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Understanding and quantifying wildfire behavior is of interest to the scientific community, as well as public health and fire management professionals. To achieve this end, there is a demand for statistical descriptions of wildfire behavior and its relationship to the environment. However, wildfire behavior can be complex, described by multiple characteristics such as final size, duration and growth rates, and influenced by processes that can be regionally dependent. Further challenges arise due to the poor quality and availability of cumulative burn area time series data, which often contain missing and erroneous measurements. To address these issues, a variety of methods are presented. Multiple wildfire behaviors are represented using a simple decomposition of cumulative burn area time series that measures four meaningful quantities from the growth curve. The relationship between wildfire activity and the environment are approximated using regionally specific generalized linear models. Weather and landscape data are used to predict various measures of wildfire behavior. Validation results suggested that most of the models generalized well to independent data, and have potentially useful applications in climatological research. Data quality issues common to cumulative burn area time series are addressed using Bayesian state-space models, which reconstruct growth curves from multiple corrupted burn area time series. Two state space models are presented, a stationary version that assumes idealized fire growth, and a non-stationary version that produces reconstructions with time-varying growth rates. The relative computational costs and goodness-of-fit is illustrated by reconstructing the growth curves of 13 wildfires from 2014 wildfire season using growth data coming from two sources, fire perimeters from the Geospatial Multi-Agency Coordination (GeoMAC) and cumulative hotspot detects from the Hazard Mapping System (HMS). The stationary model had minimal computational costs, but rarely produced adequate descriptions of the burn area observations. The non-stationary model had much higher computational costs, but produced realistic estimates of the time series. An informal sensitivity analysis suggested that the reconstructed curves would be robust to changes in the priors. The main application of the state-space models is to reconstruct burn area time series, which can in turn be used for statistical analysis or validation of currently existing growth models. The framework can be modified for other purposes as well including forecasting burn area, and predicting the extinguishment date of a fire.
This reference work encompasses the current, accepted state of the art in the science of wildfires and wildfires that spread to communities, known as wildland-urban interface (WUI) fires. 171 author contributions include accepted knowledge on these topics from throughout the world, all written by the leading researchers, experts, practitioners, and academics. This encyclopedia is an invaluable reference for newcomers to the field, as well as researchers, students, developers, and professionals who are interested in exploring this dynamic area. General Sections include: Combustion Coordination System Locations Fire Whirls Firebrands and Embers Incident Management Team (IMT) Support Locations Incident Response Support Locations On-the-Incident Locations Soot and Effects on Wildland/WUI Fire Behavior Weathering Effects on Fire Retardant Wood Treatments Wildland Firefighting Locations Wildland Fuel Treatments
Forest fires are one of the biggest ecological disasters in Canada. Counts and sizes of fires vary substantially from year to year. In this study, the data is collected from Northwest Territories in Canada. We assume that fire counts appear to follow the Gamma-Poisson distribution, and fire sizes approximately follow the Gamma-Exponential distribution. The Maximum Likelihood Estimation and Random Search are used to estimate the parameters of two models. Identifiability issues regarding parameters in the two models are explored. The Kolmogorov-Smirnov test is used to check for goodness of fit. For fire sizes data, although the Kolmogorov-Smirnov test shows a low p-value, by plotting theoretical and empirical distribution, we can see that the Gamma-Exponential distribution fits adequately.
This is a print on demand edition of a hard to find publication. Land management agencies (LMA) need to understand and monitor the consequences of their fire suppression decisions. The authors developed a framework for retrospective fire behavior modeling and impact assessment to determine where ignitions would have spread had they not been suppressed, and to assess the cumulative effects that would have resulted. This guidebook is used for applying this methodology and is for those interested in quantifying the impacts of fire suppression. Land managers who use this methodology can track the cumulative effects of suppression, frame future suppression decisions and cost-benefit analyses in the context of past experiences, and communicate tradeoffs to the public, non-gov. organ., and LMA.
Drought and wildfire occurrences are predicted to compound due to global climate change, especially in Mediterranean climates. Therefore, researching potential wildfire determinants is imperative in preparing for and managing future wildfires. The primary goal of this research was to determine if specific environmental, spatial, and human-based variables can explain large wildfire occurrences in Southern California during four designated wildfire regimes, which are drought and post-drought years within the two fire seasons (i.e., dry and Santa Ana (SA) wind fire seasons), between 2012 and 2019 utilizing binary logistic regression models. The secondary goal was to map the predictive patterns of large wildfire occurrences in Southern California. This research used remotely sensed land surface temperature (LST), normalized difference vegetation index (NDVI), and evapotranspiration (ET) datasets. This research also used other raster datasets, such as precipitation, wind, aspect, slope, and digital elevation model (DEM). Various vector derived raster datasets were also used, such as distance to roads, powerlines, cities, and campgrounds, ecoregions, and the wildland-urban interface (WUI). Wildfire occurrences are influenced by anthropogenic, environmental, and spatial factors; however, once ignition occurs and wildfires begin to spread, the environmental factors become more significant in fueling large wildfires. The results indicated that lower NDVI values were the strongest predictor when wildfires were smaller in terms of area burned and when less wildfires occurred. Higher wind speeds were the strongest predictor when wildfires were larger. However, higher LST values were the strongest predictor when wind was not a significant contributor to the model. These conclusions determine that large wildfires are mostly explained by wind, and when wind is not a significant contributor, then LST takes on that role, as these two variables have the ability to dry vegetation and to spread wildfires. This research further establishes the potential for early detections of large wildfires based on wildfire prediction patterns, provides useful information for resource issuance and wildfire management, and enhances general knowledge of the predicted extreme wildfire events in Southern California.
The objective of this study was to provide managers with national-level data on current conditions of vegetation and fuels developed from ecologically based methods to address these questions: How do current vegetation and fuels differ from those that existed historically? Where on the landscape do vegetation and fuels differ from historical levels? In particular, where are high fuel accumulations? When considered at a coarse scale, which areas estimated to have high fuel accumulations represent the highest priorities for treatment?