The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nSnow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nSnow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.

Carbon Crops Study for Greenhouse gas Reduction through Agricultural Carbon Enhancement network and Resilient Economic Agricultural Practices in Morris, Minnesota The overall goal of the Carbon Crop study, established in 2000, was to assess strategies for increasing soil C sequestration including converting to no till systems and including perennial grasses (e.g., switchgrass and big bluestem) Overall, the goal of the study has remained constant, although individual treatments were changed after an incremental soil sampling, in response to new hypotheses and questions. Soil sampling is conducted as treatment changes are implemented. In 2012, two of the perennial grass systems (spring harvest of Switchgrass and Big Bluestem) were changed to corn/soybean rotations, beginning with a soybean entry point, to determine if the SOC accrued under the perennial system was lost by converting to a short annual rotation managed without tillage. The second change made was to compare the productivity between recent and traditional switchgrass cultivars. The final change was conversion of autumn harvest of Big Bluestem treatment replaced with an annual biomass crop – Sorghum-Sudan grass. Soil samples were taken to 1 m in 2000, 2006, 2011, and 2016. Nitrous oxide and carbon dioxide fluxes from the soil were measured from June 2009 through March 2012.

National Snow & Ice Data Center - scientific data about the Earth's frozen regions. 1044 data sets available.https://web.archive.org/web/*/http://nsidc.org/data

This data has been updated and corrected for errors. The most up to date data can be found in the dataset Data from: Eleven years of mountain weather, snow, soil moisture and stream flow data from the rain-snow transition zone - the Johnston Draw catchment, Reynolds Creek Experimental Watershed and Critical Zone Observatory, USA. v1.1 This dataset is supplemental to the article "A hydrological modeling dataset for the Johnston Draw catchment, Reynolds Creek Experimental Watershed, Idaho, USA," which was submitted to Water Resources Research in December 2015. The data includes time-series measurements of precipitation at three different gauges (124, 125, and 124b) at the Johnston Draw watershed, a sub-watershed of the Reynolds Creek Critical Zone Observatory. The Johnston Draw watershed was established by the USDA's Agricultural Research Service in 2002 to study the rain-snow transition zone. Data was collected at gauges 124 and 125 from October 1, 2003 through September 30, 2014 and at gauge 124b from November 11, 2006 through September 30, 2014. Precipitation for 124 and 125 were wind-corrected using the dual-gauge method described by Hanson et al. (2004). Precipitation for 124b was wind-corrected using wind data and the standard World Meteorological Organization (WMO) method as applied by Yang et al. (1999). The percent snow was calculated using the methods developed by Marks et al. (2013), using the during-storm dew point temperature (Td) where: Td < ­‐0.5 °C 100 % Snow ­ ‐0.5 °C >= Td < 0 °C 75 % Snow 0 °C <= Td < +0.5 °C 25 % Snow 0.5 °C <= Td 0 % Snow 125 and 124b are dual gauge precipitation stations. The pair are modified Belfort Universal gauges, with 124b having a wind shield and 125 remaining unshielded. Each of the precipitation records is an ASCII comma-separated text file with one header row containing Date_time, WY (Water Year), Year, Month, Day, Hour, Minute, ppt_s (shielded precipitation; mm), ppt_u (unshielded precipitation; mm), ppt_a (wind corrected precipitation; mm), and pct_snow (percent of precipitation that is snow; %) separated by commas.

Farming Systems Study for Greenhouse gas Reduction through Agricultural Carbon Enhancement network in Morris, Minnesota Tillage is decreasing globally due to recognized benefits of fuel savings and improved soil health in the absence of disturbance. However, a perceived inability to control weeds effectively and economically hinders no-till adoption in organic production systems in the Upper Midwest, USA. A strip-tillage (ST) strategy was explored as an intermediate approach to reducing fuel use and soil disturbance, and still controlling weeds. An 8-year comparison was made between two tillage approaches, one primarily using ST the other using a combination of conventional plow, disk and chisel tillage [conventional tillage (CT)]. Additionally, two rotation schemes were explored within each tillage system: a 2-year rotation (2y) of corn (Zea mays L.), and soybean (Glycine max [L.] Merr.) with a winter rye (Secale cereale L.) cover crop; and a 4-year rotation (4y) of corn, soybean, spring wheat (Triticum aestivum L.) underseeded with alfalfa (Medicago sativa L.), and a second year of alfalfa. These treatments resulted in comparison of four main management systems CT-2y, CT-4y, ST-2y and ST-4y, which also were managed under fertilized and non-fertilized conditions. Yields, whole system productivity (evaluated with potential gross returns), and weed seed densities (first 4 years) were measured. Across years, yields of corn, soybean and wheat were greater by 34% or more under CT than ST but alfalfa yields were the same. Within tillage strategies, corn yields were the same in 2y and 4y rotations, but soybean yields, only under ST, were 29% lower in the fertilized 4y than 2 yr rotation. In the ST-4y system yields of corn and soybean were the same in fertilized and non-fertilized treatments. Over the entire rotation, system productivity was highest in the fertilized CT-2y system, but the same among fertilized ST-4y, and non-fertilized ST-2y, ST-4y, and CT-4y systems. Over the first 4 years, total weed seed density increased comparatively more under ST than CT, and was negatively correlated to corn yields in fertilized CT systems and soybean yields in the fertilized ST-2y system. These results indicated ST compromised productivity, in part due to insufficient weed control, but also due to reduced nutrient availability. ST and diverse rotations may yet be viable options given that overall productivity of fertilized ST-2y and CT-4y systems was within 70% of that in the fertilized CT-2y system. Closing the yield gap between ST and CT would benefit from future research focused on organic weed and nutrient management, particularly for corn.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

This tile layer is intended for use in the web map 'Earliest date of first snow of the season for the United States'.This map shows the earliest first day of snow recorded at thousands of locations in the United States during their period of operation. Map based on an analysis of Global Historical Climatology Network (GHCN) station data by Jared Rennie, NOAA's National Centers for Environmental Information (NCEI). Light colors indicates places where the first recorded snowfall happened early in the season, in July or August. Shades of blue and purple show places where the earliest snow fell between between August and May. While the map shows the earliest date of first snow recorded at a given station, this map should not be interpreted as the “earliest ever” first snow of the season. It is simply the earliest date of first snow at a given station during its period of operation. For a more detailed assessment of the earliest date of first snow, please see this Climate.gov blog post. For more information about the Global Historical Climate Network, please visit the GHCN description page. For access to the data, please visit the GHCN data page at NOAA NCEI.

These tiles are published and intended for use in the map Historic date of first snow.These base map tiles cover the North American extent and include data which represent the historic date by which there’s a 50% chance at least 0.1” of snow will have accumulated, based on each location’s snowfall history from 1981-2010. Map based on an analysis of the current U.S. Climate Normals by Mike Squires, National Centers for Environmental Information.  White indicates places where there is a year-round chance of snow. Shades of blue and purple show places where the first day of snow historically falls between August 1st and December 31st, while dark gray shows places where, historically, the first snow doesn't take place until January 1st or later. Empty circles showing background gray indicate places where snow is so infrequent that there is not enough data to calculate a statistical first date of snow. While the map shows the historic date of first snow, the actual conditions this year may vary widely from this map because current weather patterns will determine the first snow of the year. For a more detailed assessment of the historic date of first snow, please see this Climate.gov blog post by Deke Arndt, NOAA NCEI scientist. For a broad overview of NOAA's 1981–2010 Climate Normals, see NOAA's 1981-2010 U.S. Climate Normals: An Overview published in the Bulletin of the American Meteorological Society, or for a detailed description of snow Normals, seeNOAA's 1981-2010 U.S. Climate Normals: Monthly Precipitation, Snowfall, and Snow Depth published in the Journal of Applied Meteorology and Climatology.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nSnow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127).\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

NCEI offers several types of climate information generated from examination of the data in the archives. These types of information include record temperatures, record precipitation and snowfall, climate extremes statistics, and other derived climate products.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

Abstract:Rain on Snow is a statewide coverage of rain-on-snow zones.  Rain-on-snow zones are based on average amounts of snow on the ground in early January, relative to the amount of snow that could reasonably be melted during a model storm event.  Five Rain on Snow zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation.  Rain on Snow was digitized from 1:250,000 USGS quads.Purpose:The Rain-on-snow coverage was created as a screening tool to identify forest practice applications that may be in a significant rain-on-snow zone (WAC 222-22-100).Description:Five ROS zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation. Rain on snow is a process that exhibits spatial and temporal variation under natural conditions, with the effects of vegetation on snow accumulation and melt adding additional complications in prediction. There is no map that shows the magnitude and frequency of water inputs to be expected from rain on snow events, so we have attempted to create an index map based on what we know about the process controls and their effects in the various climatic zones. If we assume that, averaged over many years, the seasonal storm tracks that bring warm, wet cyclonic storms to the Northwest have access to all parts of Washington , then the main factors controlling and/or reflecting the occurrence and magnitude of a R/S event in any particular place are: 1) Climatic region: especially the differences between windward and leeward sides of major mountain ranges, which control seasonal climatic patterns;2) Elevation: controls temperature, thus the likelihood and amount of snow on the ground, and affects orographic enhancement of storm precipitation; 3) Latitude: affects temperature, thus snow;4) Aspect: affects insolation and temperature (especially in winter), thus melting of snow; 5) Vegetation: the species composing forest communities can reflect the climate of an area (tolerance of warmth or cold, wet or dry conditions, deep and/or long lived snowpacks); the height and density of vegetation also partly controls the amount of snow on the ground. As natural vegetation integrates the effects of all of these controls, we tried to find or adapt floral indicators of the various zones of water input. We designed the precipitation zones to reflect the amount of snow likely to be on the ground at the beginning of a storm. We assumed that some middle elevation area would experience the greatest water input due to Rain on Snow, because the amount of snow available would be likely to be approximately the amount that could be melted. Higher and lower elevation zones would bear diminished effects, but for opposite reasons (no snow to melt, vs too cold to melt much). These considerations suggested a three or five zone system. We chose to designate five zones because a larger number of classes reduces the importance of the dividing lines, and thus of the inherent uncertainties of those lines. The average snow water equivalents (SWE) for the early January measurements at about 100 snow courses and snow pillows were compiled; snow depths for the first week in January at about 85 weather stations were converted into SWE. For each region (western North Cascades, Blue Mountains, etc.), the snow amounts were sorted by station elevation to derive a rough indicator of the relationship between snow accumulation and elevation. (Sub regional differences in snow accumulation patterns were also recognized.) After trying various combinations of ratios for areas where the snow hydrology is relatively well known, we adopted the following designations: 5. Highlands: >4 5 times ideal SWE; high elevation, with little likelihood of significant water input to the ground during storms (precipitation likely to be snow, and liquid water probably refreezes in a deep snow pack); effects of harvest on snow accumulation are minor; 4. Snow dominated zone: from "1.25 1.5 ideal SWE, up to "4; melt occurs during R/S (especially during early season storms), but effects can be mitigated by the lag time of percolation through the snowpack; 3. Peak rain on snow zone: "0.5 0.75 up to "1.25 ideal SWE; middle elevations: shallow snow packs are common in winter, so likelihood and effects of R/S in heavy rainstorms are greatest; typically more snow accumulation in clearings than in forest; 2. Rain dominated zone: "0.1 0.5 ideal SWE; areas at lower elevations, where rain occasionally falls on small amounts of snow; 1. Lowlands: <0.1 ideal SWE; coastal, low elevation, and rain shadow areas; lower rainfall intensities, and significant snow depths are rare. Precipitation zones were mapped on mylar overlays on 1:250,000 scale topographic maps. Because snow depth is affected by many factors, the correlation between snow and elevation is crude, and it was not possible to simply pick out contour markers for the boundaries. Ranges of elevations were chosen for each region, but allowance was made for the effects of sub regional climates, aspect, vegetative indicators of snow depth, etc. Thus, a particular boundary would be mapped somewhat lower on the north side of a ridge or in a cool valley (e.g. below a glacier), reflecting greater snow accumulations in such places. The same boundary would be mapped higher on the south side of the ridge, where inter-storm sunshine could reduce snow accumulation. Conditions at the weather stations and snow courses were used to check the mapping; but in areas where measurements are scarce, interpolation had to be performed. The boundaries of the precipitation zones were entered in the DNR's GIS. Because of the small scale of the original mapping and the imprecision of the digitizing process, some errors were introduced. It should not be expected that GIS images can be projected to large scales to define knife edge zone boundaries (which don't exist, anyway), but they are good enough to locate areas tens of acres in size. Some apparent anomalies in the map require explanation. Much of western Washington is mapped in the lowland or highland zones. This does not mean that R/S does not occur in those areas; it does, but on average with less frequency and hydrologic significance than in the middle three zones. Most of central and eastern Washington is mapped in the rain dominated zone, despite meager precipitation there; this means only that the amount of snow likely to be on the ground is small, and storm water inputs are composed dominantly of the rain itself, without much contribution from snow melt. Much of northeastern Washington is mapped in the peak Rain Snow zone, despite the fact that such events are less common there than in western Washington. This is due to the fact that there is less increase in snow depth with elevation (i.e. the snow wedge is less steep), so a wider elevation band has appropriate snow amounts; plus, much of that region lies within that elevation band where the 'ideal' amount of snow is liable to be on the ground when a model Rain Snow event occurs. This does not reflect the lower frequency of such storms in that area.

Snow is the dominant form of precipitation in the Reynolds Creek Experimental Watershed (RCEW). Seven snow course sites were established in 1961, and one additional site was added in 1970. All sites are located in the high-elevation southern extent of the basin, where snow accumulation is greatest. Snow water equivalent (SWE) and depth have been sampled at multiple locations in RCEW since 1961. These data have been collected using snow tube methods that are generally considered the standard for manual measurement of SWE and snow depth. Snow water equivalent (SWE) has been measured at eight locations in RCEW every 2 weeks throughout the snow season (December 1 to June 1) for 35 water years (1962-1996). SWE was continuously monitored at site 176x07 using a snow pillow for 14 water years (1983-1996).

The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The NESOB 1997 Daily Precipitation Composite is one of several precipitation datasets provided in the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP) Near-Surface Observation Data Set (NESOB) 1997. This precipitation composite is composed of data from several sources (i.e., National Weather Service (NWS) Cooperative Observers, National Centers for Environmental Prediction (NCEP), and the daily precipitation data extracted from the NESOB 1997 Hourly Precipitation Composite). Data from these sources were quality controlled and merged to form this precipitation composite. After the datasets were merged to form the NESOB 1997 Daily Precipitation Composite, a statistics program was executed to ensure that the quality of the individual datasets had been retained. This composite contains data for the NESOB 1997 domain (approximately 94.5 W to 100.5 W longitude and 34 N to 39 N latitude) and time period (01 April 1997 through 31 March 1998). The NCEP Daily Precipitation dataset was formed by extracting incremental precipitation values. The value reported for any daily observation represents data collected during the previous 24 hours. The Daily Precipitation Composite contains six metadata parameters and four data parameters. The metadata parameters describe the station location and time at which the data were collected. The four data parameters repeat once for each day in the monthly record. Every record has 31 days reported, regardless of the actual number of days in the month. For months with less than 31 days, the extra days are reported as missing (i.e., '-999.99 7 M'). Each 24 hour precipitation value has an associated observation hour. The observation hour is the ending UTC hour for the 24 hour period for which the precipitation value is valid.

The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The NESOB 1997 Daily Precipitation Composite is one of several precipitation datasets provided in the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP) Near-Surface Observation Data Set (NESOB) 1997. This precipitation composite is composed of data from several sources (i.e., National Weather Service (NWS) Cooperative Observers, National Centers for Environmental Prediction (NCEP), and the daily precipitation data extracted from the NESOB 1997 Hourly Precipitation Composite). Data from these sources were quality controlled and merged to form this precipitation composite. After the datasets were merged to form the NESOB 1997 Daily Precipitation Composite, a statistics program was executed to ensure that the quality of the individual datasets had been retained. This composite contains data for the NESOB 1997 domain (approximately 94.5 W to 100.5 W longitude and 34 N to 39 N latitude) and time period (01 April 1997 through 31 March 1998). The NCEP Daily Precipitation dataset was formed by extracting incremental precipitation values. The value reported for any daily observation represents data collected during the previous 24 hours. The Daily Precipitation Composite contains six metadata parameters and four data parameters. The metadata parameters describe the station location and time at which the data were collected. The four data parameters repeat once for each day in the monthly record. Every record has 31 days reported, regardless of the actual number of days in the month. For months with less than 31 days, the extra days are reported as missing (i.e., '-999.99 7 M'). Each 24 hour precipitation value has an associated observation hour. The observation hour is the ending UTC hour for the 24 hour period for which the precipitation value is valid.

The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The region selected for investigation is the best instrumented site for surface soil moisture, hydrology and meteorology in the world. This includes the USDA/ARS Little Washita Watershed, the USDA/ARS facility at El Reno, Oklahoma, the ARM/CART central facility, as well as the Oklahoma Mesonet. The National Climatic Data Center (NCDC) Summary of the Day Co-operative Dataset is one of several surface datasets provided for the Southern Great Plains (SGP) 1997 project. This NCDC Co-operative Observer (COOP) dataset contains data from sixty-two stations for the SGP 1997 time period (18 June 1997 through 18 July 1997) and in the SGP 1997 domain (approximately 97W to 99W longitude and 34.5N to 37N latitude). The primary thrust of the cooperative observing program is the recording of 24-hour precipitation amounts, but approximately 55% of the stations also record maximum and minimum temperatures. The observations are for the 24-hour period ending at the time of observation. Observer convenience or special program needs mean that observing times vary from station to station. However, the vast majority of observations are taken near either 7:00 AM or 7:00 PM local time. The NCDC Summary of the Day Co-operative Dataset (TD-3200) contains eight metadata parameters and fifteen data parameters and flags. The metadata parameters describe the date/time, network, station and location at which the data were collected. All times are UTC. Data values are valid for the 24 hours preceding the time of observation.

The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The region selected for investigation is the best instrumented site for surface soil moisture, hydrology and meteorology in the world. This includes the USDA/ARS Little Washita Watershed, the USDA/ARS facility at El Reno, Oklahoma, the ARM/CART central facility, as well as the Oklahoma Mesonet. The National Climatic Data Center (NCDC) Summary of the Day Co-operative Precipitation Dataset is one of several surface precipitation datasets provided in the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP) by UCAR/JOSS. The primary thrust of the cooperative observing program is the recording of 24-hour precipitation amounts. The observations are for the 24-hour period ending at the time of observation. Observer convenience or special program needs mean that observing times vary from station to station. However, the vast majority of observations are taken near either 7:00 AM or 7:00 PM local time. The National Weather Service (NWS) Cooperative Observer Daily Precipitation dataset was formed by extracting the daily incremental precipitation values provided in the National Climatic Data Center (NCDC) TD 3200 dataset. The Daily Precipitation data set contains six metadata parameters and four data parameters. The metadata parameters describe the station location and time at which the data were collected. The four data parameters repeat once for each day in the monthly record. Every record has 31 days reported, regardless of the actual number of days in the month. For months with less than 31 days, the extra days are reported as missing (i.e., '-999.99 7 M'). Each 24 hour precipitation value has an associated observation hour. The observation hour is the ending UTC hour for the 24 hour period for which the precipitation value is valid.

Monthly snowpack data for all SNOTEL and SNOW network stations in New Mexico for all water years from the start of historical monitoring to the present from the Natural Resources Conservation Service (NRCS) division of the USDA. This data was retrieved and aggregated by NMBGMR from https://www.wcc.nrcs.usda.gov/snow/ on February 19, 2020.

New Mexico current and historic precipitation data, plus snow and weather from Natural Resources Conservation Service.