert grassland

Desert shrub

Douglas-fir

Grassland/wetland

Great Basin pine

Great Basin shrub

Hemlock/Douglas-fir

Lodgepole pine

Mesquite savanna

Mixed conifer

Mixed grass prairie

N. Floodplain

Oak/juniper

Pine/cypress

Pinyon/juniper

Ponderosa pine

Redwood

Shortgrass prairie

Silver fir/Douglas-fir

Spruce/hemlock

Tallgrass prairie

W. Fir/spruce

W. Oakwoods

Alder/ash

Alpine tundra

Cedar/hemlock/pine

Chaparral

Pre-transition Post-transition

Desert

Desert grassland

Desert shrub

Douglas-fir

Grassland/wetland

Great Basin pine

Great Basin shrub

Hemlock/Douglas-fir

Lodgepole pine

Mesquite savanna

Mixed conifer

Mixed grass prairie

N. Floodplain

Oak/juniper

Pine/cypress

Pinyon/juniper

Ponderosa pine

Redwood

Shortgrass prairie

Silver fir/Douglas-fir

Spruce/hemlock

Tallgrass prairie

W. Fir/spruce

W. Oakwoods

Alder/ash

Alpine tundra

Cedar/hemlock/pine

Chaparral

Figure 2: The results of applying the one-step transition rules in Figure 1 to Küchler’s (1964) vegetation types for the western United States. Adapted from McKenzie et al. (1996b).

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Target application

McKenzie et al.’s (1996b) model was designed specifically to inform a qualitative fire-disturbance module for the biogeographical Mapped Atmosphere Plant Soil System (MAPSS) model (Neilson 1995). A cross-tabulation was established between the physiognomically based vegetation simulated by MAPSS and the species-oriented vegetation types in Figure 2 (McKenzie et al. 1996b) so the MAPSS output could be interpreted at the species level.

Semi-qualitative statistical models

Semi-qualitative statistical models rely on both qualitative and quantitative procedures to estimate the model’s parameters.

Fire frequency models for the Interior Columbia River Basin of the western United States (McKenzie et al. 2000)

The Pacific Northwest region of the United States exemplifies the difficulties of adapting empirical data for use in landscape-level fire models. Steep gradients in elevation, precipitation, and temperature exist across multiple scales. The diversity of climatic conditions, topography, and elevations supports a variety of ecosystem types, including coastal temperate rainforest, subalpine parkland and alpine meadows, drier mixed coniferous forests, and semi-arid shrublands and grasslands (Daubenmire 1978, Lassoie et al. 1985). A variety of fire regimes occur within the Pacific Northwest (Agee 1993), including large, stand-replacing fires (Agee and Smith 1984, Huff 1984, Henderson et al. 1989); mixed-severity, medium-frequency fires (Morrison and Swanson 1990, Taylor and Halpern 1991); and low-severity, high-frequency fires (Bork 1985, Kertis 1986). Severe fires, particularly in moist, high-elevation forests, are usually associated with synoptic weather patterns (Agee 1993, Ferguson 1997, Schmoldt et al. 1999). In drier ecosystems on or east of the crest of the Cascade Range in the Interior Columbia River Basin, altered fire regimes in the last century and the potential effects of global climate change of particular interest to modelers and managers.

Qualitative component

A hierarchical model was developed to rank the dominant vegetation cover types in the Interior Columbia River Basin based on relative fire frequency and severity, and the results were displayed as a dendrogram (Figure 3). The hierarchical structure reflects the similarity in fire frequency and severity between vegetation types that are adjacent in the dendrogram, and an aggregation algorithm whereby vegetation types can be clustered into groups, depending on the required resolution, in a “taxonomy” specifically associated with fire regimes.

Quantitative/statistical component

Multiple-regression models were used to predict broad-scale (1-km) patterns of fire frequency for forested areas in the Interior Columbia River Basin (Figure 4) using fire-return intervals from a fire history database as the response variable (Heyerdahl et al. 1995) and GIS

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Interior ponderosa pine

Pacific ponderosa pine

Sierra mixed conifer

Oregon white oak

Limber pine

Mixed conifer woodland

Interior Douglas-fir

Grand fir/white fir

Whitebark pine

Whitebark pine/subalpine larch

Western larch

Lodgepole pine

Aspen

Western white pine

Shasta red fir

Western hemlock/western redcedar

Pacific silver fir

Engelmann spruce/subalpine fir

Mountain hemlock

Interior ponderosa pine

Pacific ponderosa pine

Sierra mixed conifer

Oregon white oak

Limber pine

Mixed conifer woodland

Interior Douglas-fir

Grand fir/white fir

Interior Douglas-fir

Grand fir/white fir

Whitebark pine

Whitebark pine/subalpine larch

Whitebark pine

Whitebark pine/subalpine larch

Western larch

Lodgepole pine

Aspen

Western larch

Lodgepole pine

Aspen

Western white pine

Shasta red fir

Western white pine

Shasta red fir

Western hemlock/western redcedar

Pacific silver fir

Western hemlock/western redcedar

Pacific silver fir

Engelmann spruce/subalpine fir

Mountain hemlock

Engelmann spruce/subalpine fir

Mountain hemlock

Figure 3: Dendrogram for the dominant cover types of the Interior Columbia River Basin of the western United States. Moving from the top to the bottom of the dendrogram, the fire-free interval increases. Adapted from McKenzie et al. (2000).

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data layers for vegetation (Quigley et al. 1996), precipitation (Daly et al. 1994), and elevation as predictors. The vegetation variable was assigned a rank based on the hierarchical model and incorporated into the multiple regressions. Predictions from the models were then mapped to the landscape of the Interior Columbia River Basin at1-km resolution. Because of the varying quality of the fire history database, two models were fit to the data: one that used the full database, and another that used only the highest-quality sites---those with fully cross-dated fire records (McKenzie et al. 2000).

The models predicted fire-return intervals of between 2 and 375 yr in the Interior Columbia River Basin (Figure 5). Elevation, summer precipitation, vegetation type, and latitude were all significantly associated with fire frequency, although the vegetation type was only barely significant, and of all the predictors, explained the least amount of the variance. Unlike all

previous mapped estimates of fire frequency for the Interior Columbia River Basin, which predicted broad ranges based on vegetation alone, the statistical models produced by this study found environmental variables to be better predictors than vegetation, and estimated a mean and a variance for each 1-km cell instead of a range of values.

Figure 5: Gridded maps of the predicted fire-return intervals from two multiple-regression models. (a) Using only high-quality fire history data, (b) Using all fire history data. Reprinted, with permission, from McKenzie et al. (2000).

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Target applications

The models were designed to assist broad-scale simulations that use fire frequency as a basic input variable (e.g., Keane et al. 1996a). The limits imposed by the coarse resolution mean that the models will be less useful for fine-scale than for broad-scale applications, although local managers may be able to integrate model predictions with local qualitative data and knowledge about systems similar to theirs, in order estimate historic conditions, as a basis for emulating natural disturbance (Morgan et al. 1994, Landres et al. 1999, Swetnam et al. 1999). The statistical approach allows modelers to incorporate means and variances in applications where fire starts are simulated stochastically. For example, in mechanistic models (e.g., Keane et al. 1996b), predicted fire-return intervals (FRIs) for each pixel in the model could be used directly or used to represent the mean value in a candidate probability distribution such as the Weibull (Grissino-Meyer 1999). From this distribution, input fire-return intervals could be chosen randomly. Broad-scale modeling will probably need to incorporate semi-qualitative elements for the foreseeable future because of the unavailability of sufficient high-quality empirical data (Keane and Long 1998, McKenzie 1998). Our results suggest that heuristic, knowledge-based methods (the hierarchical vegetation model) and rigorous statistical methods can be successfully combined.

Quantitative, non-equilibrium models

Quantitative non-equilibrium models use statistical techniques, but simulate temporal dynamics rather than a static (equilibrium) view. Typically time-series analysis is involved, either explicitly if temporal autocorrelation is to be incorporated, or as a preprocessor to remove autocorrelation so that other statistical methods can be used (e.g., Cook and Kairiukstis 1990).

Fire and climate in the inland Pacific Northwest region of the United States (Hessl et al. 2003)

The geographic extent of this study spans two national forests within the Interior Columbia River Basin: the Okanogan-Wenatchee and the Colville, which extend across the Northern Cascades and Okanogan Highlands physiographic provinces (Franklin and Dyrness 1988). In the Northern Cascades, the topography is extremely rugged, with deep and steep-sided valleys and eastward- and westward-flowing streams. Further east, the Okanogan Highlands present moderate slopes and broad, rounded summits. A variety of soil types appear in both provinces, reflecting the influence of Pleistocene glaciers, with glacial soils predominant on valley bottoms and residual soils on hillslopes and ridgetops. The climate is intermediate between the maritime climate west of the Cascade Crest and the continental climate east of the Rocky Mountains, and is characterized by summer drought.

Climate reconstructions (Stahle et al. 1998, Cook et al. 1999, Gedalof and Smith 2001) were compared with data on fire extent and occurrence documented in a spatially explicit fire history dataset from five watersheds in the Okanogan--Wenatchee and Colville National Forests of Washington State (Everett et al. 2000; Figure 6). Correlation analysis, superposed-epoch analysis (Grissino-Meyer 1995), and cross-spectral analysis (Bloomfield 2000) were used to identify significant relationships between fire occurrence (as measured by the percentage of trees that

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recorded a fire in a particular year) and three climate indices: the Palmer Drought Severity Index (Alley 1984), for which lower values indicate drier conditions the magnitude of the El Niño Southern Oscillation, and the magnitude of the Pacific Decadal Oscillation (Mantua and Hare 2002). Relationships between these climatic indices and fire were established at both the watershed and the regional scales. In addition, mean fire-free intervals and Weibull median probability intervals (Grissino-Meyer 1999), the latter a robust estimator of central tendency in fire-interval distributions, were estimated on a gradient of spatial scales from point to watershed. Composite fire intervals at spatial extents from point-level to the entire watershed were fit to a two-parameter Weibull distribution to estimate the “hazard function” (Johnson and Gutsell 1994), which represents the risk of fire in a given year. If the slope of this function is zero, fires are considered equally likely in any year following a fire, whereas a positive slope indicates

Figure 6: Five focal watersheds in eastern Washington State (United States) for which spatially explicit fire records are available. Hatched areas represent national forests. The inset shows the detailed topography and the spatial pattern of fire-scarred trees in the Swauk watershed. Adapted from Hessl et al. (2003).

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increasing hazard over time, possibly associated with the buildup of fuels (Johnson and Gutsell 1994).

Fire occurrence was negatively correlated with the Palmer Drought Severity Index and thus was positively correlated with drought over the period of record (1700-1975), but the correlation was stronger before 1900 (Figure 7). No significant correlation was identified with either the El Niño Southern Oscillation or the Pacific Decadal Oscillation. Superposed-epoch analysis also identified a significant relationship between fire occurrence and the Palmer Drought Severity

Figure 7: Ten-year average of the Palmer Drought Severity Index (PDSI) reconstructed from tree-rings for the 1684-1978 period (Cook et al. 1999) (solid line) and the 10-yr average of the percentage of trees scarred by fire over time (dotted line). Adapted from Hessl et al. (2003).

-1.5

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Index (Figure 8); in each case, the mean index was lower in or around the year of the fire (lag year 0), confirming the study’s observation that fires were associated with increasing drought. Cross-spectral analysis identified a significant association between fire occurrence (aggregated to 5-yr sums) and the Pacific Decadal Oscillation, at a period (1/spectral frequency) of approximately 47 yr – roughly the length of a complete cycle of the Pacific Decadal Oscillation – with a lag of approximately 5 yr. This last result is promising in that it and the superposed-epoch analysis suggest that the causes of temporal variability in fire regimes may be identified at multiple scales. However, these results are preliminary and must be carefully validated.

Comparison of composite fire intervals for watersheds as a whole with the fire-free intervals for individual points, while accounting for the different areas represented by the different

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watersheds, suggests the possibility of modeling the relationship between increasing sample area and decreasing estimates of fire-free intervals. Initial simulations indicated that a strong nonlinear inverse relationship exists between these two variables in neutral landscapes (landscapes simulated to have no specific constraints on fire regimes – (Gardner and O’Neill 1991) and that models of this type should be pursued further.

Target applications

If modelers have access to empirical fire history data at appropriate scales, some difficult problems associated with data aggregation, such as the scaling of fire-frequency estimates from point to stand to landscape, are mitigated (McKenzie et al. 1996a, 2000). Additionally, because constraints on fire regimes change across scales, appropriate parameters can be more easily applied in mechanistic models if they have been identified at multiple scales. For example, fine-scale models might take advantage of fire probabilities based on Weibull distributions to simulate fire starts (e.g., Keane et al. 1996b), whereas broad-scale models might use empirical data on fire distributions based on climatological inputs. These spatially explicit, extensive fire history data can be used to identify fire-climate interactions and the key spatial scales of variability in fire frequency (Table 3), to estimate key mechanisms at different scales (Table 4), and to predict the characteristics of fire regimes in unsampled watersheds throughout the study area so as to provide the extensive coverage needed for broad-scale simulations.

The spatial and temporal dynamics of fire and forest succession in a montane ecosystem (Prichard 2003)

This model focused on Thunder Creek, a large (30 000 ha) watershed in the heart of North Cascades National Park, Washington State. The watershed lies within the rain shadow of several large peaks, thus local climate is considerably drier than in watersheds west of these mountains. An existing fire history dataset (Agee et al. 1990) provided a record of the mixed-severity fire regime that typifies transitional climatic zones of the North Cascades, but it is temporally limited to the age of the existing forests.

Records of lake sediment charcoal can provide an extensive temporal record of fire; in the North Cascades, this record extends throughout the Holocene (>10 000 yr BP to the present; Cwynar 1987). Within the same sediment record, macrofossils and pollen can be used to evaluate changes in species composition and abundance associated with historic fires (Dunwiddie 1986, Gavin and Brubaker 1999). Although lake sediments have excellent temporal resolution and extent, they represent an indefinite area surrounding lake basins and do not record spatial variability in fire history and vegetation (Dunwiddie 1987, Whitlock and Millspaugh 1996).

In this study, the spatial and temporal dynamics of fire and vegetation were investigated in a 4-km2 area of the Thunder Creek Watershed. Forest succession after fires was reconstructed using age-structure analysis based on tree ages, increment cores, and tree-size distributions along a precipitation gradient parallel to Thunder Creek and along an elevation gradient up the steep northeastern slope of the watershed. Holocene fire and vegetation histories were reconstructed from lake sediment records sampled from a small (0.4-ha) lake in a montane forest zone. Charcoal records were used to reconstruct a continuous record of local (1- to 3-ha) fires over the past 10 000 yr, and macrofossils of conifer needles, twigs, and seeds were used to assess

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fluctuations in forest assemblages in association with changes in fire frequency. Charcoal accumulation rates and total macrofossil accumulation rates were compared with superposed-epoch analysis (see above) to evaluate whether charcoal peaks represented local fires.

The successional dynamics after fire varied dramatically across the 4-km2 study area. Even at similar elevations, current forest assemblages are strikingly different at the dry end of the precipitation gradient parallel to Thunder Creek, where lodgepole pine (Pinus contorta Dougl.) and Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) codominate the stands, and at the moist end of the precipitation gradient, where western hemlock (Tsuga heterophylla [Raf.] Sarg.), western red cedar (Thuja plicata Donn) and Douglas-fir codominate (Figure 9).

Table 3. Characteristics of a multi-scale analysis of the influences on fire occurrence.

Application Watershed-scale modeling of fuels and fire (sub-district level)

Fuel succession modeling (district to National Forest level)

Fire event prediction using fire--climate relationships (National Forest to regional level)

Fine scale

(20--75 ha)

Medium scale

(200--15 000 ha)

Broad scale

(0.5--1.0 million ha)

Unit of analysis

Trees or points Watersheds National Forests

Climatic variables

Reconstructions of climatic variables from watershed-level chronologies

Climatic reconstructions from broad-scale climatic data

Regional climatic variables (Palmer Drought Severity Index, El Niño Southern Oscillation, Pacific Decadal Oscillation)

Biophysical variables

Aspect, slope, elevation, solar radiation (30-m resolution)

Potential vegetation, solar radiation (1-km resolution), slope, elevation, aspect (summarized by watershed)

Geographic gradients of medium-scale variables

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Quartzite South Deep

SwaukEntiat

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Figure 8: Superposed-epoch analysis for each watershed in the area studied by Hessl et al. 2003), showing departures from the mean annual Palmer drought severity index (PDSI) during fires that affected ≥10% of the trees used to reconstruct the fire history. “Lag year” represents the time before (negative values) or after (positive values) the fire; the fire itself occurs in lag year 0. PDSI is shown during, prior to, and following the fire year. The horizontal dashed and solid lines represent 95% and 99% confidence intervals, respectively. Adapted from Hessl et al. (2003).

Throughout the 10 000+ yr lake sediment core, rapid sedimentation rates yielded a high-resolution (average 10.5 yr cm-1) record of fires. Superposed-epoch analysis of the charcoal accumulation rates and total macrofossil accumulation rates demonstrated a statistically significant (p = 0.001) decrease in macrofossil accumulation rates following peaks in charcoal accumulation rates. Because macrofossils represent the local vegetation that typically lies within 30 m of a lake (Dunwiddie 1987), this association of macrofossils with peaks in charcoal

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accumulation rates suggests that the charcoal record represents local fires. In the Thunder Creek watershed, fire regimes appear to have varied during the broad climatic changes of the last 10 000 yr. The mean fire-return interval for the Holocene period is 227 yr, with more frequent fires in the early Holocene (an interval of 158 yr) than in the mid-Holocene (214 yr) and late Holocene (308 yr).

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A

B

Figure 9: Age-class frequency distributions for (A) dry forest and (B) moist forest in the Thunder Creek watershed. Pico = lodgepole pine, Pimo = western white pine (Pinus monticola Dougl.), Psme = Douglas-fir, Thpl = western red cedar, Tshe = western hemlock. Adapted from Prichard (2003).

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Table 4. Candidate mechanisms associated with temporal and spatial patterns of fire.

Pattern Fire pattern Probable driving factor

Dispersed Fires are less likely soon after a fire.

Fuel buildup is the primary constraint.

Random Fires are equally likely at any time.

The dominant factor is stochastic (e.g., local extreme weather conditions).

Aggregated Fires are more likely soon after a fire.

Periods of favorable climate (weather) for fire, potentially corresponding to cycles of productivity and drought.

Synchronous Multiple fires occur across polygons with similar aspect, or across watersheds or regions in the same year.

Local to regional-scale climate patterns, depending on the geographic extent of the synchrony.

Asynchronous No relationship between the timing of fires across the landscape.

Climate is not an important driver; topography, fuels, or human influence may be dominant.

Target applications

This study provides a unique reconstruction of the historic range of variability in mixed-severity fire regimes by combining the spatial dynamics of vegetation relatively soon after a fire (<150 yr) with the temporal dynamics of fire and vegetation throughout the Holocene period. Mixed-severity fire regimes, common in transitional climates such as that of the Thunder Creek watershed, are often more complex in terms of spatial patchiness and temporal variability than are low- or high-severity regimes (Agee 1998), and are consequently more difficult to simulate. Furthermore, in spite of how common mixed-severity fire regimes are in mountain forest ecosystems, they have received little attention in fire history studies or modeling (Agee 1993). Although the Holocene results lie outside the typical temporal range of landscape modeling, this study will provide both a baseline and a historic record that can guide long-term simulations and future monitoring of fire and forest dynamics in the Cascade Range. For example, these research results are currently being used to validate the application of a process-based model of fires and succession for mixed-severity fire regimes (Keane et al. 1999, Fagre et al. 2003).

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LINKING EMPIRICAL STUDIES TO LANDSCAPE MODELING

We suggest that modelers should address several considerations when designing empirical research to support landscape-level modeling:

Determine the extent and resolution of the modeling

Equilibrium models may adequately support simulations based on current conditions, whereas simulations that incorporate climatic change may require methods based on dynamic time series. If extrapolation from existing data is part of the simulation exercise, empirical models should be robust enough that they are not just valid for the conditions under which they were built. Qualitative models should provide for the inclusion of categories not represented in the initial data. For example, the hierarchical model of fire regimes (Figure 3) allowed the prediction of fire frequency for vegetation types not represented in the fire history database.

Determine the robustness of the existing data sources

What is the predictive ability of models based on available data? For example, the transition-rule model (Figure 1) was qualitative because insufficient quantitative data were available at the appropriate spatial scale. Similarly, the Interior Columbia River Basin fire-frequency model could not incorporate spatial autocorrelation, even though fire is known to be contagious, because the spatial pattern in the raw data was clustered. In contrast, the fire history data from eastern Washington permits spatial modeling within watersheds, but not among them. However, the qualitative change in fire regimes that seems to have occurred around 1900 prevents extrapolation of quantitative fire--climate relationships from an earlier period (1700-1900) to the present day. The Thunder Creek study records broad-scale temporal variation in fire regimes over 10000+ yr, but extrapolation to regions outside the North Cascade Range would be unwise.

Tune the complexity of the empirical model to the structure of the simulation

Most simulation models use explicit time steps, between which values of each variable change as a mechanistic function of the variable’s current state and the state of other variables. An autoregressive model built from empirical data for one of these variables would be superfluous unless it could be combined with mechanistic functions – a process that entails considerable additional complexity, with little theory to support it. Similarly, some mechanistic models use biogeochemical cycling to compute litterfall, mortality, decomposition, and the resulting load of dead fuel (e.g., Keane et al. 1999). An empirical model of fuel succession, in which a statistical relationship between the elapsed time since disturbance and fuel abundance is modeled, would be superfluous because its function is superseded by routines within the simulation model. In both these examples, the empirical models could be of considerable scientific interest, but not of particular value to the simulation.

Report the uncertainties and limitations of the empirical model

Stochastic simulation models can explicitly incorporate the error structure of any empirical relationships. Deterministic models can at least use knowledge of this error structure to design

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sensitivity analyses efficiently. The statistical error structure of a quantitative empirical model (e.g., variances, confidence intervals, patterns of residuals) is the easiest type to report, but other errors based on dubious assumptions, extrapolations, or inadequate data collection should also be evaluated. For example, in the Interior Columbia River Basin fire-frequency study, substantial uncertainties in the fire history data were suspected because of the dearth of data points for many of the reconstructions. A sensitivity analysis was therefore conducted of the regional-scale prediction errors associated with inadequate cross-dating of fire scars (Madany et al. 1982, McKenzie et al. 2000).

CONCLUSIONS

Emulating historic fire disturbance within the landscape requires a quantitative understanding of fire regimes at multiple scales (Perera and Buse 2003, Suffling and Perera 2003, Keane et al. 2003). In the present chapter, we illustrated some empirical approaches for generating statistical models and new data layers for landscape-level fire simulations. Many other empirical studies with different spatial, temporal, and taxonomic resolution would inform or help parameterize landscape simulations. Some examples include:

• a fuelbed classification with sufficient links between vegetation and fuel load that it can be applied to existing vegetation coverage and to initialize fire-behavior modules (Sandberg et al. 2001);

• statistical models of fire-climate interactions in the American Southwest (Swetnam and Betancourt 1990), the southern Rocky Mountains in Colorado, USA (Veblen et al. 2000), and the Canadian boreal forest (Skinner et al. 1999, 2002);

• other studies that use palaeoecological records (Long et al. 1998, Gavin and Brubaker 1999, Millspaugh et al. 2000).

The limitations of each of our examples suggest future data needs and research directions. The models of fire frequency would be improved by more extensive collection of fire history data, especially in ecosystems with long fire-return intervals. Even so, there are limits to the interpretation of fire history data collected at different spatial scales; fire history models from eastern Washington State indicate that both fire frequency and the constraints on fire occurrence are scale-dependent. To be most useful to modelers, we need to identify scaling laws that can translate local fire history information to the broad scales at which models of landscape disturbance are applied. Because we may never have enough empirical data to characterize every forested landscape adequately for modeling, we must instead develop robust methods for making inferences across spatial and temporal scales and across varying levels of taxonomic (e.g., species vs. life-form) resolution.

Landscape-level modeling of fire is one method of simulating natural forest disturbance. We have focused on applications of empirical models to landscape simulation, but empirical models can also directly inform the emulation of natural disturbance on real landscapes to help achieve management objectives. Our work suggests that for management applications and to support policy development, we need to be able to integrate empirical models from multiple scales, not

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only because of the hierarchical nature of administrative agencies but also because of the varying scales that we identified for landscape patterns and processes, in part, by empirical modeling.

For example, in the dry forest ecosystems of the Pacific Northwest of the United States, which the majority of our research represents, the principal means of emulating natural disturbance are prescribed fire under controlled conditions and mechanical treatments designed to emulate the disturbance processes that gave rise to historical landscapes. Our analysis of historical low-severity fire regimes can be directly applied to temporal patterns of prescribed fire by identifying the extent and frequency of the fires, and indirectly applied to silvicultural prescriptions by predicting the density and age structure of trees in the post-treatment stands. These prescriptions for emulating natural disturbance are best applied at the (local) scale of National Forest districts.

The fire-frequency models for the Interior Columbia River Basin are best applied to broad-scale (regional) management, in which an array of individual strategies may be necessary to emulate natural disturbance in different landscapes. Particularly in ecosystems characterized by high-severity, low-frequency fires, emulation will be very difficult because unlike in much of the Canadian boreal forest, fire-return intervals are up to four times as long (200 to more than 300 yr) as even the longest silvicultural rotations, and prescribed crown fire is not currently a management option.

The fire-succession study in the Thunder Creek watershed suggests that ecosystem response to moderate-severity fires is complex, and that simple attempts to emulate natural disturbance (e.g., clearcutting followed by planting economically desirable species) are unlikely to reproduce the species composition and spatial pattern expected after a fire. At a minimum, a careful selection of the residual trees that will serve as a seed source and an attempt to recapture pre-fire species composition through replanting would be necessary.

Finally, our empirical studies exemplify the variety of methods and data sources necessary for both a multi-scale understanding of natural disturbance and identification of the range of opportunities and limitations for emulating them via management. At local scales, detailed quantitative knowledge of ecosystems is the key to successfully emulating and possibly restoring natural disturbance regimes. At the policy level, understanding the variability in natural disturbance regimes across geographic regions will inform more flexible, adaptive, and efficient strategies for managing ecosystems.

ACKNOWLEDGMENTS

We thank Robert Keane and Ajith Perera for encouraging us to develop the ideas in this chapter and for providing the initial venues for their presentation. Alynne Bayard, Lara Kellogg, and Robert Norheim produced the maps. Ze’ev Gedalof and Charles Halpern provided helpful comments on an earlier draft. Research was funded by the USDA Forest Service’s Pacific Northwest Research Station, the Canon National Park Science Scholars Program, and the Global Change Research Program of the United States Geological Survey.

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