Spatial datasets of probabilistic wildfire risk components for the United States (270m) (2nd Edition)

Short, Karen C.; Finney, Mark A.; Vogler, Kevin C.; Scott, Jill R.; Gilbertson-Day, Julie W.; Grenfell, Isaac C.

doi:10.2737/rds-2016-0034-2

Cited by 32 publications

(32 citation statements)

References 8 publications

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“…3A). This contrasted with the static burn probability map (Short et al 2020); although proportional burned area increased steadily through the 8 th decile bin, roughly equal proportions of the upper 3 decile bins burned (Fig. 3A).…”

Section: Hindcast Evaluationmentioning

confidence: 61%

“…Spatial tools quantifying wildfire hazard fall into two broad categories: static land cover- and climate-based maps depicting the long-term average annual probability of burning (e.g., Short et al 2020), and short-term (daily to weekly) indices based on rapidly changing variables such as fuel moisture, humidity, wind speed, and temperature (e.g., the Keetch-Byram Drought Index [Keetch and Byram 1968] and the various indices under the National Fire Danger Rating System [Deeming et al 1972, Burgan et al 1998]). These tools reflect a historical focus on “flammability-limited” forested ecosystems, where fuel quantity changes little from one year to the next but fire danger can change rapidly within a season as the moisture content of fuels responds to atmospheric conditions (Krawchuk and Moritz 2011, Abatzoglou and Kolden 2013).…”

Section: Introductionmentioning

confidence: 99%

“…We anticipated that short-term drought indices and herbaceous vegetation production in the spring would become increasingly informative as the forecast date approached the onset of the fire season, and therefore forecasts made earlier in the year, when they would potentially be more useful for planning, would be less accurate than forecasts made later in the spring. Finally, to explore the utility of our approach relative to currently available spatial fire risk products we compare the skill of our hindcasts to a static burn probability map widely used by land management agencies (Short et al 2020) in predicting the spatial patterns of large wildfires over the last 3 decades.…”

Section: Introductionmentioning

confidence: 99%

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Where there’s smoke, there’s fuel: dynamic vegetation data improve predictions of wildfire hazard in the Great Basin

Smith

et al. 2021

Preprint

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Wildfires are a growing management concern in western US rangelands, where invasive annual grasses have altered fire regimes and contributed to an increased incidence of catastrophic large wildfires. Fire activity in arid, non-forested regions is thought to be largely controlled by interannual variation in fuel amount, which in turn is controlled by antecedent weather. Thus, long-range forecasting of fire activity in rangelands should be feasible given annual estimates of fuel quantity. Using a 32 yr time series of spatial data, we employ machine learning algorithms to predict the relative probability of large (>400 ha) wildfire in the Great Basin based on fine-scale annual and 16-day estimates of cover and production of vegetation functional groups, weather, and multitemporal scale drought indices. We evaluate the predictive utility of these models with a leave-one-year-out cross-validation, building spatial forecasts of fire probability for each year that we compare against actual maps of large wildfires. Herbaceous vegetation aboveground biomass production, bare ground cover, and long-term drought indices were the most important predictors of fire probability. Across 32 fire seasons, >80% of the area burned in large wildfires coincided with predicted fire probabilities ≥0.5. At the scale of the Great Basin, several metrics of fire season severity were moderately to strongly correlated with average fire probability, including total area burned in large wildfires, number of large wildfires, and average and maximum fire size. Our findings show that recent years of exceptional fire activity in the Great Basin were predictable based on antecedent weather and biomass of fine fuels, and reveal a significant increasing trend in fire probability over the last three decades driven by widespread changes in fine fuel characteristics.

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Section: Hindcast Evaluationmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Where there’s smoke, there’s fuel: dynamic vegetation data improve predictions of wildfire hazard in the Great Basin

Smith

et al. 2021

Preprint

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“…Large wildfire burn probability raster files are downloaded from U.S. Department of Agriculture (Short et al 2020). Then, the "Extract by Mask" tool in ArcGIS ® is used to delineate the large fire probabilities in Louisiana and surroundings (50-km buffer).…”

Section: Assessing Historical Wildfire Burn Probabilitymentioning

confidence: 99%

“…The wildfire probability calculation follows the method of Dillon et al (2015), which uses large fire (i.e., > 300 acres) probability from FSim (Finney et al 2011) supplemented by a collection of small fire (i.e., < 300 acres) probabilities from the Fire Protection Agency (FPA) fire occurrence data (Short 2013). The total probability is calculated as the sum of large and small fire probability.Large wildfire burn probability raster files are downloaded from U.S. Department of Agriculture (Short et al 2020). Then, the "Extract by Mask" tool in ArcGIS ® is used to delineate the large fire probabilities in Louisiana and surroundings (50-km buffer).…”

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confidence: 99%

Future Property Risk Estimation For Wildfire In Louisiana, USA

Mostafiz

Friedland

Rohli

et al. 2022

Preprint

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Background: Wildfire is an important but understudied natural hazard. As with other natural hazards, wildfire research is all too often conducted at too broad a spatial scale to identify local or regional patterns. This study addresses these gaps by examining the current and future wildfire property risk at the census-block level in Louisiana, a U.S. state with relatively dense population and substantial vulnerability to loss from this hazard, despite its wet climate. Here wildfire risk is defined as the product of exposure and vulnerability to the hazard, where exposure is a function of the historical and anticipated future wildfire frequency and extent, and the latter is a function of population, structure and content property value, damage probability, and percent of property damaged. Results: Historical (1992−2015) average annual statewide property loss due to wildfire was $5,556,389 (2010$), with the greatest risk to wildfire in southwestern inland, east-central, extreme northwestern, and coastal southwestern Louisiana. Based on existing climate and environmental model output, this research projects that wildfire will increase by 25 percent by 2050 in Louisiana from current values. When combined with projections of population and property value, it is determined that the geographic distribution of risk by 2050 will remain similar to that today – with highest risk in southwestern inland Louisiana and east-central Louisiana. However, the magnitude of risk will increase across the state, especially in those areas. Projected annual loss will be $11,167,496 by 2050 (2010$) due to population growth, intensification of development at the wildland-urban interface, and climate change. The wildfire-induced property damage is notable because it is projected to increase by 101 percent. These values do not include crop, forestry, or indirect losses (e.g., cost of evacuation and missed time at work), which are likely to be substantial. Conclusions: The results suggest that increased efforts are needed to contain wildfires, to reduce the future risk. Otherwise, wildfire managers, environmental planners, actuaries, community leaders, and individual property owners in Louisiana will need to anticipate and budget for additional efforts to mitigate the economic (and presumably other) impacts associated with a substantial and increasing hazard that often goes underestimated.

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Assessment of Regional Nursing Home Preparedness for and Regulatory Responsiveness to Wildfire Risk in the Western US

et al. 2023

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ImportanceIt is uncertain whether emergency preparedness and regulatory oversight for US nursing homes are aligned with local wildfire risk.ObjectiveTo evaluate the likelihood that nursing homes at elevated risk of wildfire exposure meet US Centers for Medicare &amp; Medicaid Services (CMS) emergency preparedness standards and to compare the time to reinspection by exposure status.Design, Setting, and ParticipantsThis cross-sectional study of nursing homes in the continental western US from January 1, 2017, through December 31, 2019, was conducted using cross-sectional and survival analyses. The prevalence of high-risk facilities within 5 km of areas at or exceeding the 85th percentile of nationalized wildfire risk across areas overseen by 4 CMS regional offices (New Mexico, Mountain West, Pacific/Southwest, and Pacific Northwest) was determined. Critical emergency preparedness deficiencies cited during CMS Life Safety Code Inspections were identified. Data analysis was performed from October 10 to December 12, 2022.Main Outcomes and MeasuresThe primary outcome classified whether facilities were cited for at least 1 critical emergency preparedness deficiency during the observation window. Regionally stratified generalized estimating equations were used to evaluate associations between risk status and the presence and number of deficiencies, adjusted for nursing home characteristics. For the subset of facilities with deficiencies, differences in restricted mean survival time to reinspection were evaluated.ResultsOf the 2218 nursing homes in this study, 1219 (55.0%) were exposed to elevated wildfire risk. The Pacific/Southwest had the highest percentage of both exposed (680 of 870 [78.2%]) and unexposed (359 of 486 [73.9%]) facilities with 1 or more deficiencies. The Mountain West had the largest difference in the percentage of exposed (87 of 215 [40.5%]) vs unexposed (47 of 193 [24.4%]) facilities with 1 or more deficiencies. Exposed facilities in the Pacific Northwest had the greatest mean (SD) number of deficiencies (4.3 [5.4]). Exposure was associated with the presence of deficiencies in the Mountain West (odds ratio [OR], 2.12 [95% CI, 1.50-3.01]) and the presence (OR, 1.84 [95% CI, 1.55-2.18]) and number (rate ratio, 1.39 [95% CI, 1.06-1.83]) of deficiencies in the Pacific Northwest. Exposed Mountain West facilities with deficiencies were reinspected later, on average, than unexposed facilities (adjusted restricted mean survival time difference, 91.2 days [95% CI, 30.6-151.8 days]).Conclusions and RelevanceIn this cross-sectional study, regional heterogeneity in nursing home emergency preparedness for and regulatory responsiveness to local wildfire risk was observed. These findings suggest that there may be opportunities to improve the responsiveness of nursing homes to and regulatory oversight of surrounding wildfire risk.

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Spatial datasets of probabilistic wildfire risk components for the United States (270m) (2nd Edition)

Cited by 32 publications

References 8 publications

Where there’s smoke, there’s fuel: dynamic vegetation data improve predictions of wildfire hazard in the Great Basin

Where there’s smoke, there’s fuel: dynamic vegetation data improve predictions of wildfire hazard in the Great Basin

Future Property Risk Estimation For Wildfire In Louisiana, USA

Assessment of Regional Nursing Home Preparedness for and Regulatory Responsiveness to Wildfire Risk in the Western US

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