Filters: Tags: fire regimes (X) > Types: Map Service (X)
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Executive Summary: Portions of broad-scale ecoregions of the Great Plains, and Southern Semiarid Highlands were generally projected as mostly suitable for large fires of low severity within 31 years. Under a 2070 future climate scenario of high CO2 emission (HadGEM2-ES RCP8.5) a significant increase in suitability for large low severity wildfires was seen in Wyoming and Montana, which was accompanied by a decrease in suitability for the Madrean Archipelago and portions of central and west Texas. Broad scale niche model for the Southwestern Willow Flycatcher under current climate was centered within the known breeding range mostly along riparian areas. Under a 2070 future climate scenario of high CO2 emission (HadGEM2-ES...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: 2014,
AZ-01,
AZ-02,
AZ-03,
AZ-04,
In regulated rivers of the southwest, reduced flooding and the invasion of tamarisk contributes to accumulation of greater fuel loads and increased riparian fire frequency. As a result, some desert riparian areas, historically considered barriers to wildfire, have been converted into pathways for wildfire spread. Fire-smart management strategies are needed to protect sensitive riparian species and reduce fire risk from increased fire frequency due to interactions of climate change, tamarisk invasion, and tamarisk beetle activity. Fire niche simulations will be used to project impacts of fire frequency and climate change, which can be used to highlight areas of the Desert LCC where Southwestern Willow Flycatcher,...
Categories: Data,
Project;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: 2014,
AZ-01,
AZ-02,
AZ-03,
AZ-04,
This raster dataset represents spatially explicit predictions of burn severity (dNBRPredict.tif) in the Mojave Desert based on models developed from data on the difference normalized burn ratio (dNBR) within perimeters of fires greater than 405 hectares that burned between 1984 to 2010. Raster resolution equals 30 meters, projection equals UTM Zone 11N.
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Grass-fire cycle,
Mojave Desert,
Mojave Desert,
Predictive models,
Random forests,
This raster dataset represents spatially explicit predictions of probability of ignition in the Mojave Desert based on models developed from data on perimeters of fires greater than 405 hectares that burned between 1972 to 2010. Raster resolution equals 30 meters, projection equals UTM Zone 11N.
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Grass-fire cycle,
Maxent,
Mojave Desert,
Mojave Desert,
Predictive models,
Arid ecosystems are often vulnerable to transformation to invasive-dominated states following fire, but data on persistence of these states are sparse. The grass/fire cycle is a feedback process between invasive annual grasses and fire frequency that often leads to the formation of alternative vegetation states dominated by the invasive grasses. However, other components of fire regimes, such as burn severity, also have the potential to produce long-term vegetation transformations. Our goal was to evaluate the influence of both fire frequency and burn severity on the transformation of woody-dominated communities to communities dominated by invasive grasses in major elevation zones of the Mojave Desert of western...
Categories: Data;
Types: Citation,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Mojave desert,
USGS Science Data Catalog (SDC),
biological invasions,
chronosequence,
community structure,
This raster dataset represents spatially explicit predictions of fire frequency in the Mojave Desert based on models developed from data on perimeters of fires greater than 405 hectares that burned between 1972 through 2010. Raster resolution equals 30 meters, projection equals UTM Zone 11N.
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Grass-fire cycle,
Mojave Desert,
Mojave Desert,
Predictive models,
Random forests,
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