Soil moisture influences on Sierra Nevada dead fuel moisture content and fire risks

2021 ◽  
Vol 496 ◽  
pp. 119379
Author(s):  
Ekaterina Rakhmatulina ◽  
Scott Stephens ◽  
Sally Thompson
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Sierra Nevada ◽  
Fuel Moisture ◽  
Fuel Moisture Content

Soil Moisture and Live Fuel Moisture Content as key remote sensing variables to unlock improved wildfire predictions

2021 ◽  
Author(s):  
Florian Briquemont ◽  
Akli Benali
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Fire Risk ◽  
Simulation Models ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Live Fuel Moisture ◽  
Risk Maps ◽  
Fuel Moisture Content ◽  
The Impact

<p>Large wildfires are amongst the most destructive natural disasters in southern Europe, posing a serious threat to both human lives and the environment.</p><p>Although wildfire simulations and fire risk maps are already very a useful tool to assist fire managers in their decisions, the complexity of fire spread and ignition mechanisms can greatly hinder their accuracy. An important step in improving the reliability of wildfire prediction systems is to implement additional drivers of fire spread and fire risk in simulation models.</p><p>Despite their recognized importance as factors influencing fuel flammability and fire spread, soil moisture and live fuel moisture content are rarely implemented in the simulation of large wildfires due to the lack of sufficient and accurate data. Fortunately, new satellite products are giving the opportunity to assess these parameters on large areas with high temporal and spatial resolution.</p><p>The purpose of this study is twofold. First, we aimed to evaluate the capabilities of satellite data to estimate soil moisture and live fuel moisture content in different landcovers.  Secondly, we focused on the potential of these estimates for assessing fire risk and fire spread patterns of large wildfires in Portugal. Ultimately, the goal of this study is to implement these estimated variables in fire spread simulations and fire risk maps.<br><br>We compared datasets retrieved from Sentinel 1, SMAP (Soil Moisture Active Passive radiometer) and MODIS (Moderate Resolution Imaging Spectrometer) missions. Several estimators of LFMC based on spectral indices were tested and their patterns were compared with field data. Based on these estimators, we assessed the impact of LFMC and soil moisture on the extent and occurrence of large wildfires. Finally, we built a database of detailed historical wildfire progressions, which we used to evaluate the influence of soil moisture and LFMC on the velocity and direction of the fire spread.</p>

Assessing Grassland Moisture and Biomass in Tasmania - the Application of Remote-Sensing and Empirical-Models for a Cloudy Environment

1995 ◽  
Vol 5 (3) ◽  
pp. 165 ◽  
Author(s):  
MA Chladil ◽  
M Nunez
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Fire Season ◽  
Fuel Moisture ◽  
Noaa Avhrr ◽  
Dryness Index ◽  
Ground Truthing ◽  
Soil Dryness ◽  
Fuel Moisture Content ◽  
Semi Empirical

The operational feasibility of NOAA/AVHRR data and two semi-empirical moisture models were evaluated in the grasslands of southeastern Tasmania (Australia) during the 1988/89 fire season. A limited ground-truthing experiment compared the grassland dry biomass, soil moisture and fuel moisture with the satellite derived NDVI and the Soil Dryness Index (SDI) and the Grassland Curing Index (GCI). The NDVI gave good results for fuel moisture content (FMC) and soil moisture content (SMC) but unreliable image availability precludes the use of NDVI as a stand alone system for fire managers. The SDI and GCI also performed well in predicting SMC and FMC. Very good results were obtained when the NDVI and the GCI were combined. These results suggest the combination of data will provide both the accuracy and the continuity of information needed for operational use by fire managers. The methods used here could be cheaply and quickly repeated for use in other similar fire prone and cloudy environments.

Predicting 1-H Dead Fuel Moisture Content at Regional Scales Using Machine Learning from Himawari-8 Data

2021 ◽  
Author(s):  
Chunquan Fan ◽  
Binbin He ◽  
Peng Kong ◽  
Hao Xu ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Moisture Content ◽  
Fuel Moisture ◽  
Fuel Moisture Content

scholarly journals Evaluating the Effects of Projected Climate Change on Forest Fuel Moisture Content

2014 ◽  
Vol 37 ◽  
pp. 65-69
Author(s):  
Kellen Nelson ◽  
Daniel Tinker
Keyword(s):  
Climate Change ◽  
Moisture Content ◽  
Stand Structure ◽  
Canopy Cover ◽  
Fire Season ◽  
Climate Change Scenarios ◽  
Fuel Moisture ◽  
Mature Forest ◽  
Forest Fuel ◽  
Fuel Moisture Content

Understanding how live and dead forest fuel moisture content (FMC) varies with seasonal weather and stand structure will improve researchers’ and forest managers’ ability to predict the cumulative effects of weather on fuel drying during the fire season and help identify acute conditions that foster wildfire ignition and high rates of fire spread. No studies have investigated the efficacy of predicting FMC using mechanistic water budget models at daily time scales through the fire season nor have they investigated how FMC may vary across space. This study addresses these gaps by (1) validating a novel mechanistic live FMC model and (2) applying this model with an existing dead FMC model at three forest sites using five climate change scenarios to characterize how FMC changes through time and across space. Sites include post-fire 24-year old forest, mature forest with high canopy cover, and mature forest affected by the mountain pine beetle with moderate canopy cover. Climate scenarios include central tendency, warm/dry, warm/wet, hot/dry, and hot/wet.

Effects of distribution of bulk density and moisture content on shrub fires

2013 ◽  
Vol 22 (5) ◽  
pp. 625 ◽  
Author(s):  
Ambarish Dahale ◽  
Selina Ferguson ◽  
Babak Shotorban ◽  
Shankar Mahalingam
Keyword(s):  
Moisture Content ◽  
Spatial Variation ◽  
Bulk Density ◽  
Solid Fuel ◽  
Numerical Solutions ◽  
Propagation Speed ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Combustion Heat ◽  
Fuel Moisture Content

Formulation of a physics-based model, capable of predicting fire spread through a single elevated crown-like shrub, is described in detail. Predictions from the model, obtained by numerical solutions to governing equations of fluid dynamics, combustion, heat transfer and thermal degradation of solid fuel, are found to be in fairly good agreement with experimental results. In this study we utilise the physics-based model to explore the importance of two parameters – the spatial variation of solid fuel bulk density and the solid fuel moisture content – on the burning of an isolated shrub in quiescent atmosphere. The results suggest that vertical fire spread rate within an isolated shrub and the time to initiate ignition within the crown are two global parameters significantly affected when the spatial variation of the bulk density or the variation of fuel moisture content is taken into account. The amount of fuel burnt is another parameter affected by varying fuel moisture content, especially in the cases of fire propagating through solid fuel with moisture content exceeding 40%. The specific mechanisms responsible for the reduction in propagation speed in the presence of higher bulk densities and moisture content are identified.

Modelling fine fuel moisture content and the likelihood of fire spread in blue gum (Eucalyptus globulus) litter

2014 ◽  
pp. 353-359
Author(s):  
Anita Pinto ◽  
Juncal Espinosa-Prieto ◽  
Carlos Rossa ◽  
Stuart Matthews ◽  
Carlos Loureiro ◽  
...  
Keyword(s):  
Moisture Content ◽  
Eucalyptus Globulus ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Fuel Moisture Content ◽  
Blue Gum
Sign in / Sign up

Export Citation Format

Share Document