This is a map of various vegetation canopy structure metrics derived from high-density airborne LiDAR scans collected in August - September 2015 and 2019. The different raster bands represent statistical summaries of the normalized LiDAR point cloud. In the normalized point cloud, ground elevations

This is a styled basemap showing snow depth on April 7th 2019 derived from repeat LiDAR data collection by the Airborne Snow Observatory. This dataset is derived directly from: Painter, T. 2019. ASO L4 Lidar Snow Depth 3m UTM Grid, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Ce

This is a styled basemap showing snow depth on March 31st, 2018 derived from repeat LiDAR data collection by the Airborne Snow Observatory. This dataset is derived directly from: Painter, T. 2018. ASO L4 Lidar Snow Depth 3m UTM Grid, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data

This dataset represents a 1/3 m resolution vegetation canopy height model for the upper East River Watershed in Western Colorado. Source datasets include August 2015 and August 2019 discrete-return LiDAR point clouds collected by Quantum Geospatial for terrain mapping purposes on behalf of the Color

This dataset is a gap-filled meteorological dataset (years 2011-2020) that includes modeled potential evapotranspiration from a station near the pumphouse on the East River (KCOMTCRE2 WeatherUnderground ) near the top of the ridge at the Pumphouse PLM wells. These datasets are useful as model inputs

This data set provides snowmelt timing maps (STMs), cloud interference maps, and a map with the count of calculated snowmelt timing values for North America. The STMs are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard 8-day composite snow-cover product MOD10A2 collection

This map represents estimated watersheds for stream segments derived from a hydrologically corrected digital elevation model. The flow lines were derived in GRASS GIS using a single direction algorithm that does not allow channel braiding. Each watershed is identified by a unique integer that matche

This map represents estimated watersheds for stream segments derived from a hydrologically corrected digital elevation model. The flow lines were derived in GRASS GIS using a single direction algorithm that does not allow channel braiding. Each watershed is identified by a unique integer that matche

This map represents estimated stream flowlines from a hydrologically corrected digital elevation model. The lines were derived in GRASS GIS using a single flow-direction (D8) algorithm that does not allow channel braiding. Each stream segment is identified by a unique integer. Stream lines were deli

This map represents estimated stream flowlines from a hydrologically corrected digital elevation model. The lines were derived in GRASS GIS using a multi-direction algorithm that allows channel braiding. Each stream segment is identified by a unique integer. Stream lines were delineated for drainage

This map represents estimated stream flowlines from a hydrologically corrected digital elevation model. The lines were derived in GRASS GIS using a single-flow direction algorithm that does not allow channel braiding. Each stream segment is identified by a unique integer.Stream lines were delineated

This map represents estimated stream flowlines from a hydrologically corrected digital elevation model. The lines were derived in the GRASS GIS module r.watershed using a multi-flow direction algorithm that allows channel braiding. Each stream segment is identified by a unique integer.

This map represents estimated flow accumulation from a hydrologically corrected digital elevation model. The map was derived in GRASS GIS using a single flow-direction (D8) algorithm that does not allow channel braiding. Values represent the area (in square meters) of land that contributes flow to a

This dataset represents estimated flow accumulation from a hydrologically corrected digital elevation model. The map was derived in GRASS GIS using a multi-flow direction algorithm that allows channel braiding. Values represent the area (in square meters) of land that contributes flow to a given cel

Many biotic interactions influence community structure, yet most distribution models for plants have focused on plant competition or used only abiotic variables to predict plant abundance. Furthermore, biotic interactions are commonly context-dependent across abiotic gradients. For example, plant-pl

Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series

Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series

Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series

Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series

Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series