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Climate change is causing an increase in the amount of forested area burned by wildfires in the western U.S. The warm, dry post-fire conditions of the region may limit tree regeneration in some areas, potentially causing a shift to non-forest vegetation. Managers are increasingly challenged by the combined impacts of greater wildfire activity, the significant uncertainty about whether forests will recover, and limited resources for reforestation efforts. Simultaneously, there has been an increased focus on post-fire reforestation efforts as tree planting has become a popular climate change mitigation strategy across the nation. Therefore, with increased interest and need, it is crucial to identify where varying...
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Model Name (Name of the model, including acronyms): WEMo - Wave Exposure Model Model Version (Version of the model, (i.e., 2.0)): 4.0 Abstract (Description or summary of the model, including its purpose or goals and the type or formalism of the model (e.g., system dynamic, agent based model, machine learning), preferably accompanied by relevant keywords): Originator(s) (Name(s) of the model developer(s)/author(s)): Contact Name(s) and Email Address(es) (Names of person(s) responsible for maintenance of the model along with their email): National Centers for Coastal Ocean Science (nccos.webcontent@noaa.gov ) Release Date (Date model was originally released (YYYYMMDD, YYYYMM, YYYY)): Last Update (Date of last update...
Tags: ArcGIS
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The U.S. Geological Survey (USGS), in cooperation with the California State Water Resources Control Board (SWRCB), compiled Fall 2017 fluid level elevation data from idle oil and gas wells in the Oxnard Oil Field to estimate vertical hydraulic head difference between oil production and overlying groundwater aquifer zones. Fluid elevations came from two sources, measurements in idle oil and gas wells and groundwater elevations in water wells in the overlying aquifer estimated at the points of idle well measurements using geographic information system (GIS) procedures. The fluid elevations from idle oil and gas wells were compiled by the California Geologic Energy Management Division (CalGEM) as part of their Idle...
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In Shoshone National Forest, 12 to 15 miles southwest of Clark Post Office, looking southward across valley of upper Clark Fork of the Yellowstone River into basins of Sunlight and Dead Indian Creeks. Volcanic peaks of Absaroka Mountains in background; shows broad Pleistocene valley eroded in the Paleozoic rocks and, sharply cut into the bottom of this valley, the narrow branching box canyons of a later (Pleistocene?) cycle of erosion, more than 1,000 feet in Archean granite and gneiss. The broad bench, or bottom of the ancient valley, is probably to be correlated with the highest (No. 1) bench on the plains to the east. Park County, Wyoming. June 12, 1922. Plate 13-C, U.S. Geological Survey Professional paper 174....
Tier 3 Datasets: Less commonly used; Spotty spatial coverage; Other datasets not identified in Tier 1 & 2
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A collection of hydrodynamic model simulations, their inputs, outputs by USGS Woods Hole Coastal and Marine Science Center. For more information on the Coupled Ocean Atmospheric Wave Sediment Transport (COAWST) modeling system see https://code.usgs.gov/coawstmodel/COAWST. Aboout the Estuarine Processes, Hazards, and Ecosystems Team At the U.S. Geological Survey's Woods Hole Coastal and Marine Science Center we undertake interdisciplinary projects that aim to quantify and understand estuarine processes through observations and numerical modeling. Both the spatial and temporal scales of these mechanisms are important, and therefore require modern instrumentation and state-of-the-art hydrodynamic models. These are...
Model Name (Name of the model, including acronyms): Sagebrush Hurdle Model Model Version (Version of the model, (i.e., 2.0)): Abstract (Description or summary of the model, including its purpose or goals and the type or formalism of the model (e.g., system dynamic, agent based model, machine learning), preferably accompanied by relevant keywords): Originator(s) (Name(s) of the model developer(s)/author(s)): Douglas Shinneman (dshinneman@usgs.gov) Contact Name(s) and Email Address(es) (Names of person(s) responsible for maintenance of the model along with their email): Douglas Shinneman Release Date (Date model was originally released (YYYYMMDD, YYYYMM, YYYY)): 201906 Last Update (Date of last update (YYYYMMDD, YYYYMM,...
Description for MCF Solar goes here
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This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated...
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Handwritten names on album caption: Vogel, Diane, Mohler, Ferher. Mar 10/17 Note: In 1917, the USGS offices were located in the Adams Building, 1333-1335 F. Street, NW, Washington, D.C.
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This map was developed to examine multi-scale spatial relationships between percentage of sagebrush and other response variables of interest. A map of sagebrush in the western United States was used as a base layer for a moving window analysis to calculate the percentage of the area classified as sagebrush within a 50-km search radius.
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This data set includes the relative production scenarios for eight (8) grass species based on linear models from Epstein, et al. (1998). We selected two indicator species for each community: shortgrass prairie: blue grama (Bouteloua gracilis; BOGR) and buffalo grass (Bouteloua dactyloides; BODA); mixedgrass prairie: sideoats grama (Bouteloua curtipendula; BOCU) and little bluestem (Schizachyrium scoparium; SCSC); tallgrass prairie: big bluestem (Andropogon gerardii; ANGE) and Indiangrass (Sorghastrum nutans; SONU); and semiarid grasslands: black grama (Bouteloua eriopoda; BOER) and tobosagrass (Pleuraphis mutica; PLMU). Soil texture (percent by weight) came from the Earth Systems Science Center (2008) which provided...
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Album caption: Same as above. Mesa Verde scarp, Coyote Creek Valley and decline of San Juan Mtns. to south. Looking S. 60 degrees east. Colorado. n.d. No index card available.
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Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS)...
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Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS)...
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Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS)...
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Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS)...


map background search result map search result map Proportion of Sagebrush Land Cover (50-km scale) in the western US USGS Topo Map Vector Data (Vector) 19961 Hebron, Indiana 20190704 for 7.5 x 7.5 minute Shapefile USGS Topo Map Vector Data (Vector) 11294 Danbury West, Wisconsin 20190326 for 7.5 x 7.5 minute Shapefile USGS Topo Map Vector Data (Vector) 25135 Lawler, Iowa 20190321 for 7.5 x 7.5 minute Shapefile Potential productivity and change estimates for eight grassland species to evaluate vulnerability to climate change in the southern Great Plains CoSMoS v3.1 water level projections: 20-year storm in San Luis Obispo County Fluid levels in the Oxnard Oil Field, Ventura County, California Science to Inform Post-fire Conifer Regeneration and Reforestation Strategies Under Changing Climate Conditions USGS employees and presses, Adams Building. Washington, D.C. 1917. USGS Lidar Point Cloud OH_Statewide_Phase1_2019_B19 BN13480738 USGS Lidar Point Cloud OH_Statewide_Phase1_2019_B19 BN13510645 USGS Lidar Point Cloud OH_Statewide_Phase1_2019_B19 BN13510667 USGS Lidar Point Cloud OH_Statewide_Phase1_2019_B19 BN13510681 Mesa Verde scarp, Coyote Creek Valley and decline of San Juan Mountains to south. Colorado. n.d. USGS Lidar Point Cloud OH_Statewide_Phase1_2019_B19 BN13510645 USGS Lidar Point Cloud OH_Statewide_Phase1_2019_B19 BN13510667 USGS Lidar Point Cloud OH_Statewide_Phase1_2019_B19 BN13510681 USGS Lidar Point Cloud OH_Statewide_Phase1_2019_B19 BN13480738 Fluid levels in the Oxnard Oil Field, Ventura County, California USGS employees and presses, Adams Building. Washington, D.C. 1917. CoSMoS v3.1 water level projections: 20-year storm in San Luis Obispo County Mesa Verde scarp, Coyote Creek Valley and decline of San Juan Mountains to south. Colorado. n.d. Potential productivity and change estimates for eight grassland species to evaluate vulnerability to climate change in the southern Great Plains Science to Inform Post-fire Conifer Regeneration and Reforestation Strategies Under Changing Climate Conditions Proportion of Sagebrush Land Cover (50-km scale) in the western US