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Solar radiation grids were produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This solar radiation grid was produced using the Area Solar Radiation tool in ArcGIS 10.1, using inputs of the associated 30m DEM.
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For his MS thesis, Brendan Rogers used the vegetation model MC1 to simulate vegetation dynamics, associated carbon and nitrogen cycle, water budget and wild fire impacts across the western 2/3 of the states of Oregon and Washington using climate input data from the PRISM group (Chris Daly, OSU) at a 30arc second (800m) spatial grain. The model was run from 1895 to 2100 assuming that nitrogen demand from the plants was always met so that the nitrogen concentrations in various plant parts never dropped below their minimum reported values. A CO2 enhancement effect increased productivity and water use efficiency as the atmospheric CO2 concentration increased. Future climate change scenarios were generated through statistical...
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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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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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The integrity of Amazon forests are currently threatened by climate change, deforestation, and fire. However, it is unclear how these agents of change interact over large spatial and temporal domains and reducing this uncertainty is important for projecting changes in carbon stocks and species biogeography, and could better inform continental scale conservation programs. With this in mind, above ground biomass and tree cover data were produced using the dynamic global vegetation model, LPJmL, with 9 different global climate models (using the SRES A2 emissions storyline) and 2 different deforestation scenarios (from Soares et al.). The existing fire module was modified to include 'escaped fire' associated with deforestation,...
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The integrity of Amazon forests are currently threatened by climate change, deforestation, and fire. However, it is unclear how these agents of change interact over large spatial and temporal domains and reducing this uncertainty is important for projecting changes in carbon stocks and species biogeography, and could better inform continental scale conservation programs. With this in mind, above ground biomass and tree cover data were produced using the dynamic global vegetation model, LPJmL, with 9 different global climate models (using the SRES A2 emissions storyline) and 2 different deforestation scenarios (from Soares et al.). The existing fire module was modified to include 'escaped fire' associated with deforestation,...
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The integrity of Amazon forests are currently threatened by climate change, deforestation, and fire. However, it is unclear how these agents of change interact over large spatial and temporal domains and reducing this uncertainty is important for projecting changes in carbon stocks and species biogeography, and could better inform continental scale conservation programs. With this in mind, above ground biomass and tree cover data were produced using the dynamic global vegetation model, LPJmL, with 9 different global climate models (using the SRES A2 emissions storyline) and 2 different deforestation scenarios (from Soares et al.). The existing fire module was modified to include 'escaped fire' associated with deforestation,...
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The integrity of Amazon forests are currently threatened by climate change, deforestation, and fire. However, it is unclear how these agents of change interact over large spatial and temporal domains and reducing this uncertainty is important for projecting changes in carbon stocks and species biogeography, and could better inform continental scale conservation programs. With this in mind, aboveground biomass and tree cover data were produced using the dynamic global vegetation model, LPJmL, with 9 different global climate models (using the SRES A2 emissions storyline) and 2 different deforestation scenarios (from Soares et al.). The existing fire module was modified to include 'escaped fire' associated with deforestation,...
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The integrity of Amazon forests are currently threatened by climate change, deforestation, and fire. However, it is unclear how these agents of change interact over large spatial and temporal domains and reducing this uncertainty is important for projecting changes in carbon stocks and species biogeography, and could better inform continental scale conservation programs. With this in mind, aboveground biomass and tree cover data were produced using the dynamic global vegetation model, LPJmL, with 9 different global climate models (using the SRES A2 emissions storyline) and 2 different deforestation scenarios (from Soares et al.). The existing fire module was modified to include 'escaped fire' associated with deforestation,...
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The integrity of Amazon forests are currently threatened by climate change, deforestation, and fire. However, it is unclear how these agents of change interact over large spatial and temporal domains and reducing this uncertainty is important for projecting changes in carbon stocks and species biogeography, and could better inform continental scale conservation programs. With this in mind, aboveground biomass and tree cover data were produced using the dynamic global vegetation model, LPJmL, with 9 different global climate models (using the SRES A2 emissions storyline) and 2 different deforestation scenarios (from Soares et al.). The existing fire module was modified to include 'escaped fire' associated with deforestation,...
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The integrity of Amazon forests are currently threatened by climate change, deforestation, and fire. However, it is unclear how these agents of change interact over large spatial and temporal domains and reducing this uncertainty is important for projecting changes in carbon stocks and species biogeography, and could better inform continental scale conservation programs. With this in mind, aboveground biomass and tree cover data were produced using the dynamic global vegetation model, LPJmL, with 9 different global climate models (using the SRES A2 emissions storyline) and 2 different deforestation scenarios (from Soares et al.). The existing fire module was modified to include 'escaped fire' associated with deforestation,...
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The integrity of Amazon forests are currently threatened by climate change, deforestation, and fire. However, it is unclear how these agents of change interact over large spatial and temporal domains and reducing this uncertainty is important for projecting changes in carbon stocks and species biogeography, and could better inform continental scale conservation programs. With this in mind, aboveground biomass and tree cover data were produced using the dynamic global vegetation model, LPJmL, with 9 different global climate models (using the SRES A2 emissions storyline) and 2 different deforestation scenarios (from Soares et al.). The existing fire module was modified to include 'escaped fire' associated with deforestation,...


map background search result map search result map Simulated PNW biomass consumed (g C/m2) under MIROC 3.2 medres A2 (2070-2099 average) 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) Percent change in above ground tree cover for the Amazon Basin under UKMO HADCM3 climate and GOVernance deforestation scenarios with no fire (2020s) Percent change in above ground tree cover for the Amazon Basin under UKMO HADCM3 climate scenario and current deforestation with no fire (2080s) Percent change in above ground tree cover for the Amazon Basin under MPI ECHAM 5 climate and GOVernance deforestation scenarios with fire (2020s) Aboveground biomass (Mg C/ha) for the Amazon Basin under UKMO HADGEM1 climate, no deforestation, and no fire scenarios (2040s) Aboveground biomass (Mg C/ha) for the Amazon Basin under UKMO HADGEM1 climate, no deforestation, and fire scenarios (2060s) Aboveground biomass (Mg C/ha) for the Amazon Basin under UKMO HADGEM1 climate, current deforestation (BAU), and fire scenarios (2080s) Aboveground biomass (Mg C/ha) for the Amazon Basin under UKMO HADCM3 climate, GOVernance deforestation, and no fire scenarios (2060s) Aboveground biomass (Mg C/ha) for the Amazon Basin under CCSM 3.0 climate, GOVernance deforestation, and no fire scenarios (2020s) Simulated PNW biomass consumed (g C/m2) under MIROC 3.2 medres A2 (2070-2099 average) Percent change in above ground tree cover for the Amazon Basin under UKMO HADCM3 climate and GOVernance deforestation scenarios with no fire (2020s) Percent change in above ground tree cover for the Amazon Basin under UKMO HADCM3 climate scenario and current deforestation with no fire (2080s) Percent change in above ground tree cover for the Amazon Basin under MPI ECHAM 5 climate and GOVernance deforestation scenarios with fire (2020s) Aboveground biomass (Mg C/ha) for the Amazon Basin under UKMO HADGEM1 climate, no deforestation, and no fire scenarios (2040s) Aboveground biomass (Mg C/ha) for the Amazon Basin under UKMO HADGEM1 climate, no deforestation, and fire scenarios (2060s) Aboveground biomass (Mg C/ha) for the Amazon Basin under UKMO HADGEM1 climate, current deforestation (BAU), and fire scenarios (2080s) Aboveground biomass (Mg C/ha) for the Amazon Basin under UKMO HADCM3 climate, GOVernance deforestation, and no fire scenarios (2060s) Aboveground biomass (Mg C/ha) for the Amazon Basin under CCSM 3.0 climate, GOVernance deforestation, and no fire scenarios (2020s) 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)