Dead fuel moisture research: 1991–2012

Matthews, Stuart

doi:10.1071/wf13005

Cited by 176 publications

(111 citation statements)

References 101 publications

(115 reference statements)

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“…There are also a broad variety of mechanistic models of fuel moisture. Many of these models are difficult to implement as they require as many as 26 different parameters (Matthews, 2013). Most mechanistic models rely on Nelson's equilibrium moisture equation (e.g.…”

Section: Comparison With Empirical Modelsmentioning

confidence: 99%

“…New models are often either compared against other models rather than field data (Sharples et al, 2009;Snyder et al, 2006), or are compared against only brief time series of field data (Matthews, 2013;Viney, 1991). This approach assumes that 'benchmark models' such as FM Nelson , FM vanWagneror FM Simard provide accurate estimates of field FM (Table 3).…”

Section: Implications For Fire Managementmentioning

confidence: 99%

“…risk and fire propagation, and drought indices are therefore used as surrogates of dead fine fuel moisture. Viney (1991) and Matthews (2013) reviewed 37 published models for predicting dead fine fuel moisture. A common theme across the reviewed models was a focus on hourly time scales and a paucity of models that operate at daily time steps, as well as a lack of longterm or multiple site validation (Slijepcevic et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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A semi-mechanistic model for predicting the moisture content of fine litter

Dios

Fellows

Nolan

et al. 2015

Agricultural and Forest Meteorology

103

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a b s t r a c tThe moisture content of vegetation and litter (fuel moisture) is an important determinant of fire risk, and predictions of dead fine fuel moisture content (fuel with a diameter <25.4 mm) are particularly important. A variety of indices, as well as empirical and mechanistic models, have been proposed to predict fuel moisture, but these approaches have seldom been validated across temporally extensive datasets, or widely contrasting vegetation types. Here, we describe a semi-mechanistic model, based on the exponential decline of fuel moisture content with atmospheric vapor pressure deficit, that predicts daily minimum fuel moisture content. We calibrated the model at one site in New South Wales, Australia, and validated it at three contrasting ecosystem types in California, USA, where 10-h fuel moisture content was continuously measured every 30 min over a year. We found that existing drought indices did not accurately predict fuel moisture, and that empirical and equilibrium models provided biased estimates. The mean absolute error (MAE) of the fuel moisture content predicted by our model across sites and years was 3.7%, which was substantially lower than for other, commonly used models. Our model's MAE dropped to 2.9% when fuel moisture was below 20%, and to 1.8% when fuel moisture was below 10%. Our model's MAE was comparable to instrumental MAE (3.1-2.5%), indicating that further improvement may be limited by measurement error. The simplicity, accuracy and precision of our model makes it suitable for a range of applications, such as operational fire management and the prediction of fire risk in vegetation models, without the need for site-specific calibrations.

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Section: Comparison With Empirical Modelsmentioning

confidence: 99%

Section: Implications For Fire Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A semi-mechanistic model for predicting the moisture content of fine litter

Dios

Fellows

Nolan

et al. 2015

Agricultural and Forest Meteorology

103

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“…However, direct measurements of LFM for the purposes of wildfire danger assessment are not widely available because fuel moisture sampling is labor inten-sive. Instead, LFM, and consequently wildfire danger, are estimated from weather data (Bradshaw et al, 1983;Forestry Canada Fire Danger Group, 1992;Viegas et al, 2001;Castro et al, 2003;Carlson et al, 2007;Dennison et al, 2008;Matthews, 2014), related to drought indices (Dimitrakopoulos and Bemmerzouk, 2003;Pellizzaro et al, 2007), or estimated using satellite remote sensing techniques (Chuvieco et al, 2002;Caccamo et al, 2012;Jurdao et al, 2012).…”

mentioning

confidence: 99%

Soil Moisture Affects Growing-Season Wildfire Size in the Southern Great Plains

Krueger

Ochsner

Engle

et al. 2015

Soil Science Society of America Journal

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The increasing availability of soil moisture data presents an opportunity for its use in wildfire danger assessments, but research regarding the influence of soil moisture on wildfires is scarce. Our objective was to identify relationships between soil moisture and wildfire size for Oklahoma wildfires during the growing (May-October) and dormant seasons (November-April). we hypothesized that soil moisture influences wildfire size when vegetation is growing but is less important when most vegetation is dead or dormant. soil moisture, as fraction of available water capacity (FAw), and commonly measured weather variables were determined for 38,419 wildfires from 2000-2012. wildfires were grouped by size class (<4.05, ³4.05 and <40.5, ³40.5 and <121, ³121 and <405, and ³405 ha), and the Kruskal-wallis test with multiple comparisons was used to identify differences in each variable between wildfire size classes and seasons. Large fires occurred at lower FAw than small fires during both seasons (P < 0.001), but growing-season wildfires ³405 ha occurred over a narrow range of FAw (0.05-0.46) whereas dormant-season fires of this size occurred across the entire range of FAw (0.05-1.05). For growing-season fires ³ 121 ha, 91% occurred at FAw < 0.5 and 77% occurred at FAw < 0.2. Our finding that large growing-season wildfires occurred exclusively under conditions of low soil moisture highlights the need to develop methods to use soil moisture data in wildfire danger assessments.Abbreviations: AWC, available water capacity; FAW, fraction of available water capacity; KBDI, Keetch-Byram Drought Index; LFM, live fuel moisture; PAW, plant available water.

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“…Estes elevados valores justificam a baixa velocidade de propagação, comprimento das chamas e consumo do material combustível, já que, quanto maior o teor de umidade e mais compactado o leito do material combustível, maior é a dificuldade do fogo se propagar (ROTHERMEL, 1972;SOARES;BATISTA, 2007;LOUREIRO, 2013;WHITE et al, 2016a). O elevado teor de umidade no material combustível reduz a eficiência termal, já que será necessário um elevado gasto de energia para inicialmente evaporar a água e, a partir de então, dar-se origem o processo de combustão (MATTHEWS, 2014;WHITE et al, 2016a). Já combustíveis mais compactados apresentam uma menor relação área superficial/volume, o que ocasiona menor transferência de calor durante o processo de combustão.…”

Section: Discussõesunclassified

Avaliação E Simulação Do Comportamento Do Fogo Em Diferentes Fitofisionomias De Uma Área De Mata Atlântica Do Nordeste Brasileiro

White

Oliveira

Ribeiro

2017

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ResumoIncêndios florestais constituem um dos maiores problemas ambientais do mundo moderno. Eles podem reduzir a biodiversidade, contribuir com a emissão de gases do efeito estufa e alterar as propriedades químicas e físicas do solo. Em busca de novos conhecimentos que auxiliem os trabalhos de prevenção e combate aos incêndios, novos estudos vêm sendo desenvolvidos baseando-se na modelagem do comportamento do fogo. O objetivo deste trabalho foi avaliar parâmetros como velocidade de propagação do fogo, comprimento das chamas e consumo do material combustível em diferentes fitofisionomias presentes no Refúgio de Vida Silvestre Mata do Junco, Capela, Sergipe, Brasil. Para tal, foram coletadas amostras de material combustível superficial que foram levadas ao laboratório para realização de 45 queimas experimentais. Os dados obtidos em laboratório foram comparados às simulações realizadas pelos softwares Eucalyptus Fire Safety System e BehavePlus buscando-se avaliar a eficiência dos mesmos. Foram observadas diferenças significativas nos parâmetros do comportamento do fogo entre os ambientes selecionados para o estudo. Nas fitofisionomias de bambuzal e vegetação regenerante, o fogo apresentou maior velocidade de propagação e comprimento das chamas quando comparado com as áreas de mata fechada. O consumo do material combustível foi maior nos bambuzais. Em relação aos softwares utilizados, houveram diferenças significativas entre os valores reais e os simulados por ambos. Os resultados obtidos demostram a necessidade de se construir ou ajustar modelos matemáticos que estimem com maior eficiência o comportamento do fogo a fim de se auxiliar os trabalhos de prevenção e combate. Palavras-chave: Incêndios florestais; BehavePlus; Eucalyptus Fire Safety System. Abstract Evaluation and Simulation of Fire Behavior in Different Vegetation Types in an Atlantic Forest Area of Northeast Brazil.Wildfires are one of the most important environmental problems of the modern world. They represent a threat to the preservation of biodiversity, increase the release of greenhouse gases and change the chemical and physical properties of the soil. The knowledge of how the fire will behave can be used to assist fire prevention and suppression activities, that is why new studies have been done trying to describe and model fire behavior parameters. The aim of this work was to evaluate parameters such as rate of spread, flame length and fuel consumption in different vegetation types from the Mata do Junco Wildlife Refuge, Capela, Sergipe, Brazil. For this purpose, surface fuel load samples were collected in different vegetation types and brought to the laboratory to perform 45 experimental burns. The data from the burns were compared with simulations done in the software Eucalyptus Fire Safety System and BehavePlus in order to assess their efficiency. Significant differences in the fire behavior parameters was verified in the three different vegetation types. In the bamboo fields and regenerating vegetation, the fire had higher rate of spread and f...

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Dead fuel moisture research: 1991–2012

Cited by 176 publications

References 101 publications

A semi-mechanistic model for predicting the moisture content of fine litter

A semi-mechanistic model for predicting the moisture content of fine litter

Soil Moisture Affects Growing-Season Wildfire Size in the Southern Great Plains

Avaliação E Simulação Do Comportamento Do Fogo Em Diferentes Fitofisionomias De Uma Área De Mata Atlântica Do Nordeste Brasileiro

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