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.

Wildfire Management and Forecasting Fire Potential: The Roles of Climate Information and Social Networks in the Southwest United States

2012 ◽  
Vol 4 (2) ◽  
pp. 90-102 ◽  
Author(s):  
Gigi Owen ◽  
Jonathan D. McLeod ◽  
Crystal A. Kolden ◽  
Daniel B. Ferguson ◽  
Timothy J. Brown
Keyword(s):  
United States ◽  
Fire Ecology ◽  
Management Practices ◽  
Fire Management ◽  
Wildland Fire ◽  
Fire Risk ◽  
The United States ◽  
Fire Season ◽  
Types Of Information ◽  
Wildland Fire Management

Abstract Continuing progress in the fields of meteorology, climatology, and fire ecology has enabled more proactive and risk-tolerant wildland fire management practices in the United States. Recent institutional changes have also facilitated the incorporation of more advanced climate and weather research into wildland fire management. One of the most significant changes was the creation of Predictive Services in 1998, a federal interagency group composed, in part, of meteorologists who create climate- and weather-based fire outlooks tailored to fire manager needs. Despite the numerous forecast products now available to fire managers, few studies have examined how these products have affected their practices. In this paper the authors assess how fire managers in the Southwest region of the United States perceive and incorporate different types of information into their management practices. A social network analysis demonstrates that meteorologists have become central figures in disseminating information in the regional interagency fire management network. Interviews and survey data indicate that person-to-person communication during planning phases prior to the primary fire season is key to Predictive Services’ success in supporting fire managers’ decision making. Over several months leading up to the fire season, predictive forecasts based on complex climate, fuels, and fire-risk models are explained to fire managers and updated through frequent communication. The study’s findings suggest that a significant benefit of the information sharing process is the dialogue it fosters among fire managers, locally, regionally, and nationally, which better prepares them to cooperate and strategically plan for the fire season.

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.

scholarly journals Nature inequity and higher COVID-19 case rates in less-green neighbourhoods in the United States

2021 ◽  
Author(s):  
Erica N. Spotswood ◽  
Matthew Benjamin ◽  
Lauren Stoneburner ◽  
Megan M. Wheeler ◽  
Erin E. Beller ◽  
...  
Keyword(s):  
United States ◽  
Low Income ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
The United States ◽  
Incidence Rates ◽  
Lower Income ◽  
Urban Nature ◽  
Urbanized Areas ◽  
Block Groups

AbstractUrban nature—such as greenness and parks—can alleviate distress and provide space for safe recreation during the COVID-19 pandemic. However, nature is often less available in low-income populations and communities of colour—the same communities hardest hit by COVID-19. In analyses of two datasets, we quantified inequity in greenness and park proximity across all urbanized areas in the United States and linked greenness and park access to COVID-19 case rates for ZIP codes in 17 states. Areas with majority persons of colour had both higher case rates and less greenness. Furthermore, when controlling for sociodemographic variables, an increase of 0.1 in the Normalized Difference Vegetation Index was associated with a 4.1% decrease in COVID-19 incidence rates (95% confidence interval: 0.9–6.8%). Across the United States, block groups with lower income and majority persons of colour are less green and have fewer parks. Our results demonstrate that the communities most impacted by COVID-19 also have the least nature nearby. Given that urban nature is associated with both human health and biodiversity, these results have far-reaching implications both during and beyond the pandemic.

scholarly journals Confidence Interval Estimation for Quantitative Precipitation Forecasts (QPF) Using Short-Range Ensemble Forecasts (SREF)

2006 ◽  
Vol 21 (1) ◽  
pp. 24-41 ◽  
Author(s):  
Jung-Sun Im ◽  
Keith Brill ◽  
Edwin Danaher
Keyword(s):  
United States ◽  
Confidence Interval ◽  
Lead Time ◽  
Short Range ◽  
Grid Point ◽  
The United States ◽  
Regression Equations ◽  
Ensemble Forecasts ◽  
Forecast Lead Time ◽  
Quantitative Precipitation Forecasts

Abstract The Hydrometeorological Prediction Center (HPC) at the NCEP has produced a suite of deterministic quantitative precipitation forecasts (QPFs) for over 40 yr. While the operational forecasts have proven to be useful in their present form, they offer no information concerning the uncertainties of individual forecasts. The purpose of this study is to develop a methodology to quantify the uncertainty in manually produced 6-h HPC QPFs (HQPFs) using NCEP short-range ensemble forecasts (SREFs). Results presented herein show the SREFs can predict the uncertainty of HQPFs. The correlation between HQPF absolute error (AE) and ensemble QPF spread (SP) is greater than 0.5 at 90.5% of grid points in the continental United States, exceeding 0.8 at 10% of these, for the 6-h forecast in winter. On the basis of the high correlation, the linear regression equations of AE on SP are derived at each point on a grid covering the United States. In addition, the regression equations for data categorized according to the observed and forecasted precipitation amounts are obtained and evaluated. Using the regression model equation parameters for 15 categorized ranges of HQPF at each horizontal grid point for each season and individual forecast lead time, an AE associated with an individual SP is predicted, as is the 95% confidence interval (CI) of the AE. Based on the AE CI forecast and the HQPF itself, the 95% CI of the HQPF is predicted as well. This study introduces an efficient and advanced method, providing an estimate of the uncertainty in the deterministic HQPF. Verification demonstrates the usefulness of the CI forecasts for a variety of classifications, such as season, CI range, HQPF, and forecast lead time.

scholarly journals Understanding Forest Fire Management in Indonesia from a Global Perspective

2020 ◽  
Vol 37 (1) ◽  
Author(s):  
Indra Agus Riyanto ◽  
Ahmad Cahyadi ◽  
Faricha Kurniadhini ◽  
Hafidz Bachtiar ◽  
Dwiki Apriyana ◽  
...  
Keyword(s):  
United States ◽  
Disaster Management ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Management ◽  
The United States ◽  
Global Perspective ◽  
Forest Fire Management ◽  
Fire Disaster ◽  
Forest Fire Disaster

Forest fires are one of the global issues that attract worldwide attention. Russia, Brazil, Canada, the United States, and Indonesia are among the countries with the largest forest cover and long records of massive forest fires. Forest fire management is, therefore, critical to decreasing the severity level of these fires. Current conditions indicate that, compared with the four other countries, Indonesia has significantly reduced forest fires within the past five years. Consequently, adopting a global perspective to study the characteristics of forest fire disaster management has become necessary. For each management parameter, this research employed a literature review and descriptive analysis. The results showed that Indonesia had an advantage in the field of legal regulation. Indonesia tends to change its regulations within a short span of time, resulting in the number of forest fire incidents decreasing significantly compared with Russia, Brazil, Canada, and the United States. However, the country still has several weaknesses, namely in emergency responses, forest fire monitoring technology, and inter-institutional integration in forest fire disaster management.

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 mizuRoute version 1: a river network routing tool for a continental domain water resources applications

2016 ◽  
Vol 9 (6) ◽  
pp. 2223-2238 ◽  
Author(s):  
Naoki Mizukami ◽  
Martyn P. Clark ◽  
Kevin Sampson ◽  
Bart Nijssen ◽  
Yixin Mao ◽  
...  
Keyword(s):  
United States ◽  
Water Resources ◽  
The United States ◽  
Hydrologic Model ◽  
River Network ◽  
Surface Model ◽  
Network Data ◽  
Unit Hydrograph ◽  
Spatially Distributed ◽  
Grid Based

Abstract. This paper describes the first version of a stand-alone runoff routing tool, mizuRoute. The mizuRoute tool post-processes runoff outputs from any distributed hydrologic model or land surface model to produce spatially distributed streamflow at various spatial scales from headwater basins to continental-wide river systems. The tool can utilize both traditional grid-based river network and vector-based river network data. Both types of river network include river segment lines and the associated drainage basin polygons, but the vector-based river network can represent finer-scale river lines than the grid-based network. Streamflow estimates at any desired location in the river network can be easily extracted from the output of mizuRoute. The routing process is simulated as two separate steps. First, hillslope routing is performed with a gamma-distribution-based unit-hydrograph to transport runoff from a hillslope to a catchment outlet. The second step is river channel routing, which is performed with one of two routing scheme options: (1) a kinematic wave tracking (KWT) routing procedure; and (2) an impulse response function – unit-hydrograph (IRF-UH) routing procedure. The mizuRoute tool also includes scripts (python, NetCDF operators) to pre-process spatial river network data. This paper demonstrates mizuRoute's capabilities to produce spatially distributed streamflow simulations based on river networks from the United States Geological Survey (USGS) Geospatial Fabric (GF) data set in which over 54 000 river segments and their contributing areas are mapped across the contiguous United States (CONUS). A brief analysis of model parameter sensitivity is also provided. The mizuRoute tool can assist model-based water resources assessments including studies of the impacts of climate change on streamflow.

An evaluation of fire danger and behaviour indices in the Great Lakes Region calculated from station and gridded weather information

2014 ◽  
Vol 23 (2) ◽  
pp. 202 ◽  
Author(s):  
John D. Horel ◽  
Robert Ziel ◽  
Chris Galli ◽  
Judith Pechmann ◽  
Xia Dong
Keyword(s):  
United States ◽  
Great Lakes ◽  
The United States ◽  
Fire Danger ◽  
Great Lakes Region ◽  
Fire Weather ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Weather Index ◽  
Weather Parameters

A web-based set of tools has been developed to integrate weather, fire danger and fire behaviour information for the Great Lakes region of the United States. Weather parameters obtained from selected observational networks are combined with operational high-resolution gridded analyses and forecast products from the United States National Weather Service. Fuel moisture codes and fire behaviour indices in the Fire Weather Index subsystem of the Canadian Forest Fire Danger Rating System are computed from these sources for current and forecast conditions. Applications of this Great Lakes Fire and Fuels System are demonstrated for the 2012 fire season. Fuel moisture codes and fire behaviour indices computed from gridded analyses differ from those derived from observations in a manner similar to the analysis errors typical for the underlying weather parameters. Indices that are particularly sensitive to seasonally accumulating precipitation, such as the Drought Code, exhibit the largest differences. The gridded analyses and forecasts provide considerable additional information for fire weather professionals to evaluate weather and fuel state in the region. The potential utility of these gridded analyses and forecasts throughout the continental United States is highlighted.

scholarly journals A Statistical Model for Estimating Midday NDVI from the Geostationary Operational Environmental Satellite (GOES) 16 and 17

2019 ◽  
Vol 11 (21) ◽  
pp. 2507 ◽  
Author(s):  
Kathryn I. Wheeler ◽  
Michael C. Dietze
Keyword(s):  
United States ◽  
Statistical Model ◽  
Vegetation Index ◽  
Near Infrared ◽  
The United States ◽  
Western Hemisphere ◽  
High Temporal Resolution ◽  
Atmospheric Scattering ◽  
Geostationary Operational Environmental Satellite ◽  
Uncertainty Estimates

The newest version of the Geostationary Operational Environmental Satellite series (GOES-16 and GOES-17) includes a near infrared band that allows for the calculation of normalized difference vegetation index (NDVI) at a 1 km at nadir spatial resolution every five minutes throughout the continental United States and every ten minutes for much of the western hemisphere. The usefulness of individual NDVI observations is limited due to the noise that remains even after cloud masks and data quality flags are applied, as much of this noise is negatively biased due to scattering within the atmosphere. Fortunately, high temporal resolution NDVI allows for the identification of consistent diurnal patterns. Here, we present a novel statistical model that utilizes this pattern, by fitting double exponential curves to the diurnal NDVI data, to provide a daily estimate of NDVI over forests that is less sensitive to noise by accounting for both random observation errors and atmospheric scattering biases. We fit this statistical model to 350 days of observations for fifteen deciduous broadleaf sites in the United States and compared the method to several simpler potential methods. Of the days 60% had more than ten observations and were able to be modeled via our methodology. Of the modeled days 72% produced daily NDVI estimates with <0.1 wide 95% confidence intervals. Of the modeled days 13% were able to provide a confident NDVI value even if there were less than five observations between 10:00–14:00. This methodology provides estimates for daily midday NDVI values with robust uncertainty estimates, even in the face of biased errors and missing midday observations.

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