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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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The Standardized Precipitation Index (SPI) is a probability index that can be calculated for different time periods to indicate periods of abnormal wetness or dryness. SPI is derived solely from monthly precipitation and can be compared across regions with different climates. The SPI is an index based on the probability of recording a given amount of precipitation, and the probabilities are standardized so that an index of zero indicates the median precipitation amount (half of the historical precipitation amounts are below the median, and half are above the median). This dataset shows the average 12-month SPI (in classes ranging from extremely wet to extremely dry) for the three-month forecast period indentified...
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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...
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The Institute for Bird Populations (IBP) coordinates the Monitoring Avian Productivity and Survivorship (MAPS) Program to assess and monitor the vital rates and population dynamics of North American landbirds. The MAPS Program utilizes a standardized constant-effort mist netting and banding protocol (DeSante et al. 2010) at a continent-wide network of monitoring stations. Each summer dedicated volunteers operate bird-banding stations to collect data on individual "birds-in-the-hand" representing over 200 species. Analyses of MAPS data provide critical information relating to the ecology, conservation, and management of North American landbird populations and the factors responsible for changes in their populations....
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The MAPSS team together with long-time collaborator Chris Daly of the Spatial Climate Analysis Service is using Daly's PRISM model to produce high-resolution data grids of observed fire weather. The PRISM model produces interpolations of weather station data that are sensitive to topography, which is especially important in the complex, fire-prone terrain of the mountainous West. Input station data are gathered primarily from the National Weather Service (NWS) Cooperative Observer Program (COOP) and U.S. Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS) SNOTEL networks. For mapped examples of the PRISM-generated historical weather data grids see the Spatial Climate Analysis Service's Web...
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A map service on the www depicting ownership parcels of the surface estate. Each surface ownership parcel is tied to a particular legal transaction. The same individual or organization may currently own many parcels that may or may not have been acquired through the same legal transaction. Therefore, they are captured as separate entities rather than merged together. Surface Ownership provides the land status user with a current snapshot of ownership within National Forest boundaries. The purpose of the data is to provide display, identification, and analysis tools for determining current boundary information for Forest Service managers, GIS Specialists, and others. Map service is symbolized showing US Forest Service...
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This map 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). Different colors indicate the level of consensus among five different MC1 simulations (i.e., one for each forecast provided by five different weather models), ranging from one of five to five of five simulations predicting high fire potential. 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...
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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....
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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....
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This map 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). Different colors indicate the level of consensus among five different MC1 simulations (i.e., one for each forecast provided by five different weather models), ranging from one of five to five of five simulations predicting high fire potential. 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...
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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....
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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...
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A map service on the www depicting status of parcels for Forest Service land congressionally designated as wilderness such as National Wilderness Areas. This map service provides display, identification, and analysis tools for determining current boundary information for Forest Service managers, GIS Specialists, and others. Best available source with a target scale of 1:24000 for Continental U.S., Puerto Rico, and Hawaii and 1:63360 for Alaska.


map background search result map search result map US Forest Service Wilderness Areas Status US Forest Service Surface Ownership MC1 DGVM fire potential consensus forecast January-November 2012 (number of weather forecasts resulting in high potential) Palmer drought severity index forecast June - August 2012 (based on ECPC 7-mo weather forecast) Palmer drought severity index forecast May - July 2012 (based on CCM3V6 7-mo weather forecast) MC1 DGVM fire potential consensus forecast January-May 2012 (number of weather forecasts resulting in high potential) Palmer drought severity index forecast April - June 2012 (based on ECHAM 7-mo weather forecast) MC1 DGVM fire potential forecast JANUARY - JUNE 2012 (based on COLA 7-month weather forecast) Standardized precipitation index forecast June - December 2011 (based on ECHAM 7-mo weather forecast) MC1 DGVM fire potential forecast January - July 2011 (based on COLA 7-mo weather forecast) Pacific Northwest MAPS spatial demographic coverages -Wilson's warbler MCI DGVM high fire potential consensus forecast October-December, 2010 (number of weather forecasts resulting in high potential) Precipitation (Proportion July - Sep) - 2020-2050 - RCP8.5 - Min Temperature (Mean: Annual) - 2020-2050 - RCP4.5 - Max Temperature (Mean: Annual) - 2020-2050 - RCP8.5 - Min Precipitation (Proportion May - Oct) - 1980-2010 Precipitation (Proportion May - Oct) - 2070-2100 - RCP4.5 - Min Precipitation (Proportion May - Oct) - 2020-2050 - RCP4.5 - Min Precipitation (Mean: Apr - June) - 2070-2100 - RCP4.5 - Max Precipitation (Mean: Dec - Mar) - 2020-2050 - RCP4.5 - Min Pacific Northwest MAPS spatial demographic coverages -Wilson's warbler MC1 DGVM fire potential forecast January - July 2011 (based on COLA 7-mo weather forecast) MCI DGVM high fire potential consensus forecast October-December, 2010 (number of weather forecasts resulting in high potential) Precipitation (Proportion July - Sep) - 2020-2050 - RCP8.5 - Min Temperature (Mean: Annual) - 2020-2050 - RCP4.5 - Max Temperature (Mean: Annual) - 2020-2050 - RCP8.5 - Min Precipitation (Proportion May - Oct) - 1980-2010 Precipitation (Proportion May - Oct) - 2070-2100 - RCP4.5 - Min Precipitation (Proportion May - Oct) - 2020-2050 - RCP4.5 - Min Precipitation (Mean: Apr - June) - 2070-2100 - RCP4.5 - Max Precipitation (Mean: Dec - Mar) - 2020-2050 - RCP4.5 - Min MC1 DGVM fire potential consensus forecast January-November 2012 (number of weather forecasts resulting in high potential) Palmer drought severity index forecast June - August 2012 (based on ECPC 7-mo weather forecast) Palmer drought severity index forecast May - July 2012 (based on CCM3V6 7-mo weather forecast) MC1 DGVM fire potential consensus forecast January-May 2012 (number of weather forecasts resulting in high potential) Palmer drought severity index forecast April - June 2012 (based on ECHAM 7-mo weather forecast) MC1 DGVM fire potential forecast JANUARY - JUNE 2012 (based on COLA 7-month weather forecast) Standardized precipitation index forecast June - December 2011 (based on ECHAM 7-mo weather forecast) US Forest Service Wilderness Areas Status US Forest Service Surface Ownership