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Streamflow in the Colorado River is heavily influenced by high-elevation snowpack. Warming temperatures in spring can reduce snow-fed flows, with serious implications for the water supplies that support communities and wildlife. While it is already well-known that precipitation has a significant influence on river flow, recent observations suggest that temperature and the amount of water in soil may also influence streamflow. In the face of a changing climate, it is important that resource managers understand how factors such as changing temperatures and precipitation will affect this vital water source. To address this need, researchers are examining records of streamflow, temperature, soil moisture, and precipitation...
Members of the Eastern Shoshone and Northern Arapaho Tribes have been working with an interdisciplinary team of social, ecological, and climate scientists from the North Central CSC, the High Plains Regional Climate Center, and the National Drought Mitigation Center along with other university and agency partners to prepare regular climate and drought summaries to aid in managing water resources on the Wind River Reservation and in surrounding areas.
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The threat of droughts and their associated impacts on the landscape and human communities has long been recognized in the United States, especially in high risk areas such as the South Central region. There is ample literature on the effects of long-term climate change and short-term climate variability on the occurrence of droughts. However, it is unclear whether this information meets the needs of relevant stakeholders and actually contributes to reducing the vulnerability or increasing the resilience of communities to droughts. For example, are the methods used to characterize the severity of drought – known as drought indices – effective tools for predicting the actual damage felt by communities? As droughts...
The Eastern Shoshone and Northern Arapaho Tribes on the Wind River Indian Reservation in Wyoming are preparing for drought and other climate fluctuations with help from a broad coalition of scientists. Read More: https://www.drought.gov/drought/sites/drought.gov.drought/files/media/whatisnidis/Newsletter/October%202015%20v4.pdf
Abstract (from http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174045): Several studies have projected increases in drought severity, extent and duration in many parts of the world under climate change. We examine sources of uncertainty arising from the methodological choices for the assessment of future drought risk in the continental US (CONUS). One such uncertainty is in the climate models’ expression of evaporative demand (E0), which is not a direct climate model output but has been traditionally estimated using several different formulations. Here we analyze daily output from two CMIP5 GCMs to evaluate how differences in E0 formulation, treatment of meteorological driving data, choice of GCM,...
Categories: Publication; Types: Citation; Tags: Drought, Drought, North Central CASC
The responses of individual species to environmental changes can be manifested at multiple levels that range from individual-level (i.e., behavioral responses) to population-level (i.e., demographic) impacts. Major environmental changes that ultimately result in population level impacts are often first detected as individual-level responses. For example, herbivores respond to limited forage availability during drought periods by increasing the duration of foraging periods and expanding home range areas to compensate for the reduction in forage. However, if the individual-level responses are not sufficient to compensate for reduced forage availability, reduced survival and reproductive rates may result. We studied...
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Forests are of tremendous ecological and economic importance. They provide natural places for recreation, clean drinking water, and important habitats for fish and wildlife. However, the warmer temperatures and harsher droughts in the west that are related to climate change are causing die-offs of many trees. Outbreaks of insects, like the mountain pine beetle, that kill trees are also more likely in warmer, drier conditions. To maintain healthy and functioning forest ecosystems, one action forest managers can take is to make management decisions that will help forests adapt to future climate change. However, adaptation is a process based on genetic change and few tools are currently available for managers to use...
Severe droughts cause widespread tree mortality and decreased growth in forests across the globe. Forest managers are seeking strategies to increase forest resistance (minimizing negative impacts during the drought) and resilience (maximizing recovery rates following drought). Limited experimental evidence suggests that forests with particular structural characteristics have greater capacity to resist change and or recover ecosystem function in the face of drought. However, the applicability of these results to practical forest conservation and management remains unclear. This project utilized an existing network of eight long-term, operational-scale, forest management experiments from Arizona to Maine to examine...
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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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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 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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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 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 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...


map background search result map search result map Community Resilience to Drought Hazard: An Analysis of Drought Exposure, Impacts, and Adaptation in the South Central U.S. Examining the Influence of Temperature and Precipitation on Colorado River Water Resources: Reconstructing the Past to Understand the Future 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-August 2012 (number of weather forecasts resulting in high potential) 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 April - October (based on ECPC 7-mo 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) MCI DGVM high fire potential consensus forecast October-December, 2010 (number of weather forecasts resulting in high potential) Using Genetic Information to Understand Drought Tolerance and Bark Beetle Resistance in Whitebark Pine Forests Using Genetic Information to Understand Drought Tolerance and Bark Beetle Resistance in Whitebark Pine Forests Examining the Influence of Temperature and Precipitation on Colorado River Water Resources: Reconstructing the Past to Understand the Future Community Resilience to Drought Hazard: An Analysis of Drought Exposure, Impacts, and Adaptation in the South Central U.S. 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) 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-August 2012 (number of weather forecasts resulting in high potential) 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 April - October (based on ECPC 7-mo weather forecast) Standardized precipitation index forecast June - December 2011 (based on ECHAM 7-mo weather forecast)