A comprehensive set of measurements and calculated metrics
describing physical, chemical, and biological conditions in the river
corridor is presented. These data were collected in a catchment-wide,
synoptic campaign in the H. J. Andrews Experimental
Forest (Cascade Mountains, Oregon, USA) in summer 2016 during low-discharge
conditions. Extensive characterization of 62 sites including surface water,
hyporheic water, and streambed sediment was conducted spanning 1st- through
5th-order reaches in the river network. The objective of the sample design
and data acquisition was to generate a novel data set to support scaling of
river corridor processes across varying flows and morphologic forms present
in a river network. The data are available at 10.4211/hs.f4484e0703f743c696c2e1f209abb842 (Ward, 2019).
Leverhulme TrustWhere rivers, groundwater and disciplines meet: a hyporheic research networkNE/L003872/1U.S. Department of EnergyPacific Northwest National LaboratoryDE-SC0019377National Science FoundationDEB-1440409EAR-1652293EAR-1417603EAR-1446328University of BirminghamInsititute of Advanced StudiesEuropean CommissionHiFreq - Smart high-frequency environmental sensor networks for quantifying nonlinear hydrological process dynamics across spatial scales (734317)Introduction
River corridor science is the study of the exchange of water, solutes,
particulate matter, energy, and biota between surface and subsurface
domains, collectively called river corridor exchange (e.g., Brunke and
Gonser, 1997; Boulton et al., 1998; Harvey and Gooseff, 2015; Tonina and
Buffington, 2009; Krause et al., 2011, 2017). These beneficial functions are
primarily derived from the interactions between physical, chemical, and
biological processes in the river corridor (e.g., McDonnell et al., 2007;
Boano et al., 2014; Ward, 2015; Bernhardt et al., 2017). In a recent review,
Ward (2015) identified two key deficiencies that must be addressed to
advance our predictive understanding of the functioning of the river
corridor. First, although the physical, chemical, and biological processes
are known to be tightly coupled and co-evolved, they are seldom
co-investigated. More comprehensive characterizations of
physical–chemical–biological conditions are required to enable the study of
coupled processes that span these sub-systems. Second, most comprehensive,
interdisciplinary studies are conducted at single locations within an
extensive river network and are limited in their range of spatial and
temporal scales. Combined, these limitations have hindered our predictive
understanding of ecosystem services and functions at the scale of river
networks (Ward and Packman, 2019). While interactions between physical,
chemical, and biological processes is necessary to improve our predictive
understanding at the scale of river networks, this knowledge is not
sufficient to achieve that goal.
In addition to local-scale understanding of process interactions and
controls, predictive understanding of process dynamics in river networks
requires an understanding of spatial structure of processes and their
interactions. Traditional studies of river corridors focus on interpretation
of time-series analysis of repeated at fixed points. However, an emerging
class of data sets and approaches emphasize the value of spatially
distributed sampling campaigns in understanding the structure and function
of river corridors (e.g., Kaufmann et al., 1991; Wolock et al., 1997; Dent and Grimm, 1999; Temnerud and Bishop, 2005; Likens et al., 2006; Hale and Godsey, 2019). Spatially distributed studies along river corridors may provide
increased information about biogeochemical processes in comparison to equal
effort in characterization of local-scale processes at a size (Lee-Cullin et
al., 2018). Similarly, these data sets are driving innovation in the
frameworks used to interpret spatially distributed data sets, including foci
on spatiotemporal variance (Abbott et al., 2018), the application of
geostatistical approaches to characterize scale-dependent relationships
linking stream water chemistry and basin characteristics (Zimmer et al.,
2013; McGuire et al., 2014; Dupas et al., 2019), and additional spatial
statistics methods (Isaak et al., 2014; Lowe et al., 2006).
While each of the studies cited above has made advances, they remain
limited in two important dimensions. First, the studies cited above
primarily focus on spatial patterns in stream water chemistry with limited
characterization of biological and physical dimensions of the river
corridor. Second, these studies are almost exclusively focused on
measurements in the surface water domain rather than explicitly considering
hyporheic waters and the streambed sediments themselves. Consequently,
interpretations of causal mechanisms are limited by incomplete
characterization and an emphasis on in-stream water. we have a limited
ability to predict river corridor processes and the associated ecosystem
functions at the spatiotemporal scales of river networks, where water
resource managers and policy makers typically operate (Krause et al., 2011).
In response, we endeavored to collect river corridor data that directly
address the two limitations by acquiring simultaneous, multidisciplinary
measurements distributed across a river network. The result is a novel river
corridor data set documented herein that presents new opportunities for
exploring multiscale, interacting river corridor patterns and processes.
Specifically, this paper presents the collection of a synoptic-in-time,
distributed-in-space characterization of physical, chemical, and biological
conditions in the river corridor of the 5th-order Lookout Creek stream
network within the H. J. Andrews Experimental Forest and Long Term Ecological
Research site (Cascade Mountains, Oregon, USA).
Study location and campaign designStudy catchment
The H. J. Andrews Experimental Forest (HJA) is a 5th-order catchment draining
about 6400 ha. The forest is located in the Western Cascades, Oregon, USA.
Elevation in the basin ranges from about 410 to 1630 m a.m.s.l., and the
landscape is heavily forested, including 400-year-old Douglas fir forests and
areas of younger regrowth forest after wildfire or replanting after
forest harvest. Additional detail about the climate, morphology, geology,
and ecology of the site and region are well described by others (Dyrness,
1969; Swanson and James, 1975; Swanson and Jones, 2002; Jefferson et al.,
2004; Deligne et al., 2017).
Within the study catchment, there are three predominant landforms (Table 1;
Figs. 1, 2). First, lower elevations are typically underlain by thermally
weakened upper Oligocene–lower Miocene basaltic flows. These landforms are
typified by highly dissected landscapes resulting from rapidly incising
V-shaped valleys that are steep and narrow, with colluvium emplaced by high-energy hillslope failures and debris flows. Second, high elevations are
typically underlain by plieocascade volcanics. These higher elevations have
well-defined, U-shaped valleys resulting from glacial processes, with
cirques at the head of valleys and highly compacted glacial tills filling
the valley bottoms. Third, several deep-seated earthflows are emplaced on
the upper Oligocene–lower Miocene basaltic flows. These earthflow
landforms typically lack well-developed drainage networks because they are
too young to have developed large valleys and thus have minimal lateral
constraint or visible bedrock along the streams.
The HJA has been the site of forest management, watershed, and ecosystem
research since it was established as a U.S. Forest Service research site in
1948, and has been one of the National Science Foundation's Long Term
Ecological Research sites since 1980. As a result of these efforts and
sustained commitment to data stewardship, the HJA hosts an extensive
catalogue of data, maps, images, models, and software that are complementary
to the data presented in this publication and provide context within which
these data can be interpreted (see HJA data catalogue at
https://andrewsforest.oregonstate.edu/data, last access: 19 September 2019). For example, there are many
complementary datasets of interest to readers of this paper, including
stream discharge (HF004), stream chemistry (CF002), meteorological data
(MS001), precipitation and dry deposition chemistry (CP002), aquatic
invertebrate inventories (SA012, SA013, SA017), and soil properties and
chemistry (SP001, SP006, SP026). We note these data are only a subset of the
available information and encourage users of the data to explore the HJA
data catalogue for additional information.
Synoptic campaign design
This study was designed to replicate characterizations of the river corridor
at a total of 62 sites spanning 1st- through 5th-order reaches in the HJA.
Site selection was based on (1) the presence of flowing surface waters, (2) stratification across stream orders, (3) coverage of the three major
landform units in the HJA, and (4) accessibility of sites. All sampling of
water and streambed sediment was conducted within the period 26 July through
3 August 2016 with no flow or precipitation events were recorded during the sampling
campaign. All solute tracer experiments occurred during the period 31 July
through 12 August 2016, again with no recorded flow or precipitation events.
In addition to broad spatial coverage of the river network, we selected four
subcatchments for a more detailed characterization consisting of replication
along the study reach at four to six locations per subcatchment. These four
subcatchments were selected to have one subcatchment in the three predominant
landforms in the study catchment, plus a fourth subcatchment located where a
large debris flow scoured a section of the river corridor to bedrock in 1996
(Johnson, 2004). The objective of including two subcatchments in the
low-elevation landform was to provide a space-for-time comparison (i.e.,
WS01 and WS03 provide two realizations of the same landform type at
different states in response to the large debris flow that typifies a key
geologic disturbance in the system).
Synoptic sites and lidar-derived stream network (see details on network definition in Sect. 3.1.1).
Headwater catchments in the major landform units at the H. J. Andrews Experimental Forest, including multiple synoptic sites along an intensively studied reach. WS01 and WS03 are located in the upper Oligocene–lower Miocene basaltic flows, an unnamed creek on a deep-seated earthflow, and Cold Creek in more modern plieocascade volcanics. Characteristics of each landform and catchment are detailed in Table 1.
Summary of site characteristics for the four headwater catchments
where more intensive sampling was conducted. The descriptions of these
headwater catchments are considered representative of the major landform
types within the HJA (after Dyrness, 1969; Swanson and James, 1975; Swanson
and Jones, 2002). See catchment topography in Fig. 2 for each site.
SiteStudy reachGeologic settingValley formColluvium presenceand descriptionNotable river corridor descriptionConstraintLateral inflowsSpatially intermittent?WS01LowerUpper Oligocene – lower Miocene basaltic flows, volcanoclastic rocks. Thermally altered (weakened) by subsequent volcanic activity enabling rapid downcutting of the valley bottoms.V-shaped valley w/ wide (10–20 m) valley bottomInceptisols. Abundant deposition from hillslope debris flows. Highly porous. Minimally compacted.Pool-riffle-step and Pool-step-riffle morphology. Channel splits. Gravel wedges. Long, continuous sections of deposition from high-energy debris flow events.Observed lateral (valley walls) and vertical (streambed) constraint of active channelProportional to lateral tributary area of hillslopes. Hillslopes underlain by intact bedrock.Yes. Diurnal fluctuation in-stream discharge enable rapid shift from continuous to intermittant over repeated 24 h cycles.MiddleUpperWS03LowerV-shaped valley w/ narrow (2–10 m) valley bottomDeposition of colluvium from 1996 scouring eventYesMiddleIntermittent inceptisol- based colluvium on bedrockIsolated gravel wedges formed by large woody debrisYes, below featuresUpperMinimal colluvium present100 % surface flow (no colluvium)NoUnnamed CreekUpperDeep-seated earth failure on upper Oligocene–lower Miocene basaltic flowsEarly downcutting & valley formation in unstructure colluvial materialExtensive colluvium. Flat and wide valley bottom with lateral meandering of active channel in incising valley bottomMeanders, cut banks more typical of alluvial valleys compared to other study catchments proposedNo visible bedrock in active channelNo known groundwater nor lateral inflows. Minimal lateral tributary area in study reachUnknown at this time. Expected due to the site of colluvial deposit.LowerCold CreekUpperPlieocascade volcanics atop middle and upper Miocene volcanics (andesite, basalt)U-shaped valley (glacial cirque)Compacted glacial tillsLarge woody debris on till forms pools, steps with intermediate graveland cobble rifflesBedrock visible at one locationProportional to hillslope areaUnknown at this time. Not expected given apparent contributions from aqufier.LowerAquifer extends beyond catchment
Left: Summary of sample collection (site characterization,
streambed sediment, stream water, hyporheic water) and analyses included in
this data set. Center: mapping of data types to their characterization of
physical, chemical, and/or biological systems (after definitions of Ward,
2015). Right: data archival summary.
The stream network was derived from a 1 m digital terrain model based on
airborne lidar collected in 2008 (Spies, 2016). We used the one-directional
flow accumulation algorithm (Seibert and McGlynn, 2007) implemented in a
modified version of TopoToolbox (Schwanghart and Kuhn, 2010; Schwanghart and
Scherler, 2014) to derive the direction of flow and accumulation of drainage
area within the basin. We defined the stream network as any location
draining more than 5 ha. The threshold was established based on iteratively
comparing the derived stream network to our experience working in headwater
catchments and their extent (consistent with analyses by Ward et al., 2018).
The TopoToolbox algorithm defined study reaches as the segment between two
junctions. In our analysis, we defined 686 river corridor segments including
a total length of about 209 km of valley containing about 242 km of stream.
For each study reach, we tabulated the sinuosity of the stream within the
valley. Next, we discretized each reach into 10 m segments, extracting
valley slope, stream sinuosity, and stream slope for each segment (after
Corson-Rikert et al., 2016; Ward et al., 2018). Each synoptic site was
assigned a stream order and average valley slope, streambed slope, and
sinuosity for the reach within which it was located.
Hydraulic and valley geometry
At each synoptic site, field observations of valley width were collected
using a tape measure, with valley edge being visually defined in the field
based on the hillslope break point between the relatively flat valley bottom
and steeper valley walls. Total wetted channel width was measured
perpendicular to the direction of flow at the synoptic site, and average
channel depth was recorded based on at least five measurements of depth
spaced evenly across the channel.
Hydraulic conductivity
At the approximate centerline of the synoptic site, a Solinst 615N
drive-point piezometer (615N, Solinst Canada, Ltd., Georgetown, ON, Canada)
was driven to a depth of about 65 cm below the streambed. The piezometer was
screened over the distance of 50–65 cm below the streambed. The piezometer
was developed and purged by pumping slowly using a peristaltic pump until
the water was visually clear, typically about 5 min. Then hyporheic
water sampling occurred as described below (Sect. 3.2). Then a series of
three to six replicates of a falling head test were conducted using the piezometer,
with water levels measured using a Van Essen Micro-Diver (DI601, Van Essen
Instruments, Mukilteo, WA, USA), recording at 0.5 s intervals and corrected
for any variation in atmospheric pressure collecting data every 10 min.
Falling head data were used to estimate hydraulic conductivity after
Hvorslev (1951). We report the geometric mean of the replicate tests for
each synoptic site. Finally, we note that at five sites there was minimal
(∼< 10 cm) to no colluvium present in the valley
bottom. At these sites we did not sample hyporheic water nor measure
hydraulic conductivity, but we did collect streambed sediment from small
in-channel deposits at the synoptic site. These sites are necessary for
complete representation of the river corridor of the study catchment as
there are many locations in the valley bottom that have minimal or no
colluvium.
Macroinvertebrate community
Benthic macroinvertebrate colonization pots were installed at 44 of the 62
synoptic sites using the design of Crossman et al. (2012) during the
synoptic campaign. Colonization pots were constructed of wire mesh with 1.25 cm openings formed into cylinders approximately 15 cm in height and 8 cm in
diameter, including a screened bottom. Hence, at sites where surface
sediment grain sizes were larger than 8 cm, they could not be installed.
Substrate was excavated by hand and placed in each pot prior to installing
so that the top of each pot was level with the streambed. Colonization pots
remained in situ for about 6 weeks following installation. Removal was
achieved by pulling a cable to raise a specially constructed tarpaulin bag
around the sides of the pot before extraction, thereby minimizing sample
loss. All substrate and macroinvertebrates were placed in a 90 % ethanol
solution for preservation. Additionally at 10 sites, surface samples of
macroinvertebrates were collected with a Surber sampler with a 330 µm mesh net, collected in triplicate at proximal locations and pooled for identification during the synoptic campaign. Surface samples were processed
using identical preservation methods, and identification was conducted by
the same researcher.
After separation of macroinvertebrates, sediment samples were oven-dried and
sieved to assemble grain size distributions for each colonization pot.
Importantly, because the pots were packed by hand in flowing water, we
expect these grain size distributions are biased toward the coarse fraction
of streambed sediment, as finer materials would have washed away during
packing. Additionally, large cobbles would not have fit into the pots and
excluded from collection.
Identification was performed under the stereomicroscope, except for the
Chironomidae (family larvae and early larval instars of the Plecoptera
(order) and Ephemeroptera (order)), which were mounted in the Euparal and
examined under the light microscope as described by Andersen (2013).
Macroinvertebrates were identified to the lowest possible taxonomic level,
including the differentiation of adult and juvenile stages. Identification
was performed using established keys (Merritt and Cummins, 1996; Andersen,
2013; Malicky, 1983; Langton, 1991; Epler, 2001).
Water sampling & analysesSample collection from stream and hyporheic zone
All water samples were collected using a peristaltic pump to sample water at
a flow rate of about 0.5 L min-1. The pump intake was located either in the
stream thalweg for surface samples or in the developed piezometer for
hyporheic samples. Tubing was rinsed with water from the stream or hyporheic
zone for at least 5 min prior to sample collection to minimize
cross-contamination between sites. We did not record the pumping rates nor
volumes for this rinse, and acknowledge it may have an impact on the flow field
prior to sample collection. However, we expect this would be minimal because
the sediment is generally highly hydraulically conductive.
First, water temperature and dissolved oxygen were recorded using a YSI
ProODO handheld probe (YSI, Inc., Yellow Springs, OH, USA) with an optical
dissolved oxygen (DO) sensor and thermistor. For stream samples, the probe
was held in the water column at the synoptic site near the pump intake. For
hyporheic samples, water was pumped into a small flow-through cell until it
overflowed, and then the sensor was placed into the cell while flow continued. For
both stream and hyporheic observations the sensor remained in place in the
flowing water until probe readings for temperature and DO stabilized.
Specific conductivity was also measured with a handheld conductivity probe
(YSI EC300; YSI, Inc., Yellow Springs, OH, USA) using the same approaches.
Physical water samples for subsequent laboratory analyses were collected
from the stream and hyporheic zone using identical methods. (1) Unfiltered samples for water isotope analysis (Sect. 3.2.2) were collected
in 20 mL glass scintillation vials with conical inserts and were capped
without headspace to minimize fractionation. (2) Samples for dissolved water
chemistry and nutrients (Sect. 3.2.3) were collected by field filtering
using handheld 65 mL syringes. Syringes were triple rinsed with sample water
prior to collection of any sample volume. Samples for dissolved organic
carbon (DOC) analyses were field-filtered using a 0.2 µm cellulose
acetate filter. Acid-washed amber HDPE bottles were triple-rinsed with
filtered sample water prior to sample collection. DOC samples were placed in
a cooler with ice in the field and remained chilled until analysis. Samples
for dissolved nutrients, anions, and cations were field-filtered using a
0.45 µm cellulose acetate filter. Sample bottles were triple-rinsed
with filtered sample water prior to sample collection. Dissolved nutrient
samples were placed on dry ice in the field immediately after collection and
remained frozen until analysis. (3) Samples for microbial analysis (Sect. 3.2.4) were collected following Crevecoeur et al. (2015) by pumping water
through a Sterivex (Millipore) cartridge with a 0.22 µm Durapore (PVDF) filter membrane until either 1 L of water was filtered or 45 min
elapsed. Cartridges were immediately sparged to remove site water, filled
with RNAlater stabilization solution (Ambion), and frozen in the field on
dry ice. Samples remained frozen on dry ice until transferred and stored in
a -80∘C freezer until analysis.
Water stable isotope ratios
We analyzed water stable isotopes to facilitate characterization of water
ages using a cavity ring-down spectroscopy method (Picarro L2130-I, Picarro
Inc.), following laboratory protocols described by Nickolas et al. (2017).
Briefly, samples were run under high-precision analysis mode using a 10 µL syringe for six injections per sample. We discarded the first three injections to eliminate memory effects. We used internal standards to
develop calibration equations for stable isotopes of oxygen and hydrogen.
The internal standards were calibrated using primary IAEA standards for
Vienna Standard Mean Ocean Water (VSMOW2: δ18O=0.0 ‰, δ2H=0.0 ‰),
Standard Light Antarctic Precipitation (SLAP2: δ18O=-55.5 ‰, δ2H=-427.5 ‰), and Greenland Ice Sheet Precipitation
(GIPS: δ18O=-24.76 ‰, δ2H=-189.5 ‰). All stable isotopic values were reported as
delta (δ) values in parts per thousand (‰),
which represent the deviation from the adopted VSMOW2 standard. Internal
laboratory precision of the mean reported δ18O and δ2H values was estimated as 0.03 ‰ and
0.058 ‰ for δ18O and δ2H,
respectively, based on the analysis of >50 duplicate samples. The
external accuracy – representing the overall accuracy of the laboratory –
was estimated as 0.058 ‰ and 0.241 ‰ for δ18O and δ2H by comparing > 60
estimated values for a known standard. A total of seven samples collected for
water isotope analysis were lost due to breakage of collection vials during
transport. Paired surface and hyporheic samples were recollected on 1–3 August 2016 for these locations.
Dissolved water chemistry and nutrients
Dissolved nutrients (PO43-, NO2-+NO3-, and
NH3) were analyzed on a San++ automated wet chemistry analyzer–segmented flow analyzer (Skalar Analytical B.V., Netherlands). Anions
(Cl-, SO42-) and cations (Na+, K+, Mg2+, Ca2+) were analyzed on a Dionex ICS5000 ion chromatography system (Thermo Fisher Scientific). Samples were thawed on the laboratory bench prior to analysis (typically 2–4 h) and were analyzed at room temperature.
DOC concentrations (as non-purgeable organic carbon, NPOC) and total
dissolved nitrogen (TDN) were analyzed via acid-catalyzed high-temperature
combustion using a Shimadzu TOC-L analyzer with a TN module (Shimadzu
Scientific Instruments, Kyoto, Japan). Samples were allowed to come to room
temperature prior to analysis.
Dissolved organic matter (DOM) optical quality was analyzed via absorbance
and fluorescence spectroscopy. UV–visible absorbance spectra ranging from
220 to 800 nm were collected using semi-micro, Brand-Tech cuvettes with a
1 cm path length on a Shimadzu dual-beam UV 1800 spectrophotometer (Shimadzu
Scientific Instruments, Kyoto, Japan). Samples were allowed to come to room
temperature prior to analyses. E-Pure water (18 MΩ, Barnstead E-Pure
system) was used as a blank and cuvettes were triplicate rinsed with E-Pure water and
rinsed with sample water between readings.
Excitation-emission matrices (EEMs) were measured over excitation
wavelengths of 250–450 nm and emission wavelengths of 320–550 nm on a Horiba
Aqualog fluorometer (Horiba Scientific, Kyoto, Japan). Following the methods
of Cory et al. (2010b), EEMs were generated for each sample using a 4 s
integration time using a quartz cuvette with a 1 cm path length and E-Pure
water as a blank. Samples were allowed to come to room temperature prior to
analysis. Cuvettes were rinsed with E-Pure water at least 10 times and
triplicate rinsed with sample water between readings. EEMs were corrected
for instrument-specific excitation and emission corrections and the
inner-filter effect (Cory et al., 2010). E-Pure water blank EEMs were
collected and used to correct for Raman scattering. Fluorescence intensities
from corrected-sample EEMs were converted to Raman units (Stedmon and Bro,
2008). EEM corrections and processing were performed using MATLAB
consistent with Cory et al. (2010).
Using EEMs and UV–visible absorbance spectra, several DOM quality indices
were calculated for each sample. Specific UV absorbance at 254 nm (SUVA254)
was calculated using absorbance readings at 254 nm normalized for path
length (m-1) and DOC concentration (mg L-1). Higher SUVA254
values are associated with higher aromaticity of DOM (Weishaar et al.,
2003). Spectral slope ratio (SR) was calculated from absorbance spectra
following the methods of Helms et al. (2008). SR values correspond inversely
to relative DOM molecular weight. Fluorescence index (FI) was calculated
following Cory and McKnight (2005) as the ratio of emission (em) intensities
for 470 and 520 nm at the 370 nm excitation (ex) wavelength. FI values
correspond to DOM source with lower FI values corresponding to
allochthonous, terrestrially derived DOM and higher FI values corresponding
to autochthonous, microbially derived DOM (McKnight et al., 2001).
Intensities of specific EEM peaks and absorbance wavelengths were selected
and reported as well-documented proxies for character and sources of DOM.
Following Coble (1996) and Cory and Kaplan (2012), EEM peak A (ex 250,
420/em 500) and peak C (ex 250, 365/em 466) were reported as proxies for
humic-like, terrestrially derived fluorescent DOM (FDOM). EEM peak T (ex
250, 285/em 344) was reported as a proxy for protein-like FDOM (Cory and
Kaplan, 2012). Specific decadic and Napierian absorption coefficients
reported serve as proxies for colored DOM (CDOM), and can be used as
indicators for specific sources and reactive fractions of the DOM pool
(Spencer et al., 2009). Decadic absorption coefficients (m-1) were
calculated from absorbance readings at specific wavelengths normalized for
path length (m). Napierian absorption coefficients (m-1) are
reported on a natural log scale and are calculated from absorbance readings
at specific wavelengths normalized for path length (m) and multiplied by
a factor of 2.303.
Microbial ecology
To characterize the bacterial communities collected from the surface water
and hyporheic zone, we first isolated the filter membrane from the Sterivex
cartridge. We extracted DNA from the filters using the DNeasy PowerWater kit
(Qiagen). Following DNA extractions, we used polymerase chain reaction (PCR) to amplify the V4-V5 region
of the 16S rRNA gene using barcoded primers (515F and 806R) designed for the
Illumina MiSeq sequencing platform (Caporaso et al., 2012). The sequence
libraries were cleaned using the AMPure XP purification kit (Agencourt) and
quantified using the PicoGreen dsDNA quantification kit (Quant-iT,
Invitrogen). Libraries were pooled at 10 ng per library. Pooled DNA libraries were sequenced on the Illumina MiSeq platform at the
Center for Genomics and Bioinformatics sequencing facility at Indiana
University using paired-end reads (Illumina Reagent Kit v2, 500-reaction
kit).
Sediment sampling & analysesSample collection
Streambed sediment samples were collected near the piezometer at each
synoptic site. Sample collection involved manually removing the armor layer
from the bed and then using a small specimen cup and putty knife to remove
bed sediment without loss of fines. Samples were sieved to remove coarse
material using a 2 mm sieve. Sieved material was placed in a sterile 50 mL
centrifuge tube and frozen on dry ice immediately after collection. Samples
were retained on dry ice or in a -80∘C freezer until analysis.
Duplicate sediment samples were collected for analysis of extracellular
enzymatic activity at nine sites. Samples collected in this fashion were used
for extracellular enzymatic activity and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) analyses, detailed in
subsequent sections.
Enzymes examined in this study and the reactions they catalyze.
EnzymeModel substrateProductReactionβ-D-glucosidase (GLU)4-MUF-β-D-glucopyranosideMUF1Hydrolysis of glucose from cellobiose and celluloseAlkaline phosphatase (AP)4-MUF-phosphateMUF1Hydrolysis of phosphate fromphosphosaccarides and phospholipidsLeucine aminopeptidase (LAP)L-Leucine-AMCAMC2Hydrolysis of leucine from polypeptidesN-acetylglucosaminidase (NAG)MUF-N-acetyl-β -D-glucosaminideMUF1Degradation of chitin and other β-1,4-linked glucosamine polymers
Enzyme activities were determined using laboratory assays in which sediment
extracts were exposed to model substrates that are hydrolyzed by the enzymes
(Table 3). Protocols were based on those described by Sinsabaugh et al. (1997) and Belanger et al. (1997). Frozen sediment samples were thawed to
room temperature and then 10 mL of 5 mM sodium bicarbonate buffer solution
was added to approximately 1 mL subsamples of sediment in 15 mL centrifuge
tubes. These tubes were homogenized with a vortex mixer for 15 s and then
centrifuged for 15 min at 400 g. Samples were then stored in a refrigerator
overnight and the following day 200 µL of the supernatant was
pipetted in triplicate onto 96-well microplates. To ensure that any increase
in fluorescence was due to enzyme activity, a set of control samples which
had been boiled for 5 min to denature enzymes was also added to the
plates. A set of standard solutions with known concentrations of fluorescent
product were also added to each plate to generate a standard curve.
Background fluorescence readings were recorded and substrate solution was
added to start the enzyme reaction. Each well in the microplate received 50 µL of a 200 µM substrate solution. Fluorescence measurements
(440 nm emission intensity and 365 nm excitation wavelength) were recorded
every ∼ 30 min for at least 3 h. Microplates were protected
from light and kept at room temperature between readings. Fluorescence was
measured using a BioTek Synergy Mx microplate reader. The accumulation of
fluorescent products (AMC or MUF; see Table 3) from the hydrolysis reactions
was measured over time and enzyme activity was calculated as the slope of a
regression of AMC or MUF concentration against time.
About 1 mL of each sediment sample was dried, weighed, and then combusted at
550 ∘C and reweighed to determine ash-free dry mass (AFDM) and percent organic content for the sample (Wallace et al., 2006). Extracellular
enzymatic activity rates were then normalized to organic matter content and
are reported in units of µmol g AFDM-1 h-1.
Organic matter characterizationFT-ICR-MS solvent extraction and data acquisition
We performed electrospray ionization (ESI) and Fourier transform ion
cyclotron resonance (FT-ICR) mass spectrometry (MS) using a 12 Tesla Bruker
solariX FT-ICR-MS instrument located at the Environmental Molecular Sciences Laboratory
(EMSL) in Richland, WA, USA. Prior to mass spectrometry, organic matter was
extracted from sediments by adding 1 mL of water (18 MΩ ionic purity)
to 500 mg of sediments (after Tfaily et al., 2017). Each sediment sample was
extracted three times with the above procedure. Supernatant from all extractions was combined and diluted to 5 mL to generate a final aliquot for analysis. These
aliquots were acidified to pH 2 with 85 % phosphoric acid and extracted
with PPL cartridges (Bond Elut), following Dittmar et al. (2008). We
performed weekly calibration after Tfaily et al. (2017) and instrument
settings were optimized using Suwannee River Fulvic Acid (IHSS). The
instrument was flushed between samples using a mixture of water and
methanol. Blanks were analyzed at the beginning and the end of the day to
monitor for background contaminants.
Samples were injected directly into the mass spectrometer and the ion
accumulation time was set to 0.1 s. Data were collected from 98 to 900 m/z
at 4M, yielding 144 scans that were co-added. A standard Bruker ESI source
was used to generate negatively charged molecular ions. Samples were
introduced to the ESI source equipped with a fused silica tube (30 µm i.d.) through an Agilent 1200 series pump (Agilent Technologies) at a flow rate of 3.0 µL min-1. Experimental conditions were as follows: needle voltage of +4.4 kV; Q1 set to 50 m/z; and the heated resistively coated
glass capillary operated at 180 ∘C.
FT-ICR-MS data processing
A total of 144 individual scans were averaged for each sample and
internally calibrated using an organic matter homologous series separated by
14 Da (-CH2 groups). The mass measurement accuracy was less than 1 ppm for
singly charged ions across a broad m/z range (100–1200 m/z). The mass
resolution was ∼ 240 K at 341 m/z. The transient was 0.8 s. Data analysis software (BrukerDaltonik version 4.2) was used to
convert raw spectra to a list of m/z values applying FTMS peak picker module with a signal-to-noise ratio (S / N) threshold set to 7 and absolute intensity threshold to the default value of 100. Peaks were treated as presence/absence data because peak intensity differences are reflective of
ionization efficiency as well as relative abundance (Kujawinski and Behn,
2006; Minor et al., 2012; Tfaily et al., 2015, 2017).
Putative chemical formulae were then assigned using in-house software
following the compound identification algorithm (CIA), proposed by
Kujawinski and Behn (2006), modified by Minor et al. (2012), and previously
described in Tfaily et al. (2017). Chemical formulae were assigned based on
the following criteria: S / N > 7, and mass measurement error
< 1 ppm, taking into consideration the presence of C, H, O, N, S, and
P and excluding other elements. To ensure consistent formula assignment, we
aligned all sample peak lists for the entire dataset to each other in order
to facilitate consistent peak assignments and eliminate possible mass shifts
that would impact formula assignment. We implemented the following rules to
further ensure consistent formula assignment: (1) we consistently picked the
formula with the lowest error and with the lowest number of heteroatoms and
(2) the assignment of one phosphorus atom requires the presence of at least
four oxygen atoms.
The chemical character of thousands of peaks in each sample's ESI FT-ICR-MS
spectrum was evaluated on van Krevelen diagrams. Compounds were plotted on
the van Krevelen diagram on the basis of their molar H : C ratios (y axis) and molar O : C ratios (x axis) (Kim et al., 2003). Van Krevelen diagrams provide a means to visualize and compare the average properties of organic compounds
and assign compounds to the major biochemical classes (e.g., lipid,
protein, lignin, carbohydrate, and condensed aromatic). In this
study, biochemical compound classes are reported as relative abundance
values based on counts of C, H, and O for the following H : C and O : C ranges: lipids (0 < O : C ≤ 0.3, 1.5 ≤ H : C ≤ 2.5), unsaturated
hydrocarbons (0 ≤ O : C ≤ 0.125, 0.8 ≤ H : C < 2.5), proteins (0.3 < O : C ≤ 0.55, 1.5 ≤ H : C ≤ 2.3), amino
sugars (0.55 < O : C ≤ 0.7, 1.5 ≤ H : C ≤ 2.2), lignin
(0.125 < O : C ≤ 0.65, 0.8 ≤ H : C < 1.5), tannins
(0.65 < O : C ≤ 1.1, 0.8 ≤ H : C < 1.5), and condensed
hydrocarbons (0 ≤ 200 O : C ≤ 0.95, 0.2 ≤ H : C < 0.8)
(Tfaily et al., 2015).
Finally, we calculated the Gibbs free energy of OC oxidation under standard
conditions (ΔGoCox) from the nominal oxidation state of carbon
(NOSC) after La Rowe and Van Cappellen (2011). Though the exact calculation
of ΔGoCox necessitates an accurate quantification of all species
involved in every chemical reaction in a sample, the use of NOSC as a
practical basis for determining ΔGoCox has been validated (Arndt et
al., 2013; LaRowe and Van Cappellen, 2011; Graham et al., 2017; Boye et al., 2017; Stegen et al., 2018).
Stream solute tracer
Two injections of a conservative solute tracer (NaCl) were conducted at 46
synoptic sites, one each at the upstream and downstream reach boundaries to
quantify discharge and short-term hyporheic flux. First, we fixed the
upstream end of the study reach at the same transect as the piezometer and
sampling location. Next, we set the downstream station at a distance of
about 20 wetted channel widths downstream from the piezometer and sampling
location, a length selected to capture a representative valley segment
(after Anderson et al., 2005). Minor variation in distance was allowed to
place two specific conductivity sensors in well-mixed locations within the
stream channel, with the total length reported for each tracer study reach.
For each injection, mixing lengths for the solute tracer were visually
estimated (after Payn et al., 2009; Ward et al., 2013b, a), and small
releases of a visual tracer were used to confirm mixing lengths when visual
estimates were uncertain. A known mass of NaCl was dissolved in stream water
and released as an instantaneous injection one mixing length upstream from
the reach boundary. Initially, the downstream slug was released and measured
only at the downstream location to enable dilution gauging estimates of
discharge at the downstream end of the study reach. Next, the upstream slug
was released and monitored at both locations to enable dilution gauging at
the upstream transect, and evaluation of both recovered and lost tracer
along the study reach. The experimental design closely follows Payn et al. (2009) and Ward et al. (2013b).
Solute tracer data at the reach boundaries were recorded as specific
conductance (Onset Computer Corporation, Bourne, MA, USA). We used a four-point calibration curve constructed by dissolving known masses of NaCl in
stream water to convert specific conductance to salt concentration (C= 0.5022 S, where C is NaCl concentration in milligrams per liter and S is specific conductance;
r2>0.99). Notably, this equation does not include a
y intercept as we first subtracted background S from all observations prior to conversion. In addition to providing the full solute tracer time series in
the data set, we also provide estimates of discharge (Q) based on dilution
gauging, truncating the recovered tracer time series after 99 % recovery
(after Mason et al., 2012; Ward et al., 2013b, a). We report in the data
set Q for both the upstream and downstream ends of the study reach, and the
change in Q along the study reach. Several additional metrics describing
solute tracer time series are detailed in Ward et al. (2019).
Data availability
These data are archived in the Consortium of Universities for the
Advancement of Hydrologic Science, Inc. (CUAHSI) HydroShare data repository,
accessible as 10.4211/hs.f4484e0703f743c696c2e1f209abb842 (Ward, 2019). In addition to tabular data, time series for solute tracer experiments and
detailed results from the FT-ICR-MS analyses are archived. Raw sequence data
for 16S DNA analyses are archived at the U.S. National Center for
Biotechnology Information (NCBI) as a BioProject (Accession: PRJNA534507).
Conclusions
We provide here a detailed characterization of physical, chemical, and
biological parameters that are germane to the study of river corridor
exchange and associated ecosystem functions and services. These data
represent state-of-the-science characterization conducted at a heretofore
unpresented resolution in space, and the only known data set that integrates
across physical, chemical, and biological dimensions of the river corridor,
including coverage across 5 stream orders. Taken together, these data will
enable the testing of hypothesized processes and relationships in the river
corridor across spatial scales, and will be useful in the generation of
testable hypotheses about river corridor exchanges in future studies.
Author contributions
All co-authors participated in the field collection, laboratory analysis,
and/or curation of the data set. ASW was primarily responsible for the
writing of this paper and assembly of the archival database. ASW and
JPZ conceived of the study design with input from all co-authors. All
authors contributed to the writing of this paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Linking landscape organisation and hydrological functioning: from hypotheses and observations to concepts, models and understanding (HESS/ESSD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
Data and facilities
were provided by the H. J. Andrews Experimental Forest and Long Term Ecological
Research program, administered cooperatively by the USDA Forest Service
Pacific Northwest Research Station, Oregon State University, and the
Willamette National Forest. Adam S. Ward's time in preparation of this
paper was supported by the University of Birmingham's Institute of
Advanced Studies. A portion of the research was performed using EMSL (grid 436923.9), a DOE Office of Science User Facility sponsored by the Office of Biological and Environmental Research. Finally, the authors acknowledge
this would not have been possible without support from their home
institutions.
Financial support
This research has been supported by the Leverhulme Trust (“Where rivers, groundwater and disciplines meet: a hyporheic research network”), the UK Natural Environment Research Council (grant no. NE/L003872/1), the U.S. Department of Energy (Pacific Northwest National Laboratory, grant no. DE-SC0019377), the National Science Foundation (grant nos. DEB-1440409, EAR-1652293, EAR-1417603, and EAR-1446328), the University of Birmingham (grant no. Institute of Advanced Studies), and the European Commission (HiFreq, grant no. 734317).
Review statement
This paper was edited by Loes van Schaik and reviewed by Eric Moore and one anonymous referee.
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