The wildland - urban interface fire problem  -  current approaches and research needs

Mell, William E.; Manzello, Samuel L.; Maranghides, Alexander; Butry, David T.; Rehm, Ronald G.

doi:10.1071/wf07131

Cited by 369 publications

(256 citation statements)

References 18 publications

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“…Knowledge for estimating and mitigating risk from wildfires is most critical at the wildland urban interface (WUI): the boundary between flammable vegetation and the houses and other assets in the urban land (Safford et al, 2009;Mell et al, 2010). As urban populations expand, so do the number of houses in the WUI in Australia (Hughes and Mercer, 2009) and in the USA (Schoennagel et al, 2009), and house losses increase accordingly (Schoennagel et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

The spatial domain of wildfire risk and response in the wildland urban interface in Sydney, Australia

Price

Bradstock

2013

Nat. Hazards Earth Syst. Sci.

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Abstract. In order to quantify the risks from fire at the wildland urban interface (WUI), it is important to understand where fires occur and their likelihood of spreading to the WUI. For each of the 999 fires in the Sydney region we calculated the distance between the ignition and the WUI, the fire's weather and wind direction and whether it spread to the WUI. The likelihood of burning the WUI was analysed using binomial regression. Weather and distance interacted such that under mild weather conditions, the model predicted only a 5 % chance that a fire starting > 2.5 km from the interface would reach it, whereas when the conditions are extreme the predicted chance remained above 30 % even at distances > 10 km. Fires were more likely to spread to the WUI if the wind was from the west and in the western side of the region. We examined whether the management responses to wildfires are commensurate with risk by comparing the distribution of distance to the WUI of wildfires with roads and prescribed fires. Prescribed fires and roads were concentrated nearer to the WUI than wildfires as a whole, but further away than wildfires that burnt the WUI under extreme weather conditions (high risk fires). Overall, 79 % of these high risk fires started within 2 km of the WUI, so there is some argument for concentrating more management effort near the WUI. By substituting climate change scenario weather into the statistical model, we predicted a small increase in the risk of fires spreading to the WUI, but the increase will be greater under extreme weather. This approach has a variety of uses, including mapping fire risk and improving the ability to match fire management responses to the threat from each fire. They also provide a baseline from which a cost-benefit analysis of complementary fire management strategies can be conducted.

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Section: Introductionmentioning

confidence: 99%

The spatial domain of wildfire risk and response in the wildland urban interface in Sydney, Australia

Price

Bradstock

2013

Nat. Hazards Earth Syst. Sci.

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“…However, excessive urban growth in SoCal significantly increases the wildland-urban interface, and thus seriously compounds wildfire hazards, resulting in loss of human life and property [1,2]. Thus, improving fire danger assessment systems with a high spatial resolution and a wide coverage across the vast wildland is essential for decision makers and fire agencies to develop and implement pro-active policies.…”

Section: Introductionmentioning

confidence: 99%

“…Our novel approach is to test the LFM relationship with enhanced vegetation index (EVI) that is averaged over various spatial extents centered at each in-situ LFM sampling location. The objectives of this study are to: (1) Compare seasonal and interannual characteristics of LFM with those of VIs calculated from satellite data in SoCal; (2) develop an empirical model function of LFM based on an optimal vegetation index together with temperature data; and, (3) evaluate the feasibility, as well as limitations of the empirical model for wildfire danger assessments.…”

Section: Introductionmentioning

confidence: 99%

Estimating Live Fuel Moisture from MODIS Satellite Data for Wildfire Danger Assessment in Southern California USA

et al. 2018

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Abstract:The goal of the research reported here is to assess the capability of satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer onboard both Terra and Aqua satellites, in order to replicate live fuel moisture content of Southern California chaparral ecosystems. We compared seasonal and interannual characteristics of in-situ live fuel moisture with satellite vegetation indices that were averaged over different radial extents around each live fuel moisture observation site. The highest correlations are found using the Aqua Enhanced Vegetation Index for a radius of 10 km, independently verifying the validity of in-situ live fuel moisture measurements over a large extent around each in-situ site. With this optimally averaged Enhanced Vegetation Index, we developed an empirical model function of live fuel moisture. Trends in the wet-to-dry phase of vegetation are well captured by the empirical model function on interannual time-scales, indicating a promising method to monitor fire danger levels by combining satellite, in-situ, and model results during the transition before active fire seasons. An example map of Enhanced Vegetation Index-derived live fuel moisture for the Colby Fire shows a complex spatial pattern of significant live fuel moisture reduction along an extensive wildland-urban interface, and illustrates a key advantage in using satellites across the large extent of wildland areas in Southern California.

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“…The fire impact may be at various intensity levels and may include convective heating or direct contact of the flames, radiant heat flux from nearby flames and airborne firebrands (Cohen 2000;Koo et al 2010;Mell et al 2010). Focusing on the effect of meteorology on fire characteristics, Blanchi et al (2010) analysed the relationship between house loss and the fire weather under which it occurred and found that virtually all of the losses occurred under extreme conditions.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic prediction of wildfire economic losses to housing in Cyprus using Bayesian network analysis

Papakosta

Xanthopoulos²,

Straub

2017

Int. J. Wildland Fire

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Abstract. Loss prediction models are an important part of wildfire risk assessment, but have received only limited attention in the scientific literature. Such models can support decision-making on preventive measures targeting fuels or potential ignition sources, on fire suppression, on mitigation of consequences and on effective allocation of funds. This paper presents a probabilistic model for predicting wildfire housing loss at the mesoscale (1 km 2 ) using Bayesian network (BN) analysis. The BN enables the construction of an integrated model based on causal relationships among the influencing parameters jointly with the associated uncertainties. Input data and models are gathered from literature and expert knowledge to overcome the lack of housing loss data in the study area. Numerical investigations are carried out with spatiotemporal datasets for the Mediterranean island of Cyprus. The BN is coupled with a geographic information system (GIS) and the resulting estimated house damages for a given fire hazard are shown in maps. The BN model can be attached to a wildfire hazard model to determine wildfire risk in a spatially explicit manner. The developed model is specific to areas with house characteristics similar to those found in Cyprus, but the general methodology is transferable to any other area, as well as other damages.

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The wildland - urban interface fire problem - current approaches and research needs

Cited by 369 publications

References 18 publications

The spatial domain of wildfire risk and response in the wildland urban interface in Sydney, Australia

The spatial domain of wildfire risk and response in the wildland urban interface in Sydney, Australia

Estimating Live Fuel Moisture from MODIS Satellite Data for Wildfire Danger Assessment in Southern California USA

Probabilistic prediction of wildfire economic losses to housing in Cyprus using Bayesian network analysis

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