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\begin{document}

\title{The East River Community Observatory Data Collection: Diverse,
multiscale data from a mountainous watershed in the East River, Colorado}



\author[1]{Zarine Kakalia}%
\author[1]{Charuleka Varadharajan}%
\author[2]{Erek Alper}%
\author[1]{Eoin Brodie}%
\author[1]{Madison Burrus}%
\author[3]{Rosemary Carroll}%
\author[1]{Danielle Christianson}%
\author[1]{Valerie Hendrix}%
\author[1]{Matthew Henderson}%
\author[1,1]{Susan Hubbard}%
\author[2]{Douglas Johnson}%
\author[2]{Roelof Versteeg}%
\author[4]{Kenneth Williams}%
\author[1]{Deborah Agarwal}%
\affil[1]{Lawrence Berkeley National Laboratory}%
\affil[2]{Subsurface Insights LLC}%
\affil[3]{Desert Research Institute}%
\affil[4]{E O Lawrence Berkeley National Laboratory}%


\vspace{-1em}



  
  \date{May 29, 2024}


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\selectlanguage{english}
\begin{abstract}
The U.S. Department of Energy's (DOE) East River community observatory
(ER) in the Upper Colorado River Basin was established in 2015 as a
representative mountainous, snow-dominated watershed to study
hydrobiogeochemical responses to hydrological perturbations in headwater
systems. Led by the Watershed Function Science Focus Area (SFA), the ER
has both long-term and spatially-extensive observations paired with
experimental campaigns. The Watershed Function SFA, led by Berkeley
Laboratory, includes researchers from over 30 organizations who conduct
cross-disciplinary process-based investigations and mechanistic modeling
of watershed behavior in the ER. The data generated at the ER are
extremely heterogeneous, and include hydrological, biogeochemical,
climate, vegetation, geological, remote sensing, and model data that
together comprise an unprecedented collection of data and value-added
products within a mountainous watershed, across multiple spatiotemporal
scales, compartments, and life zones. Within 5 years of data collection,
these datasets have already revealed insights into numerous aspects of
watershed function such as factors influencing snow accumulation and
melt timing, water balance partitioning, and impacts of floodplain
biogeochemistry and hillslope ecohydrology on riverine geochemical
exports. Data generated by the SFA are managed and curated through its
Data Management Framework. The SFA has an open data policy, and over
sixty ER datasets are publicly available through relevant data
repositories. A public interactive map of data collection sites run by
the SFA is available to inform the broader community about SFA field
activities. Here, we describe the ER and the SFA measurement network,
present the public data collection generated by the SFA and partner
institutions, and highlight the value of collecting multidisciplinary
multiscale measurements in representative catchment observatories.%
\end{abstract}%



\sloppy


\textbf{Abstract}

The U.S. Department of Energy's (DOE) East River community observatory
(ER) in the Upper Colorado River Basin was established in 2015 as a
representative mountainous, snow-dominated watershed to study
hydrobiogeochemical responses to hydrological perturbations in headwater
systems. Led by the Watershed Function Science Focus Area (SFA), the ER
has both long-term and spatially-extensive observations paired with
experimental campaigns. The Watershed Function SFA, led by Berkeley
Laboratory, includes researchers from over 30 organizations who conduct
cross-disciplinary process-based investigations and mechanistic modeling
of watershed behavior in the ER. The data generated at the ER are
extremely heterogeneous, and include hydrological, biogeochemical,
climate, vegetation, geological, remote sensing, and model data that
together comprise an unprecedented collection of data and value-added
products within a mountainous watershed, across multiple spatiotemporal
scales, compartments, and life zones. Within 5 years of data collection,
these datasets have already revealed insights into numerous aspects of
watershed function such as factors influencing snow accumulation and
melt timing, water balance partitioning, and impacts of floodplain
biogeochemistry and hillslope ecohydrology on riverine geochemical
exports. Data generated by the SFA are managed and curated through its
Data Management Framework. The SFA has an open data policy, and over
sixty ER datasets are publicly available through relevant data
repositories. A public interactive map of data collection sites run by
the SFA is available to inform the broader community about SFA field
activities. Here, we describe the ER and the SFA measurement network,
present the public data collection generated by the SFA and partner
institutions, and highlight the value of collecting multidisciplinary
multiscale measurements in representative catchment observatories.

\textbf{Site Description}

The East River community observatory (ER) in the Upper Colorado Basin,
United States (39.033\selectlanguage{ngerman}° N 107.12° W, 38.83° N 106.88° W) is a 300 square
kilometer headwater catchment representative of watersheds in the Rocky
mountains of the Western United States (Hubbard et al., 2018). Since
2015, the ER has been the primary field site for the U.S. Department of
Energy's Watershed Function Scientific Focus Area
(SFA;\emph{http://watershed.lbl.gov}), and now serves as a community
testbed for over 30 collaborating institutions that collectively aim to
understand the impacts of perturbations, such as drought and early
snowmelt, on the hydrobiogeochemical dynamics of mountainous, headwater
catchments at seasonal to decadal timescales. The ER spans first-order
mountain streams and meandering floodplains across four drainages - the
East River, Washington Gulch, Slate River, and Coal Creek (Figure 1a).
The ER lithology consists of igneous formations intruding into
carbon-rich marine shale in the Mancos Formation, as well as sedimentary
strata grading older (Permian) to younger (Tertiary) as one moves east
to west across the ER domain with pockets of significant mineralization
(Carroll et al., 2018; Gaskill, 1991). In addition to geologic
variability, the ER is characterized by steep elevation, hydrologic and
vegetation gradients along floodplain, montane, subalpine, and alpine
life zones, which makes it an ideal location to understand how different
mountain subsystems contribute to overall watershed behavior.

\textbf{Measurements and field infrastructure}

The SFA and collaborators have collected extensive sample- and
sensor-based measurements at several locations across the East River and
adjacent drainages (Figure 1a). Regions of particular emphasis include
``SFA-intensive'' sites located within representative meanders and a
hillslope in a lower montane (Pumphouse) subregion of the pristine East
River drainage basin, where several cross-disciplinary, co-located
measurements are being conducted (Figure 1b). Additional ``satellite''
sites, targeting specific research questions are located near the Brush
Creek confluence floodplain, an elevation gradient of research meadows
along Washington Gulch and on the flanks of Cinnamon Mountain and Mount
Baldy, Snodgrass Mountain, and mining-impacted sites in both Coal Creek
and the Redwell Basin.

The ER instrumentation network maintained by the SFA and collaborators
includes 15 stream-gauging and water quality stations used to obtain
paired concentration-discharge measurements, 6 weather stations with
soil moisture and temperature probes, 18 instrumented groundwater wells
(e.g Figure 2a-2c), and about \textasciitilde{}40 piezometers,
\textasciitilde{}15 ecohydrological sensor stations, and
\textasciitilde{}40 digital phenocam locations (Varadharajan et al.
2020). An eddy flux tower is maintained in the East River floodplain by
the National Center for Atmospheric Research (NCAR). Extensive
measurements of depth-resolved snow density, snow water equivalent and
water isotopes as well as whole-pit chemistry, snowmelt water isotopes,
rain chemistry and water isotopes have been conducted over multiple
years to inform stream water source (Fang et al., 2019). Snowmelt
manipulation experiments in vegetation plots in different mountain life
zones were used to study the impacts of snowmelt timing on vegetation
phenology. Metagenomic analyses of microbial communities have been
conducted for soils and sediments representing various locations across
the floodplain meanders and lower montane hillslopes that contribute
water and various elements to the river (Lavy et al., 2020; Matheus
Carnevali et al., 2020; Sorensen et al., 2020). As a result, and with
watershed-scale surveys in progress, the East River watershed in
Colorado is fast becoming the most genomically characterized
high-elevation watershed in the world, with over 5,000
metagenome-assembled genomes to date enabled by DOE JGI-CSP and FICUS
awards. In addition, several multi-institutional remote sensing
campaigns have been conducted at the ER which include a 2015 Light
Detection and Ranging (LiDAR) survey led by the SFA (Wainwright \&
Williams, 2017), Airborne Snow Observatory (ASO) flights by the National
Aeronautics and Space Administration's Joint Propulsion Laboratory (NASA
JPL) in 2016, 2018 and 2019 (Painter et al., 2016), a 2017 USGS Airborne
Electromagnetic survey, and a 2018 National Ecological Observation
Network (NEON) hyperspectral survey paired with an extensive
ground-based campaign conducted in coordination with Stanford University
(Chadwick et al., 2020). In 2021, a two-year deployment of the DOE's
Atmospheric Radiation Measurement (ARM) Program mobile Surface
Atmosphere Integrated Field Laboratory (SAIL) will use more than three
dozen instruments to collect a suite of meteorology, clouds, aerosol and
other atmospheric measurements in the ER (\emph{http://sail.lbl.gov}).
The SFA's ER measurement locations are viewable through a public,
user-friendly field information portal
(\emph{https://wfsfa-data.lbl.gov/watershed/}).

The ER watershed also has significant infrastructure maintained by
several federal, state, and local agencies that have different data
systems (some of these are indicated in Figure 1a). Notably, the Rocky
Mountain Biological Laboratory (RMBL, https://www.rmbl.org/) is situated
in the townsite of Gothic, and has over 90 years of data collection
activities in the watershed. Snow measurements and associated
meteorological data are available from the National Resources
Conservation Service (NRCS) snow Telemetry (SNOTEL) sites `Butte' and
`Schofield Pass', Crested Butte Cooperative Observer Network (COOP), and
a number of Weather Underground stations. The USGS maintains gaging
stations and collects water quality measurements across the East-Taylor
watersheds, and makes the data available through NWIS (HUC: 14020001).
Additional water quality data are available from the National Water
Quality Portal, which includes measurements by the U.S. Environmental
Protection Agency (EPA), Colorado Department of Public Health and
Environment (CDPHE), and local groups including the Coal Creek Watershed
Coalition and the Rivers of Colorado Water Watch. The EPA also maintains
a National Atmospheric Deposition Program (NADP) Colorado Clean Air
Status and Trends Network (CASTNET) station at Gothic.

\textbf{Overview of ER Datasets and Research Themes}

The Watershed Function SFA and its collaborators generate vastly
diverse, multiscale datasets at the ER including hydrological,
(bio)geochemical, climate, vegetation, geophysical, microbiological,
remote sensing, and model data. Additionally, several model datasets are
generated from numerical simulations of different watershed subsystems
and their aggregated behavior. Detailed descriptions of the data types,
data variables collected, and methods used are listed in Table 1. The
most common publicly available data types from the ER during 2015-2020
are biogeochemistry and hydrology data (Figure 3).

The simultaneous collection of these data types across the watershed
allows researchers across institutions to share knowledge and draw
conclusions. Examples of research topics that these data were collected
to address include correlations of hydrologic data with variation in
climate and microclimate; effects of snow accumulation and melt timing
on vegetation, microbial and watershed function; stream gas exchange
rates, groundwater and surface water interactions, biogeochemical
hotspots and hydrodynamics; cycling and sourcing of metal nutrients by
plants, nitrogen release dynamics from shale bedrock; sources of
elemental contributions to the East River and weathering processes
impact on river water; solute concentration responses and water
partitioning to quantify seasonal stream water; and more broadly an
aggregated understanding of watershed hydrobiogeochemical processes
given their variation over space and different watershed functional
zones and compartments.

\textbf{Data Policy, Curation Process, and Availability}

The SFA has a Data Management Framework component that provides services
and infrastructure to support the project's data lifecycle. The
framework comprises systems, workflows and scripts to acquire and store
data in a queryable database, conduct QA/QC, integrate project data with
external data for real-time queries, discover and download data, and to
publish data with digital object identifiers (DOIs) in the DOE's
Environmental Systems Science Data Infrastructure for a Virtual
Ecosystem (ESS-DIVE) repository (Varadharajan et al., 2019).
Additionally, the SFA has developed an integrated field-data workflow to
acquire critical metadata from these diverse data streams, and to manage
data at each stage of the scientific process. The field-data workflow
outlines guidelines for managing metadata and identifiers for field
locations, samples, sensors, and data package creation and publication.
While developed by the SFA, the workflow is available to other
collaborating organizations, and is built based on community feedback
and established data management best practices.

The SFA has an open data policy where project-generated data are made
publicly available following the U.S. Department of Energy's guidelines
(\emph{https://watershed.lbl.gov/data/data-policy/}). Several datasets
generated by the SFA and collaborators at the ER are publicly available
in online repositories, including ESS-DIVE, NCBI, United States
Geological Survey (USGS), NASA DAAC: National Snow and Ice Data Center,
Figshare, and HydroShare (Table 2). The SFA alone has 36 public datasets
with associated metadata available on ESS-DIVE that include many of the
long-term monitoring and spatially-extensive remote sensing and
associated ground campaign datasets. A collection of data packages
generated across institutions at the ER can be accessed on ESS-DIVE
through the East River watershed portal
(\emph{https://data.ess-dive.lbl.gov/portals/east-river-watershed}). The
data are typically licenced under Creative Commons by Attribution
(CC-By4.0) or Creative Commons Public Domain (CC0) usage policies. Large
datasets such as the remote sensing or model products are stored and
distributed through public data transfer nodes on the DOE's National
Energy Scientific Computing Center (NERSC).

\textbf{Scientific Impact of ER Datasets}

The multidisciplinary data from the ER have already advanced significant
understanding of hydrological processes in mountainous catchments, and
they have been used in numerous publications of which a select few are
highlighted here. For example by combining measurements of river, rain,
groundwater and snow chemistry, stream discharge, remote sensing (LIDAR,
ASO), Carroll et al. (2018, 2019) found that groundwater recharge, an
important contributor to streamflow, is dependent on elevation and
vegetation and increases in higher elevations, such as the upper
subalpine zone where there is greater snow accumulation and lower canopy
cover. Through analyses of data on groundwater chemistry, water table
depth, and rock mineralogy, Wan et al. (2019) found that the seasonal
water table depth determines the weathering zone and weathering front in
sedimentary bedrock, and that the Mancos shale can be a significant
contributor to river nitrogen exports. Combining snow measurements with
metagenome analysis, (Sorensen et al., 2020) found that snowmelt
triggers a pulse of nitrogen in hillslope soils concomitant with a
collapse in microbial biomass, and changes in microbial community
composition. Using model simulations of floodplain meanders and regions
of hyporheic exchange, Dwivedi et al. (2017, 2018) found these
subsystems exert critical controls on nitrogen cycling and other solute
exports to the river. These findings, and others from the ER community,
highlight the value of designing multidisciplinary watershed
observatories using open science by design principles, and publishing
data generated in open, public repositories (Stegen et al., 2019).

\textbf{Data Availability Statement}

The data that support the findings of this study are openly available in
several repositories as listed in Table 2.

\textbf{Acknowledgements}

This research is supported as part of the Watershed Function Scientific
Focus Area funded by the U.S. Department of Energy, Office of Science,
Office of Biological and Environmental Research under Contract No.
DE-AC02-05CH11231. This research used resources of the National Energy
Research Scientific Computing Center (NERSC) and the U.S. Department of
Energy's Joint Genome Institute (JGI), which are U.S. Department of
Energy Office of Science user facilities operated under Contract No.
DE-AC02-05CH11231. Support to Subsurface Insights for development of
some of the core abilities used here under awards DE-SC0009732 and
DE-SC0018447 is gratefully acknowledged. We also acknowledge the members
of the Watershed SFA community who provided information about their
datasets for this paper, including Erica Woodburn, Max Berkelhammer,
Rich Wanty, Curtis Beutler, Heidi Steltzer, Amanda Henderson, Brian
Enquist, Baptiste Dafflon, Li Li, Qina Yan, Kamini Singha, Martin
Briggs, Reed Maxwell, David Gochis, Anna Ryken, Bhavna Arora, Sebastian
Uhlemann, Michael Wilkins, Amelia Nelson, Ulas Karaoz, Janice Brahney,
Yuxin Wu, Dana Chadwick, Boris Faybishenko, John Christensen, Wendy
Brown, Nick Bouskill, Pat Sorensen, Jiancong Chen, Jiamin Wan, James St.
Clair, Savannah (Bryant) Scott, Matthew Winnick, Anna Chovanes, Xiaoqin
Wu, Ate Visser, Mark Raleigh, Jeffrey Deems, Joel Rowland, Michelle
Newcomer, and Mariah Carbone.



\textbf{FIGURES}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Graphical Abstract (caption not required}

\textbf{Figure 1a:} Map of ER site and measurements of the SFA and some
collaborators. The yellow line indicates the East River Community
Observatory domain. The red lines indicate the major drainage boundaries
of the East River, Washington Gulch (Wash. Gul.), Slate River, and Coal
Creek. The blue lines show stream flow lines determined from the
National Hydrography Dataset (U.S. Geological Survey, 2001).
Measurements by community partners in the Slate River and Redwell basin
are not shown. The white box shows the intensively sampled Pumphouse
subregion shown in greater detail in Figure 1b.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 1b} Diverse measurements conducted in the SFA-intensive
Pumphouse subregion include a hillslope transect and near the meanders
of the East River.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 2} . Photographs of some field locations and
infrastructure installations in the ER: (A) Hillslope borehole,
subsurface water and carbon inventory well (B) Weather station,
atmospheric weather sensor in the alpine zone, (C) Pumphouse ISCO,
sampler for subsurface discharge and solute flux in the subalpine zone,
(D) A plot to study impacts of early snowmelt on vegetation at a
subalpine location along an elevation gradient\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 3.} Categories of published data packages from the East
River Watershed. The numbers shown in the X axis represent the number of
published data packages with a given data type out of 67 data packages
total. Some data packages have data from more than one data type.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Table 1} . ER Data types, variables, and methods of data
collection\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\textbf{Data Type}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\textbf{Data Variables}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\textbf{Methods/Instrumentation}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Hydrology\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Evapotranspiration, Groundwater Pressure, Water Table Depths,
Groundwater Temperature, Stream Water Temperature, Stream Discharge,
Soil Moisture, Soil Temperature, Snow Cover, Snow Chemistry, Snow
Density, Water Isotopes, Ecosystem Respiration, Stream Gas Exchange
Rates, Hydraulic Conductivity\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
ParFlow-CLM, PRISM, SNOTEL, GSFLOW, Groundwater Wells, Snowpits,
Pressure Transducers, Acoustic Doppler Current Profiler, Dissolved Gas
Sample Collection, Mass Spectrometry\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Biogeochemistry\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Surface Water Geochemistry, Groundwater Geochemistry, Isotopes, Anions,
Cations, Total Dissolved Nitrogen, Dissolved Organic Carbon, Dissolved
Inorganic Carbon, Ammonia, Hydrochloride, Dissolved Gas Concentrations,
Mineralogy, Metagenomics, Microbial Biomass, Spectroscopy,
Transcriptomics\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Soil Lysimeter, Nucleic Acids Sequencing, Piezometers, Soil Sensors,
Radiation Sensors, Infrared Sensors, Flux devices, MicroMET, MC-ICPMS,
TIMS, Chromatography, Laser Absorbance Spectroscopy\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Climate\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Atmospheric deposition, Meteorology, Precipitation, Energy Fluxes, Heat
Flux\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Weather Stations, Microclimate Loggers\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Vegetation\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Plant growth, Biophysical Characteristics of Plant Canopy, Plant
Nutrients, Snowmelt Date, Growing Season Microclimate, Leaf Litter
Quality, Mass Loss, Sap Flux, Canopy Temperature\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Direct physical observations of phenophases and plant canopy
characteristics, Sap Flux Sensors, Thermal Cameras,\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Geology and Geophysics\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Soil Electrical Conductivity, Electrical Resistivity Tomography,
Seismic, Bedrock Properties, Porosity, Permeability, Hydraulic Flow,
Soil Thickness, Soil Organic Carbon\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Sensor Network, Groundwater Well 2-D Spatial Geotiff, Sampling Stations,
self-developed model (SCALE)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Remote Sensing\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Hyperspectral, Land Surface Elevation, Normalized Difference Vegetation
Index\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Ground-penetrating radar, LiDAR, Electromagnetic and Spectroscopic
Imaging, Drones\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} 67 public datasets generated by the Watershed Function
SFA and collaborating institutions are available in many data
repositories. The datasets can be accessed using a digital object
identifier (DOI) by appending a prefix\emph{http://dx.doi.org/DOI}.
Further details for these data, including descriptions, methods, and
author contact information are available on the dataset landing pages.
For continued access to newly published datasets, view the East River
Watershed portal
(\emph{https://data.ess-dive.lbl.gov/portals/east-river-watershed}).\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Measurement Type} & \textbf{Data Package Title} &
\textbf{Citation} & \textbf{Data DOI}\tabularnewline
\textbf{ESS-DIVE (Environmental Systems Science Data Infrastructure for
a Virtual Ecosystem): 53 datasets} & \textbf{ESS-DIVE (Environmental
Systems Science Data Infrastructure for a Virtual Ecosystem): 53
datasets} & \textbf{ESS-DIVE (Environmental Systems Science Data
Infrastructure for a Virtual Ecosystem): 53 datasets} & \textbf{ESS-DIVE
(Environmental Systems Science Data Infrastructure for a Virtual
Ecosystem): 53 datasets}\tabularnewline
Site Description & Location Identifiers and Site Metadata for Field
Locations at the East River Watershed, Colorado & Varadharajan et al.
2020 & 10.15485/1660962+\tabularnewline
Site Description & Sub-Basin Delineation for the Upper East River,
Colorado, United States & (R. Carroll, Bill, et al., 2019) &
\emph{10.21952/WTR/1508403}+\tabularnewline
Biogeochemistry & Anion Data for the East River Watershed, Colorado. &
Williams et al. 2020 & 10.15485/1668054+\tabularnewline
Biogeochemistry & Annual floodplain sediment deposition recorded using
feldspar clay marker horizons along the East River, Colorado, 2015-2017.
& (Sutfin \& Rowland, 2019a) & \emph{10.15485/1577279}\tabularnewline
Biogeochemistry & Cation Data for the East River Watershed, Colorado &
Dong et al. 2020 & 10.15485/1668055+\tabularnewline
Biogeochemistry & Classified channel masks of the East River, Colorado,
U.S.A and areas of floodplain erosion and accretion ranging from 1955 to
2015. & (Rowland \& Stauffer, 2020a) &
\emph{10.15485/1642909}\tabularnewline
Biogeochemistry & Depth and elevation of floodplain fine sediment along
the East River near Crested Butte, Colorado measured in 2016 and 2017. &
(Sutfin \& Rowland, 2019b) & \emph{10.15485/1574502}\tabularnewline
Biogeochemistry & Depth-resolved seasonal porewater chemistry
measurements from 3 locations around Meander A of the East River,
Colorado. Seasonal controls on dynamic hyporheic zone redox
biogeochemistry. & (Saup et al., 2019) &
\emph{10.15485/1504779}\tabularnewline
Biogeochemistry & Dissolved Inorganic Carbon and Dissolved Organic
Carbon Data for the East River Watershed, Colorado. & (Dong, Beutler,
Brown, et al., 2020) & \emph{10.15485/1660459}+\tabularnewline
Biogeochemistry & Distinct Source Water Chemistry Shapes Contrasting
Concentration Discharge Patterns, Water Resources Research: Dataset &
(Zhi et al., 2019) & \emph{10.21952/WTR/1528928}+\tabularnewline
Biogeochemistry & Distributed hydrological, chemical, and
microbiological measurements around Meander A of East River, Colorado.
Seasonal controls on dynamic hyporheic zone redox biogeochemistry. &
(Nelson et al., 2019) & \emph{10.15485/1507800}\tabularnewline
Biogeochemistry & Geochemical characterization of floodplain sediments
from Meander C and O in the East River Watershed, CO, USA & (Fox et al.,
2019) & \emph{10.21952/WTR/1572186}+\tabularnewline
Biogeochemistry & Hyporheic, Floodplain, and Surface Water (on
Floodplain and River) Geochemical Datasets, and Shapefiles on Meander C
at the East River, Colorado & (Newcomer, Raberg, et al., 2020) &
\emph{10.15485/1647038}+\tabularnewline
Biogeochemistry & Kinetics Data of Iron(II), Manganese(II), Dissolved
Organic Carbon and Nitrate from Batch Incubation Experiments Using
Hyporheic Sediments from East River, Colorado. & (Dong, Fox, et al.,
2020) & \emph{10.15485/1659482}+\tabularnewline
Biogeochemistry & Natural abundance nitrate isotopes from Rifle and the
East River Watershed, Colorado & (Bouskill et al., 2020) &
\emph{10.15485/1660462}+\tabularnewline
Biogeochemistry & River bank erosion and accretion rates, and planform
metrics of the East River, downstream of Gothic Colorado over intervals
between 1955 and 2015. & (Rowland \& Stauffer, 2020b) &
\emph{10.15485/1642910}\tabularnewline
Biogeochemistry & Sample Collection Metadata for Soil Cores from the
East River Watershed, Colorado collected in 2017 & (P. Sorensen et al.,
2019a) & \emph{10.21952/WTR/1573029}+\tabularnewline
Biogeochemistry & Sapflow and xylem water isotopes from Snodgrass
Mountain, East River Watershed, Colorado USA & (Berkelhammer, 2020) &
\emph{10.15485/1647654}\tabularnewline
Biogeochemistry & Soil Environmental Conditions in the East River
Watershed, CO & (Winnick et al., 2020) &
\emph{10.15485/1660455}\tabularnewline
Biogeochemistry & Soil Nitrogen, Water Content, Microbial Biomass, and
Archaeal, Bacterial and Fungal Communities from the East River
Watershed, Colorado collected in 2016-2017 & (P. Sorensen et al., 2019b)
& \emph{10.15485/1577267}+\tabularnewline
Biogeochemistry & Total Dissolved Nitrogen and Ammonia Data for the East
River Watershed, Colorado. & (Dong, Beutler, Bouskill, et al., 2020) &
\emph{10.15485/1660456}+\tabularnewline
Biogeochemistry & Using geochemical indicators to distinguish high
biogeochemical activity in floodplain soils and sediments, Science of
the Total Environment: Dataset & (Sitchler et al., 2019) &
\emph{10.21952/WTR/1506944}+\tabularnewline
Biogeochemistry & Using strontium isotopes to evaluate the spatial
variation of groundwater recharge, Science of The Total Environment:
Dataset & (Christensen et al., 2019) &
\emph{10.21952/WTR/1495381}+\tabularnewline
Biogeochemistry, Climate, Hydrology & Identifying geochemical hot
moments and their controls on a contaminated river floodplain system
using wavelet and entropy approaches, Environmental modelling \&
software: Dataset & (Arora, Dwivedi, et al., 2019) &
\emph{10.21952/WTR/1506938}+\tabularnewline
Biogeochemistry, Climate, Hydrology & The East River, Colorado,
Watershed: A Mountainous Community Testbed for Improving Predictive
Understanding of Multiscale Hydrological--Biogeochemical Dynamics:
Dataset & (S. Hubbard et al., 2019) &
\emph{10.21952/WTR/1506942}+\tabularnewline
Biogeochemistry, Hydrology & Conifer water use patterns in the East
River Watershed, Colorado US, based on stable water isotopes and
cellulose isotopes. Space and Time Dynamics of Transpiration in the East
River Watershed: Biotic and Abiotic Controls (DE-SC0019210). &
(Berkelhammer et al., 2020) & \emph{10.15485/1596123}\tabularnewline
Biogeochemistry, Hydrology & Copper Creek Baseflow Age Experiment using
Environmental Gas Tracers & (Rosemary Carroll et al., 2019) &
\emph{10.21952/WTR/1572196}+\tabularnewline
Biogeochemistry & Genome-resolved metagenomics and metatranscriptomics
of microbial communities in three meander-bound floodplain soils along
the East River, Colorado. & (P. Matheus Carnevali et al., 2020) &
\emph{10.15485/1631979}+\tabularnewline
Biogeochemistry, Hydrology & Differential concentration discharge for
the upper and lower reaches of the East River Watershed, Colorado. &
(Arora et al., 2020) & \emph{10.15485/1657887}+\tabularnewline
Biogeochemistry, Hydrology & Geochemical exports to river from the
intrameander hyporheic zone under transient hydrologic conditions, Water
Resources Research: Dataset & (Dwivedi, 2019) &
\emph{10.21952/WTR/1506940}+\tabularnewline
Biogeochemistry, Hydrology & Hillslope subsurface flow and transport
data for the PLM transect in East River, Colorado & (Tokunaga et al.,
2019) & \emph{10.21952/WTR/1506941}+\tabularnewline
Biogeochemistry, Hydrology & Influence of hydrological, biogeochemical
and temperature transients on subsurface carbon fluxes in a flood plain
environment, Biogeochemistry: Dataset & (Arora, Bill, et al., 2019) &
\emph{10.21952/WTR/1506937}+\tabularnewline
Biogeochemistry, Hydrology, Geology and Geophysics & Predicting
sedimentary bedrock subsurface weathering fronts and weathering rates &
(Wan, Tokunaga, Williams, Brown, et al., 2019) &
\emph{10.15485/1601290}+\tabularnewline
Biogeochemistry, Remote Sensing & Gap-filled water quality, Normalized
Differenced Vegetation Index, total nitrogen (nitrate and ammonia)
deposition, and land cover data trends for the Continental United
States. & (Newcomer, Bouskill, et al., 2020) &
\emph{10.15485/1647366}+\tabularnewline
Climate, Remote Sensing, Vegetation & Hybrid predictive modeling
approach simulated evapotranspiration and ecosystem respiration data. &
(Chen et al., 2020) & \emph{10.15485/1633810}+\tabularnewline
Hydrology & Stable Water Isotope Data for the East River Watershed,
Colorado & Williams et al. 2020 &
\emph{10.15485/1668053+}\tabularnewline
Hydrology & End-Member Mixing Analysis Data Package for the East River
Watershed, CO USA & (Rosemary Carroll et al., 2018) &
\emph{10.21952/WTR/1465929+}\tabularnewline
Hydrology & Discharge data collected within the East River for the
Lawrence Berkeley National Laboratory Watershed Function Science Focus
Area (water years 2015-present) & (Rosemary \& Williams, 2019) &
\emph{10.21952/WTR/1495380+}\tabularnewline
Hydrology & The Importance of Interflow to Groundwater Recharge in a
Snowmelt-Dominated, Alpine Watershed, Geophysical Research Letters:
Modeling and Data Package. & (R. Carroll, Deems, et al., 2019) &
\emph{10.21952/WTR/1508390}+\tabularnewline
Hydrology & Estimated Darcy Velocities Using Temperature Time Series for
Meander A of East River, Colorado. & (Bryant et al., 2019) &
\emph{10.15485/1498798}\tabularnewline
Hydrology & Groundwater level elevation and temperature across Meander C
at the Lower Montane in the East River Watershed, Colorado. & Dafflon et
al. 2020 & \emph{10.15485/1647041}\tabularnewline
Hydrology & Groundwater level elevation and temperature at the Lower
Montane in the East River Watershed, Colorado. & (Dafflon \& Dwivedi,
2020) & \emph{10.15485/1647040}+\tabularnewline
Geophysics and Geology & Geophysical borehole logging data of wells
ER-GLS1, ER-GUM1, ER-PLM7, and ER-PLM8 at the East River Watershed,
Colorado. & (Uhlemann et al., 2020) &
\emph{10.15485/1650355}+\tabularnewline
Geophysics and Geology & Time-domain reflectometer survey across the
East River Watershed, Colorado. & (Falco et al., 2020) &
\emph{10.15485/1648526}+\tabularnewline
Remote Sensing & Custom NEON AOP reflectance mosaics and maps of shade
masks, canopy water content. & (Brodrick et al., 2020) &
\emph{10.15485/1618131}\tabularnewline
Remote Sensing & LiDAR collection in August 2015 over the East River
Watershed, Colorado, USA & (Wainwright \& Williams, 2017) &
\emph{10.21952/WTR/1412542}\tabularnewline
Remote Sensing, Vegetation & NEON AOP foliar trait maps, maps of model
uncertainty estimates, and conifer map, East River, CO 2018. &
(Chadwick, Brodrick, et al., 2020) &
\emph{10.15485/1618133}\tabularnewline
Remote Sensing & NEON AOP Imaging Spectroscopy Survey of Upper East
River Watersheds: Raw-Space Radiance and Observational Variable Dataset
& (Goulden, Hulslander, et al., 2020) &
\emph{10.15485/1617204}\tabularnewline
Remote Sensing & NEON AOP Survey of Upper East River Watersheds: LiDAR
Surface, Terrain, Canopy Height Rasters and LAZ Files & (Goulden, Hass,
et al., 2020) & 1\emph{0.15485/1617203}\tabularnewline
Remote Sensing & Post survey report for AOP Assignable Asset collection
of Crested Butte, CO & (Goulden \& Musinsky, 2020) &
\emph{10.15485/1617202}+\tabularnewline
Vegetation & Site-level Foliar C, N, delta13C data from samples
collected during field survey associated with NEON AOP survey, East
River, CO 2018. & (Chadwick, Grant, Bill, et al., 2020) &
\emph{10.15485/1631278}\tabularnewline
Vegetation & Leaf mass per area and leaf water content measurements from
field survey in association with NEON AOP survey, East River, CO 2018. &
(Chadwick, Grant, Henderson, Scott, et al., 2020) &
\emph{10.15485/1618132}\tabularnewline
Vegetation & Locations, metadata, and species cover from field sampling
survey associated with NEON AOP survey, East River, CO 2018. &
(Chadwick, Grant, Henderson, Breckheimer, et al., 2020) &
\emph{10.15485/1618130}+\tabularnewline
\textbf{Figshare: 1 dataset} & \textbf{Figshare: 1 dataset} &
\textbf{Figshare: 1 dataset} & \textbf{Figshare: 1
dataset}\tabularnewline
Biogeochemistry & Depth profiles of soil CO2 Concentrations, soil
temperature, and soil moisture (Rocky Mountain Biological Laboratory,
Gothic, Colorado, 2011-2016) & (Carbone, 2019) &
\emph{10.6084/m9.figshare.7834406.v1}\tabularnewline
\textbf{HydroShare: 1 dataset} & \textbf{HydroShare: 1 dataset} &
\textbf{HydroShare: 1 dataset} & \textbf{HydroShare: 1
dataset}\tabularnewline
Hydrogeology, Remote Sensing & Data from Malenda et al. (2019),
Floodplain hydrostratigraphy from sedimentology, geophysics, and remote
sensing & (Malenda H, n.d.) &
\emph{10.4211/hs.394a6900a0bd4911b642f9ba94046780}\tabularnewline
\textbf{NCBI (National Center for Biotechnology Information): 2
datasets} & \textbf{NCBI (National Center for Biotechnology
Information): 2 datasets} & \textbf{NCBI (National Center for
Biotechnology Information): 2 datasets} & \textbf{NCBI (National Center
for Biotechnology Information): 2 datasets}\tabularnewline
Biogeochemistry & Asgard archaea illuminate the origin of eukaryotic
cellular complexity & (Zaremba-Niedzwiedzka et al., 2017) &
\emph{10.1038/nature21031}\tabularnewline
Biogeochemistry & Hydrogen-based metabolism as an ancestral trait in
lineages sibling to the Cyanobacteria & (P. B. Matheus Carnevali et al.,
2019) & \emph{10.1038/s41467-018-08246-y+}\tabularnewline
\textbf{USGS Sciencebase: 6 datasets} & \textbf{USGS Sciencebase: 6
datasets} & \textbf{USGS Sciencebase: 6 datasets} & \textbf{USGS
Sciencebase: 6 datasets}\tabularnewline
Biogeochemistry & Chemical and geophysical data collected along
Oh-be-joyful Creek, Gunnison National Forest, Colorado & (Briggs, 2018)
& \emph{10.5066/F71Z42NF}\tabularnewline
Biogeochemistry & Thermal infrared and photogrammetric data collected by
small unoccupied aircraft system for hydrogeologic analysis of
Oh-be-joyful Creek, Gunnison National Forest, Colorado, August 2017 &
(Dawson et al., 2018) & \emph{10.5066/P931G95D}\tabularnewline
Biogeochemistry, Hydrogeology & Geochemical analyses of surface water,
groundwater and springs surrounding Mount Emmons near Crested Butte,
Colorado & (Johnson et al., 2019) &
\emph{10.5066/P9CQJ0XR}\tabularnewline
Biogeochemistry, Hydrogeology & Hydrogeochemical data for the
characterization of stream, groundwater, and beaver-induced floodplain
exchange in the East River Science Focus Area, Crested Butte, CO &
(Briggs, Wang, et al., 2019) & \emph{10.5066/P9Q1Z1TK}\tabularnewline
Hydrogeology & Fiber-optic distributed temperature data collected along
the streambed of the East River, Crested Butte, CO, USA & (Briggs et
al., 2017) & \emph{10.5066/F7J9658M}\tabularnewline
Remote Sensing & Thermal infrared, multispectral, and photogrammetric
data collected by drone for hydrogeologic analysis of the East River and
Coal Creek beaver-impacted corridors near Crested Butte, Colorado &
(Briggs, Dawson, et al., 2019) & \emph{10.5066/P9YWSJ2J}\tabularnewline
\textbf{National Snow and Ice Data Center (NSIDC): 4 datasets} &
\textbf{National Snow and Ice Data Center (NSIDC): 4 datasets} &
\textbf{National Snow and Ice Data Center (NSIDC): 4 datasets} &
\textbf{National Snow and Ice Data Center (NSIDC): 4
datasets}\tabularnewline
Remote Sensing & ASO L4 Lidar Point Cloud Digital Terrain Model 3m UTM
Grid, Version 1. {[}USCOGE/USCOCB{]} & (T. H. Painter \& Bormann, 2020)
& \emph{10.5067/2EHMWG4IT76O}\tabularnewline
Remote Sensing & ASO L4 Lidar Snow Depth 3m UTM Grid, Version 1.
{[}USCOGE/USCOCB{]} & (T. H. Painter, 2018) &
\emph{10.5067/KIE9QNVG7HP0}\tabularnewline
Remote Sensing & ASO L4 Lidar Snow Depth 50m UTM Grid, Version 1.
{[}USCOGE/USCOCB{]} & (T. Painter, 2018a) &
\emph{10.5067/STOT5I0U1WVI.}\tabularnewline
Remote Sensing & ASO L4 Lidar Snow Water Equivalent 50m UTM Grid,
Version 1. {[}USCOGE/USCOCB{]} & (T. Painter, 2018b) &
\emph{10.5067/M4TUH28NHL4Z}\tabularnewline
\bottomrule
\end{longtable}

+ Datasets generated by the Watershed Function Science Focus Area.

\textbf{References}

\begin{enumerate}
\tightlist
\item
  Arora, B., Bill, M., Conrad, M., Dong, W., Faybishenko, B., Molins,
  S., Spycher, N., Steefel, C., Tokunaga, T., Wan, J., \& Williams, K.
  (2019). \emph{Influence of hydrological, biogeochemical and
  temperature transients on subsurface carbon fluxes in a flood plain
  environment, Biogeochemistry: Dataset}{[}Data set{]}. Watershed
  Function SFA. https://doi.org/10.21952/WTR/1506937
\item
  Arora, B., Burrus, M., Newcomer, M., Steefel, C., Carroll, R.,
  Dwivedi, D., Dong, W., Williams, K., \& Hubbard, S. (2020).
  \emph{Differential concentration discharge for the upper and lower
  reaches of the East River Watershed, Colorado} {[}Data set{]}.
  Environmental System Science Data Infrastructure for a Virtual
  Ecosystem; Watershed Function SFA. https://doi.org/10.15485/1657887
\item
  Arora, B., Dwivedi, D., Hubbard, S., Steefel, C., \& Williams, K.
  (2019). \emph{Identifying geochemical hot moments and their controls
  on a contaminated river floodplain system using wavelet and entropy
  approaches, Environmental modelling \& software: Dataset} {[}Data
  set{]}. Watershed Function SFA. https://doi.org/10.21952/WTR/1506938
\item
  Berkelhammer, M. (2020).\emph{Sapflow and xylem water isotopes from
  Snodgrass Mountain, East River Watershed, Colorado USA} {[}Data
  set{]}. Environmental System Science Data Infrastructure for a Virtual
  Ecosystem; Space and time dynamics of transpiration in the East River
  watershed: biotic and abiotic controls (DE-SC0019210).
  https://doi.org/10.15485/1647654
\item
  Berkelhammer, M., Ritter, F., Williams, K., Carroll, R., Still, C.,
  Winnick, M., Anderson, L., \& Carbone, M. (2020). \emph{Conifer water
  use patterns in the East River Watershed, Colorado US, based on stable
  water isotopes and cellulose isotopes} {[}Data set{]}. Space and Time
  Dynamics of Transpiration in the East River Watershed: Biotic and
  Abiotic Controls. https://doi.org/10.15485/1596123
\item
  Bouskill, N. J., Sorensen, P., Conrad, M., Bill, M., Maavara, T.,
  Newman, A., Brodie, E., \& Williams, K. (2020). \emph{Natural
  abundance nitrate isotopes from Rifle and the East River Watershed,
  Colorado} {[}Data set{]}. Environmental System Science Data
  Infrastructure for a Virtual Ecosystem; Watershed Function SFA.
  https://doi.org/10.15485/1660462
\item
  Briggs, M. (2018).\emph{Chemical and geophysical data collected along
  Oh-be-joyful Creek, Gunnison National Forest, Colorado} {[}Data
  set{]}. U.S. Geological Survey. https://doi.org/10.5066/F71Z42NF
\item
  Briggs, M., Dawson, C. B., Holmquist-Johnson, C., Adams, J. D., \&
  White, E. A. (2019).\emph{Thermal infrared, multispectral, and
  photogrammetric data collected by drone for hydrogeologic analysis of
  the East River and Coal Creek beaver-impacted corridors near Crested
  Butte, Colorado}{[}Data set{]}. U.S. Geological Survey.
  https://doi.org/10.5066/P9YWSJ2J
\item
  Briggs, M., Pai, H., Malenda, H., Randell, J., Singha, K., Tyler, S.,
  \& Williams, K. (2017). \emph{Fiber-optic distributed temperature data
  collected along the streambed of the East River, Crested Butte, CO,
  USA} {[}Data set{]}. U.S. Geological Survey.
  https://doi.org/10.5066/F7J9658M
\item
  Briggs, M., Wang, C., Slater, L., Day, F. D., \& Fosburg, D. (2019).
  \emph{Hydrogeochemical data for the characterization of stream,
  groundwater, and beaver-induced floodplain exchange in the East River
  Science Focus Area, Crested Butte, CO} {[}Data set{]}. U.S. Geological
  Survey. https://doi.org/10.5066/P9Q1Z1TK
\item
  Brodrick, P., Goulden, T., \& Chadwick, K. D. (2020). \emph{Custom
  NEON AOP reflectance mosaics and maps of shade masks, canopy water
  content} {[}Data set{]}. Environmental System Science Data
  Infrastructure for a Virtual Ecosystem; Watershed Function SFA.
  https://doi.org/10.15485/1618131
\item
  Bryant, S., Briggs, M., Nelson, A., Saup, C., Wilkins, M., Williams,
  K., \& Sawyer, A. (2019).\emph{Estimated Darcy Velocities Using
  Temperature Time Series for Meander A of East River, Colorado} {[}Data
  set{]}. Seasonal controls on dynamic hyporheic zone redox
  biogeochemistry. https://doi.org/10.15485/1498798
\item
  Carbone, M. (2019).\emph{Depth profiles of soil CO2 Concentrations,
  soil temperature, and soil moisture (Rocky Mountain Biological
  Laboratory, Gothic, Colorado, 2011-2016)} {[}Data set{]}. figshare.
  https://doi.org/10.6084/M9.FIGSHARE.7834406.V1
\item
  Carroll, R., Bill, M., Dong, W., \& Williams, K. (2019).
  \emph{Sub-Basin Delineation for the Upper East River, Colorado, United
  States} {[}Data set{]}. Watershed Function SFA.
  https://doi.org/10.21952/WTR/1508403
\item
  Carroll, R., Deems, J., Niswonger, R., Schumer, R., \& Williams, K.
  (2019). \emph{The Importance of Interflow to Groundwater Recharge in a
  Snowmelt-Dominated, Alpine Watershed, Geophysical Research Letters:
  Modeling and Data Package} {[}Data set{]}. Watershed Function SFA.
  https://doi.org/10.21952/WTR/1508390
\item
  Carroll, R., Manning, A., Niswonger, R., Marchetti, D., \& Williams,
  K. (2019). \emph{Copper Creek Baseflow Age Experiment using
  Environmental Gas Tracers in the East River Watershed, CO} {[}Data
  set{]}. Watershed Function SFA. https://doi.org/10.21952/WTR/1572196
\item
  Carroll, R. W. H., Bearup, L. A., Brown, W., Dong, W., Bill, M., \&
  Willlams, K. H. (2018). Factors controlling seasonal groundwater and
  solute flux from snow-dominated basins. \emph{Hydrological Processes},
  \emph{32}(14), 2187--2202.
  https://onlinelibrary.wiley.com/doi/abs/10.1002/hyp.13151
\item
  Carroll, R., Williams, K., Bill, M., Dong, W., \& Brown, W. (2018).
  \emph{End-Member Mixing Analysis Data Package for the East River
  Watershed, CO USA} {[}Data set{]}. Lawrence Berkeley National
  Laboratory (LBNL), Berkeley, CA (United States).
  https://doi.org/10.21952/WTR/1465929
\item
  Chadwick, K. D., Brodrick, P., Grant, K., Henderson, A., Bill, M.,
  Breckheimer, I., Williams, C. F. R., Goulden, T., Falco, N.,
  McCormick, M., Musinsky, J., Pierce, S., Hastings Porro, M., Scott,
  A., Brodie, E., Hancher, M., Steltzer, H., Wainwright, H., Williams,
  K., \& Maher, K. (2020). \emph{NEON AOP foliar trait maps, maps of
  model uncertainty estimates, and conifer map, East River, CO 2018}
  {[}Data set{]}. A Multiscale Approach to Modeling Carbon and Nitrogen
  Cycling within a High Elevation Watershed.
  https://doi.org/10.15485/1618133
\item
  Chadwick, K. D., Grant, K., Bill, M., Henderson, A., Scott, A., \&
  Maher, K. (2020).\emph{Site-level Foliar C, N, delta13C data from
  samples collected during field survey associated with NEON AOP survey,
  East River, CO 2018} {[}Data set{]}. A Multiscale Approach to Modeling
  Carbon and Nitrogen Cycling within a High Elevation Watershed.
  https://doi.org/10.15485/1631278
\item
  Chadwick, K. D., Grant, K., Henderson, A., Breckheimer, I., Williams,
  C. F. R., Falco, N., Chen, J., Henry, H., Khurram, A., Lamb, J.,
  McCormick, M., McOmber, H., Pierce, S., Polussa, A., Hastings Porro,
  M., Scott, A., Wu Singh, H., Whitney, B., Brodie, E., \ldots{} Maher,
  K. (2020). \emph{Locations, metadata, and species cover from field
  sampling survey associated with NEON AOP survey, East River, CO 2018}
  {[}Data set{]}. Watershed Function SFA.
  https://doi.org/10.15485/1618130
\item
  Chadwick, K. D., Grant, K., Henderson, A., Scott, A., McCormick, M.,
  Pierce, S., Hastings Porro, M., \& Maher, K. (2020). \emph{Leaf mass
  per area and leaf water content measurements from field survey in
  association with NEON AOP survey, East River, CO 2018} {[}Data set{]}.
  Environmental System Science Data Infrastructure for a Virtual
  Ecosystem; A Multiscale Approach to Modeling Carbon and Nitrogen
  Cycling within a High Elevation Watershed.
  https://doi.org/10.15485/1618132
\item
  Chen, J., Dafflon, B., Tran, A., Falco, N., \& Hubbard, S. (2020).
  \emph{Hybrid predictive modeling approach simulated evapotranspiration
  and ecosystem respiration data}{[}Data set{]}. Watershed Function SFA.
  https://doi.org/10.15485/1633810
\item
  Christensen, J., Brown, S., Dafflon, B., Dong, W., Faybishenko, B.,
  Hubbard, S., Tokunaga, T., Wan, J., \& Williams, K. (2019).
  \emph{Using strontium isotopes to evaluate the spatial variation of
  groundwater recharge, Science of The Total Environment: Dataset}
  {[}Data set{]}. Watershed Function SFA.
  https://doi.org/10.21952/WTR/1495381
\item
  Dafflon, B., \& Dwivedi, D. (2020). \emph{Groundwater level elevation
  and temperature at the Lower Montane in the East River Watershed,
  Colorado} {[}Data set{]}. Watershed Function SFA.
  https://doi.org/10.15485/1647040
\item
  Dana Chadwick, K., Brodrick, P. G., Grant, K., Goulden, T., Henderson,
  A., Falco, N., Wainwright, H., Williams, K. H., Bill, M., Breckheimer,
  I., Brodie, E. L., Steltzer, H., Rick Williams, C. F., Blonder, B.,
  Chen, J., Dafflon, B., Damerow, J., Hancher, M., Khurram, A., \ldots{}
  Maher, K. (2020). Integrating airborne remote sensing and field
  campaigns for ecology and Earth system science. In \emph{Methods in
  Ecology and Evolution}. https://doi.org/10.1111/2041-210x.13463
\item
  Dafflon B ; Malenda H ; Dwivedi D (2020): Groundwater level elevation
  and temperature across Meander C at the Lower Montane in the East
  River Watershed, Colorado. Watershed Function SFA.
  doi:10.15485/1647041
\item
  Dawson, C. B., Holmquist-Johnson, C., \& Briggs, M. (2018).
  \emph{Thermal infrared and photogrammetric data collected by small
  unoccupied aircraft system for hydrogeologic analysis of Oh-be-joyful
  Creek, Gunnison National Forest, Colorado, August 2017} {[}Data
  set{]}. U.S. Geological Survey. https://doi.org/10.5066/P931G95D
\item
  Dong, W., Beutler, C., Bouskill, N., Brown, W., Newman, A., Versteeg,
  R., \& Williams, K. H. (2020). \emph{Total Dissolved Nitrogen and
  Ammonia Data for the East River Watershed, Colorado} {[}Data set{]}.
  Watershed Function SFA. https://doi.org/10.15485/1660456
\item
  Dong, W., Beutler, C., Brown, W., Newman, A., Versteeg, R., \&
  Williams, K. H. (2020).\emph{Dissolved Inorganic Carbon and Dissolved
  Organic Carbon Data for the East River Watershed, Colorado} {[}Data
  set{]}. Environmental System Science Data Infrastructure for a Virtual
  Ecosystem; Watershed Function SFA. https://doi.org/10.15485/1660459
\item
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\end{enumerate}\selectlanguage{english}
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