Global increase in wildfire risk due to climate driven declines in fuel moisture

2021 ◽  
Author(s):  
T. Michael Ellis ◽  
David M.J.S. Bowman ◽  
Piyush Jain ◽  
Mike D. Flannigan ◽  
Grant J. Williamson
Keyword(s):  
Fuel Moisture ◽  
Wildfire Risk ◽  
Global Increase

Detection of non-linearities in the dependence of burn area on fuel age and climatic variables

2003 ◽  
Vol 12 (1) ◽  
pp. 1 ◽  
Author(s):  
Frederic Paik Schoenberg ◽  
Roger Peng ◽  
Zhijun Huang ◽  
Philip Rundel
Keyword(s):  
Los Angeles ◽  
Los Angeles County ◽  
Fire Risk ◽  
Climatic Variables ◽  
Fuel Moisture ◽  
Wildfire Risk

Evidence from Los Angeles County in California, USA suggests that the relationships between wildfire burn area and fuel age, temperature, precipitation, and fuel moisture are not linear. Instead, the relationships appear to have thresholds.The data seem to support the notion that fire risk is nearly constant provided various conditions are met: that fuel age and temperature exceed a given threshold, and that fuel moisture and precipitation are sufficiently low. There appears to be little distinction in terms of wildfire risk between conditions that are sufficient for wildfires and those that are extreme.

scholarly journals Mapping Soil Moisture as an Indicator of Wildfire Risk Using Landsat 8 Images in Sri Lanna National Park, Northern Thailand

2016 ◽  
Vol 8 (10) ◽  
pp. 107 ◽  
Author(s):  
Kansuma Burapapol ◽  
Ryota Nagasawa
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Linear Regression Analysis ◽  
Landsat 8 ◽  
Fuel Moisture ◽  
Wildfire Risk ◽  
Dry Conditions ◽  
Scatter Plots

<p>Severely dry climate plays an important role in the occurrence of wildfires in Thailand. Soil water deficits increase dry conditions, resulting in more intense and longer burning wildfires. The temperature vegetation dryness index (TVDI) and the normalized difference drought index (NDDI) were used to estimate soil moisture during the dry season to explore its use for wildfire risk assessment. The results reveal that the normalized difference wet index (NDWI) and land surface temperature (LST) can be used for TVDI calculation. Scatter plots of both NDWI/LST and the normalized difference vegetation index (NDVI)/LST exhibit the triangular shape typical for the theoretical TVDI. However, the NDWI is more significantly correlated to LST than the NDVI. Linear regression analysis, carried out to extract the maximum and minimum LSTs (LST<sub>max</sub>, LST<sub>min</sub>), indicate that LST<sub>max </sub>andLST<sub>min</sub> delineated by the NDWI better fulfill the collinearity requirement than those defined by the NDVI. Accordingly, the NDWI-LST relationship is better suited to calculate the TVDI. This modified index, called TVDI<sub>NDWI-LST</sub>, was applied together with the NDDI to establish a regression model for soil moisture estimates. The soil moisture model fulfills statistical requirements by achieving 76.65% consistency with the actual soil moisture and estimated soil moisture generated by our model. The relationship between soil moisture estimated from our model and leaf fuel moisture indicates that soil moisture can be used as a complementary dataset to assess wildfire risk, because soil moisture and fuel moisture content (FMC) show the same or similar behavior under dry conditions. <strong></strong></p>

Evaluation of standard methods for collecting and processing fuel moisture samples

2016 ◽  
Author(s):  
Sally M. Haase ◽  
José Sánchez ◽  
David R. Weise
Keyword(s):  
Fuel Moisture ◽  
Standard Methods

Spatial dataset of probabilistic wildfire risk components for the conterminous United States

2016 ◽  
Author(s):  
Karen C. Short ◽  
Mark A. Finney ◽  
Joe H. Scott ◽  
Julie W. Gilbertson-Day ◽  
Isaac C. Grenfell
Keyword(s):  
United States ◽  
Wildfire Risk ◽  
Spatial Dataset

Wildfire risk and hazard: procedures for the first approximation

2010 ◽  
Author(s):  
David E. Calkin ◽  
Alan A. Ager ◽  
Julie Gilbertson-Day
Keyword(s):  
Wildfire Risk ◽  
First Approximation

Application of BMP-2 for bone graft in Dentistry

RSBO ◽  
2016 ◽  
Vol 12 (1) ◽  
pp. 88
Author(s):  
Lídia Audrey Rocha Valadas Marques ◽  
Edvan Alves da Costa Júnior ◽  
Mara Assef Leitão Lotif ◽  
Edilson Martins Rodrigues Neto ◽  
Francisco Filipe Carvalho da Silva ◽  
...  
Keyword(s):  
Life Expectancy ◽  
Bone Graft ◽  
Bone Regeneration ◽  
Tooth Loss ◽  
Clinical Use ◽  
Increase Life Expectancy ◽  
Bone Replacement ◽  
Morphogenetic Protein ◽  
The Face ◽  
Global Increase

Introduction: The global increase life expectancy and the resulting tooth loss has required searching for new rehabilitation alternatives in Dentistry. Biomaterials can be defined as any material that acts replacing a lost bone defect and its function. In Dentistry, many studies have aimed to improve bone regeneration through the use of BMPs for bone replacement. Objective: To review the literature on the use and clinical viability of human morphogenetic protein for the jaws reconstruction. Material and methods: The following databases were searched: Pubmed, Bireme, Lilacs, and Scielo and 30 articles published between 1965 and 2013 were found using the following descriptors: “dental implants”, “maxillary sinus”, and “biocompatible materials”. Results: Several studies demonstrate the biological advantages of rhBMP-2 on bone regeneration of the jaws. In recent years, morphogenetic protein has presented a large clinical use. Conclusion: Despite being a high-cost biomaterial, rhBMP-2 is a viable and very effective alternative for reconstruction of defects of the face.

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

2001 ◽  
Vol 10 (2) ◽  
pp. 241 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Fire Management ◽  
Prediction Models ◽  
Dew Point ◽  
Seasonal Effects ◽  
Experimental Program ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

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