International Journal of Wildland Fire最新文献

The record number of wildfires in the United States in recent years has led to an increased focus on developing tools to accurately forecast their impacts at high spatial and temporal resolutions.

The Warn-on-Forecast System for Smoke (WoFS-Smoke) was developed to improve these forecasts using wildfire properties retrieved from satellites to generate smoke plumes in the system.

The WoFS is a regional domain ensemble data assimilation and forecasting system built around the concept of creating short-term (0–6 h) forecasts of high impact weather. This work extends WoFS-Smoke by ingesting data from the GOES-16 satellite at 15-min intervals to sample the rapidly changing conditions associated with wildfires.

Comparison of experiments with and without GOES-16 data show that ingesting high temporal frequency data allows for wildfires to be initiated in the model earlier, leading to improved smoke forecasts during their early phases. Decreasing smoke plume intensity associated with weakening fires was also better forecast.

The results were consistent for a large fire near Boulder, Colorado and a multi-fire event in Texas, Oklahoma, and Arkansas, indicating a broad applicability of this system.

The development of WoFS-Smoke using geostationary satellite data allows for a significant advancement in smoke forecasting and its downstream impacts such as reductions in air quality, visibility, and potentially properties of severe convection.

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 nature of peat moisture.

We conducted a computational study into the spread behaviour of smouldering peat with horizontally varying moisture contents.

We developed a discrete cellular automaton model called BARA, and calibrated it against laboratory experiments.

BARA demonstrated high accuracy in predicting fire spread under non-uniform moisture conditions, with >80% similarity between observed and predicted shapes, and captured complex phenomena. BARA simulated 1 h of peat smouldering in 3 min, showing its potential for field-scale modelling.

Our findings demonstrate: (i) the critical role of moisture distribution in determining smouldering behaviour; (ii) incorporating peat moisture distribution into BARA’s simple rules achieved reliable predictions of smouldering spread; (iii) given its high accuracy and low computational requirement, BARA can be upscaled to field applications.

BARA contributes to our understanding of peatland wildfires and their underlying drivers. BARA could form part of an early fire warning system for peatland.

The decision making process undertaken during wildfire responses is complex and prone to uncertainty. In the US, decisions federal land managers make are influenced by numerous and often competing factors.

To assess and validate the presence of decision factors relevant to the wildfire decision making context that were previously known and to identify those that have emerged since the US federal wildfire policy was updated in 2009.

Interviews were conducted across the US while wildfires were actively burning to elucidate time-of-fire decision factors. Data were coded and thematically analysed.

Most previously known decision factors as well as numerous emergent factors were identified.

To contextualise decision factors within the decision making process, we offer a Wildfire Decision Framework that has value for policy makers seeking to improve decision making, managers improving their process and wildfire social science researchers.

Managers may gain a better understanding of their decision environment and use our framework as a tool to validate their deliberations. Researchers may use these data to help explain the various pressures and influences modern land and wildfire managers experience. Policy makers and agencies may take institutional steps to align the actions of their staff with desired wildfire outcomes.

The cloud-penetrating and fog-penetrating capability of Synthetic Aperture Radar (SAR) give it the potential for application in forest fire progress monitoring; however, the low extraction accuracy and significant salt-and-pepper noise in SAR remote sensing mapping of the burned area are problems.

This paper provides a method for accurately extracting the burned area based on fully exploiting the changes in multiple different dimensional feature parameters of dual-polarised SAR images before and after a fire.

This paper describes forest fire progress monitoring using dual-polarisation SAR images combined with multi-scale segmentation and unsupervised classification. We first constructed polarisation feature and texture feature datasets using multi-scene Sentinel-1 images. A multi-scale segmentation algorithm was then used to generate objects to suppress the salt-and-pepper noise, followed by an unsupervised classification method to extract the burned area.

The accuracy of burned area extraction in this paper is 91.67%, an improvement of 33.70% compared to the pixel-based classification results.

Compared with the pixel-based method, our method effectively suppresses the salt-and-pepper noise and improves the SAR burned area extraction accuracy.

The fire monitoring method using SAR images provides a reference for extracting the burned area under continuous cloud or smoke cover.

Direct fuel moisture content measurements are critical for characterising spatio-temporal variations in fuel flammability and for informing fire danger assessments. However, among-sampler variability (systematic differences in measurements between samplers) likely contributes to fuel moisture measurement variability in most field campaigns.

We assessed the magnitude of among-sampler variability in plot-scale Calluna vulgaris fuel moisture measurements.

Seventeen individuals collected samples from six fuel layers hourly from 10:00 hours to 18:00 hours. We developed mixed effects models to estimate the among-sampler variability.

Fuel moisture measurements were highly variable between individuals sampling within the same plot, fuel layer, and time of day. The importance of among-sampler variability in explaining total measured fuel moisture variance was fuel layer dependent. Among-sampler variability explained the greatest amount of measurement variation in litter (58%) and moss (45%) and was more important for live (19%) than dead (4%) Calluna.

Both consideration of samplers within the experimental design and incorporation of sampler metadata during statistical analysis will improve understanding of spatio-temporal fuel moisture dynamics obtained from field-based studies.

Accounting for among-sampler variability in fuel moisture campaigns opens opportunities to utilise sampling teams and citizen science research to examine fuel moisture dynamics over large spatio-temporal scales.

Vegetation fire may change Phosphorus (P) cycling in terrestrial ecosystems through converting biomass into fire residues.

The aim of this study was to understand the chemistry and mobility of P in fire residues to help reveal P thermochemistry during biomass burning and post-fire P cycling.

A combination of sequential extraction, liquid ³¹P NMR and P K-edge XANES was used to obtain quantitative P speciation and explain P solubilisation behaviours of charcoal.

Despite varying diverse P species existing in raw biomass, only two P structural moieties – orthophosphate and pyrophosphate – were identified in charcoal. However, relative abundance of pyrophosphate differs greatly among charcoal samples from different biomass types, ranging between 0 and 40% of total extractable P. Although P K-edge XANES data indicates abundant soluble phosphate minerals, most P (70–90%) is likely occluded physically in the charcoal. The bicarbonate-extractable P (the Olsen-P) varies significantly and cannot be explained by surface P concentration or elemental stoichiometry alone.

The results suggest the importance of starting biomass P speciation (i.e. molecular structure and complexation environment) and thermal conditions in controlling P speciation and availability in charcoal. The different P chemistry between charcoal and ash suggests the importance of fire types and severity in disturbing the P cycle.

The Lupande Game Management Area (GMA) and the adjacent South Luangwa National Park (NP) in Zambia allow comparison of fire regimes in African savannas with different human densities.

To investigate humans’ effects on fire regimes within a sub-Saharan savanna ecosystem.

We delineated burned areas for the Lupande GMA and South Luangwa NP using 156 Landsat images from 1989 to 2017. We performed comparisons of fire regimes between the Lupande GMA and South Luangwa NP using various burned area variables and assessed their association with precipitation.

Overall, and compared with the South Luangwa NP, the Lupande GMA had a greater extent of burned area and a higher frequency of repeat burns. The Lupande GMA experienced fires earlier in the fire season, which are typically less damaging to woody vegetation. We observed a significant positive relationship between precipitation and burned area trends in South Luangwa NP but not in the Lupande GMA, suggesting that precipitation increases burned area in South Luangwa NP.

Results support the theory that human fire management mitigates climate’s effect, particularly rainfall, on interannual burned area variation.

This study shows that human-dominated fire regimes in savannas can alter the influence of precipitation.

We investigate the associations between major Australian climate drivers and extreme weekly fire danger throughout the year.

We use a composite-based approach, relating the probability of top-decile observed potential fire intensity to the positive and negative modes of the El Niño Southern Oscillation, Indian Ocean Dipole, Madden–Julian Oscillation, Southern Annular Mode, split-flow blocking and Subtropical Ridge Tasman Highs, both concurrently and at a variety of lag times.

The chance of extreme fire danger increases over broad regions of the continent in response to El Niño and positive Indian Ocean Dipole events, the negative mode of the Southern Annular Mode, split-flow Blocking Index and Subtropical Ridge Tasman High, and Madden–Julian Oscillation phases 5, 6, 2 and 8 in Austral summer, autumn, winter and spring respectively. These relationships exist not only concurrently, but also when a climate event occurs up to 6 months ahead of the season of interest.

These findings highlight the importance of considering the influence of diverse climate drivers, at a range of temporal lag periods, in understanding and predicting extreme fire danger.

The results of this study may aid in the development of effective fire management strategies and decision-making processes to mitigate the impacts of fire events in Australia.

Wildland firefighters have physically and psychologically demanding jobs that can result in social, economic and health-related stress. Previous studies have examined the physiological and physical effects of a career in wildland fire, but fewer studies have addressed psychological effects, and to date, none have directly analysed the hiring and work experiences of wildland firefighters.

We surveyed work experiences, health and well-being, and morale of wildland firefighters, explored factors that can improve recruitment and retention, and summarised broad patterns.

We conducted a voluntary anonymous survey of 708 federal wildland firefighters via an online platform over 2 months in 2022.

Respondents reported dissatisfaction with recruitment and hiring processes, low base salaries, poor mental health outcomes, and health and safety concerns. Respondents also reported the high importance of training, performance feedback and work environment to their retention in the field. We found significant effects of wildland firefighting on family status.

Wildland firefighters report experiencing low morale, financial stress, personal life strain and poor mental health outcomes.

These data provide a framework to establish future policy and research priorities and highlight the need for organisational actions and change.

Potential Wildfire Operational Delineations (PODs) were developed as a pre-season planning tool to promote safe and effective fire response. Past research on PODs has identified uses in an incident management context. There has been little research on how PODs are being utilised in non-incident management contexts to align forest and wildfire planning objectives.

We sought to understand how actors are adopting and adapting the PODs framework to inform non-incident management, and to identify facilitators, barriers and recommendations.

We investigated three cases, the San Juan National Forest, the San Isabel National Forest and the Washington Department of Natural Resources, through 13 semi-structured, key informant interviews.

We found that PODs were helpful for validating fuels treatment plans and supporting communication among agency staff, and with private landowners and collaborators. Challenges included lack of technical knowledge and skills, unclear leadership direction, potential misalignment with other forest management goals and community and agency buy-in to using PODs.

We offer insights into how PODs are being utilised within our case studies and align these findings with diffusion of innovation literature.

This preliminary research is important given increased funding for PODs in recent legislation and the possibility of broader adoption for fuels treatment planning in the future.