Evaluating predictive models of critical live fuel moisture in the Santa Monica Mountains, California

Philip E. Dennison; Max A. Moritz; Robert S. Taylor

doi:10.1071/wf07017

Evaluating predictive models of critical live fuel moisture in the Santa Monica Mountains, California

International Journal of Wildland Fire ◽

10.1071/wf07017 ◽

2008 ◽

Vol 17 (1) ◽

pp. 18 ◽

Cited By ~ 51

Author(s):

Philip E. Dennison ◽

Max A. Moritz ◽

Robert S. Taylor

Keyword(s):

Predictive Models ◽

Fire History ◽

Fuel Moisture ◽

Spring Precipitation ◽

Santa Monica Mountains ◽

Santa Monica ◽

Show Evidence ◽

Large Fires ◽

Live Fuel Moisture ◽

Wind Events

Large wildfires in the Santa Monica Mountains of southern California occur when low levels of live and dead fuel moisture coincide with Santa Ana wind events. Declining live fuel moisture may reach a threshold that increases susceptibility to large wildfires. Live fuel moisture and fire history data for the Santa Monica Mountains from 1984 to 2005 were used to determine a potential critical live fuel moisture threshold, below which large fires become much more likely. The ability of live fuel moisture, remote sensing, and precipitation variables to predict the annual timing of 71 and 77% live fuel moisture thresholds was assessed. Spring precipitation, measured through the months of March, April, and May, was found to be strongly correlated with the annual timing of both live fuel moisture thresholds. Large fires in the Santa Monica Mountains only occurred after the 77% threshold was surpassed, although most large fires occurred after the less conservative 71% threshold. Spring precipitation has fluctuated widely over the past 70 years but does not show evidence of long-term trends. Predictive models of live fuel moisture threshold timing may improve planning for large fires in chaparral ecosystems.

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Critical live fuel moisture in chaparral ecosystems: a threshold for fire activity and its relationship to antecedent precipitation

International Journal of Wildland Fire ◽

10.1071/wf08055 ◽

2009 ◽

Vol 18 (8) ◽

pp. 1021 ◽

Cited By ~ 55

Author(s):

Philip E. Dennison ◽

Max A. Moritz

Keyword(s):

Los Angeles ◽

Fire History ◽

Regression Tree ◽

Fire Regimes ◽

Winter Precipitation ◽

Fire Season ◽

Climate Change Scenarios ◽

Fuel Moisture ◽

Spring Precipitation ◽

Live Fuel Moisture

Large wildfires in southern California typically occur during periods of reduced live fuel moisture (LFM) and high winds. Previous work has found evidence that a LFM threshold may determine when large fires can occur. Using a LFM time series and a fire history for Los Angeles County, California, we found strong evidence for a LFM threshold near 79%. Monthly and 3-month total precipitation data were used to show that the timing of this threshold during the fire season is strongly correlated with antecedent rainfall. Spring precipitation, particularly in the month of March, was found to be the primary driver of the timing of LFM decline, although regression tree analysis revealed that high winter precipitation may delay the timing of the threshold in some years. This work further establishes relationships between precipitation and fire potential that may prove important for anticipating shifts in fire regimes under climate-change scenarios.

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Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem

International Journal of Wildland Fire ◽

10.1071/wf08020 ◽

2009 ◽

Vol 18 (4) ◽

pp. 430 ◽

Cited By ~ 57

Author(s):

Emilio Chuvieco ◽

Isabel González ◽

Felipe Verdú ◽

Inmaculada Aguado ◽

Marta Yebra

Keyword(s):

Logistic Regression ◽

Moisture Content ◽

Fire Danger ◽

Burned Area ◽

Mediterranean Ecosystem ◽

Fuel Moisture ◽

Fire Occurrence ◽

Large Fires ◽

Live Fuel Moisture ◽

Fuel Moisture Content

The present paper presents and discusses the relationships between live Fuel Moisture Content (FMC) measurements and fire occurrence (number of fires and burned area) in a Mediterranean area of central Spain. Grasslands and four shrub species (Cistus ladanifer L., Rosmarinus officinalis L., Erica australis L. and Phillyrea angustifolia L.) were sampled in the field from the spring to the summer season over a 9-year period. Higher seasonal FMC variability was found for the herbaceous species than for shrubs, as grasslands have very low values in summertime. Moisture variations of grasslands were found to be good predictors of number of fires and total burned surface, while moisture variation of two shrubs (C. ladanifer L. and R. officinalis L.) was more sensitive to both the total burned area and the occurrence of large fires. All these species showed significant differences between the FMC of high and low occurrence periods. Three different logistic regression models were built for the 202 periods of analysis: one to predict periods with more and less than seven fires, another to predict periods with and without large fires (>500 ha), and the third to predict periods with more and less than 200 ha burned. The results showed accuracy in predicting periods with a high number of fires (94%), and extensive burned area (85%), with less accuracy in estimating periods with large fires (58%). Finally, empirical functions based on logistic regression analysis were successfully related to fire ignition or potential burned area from FMC data. These models should be useful to integrate FMC measurements with other variables of fire danger (ignition causes, for instance), to provide a more comprehensive assessment of fire danger conditions.

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SYCAMORE CANYON FALLS: A BEDROCK-CONTROLLED KNICKPOINT IN THE SANTA MONICA MOUNTAINS, CALIFORNIA

10.1130/abs/2020cd-347151 ◽

2020 ◽

Author(s):

James L. Pinto ◽

◽

Daniel J. Holiday ◽

Brandon J. Carignan ◽

Roger L. Putnam

Keyword(s):

Santa Monica Mountains ◽

Santa Monica

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Improved fuel moisture prediction in non-native tropical Megathyrsus maximus grasslands using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived vegetation indices

International Journal of Wildland Fire ◽

10.1071/wf16131 ◽

2017 ◽

Vol 26 (5) ◽

pp. 384

Author(s):

L. M. Ellsworth ◽

A. P. Dale ◽

C. M. Litton ◽

T. Miura

Keyword(s):

Vegetation Index ◽

Vegetation Indices ◽

Fuel Moisture ◽

Fire Behaviour ◽

Native Grass ◽

Tropical Ecosystems ◽

Moderate Resolution ◽

Resolution Imaging ◽

Live Fuel Moisture ◽

Moderate Resolution Imaging Spectroradiometer

The synergistic impacts of non-native grass invasion and frequent human-derived wildfires threaten endangered species, native ecosystems and developed land throughout the tropics. Fire behaviour models assist in fire prevention and management, but current models do not accurately predict fire in tropical ecosystems. Specifically, current models poorly predict fuel moisture, a key driver of fire behaviour. To address this limitation, we developed empirical models to predict fuel moisture in non-native tropical grasslands dominated by Megathyrsus maximus in Hawaii from Terra Moderate-Resolution Imaging Spectroradiometer (MODIS)-based vegetation indices. Best-performing MODIS-based predictive models for live fuel moisture included the two-band Enhanced Vegetation Index (EVI2) and Normalized Difference Vegetation Index (NDVI). Live fuel moisture models had modest (R2=0.46) predictive relationships, and outperformed the commonly used National Fire Danger Rating System (R2=0.37) and the Keetch–Byram Drought Index (R2=0.06). Dead fuel moisture was also best predicted by a model including EVI2 and NDVI, but predictive capacity was low (R2=0.19). Site-specific models improved model fit for live fuel moisture (R2=0.61), but limited extrapolation. Better predictions of fuel moisture will improve fire management in tropical ecosystems dominated by this widespread and problematic non-native grass.

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