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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-30-4889-2026</article-id><title-group><article-title>Hysteresis between groundwater and surface water levels indicates the states of hydrological turnover affecting solute transport  and redox processes</article-title><alt-title>Hysteresis between groundwater and surface water levels</alt-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Bäthke</surname><given-names>Lars</given-names></name>
          <email>baethke@uni-trier.de</email>
        <ext-link>https://orcid.org/0009-0006-5150-3304</ext-link></contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Schuetz</surname><given-names>Tobias</given-names></name>
          <email>tobias.schuetz@uni-trier.de</email>
        <ext-link>https://orcid.org/0000-0002-7500-2145</ext-link></contrib>
        <aff id="aff1"><label>1</label><institution>Department of Hydrology, Faculty of Regional and Environmental Sciences, University of Trier, Trier, Germany</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Lars Bäthke (baethke@uni-trier.de) and Tobias Schuetz (tobias.schuetz@uni-trier.de)</corresp></author-notes><pub-date><day>4</day><month>August</month><year>2026</year></pub-date>
      
      <volume>30</volume>
      <issue>15</issue>
      <fpage>4889</fpage><lpage>4907</lpage>
      <history>
        <date date-type="received"><day>9</day><month>April</month><year>2025</year></date>
           <date date-type="rev-request"><day>5</day><month>May</month><year>2025</year></date>
           <date date-type="rev-recd"><day>17</day><month>April</month><year>2026</year></date>
           <date date-type="accepted"><day>17</day><month>April</month><year>2026</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2026 Lars Bäthke</copyright-statement>
        <copyright-year>2026</copyright-year>
      <license license-type="open-access"><license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p></license></permissions><self-uri xlink:href="https://hess.copernicus.org/articles/30/4889/2026/hess-30-4889-2026.html">This article is available from https://hess.copernicus.org/articles/30/4889/2026/hess-30-4889-2026.html</self-uri><self-uri xlink:href="https://hess.copernicus.org/articles/30/4889/2026/hess-30-4889-2026.pdf">The full text article is available as a PDF file from https://hess.copernicus.org/articles/30/4889/2026/hess-30-4889-2026.pdf</self-uri>
      <abstract><title>Abstract</title>

      <p id="d2e90">Small streams are highly sensitive to variations in discharge, a sensitivity predicted to increase in future climate scenarios, impacting ecological health of streams and water management practices. Prolonged low-flow conditions alter groundwater-surface water (GW-SW) exchange patterns, leading to extended losing phases and a reduced duration of gaining periods. This study examines the relationship between hydrological turnover (HT) and stream-stage-groundwater-level hysteresis patterns under various system states in a third-order tributary of the River Mosel in Trier, Germany, using high-resolution stream-stage and groundwater-level data (GW1, GW2) together with complementary chemical observations collected over two years.</p>

      <p id="d2e93">Our results reveal distinct seasonal dynamics in GW-SW exchange. Counterclockwise hysteresis, prevalent during summer and drought conditions, coincides with conditions indicative of hyporheic zone expansion and bank storage, potentially affecting flow paths and redox dynamics. We established a strong correlation between HT and hysteresis characteristics, identifying the <inline-formula><mml:math id="M1" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index as a valuable diagnostic tool for tracking seasonal changes in GW-SW connectivity, storage and hyporheic zone behaviour based on hydraulic preconditions. The <inline-formula><mml:math id="M2" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index quantifies the direction and magnitude of hysteresis based on the integrated difference between rising and falling limbs, thereby capturing time lags and asymmetries in GW–SW interactions.</p>

      <p id="d2e110">In the context of climate change, drought conditions alter exchange dynamics. Thus, the hyporheic zone plays a vital role in solute cycling and GW-SW connectivity. The <inline-formula><mml:math id="M3" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index, combined with chemical and hydrological monitoring, provides a robust framework for understanding these dynamics in small stream ecosystems.</p>
  </abstract>
    
<funding-group>
<award-group id="gs1">
<funding-source>Universität Trier</funding-source>
<award-id>n/a</award-id>
</award-group>
</funding-group>
</article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d2e129">The interaction between GW and SW systems has an impact on the dynamics of streamflow, solute transport, and nutrient cycling, with significant implications for water resource management and ecological health. Within the broader hydrological system, delays in streamflow response are closely linked to stream-catchment connectivity governing the timing and magnitude of water, solute, carbon, and nutrient exchanges between surface and groundwater. In combination, these factors significantly impact stream water quality and a catchment's response to rainfall events (e.g., Brunner et al., 2009, 2011; Covino et al., 2011; Zuecco et al., 2019). Connectivity patterns frequently manifest hysteretic behaviour, a non-linear, loop-like relationship between dependent and independent variables such as groundwater levels and river state (e.g., Pavlin et al., 2021; Camporese et al., 2014; Penna et al., 2011; Outram et al., 2014; McGuire and McDonnell, 2010; Gelmini et al., 2022; Zuecco et al., 2016, 2019).</p>
      <p id="d2e132">Hysteresis describes how a system's response depends on both current inputs and prior states and are defined by non-linear behaviour in response to inputs (e.g. Phillips, 2003; Camporese et al., 2014). This “hysteretic” behaviour, commonly observed in interactions between groundwater and surface water levels, reveals the complex interplay among storage capacities, groundwater hydraulics and flow dynamics (Zuecco et al., 2019). For example, hysteresis can manifest as a delayed groundwater response to surface water discharge variations (Zuecco et al., 2016; Pavlin et al., 2021), highlighting the impact of the variability of hydraulic gradients between stream and aquifer within a catchment (Welch et al., 2015). Temporally asynchronous water level changes characterize subsurface flow paths and generating characteristic hysteresis loops between surface and groundwater (Penna et al., 2011). An improved understanding of hysteretic behaviour thus might enhance our understanding of stream flow exchange with the riverbanks and the hyporheic zone, a nonlinear mechanism involved in stream flow generation and its chemical signature (McGuire and McDonnell, 2010).</p>
      <p id="d2e135">Within the context of GW-SW interaction, hyporheic exchange fluxes are modulated by event characteristics and groundwater preconditions (Trauth and Fleckenstein, 2017). Defined as the boundary between ground and surface waters, the hyporheic zone expands and contracts depending on the relative difference between groundwater levels and stream discharge (e.g., Wroblicky et al., 1998; Arntzen et al., 2006). Thus, significantly affecting exchange volumes (Cardenas and Wilson, 2007; Malzone et al., 2016). An increase in stream stage typically increases the hyporheic volume, whereas it shrinks during recession or low flow conditions (Soulsby et al., 2001). Hyporheic exchange in the riparian zone induced by fluctuations in the stream stage are referred to as bank storage. This extends the hyporheic volume further into the stream bank, distinct from in-stream hyporheic exchange (Cooper and Rorabaugh, 1963). Bank storage often induces transport and mixing of chemical species between groundwater and surface waters, leading to processing of these solutes and a delayed return of these potentially modified chemicals (Gu et al., 2012). Bank storage-induced GW-SW mixing thus has the potential to alter solute fluxes out of the hyporheic zone, influencing the chemical signature of groundwater discharge (McCallum et al., 2010).</p>
      <p id="d2e138">The interaction between stream and groundwater levels propagates surface water fluctuations into groundwater systems, enhancing GW-SW exchange (Xin et al., 2018). Tools such as the hysteresis index developed by Zuecco et al. (2016) and similar methods by Lloyd et al. (2016b) systematically quantify and compare hysteresis during runoff events. These tools support the classification and characterization of hydrological responses in catchments (Gelmini et al., 2022). However, stream flow responsiveness depends on multiple factors, such as local groundwater levels, with stream water level fluctuations modifying hydraulic gradients that drive GW-SW interactions at the stream-groundwater interface (Boano et al., 2014; Cardenas, 2008).</p>
      <p id="d2e142">Several studies advocate that the exchange between GW and SW along streams needs to be addressed as bidirectional, consisting of gross gains and losses, with the cumulative bidirectional flux understood as a hydrological turnover (HT) (Payn et al., 2009, 2012; Covino et al., 2011; Mallard et al., 2014; Jimenez-Fernandez et al., 2022; Jähkel et al., 2022; Bäthke and Schuetz, 2024). Conceptually rooted in the nutrient spiralling framework (Stream Solute Workshop, 1990), HT in this study refers to the intensity of exchange-driven renewal of water between the stream and adjacent storage compartments such as the hyporheic zone and near-stream groundwater. Unlike residence time, HT does not describe how long water remains within the system, but rather how rapidly water is exchanged and mixed across hydrological compartments (Covino et al., 2011). Consequently, HT is a process-based and state-dependent metric that does not have a single characteristic time scale at the reach scale. Instead, it emerges from the temporal dynamics of exchange processes, particularly during hydrological events (Payn et al., 2009; Ward et al., 2013). Thus, HT shapes stream chemical signatures, making it a critical framework for understanding the bidirectional GW-SW interactions explored in this study. The process of GW-SW interaction in headwater streams integrates the movement of water masses between the near-stream aquifer, the hyporheic zone, and the stream channel (Payn et al., 2009; Ward et al., 2013, 2019). Despite the recognition of hydrological turnover (HT) as the cumulative expression of bidirectional GW-SW exchange (Payn et al., 2009; Covino et al., 2011; Mallard et al., 2014), its temporal variability and the range of HT magnitudes that can occur within a single catchment and even within one stream reach is insufficiently quantified (Jähkel et al., 2022; Jimenez-Fernandez et al., 2022; Bäthke and Schuetz, 2024). A key limitation in HT monitoring is methodological: direct HT quantification requires tracer-based dilution gauging and therefore provides only snapshots of exchange, while GW–SW interactions are known to shift substantially with changing storage states, hydraulic gradients, and event forcings across seasons (e.g. Ward et al., 2013, 2019).</p>
      <p id="d2e145">Here, we address this gap for a headwater stream reach by combining process-based HT measurements with high-frequency stream–groundwater hysteresis metrics. We focus on hysteresis between near-stream groundwater levels and stream stage because this signature captures the relative timing of groundwater and streamflow responses and has been shown to provide a sensitive, event-scale diagnostic indicator of changing hydrological connectivity and storage activation under contrasting antecedent conditions (Zuecco et al., 2019; Gelmini et al., 2022). This site-specific approach allows us to assess whether event-scale hysteresis reflects changing exchange states at the reach scale and thereby improves our understanding of the spatio-temporal dynamics of groundwater–surface water exchange at the study site.</p>
      <p id="d2e148">By quantifying hysteresis loops and HT over two seasons, we determine the impact of groundwater-surface water exchange variability on solute transport and stream chemistry. Through 68 hysteresis loops and 28 HT measurements, including nine during the 2020 drought, we analyse how HT and hysteresis characteristics respond to seasonality and hydraulic gradients. Stream and near-stream groundwater sampling (DOC, NO<inline-formula><mml:math id="M4" display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula>, Mn, Fe) reveal changes in redox zonation with hysteretic behaviour. Silica serves as a proxy for groundwater, providing insights into subsurface contributions to stream chemistry. Potassium, as a local proxy for stream water, highlights the influence of GW-SW interactions on mixing and solute transport through the stream bank. In this study, we aim to evaluate whether the hysteresis between groundwater and stream water levels at the event scale reflects seasonal changes in system state and hydrological turnover, building on the conceptual framework introduced in Bäthke and Schuetz (2024). We conceptualize stream–groundwater hysteresis as an integrated hydraulic response to seasonally and event-scale varying storage states, transmissivity, hydraulic gradients, and bank storage within the riparian corridor (Cardenas, 2015; Brunner et al., 2009; Wondzell, 2011; Gu et al., 2012). Hysteresis is interpreted as a diagnostic indicator linking hydraulic boundary conditions to hydrological turnover (HT) and associated biogeochemical responses. During summer and drought conditions, low groundwater levels and steep losing hydraulic gradients promote stream water infiltration into the streambed and banks. Reduced transmissivity and limited antecedent moisture lead to delayed groundwater responses relative to stream stage fluctuations, associated with enhanced bank storage activation and high relative HT. Conversely, during winter and wet conditions, elevated groundwater tables and reduced gradients favour gaining or weakly losing conditions. Increased hydraulic connectivity leads to more synchronous stream – groundwater responses, with the exchange dominated by groundwater discharge rather than bank storage-driven turnover.</p>
      <p id="d2e163">Within this study, we analyse whether seasonal shifts in hysteresis direction and magnitude reflect transitions between storage-controlled and exchange-controlled GW–SW interaction. Thus, we hypothesize that (1) the direction and magnitude of hysteresis between groundwater and stream water levels are indicative of seasonal hydrological states, and (2) consequently, the direction and magnitude of the hysteresis is related to hydrological turnover, thereby facilitating (3) redox fluctuations creating oxic-anoxic transition in the riparian zone, shaping redox hot spots.</p>
</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Materials and Methods</title>
<sec id="Ch1.S2.SS1">
  <label>2.1</label><title>Study Area</title>
      <p id="d2e181">The Olewigerbach is a third-order tributary of the River Mosel, located south of the city of Trier in southwest Germany. The catchment drains a 25 km<sup>2</sup> watershed with a total stream length of 14 km (Krein and Schorer, 2000). The area above the experimental site drains 8.01 km<sup>2</sup>. The elevation difference between the headwaters and the outlet is approximately 300 m. The stream has a pluvial regime, receiving an average annual precipitation of 745 mm and exhibiting a mean discharge of 106 L s<sup>−1</sup> recorded between 2010 and 2023 at the study site. The catchment's geology is dominated by Devonian schists, primarily argillaceous slates. The shallow soil layer (<inline-formula><mml:math id="M8" display="inline"><mml:mo lspace="0mm">∼</mml:mo></mml:math></inline-formula> 1.3 m) contains impermeable clay pockets underlain by colluvial or bedrock argillaceous slates (Krein and Symader, 2000). The study area is situated in the headwaters of the catchment at an elevation of approximately 290 m above sea level, characterized by steep hillslopes.</p>
      <p id="d2e221">Drought conditions were defined as periods when streamflow fell below 5 % (5.3 L s<sup>−1</sup>) of the mean discharge, following thresholds set by Yevjevich (1967) and Zelenhasić and Salvai (1987).</p>
</sec>
<sec id="Ch1.S2.SS2">
  <label>2.2</label><title>Experimental Setup</title>
      <p id="d2e244">Field measurements were conducted from 2020 to 2023. The dataset comprises 28 differential discharge gauging campaigns, utilizing NaCl slug injections, to estimate HT during runoff events, over the stream section equipped with sampling wells. We monitored 68 runoff events with precipitation totals ranging up to 60 mm. Corresponding streamflow responses showed peak discharges between 20 and 700 L s<sup>−1</sup>. Antecedent soil moisture conditions varied from dry at summer drought events to near saturation at winter events (Table 1 and Fig. 3). While these event descriptions provide hydrological context, the classification of events in this study is based on hysteresis behaviour and HT rather than on precipitation or discharge metrics. Hysteresis loops were recorded based on stream water levels and groundwater levels measured at two groundwater wells (GW1 and GW2) located close to the stream (Fig. 1).</p>

      <fig id="F1"><label>Figure 1</label><caption><p id="d2e261">Spatial representation of topographic slopes of the Olewiger Bach catchment in the south of the city of Trier, Germany. Red: Gauging station with detailed sampling site sketch, with stream and sampling well setup. HT concept and groundwater connectivity through riparian zone. Blue: Location of the weather station. Grey: Location of additional sampling wells downstream.</p></caption>
          <graphic xlink:href="https://hess.copernicus.org/articles/30/4889/2026/hess-30-4889-2026-f01.png"/>

        </fig>

      <p id="d2e270">Groundwater levels were continuously logged in 10 min intervals using Orpheus Mini Level Loggers (OTT GmbH). At the upstream gauging station three piezometers were installed in a transect: one in the stream (289.47 m a.s.l.) and two at the groundwater wells, GW1 (288.63 m a.s.l.) and GW2 (288.65 m a.s.l.). GW1 is located 1.5 m from the streambank, and GW2 is 3.7 m away. Groundwater levels were monitored at a depth of 1.3 m. Based on the recorded hydrograph data; 68 events were selected for hysteresis analysis using the <inline-formula><mml:math id="M11" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index developed by Zuecco et al. (2016). Corresponding HT measurements (<inline-formula><mml:math id="M12" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">28</mml:mn></mml:mrow></mml:math></inline-formula>) were available for 20 of these events (Table 1). We identified three severe drought periods: 27 March to 1 April 2020; 24 April to 28 September 2020; and 6 September to 20 October 2021 (Fig. 3). Precipitation and modelled soil moisture data (AMBAV, DWD) for the study area were obtained from the Deutscher Wetterdienst (DWD) weather station located near the Olewigerbach catchment (Station: Trier-Petrisberg; ID: 05100; <uri>https://opendata.dwd.de/climate_environment/</uri>, last access: 17 April 2026).</p>

<table-wrap id="T1" specific-use="star"><label>Table 1</label><caption><p id="d2e299">Characteristics of runoff events occurring prior to one or multiple tracer experiments: event precipitation, Total Runoff [mm], Event Runoff [mm], Baseflow Index [BFI] as 1 – (Total Runoff/Event Runoff), and the runoff coefficient calculated as the ratio of direct event runoff volume to precipitation-derived input volume (runoff coefficient <inline-formula><mml:math id="M13" display="inline"><mml:mo>=</mml:mo></mml:math></inline-formula> event runoff volume/(precipitation <inline-formula><mml:math id="M14" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> catchment area)), Soil moisture indicated as usable field capacity in %.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="7">
     <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:colspec colnum="6" colname="col6" align="right"/>
     <oasis:colspec colnum="7" colname="col7" align="right"/>
     <oasis:thead>
       <oasis:row>
         <oasis:entry colname="col1">Date</oasis:entry>
         <oasis:entry colname="col2">Precipitation</oasis:entry>
         <oasis:entry colname="col3">Total. Runoff</oasis:entry>
         <oasis:entry colname="col4">Event. Runoff</oasis:entry>
         <oasis:entry colname="col5">Runoff coeff.</oasis:entry>
         <oasis:entry colname="col6">BFI</oasis:entry>
         <oasis:entry colname="col7">Usable Field capacity</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">[dd.mm.yyyy]</oasis:entry>
         <oasis:entry colname="col2">[mm]</oasis:entry>
         <oasis:entry colname="col3">[mm]</oasis:entry>
         <oasis:entry colname="col4">[mm]</oasis:entry>
         <oasis:entry colname="col5">[–]</oasis:entry>
         <oasis:entry colname="col6"/>
         <oasis:entry colname="col7">0–30 cm [%]</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">03.08.2020</oasis:entry>
         <oasis:entry colname="col2">3.80</oasis:entry>
         <oasis:entry colname="col3">0.62</oasis:entry>
         <oasis:entry colname="col4">0.38</oasis:entry>
         <oasis:entry colname="col5">0.099</oasis:entry>
         <oasis:entry colname="col6">0.39</oasis:entry>
         <oasis:entry colname="col7">2.3</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">17.08.2020</oasis:entry>
         <oasis:entry colname="col2">8.30</oasis:entry>
         <oasis:entry colname="col3">0.24</oasis:entry>
         <oasis:entry colname="col4">0.16</oasis:entry>
         <oasis:entry colname="col5">0.019</oasis:entry>
         <oasis:entry colname="col6">0.34</oasis:entry>
         <oasis:entry colname="col7">9.3</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">18.08.2020</oasis:entry>
         <oasis:entry colname="col2">2.20</oasis:entry>
         <oasis:entry colname="col3">0.03</oasis:entry>
         <oasis:entry colname="col4">0.01</oasis:entry>
         <oasis:entry colname="col5">0.003</oasis:entry>
         <oasis:entry colname="col6">0.78</oasis:entry>
         <oasis:entry colname="col7">9.3</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">02.09.2020</oasis:entry>
         <oasis:entry colname="col2">3.00</oasis:entry>
         <oasis:entry colname="col3">0.09</oasis:entry>
         <oasis:entry colname="col4">0.02</oasis:entry>
         <oasis:entry colname="col5">0.006</oasis:entry>
         <oasis:entry colname="col6">0.80</oasis:entry>
         <oasis:entry colname="col7">2.6</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">24.09.2020</oasis:entry>
         <oasis:entry colname="col2">8.00</oasis:entry>
         <oasis:entry colname="col3">0.14</oasis:entry>
         <oasis:entry colname="col4">0.03</oasis:entry>
         <oasis:entry colname="col5">0.004</oasis:entry>
         <oasis:entry colname="col6">0.75</oasis:entry>
         <oasis:entry colname="col7">5.3</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">06.10.2020</oasis:entry>
         <oasis:entry colname="col2">16.30</oasis:entry>
         <oasis:entry colname="col3">1.45</oasis:entry>
         <oasis:entry colname="col4">0.43</oasis:entry>
         <oasis:entry colname="col5">0.026</oasis:entry>
         <oasis:entry colname="col6">0.70</oasis:entry>
         <oasis:entry colname="col7">87.6</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">09.10.2020</oasis:entry>
         <oasis:entry colname="col2">22.60</oasis:entry>
         <oasis:entry colname="col3">3.68</oasis:entry>
         <oasis:entry colname="col4">0.98</oasis:entry>
         <oasis:entry colname="col5">0.043</oasis:entry>
         <oasis:entry colname="col6">0.73</oasis:entry>
         <oasis:entry colname="col7">72</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">23.10.2020</oasis:entry>
         <oasis:entry colname="col2">8.20</oasis:entry>
         <oasis:entry colname="col3">0.44</oasis:entry>
         <oasis:entry colname="col4">0.17</oasis:entry>
         <oasis:entry colname="col5">0.020</oasis:entry>
         <oasis:entry colname="col6">0.62</oasis:entry>
         <oasis:entry colname="col7">84.6</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">08.02.2021</oasis:entry>
         <oasis:entry colname="col2">19.50</oasis:entry>
         <oasis:entry colname="col3">8.71</oasis:entry>
         <oasis:entry colname="col4">0.68</oasis:entry>
         <oasis:entry colname="col5">0.035</oasis:entry>
         <oasis:entry colname="col6">0.92</oasis:entry>
         <oasis:entry colname="col7">110</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">13.03.2021</oasis:entry>
         <oasis:entry colname="col2">18.20</oasis:entry>
         <oasis:entry colname="col3">1.19</oasis:entry>
         <oasis:entry colname="col4">0.53</oasis:entry>
         <oasis:entry colname="col5">0.029</oasis:entry>
         <oasis:entry colname="col6">0.56</oasis:entry>
         <oasis:entry colname="col7">96</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">19.03.2021</oasis:entry>
         <oasis:entry colname="col2">30.90</oasis:entry>
         <oasis:entry colname="col3">6.22</oasis:entry>
         <oasis:entry colname="col4">0.44</oasis:entry>
         <oasis:entry colname="col5">0.014</oasis:entry>
         <oasis:entry colname="col6">0.93</oasis:entry>
         <oasis:entry colname="col7">98</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">11.04.2021</oasis:entry>
         <oasis:entry colname="col2">42.10</oasis:entry>
         <oasis:entry colname="col3">3.44</oasis:entry>
         <oasis:entry colname="col4">1.59</oasis:entry>
         <oasis:entry colname="col5">0.038</oasis:entry>
         <oasis:entry colname="col6">0.54</oasis:entry>
         <oasis:entry colname="col7">102</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">26.05.2021</oasis:entry>
         <oasis:entry colname="col2">15.30</oasis:entry>
         <oasis:entry colname="col3">2.20</oasis:entry>
         <oasis:entry colname="col4">0.39</oasis:entry>
         <oasis:entry colname="col5">0.025</oasis:entry>
         <oasis:entry colname="col6">0.82</oasis:entry>
         <oasis:entry colname="col7">65.3</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">04.06.2021</oasis:entry>
         <oasis:entry colname="col2">19.80</oasis:entry>
         <oasis:entry colname="col3">0.97</oasis:entry>
         <oasis:entry colname="col4">0.30</oasis:entry>
         <oasis:entry colname="col5">0.015</oasis:entry>
         <oasis:entry colname="col6">0.69</oasis:entry>
         <oasis:entry colname="col7">32.6</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">29.06.2021</oasis:entry>
         <oasis:entry colname="col2">7.00</oasis:entry>
         <oasis:entry colname="col3">0.32</oasis:entry>
         <oasis:entry colname="col4">0.11</oasis:entry>
         <oasis:entry colname="col5">0.016</oasis:entry>
         <oasis:entry colname="col6">0.65</oasis:entry>
         <oasis:entry colname="col7">40.6</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">05.07.2021</oasis:entry>
         <oasis:entry colname="col2">18.10</oasis:entry>
         <oasis:entry colname="col3">1.36</oasis:entry>
         <oasis:entry colname="col4">0.80</oasis:entry>
         <oasis:entry colname="col5">0.044</oasis:entry>
         <oasis:entry colname="col6">0.41</oasis:entry>
         <oasis:entry colname="col7">64</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">27.07.2021</oasis:entry>
         <oasis:entry colname="col2">28.20</oasis:entry>
         <oasis:entry colname="col3">4.77</oasis:entry>
         <oasis:entry colname="col4">1.44</oasis:entry>
         <oasis:entry colname="col5">0.051</oasis:entry>
         <oasis:entry colname="col6">0.70</oasis:entry>
         <oasis:entry colname="col7">106</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">08.08.2021</oasis:entry>
         <oasis:entry colname="col2">4.20</oasis:entry>
         <oasis:entry colname="col3">0.34</oasis:entry>
         <oasis:entry colname="col4">0.15</oasis:entry>
         <oasis:entry colname="col5">0.036</oasis:entry>
         <oasis:entry colname="col6">0.56</oasis:entry>
         <oasis:entry colname="col7">104</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">26.11.2021</oasis:entry>
         <oasis:entry colname="col2">15.40</oasis:entry>
         <oasis:entry colname="col3">0.88</oasis:entry>
         <oasis:entry colname="col4">0.27</oasis:entry>
         <oasis:entry colname="col5">0.017</oasis:entry>
         <oasis:entry colname="col6">0.69</oasis:entry>
         <oasis:entry colname="col7">100</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">07.12.2021</oasis:entry>
         <oasis:entry colname="col2">29.40</oasis:entry>
         <oasis:entry colname="col3">5.78</oasis:entry>
         <oasis:entry colname="col4">1.58</oasis:entry>
         <oasis:entry colname="col5">0.054</oasis:entry>
         <oasis:entry colname="col6">0.73</oasis:entry>
         <oasis:entry colname="col7">103</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

      <p id="d2e894">Streamflow data were monitored at the sampling site (Fig. 1). We collected 144 water samples – 48 each from the stream, GW1, and GW2 – across three identical sampling spots, including two additional downstream locations without piezometers. Sampling was conducted under various baseflow conditions (Table1; BFI). The experimental infrastructure is drawn out in detail in Bäthke and Schuetz (2024).</p>
<sec id="Ch1.S2.SS2.SSSx1" specific-use="unnumbered">
  <title>Tracer Injection Protocol</title>
      <p id="d2e903">Instantaneous NaCl tracer injections were performed for HT estimation, during recession, utilizing salt dilution gauging (Day, 1976; Covino et al., 2011; Mallard et al., 2014). At the monitored stream section (<inline-formula><mml:math id="M15" display="inline"><mml:mo lspace="0mm">∼</mml:mo></mml:math></inline-formula> 250 m) two independent discharge measurements were conducted, at the upstream and downstream ends of the stream reach. Injection distance for the measurement devices was approximately 30m (Bäthke and Schuetz, 2024). The <inline-formula><mml:math id="M16" display="inline"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mi mathvariant="normal">Loss</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> estimation is based on an additional tracer injection injected at the upstream site and measured at the downstream site, following the instructions of Payn et al. (2009). The wells are located in 1.5 and 3 m lateral distance from the upstream measurement point. Electrical conductivity during tracer injections ranged from 203–320 <inline-formula><mml:math id="M17" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>S cm<sup>−1</sup> at baseline, with peaks 100–300 <inline-formula><mml:math id="M19" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>S cm<sup>−1</sup> above baseline levels. <list list-type="bullet"><list-item>
      <p id="d2e967">Injection mass ranged from 50 to 2000 g of pre-dissolved NaCl, adjusted based on discharge and background conductivity.</p></list-item><list-item>
      <p id="d2e971">Breakthrough curves (BTCs) with peaks outside 1.25–2.0 times baseline conductivity was excluded or repeated.</p></list-item><list-item>
      <p id="d2e975">Measurements covered discharge rates from <inline-formula><mml:math id="M21" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 1 to 400 L s<sup>−1</sup>.</p></list-item><list-item>
      <p id="d2e998">Conductivity was logged at 1 s intervals, except during extreme low-flow conditions (<inline-formula><mml:math id="M23" display="inline"><mml:mrow><mml:mi mathvariant="italic">&lt;</mml:mi><mml:mn mathvariant="normal">5</mml:mn></mml:mrow></mml:math></inline-formula> L s<sup>−1</sup>), when a 5 s resolution was used to conserve device storage capacity.</p></list-item></list> Electrical conductivity data were used to calculate mass equivalents, which were calibrated to determine HT (Eq. 1). The hyporheic zone covers the space between the riparian groundwater and the stream (Wondzell, 2011). Bäthke and Schuetz (2024) show that high HT magnitudes manifest in low variability between stream and groundwater silica concentrations, with silica concentrations serving as a proxy of prolonged contact with the subsurface (see as well e.g. Burns et al., 2003). Prior measurements have shown elevated potassium concentrations in the stream compared to groundwater, with its source upstream of the sampling site. Soils containing sufficient amounts of clay minerals (e.g. illite, vermiculite) may reduce the potassium in solution via sorption (Sparks and Huang, 1985). Thus, we measured potassium as a proxy for stream water. To study redox changes within the riparian zone, we conducted additional sampling for DOC, NO<inline-formula><mml:math id="M25" display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula>, Mn, Fe. Such redox tracers can track redox dynamics as conceptualized by biogeochemical hydrological coupling (Peiffer et al., 2021) and the classical redox sequence in oxygen-limited environments (Zehnder and Stumm, 1988). Groundwater and stream samples were collected during stream flow recession towards baseflow conditions, immediately prior to the dilution gauging experiments (<inline-formula><mml:math id="M26" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">48</mml:mn></mml:mrow></mml:math></inline-formula>) and not during rainfall events. Laboratory analyses were carried out after filtration through a 0.45 <inline-formula><mml:math id="M27" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>m Macherey and Nagel glass fibre filter. Nitrate, potassium (<inline-formula><mml:math id="M28" display="inline"><mml:mrow><mml:mo>±</mml:mo><mml:mn mathvariant="normal">5</mml:mn></mml:mrow></mml:math></inline-formula> %) and silica (<inline-formula><mml:math id="M29" display="inline"><mml:mrow><mml:mo>±</mml:mo><mml:mn mathvariant="normal">8</mml:mn></mml:mrow></mml:math></inline-formula> %) was determined by ion chromatography (<inline-formula><mml:math id="M30" display="inline"><mml:mrow><mml:mo>±</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:math></inline-formula> %),  iron and manganese (<inline-formula><mml:math id="M31" display="inline"><mml:mrow><mml:mo>±</mml:mo><mml:mn mathvariant="normal">5</mml:mn></mml:mrow></mml:math></inline-formula> %) by atomic absorption spectroscopy (AAS, contrAA 300), and dissolved organic carbon (DOC) by combustion followed by IR detection (TOC analyzer, <inline-formula><mml:math id="M32" display="inline"><mml:mrow><mml:mo>±</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:math></inline-formula> %). Reported <inline-formula><mml:math id="M33" display="inline"><mml:mo>±</mml:mo></mml:math></inline-formula> values represent the standard deviation of the replicate analyses.</p>
</sec>
<sec id="Ch1.S2.SS2.SSS1">
  <label>2.2.1</label><title>Hydrologic Turnover</title>
      <p id="d2e1122">The conducted tracer experiments provided BTCs to quantify net changes in discharge along the reach (<inline-formula><mml:math id="M34" display="inline"><mml:mo lspace="0mm">∼</mml:mo></mml:math></inline-formula> 250 m) as well as gross gains and losses of stream water to and from groundwater. With discharge estimation at the upper and lower end of the selected stream reach:

              <disp-formula id="Ch1.E1" content-type="numbered"><label>1</label><mml:math id="M35" display="block"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:msub><mml:mi>M</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msubsup><mml:mo>∫</mml:mo><mml:mn mathvariant="normal">0</mml:mn><mml:mi>t</mml:mi></mml:msubsup><mml:msub><mml:mi>C</mml:mi><mml:mrow><mml:msub><mml:mi>M</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:msub><mml:mfenced close=")" open="("><mml:mi>t</mml:mi></mml:mfenced><mml:mi mathvariant="normal">d</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac></mml:mstyle></mml:mrow></mml:math></disp-formula>

            Where <inline-formula><mml:math id="M36" display="inline"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> [L s<sup>−1</sup>] is the discharge at the measurement location discharge entering a stream reach and <inline-formula><mml:math id="M38" display="inline"><mml:mi>Q</mml:mi></mml:math></inline-formula> discharge leaving the stream reach. <inline-formula><mml:math id="M39" display="inline"><mml:mrow><mml:msub><mml:mi>M</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> [g] is initial tracer mass injected and <inline-formula><mml:math id="M40" display="inline"><mml:mrow><mml:msub><mml:mi>C</mml:mi><mml:mrow><mml:msub><mml:mi>M</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:msub><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> [g L<sup>−1</sup>] the integrated tracer concentration from the BTC. Hydrological turnover (HT) assumes that the fractional loss of tracer mass represents the fractional loss of stream flow to the subsurface and does not enter the stream channel again during the duration of the experiment (Payn et al., 2009; Covino et al., 2011). Gross losses (<inline-formula><mml:math id="M42" display="inline"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mi mathvariant="normal">Loss</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) were calculated by:

              <disp-formula id="Ch1.E2" content-type="numbered"><label>2</label><mml:math id="M43" display="block"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mi mathvariant="normal">Loss</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>Q</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msub><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:msub><mml:mi>M</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>-</mml:mo><mml:msubsup><mml:mo>∫</mml:mo><mml:mn mathvariant="normal">0</mml:mn><mml:mi>t</mml:mi></mml:msubsup><mml:mi>M</mml:mi><mml:mfenced close=")" open="("><mml:mi>t</mml:mi></mml:mfenced><mml:mi mathvariant="normal">d</mml:mi><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mi>M</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mstyle></mml:mrow></mml:math></disp-formula></p>
      <p id="d2e1322">Net discharge changes (<inline-formula><mml:math id="M44" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>Q</mml:mi></mml:mrow></mml:math></inline-formula>) and HT were then determined as:

                  <disp-formula specific-use="gather" content-type="numbered"><mml:math id="M45" display="block"><mml:mtable displaystyle="true"><mml:mlabeledtr id="Ch1.E3"><mml:mtd><mml:mtext>3</mml:mtext></mml:mtd><mml:mtd><mml:mrow><mml:mstyle displaystyle="true" class="stylechange"/><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>Q</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi>Q</mml:mi><mml:mi mathvariant="normal">Gain</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msub><mml:mi>Q</mml:mi><mml:mi mathvariant="normal">Loss</mml:mi></mml:msub></mml:mrow></mml:mtd></mml:mlabeledtr><mml:mlabeledtr id="Ch1.E4"><mml:mtd><mml:mtext>4</mml:mtext></mml:mtd><mml:mtd><mml:mrow><mml:mstyle class="stylechange" displaystyle="true"/><mml:msub><mml:mi mathvariant="normal">HT</mml:mi><mml:mi>a</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mo>|</mml:mo><mml:msub><mml:mi>Q</mml:mi><mml:mi mathvariant="normal">Loss</mml:mi></mml:msub><mml:mo>|</mml:mo><mml:mo>+</mml:mo><mml:msub><mml:mi>Q</mml:mi><mml:mi mathvariant="normal">Gain</mml:mi></mml:msub></mml:mrow></mml:mtd></mml:mlabeledtr></mml:mtable></mml:math></disp-formula></p>
      <p id="d2e1396">The absolute differences in <inline-formula><mml:math id="M46" display="inline"><mml:mi>Q</mml:mi></mml:math></inline-formula> (<inline-formula><mml:math id="M47" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>Q</mml:mi></mml:mrow></mml:math></inline-formula>, Eq. 3) between upstream (<inline-formula><mml:math id="M48" display="inline"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>) and downstream (<inline-formula><mml:math id="M49" display="inline"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) discharge measurement sites in combination with <inline-formula><mml:math id="M50" display="inline"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mi mathvariant="normal">Loss</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> (Eq. 2) results in <inline-formula><mml:math id="M51" display="inline"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mi mathvariant="normal">Gain</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> as the residual, i.e. the part of <inline-formula><mml:math id="M52" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>Q</mml:mi></mml:mrow></mml:math></inline-formula> which is not explained by <inline-formula><mml:math id="M53" display="inline"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mi mathvariant="normal">Loss</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> (Eq. 2). Finally, HT is normalized to a relative value based on reach length (<inline-formula><mml:math id="M54" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula>) in m:

              <disp-formula id="Ch1.E5" content-type="numbered"><label>5</label><mml:math id="M55" display="block"><mml:mrow><mml:mi mathvariant="normal">HT</mml:mi><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:mstyle displaystyle="false"><mml:mfrac style="text"><mml:mrow><mml:msub><mml:mi mathvariant="normal">HT</mml:mi><mml:mi>a</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mstyle><mml:mspace linebreak="nobreak" width="0.125em"/><mml:mn mathvariant="normal">100</mml:mn></mml:mrow><mml:mi>d</mml:mi></mml:mfrac></mml:mstyle></mml:mrow></mml:math></disp-formula></p>
</sec>
<sec id="Ch1.S2.SS2.SSS2">
  <label>2.2.2</label><title>The Hysteresis Index</title>
      <p id="d2e1538">The hysteresis index (<inline-formula><mml:math id="M56" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index) quantifies differences in water levels between rising and falling limbs of a hysteresis loop and is calculated in four steps (Zuecco et al., 2016). The extraction of stream water level data was done manually. Computation is done by utilizing the python script established by Jehn (2019, <uri>https://github.com/zutn/Hysteresis-Index-Zuecco</uri>, last access: 20 January 2025). The script contains all computational steps as follows: <list list-type="bullet"><list-item>
      <p id="d2e1553">Normalization:<disp-formula specific-use="gather" content-type="numbered"><mml:math id="M57" display="block"><mml:mtable displaystyle="true"><mml:mlabeledtr id="Ch1.E6"><mml:mtd><mml:mtext>6</mml:mtext></mml:mtd><mml:mtd><mml:mrow><mml:mstyle class="stylechange" displaystyle="true"/><mml:mi>u</mml:mi><mml:mfenced close=")" open="("><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:mi>x</mml:mi><mml:mfenced close=")" open="("><mml:mi>t</mml:mi></mml:mfenced><mml:mo>-</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi mathvariant="normal">min</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mi mathvariant="normal">max</mml:mi></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi mathvariant="normal">min</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mstyle></mml:mrow></mml:mtd></mml:mlabeledtr><mml:mlabeledtr id="Ch1.E7"><mml:mtd><mml:mtext>7</mml:mtext></mml:mtd><mml:mtd><mml:mrow><mml:mstyle displaystyle="true" class="stylechange"/><mml:mi>v</mml:mi><mml:mfenced close=")" open="("><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:mi>y</mml:mi><mml:mfenced close=")" open="("><mml:mi>t</mml:mi></mml:mfenced><mml:mo>-</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi mathvariant="normal">min</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>y</mml:mi><mml:mi mathvariant="normal">max</mml:mi></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi mathvariant="normal">min</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mstyle></mml:mrow></mml:mtd></mml:mlabeledtr></mml:mtable></mml:math></disp-formula></p></list-item><list-item>
      <p id="d2e1646">Integral computation for rising (<inline-formula><mml:math id="M58" display="inline"><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mrow><mml:mi>r</mml:mi><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>]</mml:mo></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>) and falling (<inline-formula><mml:math id="M59" display="inline"><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mrow><mml:mi>f</mml:mi><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>]</mml:mo></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>) limbs, with A as area of the normalized Integral:<disp-formula specific-use="gather" content-type="numbered"><mml:math id="M60" display="block"><mml:mtable displaystyle="true"><mml:mlabeledtr id="Ch1.E8"><mml:mtd><mml:mtext>8</mml:mtext></mml:mtd><mml:mtd><mml:mrow><mml:mstyle displaystyle="true" class="stylechange"/><mml:msub><mml:mi>A</mml:mi><mml:mrow><mml:mi>r</mml:mi><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>]</mml:mo></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msubsup><mml:mo>∫</mml:mo><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:msubsup><mml:msub><mml:mi>v</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>u</mml:mi><mml:mo>)</mml:mo><mml:mi mathvariant="normal">d</mml:mi><mml:mi>u</mml:mi></mml:mrow></mml:mtd></mml:mlabeledtr><mml:mlabeledtr id="Ch1.E9"><mml:mtd><mml:mtext>9</mml:mtext></mml:mtd><mml:mtd><mml:mrow><mml:mstyle class="stylechange" displaystyle="true"/><mml:msub><mml:mi>A</mml:mi><mml:mrow><mml:mi>f</mml:mi><mml:mfenced open="[" close="]"><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msubsup><mml:mo>∫</mml:mo><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:msubsup><mml:msub><mml:mi>v</mml:mi><mml:mi>f</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mi>u</mml:mi><mml:mo>)</mml:mo><mml:mi mathvariant="normal">d</mml:mi><mml:mi>u</mml:mi></mml:mrow></mml:mtd></mml:mlabeledtr></mml:mtable></mml:math></disp-formula></p></list-item><list-item>
      <p id="d2e1791">Difference computation:<disp-formula id="Ch1.E10" content-type="numbered"><label>10</label><mml:math id="M61" display="block"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:msub><mml:mi>A</mml:mi><mml:mrow><mml:mfenced close="]" open="["><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>A</mml:mi><mml:mrow><mml:mi>r</mml:mi><mml:mfenced close="]" open="["><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:mspace linebreak="nobreak" width="0.125em"/><mml:msub><mml:mi>A</mml:mi><mml:mrow><mml:mi>f</mml:mi><mml:mfenced open="[" close="]"><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:msub></mml:mrow></mml:math></disp-formula></p></list-item><list-item>
      <p id="d2e1847">Summation of differences to obtain the <inline-formula><mml:math id="M62" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index:<disp-formula id="Ch1.E11" content-type="numbered"><label>11</label><mml:math id="M63" display="block"><mml:mrow><mml:mi>h</mml:mi><mml:mo>=</mml:mo><mml:munderover><mml:mo movablelimits="false">∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:munderover><mml:mi mathvariant="normal">Δ</mml:mi><mml:msub><mml:mi>A</mml:mi><mml:mrow><mml:mo>[</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>]</mml:mo></mml:mrow></mml:msub></mml:mrow></mml:math></disp-formula></p></list-item></list></p>
      <p id="d2e1894">The <inline-formula><mml:math id="M64" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index value is quantified by the sum of the area within the normalized loop <inline-formula><mml:math id="M65" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:msub><mml:mi>A</mml:mi><mml:mrow><mml:mfenced open="[" close="]"><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>, where n is the number of chosen intervals. With <inline-formula><mml:math id="M66" display="inline"><mml:mi>x</mml:mi></mml:math></inline-formula> as the SW-level and <inline-formula><mml:math id="M67" display="inline"><mml:mi>y</mml:mi></mml:math></inline-formula> as the GW-level at time <inline-formula><mml:math id="M68" display="inline"><mml:mi>t</mml:mi></mml:math></inline-formula> (Fig. 2). Positive <inline-formula><mml:math id="M69" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula> values indicate clockwise hysteresis, while negative values indicate anticlockwise hysteretic behaviour. A value equal to zero indicates no hysteresis or a symmetrical eight shaped. The extent of the hysteretic loop is given by the absolute value of <inline-formula><mml:math id="M70" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>; the larger the hysteretic loop, the further <inline-formula><mml:math id="M71" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula> is away from zero (Zuecco et al., 2016; Gelmini et al., 2022).</p>

      <fig id="F2" specific-use="star"><label>Figure 2</label><caption><p id="d2e1969">Schematic <inline-formula><mml:math id="M72" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index, with negative index values (blue), counterclockwise rotation. (Red) positive index values, clockwise rotation. Magnitude of area within the hysteresis cycle indicates absolute index value, extended area index value approaches <inline-formula><mml:math id="M73" display="inline"><mml:mrow><mml:mo>±</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula>, contracted area index value near 0. Dotted lines chosen intervals. Based on Lloyd et al. (2016b) and Zuecco et al. (2016).</p></caption>
            <graphic xlink:href="https://hess.copernicus.org/articles/30/4889/2026/hess-30-4889-2026-f02.png"/>

          </fig>

</sec>
<sec id="Ch1.S2.SS2.SSS3">
  <label>2.2.3</label><title>Local Hydraulic Gradient</title>
      <p id="d2e2004">We assessed the local near stream hydraulic gradients in three different ways. First, we calculated the hydraulic gradient at each time step during the selected hysteresis events. The mean gradient from start to end of the hysteresis event is then associated to the corresponding hysteresis event, resulting in one mean hydraulic gradient per well during each hysteresis event <inline-formula><mml:math id="M74" display="inline"><mml:mrow><mml:msub><mml:mi>i</mml:mi><mml:mrow><mml:msub><mml:mi mathvariant="normal">GW</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mn mathvariant="normal">1</mml:mn><mml:mo>,</mml:mo><mml:mn mathvariant="normal">2</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> [–] with <inline-formula><mml:math id="M75" display="inline"><mml:mrow><mml:msub><mml:mi>h</mml:mi><mml:mi mathvariant="normal">S</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> [m] as the mean piezometer height in the stream, <inline-formula><mml:math id="M76" display="inline"><mml:mrow><mml:msub><mml:mi>h</mml:mi><mml:mrow><mml:mi mathvariant="normal">GW</mml:mi><mml:mo>(</mml:mo><mml:mn mathvariant="normal">1</mml:mn><mml:mo>,</mml:mo><mml:mn mathvariant="normal">2</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> [m] as the mean piezometer height at the wells GW1 and GW2 and the distance between the stream and the respective well as <inline-formula><mml:math id="M77" display="inline"><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mn mathvariant="normal">1</mml:mn><mml:mo>,</mml:mo><mml:mn mathvariant="normal">2</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> [m].

              <disp-formula id="Ch1.E12" content-type="numbered"><label>12</label><mml:math id="M78" display="block"><mml:mrow><mml:msub><mml:mi>i</mml:mi><mml:mrow><mml:msub><mml:mi mathvariant="normal">GW</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mn mathvariant="normal">1</mml:mn><mml:mo>,</mml:mo><mml:mn mathvariant="normal">2</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mn mathvariant="normal">1</mml:mn><mml:mi>n</mml:mi></mml:mfrac></mml:mstyle><mml:mspace width="0.125em" linebreak="nobreak"/><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:msubsup><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:msub><mml:mi>h</mml:mi><mml:mi mathvariant="normal">GW</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:mn mathvariant="normal">1</mml:mn><mml:mo>,</mml:mo><mml:mn mathvariant="normal">2</mml:mn><mml:mo>)</mml:mo><mml:mo>-</mml:mo><mml:msub><mml:mi>h</mml:mi><mml:mi mathvariant="normal">S</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mn mathvariant="normal">1</mml:mn><mml:mo>,</mml:mo><mml:mn mathvariant="normal">2</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mstyle></mml:mrow></mml:math></disp-formula></p>
      <p id="d2e2168">Second, we calculated the mean hydraulic gradient <inline-formula><mml:math id="M79" display="inline"><mml:mrow><mml:msub><mml:mi>i</mml:mi><mml:mrow><mml:msub><mml:mi mathvariant="normal">GW</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mn mathvariant="normal">1</mml:mn><mml:mo>,</mml:mo><mml:mn mathvariant="normal">2</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> [–] between both groundwater wells during the defined hysteresis events:

              <disp-formula id="Ch1.E13" content-type="numbered"><label>13</label><mml:math id="M80" display="block"><mml:mrow><mml:msub><mml:mi>i</mml:mi><mml:mi mathvariant="normal">GW</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mn mathvariant="normal">1</mml:mn><mml:mi>n</mml:mi></mml:mfrac></mml:mstyle><mml:mspace width="0.125em" linebreak="nobreak"/><mml:munderover><mml:mo movablelimits="false">∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:munderover><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:msub><mml:mi>h</mml:mi><mml:mrow><mml:mi mathvariant="normal">GW</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>h</mml:mi><mml:mrow><mml:mi mathvariant="normal">GW</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mn mathvariant="normal">2.2</mml:mn></mml:mfrac></mml:mstyle></mml:mrow></mml:math></disp-formula></p>
</sec>
</sec>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Results</title>
<sec id="Ch1.S3.SS1">
  <label>3.1</label><title>Hysteresis behaviour</title>
      <p id="d2e2266">The majority of the events analysed in summer showed groundwater levels of the near stream well (GW1) below the stream water level (Fig. 3). However, in winter groundwater is often observed to be higher than the stream, especially at events during high soil moisture conditions. Analysing the hysteresis between the stream and the respective groundwater well, we observed differences between hysteretic behaviour during all seasons. During summer events the hydraulic head becomes negative, but also the hydraulic head difference between the two observed wells (Fig. 3). Precipitation events correspond with the on-site logged hydrograph dynamics as well as the soil moisture data (Fig. 4c).</p>

      <fig id="F3" specific-use="star"><label>Figure 3</label><caption><p id="d2e2271">Upper panels: Exemplary events of recorded water-table hysteresis between stream and wells. Left: Characteristic Winter water levels (blue: stream, black: GW1, grey: GW2). Sketch of the experimental setup. Time of HT measurement marked by dashed line. Lower panels: Normalized water levels plotted against each other as hysteretic loops. <inline-formula><mml:math id="M81" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index of the presented hysteretic loops of GW1 (black) and GW2 (grey). Right: Characteristic Summer water levels (blue: stream, black: GW1, grey: GW2). HT measurement marked by dashed line. Normalized water level plotted against each other as hysteric loop. <inline-formula><mml:math id="M82" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Indices of the presented hysteretic loops of GW1 (black) and GW2 (grey).</p></caption>
          <graphic xlink:href="https://hess.copernicus.org/articles/30/4889/2026/hess-30-4889-2026-f03.png"/>

        </fig>

      <fig id="F4"><label>Figure 4</label><caption><p id="d2e2296"><bold>(a)</bold> Catchment precipitation (mm) derived from a near weather station. <bold>(b)</bold> On- site stream flow (L s<sup>−1</sup>). <bold>(c)</bold> Soil moisture as the ratio to field capacity (–) from a near weather station, two depths: 0–30 cm (grey) and 30–60 cm (brown). Data presented over the observation period, with black dotted lines marking the identified periods of drought; <inline-formula><mml:math id="M84" display="inline"><mml:mi>Q</mml:mi></mml:math></inline-formula> below 95 % of mean <inline-formula><mml:math id="M85" display="inline"><mml:mi>Q</mml:mi></mml:math></inline-formula> (mQ).</p></caption>
          <graphic xlink:href="https://hess.copernicus.org/articles/30/4889/2026/hess-30-4889-2026-f04.png"/>

        </fig>

      <p id="d2e2340">Higher catchment soil moisture contents corresponded to higher stream flow and hydrograph reaction velocity to rain events. During drought conditions at stream flow below 5 % of mean discharge, we observed the highest HT [% m<sup>−1</sup>] values (Fig. 5), while absolute net exchange presents itself as very low (Fig. 5).</p>

      <fig id="F5" specific-use="star"><label>Figure 5</label><caption><p id="d2e2357"><bold>(a)</bold> Overview of catchment precipitation (mm) derived from a near weather station. <bold>(b)</bold> <inline-formula><mml:math id="M87" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index of hysteresis between stream and GW1 (red), <bold>(c)</bold>
<inline-formula><mml:math id="M88" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index of hysteresis between stream and GW2 (dark red) with moving average (black line) of index values (<inline-formula><mml:math id="M89" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">68</mml:mn></mml:mrow></mml:math></inline-formula>) over the observation period. Hydraulic gradient between stream and GW1 <bold>(d)</bold>, GW2 <bold>(e)</bold>, with moving average (black line) of hydraulic gradients (Eq. 12) over the observation period. <bold>(f)</bold> Hydrological turnover (HT) in % m<sup>−1</sup> point measurements (<inline-formula><mml:math id="M91" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">28</mml:mn></mml:mrow></mml:math></inline-formula>) during the observation period. Grey bars indicating net losses and gains, cyan dots net changes. Dotted black lines marking the transitions between summer and winter season. Blue background marks HT measurements during drought conditions (<inline-formula><mml:math id="M92" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">9</mml:mn></mml:mrow></mml:math></inline-formula>).</p></caption>
          <graphic xlink:href="https://hess.copernicus.org/articles/30/4889/2026/hess-30-4889-2026-f05.png"/>

        </fig>

      <p id="d2e2446">We observed extended HT during drought, and distinct seasonality of the <inline-formula><mml:math id="M93" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index (Fig. 5) between winter and summer (including the drought period). The shift between seasons of the <inline-formula><mml:math id="M94" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index corresponds with the crossing of its moving average of the zero line (Fig. 5b and c), with <inline-formula><mml:math id="M95" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-values during the summer mostly negative index values (counterclockwise hysteresis) and during the winter positive index values (clockwise hysteresis) (Fig. 3). However, there is a shift in seasonality between the two groundwater wells, with the well closer to the stream (GW1) showing the seasonal shift earlier and transitions later in the year compared to the second well (GW2). The distance between wells is 2.2 m, which appears to correspond to one month in delay. Summer rainfall events show a marginal impact on low flows, just initializing sharp spikes in stream flow for a very short period (Fig. 3). During winter, frequent rain events sustain higher baseflow (Fig. 4b). Antecedent moisture conditions provide the boundary for discharge generation (Table 1). The Olewigerbach is during both seasons a highly reactive stream with increased discharge amplitudes (Fig. 4b). Seasonality, reflected in precipitation and soil moisture data, aligns with <inline-formula><mml:math id="M96" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values and hydraulic gradients at the Olewigerbach (Fig. 5).</p>
</sec>
<sec id="Ch1.S3.SS2">
  <label>3.2</label><title>Hysteresis Index in relation to HT</title>
      <p id="d2e2485">Comparing the <inline-formula><mml:math id="M97" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Indices, both wells at the experimental site show significant differences (Wilcoxon rank-sum test) in <inline-formula><mml:math id="M98" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Indices between summer and winter (Fig. 6).</p>

      <fig id="F6" specific-use="star"><label>Figure 6</label><caption><p id="d2e2504">(Left) Violine plots of <inline-formula><mml:math id="M99" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values (GW1), below median value of the integrated boxplot per season. (Right) Violine plots of <inline-formula><mml:math id="M100" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values (GW2), below median value of the integrated boxplot per season. Comparison between Winter (<inline-formula><mml:math id="M101" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">37</mml:mn></mml:mrow></mml:math></inline-formula>), summer (<inline-formula><mml:math id="M102" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">22</mml:mn></mml:mrow></mml:math></inline-formula>), and drought (<inline-formula><mml:math id="M103" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">9</mml:mn></mml:mrow></mml:math></inline-formula>). Significant difference between groups indicated by <sup>*</sup> (<inline-formula><mml:math id="M105" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>*</mml:mo><mml:mo>*</mml:mo><mml:mo>*</mml:mo></mml:mrow></mml:msup><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.001</mml:mn></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id="M106" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>*</mml:mo><mml:mo>*</mml:mo></mml:mrow></mml:msup><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.01</mml:mn></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id="M107" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mo>*</mml:mo></mml:msup><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula>, Wilcoxon rank-sum test). Red dotted line showing decline in median <inline-formula><mml:math id="M108" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index value between seasons.</p></caption>
          <graphic xlink:href="https://hess.copernicus.org/articles/30/4889/2026/hess-30-4889-2026-f06.png"/>

        </fig>

      <p id="d2e2636">The first well tends towards more negative <inline-formula><mml:math id="M109" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values (counterclockwise) compared to the second well. Seasonally, both wells show significant differences between <inline-formula><mml:math id="M110" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values. During the summer period, the <inline-formula><mml:math id="M111" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values present decreasing values, shifting from clockwise to counterclockwise behaviour (Fig. 6). Additionally, compared with the <inline-formula><mml:math id="M112" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values tend further towards negative values during the drought periods. However, the drought dataset is small in observed events (<inline-formula><mml:math id="M113" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">9</mml:mn></mml:mrow></mml:math></inline-formula>). <inline-formula><mml:math id="M114" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values are substantially larger during drought conditions compared to winter, indicating an increase in hysteresis loop size.</p>

      <fig id="F7" specific-use="star"><label>Figure 7</label><caption><p id="d2e2690">Spearman correlation between <inline-formula><mml:math id="M115" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index value (GW1: left, GW2: right) and HT <bold>(a, b)</bold>, gross loss <bold>(c, d)</bold>, gross gain <bold>(e, f)</bold> in % m<sup>−1</sup> and discharge <bold>(g, h)</bold> in L s<sup>−1</sup>. Black data points for drought (<inline-formula><mml:math id="M118" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">7</mml:mn></mml:mrow></mml:math></inline-formula>), dark grey for summer (<inline-formula><mml:math id="M119" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">8</mml:mn></mml:mrow></mml:math></inline-formula>) and light grey for winter (<inline-formula><mml:math id="M120" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">14</mml:mn></mml:mrow></mml:math></inline-formula>).</p></caption>
          <graphic xlink:href="https://hess.copernicus.org/articles/30/4889/2026/hess-30-4889-2026-f07.png"/>

        </fig>

      <p id="d2e2779">Thus, showing the increased extended duration of the total counterclockwise hysteresis between the stream water level and the groundwater level. Comparing summer HT to drought event HT, the significance level of the correlation decreases from summer to drought (Fig. 6). Comparing the <inline-formula><mml:math id="M121" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values of both wells to HT, as the part of discharge exchanged per distance (% m<sup>−1</sup>), both wells behave similar. The <inline-formula><mml:math id="M123" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index shows a significant correlation with overall hydrological turnover (Fig. 7a, b), as well as with its components, gross loss (Fig. 7c, d) and gross gain (Fig. 7e, f). A comparable pattern is evident across all presented correlations: drought measurements cluster at the lowest levels of the <inline-formula><mml:math id="M124" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index range, showing the highest observed HT values (Fig. 7). These drought measurements were taken during periods of lowest discharge, whereas winter measurements correspond to periods of highest discharge. Additionally, we observe a positive correlation between discharge and <inline-formula><mml:math id="M125" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values at both wells (Fig. 7g and h).</p>
</sec>
<sec id="Ch1.S3.SS3">
  <label>3.3</label><title>Hysteresis Index in relation to Hydraulic Gradients</title>
      <p id="d2e2830">The mean hydraulic gradient between groundwater wells and the stream during single events, defined as negative toward the stream, shows a significant correlation with <inline-formula><mml:math id="M126" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values (Fig. 8a and b; Eq. 12).</p>

      <fig id="F8" specific-use="star"><label>Figure 8</label><caption><p id="d2e2842">Left: Schematic of hydraulic gradient. Right: Hysteresis direction indicated by red arrows. Mean hydraulic gradient against <inline-formula><mml:math id="M127" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values at GW1 <bold>(a)</bold> and GW2 <bold>(b)</bold> of the hysteresis event (<inline-formula><mml:math id="M128" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">68</mml:mn></mml:mrow></mml:math></inline-formula>). Hydraulic gradient between GW1 and GW2 against <inline-formula><mml:math id="M129" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values at GW1 <bold>(c)</bold> and at GW2 <bold>(d)</bold>. Dot size indicating corresponding HT measurements. Light gray blue couture (Winter), dark grey and red couture (Summer), black (drought). Plots are separated in quadrants: (I) Gaining &amp; counterclockwise; (II) Gaining and clockwise; (III) Losing &amp; counterclockwise; (IV) Losing &amp; clockwise hysteresis.</p></caption>
          <graphic xlink:href="https://hess.copernicus.org/articles/30/4889/2026/hess-30-4889-2026-f08.png"/>

        </fig>

      <p id="d2e2890">Steeper negative gradients and more negative <inline-formula><mml:math id="M130" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values were primarily observed during summer and drought events. In contrast, winter events exhibited both clockwise and counterclockwise hysteresis under less steep gradients. Positive gradients generally indicate gaining conditions, while negative gradients reflect losing conditions. Between the wells (Fig. 8c and d; Eq. 13), most summer and drought events displayed negative gradients and counterclockwise hysteresis. Based on hydraulic gradient and <inline-formula><mml:math id="M131" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index we divided the observations into four behavioural clusters (I–IV): <list list-type="bullet"><list-item>
      <p id="d2e2909"><italic>Cluster I</italic>: Mostly Winter events and heavy summer rain events; characterized by flat or positive gradients and counterclockwise hysteresis (groundwater rises before surface water).</p></list-item><list-item>
      <p id="d2e2915"><italic>Cluster II</italic>: Winter events; positive gradients with clockwise hysteresis (surface water rises before groundwater).</p></list-item><list-item>
      <p id="d2e2921"><italic>Cluster III</italic>: Predominantly summer and all drought events; negative gradients and counterclockwise hysteresis.</p></list-item><list-item>
      <p id="d2e2927"><italic>Cluster IV</italic>: Smallest group; characterized by negative gradients and clockwise hysteresis; stream-GW pairs include four summer events with negative gradients and clockwise hysteresis. Between wells, five winter and two summer events shared these characteristics.</p></list-item></list> Gradients between wells were generally smaller and showed higher short-term variability during rain events, compared to stream-GW gradients. Based on these mostly seasonal Clusters we grouped the tracers into a conservative mixing set (geogenic silica, Potassium) and a redox-related set to reflect the hypotheses 1 and 2 as well as 3, respectively: mixing tracers diagnose HT and its seasonal variability in groundwater-surface water exchange. This separation enables redox tracers (DOC, NO<inline-formula><mml:math id="M132" display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula>, Fe, Mn) to capture the biogeochemical responses associated with the different identified exchange patterns. We emphasize that event metrics are used as hydrological context, while event classification itself is based on hysteresis behaviour.</p>
</sec>
<sec id="Ch1.S3.SS4">
  <label>3.4</label><title>Hysteresis Index and Redox dynamics</title>
      <p id="d2e2953">Hysteretic behaviour and the associated HT induced mixing through the hyporheic zone and the stream bank, allow for exchange of solutes between GW and SW. Hence, we compared the sampling results by distinguishing seasons (Summer and Winter), Hysteresis direction (<inline-formula><mml:math id="M133" display="inline"><mml:mrow><mml:mi>h</mml:mi><mml:mi mathvariant="italic">&lt;</mml:mi><mml:mn mathvariant="normal">0</mml:mn></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id="M134" display="inline"><mml:mrow><mml:mi>h</mml:mi><mml:mi mathvariant="italic">&gt;</mml:mi><mml:mn mathvariant="normal">0</mml:mn></mml:mrow></mml:math></inline-formula>) and hydraulic conditions based on the gradient data (Losing or Gaining) (Fig. 9).</p>

      <fig id="F9" specific-use="star"><label>Figure 9</label><caption><p id="d2e2982">Boxplots of water chemistry parameters separated by <bold>(a)</bold> season (Summer/Winter), <bold>(b)</bold> hysteresis direction (<inline-formula><mml:math id="M135" display="inline"><mml:mrow><mml:mi>h</mml:mi><mml:mi mathvariant="italic">&lt;</mml:mi><mml:mn mathvariant="normal">0</mml:mn></mml:mrow></mml:math></inline-formula>/<inline-formula><mml:math id="M136" display="inline"><mml:mrow><mml:mi>h</mml:mi><mml:mi mathvariant="italic">&gt;</mml:mi><mml:mn mathvariant="normal">0</mml:mn></mml:mrow></mml:math></inline-formula>), and <bold>(c)</bold> hydraulic conditions (Gaining/Losing). <bold>(a)</bold> Mixing proxies between groundwater and surface water represented by Silica (GW) and Potassium (SW). <bold>(b)</bold> Redox-sensitive compounds representing the redox chain: DOC, Nitrate, Manganese, and Iron. Significant differences (<sup>*</sup> <inline-formula><mml:math id="M138" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mi mathvariant="italic">&lt;</mml:mi><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula>, <sup>**</sup> <inline-formula><mml:math id="M140" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mi mathvariant="italic">&lt;</mml:mi><mml:mn mathvariant="normal">0.01</mml:mn></mml:mrow></mml:math></inline-formula>, <sup>***</sup> <inline-formula><mml:math id="M142" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mi mathvariant="italic">&lt;</mml:mi><mml:mn mathvariant="normal">0.001</mml:mn></mml:mrow></mml:math></inline-formula>; Wilcoxon rank-sum test) between the sampling locations Stream, GW1, and GW2 are indicated by stars above the boxplots. Transect between Stream, GW1, and GW2; <inline-formula><mml:math id="M143" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">48</mml:mn></mml:mrow></mml:math></inline-formula>.</p></caption>
          <graphic xlink:href="https://hess.copernicus.org/articles/30/4889/2026/hess-30-4889-2026-f09.png"/>

        </fig>

      <p id="d2e3115">We observed differences for distribution of potassium samples between seasons. The winter events, positive <inline-formula><mml:math id="M144" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index show consistently the highest values in the stream with similar concentrations in both wells. However, for summer events, negative <inline-formula><mml:math id="M145" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Indices and losing conditions exhibit high potassium values at the stream gradually declining with distance towards the wells. In winter potassium is in a similar range at the wells (Fig. 9a). Generally, silica is rising from groundwater to stream in concentration while potassium shows the opposite (Fig. 9a). We detected redox-sensitive species (Fe, Mn, NO<inline-formula><mml:math id="M146" display="inline"><mml:mrow><mml:msubsup><mml:mi/><mml:mn mathvariant="normal">3</mml:mn><mml:mo>-</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula>) in all samples, with the highest concentrations of iron and manganese consistently observed in near-stream groundwater wells. This suggests heterogeneous  redox conditions in the riparian groundwater compared to the stream. For DOC concentrations tended to decline from stream to GW during the summer months while the opposite occurred during winter. Nitrate concentrations were generally variable with, on average, higher stream concentrations compared to GW (Fig. 9b). Nitrate is constantly present during our observations, regardless of redox potential indicated by the concentrations of iron and manganese ions. Manganese and iron are simultaneously present at the groundwater wells, often in similarly high concentrations, and show clear temporal patterns. During summer, concentrations of both elements were higher at GW1 than at GW2, suggesting intensified redox activity closer to the stream. These conditions coincided with hydraulic gradients indicative of losing conditions and counterclockwise hysteresis, indicating that stream-derived organic carbon inputs may stimulate microbial activity in the near-stream sediments.</p>
</sec>
</sec>
<sec id="Ch1.S4">
  <label>4</label><title>Discussion</title>
      <p id="d2e3153">We utilized the hysteresis index introduced by Zuecco et al. (2016) to analyse changes in the direction and magnitude of hysteresis between stream water levels and groundwater levels at the Olewigerbach catchment. This study relies on two available groundwater wells located in the close vicinity of the stream reach under study. While these wells provide valuable process-based insights into GW–SW exchange and redox dynamics, they cannot capture spatial heterogeneity across the catchment. The geological setting with fissured schists as the near surface bedrock does not provide a contiguous shallow aquifer, and thus no groundwater monitoring network at the catchment-scale. Therefore, larger-scale GW monitoring is absent. We instead utilize the standardized soil moisture model provided by the public weather service for each weather station (DWD) as an alternative proxy for subsurface storage dynamics (Fig. 4). Consequently, the conceptual framework we propose (<inline-formula><mml:math id="M147" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index/HT relationships) should be understood as locally constrained and hypothesis-generating, rather than directly representative of larger scales.</p>
<sec id="Ch1.S4.SS1">
  <label>4.1</label><title>Hysteresis dynamics and hydrologic turnover</title>
      <p id="d2e3170">High-resolution hydraulic gradient data allowed comparison of streamflow events with sufficient resolution to minimize noise. This allowed us to find seasonal variations in hysteretic patterns during the observation period (Fig. 6), showing that hysteresis between GW and SW levels are connected to seasonal hydrological states, comparable to those found in the literature (e.g., Zuecco et al., 2016; Lloyd et al., 2016a; Gelmini et al., 2022). The diagnostic use of hysteresis utilizes lagged hydraulic responses as indicators of system state rather than causal mechanisms.</p>
      <p id="d2e3173">Although the <inline-formula><mml:math id="M148" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index showed only slight differences in hysteresis between the wells (Figs. 3–5), correlation analysis reveals significant spatial and temporal variability in groundwater-stream interactions (Figs. 6 and 7). These variations reflect the dynamic nature of the hyporheic zone, which changes seasonally, particularly between winter, summer, and drought conditions (e.g., Wondzell, 2011; Cardenas, 2015; Harvey and Bencala, 1993; Malzone et al., 2016; Wroblicky et al., 1998). As observed by Gelmini et al. (2022), our study identifies clear seasonality in hysteretic behaviour. Specifically, HT varies with seasonal changes in hydraulic gradients and discharge magnitudes (Figs. 7 and 8). The <inline-formula><mml:math id="M149" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index serves as an event-based parameter to describe GW-SW interactions by incorporating hydraulic preconditions and event characteristics (Table 1). Distinct <inline-formula><mml:math id="M150" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index value ranges correspond to increased portions of stream water engaged in turnover processes (Fig. 7), aligning with studies emphasizing pressure-head variations and their influence on seasonal groundwater-stream connectivity (Brunner et al., 2009, 2011). The seasonal transition is marked by declining <inline-formula><mml:math id="M151" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values (including directional reversals) from winter to summer, coinciding with decreasing stream discharge and increasing hydrological turnover. These findings indicate that both the direction and magnitude of hysteresis control the intensity and direction of groundwater–surface-water exchange, as evidenced by the HT measurements. This is consistent with findings from Payn et al. (2009), Covino et al. (2011), and others. Beyond direction, the <inline-formula><mml:math id="M152" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index also reflects temporal dynamics between groundwater and surface water levels during events (Zuecco et al., 2016; Lloyd et al., 2016b), offering a physical framework for understanding HT processes. Thus, emphasizing that HT reflects exchange-driven renewal processes rather than storage duration, distinguishing it conceptually from residence time metrics. Hydraulic gradients between the stream and shallow groundwater serve as preconditions for runoff events, modulating GW-SW interactions (e.g., Zimmer and McGlynn, 2017; Voltz et al., 2013) a distinct seasonal pattern emerges, steeper hydraulic gradients during summer events correspond to counterclockwise hysteresis, while gentler gradients in autumn and winter are associated with clockwise hysteresis (Fig. 8). In addition, the riparian zone at the study site is covert by vegetation, which further influence GW–SW interactions. Root water uptake and evapotranspiration can modify near-stream hydraulic gradients, particularly during low-flow and drought conditions (Tabacchi et al., 2000), thereby potentially reinforcing strong gradients and affecting exchange fluxes between stream and the riparian zone (Wondzell et al., 2010; Boano et al., 2014). While these processes were not explicitly quantified in this study, they likely interact with the observed hysteresis patterns and hydrological turnover and should be considered when interpreting GW–SW exchange dynamics.</p>
      <p id="d2e3211">This relationship is reflected in our results, where counterclockwise hysteresis (negative <inline-formula><mml:math id="M153" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index) predominantly occurs under steep negative hydraulic gradients, indicating losing conditions, whereas positive or near-zero gradients are commonly associated with clockwise hysteresis and gaining conditions (Fig. 8 and corresponding <inline-formula><mml:math id="M154" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values in Sect. 3.3). Soil moisture controls the unsaturated hydraulic conductivity, governing occurrence of infiltration and overland flow (Horton, 1933). Thus, controlling threshold behaviour and the hydraulic response to rainfall events (e.g. Zehe and Sivapalan, 2009). Based on this hypothesis we identified three system states during the observations, winter, summer and drought conditions. These patterns reflect groundwater storage states and hyporheic zone variability. Hydraulic gradient changes not only influence hysteretic loop direction but also reflect groundwater table dynamics, hyporheic zone extent, and riparian bank storage (e.g., Brunner et al., 2009; Gu et al., 2012; Malzone et al., 2016). Elevated stream stages promote counterclockwise hysteresis and hyporheic zone expansion through increased bankstorage (Gu et al., 2012). Conversely, groundwater discharge initially reduces the hyporheic zone's extent (Cardenas and Wilson, 2007).</p>

      <fig id="F10" specific-use="star"><label>Figure 10</label><caption><p id="d2e3231">Conceptual comparison of summer and winter conditions at the study site. Top: Seasonal Turnover at <inline-formula><mml:math id="M155" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index (Summer in red; Winter in Blue). Sketch of the Stream with adjacent groundwater Profile, under clockwise conditions and counterclockwise hysteric conditions. Red frame marking typical summer condition and blue frame marking typical winter condition.</p></caption>
          <graphic xlink:href="https://hess.copernicus.org/articles/30/4889/2026/hess-30-4889-2026-f10.png"/>

        </fig>

      <p id="d2e3247">Our results demonstrate that dynamic local gradients strongly influence hyporheic exchange fluxes in the form of observed HT magnitude (Fig. 8). Thus, the Olewigerbach exhibits distinct seasonal system states that shape the water volume at the surface-groundwater boundary layer and thus shaping exchange in the form of HT. HT significantly correlates with <inline-formula><mml:math id="M156" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values, connecting system state driven response with GW-SW exchange. We propose addressing HT as a proportion of total discharge, considering the extent of hyporheic zone involvement relative to streamflow conditions. Under low surface discharge, hyporheic zone expansion potentially increases the ratio of hyporheic area to streamflow, engaging a greater fraction of stream water in HT.</p>
      <p id="d2e3257">This is demonstrated by the observed clustering of hysteresis index and hydraulic gradients reveal distinct hydrological states driven by seasonal conditions and event timing. We show that plotting events with hydraulic gradient against <inline-formula><mml:math id="M157" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index value four behavioural clusters can be identified (Fig. 8). Cluster I is dominated by winter events with counterclockwise hysteresis and flat or positive gradients, suggests storage influence where groundwater responds ahead of streamflow. This may be indicative of a hyporheic response occurring during early infiltration phases. Cluster II, with winter-dominated events and clockwise hysteresis under gaining conditions, likely indicates surface runoff or upstream input overwhelming the local GW response. These conditions reduce the influence of subsurface exchange, leading to stream-dominated hysteretic behaviour. Cluster III shows the strongest expression of hyporheic connectivity under summer and drought conditions. This description of present-day summer and drought system states may support the interpretation of possible HT process shifts if hydrological regime changes occur in the future. Steep negative gradients and counterclockwise loops here point to deeper, prolonged infiltration into the stream bank enabling intense mixing, reflected by the high HT values during those conditions (Fig. 7). Cluster IV shows the temporal variability and spatial heterogeneity of riparian processes. Particularly, the flatter gradients between the two wells are more susceptible to transient fluctuations during rainfall events, resulting in mixed hysteresis responses.</p>
      <p id="d2e3267">Additionally, our results show that gaining and losing conditions are not exclusively associated with one hysteresis direction. Similar observations were reported by Gelmini et al. (2022), who found that hysteresis behaviour, captured by the <inline-formula><mml:math id="M158" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index can vary depending on antecedent conditions and event timing, rather than being strictly determined by the direction of exchange. However, this cluster fitted the least number of events during our observed period (Figs. 8 and 10). These findings show that combining <inline-formula><mml:math id="M159" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index and hydraulic gradients allows detailed classification of hydrological states at event scale. Our results support Hypothesis (1): the direction and magnitude of the <inline-formula><mml:math id="M160" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index exhibit a clear and consistent seasonal pattern, with clockwise hysteresis dominating under winter gaining conditions and counterclockwise hysteresis prevailing during summer and drought losing conditions. We also accept Hypothesis (2), since hydrological turnover (HT), as an independent measure of exchange, strongly co-varied with <inline-formula><mml:math id="M161" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values. Periods of intensified HT aligned with negative <inline-formula><mml:math id="M162" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index values under steep losing gradients, whereas low HT occurred under positive, or near-zero gradients associated with gaining conditions.</p>
</sec>
<sec id="Ch1.S4.SS2">
  <label>4.2</label><title>Solute transport and redox conditions</title>
      <p id="d2e3313">Counterclockwise hysteresis and high HT are associated with enhanced riparian bank storage. Vertical expansion of the hyporheic zone at Olewigerbach is likely limited by shallow sediments and small stream size (Malzone et al., 2016). Instead, horizontal bankstorage expansion dominates, forming storage capacities (Fig. 10) along the streambank (Gu et al., 2012). The occurrence of bank storage is associated with counterclockwise hysteresis between stream water and groundwater levels, which occurs primarily during summer (Cluster III), while winter is dominated by clockwise hysteresis patterns (Fig. 10). This is further promoted by the losing conditions observed at the site, as similarly described in the Selke catchment (Trauth et al., 2015). Short-time alternating water level gradients during and after stream flow events create oxic-anoxic transition zones in the riparian zone could promote microbial processes (Knorr et al., 2009). Thus, these transition zones created by redox fluctuation within the riparian zone are shaping redox hot spots. This is supported by elevated concentrations of DOC, Fe<sup>2+</sup>, and Mn<sup>2+</sup> at GW1 during summer losing conditions, fitting with the concept of the hydrological framework of redox-active compounds in aquatic systems explaining the simultaneous occurrence of ions along the redox ladder. Thus, our observed enhanced biogeochemical activity is likely to be driven by redox fluctuations (Fig. 9). Such conditions influence chemical turnover, pollutant degradation and solute transport. In redox-sensitive environments, such as the riparian zone microbial reduction processes are understood to occur in a sequence with electron acceptors becoming progressively depleted (Zehnder and Stumm, 1988). This sequence is influenced by factors such as redox potential, pH, electron acceptor availability, and the presence of bioavailable organic matter. While our study lacks direct measurements of dissolved oxygen (DO) and pH, which would have strengthened the interpretation of redox processes, we infer redox conditions based on observed concentrations of DOC, Fe<sup>2+</sup>, and Mn<sup>2+</sup>. However, point-scale measurements of DO or pH in riparian groundwater would still represent spatially mixed signals, because small-scale heterogeneity in flow paths and reaction zones are difficult to resolve with standard sampling alone (Frei et al., 2012; Knorr et al., 2009; Peiffer et al., 2021; Alewell et al., 2008). We interpret that, during summer and under counterclockwise hysteresis conditions, microbial activity is increased, although the significance tests indicate that DOC concentrations are comparable across the sampling sites and that Fe<sup>2+</sup> and Mn<sup>2+</sup> do not differ significantly between the groundwater wells. Thus, during summer and under counterclockwise hysteresis conditions the increased microbial activity and oxygen depletion is based on the co-occurrence of high DOC concentrations, elevated Fe<sup>2+</sup> and Mn<sup>2+</sup> in GW1 during losing conditions and counterclockwise hysteresis, likely driven by enhanced inflow of labile organic carbon from the stream into near-stream sediments (Smith and Arah, 1984). This is evidenced by elevated DOC concentrations and redox-sensitive solutes at GW1 (Fig. 9). However, our observations revealed the co-occurrence of nitrate, manganese, and iron in groundwater and stream samples. This simultaneous presence of multiple electron acceptors suggests that the redox sequence is not strictly adhered to under field conditions, a finding consistent with Alewell et al. (2008) and others. Such deviations from idealized zonation likely result from heterogeneous flow paths, dynamic hydraulic gradients (Peiffer et al., 2021; Frei et al., 2012; Knorr et al., 2009), and continuous mixing at the GW-SW interface (Gu et al., 2012). Such conditions foster overlapping redox states, enabling parallel rather than sequential redox processes. This highlights the riparian zone as a hydrologically and biogeochemically active layer, supporting diverse redox environments (e.g. Hill and Cardaci, 2004; Carlyle and Hill, 2001). Our findings emphasize the coexistence of multiple redox processes. The resulting shifts between oxic and anoxic conditions are critical for degrading pollutants and supporting biogeochemical activity in the riparian zone. These dynamics underscore the importance of water-stage fluctuations in enhancing chemical exchanges and microbial activity at the GW-SW boundary (Gu et al., 2012), supporting Hypothesis (3)   Counterclockwise hysteresis under summer losing conditions coincided with elevated DOC, Fe<sup>2+</sup> and Mn<sup>2+</sup> at the near-stream well, indicating that local hysteresis-driven exchange promotes redox fluctuations and the formation of riparian redox hot spots. Overall, the observed SW–GW level hysteresis reflects the system's exchange-driven redox response and should therefore be interpreted as a diagnostic indicator, not as a mechanistic explanation.</p>
</sec>
</sec>
<sec id="Ch1.S5" sec-type="conclusions">
  <label>5</label><title>Conclusion</title>
      <p id="d2e3447">Seasonal variability in GW-SW interactions at the Olewigerbach is driven by changes in discharge, hydraulic gradients, and hyporheic zone dynamics.</p>
      <p id="d2e3450">Our results demonstrate that the <inline-formula><mml:math id="M173" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index provides a useful diagnostic tool for characterizing these seasonal shifts, particularly due to its strong correspondence with independently measured hydrological turnover (HT). By analysing hysteresis between stream stage and groundwater levels in two riparian wells across 68 events, and quantifying HT for 28 of these events, we show that hysteresis reliably reflects short-term GW–SW exchange processes. We conclude our findings by the following remarks: <list list-type="bullet"><list-item>
      <p id="d2e3462">The direction and magnitude of hysteresis between stream and groundwater levels at the study site reflects distinct seasonal hydraulic states, separating winter gaining conditions from summer and drought losing conditions.</p></list-item><list-item>
      <p id="d2e3466">Event-scale hysteresis at the observed stream section tracks measured hydrological turnover, with high turnover occurring under steep losing gradients and counterclockwise loops, and low turnover under gaining conditions and clockwise loops.</p></list-item><list-item>
      <p id="d2e3470">Variations in hydraulic gradients reorganize the hyporheic zone, influencing stream water penetration into the riparian sediments shaping bank storage and mixing.</p></list-item><list-item>
      <p id="d2e3474">The chemical signatures in the stream-to-groundwater transect demonstrate that increased mixing under losing conditions is likely to promote redox-active microenvironments.</p></list-item><list-item>
      <p id="d2e3478">Together, hysteresis patterns, HT measurements, and solute data provide a coherent diagnostic framework for interpreting event based short-term GW–SW exchange processes and their biogeochemical consequences.</p></list-item></list> The <inline-formula><mml:math id="M174" display="inline"><mml:mi>h</mml:mi></mml:math></inline-formula>-Index revealed clear differences winter gaining and summer losing conditions, and drought extremes. Thus, hysteresis behaviour can serve as an indicator of changes in hyporheic zone dynamics. The chemical transects from the stream through the groundwater wells reinforced the critical role of hydraulic gradients in modulating mixing and connectivity within the riparian zone. Thus, leading to frequent shifts between oxic and anoxic conditions, critical for degrading pollutants and supporting biogeochemical activity. If long term climate trends persist, where prolonged droughts and reduced discharge magnitudes are likely to become more frequent, our study suggests that the hyporheic zone will increasingly sustain nutrient cycling and be a prominent component of GW-SW connectivity in gravel bed rivers throughout central Europe. The patterns of elevated HT and large gross exchanges observed during drought conditions indicate that turnover-induced mixing may dominate solute exchange and reaction processes under such scenarios. The observed relationship may help interpret shifts observed in future monitoring records, providing contextual information that may support water-management assessments.</p>
</sec>

      
      </body>
    <back><notes notes-type="codedataavailability"><title>Code and data availability</title>

      <p id="d2e3493">The data used to generate the figures in this study are publicly available at <ext-link xlink:href="https://doi.org/10.5281/zenodo.19771812" ext-link-type="DOI">10.5281/zenodo.19771812</ext-link> (Bäthke, 2026). Meteorological data are publicly available from the Deutscher Wetterdienst (DWD) open data portal (<uri>https://opendata.dwd.de/climate_environment/</uri>, last access: 24 June 2026). The Python code for the hysteresis index is available at <ext-link xlink:href="https://doi.org/10.5281/zenodo.3441882" ext-link-type="DOI">10.5281/zenodo.3441882</ext-link> (Jehn, 2019).</p>
  </notes><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d2e3508">LB and TS conceptualized the study. Further, LB collected and analysed field and Lab data. Both LB and TS contributed to the final version of the manuscript.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d2e3514">The contact author has declared that neither of the authors has any competing interests.</p>
  </notes><notes notes-type="disclaimer"><title>Disclaimer</title>

      <p id="d2e3520">Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. The authors bear the ultimate responsibility for providing appropriate place names. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.</p>
  </notes><ack><title>Acknowledgements</title><p id="d2e3526">The authors thank Dr. Hongkai Gao for his exceptional support during the review process and his personal initiative in handling this manuscript. We further thank Dr. Benjamin S. Gilfedder for his valuable advice. Sven Ulrich is thanked for his help during initial data acquisition. Lars Bäthke was supported by a PhD stipend of Studienwerk Villigst e. V., Germany.</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d2e3531">The publication was supported by the Open Access Fund of Universität Trier, Germany.</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d2e3537">This paper was edited by Hongkai Gao and reviewed by three anonymous referees.</p>
  </notes><ref-list>
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