Determining fuel moisture thresholds to assess wildfire hazard: A contribution to an operational early warning system

<div><p>Fuel moisture content (FMC) is an important fuel property for assessing wildfire hazard, since it influences fuel flammability and fire behavior. The relationship between FMC and fire activity differs among land covers and seems to be a property of each ecosystem. Our objectives were to analyze pre-fire FMC among different land covers and to propose a wildfire hazard classification for the Sierras Chicas in the Chaco Serrano subregion (Argentina), by analyzing pre-fire FMC distributions observed for grasslands, shrublands and forests and using percentiles to establish thresholds. For this purpose, we used a fire database derived from Landsat imagery (30 m) and derived FMC maps every 8 days from 2002 to 2016 using MODIS reflectance products and empirical equations of FMC. Our results indicated that higher FMC constrains the extent of wildfires, whereas at lower FMC there are other factors affecting their size. Extreme and high fire hazard thresholds for grasslands were established at FMC of 55% and 67% respectively, at 72% and 105% for forests and at 106% and 121% for shrublands. Our FMC thresholds were sensitive to detect extreme fire hazard conditions during years with high fire activity in comparison to average conditions. The differences in the distributions of pre-fire FMC among land covers and between ecosystems highlighted the need to locally determine land cover-specific FMC thresholds to assess wildfire hazard. Our wildfire hazard classification applied to FMC maps in an operational framework will contribute to improving early warning systems in the Sierras Chicas. However, moisture alone is not sufficient to represent true fire hazard in Chaco forests and the combination with other variables would provide better hazard assessments. These operational wildfire hazard maps will help to better allocation of fire protective resources to minimize negative impact on people, property and ecosystems. To the best of our knowledge, this is the first study analyzing pre-fire FMC over several fire seasons in a non-Mediterranean ecosystem, aiming at assessing wildfire hazard.</p></div

Percentage environmental layers.

<p>References: a) high vegetation, b) low vegetation, c) pasture, d) shadow, e) water, f) bare soil, g) urban constructions, and h) drinking water.</p

Probability map for <i>Ae</i>. <i>aegypti</i> breeding sites generated by an ecological niche model.

<p>The blue areas represent null risk of breeding sites and red areas represent the most suitable habitats for vector breeding sites.</p

Study area.

<p>Left top, worldwide location. Left bottom, location of Salta province in Argentina. Right top, Salta-Bolivia border. Bottom right, detail of Tartagal city and neighborhoods included in the study, neighborhoods (a) Northeastern added in 2013 and (b) Southwest added in 2011.</p

Relative contribution of each environmental variable (contributions larger than 1%) to the Maxent model.

<p>Relative contribution of each environmental variable (contributions larger than 1%) to the Maxent model.</p

Distribution and total number of containers with larvae of <i>Ae</i>. <i>aegypti</i> for sites that were positive in Tartagal in the summer-autumn of each year (2009–2014).

<p>Distribution and total number of containers with larvae of <i>Ae</i>. <i>aegypti</i> for sites that were positive in Tartagal in the summer-autumn of each year (2009–2014).</p

Fluctuation of the Stegomyia indexes (HI and BI) during the study sample period in Tartagal.

<p>References: FC (Focal Cycle); M (random sampling).</p

Land cover map of Tartagal city derived from an unsupervised classification (K-means) of SPOT 5 images, with seven different classes.

<p>Reference: unclassified (black), water (blue), shadows (black), high vegetation (dark green), low vegetation (light green), pasture and crops (yellow), bare soil (brown) and urban (grey).</p

Environmental, climatic and demographic variables used to create <i>Aedes aegypti</i> distribution models for Tartagal.

<p>Environmental, climatic and demographic variables used to create <i>Aedes aegypti</i> distribution models for Tartagal.</p

Distances environmental layers.

<p>References: a) bare soil, b) high vegetation, c) low vegetation, d) water, e) urban constructions, f) pasture, g) critical points, and h) shadows.</p