2017 Meander C sediment characterization from the East River, Colorado - Frances Griswold; Genevieve Goebel; Sam Ying; Malak M. Tfaily; Scott Fendorf; Craig R. Anderson; Marco Keiluweit; P. M. Fox; P. Nico (ess_dive, 2023)
This dataset includes characterization data collected on sediment samples from Meander C as part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected to investigate the nature of mineral-organic associations across the meander transect and with depth. Samples were collected in 2017, and analyzed using sequential extractions (water, pyrophosphate, HCl, and dithionite) and density fractionation. Data collected include: texture, pH, root biomass, and metals (Fe, Al, Ca, Si) of bulk sediment samples; C and N of heavy fraction; and extractable OC, Fe, and Al, and organic matter characterization using high-resolution mass spectrometry (FT-ICR-MS) of sequential extractions. Sample locations are included in the ‘sample_metadata.xlsx” file. Sediment characterization data is included in the accompanying csv files: bulk and density fractions (MCB2017_density-and-bulk-fractions.csv), sequential extractions (MCB2017_sequential-extractions.csv), and FT-ICR-MS data from sequential extractions in three files to be read by the R package ftmsRanalysis (MCB2017_FTICRMS-sequential-edata.csv, MCB2017_FTICRMS-sequential-emeta.csv, and MCB2017_FTICRMS-sequential-fdata.csv). Sediment was collected in triplicate; ID’s refer to individual samples.

In-situ electrochemical and water quality data; Slate River and East River floodplains, Crested Butte, CO; May 2022-September 2022. - Kristin Boye; Samuel Pierce (other, 2023)
This data package includes a time-series of field measurements from May to September 2022 in groundwater and surface water from the Slate River and East River floodplains in Crested Butte, CO, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The data package includes 5 data files, one for each measured variable: specific conductivity, pH, dissolved oxygen, water temperature and alkalinity. All measurements were recorded in the field immediately after water sampling. Groundwater samples were extracted from a network of installed rhizons (Rhizosphere Research Products, part no. 19.60.21F, 0.6 micrometer mesh size) and piezometer wells within the floodplain. In addition to the data files, there is a terminology file explaining the terms used, a file level metadata file, and a sensor file with metadata about the sensors used. All files are in csv format.

SnowEx20 Grand Mesa IOP Computed Tomography Snow Microstructure, Version 1 (other, 2023)

SnowEx21 Senator Beck Basin and Grand Mesa, CO AVIRIS-NG Surface Spectral Reflectance, Version 1 (other, 2023)

Air Temperature Freezing Degree-days Early Season Standard Deviation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in spring accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Freezing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute freezing degree days, temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days and multiplied by negative 1. This map represents the standard deviation of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D026')  metadata <- sdp_get_metadata('R4D026')    For more information about rSDP, visit the package homepage.

Groundwater and Surface Water Flow (GSFLOW) model files for the East River, Colorado - Richard Niswonger; E. R. Woodburn; E. R. Woodburn; Richard Niswonger; Charuleka Varadharajan; Craig Ulrich; Charuleka Varadharajan; Craig Ulrich; K. H. Williams; K. H. Williams (ess_dive, 2023)
The data package contains model input files and executables for the East River, Colorado (750 km2) located in the headwaters of the Upper Colorado River Basin. The code applied is the U.S. Geological Survey (USGS) Groundwater and Surface Water Flow (GSFLOW) model. The model contains a 100-m grid resolution to represent topographic complexity and a daily timestep accounts for energy and water partitioning between the snowpack, vegetation, soil zone and bedrock. The land surface model is dynamically linked to a three-dimensional groundwater flow model that allows for streamflow gaining and losing conditions. The groundwater model accounts for nine stratigraphic units and extends 400 m below land surface. Using this modeling framework we explore historical water budgets (water year 1987-2022) and the influence of seasonal warming and associated mechanisms driving groundwater declines and streamflow loss. A Readme_062424.txt file provides instructions on how to download all files. Input and output files are provided for the historical simulation representative of water years 1987 to 2022 (i.e. baseline) and the all-year +4C warming scenario. Instructions are provided to run the seasonal warming scenarios with warming applied only to the autumn, winter, spring or summer months. Modeled output used in figures for Carroll et al., 2024 are also provided with metadata describing where these data were obtained. This dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. Updated on 06-24-2024: The dataset was updated to include model outputs (updated base.zip and 4C.zip files), figure source files (Figure_Source_Files.zip), an updated Readme file (Readme_062424.txt), climate input files (climate_input_files.zip), and the adoption of ESS-DIVE File Level Metadata and CSV reporting formats (inclusion of flmd.csv and dd.csv files).

The role of environmental variation in mediating fitness tradeoffs for an amphibian polyphenism (2023)
Fitness tradeoffs are a foundation of ecological and evolutionary theory because tradeoffs can explain life history variation, phenotypic plasticity, and the existence of polyphenisms.  Using a 32-year mark-recapture dataset on lifetime fitness for 1,093 adult Arizona tiger salamanders (Ambystoma mavortium nebulosum) from a high elevation, polyphenic population, we evaluated the extent to which two life history morphs (aquatic paedomorphs vs terrestrial metamorphs) exhibited fitness tradeoffs in breeding and body condition with respect to environmental variation (e.g., climate) and internal state-based variables (e.g., age). Both morphs displayed a similar response to higher probabilities of breeding during years of high spring precipitation (i.e., not indicative of a morph-specific fitness tradeoff). There were likely no climate-induced fitness tradeoffs on breeding state for the two life history morphs because precipitation and water availability are vital to amphibian reproduction.  Body condition displayed a contrasting response for the two morphs that was indicative of a climate-induced fitness tradeoff. While metamorphs exhibited a positive relationship with summer snowpack conditions, paedomorphs were unaffected. Fitness tradeoffs from summer snowpack are likely due to extended hydroperiods in temporary ponds, where metamorphs gain a fitness advantage during the summer growing season by exploiting resources that are unavailable to paeodomorphs. However, paedomorphs appear to have the overwintering fitness advantage because they consistently had higher body condition than metamorphs at the start of the summer growing season.  Our results reveal that climate and habitat type (metamorphs as predominately terrestrial, paedomorphs as fully aquatic) interact to confer different advantages for each morph. These results advance our current understanding of fitness tradeoffs in this well-studied polyphenic amphibian by integrating climate-based mechanisms. Our conclusions prompt future studies to explore how climatic variation can maintain polyphenisms and promote life history diversity, as well as the implications of climate change for polyphenisms. We used a trait by environment modeling approach to determine the effects of natural environmental variation on the different fitness parameters of two life history morphs in a polyphenic population of tiger salamanders. We provide an excel datafile on the lifetime detection histories of individual salamander, an excel datafile on annual climatic conditions for the study population, the associated R code for all analyses, and a READ ME document to explain the different datafiles.

Supplementary material from "The social microbiome: gut microbiome diversity and abundance are negatively associated with sociality in a wild mammal" (2023)
The gut microbiome has a well-documented relationship with host fitness. Greater microbial diversity and abundance of specific microbes have been associated with improved fitness outcomes. Intestinal microbes also may be associated with patterns of social behaviour. However, these associations have been largely studied in captive animal models; we know less about microbiome composition as a potential driver of individual social behaviour and position in the wild. We used linear mixed models to quantify the relationship between fecal microbial composition, diversity and social network traits in a wild population of yellow-bellied marmots (Marmota flaviventer). We focused our analyses on microbes previously linked to sociability and neurobehavioural alterations in captive rodents, primates and humans. Using 5 years of data, we found microbial diversity (Shannon–Wiener and Faith's phylogenetic diversity) has a modest yet statistically significant negative relationship with the number of social interactions an individual engaged in. We also found a negative relationship between Streptococcus spp. relative abundance and two social network measures (clustering coefficient and embeddedness) that quantify an individual's position relative to others in their social group. These findings highlight a potentially consequential relationship between microbial composition and social behaviour in a wild social mammal.

Air Temperature Freezing Degree-days Late Season Timeseries - Ian K Breckheimer (s3, 2023)
These are map of annual fall accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Freezing degree-days were accumulated from day-of-year 197 to day-of-year 305 (July 16th to November 1st in a non-leap year). To compute freezing degree days, temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days and multiplied by negative 1. These maps represent annual values. 20-year means computed from 2002 to 2021 are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D011')  metadata <- sdp_get_metadata('R4D011')    For more information about rSDP, visit the package homepage.

Air Temperature Freezing Degree-days Annual Standard Deviation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of variability in accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Freezing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute freezing degree days, temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days and multiplied by negative 1. This map represents the standard deviation of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D024')  metadata <- sdp_get_metadata('R4D024')    For more information about rSDP, visit the package homepage.

Supplementary material from "Emergent social structure is typically not associated with survival in a facultatively social mammal" (2023)
For social animals, group social structure has important consequences for disease and information spread. While prior studies showed individual connectedness within a group has fitness consequences, less is known about the fitness consequences of group social structure for the individuals who comprise the group. Using a long-term dataset on a wild population of facultatively social yellow-bellied marmots (Marmota flaviventer), we showed social structure had largely no relationship with survival, suggesting consequences of individual social phenotypes may not scale to the group social phenotype. An observed relationship for winter survival suggests a potentially contrasting direction of selection between the group and previous research on the individual level; less social individuals, but individuals in more social groups experience greater winter survival. This work provides valuable insights into evolutionary implications across social phenotypic scales.

Dryad dataset awaiting publication (2023)
:unas

Minimum 2m Air Temperature Monthly Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of monthly averages of daily minimum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and microclimate sensor data using Bayesian regression-kriging, incorporating topographic and vegetation structure covariates. These data were derived from daily maps, which are also available. To reduce storage space, original estimates were multiplied by 10000 and stored as integer data. Divide the map values by 10000 to retrieve the original estimates.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D008')  metadata <- sdp_get_metadata('R4D008')    For more information about rSDP, visit the package homepage.

Snow-free Freezing Degree-days Early Season Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of annual accumulated spring snow-free freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free freezing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. These maps represent annual values computed from 2002 to 2022. 20-year means (2002-2021) are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D016')  metadata <- sdp_get_metadata('R4D016')    For more information about rSDP, visit the package homepage.

BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH - Maciej Stachura; Eryan Dai; Darren Jackson; Jack Elston; Jack Elston; Maciej Stachura; Darren Jackson; Gijs de Boer; Eryan Dai; Janet Intrieri; Gijs de Boer; Eryan Dai; et al. (other, 2023)
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. The file name convention for the Level 3 NetCDF files is as follows. uas_L3_yyyymmdd_hhmmss_vx.x.nc where L3 = Level 3 data yyyymmdd = year,month,day hhmmss = hour,minute,second x.x = version number Time is the flight start time in UTC. Version number description is provided in the NetCDF global attributes. Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour. November 2023 update: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data. Version 2.1 adds data file uas_L3_20221018_171650_v2.1.nc that was missing in Version 2.0. December 2023 update: Version 2.2 updated soil moisture data with a wet bias in v2.1 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.

BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH (other, 2023)
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. The file name convention for the Level 3 NetCDF files is as follows. uas_L3_yyyymmdd_hhmmss_vx.x.nc where L3 = Level 3 data yyyymmdd = year,month,day hhmmss = hour,minute,second x.x = version number Time is the flight start time in UTC. Version number description is provided in the NetCDF global attributes. Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour. November 2023 update: Version 2.0 added which has flight data from 2022 and 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data and a mask was applied to the soil moisture retrieval that masks data over water surfaces. Version 2.1 adds data file uas_L3_20221018_171650_v2.1.nc that was missing in Version 2.0.

Bioaerosol Properties using WIBS from SAIL on Mt. Crested Butte, CO - Allison Aiken; Abu Sayeed Md Shawon; Katherine Benedict (other, 2023)
We deployed a Wideband Integrated Bioaerosol Sensor (WIBS-Neo, DMT) to measure different properties of fluorescent bioaerosol particles (FBAP) during SAIL. Our measurements were collected at the supplementary site on Crested Butte Mountain from inside the ARM Aerosol Observing System (AOS) sampling from the AOS inlet. We had two deployment periods at SAIL during biologically active seasons: summer 2022 (15 June 2022 - 13 September 2022) and spring 2023 (21 March 2023 - 19 June 2023). The WIBS is an online single-particle measurement that detects FBAPs (within a size range of 0.5 - 30 microns in diameter) based on the excitation and emission wavelengths of the individual particles. Using two xenon lamps, the WIBS excites FBAPs at 280 nm and 370 nm. Their emission is detected across two wavebands of 310-400 nm and 420-650 nm. We classified the FBAPs into seven different categories (A, B, C, AB, BC, AC, and ABC) using the classification scheme in Perring et. al. (2015) [1]. Here, we report one-minute averaged number concentration of FBAPs (total and by category) and NFBAPs. We also present one-minute-averaged size distributions and the asymmetry factor (AF, a surrogate for shape) of all FBAPs and NFBAPs. The logarithmic bin width of the size bins is the same as the average bin width of the AOS's optical particle counter (OPC, Grimm) for the range of sizes in which they overlap (26 bins from 0.5 - 30 microns). AF of the particles ranges from 0-100 and is divided into five bins with a linear spacing at increments of 20. The smallest AF bin represents more spherical particles while the largest bin represents more rod-shaped particles. We recommend referring to the readme file for more details of the variables present in the files. [1] Perring, A. E., et al. (2015), Airborne observations of regional variation in fluorescent aerosol across the United States, J. Geophys. Res. Atmos., 120, 1153-1170, doi:10.1002/2014JD022495. Abstract and description of the campaign can be found here: https://www.arm.gov/research/cam

SAIL-Net Raw and Post Corrected POPS Data Fall 2021 - Summer 2023 - E. J. T. Levin; Lay James Gibson (other, 2023)
SAIL-Net is a DOE funded project in the East River Watershed near Crested Butte, Colorado with the goal of advancing our understanding of aerosol-cloud interactions in complex, mountainous regions. Through the deployment of a network of six low cost microphysics nodes in Fall 2021 in the same domain at the SAIL campaign, SAIL-Net provides data on aerosol size distributions, cloud condensation nuclei (CCN), and ice nucleation particles (INP). This network enables the investigation of small-scale variations in complex terrain.Two datasets are provided - one containing raw data and the other containing post-corrected data.The raw dataset provides the raw data recorded from the POPS which were deployed at each of the six sites. These data are organized by site and broken down into daily data files. The six site names used here are: “gothic”, “irwin”, “cbtop”, “cbmid”, “pumphouse”, and “snodgrass”. These data are not cleaned or post-corrected, but some flags have been added. The data are reported at 1 second time resolution. The post-corrected dataset provides the post-corrected and cleaned data recorded from the POPS which were deployed at each of the six sites. This data are also organized by site (same as those found in the raw data) and broken down into daily data files. Unlike the raw POPS data, these data have already been cleaned to remove what we believe are bad values. These data should be ready to use with no cleaning. For a full description of the cleaning and post-correction process, see the readme.

Raw soil carbon dioxide, moisture, temperature and micrometeorological data in the East River Watershed, Colorado June 2021-October 2022. (DE-SC0021139). Ecohydrological controls on root and microbial respiration in the East River watershed of Colorado (ess_dive, 2023)
This dataset contains raw data from four tripod stations along an elevation gradient on Snodgrass Mountain in the East River Watershed, CO, USA. Each station contains a datalogger connected to 3 soil Carbon Dioxide CO2 gas probes, 3 soil temperature/moisture sensors and a micrometeorological station. Sensors are scanned every minute, and the 30 minute average is reported. The file snodgrass_soil_ESS.csv contains raw data and a row of column descriptors and units of measurements. No data processing or QA/QC was done on the raw data sets. This research was performed to investigate the ecohydrological linkages of belowground carbon processes in the East River watershed forested communities to better understand how these ecosystems will respond to a changing cold-season moisture input.

Specific conductivity, pH, dissolved oxygen, water temperature and alkalinity in-situ data; Slate River floodplain, Crested Butte, CO; March 2021-October 2021. (ess_dive, 2023)
This data package includes a time-series of field measurements from March to October 2021 in groundwater and surface water from the Slate River floodplain in Crested Butte, CO, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The data package includes 5 data files, one for each measured variable: specific conductivity, pH, dissolved oxygen, water temperature and alkalinity. All measurements were recorded in the field immediately after water sampling. Groundwater samples were extracted from a network of installed rhizons (Rhizosphere Research Products, part no. 19.60.21F, 0.6 micrometer mesh size) and piezometer wells within the floodplain. In addition to the data files, there is a terminology file explaining the terms used, a file level metadata file, and a sensor file with metadata about the sensors used. All files are in csv format.

Maximum 2m Air Temperature Daily Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of daily maximum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and microclimate sensor data using Bayesian regression-kriging, incorporating topographic and vegetation structure covariates. To reduce storage space, original estimates were multiplied by 10000 and stored as integer data. Divide the map values by 10000 to retrieve the original estimates.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D004')  metadata <- sdp_get_metadata('R4D004')    For more information about rSDP, visit the package homepage.

Maximum 2m Air Temperature Monthly Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of monthly averages of daily maximum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and microclimate sensor data using Bayesian regression-kriging, incorporating topographic and vegetation structure covariates. These data were derived from daily maps, which are also available. To reduce storage space, original estimates were multiplied by 10000 and stored as integer data. Divide the map values by 10000 to retrieve the original estimates.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D007')  metadata <- sdp_get_metadata('R4D007')    For more information about rSDP, visit the package homepage.

Air Temperature Growing Degree-days Early Season Mean (2002-2022) - Ian K Breckheimer (s3, 2023)
This is a map of accumulated growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Temperature estimates represent conditions approximately 2m above the ground surface. Growing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute GDD, daily maximum temperatures above 5 degrees Celcius (minimal growth conditions for most plants) were summed across all focal days. This map represents the mean of annual values computed from 2002 to 2021. Annual estimates are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D031')  metadata <- sdp_get_metadata('R4D031')    For more information about rSDP, visit the package homepage.

Air Temperature Freezing Degree-days Annual Mean (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Freezing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute freezing degree days, temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days and multiplied by negative 1. This map represents the mean of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D023')  metadata <- sdp_get_metadata('R4D023')    For more information about rSDP, visit the package homepage.

NOAA PSL Soil Moisture and Surface Temperature Probe Data for SPLASH (other, 2023)
This dataset contains measurements from a hand-held FieldScout TDR Soil Moisture Meter within the 0-10 cm soil depth of: Time (UTC), GPS locations, Electrical Conductivity (EC), compensated percent volumetric water content (VWC), soil surface temperature (T), and rod length (inches) obtained during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected around the SPLASH campaign areas near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from between June 1st, 2022 and September 18th, 2023, under support from the NOAA Physical Sciences Laboratory and NOAA Weather Program Office under award NA21OAR4590363. Two file formats are provided: one version is text csv format and the second version is in NetCDF. Volumetric water content calculations: Data were calibrated and adjusted, with a soil-specific sample set, to improve accuracy and compensate for the meter's default "standard" soil type used in the sampling. VWC data was correlated by measuring the weight of a known volume of soil from a range of saturation values. Samples were measured and weighed, dried at 105 degrees C for 48 hours, then weighed again. Calculations of VWC (VWC = 100*(Mwet - Mdry)/(w*Vtot) )were plotted against TDR readings. Where: Mwet, Mdry = mass (g) of wet and dry soil respectively Vtot = total soil volume (ml) w = density of water (1g/ml) A regression analysis to correlate TDR readings to the samples is below and was applied to the dataset. vwc_calculated = vwc_probe * slope + intercept slope = 1.20665, intercept = 0.0837017 m3/m3, slope_std_error = 0.09229, intercept_std_error = 0.0217403 m3/m3 Definitions: TDR (Time Domain Reflectometry): A technique for measuring soil moisture content that uses the fact that water has a much higher dielectric permittivity than air, soil minerals, and organic matter. VWC (Volumetric Water Content): The ratio of the volume of water in a given volume of soil to the total soil volume expressed as a decimal or a percentage. The percent of the soil volume that is filled with water. At saturation, the VWC will equal the soil porosity (Saturation is typically around 50%). EC (Electrical Conductivity): A measure of how well the soil solution conducts electricity. The EC is influenced by the amount of salt and water in the soil. The VWC measured by TDR is an average over the length of the waveguide. Soil Characteristics: Soil at both Kettle Ponds (KEP1 and KPA) locations and Avery Picnic (AYP) were lab tested for composition as follows: Sample ID Depth(in.) Sand(%) Silt(%) Clay(%) Soil Texture ------------------------------------------------------------------------------------------------------ KEP1 2 43 35 22 Loam AYP 2 40 35 25 Loam KPA 2 35 42 22 Loam ------------------------------------------------------------------------------------------------------

Atmospheric Surface Flux Station #30 measurements (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), September 2021-July 2023 - Jackson Osborn; Gijs de Boer; S. Morris; J. Hamilton; Brian Butterworth; Erik Hulm; M. K. Gallagher; Matthew Shupe; Janet Intrieri; Benjamin Schmatz; Christopher Cox (other, 2023)
Raw (Level 1) measurements from the Atmospheric Surface Flux Station #30 (ASFS-30) deployed at the Kettle Ponds Annex site (38°56.3686' N, 106°58.1781' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from September 2021 through July 2023. The ASFS measured variables comprising the surface energy budget, surface momentum flux, near-surface meteorology, and soil properties. These measurements are included in three netCDF files per day. The "slow" files are for 1-minute averages of the measured variables, including near-surface meteorology, surface height change (due to accumulating/ablating snow), and upwelling and downwelling shortwave and longwave radiative fluxes. The "fast" files are for data at 20 Hz resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with technical corrections applied but no quality assurance. A detailed documentation of the measurement will be provided in a forthcoming publication. For scientific purposes, we recommend using the Level 2 data files when they are available, as these will include full quality control as well as higher-order derived products.

Water Level Data from Wells PLM1 and PLM6 for the East River Watershed, Colorado (ess_dive, 2023)
This dataset (Williams et al., 2020) contains the original un-QA/QC-ed water level data for PLM1 and PLM6 and has been obsoleted. The data contained within this dataset is not to be used. Refer to Faybishenko et al., 2022 (DOI: 10.15485/1866836) for the latest QA/QC-ed data available via ESS-DIVE.This data set contains water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location. These wells used to monitor subsurface water and carbon inventories and fluxes at the East River Watershed, Colorado, USA. Complete metadata information on the PLM1 and PLM6 wells are available in the related data package reference Varadharajan C, et al (2020). https://doi.org/10.15485/1660962.Data are reported in .csv files per well. The latitude and longitude of each location are given in a file called locations.csv. These data are used for determining the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistry can be used to estimate rates of solute export from the hillslope to the floodplain and river.These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales.

NOAA PSL Soil Moisture and Surface Temperature Probe Data for SPLASH - William Currier; Jake Longenecker; Jake Longenecker; William Currier; Darren Jackson; Maciej Stachura; Maciej Stachura; Darren Jackson; Gijs de Boer; Janet Intrieri; Gijs de Boer; Janet Intrieri (other, 2023)
This dataset contains measurements from a hand-held FieldScout TDR Soil Moisture Meter within the 0-10 cm soil depth of: Time (UTC), GPS locations, Electrical Conductivity (EC), compensated percent volumetric water content (VWC), soil surface temperature (T), and rod length (inches) obtained during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected around the SPLASH campaign areas near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from between June 1st, 2022 and September 18th, 2023, under support from the NOAA Physical Sciences Laboratory and NOAA Weather Program Office under award NA21OAR4590363. Two file formats are provided: one version is text csv format and the second version is in NetCDF. Volumetric water content calculations: Data were calibrated and adjusted, with a soil-specific sample set, to improve accuracy and compensate for the meter's default "standard" soil type used in the sampling. VWC data was correlated by measuring the weight of a known volume of soil from a range of saturation values. Samples were measured and weighed, dried at 105 degrees C for 48 hours, then weighed again. Calculations of VWC (VWC = 100*(Mwet - Mdry)/(w*Vtot) )were plotted against TDR readings. Where: Mwet, Mdry = mass (g) of wet and dry soil respectively Vtot = total soil volume (ml) w = density of water (1g/ml) A regression analysis to correlate TDR readings to the samples is below and was applied to the dataset. vwc_calculated = vwc_probe * slope + intercept slope = 1.20665, intercept = 0.0837017 m3/m3, slope_std_error = 0.09229, intercept_std_error = 0.0217403 m3/m3 Definitions: TDR (Time Domain Reflectometry): A technique for measuring soil moisture content that uses the fact that water has a much higher dielectric permittivity than air, soil minerals, and organic matter. VWC (Volumetric Water Content): The ratio of the volume of water in a given volume of soil to the total soil volume expressed as a decimal or a percentage. The percent of the soil volume that is filled with water. At saturation, the VWC will equal the soil porosity (Saturation is typically around 50%). EC (Electrical Conductivity): A measure of how well the soil solution conducts electricity. The EC is influenced by the amount of salt and water in the soil. The VWC measured by TDR is an average over the length of the waveguide. Soil Characteristics: Soil at both Kettle Ponds (KEP1 and KPA) locations and Avery Picnic (AYP) were lab tested for composition as follows: Sample ID Depth(in.) Sand(%) Silt(%) Clay(%) Soil Texture ------------------------------------------------------------------------------------------------------ KEP1 2 43 35 22 Loam AYP 2 40 35 25 Loam KPA 2 35 42 22 Loam ------------------------------------------------------------------------------------------------------

Snowpack Onset Day of Year Standard Deviation (1993-2022) - Ian K Breckheimer (s3, 2023)
This dataset represents an estimate of interannual variability in the day of year (i.e., "Julian Day") of the onset of the seasonal snowpack.  Specifically these are estimates of the last day of bare ground derived from long-term time-series of Landsat, and OLI imagery starting in 1993.  To facilitate computation of snow cover duration, day of year is scaled from January 1st of the following year.  This means that negative values represent days before January 1st.  For example, a snow onset on November 11th, 2021 (Day of Year 315) would be represented in the 2022 map with a value of -50 (315 - 365). These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ), with a time-series analysis of a spectral snow index (NDSI) using a hierarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022), and greater than 90% of temporal variation across the full 1993 - 2022 time-series. This data represents the 30-year standard deviation from 1993 - 2022. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D060')  metadata <- sdp_get_metadata('R4D060')    For more information about rSDP, visit the package homepage.

Air Temperature Growing Degree-days Late Season Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of annual accumulated fall growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Temperature estimates represent conditions approximately 2m above the ground surface. Growing degree-days were accumulated from day-of-year 197 to day-of-year 305 (July 16th to November 1st in a non-leap year). To compute GDD, daily maximum temperatures above 5 degrees Celcius (minimal growth conditions for most plants) were summed across all focal days. These data represent annual estimates. 20-year means (2002-2021) are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D014')  metadata <- sdp_get_metadata('R4D014')    For more information about rSDP, visit the package homepage.

Snowpack Duration Yearly Timeseries - Ian K Breckheimer (s3, 2023)
These maps represent annual estimates of the number of days of continuous seasonal snowpack from 1993 - 2022. The maps are derived from estimates of the first and last day of bare ground derived from long-term time-series of Landsat, and OLI imagery starting in 1993.  Annual estimates represent the number of days of continuous snow cover from the fall of the previous year to the spring of the current year. For example, the 1993 estimate is the number of days of snowpack cover from fall 1992 to spring 1993. This is often called a "water year" estimate. These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ) with a time-series analysis of a spectral snow index (NDSI) using a hierarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022), and greater than 90% of temporal variation across the full 1993 - 2022 time-series. This data represents annual estimates from 1993 - 2022. The 30-year mean is also available. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D003')  metadata <- sdp_get_metadata('R4D003')    For more information about rSDP, visit the package homepage.

Snowpack Onset Day of Year Mean (1993 - 2022) - Ian K Breckheimer (s3, 2023)
This dataset represents an estimate of the mean day of year (i.e., "Julian Day") of the onset of the seasonal snowpack.  Specifically these are estimates of the last day of bare ground derived from long-term time-series of Landsat TM, ETM, and OLI imagery starting in 1993.  To facilitate computation of snow cover duration, day of year is scaled from January 1st of the following year.  This means that negative values represent days before January 1st. For example, a snow onset on November 11th, 2021 (Day of Year 315) would be represented in the 2022 map with a value of -50 (315 - 365). These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ), with a time-series analysis of a spectral snow index (NDSI) using a hierarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022), and greater than 90% of temporal variation across the full 1993 - 2022 time-series. This data represents the 30-year mean from 1993 - 2022. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D059')  metadata <- sdp_get_metadata('R4D059')    For more information about rSDP, visit the package homepage.

Dryad dataset awaiting publication (2023)
:unas

Snowpack Duration Standard Deviation (Water Year 1993 - 2022) - Ian K Breckheimer (s3, 2023)
This dataset represents an estimate of interannual variability in the number of days of continuous seasonal snowpack from 1993 - 2022. This map is derived from estimates of the first and last day of bare ground derived from long-term time-series of Landsat TM, ETM, and OLI imagery starting in 1993.  These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ) with a time-series analysis of a spectral snow index (NDSI) using a hierarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022), and greater than 90% of temporal variation across the full 1993 - 2022 time-series. This data represents the mean of annual estimates from 1993 - 2022. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D058')  metadata <- sdp_get_metadata('R4D058')    For more information about rSDP, visit the package homepage.

Air Temperature Growing Degree-days Annual Standard Deivation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in accumulated growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Temperature estimates represent conditions approximately 2m above the ground surface. Growing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute GDD, daily maximum temperatures above 5 degrees Celcius (minimal growth conditions for most plants) were summed across all focal days. This map represents the standard deviation of annual values computed from 2002 to 2021. Estimates for individual years are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D030')  metadata <- sdp_get_metadata('R4D030')    For more information about rSDP, visit the package homepage.

Snow-free Growing Degree-day 0-60 Days Post Snow Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of annual accumulated snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated in the 60 days after the disappearance of seasonal snowpack.  In the absence of snow, the accumulation period started on day-of-year 60 (March 1st in a non-leap year). To compute SFGDD, daily estimates of temperatures above a base of 5 degrees Celcius (a minimum growth threshold for many plants) were summed across all days estimated to be free of snow cover. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. These maps represent annual values. 20-year means computed from 2002 to 2021 are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D022')  metadata <- sdp_get_metadata('R4D022')    For more information about rSDP, visit the package homepage.

SnowEx21 Senator Beck Basin and Grand Mesa, CO AVIRIS-NG Surface Spectral Reflectance, Version 1 (other, 2023)

Snowpack Persistence Day of Year Mean (1993 - 2022) - Ian K Breckheimer (s3, 2023)
This dataset represents an estimate of the mean day of year (i.e., "Julian Day") of the persistence of the seasonal snowpack from 1993 - 2022.  Specifically these are estimates of the first day of bare ground derived from long-term time-series of Landsat, and OLI imagery starting in 1993.  These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ) with a time-series analysis of a spectral snow index (NDSI) using a hierarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022), and greater than 90% of temporal variation across the full 1993 - 2022 time-series. This data represents the mean of annual estimates from 1993 - 2022. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D061')  metadata <- sdp_get_metadata('R4D061')    For more information about rSDP, visit the package homepage.

Minimum 2m Air Temperature Daily Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of daily minimum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and microclimate sensor data using Bayesian regression-kriging, incorporating topographic and vegetation structure covariates. To reduce storage space, original estimates were multiplied by 10000 and stored as integer data. Divide the map values by 10000 to retrieve the original estimates.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D005')  metadata <- sdp_get_metadata('R4D005')    For more information about rSDP, visit the package homepage.

Aeolian mass flux data for the Colorado Plateau (other, 2023)
These data were compiled to measure airborne horizontal mass flux of sediments moved by wind across soils, climates, vegetation types, and land uses on the Colorado Plateau. Objectives of our study were to quantify spatial and temporal patterns in wind erosion and further our understanding of how soil and site setting, climate, and land uses are controlling wind erosion and horizontal mass flux. These data represent seasonal cumulative horizontal mass flux as measured using passive aspirated sediment traps, Big Spring Number Eight (BSNE) samplers. These data were collected in Grand and San Juan counties, Utah, and Mesa County, Colorado, USA between August 2017 and November 2020. These data were collected by the U.S. Geological Survey, [...]

Snowpack Duration Mean (Water Year 1993 - 2022) - Ian K Breckheimer (s3, 2023)
This dataset represents an estimate of the mean number of days of continuous seasonal snowpack from 1993 - 2022. This map is derived from estimates of the first and last day of bare ground derived from long-term time-series of Landsat TM, ETM, and OLI imagery starting in 1993.  These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ) with a time-series analysis of a spectral snow index (NDSI) using a hierarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022), and greater than 90% of temporal variation across the full 1993 - 2022 time-series. This data represents the mean of annual estimates from 1993 - 2022. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D057')  metadata <- sdp_get_metadata('R4D057')    For more information about rSDP, visit the package homepage.

Snowpack Proportional Reduction in Growing Degree Days (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of the influence of snow on the energy available for plant growth (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. Snow-free growing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute growing degree days, daily estimates of temperatures above a base of 5 degrees Celcius (a minimum growth threshold for many plants) were summed across all days estimated to be free of snow cover. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of growing potential for snow-dominated climates than growing degree days (FDD). This map represents the proportional reduction in growing potential from 2002 to 2021 using the formula (GDD - SFGDD) / GDD.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D054')  metadata <- sdp_get_metadata('R4D054')    For more information about rSDP, visit the package homepage.

Snowpack Onset Day of Year Yearly Timeseries - Ian K Breckheimer (s3, 2023)
This dataset represents an estimate of the day of year (i.e. ,  "Julian Day") of the onset of the seasonal snowpack.  Specifically these are estimates of the last day of bare ground derived from long-term time-series of Landsat TM,  ETM,  and OLI imagery starting in 1993.  To facilitate computation of snow cover duration,  day of year is scaled from January 1st of the following year.  This means that negative values represent days before January 1st.  For example,  a snow onset on November 11th,  2021 (Day of Year 315) would be represented in the 2022 map with a value of -50 (315 - 365). These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ),  with a time-series analysis of a spectral snow index (NDSI) using a heirarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022),  and greater than 90% of temporal variation across the full 1993 - 2022 time-series. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D002')  metadata <- sdp_get_metadata('R4D002')    For more information about rSDP, visit the package homepage.

BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH (other, 2023)
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 19th, 2022. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. The file name convention for the Level 3 NetCDF files is as follows. uas_L3_yyyymmdd_hhmmss_vx.x.nc where L3 = Level 3 data yyyymmdd = year,month,day hhmmss = hour,minute,second x.x = version number Time is the flight start time in UTC. Version number description is provided in the NetCDF global attributes. Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour. November 2023 update: Version 2.0 added which has flight data from 2022 and 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data and a mask was applied to the soil moisture retrieval that masks data over water surfaces.

BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH (other, 2023)
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 o N,106.996855 o W) and Kettle Ponds (38.942005 o N,106.973006 o W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 19th, 2022. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. The file name convention for the Level 3 NetCDF files is as follows. uas_L3_yyyymmdd_hhmmss_vx.x.nc where L3 = Level 3 data yyyymmdd = year,month,day hhmmss = hour,minute,second x.x = version number Time is the flight start time in UTC. Version number description is provided in the NetCDF global attributes. Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour.

Potentilla demographic and environmental data for Rainbow Meadows, Elk Meadows, and Cabin Clearing, 2018 - 2020. - Kelly A Carscadden (other, 2023)
To understand parent-hybrid dynamics in cinquefoil (Potentilla) species in the Colorado Rocky Mountains, I am estimating environmental overlap among parents and hybrids, interbreeding among parents and hybrids, and hybrid population growth in multiple natural populations at NWT and (not included here) the Rocky Mountain Biological Laboratory (RMBL). This data was collected to test broad hypotheses about hybrid-parent dynamics in changing montane environments.

Per-Sample TPM with Annotations (other, 2023)
Metatranscriptomic data generated from soil collected at the East River watershed, Crested Butte, CO. Normalized by TPM. Annotations included, and labeled with genome ID for all ORFs appearing on binned scaffolds. (Unbinned sequences labeled with UNK)

Herbarium-Derived Phenological Data in North America - Isaac Park; C. C. Davis; Tadeo Hernan Ramirez-Parada; S. J. Mazer; Sydne Record (other, 2023)
We present infrastructure for developing large-scale and long-term phenological datasets across multiple herbaria, as well as a sample dataset that has been acquired from the digital archives of 440 distinct herbaria across North America and further processed to evaluate phenological status. This dataset contains 2,319,672 specimen records of plants collected while reproductively active. These data have been modified to explicitly codify the observed phenological status of each specimen at the time of collection, and to remove specimens for which information essential to assessing their phenology or the corresponding climate conditions in the year and location of collection were missing. As different collectors have used distinct taxonomic schema over space and time in documenting the specimens being collected, these data were also rectified into a single unified taxonomic schema to ensure that consistent taxon names were used throughout the dataset. Further, this data has been united with long-term and annual climate conditions in the year and location of collection, as derived from PRISM climate data (https://www.prism.oregonstate.edu/). To date, this data includes 2,319,672 specimens across 25,429 plant taxa. However, this represents a living dataset that will continue to be updated as digitization efforts proceed and additional digital specimen records become available.

SnowEx20 Grand Mesa IOP Computed Tomography Snow Microstructure, Version 1 (other, 2023)

QA/QC of the East River, Colorado, discharge and geochemical time series datasets (Almont, BCC, and Pump House) to be used for modeling of hydrogeochemical balance. - Boris Faybishenko; P. M. Fox (other, 2023)
The following datasets were QA/QC-ed (Quality Assurance/Quality Control): 1. Brush Creek Confluence (BCC) discharge data (from Helen Malenda, USGS, Colorado School of Mines), which were calculated using the pressure transducer data and rating curves. The original 15 min time series data were presented as mean daily discharge. 2. Almont discharge data from United States Geological Survey (USGS). The original data were in 15 min time intervals, and were averaged to mean daily discharge time series. 3. Pump House discharge data as mean daily discharge (downloaded from the SFA portal). 4. BCC and Pump House chemistry data from SFA data portal and/or original spreadsheets provided by Roelof Versteeg. The following challenging QA/QC problems of the datasets were resolved: Missing data with the duration of gaps up to >1 month; Duplicated dates; Anomalies and outliers of discharge and concentrations; Time stamps of measurements of the discharge and concentrations are not aligned (hydrogeochemical balance calculations require the timestamps to be aligned). All QA/QC-ed datasets are given as csv files. The csv files were prepared using the xts files with multiple worksheets, which are also included in the data packages. Figures of the QA/QC-ed datasets are given in the jpeg and pdf formats. The QA/QC-ed datasets have been used to quantify discharge and chemical concentrations in river water in order to understand riverine exports of water and dissolved constituents in the East River watershed. These datasets served as a basis in the presentation given by P. Fox et al. at the 2021 Goldschmidt Conference.

Community-level flowering & fitness data across an elevational gradient, Rocky Mountain Biological Lab, 2021-2022 - Leah N. Veldhuisen (other, 2023)
We collected data at three sites in Washington Gulch near the Rocky Mountain Biological Laboratory (RMBL, Gothic, Colorado, USA) from June to August 2021 and 2022. RMBL is located in the East River valley of the West Elk mountains, approximately 10 kilometers from Crested Butte, Colorado. Study sites were located at 2815 m (38°53'50"N, 106°58'43"W), 3165 m (38°57'38"N, 107°01'53"W) and 3380 m (38°58'10"N, 107°01'53"W) in elevation. All sites are approximately 50 m 2 and have similar slope and aspect (Sloat et al. 2015). We randomly defined five 1.2 m x 1.2 m plots within each site. Within the plots, we marked all newly open flowering units once per week from early June to mid-August. We counted flowering units as either a single flower (most species), a flowering head (Asteraceae), or an umbel (Apiaceae). We only counted flowering units once as they opened, so counts were not cumulative. We quantified fitness as successful fruit and seed development at the end of the season. When flowers began to produce fruits, we counted the number of units with fruit, the number of developed fruits produced by each flowering unit, and the number of seeds per fruit. We also recorded the week of flowering for each fruit. We could not count fruits and seeds for all species because they disappeared, were consumed too quickly, or did not produce seeds during our field season.

Water Level Data from Wells PLM1 and PLM6 for the East River Watershed, Colorado (ess_dive, 2023)
This dataset (Williams et al., 2020) contains the original un-QA/QC-ed water level data for PLM1 and PLM6 and has been obsoleted. The data contained within this dataset is not to be used. Refer to Faybishenko et al., 2022 (DOI: 10.15485/1866836) for the latest QA/QC-ed data available via ESS-DIVE.This data set contains water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location. These wells used to monitor subsurface water and carbon inventories and fluxes at the East River Watershed, Colorado, USA. Complete metadata information on the PLM1 and PLM6 wells are available in the related data package reference Varadharajan C, et al (2020). https://doi.org/10.15485/1660962.Data are reported in .csv files per well. The latitude and longitude of each location are given in a file called locations.csv. These data are used for determining the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistry can be used to estimate rates of solute export from the hillslope to the floodplain and river.These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales.

Downscaled precipitation and mean air temperature datasets; East-Taylor subbasin; 2008-2019; daily temporal resolution; 400 m spatial resolution. - J. B. Brown; Dipankar Dwivedi; Carl I. Steefel; Utkarsh Mital (other, 2023)
This dataset provides gridded meteorological forcing data (specifically, daily precipitation and daily mean air temperature). The dataset has been generated by downscaling the Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset from a spatial resolution of 800 m to 400 m. The time period is 2008-2019, and the mapped area is East Taylor subbasin in Upper Colorado. The data are in the form of NetCDF files, arranged by year. Compared to the PRISM dataset, the dates of the downscaled dataset are shifted backwards by one (e.g., downscaled data for May 25 corresponds to PRISM data for May 26). This temporal shifting makes the meteorological forcing correspond more closely to the prescribed date. The NetCDF format is a standard raster format that can be read using any Geographic Information System (GIS) software; plenty of modules exist in popular scripting languages (such as Python, R and Matlab) that can also be used to read NetCDF files. East_Taylor_Tavg_PRISM400 corresponds to mean air temperature. East_Taylor_Precip_PRISM400.zip corresponds to precipitation. Dataset update on March 9, 2022: Uploaded new data files that are identical to the earlier files but are now in the NetCDF format, arranged by year. Earlier, the files were in the GeoTiff format, arranged by date.

Groundwater and Surface Water Flow (GSFLOW) model files for the East River, Colorado (ess_dive, 2023)
The data package contains model input files and executables for the East River, Colorado (750 km2) located in the headwaters of the Upper Colorado River Basin. The code applied is the U.S. Geological Survey (USGS) Groundwater and Surface Water Flow (GSFLOW) model. A Readme.txt file provides instructions on how to download all files and and execute mode runs. The model contains a 100-m grid resolution to represent topographic complexity and a daily timestep accounts for energy and water partitioning between the snowpack, vegetation, soil zone and bedrock. The land surface model is dynamically linked to a three-dimensional groundwater flow model that allows for streamflow gaining and losing conditions. The groundwater model accounts for nine stratigraphic units and extends 400 m below land surface. Using this modeling framework we explore historical water budgets (water year 1987-2022) and the influence of seasonal warming and associated mechanisms driving groundwater declines and streamflow loss.

Aerobic respiration controls on shale weathering, Geochimica et Cosmochimica Acta, 2023: Dataset - Zhi Li; Zhi Li; Lucien Stolze; Zhi Li; Lucien Stolze; Sergio Carrero; Lucien Stolze; Benjamin Gilbert; Sergio Carrero; Bhavna Arora; Benjamin Gilbert; Bhavna Arora; et al. (ess_dive, 2023)
This data package was generated in order to support the development of a deep-time weathering model and to assess the coupling between shale weathering and aerobic respiration in the paper “Aerobic respiration controls on shale weathering” by Stolze et al., Geochimica et Cosmochimica Acta (2023). The package contains two csv files providing the average CO2(g) concentration profiles [ppm] and mineral concentration profiles [wt%], respectively. The CO2(g) concentration profiles were measured in the vicinity of the monitoring well PLM2 between January 2018 and April 2019. The gas samples were collected in the unsaturated zone to a depth of 1.52 m. The mineral concentration profiles were determined by X-Ray diffraction (XRD). The XRD measurements were performed on sub-core samples collected in the monitoring well PLM3 down to a depth of 7.01 m. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. Update on 2024-05-28: Revised versions of the CSV data files (CO2_data_GCA_Stolze_et_al_2023.csv and XRD_data_GCA_Stolze_et_al_2023.csv) were made to apply ESS-DIVE's CSV reporting format guidelines. Updated versions of the File Level Metadata (v2_20240528_flmd.csv) and Data Dictionary (v2_20240528_dd.csv) files were updated to reflect the changes made to the CSV files.

Aerobic respiration controls on shale weathering, Geochimica et Cosmochimica Acta, 2023: Dataset (ess_dive, 2023)
This data package was generated in order to support the development of a deep-time weathering model and to assess the coupling between shale weathering and aerobic respiration in the paper “Aerobic respiration controls on shale weathering” by Stolze et al., Geochimica et Cosmochimica Acta (2023). The package contains two csv files providing the average CO2(g) concentration profiles [ppm] and mineral concentration profiles [wt%], respectively. The CO2(g) concentration profiles were measured in the vicinity of the monitoring well PLM2 between January 2018 and April 2019. The gas samples were collected in the unsaturated zone to a depth of 1.52 m. The mineral concentration profiles were determined by X-Ray diffraction (XRD). The XRD measurements were performed on sub-core samples collected in the monitoring well PLM3 down to a depth of 7.01 m. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. Update on 2024-05-28: Revised versions of the CSV data files (CO2_data_GCA_Stolze_et_al_2023.csv and XRD_data_GCA_Stolze_et_al_2023.csv) were made to apply ESS-DIVE's CSV reporting format guidelines. Updated versions of the File Level Metadata (v2_20240528_flmd.csv) and Data Dictionary (v2_20240528_dd.csv) files were updated to reflect the changes made to the CSV files.

Dissolved Oxygen and Temperature Data from the Hyporheic Zone of the East River Watershed July 2017 to October 2018 - Ruby N. Ghosh; Charles McIntire; Michael Freeman; Terry Ball (ess_dive, 2023)
Dissolved oxygen (DO) is critical for aquatic ecosystems. Our focus is on the long-term DO dynamics in hyporheic zone of rivers, which are a function of both transport (hydrologic exchange between river and hyporheic zone) and uptake by biogeochemical reactions or respiration. The study site is the alpine East River watershed in Colorado, USA, meander A downstream from the pump house. Opti O2 probes were deployed in the water column and directly within the river-bed at 10, 20, and 35 cm depth (38°55'23.38"N, 106°57'4.21"W, 9046.88m) to monitor DO and temperature. A continuous data stream from July 24, 2017 to Oct 24, 2018 was autonomously telemetered to the cloud. This 14-month DO and temperature time series were obtained without any servicing for maintenance or data downloads; additionally the ability to remotely verify probe performance during field deployment was essential to confirm data validity during winter freeze-in and hydrological/weather events, such as spring melt and summer monsoons. We investigate the variations in dissolved oxygen dynamics of this snow-pack dominated watershed during a comparatively low flow water year (2018) and a relatively normal water year (2017), enabled by distinctive, in-situ, high frequency (∆t = 5min) sensors that provided a continuous time-series from the undisturbed study site over multiple seasons.

Water Level Data from Wells PLM1 and PLM6 for the East River Watershed, Colorado - Tetsu Tokunaga; Roelof Versteeg; Roelof Versteeg; Tetsu Tokunaga; Roelof Versteeg; Tetsu Tokunaga; Roelof Versteeg; Tetsu Tokunaga; Tetsu Tokunaga; Roelof Versteeg; Wenming Dong; Wenming Dong; et al. (ess_dive, 2023)
This dataset (Williams et al., 2020) contains the original un-QA/QC-ed water level data for PLM1 and PLM6 and has been obsoleted. The data contained within this dataset is not to be used. Refer to Faybishenko et al., 2022 (DOI: 10.15485/1866836) for the latest QA/QC-ed data available via ESS-DIVE.This data set contains water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location. These wells used to monitor subsurface water and carbon inventories and fluxes at the East River Watershed, Colorado, USA. Complete metadata information on the PLM1 and PLM6 wells are available in the related data package reference Varadharajan C, et al (2020). https://doi.org/10.15485/1660962.Data are reported in .csv files per well. The latitude and longitude of each location are given in a file called locations.csv. These data are used for determining the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistry can be used to estimate rates of solute export from the hillslope to the floodplain and river.These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales.

Water Level Data from Wells PLM1 and PLM6 for the East River Watershed, Colorado (ess_dive, 2023)
This dataset (Williams et al., 2020) contains the original un-QA/QC-ed water level data for PLM1 and PLM6 and has been obsoleted. The data contained within this dataset is not to be used. Refer to Faybishenko et al., 2022 (DOI: 10.15485/1866836) for the latest QA/QC-ed data available via ESS-DIVE.This data set contains water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location. These wells used to monitor subsurface water and carbon inventories and fluxes at the East River Watershed, Colorado, USA. Complete metadata information on the PLM1 and PLM6 wells are available in the related data package reference Varadharajan C, et al (2020). https://doi.org/10.15485/1660962.Data are reported in .csv files per well. The latitude and longitude of each location are given in a file called locations.csv. These data are used for determining the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistry can be used to estimate rates of solute export from the hillslope to the floodplain and river.These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales.

Snowpack_Snodgrass Elevation transect_Winters (2021-22, 2022-23)_AS.csv (other, 2023)
The data set “Snowpack_Snodgrass_AS.csv” consists of the snowpack data measured by the PhenoCams at each site up Snodgrass Mtn, Crested Butte, Co. To calculate snow depth each fall, we placed a 3-meter PVC pipe in the ground in the field of view of the PhenoCam. We spray painted 25 cm alternating increments of red and white, with each meter labeled with a black stripe. Using the downloaded PhenoCam images, a calibration curve of pole height to pixels was calculated. The amount of non-snow-covered pole pixels for each image was quantified, and the snow depth was calculated using the calibration curve. Sites are abbreviated versions of site names and phenocam numbers, i.e., hc7= high_conifer_Snodgrass_7.

Radon Isotopes and Stable Water Isotopes from Coal Creek Watershed, Colorado (2021) - William Payton Gardner; Wesley M. Brown; Nicholas Thiros; J. N. Christensen; Curtis A. Beutler; K. H. Williams (ess_dive, 2023)
The radon isotope and stable water isotope data for Coal Creek Watershed, Colorado, consists of d2H, d18O, and 222Rn values from samples collected at 8 stream location along Coal Creek, samples from 7 groundwater springs within the watershed, and precipitation isotope samples collected by Next Generation Water Observing System (NGWOS) from a collector within the watershed. All stream and spring samples were collected between June and October, 2021, and precipitation isotope samples were collected between November 2020 and September 2021. These data were collected to evaluate how groundwater contributions to Coal Creek originating from a fractured hillslope and alluvial fan respond to summer monsoon rains and seasonal drying. Understanding of groundwater-surface water interactions in montane systems in critical for the future of water availability in the Western US as groundwater contributions are expected to become more important for sustaining summer stream flows. This data package contains: (1) a csv of all radon samples; (2) a csv of all stream and spring isotope samples; (3) a csv of precipitation isotope samples; and (4) a csv of locations for each sampling site. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

Snow-free Growing Degree-days 0-60 Days Post Snow Mean (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of accumulated snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated in the 60 days after the disappearance of seasonal snowpack.  In the absence of snow, the accumulation period started on day-of-year 60 (March 1st in a non-leap year). To compute SFGDD, daily estimates of temperatures above a base of 5 degrees Celsius (a minimum growth threshold for many plants) were summed across all days estimated to be free of snow cover. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. This map represents the mean of annual values computed from 2002 to 2021. Estimates from individual years are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D049')  metadata <- sdp_get_metadata('R4D049')    For more information about rSDP, visit the package homepage.

BST/NOAA PSL Level 2 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH - Darren Jackson; Eryan Dai; Maciej Stachura; Jack Elston; Maciej Stachura; Gijs de Boer; Darren Jackson; Eryan Dai; Jack Elston; Janet Intrieri; Gijs de Boer; Janet Intrieri; et al. (other, 2023)
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies LLC. Each zip file contains a set of four Level 2 NetCDF files which provides the highest spatial resolution available for each of four products for a given flight location. With the Level 2 data, each flight location and variable can have different spatial resolutions depending on the sensor type, retrieval algorithm, and flight altitude. The file name convention for the zip files is as follows. uas_L2_yyyymmdd_hhmmss_vX.X.zip where L2 = Level 2 data yyyymmdd = year,month,day hhmmss = hour,minute,second vX.X = version number Time is the flight start time in UTC. The NetCDF file format contained in the zip files has a similar format to the zip files with convention uas_ _L2_yyyymmdd_hhmmss.nc where = vsm, dem, ndvi, or stmp vsm = volumetric soil moisture dem = digital elevation ndvi = normalized difference vegetation index stmp = surface temperature Note that each flight location using the E2 aerial platform required two flights so starting flight times for the soil moisture NetCDF files are different from the other three products. November 2023 update: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data. December 2023 update: Version 2.1 updated soil moisture data with a wet bias in v2.0 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.

Total metals, carbon, nitrogen & anion concentration data; Slate River & East River floodplains, Crested Butte, CO; May 2022-October 2022 - Samuel Pierce (ess_dive, 2023)
This data package includes processed and undiluted measurements for metal, total carbon, total nitrogen, and anion concentrations from pore water (groundwater) and surface water samples from the Slate River and East River floodplains of Crested Butte, CO, focus field sites for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? Samples were collected between May and October of 2022. These measurements were all recorded at the Arizona Laboratory for Emerging Contaminants (ALEC) at the University of Arizona located in Tucson, AZ. Groundwater samples were extracted from a network of installed rhizon (Rhizosphere Research Products, part no. 19.60.21F, 0.6 micrometer mesh size) and piezometer wells within the river floodplain. All water samples were shaded from sun exposure during extraction from the subsurface and preserved at 4C until measured at ALEC. Analysis by ICP-MS (metals): Measurements for total metals were made on the Agilent 7700x ICP-MS (for total metals) – Agilent Technologies, Santa Clara, CA. The analytical QA/QC protocol was adapted from US EPA Method 200.8 for analysis by ICP-MS. Calibration standards were prepared from multi-element stock solutions (SPEX Certiprep, Metuchen, NJ). Calibration curves include at least 7 points with correlation coefficients > 0.995. The QC protocol includes a continuing calibration blank (CCB), a continuing calibration verification (CCV) solution and at least one quality control sample (QCS) to be analyzed just after calibration and again after every 12 samples and at the completion of the run. The QCS solutions are from an independent source, such as NIST SRM 1643e - Trace elements in water, or QCS solutions from High Purity Standards (Charleston, SC).  Acceptable QC responses must be between 90 and 110% of the certified value. Lastly, a suitable internal standard (usually Rh, In, Ga or Ge) is added using on-line addition into the sample line and mixing tee. Analysis by Shimadzu TOC-L (TOC/TN): The TOC-L system is a combustion technique where liquid samples are injected and combusted into CO2 for carbon detection by non-dispersive infrared (NDIR) and NO for detection by chemiluminescence. A calibration curve using five standard solutions between 0.1 and 7 ppm for carbon and 0.05 and 3.5 ppm for nitrogen is made for each type of measurement with a linearity >0.99. All samples, standards, and QC’s are prepared in 24mL scintillation vials that have been baked for 4hrs at 475 Cº and made using RO water (18.2mΩ). QC’s include a calibration blank check (CCB), continuing calibration check (CCC), and a certified reference material check (CRM). All QC’s are within ±10% error and are run before and after each batch of samples. Samples are diluted and rerun if any measurement concentrations are above the highest standard. Analysis by Ion Chromatography (Anions): The instrument used is the Thermo Scientific Dionex ICS-6000 using AS+AG22 column set for anion analysis with sodium carbonate eluent. A calibration curve using five standard solutions between 5 and 250 umol/L is made with a linearity >0.99. Standards and QC’s are prepared in 15mL polypropylene conical tubes, pipetted along with the samples into 1.5mL polypropylene vials. Dilutions are made using RO water (18.2mΩ). QC’s include a calibration blank check (CCB), continuing calibration check (CCC), and a certified reference material check (CRM). All QC’s are within ±10% error and are run before and after each batch of samples. Samples are diluted and rerun if any measurement concentrations are above the highest standard. All files are in csv format.

Snowpack Proportional Reduction in Late Season Freezing Degree Days (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of the influence of fall snow on late season freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. Snow-free freezing degree-days were accumulated from day-of-year 195 to day-of-year 305 (July 15th to November 1st  in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates than freezing degree days (FDD). This map represents the proportional reduction in late season freezing potential from 2002 to 2021 using the formula (FDD - SFFDD) / FDD.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D053')  metadata <- sdp_get_metadata('R4D053')    For more information about rSDP, visit the package homepage.

Maps of multiple future threats and stable areas for Gunnison sage-grouse habitats across three scenarios (2016-2070) - Nathan D Van Van Schmidt (other, 2023)
This dataset contains a series of maps of projected threats and current state of habitats for the threatened Gunnison sage-grouse (Centrocercus minimus). The maps are 30-m spatially explicit projections of current habitats (2016) and future landscape change (by 2070) for the full range of the species’ seven extant populations, which cover diverse ecoregions of the imperiled sagebrush biome of the western United States. Risk maps assessed projected changes caused by climate change-driven shifts in sagebrush habitats, mesic habitats, pinyon-juniper encroachment, and invasive annual grasses, as well as potentially synergistic risk of development and wildfire. We used a scenario-based risk assessment framework based on the U.S. Fish & Wildlife Service’s Species Status Assessment for Gunnison Sage-grouse. This aligned individual projections for each threat into three combinatory scenarios of change: Pessimistic (P; hot and dry future climate, high development), Continuation (C; moderately hot, moderate development), and Optimistic (O; warm and wet, low development). An additional set of habitat Vulnerability (V) maps integrates current and future datasets across scenarios to map areas that are currently partially degraded, fully degraded, sometimes at-risk across future scenarios, always at-risk, or stable both currently and in the future. Lastly, Overall species vulnerability maps describe cumulative vulnerability to all six threats as a mean weighted by relevance to Gunnison sage-grouse habitat selection and the sum number of threats that were at-risk in future projections.

Snow-free Freezing Degree-days Late Season Standard Deviation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in accumulated fall snow-free freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free freezing degree-days were accumulated from day-of-year 197 to day-of-year 305 (July 16th to November 1st in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. This map represents the standard deviation of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D040')  metadata <- sdp_get_metadata('R4D040')    For more information about rSDP, visit the package homepage.

River and hyporheic zone water geochemical data from OsmoSamplers, East River Pumphouse, Colorado, Nov 2017-Sept 2018 - Fredrick Colwell; Andrew Thurber; L. L. Lapham; Jessica Buser-Young (other, 2023)
This data package contains geochemical and river water temperature data from OsmoSamplers deployed at East River, Colorado pump house. There are three data files. The first one contains sulfate (SO4) and chloride (Cl) concentrations from riverwater and hyporheic zone (sediment water interface (SWI), 4 cm below seafloor (cmbsf), and 12 (cmbsf)). The second file contains methane concentrations, methane stable carbon isotope ratios and water isotopes from hyporheic zone porewaters. And the third is from a temperature logger during the deployment. Files are provided as .xls or .csv. This research was performed as part of the SBR funded project “Quantifying Subsurface Biogeochemical Variability in a High Altitude Watershed During Winter Isolation” DE-SC0018328. The novel part of this dataset is that we quantify these geochemical parameters throughout the winter isolation period, which is difficult to measure, as well as within the hyporheic zone. The data files were updated 3/1/2021 because the column label for the water isotopes was incorrect. Also, the csv files were also updated since they were not formatted correctly.

Snow-free Growing Degree-days Late Season Standard Deviation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in accumulated fall snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum air temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated from day-of-year 197 to day-of-year 305 (July 16th to November 1st in a non-leap year). To compute growing degree days, daily estimates of temperatures above a base of 5 degrees Celcius (the minimum growth threshold for many plants) were summed across all days estimated to be free of snow. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of the energy available for biological processes in snow-dominated climates. This map represents the standard deviation of annual values computed from 2002 to 2021. Estimates for individual years are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D048')  metadata <- sdp_get_metadata('R4D048')    For more information about rSDP, visit the package homepage.

A pathogen's spatial range is not constrained by geographical features in the flax rust pathosystem - Juliana Jiranek; Keenan Duggal; Jessica L. Metcalf (other, 2023)
In this study, we performed several transect surveys over the course of the 2021 summer field season to assess potential ecogeographical range determinants for Lewis flax ( Linum lewisii ) and its pathogen, flax rust ( Melamspora lini ), in the area surrounding the Rocky Mountain Biological Laboratory in Gothic, Colorado. Additionally, we used generalized additive models to examine the effects of host population density and metapopulation structure on disease presence and prevalence.

Raw soil carbon dioxide, moisture, and temperature data from a mixed conifer and aspen forest stands in the East River Watershed of Colorado June 2011-December 2021. - Mariah Carbone (ess_dive, 2023)
These datasets contain raw data from adjacent mixed-conifer (Abies lasiocarpa and Picea engelmannii) and deciduous (Populus tremuloides) forest stands in the Elk Mountains at the Rocky Mountain Biological Laboratory in Gothic, Colorado (38.9592◦ N, longitude: 106.9898◦ W and elevation of 2880 m). Each site contains five soil carbon dioxide (CO2) gas probes and three soil temperature/moisture sensors connected to a data logger. Soil temperature, moisture, and CO2 (ppm) concentrations were recorded each hour. There are three attached CSV files; two CSV files are of the raw data collected in the respective forest types, conifer: "Carbone_conifer.csv" and deciduous: "Carbone_aspen.csv". The third file "Carbone_Headers.csv" is a shared headers file for both datasets. Basic data processing and QA/QC was done on the raw data sets. This research was performed to investigate the response of the soil CO2 to inter-annual and seasonal variability of moisture inputs in mountainous ecosystems.

Marmot capture history data and growing season length data - B. D. Gerber; L. S. Cordes; Kenneth B. Armitage; D. Z. Childs; J. G. A. Martin; Paul J. CaraDonna; Daniel T. Blumstein (other, 2023)
Seasonal environmental conditions shape the behavior and life history of virtually all organisms. Climate change is modifying these seasonal environmental conditions, which threatens to disrupt population dynamics. It is conceivable that climatic changes may be beneficial in one season but result in detrimental conditions in another because life-history strategies vary between these time periods. We analyzed the temporal trends in seasonal survival of yellow-bellied marmots ( Marmota flaviventer ) and explored the environmental drivers using a 40-y dataset from the Colorado Rocky Mountains (USA). Trends in survival revealed divergent seasonal patterns, which were similar across age-classes. Marmot survival declined during winter but generally increased during summer. Interestingly, different environmental factors appeared to drive survival trends across age-classes. Winter survival was largely driven by conditions during the preceding summer and the effect of continued climate change was likely to be mainly negative, whereas the likely outcome of continued climate change on summer survival was generally positive. This study illustrates that seasonal demographic responses need disentangling to accurately forecast the impacts of climate change on animal population dynamics. We were able to impute body mass for each individual twice during each year following their first capture using a similar approach to Ozgul et al. (2010) (for more details on the modeling procedure see SI Appendix within the main paper). Body mass measurements were log-transformed.

Data for: Pollinator and habitat-mediated selection as potential contributors to ecological speciation in two closely related species - Madison Crowell; Diane R. Campbell; J. M. Powers (other, 2023)
In ecological speciation, incipient species diverge due to natural selection that is ecologically based. In flowering plants, different pollinators could mediate that selection (pollinator-mediated divergent selection) or other features of the environment that differ between habitats of two species could do so (environment-mediated divergent selection). Although these mechanisms are well understood, they have received little rigorous testing, as few studies of divergent selection across sites of closely related species include both floral traits that influence pollination and vegetative traits that influence survival. This study employed common gardens in sites of the two parental species and a hybrid site, each containing advanced generation hybrids along with the parental species, to test these forms of ecological speciation in plants of the genus Ipomopsis . Three vegetative traits (specific leaf area, leaf trichomes, and photosynthetic water-use efficiency) and five floral traits (corolla length and width, anther insertion, petal color, nectar production) were analyzed for impacts on fitness components (survival to flowering and seeds per flower, respectively). These traits exhibited strong clines across the elevational gradient in the hybrid zone, with narrower clines in theory reflecting stronger selection or higher genetic variance. Plants with long corollas and inserted anthers had higher seeds per flower at the I. tenuituba site, whereas selection favored the reverse condition at the I. aggregata site, a signature of divergent selection. In contrast, no divergent selection due to variation in survival was detected on any vegetative trait. Selection within the hybrid zone most closely resembled selection within the I. aggregata site. Across traits, the strength of divergent selection was not significantly correlated with width of the cline, which was better predicted by evolvability (standardized genetic variance). These results support the role of pollinator-mediated divergent selection in ecological speciation and illustrate the importance of genetic variance in determining divergence across hybrid zones.

Pollinator visitation on Na-enriched plants in a subalpine meadow - Thiago Gonçalves Souza; Ethan VanValkenburg; Nathan J. Sanders; Paul J. CaraDonna (other, 2023)
Many plants have evolved nutrient rewards to attract pollinators to flowers, but most research has focused on the sugar content of floral nectar resources. Concentrations of sodium in floral nectar (a micronutrient in low concentrations in nectar) can vary substantially both among and within co-occurring species. Sodium concentrations in floral nectar might play an important and underappreciated role in plant-pollinator interactions, especially because many animals, including pollinators, are sodium-limited in nature. Yet, the consequences of variation in sodium concentrations in floral nectar have gone largely unexplored. Here, we investigate whether enriching floral nectar with sodium influences the composition, diversity, and abundance of pollinator interactions. We experimentally enriched sodium concentrations in four plant species in a subalpine meadow in Colorado, USA. We found that flowers with sodium-enriched nectar received more visits from a greater diversity of pollinator visitors throughout the season. Each pollinator species foraged more frequently on flowers enriched with sodium and showed evidence of other changes to pollinator foraging behavior, including greater dietary evenness. These findings are consistent with the 'salty nectar hypothesis,' providing evidence for the importance of sodium limitation in pollinators and suggesting that even small nectar constituents can shape plant-pollinator interactions.

Atmospheric Surface Flux Station #30 measurements (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), September 2021-July 2023 (2023)
Raw (Level 1) measurements from the Atmospheric Surface Flux Station #30 (ASFS-30) deployed at the Kettle Ponds Annex site (38°56.3686' N, 106°58.1781' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from September 2021 through July 2023. The ASFS measured variables comprising the surface energy budget, surface momentum flux, near-surface meteorology, and soil properties.  These measurements are included in three netCDF files per day. The "slow" files are for 1-minute averages of the measured variables, including near-surface meteorology, surface height change (due to accumulating/ablating snow), and upwelling and downwelling shortwave and longwave radiative fluxes. The "fast" files are for data at 20 Hz resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with technical corrections applied but no quality assurance. A detailed documentation of the measurement will be provided in a forthcoming publication. For scientific purposes, we recommend using the Level 2 data files when they are available, as these will include full quality control as well as higher-order derived products.

Facilitation strength across environmental and beneficiary trait gradients in stream communities - Benjamin Tumolo (other, 2023)
Ecosystem engineers modify habitats in ways that facilitate other community members by ameliorating harsh conditions. The strength of such facilitation is predicted to be influenced by both beneficiary traits and abiotic context. One key trait of animals that could control the strength of facilitation is beneficiary body size because it should determine how beneficiaries fit within and exploit stress ameliorating habitat modifications. However, few studies have measured how beneficiary body size relates to facilitation strength along environmental gradients. We examined how the strength of facilitation by net-spinning caddisflies on invertebrate communities in streams varied along an elevation gradient and based on traits of the invertebrate beneficiaries. We measured whether use of silk retreats as habitat concentrated invertebrate density and biomass compared to surrounding rock-surface habitat and whether the use of retreat habitat varied across body sizes of community members along the gradient. We found that retreats substantially concentrated the densities of a diversity of taxa including eight different Orders, and this effect was greatest at high elevations. Caddisfly retreats also concentrated invertebrate biomass more as elevation increased. Body size of invertebrates inhabiting retreats was lower than that of surrounding rock habitats at low elevation sites, however body size between retreats and rocks converged at higher elevation sites. Additionally, the body size of invertebrates found in retreats varied within and across taxa. Specifically, caddisfly retreats functioned as a potential nursery for taxa with large maximal body sizes. However, the patterns of this taxa-specific nursery effect were not influenced by elevation unlike the patterns observed based on community level body size.Collectively, our results indicate that invertebrates use retreats in earlier life stages or when they are smaller in body size independent of life stage. Furthermore, our analysis suggests that facilitation strength intensifies as elevation increases within stream invertebrate communities. Further consideration of how trait variation and environmental gradients interact to determine the strength and direction of biotic interactions will be important as species ranges and environmental conditions continue to shift.

Cation Data for the East River Watershed, Colorado (2014-2021) (ess_dive, 2023)
This data package contains mean values for cation concentration for water samples taken from the East River Watershed in Colorado. Inductively coupled plasma mass spectrometry (ICP-MS) has been used to measure the concentrations of 37 elements of interest simultaneously for the East River Watershed, Colorado groundwater and surface water samples to inform insights on the biogeochemistry processes within the watershed. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States. Inductively coupled plasma mass spectrometry (ICP-MS) has been used to measure the concentrations of 37 elements of interest simultaneously for Rifle and East River Watershed, Colorado groundwater and surface water samples. The instrumentation, Elan DRC II, PerkinElmer SCIEX, automatically switches among the three models necessary to analyze all 37 elements. These 37 elements include: (1) Lithium (Li), Beryllium (Be), Boron (B), Soldium (Na), Magnesium (Mg), Aluminium (Al), Silicon (Si), Phosphorus (P), Titanium (Ti), Cobalt (Co), Nickel (Ni), Copper (Cu), Zinc (Zn), Germanium (Ge), Arsenic (As), Rubidium (Rb), Strontium (Sr), Zirconium (Zr), Molybdenum (Mo), Silver (Ag), Cadmium (Cd), Tin (Sn), Antimony (Sb), Caesium (Cs), Barium (Ba), Europium (Eu), Lead (Pb), Thorium (Th), Uranium (U) using santadard model, argon Ar as reaction gas, (2) Potassium (K), Calcium (Ca), Vanadium (V), Chromium (Cr), Manganese (Mn), Iron (Fe) using dynamic reaction cell (DRC) model, ammonia NH3 as reaction gas, and (3) Phosphorus (P) and Selenium (Se) using DRC model, oxygen O2 as reaction gas. Note for the samples with higher concentrations of chloride (Cl-), asenic (As) concentrations were analysed with DRC model (oxygen O2 as reaction gas) to avoid the interference of chloride. All samples were prepared/diluted with 2% (v/v) ultrapure nitric acid in Milli-Q water (18.2 mega ohm-cm), and analyzed under a rigorous quality assurance and quality control (QA/QC) process. This data package contains (1) a zip file (cation_data_2014-2021.zip) containing a total of 4,517 files: 4,516 data files of cation data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location and a locations.csv (1 file) with latitude and longitude for each location; (2) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and (3) a data dictionary (dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type. There are a total of 99 locations containing isotope data. Update on 4/11/2021, Added Detemination of Method Detection Limits (MDLs) for ICP-MS document, which can be accessed as a PDF or with Microsoft Word. Update on 6/10/2022: versioned updates to this dataset was made along with these changes: (1) updated cation data for all locations up to 2021-12-31, (2) removal of units from column headers in datafiles, (3) added row underneath headers to contain units of variables, (4) removed suffix and prefix on two variables (“aqberylliumion_asberyllium” and “aqlithiumion_aslithium”), (5) added -9999 for empty numerical cells, and (6) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format. Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv).

Water Level Data from Wells PLM1 and PLM6 for the East River Watershed, Colorado (ess_dive, 2023)
This dataset (Williams et al., 2020) contains the original un-QA/QC-ed water level data for PLM1 and PLM6 and has been obsoleted. The data contained within this dataset is not to be used. Refer to Faybishenko et al., 2022 (DOI: 10.15485/1866836) for the latest QA/QC-ed data available via ESS-DIVE.This data set contains water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location. These wells used to monitor subsurface water and carbon inventories and fluxes at the East River Watershed, Colorado, USA. Complete metadata information on the PLM1 and PLM6 wells are available in the related data package reference Varadharajan C, et al (2020). https://doi.org/10.15485/1660962.Data are reported in .csv files per well. The latitude and longitude of each location are given in a file called locations.csv. These data are used for determining the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistry can be used to estimate rates of solute export from the hillslope to the floodplain and river.These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales.

Meteorological and hydrological parameters for 17 locations of meteorological stations of the East River Watershed - Bhavna Arora; Dipankar Dwivedi; Boris Faybishenko; E. L. Brodie (ess_dive, 2023)
The Data Package includes a set of csv files of input meteorological parameters for locations of 17 meteorological stations within the East River watershed. These meteorological datasets were downloaded from the (1) PRISM database--monthly precipitation, air temperature (minimum, mean, and maximum), vapor pressure deficit (minimum and maximum), and dewpoint temperature, and (2) NCEP/NCAR Reanalysis database – wind database. The datasets were used for calculations of the Potential Evapotranspiration (ETo), Actual Evapotranspiration (ET), Standard Precipitation Index (SPI) , Standard Evapotranspiration-Precipitation Index (SPEI) for the period from 1966 to 2021. The main research questions addressed are: the evaluation of the long-term temporal trends of climatic parameters, hierarchical clustering, and areal mapping/zonation of the East River watershed. Calculations were conducted in the Rstudio environment. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. The input datasets were downloaded from (a) PRISM database (the Northwest Alliance for Computational Science and Engineering at the Oregon State University), and (b) NCEP/NCAR Reanalysis database.

Atmospheric Surface Flux Station #50 measurements (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), October 2021-June 2023 (2023)
Raw (Level 1) measurements from the Atmospheric Surface Flux Station #50 (ASFS-50) deployed at the Avery Picnic site (38°58.3455' N, 106°59.8113' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from October 2021 through June 2023. The ASFS measured variables comprising the surface energy budget, surface momentum flux, near-surface meteorology, and soil properties.  These measurements are included in three netCDF files per day. The "slow" files are for 1-minute averages of the measured variables, including near-surface meteorology, surface height change (due to accumulating/ablating snow), and upwelling and downwelling shortwave and longwave radiative fluxes. The "fast" files are for data at 20 Hz resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with technical corrections applied but no quality assurance. A detailed documentation of the measurement will be provided in a forthcoming publication. For scientific purposes, we recommend using the Level 2 data files when they are available, as these will include full quality control as well as higher-order derived products.

Potentilla flowering phenology for Cabin Clearing, Elk Meadows and Rainbow Meadows, 2019. - Kelly A Carscadden (other, 2023)
To understand parent-hybrid dynamics in cinquefoil (Potentilla) species in the Colorado Rocky Mountains, I am estimating environmental overlap among parents and hybrids, interbreeding among parents and hybrids, and hybrid population growth in multiple natural populations at NWT and (not included here) the Rocky Mountain Biological Laboratory (RMBL). Since 2018, I have been monitoring populations at Rainbow Meadows Lower, Rainbow Meadows Upper, Elk Meadows South, and Cabin Clearing - sites that vary in cinquefoil composition. In 2019, my team and I recorded reproductive phenophases (e.g., vegetative, buds, flowering, setting seed) on tagged plants in June, July, and August. When plants began flowering, we estimated floral counts. Characterizing flowering phenology of parent and hybrid species in each site sheds light on the potential for ongoing interbreeding in different environments. NWT phenological data is shared here, and soil moisture and trait data from demographic monitoring are provided separately. This data was collected to test broad hypotheses about hybrid-parent dynamics in changing montane environments.

Demographic consequences of changes in environmental periodicity - Daniel T. Blumstein; Maria Paniw; Eva Conquet; Kenneth B. Armitage; J. G. A. Martin (other, 2022)
The fate of natural populations is mediated by complex interactions among vital rates, which can vary within and among years. While the effects of random, among-year variation in vital rates have been studied extensively, relatively little is known about how periodic, non-random variation in vital rates affects populations. This knowledge gap is potentially alarming as global environmental change is projected to alter common periodic variations, such as seasonality. We investigated the effects of changes in vital-rate periodicity on populations of three species representing different forms of adaptation to periodic environments: the yellow-bellied marmot (Marmota flaviventer), adapted to strong seasonality in snowfall; the meerkat (Suricata suricatta), adapted to inter-annual stochasticity as well as seasonal patterns in rainfall; and the dewy pine (Drosophyllum lusitanicum), adapted to fire regimes and periodic post-fire habitat succession. To assess how changes in periodicity affect population growth, we parameterized periodic matrix population models and projected population dynamics under different scenarios of perturbations in the strength of vital-rate periodicity. We assessed the effects of such perturbations on various metrics describing population dynamics, including the stochastic growth rate, log λS. Overall, perturbing the strength of periodicity had strong effects on population dynamics in all three study species. For the marmots, log λS decreased with increased seasonal differences in adult survival. For the meerkats, density dependence buffered the effects of perturbations of periodicity on log λS. Finally, dewy pines were negatively affected by changes in natural post-fire succession under stochastic or periodic fire regimes with fires occurring every 30 years, but were buffered by density dependence from such changes under presumed more frequent fires or large-scale disturbances. We show that changes in the strength of vital-rate periodicity can have diverse but strong effects on population dynamics across different life histories. Populations buffered from inter-annual vital-rate variation can be affected substantially by changes in environmentally-driven vital-rate periodic patterns; however, the effects of such changes can be masked in analyses focusing on inter-annual variation. As most ecosystems are affected by periodic variations in the environment such as seasonality, assessing their contributions to population viability for future global-change research is crucial.

Integrated hydrological model results for Lower Triangle Region in East River Watershed, Colorado, WYs 2016 and 2017 - Ilhan Özgen-Xian; Utkarsh Mital; Craig Ulrich; Qina Yan; Zexuan Xu; Ralf Loritz; Sergi Molins; Ilhan Özgen-Xian; Carl I. Steefel; Sergi Molins; Zexuan Xu; Utkarsh Mital; et al. (other, 2022)
Summary This data package contains numerical simulation results of integrated hydrology in Lower Triangle Region in East River Watershed, Colorado. The system is forced with DAYMET precipitation and climate data of the region for the water years 2016 and 2017. The results are computed on triangular multi-resolution meshes with resolutions ranging from 10 meter to 80 meter. The purpose of the data is to assess the influence of surface-subsurface exchange on distributed and aggregated hydrological response. Material and Methods Data has been generated by the Advanced Terrestrial Simulator (ATS) v1.0. The output format of ATS for spatially distributed data is HDF5 and can be viewed, for example, using VisIt or ParaView. The format of point data is plain text. README content The uploaded files have been created using the unix split(1) command to limit the size of each individual package. Files can be merged under a unix system through: $ cat OZGEN_ETAL_2022.zip.partaa OZGEN_ETAL_2022.zip.partab OZGEN_ETAL_2022.zip.partac > output.zip Unzip via $ unzip output.zip or using a graphical environment.

Integrated hydrological model results for Lower Triangle Region in East River Watershed, Colorado, WYs 2016 and 2017 (other, 2022)
Summary This data package contains numerical simulation results of integrated hydrology in Lower Triangle Region in East River Watershed, Colorado. The system is forced with DAYMET precipitation and climate data of the region for the water years 2016 and 2017. The results are computed on triangular multi-resolution meshes with resolutions ranging from 10 meter to 80 meter. The purpose of the data is to assess the influence of surface-subsurface exchange on distributed and aggregated hydrological response. Material and Methods Data has been generated by the Advanced Terrestrial Simulator (ATS) v1.0. The output format of ATS for spatially distributed data is HDF5 and can be viewed, for example, using VisIt or ParaView. The format of point data is plain text. README content The uploaded files have been created using the unix split(1) command to limit the size of each individual package. Files can be merged under a unix system through: $ cat OZGEN_ETAL_2022.zip.partaa OZGEN_ETAL_2022.zip.partab OZGEN_ETAL_2022.zip.partac > output.zip Unzip via $ unzip output.zip or using a graphical environment.

SnowEx Meteorological Station Measurements from Grand Mesa, CO Raw, Version 1 (other, 2022)

SnowEx Meteorological Station Measurements from Grand Mesa, CO, Version 1 (other, 2022)

NOAA/WDS Paleoclimatology - Woodhouse and Lukas 2004 TreeFlow Colorado Streamflow Reconstructions (other, 2022)
This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Climate Reconstruction. The data include parameters of climate reconstructions|tree ring with a geographic location of Colorado, United States Of America. The time period coverage is from 567 to -52 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

LAS Digital logs for the Mancos Shale, Candy Lane 1 (CL-1) Core in Montrose County, Colorado - Kira K Timm (other, 2022)
The Candy Lane 1 (CL-1) core was acquired in November 2005 as part of a multidisciplinary effort by the U.S. Geological Survey, the Bureau of Land Management, and the Bureau of Reclamation to investigate the Mancos Shale in the Gunnison Gorge National Conservation Area, Montrose County, Colorado. The Upper Cretaceous Mancos core was taken from the NE 1/4 sec. 8, T. 50 N., R. 9 W. corresponding to approximately lat 38.61717° N., long 107.90174° W. The CL-1 core contains part of the lower portion of the Mancos Shale Formation; Mancos Formation members in ascending order include the Bridge Creek Limestone (partial), Fairport, Blue Hill, Juana Lopez, Montezuma Valley, and Niobrara (partial). After coring was completed, the USGS collected geophysical data between the depths of 13 to 552 feet. Logs include gamma ray, caliper, conductivity, resistivity, porosity, density, and temperature. The digital logs for gamma ray (GR), resistivity (RES), and density (DEN) corresponding to those published in Open File Report 2009-1294 are identified in the name of the three Log ASCII Standard (LAS) files.

Data-model files associated with the manuscript "The Effects of Spatial and Temporal Resolution of Gridded Meteorological Forcing on Watershed Hydrological Responses" - X. Chen; Pin Shuai; Dipankar Dwivedi; E. T. Coon; Utkarsh Mital (other, 2022)
This data package contains the model inputs and outputs used in "The Effects of Spatial and Temporal Resolution of Gridded Meteorological Forcing on Watershed Hydrological Responses" (Shuai et al., 2022 HESS). The data.zip file contains the data used to drive the model simulations. The model.zip file contains the XML input file for ATS. The notebook.zip file contains the Jupyter notebooks for pre- and post- processing model results. The figures.zip file contains the raw figures associated with the manuscript. Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at high temporal (hourly to daily) and spatial resolution (10s of meters) has become efficient and computationally affordable. These hyperresolution watershed models require high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow, and distributed variables such as evapotranspiration, snow water equivalent (SWE), and groundwater table driven by three publicly available, gridded meteorological forcings (GMFs) -- Daily Surface Weather and Climatological Summaries (Daymet), Parameter-elevation Regressions on Independent Slopes Model (PRISM), and North American Land Data Assimilation System (NLDAS). By comparing various spatial resolutions (ranging from 400 m to 4 km) of PRISM, the simulated streamflow only becomes marginally worse when spatial resolution of meteorological forcing is coarsened to 4 km (or 30% of the watershed area). However, the 4 km resolution has much worse performance than finer resolution in spatially distributed variables such as SWE. Using temporally disaggregated PRISM, we compared models forced by different temporal resolutions (hourly to daily), sub-daily resolution preserves the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Conversely, the simulated streamflow shows better performance using daily resolution compared to that using sub-daily resolution. Our findings suggest that the choice of GMF and its spatiotemporal resolution depends on the quantity of interest and its spatial and temporal scale, which may have important implications on model calibration and watershed management decisions.

Occurrence Download (other, 2022)
A dataset containing 10742 species occurrences available in GBIF matching the query: { "DatasetKey" : [ "is Rocky Mountain Biological Laboratory" ] } The dataset includes 10742 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0101451-220831081235567/datasets/export for details. Data from some individual datasets included in this download may be licensed under less restrictive terms.

NOAA/WDS Paleoclimatology - Simic fire data from Timber Sale, Western Slope - IMPD USTRS002 - Petar Z. Simic; M. Lopez; Ellis Q. Margolis; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

Carry-over effects of larval food stress on adult energetics and life history in a nectar-feeding butterfly - C. L. Boggs (other, 2022)
Stressful juvenile developmental conditions can affect performance and fitness later in life. In holometabolous insects such as butterflies, development under stressful conditions may lead to smaller adult size, lower reproductive output and shorter lifespan. However, how larval developmental stress affects energy intake and expenditure in adult individuals is poorly understood. We subjected last-instar larvae of Speyeria mormonia Edwards (Lepidoptera: Nymphalidae) to periodic dietary restriction (DR) to examine the allocation of energy and nutrients among different life-history processes. We measured adult food intake, resting metabolic rate (RMR), metabolic flight capacity, lifespan, and reproductive output. Consistent with pressure to disperse from a poor environment while maintaining offspring number, we predicted that stressed individuals would have increased adult food intake and higher flight capacity. Adult body size was strongly reduced. Contrary to predictions, we found no compensatory adult feeding. Mass-adjusted flight metabolic rate was reduced, suggesting poor dispersal capacity. Larval DR did not affect adult lifespan, nor did the rate of metabolic senescence change. Larval DR did affect RMR, as stressed females had a steeper slope between RMR and body mass, which may reflect differences in physiological activity due to condition. Fecundity decreased less than predicted based on body mass. Instead of investing in flight capacity, females increased relative allocation to reproduction, which may partly buffer against poor environmental conditions. Understanding the interplay of energy acquisition and allocation to life history traits across the life cycle is vital for predicting responses to environmental change.

NOAA PSL CL31 Ceilometer Backscatter and Cloud Base Height Data for SPLASH - Sergio Pezoa; Laura Bianco; Bianca Adler; James Wilczak; Ludovic Bariteau; Timothy Djalalova; James Wilczak; Sergio Pezoa; Laura Bianco; Timothy Djalalova; Bianca Adler; Ludovic Bariteau (other, 2022)
This dataset contains daily files from a CL31 ceilometer manufactured by Vaisala that was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. The files contain backscatter profiles, cloud base heights, and visibility. The ceilometer measures vertical profiles of backscatter using laser technology. From the backscatter profiles, cloud base height and vertical visibility are determined using with the Vaisala software CL-view. For details on the instrument specifics and the methods, see the manufacturer manual (cl31usersguide.pdf). The file format is the original Vaisala format (.DAT). For a description of the format see the manufacturer manual. To convert the .DAT file format to netcdf format, the open source command line Python program ‘cl2nc’ (https://github.com/peterkuma/cl2nc) can, for example, be used. The file naming conventions for the .DAT files are as follows: NOAA_PSL_CL31_Roaring Judy_yyyymmdd_HH.DAT with yyyy: Year mm: Month dd: Day HH: Hour when the first sample was written to the file The time stamp of all data is in UTC.

NOAA PSL thermodynamic profiles retrieved from ASSIST infrared radiances with the optimal estimation physical retrieval TROPoe during SPLASH (other, 2022)
This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (TROPoe, Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 min from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Rochette et al. 2009). The ASSIST was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm -1 and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer, temperature, water vapor mixing ratio, and pressure from colocated near-surface measurements and from hourly analysis profiles from the operational Rapid Refresh (RAP, Benjamin et al. 2021) weather prediction model at the closest grid point. The latter are used only outside the atmospheric boundary layer (ABL) above 4 km above ground level (AGL) and provide information in the middle and upper troposphere where little to no information content is available from the infrared radiances. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) which provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see e.g. Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. For this study, we computed the prior from operational radiosondes launched near Denver, CO, and re-centered the mean profiles of water vapor and temperature to account for the elevation difference between the East River Valley and the launch site near Denver to get a more representative prior. The file format is netcdf and the file naming conventions are NOAA_PSL_ASSIST_RoaringJudy_yyyymmdd.cdf with yyyy: Year mm: Month dd: Day The time stamp of all data is in UTC. Selected basic variables are (many more provided): Name Dimension Unit base_time Single value Seconds (since 00 UTC 1 Jan 1970) time_offset Time Second (since base_time) hour Time Hours since 00UTC this day height Height km AGL temperature Time, Height C, temperature waterVapor Time, Height g/kg, water vapor mixing ratio theta Time, Height K, potential temperature pressure Time, Height hPa, pressure rh Time, Height %, relative humidity dewpt Time, Height C, dew point temperature thetae Time, Height K, equivalent potential temperature sigma_temperature Time, Height C, 1-sigma uncertainty temperature sigma_waterVapor Time, Height g/kg, 1-sigma uncertainty water vapor cdfs_temperature Time, Height cumulative degrees of freedom for temperature cdfs_waterVapor Time, Height cumulative degrees of freedom for water vapor Bold variables are the main retrieved profiles, from which the other variables are derived. Note that the vertical resolution of the retrieved profiles decreases with height, because of the broadening of the weighting function as a function of height. Thus, there are relatively few independent pieces of information in the profiles, this is reflected in the cumulative degree of freedom variables. The majority of the information from the ASSIST is in the lowest 2-3 km, above that most information comes from the RAP model. Because of strong emission in the infrared from clouds, clouds strongly impact the ability to retrieve profiles from the ASSIST and care should be taken when analyzing the retrievals in the presence of clouds. References: Rochette, L., W. L. Smith, M. Howard, and T. Bratcher, 2009: ASSIST, atmospheric sounder spectrometer for infrared spectral technology: Latest development and improvement in the atmospheric sounding technology. Imaging spectrometry XIV, Vol. 7457 of, SPIE, 9–17. Turner, D. D., and U. Löhnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based atmospheric emitted radiance interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1. Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968. Turner, D. D., and U. Löhnert, 2021: Ground-based temperature and humidity profiling: Combining active and passive remote sensors. Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021.

Gothic Colorado WInter 2022 Snow Depth Lidar - Ian K Breckheimer; Ian K Breckheimer (other, 2022)
Gothic Colorado Winter 2022 Snow Depth Lidar from Jan7, Jan 26, and Feb 14 2022. Additionally, includes snow off.

Occurrence Download (other, 2022)
A dataset containing 905116 species occurrences available in GBIF matching the query: { "and" : [ "BasisOfRecord is Specimen", { "or" : [ "DatasetKey is The New York Botanical Garden Herbarium (NY)", "DatasetKey is University of New Mexico Herbarium", "DatasetKey is University of Colorado Museum of Natural History Herbarium Vascular Plant Collection", "DatasetKey is Rocky Mountain Biological Laboratory", "DatasetKey is Harvard University Herbaria: All Records", "DatasetKey is Colorado State University, Charles Maurer Herbarium" ] }, "OccurrenceStatus is Present", { "or" : [ "StateProvince is Colorado", "StateProvince is ArIzona", "StateProvince is Utah", "StateProvince is New Mexico", "StateProvince is Wyoming", "StateProvince is South Dakota", "StateProvince is Nebraska", "StateProvince is Kansas", "StateProvince is Oklahoma", "StateProvince is Texas" ] } ] } The dataset includes 905116 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0347300-210914110416597/datasets/export for details. Data from some individual datasets included in this download may be licensed under less restrictive terms.

Gothic Colorado WInter 2022 Snow Depth Lidar (other, 2022)
Gothic Colorado Winter 2022 Snow Depth Lidar from Jan7, Jan 26, and Feb 14 2022. Additionally, includes snow off.

NOAA/WDS Paleoclimatology - Woodhouse and Lukas 2004 TreeFlow Colorado Streamflow Reconstructions (other, 2022)
This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Climate Reconstruction. The data include parameters of climate reconstructions|tree ring with a geographic location of Colorado, United States Of America. The time period coverage is from 567 to -52 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

HEC-HMS models of major rainfall induced floods in ungauged mountain basins of Colorado (other, 2022)
This dataset includes the models used in the journal article entitled, "Parameter Estimation for Models of Major Rainfall Induced Floods in Ungauged Mountain Basins of Colorado," published in the Journal of Hydrology Regional Studies. Hydrologic Engineering Center – Hydrologic Modeling System (HEC-HMS) models are included for the following river basins and major storms in the Front Range and San Juan Range of Colorado: North Fork of the Big Thompson river (1976 storm), Big Thompson River (2013 storm), South Boulder Creek (2013 storm), Bear Creek (2013 storm), Cheyenne Creek (1997 and 2013 storms), Saguache Creek (1999 storm), Vallecito Creek (1970 and 2006 storms), and Mineral Creek (2004 and 2006 storms). In each case, the model produces streamflow hydrographs based on the provided precipitation hyetographs. For each case, calibrated and uncalibrated versions of the models are included. The models also include models that estimate the streamflow for design storms that were generated from the Regional Extreme Precipitation Study.

HEC-HMS models of major rainfall induced floods in ungauged mountain basins of Colorado (other, 2022)
This dataset includes the models used in the journal article entitled, "Parameter Estimation for Models of Major Rainfall Induced Floods in Ungauged Mountain Basins of Colorado," published in the Journal of Hydrology Regional Studies. Hydrologic Engineering Center – Hydrologic Modeling System (HEC-HMS) models are included for the following river basins and major storms in the Front Range and San Juan Range of Colorado: North Fork of the Big Thompson river (1976 storm), Big Thompson River (2013 storm), South Boulder Creek (2013 storm), Bear Creek (2013 storm), Cheyenne Creek (1997 and 2013 storms), Saguache Creek (1999 storm), Vallecito Creek (1970 and 2006 storms), and Mineral Creek (2004 and 2006 storms). In each case, the model produces streamflow hydrographs based on the provided precipitation hyetographs. For each case, calibrated and uncalibrated versions of the models are included. The models also include models that estimate the streamflow for design storms that were generated from the Regional Extreme Precipitation Study.

HEC-HMS models of major rainfall induced floods in ungauged mountain basins of Colorado (other, 2022)
This dataset includes the models used in the journal article entitled, "Parameter Estimation for Models of Major Rainfall Induced Floods in Ungauged Mountain Basins of Colorado," published in the Journal of Hydrology Regional Studies. Hydrologic Engineering Center – Hydrologic Modeling System (HEC-HMS) models are included for the following river basins and major storms in the Front Range and San Juan Range of Colorado: North Fork of the Big Thompson river (1976 storm), Big Thompson River (2013 storm), South Boulder Creek (2013 storm), Bear Creek (2013 storm), Cheyenne Creek (1997 and 2013 storms), Saguache Creek (1999 storm), Vallecito Creek (1970 and 2006 storms), and Mineral Creek (2004 and 2006 storms). In each case, the model produces streamflow hydrographs based on the provided precipitation hyetographs. For each case, calibrated and uncalibrated versions of the models are included. The models also include models that estimate the streamflow for design storms that were generated from the Regional Extreme Precipitation Study.

Data from: Genetic and spatial variation in vegetative and floral traits across a hybrid zone - Maya Midzik; Geoffrey Broadhead; Robert A. Raguso; Mascha Bischoff; Diane R. Campbell (other, 2022)
Premise: Genetic variation influences potential for evolution to rescue populations from impacts of environmental change. Most studies of genetic variation in fitness-related traits focus on either vegetative or floral traits, with few on floral scent. How vegetative and floral traits compare in potential for adaptive evolution is poorly understood. Results: Vegetative traits SLA and WUE varied greatly among planting sites, while showing weak or no genetic variation among source populations. SLA and trichomes responded plastically to snowmelt date, and SLA exhibited within-population genetic variation. All aspects of floral morphology varied genetically among source populations, and corolla length, corolla width, and sepal width varied genetically within populations. Heritability was not detected for volatiles, due to high environmental variation, although one terpene had high evolvability and two terpenes correlated genetically with sepal width, associated with high emission from that tissue. Environmental variation across sites was weak for floral morphology and stronger for volatiles and vegetative traits. Three of 4 volatiles showed inheritance departing from additive. Conclusions: Results indicate stronger genetic potential for evolutionary responses to selection in floral morphology compared with scent and vegetative traits, while finding potentially adaptive plasticity in some vegetative traits.

SnowEx Meteorological Station Measurements from Grand Mesa, CO, Version 1 (other, 2022)

Machine Learning Assisted Gap-Filled Discharge Data for the East River Community Watershed, Colorado, for Water Years 2014-2021 - Carroll Rosemary; Michelle Newcomer; K. H. Williams (ess_dive, 2022)
This dataset contains a collection of machine learning assisted gap-filled discharge data created for all discharge stations across the East River Watershed, Colorado. This data was generated by using raw discharge data collected by Rosemary Carroll, and conducting a random forest machine learning analysis to gap-fill discharge data across all years at the hourly time level. Discharge data with gaps creates problems for analysis of measured and modeled fluxes of carbon and nitrogen exported out of each sub-watershed. Gap-filled data is also required as an input to surface water models, which helps to address our main research question related to how snowmelt timing impacts the timing and magnitude of nitrogen exports. Data is provided in one csv file.

SnowEx20 Grand Mesa Snow Depth from Snow Pole Time-Lapse Imagery, Version 1 (other, 2022)

SnowEx Meteorological Station Measurements from Grand Mesa, CO Raw, Version 1 (other, 2022)

Aspen ploidy level and cover maps from NEON Airborne Observation Platform, 2018, Crested Butte, Colorado - Philip G. Brodrick; K. Dana Chadwick; B. Blonder (ess_dive, 2022)
Maps of aspen ploidy (diploid or triploid) developed using National Ecological Observatory Network (NEON) Airborne Observation Platform visible to shortwave infrared imaging spectroscopy data that were acquired over the Upper East River, Colorado in 2018. Maps of aspen distribution, as well as other land cover types that were used to screen for aspen dominated pixels are also provided. Each image file is provided as a GeoTiff with internal tiling and LZW compression. For use of ploidy maps, it is necessary to mask out non-aspen and shaded pixels. Shaded pixels are provided in an associated data package. For full details, please see associated publication and methods description. This dataset is associated with the publication in Ecological Applications.

ParFlow-CLM model simulation files for Maina et al., Journal of Hydrology, 2022 (ess_dive, 2022)
This dataset contains the files to run a ParFlow-CLM integrated hydrologic model simulation for Maina et al., HESS, 2022. It also contains the associated daily pressure output of those simulations. Simulations are for a hillslope-similarity approach based on seasonal groundwater changes in the East River, Colorado. Comparisons are made to different clustering, or functional zonation approaches. We assess the ability of these clustering approaches to identify and categorize hillslopes with similar static characteristics, hydroclimate, land surface processes, and subsurface dynamics in a mountainous watershed. ParFlow binary files (.pfb) are model specific, and can be modified/viewed with conversion to .sa, .silo, or .vtk file formats. ParFlow input scripts are in the .tcl file format. Associated post-processing scripts are included as .f90 files.

Sulfur x-ray absorption spectroscopy data from bedrock shale, soil, and floodplain sediment from the East River, Colorado watershed. - Sergio Carrero; Christian Dewey; Marco Keiluweit; P. Nico; Craig R. Anderson; H. R. Naughton; P. M. Fox (other, 2022)
This dataset includes sulfur x-ray absorption near edge spectroscopy (XANES) data collected on solid samples as a part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected in order to investigate the speciation of sulfur and degree of shale weathering in solid samples across the watershed and their impact on riverine export of sulfate. Solid samples, including shale, hillslope soil, and floodplain sediment samples collected in 2016-2018, were analyzed by bulk XANES. Density fractions of hillslope soil, including a light fraction representing particulate organic matter and a heavy fraction representing minerals are also analyzed by bulk XANES. Micro-focused XANES on a weathered shale sample (PLM6, 2.7 m) was also performed. Sample locations are included in the 'sample meta data.csv' file, and XANES data for each sample type are included in csv files.

Field data for the Vegetation Mapping Inventory Project of Black Canyon of the Gunnison National Park - Open Format Data Package (other, 2022)
These data were converted from the originally delivered Microsoft Access PLOTs database from the Vegetation Mapping Inventory Project of Black Canyon of the Gunnison National Park. These comma-delimited data tables contain(s) vegetation mapping plot classification and accuracy assessment data, as well as summary information about the data itself. If a table is empty, then it was empty in the original database.

Surface electrical resistivity tomography, magnetic, and gravity surveys in Redwell Basin and the greater East River watershed near Crested Butte, Colorado, 2017 - Bethany L Burton (other, 2022)
Surface electrical resistivity tomography (ERT), time-domain electromagnetics (TEM), nuclear magnetic resonance (NMR), magnetics, and gravity data were acquired in 2016, 2017 and 2018 in the greater East River Watershed near Crested Butte Colorado with a focused effort in Redwell Basin. Five ERT profiles were acquired within Redwell Basin and Brush Creek to map geologic structure at depths up to 40 meters, depending on the subsurface resistivity, using the Advanced Geosciences, Inc. SuperSting R8 resistivity meter. Approximately ten kilometers of total field magnetics data were acquired with a Geometrics G-858 cesium vapor magnetometer that detects changes in deep (tens of meters to kilometers) geologic structure based on variations [...]

Magnetics Data - Bethany L Burton (other, 2022)
Surface electrical resistivity tomography, magnetic, and gravity surveys were conducted in July 2017 in the greater East River Watershed near Crested Butte Colorado with a focused effort in Redwell Basin as part of a broader study of the role of bedrock groundwater in the hydrogeology of mineralized mountain watersheds. Approximately ten kilometers of total field magnetics data were acquired on July 29, 2017 with a Geometrics G-858 cesium vapor magnetometer that detects changes in deep (tens of meters to kilometers) geologic structure based on variations in the magnetic properties of different formations. This data release includes the raw and processed magnetics data. They are provided as digital data, and data fields are defined [...]

Floodplain hydrostratigraphy from sedimentology, geophysics, and remote sensing - H. F. Malenda; J. Randall; K. Singha (other, 2022)
This file includes the data published in: Malenda, H.F., Sutfin, N.A., Stauffer, S., Guryan. G., Rowland, J.C., Williams, K.H., and Singha, K. (2019). From Grain to Floodplain: Evaluating heterogeneity of floodplain hydrostatigraphy using sedimentology, geophysics, and remote sensing. Earth Surface and Planetary Landforms, doi:10.1002/esp.4613. Floodplain stratigraphy, a major structural element of alluvial aquifers, is a fundamental component of floodplain heterogeneity, hydraulic conductivity, and connectivity. Watershed-scale hydrological models often simplify floodplains by modeling them as largely homogeneous, which inherently overlooks natural floodplain heterogeneity and anisotropy and their effects on hydrologic processes such as groundwater flow and transport and hyporheic exchange. This study, conducted in the East River Basin, Colorado, USA, combines point-, meander-, and floodplain-scale data to explore the importance of detailed field studies and physical representation of alluvial aquifers. We combine sediment core descriptions, hydraulic conductivity estimates from slug tests, ground-penetrating radar (GPR), historical maps of former channels, LiDAR-based elevation and Normalized Difference Vegetation Index data to infer 3-D fluvial stratigraphy. We compare and contrast stratigraphy of two meanders with disparate geometries to explore floodplain heterogeneity and connectivity controls on flow and transport. We identify buried point bars, former channels, and overbank deposits using GPR, corroborated by point sediment descriptions collected during piezometer installment and remotely sensed products. We map heterogeneous structural features that should control resultant flow and transport; orientation and connectivity of these features would control residence times important in hydrologic models.

Strontium isotopic data from the Mount Emmons-Redwell area, Crested Butte, Colorado - Philip L Verplanck (other, 2022)
This U.S. Geological Survey (USGS) data release contains strontium isotopic data from water and rock samples collected between 2000 and 2019 from the Mount Emmons area, central Colorado. The data include strontium isotopic compositions, 87Sr/86Sr, for surface- and groundwater samples collected from streams, springs, draining mines, piezometers, and drill holes and for leachates of rock samples collected from surface outcrops and drill core. Rock sample isotopic data are results from two-step leaching of samples from various lithologies within the study area. Drill core rock and water samples were collected from holes drilled in 2017 and 2018 as part of a U.S. Geological Survey and Department of Energy investigation of groundwater flowpaths [...]

ParFlow-CLM model simulation files for Maina et al., Journal of Hydrology, 2022 - Fadji Maina; Fadji Maina; P. James Dennedy-Frank; Fadji Maina; E. R. Woodburn; P. James Dennedy-Frank; E. R. Woodburn; P. James Dennedy-Frank; Fadji Maina; E. R. Woodburn; Fadji Maina; P. James Dennedy-Frank; et al. (ess_dive, 2022)
This dataset contains the files to run a ParFlow-CLM integrated hydrologic model simulation for Maina et al., HESS, 2022. It also contains the associated daily pressure output of those simulations. Simulations are for a hillslope-similarity approach based on seasonal groundwater changes in the East River, Colorado. Comparisons are made to different clustering, or functional zonation approaches. We assess the ability of these clustering approaches to identify and categorize hillslopes with similar static characteristics, hydroclimate, land surface processes, and subsurface dynamics in a mountainous watershed. ParFlow binary files (.pfb) are model specific, and can be modified/viewed with conversion to .sa, .silo, or .vtk file formats. ParFlow input scripts are in the .tcl file format. Associated post-processing scripts are included as .f90 files.

NOAA/WDS Paleoclimatology - Woodhouse et al. 2006 Updated Streamflow Reconstructions for the Upper Colorado River Basin - NCEI (other, 2022)
This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Climate Reconstruction. The data include parameters of climate reconstructions|tree ring with a geographic location of Colorado, United States Of America. The time period coverage is from 425 to -49 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Gravity Data - Bethany L Burton (other, 2022)
Surface electrical resistivity tomography, magnetic, and gravity surveys were conducted in July 2017 in the greater East River Watershed near Crested Butte Colorado with a focused effort in Redwell Basin as part of a broader study of the role of bedrock groundwater in the hydrogeology of mineralized mountain watersheds. Ten gravity stations were acquired with a LaCoste and Romberg G-model relative gravimeter to map density variations. This data release includes principal facts for the gravity stations provided as digital data. Data fields are defined in the data dictionary.

Electrical Resistivity Tomography (ERT) Data - Bethany L Burton (other, 2022)
Surface geophysical surveys were conducted from 2016 to 2018 in the greater East River Watershed near Crested Butte Colorado with a focused effort in Redwell Basin as part of a broader study of the role of bedrock groundwater in the hydrogeology of mineralized mountain watersheds. Five electrical resistivity tomography (ERT) profiles were acquired within Redwell Basin and Brush Creek to map geologic structure at depths up to 40 meters, depending on the subsurface resistivity, using the Advanced Geosciences, Inc. SuperSting R8 resistivity meter. This data release includes the raw and processed resistivity data as well as inverted resistivity models. All are provided as digital data, and data fields for each file type are defined in [...]

Whole rock major, minor, and trace element geochemistry of the upper part of the Mount Emmons-Redwell porphyry molybdenum (Climax-type) deposit, Redwell Basin, Crested Butte, Colorado - Jeffrey L Mauk (other, 2022)
This U.S. Geological Survey (USGS) data release provides whole rock major, minor, and trace element geochemical data from the fluorine-rich Mount Emmons-Redwell porphyry molybdenum (Climax-type) deposit (Mt. Emmons-Redwell deposit), located approximately 5.6 km (3.5 mi) northwest of Crested Butte, Colorado. The Mt. Emmons-Redwell deposit partly underlies Redwell Basin on the northwest flank of Mount Emmons in the west-central region of the Colorado Mineral Belt. Mineralization of the Mt. Emmons-Redwell deposit occurs at depth as vein, veinlet, stockwork, and fracture-hosted Climax-type mineralization [308 Mt at 0.32% Mo (Galey, 1978; Guarnera and Anderson, 2007)], and is associated with the Mt. Emmons igneous complex. Samples were [...]

QA/QC-ed Groundwater Level Time Series in PLM-1 and PLM-6 Monitoring Wells, East River, Colorado - Boris Faybishenko; Roelof Versteeg (ess_dive, 2022)
Performed Quality Assurance and Quality Control (QA/QC) analysis of measured groundwater levels in monitoring wells PLM-1 and PLM-6, including identification and flagging of duplicated values of timestamps, gap filling of missing timestamps and water levels, removal of abnormal/bad and outliers of measured water levels. The purpose of the QA/QC analysis was to test the application of different QA/QC methods and the development of regular (1-hour and 1-day) time series datasets, which can be served as benchmark for testing other QA/QC techniques, and will be applicable for ecohydrological modeling. The package includes a Readme file, two R Markdown html files, a series of 8 csv files: two input files of irregular measured groundwater levels, two output files with QA/QC flagging of original data, and four QA/QC-ed regular time series datasets.

R code for Demographic consequences of changing environmental periodicity - Maria Paniw; Daniel T. Blumstein; J. G. A. Martin; Eva Conquet; Kenneth B. Armitage (other, 2022)
These R scripts contain the code to replicate the analyses performed in Demographic consequences of changing environmental periodicity , Ecology. Vital-rate estimation We used the demographic data of each species to model periodic differences in vital rates for each life-cycle stage using generalized linear models and mixed models (GLMs for the deterministic vital rates in dewy pines and GLMMs for all other vital rates). More specifically, we tested for the effect of season on marmot and meerkat vital rates and of post-fire habitat states (time since fire, TSF) on dewy pine vital rates. For all three species, we estimated stage-specific survival and probability of transition to another stage using a binomial distribution, and reproductive output using a Poisson distribution. In addition, we estimated meerkat helper emigration probability and dewy-pine flowering probability using a binomial distribution. In addition to fixed effects, we incorporated year random effects in all appropriate models to model year-specific differences among season-specific average vital-rates (for marmots and meerkats) or among vital-rate averages (for dewy pines). For the meerkats and dewy pines, we also incorporated the fixed effect of density on vital rates. For each modeled vital rate, we first selected the best random-effect structure where appropriate (i.e., testing whether a random effect on the average vital rate and the slope between seasons outperformed a random effect on the average vital rate only). We then selected the best fixed-effect variables using the Akaike Information Criterion corrected for small sample size (AICc). When the difference in AICc values was not significant (dAICc<2), we picked the model with fewer parameters or the most biologically relevant. For models using a Poisson distribution, we tested for over- and under-dispersion in the best model and fitted over- and under-dispersed models with a quasi-Poisson distribution when necessary. Population projections We used the predictions of the vital-rate models described above to parameterize periodic matrix population models (MPMs) and project the population dynamics for 100 years according to two scenarios—control and perturbed—to assess the effect of changes in vital-rate periodicity on the stochastic growth rate (log λ S ), the variance of 100 annual growth rates (var(log λ )), and the probability of quasi-extinction ( p qext , i.e., the ratio of simulations leading to quasi-extinction out of 500 simulations, with a threshold set at 15% of the minimum observed abundance or number of reproductive individuals for the marmot and meerkat populations, and at 50% of the minimum observed aboveground or seed bank abundance for the dewy pines). Yellow-bellied marmot and meerkat (seasonal dynamics) For the meerkats and marmots, we projected population dynamics under changing patterns of vital rates for which the year random effect in the GLMM was applied both on the mean vital-rate estimate (i.e., the model intercept) and the difference between seasons. That is, for the marmots, we assessed the population responses to changes in the seasonal patterns of yearling and non-reproductive and reproductive adult survival. For the meerkats, we did so for subadult, helper, and dominant survival, helper emigration, transition from helper to dominant, and helper and dominant recruitment. We used the above models to predict season- and year-specific vital rates and parameterize periodic matrix population models (MPMs) for each season. We then simulated population dynamics by projecting MPMs representing low- (LS) and high-seasonality (HS) years for each of the vital rates. Half of all years in which a vital rate was estimated were considered as LS and the other half as HS. That is, for a given vital rate, we defined the threshold between LS and HS years as the 50th percentile of all year-specific absolute differences between seasons. In addition, we used a control scenario, where we projected the population dynamics using MPMs representing both high- and low-seasonality years indistinctly. We performed 500 simulations, each starting with the same population vector, and projected the population dynamics for 100 years. For each simulation, we randomly selected 100 years corresponding to each scenario (LS, HS, or control). In each step of the simulation, all vital rates were predicted based on the same randomly selected year. This allowed us to maintain within-year vital-rate correlation. The predicted vital rates were then used to build the corresponding period-specific MPM. Dewy pine (Multi-year habitat succession and periodicity in fire regimes) For dewy pines, we simulated two distinct types of fire regimes, each with two frequencies: (1) periodic burning occurring systematically every 15 or 30 years, and (2) stochastic fires occurring on average every 15 or 30 years. In all simulations, dewy pines transition deterministically through the first four post-fire states, TSF 0 to TSF 3 . Under periodic fires, once in the fifth state, TSF >3 , the population remains in that state until the next fire (15 or 30 years after the previous fire) and then transitions to TSF 0 . Under stochastic fires, the population can transition from TSF >3 to TSF 0 conditional on fire frequency ( p ). This is done using a Markov-chain approach. In addition to the different periodic patterns in fire regimes, we perturbed periodic patterns in habitat succession in natural heathlands by introducing an additional human disturbance (i.e., using vital rates from populations under a browsing-induced disturbance) first only in the years of the last post-fire state (TSF >3 ), and then increasingly in the previous states (i.e., in TSF 3 and >3 , in TSF 2, 3, and >3 , etc.) until all post-fire states of a natural population were perturbed. We used TSF-specific vital-rate predictions from our models and rates on seed-bank dynamics to build MPMs for each TSF. We used these MPMs to project population dynamics for 100 years using 500 simulations, in each iteration randomly sampling among MPMs describing one of the two natural populations. In addition, for each iteration in the stochastic post-fire state TSF >3 , we randomly sampled a year-specific MPM. We increased browsing pressure by replacing MPMs associated with natural habitat conditions by MPMs parameterized with vital rates estimated from human-disturbed populations, for any given TSF. We then compared scenarios of increasing anthropogenic pressures to the control scenario (i.e., natural populations with no browsing perturbation) in each fire regime. Population responses to vital-rate periodic patterns under density dependence For the meerkats and dewy pines, projections of population dynamics incorporated density dependence. That is, at each iteration of the simulations, population density was estimated and used to predict vital rates and parameterize an MPM from these predictions at the next iteration. We then compared density-dependent projections to ones where the density input during parameter estimation was fixed at constant average values obtained from the observed data. Analysis of the simulations results For all three systems and for each simulation, we recorded the stochastic growth rate log λ S . We also investigated the effect of changes in vital-rate patterns on the variance in 100 annual log λ , var(log λ ), and the quasi-extinction probability p qext . We checked the overlap of the distributions of each metric (i.e., the mean and the 2.5th and 97.5th percentiles) across the 500 simulations between two scenarios. We considered a metric to differ between scenarios when 95% of the distributions (i.e., between the 2.5th and 97.5th percentiles) did not overlap. Comparing population sensitivity to changes in periodic environmental patterns vs. stochastic environmental variation We compared our results from periodic population models to “classic” assessments of population fitness sensitivity to stochastic environmental variation. To do so, for marmots and meerkats, we computed the stochastic elasticities of the population growth rate to changes in the mean and standard deviation of vital rates. We then calculated the relative importance of the stochastic elasticity of the growth rate due to changes in the variability of vital rates compared to changes in their mean. For dewy pines, as the effects of periodic patterns consisted of changing the sequences of post-fire habitat states, we explored the link between the effects of human-induced disturbances in various post-fire habitat states and the role of these states in shaping population dynamics. We thus used the megamatrix approach to calculate the elasticity of the population growth rate to each post-fire habitat state.

NOAA/WDS Paleoclimatology - Simic fire data from Needle Creek, Western Slope - IMPD USNEC001 - Petar Z. Simic; Ellis Q. Margolis; M. Lopez; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

NOAA PSL thermodynamic profiles retrieved from ASSIST infrared radiances with the optimal estimation physical retrieval TROPoe during SPLASH - Timothy Myers; Timothy Myers; James Wilczak; Timothy Djalalova; Bianca Adler; Laura Bianco; James Wilczak; Bianca Adler; Timothy Djalalova; Laura Bianco (other, 2022)
This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (TROPoe, Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 min from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Rochette et al. 2009). The ASSIST was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm -1 and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer, temperature, water vapor mixing ratio, and pressure from colocated near-surface measurements and from hourly analysis profiles from the operational Rapid Refresh (RAP, Benjamin et al. 2021) weather prediction model at the closest grid point. The latter are used only outside the atmospheric boundary layer (ABL) above 4 km above ground level (AGL) and provide information in the middle and upper troposphere where little to no information content is available from the infrared radiances. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) which provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see e.g. Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. For this study, we computed the prior from operational radiosondes launched near Denver, CO, and re-centered the mean profiles of water vapor and temperature to account for the elevation difference between the East River Valley and the launch site near Denver to get a more representative prior. The file format is netcdf and the file naming conventions are NOAA_PSL_ASSIST_RoaringJudy_yyyymmdd.cdf with yyyy: Year mm: Month dd: Day The time stamp of all data is in UTC. Selected basic variables are (many more provided): Name Dimension Unit base_time Single value Seconds (since 00 UTC 1 Jan 1970) time_offset Time Second (since base_time) hour Time Hours since 00UTC this day height Height km AGL temperature Time, Height C, temperature waterVapor Time, Height g/kg, water vapor mixing ratio theta Time, Height K, potential temperature pressure Time, Height hPa, pressure rh Time, Height %, relative humidity dewpt Time, Height C, dew point temperature thetae Time, Height K, equivalent potential temperature sigma_temperature Time, Height C, 1-sigma uncertainty temperature sigma_waterVapor Time, Height g/kg, 1-sigma uncertainty water vapor cdfs_temperature Time, Height cumulative degrees of freedom for temperature cdfs_waterVapor Time, Height cumulative degrees of freedom for water vapor Bold variables are the main retrieved profiles, from which the other variables are derived. Note that the vertical resolution of the retrieved profiles decreases with height, because of the broadening of the weighting function as a function of height. Thus, there are relatively few independent pieces of information in the profiles, this is reflected in the cumulative degree of freedom variables. The majority of the information from the ASSIST is in the lowest 2-3 km, above that most information comes from the RAP model. Because of strong emission in the infrared from clouds, clouds strongly impact the ability to retrieve profiles from the ASSIST and care should be taken when analyzing the retrievals in the presence of clouds. References: Rochette, L., W. L. Smith, M. Howard, and T. Bratcher, 2009: ASSIST, atmospheric sounder spectrometer for infrared spectral technology: Latest development and improvement in the atmospheric sounding technology. Imaging spectrometry XIV, Vol. 7457 of, SPIE, 9–17. Turner, D. D., and U. Löhnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based atmospheric emitted radiance interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1. Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968. Turner, D. D., and U. Löhnert, 2021: Ground-based temperature and humidity profiling: Combining active and passive remote sensors. Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021.

Valve Ultrastructure and Description of a new Luticola D. G. Mann (Bacillariophyceae) species from Colorado, USA - Jeremy Greifenstein; J. P. Kociolek; Rachel Shea (other, 2022)
This dataset contains the digitized treatments in Plazi based on the original journal article Shea, Rachel, Greifenstein, Jeremy, Kociolek, J. P. (2022): Valve Ultrastructure and Description of a new Luticola D. G. Mann (Bacillariophyceae) species from Colorado, USA. Phytotaxa 554 (3): 269-277, DOI: 10.11646/phytotaxa.554.3.5AbstractA new species of the diatom genus Luticola D.G.Mann is described from a wet meadow environment from Gunnison County, Colorado, based on light and scanning electron microscopy. The new species has rugose valves, with longitudinal interruptions in the striae. Unlike other species of the genus, L. coloradiana sp. nov. has a simple structure of the internal stigma, lacking the complex opening seen in all other species in the genus. The new species resembles L. plausibilis (Hustedt) D.G.Mann, described from Franz Josef Land in the Arctic, but differs in valve shape and density of the striae. Luticola coloradiana sp. nov. also resembles L. plausibiloides Metzeltin et al., a species described from subtropical Uruguay, but differs in striae density and the rugose nature of the valves. Further research is needed to assess if the similar species from the Arctic and subtropical South America also possess the unique stigma structure seen in L. coloradiana sp. nov.

Global Daily Surface Blue-sky Albedo Climatology and Land Cover Climatology Dataset from 20-year MODIS Products (500m) - Aolin Jia; N. Wang; Yunyue Yu; Jingjing Peng; Shunlin Liang (other, 2022)
Only the first days of each month were uploaded to Zenodo due to the data storage limitation, and the full dataset is available at http://glass.umd.edu/albedo_clim/. Surface albedo plays a critical role in climate, hydrological, and biogeochemical modeling and weather forecasting. Therefore, precisely mapping surface albedo climatology globally is necessary to better parameterize environmental systems. We generated a new global surface blue-sky actual and snow-free albedo climatology dataset from 20-year MODIS products from the Google Earth Engine (GEE). The 500m global surface blue-sky daily albedo climatology dataset follows the basic MODIS product format and employed the sinusoidal projection. It includes historical and snow-free blue-sky albedo climatology data. For application convenience, the land cover climatology of MODIS product (MCD12Q1) is also generated and attached. The International Geosphere-Biosphere Programme (IGBP) and PFT classification climatology of MCD12Q1 since 2001 were also generated.

oysteiop/EvolDiv: Publication release (other, 2022)
Analyses of evolvability and population divergence

Western United States UCLA Daily Snow Reanalysis, Version 1 - Steve Margulis; Y. Liu; Yilin Fang (other, 2022)

Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4 R1 - S-C. Kao; P.E. Thornton; M.M. Thornton; Y. Wei; R. Shrestha (other, 2022)
This dataset provides Daymet Version 4 R1 data as gridded estimates of daily weather parameters for North America, Hawaii, and Puerto Rico. Daymet variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset covers the period from January 1, 1980, to December 31 (or December 30 in leap years) of the most recent full calendar year for the Continental North America and Hawaii spatial regions. Data for Puerto Rico is available starting in 1950. Each subsequent year is processed individually at the close of a calendar year. Daymet variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Areas of Hawaii and Puerto Rico are available as files separate from the continental North America. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant netCDF file format. In Version 4 R1, all 2020 and 2021 files were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.

climlab/climlab: Version 0.8.1 - Andrew Williams; Sebastian Meßlinger; Paul Gierz; Brian Rose; Ryan Abernathey; Xoviat; Chris Cardinale; Moritz Kreuzer (other, 2022)
A major refactor of the internals: all the Fortran code has been moved into external companion packages climlab-rrtmg, climlab-cam3-radiation, and climlab-emanuel-convection. Climlab is now (once again!) a pure Python package. Builds of these helper packages are available through conda-forge and will be automatically installed as dependencies by conda / mamba. The climlab source repo also moved to https://github.com/climlab/climlab There should be no breaking changes to the user-facing API. The major motivation for this change was to (vastly) simplify the development and testing of new-and-improved climlab internals (coming soon).

ParFlow-CLM model simulation files for Maina et al., Journal of Hydrology, 2022 (ess_dive, 2022)
This dataset contains the files to run a ParFlow-CLM integrated hydrologic model simulation for Maina et al., HESS, 2022. It also contains the associated daily pressure output of those simulations. Simulations are for a hillslope-similarity approach based on seasonal groundwater changes in the East River, Colorado. Comparisons are made to different clustering, or functional zonation approaches. We assess the ability of these clustering approaches to identify and categorize hillslopes with similar static characteristics, hydroclimate, land surface processes, and subsurface dynamics in a mountainous watershed. ParFlow binary files (.pfb) are model specific, and can be modified/viewed with conversion to .sa, .silo, or .vtk file formats. ParFlow input scripts are in the .tcl file format. Associated post-processing scripts are included as .f90 files.

ParFlow-CLM model simulation files for Maina et al., Journal of Hydrology, 2022 (ess_dive, 2022)
This dataset contains the files to run a ParFlow-CLM integrated hydrologic model simulation for Maina et al., HESS, 2022. It also contains the associated daily pressure output of those simulations. Simulations are for a hillslope-similarity approach based on seasonal groundwater changes in the East River, Colorado. Comparisons are made to different clustering, or functional zonation approaches. We assess the ability of these clustering approaches to identify and categorize hillslopes with similar static characteristics, hydroclimate, land surface processes, and subsurface dynamics in a mountainous watershed. ParFlow binary files (.pfb) are model specific, and can be modified/viewed with conversion to .sa, .silo, or .vtk file formats. ParFlow input scripts are in the .tcl file format. Associated post-processing scripts are included as .f90 files.

Multi-year census of arthropod abundance on the plant Ligusticum porteri near Gothic, CO (other, 2022)
The purpose of this study was to track year-to-year variation in aphid abundance on the host plant Ligusticum porteri (Apiaceae). We censused arthropod abundance on the flowering stalks of L. porteri weekly in June-August from 2012 to 2020. The censuses took place in ten L. porteri populations near the Rocky Mountain Biological Laboratory in Gothic, CO. While the same populations were used across years, we randomly selected ten flowering plants in each population in each year (N = 100 plants per year). Observations focused on colonization by the aphid Aphis asclepiadis, its mutualist ants, and natural enemies. When found, we counted other arthropods as well, identifying them to Family or Order in the field. We also counted the number of host plant flowering stalks and inflorescences (umbel). In 2016, we began tracking flowering phenology using a numerical score (0-8). To track senescence, we used a qualitative score (TB=turning brown and AB=all brown) for terminal and primary umbels.

Bumble bee occurrences of North America from 1805–2020 - Leithen M'Gonigle; Laura Melissa Guzman; Hanna Jackson; Lora Morandin; Sarah Johnson; L. L. Richardson (other, 2022)
Bumble bee occurrence data used for "Climate change winners and losers among North American bumble bees": These data comprise 649 407 specimen records from 48 species and spans 1805–2020. These records have been compiled from a variety of collections and sources with reputable origin. Data contributors are listed in: https://www.leifrichardson.org/bbna.html

Occurrence of plants in plots along the Gunnison River, Colorado, 1990-2017 - Abigail M Eurich; Gregor T Auble; Michael L Scott; Polly P Gibson; Jonathan M Friedman (other, 2022)
In 1990 the US Geological Survey Fort Collins Science Center and National Park Service Water Resources Division established 133 permanent 1meter (m) X 2 m plots along the Gunnison River near Warner Point in Black Canyon of the Gunnison National Park. A one-dimensional hydraulic model was calibrated to determine the inundation necessary to inundate each plot, and this can be combined with daily flow data to determine the inundation duration or percentage of time a plot is under water. Occurrence of all plant species in these plots was determined in late July of 1990, 1994, 2001, 2006 and 2013. This data set includes 3 csv files of plot data. One file gives characteristics of plots, including plot name, inundating discharge, mean number of species occurring in the plot, mean vegetative cover, Mean Sorensen Index and mean inundation duration with flows exponentially weighted for recency using a half-life of 1.5 years. A second file gives characteristics of all the plant species that have occurred in the plots including scientific and common names, plant family, life span, occurrence of rhizomes, height, total occurrences across years, median inundating discharge of plots containing the plant, half-life for exponential weighting of flows selected by regressions relating species occurrence to flow in each sample year, slope, probability and variance of half-life regressions, and the optimum inundation duration, probability and variance of logistic regressions relating occurrence of species in all sample years to flow. A third file documents occurrence of species in plots in all five sample years as well as the inundation duration, number of species and total vegetative cover in each plot in each sample year. The 1990 data were interpreted by Auble et al. (1991 and 1994), and all five years of data were interpreted by Friedman et al. (2022). This data set also includes a library of repeat views of the river bars containing the plots photographed from the same set of 21 camera locations in 1990, 1994, 2001, 2006, 2013 and 2017, a total of 117 images. Finally, this data set also includes a csv file listing the years each view was photographed and the approximate latitude and longitude of each camera location.

Data used in Wainwright, H.M. et al. 2021, “Watershed zonation through hillslope clustering for tractably quantifying above- and belowground watershed heterogeneity and functions” (2022)
This data package contains spatial data layers and processing scripts used in Wainwright, H.M. et al. 2021, “Watershed zonation approach for tractably quantifying above-and- belowground watershed heterogeneity and functions”. The purpose of the data and paper is to develop a watershed zonation approach for characterizing watershed organization and function in a tractable manner by applying clustering methods to multiple spatial data layers. The data package contains the geotiff files of spatial data layers, and the processed data values corresponding to the figures in the paper. The Data_description file describe each file in details. The spatial data sets (geotiff) are included in the zip files.

Multi-year census of arthropod abundance on the plant Ligusticum porteri near Gothic, CO - James H Den Uyl; M. L. Mullins; M. L. Mullins; E. H. Mooney; James H Den Uyl; M. L. Mullins; E. H. Mooney; James H Den Uyl; M. L. Mullins; James H Den Uyl; E. H. Mooney; E. H. Mooney (other, 2022)
The purpose of this study was to track year-to-year variation in aphid abundance on the host plant Ligusticum porteri (Apiaceae). We censused arthropod abundance on the flowering stalks of L. porteri weekly in June-August from 2012 to 2022. The censuses took place in ten L. porteri populations near the Rocky Mountain Biological Laboratory in Gothic, CO. While the same populations were used across years, we randomly selected ten flowering plants in each population in each year (N = 100 plants per year). Observations focused on colonization by the aphid Aphis asclepiadis, its mutualist ants, and natural enemies. When found, we counted other arthropods as well, identifying them to Family or Order in the field. We also counted the number of host plant flowering stalks and inflorescences (umbel). In 2016, we began tracking flowering phenology using a numerical score (0-8). To track senescence, we used a qualitative score (TB=turning brown and AB=all brown) for terminal and primary umbels.

Multi-year census of arthropod abundance on the plant Ligusticum porteri near Gothic, CO (other, 2022)
The purpose of this study was to track year-to-year variation in aphid abundance on the host plant Ligusticum porteri (Apiaceae). We censused arthropod abundance on the flowering stalks of L. porteri weekly in June-August from 2012 to 2020. The censuses took place in ten L. porteri populations near the Rocky Mountain Biological Laboratory in Gothic, CO. While the same populations were used across years, we randomly selected ten flowering plants in each population in each year (N = 100 plants per year). Observations focused on colonization by the aphid Aphis asclepiadis, its mutualist ants, and natural enemies. When found, we counted other arthropods as well, identifying them to Family or Order in the field. We also counted the number of host plant flowering stalks and inflorescences (umbel). In 2016, we began tracking flowering phenology using a numerical score (0-8). To track senescence, we used a qualitative score (TB=turning brown and AB=all brown) for terminal and primary umbels.

Data from: Comparative impacts of long-term trends in snowmelt and species interactions on plant population dynamics - Mary V. Price; Alison K. Brody; Nickolas M. Waser; R. E. Irwin; Diane R. Campbell (other, 2022)
Climate change can impact plant fitness and population persistence directly through changing abiotic conditions and indirectly through its effects on species interactions. Pollination and seed predation are important biotic interactions that can impact plant fitness, but their impact on population growth rates relative to the role of direct climatic effects is unknown. We combined 13 years of experiments on pollen limitation of seed set and pre-dispersal seed predation in Ipomopsis aggregata , a subalpine wildflower, with a long-term demographic study that has documented declining population growth with earlier spring snowmelt date. We determined how pollen limitation and seed predation changed with snowmelt date over 21 years and incorporated those effects into an integral projection model to assess relative impacts of biotic factors on population growth. Both pollen limitation and the difference in stigma pollen load between pollen-supplemented and control plants declined over years. Neither pollen limitation nor seed predation changed detectably with snowmelt date, suggesting an absence of indirect effects of that specific abiotic factor on these indices of biotic interactions. The projected biotic impacts of pollen limitation and seed predation on population growth rate were small compared to factors associated with snowmelt date. Providing full pollination would delay the projected date when earlier snowmelt will cause populations to fall below replacement by only 14 years. Synthesis. Full pollination and elimination of seed predation would not compensate for the strong detrimental effects of early snowmelt on population growth rate, which in I. aggregata appears driven largely by abiotic environmental factors. The reduction over two decades in pollen limitation also suggests that natural selection on floral traits may weaken with continued climate change. These results highlight the value of studying both abiotic factors and biotic interactions to understand how climate change will influence plant populations.

Leaf gas exchange in Ipomopsis aggregata under manipulated snowmelt timing and summer precipitation - J. M. Powers; Ayaka Paul; Jocelyn Navarro; Diane R. Campbell (other, 2022)
Vegetative traits of plants can respond directly to changes in the environment, such as those occurring under climate change. That phenotypic plasticity could be adaptive, maladaptive, or neutral. We manipulated the timing of spring snowmelt and amount of summer precipitation in factorial combination and examined the responses of photosynthetic rate, stomatal conductance, and intrinsic water-use efficiency (iWUE) in the subalpine herb Ipomopsis aggregata. The experiment was repeated in three years differing in natural timing of snowmelt. A 50% reduction in summer precipitation reduced stomatal conductance and iWUE. Combining natural and experimental variation, earlier snowmelt reduced soil moisture, photosynthetic rate and stomatal conductance, and increased iWUE. Earlier snowmelt is a strong signal of climate change and can change expression of leaf morphology and gas exchange traits, just as reduced precipitation can. Stomatal conductance showed adaptive plasticity under some conditions.

SnowEx20 Grand Mesa Snow Depth from Snow Pole Time-Lapse Imagery, Version 1 (other, 2022)

NOAA/WDS Paleoclimatology - Simic fire data from Meyer West, Western Slope - IMPD USMYW001 - Petar Z. Simic; M. Lopez; Ellis Q. Margolis; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

NOAA/WDS Paleoclimatology - Simic fire data from Sapinero Mesa, Western Slope - IMPD USSOM001 - Ellis Q. Margolis; M. Lopez; Petar Z. Simic; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

NOAA/WDS Paleoclimatology - Simic fire data from Old Monarch Pass Low, Western Slope - IMPD USOML001 - Ellis Q. Margolis; M. Lopez; Petar Z. Simic; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

NOAA/WDS Paleoclimatology - Simic fire data from Yellow Pine Ridge South, Western Slope - IMPD USYPS001 - Ellis Q. Margolis; Petar Z. Simic; M. Lopez; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

NOAA/WDS Paleoclimatology - Simic fire data from Iola Valley, Western Slope - IMPD USIAV001 - M. Lopez; Petar Z. Simic; Ellis Q. Margolis; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

NOAA/WDS Paleoclimatology - Simic fire data from North Powderhorn 2, Western Slope - IMPD USNPH001 - Ellis Q. Margolis; Petar Z. Simic; M. Lopez; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

Data from the assessment of sediment-retention ponds near Delta, Colorado, 2019 - Rodney J Richards (other, 2022)
In 2019, soil samples were collected at selected sediment-retention ponds in the Gunnison Gorge National Conservation Area, near Delta, Colorado. This dataset includes pond location, pond characteristics, soil-moisture data, soil-chemistry data, and geophysical data. Pond locations were surveyed using real-time kinematic Global Navigation Satellite Systems (RTK-GNSS) methods and incorporated as a single shapefile in Ponds.zip. Pond characteristics were assessed, and observations were made for assigned surficial-geologic unit, vegetation, check-dam condition, check-dam height, water holding capacity, and stream condition entering and exiting the pond. Pond-characteristic data are incorporated as a single comma-separated values (.csv) file (PondInventory.csv). Soil moisture and chemistry data characterize soil moisture, specific conductance, pH, total dissolved solids, and chloride at selected depths in the soil profile. Soil moisture and chemistry data are presented in a .csv file (SoilData.csv). Geophysical surveys were conducted using a Geophex GEM-2, a hand-held multi-frequency electromagnetic induction sensor. The geophysical surveys were conducted to evaluate soil conductivity differences in and around pond locations and are presented as the unprocessed data from the instrument as a .csv file in GEM-2 data.zip.

NOAA/WDS Paleoclimatology - Simic fire data from Antelope Hill, Western Slope - IMPD USAEH001 - M. Lopez; Petar Z. Simic; Ellis Q. Margolis; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

NOAA/WDS Paleoclimatology - Simic fire data from Old Monarch Pass High, Western Slope - IMPD USOMH001 - M. Lopez; Petar Z. Simic; Ellis Q. Margolis; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

NOAA/WDS Paleoclimatology - Woodhouse 2003 Gunnison River Basin Snow Water Equivalent Reconstruction - NCEI (other, 2022)
This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Climate Reconstruction. The data include parameters of instrumental with a geographic location of Colorado, United States Of America. The time period coverage is from 381 to -49 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA - Mark S. Raleigh; Hannah M. Bonner; Eric Smyth; E. Small (other, 2022)
This dataset contains meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains during water years 2019-2021. Data collection had an emphasis on paired open-forest sites and included three forested elevations. In total, we present 270 snow pit observations, 4,019 snow depth measurements, and three years of meteorological forcing from two weather stations (one in a meadow, the other in an adjacent forest). The dataset is described in a forthcoming publication of the same name: A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA (Bonner et al., 2022). All snow observation and meteorological forcing data are available as both .nc and .mat files. Additionally, original digitized copies of snow pit observations are provided as .gsheet/.xlxs files. This dataset will continue to be updated, via this repository, as additional years of data are collected.

stemkov/pheno_skew: Phenological skews (2022)
This release contains data and code used for the analysis for "Skewness in bee and flower phenological distributions" (Stemkovski et al. in prep).

ParFlow-CLM model simulation files for Maina et al., Journal of Hydrology, 2022 (ess_dive, 2022)
This dataset contains the files to run a ParFlow-CLM integrated hydrologic model simulation for Maina et al., HESS, 2022. It also contains the associated daily pressure output of those simulations. Simulations are for a hillslope-similarity approach based on seasonal groundwater changes in the East River, Colorado. Comparisons are made to different clustering, or functional zonation approaches. We assess the ability of these clustering approaches to identify and categorize hillslopes with similar static characteristics, hydroclimate, land surface processes, and subsurface dynamics in a mountainous watershed. ParFlow binary files (.pfb) are model specific, and can be modified/viewed with conversion to .sa, .silo, or .vtk file formats. ParFlow input scripts are in the .tcl file format. Associated post-processing scripts are included as .f90 files.

Stable isotopes of carbon dioxide (CO2), dissolved inorganic carbon (DIC), water and concentrations of greenhouse gases in a mountainous watershed (Crested Butte, Colorado). - Markus Bill (other, 2022)
Temperature and precipitation variability in response to climate change affects water cycling of watersheds and can potentially impact water quality, water availability, elemental and molecular fluxes, and biogeochemical processes. Here we report the measurements of light stable isotopes and greenhouse gases analysis to a large multidisciplinary project addressing watershed function. Water hydrogen (H) and oxygen (O) stable isotopes of snow and water saturated soil were measured to constrain source contributions to groundwater and streamflow. In an effort to better constrain existing nitrogen and carbon cycling models in high-altitude ecosystems, carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4) greenhouse gases concentrations and stable isotope signatures of CO2, dissolved inorganic carbon (DIC), and water were measured. This dataset includes one CSV file and one Excel file containing data including stable isotopes of carbon dioxide (CO2), dissolved inorganic carbon (DIC), water and concentrations of greenhouse gases. The CSV and Excel files contain the same information/data but have included both versions for accessibility.

NOAA PSL CL31 Ceilometer Backscatter and Cloud Base Height Data for SPLASH (other, 2022)
This dataset contains daily files from a CL31 ceilometer manufactured by Vaisala that was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. The files contain backscatter profiles, cloud base heights, and visibility. The ceilometer measures vertical profiles of backscatter using laser technology. From the backscatter profiles, cloud base height and vertical visibility are determined using with the Vaisala software CL-view. For details on the instrument specifics and the methods, see the manufacturer manual (cl31usersguide.pdf). The file format is the original Vaisala format (.DAT). For a description of the format see the manufacturer manual. To convert the .DAT file format to netcdf format, the open source command line Python program ‘cl2nc’ (https://github.com/peterkuma/cl2nc) can, for example, be used. The file naming conventions for the .DAT files are as follows: NOAA_PSL_CL31_Roaring Judy_yyyymmdd_HH.DAT with yyyy: Year mm: Month dd: Day HH: Hour when the first sample was written to the file The time stamp of all data is in UTC.

Occurrence Download (other, 2022)
A dataset containing 648 species occurrences available in GBIF matching the query: { "DatasetKey" : [ "is Rocky Mountain Biological Laboratory Mammal Collection" ] } The dataset includes 648 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0164883-220831081235567/datasets/export for details. Data from some individual datasets included in this download may be licensed under less restrictive terms.

Gage Height data from February through September for years 2016-19 at Gunnison River near Grand Junction, Colorado - Mark F Henneberg; Laura A Hempel (other, 2022)
Gage height values from U.S. Geological Survey (USGS) streamflow-gaging station 09152500 for the specified period are presented in comma separated value (CSV) format. Values encompass the monthly date range from February 1st to September 30th for each year, 2016 through 2019.

Temperature and floral density data from seven study sites around the Rocky Mountain Biological Laboratory, Colorado: 2013-2021 - Jessica R. K. Forrest (other, 2022)
Data were collected as part of a study of population dynamics of solitary, cavity-nesting Hymenoptera. Nesting structures (“trap-nests”) were established at five study sites along an elevational gradient around the Rocky Mountain Biological Laboratory in 2013. Two additional study sites were added in 2014, and one of the original study sites was dropped at the end of 2015. At each site, a HOBO data-logger placed under a centrally located trap-nest records air temperatures hourly. Floral densities are recorded at each site, typically 1-2 times per week, throughout the growing season, for specific plant taxa known to be used as pollen sources by cavity-nesting bees.

Field data for the Vegetation Mapping Inventory Project of Black Canyon of the Gunnison National Park - Open Format Data Package (other, 2022)
These data were converted from the originally delivered Microsoft Access PLOTs database from the Vegetation Mapping Inventory Project of Black Canyon of the Gunnison National Park. These comma-delimited data tables contain(s) vegetation mapping plot classification and accuracy assessment data, as well as summary information about the data itself. If a table is empty, then it was empty in the original database.

HEC-HMS models of major rainfall induced floods in ungauged mountain basins of Colorado (other, 2022)
This dataset includes the models used in the journal article entitled, "Parameter Estimation for Models of Major Rainfall Induced Floods in Ungauged Mountain Basins of Colorado," published in the Journal of Hydrology Regional Studies. Hydrologic Engineering Center – Hydrologic Modeling System (HEC-HMS) models are included for the following river basins and major storms in the Front Range and San Juan Range of Colorado: North Fork of the Big Thompson river (1976 storm), Big Thompson River (2013 storm), South Boulder Creek (2013 storm), Bear Creek (2013 storm), Cheyenne Creek (1997 and 2013 storms), Saguache Creek (1999 storm), Vallecito Creek (1970 and 2006 storms), and Mineral Creek (2004 and 2006 storms). In each case, the model produces streamflow hydrographs based on the provided precipitation hyetographs. For each case, calibrated and uncalibrated versions of the models are included. The models also include models that estimate the streamflow for design storms that were generated from the Regional Extreme Precipitation Study.

Data for “Herbivory damage but not plant disease under experimental warming is dependent on weather for three subalpine grass species”, Rocky Mountain Biological Laboratory, Gothic, Colorado, 2015-2017. (2022)
Both theory and prior studies predict that climate warming should increase attack rates by herbivores and pathogens on plants. However, past work has often assumed that variation in abiotic conditions other than temperature (e.g., precipitation) do not alter warming responses of plant damage by natural enemies. Studies over short time periods span low variation in weather, and studies over long-time scales often neglect to account for fine-scale weather conditions. Here, we used a 20+ year field warming experiment to investigate if warming affects herbivory and disease are dependent on variation in ambient weather observed over three years. We studied three common grass species in a subalpine meadow in the Colorado Rocky Mountains, USA. We visually estimated herbivory and disease every two-weeks during the growing season and evaluated weather conditions during the previous two- or four-week time interval (two-week average air temperature, two- and four-week cumulative precipitation) as predictors of the probability and amount of damage. Herbivore attack was 13% more likely and amount of damage was 29% greater in warmed plots than controls across the focal species, but warming treatment had little affect on plant disease. Herbivory presence and damage increased the most with experimental warming when preceded by wetter, rather than drier, fine-scale weather, but preceding ambient temperature did not strongly interact with elevated warming to influence herbivory. Disease presence and damage increased, on average, with warmer weather and more precipitation regardless of warming. The effect of warming over reference climate on herbivore damage is dependent on and amplified by fine-scale weather variation, suggesting more boom-and-bust damage dynamics with increasing climate variability. However, the mean effect of regional climate change is likely reduced monsoon rainfall, for which we predict a reduction in insect herbivore damage. Plant disease was generally unrelated to warming or weather, which may be a consequence of our coarse disease estimates that did not track specific pathogen species or guilds. The results point towards temperature as an important but not sufficient determinant and regulator of species interactions, where precipitation and other constraints may determine the effect of warming.

Supplementary material from "Current and lagged climate affects phenology across diverse taxonomic groups" (2022)
The timing of life events (phenology) can be influenced by climate. Studies from around the world tell us that climate cues and species' responses can vary greatly. If variation in climate effects on phenology is strong within a single ecosystem, climate change could lead to ecological disruption, but detailed data from diverse taxa within a single ecosystem are rare. We collated first sighting and median activity within a high-elevation environment for plants, insects, birds, mammals and an amphibian across 45 years (1975–2020). We related 10 812 phenological events to climate data to determine the relative importance of climate effects on species’ phenologies. We demonstrate significant variation in climate-phenology linkage across taxa in a single ecosystem. Both current and prior climate predicted changes in phenology. Taxa responded to some cues similarly, such as snowmelt date and spring temperatures; other cues affected phenology differently. For example, prior summer precipitation had no effect on most plants, delayed first activity of some insects, but advanced activity of the amphibian, some mammals, and birds. Comparing phenological responses of taxa at a single location, we find that important cues often differ among taxa, suggesting that changes to climate may disrupt synchrony of timing among taxa.

An Equation of State Unifies Diversity, Productivity, Abundance and Biomass (data sets) (2022)
Macroecological data sets, their state variables, and various calculated metabolic quantities for tests of the Maximum Entropy Theory of Ecology. These data sets are used with all relevant permissions, as detailed in the main publication. The raw data files are: data_statevariables_only.txt - This file contains the raw data and metadata. The 3/4 scaling is assumed, so E and B are with that scaling. For plants, the metabolic rate was obtained from empirical scaling of plant size (ie. dbh), and then the scaling relationship was used to calculate B, ie. m ~ e^(4/3). For the arthropods biomass was measured directly, and so E is derived using this scaling, i.e. e ~ m^(3/4).  The 2/3 scaling is also included in separate columns. Note that because of what is measured directly, for the plants B is different for 2/3 and 3/4 scaling, while for the animals E is different. This file also includes the minimum diameter at breast height (DBH) for plant data and the minimum metabolic rate for arthropod data. data_statevariables.txt - This file includes the observed values, but also the predicted biomass calculated numerically, and calculated analytically with the 0th and first order approximations (columns are pBnum, pB0, pB1). Also includes the calculated lambdas as lambda_1, lambda_2, and beta. This is the relevant file for plotting.

Supplementary material from "Life-history traits predict responses of wild bees to climate variation" (2022)
Life-history traits, which are physical traits or behaviours that affect growth, survivorship and reproduction, could play an important role in how well organisms respond to environmental change. By looking for trait-based responses within groups, we can gain a mechanistic understanding of why environmental change might favour or penalize certain species over others. We monitored the abundance of at least 154 bee species for 8 consecutive years in a subalpine region of the Rocky Mountains to ask whether bees respond differently to changes in abiotic conditions based on their life-history traits. We found that comb-building cavity nesters and larger bodied bees declined in relative abundance with increasing temperatures, while smaller, soil-nesting bees increased. Further, bees with narrower diet breadths increased in relative abundance with decreased rainfall. Finally, reduced snowpack was associated with reduced relative abundance of bees that overwintered as prepupae whereas bees that overwintered as adults increased in relative abundance, suggesting that overwintering conditions might affect body size, lipid content and overwintering survival. Taken together, our results show how climate change may reshape bee pollinator communities, with bees with certain traits increasing in abundance and others declining, potentially leading to novel plant–pollinator interactions and changes in plant reproduction.

Life history consequences of climate change in hibernating mammals: A review (2022)
Climatic shifts to warmer and often drier conditions are challenging terrestrial species worldwide. These shifts are occurring more rapidly at higher elevations and latitudes, likely causing disproportionate effects to mammalian hibernators there. While there is some information about how these species’ ranges are responding to climatic shifts, we lack an understanding of how climate components are affecting species’ life history variation, which is key to individual success and population-level resilience. We reviewed the literature to identify the direction of life history responses to climate change in mammalian hibernators along three axes: latitudinal, elevational, and temporal. We found 39 studies involving 27 species that reported climate effects on our four target life history traits – phenology, body mass/condition and growth, reproduction, and survival. We found warmer temperatures are advancing hibernator phenology and increasing reproductive success. By contrast, warming and drying trends are having uncertain effects on body condition, and complex effects on survival - depending on season, age class, latitude, and elevation. We found no pattern of significant climate-trait outcomes by duration or decade of study. More research on drought conditions - particularly in relation to resource availability - would help inform hibernator susceptibility to increased drying trends expected to intensify globally. Notably, our results are highly biased towards small mammal hibernators in Northern hemisphere alpine/mountain ecosystems, with few long-term studies conducted on Southern hemisphere hibernators.This review highlights that phenological shifts constitute one of the most obvious consequences of climate change, yet, the timing of life history events (e.g. timing of migration, reproduction, hibernation) remains poorly understood. Further integration of insights from physiologists, evolutionary biologists, and population ecologists working on wild populations will improve our collective understanding of the effects of seasonal climatic shifts on mammalian hibernator life history traits, key drivers of their population-level persistence. We identified studies that addressed mammalian hibernator responses to anthropogenic climate change by conducting a standardized literature search in the ISI Web of Knowledge. Our final search took place on August 5th 2020 and identified studies published between 1950 and 2020. Searching for relevant studies with combinations of the following keywords - climat* AND hibern*, weather AND hibern* - generated a list of 717 citations (see PRISMA flow diagram presented in fig. 1). We qualitatively disregarded studies that addressed topics that were not in line with our review: those that reported on species that did not truly hibernate (i.e. animals that exhibit torpor bouts < 24 hours, Geiser and Ruf 1995), or species that were not mammals. We further excluded studies where no statistics were reported, for which we could not find an English version, or that did not present sufficient evidence to detect a relationship between life history traits and climate or weather. We accepted experimental work if conducted in natura, but excluded studies that focused on habitat selection processes, activity budgets, and physiological correlates of hibernation, as opposed to the  traits of interest to our study: phenology, body mass/condition and growth, reproduction, and survival. Once a working subset of studies was identified (N=39), we separated results based on the key life history traits of interest (phenology, body mass/condition and growth, reproduction, and survival) and collected the following information for each entry in our dataframe: the species common name, Latin name, Order, study type (qualitative, quantitative, or review), ecosystem (desert, forest, grassland, mountain/alpine, rainforest, savannah, tundra, or other), field site location, latitude, longitude, length of study, beginning year, end year, life history trait(s), season in which trait was measured (summer/active, winter/hibernation), age group measured (pup, yearling, adult), social/reproductive status (e.g. reproductive vs. non-reproductive, subordinate vs. dominant), general climatic driver(s) (i.e. drought index, global index, rain, snow, temperature), specific climatic driver(s) (e.g. July mean maximum temperature), direction of effect(s) (i.e. negative, positive, no effect), statistical method, statistics reported, sample size, and citation information (table S1). Because studies reported a variety of weather and climate variables, we conceptually standardized the results according to four expected future climatic conditions: warmer conditions, drier conditions, a longer growing season, and a shorter winter from earlier snowmelt. For instance, a positive effect of rainfall and negative effect of drought are conceptually the same result, so those would both be categorized here as negative effect of drier conditions. Each row summarized a unique result linking a specific life history trait and climatic driver(s); hence, studies with multiple results were represented multiple times in our dataframe and in our results. Please see ReadMe file

Biomass Inventories at Harvard Forest EMS Tower since 1993 - William Munger; Steven Wofsy (other, 2021)
In 1993, we installed 40 circular, 10 m radius biometric plots in the footprint of the EMS tower on Prospect Hill. We randomly placed the plots within 100 m increments along ten 500 m transects that extend from the tower in the northwest and southwest directions. In 2001, we removed three plots (G3, H3, H4) from the datasets and ceased measurements there due to their inundation by a beaver pond. In 1999, we installed 6 additional circular, 10 m radius biometric plots on the Simes Lot, adjacent to Prospect Hill to study the effects of a selective harvest that occurred there in the winter of 2000-01. In the summer of 2001, we expanded the harvested plots in size to 15 m radius and ceased measurements at one plot (X4) because it was unaffected by the harvest. The harvest also affected three of the original tower plots (A4, A5, B5), which were expanded in size as a part of the harvest plot group. Consequently, there are 34 tower plots and 8 harvest plots. We have taken the following ecological measurements at each site: tree growth, woody debris, litter, leaf area increment (LAI), leaf chemistry, and soil respiration and moisture.

Snow Depth on March 31st 2018 for Upper East River Derived from Airborne Snow Observatory Data - Thomas Painter (s3, 2021)
This is a 3m map of snow depth derived from repeat LiDAR data collection by the Airborne Snow Observatory. This dataset has been clipped and resampled to the standard 3m SDP grid, and 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 Center Distributed Active Archive Center. doi: https://doi.org/10.5067/KIE9QNVG7HP0. Small areas outside of the UER region, but within the extent of this map had missing data. These gaps were filled with linear regression relating 2018 and 2019 ASO snow depths (where one map was available), or regressions related snow depth to elevation (where neither 2018 nor 2019 data was available). Peak snow depths in 2018 were near the 20th percentile of the long-term average estimated from the Butte SNOTEL station.

Unsaturated and Saturated Hydraulic Parameters of Soils at the East River Watershed, Colorado Calculated using the Pedotransfer Function Method (ess_dive, 2021)
The purpose of the data package is to present the results of calculations of the following unsaturated-saturated hydraulic parameters of soils of the East River Watershed, Colorado: fully saturated water content (Qs), residual saturation (Qr), van genuchten parameters alpha and n, field capacity (FC), and saturated hydraulic conductivity (Ks). Calculations were conducted for the following soil texture types: sandy_loam, loam, silty_clay_loam, clay_loam, silt loam; the particle size distribution included the percentage of sand, silt, clay, and the bulk density (BD) of soils. Calculations were conducted for the following areas - Lower_Subalpine (6 samples), - Lower_Montane_lower_hillslope (7 samples), - Floodplain_at_base_of_lower_montane_hillslope, (11 samples), - PLM-0 (10 samples), - PLM-1 (10 samples), and - PLM-3 (13 samples) Calculations were conducted using the Pedotransfer Function Method developed by Tóth et al. (2014) with the application of the code ”euptf” in Rstudio. The data are being summarized in the Excel file ”Unsaturated_Saturated_Hydraulic_Parameters_PTF_calculated_EASt_EIver.xls" including two worksheets: “full_data_EAST_RIVER” which includes (a) the input data used for calculations, the soil texture triangle, and the plots of the water retention curves and unsaturated hydraulic conductivity for several types of soil texture, and (b) the summary table with the averaged parameters for each area. The the texture triangle was plotted using the Rstudio "soiltexture." Microsoft Excel software is required to open the Excel file. The metadata and a list of references to the Rstudio libraries used for calculations and data visualization are giving in the Word Readme file.

SnowEx20 Grand Mesa Autumn 2019 Snow Water Equivalent, Version 1 (other, 2021)

Hyporheic, Floodplain, and Surface Water (on Floodplain and River) Geochemical Datasets, and Shapefiles on Meander C at the East River, Colorado. (ess_dive, 2021)
The purpose of this dataset was to sample surface water and groundwater geochemistry at random locations in the East River, Colorado to provide a broad spatial dataset of geochemistry. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States. We conducted this random sampling at multiple time points including during the snowmelt flooding stage, during peak flood, during the falling limb of flood, and during baseflow. This datasets contain many datafiles. We include flat text files of surface water geochemistry including surface water on the floodplain and surface water in the river, geochemistry of floodplain pore-water samples, and geochemistry of hyporheic pore-water samples. All datasets are also included as shapefiles so location geochemistry is co-located with sampling locations and sampling depths. This datasets also has the locations of small channels, and hollows that are on top of floodplain Meander C. These small channels funnel surface water from the hillslope through the floodplain and out to the river. All text datasets can be analyzed with software such as Excel or R. All shapefiles can be viewed with freely available R packages such as Leaflet.

3 m Resolution Canopy Cover Estimates for the Upper Gunnison Basin derived from 2015 and 2019 LiDAR Data - Ian K Breckheimer (s3, 2021)
This is a map of vegetation canopy cover or density for the Upper Gunnison River Basin based on 2015 and 2019 LiDAR data. Cover is measured as a proportion (0 representing no cover, and 1 representing complete cover). This measure of cover is the inverse of the canopy gap fraction. This dataset was generated from the normalized, elevation-corrected LiDAR point clouds, and was calculated as Density = N / T Where N in the number of LiDAR returns greater than 2 m above the ground surface, and T is the total number of returns. Calculation was implemented in the R package lidR. The ground-classified points were first used to generate a bare-earth surface, which was subtracted from the point cloud elevation to generate the normalized point cloud.  Topographic artifacts and structures have not been removed from this dataset.

Qinayan/Soil-thickness: Soil thickness estimation - Qina (other, 2021)
This release includes Python codes and associated field sampling and remote sensing data for the estimation of the spatial distribution of soil thickness in two hillslopes in the Pump House area in the East River Watershed in the CO., the U.S.

1 m Resolution WGS84 UTM Zone 13N X Coordinate for the Upper Gunnison Domain - Ian K Breckheimer (s3, 2021)
This map records the UTM X Coordinate (measured in meters) for every pixel in the Upper Gunnison Domain, as measured using the WGS84 UTM Zone 13N Coordinate System (EPSG:32613). This map is useful for spatial prediction in models that explicitly incorporate geographic relationships, such as regression models that include a linear or non-linear spatial trend. UTM is a projected coordinate system that preserves distances.

SnowEx20 Grand Mesa Reference GIS Data Sets, Version 1 (other, 2021)

1m Resolution NDVI for the Upper Gunnison Basin derived from October 2017 NAIP Imagery - Ian K Breckheimer (s3, 2021)
This is a 1m resolution map of Normalized Differential Vegetation Index (NDVI) derived from resampled 0.6m 4-band orthoimagery collected as part of the USDA National Aerial Imagery Program. The NAIP tiles were mosaiced and bilinearly resampled to the standard UG 1m grid before calculating NDVI as (NIR-Red) / (NIR+Red). The NAIP Imagery for this area was collected on October 10th, 2017.

Dissolved Inorganic Carbon and Dissolved Organic Carbon Data for the East River Watershed, Colorado (2015-2021) (ess_dive, 2021)
This data package contains mean values for dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) for water samples taken from the East River Watershed in Colorado. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States. DOC and DIC concentrations in water samples were determined using a TOC-VCPH analyzer (Shimadzu Corporation, Japan). DOC was analyzed as non-purgeable organic carbon (NPOC) by purging HCl acidified samples with carbon-free air to remove DIC prior to measurement. After the acidified sample has been sparged, it is injected into a combustion tube filled with oxidation catalyst heated to 680 degrees C. The DOC in samples is combusted to CO2 and measured by a non-dispersive infrared (NDIR) detector. The peak area of the analog signal produced by the NDIR detector is proportional to the DOC concentration of the sample. DIC was determined by acidifying the samples with HCl first, and then purge with carbon-free air to release CO2 for analysis by NDIR detector. All files are labeled by location and variable, and data reported are the mean values upon minimum three replicate measurements with a relative standard deviation < 3%. All samples were analyzed under a rigorous quality assurance and quality control (QA/QC) process as detailed in the methods. This data package contains (1) a zip file (dic_npoc_data_2014-2021.zip) containing a total of 261 files: 260 data files of DIC and NPOC data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location and a locations.csv (1 file) with latitude and longitude for each location; (2) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and (3) a data dictionary (dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type. There are a total of 99 locations containing isotope data. Update on 10/07/2020: Updated the data files to remove times from the timestamps, so that only dates remain. The data values have not changed. Update on 4/11/2021: Added Determination of Method Detection Limits (MDLs) for DIC, NPOC and TDN Analyses document, which can be accessed as a PDF or with Microsoft Word.Update on 6/10/2022: versioned updates to this dataset was made along with these changes: (1) updated dissolved inorganic carbon and dissolved organic carbon data for all locations up to 2021-12-31, (2) removal of units from column headers in datafiles, (3) added row underneath headers to contain units of variables, (4) restructure of units to comply with CSV reporting format requirements, (5) added -9999 for empty numerical cells, and (6) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format. Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv).

1 m Resolution WGS84 UTM Zone 13N Y Coordinate for the Upper Gunnison Domain - Ian K Breckheimer (s3, 2021)
This map records the UTM Y Coordinate (in meters) for every pixel in the Upper Gunnison Domain, as measured using the WGS84 UTM Zone 13N Coordinate System (EPSG:32613). This map is useful for spatial prediction in models that explicitly incorporate geographic relationships, such as regression models that include a linear or non-linear spatial trend. UTM is a projected coordinate system that preserves distances.

SnowEx20 Grand Mesa IOP CSU 1GHz GPR Raw, Version 1 (other, 2021)

SnowEx20 SnowEx Grand Mesa Automated Meteorological and Snow Station Observations 2016-2021, version 1 (other, 2021)

Digital Data from Mineral Investigation of Sangre de Cristo Wilderness Study Area, Alamosa, Custer, Fremont, Huerfano, and Saguache Counties, Colorado, USA (other, 2021)
This Data Release provides tabular and geospatial data digitized by the U.S. Geological Survey (USGS) from a U.S. Bureau of Mines (USBoM) report titled Mineral Investigation of Sangre de Cristo Wilderness Study Area, Alamosa, Custer, Fremont, Huerfano, and Saguache Counties, Colorado. The original preliminary paper report, numbered Mineral Land Assessment 65-83 (MLA 65-83; Ellis and others, 1983), presented the results of a mineral resource potential survey of areas in and around the Sangre de Cristo Range flanking the northeastern extent of San Luis Valley in south-central Colorado. Included in this Data Release are georeferenced scans of the regional plate maps, digitized sample site locations, analytical chemistry, and structural geologic data from MLA 65-83 in a modern and searchable geospatial database. In addition to the maps and associated point locations, the dataset contains 1,262 rows and 58 columns of contextual and numeric information in the main data table, as well as a table describing 97 samples excluded from the MLA report, a table of summary deposit information, a table of USBoM inventory data for the prepared and stored rock samples, and geospatial boundary data of the Sangre de Cristo Wilderness, Wilderness Study Area (WSA), and mineralized sampling areas. Prospecting, claiming, and some mining of largely polymetallic precious- and base-metal veins occurred in the Sangre de Cristo Range from the late 1800s through the early 1930s, with sporadic prospecting and some mining through the late 1980s (Ellis and others, 1983). Many of the vein networks are found along the western side of the mountains that are bounded by the Sangre de Cristo normal fault, a principal structure delineating a major segment of the Rio Grande rift. There also are mines along the crest and the northeast side of the range such as the Rita Alta copper mine. However, the locations of many claims, prospects, and mines were hand located on topographic and mine maps without modern spatial coordinate data and there were few analytical chemistry data available with properly associated geologic context. As a mandated component of the 1964 Wilderness Act, the USGS and USBoM were charged with identifying and assessing mineral resources in Federal lands known as Wilderness Study Areas (WSA; for example, Johnson and others, 1984). The Sangre de Cristo WSA included the mountainous areas from Poncha Pass in the north to the Blanca Peak area in the south. Approximately 75 to 85 percent of the study area was covered by aerial photographic inspection and sites were visited by Ellis and others (1983) as access allowed. One thousand three hundred and ten samples were fire assayed for gold and silver and spectrographically analyzed for 40 other elements including copper, lead, molybdenum, tungsten, and uranium. Select samples were also analyzed for specific elements by various other state-of-the-art analytic methods of the time: copper, lead, and molybdenum by atomic absorption, tungsten by colorimetry, and uranium by fluorimetry. Integration of mapping, field characterization, mineralogical, and chemical data resulted in the identification of 18 more or less distinct mineralized areas. Sample locations and a variety of site and mine maps were compiled and integrated into the MLA 65-83 report along with extensive tabulation on paper with all data organized by mineralized area. Most of the study area was ultimately designated as Wilderness in 1980 and the unique sand dunes and associated watersheds on the western side of the central portion of the range, originally designated Great Sand Dunes National Monument in 1932, were redesignated as a National Park and Preserve in 2004. During the late 1990's and 2000's the USGS completed extensive research in the Rio Grande Rift (for example, Hudson and Grauch, 2013) including the Sangre de Cristo Mountains and adjacent San Luis Valley. Much of the work focused on interpretation of new geophysical data and new geologic mapping as well as new analyses of mineralized areas and associated geologic structures. Overall, the absolute age, petrogenetic, and metallogenic affinities of the mineralized areas are poorly understood. However, some combination of magmatic, hydrothermal, structural, chemical, and fluid-related processes all contributed to the formation of the Sangre de Cristo mineral system. The digital data and information provided in this Data Release allows for public access to the information contained in the archived paper data record, report, and plates. This publication is useful in developing a better understanding of the geologic and tectonic framework, occurrences and controls on mineralization, and other Earth resources such as groundwater. References Cited Above Ellis, C.E., Hannigan, B.J., and Thomson, J.R., 1983, Mineral Investigation of Sangre de Cristo Wilderness Study Area, Alamosa, Custer, Fremont, Huerfano, and Saguache Counties, Colorado: U.S. Bureau of Mines Mineral Land Assessment preliminary report MLA 65-83, 190 p., 2 plates. Hudson, M.R., and Grauch, V.J.S., 2013, Introduction, in Hudson, M.R., and Grauch, V.J.S., eds., New Perspectives on Rio Grande Rift Basins: From Tectonics to Groundwater: Geological Society of America Special Paper, v. 494, p. v-xii, https://doi.org/10.1130/SPE494. Johnson, B.R., Lindsey, D.A., Ellis, C.E., Hannigan, B.J., and Thompson, J.R., 1984, Mineral Resource Potential of the Sangre de Cristo Wilderness Study Area, South-central Colorado: U.S. Geological Survey Miscellaneous Field Studies Map MF-1635-A and Pamphlet, 13 p., https://doi.org/10.3133/mf1635A.

SnowEx20 Grand Mesa Intensive Observation Period Snow Pit Measurements, Version 1 (other, 2021)

Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020 (ess_dive, 2021)
The timing of snowmelt is a critical cue for the initiation of growth in mountain meadow ecosystems and can also impact the duration and magnitude of plant production. High frequency observations of species-level phenology are time consuming and require a high degree of expertise, and publicly available remote sensing products lack the necessary temporal resolution to assess fine-scale growing season dynamics. Near-surface sensing methods provide a middle ground with high temporal frequency and tractable spatial scales (from sub-meter to hillslope scale). This data package includes csv files of Normalized Difference Vegetation Index (NDVI) timeseries (SM_NDVI_dailymax.csv) and phenological thresholds (SM_NDVI_summary.csv) for sub-plots (1m2) and Green Chromatic Coordinate (GCC) phenological thresholds (SM_GCC_summary.csv) at the plot scale (10m x 14m). Location IDs associated with this data package are: ER-LM, WG-UM, WG-LS, ER-US, and XX-AL. Related data packages include: “Microclimate observations associated with snowmelt experiment gradient sites, East River, Colorado, 2017 to 2020” and “Colorado Elevation Gradient Snowmelt Manipulation Plant Phenology 2017-2018”.

SnowEx20 Grand Mesa Autumn 2019 Snow Depth, Version 1 (other, 2021)

Flowering phenology in subalpine meadows: Does climate variation influence community co-flowering patterns? - J. D. Thomson; Jessica R. K. Forrest; David William Inouye (other, 2021)
Climate change is expected to alter patterns of species co-occurrence, in both space and time. Species-specific shifts in reproductive phenology may alter the assemblages of plant species in flower at any given time during the growing season. Temporal overlap in the flowering periods (co-flowering) of animal-pollinated species may influence reproductive success if competitive or facilitative interactions between plant species affect pollinator services. We used a 33-year data set on flowering phenology in subalpine meadows in Colorado, USA, to determine whether interannual variation in snowmelt date, which marks the start of the growing season, affected co-flowering patterns. For two of four species considered, we found a significant relationship between snowmelt timing and composition of the assemblage of co-flowering plants. In years of early snowmelt, Lathyrus lanszwertii var. leucanthus (Fabaceae), the species we investigated in most detail, tended to overlap with earlier-flowering species and with fewer species overall. In particular, overlap with the flowering period of Lupinus polyphyllus var. prunophilus, with which Lathyrus leucanthus shares pollinators, was significantly reduced in early-snowmelt years. The observed association between timing of snowmelt and patterns of flowering overlap could not have been predicted simply by examining temporal trends in the dates of peak flowering of the dominant species in the community, as peak flowering dates have largely shifted in parallel with respect to snowmelt date. However, subtle interspecific differences in responsiveness of flowering time, duration, and intensity to interannual climate variation have likely contributed to the observed relationship. Although much of the year-to-year variation in flowering overlap remains unexplained by snowmelt date, our finding of a measurable signal of climate variation suggests that future climate change may lead to altered competitive environments for these wildflower species.

SnowEx20 Grand Mesa In Situ Dielectric Soil Moisture, Version 1 (other, 2021)

Sample Collection Metadata for Soil Cores from the East River Watershed, Colorado collected in 2017. (ess_dive, 2021)
This data package contains sample collection metadata for soil cores from the East River Watershed in Colorado used in biogeochemical analyses by the Watershed Function SFA. Soil cores were collected seasonally during autumn, winter, snowmelt, and spring at a predominately montane meadow, high altitude field site. Information provided for each soil core sample assigned an International Geo Sample Number (IGSN) includes: sample name, IGSN, material, description, collection method, core depth, location, field program, collectors, and date. The full list of 53 IGSN's is listed in the Methods section. There is also a complete core sample list and metadata for the set of baseline data.

East River Surface and Pore Water FTICR-MS Data Associated with “Implications of sample treatment on characterization of the riverine environmental metabolome” (ess_dive, 2021)
Surface and pore water samples were collected from distributed locations around Meander A in East River (Crested Butte, CO, USA) during the summer of 2018. This dataset consists of the characterization of dissolved organic matter using 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS; in .xml format) analyzed through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) using two different sample pre-treatment methods (solid phase extraction, no solid phase extraction) and two electrospray ionization methods (ESI-positive, ESI-negative) to identify the impacts of these choices on the final dataset. This study aimed to understand how the different sample processing methods influence the resulting FTICR-MS dataset. Any published work that utilizes data presented in this dataset should cite the dataset using the DOI number. If using the dataset, please acknowledge the U.S. Department of Energy (DOE) Office of Science (BER) Subsurface Biogeochemical Research (SBR) program, the Environmental Molecular Sciences Laboratory (EMSL), and Worldwide Hydrobiogeochemical Observation Network for Dynamic River Systems (WHONDRS). The publication DOI will be added to this abstract when available.

1 m Resolution topographic slope for the Upper Gunnison Basin derived from 2015 and 2019 LiDAR data. - Ian K Breckheimer (s3, 2021)
This is a 1 m resolution map of topographic slope (measured in degrees) computed using a 3*3 pixel kernel and Horn's formula. It is derived from a 1m resolution Digital Elevation Model (DEM) for the Upper Gunnison River derived from public LiDAR datasets. The primary data source was a 2019 LiDAR collection for Gunnison County. A small portion of the upper basin was not covered by this dataset, and for those areas, data from a 2015 acquisition was substituted.

Mask for the Upper Gunnison SDP Domain at 1m resolution - Ian K Breckheimer (s3, 2021)
This is a 1m resolution binary map representing areas within the Upper Gunnison Domain of the RMBL Spatial Data Platform.

1 m Resolution 4-band orthoimagery for the Upper Gunnison Basin derived from October 2017 NAIP imagery - Ian K Breckheimer (s3, 2021)
This is a 1m resolution map of Normalized Differential Vegetation Index (NDVI) derived from resampled 0.6m 4-band orthoimagery collected as part of the USDA National Aerial Imagery Program. The NAIP tiles were mosaiced and bilinearly resampled to the standard UG 1m grid before calculating NDVI as (NIR-Red) / (NIR+Red). The NAIP Imagery for this area was collected on October 10th, 2017.

MODIS/Terra+Aqua BRDF/Albedo Daily L3 Global - 500m V061 - Crystal Schaaf; Zhuosen Wang (other, 2021)

Phenology of selected cavity-nesting Hymenoptera and flowering plant taxa in the Colorado Rocky Mountains from 2008 to 2010. - Jessica R. K. Forrest; Rocky Mountain Biological Laboratory; Ecological Society of America (other, 2021)
Data come from fourteen sites in the West Elk Mountains of Colorado, USA. The study aimed to identify the factors regulating phenology of plants and cavity-nesting insects, and to determine the likelihood of asynchrony between flowering and pollinator emergence under climate change. Numbers of flowers or flowering stems of several abundant species were counted 2-3 times weekly along variable-width belt transects in May-July 2008 and 2009. Insect data are from experimental nest blocks colonized naturally by cavity-nesting insects in 2007 and 2008. Emergence traps on these nest blocks were monitored 2-3 times weekly in 2008 (for 2007 nests) and 2009-2010 (2008 nests) to characterize insect phenology. All Hymenoptera, excluding Ichneumonoidea, were identified to species, if possible. A reciprocal transplant experiment was conducted using nest blocks at two of the sites in 2007-2008, and a study of nest-height effects on emergence phenology was conducted at one site in 2008-2010. For each individual nest or nest block on each sampling date, we recorded the number of individuals, their sex, the number already dead at the time of capture, and the experimental treatment (when appropriate). Dataloggers recorded temperature and light hourly at each study site; these data are deposited separately with Ecological Archives.

SnowEx20 Grand Mesa IOP CSU 1GHz GPR, Version 1 (other, 2021)

East River Surface and Pore Water FTICR-MS Data Associated with “Implications of sample treatment on characterization of the riverine environmental metabolome” (ess_dive, 2021)
Surface and pore water samples were collected from distributed locations around Meander A in East River (Crested Butte, CO, USA) during the summer of 2018. This dataset consists of the characterization of dissolved organic matter using 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS; in .xml format) analyzed through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) using two different sample pre-treatment methods (solid phase extraction, no solid phase extraction) and two electrospray ionization methods (ESI-positive, ESI-negative) to identify the impacts of these choices on the final dataset. This study aimed to understand how the different sample processing methods influence the resulting FTICR-MS dataset. Any published work that utilizes data presented in this dataset should cite the dataset using the DOI number. If using the dataset, please acknowledge the U.S. Department of Energy (DOE) Office of Science (BER) Subsurface Biogeochemical Research (SBR) program, the Environmental Molecular Sciences Laboratory (EMSL), and Worldwide Hydrobiogeochemical Observation Network for Dynamic River Systems (WHONDRS). The publication DOI will be added to this abstract when available.

SnowEx20 Grand Mesa Autumn 2019 Snow Pits, Version 1 (other, 2021)

1 m Resolution 4-band orthomosaic for the Upper Gunnison Basin derived from September 2019 NAIP Imagery - Ian K Breckheimer (s3, 2021)
This is a 1m resolution aerial imagery orthomosaic resampled from 0.6m 4-band orthoimagery collected on September 14th 2019 as part of the USDA National Aerial Imagery Program. The original NAIP tiles were reprojected, mosaiced and bilinearly resampled to the standard UG 1m grid, and compressed using a JPEG algorithm (90% quality) to reduce the resulting file size. The 4 bands correspond to brightness in Red (band 1), Green (band 2), Blue (band 3), and Near-IR (band 4), recorded as digital numbers scaled between 0 and 255.

Hillslope subsurface flow and transport data for the PLM transect in East River, Colorado. (ess_dive, 2021)
This file contains data used to calculate subsurface flow and transport at the Pumphouse Lower Montane (PLM) hillslope transect. Information includes locations, weather data, hydraulic conductivities, hydraulic head profiles, soil water profiles, depths to the water table, specific conductance (SC) and dissolved organic carbon (DOC) in pore waters, water isotopic composition, calculated subsurface fluxes, calculated subsurface transport concentrations for SC and DOC, East River discharge and SC, transect soil particle-size distributions, and moisture characteristic curves. Files were updated on 2019-12-03: Figure 7 now includes data on the hydraulic gradient (data added in column N of the sheet for Fig. 7). There is a new Figure 14 that shows impacts of assigning different hydraulic conductivities to the soil and weathering zone. The old Figure 14 is now Figure 15. In the supporting Information, Figures S3 is added (on pressure responses of samplers) and S4 (on water table depth relations with hydraulic gradients and transmissivity fluxes. Revised Supporting Information is now attached.

Genome-resolved metagenomics and metatranscriptomics of microbial communities in three meander-bound floodplain soils along the East River, Colorado. (ess_dive, 2021)
This dataset comprises paired environmental and genomic data for soil samples collected across meander-bound floodplains G (ERMG), L (ERML) and Z (ERMZ). A total of 96 soil samples (32 per floodplain) from the ~ 10-25 cm layer were collected in September 2015, during base flow conditions. An additional 32 soil samples were collected from ERML in September 2016, including three samples from a depth just above the water table. A subset of samples (14) collected in 2016 were used for metatranscriptomics as well as metagenomics. The data package includes: 1) A spread sheet with sample names, location and depth by meander-bound floodplain. 2) Total organic carbon (TOC), total inorganic carbon (TIC), total carbon (TC = TOC + TIC), total nitrogen (TN), and the TOC to TN ratio (TOC:N) from 2015 subsamples. 3) Soil chemistry for 2016 subsamples. 4) A spread sheet with elevation, Easting, Northing, and topographic position index (TPI) for each site sampled in 2015. 5) A kml file, which can be viewed in Google Earth, with the locations of the 96 soil sampling sites along the East River Watershed Floodplain in Colorado. Metagenomics and metatranscriptomics data, as well as reconstructed genomes, are available elsewhere (see data package methods and bioRxiv preprint for links to the data). 6) Two sets of reconstructed genomes with overlapping members (see bioRxiv preprint for description and associated data). Metagenomes and metatranscriptomes are available elsewhere (see data package methods and bioRxiv preprint). To view tar.gz files, the following software can be used: https://www.gnu.org/software/tar/, https://www.7-zip.org/.

Waterlevel, geochemical, and borehole data from Rifle, Colorado from 2006-2016 (ess_dive, 2021)
The files included in this data package provide site wide water level data, geochemical data, and borehole information associated with the Rifle site in Colorado during the Lawrence Berkeley National Lab (LBNL) led Integrated Field Research Challenge (IFRC) and Scientific Focus Area (SFA) research projects. The data informs hydrogeochemical interactions occurring in the watershed. The readme file contains detailed metadata for the files, including naming conventions, column headers, and data validation.The borehole information is provided in the file called rifle_boreholes.csv. It provides names and location for boreholes/wells at the Rifle. The waterlevel data is provided in the folder rifle_waterlevels. The geochemical data is provided in two folders: the rifle_floodplain_geochemistry folder has data which was collected from wells across the floodplain and a variety of offsite locations that contribute solutes to the floodplain given their upgradient locations. The rifle_plotC_geochemistry folder has data which was collected primarily in plot C, which was a focused study area in which a large number of groundwater manipulation / biostimulation experiments were conducted. While not all geochemical constituents are included for each well location and sampling date, they include anions, cations, trace metals, dissolved organic and inorganic carbon, stable isotopes of water and sulfur (as sulfate), and water quality parameters including pH, groundwater electrical conductivity (EC) and dissolved oxygen (DO). All files are in csv or txt format.

EXAMINATION OF BULK SOIL/DETRITAL CHARCOAL AND AMS RADIOCARBON AGE DETERMINATION OF MATERIAL FROM SITES TRCR1, TRCR2, TRCR4, TRCR5, AND TRCR6 ALONG TAYLOR RIVER, COLORADO. - Peter Kováčik (other, 2021)
Six bulk soil samples and seventeen detrital charcoal samples from sites TRCR1, TRCR2, TRCR4, TRCR5, and TRCR6 along Taylor River in Gunnison County, central Colorado, were analyzed to recover organic remains suitable for radiocarbon age determination. Samples were collected from Holocene terraces at a reach downstream of Taylor Park Reservoir. Botanic components and detrital charcoal were identified, and potentially radiocarbon datable material was separated. Five radiocarbon dates were obtained on charcoal and charred botanic remains recovered from these samples.