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

Higuera PE, Abatzoglou JT, Littell JS, Morgan P - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: College of Natural Resources, University of Idaho, Moscow, Idaho, United States of America.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Cross-validation skill, model parameters, and strength of fire-climate relationships for top metrics.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 r2 or R2adj for that window (as in Fig 5B), 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 PPTJJA, TJA, and TMAM represent β1, 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 (CE = 0) lack cross-validation skill. Metrics are ordered from upper left to bottom right based on the overall model score (Table 2).
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pone.0127563.g007: Cross-validation skill, model parameters, and strength of fire-climate relationships for top metrics.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 r2 or R2adj for that window (as in Fig 5B), 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 PPTJJA, TJA, and TMAM represent β1, 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 (CE = 0) lack cross-validation skill. Metrics are ordered from upper left to bottom right based on the overall model score (Table 2).

Mentions: The mid-to-late 20th century (1943–1984) was characterized by a shift to below-average area burned, including the seven out of the ten smallest fire years in the record, and not a single year in the upper decile of the record (Fig 5A); more area burned in Dry (42%) vs. Cold (28%) forests (Fig A in S1 Appendix). This shift to decreased burning was associated with significant increases in June-August precipitation, increased Soil Moisture, and decreases in the DMC and Drought Code (Fig 6). Most models explained between ca. 20–50% of the variability in annual area burned, but the DMC and Soil Moisture explained > 60% of the variability for multiple decades (Fig 5B). Regression parameters for top-performing models either changed little (e.g., DMC, Drought Code, GDD0, PETJJA) or became more extreme (Soil Moisture) relative to earlier the period (Fig 5D and Fig 7). Top-performing models showed predictive skill beyond calibration periods (i.e., CE > 0), but CE decreased throughout the period, generally in accordance with the model (Fig 5C). The exception was in PETJJA and the TMAM, TJA, and PJJA model, which lacked cross-validation skill during the latter part of the period (Fig 5C and Fig 7).


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

Higuera PE, Abatzoglou JT, Littell JS, Morgan P - PLoS ONE (2015)

Cross-validation skill, model parameters, and strength of fire-climate relationships for top metrics.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 r2 or R2adj for that window (as in Fig 5B), 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 PPTJJA, TJA, and TMAM represent β1, 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 (CE = 0) lack cross-validation skill. Metrics are ordered from upper left to bottom right based on the overall model score (Table 2).
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Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482589&req=5

pone.0127563.g007: Cross-validation skill, model parameters, and strength of fire-climate relationships for top metrics.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 r2 or R2adj for that window (as in Fig 5B), 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 PPTJJA, TJA, and TMAM represent β1, 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 (CE = 0) lack cross-validation skill. Metrics are ordered from upper left to bottom right based on the overall model score (Table 2).
Mentions: The mid-to-late 20th century (1943–1984) was characterized by a shift to below-average area burned, including the seven out of the ten smallest fire years in the record, and not a single year in the upper decile of the record (Fig 5A); more area burned in Dry (42%) vs. Cold (28%) forests (Fig A in S1 Appendix). This shift to decreased burning was associated with significant increases in June-August precipitation, increased Soil Moisture, and decreases in the DMC and Drought Code (Fig 6). Most models explained between ca. 20–50% of the variability in annual area burned, but the DMC and Soil Moisture explained > 60% of the variability for multiple decades (Fig 5B). Regression parameters for top-performing models either changed little (e.g., DMC, Drought Code, GDD0, PETJJA) or became more extreme (Soil Moisture) relative to earlier the period (Fig 5D and Fig 7). Top-performing models showed predictive skill beyond calibration periods (i.e., CE > 0), but CE decreased throughout the period, generally in accordance with the model (Fig 5C). The exception was in PETJJA and the TMAM, TJA, and PJJA model, which lacked cross-validation skill during the latter part of the period (Fig 5C and Fig 7).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: College of Natural Resources, University of Idaho, Moscow, Idaho, United States of America.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus