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  <front>
    <journal-meta><journal-id journal-id-type="publisher">HESS</journal-id><journal-title-group>
    <journal-title>Hydrology and Earth System Sciences</journal-title>
    <abbrev-journal-title abbrev-type="publisher">HESS</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Hydrol. Earth Syst. Sci.</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1607-7938</issn><publisher>
    <publisher-name>Copernicus Publications</publisher-name>
    <publisher-loc>Göttingen, Germany</publisher-loc>
  </publisher></journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5194/hess-21-6031-2017</article-id><title-group><article-title>Hydrogeological controls on spatial patterns of groundwater discharge in
peatlands</article-title>
      </title-group><?xmltex \runningtitle{Hydrogeological controls on spatial patterns of groundwater discharge}?><?xmltex \runningauthor{D. K. Hare et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1 aff2">
          <name><surname>Hare</surname><given-names>Danielle K.</given-names></name>
          <email>hare.danielle@gmail.com</email>
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Boutt</surname><given-names>David F.</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-1397-0279</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Clement</surname><given-names>William P.</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Hatch</surname><given-names>Christine E.</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3">
          <name><surname>Davenport</surname><given-names>Glorianna</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff4">
          <name><surname>Hackman</surname><given-names>Alex</given-names></name>
          
        </contrib>
        <aff id="aff1"><label>1</label><institution>AECOM Technical Services, Rocky Hill, CT 06067, USA</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>Department of Geosciences, University of Massachusetts Amherst, 611 N.
Pleasant St., Amherst, MA 01003, USA</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Living Observatory at Tidmarsh Farms, 139 Bartlett Road, Plymouth, MA 02360, USA</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>Massachusetts Division of Ecological Restoration, 251 Causeway St.,
Suite 400, Boston, MA 02114, USA</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Danielle K. Hare (hare.danielle@gmail.com)</corresp></author-notes><pub-date><day>30</day><month>November</month><year>2017</year></pub-date>
      
      <volume>21</volume>
      <issue>12</issue>
      <fpage>6031</fpage><lpage>6048</lpage>
      <history>
        <date date-type="received"><day>14</day><month>May</month><year>2017</year></date>
           <date date-type="rev-request"><day>14</day><month>June</month><year>2017</year></date>
           <date date-type="rev-recd"><day>11</day><month>October</month><year>2017</year></date>
           <date date-type="accepted"><day>12</day><month>October</month><year>2017</year></date>
      </history>
      <permissions>
        
        
      <license license-type="open-access"><license-p>This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/3.0/">https://creativecommons.org/licenses/by/3.0/</ext-link></license-p></license></permissions><self-uri xlink:href="https://hess.copernicus.org/articles/21/6031/2017/hess-21-6031-2017.html">This article is available from https://hess.copernicus.org/articles/21/6031/2017/hess-21-6031-2017.html</self-uri><self-uri xlink:href="https://hess.copernicus.org/articles/21/6031/2017/hess-21-6031-2017.pdf">The full text article is available as a PDF file from https://hess.copernicus.org/articles/21/6031/2017/hess-21-6031-2017.pdf</self-uri>
      <abstract>
    <p id="d1e144">Peatland environments provide important ecosystem services including water
and carbon storage, nutrient processing and retention, and wildlife habitat.
However, these systems and the services they provide have been degraded
through historical anthropogenic agricultural conversion and dewatering
practices. Effective wetland restoration requires incorporating site
hydrology and understanding groundwater discharge spatial patterns.
Groundwater discharge maintains wetland ecosystems by providing relatively
stable hydrologic conditions, nutrient inputs, and thermal buffering
important for ecological structure and function; however, a comprehensive
site-specific evaluation is rarely feasible for such resource-constrained
projects. An improved process-based understanding of groundwater discharge in
peatlands may help guide ecological restoration design without the need for
invasive methodologies and detailed site-specific investigation.</p>
    <p id="d1e147">Here we examine a kettle-hole peatland in southeast Massachusetts
historically modified for commercial cranberry farming. During the time of
our investigation, a large process-based ecological restoration project was
in the assessment and design phases. To gain insight into the drivers of
site hydrology, we evaluated the spatial patterning of groundwater discharge
and the subsurface structure of the peatland complex using heat-tracing
methods and ground-penetrating radar. Our results illustrate that two
groundwater discharge processes contribute to the peatland hydrologic
system: diffuse lower-flux marginal matrix seepage and discrete
higher-flux preferential-flow-path seepage. Both types of groundwater
discharge develop through interactions with subsurface peatland basin
structure, often where the basin slope is at a high angle to the regional
groundwater gradient. These field observations indicate strong correlation
between subsurface structures and surficial groundwater discharge.
Understanding these general patterns may allow resource managers to more
efficiently predict and locate groundwater seepage, confirm these using
remote sensing technologies, and incorporate this information into
restoration design for these critical ecosystems.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <title>Introduction</title>
      <p id="d1e157">Peatlands develop in response to physical, biological, and chemical
processes and feedbacks. Groundwater discharge to surface water is one of
the most important physical controls on peatlands stability
(Siegel et al., 1995; Watters and Stanley, 2007);
yet the underlying physical hydrogeologic framework governing the
development of surface seepage distribution in these systems is not well
understood. Preferential flow paths, hydraulic conductivity (<inline-formula><mml:math id="M1" display="inline"><mml:mrow><mml:mi>K</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> anisotropy,
and geologic heterogeneities likely influence the surface expression of
discharge zones (Chason and Siegel, 1986;
Drexler et al., 1999; Smart et al., 2012). However, these variables have
been difficult to constrain due to the spatial resolution of traditional
localized groundwater wetland methods (wells, boreholes, surface point
measurements, etc.) and their impact on fragile flow paths. The underlying
hydrologic engine of these wetlands have shown to be difficult to discern in
large-scale systems.</p>
      <p id="d1e170">Thermal dynamics of ground and surface waters also govern critical wetland
functions and can be assessed in multiple ways. Surface water thermal
stability, for example, is a popular research focus in ecohydrology, as this
process is important for aquatic species that rely on the low variance of
groundwater temperature to buffer themselves from heat extremes and regulate
their metabolism (Caissie, 2006; Deitchman and
Loheide II, 2012). Temperature also controls chemical processes in ecosystem
respiration, which in turn controls carbon processing and nutrient retention
(Boulton et al., 1998;
Davidson and Janssens, 2006; Demars et al., 2011; Lafleur et al., 2005),
biodiversity (Parish et al., 2008), as well as overall
species health (Verberk et al., 2011). Upwelling zones
are linked to increased biogeochemical cycling (Sebestyen and
Schneider, 2001) and also maintain species richness through the “edge
effect” – overlap between the thermally and chemically stable groundwater
ecotone and the higher oxygen environment within the main stream channel
(Brunke and Gonser,
1997; Cirkel et al., 2010). An increase in wetland temperature has been
shown to stimulate methane production (McKenzie et al., 2007)
as well. The underlying drivers of the thermal regime of a wetland system
can be caused through varying driving processes, and are important to the
ecosystem services provided in the peatland.</p>
      <p id="d1e173">Widespread drainage of peatlands has caused wetland degradation and loss of
ecosystem services. Anthropogenic modifications such as ditching and filling
create discontinuity between surface water and groundwater systems, with
impacts on wetland function (van Loon et al.,
2009). In some parts of the world, wetland restoration is attempting to
address these historical impacts. Within the United Kingdom, for example,
efforts to return natural water table levels by filling drainage ditches in
peat mining areas have led to disagreements as to the cost–benefit of these
specific restoration designs (Grand-Clement et al., 2013).
In New England (United States), where thousands of acres of historical
peatlands were converted to commercial cranberry farming in the late 1800s
(Garrison and Fitzgerald, 2005), wetland restoration is similarly
attempting to regain natural water table levels (Price et
al., 2003). An incomplete understanding of the underlying hydrology and thermal
regime can limit the effectiveness of such efforts.</p>
      <p id="d1e176">In this research, we explore the spatial distribution of groundwater seepage
through a kettle-hole peatland from the analysis of basin structure and
hydraulic properties of the peatland matrix. To assist in wetland
restoration design at the study site, we focus on understanding the natural
processes that promote the hydrologic inputs for aquatic habitat formation
and maintenance. The goals of this study are to (1) identify groundwater
discharge locations and their hydrogeologic controls, (2) determine
temperature dynamics of the groundwater discharge locations, and (3)
evaluate the development of these seepage patterns. Through this work,
insight is gained into how the hydrologic driving mechanisms of peat-based
wetlands can support the restoration of sustainable ecosystems (e.g., process-based
design) (Beechie et al., 2010; Dahl et
al., 2007).</p>
<sec id="Ch1.S1.SS1">
  <title>Site description</title>
      <p id="d1e185">The site “Tidmarsh Farms” is comprised of three cranberry farms, and the two largest farms
are separated by Beaver Dam Road (Fig. 1). The area surrounding this
peatland site is characterized by outwash, kame deltas, and ground
moraines that show evidence of collapse features and deformation (Larson, 1982; Stone et al.,
2011). These ice collapse features are typical of environments proximal to
ice contact zones and can result in the formation of kettle holes, of which
there is extensive evidence throughout the surrounding region. All three
of the site's cranberry farms were built on kettle-hole peatlands between
the late 1800s and early 1900s. The cranberry peatlands on Tidmarsh East
were taken out of production in 2010 and another was taken out of production
in 2015. This study concerns work on Tidmarsh East (the site), which is 2.5 km<inline-formula><mml:math id="M2" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula> (Fig. 1b).</p>
      <p id="d1e197">A layer of sand 0.3–1.5 m thick overlays native surficial soils, which was laid down as a
part of a normal cranberry farming practices until the site retirement in
2010. This practice maintained a very low gradient across the site with a
slight decline to the north, with minimal microtopography.</p>
      <p id="d1e200">During this research, conducted in collaboration between the Living Observatory
and University of Massachusetts, a restoration project involving the private
landowners, governmental agencies, and nongovernmental organizations was in
assessment and design phases. Project planners were specifically interested
in the location of groundwater discharge across the site to help design the
placement of reconstructed stream channels. In addition, the restoration
design team sought to better understand the location of subsurface peat
deposits, underlying site hydrology, and potential future thermal regimes
when considering potential restoration activities. As of 2017, the site has
undergone both passive and active restoration to encourage an accelerated
ecological recovery based on the conclusions of this work. In the following
sections, we document our methods and findings specific to the spatial
distribution of the groundwater discharge at Tidmarsh East and the
implications for restoration design.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><caption><p id="d1e205"><bold>(a)</bold> Site map of the Tidmarsh Farms regional peatland showing the
study area and watershed boundary; Plymouth County, Massachusetts, and PCKD
USGS wells used for regional groundwater isotopic data (Table 1). <bold>(b)</bold> Detail
of the Tidmarsh Farms study site showing the major waterways and flow
direction in blue, site groundwater wells, isotopic sample locations, and
GPR transects. Beaver Dam Brook flows north into Plymouth Bay.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://hess.copernicus.org/articles/21/6031/2017/hess-21-6031-2017-f01.png"/>

        </fig>

</sec>
<sec id="Ch1.S1.SS2">
  <title>Site hydrology</title>
      <p id="d1e225">The farm is a part of the small 5 km<inline-formula><mml:math id="M3" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula> surficial Beaver Dam Brook
Watershed, but is also a discharge location of the 360 km<inline-formula><mml:math id="M4" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula>
Plymouth–Carver–Kingston–Duxbury (PCKD) aquifer, and thus the groundwater
flow paths contribute from a much larger hydrologic system than the surficial
watershed (Fig. 1a). The PCKD aquifer below the site is characterized by
glacial outwash sands (Masterson et al., 2009). Surface
water enters the site from four surface water bodies south of the site
(Fresh Pond, Little Island Pond, the Arm Wetland, and Beaver Dam Pond
headwaters) and drains northward into Beaver Dam Brook, an approximately
2 km reach, before discharging in Bartlett Pond and then directly into
Plymouth Bay (Fig. 1b).</p>
      <p id="d1e246">To facilitate drainage and irrigation, lateral and perimeter drainage
ditches exist throughout farmed areas. Parallel drainage ditches are located
approximately every 18–35 m throughout the entire site, and are
approximately 1 m wide and 0.5 m deep. The western agricultural cells have
drainage ditches oriented east–west (Cell 3 and 4), and in the eastern cells
(Cell 6 and 7) most drainage ditches are oriented north–south. When the
study was conducted, the site was still predominately covered in low-lying
cranberry vegetation, as well as a variety of sedges and cattails mostly
adjacent to the central stream bank and marginal drainage ditches.</p>
      <p id="d1e249">Flashboards in the dam creating the Beaver Dam Pond impoundment were
permanently removed by the landowners in the fall of 2010, after which the
southern side of the farm was allowed to return to a natural wetland state
(Fig. 1b). Data collection conducted for this research spanned 2012–2014,
beginning 2 years after farming ceased, and prior to any active wetland
restoration activity.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1" specific-use="star"><caption><p id="d1e255">USGS groundwater wells <inline-formula><mml:math id="M5" display="inline"><mml:mi mathvariant="italic">δ</mml:mi></mml:math></inline-formula>2H-H<inline-formula><mml:math id="M6" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O and <inline-formula><mml:math id="M7" display="inline"><mml:mi mathvariant="italic">δ</mml:mi></mml:math></inline-formula>18O-H<inline-formula><mml:math id="M8" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O
isotopic data used to establish the regional groundwater trend.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="5">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="right"/>
     <oasis:thead>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1">USGS Well ID</oasis:entry>  
         <oasis:entry colname="col2"><inline-formula><mml:math id="M9" display="inline"><mml:mrow><mml:msup><mml:mi mathvariant="italic">δ</mml:mi><mml:mn mathvariant="normal">18</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>O</oasis:entry>  
         <oasis:entry colname="col3"><inline-formula><mml:math id="M10" display="inline"><mml:mrow><mml:msup><mml:mi mathvariant="italic">δ</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>H</oasis:entry>  
         <oasis:entry colname="col4">Latitude  (WGS 84)</oasis:entry>  
         <oasis:entry colname="col5">Longitude (WGS 84)</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>  
         <oasis:entry colname="col1">MA-PWW 494, Plymouth</oasis:entry>  
         <oasis:entry colname="col2"><inline-formula><mml:math id="M11" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>6.15</oasis:entry>  
         <oasis:entry colname="col3"><inline-formula><mml:math id="M12" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>37.38</oasis:entry>  
         <oasis:entry colname="col4">41.8713889</oasis:entry>  
         <oasis:entry colname="col5"><inline-formula><mml:math id="M13" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>70.6586111</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">MA-EBW 30, East Bridgewater</oasis:entry>  
         <oasis:entry colname="col2"><inline-formula><mml:math id="M14" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>8.07</oasis:entry>  
         <oasis:entry colname="col3"><inline-formula><mml:math id="M15" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>46.33</oasis:entry>  
         <oasis:entry colname="col4">42.0155556</oasis:entry>  
         <oasis:entry colname="col5"><inline-formula><mml:math id="M16" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>70.9658333</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">MA-WFW 51 Wareham</oasis:entry>  
         <oasis:entry colname="col2"><inline-formula><mml:math id="M17" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>6.83</oasis:entry>  
         <oasis:entry colname="col3"><inline-formula><mml:math id="M18" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>37.45</oasis:entry>  
         <oasis:entry colname="col4">41.7550000</oasis:entry>  
         <oasis:entry colname="col5"><inline-formula><mml:math id="M19" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>70.7325000</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">MA-D4W 80 Duxbury</oasis:entry>  
         <oasis:entry colname="col2"><inline-formula><mml:math id="M20" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>8.13</oasis:entry>  
         <oasis:entry colname="col3"><inline-formula><mml:math id="M21" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>47.42</oasis:entry>  
         <oasis:entry colname="col4">42.0547222</oasis:entry>  
         <oasis:entry colname="col5"><inline-formula><mml:math id="M22" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>70.7247222</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">MA-XGW 2, Weymouth</oasis:entry>  
         <oasis:entry colname="col2"><inline-formula><mml:math id="M23" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>8.55</oasis:entry>  
         <oasis:entry colname="col3"><inline-formula><mml:math id="M24" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>50.33</oasis:entry>  
         <oasis:entry colname="col4">42.1650000</oasis:entry>  
         <oasis:entry colname="col5"><inline-formula><mml:math id="M25" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>70.9458333</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">MA-NGW 116, New Bedford</oasis:entry>  
         <oasis:entry colname="col2"><inline-formula><mml:math id="M26" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>7.55</oasis:entry>  
         <oasis:entry colname="col3"><inline-formula><mml:math id="M27" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>43.42</oasis:entry>  
         <oasis:entry colname="col4">41.6736111</oasis:entry>  
         <oasis:entry colname="col5"><inline-formula><mml:math id="M28" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>70.9577778</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">MA-F3W 23, Freetown</oasis:entry>  
         <oasis:entry colname="col2"><inline-formula><mml:math id="M29" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>7.86</oasis:entry>  
         <oasis:entry colname="col3"><inline-formula><mml:math id="M30" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>44.02</oasis:entry>  
         <oasis:entry colname="col4">41.7847222</oasis:entry>  
         <oasis:entry colname="col5"><inline-formula><mml:math id="M31" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>71.0813889</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

      <p id="d1e604">The site is located within the discharge zone of the large PCKD aquifer, and
thus short-term, drastic temporal shifts are not expected in the
hydrogeology or the processes described herein. We expect that our
observations from the study conducted over this 2-year period to be
representative of present-day conditions. The primary source of recharge to
the PCKD aquifer is through precipitation which rapidly infiltrates outwash
plain deposits (Wareham and Carver Pitted Plains) (Masterson, 2009), and thus changes in
the water table elevation can be expected. Topographic changes to the base level
due to isostatic rebound and sea level rise may also contribute to water
table elevation changes (Oakley and Boothroyd, 2012). The regional aquifer may be sensitive to
long-term climatic changes (Shuman et al., 2001; Newby et al., 2009); however, this question is
outside the scope of this study.</p>
</sec>
</sec>
<sec id="Ch1.S2">
  <title>Methods</title>
      <p id="d1e614">Seepage patterns within peatlands have been difficult to constrain due to
large site areas and complex, dynamic substrates. At Tidmarsh Farms, we use
multiple remote-sensing and direct-contact methods in this environment to
connect data from different scales into a process-based understanding of
peatland groundwater seepage. Ground-penetrating radar (GPR) is used to
evaluate the subsurface structure of the peatland basin(s), and multiple
thermal methods are used to locate and analyze surficial groundwater seepage
patterns. Stable water isotopes are used to describe the dominant water sources
supplying the seepage.</p>
      <p id="d1e617">Traditional hydrogeologic methods were also implemented including well
transects, seepage meters, and differential discharge gauging along Beaver
Dam Brook. Figure 1b illustrates the location of field measurements.
Differential discharge gauging of surface water flow was performed at the
site with a Marsh McBirney Flo-Mate 2000. A low-profile seepage meter was
used to quantify groundwater discharge in accordance with the technique
described by Rosenberry (2008).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2" specific-use="star"><caption><p id="d1e622"><bold>(a)</bold> Map of total peat thickness beneath Tidmarsh Farms based on GPR
data. GPR data collected along linear transects shown here (black lines;
pink lines in Fig. 1b) were interpolated and contoured to show peat
thickness (colors) on the 2-D surface map. Zones of medium and high curvature
(the second derivative of the thickness) of the peat–sand interface are shown
as grey and black pixels, respectively. <bold>(c, d)</bold> Three example cross
sectional profiles, or radargrams, illustrate a distinct reflector at the
basal peat–sand contact. Peat is shaded red. Sediment cores shown as yellow
lines (hand cores) and orange lines (vibracore) were used to constrain the
GPR velocity data. High curvature is highlighted in green boxes.</p></caption>
        <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://hess.copernicus.org/articles/21/6031/2017/hess-21-6031-2017-f02.pdf"/>

      </fig>

<sec id="Ch1.S2.SS1">
  <title>Resolving subsurface structure</title>
      <p id="d1e641">GPR has been successfully used to characterize peatlands' physical structure
and stratigraphy due to the strong contrast between peat and the underlying
aquifer geophysical properties (e.g., water content) (e.g.,
Comas et
al., 2005; Holden, 2004; Kettridge et al., 2008; Lowry et al., 2009; Slater
and Reeve, 2002). The GPR method relies on the transmission of
electromagnetic (EM) waves through the subsurface then records the time and
amplitude of the returning signal (reflection) to image changes in the EM
properties between subsurface materials (Knight,
2001; Lowry et al., 2009). In August 2012 we collected common-offset
reflection data using Malå ProEx 100 and 50 MHz antennas with a
transmitter–receiver separation of 1 and 2 m, respectively. Here, we only
use the 100 MHz data to generate interpolated maps of peat thickness, as
those data provide better resolution of the peat–sand interface given the
EM properties of the peat matrix and the depth of the
structure of interest, which was 0–15 m for this study site. A total of 19 GPR
line surveys were completed; all surveys used 0.3 m trace spacing and ranged
from 100 to 1000 m in total length (Fig. 1b). The vertical resolution of
the survey was 0.9 m, based on the theory that layers can only be
resolved if their thickness is greater than one-quarter of a wavelength.</p>
      <p id="d1e644">We applied a 150 MHz high-cut filter to remove the high-frequency noise, and
then a 100 ns automatic gain control to compensate for signal loss with
depth, and distinguish deeper reflections by averaging over the time window
applied and adjusting the central signal strength with respect to that
result. No topographic adjustments were made, as there is negligible
topographic variation both along the surveys and between the surveys. The
peat thickness was determined in each of the radargrams. Three
characteristic radargrams are shown in Fig. 2.</p>
      <p id="d1e647">We constrain the EM signal velocity through the peat for the GPR data
analysis, and describe the peat's structure with depth by collecting nine
sediment cores (Fig. 1b) with a vibracorer (3) and hand corer (6). Analysis
of the cores demonstrated that the layered reflections observed in the
radargrams were due to variation in the degree of humification. We
determined an average EM velocity of 0.036 m ns<inline-formula><mml:math id="M32" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> (range <inline-formula><mml:math id="M33" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> 0.030–0.040 m ns<inline-formula><mml:math id="M34" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>)
through the peat for the five full length cores that extended to the
peat–sand interface. This velocity range is consistent with other peatland
GPR studies (0.033–0.040 m ns<inline-formula><mml:math id="M35" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>) (Parsekian et al., 2012).
Using these data, a 3-D interpolation of the peat–sand interface was created
using kriging to estimate the subsurface peat basin structure (Fig. 2). The
second derivative of the maximum slope (profile curvature) was calculated
from the interpolated surface to identify changes in basal slope of the
peat–sand interface, and is shown in Fig. 2.</p>
</sec>
<sec id="Ch1.S2.SS2">
  <title>Identifying locations of groundwater discharge to surface water using
temperature</title>
      <p id="d1e700">Heat can be used as a tracer to identify upwelling groundwater, as air
temperature oscillations on diurnal and annual timescales strongly influence
surface waters, while deep (e.g., greater than approximately 10 m)
groundwater temperatures remain relatively constant through time
(Anderson
et al., 2005; Constantz, 2008). Local, shallow flow paths can be more
sensitive to climatic and seasonal changes in evaporation and precipitation
(Fraser
et al., 2001; Kurylyk et al., 2014b; Menberg et al., 2014; Reeve et al.,
2006) and may not contribute to the thermal stability of aquatic systems to
the same extent as deep (&gt; 10 m) regional aquifers. This noted,
during the thermal study periods, groundwater temperatures range from
10–11 <inline-formula><mml:math id="M36" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in on-site wells below the peat.</p>
<sec id="Ch1.S2.SS2.SSS1">
  <title>Fiber-optic distributed temperature sensing</title>
      <p id="d1e717">Raman spectra fiber-optic distributed temperature sensing (FO-DTS) is used
for spatially extensive heat tracing in aquatic systems. Tyler et al. (2009)
provides a thorough review of the details of the technology and calibration.
DTS temperature data were collected with a SensorTran Gemini HT control unit
in dual-ended mode using an AFL telecommunications
umbilical fiber-optic cable. This FO-DTS unit allows for 1 m spatial
accuracy at 0.1 <inline-formula><mml:math id="M37" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C precision over <inline-formula><mml:math id="M38" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 15 min integration times.
Each FO-DTS deployment was operated for a minimum of 5 days to ensure
multiple sufficiently strong diurnal oscillations were captured. Calibration
coils that are 50 m long were maintained at a constant temperature with an
ice-water slush bath and/or ambient bath and were compared to an independent
Onset HOBO Water Temperature Pro v2 Data Logger (U22-001)
(<inline-formula><mml:math id="M39" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula>0.2 <inline-formula><mml:math id="M40" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C accuracy).</p>
      <p id="d1e752">In July and August of 2013 four FO-DTS deployments were installed, one
within the drainage ditches of eastern peatland cells, and three within the
western cells. We capitalize on the modified structure of the agricultural
peatland surface, particularly the relatively evenly spaced drainage
ditches, to thermally sample surface water in a distributed way which is not
possible in more natural systems (e.g., Lowry et al., 2007). The deployment
sites were chosen based on previous infrared surveys (27 November
2012, discussed in Sect. 2.2.2), interviews with the farmer, and feasibility
of installation. Each deployment ranged from 1000 to 2500 m in length.
Macrophyte growth was cleared during installation and continuously monitored
through each deployment.</p>
      <p id="d1e755">The arithmetic mean and standard deviation were calculated for each
<inline-formula><mml:math id="M41" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 5-day time series of FO-DTS data at every meter along the
fiber-optic cable to identify locations of groundwater seepage. These
results can indicate the location and relative magnitude and permanence of
groundwater discharge, which is not possible with other methods, such as thermal infrared
(TIR)
or temperature probes
(Briggs
et al., 2012; Hare et al., 2015; Sebok et al., 2013; Selker et al., 2006).</p>
</sec>
<sec id="Ch1.S2.SS2.SSS2">
  <title>Infrared surveys</title>
      <p id="d1e771">TIR cameras sense and quantify surface infrared (heat)
radiation and are increasingly being used to evaluate aquatic systems
efficiently on large scales
(Chen
et al., 2009; Deitchman and Loheide, 2009; Dugdale et al., 2016; Handcock et
al., 2012; Hare et al., 2015), particularly at large sites, or sites where
in situ measurements are not possible. The hand-held TIR survey was
conducted to both expand the thermal survey and to compare this method to
the FO-DTS data. We used a high-resolution forward-looking infrared camera
(T640BX model FLIR, FLIR Systems, Inc.) with GPS and compass capabilities.
The TIR method allowed for efficient spatial coverage and allowed us to
obtain thermal data unreachable with FO-DTS
(Hare et al.,
2015).</p>
      <p id="d1e774">At Tidmarsh Farms East three TIR surveys were completed: one spanning 30–31 July
2013, one on 21 March 2014, and one reconnaissance survey on 27 November
2012. The July survey was used to make comparisons to the FO-DTS data as it
was taken during the same time period; the March survey was used to compare
seasonal variability in seepage patterns. Surveys were conducted in the
morning and evening to minimize reflection interference, and all temperature
collection practices and considerations for this site are described in
detail in
Hare et al. (2015). To create a spatial site map comprised of all TIR images, a single
temperature (color-contoured pixel) from an aquatic point of interest was
selected, and used to color an icon on the map. This allowed for
georeferenced TIR data to be used quantitatively to evaluate seepage
patterns by location. The relative magnitude of the seepage rate is estimated
based on how similar the observed temperature is to the regional groundwater
temperatures.</p><?xmltex \hack{\newpage}?>
</sec>
<sec id="Ch1.S2.SS2.SSS3">
  <title>1-D vertical temperature profiles</title>
      <p id="d1e784">The depth to which the surface diurnal temperature signal penetrates
saturated near-surface sediments depends on the period of the signal, the
fluid flow velocity and direction, and the physical properties of the
fluid-saturated sediment
(Goto
et al., 2005; Hatch et al., 2006; Irvine et al., 2017; Stallman, 1965). With
depth, the diurnal heat signal variation decreases in amplitude and its
shifts forward in time. Much of the heat transport not explained by pure
conduction is attributable to advective fluxes, which can be solved for from
thermal time series at multiple depths using simple analytical solutions to
the 1-D heat transport equation with specified boundary
conditions
(Hatch
et al., 2006; Rau et al., 2014; Schmidt et al., 2007; Silliman et al., 1995;
Stallman, 1965).</p>
      <p id="d1e787">We analyzed four 1-D vertical temperature profiles to understand the vertical
subsurface fluid flux patterns at the site. Maxim iButton temperature
loggers (0.0625 <inline-formula><mml:math id="M42" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C resolution; 1 <inline-formula><mml:math id="M43" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C accuracy) were attached to cavities
drilled into a wooden dowel and placed into the ground such that the logger
locations were <inline-formula><mml:math id="M44" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>2.5, <inline-formula><mml:math id="M45" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>5.0, <inline-formula><mml:math id="M46" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>10.0, and <inline-formula><mml:math id="M47" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>25.0 cm depth below the ground surface
and <inline-formula><mml:math id="M48" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula>2.5 cm above the surface. We coated each iButton with silicon sealant
to prevent leaking and sensor damage; however, a 25 % sensor failure rate was
still experienced. A 10 min sampling interval was used for a minimum of 7
days during July and August of 2013 for each temperature time series.</p>
      <p id="d1e844">The installation locations chosen represented the two types of seepage observed
with the FO-DTS, and 1-D vertical temperature data were collected
synchronously with DTS deployments. Two additional control deployments of 1-D
temperature profiles were installed within or below drainage ditches. We assume
that under low surficial flow conditions the system is at
quasi-steady-state, allowing us to estimate (upward) seepage flux from
measured surface water, groundwater, and intermediate-depth temperatures
using the analytical solution to the heat transport equation derived by
Turcotte and Schubert (1982) and modified
by Schmidt et al. (2007). A flux value was calculated for each collected
data time step, and was averaged for each profile for the final reported
flux value. Flux values were calculated 4 times for each profile using
the range of peat porosity and range of thermal conductivity values. The
thermal parameters utilized for the 1-D heat transport equation are shown on
Table 2.</p>
</sec>
</sec>
<sec id="Ch1.S2.SS3">
  <title>Assessment of environmental isotopes to infer groundwater flow
paths</title>
      <p id="d1e854">To trace the source of the groundwater flow paths contributing to discharge,
we use <inline-formula><mml:math id="M49" display="inline"><mml:mrow><mml:msup><mml:mi mathvariant="italic">δ</mml:mi><mml:mn mathvariant="normal">18</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>O and <inline-formula><mml:math id="M50" display="inline"><mml:mrow><mml:msup><mml:mi mathvariant="italic">δ</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>H to distinguish between local
recharge (short flow paths) and regional recharge (long flow paths). The
isotopic composition (<inline-formula><mml:math id="M51" display="inline"><mml:mrow><mml:msup><mml:mi mathvariant="italic">δ</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>H-H<inline-formula><mml:math id="M52" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O, <inline-formula><mml:math id="M53" display="inline"><mml:mrow><mml:msup><mml:mi mathvariant="italic">δ</mml:mi><mml:mn mathvariant="normal">18</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>O-H<inline-formula><mml:math id="M54" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O)
of hydrogen and oxygen of the water molecule was analyzed for water samples
collected from surface water (monthly), shallow ground water (seasonally),
deep groundwater (seasonally), groundwater seepage (August 2013), and pore
waters (October 2013). The four pore water samples were acquired through a
manual press of samples from Russian peat cores 0–1 m below the ground
surface and subsequently filtered for analysis.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T2"><caption><p id="d1e923">Parameters within the 1-D heat transport equation derived by
Turcotte and Schubert (1982) and modified by
Schmidt et al. (2007). <inline-formula><mml:math id="M55" display="inline"><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mi mathvariant="normal">s</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is the thermal conductivity of the
solid, <inline-formula><mml:math id="M56" display="inline"><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mi mathvariant="normal">f</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is the thermal conductivity of the fluid, and <inline-formula><mml:math id="M57" display="inline"><mml:mi>n</mml:mi></mml:math></inline-formula> is the porosity of
the matrix. The density of the fluid and heat capacity of the fluid
multiplied together are the volumetric heat capacity of the fluid (<inline-formula><mml:math id="M58" display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">ρ</mml:mi><mml:mi mathvariant="normal">f</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M59" display="inline"><mml:mrow><mml:msub><mml:mi>c</mml:mi><mml:mi mathvariant="normal">f</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, J m<inline-formula><mml:math id="M60" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> K<inline-formula><mml:math id="M61" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>).</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="2">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="left"/>
     <oasis:tbody>
       <oasis:row>  
         <oasis:entry colname="col1"><inline-formula><mml:math id="M66" display="inline"><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mi mathvariant="normal">s</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> (J s<inline-formula><mml:math id="M67" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> m<inline-formula><mml:math id="M68" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> K<inline-formula><mml:math id="M69" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>)</oasis:entry>  
         <oasis:entry colname="col2">0.4<inline-formula><mml:math id="M70" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">a</mml:mi></mml:msup></mml:math></inline-formula>–0.6<inline-formula><mml:math id="M71" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">b</mml:mi></mml:msup></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1"><inline-formula><mml:math id="M72" display="inline"><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mi mathvariant="normal">f</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> (J s<inline-formula><mml:math id="M73" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> m<inline-formula><mml:math id="M74" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> K<inline-formula><mml:math id="M75" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>)</oasis:entry>  
         <oasis:entry colname="col2">0.6<inline-formula><mml:math id="M76" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">c</mml:mi></mml:msup></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1"><inline-formula><mml:math id="M77" display="inline"><mml:mi>n</mml:mi></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col2">0.5<inline-formula><mml:math id="M78" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">a</mml:mi></mml:msup></mml:math></inline-formula>–0.8<inline-formula><mml:math id="M79" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">d</mml:mi></mml:msup></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1"><inline-formula><mml:math id="M80" display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">ρ</mml:mi><mml:mi mathvariant="normal">f</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> at 10 <inline-formula><mml:math id="M81" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C (kg m<inline-formula><mml:math id="M82" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>)</oasis:entry>  
         <oasis:entry colname="col2">999.7</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1"><inline-formula><mml:math id="M83" display="inline"><mml:mrow><mml:msub><mml:mi>c</mml:mi><mml:mi mathvariant="normal">f</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> at 10 <inline-formula><mml:math id="M84" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C (kJ kg<inline-formula><mml:math id="M85" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> K<inline-formula><mml:math id="M86" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>)</oasis:entry>  
         <oasis:entry colname="col2">4193</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table><table-wrap-foot><p id="d1e1002"><inline-formula><mml:math id="M62" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">a</mml:mi></mml:msup></mml:math></inline-formula> McKenzie et al. (2007). <inline-formula><mml:math id="M63" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">b</mml:mi></mml:msup></mml:math></inline-formula> Letts et al. (2000). <inline-formula><mml:math id="M64" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">c</mml:mi></mml:msup></mml:math></inline-formula> Schmidt et
al. (2007).
<inline-formula><mml:math id="M65" display="inline"><mml:msup><mml:mi/><mml:mi mathvariant="normal">d</mml:mi></mml:msup></mml:math></inline-formula> Rezanezhad et al. (2016).</p></table-wrap-foot></table-wrap>

      <p id="d1e1310">Values of <inline-formula><mml:math id="M87" display="inline"><mml:mrow><mml:msup><mml:mi mathvariant="italic">δ</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>H-H<inline-formula><mml:math id="M88" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O and <inline-formula><mml:math id="M89" display="inline"><mml:mrow><mml:msup><mml:mi mathvariant="italic">δ</mml:mi><mml:mn mathvariant="normal">18</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>O-H<inline-formula><mml:math id="M90" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O were measured by
wavelength scanned cavity ring-down spectrometry on unacidified samples
with a Picarro L-1102i WS-CRDS analyzer (Picarro, Sunnyvale, CA). Samples
were vaporized at 110 <inline-formula><mml:math id="M91" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C. International reference standards
(IAEA, Vienna, Austria) were used to calibrate the instrument to the Vienna Standard Mean Ocean
Water and Standard Light Antarctic Precipitation (VSMOW–SLAP) scale and working standards were used with each analytical run.
Three standards that isotopically bracket the sample values are run
alternately with the samples. Secondary laboratory reference waters (from
Boulder, Colorado; Tallahassee, Florida; and Amherst, Massachusetts) were
calibrated with Greenland Ice Sheet Precipitation (GISP), SLAP, and VSMOW
from the IAEA. The isotopic composition results use a rolling calibration,
which calculates each sample's error by the three standards run closest in
time to the sample. Long-term averages of internal laboratory-standard
analytical results yield an instrumental precision of 0.51 ‰ for <inline-formula><mml:math id="M92" display="inline"><mml:mrow><mml:msup><mml:mi mathvariant="italic">δ</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>H-H<inline-formula><mml:math id="M93" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O and 0.08 ‰ for <inline-formula><mml:math id="M94" display="inline"><mml:mrow><mml:msup><mml:mi mathvariant="italic">δ</mml:mi><mml:mn mathvariant="normal">18</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>O-H<inline-formula><mml:math id="M95" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O.</p>
      <p id="d1e1403">The USGS wells were sampled for groundwater isotopic compositions within the
PCKD aquifer, providing regional groundwater values for the aquifer and
defining the expected annual range of isotopic values for local
precipitation (Table 1). The regional groundwater trend line was generated
by fitting a linear regression through the USGS well isotope data from the
regional PCKD aquifer.</p>
</sec>
</sec>
<sec id="Ch1.S3">
  <title>Results</title>
      <p id="d1e1413">As an initial evaluation of the groundwater contribution to the site, we
conducted differential discharge gauging measurements on 15 September
2013. The locations of these measurements are indicated by the
purple circles in Fig. 1b. The stream gained 6 L s<inline-formula><mml:math id="M96" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> discharge
through Cell 7 from the Arm Pond input to the confluence with Beaver Dam
Brook (1.5 km), equal to an average of 0.004 L s<inline-formula><mml:math id="M97" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> per meter of river
length (Fig. 1b). Cell 3 and 4 gained 113 L s<inline-formula><mml:math id="M98" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> from the Beaver Dam
Pond input to the confluence with the east side river (1 km), equal to an
average of 0.113 L s<inline-formula><mml:math id="M99" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> per meter of river length. At
other wetland sites seepage flux magnitudes and directions have shown to be
temporally transient (Fraser et al., 2001; Sebestyen and Schneider, 2001); however, due to the consistent
high hydraulic gradient in the regional aquifer and the small watershed, we
assume that temporal dynamics are insignificant within our data set and
sufficiently static to describe the present-day conditions. This assumption
is supported by the two seasonally distinct infrared surveys resulting with
similar seepage distribution results.</p>
<sec id="Ch1.S3.SS1">
  <title>Resolving peatland basin structure</title>
      <p id="d1e1469">The interpolation of the basal surface, or the peat–sand contact beneath the
peat from GPR data, indicates four isolated peat depressions at the site,
two depressions in Cell 6 and Cell 7 and two in Cell 3 and Cell 4. Cells 6 and
7 have a maximum peat thickness of <inline-formula><mml:math id="M100" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 7 m and a gradual
curvature of the peat–sand interface than the western cells, Cell 3 and Cell
4 (Fig. 2). The western cells show a maximum peat thickness of
<inline-formula><mml:math id="M101" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 10 m, and relatively high curvature values. The basin
structure of the western cells is also more complex than Cell 6 and 7, as
Cell 3 and Cell 4 have pronounced undulations in the basal peat–sand contact
surface, creating dramatic changes in basin shape. Particularly, there is a
notably high curvature of the basal peat–sand interface along the western
edge approximately 30 m from the margin. The GPR profiles illustrate
multiple series of normal faults beneath the peat body that are consistent
with ice melt-out and/or collapse features (Fig. 2c) typical of kettle-hole
origin (Kruger et al., 2009).</p>
</sec>
<sec id="Ch1.S3.SS2">
  <title>Thermal evaluation of groundwater seepage</title>
      <p id="d1e1492">Surface water temperatures in the main channel and ambient drainage ditch
environments generally show high standard deviation, indicative of a
coupling between these surface waters and air temperatures, and mean water
temperatures closely tied to the seasonal surface temperature average, also
indicative of surface water dominance. FO-DTS surveys were designed to
detect low standard deviation and consistent mean temperature anomalies from
these background conditions, which is indicative of groundwater inflows. The
temperature results of both these surveys are presented in
Hare et al. (2015). Results from both TIR and FO-DTS identified two categories of
thermal anomalies: type 1 anomalies manifest as temperatures with relatively
low standard deviation through time, and an anomalous heat signature that is
seasonally warmer or cooler than regional groundwater temperature by
approximately <inline-formula><mml:math id="M102" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula>3–5 <inline-formula><mml:math id="M103" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C; and type 2 thermal anomalies also have a low
standard deviation, but temperatures more closely resemble regional
groundwater temperatures (10–11 <inline-formula><mml:math id="M104" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C). Figure 3 shows time series data
collected with the FO-DTS and illustrates each of the major thermal
signatures shown on-site: temperatures of groundwater, the main channel, a
drainage ditch, and the two thermal anomalies. We interpret these two
anomalies to correspond to two modes of seepage, type 1 thermal anomalies
correspond to matrix seepage, and type 2 thermal anomalies correspond to
preferential flow-path (PFP) seepage. The two seepage types are clearly
differentiated through thermal signatures, and can be isolated using the
average and standard deviation of temperatures with time. The TIR surveys
also revealed these two distinct types of seepage, which were present in
both the summer and winter surveys (Fig. 4).</p>
      <p id="d1e1520">TIR surveys and FO-DTS data indicate that most groundwater input likely
occurs along the western edge of the Cell 3 and Cell 4, where peat is
thinner or where there is strong sand–peat contact curvature in peat basin
shape (Fig. 5). Isolated locations of consistent temperatures similar to
groundwater temperatures and anomalously low standard deviations exist along
the linear location of highest peat–sand contact curvature near the western
edge of the cells, as well as along edge areas with the thinnest peat. The
isolated, unique locations of PFP seepage that occur within the deeper peat
represent a distinct seepage process from matrix seepage and PFP seeps along
the edge of the peat.</p>
      <p id="d1e1523">During the March infrared survey, a high density of <inline-formula><mml:math id="M105" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 1–5 cm
diameter flowing macropores within the peat was discovered in Cell 3. The
water discharging from these macropores exhibited groundwater seepage
temperatures (Fig. 6) and led us to term this mode of PFP seepage. This
observation is similar to the peat macropores or “peat pipes” described in
previous peatland research (e.g., Briggs et al.,
2016; Cunliffe et al., 2013; Holden, 2004; Smart et al., 2012; Vandenbohede
et al., 2014), but the concentration of macropores in this singular location
makes the northwest cell macropores observation unique. We measured high
3.0 L min<inline-formula><mml:math id="M106" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> flux PFP seeps with a seepage meter. Despite the very few
locations of PFPs, their high fluxes have the potential to contribute
significantly to the groundwater gain across the site
(Poulsen et al., 2015). The
peat thickness map (Fig. 5) indicates that the zone of high macropore
density is an area of peat thinning reaching a minimum peat thickness of 3 m, and also a location of high curvature (center of cell 3).
Rossi et al. (2012) describes similar correlation to
peat thinning at a site in Finland.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3"><caption><p id="d1e1547">Fiber-optic distributed temperature sensing (FO-DTS) temperature
time series from four 1 m segments of cable to illustrate the characteristic
thermal signatures at Tidmarsh Farms. The greatest amplitude and variability
occurs in the drainage ditches with little flow and significant solar
heating (red), followed by the main channel of Beaver Dam Brook (green). Two
seepage types are also plotted over 2.5 days: matrix (type 1) seepage, with
very low variability (low standard deviation) over time and a mean
temperature a few degrees higher than groundwater (light blue) and
preferential flow-path (type 2) seepage with a mean temperature nearly equal
to groundwater (dark blue).</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://hess.copernicus.org/articles/21/6031/2017/hess-21-6031-2017-f03.pdf"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4" specific-use="star"><caption><p id="d1e1559">Thermal infrared (TIR) images recorded on 30–31 July 2013
(Summer) and 21 March  2014 (Winter) at Tidmarsh Farms. Visible-light images
are shown in the bottom left of March images, but not July, as these surveys
were conducted at night to limit issues associated with reflectance. TIR
images illustrate the two types of seepage in both seasons: type 1
preferential flow-path seepage that is characterized by discrete discharge
points very close to groundwater temperature with high flux, and type 2
matrix seeps that are diffuse and 3–5 <inline-formula><mml:math id="M107" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C warmer or cooler than groundwater and
lower flux.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://hess.copernicus.org/articles/21/6031/2017/hess-21-6031-2017-f04.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5" specific-use="star"><caption><p id="d1e1579">Map of seepage at Tidmarsh Farms determined with fiber-optic
distributed temperature sensing (FO-DTS, squares) and thermal infrared (TIR)
surveys (circles). Background shaded region(s) match the bounded area from
Fig. 1b, and darker background shading delineates zones of high curvature
(the 2nd derivative of the thickness) of the peat–sand interface (Fig. 2).
For both methods, light purple to pink symbols indicate matrix (type 1)
seepage, and dark blue indicates locations of PFP (type 2) seepage. From
FO-DTS data, a location was tagged as seepage if the standard deviation was
less than 1.5 and the temperature was less than 15 <inline-formula><mml:math id="M108" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for the matrix and less
than or equal to 11 <inline-formula><mml:math id="M109" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for PFP seepage. From TIR surveys, seepage was
distinguished by temperatures of 9–11 <inline-formula><mml:math id="M110" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for interior seepage, and 11–15 <inline-formula><mml:math id="M111" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C
for matrix seepage. The location of GPR line 7.1 is shown on this figure to
reference data for the conceptual model in Fig. 9.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://hess.copernicus.org/articles/21/6031/2017/hess-21-6031-2017-f05.pdf"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F6" specific-use="star"><caption><p id="d1e1626">Thermal infrared (TIR) image from 21 March  2014 at Tidmarsh Farms
illustrating PFP (type 2) seepage. Many macropores are observed in both the
infrared (slightly smaller) and the visual image. These seeps are located in
the middle of cell 3 (Fig. 1b), where peat is <inline-formula><mml:math id="M112" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 3 m thick and
dramatically thinning.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://hess.copernicus.org/articles/21/6031/2017/hess-21-6031-2017-f06.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F7" specific-use="star"><caption><p id="d1e1644">Temperature profiles vs. depth at Tidmarsh Farms recorded in 30–31 July
2013. For each profile, the range of air temperatures and groundwater
(GW) temperatures are shown as bands of pink (air) and dark blue (GW).
Locations 1 and 2 (profiles 1 and 2) show the influence of upwelling
GW, expressed as type 1 preferential flow-path (PFP) seepage
(profile 2) and type 2 matrix seepage (profile 1). The convex upward shape
of temperature–depth profiles 3 and 4 is also consistent with upwelling
GW.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://hess.copernicus.org/articles/21/6031/2017/hess-21-6031-2017-f07.pdf"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F8"><caption><p id="d1e1656">Plot of the stable isotopes <inline-formula><mml:math id="M113" display="inline"><mml:mrow><mml:msup><mml:mo>∂</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>H-H<inline-formula><mml:math id="M114" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O and
<inline-formula><mml:math id="M115" display="inline"><mml:mrow><mml:msup><mml:mo>∂</mml:mo><mml:mn mathvariant="normal">18</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>O-H<inline-formula><mml:math id="M116" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O from the Tidmarsh Farms area surface water
(diamonds), groundwater (circles), shallow well, and seep sources
(triangles). The regional groundwater trend line was derived from samples
from relatively shallow, regional USGS wells (blue dots), consistent with a
relatively humid climate at the site.</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://hess.copernicus.org/articles/21/6031/2017/hess-21-6031-2017-f08.pdf"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS3">
  <title>1-D vertical temperature profiles</title>
      <p id="d1e1711">The two seepage types and two ambient drainage ditch locations were
monitored with 1-D vertical temperature profiles for 7 to 10 days. We
expected to observe significant upwelling at this site, which we could
easily identify by a rapid attenuation of the diurnal signal with depth
coupled with a characteristic convex upward shape of mean temperature with
depth (e.g., Schmidt et al., 2007). Temperatures from all four
1-D vertical temperature profiles are distinct from one another; however, all
the temperature profiles, including the “ambient” drainage ditches, are
consistent with upwelling of groundwater (convex upward shape of mean
temperature with depth in Fig. 7). The surface temperature of the ambient
drainage ditches (temperature profiles 3 and 4) is similar to the diurnal
temperature cycles measured with FO-DTS, and were used as background data
for the heat signature of the site. The 1-D fluid flux calculations of the
temperature time series of the two drainage ditch locations yielded a range
of <inline-formula><mml:math id="M117" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.028 to <inline-formula><mml:math id="M118" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.031 and <inline-formula><mml:math id="M119" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.067 to <inline-formula><mml:math id="M120" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.074 m d<inline-formula><mml:math id="M121" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>.</p>
      <p id="d1e1754">Temperature profiler 1 was installed at a location with a surficial
temperature of 13–14 <inline-formula><mml:math id="M122" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in August 2013. The total peat thickness at this
location is 50 cm, and, consistent with groundwater upwelling, minimal
diurnal signal propagates to depth, and surface water exhibits relatively
low variance in temperatures over time. Thermal time series estimates of
flux show a modest <inline-formula><mml:math id="M123" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.146 to <inline-formula><mml:math id="M124" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula>0.163 m d<inline-formula><mml:math id="M125" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> upwelling through the peat at
this seepage location.</p>
      <p id="d1e1792">Finally, temperature profiler 2 was installed in a location with a surficial
temperature consistent with groundwater temperatures of 10–11 <inline-formula><mml:math id="M126" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in August
2013, and temperatures with depth exhibit a groundwater thermal signal
throughout the entire profile. Even close to the bed interface, the
streambed thermistor (2.5 cm) shows slight thermal shifts (<inline-formula><mml:math id="M127" display="inline"><mml:mrow><mml:mi mathvariant="italic">σ</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.096</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M128" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C), which are near to the resolution of the instrument (0.0625 <inline-formula><mml:math id="M129" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C).
This unique temperature profile is indicative of high upward flux rates, as
the diurnal signal cannot be resolved and there is essentially no downward
conduction from above; therefore, we were unable to use the steady-state
analytical solution to estimate a flux rate. However, in July 2015, we
deployed a seepage meter at this location and measured fluxes in excess of
3 m d<inline-formula><mml:math id="M130" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>, rates which exceed the limits for analytical flux calculations.</p>
</sec>
<sec id="Ch1.S3.SS4">
  <title>Groundwater discharge source areas</title>
      <p id="d1e1853">Groundwater discharge to the wetland complex is a mixture of shallow and
deep regional flow paths. Isotopic analyses of waters from wells in the
upgradient portion of the PCKD aquifer (blue circles in Fig. 8) fall
along a regional groundwater trend line. We interpret this regional trend
line to be characteristic of the annual isotopic composition of recharge
water to the region as well as local groundwater recharge in the topographic
watershed of Tidmarsh. These upgradient groundwater isotopic values plot
left of the global meteoric water line (GWML) (Craig, 1961),
which reflects local and regional vapor recycling and a characteristic
mixture of vapor sources (Koster et al., 1993). The one
exception to this line is the USGS well MA-PWW 494 in Plymouth, MA which is
similar to Tidmarsh in that it is downgradient of the recharge area of the
PCKD aquifer. This water falls to the right of the regional groundwater
trend line. Discharging and shallow groundwaters at the wetland site plot
close to but off the regional groundwater trend line. The blue diamonds
(Fig. 8) represent a monthly sampling of wetland surface waters that depict
a significant clustering to the right of the regional groundwater trend and
evolve along a line tangential to that intersecting the deep TM groundwater.
Uncharacteristically, the deepest sampled groundwater at the site
(&gt; 15 m) falls to the right of the GMWL (orange circle),
suggesting this water has experienced a significant enrichment in the heavy
isotopes due to evaporation processes. Repeated sampling of this water
reveals a consistent isotopic composition that suggests the deep groundwater
beneath Tidmarsh is isotopically enriched due to evaporation from open-water
bodies in upgradient kettle ponds. The headwater seepage area and the
strong discharge seepage area (large pink and red triangles in Fig. 8) in
the interior of the wetland complex fall along a line that represents either
a mixture of this evaporated water and the regional groundwater trend
(finely dashed line) or itself is simply evaporatively evolved water.
 Both interpretations suggest that the source of water to the shallow
groundwater wells and the large volume springs in the interior of the
wetland complex are distinct. This indicates that the local flow path from
the southwest to the northeast is the large-scale hydraulic gradient that
dominates the observed seepage patterns. The orientation of peatland basin
slope break and the regional groundwater gradient also intercepts the
southwest corner of the peatland where numerous high-flux groundwater seeps
are located.</p>
</sec>
</sec>
<sec id="Ch1.S4">
  <title>Discussion</title>
<sec id="Ch1.S4.SS1">
  <title>Groundwater discharge types</title>
      <p id="d1e1868">Two types of groundwater discharge (or seepage) were identified using
thermal methods, as detailed in Sect. 3.2: PFP discharge areas that have
regional groundwater temperature (e.g., 10–11 <inline-formula><mml:math id="M131" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C) and matrix seepage
locations with groundwater discharge at temperatures that are offset
(<inline-formula><mml:math id="M132" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula>3–5 <inline-formula><mml:math id="M133" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C) from regional groundwater temperature but have very low
variance compared to expected diurnal variations and are also significantly
distinct from local surface water temperatures. Both seepage types appear to
strongly buffer stream temperatures, illustrated by low variance when
examined through time (FO-DTS data). A low variance could have also been
caused by mobile sediment (Sebok et al., 2015); however, within
this peatland environment this process is not expected, nor was it observed.
The identification of these two distinct seepage types using multiple
methods and during distinct seasons indicates different mechanisms for
generation of each of these seepage patterns. Figure 5 combines both matrix
and PFP seepage observed with either FO-DTS or TIR to evaluate spatial
patterning and consistencies, and shows how the two types are related to one
another as well as to patterns of high basal curvature.</p>
      <p id="d1e1896">Consistent (low standard deviation) and groundwater-like temperatures
(10–11 <inline-formula><mml:math id="M134" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C) of the PFP seepage indicate very high flux
(&gt; 3 m d<inline-formula><mml:math id="M135" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> was confirmed with seepage meter measurements).
Given the low vertical <inline-formula><mml:math id="M136" display="inline"><mml:mi>K</mml:mi></mml:math></inline-formula> of peat matrices, sustaining such high fluxes would
require seemingly implausible hydraulic gradients, certainly far above the
vertical hydraulic gradients observed on-site. Therefore, it is highly
likely that this seepage does not occur as flow through the peat matrix, but
instead as focused, high-discharge, conduit flow, consistent with
“short-circuit discharges” described by Conant Jr. (2004).
Focused flow in conduits through the peat was observed in the field at
Tidmarsh Farms (Fig. 6), and by  Briggs et al. (2016), and has been
documented through visual descriptions of peat pipes, or macropores at other
locations (Baird,
1997; Beckwith et al., 2003; Cunliffe et al., 2013; Holden, 2004; Smart et
al., 2012; Wallage and Holden, 2011). However, the spatial extent of these
preferential flow zones has not been previously demonstrated. Due to their
high flux, physical isolation, and focused nature, we refer to these types of
seepage as PFP seeps.</p>
      <p id="d1e1927">Data represented by matrix seepage show that surface water diurnal
temperatures are also buffered in these zones and are distinct from most
ambient surface temperatures. This observation could indicate shallow
aquifer groundwater discharge, which is more influenced by atmospheric
temperatures than deeper regional flow
(Kurylyk et al., 2014a; Menberg et al., 2014). However, consistent
temperatures in the site's shallow groundwater wells and 1-D temperature
profiles indicate that these seepage temperatures are controlled by a lower
flux rather than distinct atmospherically influenced shallow flow paths. These
matrix seeps indicate that while vertical upwelling fluxes are present, they
are much smaller than PFP discharge zones and must be controlled by a
different mechanism. Thermal profilers yielded vertical flux rates
consistent with a low to moderate upwelling though porous media according to
Conant Jr. (2004), which would be typical of the hydraulic properties associated with
peat, and thus is the reason we refer to locations with this
signature as “matrix” seeps. The two seepage types, PFP and matrix
seepage, are similar to the “point” and “diffuse” peat seepage
categories defined by Rossi et al. (2012) but, rather than focusing on the
area of influence, instead highlight the physical structure that governs the
process which ultimately generates seepage in these peatland seepage zones.</p>
</sec>
<sec id="Ch1.S4.SS2">
  <title>Subsurface structural control on the spatial distribution of seepage
types</title>
      <p id="d1e1936">Matrix seeps were plentiful within approximately 30 m of the peatland edge
(Fig. 5), consistent with margin seepage observed in lake environments
(Rosenberry
et al., 2010; Sebestyen and Schneider, 2004; Sebok et al., 2013; Winter,
2001) and other wetlands (Freeze, 1988; Labaugh et al.,
1998). The peat is 0.1–3.0 m thick along the margin where matrix seepage
occurs (Fig. 3), which is generally significantly thinner than locations of
observed interior PFP seepage. Matrix seeps generally occur in the thinnest
peat zones and typically decrease rapidly with distance from the peatland
edge toward the interior slope change, after which no thermally distinct
groundwater discharge points are observed (Fig. 5). While evidence for PFP
seepage does occur as well in these shallow areas, matrix seepage is more
consistent within this shallow peat environment. This is shown as a
conceptual model in Fig. 9, based on temperature data collected proximal
to GPR line 7.1 (radargram shown in Fig. 2c). Similar landscape-scale
observations have been made within lakes and wetlands
(e.g.,
Cherkauer and Zager, 1989; Sebok et al., 2013), and as kettle-hole peatlands
typically form from initially open-water bodies, there are logical
similarities in basic processes between the two environments.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F9"><caption><p id="d1e1941">Conceptual model illustrating the mechanism for development of
matrix (type 1) seepage (pink arrows) and preferential flow path (PFP) or
interior (type 2) seepage (blue arrows), corresponding to locations in
winter TIR images. Thick black lines represent groundwater flow direction,
and the yellow-green box indicates the region of high basin curvature. The
brown basin represents peat in a typical basin shape based on GPR line 7.1
(Fig. 2c). Conceptual matrix and PFP seepage locations are based on the
temperature data recorded proximal to GPR line 7.1 and winter TIR images
from this same transect. PFP seeps found in the thicker peat are associated
with locations of high basin curvature where strong vertical gradients drive
focused, higher-flux seepage through pre-existing weaknesses in the peat
matrix.</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://hess.copernicus.org/articles/21/6031/2017/hess-21-6031-2017-f09.png"/>

        </fig>

      <p id="d1e1950">Discrete seepage zones may reflect zones of higher effective <inline-formula><mml:math id="M137" display="inline"><mml:mi>K</mml:mi></mml:math></inline-formula> than the
surrounding peat matrix, which could be explained by littoral-zone migration
in the lake to wetland evolution as the water table fluctuates and migrates.
In lake environments, diffuse matrix seepage occurs because of an increase
in <inline-formula><mml:math id="M138" display="inline"><mml:mi>K</mml:mi></mml:math></inline-formula> at the edge of the lake caused by “erosional deposition,” whereby
focused wave and current action disrupt and erode sediments, particularly
mobilizing the finest sediments elsewhere and concentrating larger
particles, indicative of these higher-energy environments in these
locations. Preferentially stronger flow paths are thus concentrated at the
break in land surface slope
(Blume et al., 2013; Casson et al., 2010; Cherkauer and McKereghan, 1991;
McBride and Pfannkuch, 1975; Rosenberry et al., 2010; Winter, 1981).
Previous work proposes that seepage flux decreases exponentially with
distance from the shore of a lake (Cherkauer and
Zager, 1989; McBride and Pfannkuch, 1975), which is qualitatively confirmed
by our data. Paleoclimate reconstructions have demonstrated that the
regional water table around Tidmarsh has been increasing in elevation since
the Laurentide ice sheet retreated <inline-formula><mml:math id="M139" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 10 kyr ago, with 2–3
significant low stands
(Newby et al.,
2000, 2009). Therefore, we hypothesize that the extent of the matrix seepage
observed along the western edge of the peatland is a result of this lake
transgression and coincident decrease in deposition of organic material.
Here the lower <inline-formula><mml:math id="M140" display="inline"><mml:mi>K</mml:mi></mml:math></inline-formula> of the peat matrix intersects with shallow groundwater
flow paths, strongly affecting lateral hydraulic gradients and driving upward
flux; a process which likely generates much of the observed matrix seepage
(Fig. 9). This observation is supported by similar seepage processes observed
in riverine systems (Sophocleous, 2002), wetland
(Larsen et al., 2007), lake
(Bakker and Anderson, 2002; Winter, 1981),
and hillslope environments (Shaw et al., 2017;
Winter et al., 1998).</p>
      <p id="d1e1981">In contrast to the matrix seepage, PFP seepage was less common and spatially
disconnected from similar flux seeps (Fig. 5). Similar to matrix seepage,
PFP seepage exhibits low standard deviation of temperature (Fig. 3), but PFP
seep temperatures were much closer to average regional groundwater
temperature. This indicates that PFP seepage waters have very short
residence times within peatland sediments, which may have important
implications for nutrient transformations within them. At some PFP seeps the
peat is generally thicker and located more toward the interior of the
peatland rather than along the margin where matrix seepage zones are found
in addition to being found between the peatland edge and the area with high
basal curvature values (Figs. 5 and 9). Typical interior PFP flow-path
lengths from the sandy aquifer below the peat to the surface should be much
greater than for matrix seeps; however, the thermal signature seems to
contradict this; therefore, PFP seepage zones must be generated through a
unique hydraulic process from matrix seeps. Since PFP seeps at Tidmarsh
Farms correlate with significant slope changes, or locations of high
curvature, these isolated seepage zones must be generated by an abrupt
change in horizontal <inline-formula><mml:math id="M141" display="inline"><mml:mi>K</mml:mi></mml:math></inline-formula>, and the PFP seep locations closer to the edge may be
a result of zones of inherent matrix weaknesses, such as varying degrees of
humification caused by vegetative difference and water level, or other
disruptions in the peat matrix, including plant rooting and desiccation
“cracks” as proposed by Smart et al. (2012) (Fig. 9).</p>
      <p id="d1e1992">An abrupt change from high to low <inline-formula><mml:math id="M142" display="inline"><mml:mi>K</mml:mi></mml:math></inline-formula> has long been known to promote the
transition from horizontal to vertical flow (Freeze and
Witherspoon, 1967). Lowry et al. (2009)
hypothesized this process to explain developed seepage within the interior
of a peatland through using 3-D numerical groundwater flow models. As
horizontally flowing regional groundwater encounters a low-conductivity
peatland, it is forced to go through or around it, causing pressure to
increase where the abrupt change in the <inline-formula><mml:math id="M143" display="inline"><mml:mi>K</mml:mi></mml:math></inline-formula> from the sand to catotelm peat
matrix occurs (Fig. 9). PFP seeps develop as fast pathways to the surface and
as pressure-relief valves, where these localized increases in aquifer pressure
at the base of the peat matrix translate into strong, sustained discharge of
unaltered regional groundwater to the surface.</p>
      <p id="d1e2009">Rosenberry et al. (2010) note that in lake bottoms, a
significant upward seepage velocity can maintain a locally high <inline-formula><mml:math id="M144" display="inline"><mml:mi>K</mml:mi></mml:math></inline-formula>, as the
upward force may suspend smaller particles within the water column.
Particulate organic matter and lacustrine sediment have a very low settling
velocity, and therefore if the upward force that groundwater seepage induces is
greater than the settling velocity, only organic matter with a high mass
will be able to accumulate over these lake seepage locations. This would
cause the peat matrix to have a relatively high porosity and a high
permeability compared to its surrounding very low permeability matrix. These
locations will continue to be zones of weakness through the formation of the
peatland. Thus, we hypothesize that high-flux PFP seepage zones persist
through the transition from lake to peatland environment due to the
inability of fine sediments and organic matter to accumulate over these high-flux locations. Still, these locations of consistently high hydraulic
gradient will also continually take advantage of inherent matrix
weaknesses. However, the underlying mechanics of PFP seepage in the
deeper interior peat are caused by the interception of the regional
groundwater gradient and high-curvature peat subsurface structure (Fig. 9).</p>
      <p id="d1e2019">The orientation of peatland basin slope break (high basin profile curvature)
and the southwest-to-northeast regional groundwater gradient dictates the
observed pattern of strong seepage along the western boundary, which is
supported by isotopic analysis. PFP and matrix seep waters both exhibit
isotopic signatures consistent with a mixture of local groundwater and
regional recharge signature (Fig. 8). This observation is further reinforced
by the increase in net groundwater gain through the western cells, as well
as a large number of PFP seeps in the southwestern portion of the site (Fig. 5).</p>
</sec>
</sec>
<sec id="Ch1.S5" sec-type="conclusions">
  <title>Conclusions</title>
      <p id="d1e2030">Subsurface basin shape exhibits significant control on the spatial
distribution of groundwater discharge within peatland environments. As
horizontal groundwater flow intercepts the peat matrix, two types of seepage
develop: matrix and preferential flow-path seepage. Matrix seepage is
defined by a low standard deviation in temperature and surface temperature
similar to groundwater <inline-formula><mml:math id="M145" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula>3–5 <inline-formula><mml:math id="M146" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C, consistent with relatively low-flux
seepage. Low fluxes are produced where the regional groundwater flow paths
intercept the low-<inline-formula><mml:math id="M147" display="inline"><mml:mi>K</mml:mi></mml:math></inline-formula> peat at the basin `shoreline', inducing upward flow
through relatively thin (0.1–3.0 m) peat. The second type of observed
discharge, PFP seepage, has a surface temperature essentially
indistinguishable from deep regional groundwater temperature. This indicates
very strong upwelling fluxes at these locations and little time for
conductive heat losses or gains. Locations of PFP seeps appear along the
periphery of the peatland, but more notably also correlate with high rates
of basal peat slope change (curvature) of the peat basin (Fig. 9). These
seeps develop where the regional groundwater flow path intercepts a
secondary slope change and where there is a stark change in <inline-formula><mml:math id="M148" display="inline"><mml:mi>K</mml:mi></mml:math></inline-formula> between the
high-<inline-formula><mml:math id="M149" display="inline"><mml:mi>K</mml:mi></mml:math></inline-formula> sand aquifer material and the low-<inline-formula><mml:math id="M150" display="inline"><mml:mi>K</mml:mi></mml:math></inline-formula> peat. Together, these physical
features generate large pressures, induce localized zones of high vertical
hydraulic gradient, and drive large seepage fluxes upward. Because PFP seeps
typically occur in locations with thicker peat and yet maintain close to
groundwater temperatures, they must have a much higher vertical hydraulic
gradient and/or higher effective <inline-formula><mml:math id="M151" display="inline"><mml:mi>K</mml:mi></mml:math></inline-formula> than the matrix seeps. Through multiple
lines of evidence, we conclude that the development and spatial distribution
of minerotrophic peatland seepage is strongly controlled by the interactions
among the subsurface basin structure, physical processes within the peat
structure, and hydraulic gradient.</p>
      <p id="d1e2085">Through our results, we establish a predictable pattern of seepage,
consistent across the coastal site that is explained by knowledge of basin
shape and regional hydraulic gradient. This information provides valuable
insights for water resource managers to better understand the natural forces
driving groundwater seepage. This knowledge, in turn, may be used in the
restoration design of degraded peatland systems. Knowing where seepage is
expected to occur naturally across a site allows for the development of more
sustainable restoration designs that work with the land, and not against it.
In retired cranberry farms, for example, channels may be relocated to
intercept springs to maintain cooler water temperatures. This knowledge can
also guide the location of targeted intensive grading. For example, as was
done at Tidmarsh Farms, the dense cranberry mat can be broken up
mechanically to encourage groundwater expression on former dry farm surfaces
and access native seed banks below. Incorporating this data into a
restoration design will greatly aid the ability to predict and achieve
desired ecosystem outcomes, making restoration projects more efficient, both
ecologically and monetarily.</p>
      <p id="d1e2088">This research provides a process-based investigation of the subsurface
hydrodynamics within a peatland. While a peat matrix exhibits strongly
heterogeneous and anisotropic tendencies, large-scale patterns occur and can
be predicted. These patterns are dependent on basin shape, peat accumulation
history, and underlying aquifer flow paths. The importance of groundwater
flow paths surrounding the peatland and resulting seepage patterns
emphasizes that peatlands are not isolated entities from the groundwater
system and cannot be treated as such. Observed large-scale seepage
patterning provides insight that may help explain vegetation patterning,
macropore development, and other localized peat dynamics that have been
unidentified in the past, and greatly aid peatland management and
restoration to establish more naturally sustainable, efficient practices.</p>
</sec>

      
      </body>
    <back><notes notes-type="dataavailability">

      <p id="d1e2095">The underlying research data from this manuscript can be accessed through email request to the corresponding author.</p>
  </notes><notes notes-type="competinginterests">

      <p id="d1e2101">The authors declare that they have no conflict of interest.</p>
  </notes><notes notes-type="disclaimer">

      <p id="d1e2107">The views and opinions expressed in this article are those of the authors
and do not necessarily reflect the official policy or position of AECOM
Technical Services.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e2113">This research would like to acknowledge the support of Evan Shulman for site
access, field support, and good humor, as well as Henry Eshbaugh, Eric Van Dam, Steve
Hurley, and the many volunteers who support the data collection efforts and site
co-ordination. Much gratitude is given to John Lane and Martin Briggs of the
US Geological Survey, Branch of Hydrogeophysics, for equipment rental and
technical support. Also, we thank Jon Woodruff of the University of
Massachusetts, Amherst, for use of his laboratory equipment and data processing
software as well as his student support and technical guidance.<?xmltex \hack{\newline}?><?xmltex \hack{\newline}?>
Edited by: Philippe Ackerer<?xmltex \hack{\newline}?>
Reviewed by: Neil Terry and two anonymous referees</p></ack><ref-list>
    <title>References</title>

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    <!--<article-title-html>Hydrogeological controls on spatial patterns of groundwater discharge in peatlands</article-title-html>
<abstract-html><p class="p">Peatland environments provide important ecosystem services including water
and carbon storage, nutrient processing and retention, and wildlife habitat.
However, these systems and the services they provide have been degraded
through historical anthropogenic agricultural conversion and dewatering
practices. Effective wetland restoration requires incorporating site
hydrology and understanding groundwater discharge spatial patterns.
Groundwater discharge maintains wetland ecosystems by providing relatively
stable hydrologic conditions, nutrient inputs, and thermal buffering
important for ecological structure and function; however, a comprehensive
site-specific evaluation is rarely feasible for such resource-constrained
projects. An improved process-based understanding of groundwater discharge in
peatlands may help guide ecological restoration design without the need for
invasive methodologies and detailed site-specific investigation.</p><p class="p">Here we examine a kettle-hole peatland in southeast Massachusetts
historically modified for commercial cranberry farming. During the time of
our investigation, a large process-based ecological restoration project was
in the assessment and design phases. To gain insight into the drivers of
site hydrology, we evaluated the spatial patterning of groundwater discharge
and the subsurface structure of the peatland complex using heat-tracing
methods and ground-penetrating radar. Our results illustrate that two
groundwater discharge processes contribute to the peatland hydrologic
system: diffuse lower-flux marginal matrix seepage and discrete
higher-flux preferential-flow-path seepage. Both types of groundwater
discharge develop through interactions with subsurface peatland basin
structure, often where the basin slope is at a high angle to the regional
groundwater gradient. These field observations indicate strong correlation
between subsurface structures and surficial groundwater discharge.
Understanding these general patterns may allow resource managers to more
efficiently predict and locate groundwater seepage, confirm these using
remote sensing technologies, and incorporate this information into
restoration design for these critical ecosystems.</p></abstract-html>
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