Relationships between methane (
Considerable effort has been directed to the study of carbon dioxide
(CO
Net soil
A spatially explicit understanding of heterogeneity in
Functional landscape elements have proven useful for assessing spatial
heterogeneity and influences of scale in hydrology (Wood et al., 1988),
ecology (Forman and Godron, 1981), and biogeochemistry (Corre et al., 1996;
Reynolds and Wu, 1999). Functional landscape elements and terrain metrics
that represent topographically driven hydrologic gradients have been used to
analyze and scale biogeochemical cycles (e.g., carbon: Creed et al., 2002;
Riveros-Iregui and McGlynn, 2009; Pacific et al., 2011; nitrogen: Hedin et
al., 1998b; Creed and Beall, 2009; Duncan et al., 2013; Anderson et al.,
2015; phosphorus: Devito et al., 2000; sulfate: Welsch et al., 2004), but
limited analogous work has been done for How do environmental variables relate to How does landscape structure relate to relative magnitude and direction of
Map of upper Stringer Creek watershed (394 ha), located in central Montana, showing sampling locations and meteorological towers. Inset shows profiles of transects T1 and T2, where site number increases away from the creek on the west and east sides.
Streamflow and precipitation inputs to upper Stringer Creek over the 2013 growing season. Gas sampling began on 29 May, shortly after the first hydrograph peak.
Tenderfoot Creek Experimental Forest (46.55
The watershed experiences a continental climate, with 70 % of the 800 mm
annual precipitation typically falling as snow from November to May. Growing
season length ranges from 45 to 70 days (Schmidt and Friede, 1996), and mean
daily summer temperature is 11
The geology of the Stringer Creek watershed is comprised of Flathead sandstone, Wolsey shale, and granite gneiss. Soils are shallow (< 1 m) typic cryocrepts in the uplands and aquic cryobalfs in the riparian areas. The seasonal dry-down of the upland soils vs. the riparian areas (which typically maintain a shallow water table throughout the year; Jencso et al., 2009) reflects the differentiation in soil types. Upland soils have a sandy loam texture but vary in rock and organic matter content across landscape positions.
Plant communities transition from wet riparian meadows in the valley bottom
through drier meadows to the upland conifer forest. The vegetation in the
riparian area is predominately grasses (
Ten-meter and 3 m digital elevation models (DEMs) were constructed
by coarsening 1 m
We examined the spatial and temporal variability of
Soil cores were collected on 8 and 9 July 2012 within 2 m of each gas sampling site for soil analysis. Soil cores were
extracted using a 100 cm
Weekly measurements of environmental variables were collected in conjunction with gas samples at each site from May to September 2013 between 09:00 and 18:00 UTC. Environmental variables that were measured included volumetric soil water content (VWC), soil temperature (12 cm soil thermometer, Reotemp Instrument Corporation, CA, USA), and barometric pressure (Atmospheric Data Center Pro, Brunton, Boulder, CO, USA). VWC was measured three times at each site during each round of sampling using a Hydrosense II portable soil water content meter (12 cm, Campbell Scientific Inc., UT, USA). The mean of the three samples was used for data analysis. We measured real-time water content hourly at individual riparian (T1E1), transition (T1E2), and hillslope (T1E3) sites using water content probes (CSI model 616, Campbell Scientific Inc., UT, USA) that were inserted from 0 to 12 cm in the soil (Fig. 1).
Groundwater table data were recorded in wells located along the two
riparian–hillslope transects to augment the weekly measurements of near-surface
soil water content (Fig. 1). Groundwater wells (created from 3.81 cm
diameter polyvinyl chloride (PVC), screened from completion depth to within
10 cm of ground surface) were installed along the riparian–hillslope
transects (co-located with gas wells). Capacitance rods (
Soil gas wells constructed of 5.25 cm diameter, 15 cm long sections of PVC
were installed to sample soil air for concentration measurements of
Weekly gas samples were taken from the closed recirculation loop after
observed soil
Gas samples were analyzed for
Methane fluxes were calculated using the gradient method (Fick's first law)
and measured soil concentrations at 5 cm (Eq. 3).
Relationship between effective soil diffusivity for methane
We used two-sample
Average soil characteristics with 1 standard deviation in parentheses.
We assessed two sets of predictor variables for multiple-regression
modeling: (1) both terrain metrics and local soil measurements (VWC
Terrain analysis was performed using both 3 and 10 m DEMs, and although
higher-resolution mapping can be beneficial in some scenarios, the 10 m flow
accumulation results have been shown to be more reflective of the lateral
transport of water in TCEF and were used in this analysis (Jencso et al.,
2009). The slopes in the upper Stringer Creek watershed range from moderate
(2 %) to steep (66 %). Sampling sites encompassed the range of aspects
in the watershed (72–312
Soil molar C
Soil temperatures ranged from 0 to 8
VWC had a strong seasonal pattern and was
significantly different between riparian and upland landscape positions
(two-sample
Real-time water content sensors (solid lines) that were distributed across landscape positions during the growing season of 2013 show the seasonal dry-down of the landscape, with a muted signal in the riparian area. These high-frequency sensor data corroborate the distributed volumetric water content (VWC) measurements made at every site during discrete sampling (filled symbols). Riparian sites increase in variability throughout the season, and hillslope positions gradually dry down to low soil moisture conditions.
Methane dynamics and the seasonal decline of the groundwater (GW)
table at three sites located along a riparian–hillslope transect during the
2013 growing season.
Measurements of
Groundwater (GW) table dynamics can be described by three general responses that were related to proximity to the creek (Fig. 6). Riparian locations maintained a GW table throughout the season, with near-surface saturation during snowmelt, and GW tables 20–50 cm below the soil surface late in the season. GW wells closest to the stream (T1E1 and T1W1) had a water table within 22–25 cm of the surface throughout the season. Toe slope positions (near the strong break in slope on the east side) responded rapidly to snowmelt, and retained a GW table through late July. Wells in this transition zone (e.g., T1E2, Fig. 6c) had variable GW dynamics, which included GW response to the rain events (up to 11 mm) in the first week of August. At another transition location, a well that was influenced by the large local riparian extent and low local gradient (T2W3) maintained a GW table within 70 cm of the surface throughout the season. Upland positions above the break in slope exhibited transient GW tables during peak snowmelt, and by mid- to late June no longer had GW tables present. During snowmelt these wells had a GW table for up to 28 days, and no wells had a GW table after 26 June.
Cumulative
The shallow soil was well oxygenated; in the uplands 5 cm
Methane fluxes (
Net
Methane fluxes were not significantly correlated with %
Methane flux statistics (
Coefficients of the parameters used to model cumulative seasonal
influx (ln
Cumulative seasonal
We assessed the degree to which terrain metrics were correlated with
environmental variables and
Measured cumulative
Modeled and observed seasonal
Multiple regressions that included soil data explained up to 60 % of the
observed variability in ln
We created a spatially explicit model of upper Stringer Creek
ln
We utilized understanding of watershed hydrology processes at TCEF (Jencso
et al., 2009, 2010; Kelleher et al., 2017; Nippgen et al.,
2015) to design a sampling campaign which captured
Research on soil–atmosphere
Rates of both soil
Depth to groundwater table, VWC, and
Soil moisture has a strong influence on the microbial populations that drive
methane cycling (Conrad, 1996; Potter et al., 1996; Smith et al., 2003; Luo
et al., 2013; Du et al., 2015), but the differential response of
methanotrophs and methanogens to soil moisture status can make it difficult
to find simple relationships between net
Riparian zones are often characterized by high rates of biogeochemical
cycling due to organic carbon availability, fluctuating water tables, and
correspondingly variable redox conditions. At TCEF, soil in the riparian
area is saturated during the snowmelt period, and the hydrologic connection
to the uplands provides a downslope pulse of dissolved organic carbon
(Pacific et al., 2010). This seasonal input of carbon could lead to
increased methanogenesis, yet soil
Transition zones or boundaries between landscape elements can exhibit steep
gradients in hydrologic conditions and nutrients (Hedin et al., 1998a). We
determined that this was also true for
Flux of
Greenhouse gases have been modeled using a range of frameworks including
empirical (data-driven), mechanistic (process-based), and atmospheric
inverse modeling (see Blagodatsky and Smith (2012) and Wang et al. (2012)
for detailed reviews). Although these modeling efforts have significantly
advanced our understanding of GHG dynamics at landscape to regional scales,
most of them do not reflect spatial patterns (or variability) in the lateral
redistribution of water (Tague and Band, 2001; Groffman, 2012). The spatial
patterns of soil properties (Konda et al., 2010), microbial assemblages
(Florinsky et al., 2004), and resultant biogeochemistry influenced by
landscape position and topography (Creed and Beall, 2009; Riveros-Iregui and
McGlynn, 2009; Creed et al., 2013; Anderson et al., 2015) have been
investigated and used to scale point observations to the larger landscape in
a limited number of studies. Remote sensing and vegetation classification
have also been suggested as empirical methods to scale
We used an empirical model based on topographic indices to scale
This spatially distributed model (ln
Terrain analysis reflects the long-term conditions of a given location
relative to its landscape setting. Lower VWC (at the point scale) and
relative water availability (as represented by TWI at the landscape scale)
corresponded to more
Consistent with previous research on
The strong gradients of water availability at TCEF impose both a direct
(local) and indirect (distal/historic) effect on the microbial communities
and physical transport processes regulating biogeochemical fluxes. We
implemented a sampling design that utilized these hydrologic gradients to
study the influence of landscape heterogeneity on watershed
Landscape elements can be useful in characterizing areas that behave
similarly to net sources or sinks of
The effect of soil moisture on
Data are publicly available and can be accessed at
All topographic variables included in initial exploratory data
analysis. If a set of topographic variables had a Pearson's correlation
coefficient greater than 0.6, then the variable with a lower correlation
with ln
All environmental variables considered in the initial exploratory
data analysis. If a set of variables had a Pearson's correlation coefficient
greater than 0.6, the variable with a lower correlation with ln
Bivariate plots of
Results from the variable jack-knife analysis to determine which
variables were the most important from a given parameter set:
BLM and JED conceived of the initial project as project principal investigators. The project became a collaborative effort with KEK, who conducted most of the fieldwork. JED led collection of the LI-COR data for the diffusivity relationship and analyzed the gas samples. KEK conducted data analysis and prepared the manuscript with contributions from her advisor (BLM) and revisions from both co-authors.
This work was principally supported by NSF grant 1114392 awarded to John E. Dore and Brian L. McGlynn and an NSF GRFP fellowship DGE 1644868 awarded to Kendra E. Kaiser. Additional support to John E. Dore from NSF EPSCoR Cooperative Agreement #EPS-1101342 is gratefully acknowledged. The authors appreciate logistical collaboration with the USDA Forest Service, particularly Helen Smith of the Rocky Mountain Research Station and Carol Hatfield of the Lewis and Clark National Forest. We thank Chris Allen, William Avery, Andrew Birch, Keenan Brame, Mark Burr, Patrick Clay, Tim Covino, Calvin Dore, Helena Dore, Missey Dore, Rob Edwards, James Irvine, Kelsey Jencso, Ryan Jones, Liyin Liang, Tracey Lorenzo, Tim McDermott, Alex Michaud, Fabian Nippgen, Sarah Ohlen, Hayden Wilson, Ellie Zignego, and Margaret Zimmer for field and/or laboratory assistance, and Dean Urban for his suggestions on how to improve assessment of model performance. We would particularly like to thank Erin Seybold for her critical contributions in the field and assistance with data quality control. Edited by: Edzo Veldkamp Reviewed by: two anonymous referees