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The Alaska Reference Database originated as the standalone Alaska Fire Effects Reference Database, a ProCite reference database maintained by former BLM-Alaska Fire Service Fire Ecologist Randi Jandt. It was expanded under a Joint Fire Science Program grant for the FIREHouse project (The Northwest and Alaska Fire Research Clearinghouse). It is now maintained by the Alaska Fire Science Consortium and FRAMES, and is hosted through the FRAMES Resource Catalog. The database provides a listing of fire research publications relevant to Alaska and a venue for sharing unpublished agency reports and works in progress that are not normally found in the published literature.

Displaying 1 - 25 of 43

Badhan, Shamsaei, Ebrahimian, Bebis, Lareau, Rowell
The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution…
Year: 2024
Type: Document
Source: FRAMES

Arrogante-Funes, Aguado, Chuvieco
Background: Fire is a natural disturbance that significantly impacts ecosystems and plays a crucial role in the distribution and preservation of biota worldwide. The effects of fires on bird diversity can be both positive, as they can create new habitats, and negative, as they…
Year: 2024
Type: Document
Source: FRAMES

Song, Xu, Li, Oppong
Wildfire causes environmental, economic, and human problems or losses. This study reviewed wildfires induced by lightning strikes. This review focuses on the investigations of lightning mechanisms in the laboratory. Also, the paper aims to discuss some of the modeling studies on…
Year: 2024
Type: Document
Source: FRAMES

Purnomo, Christensen, Fernandez-Anez, Rein
Background: Smouldering peatland wildfires can last for months and create a positive feedback for climate change. These flameless, slow-burning fires spread horizontally and vertically and are strongly influenced by peat moisture content. Most models neglect the non-uniform…
Year: 2024
Type: Document
Source: FRAMES

Campbell-Lochrie, Gallagher, Skowronski, Hadden
Background: Fifty years after its initial publication, Rothermel’s model continues to underpin many operational fire modelling tools. Past authors have, however, suggested a possible oversensitivity of the Rothermel model to fuel depth in certain fuel types. Aims: To evaluate…
Year: 2024
Type: Document
Source: FRAMES

Zhang, Wang, Yang, Liu
Global climate change and extreme weather has a profound impact on wildfire, and it is of great importance to explore wildfire patterns in the context of global climate change for wildfire prevention and management. In this paper, a wildfire spatial prediction model based on…
Year: 2024
Type: Document
Source: FRAMES

Ahmed, Trouve, Forthofer, Finney
Our objective in the present study is to provide basic insights into the coupling between external-gas and solid biomass vegetation processes that control the dynamics of flame spread in wildland fire problems. We focus on a modeling approach that resolves processes occurring at…
Year: 2024
Type: Document
Source: FRAMES

Xu, Li, Zhang, Liu, Zhang
In the context of large-scale fire areas and complex forest environments, the task of identifying the subtle features and aspects of fire can pose a significant challenge for the deep learning model. As a result, to enhance the model’s ability to represent features and its…
Year: 2024
Type: Document
Source: FRAMES

Li, Tang, Li, Dou, Li
Background: Extreme wildfires pose a serious threat to forest vegetation and human life because they spread more rapidly and are more intense than conventional wildfires. Detecting extreme wildfires is challenging due to their visual similarities to traditional fires, and…
Year: 2024
Type: Document
Source: FRAMES

Volkova, Fernández
Fire is an important component of many forest ecosystems, yet climate change is now modifying fire regimes all over the world, driving a need to understand the impact of fires on the physical and biological processes. In 2022, Elsevier launched a Special Collection that spanned…
Year: 2024
Type: Document
Source: FRAMES

Shinohara
Fire whirls cause strong wind damage in large outdoor fires such as wildland fires and urban fires. A model to predict the maximum tangential wind velocity in laboratory-scale fire whirls without flames in a crosswind is developed based on a generation mechanism and the…
Year: 2024
Type: Document
Source: FRAMES

Qayyum, Jamil, Alsboui, Hijjawi
Background: Understanding the intricacies of wildfire impact across diverse geographical landscapes necessitates a nuanced comprehension of fire dynamics and areas of vulnerability, particularly in regions prone to high wildfire risks. Machine learning (ML) stands as a…
Year: 2024
Type: Document
Source: FRAMES

Qayyum, Samee, Alabdulhafith, Aziz, Hijjawi
Background: Predicting wildfire progression is vital for countering its detrimental effects. While numerous studies over the years have delved into forecasting various elements of wildfires, many of these complex models are perceived as “black boxes”, making it challenging to…
Year: 2024
Type: Document
Source: FRAMES

Shi, Levy, Remer, Mattoo, Arnold
Starting from point sources, wildfire smoke is important in the global aerosol system. The ability to characterize smoke near-source is key to modeling smoke dispersion and predicting air quality. With hemispheric views and 10-min refresh, imagers in Geostationary (GEO) orbit…
Year: 2024
Type: Document
Source: FRAMES

Lu, Liu, Ke, Zhang, Ma, Fan
The vertical distribution of biomass burning aerosol (BBA) is important in regulating their impacts on weather and climate. The plume-rise process affects the injection height of BBA and interacts with the air parcel lifting and cloud processes. However, these processes are not…
Year: 2024
Type: Document
Source: FRAMES

Rundel
[no description entered]
Year: 1981
Type: Document
Source: TTRS

Noble, Slatyer
[no description entered]
Year: 1981
Type: Document
Source: TTRS

Albini
This note extends a predictive model for estimating spot fire distance from burning trees (Albini, Frank A. 1979. Spot fire distance from burning trees--a predictive model. USDA For. Serv. Gen. Tech. Rep. INT-56, 73 p. Intermt. For. and Range Exp. Stn., Ogden, Utah). A formula…
Year: 1981
Type: Document
Source: TTRS

Potter, Newstead, Quintilio, Lee
From the text: 'As an aid to improved presuppression and initial-attack planning, a simple fire containment model programmed for the Texas Instruments Model 59 (TI-59) hand-held calculator has been developed at the Northern Forest Research Centre. The model was derived in part…
Year: 1981
Type: Document
Source: TTRS

Steward, Richard, O'Donnell
The rate of burning of wooden dowels injected into the centre of various test fires was determined by direct weighing. The curves giving the rate of burning vs. time of exposure are presented for dowels of length 88.9 mm and diameters ranging from 2.54 mm to 50.8 mm. Five types…
Year: 1981
Type: Document
Source: TTRS

Burgan, Rothermel
A site-specific fire behavior fuel modeling system being developed by the Fire behavior Research Work Unit at the Northern Forest Fire Laboratory, combines simplified field measurement techniques with computer-assisted fuel model development and testing. The fuel models will be…
Year: 1981
Type: Document
Source: TTRS

Garmon
[no description entered]
Year: 1981
Type: Document
Source: TTRS

Ratliff, Pieper
An approach for deciding final clusters of a dendrogram is provided. Whether developed by agglomerative or divisive cluster analysis, decisions start at the 2-cluster level of the dendrogram. Cluster density is viewed as compactness and, therefore, is related to interindividual…
Year: 1981
Type: Document
Source: TTRS

Ferguson, Leech
'In conclusion, we believe that David and West overstate the difficulties associated with the use of GLS and understate those with using OLS in the conventional manner. GLS is not a panacea, nor is it a technique for neophytes, but it does offer substantial advantages over OLS…
Year: 1981
Type: Document
Source: TTRS