2. METHODS
Como Creek (Figure 1) is located in the Niwot Ridge Long Term Ecological
Research (LTER) site in the state of Colorado. Located just east of the
North American continental divide, it has an area of approximately 5
km2, with elevations ranging from 3,000-3,600 m above
sea level. This makes it one of the highest instrumented catchments
anywhere in the world. It experiences long cold winters, and short cool
summers; mean air temperatures for January are -12ºC, and mean
temperatures for July are 12ºC. The average annual precipitation is 730
mm, with roughly two thirds as snowfall (snowfall values are reported in
snow-water equivalent; SWE). The majority of the watershed is forested,
consisting of Engelmann spruce (Picea engelmannii), subalpine fir (Abies
lasiocarpa), limber pine (Pinus flexilis), lodgepole pine (Pinus
contorta var. latifolia) and quaking aspen (Populus tremuloides).
Approximately 10% of the watershed is above treeline, consisting of
alpine tundra and scree slopes.
We obtained four years (Oct 2018 - Sep 2021) of high frequency data from
the National Ecological Observatory Network (NEON). NEON
(https://neonscience.org) is a
National Science Foundation-funded network of monitoring sites
throughout the United States providing long-term, open-access ecological
data (Goodman et al., 2015). Since 2017, NEON has maintained a
monitoring reach along Como Creek, instrumented with a standardized
suite of automated sensors. Stream stage is recorded using AquaTroll 600
vented pressure transducers (In-situ; Fort Collins CO). Bi-weekly manual
Q measurements are used to develop rating curves and estimate continuous
Q. Rating curves are created for each water year (defined as October 1
through September 30). Water quality measurements, including specific
conductance (SpC), dissolved oxygen (DO), and fluorescent dissolved
organic matter (fDOM) are measured at one minute intervals using an EXO2
multiparameter sonde (YSI; Yellow Springs OH). Stream
NO3-N is measured using a submersible ultraviolet
nitrate analyzer (SeaBird Scientific, Bellevue WA) configured to take a
20 measurement burst at 15 minute intervals. The first 10 bursts of each
measurement are discarded to allow the SUNA lamp sufficient time to warm
up. Concentrations reported in μM were converted to mg-N
L-1 using the molar mass. The sensors remain installed
throughout the winter, measuring concentrations in the liquid water
under the ice and snow cover. Both the EXO2 and SUNA were equipped with
automated wipers to prevent biofouling. They are also manually cleaned
bi-weekly, and calibrated monthly. A time-lapse video of the stream
using images from the NEON monitoring location is included as Video 1.
Additional images, including real-time, are available from the PhenoCam
Network
(https://phenocam.sr.unh.edu/webcam/sites/NEON.D13.COMO.DP1.20002/).
NEON also collects bi-weekly grab samples of stream water chemistry.
Samples are collected and stored on ice until analysis at the EcoCore
laboratory at Colorado State University. In addition to major cations
and anions, grab samples are analyzed for DOC, total organic carbon
(TOC), NO2+NO3-N (of which
NO3-N is the overwhelming majority in Como Creek),
ammonium (NH4-N), total dissolved nitrogen (TDN), and
total nitrogen (TN). Grab samples of these additional species allowed us
to contextualize the sensor-based DOC and NO3-N
measurements in terms of the total C and N budgets.
We used the neonUtilities R package (Lunch et al., 2021), to download
the following publicly available NEON datasets: Continuous discharge
(NEON 2021a), Water quality (NEON 2021b), Nitrate in surface water (NEON
2021c), Temperature in surface water (NEON 2021d) and Chemical
properties of surface water (NEON 2021e). Quality flagged measurements
were excluded from our analysis; this constituted a relatively small
fraction of the total data (~5%) and the majority of
these were periods in winter when the stream froze to a depth where the
sensors became encapsulated in ice and were no longer measuring
concentrations in the liquid water beneath. Because they occured when
the stream was not flowing, their omission does not substantially impact
annual flux estimates. In a few instances, NEON maintenance and
calibration records were used to correct for drift or calibration
offsets in the data. Datasets published at higher frequencies (e.g.
water quality) were averaged to 15 minute intervals to match nitrate in
surface water, which had the lowest temporal resolution. A linear
regression between bi-weekly manual DOC measurements and corresponding
sensor fDOM measurements was used to estimate continuous DOC from the
fDOM time-series. Multiplying DOC and NO3-N
concentrations by the corresponding Q measurement, we calculated a
continuous record of DOC and NO3-N flux. The na.spline
function in the zoo R package was used to fill short gaps of less than 6
hours. Two larger concentration gaps (fDOM from 24 May to 4 June 2018
and NO3-N from 27 June to 1 Aug 2019) were filled using
linear interpolation. The effects of uncertainty in this concentration
approximation on flux values are expected to be relatively small given
that Q is expected to be the primary driver. All other major gaps were
left unfilled because they occurred during periods of little to no flow
and were deemed to exert almost no influence on annual flux budgets.
To quantitatively compare interannual variability, we determined the
date of the centroid of the annual melt pulse, the annual water yield
(WY; cumulative Q divided by watershed area), and the annual export of
DOC and NO3-N. The NEON precipitation gage is located
next to the eddy flux tower, high on Niwot ridge, at an elevation much
higher than most of the catchment. Instead, we obtained precipitation
and snowpack data was from the National Water and Climate Center Snow
Telemetry (SNOTEL;https://www.wcc.nrcs.usda.gov/snow/)
Niwot station (ID 663), which is located near the center of the Como
Creek catchment and likely more representative. It also has a much
longer period of record. From these data we calculated the total annual
precipitation, maximum depths of annual snowpack, and the date in the
spring when the snowpack depth dropped below 10 cm. We then compared
these values with the annual melt pulse and solute flux metrics
calculated above.
For each solute we determined the coefficients of variation (CV) for
discharge and concentration to determine which constituent component of
flux exhibited the larger degree of variation, and thus acted as the
primary control. We calculated Gini coefficients (G) to quantitatively
characterize the temporal inequality in flux (Jawitz and Mitchell et
al., 2011). Commonly used to characterize the distribution of wealth, a
Gini coefficient of zero represents complete equality (i.e. a constant
mass flux rate) while a value of one represents complete inequality
(i.e. entire flux in one instant).
For each solute we generated C-Q plots and fit the log-transformed data
with a linear regression (i.e. a power function in un-transformed data)
to determine whether they exhibited an enrichment, dilution, or
chemostatic response (Godsey et al., 2009). A positive slope indicates
enrichment, a near-zero slope indicating relative chemostasis, and a
negative slope indicates dilution; a slope of exactly -1 is a special
case indicating perfect dilution of a constant flux of solute. We
carefully examined the C-Q relationships for any signs of hysteretic
behavior at both annual and event time scales, noting the
directionality. Clockwise hysteresis indicates relative enrichment of
earlier arriving water, while counter-clockwise hysteresis indicates
relative enrichment of later arriving water (Evans and Davies, 1998).
The NEON sensor array has only been deployed since fall 2017, providing
four years of data at the time of this analysis. To better characterize
drivers of inter-annual variability, and better contextualize the
results in the context of long term trends, we supplemented this data
with historical LTER records of daily average Q (Williams et al., 2021)
and weekly grab samples from 2004 through 2014 (Williams 2021). This
data was publicly available from the Environmental Data Initiative (EDI)
data portal
(https://portal.edirepository.org/).
To understand the potential for the different sampling frequencies of
the historic data to influence estimates of annual flux, we downsampled
our sensor-based solute measurements to match the intervals used in the
historic sampling (daily average Q and concentration measurements from
every Monday at 12:00). Using these values, we re-calculated the
estimated annual fluxes of DOC and NO3-N and compared
with the estimates made using the 15 minute data.