Research Database
Displaying 21 - 40 of 228
Human Mediation of Wildfires and Its Representation in Terrestrial Ecosystem Models
Year: 2025
Increasing wildfires are causing global concerns about ecosystem functioning and services. Although some wildfires are caused by natural ignitions, it is also important to understand how human ignitions and human-related factors can contribute to wildfires. While dynamic global vegetation models (DGVMs) have incorporated fire-related modules to simulate wildfires and their impacts, few models have fully considered various human-related factors causing human ignitions. Using global examples, this study aims to identify key factors associated with human impacts on wildfires and provides…
Publication Type: Journal Article
Decreasing frequency of low and moderate fire weather days may be contributing to large wildfire occurrence in the northern Sierra Nevada
Year: 2025
Previous analyses identified large-scale climatic patterns contributing to greater fuel aridity as drivers of recent dramatic increases in wildfire activity throughout California. This study revisits an approach to investigate more local fire weather patterns in the northern Sierra Nevada; a region within California that has experienced exceptionally high wildfire activity recently. The annual percentages of fire season days above 90th and 95th percentile Energy Release Component (ERC) values were very low prior to 1994 (Fig. 3). Since 1994, years with noticeable percentages of exceedances (…
Publication Type: Journal Article
Wildland fire entrainment: The missing link between wildland fire and its environment
Year: 2025
Wildfires are growing in destructive power, and accurately predicting the spread and intensity of wildland fire is essential for managing ecological and societal impacts. No current operational models used for fire behavior prediction resolve critical fire-atmospheric coupling or nonlocal influences of the fire environment, rendering them inadequate in accounting for the range of wildland fire behavior scenarios under increasingly novel fuel and climate conditions. Here, we present a new perspective on a dominant fire-atmospheric feedback mechanism, which we term wildland fire entrainment (…
Publication Type: Journal Article
A cellular necrosis process model for estimating conifer crown scorch
Year: 2025
Fire-caused tree mortality has major impacts on forest ecosystems. One primary cause of post-fire tree mortality in non-resprouting species is crown scorch, the percentage of foliage in a crown that is killed by heat. Despite its importance, the heat required to kill foliage is not well-understood. We used the “lag” model to describe time- and temperature-dependent leaf cell necrosis as a method of predicting leaf scorch. The lag model includes two rate parameters that describe 1) the process of cells accumulating non-lethal damage, and 2) damage becoming lethal to the cell. To parameterize…
Publication Type: Journal Article
Comparative Analysis of Ensemble and Deterministic Models for Fire Weather Index (FWI) System Forecasting
Year: 2025
Accurate fire weather forecasting is essential for effective wildfire management, particularly in regions increasingly affected by extreme fire activity such as British Columbia and Alberta, Canada. This study evaluates the predictive performance of three ensemble forecasting systems–the Ensemble Prediction System (ENS), the Global Ensemble Forecast System (GEFS), and the Canadian Global Ensemble Prediction System (GEPS)–and one deterministic model (High Resolution Forecast, HRES) –in forecasting components of the Canadian Fire Weather Index (FWI) System with 1–15 days lead time during the…
Publication Type: Journal Article
Fire Intensity and spRead forecAst (FIRA): A Machine Learning Based Fire Spread Prediction Model for Air Quality Forecasting Application
Year: 2025
Fire activities introduce hazardous impacts on the environment and public health by emitting various chemical species into the atmosphere. Most operational air quality forecast (AQF) models estimate smoke emissions based on the latest available satellite fire products, which may not represent real-time fire behaviors without considering fire spread. Hence, a novel machine learning (ML) based fire spread forecast model, the Fire Intensity and spRead forecAst (FIRA), is developed for AQF model applications. FIRA aims to improve the performance of AQF models by providing realistic, dynamic fire…
Publication Type: Journal Article
Modeling Neighborhoods as Fuel for Wildfire: A Review
Year: 2025
Wildfire’s destruction of homes is an increasingly serious global problem. Research indicates that characterizing home hardening and defensible space at the individual structure level may reduce loss through enriched understanding of structure susceptibility in the built environment. However, improved data and methods are required to accurately characterize these features at scale. This paper does three things: (1) Identifies features correlated with structure loss. (2) Compares methods of characterizing structure susceptibility, including home assessments and emerging fire spread models. (3…
Publication Type: Journal Article
Mobile radar provides insights into hydrologic responses in burn areas
Year: 2025
Background. Wildfires often occur in mountainous terrain, regions that pose substantial challenges to operational meteorological and hydrologic observing networks. Aims. A mobile, postfire hydrometeorological observatory comprising remote-sensing and in situ instrumentation was developed and deployed in a burnt area to provide unique insights into rainfall-induced post-fire hazards. Methods. Mobile radar-based rainfall estimates were produced throughout the burn area at 75-m resolution and compared with rain gauge accumulations and basin response variables. Key results. The mobile radar was…
Publication Type: Journal Article
Evaluating a simulation-based wildfire burn probability map for the conterminous US
Year: 2025
Background: Wildfire simulation models are used to derive maps of burn probability (BP) based on fuels, weather, topography and ignition locations, and BP maps are key components of wildfire risk assessments.Aims: Few studies have compared BP maps with real-world fires to evaluate their suitability for near-future risk assessment. Here, we evaluated a BP map for the conterminous US based on the large fire simulation model FSim.Methods: We compared BP with observed wildfires from 2016 to 2022 across 128 regions representing similar fire regimes (‘pyromes’). We…
Publication Type: Journal Article
Short-term impacts of operational fuel treatments on modelled fire behaviour and effects in seasonally dry forests of British Columbia, Canada
Year: 2025
Background: In response to increasing risk of extreme wildfire across western North America, forest managers are proactively implementing fuel treatments.Aims: We assessed the efficacy of alternative combinations of thinning, pruning and residue fuel management to mitigate potential fire behaviour and effects in seasonally dry forests of interior British Columbia, Canada.Methods: Across five community forests, we measured stand attributes before and after fuel treatments in 2021 and 2022, then modelled fire behaviour and effects using the…
Publication Type: Journal Article
Using focus groups for knowledge sharing: Tracking emerging pandemic impacts on USFS wildland fire operations
Year: 2024
In early 2020 the US Forest Service (USFS) recognized the need to gather real-time information from its wildland fire management personnel about their challenges and adaptations during the unfolding COVID-19 pandemic. The USFS conducted 194 virtual focus groups to address these concerns, over 32 weeks from March 2020 to October 2020. This management effort provided an opportunity for an innovative practice-based research study. Here, we outline a novel methodological approach (weekly, iterative focus groups, with two-way communication between USFS staff and leadership), which culminated in a…
Publication Type: Journal Article
Tribal stewardship for resilient forest socio-ecosystems
Year: 2024
The Yurok Tribe, along with other tribal communities in northwest California, non-profit organizations, universities, and governmental agencies are working to restore forests and woodlands to be more resilient to wildfires, drought, pests and diseases. Our current work within ancestral Yurok territory is designing and evaluating effects of forest treatments including fuels reduction, tree harvesting, and intentional burning based upon indigenous knowledge and associated traditional stewardship practices. Central to these evaluations are the potential availability, quantity, and quality of…
Publication Type: Journal Article
Canada Under Fire – Drivers and Impacts of the Record-Breaking 2023 Wildfire Season
Year: 2024
The 2023 wildfire season in Canada was unprecedented in its scale and intensity. Spanning from late April to early November and extending across much of the forested regions of Canada, the season resulted in a record-breaking total area burned of approximately 15 million hectares, over seven times the historic national annual average. The impacts were profound with more than 200 communities evacuated (approximately 232,000 people), periods of dense smoke that caused significant public health concerns, and unprecedented demands on fire-fighting resources. The exceptional area burned can be…
Publication Type: Journal Article
Probabilistic Forecasting of Lightning Strikes over the Continental USA and Alaska: Model Development and Verification
Year: 2024
Lightning is responsible for the most area annually burned by wildfires in the extratropical region of the Northern Hemisphere. Hence, predicting the occurrence of wildfires requires reliable forecasting of the chance of cloud-to-ground lightning strikes during storms. Here, we describe the development and verification of a probabilistic lightning-strike algorithm running on a uniform 20 km grid over the continental USA and Alaska. This is the first and only high-resolution lightning forecasting model for North America derived from 29-year-long data records. The algorithm consists of a large…
Publication Type: Journal Article
Estimating the influence of field inventory sampling intensity on forest landscape model performance for determining high-severity wildfire risk
Year: 2024
Historically, fire has been essential in Southwestern US forests. However, a century of fire-exclusion and changing climate created forests which are more susceptible to uncharacteristically severe wildfires. Forest managers use a combination of thinning and prescribed burning to reduce forest density to help mitigate the risk of high-severity fires. These treatments are laborious and expensive, therefore optimizing their impact is crucial. Landscape simulation models can be useful in identifying high risk areas and assessing treatment effects, but uncertainties in these models can limit…
Publication Type: Journal Article
A fire-use decision model to improve the United States’ wildfire management and support climate change adaptation
Year: 2024
The US faces multiple challenges in facilitating the safe, effective, and proactive use of fire as a landscape management tool. This intentional fire use exposes deeply ingrained communication challenges and distinct but overlapping strategies of prescribed fire, cultural burning, and managed wildfire. We argue for a new conceptual model that is organized around ecological conditions, capacity to act, and motivation to use fire and can integrate and expand intentional fire use as a tool. This result emerges from more considered collaboration and communication of values and needs to address…
Publication Type: Journal Article
Wildfire management decisions outweigh mechanical treatment as the keystone to forest landscape adaptation
Year: 2024
BackgroundModern land management faces unprecedented uncertainty regarding future climates, novel disturbance regimes, and unanticipated ecological feedbacks. Mitigating this uncertainty requires a cohesive landscape management strategy that utilizes multiple methods to optimize benefits while hedging risks amidst uncertain futures. We used a process-based landscape simulation model (LANDIS-II) to forecast forest management, growth, climate effects, and future wildfire dynamics, and we distilled results using a decision support tool allowing us to examine tradeoffs between alternative…
Publication Type: Journal Article
Future fire events are likely to be worse than climate projections indicate – these are some of the reasons why
Year: 2024
BackgroundClimate projections signal longer fire seasons and an increase in the number of dangerous fire weather days for much of the world including Australia.AimsHere we argue that heatwaves, dynamic fire–atmosphere interactions and increased fuel availability caused by drought will amplify potential fire behaviour well beyond projections based on calculations of afternoon forest fire danger derived from climate models.MethodsWe review meteorological dynamics contributing to enhanced fire behaviour during heatwaves, drawing on examples of…
Publication Type: Journal Article
Wildfire probability estimated from recent climate and fine fuels across the big sagebrush region
Year: 2024
BackgroundWildfire is a major proximate cause of historical and ongoing losses of intact big sagebrush (Artemisia tridentata Nutt.) plant communities and declines in sagebrush obligate wildlife species. In recent decades, fire return intervals have shortened and area burned has increased in some areas, and habitat degradation is occurring where post-fire re-establishment of sagebrush is hindered by invasive annual grasses. In coming decades, the changing climate may accelerate these wildfire and invasive feedbacks, although projecting future wildfire dynamics requires a better…
Publication Type: Journal Article
Predicting daily firefighting personnel deployment trends in the western United States
Year: 2024
Projected increases in wildfire frequency, size, and severity may further stress already scarce firefighting resources in the western United States that are in high demand. Machine learning is a promising field with the ability to model firefighting resource usage without compromising dataset size or complexity. In this study, the Categorical Boosting (CatBoost) model was used with historical (2012-2020) wildfire data to train three models that calculate predicted daily counts of 1) total assigned personnel (total personnel), 2) assigned personnel that are at the fire (ground personnel), and…
Publication Type: Journal Article