Evaluation of the predictive capacity of dead fuel moisture models for Eastern Australia grasslands

Cruz, Miguel G.; Kidnie, Susan; Matthews, Stuart; Hurley, Richard; Slijepcevic, A.; Nichols, David A.; Gould, James S.

doi:10.1071/wf16036

Cited by 17 publications

(14 citation statements)

References 20 publications

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“…The spread of M with relative humidity in the Australia dataset was more cloud-like than that of either the Canada or Alaska datasets, partly because M ranges to only 14%, about half that of the Alaska dataset and only about halfway to the fiber saturation point. In comparison to six fine dead or grass moisture content models tested against the same Australia dataset by Cruz et al (Table 3 from [18]), RMSE and MAE of the Alaska models ranked in the middle and were only slightly poorer than the best models, suggesting that variance in M limits other models as well. It also suggests that the Alaska models may not be wholly unsuited for use in Australian grasslands.…”

Section: Suitability Of the Models To Other Biomesmentioning

confidence: 84%

“…The models were ranked and compared to one another using several performance statistics on the measured versus predicted values of M generally following Cruz et al [18] and Cruz et al [45]: Correlation Coefficient (r), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The Median Symmetric Accuracy (MSA) was used as a measure of relative error [46].…”

Section: Model Calibration and Comparisonmentioning

confidence: 99%

“…The Australia dataset comprises measurements between 2013 and 2016 from five grassland sites in Victoria, New South Wales, and Queensland, Australia [17,18]. The sites ranged in south latitude between 27.5 to 37.5 • .…”

Section: Suitability Of the Models To Other Biomesmentioning

confidence: 99%

“…Marsden-Smedley and Catchpole [15] modelled M in Tasmanian buttongrass using an M e equation by Nelson [16]. And Kidnie et al [17] and Cruz et al [18] mention the assumption that moisture content in standing dead grass instantaneously responds to changes in weather. If M is typically close to M e then it follows that moisture content in standing dead grass fuel beds could be simply modelled by an M e equation that has been calibrated from empirical data, the aim of this paper.…”

Section: Introductionmentioning

confidence: 99%

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Moisture Sorption Models for Fuel Beds of Standing Dead Grass in Alaska

Miller

2018

Fire

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Sorption models were developed to predict the moisture content in fuelbeds of standing dead grass from ambient weather measurements. Intuition suggests that the response time of standing dead grass to diurnal changes in weather is negligible and that moisture content tracks the equilibrium moisture content under most field conditions. This assumption suggests that moisture content could be modelled by empirically fitting coefficients to equations of equilibrium moisture content using field measurements. Here, six equations commonly used in wildland fire management and other industries were fit using 293 measurements of weather and moisture content in standing dead grass from Alaska, U.S.A. Predictors were air temperature and either relative humidity or dewpoint depression. Mean absolute errors of the best three models were approximately 1.16% of moisture content. The models predicted well the moisture content of an independently collected dataset from Canada but less so a set from Australia. The models may be used in wildland fire danger rating and fire behavior prediction systems.

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Section: Suitability Of the Models To Other Biomesmentioning

confidence: 84%

Section: Model Calibration and Comparisonmentioning

confidence: 99%

Section: Suitability Of the Models To Other Biomesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Moisture Sorption Models for Fuel Beds of Standing Dead Grass in Alaska

Miller

2018

Fire

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“…To that end, empirical approaches lack generality [28]. Physical models, such as the FIRETEC [29], AIOLOS-F [30], FIRESTAR [31] and WFDS [32] are more robust since they take into account the mechanism of fire propagation by combining thermodynamics, air-dynamics and botany [33]. However, calculating the balanced equations of the conservation of energy and momentum is usually time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data

Liu

Quan

et al. 2018

Remote Sensing

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Fire Spread Rate (FSR) can indicate how fast a fire is spreading, which is especially helpful for wildfire rescue and management. Historically, images obtained from sun-orbiting satellites such as Moderate Resolution Imaging Spectroradiometer (MODIS) were used to detect active fire and burned area at the large spatial scale. However, the daily revisit cycles make them inherently unable to extract FSR in near real-time (hourly or less). We argue that the Himawari-8, a next generation geostationary satellite with a 10-min temporal resolution and 0.5–2 km spatial resolution, may have the potential for near real-time FSR extraction. To that end, we propose a novel method (named H8-FSR) for near real-time FSR extraction based on the Himawari-8 data. The method first defines the centroid of the burned area as the fire center and then the near real-time FSR is extracted by timely computing the movement rate of the fire center. As a case study, the method was applied to the Esperance bushfire that broke out on 17 November, 2015, in Western Australia. Compared with the estimated FSR using the Commonwealth Scientific and Industrial Research Organization (CSIRO) Grassland Fire Spread (GFS) model, H8-FSR achieved favorable performance with a coefficient of determination (R2) of 0.54, mean bias error of –0.75 m/s, mean absolute percent error of 33.20% and root mean square error of 1.17 m/s, respectively. These results demonstrated that the Himawari-8 data are valuable for near real-time FSR extraction, and also suggested that the proposed method could be potentially applicable to other next generation geostationary satellite data.

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Grass Curing/Cured Fuels

Duff¹,

Bessell²,

Cruz³

2019

Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires

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Curing refers to the process of grass senescence, or die-off, and the transition of live fuels into the dead fuel component of the fuel bed. It is most commonly used to refer to grasses; however in some systems the term may also include shrubs and other herbs. The grass curing level represents the proportion of dead fuels within the fuel bed, with a fully, or 100%, cured grassland being composed solely of dead fuels.

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Evaluation of the predictive capacity of dead fuel moisture models for Eastern Australia grasslands

Cited by 17 publications

References 20 publications

Moisture Sorption Models for Fuel Beds of Standing Dead Grass in Alaska

Moisture Sorption Models for Fuel Beds of Standing Dead Grass in Alaska

Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data

Grass Curing/Cured Fuels

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