The Changing Strength and Nature of Fire-Climate Relationships in the Northern Rocky Mountains, U.S.A., 1902-2008

<div><p>Time-varying fire-climate relationships may represent an important component of fire-regime variability, relevant for understanding the controls of fire and projecting fire activity under global-change scenarios. We used time-varying statistical models to evaluate if and how fire-climate relationships varied from 1902-2008, in one of the most flammable forested regions of the western U.S.A. Fire-danger and water-balance metrics yielded the best combination of calibration accuracy and predictive skill in modeling annual area burned. The strength of fire-climate relationships varied markedly at multi-decadal scales, with models explaining < 40% to 88% of the variation in annual area burned. The early 20th century (1902-1942) and the most recent two decades (1985-2008) exhibited strong fire-climate relationships, with weaker relationships for much of the mid 20th century (1943-1984), coincident with diminished burning, less fire-conducive climate, and the initiation of modern fire fighting. Area burned and the strength of fire-climate relationships increased sharply in the mid 1980s, associated with increased temperatures and longer potential fire seasons. Unlike decades with high burning in the early 20th century, models developed using fire-climate relationships from recent decades overpredicted area burned when applied to earlier periods. This amplified response of fire to climate is a signature of altered fire-climate-relationships, and it implicates non-climatic factors in this recent shift. Changes in fuel structure and availability following 40+ yr of unusually low fire activity, and possibly land use, may have resulted in increased fire vulnerability beyond expectations from climatic factors alone. Our results highlight the potential for non-climatic factors to alter fire-climate relationships, and the need to account for such dynamics, through adaptable statistical or processes-based models, for accurately predicting future fire activity.</p></div

Results

Result

Data

Dat

Figures

This archive contains the figures from the main text, available in 300 dpi .tif, .jpg and .fig (matlab) formats

Cross-validation skill, model parameters, and strength of fire-climate relationships for top metrics.

<p>Small symbols represent cross-validation skill and the regression parameter for 21-yr calibration windows, stratified by Period 1 (1902–1942, circles), Period 2 (1943–1984, squares) and Period 3 (1984–2008, triangles). The grayscale of each small symbol represents <i>r</i>^<i>2</i> or <i>R</i>^<i>2</i>_<i>adj</i> for that window (as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127563#pone.0127563.g005" target="_blank">Fig 5B</a>), and large symbols represent the centroid of all values within each period, +/- one standard deviation. Regression parameters represent the slope of the model, β₁, for single-variable regression models. GDD₀ represents β₂ from the combined DMC, GDD₀ model, while PPT_JJA, T_JA, and T_MAM represent β_1, β₂, and β₃ from the three-variable model, respectively. Parameter values indicated the unit (standard deviation) change in log-transformed area burned for a unit (standard deviation) change in the predictor variable: more extreme values indicated a greater influence on annual area burned. Values below the dashed vertical line on the x axis (<i>CE</i> = 0) lack cross-validation skill. Metrics are ordered from upper left to bottom right based on the overall model score (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127563#pone.0127563.t002" target="_blank">Table 2</a>).</p

Generalized conceptual model for causes and signatures of shifting fire activity.

<p>Scenarios include random variability in “climate” (i.e., a hypothetical metric linked to annual fire activity) which directly determines “fire activity” (e.g., annual area burned or number of large fires). Period 1 is identical in all scenarios, but the y axes are scaled based on values in Period 2. See “<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127563#sec001" target="_blank">Introduction</a>” for a description of each scenario. In all cases of varying fire-climate relationships, a coefficient of efficiency (<i>CE</i>) statistic < 0 indicates a lack of predictive skill (for periods outside of the calibration period). β₀ (intercept) and β₁ (slope) represent regression parameters; directional changes in parentheses represent hypothetical scenarios not illustrated in the figure.</p

Appendix A. Radiocarbon ages and associated metadata.

Radiocarbon ages and associated metadata

Scripts

Script

ClimateData

ClimateDat

MW1B charcoal.xlsx

Charcoal data from site MW1B