# Hydro-ecological controls on dissolved carbon dynamics in groundwater and export to streams in a temperate pine forest

Loris Deirmendjian, Denis Loustau, Laurent Augusto, Sebastien Lafont, Christophe Chipeaux, Dominique Poirier, Gwenaël Abril

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## To cite this version:

Loris Deirmendjian, Denis Loustau, Laurent Augusto, Sebastien Lafont, Christophe Chipeaux, et al. Hydro-ecological controls on dissolved carbon dynamics in groundwater and export to streams in a temperate pine forest. Biogeosciences, 2018, 15 (2), pp.669 - 691. 10.5194/bg-15-669-2018. hal-01832165

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# Hydro-ecological controls on dissolved carbon dynamics in groundwater and export to streams in a temperate pine forest

Loris Deirmendjian et al.

Correspondence to: Loris Deirmendjian (lorisdeir@gmail.com)

The copyright of individual parts of the supplement might differ from the CC BY 3.0 License.
# Hydro-ecological controls on dissolved carbon dynamics in groundwater and export to streams in a temperate pine forest

Loris Deirmendjian<fnref n="1" />, Denis Loustau<fnref n="2" />, Laurent Augusto<fnref n="2" />, Sébastien Lafont<fnref n="2" />, Christophe Chipeaux<fnref n="2" />, Dominique Poirier<fnref n="1" />, and Gwenaël Abril<fnref n="1" />,<fnref n="3" />,a

**Correspondence:** Loris Deirmendjian (lorisdeir@gmail.com)

Received: 14 March 2017 – Discussion started: 12 April 2017

Revised: 10 December 2017 – Accepted: 28 December 2017 – Published: 1 February 2018

**Abstract.** We studied the export of dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) from forested shallow groundwater to first-order streams, based on groundwater and surface water sampling and hydrological data. The selected watershed was particularly convenient for such study, with a very low slope, with pine forest growing on sandy permeable podzol and with hydrology occurring exclusively through drainage of shallow groundwater (no surface runoff). A forest plot was instrumented for continuous eddy covariance measurements of precipitation, evapotranspiration, and net ecosystem exchanges of sensible and latent heat fluxes as well as CO₂ fluxes. Shallow groundwater was sampled with three piezometers located in different plots, and surface waters were sampled in six first-order streams; river discharge and drainage were modeled based on four gauging stations. On a monthly basis and on the plot scale, we found a good consistency between precipitation on the one hand and the sum of evapotranspiration, shallow groundwater storage and drainage on the other hand. DOC and DIC stocks in groundwater and exports to first-order streams varied drastically during the hydrological cycle, in relation with water table depth and amplitude. In the groundwater, DOC concentrations were maximal in winter when the water table reached the superficial organic-rich layer of the soil. In contrast, DIC (in majority excess CO₂) in groundwater showed maximum concentrations at low water table during late summer, concomitant with heterotrophic conditions of the forest

plot. Our data also suggest that a large part of the DOC mobilized at high water table was mineralized to DIC during the following months within the groundwater itself. In first-order streams, DOC and DIC followed an opposed seasonal trend similar to groundwater but with lower concentrations. On an annual basis, leaching of carbon to streams occurred as DIC and DOC in similar proportion, but DOC export occurred in majority during short periods of the highest water table, whereas DIC export was more constant throughout the year. Leaching of forest carbon to first-order streams represented a small portion (approximately 2%) of the net land CO₂ sink at the plot. In addition, approximately 75% of the DIC exported from groundwater was not found in streams, as it returned very fast to the atmosphere through CO₂ degassing.

## 1 Introduction

Since the beginning of the industrial era, human activities have greatly modified the fluxes of carbon between the atmosphere and the continents, as well as those occurring along the aquatic continuum that connect land and the coastal ocean (Ciais et al., 2013; Cole et al., 2007; Regnier et al., 2013). Globally, land (vegetation and soil) is a major reservoir of carbon that acts as a net annual sink of atmospheric CO₂, therefore modulating the climate system (Ciais et al.,

<footnote n="1">¹Laboratoire Environnements et Paléoenvironnements Océaniques et Continentaux (EPOC), CNRS, Université de Bordeaux, Allée Geoffroy Saint-Hilaire, 33615 Pessac CEDEX, France</footnote>

<footnote n="2">²INRA, UMR 1391 Interactions Sol-Plante-Atmosphère (ISPA), 33140 Villenave-d'Ornon, France</footnote>

<footnote n="3">³Departamento de Geoquímica, Universidade Federal Fluminense, Outeiro São João Batista s/n, 24020015, Niterói, RJ, Brazil</footnote>

<footnote>aalso at: Laboratoire d'Océanographie et du Climat, Expérimentations et Approches Numériques (LOCEAN), Centre IRD France-Nord, 32, Avenue Henri Varagnat, 93143 Bondy, France</footnote>
Supplementary Material

<table><caption>Table S1: Sampling dates of groundwater and surface waters. X correspond to a sampling of pCO₂, total alkalinity and DOC. HF, GS, LS and EW represent respectively high flow (Jan. 2014-Mar. 2014 and Feb. 2015-Mar. 2015), growing</caption><thead><tr><th rowspan="2">Period</th><th rowspan="2">Date</th><th colspan="3">Groundwater</th><th>Surface water</th></tr><tr><th>Piezometer Bilos</th><th>Piezometer 2</th><th>Piezometer 3</th><th>First-order streams</th></tr></thead><tbody><tr><td>HF</td><td>29/01/2014</td><td></td><td></td><td></td><td>X</td></tr><tr><td>HF</td><td>12/02/2014</td><td>X</td><td></td><td></td><td></td></tr><tr><td>HF</td><td>07/03/2014</td><td></td><td></td><td></td><td>X</td></tr><tr><td>HF</td><td>17/03/2014</td><td>X</td><td></td><td></td><td></td></tr><tr><td>GS</td><td>24/04/2014</td><td></td><td></td><td></td><td>X</td></tr><tr><td>GS</td><td>16/05/2014</td><td>X</td><td></td><td></td><td></td></tr><tr><td>GS</td><td>21/05/2014</td><td></td><td></td><td></td><td>X</td></tr><tr><td>GS</td><td>25/06/2014</td><td></td><td></td><td></td><td>X</td></tr><tr><td>GS</td><td>17/07/2014</td><td>X</td><td></td><td></td><td>X</td></tr><tr><td>GS</td><td>27/08/2014</td><td>X</td><td>X</td><td></td><td>X</td></tr><tr><td>LS</td><td>24/09/2014</td><td>X</td><td>X</td><td></td><td>X</td></tr><tr><td>LS</td><td>31/10/2014</td><td>X</td><td>X</td><td></td><td>X</td></tr><tr><td>EW</td><td>21/11/2014</td><td>X</td><td>X</td><td></td><td>X</td></tr><tr><td>EW</td><td>16/12/2014</td><td>X</td><td>X</td><td></td><td>X</td></tr><tr><td>EW</td><td>27/01/2015</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td>HF</td><td>04/03/2015</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td>GS</td><td>10/04/2015</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td>GS</td><td>07/05/2015</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td>GS</td><td>03/06/2015</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td>GS</td><td>09/07/2015</td><td>X</td><td>X</td><td>X</td><td>X</td></tr></tbody></table>
2013; Heimann and Reichstein, 2008), and is thought to offer a mitigation strategy to reduce global warming (Schimel et al., 2001). In European forests, 70% of the net land sink is sequestered in plants as woody biomass increments and 30% is sequestered in soils (Luyssaert et al., 2010). However, large uncertainty concerning the drivers and future of the soil organic carbon remains (Luyssaert et al., 2010). Therefore, investigating the mechanisms that impact storage and export of soil carbon from forest ecosystems is of first interest in both ecosystem and climate research.

Streams and small rivers are important links between terrestrial and aquatic ecosystems because they receive inputs of carbon from land and then transform these materials at the land-stream interface and in stream channels as water flows to larger rivers (McClain et al., 2003; Raymond et al., 2013). The carbon dynamics in forest stream ecosystems results from the interaction among biological activity, weathering, water infiltration, drainage and retention-mobilization mechanisms in soils (Jones and Mulholland, 1998; Kawasaki et al., 2005; Shibata et al., 2001). Indeed, biogeochemical cycling within and across the terrestrial-aquatic interface is dynamically linked to the water cycle (Battin et al., 2009; Johnson et al., 2006) because dissolved carbon is primarily mobilized and transported by the movement of water (Hagerdon et al., 2000; Hope et al., 1994; Kawasaki et al., 2005). Furthermore, numerous works in different environments came to the same conclusion that streams and small rivers are hotspots of CO2 degassing (Butman and Raymond, 2011; Johnson et al., 2008; Kokic et al., 2015; Polsenaere and Abril, 2012; Wallin et al., 2013). In small streams, the CO2 degassing flux primary results from inputs of groundwater enriched in CO2 (Hotchkiss et al., 2015), which comes from plant root respiration and from microbial respiration in soils and groundwater.

The quantification of dissolved carbon fluxes transported by water from terrestrial to aquatic environments is fundamental to resolving the carbon balance on the catchment scale (Billett et al., 2004; Jonsson et al., 2007; Kindler et al., 2011; Magin et al., 2017; Shibata et al., 2005). Leaching of carbon from terrestrial ecosystems to streams could potentially represent up to 160% of the net ecosystem exchange (NEE) in a Scotland peat catchment (Billett et al., 2004), 6% in a Sweden boreal catchment dominated by coniferous forest (Jonsson et al., 2007), on average 6% in five forest plots across Europe (Kindler et al., 2011), 2% in a Japanese temperate catchment dominated by deciduous forest (Shibata et al., 2005) or 2.7% of the net primary production in different woody and tilled subcatchments across southwestern Germany (Magin et al., 2017). Such large variations in carbon export rates are not well understood and it is therefore important to extend this investigation to other landscapes and climatic zones. More studies focused on the processes that govern the mobilization of soil carbon to surface waters are necessary to improve and predict carbon budgets in terrestrial ecosystems.

Some authors reported high concentrations of dissolved inorganic carbon (DIC) (Kawasaki et al., 2005; Venkiteswaran et al., 2014) and dissolved organic carbon (DOC) (Artinger et al., 2000; Baker et al., 2000) in forest-dominated groundwater (i.e., in the saturated zone of the soil). However, estimations of terrestrial carbon leaching from direct simultaneous measurements in groundwater and streams are scarce. These studies are generally restricted to submarine and coastal environments (Atkins et al., 2013; Sadat-Noori et al., 2016; Santos et al., 2012) and boreal lakes (Einarsdottir et al., 2017), but rarely streams. The few studies that estimate exports of carbon from forested landscapes to streams are generally (i) based on carbon observations in soil water (i.e., in the unsaturated zone of the soil) combined with a soil water model that simulates the volume of soil water leached to streams (Öquist et al., 2009; Kindler et al., 2011; Leith et al., 2015), (ii) based on carbon observations in stream combined with stream discharge (Billett et al., 2004; Dawson et al., 2002; Olefeldt et al., 2013; Shibata et al., 2001, 2005), or (iii) as described by the active pipe concept (Cole et al., 2007), as the sum of the three major riverine carbon fluxes: CO2 degassing, organic carbon burial in sediments and carbon export downstream (Jonsson et al., 2007). These studies do not provide a complete understanding of the link between carbon hydrological export and the physicochemical and biological processes occurring in soils and groundwater. In addition, the approaches based on only stream sampling may miss part of the DIC export flux as excess CO2 that might rapidly degas upstream of the sampling points (Venkiteswaran et al., 2014).

In this study, we instrumented a temperate watershed that offers the convenience of a homogeneous lithology (permeable sandy soil), vegetation (pine forest), topography (very flat coastal plain), and a simple hydrology functioning exclusively as shallow groundwater drainage. This simple configuration with no surface runoff allows us to identify what are the main factors that control the DIC-DOC leaching to streams, the DIC:DOC ratio in groundwater and streams, and their variation in space and over time. On the plot scale, we relate DIC and DOC temporal dynamics in groundwater with hydrology and metabolic activity of the forest ecosystem. On the watershed scale, we quantify DIC and DOC transfers through the groundwater-stream interface, and we describe the fate of this carbon in first-order streams.

## 2 Materials and methods

### 2.1 Study site

The Leyre watershed (2100 km²) is located in the southwest of France, in the "Landes de Gascogne" area (Fig. 1). The landscape is a very flat coastal plain with a mean slope lower than 0.125 % (generally NW-SE) (Jolivet et al., 2007), but with local gentle slopes (notably near some streams). The
5

season (Apr. 2014-Aug. 2014 and Apr. 2015-Aug. 2015), late summer (Sep. 2014-Oct. 2014 and Sep. 2015-Oct. 2015) and early winter (Nov. 2014-Jan. 2015 and Nov. 2015-Dec. 2015) periods.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Map of the Leyre watershed with topography showing the location of the gauging stations (the Grande Leyre, the Petite Leyre, the Grand Arriou and the Bourron rivers), the Bilos site, and the other sampled piezometers and first-order streams. The rain gauge and eddy tower are located at the Bilos plot. White circles indicate the first-order streams where additional discharge measurements were made in April 2014 and February 2015.</figcaption></figure>

mean altitude is lower than 50 m (Fig. 1) (Jolivet et al., 2007). The lithology is relatively homogeneous and composed of sandy permeable surface layers dating from the Pliocene–Quaternary period (Bertran et al., 2009, 2011; Legigan, 1979).

The podzolic soil is characterized by a low pH (4), low nutrient availability, and a high organic carbon content that can reach 55 $g kg^{-1}$ of soil (Augusto et al., 2010). Three types of podzols are present: wet Landes (humic podzol), mesophyllous Landes (duric podzol) and dry Landes (loose podzol), which represent 47, 36 and 17 % of the watershed area, respectively (Augusto et al., 2010; Jolivet et al., 2007). Moreover, there is a gradient of soil carbon content from dry Landes ($C = 6$ to $17 kg m^{-2}$) to mesophyllous Landes ($C = 13$ to $30 kg m^{-2}$) and wet Landes ($C = 15$ to $30 kg m^{-2}$) (Augusto et al., 2010). In the dry Landes of the upper parts of the watershed, the water table is always more than 2 m deep.

In the wet Landes of the lower parts, and in the vast interfluves, the groundwater is found near the soil surface in winter (0.0–0.5 m depth) and generally remains 1.0–1.5 m deep in summer. The mesophyllous Landes corresponds to the intermediate situation (Augusto et al., 2006).

The region was a vast wetland until the 19th century, when a wide forest of maritime pine (*Pinus pinaster*) was sown, following landscape drainage in 1850. Currently, the catchment is mainly occupied by pine forest (approximately 80 %), with a modest proportion of croplands (approximately 15 %) (Jolivet et al., 2007). The typical rotation period of pine forest is ~40 years, ending in clear-cutting, tilling and replanting (Kowalski et al., 2003). The climate is oceanic with a mean annual air temperature of 13 °C and a mean annual precipitation of 930 mm (Moreaux et al., 2011). Moreover, the average annual evapotranspiration of maritime pine is in the range of 234–570 mm (Govind et al., 2012). Owing to the low slope and the high permeability of the soil (hydraulic conductivity is approximately $40 cm h^{-1}$; Corbier et al., 2010), the infiltration of rain water is fast ($55 cm h^{-1}$ on average; Vernier and Castro, 2010) and surface runoff does not occur, as the excess rainfall percolates into the soil and recharges the shallow groundwater, causing the water table to rise. Moreover, very low content in feldspars and all over clay minerals in the sandy podzols induces a low water soil retention (Augusto et al., 2010). The superficial sandy soil contains a free and continuous water table strongly interconnected with the superficial river network; drainage is also facilitated by a dense network of drainage ditches built in the 19th century and currently maintained by forest managers in order to optimize tree growth (Thivolle-Cazat and Najar, 2001). In this study, we sampled first-order streams defined as streams and ditches with no tributaries and/or being seasonally dry.

## 2.2 Eddy covariance measurements on the forest plot scale

To quantify exchanges of carbon and water between the atmosphere and the pine forest plot, we used the site of Bilos (Fig. 1) (0.6 km$^2$, 44°29′38.08″ N, 0°57′21.9″ W, altitude: 40 m) as part of the ICOS research infrastructure (http://icos-ri.eu). In December 1999, the 50-year-old pine forest was clear-cut (Kowalski et al., 2003). The site was ploughed to 30 cm depth and fertilized with 60 kg of $P_2O_5$ per ha in 2001 (Moreaux et al., 2011). In November 2004, the site was divided into two parts, which were seeded with maritime pine (*Pinus pinaster*) with a 1-year lag in 2004 and 2005 (Moreaux et al., 2011). The forest plot was thus 10 and 11 years old during our sampling. The site was equipped with an eddy covariance measurement system soon after clear-cutting, and the system has been maintained since. The eddy covariance technique allows us to continuously determine the exchange of sensible heat, $CO_2$ and $H_2O$ between the ecosystem and the atmosphere by measuring the turbulent-scale covariance
<table><caption>Table S2: Periods of calculation for carbon stocks, carbon exports and carbon degassing. X corresponds to a calculation.</caption><thead><tr><th rowspan="2">Period</th><th rowspan="2">Date</th><th colspan="2">DIC<sub>stock</sub> and DOC<sub>stock</sub></th><th colspan="2">DIC<sub>export</sub> and DOC<sub>export</sub></th><th rowspan="2">F<sub>degass</sub></th></tr><tr><th>Piezometer Bilos</th><th>Piezometer Bilos</th><th>Piezometer 2</th><th>Piezometer 3</th><th>Streams</th></tr></thead><tbody><tr><td>HF</td><td>12/02/14 and 17/03/14</td><td>X</td><td>X</td><td></td><td></td><td>X<sup>a</sup></td></tr><tr><td>GS</td><td>17/03/14 and 16/05/14</td><td>X</td><td>X</td><td></td><td></td><td>X<sup>b</sup></td></tr><tr><td>GS</td><td>16/05/14 and 17/07/14</td><td>X</td><td>X</td><td></td><td></td><td>X<sup>c</sup></td></tr><tr><td>GS</td><td>17/07/14 and 27/08/14</td><td>X</td><td>X</td><td></td><td></td><td>X</td></tr><tr><td>LS</td><td>27/08/14 and 24/09/14</td><td>X</td><td>X</td><td>X</td><td></td><td>X</td></tr><tr><td>LS</td><td>24/09/14 and 31/10/14</td><td>X</td><td>X</td><td>X</td><td></td><td>X</td></tr><tr><td>EW</td><td>31/10/14 and 21/11/14</td><td>X</td><td>X</td><td>X</td><td></td><td>X</td></tr><tr><td>EW</td><td>21/11/14 and 16/12/14</td><td>X</td><td>X</td><td>X</td><td></td><td>X</td></tr><tr><td>EW</td><td>16/12/14 and 27/01/15</td><td>X</td><td>X</td><td>X</td><td></td><td>X</td></tr><tr><td>HF</td><td>27/01/15 and 04/03/15</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td>HF</td><td>04/03/15 and 10/04/15</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td>GS</td><td>10/04/15 and 07/05/15</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td>GS</td><td>07/05/15 and 03/06/15</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td>GS</td><td>03/06/15 and 09/07/15</td><td>X</td><td>X</td><td>X</td><td>X</td><td>X</td></tr></tbody></table>

a, b, c for these periods the day of sampling of surface waters do not correspond exactly to the day of sampling of groundwater (Tab. S1). Carbon stocks in groundwater can be calculated only for Bilos plot since we do not have data about the total height of the permeable layer in the other plots. HF, GS, LS and EW represent respectively high flow (Jan. 2014-Mar. 2014 and Feb. 2015-Mar. 2015), growing season (Apr. 2014-Aug. 2014 and Apr. 2015-Aug. 2015), late summer (Sep. 2014-Oct. 2014 and Sep. 2015-Oct. 2015) and early winter (Nov. 2014-Jan. 2015 and Nov. 2015-Dec. 2015) periods.

10
between vertical wind velocity and the scalar concentration of sensible heat, CO₂ and H₂O.

Wind velocity, temperature and CO₂-water vapor fluctuations were measured with a sonic anemometer (model R3, Gill instruments Lymington, UK) and an open-path dual CO₂-H₂O infrared gas analyzer (model Li7500, LI-COR, Lincoln, USA) at the top of a 9.6 m tower (1 January to 10 May 2014) and with another sonic anemometer (model HS50, Gill instruments) and an enclosed dual CO₂-H₂O infrared gas analyzer (model Li7200, LI-COR®) at the top of a 15 m tower (9 July 2014 to 31 December 2015). Consequently, there were no eddy covariance measurements available between 11 May and 8 July 2014 and thus between these two dates the latent heat fluxes were determined following the procedure of Thornthwaite (1948).

Raw data were processed following a standard methodology (Aubinet et al., 1999). The post-processing software EddyPro v6.0 (http://www.licor.com) was used to treat raw data and compute average fluxes (30 min period) by applying the following steps: (1) spike removal in anemometer or gas analyzer data using statistical analysis; (2) coordinating rotation to align the coordinate system with the stream lines of the 30 min averages; (3) blocking average detrending of sonic temperature, H₂O and CO₂ channels; (4) determining time lag values for H₂O and CO₂ channels using a cross-correlation procedure; (5) computing mean values, turbulent fluxes and characteristic parameters; and (6) making spectral corrections (Ibrom et al., 2007). Thereafter, CO₂ and H₂O fluxes were filtered in order to remove points corresponding to technical problems, meteorological conditions not satisfying eddy correlation theory or data out of realistic bounds. Different statistical tests were applied for this filtering: stationarity and turbulent conditions were tested with the steady-state test and the turbulence characteristic test recommended by Foken and Wichura (1996) and Kaimal and Finnigan (1994). Only values of CO₂ and H₂O fluxes that pass all the filters were retained. Then, missing values of CO₂ and H₂O fluxes were gap-filled. The NEE of CO₂ was partitioned into two components, gross primary production (GPP) and ecosystem respiration (R<sub>eco</sub>), with the R package REddyProc (version 0.8-2) by applying the following steps (Reichstein et al., 2005).

(i) During nighttime GPP = 0 so NEE = R<sub>eco</sub>. (ii) Statistical regression between R<sub>eco</sub> and nighttime air temperature and meteorological conditions is adjusted with a Arrhenius-type equation (Lloyd and Taylor, 1994). (iii) Daytime R<sub>eco</sub> is obtained by extrapolating nighttime fluxes using the temperature response. (iv) GPP is calculated as the difference between daytime NEE and R<sub>eco</sub>; additional checks are performed to avoid unrealistic values of GPP. Finally, a positive NEE indicates an upward flux whereas a negative NEE indicates a downward flux; GPP is positive or zero and R<sub>eco</sub> is positive. NEE = R<sub>eco</sub> - GPP.

## 2.3 Groundwater and surface water monitoring

To compare groundwater carbon dynamics on both the plot and the watershed scales, we selected three piezometers in different forest types (Fig. 1). According to the depth and amplitude of the water table, the three piezometers were representative of dry Landes (piezometer 2), mesophyllous Landes (piezometer 3) and a situation between mesophyllous and wet Landes (piezometer Bilos). Moreover, the piezometer 2 is located in a riparian mixed pine and oak forest near a first-order stream whereas piezometer 3 is located in another pine forest (approximately the same age as the Bilos pine forest). We also selected six first-order streams whose water-sheds were dominated largely by pine forest (~90%), which limits biogeochemical signal from crops. Shallow groundwater and stream waters were sampled for partial pressure of CO₂ (pCO₂), total alkalinity and DOC with approximately monthly time intervals (Table S1 in the Supplement).

## 2.4 Chemical analysis

We measured the pCO₂ directly in the field and total alkalinity and DOC back in the laboratory. The pCO₂ in the groundwater and streams was measured directly using an equilibrator (Frankignoulle and Borges, 2001; Polsenaere et al., 2013). This equilibrator was connected to an infrared gas analyzer (LI-COR®, LI-820), which was calibrated one day before sampling, on two linear segments because of its nonlinear response in the range of observed pCO₂ values (0–90 000 ppmv). This nonlinearity was due to saturation of the infrared cell at pCO₂ values above 20 000 ppmv. We used certified standards (Air Liquide™ France) of 2079 ± 42; 19 500 ± 390 and 90 200 ± 1800 ppmv, as well as nitrogen flowing through soda lime for zero. For the first linear segment (0–20 000 ppmv), which corresponded to the river waters, we set the LI-COR (LI-820) to zero and spanned the LI-COR (LI-820) at 19 500 ppmv, and then we checked for linearity at 2042 ppmv. For the second segment (20 000–90 000 ppmv), which corresponded to the sampled groundwater, we measured the response of the LI-COR (LI-820) with the standard at 90 000 ppmv and used this measured value to make a post correction of the measured value in the field. Before sampling, the groundwater was pumped from the piezometer during the time necessary to obtain stable readings with portable probes of electrical conductivity, temperature, pH and dissolved oxygen concentration.

Total alkalinity was analyzed using automated electrolysis on 50 mL filtered samples with 0.1N HCl as the titrant. The equivalence point was determined to be from a pH between 4 and 3 with the Gran method (Gran, 1952). The precision based on replicate analyses was better than ±5 µM. For samples with a very low pH (<4.5), we bubbled the water with atmospheric air in order to degas the CO₂. Consequently, the initial pH increased above the value of 5, and total alkalinity titration could be performed (Abril
<figure><img src="image_3.png" /></figure>
et al., 2015). We calculated DIC from pCO₂, total alkalinity and temperature measurements using carbonic acid dissociation constants of Millero (1979) and the CO₂ solubility from Weiss (1974) as implemented in the CO₂SYS program (Lewis et al., 1998). Contrary to the pCO₂ calculation from pH and total alkalinity (Abril et al., 2015), the DIC calculation from measured pCO₂ and total alkalinity was weakly affected by the presence of organic alkalinity because 80 ± 20% of DIC in our samples was dissolved CO₂. The DOC samples were obtained in the field through pre-combusted GF/F filters after filtration (porosity of 0.7 µm). The samples were acidified with 50 µL of HCl 37% to reach a pH of 2 and stored in pre-combusted Pyrex 25 mL vials at 4 °C in the dark before analysis. The DOC concentrations were measured with a Shimadzu TOC 500 analyzer with repeatability better than 0.1 mg L⁻¹.

## 2.5 Hydrological monitoring

The precipitation was measured continuously at the Bilos plot using automatic rain gauges with a 30 min integration: one tipping bucket rain gauge SBS500 (Campbell Scientific, Logan, USA) was located in a small clear-cut area at 3 m above ground from 1 January to 10 May 2014 and one total rain weighing sensor, TRwS 405 (MPS system, Bratislava, Slovakia) was located at the top of the canopy on a 6 m tower from 1 July 2014 to 31 December 2015. Hence, between 11 May and 31 June 2014, no precipitation measurements were available at the Bilos site. Thus, during this period, we used data from Météo France© at Belin-Béliet (approximately 30 km from the Bilos site). The precipitation measurements were also checked weekly in the field with manual reports.

The groundwater table depth was measured continuously at the Bilos plot using high-performance level pressure sensors (PDCR/PTX 1830, Druck and CS451451, Campbell Scientific) in one piezometer located at the Bilos site. Temperature and air pressure fluctuations were fully compensated for in the pressure measurements. The measurements were obtained at 60 s intervals and integrated in a 30 min period. They were checked weekly with a manual probe. The groundwater table depth was also measured punctually with a manual piezometric probe in piezometers 2 and 3 before each groundwater sampling.

Our study benefited from four calibrated gauging stations of DIREN (French water survey agency), with a daily temporal resolution, located at two second-order streams (Bourron and Grand Arriou rivers), one third-order stream (Pétite Leyre river) and one fourth-order stream (Grande Leyre river) (Fig. 1). We also performed additional discharge measurements in first-order streams (Fig. 1). For each stream order, we calculated the drainage with a daily temporal resolution for a 2-year period (i.e., discharge divided by the corresponding catchment area, in m³ km⁻² day⁻¹ or in mm day⁻¹) (Deirmendjian and Abril, 2018). We then determined the increase in drainage between two streams of successive orders. Because of the specific characteristics of the Leyre watershed with no surface runoff, we observed a regular increase in drainage values between two streams of successive orders. In addition, the proportion of additional drainage occurring in each stream order was relatively constant temporally. Our analysis based on daily discharge monitoring in second-, third-, and fourth-order streams and seasonal gauging of first-order streams revealed that monthly drainage values in first-order streams were on average 2.3 times lower than those measured in fourth-order streams and allowed us to reconstruct robust monthly drainage values in first-order streams (Deirmendjian and Abril, 2018). We wrote the water mass balance equation at the Bilos forest plot as follows:

$$P = D + ETR + GWS + \Delta S, \quad (1)$$

where P, D, ETR, GWS and ΔS were, respectively, precipitation, drainage, evapotranspiration, groundwater storage and change of soil water content in the unsaturated zone, all expressed in millimeters per day. P was the cumulative precipitation measured over a given period t at the Bilos site. D was the drainage at the Bilos site deduced from daily observation at four gauging stations and the hydrological model (Deirmendjian and Abril, 2018). ETR was the cumulative evapotranspiration obtained from eddy covariance measurements of latent heat fluxes over a period t at the Bilos site. GWS was calculated as the net change in water table depth over the period t times the soil effective porosity at the Bilos site of 0.2 (Augusto et al., 2010; Moreaux et al., 2011). Finally, no reliable measurements of soil water content were available, and with the ΔS term likely being small, the variation in soil water content in the unsaturated zone was neglected in the water mass balance.

## 2.6 Carbon stocks in groundwater, exports to streams and degassing to the atmosphere

We calculated four different terms that describe the dynamics of carbon at the Bilos plot: the stocks of DIC (DICstock) and DOC (DOCstock) in groundwater and the exports of DIC (DICexport) and DOC (DOCexport) from groundwater to first-order streams, all integrated between two sampling dates (Table S2). Because we do not know the total height of the permeable surface soil layer in piezometers 2 and 3, we calculated the stocks of carbon in the groundwater only at the Bilos site. However, in order to account for spatial differences among the dry, mesophyllous and wet Landes, specific DIC and DOC exports were calculated for the three study site piezometers. We wrote

$$\text{DIC}_{\text{stock}} = (S_i + S_f) / 2 = (\text{DIC}_i \times V_i + \text{DIC}_f \times V_f) / 2, \quad (2)$$

where DICstock was the mean stock of DIC in groundwater between two sampling dates in millimoles per square meter. Sf and Si were the final and the initial stocks of DIC in
<table><caption>**Table 1.** Water budget on the Bilos plot scale for the years 2014 and 2015, as well as for high flow (January–March 2014 and February–March 2015), growing season (April–August 2014 and April–August 2015), late summer (September–October 2014 and September–October 2015) and early winter (November 2014–January 2015 and November–December 2015). Numbers represent the mean ± SD and the range (in square brackets).</caption><thead><tr><th></th><th>Precipitation<br>(mm day<sup>-1</sup>)</th><th>Evapotranspiration<br>(mm day<sup>-1</sup>)</th><th>Drainage<br>(mm day<sup>-1</sup>)</th><th>Groundwater storage<br>(mm day<sup>-1</sup>)</th></tr></thead><tbody><tr><td>2014</td><td>3.0 ± 2.1<br>[0.2–8.0]</td><td>2.5 ± 1.4<br>[0.3–5.3]</td><td>0.5 ± 0.5<br>[0.1–1.9]</td><td>-0.2 ± 2.3<br>[-2.9–4.5]</td></tr><tr><td>2015</td><td>1.9 ± 1.2<br>[0.2–4.1]</td><td>1.7 ± 1.0<br>[0.3–3.4]</td><td>0.3 ± 0.3<br>[0.1–0.9]</td><td>-0.5 ± 1.9<br>[-3.1–2.6]</td></tr><tr><td>High flow</td><td>4.7 ± 2.1<br>[2.2–8.0]</td><td>2.4 ± 1.0<br>[0.9–3.6]</td><td>1.1 ± 0.4<br>[0.7–1.9]</td><td>-0.2<br>[-2.9–4.0]</td></tr><tr><td>Growing season</td><td>1.8 ± 0.8<br>[0.8–2.9]</td><td>3.0 ± 0.9<br>[1.6–5.3]</td><td>0.3 ± 0.2<br>[0.1–0.7]</td><td>-1.9<br>[-3.1–0.5]</td></tr><tr><td>Late summer</td><td>1.1 ± 0.5<br>[0.2–1.5]</td><td>1.5 ± 0.5<br>[1.0–2.2]</td><td>0.1 ± 0.007<br>[0.1–0.1]</td><td>0.1<br>[-1.2–0.7]</td></tr><tr><td>Early winter</td><td>2.7 ± 1.5<br>[0.2–4.7]</td><td>0.5 ± 0.2<br>[0.3–0.7]</td><td>0.2 ± 0.07<br>[0.1–0.3]</td><td>1.9<br>[0.7–4.5]</td></tr></tbody></table>

groundwater in millimoles per square meter. DICᵢ and DICf were the initial and the final concentration of DIC in groundwater in millimoles per cubic meter, respectively. Vᵢ and Vf were the initial and the final volumes of groundwater in cubic meters per square meter. The volume of groundwater (V) was calculated as follows:

$$V = (h + H) \times \Phi_{\text{effective}}, \qquad (3)$$

where *h* and *H* (*H* is negative) were the total height of the permeable surface layer (equals to 10 m; Corbier et al., 2010) and the height of groundwater table, respectively. Φ<sub>effective</sub> was the effective porosity of the soil and it was equal to 0.2. Export of DIC in first-order streams through drainage of shallow groundwater was calculated from discharge and concentration as follows:

$$\text{DIC}_{\text{export}} = D \times (\text{DIC}_{\text{i}} + \text{DIC}_{\text{f}}) / 2, \qquad (4)$$

where *D* was the mean drainage of shallow groundwater by first-order streams between the initial and the final sampling dates in meters per day. DICᵢ and DICf were the initial and final concentrations of DIC in groundwater in millimoles per cubic meter. We calculated DOC<sub>stock</sub> and DOC<sub>export</sub> in the same manner as DIC<sub>stock</sub> and DIC<sub>export</sub>. In addition, we also calculated the DIC exported from first-order streams to second-order streams by replacing the concentrations of carbon in the groundwater with the carbon concentrations in first-order streams in Eq. (4). Between two sampling dates, the degassing of CO₂ in first-order streams could thus be obtained from the difference between the DIC exported from groundwater and from first-order streams.

# 3 Results

## 3.1 Hydrological parameters and water mass balance

Water mass balance at the Bilos site was calculated on a monthly basis over a 2-year period (2014–2015) (Table 1; Figs. 2c–3). Monthly precipitation on the one hand and the sum of evapotranspiration, groundwater storage and drainage on the other hand closely followed the 1:1 line (Fig. 3), showing the consistency of the water mass balance estimated with different techniques and independent devices, even with a monthly temporal resolution insufficient to account for very sudden processes. During the years 2014 and 2015, we could define four different hydrological periods that were high-flow, growing season, late summer and early winter periods (Fig. 2). High-flow periods were characterized by two relatively short flood events in January–March 2014 (peak of 120 m³ s⁻¹) and in February–March 2015 (peak of 80 m³ s⁻¹), high drainage values (maximum of 1.9 mm day⁻¹ in February 2014) and a water table close to the soil surface (Table 1; Fig. 2a, c). These short periods of high flow in winter were followed by the forest growing season in spring and summer in May 2014–August 2014 and April–August 2015 characterized by the highest GPP and R<sub>eco</sub> (peak of 880 and 660 mmol m⁻² day⁻¹, respectively, in May 2015) and highest evapotranspiration (peak of 5.3 mm day⁻¹ in April 2014); during this forest growing period, the groundwater table decreased and groundwater storage was negative (Tables 1, 3; Fig. 2). Growing season periods were followed by late summer periods that were characterized by low precipitations (minimum of 0.2 mm day⁻¹ in September 2014) and the lowest groundwater table depth in September–October 2014 and in September–October 2015 (Table 1; Fig. 2a, c). Then, late summer periods were fol-
<figure><img src="image_4.png" /></figure>

**Figure 2.** Seasonal variations in hydrological parameters in the Leyre watershed. (a) Discharge of the Grande Leyre, the Petite Leyre, the Grand Arriou and the Bourron rivers associated with water table at the Bilos site. (b) Metabolic parameters (NEE, GPP, $R_{eco}$) estimated at the Bilos site. (c) Monthly precipitation, evapotranspiration and groundwater storage at the Bilos site as well as the drainage of first-order streams. Inputs of water (precipitation and positive groundwater storage) in the studied ecosystem are represented on a positive scale whereas outputs of water (drainage, evapotranspiration and negative groundwater storage) are represented on a negative scale. HF, GS, LS and EW represent high-flow (January–March 2014 and February–March 2015), growing season (April–August 2014 and April–August 2015), late summer (September–October 2014 and September–October 2015) and early winter (November 2014–January 2015 and November–December 2015) periods, respectively.

lowed by early winter periods that were associated with heavy precipitations (peak of 4.7 mm day⁻¹ in November 2014) and a rising groundwater table (positive groundwater storage) in November 2014–January 2015 and in November–December 2015 (Table 1; Fig. 2a, c). We considered that growing season and late summer and early winter periods, merged together represented periods of base flow.

Periods of groundwater discharge with negative groundwater storage (February–September 2014 and March–August 2015) were characterized by evapotranspiration higher than precipitation (Fig. 2a, c). Conversely, periods of groundwater recharge with positive groundwater storage

(October 2014–February 2015 and September–December 2015) were characterized by precipitations higher than evapotranspiration (Fig. 2a, c). Consequently, on the plot scale, significant correlation between groundwater storage and precipitations and between groundwater storage and evapotranspiration were observed (Table 2), attesting to evapotranspiration and precipitation playing a significant role in the groundwater storage.
<table><caption>**Table 2.** Linear correlation (Pearson) between the studied parameters on the Bilos plot scale during the sampling period. Numbers represent the Pearson's correlation coefficient at the Bilos plot between mean carbon concentrations (mmol m⁻³) in the Bilos groundwater and in the six first-order streams, carbon stocks (mmol m⁻² day⁻¹), carbon exports (mmol m⁻² day⁻¹), carbon degassing (mmol m⁻² day⁻¹) in the six first-order streams, hydrological parameters (mm day⁻¹, which are *P*, *GWS*, *ETR* and *D* for precipitation, groundwater storage, evapotranspiration and drainage, respectively), water table depth (mm) and metabolic parameters (mmol m⁻² day⁻¹). Here, degassing was calculated from the DIC data of the Bilos groundwater only. Each parameter was integrated between two sampling dates (Table S2). Values in bold font indicate correlation with a *p* value <0.05, whereas values in italic font indicate correlation with a *p* value <0.001.</caption><thead><tr><th colspan="2">Concentration in groundwater (mmol m⁻³)</th><th colspan="2">Concentration in streams (mmol m⁻³)</th><th colspan="2">Stock in groundwater (mmol m⁻²)</th><th colspan="2">Export from groundwater to streams (mmol m⁻² day⁻¹)</th><th colspan="2">Degassing in streams (mmol m⁻² day⁻¹)</th><th colspan="3">Hydrological parameters (mm day⁻¹)</th><th colspan="2">Water table depth (mm)</th><th colspan="2">Metabolic parameters (mmol m⁻² day⁻¹)</th></tr><tr><th>DIC<sub>gw</sub></th><th>DOC<sub>gw</sub></th><th>DIC<sub>stream</sub></th><th>DOC<sub>stream</sub></th><th>DIC<sub>stock</sub></th><th>DOC<sub>stock</sub></th><th>DIC<sub>export</sub></th><th>DOC<sub>export</sub></th><th>F<sub>degass</sub></th><th>P</th><th>GWS</th><th>ETR</th><th>D</th><th>H</th><th>NEE</th><th>GPP</th><th>R</th></tr></thead><tbody><tr><td>DIC<sub>gw</sub></td><td>1</td><td>0.86</td><td>-0.34</td><td>0.99</td><td><strong>-0.65</strong></td><td>-0.44</td><td><strong>-0.62</strong></td><td>-0.48</td><td>-0.02</td><td>0.45</td><td>-0.41</td><td><strong>-0.68</strong></td><td>-0.83</td><td><strong>0.52</strong></td><td>-0.31</td><td>-0.09</td><td></td></tr><tr><td>DOC<sub>gw</sub></td><td></td><td>-0.41</td><td>0.43</td><td><strong>-0.64</strong></td><td>0.98</td><td><strong>0.69</strong></td><td>0.95</td><td><strong>0.56</strong></td><td>0.17</td><td>-0.28</td><td>0.41</td><td>0.93</td><td>0.85</td><td>-0.19</td><td>-0.13</td><td>-0.36</td><td></td></tr><tr><td>DIC<sub>stream</sub></td><td></td><td>1</td><td><strong>-0.55</strong></td><td>0.82</td><td>-0.42</td><td>-0.35</td><td>-0.34</td><td><strong>-0.54</strong></td><td>-0.14</td><td>0.23</td><td>-0.25</td><td>-0.44</td><td><strong>-0.75</strong></td><td>0.43</td><td>-0.32</td><td>-0.16</td><td></td></tr><tr><td>DOC<sub>stream</sub></td><td></td><td></td><td>1</td><td>-0.33</td><td>0.45</td><td>0.45</td><td>0.38</td><td><strong>0.66</strong></td><td>0.30</td><td>0.35</td><td>-0.39</td><td>0.46</td><td><strong>-0.70</strong></td><td>0.15</td><td>-0.41</td><td><strong>-0.53</strong></td><td></td></tr><tr><td>DIC<sub>stock</sub></td><td></td><td></td><td></td><td></td><td><strong>-0.63</strong></td><td>0.37</td><td><strong>-0.62</strong></td><td>-0.44</td><td>-0.04</td><td>0.45</td><td>-0.44</td><td><strong>0.67</strong></td><td><strong>-0.79</strong></td><td>-0.48</td><td>0.28</td><td>-0.07</td><td></td></tr><tr><td>DOC<sub>stock</sub></td><td></td><td></td><td></td><td></td><td></td><td>-0.82</td><td>0.97</td><td><strong>0.67</strong></td><td>0.21</td><td>-0.23</td><td>0.32</td><td>-0.97</td><td>0.88</td><td>0.20</td><td>-0.14</td><td>-0.39</td><td></td></tr><tr><td>DIC<sub>export</sub></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td><strong>0.72</strong></td><td>0.86</td><td>0.26</td><td>0.01</td><td>0.02</td><td>0.83</td><td><strong>0.76</strong></td><td>-0.18</td><td>-0.15</td><td>-0.39</td><td></td></tr><tr><td>DOC<sub>export</sub></td><td></td><td></td><td></td><td></td><td></td><td></td><td><strong>0.57</strong></td><td>0.24</td><td>-0.22</td><td>0.28</td><td>0.98</td><td>0.87</td><td>0.78</td><td>0.06</td><td>-0.35</td><td><strong>-0.50</strong></td><td></td></tr><tr><td>F<sub>degass</sub></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0.45</td><td>0.24</td><td>0.17</td><td><strong>0.70</strong></td><td>0.23</td><td>0.33</td><td>-0.44</td><td>-0.43</td><td></td></tr><tr><td>GWS</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td><strong>0.76</strong></td><td>-0.30</td><td>0.29</td><td>-0.16</td><td>-0.15</td><td><strong>0.62</strong></td><td><strong>-0.63</strong></td><td><strong>-0.51</strong></td><td></td></tr><tr><td>ETR</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>-0.73</td><td>-0.22</td><td>0.88</td><td>0.17</td><td><strong>-0.63</strong></td><td><strong>0.63</strong></td><td><strong>0.50</strong></td><td></td></tr><tr><td>D</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>-0.23</td><td>-0.15</td><td>-0.41</td><td>-0.27</td><td>-0.06</td><td>0.31</td><td></td></tr><tr><td>H</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>-0.27</td><td>-0.06</td><td>0.31</td><td>-0.85</td><td><strong>-0.55</strong></td><td></td></tr><tr><td>NEE</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>-0.85</td><td>0.97</td><td><strong>-0.55</strong></td><td></td><td></td></tr><tr><td>GPP</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1</td><td>0.97</td><td></td><td></td><td></td></tr><tr><td>R</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><caption>**Table 3.** Metabolic parameters (GPP, $R_{eco}$ and NEE) estimated at the Bilos plot with the eddy covariance techniques. Numbers represent the mean ± SD and the range (between square brackets) for the years 2014–2015 and for high-flow (January–March 2014 and February–March 2015), growing season (April–August 2014 and April–August 2015), late summer (September–October 2014 and September–October 2015) and early winter (November 2014–January 2015 and November–December 2015) periods. Positive NEE indicates an upward flux whereas a negative NEE indicates a downward flux; GPP is positive or zero and $R_{eco}$ is positive. NEE = $R_{eco}$. – GPP.</caption><thead><tr><th></th><th>GPP<br>(mmol m<sup>-2</sup> day<sup>-1</sup>)</th><th><i>R</i><sub>eco</sub><br>(mmol m<sup>-2</sup> day<sup>-1</sup>)</th><th>NEE<br>(mmol m<sup>-2</sup> day<sup>-1</sup>)</th></tr></thead><tbody><tr><td>2014–2015</td><td>400 ± 210<br>[160–880]</td><td>310 ± 150<br>[110–660]</td><td>-90 ± 110<br>[-340–100]</td></tr><tr><td>High flow</td><td>300 ± 80<br>[180–420]</td><td>180 ± 50<br>[105–260]</td><td>-120 ± 50<br>[-160–30]</td></tr><tr><td>Growing season</td><td>640 ± 150<br>[380–880]</td><td>490 ± 100<br>[320–640]</td><td>-160 ± 140<br>[-330–100]</td></tr><tr><td>Late summer</td><td>350 ± 120<br>[240–540]</td><td>300 ± 80<br>[200–410]</td><td>-50 ± 60<br>[-160–10]</td></tr><tr><td>Early winter</td><td>210 ± 30<br>[160–260]</td><td>230 ± 50<br>[170–320]</td><td>20 ± 20<br>[-10–65]</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Monthly water mass balance at the Bilos site for the 2014–2015 period. Pearson coefficient $R = 0.85$ and $p$ value < 0.001. Blue points represent months when groundwater storage was extremely negative in March 2014, April 2014, March 2015, April 2015, June 2015 and July 2015 (Fig. 2c). These blue points are further away from the 1:1 line than the other months (represented in black). The drainage of the Leyre river is delayed compared to the drainage of the Bilos plot. Thus, when the loss of groundwater is extremely high (negative groundwater storage), estimated drainage does not correspond exactly to the measured groundwater storage.</figcaption></figure>

## 3.2 Net ecosystem exchange of CO₂ in the forest plot (Bilos plot)

GPP, $R_{eco}$ and NEE exhibited a strong seasonal variability (Table 3; Fig. 2b). GPP, $R_{eco}$ and NEE were 400 ± 220, 310 ± 150 and -90 ± 110 mmol m⁻² day⁻¹, respectively, throughout the years 2014 and 2015 (we excluded the 16 May–7 July 2014 period when no eddy covariance measurements were available), equivalent to 1750 ± 960, 1360 ± 660 and 390 ± 480 g C m⁻² yr⁻¹ (Table 3; Fig. 2b). These results were close to Moreaux et al. (2011) estimates of 1720, 1480 and 340 g C m⁻² yr⁻¹ as measured at a younger forest stage in the same forest plot. GPP increased from early winter (210 ± 30 mmol m⁻² day⁻¹) to growing season (640 ± 150 mmol m⁻² day⁻¹) periods (Table 3; Fig. 2b). $R_{eco}$ followed the same temporal trend (Table 3; Fig. 2b). During late summer and early winter periods, NEE could be positive ($R_{eco} >$ GPP), meaning that the pine forest ecosystems had switched from autotrophic to heterotrophic metabolism, notably in October, November and December 2014 (Table 3; Fig. 2b). NEE was always negative ($R_{eco} <$ GPP) during high-flow and growing season periods, except in July 2015, probably as a consequence of temporary low precipitation (Table 3; Fig. 2b–c).

## 3.3 Dissolved carbon evolution in shallow groundwater

In shallow groundwater, total alkalinity was low and originated from slow weathering of silicate minerals with vegetation-derived CO₂ (Polsenaere and Abril, 2012). The mean proportion of total alkalinity in the DIC pool in shallow groundwater was 5%, the large majority of the DIC being composed of dissolved CO₂ resulting from microbial and plant root respiration in the soil. Although the sampling frequency was monthly or more, it allowed the detection of significant changes in the groundwater DIC and DOC con-
<table><caption>**Table 4.** Carbon concentrations in the sampled groundwater and in the sampled first-order streams during the sampling period (January 2014–July 2015) for high-flow (January–March 2014 and February–March 2015), growing season (April–August 2014 and April–August 2015), late summer (September–October 2014 and September–October 2015) and early winter (November 2014–January 2015 and November–December 2015) periods. Numbers represent the mean ± SD, the range (between square brackets) and the number (N) of samples for each hydrological period.</caption><thead><tr><th rowspan="2"></th><th colspan="4">DOC (mmol m<sup>-3</sup>)</th><th colspan="4">DIC (mmol m<sup>-3</sup>)</th></tr><tr><th>Piezometer Bilos</th><th>Piezometer 2</th><th>Piezometer 3</th><th>Streams</th><th>Piezometer Bilos</th><th>Piezometer 2</th><th>Piezometer 3</th><th>Streams</th></tr></thead><tbody><tr><td>High flow</td><td>3500 ± 200<br>[3200–3700]</td><td>280</td><td>1500</td><td>490 ± 10<br>[460–510]</td><td>1160 ± 470<br>[570–1700]</td><td>1380</td><td>1510</td><td>280 ± 40<br>[220–310]</td></tr><tr><td></td><td>N = 3</td><td>N = 1</td><td>N = 1</td><td>N = 15</td><td>N = 3</td><td>N = 1</td><td>N = 1</td><td>N = 15</td></tr><tr><td>Growing season</td><td>750 ± 440<br>[320–950]</td><td>380 ± 40<br>[300–400]</td><td>880 ± 400<br>[550–830]</td><td>360 ± 100<br>[200–540]</td><td>2570 ± 240<br>[2350–3030]</td><td>1450 ± 380<br>[1000–2100]</td><td>2030 ± 220<br>[1650–2160]</td><td>330 ± 120<br>[210–550]</td></tr><tr><td></td><td>N = 7</td><td>N = 5</td><td>N = 4</td><td>N = 41</td><td>N = 7</td><td>N = 5</td><td>N = 4</td><td>N = 41</td></tr><tr><td>Late summer</td><td>540 ± 60<br>[480–600]</td><td>420 ± 80<br>[340–500]</td><td></td><td>370 ± 30<br>[340–400]</td><td>5240 ± 140<br>[5100–5400]</td><td>3900 ± 100<br>[3800–4000]</td><td></td><td>1030 ± 240<br>[790–1270]</td></tr><tr><td></td><td>N = 2</td><td>N = 2</td><td>N = 0</td><td>N = 4</td><td>N = 2</td><td>N = 2</td><td>N = 0</td><td>N = 4</td></tr><tr><td>Early winter</td><td>640 ± 50<br>[580–670]</td><td>470 ± 110<br>[350–620]</td><td>760</td><td>510 ± 30<br>[480–550]</td><td>2600 ± 980<br>[1850–4000]</td><td>2370 ± 1500<br>[940–4500]</td><td>2040</td><td>300 ± 90<br>[240–430]</td></tr><tr><td></td><td>N = 3</td><td>N = 3</td><td>N = 1</td><td>N = 17</td><td>N = 3</td><td>N = 3</td><td>N = 1</td><td>N = 17</td></tr></tbody></table>

centrations, consistent from one year to another at Bilos site and from one site to another during the second hydrological year of the study (Figs. 4–5). One first and relevant key result was the opposite temporal evolution of DIC and DOC concentrations in groundwater with water table depth (Table 2; Figs. 4–5). Indeed, DIC and DOC concentrations in groundwater exhibited strong temporal variations in relation with the hydrological cycle (Table 4; Figs. 4–5). On the one hand, during high-flow and growing season periods of 2014, the increase in DIC in Bilos groundwater (570 to 3030 µmol L⁻¹) was associated with a fast decrease in DOC in Bilos groundwater (3625 to 950 µmol L⁻¹), in parallel with a decline in the water table (Fig. 5). In 2015, the same temporal trend was observed at the same period, but with a lesser extent (Fig. 5). On the other hand, during late summer, the increase in DIC concentrations in Bilos groundwater (2700 to 5400 µmol L⁻¹) was this time not related with any decrease in DOC concentrations in groundwater (Fig. 5b–c). This maximum of DIC concentrations in groundwater corresponded to the late summer period when the overlying forest ecosystem had switched from autotrophic to heterotopic metabolism (Figs. 2b, 5). During early winter and high-flow periods, DIC concentrations in Bilos groundwater decreased from 4000 µmol L⁻¹ (November 2014) to 1700 µmol L⁻¹ (March 2015), in parallel with a rise in the water table (Fig. 5a–b). Concomitantly, a fast increase in DOC concentrations from 670 to 3600 µmol L⁻¹ occurred in Bilos groundwater between the same time periods (Fig. 5a, c).

The DIC concentrations in the three sampled piezometers exhibited a modest spatial heterogeneity (Table 4; Fig. 5b). DIC concentrations were low (e.g., 570 µmol L⁻¹ in the Bilos piezometer in February 2014) during periods of high flow and were high (e.g., 5370 µmol L⁻¹ in the Bilos piezometer

<figure><img src="image_6.png" /><figcaption>**Figure 4.** The concentrations of DIC and DOC in the three sampled groundwaters as a function of water table depth.</figcaption></figure>

in September 2014) during late summer (Table 4; Fig. 5a–b). In contrast to DIC, the DOC concentrations exhibited a significant spatial heterogeneity, particularly during high-flow periods (Table 4; Fig. 5b–c). During these periods of high flow, DOC concentrations were higher in the Bilos piezometer (3800 ± 200 µmol L⁻¹) than in piezometers 2 (280 µmol L⁻¹) and 3 (1500 µmol L⁻¹) (Table 4; Fig. 5a, c). During the other hydrological periods (periods of base flow), DOC concentrations in piezometer 2 were still lower than the
<figure><img src="image_7.png" /><figcaption>**Figure 5.** (a) Discharge of the Grande Leyre, the Petite Leyre, the Grand Arriou and the Bourron rivers associated with the water table at the Bilos site. Temporal variations throughout the sampling period in (b) the DIC concentrations, in the sampled piezometers, in the sampled first-order streams (medium dashed line; errors bars represent standard deviation of the six first-order streams), and in (c) the DOC concentrations, in the sampled piezometers and in the sampled first-order streams (medium dashed line; errors bars represent standard deviation of the six first-order streams). HF, GS, LS and EW represent high-flow (January–March 2014 and February–March 2015), growing season (April–August 2014 and April–August 2015), late summer (September–October 2014 and September–October 2015) and early winter (November 2014–January 2015 and November–December 2015) periods, respectively.</figcaption></figure>

two other piezometers (Bilos and 3) (Table 5). However, during periods of base flow, groundwater DOC concentrations at the three sampled sites remained more or less constant (Table 4; Fig. 5a, c).

## 3.4 Dissolved carbon evolution in first-order streams

In first-order streams, the DIC concentrations exhibited smaller temporal variations and significantly lower values than in groundwater, attesting to degassing occurring at the groundwater-stream interface (Table 4; Fig. 5b). In contrast
**Table 5.** Export of DIC and DOC from the sampled groundwater to first-order streams, as well as degassing in first-order streams, for the sampling period and for high-flow (January–March 2014 and February–March 2015), growing season (April–August 2014 and April–August 2015), late summer (September–October 2014 and September–October 2015) and early winter (November 2014–January 2015 and November–December 2015) periods. Numbers represent the mean ± SD whereas numbers in square brackets represent the range. Here, degassing was calculated with the DIC data from the three sampled groundwaters.

<table><thead><tr><th rowspan="2"></th><th colspan="4">DOC<sub>export</sub> (mmol m<sup>-2</sup> day<sup>-1</sup>)</th><th colspan="4">DIC<sub>export</sub> (mmol m<sup>-2</sup> day<sup>-1</sup>)</th><th rowspan="2">Degassing (mmol m<sup>-2</sup> day<sup>-1</sup>)</th></tr><tr><th>Bilos piezometer</th><th>Piezometer 2</th><th>Piezometer 3</th><th>Streams<sup>b</sup></th><th>Bilos piezometer</th><th>Piezometer 2</th><th>Piezometer 3</th><th>Streams<sup>b</sup></th><th>Streams</th></tr></thead><tbody><tr><td>High flow</td><td>3.4 ± 1.1<br>[2.3–4.9]</td><td>0.4 ± 0.02<br>[0.3–0.4]</td><td>1.5 ± 0.2<br>[1.2–1.7]</td><td>0.6 ± 0.1<br>[0.5–0.7]</td><td>1.8 ± 0.4<br>[1.3–2.2]</td><td>1.4 ± 0.2<br>[1.3–1.6]</td><td>1.8 ± 0.1<br>[1.7–1.9]</td><td>0.3 ± 0.1<br>[0.3–0.4]</td><td>1.4 ± 0.2<br>[0.8–1.9]</td></tr><tr><td>Growing season</td><td>0.4 ± 0.4<br>[0.1–1.2]</td><td>0.05 ± 0.02<br>[0.1–0.2]</td><td>0.2 ± 0.2<br>[0.1–0.4]</td><td>0.1 ± 0.1<br>[0.05–0.3]</td><td>0.7 ± 0.3<br>[0.4–1.3]</td><td>0.3 ± 0.1<br>[0.3–0.5]</td><td>0.6 ± 0.1<br>[0.4–0.7]</td><td>0.1 ± 0.03<br>[0.05–0.2]</td><td>0.5 ± 0.2<br>[0.3–1.3]</td></tr><tr><td>Late summer</td><td>0.1 ± 0.01<br>[0.1–0.1]</td><td>0.1 ± 0.04<br>[0.1–0.1]</td><td></td><td>0.1 ± 0.01<br>[0.05–0.1]</td><td>0.6 ± 0.03<br>[0.6–0.7]</td><td>0.4 ± 0.05<br>[0.4–0.5]</td><td></td><td>0.1 ± 0.01<br>[0.1–0.1]</td><td>0.4 ± 0.1<br>[0.4–0.6]</td></tr><tr><td>Early winter</td><td>0.1 ± 0.02<br>[0.1–0.2]</td><td>0.1 ± 0.03<br>[0.1–0.1]</td><td>0.2</td><td>0.1 ± 0.02<br>[0.1–0.1]</td><td>0.7 ± 0.1<br>[0.5–0.8]</td><td>0.6 ± 0.2<br>[0.4–0.8]</td><td>0.6</td><td>0.1 ± 0.02<br>[0.1–0.1]</td><td>0.5 ± 0.1<br>[0.5–0.6]</td></tr><tr><td>2014–2015</td><td>0.9 ± 1.4<br>[0.1–4.9]</td><td>0.1 ± 0.1<br>[0.05–0.4]</td><td>0.6 ± 0.5<br>[0.1–1.7]</td><td>0.2 ± 0.2<br>[0.05–0.7]</td><td>0.9 ± 0.5<br>[0.4–2.2]</td><td>0.6 ± 0.4<br>[0.3–1.6]</td><td>1.0 ± 0.6<br>[0.4–1.9]</td><td>0.2 ± 0.1<br>[0.05–0.4]</td><td>0.6 ± 0.3<br>[0.2–1.3]</td></tr><tr><td>Entire watershed<br>(2014–2015)</td><td></td><td>0.7 ± 0.7<sup>a</sup></td><td></td><td>0.2 ± 0.2</td><td></td><td>0.9 ± 0.5<sup>a</sup></td><td></td><td>0.2 ± 0.1</td><td>0.7 ± 0.5</td></tr></tbody></table>

to DIC, the DOC concentrations in first-order streams were of the same order of magnitude as in piezometer 2 (dry Landes) and significantly lower than in the two other piezometers (wet to mesophyllous Landes), in particular during periods of high flow (Table 4; Fig. 5c). As in groundwater, DOC and DIC concentrations in first-order streams were significantly anticorrelated (Table 2), suggesting that carbon dynamics in first-order streams was mostly impacted by groundwater inputs.

## 3.5 Carbon stocks in groundwater and exports to streams

At the Bilos site, the stocks of DIC and DOC in groundwater followed the same temporal trend as DIC and DOC concentrations (Figs. 5–6). The stock of DIC increased from high-flow (1140 mmol m<sup>-2</sup> the 12 February 2014) to late summer (8700 mmol m<sup>-2</sup> the 24 September 2014) periods, whereas at the same time intervals, the stock of DOC decreased from 7240 to 780 mmol m<sup>-2</sup> (Fig. 6). Furthermore, between 12 February and 16 May 2014 (95 days), we observed an increase of 4500 mmol m<sup>-2</sup> in DIC stocks very close to the decrease in DOC stocks of 5500 mmol m<sup>-2</sup>. This suggests that during the following months after the DOC peak in groundwater at high-flow period, DOC is degraded to DIC within the groundwater itself. During this period, the degradation rate of DOC in the groundwater could be estimated at approximately 60 mmol m<sup>-2</sup> day<sup>-1</sup>.

The export of DOC occurred mostly during high-flow periods (e.g., 90% of the total DOC export in the Bilos plot occurred during high-flow periods), for each sampled ground-

water (Table 5). During high-flow periods, the groundwater DOC concentrations and exports exhibited an important spatial heterogeneity at the three sampled sites (Table 5). During these periods of high flow, DOC export was higher in the Bilos piezometer (3.4 ± 1.1 mmol m<sup>-2</sup> day<sup>-1</sup>) than in piezometer 2 (0.4 ± 0.02 mmol m<sup>-2</sup> day<sup>-1</sup>) and in piezometer 3 (1.5 ± 0.2 mmol m<sup>-2</sup> day<sup>-1</sup>) (Table 5). These contrasts in DOC exports were related to the water table depth and amplitude (Fig. 4) and the gradient in soil carbon between the different podzols. In contrast to DOC exports, approximately the same quantity of DIC was exported during high-flow periods (e.g., 50% of the total DIC export in the Bilos plot occurred during high-flow periods) as during the other hydrological periods for each sampled groundwater (Table 5). Groundwater DIC exports exhibited a smaller spatial heterogeneity than DOC exports although DOC and DIC concentrations showed opposite seasonal trends in groundwater (Tables 4–5, Fig. 5b–c); the time-integrated value of carbon export for the sampling period was 0.9 ± 0.5 mmol m<sup>-2</sup> day<sup>-1</sup> (3.9 ± 2.2 g C m<sup>-2</sup> yr<sup>-1</sup>) for DIC and 0.7 ± 0.7 mmol m<sup>-2</sup> day<sup>-1</sup> (3.1 ± 3.1 g C m<sup>-2</sup> yr<sup>-1</sup>) for DOC (Table 5). As drainage of groundwater was the only hydrological pathway in the Leyre watershed, terrestrial carbon leaching to streams was estimated to be 1.6 ± 0.9 mmol m<sup>-2</sup> day<sup>-1</sup> (7.0 ± 3.9 g C m<sup>-2</sup> yr<sup>-1</sup>).

## 3.6 Degassing in first-order streams

Degassing in first-order streams was 0.7 ± 0.5 mmol m<sup>-2</sup> day<sup>-1</sup> (3.1 ± 2.2 g C m<sup>-2</sup> yr<sup>-1</sup>) throughout the sampling period (Table 5). Degassing

<footnote><sup>a</sup> Mean carbon export weighted by surface, assuming that the Bilos piezometer is representative of the wet Landes, that piezometer 2 is representative of the dry Landes and that piezometer 3 is representative of the mesophyllous Landes using the relative surface area of each type of Landes. <sup>b</sup> Carbon exports from first- to second-order streams calculated from the drainage of first-order streams (mm day<sup>-1</sup>) and the mean concentrations of DOC and DIC in first-order streams (mmol m<sup>-3</sup>).</footnote>
<figure><img src="image_8.png" /><figcaption>**Figure 6.** The mean DIC and DOC stocks between two sampling dates in Bilos groundwater. HF, GS, LS and EW represent high-flow (January–March 2014 and February–March 2015), growing season (April–August 2014 and April–August 2015), late summer (September–October 2014 and September–October 2015) and early winter (November 2014–January 2015 and November–December 2015) periods, respectively.</figcaption></figure>

was more important during periods of high flow than during the other hydrological periods (Table 5). In addition, degassing in first-order streams was positively correlated to the export of DIC (Table 2), revealing that degassing was mostly impacted by groundwater inputs. Over a hydrological year, 75% of the DIC exported from the Leyre watershed based on the three groundwater sampling sites almost immediately returned in the atmosphere through CO₂ degassing in first-order streams (Table 5).

# 4 Discussion

## 4.1 Water mass balance and the role of groundwater in the hydrological carbon export

Our hydrological dataset monitored continuously during 18 months allows us to separate the water budget in four terms on the monthly timescale (Table 1; Figs. 2c–3). The water budget at the Bilos plot was primarily impacted by precipitation and evapotranspiration (Table 1; Fig. 2c).

The transfer of precipitation to rivers involves temporary water storage in groundwater (Alley et al., 2002; Oki and Kanae, 2006). However, the lag time between precipitation and groundwater storage was short at our study site, as attested to by the significant correlation between these two parameters (Table 2). Thus, when precipitation is high (during early winter and high-flow periods), water infiltration in the sandy podzols is faster than water capture by vegetation.

Consequently, rainwater infiltration rapidly causes the water table to rise and thus increases the groundwater storage (Fig. 2a, c). This fast infiltration is due to the sandy permeable texture of soils with a low soil water retention (Augusto et al., 2010; Vernier and Castro, 2010).

The evapotranspiration was high during the growing season and late summer periods when the precipitation was low (Table 1; Fig. 2c). For that reason, the groundwater storage decreases with increasing evapotranspiration (Table 2; Fig. 2c), revealing that soil water uptake by the pine trees directly lowers the water table. Soil water retention properties usually vary with depth and thus soil water uptake by plant roots generally occurs from areas in the soil with the highest water potential (Domec et al., 2010; Warren et al., 2005). Previous studies suggest that the ordinary soil depth at which most water is taken up in pines is usually 30–40 cm (Klein et al., 2014; Querejeta et al., 2001) where nutrient concentrations are also the highest (Achat et al., 2008). In an experimental Scots pine plot in a flat and sandy area of Belgium, similar to our study site, Vincke and Thiry (2008) reported that water table uptake could contribute to 60% of the evapotranspiration thanks to capillary rise from the groundwater to the rooted soil layers. Contrary to the pine trees, direct groundwater table uptake has been observed for deciduous trees in a flat and sandy area of Portugal (Mendes et al., 2016), a process that occurred through a dimorphic root system, which allows the access and use of groundwater resources (David et al., 2013) in particular during drought periods.
riods (Del Castillo et al., 2016). Evapotranspiration strongly controls the groundwater storage in pine forests and, as a result, the water table generally rises after clear-cutting (Bosch and Hewlett, 1982; Sun et al., 2000; Xu et al., 2002). At our study site, drainage also significantly increased after wood harvesting due to reduced evapotranspiration, (Kowalski et al., 2003; Loustau and Guillot, 2009). Indeed, the network of drainage ditches created by foresters very rapidly redirects the excess water when the groundwater level rises (Thivolle-Cazat and Najar, 2001). Since most pine roots are located in the first meter of the soil to avoid winter anoxia caused by a rising water table (Bakker et al., 2006, 2009), the pine trees did not exhibit any transpiration reduction when the groundwater level was high (Fig. 2a, c; Loustau et al., 1990).

We observed a lag time between groundwater storage and drainage at our study site (Figs. 2a, c-3), confirmed by the non-significant correlation between these two parameters (Table 2). This lag of 2-3 months was due to the time necessary for water to travel through the soil depending on the spatial temporal gradient of hydraulic head, hydraulic conductivity and porosity of the system (Ahuja et al., 2010; Alley et al., 2002). At our study site, shallow groundwater acts as a buffer system, the drainage mostly being controlled by water table depth and the capacity of the porous soil to store or export water (Alley et al., 2002). Indeed, groundwater flow in a shallow sandy aquifer is largely controlled by the drainage pattern of the streams and ditches and thus by the water table depth and topography of the area (Vissers and van der Perk, 2008). At our study site, the buffer capacity of groundwater explains why the Leyre river discharge increased only in late winter, 2-3 months after the start of high precipitation and water table rise (Fig. 2a, c). Sudden hydrological events are thus buffered by this temporary groundwater storage in the porous soil. As a consequence, temporary groundwater storage mediates almost all the carbon exports to the watershed. Moreover, storms would not have such a crucial impact on the way we estimate carbon exports from groundwater to first-order streams based on monthly sampling frequency. Indeed, with our monthly resolution we observed consistent seasonal effect of DIC and DOC in shallow groundwater and streams (Fig. 5), representative for the different processes that control carbon dynamics in groundwater. Conversely, in steeper and less permeable catchments, carbon exports are quickly affected by storms and pulsed hydrological events (e.g., Raymond and Saiers, 2010; Wilson et al., 2013). Finally, the water mass balance on the Bilos plot scale being consistent with drainage modeled on the watershed scale (Figs. 2c-3), we used this drainage to estimate carbon exports on the plot scale (Table 5).

## 4.2 Soil carbon leaching to groundwater

Dissolved carbon concentrations varied considerably in groundwater (Table 4; Figs. 4-5) according to seasonal changes in hydrology and forest metabolism and depending

on the characteristics of the sampling site. Because the sampling frequency was approximately 1 month, we may have lost some short transitional periods significant for the annual carbon budget. This is most probable during the short period of high flow, when DOC mobilization and export were the highest (Table 5). However, the sampling frequency was sufficient to detect the major trends in groundwater DIC and DOC concentrations, consistent from one hydrological cycle to another at the Bilos site and from one site to another during the second hydrological year of the study, although topographic differences explained spatial differences in DOC and DIC concentrations (Fig. 5). Thanks to the high permeability of the soil and the buffering capacity of the groundwater in response to hydrology, we could observe distinct biogeochemical processes that govern carbon leaching throughout the hydrological cycle.

Dissolved organic matter generally includes a small proportion of low-molecular-weight compounds such as carbohydrates and amino acids and a larger proportion of complex, high-molecular-weight compounds (Evans et al., 2005; Kawasaki et al., 2005). Dissolved organic matter is often quantified by its carbon content and referred to as DOC and nearly all DOC in soils comes from photosynthesis (Bolan et al., 2011). Indeed, DOC in soils in forests originates from throughfall and stemflow, leaf litter leaching, root exudation and decaying fine roots in soils (Bolan et al., 2011). However, a large fraction of DOC in soil solution is sorbed onto minerals and, before being exported to streams, DOC must be mobilized from the soil (Sanderman and Amundson, 2009). Surface precipitation has been described as an important process that transports DOC downward from the topsoil to the saturated zone (Kawasaki et al., 2005; Shen et al., 2015). The transfer of DOC in groundwater also depends on the level of hydraulic connectivity between subsoil horizons and water table depth (Kalbitz et al., 2000). However, up to 90% of surface-derived DOC can be removed by re-adsorption to minerals prior to reaching the saturated zone (Shen et al., 2015). Furthermore, when sorptive retention of DOC occurs, it contributes to carbon accumulation in subsoils due to the stabilization of organic matter against biological degradation (Kaiser and Guggenberger, 2000; Kalbitz and Kaiser, 2008). During base-flow conditions, the DOC concentrations in groundwater were relatively stable at our study sites, even after rainy periods (Table 4; Figs. 2c, 5c), which suggests that soil DOC in upper horizons was not preferentially mobilized to groundwater by rainwater infiltration. Spatially, groundwater DOC was on average higher at the mesophyllous to wet Landes station (Bilos and piezometer 3), than at the dry Landes (piezometer 2) during periods with a low water table (Table 4). Indeed, several studies have reported decreasing DOC concentrations in groundwater in concurrence with increasing subsoil thickness and water table depth (Datry et al., 2004; Goldscheider et al., 2006; Pabich et al., 2001), with DOC concentrations at or close to zero reported in deep (>1 km) and old groundwater (Pabich et al., 2001). At our
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Conceptual model at the vegetation-soil-groundwater-stream interface in sandy ecosystems with shallow groundwater. OH, WT and D are the organic horizon of the soil, the water table and the drainage, respectively. Hydro-biogeochemical processes are represented by dashed arrows. Carbon exports are represented in full arrows; the thickness of the arrow indicates the magnitude of flux. High-flow periods are in January–March 2014 and February–March 2015, growing season in April–August 2014 and April–August 2015, late summer in September–October 2014 and September–October 2015, and early winter in November 2014–January 2015 and November–December 2015.</figcaption></figure>

study site, the fraction of groundwater DOC that predomina-
nates at a low water table was probably more recalcitrant,
more stabilized and more aged than during high flow. In-
deed, in small forested watersheds, the ¹⁴C age of ground-
water DOC generally varies from old DOC at base flow to
relatively modern DOC during high flow (Schiff et al., 1997).

In the podzol soils of the Landes de Gascogne, the satu-
ration of the superficial organic-rich horizon of the soil was
necessary to generate very high DOC concentrations in the
groundwater (Figs. 4–5, 7). This suggests changes in the
chemical conditions that altered the DOC retention capac-
ity of the soil. In temperate forested ecosystems, leaching
of DOC from subsoils is generally controlled by retention
in the mineral B horizon of the soil with a high content of
extractable aluminum and iron oxides (Kindler et al., 2011;
Michalzik et al., 2001). In sandy podzols that contain almost
no clay minerals (Augusto et al., 2010), DOC retention in
soil is mainly controlled by organometallic complex (Lund-
ström et al., 2000; Sauer et al., 2007). These Al–Fe oxides are
considered to be the most important sorbents for dissolved
organic matter in soils (Kaiser et al., 1996). In podzols such
as our study site, the content in Al–Fe oxides, and their de-
gree of complexation by soil organic matter increases with
depth (Achat et al., 2011; Ferro-Vázquez et al., 2014). When
the water table rises and reaches the organic-rich horizon of
the soil, reducing conditions in the saturated soil will pre-
vail. Indeed, we observed anoxic conditions in groundwa-
ter all year round at the Bilos site (data not shown). Under
such reducing conditions in the saturated soil, dissolution of
Fe oxides can occur, limiting the sorptive retention of DOC
(Camino-Serrano et al., 2014; Fang et al., 2016; Hagedorn
et al., 2000). DOC is then released to groundwater, trans-
ported downward, partly retained in the mineral horizon of

the soil and exported to streams. During these high-flow peri-
ods, groundwater DOC peaked at a significantly higher value
at the mesophyllous to wet Landes station (Bilos) than at the
mesophyllous Landes (piezometer 3) and at the dry Landes
(piezometer 2) (Table 4). This is a consequence of the wa-
ter table depth and amplitude and the different carbon con-
tent in the superficial layers of the soil (Fig. 4). We calcu-
lated a stock of soil organic carbon in the 0–60 cm layer of
9.7 kg m⁻² at the Bilos plot (Pierre Trichet and Denis Lous-
tau, personal communication, 2014) whereas the stocks of
DOC and DIC in Bilos groundwater were, on a yearly aver-
age, 0.03 and 0.06 kg m⁻², respectively (Fig. 6). As dissolved
carbon in groundwater represents approximately 1 % of the
soil carbon, only a small part of the soil organic carbon con-
tent is leached into groundwater and potentially exported to
streams.

During the 3 months (March–May 2014) following the
flood peak of 2014, DOC concentrations and stocks in Bilos
groundwater decreased regularly in parallel with an increase
in DIC concentrations and stocks in groundwater (Figs. 5–
6). The DOC degradation and DIC accumulation rates in
Bilos groundwater were very similar and were estimated at
approximately 60 mmol m⁻² day⁻¹, or 6.5 mmol m⁻³ day⁻¹.
This DOC degradation occurred during decreasing water
table periods, although these periods are characterized by
moderate groundwater temperature (<13 °C). Moreover, this
DOC degradation rate is consistent with findings of Craft et
al. (2002), who reported respiration rates within the range
of 3–100 mmol m⁻³ day⁻¹ within a floodplain aquifer of a
large gravel-bed river in northwestern Montana in the US.
Similarly, in a semiarid mountain catchment in New Mex-
ico, Baker et al. (2000) also observed that groundwater DOC
peaked during periods of high flow and resulted in higher
rates of heterotrophic metabolism, presumably because of the supply of labile DOC via more intense hydrologic con- nections between the soil and the groundwater. The bioavail- ability of groundwater DOC is related to the content of com- pounds of low molecular weight, such as amino acids or car- bohydrates, and compounds of high molecular weight such as fulvic or humic acids are more recalcitrant to decomposi- tion by microbes. Our results suggest that DOC degradation within the groundwater occurred the following months after the mobilization of biodegradable DOC during a high water table.

The increase in DIC concentrations in groundwater dur-
ing late summer of 2014 (September–October 2014) was due
to another process, this time not associated with any DOC
degradation in groundwater (Fig. 5b–c). At our study site,
the late summer period, when the forest ecosystem is a net
source of CO2 for the atmosphere (positive NEE), also cor-
responds to a maximum in CO2 concentration in groundwa-
ter (Figs. 2b, 5b) and thus a maximum contribution of soil
respiration to groundwater DIC. Transfer of CO2 from soil
air to groundwater requires input of fluid, i.e., gas or water
(Tsypin and Macpherson, 2012). Typical pathways are down-
ward CO2 transport from soil in the dissolved (Kessler and
Harvey, 2001) or gaseous form (Appelo and Postma, 2005),
upward flux of deep CO2 of various origins through gas vents
(Chiodini et al., 1999) or leakage from adjacent aquifers. At
our study site there is no evidence of a deep CO2 source
or leakage from adjacent aquifers (Bertran et al., 2011). In
addition, during late dry summer, no rainy events occurred
(Fig. 2c), and the high temperatures observed during this pe-
riod are favorable for a high production of gaseous CO2 in the
unsaturated region of the soil, which follows the Arrhenius
equation (Lloyd and Taylor, 1994; Reth et al., 2005). During
high temperature periods in summer, the amount of CO2 in
equilibrium with groundwater lower than in soil upper hori-
zons favored a downward flux of gaseous CO2 (Tsypin and
Macpherson, 2012), which suggests that soil CO2 must have
been transported to groundwater in gaseous form by sim-
ple downward diffusion (Fig. 7). In a North American tall-
grass prairie resting on limestone, downward movement of
CO2 gas followed by equilibration with groundwater at the
water table was favorable during a drought period, whereas
transport of soil CO2 in the dissolved form with diffuse flow
of recharge water was the most effective during wet peri-
ods (Tsypin and Macpherson, 2012). In temperate forested
landscapes, other authors noticed that during dry periods,
a strong reduction in soil CO2 flux to the atmosphere (up-
ward diffusion) is associated with a decline in soil water
content that stresses roots and microorganisms (Davidson et
al., 1998; Epron et al., 1999). This suggests that the peak of
groundwater pCO2 observed in October 2014 (Fig. 5b) origi-
nates from soil CO2 that was produced before, certainly dur-
ing July–August 2014 when the temperature was the high-
est and precipitation was sufficient to maintain a soil moist-
ture that did not limit soil respiration (Fig. 2b–c). The lag

time of 2–3 months between the peak of groundwater CO2
and soil CO2 has been documented by Tsypin and Macpher-
son (2012), who concluded that it corresponded to the travel
time of soil-generated CO2 to the water table. In the sandy
podzols, during the drought period, the high porosity in the
sandy soil may favor downward diffusion of CO2 and its dis-
solution in groundwater. Thereafter, during the early winter
period, concentrations of DIC in groundwater decreased as a
consequence of dilution with rainwater with low DIC content
(Fig. 7).

4.3 Carbon transfer at the
groundwater–stream–atmosphere interface

In the Leyre watershed, carbon exports are influenced by the
soil types, which are characterized by a different water ta-
ble depth and amplitude (Fig. 4), as well as a gradient of
carbon content in the different soil types (Augusto et al.,
2006). However, these last parameters have a stronger ef-
fect on the spatial heterogeneity of DOC exports than DIC
exports (Table 5). Indeed, drainage and DOC concentrations
in groundwater have a cumulative positive effect on DOC
exports (Tables 1–2, 4–5; Fig. 5b–c); in contrast, drainage
and DIC concentrations in groundwater have antagonistic
effects on DIC exports (Tables 1–2, 4–5; Fig. 5b–c). As
a consequence, groundwater exports the majority of DOC
during the 2–3 months of high flow, but approximately the
same quantity of DIC is exported during periods of high
flow and periods of base flow (Table 5). In addition, dur-
ing the study period the discharge varied by up to 100-fold
(Fig. 2a); the corresponding variations in DIC and DOC con-
centrations and exports from the groundwater varied up to
10-fold (Tables 4–5; Figs. 4–5). As reported in other stud-
ies (Fiedler et al., 2006; Öquist et al., 2009), carbon export
rates were mainly determined by discharge, the variations
in carbon concentrations and exports being relatively small
compared to the flow variation. However, for the whole sam-
pling period, the mean weighted carbon export is almost the
same for both DIC (0.9 ± 0.5 mmol m⁻² day⁻¹) and DOC
(0.7 ± 0.7 mmol m⁻² day⁻¹) (Table 5), and the forest ecosys-
tem exports in total 1.6 ± 0.9 mmol m⁻² day⁻¹ (equivalent
to 7.0 ± 3.9 g C m⁻² yr⁻¹), 40 % as DOC and 60 % as DIC
(Table 6). This terrestrial carbon leaching from groundwa-
ter to streams is of the same order of magnitude as carbon
leaching from subsoils (11.9 ± 5.9 g C m⁻² yr⁻¹) in five tem-
perate forest plots across Europe (Kindler et al., 2011), in a
temperate Japanese deciduous forest from soils to streams
(4.0 g C m⁻² yr⁻¹) (Shibata et al., 2005), or in European
forests (9.6 ± 3.2 g C m⁻² yr⁻¹) (Luyssaert et al., 2010).

As in groundwater, DOC and DIC concentrations in first-
order streams were significantly anticorrelated (Table 2),
suggesting that dissolved carbon dynamics in streams are
mostly impacted by groundwater inputs (Kawasaki et al.,
2005; Öquist et al., 2009). We could observe higher DOC
concentrations in streams during early winter and high-flow
periods than during growing season and late summer periods (Table 4). Increase in DOC concentrations with discharge and a high water table has been reported in the Leyre watershed (Polsenaere et al., 2013) and in many other forested catchments (Alvarez-Cobelas et al., 2012; Dawson et al., 2002; Raymond and Saiers, 2010; Striegl et al., 2005). At our study site, during periods of high flow, first-order streams exported $0.2 \pm 0.2 \text{ mmol m}^{-2} \text{ day}^{-1}$ to second-order streams, a flux significantly lower than DOC exports ($0.7 \pm 0.7 \text{ mmol m}^{-2} \text{ day}^{-1}$) from groundwater to first-order streams (Table 5). As a consequence, during the sampling period, 70% of the groundwater DOC was either degraded or re-immobilized at the groundwater-stream interface (Table 5). Indeed, when groundwater DOC enters the superficial river network through drainage part of it might be rapidly recycled by photooxidation (Macdonald and Minor, 2013; Moody and Worrall, 2016) or by respiration within the stream (Hall et al., 2016; Roberts et al., 2007). Alternatively, DOC can be re-adsorbed on Fe or Al oxides that are particularly abundant at the riverbed oxic–anoxic interface. As a matter of fact, flocculation with Fe or Al can remove DOC from solution (Sharp et al., 2006). In contrast, DOC concentrations and exports were similar and stable in groundwater and streams during periods of base flow (Table 5). This suggests that groundwater DOC behaved conservatively during low-flow stages (Schiff et al., 1997) and that DOC in streams was more labile during high-flow stages (Aravena et al., 2004). Indeed, in a small temperate and forested catchment in Pennsylvania (US), McLaughlin and Kaplan (2013) reported an increase in concentrations of labile DOC of up to 27-fold during high-flow stages compared to base-flow conditions.

DIC concentration in streams increased during the late summer period in parallel with those in groundwater (Table 4; Fig. 5b). Indeed, concentrations of DIC show an inverse relationship with discharge in the Leyre watershed (Polsenaere et al., 2013) and in other temperate catchments (Billett et al., 2004; Dawson and Smith, 2007) as the result of dilution with rain water and a lower contribution of deep CO₂-enriched groundwater during high-flow periods. The discharge of DIC-rich groundwater supersaturated with CO₂, together with the oxidation of dissolved organic matter in surface waters, results in a large CO₂ supersaturation of rivers (Hotchkiss et al., 2015; Stets et al., 2009). The quick loss of DIC between groundwater and first-order streams is due to efficient degassing of CO₂ from headwaters (Fiedler et al., 2006; Venkiteswaran et al., 2014). This rapid degassing is also attested to by the change in the $\delta^{13}C$ signature of the DIC (Deirmendjian and Abril, 2018; Polsenaere and Abril, 2012; Venkiteswaran et al., 2014). Furthermore, the positive correlation between degassing and export of DIC (Table 2) confirms that groundwater DIC is the main source of CO₂ degassing in superficial stream waters (Hotchkiss et al., 2015; Öquist et al., 2009). Very fast degassing was confirmed by observations in spring wa-

ters that lose up to 70% of their CO₂ a few dozen meters downstream (Deirmendjian and Abril, 2018; Öquist et al., 2009). Venkiteswaran et al. (2014) concluded that most of the stream CO₂ originating from groundwater drainage was degassed before typical in-stream sampling occurs. Throughout the sampling period degassing was, on a yearly average, approximately $0.7 \pm 0.5 \text{ mmol m}^{-2} \text{ day}^{-1}$ (equivalent to $3.1 \pm 2.2 \text{ g C m}^{-2} \text{ yr}^{-1}$). CO₂ degassing was higher during high-flow periods than during periods of base flow (Table 5), as a consequence of higher discharge and inputs of groundwater DIC to streams (Tables 1, 4) and higher water turbulence. As a matter of fact, degassing depends on water velocity that induces water turbulence and thus increases the gas transfer velocity (Alin et al., 2011; Raymond et al., 2012). Overall, during the whole sampling period CO₂ degassing in streams represented approximately 75% of the DIC exported from groundwater and thus a significant part of the carbon exported from forest rapidly returns to the atmosphere in the form of CO₂ through degassing.

Leaching of terrestrial carbon from the pine forest in the Leyre watershed calculated as the DOC and DIC export per catchment area was $1.6 \pm 0.9 \text{ mmol m}^{-2} \text{ day}^{-1}$ (equivalent to $7.0 \pm 3.9 \text{ g C m}^{-2} \text{ yr}^{-1}$). Eddy covariance measurements at the Bilos plot (Table 3) provided a forest net uptake of atmospheric CO₂ of approximately $-90 \pm 110 \text{ mmol m}^{-2} \text{ day}^{-1}$ (equivalent to $390 \pm 480 \text{ g C m}^{-2} \text{ yr}^{-1}$). In the same way that groundwater DOC and DIC stocks represent a minor fraction of soil carbon, carbon leaching represents a very small (approximately 2%) fraction of forest NEE, a conclusion consistent with other studies in temperate forest ecosystems (Kindler et al., 2011; Magin et al., 2017; Shibata et al., 2005). Such weak export of carbon from forest ecosystems, at least in temperate regions, is at odds with recent studies that attempt to integrate the contribution of inland waters in the continents’ carbon budget (Ciais et al., 2013). Indeed, on the global scale, the quantity of terrestrial carbon necessary to account for the sum of CO₂ degassing from inland waters, organic carbon burial in sediments and carbon export to the ocean represents more than $2 \text{ Pg C yr}^{-1}$, a number similar to the actual net land sink of atmospheric CO₂ (Ciais et al., 2013). Understanding why local and global carbon mass balances strongly diverge on the proportion of land NEE exported to aquatic systems appears to be a major challenge for the next years of research in this field.

## 5 Conclusion

The monitoring of DIC and DOC concentrations in groundwater and first-order streams in podzol-dominated catchment overlaid by pine forest brings new insights on the nature of processes that control carbon leaching from soils, transformation in groundwater, and export to surface waters and back to the atmosphere (Fig. 7). This terrestrial–aquatic–
atmosphere interface is believed to behave as a hotspot in the continental carbon cycle. The permeable character of the soil at the study site enables a clear temporal decomposition of processes involving carbon in groundwaters in relation with water table depth and amplitude, forest ecosystem production, and respiration. Hydrology has a strong influence on the carbon concentrations in shallow groundwater. High precipitation caused the water table to rise and saturate the topsoil, inducing a large mobilization of soil organic matter as DOC in the shallow groundwater, a process also favored by temporarily reducing conditions in the topsoil. These periods of a high water table are also associated with low DIC concentrations in groundwater caused by the groundwater dilution with rainwater. Conversely, groundwater was enriched in DIC during base-flow stages, as the result of two distinct processes. First, microbial consumption of DOC occurs within the groundwater in spring and summer, the following months after the periods of a high water table. Second, heterotrophic conditions in the forest ecosystem during late summer favor the downward diffusion of soil CO₂ to shallow groundwater.

In the absence of surface runoff, the comparison of dissolved carbon concentrations between groundwater and streams, associated with drainage data, allows us to understand and quantify the processes at the groundwater-stream-atmosphere interface. In the studied catchment, this method reveals a fast degassing of DIC as CO₂ throughout the year in first-order streams. During base-flow periods, groundwater DOC was exported conservatively to streams, probably because groundwater DOC was more recalcitrant, more stabilized and more aged during this period. However, during stages of a high water table, DOC concentrations in groundwater increased at some sites but not at others. This spatial heterogeneity of carbon export in the landscape did not fully translate in streams because of fast DOC degradation within the groundwater and/or DOC re-adsorption processes in soils close to the groundwater-streams interface.

Although spatial extrapolation of quantitative information from the plot scale to first-order streams in the watershed may have generated some uncertainty, we could make a comparison of groundwater carbon export to stream with other carbon fluxes in the landscape. Representing 2% of the local forest NEE, DIC and DOC exports to surface waters do not seem to be a significant component of the carbon budget at our study site. More detailed work at the land-water interface is necessary in order to reconcile the contradictory findings on local and global scales on the significance of hydrological carbon export in the continental carbon budget.

**Data availability.** Bilos site (FR-BIL) is a station as part of the ICOS project (https://www.icos-ri.eu/). Eddy covariance, meteorological and water table depth data are measured continuously at the Bilos pine plot. These data are available upon request to Denis Loustau (denis.loustau@inra.fr). River discharge data are measured continuously by the French water survey agency (DIREN) and

are available at http://www.hydro.eaufrance.fr. Carbon raw data are stored on data servers and repositories and available upon request to the corresponding author.

**The Supplement related to this article is available online at https://doi.org/10.5194 bg-15-669-2018-supplement.**

*Competing interests.* The authors declare that they have no conflict of interest.

**Acknowledgements.** This research is part of the CNP-Leyre project funded by the Cluster of Excellence COTE at the Université de Bordeaux (ANR-10-LABX-45). We thank Luiz Carlos Cotovicz Junior, Katixa Lajaunie-Salla, Baptiste Voltz, Gwenaëlle Chaillou and Damien Buquet (EPOC Bordeaux) for their assistance in the field. We thank Pierre Anshutz (EPOC, Bordeaux), Alain Mollier and Christian Morel (ISPA INRA) for their involvement in the CNP-Leyre project, and Céline Charbonnier for alkalinity titrations in the laboratory. Pierre Trichet (ISPA INRA) provided SOC data at the Bilos site.

Edited by: Nobuhito Ohte

Reviewed by: Masanori Katsuyama and one anonymous referee

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