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
The ER watershed also has 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 (located
downstream of the SFA gages), 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)
and Clean Air Status and Trends Network (CASTNET) station at Gothic.
Thus collectively, the SFA and its collaborators generate diverse,
multiscale datasets at the ER including hydrological, (bio)geochemical,
climate, vegetation, geophysical, microbiological, and remote sensing
data. Additionally, several model datasets, including inputs, outputs
and preprocessing codes 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.
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.
4. Scientific Impact of ER Datasets
The multidisciplinary data from the ER have advanced the understanding
of hydrological processes in mountainous catchments, and the
simultaneous collection of diverse data types across the watershed
allows researchers across institutions to share knowledge and draw
conclusions. Examples of datasets collected through periodic sampling or
from sensor instrumentation that are frequently used in analysis and
modeling include daily river stage observations and discharge estimates
(dataset 43; observed measurement errors ranging from
~2-15% ), weekly-daily surface and groundwater
geochemistry (e.g. datasets 3, 5, 9, 15, 21, 41; measurement
uncertainties in Tables S2-S3), and instantaneous groundwater elevations
and temperature in the hillslope and floodplain (e.g. datasets 30, 46).
Examples of research topics that ER datasets are used for include water
partitioning as a function of hydrological perturbations such as drought
and early snowmelt (e.g. datasets 18, 26, 39-44), surface-groundwater
interactions (e.g. datasets 63-68), nitrogen cycling and export to the
river (e.g. datasets 15, 20, 21, 27, 31-32, 52, 56), the impact of
weathering and other processes on river water composition (e.g. datasets
10, 28, 32), and more broadly an aggregated understanding of watershed
hydrobiogeochemical processes given their variation over space and
different watershed functional zones and compartments (e.g. datasets
49-54).
The ER datasets 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 (datasets 42-44, 51, 70-72). Specifically, groundwater
recharge 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 (datasets 30-32). 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 (datasets 17, 20). 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 (datasets 29, 46-47).
The interdisciplinary data from the ER can also be used in future
investigations that address science questions identified by the broader
community such as the impacts of climate change and extreme events on
the critical zone, and the scale dependence of hydrology
(Blöschl et al. 2019). These
data along with future measurements from the SAIL campaign will provide
integrated observational datasets for benchmarking atmospheric and
hydrological models in mountainous watersheds, thus addressing an
identified data gap in modeling mountain rain and snow
(Lundquist et al. 2019).
More broadly the data from the ER will help understand the impacts of
hydrological perturbations on water availability and quality in the
Western United States.
The findings from the ER community, and the potential for gaining future
scientific insights using the data, 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).