53 results — topic: ESS-DIVE File Level Metadata Reporting Format
Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025
This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the h
CHESS 2025: Field-collected vegetation attributes and site photos
This dataset represents field observations of vegetation samples collected as part of the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Samples were collected in the field using tablet computers and digital forms, with target data differing by sample type (i
CHESS 2025: Leaf Area Index (LAI) for meadow, shrub, tree, and understory vegetation
This dataset contains Leaf Area Index (LAI) measurements made as part of the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Data were collected in the Upper Gunnison Basin, Colorado, across three study domains: the Upper East River (CRBU), Almont Triangle (AL
CHESS 2025: Location data for field observations and sampling
This dataset represents geolocation data associated with field observations and sampling from the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Location data were collected using Trimble DA2 Global Navigation Satellite System (GNSS) receivers with Trimble Ca
Depth-resolved sagebrush root metabolomics, rhizosphere microbial communities, and geochemistry at the East River Watershed
This data set consists of results from soil nutrient profile, untargeted metabolomics, mass spec imaging, and amplicon sequencing. Data for soil nutrient profile includes common cations (Ca, Mg, Na, and K etc.) extracted from 3 digesting steps – ammonia acetate (for exchangeable cations), nitric aci
Mountain Basin Controls on the Snow-to-Streamflow Signal: An AIC-Weighted Multiple Linear Regression Framework
A regression-based analysis quantifies how basin characteristics modulate the snow-to-streamflow signal. First, we use the ERA5-Land reanalysis gridded product (European Centre for Medium Range Weather Forecasts reanalysis 5 -Land component) for 4,655 hydrologic unit code - 10 (HUC10) mountain basin
Data from Stewart et al. 2026 "Organic Colloid Composition in Variable-Redox Porewaters within a Mountainous Floodplain"
Redox gradients, often driven by changes in sediment moisture levels in porous, heterogeneous groundwater systems, create dynamic conditions that may promote the production and transport of colloids within natural waters. While much research has focused on the inorganic composition of colloids, the
Geophysical survey associated with NEON AOP survey, East River, CO 2018
The package contains data layers developed and used in Falco et al. 2024: “EcoImaging: Advanced Sensing to Investigate Plant and Abiotic Hierarchical Spatial Patterns in Mountainous Watersheds". The package is part of the DOE Watershed Function Science Focus Area (SFA) project and includes geophysic
Geochemistry and Strontium Isotopes for Coal Creek Watershed, Colorado, 2021-2022
The geochemistry and strontium isotope data for Coal Creek Watershed, Colorado, consists of cation, anion, and 87Sr/87Sr isotope values from samples collected at 8 stream location along Coal Creek, samples from two groundwater springs within the watershed, and a shallow subsurface piezometer. All st
Site and endmember spectra of terrestrial vegetation and soils for the Colorado Headwaters Ecological Spectroscopy Study, June-July 2025
This dataset provides site and endmember spectra collected during the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS) campaign. The site spectra were collected to help validate airborne hyperspectral data acquired by the National Ecological Observatory Network's aerial observation pla
Metagenome-assembled genomes from Slate River floodplain sediments near Crested Butte, CO, USA (June to October 2020)
Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken June to October 2020 at two loca
Metagenome-assembled genomes from Slate River floodplain sediments near Crested Butte, CO, USA (June 2018)
Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken June 2018 at two locations (OBJ
Metagenome-assembled genomes from Slate River floodplain sediments near Crested Butte, CO, USA (September 2019)
Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken September 2019 at one locations
Groundwater and river water elevations and temperature from 2017 to 2022 across Meander Z in the East River Watershed, Colorado
This dataset includes groundwater and river water elevations and temperature data collected in the East River watershed located in the Upper Colorado River Basin. The data were collected in order to investigate the coupling between hydrology and biogeochemical processes in the floodplain. Data was c
Montane Conifer, Aspen, Meadow, and Sagebrush Metagenome Resolved Genomes and Traits in East River Watershed, Colorado, USA
Climate change is driving vegetation shifts in mountain watersheds, with unknown impacts on biogeochemical cycles. We hypothesize that these shifts will reshape soil microbiomes and associated biogeochemical processes. As a part of Lawrence Berkeley National Laboratory (LBNL) Watershed Science Focus
Metagenome-assembled genomes measured at 3 depths during snowmelt period in East River, CO (March, May, and June, September 2017)
Snowmelt is a critical biogeochemical period that accounts for large nitrogen (N) export events from high-elevation watersheds. Soil microbial populations bloom and immobilize N during snowmelt, yet the population size crashes in spring, which releases a pulse of soil N. We sought to discover the N
Time-lapse imagery in 2017 and 2018 at the Lower Montane site in the East River Watershed, Colorado
Time-lapse imagery was collected using an automated RGB camera mounted on a pole at the base of the northeast-facing hillslope at the Lower Montane site in the East River Watershed, Colorado. The imagery was intended to support a better understanding of plant dynamics and their controls during the g
Snow Depth Datasets for Snodgrass Catchment, Colorado, Water Year 2022-2023
This data package presents snow depths data from distributed temperature probes at 18 locations near Snodgrass catchment, Colorado. These data show that snow melt-out dates are approximately one or two weeks later under evergreen forests compared to other vegetation types even at the same elevation.
Metagenome-assembled genomes from topsoils along a hillslope water gradient across early snowmelt to late summer in East River, CO
Drought is changing the American Mountain West at unprecedented rates with unknown consequences to soil microbiome composition and function. As a part of LBNL Watershed Science Focus Area (SFA), we investigated shifts in microbial community and transcriptional activity on a subalpine conifer-meadow
Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”
This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”. The study addressed common challenges faced in environmental sensing a
