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This dataset shows the predicted area of high fire potential for the current year up to the end of the forecast period as simulated by a modified version of the MC1 Dynamic General Vegetation Model (DGVM). The area of high fire potential is where PDSI and MC1-calculated values of potential fire behavior (fireline intensity for forest and shrubland and rate of spread of spread for grassland) exceed calibrated threshold values. Potential fire behavior in MC1 is estimated using National Fire Danger Rating System (NFDRS) formulas, monthly climatic (temperature, precipitation, and relative humidity) data, and fuel moisture and loading estimates. Monthly climatic data includes recorded values up to the last observed...
This dataset shows the predicted area of high fire potential for the current year up to the end of the forecast period as simulated by a modified version of the MC1 Dynamic General Vegetation Model (DGVM). The area of high fire potential is where PDSI and MC1-calculated values of potential fire behavior (fireline intensity for forest and shrubland and rate of spread of spread for grassland) exceed calibrated threshold values. Potential fire behavior in MC1 is estimated using National Fire Danger Rating System (NFDRS) formulas, monthly climatic (temperature, precipitation, and relative humidity) data, and fuel moisture and loading estimates. Monthly climatic data includes recorded values up to the last observed...
The Palmer Drought Severity Index (PDSI) is a measure of drought derived from both precipitation and temperature. Negative (i.e., dry) values of PDSI are closely associated with a high potential for wildland fire. PDSI is based on a supply-and-demand model of soil moisture originally developed by Wayne Palmer, who published his method in the 1965 paper Meteorological Drought for the Office of Climatology of the U.S. Weather Bureau.The index has proven to be most effective in indicating long-term drought (or wetness) over a matter of several months. PDSI calculations are standardized for an individual station (or grid cell) based on the long-term variability of precipitation and temperature at that location....
The Palmer Drought Severity Index (PDSI) is a measure of drought derived from both precipitation and temperature. Negative (i.e., dry) values of PDSI are closely associated with a high potential for wildland fire. PDSI is based on a supply-and-demand model of soil moisture originally developed by Wayne Palmer, who published his method in the 1965 paper Meteorological Drought for the Office of Climatology of the U.S. Weather Bureau.The index has proven to be most effective in indicating long-term drought (or wetness) over a matter of several months. PDSI calculations are standardized for an individual station (or grid cell) based on the long-term variability of precipitation and temperature at that location....
The Palmer Drought Severity Index (PDSI) is a measure of drought derived from both precipitation and temperature. Negative (i.e., dry) values of PDSI are closely associated with a high potential for wildland fire. PDSI is based on a supply-and-demand model of soil moisture originally developed by Wayne Palmer, who published his method in the 1965 paper Meteorological Drought for the Office of Climatology of the U.S. Weather Bureau.The index has proven to be most effective in indicating long-term drought (or wetness) over a matter of several months. PDSI calculations are standardized for an individual station (or grid cell) based on the long-term variability of precipitation and temperature at that location....
Future climates are simulated by general circulation models (GCM) using climate change scenarios (IPCC 2014). To project climate change for the sagebrush biome, we used 11 GCMs and two climate change scenarios from the IPCC Fifth Assessment, representative concentration pathways (RCPs) 4.5 and 8.5 (Moss et al. 2010, Van Vuuren et al. 2011). RCP4.5 scenario represents a future where climate policies limit and achieve stabilization of greenhouse gas concentrations to 4.5 W m-2 by 2100. RCP8.5 scenario might be called a business-as-usual scenario, where high emissions of greenhouse gases continue in the absence of climate change policies. The two selected time frames allow comparison of near-term (2020-2050) and longer-term...
Categories: Data;
Types: Citation,
Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Arizona,
CRS,
California,
Climate,
Colorado,
Future climates are simulated by general circulation models (GCM) using climate change scenarios (IPCC 2014). To project climate change for the sagebrush biome, we used 11 GCMs and two climate change scenarios from the IPCC Fifth Assessment, representative concentration pathways (RCPs) 4.5 and 8.5 (Moss et al. 2010, Van Vuuren et al. 2011). RCP4.5 scenario represents a future where climate policies limit and achieve stabilization of greenhouse gas concentrations to 4.5 W m-2 by 2100. RCP8.5 scenario might be called a business-as-usual scenario, where high emissions of greenhouse gases continue in the absence of climate change policies. The two selected time frames allow comparison of near-term (2020-2050) and longer-term...
Categories: Data;
Types: Citation,
Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Arizona,
CRS,
California,
Climate,
Colorado,
Future climates are simulated by general circulation models (GCM) using climate change scenarios (IPCC 2014). To project climate change for the sagebrush biome, we used 11 GCMs and two climate change scenarios from the IPCC Fifth Assessment, representative concentration pathways (RCPs) 4.5 and 8.5 (Moss et al. 2010, Van Vuuren et al. 2011). RCP4.5 scenario represents a future where climate policies limit and achieve stabilization of greenhouse gas concentrations to 4.5 W m-2 by 2100. RCP8.5 scenario might be called a business-as-usual scenario, where high emissions of greenhouse gases continue in the absence of climate change policies. The two selected time frames allow comparison of near-term (2020-2050) and longer-term...
Categories: Data;
Types: Citation,
Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Arizona,
CRS,
California,
Climate,
Colorado,
This dataset includes magnetotelluric (MT) sounding data collected in July 2019 in the Silverton Caldera complex, Colorado, in the Southern Rocky Mountain Volcanic Field, by the U.S. Geological Survey (USGS). Along with geologic mapping, airborne magnetics, airborne electromagnetics, and audiomagnetotellurics, the USGS collected MT data at 24 sites along five profiles ranging from 2 to 5 kilometers in length: across Red Mountain of the Silverton caldera, within the caldera in Eureka Graben, across the south-eastern margin of the caldera along Arrastra Gulch, across the southern margin of the caldera along the western margin of Kendall Mountain, and across the south-western margin of the caldera along South Fork...
Audiomagnetotelluric sounding data in the Silverton Caldera complex, Colorado, 2019; Station AMTMB06
This dataset includes audiomagnetotelluric (AMT) sounding data collected in July 2019 in the Silverton Caldera complex, Colorado, in the Southern Rocky Mountain Volcanic Field, by the U.S. Geological Survey (USGS). Along with geologic mapping, airborne magnetics, airborne electromagnetics, and magnetotellurics, the USGS collected AMT data at 26 sites along five profiles ranging from 2 to 5 kilometers in length: across Red Mountain of the Silverton caldera, within the caldera in Eureka Graben, across the south-eastern margin of the caldera along Arrastra Gulch, across the southern margin of the caldera along the western margin of Kendall Mountain, and across the south-western margin of the caldera along South Fork...
Audiomagnetotelluric sounding data in the Silverton Caldera complex, Colorado, 2019; Station AMTSF04
This dataset includes audiomagnetotelluric (AMT) sounding data collected in July 2019 in the Silverton Caldera complex, Colorado, in the Southern Rocky Mountain Volcanic Field, by the U.S. Geological Survey (USGS). Along with geologic mapping, airborne magnetics, airborne electromagnetics, and magnetotellurics, the USGS collected AMT data at 26 sites along five profiles ranging from 2 to 5 kilometers in length: across Red Mountain of the Silverton caldera, within the caldera in Eureka Graben, across the south-eastern margin of the caldera along Arrastra Gulch, across the southern margin of the caldera along the western margin of Kendall Mountain, and across the south-western margin of the caldera along South Fork...
Audiomagnetotelluric sounding data in the Silverton Caldera complex, Colorado, 2019; Station AMTAG04
This dataset includes audiomagnetotelluric (AMT) sounding data collected in July 2019 in the Silverton Caldera complex, Colorado, in the Southern Rocky Mountain Volcanic Field, by the U.S. Geological Survey (USGS). Along with geologic mapping, airborne magnetics, airborne electromagnetics, and magnetotellurics, the USGS collected AMT data at 26 sites along five profiles ranging from 2 to 5 kilometers in length: across Red Mountain of the Silverton caldera, within the caldera in Eureka Graben, across the south-eastern margin of the caldera along Arrastra Gulch, across the southern margin of the caldera along the western margin of Kendall Mountain, and across the south-western margin of the caldera along South Fork...
The LANDFIRE existing vegetation layers describe the following elements of existing vegetation for each LANDFIRE mapping zone: existing vegetation type, existing vegetation canopy cover, and existing vegetation height. Vegetation is mapped using predictive landscape models based on extensive field reference data, satellite imagery, biophysical gradient layers, and classification and regression trees. DATA SUMMARY: The existing vegetation height (EVH) data layer is an important input to LANDFIRE modeling efforts. Canopy height is generated separately for tree, shrub and herbaceous cover life forms using training data and a series of geospatial data layers. EVH is determined by the average height weighted by species...
The LANDFIRE existing vegetation layers describe the following elements of existing vegetation for each LANDFIRE mapping zone: existing vegetation type, existing vegetation canopy cover, and existing vegetation height. Vegetation is mapped using predictive landscape models based on extensive field reference data, satellite imagery, biophysical gradient layers, and classification and regression trees.DATA SUMMARY: The existing vegetation type (EVT) data layer represents the current distribution of the terrestrial ecological systems classification developed by NatureServe for the western Hemisphere (http://www.natureserve.org/publications/usEcologicalsystems.jsp). A terrestrial ecological system is defined as a group...
LANDFIRE disturbance data are developed to provide temporal and spatial information related to landscape change for determining vegetation transitions over time and for making subsequent updates to LANDFIRE vegetation, fuel and other data. Disturbance data include attributes associated with disturbance year, type, and severity. These data are developed through use of Landsat satellite imagery, local agency derived disturbance polygons, and other ancillary data. DATA SUMMARY: The disturbance data are developed through a multistep process. Inputs to this process include; Landsat imagery and derived NBR (normalized burn ratio) data; polygon data developed by local agencies for the LANDFIRE Refresh effort; fire data...
Broad-scale alterations of historical fire regimes and vegetation dynamics have occurred in many landscapes in the U.S. through the combined influence of land management practices, fire exclusion, ungulate herbivory, insect and disease outbreaks, climate change, and invasion of non-native plant species. The LANDFIRE Project produces maps of simulated historical fire regimes and vegetation conditions using the LANDSUM landscape succession and disturbance dynamics model. The LANDFIRE Project also produces maps of current vegetation and measurements of current vegetation departure from simulated historical reference conditions. These maps support fire and landscape management planning outlined in the goals of the National...
Broad-scale alterations of historical fire regimes and vegetation dynamics have occurred in many landscapes in the U.S. through the combined influence of land management practices, fire exclusion, ungulate herbivory, insect and disease outbreaks, climate change, and invasion of non-native plant species. The LANDFIRE Project produces maps of simulated historical fire regimes and vegetation conditions using the LANDSUM landscape succession and disturbance dynamics model. The LANDFIRE Project also produces maps of current vegetation and measurements of current vegetation departure from simulated historical reference conditions. These maps support fire and landscape management planning outlined in the goals of the National...
Broad-scale alterations of historical fire regimes and vegetation dynamics have occurred in many landscapes in the U.S. through the combined influence of land management practices, fire exclusion, ungulate herbivory, insect and disease outbreaks, climate change, and invasion of non-native plant species. The LANDFIRE Project produces maps of simulated historical fire regimes and vegetation conditions using the LANDSUM landscape succession and disturbance dynamics model. The LANDFIRE Project also produces maps of current vegetation and measurements of current vegetation departure from simulated historical reference conditions. These maps support fire and landscape management planning outlined in the goals of the National...
The LANDFIRE vegetation layers describe the following elements of existing and potential vegetation for each LANDFIRE mapping zone: environmental site potentials, biophysical settings, existing vegetation types, canopy cover, and vegetation height. Vegetation is mapped using predictive landscape models based on extensive field reference data, satellite imagery, biophysical gradient layers, and classification and regression trees.DATA SUMMARYThe biophysical settings (BpS) data layer represents the vegetation that may have been dominant on the landscape prior to Euro-American settlement and is based on both the current biophysical environment and an approximation of the historical disturbance regime. It is a refinement...
Broad-scale alterations of historical fire regimes and vegetation dynamics have occurred in many landscapes in the U.S. through the combined influence of land management practices, fire exclusion, ungulate herbivory, insect and disease outbreaks, climate change, and invasion of non-native plant species. The LANDFIRE Project produces maps of simulated historical fire regimes and vegetation conditions using the LANDSUM landscape succession and disturbance dynamics model. The LANDFIRE Project also produces maps of current vegetation and measurements of current vegetation departure from simulated historical reference conditions. These maps support fire and landscape management planning outlined in the goals of the National...
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