A simple index for assessing fire danger rating

Sharples, Jason J.; McRae, Richard H. D.; Weber, R. O.; Gill, A. Malcolm

doi:10.1016/j.envsoft.2008.11.004

Cited by 118 publications

(91 citation statements)

References 13 publications

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“…Understanding sensitivity and uncertainty is central to environmental modelling (Nossent et al, 2011;Refsgaard et al, 2007;Smith and Smith, 2007;Warmink et al, 2010), and the use of meteorological and soil data is common to a range of subject areas (Moussiopolous et al, 2004;Post et al, 2006;Sharples et al, 2009). The effect of different meteorological and soil inputs on model results is therefore of significant interest beyond crop modelling.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought

Pogson

Hastings

Smith

2012

Environmental Modelling & Software

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Crop growth models are increasingly used as part of research into areas such as climate change and bioenergy, so it is particularly important to understand the effects of environmental inputs on model results. Rather than investigating the effects of separate input parameters, we assess results obtained from a crop growth model using a selection of entire meteorological and soil input datasets, since these define modelled conditions. Yields are found to vary significantly only where the combination of inputs makes the crop vulnerable to drought, rather than being especially sensitive to any single input. Results highlight the significance of soil water parameters, which are likely to become increasingly critical in areas affected by climate change. Differences between datasets demonstrate the need to consider the dataset-dependence of parameterised model terms, both for model validation and predictions based on alternative datasets.

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Section: Introductionmentioning

confidence: 99%

Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought

Pogson

Hastings

Smith

2012

Environmental Modelling & Software

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“…Intuitively, fire danger decreases as fuel moisture content increases, whereas increases as wind speed increases. Sharples et al (2009a) compared three popular fire weather index (FWI) in the literature and produced a simple FWI which has advantages over others called F index. Additionally, Sharples et al (2009b) introduced a dimensionless fuel moisture index (FMI), which was compared to several existing models for determining the moisture content of fine, dead fuels.…”

Section: F (Fire Weather Index)mentioning

confidence: 99%

“…Fire danger index introduced by Sharples et al (2009a) is a combination of information on wind speed and fuel moisture content, where the latter is derived through consideration of temperature and relative humidity. Intuitively, fire danger decreases as fuel moisture content increases, whereas increases as wind speed increases.…”

Section: F (Fire Weather Index)mentioning

confidence: 99%

Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

Şatır

Berberoğlu

Dönmez

2015

Geomatics, Natural Hazards and Risk

183

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Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN) method was used to map forest fire probability in Upper Seyhan Basin (USB) in Turkey. Multi-layer perceptron (MLP) approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC) analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R D ¡0.43), tree cover (R D 0.93) and temperature (R D 0.42) were strongly correlated with forest fire probability in the USB region. KEYWORDSForest fire probability and hazard; landscape feature; weighting; artificial neural network; Mediterranean region; fire weather index

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“…The Fire Danger Rating (FDR) is an assessment of the potential bushfire behavior and it used to provide a measure of the bushfire condition, threat and the potential impact on the community and environment should a bushfire occur on a given day [25] [13].Since black Saturday in Victoria in 2009, a standardized fire danger rating (FDR) was adopted in all states of Australia. Its mission is to provide fire weather forecasts including temperature, wind speed and direction, relative humidity and dryness of vegetation during the fire season to bushfire agencies and authorities to determine the appropriate Fire Danger Rating Low to moderate 0-11…”

Section: Iiifire Danger Indexmentioning

confidence: 99%

“…McArthur Forest Fire Danger is widely used in Australia to predict fire danger [13] [14]. Fire danger index is defined as a resultant of both variables and constants fire danger factors which influence the start of fire, rate of spread and the difficulty of manage and control of the bushfire.…”

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confidence: 99%

The Influence of Instruments Uncertainty on the Forest Fire Danger Indices (FFDI)

Alzoubi¹,

Oo²,

Stojcevski³

2014

IJAREEIE

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ABSTRACT:The McArthur Forest Fire Danger Indices (FFDI) have been used in Australia since was developed in 1960s and used by fire authority to measure the level of fire danger. Its value depend on the drought factor which is based on dryness of the fuel and other weather variable factors such as temperature, relative humidity, rainfall, wind speed and wind directions. The accuracy measurement of FFDI output depends on the accuracy of the input variables, inaccuracy and uncertainty of these inputs may result in incorrect of the FFDI value. As the value of the FFDI increased the more effected from the inaccuracy of the wind speed, temperature and humidity. Bushfires prediction systems sensitivity depends on the instruments accuracy used to measure the input data.

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A simple index for assessing fire danger rating

Cited by 118 publications

References 13 publications

Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought

Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought

Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

The Influence of Instruments Uncertainty on the Forest Fire Danger Indices (FFDI)

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