Filters: Tags: Contiguous United States (X) > partyWithName: Michael C Duniway (X)
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A raster dataset representing multi-year mean (1998-2018) capacity factors (CF) for a solar photovoltaic system based on current technology, for the Conterminous United States. These data are calculated using ½ hourly irradiance values from the National Solar Radiation Database (NSRDB) Sengupta et al. (2018), and the Systems Advisor Model (Blair et al. 2014). Cell values represent the estimated capacity factor (a ratio of net generation to the maximum generation) for photovoltaic energy production for a 1-axis tracking system (technology details found in Maclaurin et al. 2019). The continuous raster were put into 8 quantile bins for interpretation and reporting. For more information and further data, please visit...
A raster dataset representing the soil organic carbon content of surface soil horizons (top 15 cm or ~6 inches) in the conterminous United States. Soil organic carbon is a readily component of soil organic matter, which plays an important role the functioning of soils, including formation of soil structure, soil nutrient content, soil moisture retention, and carbon sequestration. Soil carbon content here is measured as percent by mass. This dataset was created using the soil percent organic carbon 100 m spatial resolution predictive rasters for 0, 5, and 15 cm depths developed by Ramcharan et al. (2018). The average soil organic carbon over the top 15 cm was calculated using the trapezoidal rule, and then put into...
Knowledge of where energy resources occur and where there is existing development or new development potential, in conjunction with model-predicted golden eagle relative nest site density (Dunk et al. 2019), can be used to identify areas with higher or lower potential resource conflict. Depicted on the map is a 16-class raster that displays the spatial overlap of wind resources (4 classes, low to high) and golden eagle relative nest site density (4 classes, lower to higher). This raster displays the intersection of multi-year mean capacity factors (MCF) for wind turbines and the golden eagle relative nest site density within ecoregion raster. We have divided each probability into equal intervals, and then intersected...
A raster dataset representing slope in the conterminous United States. This dataset was created from the radian slope raster of 100-meter spatial resolution developed by Ramcharan et al. (2018). Those data were derived from a conterminous 100-m Digital Elevation Model (DEM) for the United States (http://nationalmap.gov/) using SAGA GIS software. Slope values are put into 9 slope classes to facilitate interpretation and reporting. Ramcharan, A., Hengl, T., Nauman, T., Brungard, C., Waltman, S., Wills, S., and Thompson, J., 2018, Soil Property and Class Maps of the Conterminous United States at 100-Meter Spatial Resolution: Soil Science Society of America Journal, v. 82, p. 186-201. http://dx.doi.org/10.2136/sssaj2017.04.0122.
A raster dataset representing wind speed in km per hour at 100-meter height for the Conterminous United States, averaged from 2007 to 2013 using the Weather Research and Forecasting Model run on a 2-km grid over the continental United States at a 5-min resolution. Data from Draxl et al. (2015), and then put into 8 quantile bins for interpretation and reporting. For more information and further data, please visit https://maps.nrel.gov/. Draxl, C., Clifton, A., Hodge, B.-M., and McCaa, J., 2015, The Wind Integration National Dataset (WIND) Toolkit: Applied Energy, v. 151, p. 355-366. https://doi.org/10.1016/j.apenergy.2015.03.121.
A raster dataset representing multi-year mean (2007 to 2013) capacity factors (CF) for a 5.5 MW, 175 meter rotor-diameter, 120 meter hub-height wind turbine (turbine details are from Lopez at al. Forthcoming) for the Conterminous United States. The weather data are modeled using the Weather Research and Forecasting Model run on a 2-km grid over the continental United States at a 5-min resolution (Draxl et al. 2015). Cell values represent the estimated capacity factor (a ratio of net generation to the maximum generation) for wind energy turbine production using the Systems Advisor Model (Blair et al. 2014). The continuous raster were put into 8 quantile bins for interpretation and reporting. For more information...
A raster dataset representing the clay content of surface soil horizons (top 15 cm or ~6 inches) in the conterminous United States. Soil texture, which is described by the proportion of sand, silt, and clay in the non-rock portion of the soil (soil particles < 2mm), is an important determinate of plant suitability, water movement into the soil, erosion vulnerability, and many other things. Clays are the finest soil particle size class and includes soil particles less than 0.002 mm in diameter. This dataset was created using the percent clay 100-meter spatial resolution predictive rasters for 0, 5, and 15 cm depths developed by Ramcharan et al. (2018). The average percent clay over the top 15 cm was calculated using...
Knowledge of where energy resources occur and where there is existing development or new development potential, in conjunction with model-predicted golden eagle relative nest site density (Dunk et al. 2019), can be used to identify areas with higher or lower potential resource conflict. Depicted on the map is a 16-class raster that displays the spatial overlap of solar resources (4 classes, low to high) and golden eagle relative nest site density (4 classes, lower to higher). This raster displays the intersection of multi-year mean capacity factors (MCF) for solar photovoltaic systems and the golden eagle relative nest site density within ecoregion raster. We have divided each probability into equal intervals, and...
A 4.5 km resolution raster dataset representing mean Global Horizontal Irradiance (GHI) in kWh (kilowatt-hours) per square-meter per day at each point in the National Solar Radiation Database (NSRDB) from 1998 to 2018 as calculated by Sengupta et al. (2018), and then put into 8 quantile bins for interpretation and reporting. For more information and further data, please visit https://maps.nrel.gov/. Sengupta, M., Xie, Y., Lopez, A., Habte, A., Maclaurin, G., and Shelby, J., 2018, The National Solar Radiation Data Base (NSRDB): Renewable and Sustainable Energy Reviews, v. 89, p. 51-60. https://doi.org/10.1016/j.rser.2018.03.003.
A raster dataset representing the sand content of surface soil horizons (top 15 cm or ~6 inches) in the conterminous United States. Soil texture, which is described by the proportion of sand, silt, and clay in the non-rock portion of the soil (soil particles < 2mm), is an important determinate of plant suitability, water movement into the soil, erosion vulnerability, and many other things. Sand includes soil particles between 0.05 and 2.0 mm in diameter. This dataset was created using the percent sand 100-meter spatial resolution predictive rasters for 0, 5, and 15 cm depths developed by Ramcharan et al. (2018). The average percent sand over the top 15 cm was calculated using the trapezoidal rule, and then put into...
This is a 16-class categorical raster that displays the intersection of multi-year mean capacity factors (CF) for wind (from the work by Blair et al. 2016 and Maclaurin et al. 2019) and the greater sage grouse breeding habitat probability raster (Doherty 2016). We have divided each probability into quartiles, and then intersected those two 4-class rasters to create a new raster that classifies most areas in the intermountain west into joined wind system development and greater sage grouse breeding habitat probability (<25, 25-50, 50-75, and >75% for both; 16 classes). For more information and further renewable data, please visit https://maps.nrel.gov/. The purpose of this dataset is to represent the matrix of wind...
This is a 16-class categorical raster that displays the intersection of multi-year mean capacity factors (MCF) for wind (Maclaurin et al. 2019) and the pygmy rabbit habitat probability raster (Smith et al. 2019). We have divided each source continuous raster into four classes, and then intersected those two 4-class rasters to create a new raster that classifies most areas in the Intermountain West into joined wind system development and pygmy rabbit habitat probability (four quantiles for wind MCF and <0.3167, 0.3167-0.4661, 0.4661-0.67073, and >0.67073 for habitat probability; 16 classes). For more information and further renewable data, please see: https://maps.nrel.gov/. Maclaurin, G, Grue, N., Lopez, A., Heimiller,...
This is a 2-class categorical raster displays pygmy rabbit (Brachylagus idahoensis) habitat suitability from Smith et al. (2019). The pygmy rabbit is a species of conservation concern due to its obligate relationship with the sagebrush ecosystem. Pygmy rabbit habitat suitability is classified to maximize the ability of the species distribution model to predict the presence of pygmy rabbits while not overpredicting in background areas where presence is unknown. These classifications are based on 10,420 records of pygmy rabbit occurrence from the eight range states (excluding Washington). An estimated minimum occupied area was used to create an inductive species distribution model for pygmy rabbits across their full...
This raster dataset represents depth to a soil restrictive layer in the conterminous United States. A soil restrictive layer is a soil horizon that significantly limits the soil depth available for plant rooting or movement of air and water. Depth data are binned into 5 standard depth classes for display and analysis. This dataset was created by aggregating current USDA-National Cooperative soil survey data within 800m² grid cells (Soil Survey Geographic [SSURGO] backfilled with State Soil Geographic [STATSGO] where SSURGO is not available) using area weighted average of components by map unit and area weighted average of all map units within each grid cell. These data were developed by UC Davis and USDA Natural...
This is a 16-class categorical raster that displays the intersection of multi-year mean capacity factors (MCF) for solar photovoltaic systems (Maclaurin et al. 2019) and the pygmy rabbit habitat probability raster (Smith et al. 2019). We have divided each source continuous raster into four classes, and then intersected those two 4-class rasters to create a new raster that classifies most areas in the intermountain west into joined photovoltaic system development and pygmy rabbit habitat probability (four quantiles for photovoltaic MCF and <0.3167, 0.3167-0.4661, 0.4661-0.67073, and >0.67073 for habitat probability; 16 classes). For more information and further renewable data, please see: https://maps.nrel.gov/...
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