scholarly journals Severe Fire Danger Index: A Forecastable Metric to Inform Firefighter and Community Wildfire Risk Management

Fire ◽  
2019 ◽  
Vol 2 (3) ◽  
pp. 47 ◽  
Author(s):  
W. Matt Jolly ◽  
Patrick H. Freeborn ◽  
Wesley G. Page ◽  
Bret W. Butler
Keyword(s):  
United States ◽  
Early Warning ◽  
Wildland Fire ◽  
Fire Danger ◽  
Numerical Weather ◽  
Area Burned ◽  
Active Fire ◽  
Radiative Power ◽  
Very High ◽  
Fire Radiative Power

Despite major advances in numerical weather prediction, few resources exist to forecast wildland fire danger conditions to support operational fire management decisions and community early-warning systems. Here we present the development and evaluation of a spatial fire danger index that can be used to assess historical events, forecast extreme fire danger, and communicate those conditions to both firefighters and the public. It uses two United States National Fire Danger Rating System indices that are related to fire intensity and spread potential. These indices are normalized, combined, and categorized based on a 39-yr climatology (1979–2017) to produce a single, categorical metric called the Severe Fire Danger Index (SFDI) that has five classes; Low, Moderate, High, Very High, and Severe. We evaluate the SFDI against the number of newly reported wildfires and total area burned from agency fire reports (1992–2017) as well as daily remotely sensed numbers of active fire pixels and total daily fire radiative power for large fires (2003–2016) from the Moderate-Resolution Imaging Spectroradiometer (MODIS) across the conterminous United States. We show that the SFDI adequately captures geographic and seasonal variations of fire activity and intensity, where 58% of the eventual area burned reported by agency fire records, 75.2% of all MODIS active large fire pixels, and 81.2% of all fire radiative power occurred when the SFDI was either Very High or Severe (above the 90th percentile). We further show that SFDI is a strong predictor of firefighter fatalities, where 97 of 129 (75.2%) burnover deaths from 1979 to 2017 occurred when SFDI was either Very High or Severe. Finally, we present an operational system that uses short-term, numerical weather predictions to produce daily SFDI forecasts and show that 76.2% of all satellite active fire detections during the first 48 h following the ignition of nine high-profile case study fires in 2017 and 2018 occurred under Very High or Severe SFDI conditions. The case studies indicate that the extreme weather events that caused tremendous damage and loss of life could be mapped ahead of time, which would allow both wildland fire managers and vulnerable communities additional time to prepare for potentially dangerous conditions. Ultimately, this simple metric can provide critical decision support information to wildland firefighters and fire-prone communities and could form the basis of an early-warning system that can improve situational awareness and potentially save lives.

scholarly journals Meteosat SEVIRI Fire Radiative Power (FRP) products from the Land Surface Analysis Satellite Applications Facility (LSA SAF) – Part 1: Algorithms, product contents and analysis

2015 ◽  
Vol 15 (11) ◽  
pp. 15831-15907 ◽  
Author(s):  
M. J. Wooster ◽  
G. Roberts ◽  
P. H. Freeborn ◽  
W. Xu ◽  
Y. Govaerts ◽  
...  
Keyword(s):  
Surface Analysis ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Land Surface ◽  
Active Fire ◽  
Fire Activity ◽  
Radiative Power ◽  
Satellite Applications ◽  
The Impact ◽  
Fire Radiative Power

Abstract. Characterising changes in landscape scale fire activity at very high temporal resolution is best achieved using thermal observations of actively burning fires made from geostationary Earth observation (EO) satellites. Over the last decade or more, a series of research and/or operational "active fire" products have been developed from these types of geostationary observations, often with the aim of supporting the generation of data related to biomass burning fuel consumption and trace gas and aerosol emission fields. The Fire Radiative Power (FRP) products generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from data collected by the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) are one such set of products, and are freely available in both near real-time and archived form. Every 15 min, the algorithms used to generate these products identify and map the location of new SEVIRI observations containing actively burning fires, and characterise their individual rates of radiative energy release (fire radiative power; FRP) that is believed proportional to rates of biomass consumption and smoke emission. The FRP-PIXEL product contains the highest spatial resolution FRP dataset, delivered for all of Europe, northern and southern Africa, and part of South America at a spatial resolution of 3 km (decreasing away from the west African sub-satellite point) at the full 15 min temporal resolution. The FRP-GRID product is an hourly summary of the FRP-PIXEL data, produced at a 5° grid cell size and including simple bias adjustments for meteorological cloud cover and for the regional underestimation of FRP caused, primarily, by the non-detection of low FRP fire pixels at SEVIRI's relatively coarse pixel size. Here we describe the enhanced geostationary Fire Thermal Anomaly (FTA) algorithm used to detect the SEVIRI active fire pixels, and detail methods used to deliver atmospherically corrected FRP information together with the per-pixel uncertainty metrics. Using scene simulations and analysis of real SEVIRI data, including from a period of Meteosat-8 "special operations", we describe some of the sensor and data pre-processing characteristics influencing fire detection and FRP uncertainty. We show that the FTA algorithm is able to discriminate actively burning fires covering down to 10−4 of a pixel, and is more sensitive to fire than algorithms used within many other widely exploited active fire products. We also find that artefacts arising from the digital filtering and geometric resampling strategies used to generate level 1.5 SEVIRI data can significantly increase FRP uncertainties in the SEVIRI active fire products, and recommend that the processing chains used for the forthcoming Meteosat Third Generation attempt to minimise the impact of these types of operations. Finally, we illustrate the information contained within the current Meteosat FRP-PIXEL and FRP-GRID products, providing example analyses for both individual fires and multi-year regional-scale fire activity. A companion paper (Roberts et al., 2015) provides a full product performance evaluation for both products, along with examples of their use for prescribing fire smoke emissions within atmospheric modelling components of the Copernicus Atmosphere Monitoring Service (CAMS).

Towards improving wildland firefighter situational awareness through daily fire behaviour risk assessments in the US Northern Rockies and Northern Great Basin

2017 ◽  
Vol 26 (7) ◽  
pp. 574 ◽  
Author(s):  
W. Matt Jolly ◽  
Patrick H. Freeborn
Keyword(s):  
Great Basin ◽  
Situational Awareness ◽  
Wildland Fire ◽  
Weather Conditions ◽  
Fire Spread ◽  
Fire Danger ◽  
Fire Behaviour ◽  
Radiative Power ◽  
In Fire ◽  
Fire Danger Indices

Wildland firefighters must assess potential fire behaviour in order to develop appropriate strategies and tactics that will safely meet objectives. Fire danger indices integrate surface weather conditions to quantify potential variations in fire spread rates and intensities and therefore should closely relate to observed fire behaviour. These indices could better inform fire management decisions if they were linked directly to observed fire behaviour. Here, we present a simple framework for relating fire danger indices to observed categorical wildland fire behaviour. Ordinal logistic regressions are used to model the probabilities of five distinct fire behaviour categories that are then combined with a safety-based weight function to calculate a Fire Behaviour Risk rating that can plotted over time and spatially mapped. We demonstrate its development and use across three adjacent US National Forests. Finally, we compare predicted fire behaviour risk ratings with observed variations in satellite-measured fire radiative power and we link these models with spatial fire danger maps to demonstrate the utility of this approach for landscape-scale fire behaviour risk assessment. This approach transforms fire weather conditions into simple and actionable fire behaviour risk metrics that wildland firefighters can use to support decisions that meet required objectives and keep people safe.

scholarly journals Characterization of wildfire NO<sub>x</sub> emissions using MODIS fire radiative power and OMI tropospheric NO<sub>2</sub> columns

2011 ◽  
Vol 11 (12) ◽  
pp. 5839-5851 ◽  
Author(s):  
A. K. Mebust ◽  
A. R. Russell ◽  
R. C. Hudman ◽  
L. C. Valin ◽  
R. C. Cohen
Keyword(s):  
Ozone Monitoring Instrument ◽  
Fire Emissions ◽  
Active Fire ◽  
Radiative Power ◽  
Flaming Combustion ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Current Emission ◽  
Fire Radiative Power

Abstract. We use observations of fire radiative power (FRP) from the Moderate Resolution Imaging Spectroradiometer~(MODIS) and tropospheric NO2 column measurements from the Ozone Monitoring Instrument (OMI) to derive NO2 wildfire emission coefficients (g MJ−1) for three land types over California and Nevada. Retrieved emission coefficients were 0.279±0.077, 0.342±0.053, and 0.696±0.088 g MJ−1 NO2 for forest, grass and shrub fuels, respectively. These emission coefficients reproduce ratios of emissions with fuel type reported previously using independent methods. However, the magnitude of these coefficients is lower than prior estimates. While it is possible that a negative bias in the OMI NO2 retrieval over regions of active fire emissions is partly responsible, comparison with several other studies of fire emissions using satellite platforms indicates that current emission factors may overestimate the contributions of flaming combustion and underestimate the contributions of smoldering combustion to total fire emissions. Our results indicate that satellite data can provide an extensive characterization of the variability in fire NOx emissions; 67 % of the variability in emissions in this region can be accounted for using an FRP-based parameterization.

scholarly journals Introducing Spatially Distributed Fire Danger from Earth Observations (FDEO) Using Satellite-Based Data in the Contiguous United States

2020 ◽  
Vol 12 (8) ◽  
pp. 1252 ◽  
Author(s):  
Alireza Farahmand ◽  
E. Natasha Stavros ◽  
John T. Reager ◽  
Ali Behrangi
Keyword(s):  
United States ◽  
Lead Time ◽  
Vegetation Index ◽  
Fire Management ◽  
The United States ◽  
Fire Danger ◽  
Fire Season ◽  
Earth Observations ◽  
Area Burned ◽  
Spatially Distributed

Wildfire danger assessment is essential for operational allocation of fire management resources; with longer lead prediction, the more efficiently can resources be allocated regionally. Traditional studies focus on meteorological forecasts and fire danger index models (e.g., National Fire Danger Rating System—NFDRS) for predicting fire danger. Meteorological forecasts, however, lose accuracy beyond ~10 days; as such, there is no quantifiable method for predicting fire danger beyond 10 days. While some recent studies have statistically related hydrologic parameters and past wildfire area burned or occurrence to fire, no study has used these parameters to develop a monthly spatially distributed predictive model in the contiguous United States. Thus, the objective of this study is to introduce Fire Danger from Earth Observations (FDEO), which uses satellite data over the contiguous United States (CONUS) to enable two-month lead time prediction of wildfire danger, a sufficient lead time for planning purposes and relocating resources. In this study, we use satellite observations of land cover type, vapor pressure deficit, surface soil moisture, and the enhanced vegetation index, together with the United States Forest Service (USFS) verified and validated fire database (FPA) to develop spatially gridded probabilistic predictions of fire danger, defined as expected area burned as a deviation from “normal”. The results show that the model predicts spatial patterns of fire danger with 52% overall accuracy over the 2004–2013 record, and up to 75% overall accuracy during the fire season. Overall accuracy is defined as number of pixels with correctly predicted fire probability classes divided by the total number of the studied pixels. This overall accuracy is the first quantified result of two-month lead prediction of fire danger and demonstrates the potential utility of using diverse observational data sets for use in operational fire management resource allocation in the CONUS.

scholarly journals Current Trend of Carbon Emissions from Wildfires in Siberia

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 559
Author(s):  
Evgenii Ponomarev ◽  
Nikita Yakimov ◽  
Tatiana Ponomareva ◽  
Oleg Yakubailik ◽  
Susan G. Conard
Keyword(s):  
Carbon Emissions ◽  
Current Trend ◽  
Fire Season ◽  
Fire Intensity ◽  
Fire Emissions ◽  
Active Fire ◽  
Radiative Power ◽  
Annual Carbon ◽  
Global Carbon ◽  
Fire Radiative Power

Smoke from wildfires in Siberia often affects air quality over vast territories of the Northern hemisphere during the summer. Increasing fire emissions also affect regional and global carbon balance. To estimate annual carbon emissions from wildfires in Siberia from 2002–2020, we categorized levels of fire intensity for individual active fire pixels based on fire radiative power data from the standard MODIS product (MOD14/MYD14). For the last two decades, estimated annual direct carbon emissions from wildfires varied greatly, ranging from 20–220 Tg C per year. Sporadic maxima were observed in 2003 (>150 Tg C/year), in 2012 (>220 Tg C/year), in 2019 (~180 Tg C/year). However, the 2020 fire season was extraordinary in terms of fire emissions (~350 Tg C/year). The estimated average annual level of fire emissions was 80 ± 20 Tg C/year when extreme years were excluded from the analysis. For the next decade the average level of fire emissions might increase to 250 ± 30 Tg C/year for extreme fire seasons, and to 110 ± 20 Tg C/year for moderate fire seasons. However, under the extreme IPCC RPC 8.5 scenario for Siberia, wildfire emissions might increase to 1200–1500 Tg C/year by 2050 if there were no significant changes in patterns of vegetation distribution and fuel loadings.

Relationships between climate and macroscale area burned in the western United States

2013 ◽  
Vol 22 (7) ◽  
pp. 1003 ◽  
Author(s):  
John T. Abatzoglou ◽  
Crystal A. Kolden
Keyword(s):  
United States ◽  
Snow Water Equivalent ◽  
Climatic Factors ◽  
Fire Danger ◽  
Western United States ◽  
Fire Season ◽  
Drought Indices ◽  
Climate Variables ◽  
Area Burned ◽  
Fire Activity

Increased wildfire activity (e.g. number of starts, area burned, fire behaviour) across the western United States in recent decades has heightened interest in resolving climate–fire relationships. Macroscale climate–fire relationships were examined in forested and non-forested lands for eight Geographic Area Coordination Centers in the western United States, using area burned derived from the Monitoring Trends in Burn Severity dataset (1984–2010). Fire-specific biophysical variables including fire danger and water balance metrics were considered in addition to standard climate variables of monthly temperature, precipitation and drought indices to explicitly determine their optimal capacity to explain interannual variability in area burned. Biophysical variables tied to the depletion of fuel and soil moisture and prolonged periods of elevated fire-danger had stronger correlations to area burned than standard variables antecedent to or during the fire season, particularly in forested systems. Antecedent climate–fire relationships exhibited inter-region commonality with area burned in forested lands correlated with winter snow water equivalent and emergent drought in late spring. Area burned in non-forested lands correlated with moisture availability in the growing season preceding the fire year. Despite differences in the role of antecedent climate in preconditioning fuels, synchronous regional fire activity in forested and non-forested lands suggests that atmospheric conditions during the fire season unify fire activity and can compound or supersede antecedent climatic stressors. Collectively, climate–fire relationships viewed through the lens of biophysical variables provide a more direct link to fuel flammability and wildfire activity than standard climate variables, thereby narrowing the gap in incorporating top-down climatic factors between empirical and process-based fire models.

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