International Journal of Wildland Fire最新文献

To assess development of a robust emissions accounting framework for expansive miombo woodland savannas covering ~2 million km² of southern Africa that typically are burnt under relatively severe late dry season (LDS) conditions.

A detailed site-based study of fuel accumulation, combustion and greenhouse gas (GHG) emission factor parameters under early dry season (EDS) and LDS conditions along a central rainfall-productivity and associated miombo vegetation structural and floristics gradient, from lower rainfallsites in northern Botswana to higher rainfall sites in northern Zambia.

Assembled field data inform core components of the proposed emissions reduction framework: fuel and combustion conditions sampled across the vegetation/productivity gradient can be represented by three defined Vegetation Fuel Types (VFTs); fuel accumulation, combustion and emissions parameters are presented for these. Applying this framework for an illustrative case, GHG emissions (t CO₂-e) from EDS fires were one-third to half those of LDS fires per unit area in eligible miombo VFTs.

Our accounting framework supports undertaking EDS fire management to significantly reduce emissions and, realistically, burnt extent at landscape scales. We consider application of presented data to development of formal emissions abatement accounting methods, linkages with potential complementary woody biomass and soil organic carbon sequestration approaches, and necessary caveats concerning implementation issues.

Near-term forecasts of fire danger based on predicted surface weather and fuel dryness are widely used to support the decisions of wildfire managers. The incorporation of synoptic-scale upper-air patterns into predictive models may provide additional value in operational forecasting.

In this study, we assess the impact of synoptic-scale upper-air patterns on the occurrence of large wildfires and widespread fire outbreaks in the US Pacific Northwest. Additionally, we examine how discrete upper-air map types can augment subregional models of wildfire risk.

We assess the statistical relationship between synoptic map types, surface weather and wildfire occurrence. Additionally, we compare subregional fire danger models to identify the predictive value contributed by upper-air map types.

We find that these map types explain variation in wildfire occurrence not captured by fire danger indices based on surface weather alone, with specific map types associated with significantly higher expected daily ignition counts in half of the subregions.

We observe that incorporating upper-air map types enhances the explanatory power of subregional fire danger models.

Our approach provides value to operational wildfire management and provides a template for how these methods may be implemented in other regions.

To effectively reduce future wildfire risk, several management strategies must be evaluated under plausible future scenarios, requiring models that provide estimates of how likely wildfires are to spread to community assets (wildfire likelihood) in a computationally efficient manner. Approaches to quantifying wildfire likelihood using fire simulation models cannot practically achieve this because they are too computationally expensive.

This study aimed to develop an approach for quantifying wildfire likelihood that is both computationally efficient and able to consider contagious and directionally specific fire behaviour properties across multiple spatial ‘neighbourhood’ scales.

A novel, computationally efficient index for quantifying wildfire likelihood is proposed. This index is evaluated against historical and simulated data on a case study in South Australia.

The neighbourhood index explains historical burnt areas and closely replicates patterns in burn probability calculated using landscape fire simulation (ρ = 0.83), while requiring 99.7% less computational time than the simulation-based model.

The neighbourhood index represents patterns in wildfire likelihood similar to those represented in burn probability, with a much-reduced computational time.

By using the index alongside existing approaches, managers can better explore problems involving many evaluations of wildfire likelihood, thereby improving planning processes and reducing future wildfire risks.

Spot fires play a significant role in the rapid spread of wildland and wildland–urban interface fires.

This paper presents an experimental and modelling study on the flaming and smouldering burning of wood firebrands under forced convection.

The firebrand burning experiments were conducted with different wind speeds and firebrand sizes.

The burning rate of firebrands under forced convection is quantified by wood pyrolysis rate, char oxidation rate and a convective term. The firebrand projected area is correlated with firebrand diameter, char density, wind speed, and flaming or smouldering burning. A surface temperature model is derived in terms of condensed-phase energy conservation. We finally establish a simplified firebrand transport model based on the burning rate, projected area and surface temperature of firebrands.

The mass loss due to wood pyrolysis is much greater than that due to char oxidation in self-sustaining burning. The burning rate is proportional to U^1/2, where U is wind speed. The projected area for flaming firebrands decreases more rapidly than that for smouldering ones. The firebrand surface temperature is mainly determined by radiation.

Knowledge about firebrand burning characteristics is essential for predicting the flight distance and trajectory in firebrand transport.

Fire behaviour assessments of past wildfire events have major implications for anticipating post-fire ecosystem responses and fuel treatments to mitigate extreme fire behaviour of subsequent wildfires.

This study evaluates for the first time the potential of remote sensing techniques to provide explicit estimates of fire type (surface fire, intermittent crown fire, and continuous crown fire) in Mediterranean ecosystems.

Random Forest classification was used to assess the capability of spectral indices and multiple endmember spectral mixture analysis (MESMA) image fractions (char, photosynthetic vegetation, non-photosynthetic vegetation) retrieved from Sentinel-2 data to predict fire type across four large wildfires

MESMA fraction images procured more accurate fire type estimates in broadleaf and conifer forests than spectral indices, without remarkable confusion among fire types. High crown fire likelihood in conifer and broadleaf forests was linked to a post-fire MESMA char fractional cover of about 0.8, providing a direct physical interpretation.

Intrinsic biophysical characteristics such as the fractional cover of char retrieved from sub-pixel techniques with physical basis are accurate to assess fire type given the direct physical interpretation.

MESMA may be leveraged by land managers to determine fire type across large areas, but further validation with field data is advised.

Fire simulators are increasingly used to predict fire spread. Australian fire agencies have been concerned at not having an objective basis to choose simulators for this purpose.

We evaluated wildland fire simulators currently used in Australia: Australis, Phoenix, Prometheus and Spark. The evaluation results are outlined here, together with the evaluation framework.

Spatial metrics and visual aids were designed in consultation with simulator end-users to assess simulator performance. Simulations were compared against observations of fire progression data from 10 Australian historical fire case studies. For each case, baseline simulations were produced using as inputs fire ignition and fuel data together with gridded weather forecasts available at the time of the fire. Perturbed simulations supplemented baseline simulations to explore simulator sensitivity to input uncertainty.

Each simulator showed strengths and weaknesses. Some simulators displayed greater sensitivity to different parameters under certain conditions.

No simulator was clearly superior to others. The evaluation framework developed can facilitate future assessment of Australian fire simulators.

Collection of fire behaviour observations for routine simulator evaluation using this framework would benefit future simulator development.

Tropical savannas represent a large proportion of the area burnt each year globally, with growing evidence that management to curtail fire frequency and intensity in some of these regions can contribute to mitigation of climate change. Approximately 25% of Australia’s fire-prone tropical savanna region is currently managed for carbon projects, contributing significantly to Australia’s National Greenhouse Gas Inventory.

To improve the accuracy of Australia’s national carbon accounting model (FullCAM) for reporting of fire emissions and sequestration of carbon in savanna ecosystems.

Field data from Australian savannas were collated and used to calibrate FullCAM parameters for the prediction of living biomass, standing dead biomass and debris within seven broad vegetation types.

Revised parameter sets and improved predictions of carbon stocks and fluxes across Australia’s savanna ecosystems in response to wildfire and planned fire were obtained.

The FullCAM model was successfully calibrated to include fire impacts and post-fire recovery in savanna ecosystems.

This study has expanded the capability of FullCAM to simulate both reduced emissions and increased sequestration of carbon in response to management of fire in tropical savanna regions of Australia, with implications for carbon accounting at national and project scales.

Extreme wildfires have increased in recent decades, yet the consequences of extreme fire behaviour are not fully comprehended. The study of prescribed burning provides opportunities to advance understanding of some overlooked processes in fire behaviour, such as the role of the release of volatile organic compounds (VOC).

The aim of this study was to assess VOC (α-pinene, β-pinene, and limonene), NH₃, CO and CO₂ emissions during prescribed fires in pine barrens vegetation at the Albany Pine Bush Preserve, USA.

Measurements performed by open-path Fourier transform infra-red spectroscopy (OP-FTIR) quantified VOC concentrations and characterised emissions during four independent prescribed burns.

Combustion products (e.g. CO₂, CO, CH₄) and VOC exhibited similar emission behaviour during thermal degradation, though VOC concentrations appeared to be independent of the type of biomass burned, unlike those of combustion products; Pinus strobus L. emitted two orders of magnitude higher than Pinus rigida Mill.; VOC and CO are statistically correlated (R² = 0.84).

These results confirmed that OP-FTIR is a feasible approach for gathering qualitative and quantitative information regarding VOC emission during prescribed fires.

Quantification of VOC concentrations during prescribed fires helps characterise its relationships with greenhouse gas emissions (e.g. CO₂ and CO) at different burning conditions (e.g. wind, biomass type), which could be incorporated into existing fire behaviour models to enhance their ability to better predict fire propagation.

The Australian Fire Danger Rating System (AFDRS) was implemented operationally throughout Australia in September 2022, providing calculation of fire danger forecasts based on peer-reviewed fire behaviour models. The system is modular and allows for ongoing incorporation of new scientific research and improved datasets.

Prior to operational implementation of the AFDRS, a Research Prototype (AFDRS_RP), described here, was built to test the input data and systems and evaluate the performance and potential outputs.

Fire spread models were selected and aligned with fuel types in a process that captured bioregional variation in fuel characteristics. National spatial datasets were created to identify fuel types and fire history in alignment with existing spatial weather forecast layers.

The AFDRS_RP demonstrated improvements over the McArthur Forest and Grass Fire Danger systems due to its use of improved fire behaviour models, as well as more accurately reflecting the variation in fuels.

The system design was robust and allowed for the incorporation of updates to the models and datasets prior to implementation of the AFDRS.

Wildfires represent a significant threat to peatlands globally, but whether peat fires can be initiated by a lofted firebrand is still unknown.

We investigated the ignition threshold of peat fires by a glowing firebrand through laboratory-scale experiments.

The oven-dried weight (ODW) moisture content (MC) of peat samples varied from 5% ODW to 100% ODW, and external wind (ν) with velocities up to 1 m/s was provided in a wind tunnel.

When MC < 35%, ignition is always achieved, regardless of wind velocity. However, if MC is between 35 and 85%, an external wind (increasing with peat moisture) is required to increase the reaction rate of the firebrand and thus heating to the peat sample. Further increasing the MC to be higher than 85%, no ignition could be achieved by a single laboratory firebrand. Finally, derived from the experimental results, a 90% ignition probability curve was produced by a logistic regression model.

This work indicates the importance of maintaining a high moisture content of peat to prevent ignition by firebrands and helps us better understand the progression of large peat fires.