Metabolic processes control carbon dioxide dynamics in a boreal forest ditch affected by clear-cut forestry

Boreal watercourses are large emitters of carbon dioxide (CO₂) to the atmosphere. For forestry intensive areas of the Nordic and Baltic countries, a high share of these watercourses are man-made ditches, created to improve drainage and increase forest productivity. Previous studies have suggested that terrestrial sources sustain the CO₂ in these ditches and variability in hydrology is the main temporal control. However, few studies have explored ditch CO₂ dynamics and its associated controls in catchments being exposed to forest harvest. An altered hydrology, increased nutrient export and light availability following forest harvest are all factors that potentially can change both levels, dynamics, and source controls of ditch CO₂. Here, high-frequency (30 min) CO₂ concentration dynamics together with other hydrochemical variables were studied in a forest ditch draining a fully harvested catchment in the Trollberget Experimental Area, northern Sweden. We collected data during the snow-free season from May to October. Ditch CO₂ concentrations displayed a clear seasonal pattern with higher CO₂ concentrations during summer than in spring and autumn. Concentrations ranged from 1.8 to 3.5 mg C L⁻¹ (median: 2.4 mg C L⁻¹, IQR = 0.5 mg C L⁻¹). Strong diel cycles in CO₂ developed during early summer, with daily amplitudes in CO₂ reaching up to 1.1 mg C L⁻¹. These pronounced daily cycles in CO₂ were closely related to the daily sum of shortwave radiation and water temperature. Variations in hydrology had generally a low impact on the CO₂ dynamics but did vary among seasons and between individual hydrological events. It was evident from our study that growing season CO₂ concentrations in a forest ditch affected by clear-cut harvest were highly variable and mainly controlled by light and temperature induced metabolism. These high dynamics and the associated controls need to be considered when scaling up ditch CO₂ emissions across boreal landscapes affected by intensive forestry.

1. Introduction

Headwater streams and ditches are important sources of atmospheric CO₂ emissions, estimated to contribute more than 70% of the total global fluvial CO₂ emissions (Raymond et al., 2013). The importance of headwaters for emitting CO₂ holds particularly true for boreal ecosystems, as they are rich in soil carbon and often closely connected with dense aquatic networks (Wallin et al., 2018). For forestry intensive areas of the Nordic and Baltic countries, a high share of these watercourses are man-made ditches, created to improve drainage and increase forest productivity during the 20^th century (Päivaänen and Hånell, 2012; Norstedt et al., 2021). Given the widespread occurrence of ditch networks combined with high concentrations and emission rates of CO₂, boreal ditches are important sources of atmospheric CO₂ (Peacock et al., 2021). As an intrinsic characteristic, boreal headwaters show close hydrochemical connectivity with adjacent soils and receive continuously high inputs of terrestrial derived carbon from which inflows are largely controlled by variations in hydrological inputs (Billett et al., 2006; Öquist et al., 2009; Crawford et al., 2013; Leith et al., 2015; Wallin et al., 2015). This carbon can enter the aquatic network directly as CO₂ that is produced in the soil via the mineralization of organic matter or from root-associated respiration (Campeau et al., 2019). In addition to terrestrial CO₂ inputs, CO₂ could also be produced in-situ in the watercourse from microbial decomposition or photochemical oxidation of organic matter transported from catchment soils (Köhler et al., 2002; Schelker et al., 2016a; Crawford et al., 2017). Aquatic CO₂ can also serve as the carbon source in primary production (photosynthesis), hence being consumed during daytime. For boreal headwaters, in-situ decomposition of organic matter is often found to be of minor importance for the observed CO₂ (Winterdahl et al., 2016) due to low residence time of the water (Catalán et al., 2016), limited light availability caused by dense tree canopies (Burrows et al., 2021), and low water temperatures (Tank et al., 2010). In addition, due to often unproductive conditions with low nutrient levels as well as restricted light availability, primary production typically also has low influence on CO₂ in boreal watercourses (Lamberti and Steinman, 1997; Roberts et al., 2007).

A critical aspect to consider when unraveling the different controls and their relative importance on CO₂ in watercourses is the time scale of interest. Different processes that control aquatic CO₂ are operating from hourly to seasonal scales (Riml et al., 2019). Thus, interpreting the primary controls on CO₂ concentration dynamics in watercourses requires continuous data collected at sufficient frequency (e.g., hourly) covering complete seasons (Wallin et al., 2020; Gómez-Gener et al., 2021). The development of sensors that monitor high-frequent CO₂ data has enabled studies that explored controls on dissolved CO₂ in watercourses draining various ecosystems (e.g., forest, agriculture, and wetlands) and across different climatic zones (e.g., boreal, alpine, subtropical, etc.) (e.g., Johnson et al., 2010; Dinsmore et al., 2013; Peter et al., 2014; Crawford et al., 2017; Rocher-Ros et al., 2020; Wallin et al., 2020). Studies performed in streams draining nutrient-poor forest ecosystems have shown that CO₂ dynamics are generally driven by variability in stream discharge (Johnson et al., 2007; Dinsmore et al., 2013; Crawford et al., 2017; Riml et al., 2019). The hydrological response in stream CO₂ is dependent on catchment-specific characteristics and variations in groundwater flow paths controlling connectivity to terrestrial CO₂ sources (Leith et al., 2015; Campeau et al., 2018). In contrast, for ditch or stream systems draining landscapes with open canopies that are fully exposed to light, in-situ metabolic processes have been found to have stronger control on aquatic CO₂. In such systems diel cycles in CO₂ can be particularly pronounced, with large concentration differences between day and night (Nimick et al., 2011; Crawford et al., 2017; Attermeyer et al., 2021; Gómez-Gener et al., 2021). These diel cycles reflect the interplay of in-situ primary production (photosynthesis) and respiration within the watercourse (or in the adjacent soils).

Previous findings of low in-situ contribution to CO₂ dynamics in boreal watercourses stem mainly from studies in low-intensively or unmanaged forest systems (Marx et al., 2017; Campeau et al., 2019). In contrast, few studies (e.g., Klaus et al., 2018) have explored CO₂ dynamics in headwaters draining areas affected by clear-cut forestry, which is a common management operation for many production forests in the boreal countries. In Sweden, about 1% (or ca 230,000 ha, based on a 5-year mean 2016-2020) of the productive forest land is harvested every year (Swedish Forest Agency, 2020), and as a consequence, many forest drainage networks are affected by this practice. Following harvest, the catchment hydrology is altered due to reduced evapotranspiration leading typically to higher groundwater levels and increased runoff (Andréassian, 2004; Sørensen et al., 2009; Schelker et al., 2013). Also, dissolved organic carbon (DOC) concentrations are often increased following harvest due to higher rates of decomposition of organic matter in soils and due to greater lateral mobilization from terrestrial sources (Nieminen, 2004; Laudon et al., 2009; Schelker et al., 2012). Similarly, forest harvest often results in enhanced export of nutrients, especially nitrogen, caused by reduced uptake in vegetation and increased mineralization of organic matter (Nieminen, 2004; Schelker et al., 2016b). There is limited literature concerning the influence of forest harvesting on dissolved CO₂ concentrations and emissions in connecting aquatic systems. However, Klaus et al. (2018) found that harvest increased dissolved CO₂ concentrations in groundwater of the surrounding catchment soils but did not affect CO₂ emissions from recipient streams. To what extent this discrepancy in observed patterns between groundwater and watercourses stems from changes in the in-situ stream CO₂ controls following harvest is currently unknown.

This study aims to investigate the impact of clear-cut forestry on the dynamics of dissolved CO₂ concentrations in draining watercourses. We hypothesize that dissolved CO₂ dynamics following forest harvest are altered and become more variable on short time scales (daily) due to changes in light and nutrient regimes which in turn increase the potential for in-situ metabolism. To test this hypothesis we (1) quantified ditch CO₂ concentration levels and dynamics for a full growing season in a forest ditch within a catchment recently being clear-cut harvested, (2) identified and explored the main temporal controls and how they vary with season, and (3) compared observed CO₂ concentration patterns from the clear-cut ditch with patterns observed in a stream draining an unmanaged forest catchment in close proximity.

2. Study area

The study was conducted in the Trollberget Experimental Area (TEA) (64°10'N, 19°46'E), located 50 km northwest of the city of Umeå, Sweden (Figure 1A)¹. The experimental area was set up in 2018 to study the environmental impacts of different types of forest management practices on aquatic ecosystems and has been embedded within the framework of the Krycklan Catchment Study (KCS) (Laudon et al., 2021). The mean annual air temperature for the area is 2.4°C and with a mean annual precipitation of 623 mm (about 30% as snow, based on data from 1980–2020 collected at the nearby Svartberget Climate station) (Laudon et al., 2021). Across the period of the growing season, the number of sunlight hours changes drastically at these northern latitudes (from about 20 h in early June to about 8 h in October).

FIGURE 1

Figure 1. The (A) Krycklan catchment study including the TEA, with the (C) DC2 and (B) C2 sub-catchments highlighted.

Within TEA, we studied a 4.4 ha large catchment (DC2) characterized by a dense ditch network (total length, 1.1 km, density, 0.025 m m⁻²) (Laudon et al., 2021, Figure 1C). The DC2 catchment is dominated by till soils (almost 100%) with well-developed podzols including a 10–20 cm humic/partly humic layer on top. Until July 2020, DC2 was completely forest covered, mainly by Norway spruce (Picea abies) and Scots pine (Pinus sylvestris). In July 2020, the catchment was completely harvested.

A second unmanaged sub-catchment (C2, located about 10 km from DC2) of the Krycklan catchment study was used in the study for comparative analysis with the clear-cut catchment of DC2 (Laudon et al., 2013; Leith et al., 2015; Figure 1B). C2 is 100% forested and slightly larger (12 ha) than DC2 but is otherwise similar in terms of soil types and forest composition. Both DC2 and C2 have been affected by historical ditching activity that occurred in the early 20^th century to improve drainage. Catchment characteristics of DC2 and C2 are shown in Supplementary Table 1.

3. Methods

3.1. Sensor measurements

Measurements in DC2 were conducted during the snow-free period from 8 May to 28 October 2021 (in total 174 days), encompassing a full growing season. CO₂ concentration was continuously measured together with water temperature, and electrical conductivity (EC) just upstream of a V-notch weir installed at the outlet of DC2. The sensors were deployed underwater attached to a wooden structure of the weir. CO₂ concentration was monitored using an eosGP sensor (range 0–2%, Eosense, Dartmouth, Canada) wrapped with copper tape to prevent biofouling. Inspection and cleaning of the sensors were performed monthly. Sensor accuracy is, according to the manufacturer, <1% of the calibrated range (0–2% CO₂) + 1% of the reading corresponding to a maximum error of ca 0.1 mg C L⁻¹ based on the maximum CO₂ measured in the current study. Water temperature and EC were monitored using a thermocouple (Type T) and a CS547A-L conductivity sensor (Campbell, UK), respectively. All sensors were connected to a CR1000X data logger (Campbell, UK) measuring at a 1 min interval and storing mean values at a temporal resolution of 30 min.

Volume fraction outputs (ppmv of CO₂) from the sensor were corrected for variations in water temperature, water level and atmospheric pressure (Johnson et al., 2010; Wallin et al., 2020) and expressed in milligrams of carbon per liter (mg C L⁻¹). Finally, sensor derived CO₂-C concentration data were calibrated against manually taken CO₂ measurements (n = 35, see water sampling section) that were carried out in the ditch during the entire study period. Calibration was conducted through the application of a regression equation, encompassing nearly the entire range of measurements (Supplementary Figure 1). Water discharge was measured at the outlet V-notch weir using an established stage height-discharge relationship. Stream discharge gauging for rating curve definition was done using time-volume (bucket) measurements covering a wide discharge range (n = 10). Stage height was continuously recorded (60 min) using a capacitance sensor (TruTrack Logger Type WT-HR 64K). Discharge per unit of catchment area (Q) was calculated and reported in mm h⁻¹ or mm d⁻¹. Precipitation and air temperature were measured nearby (300 m) the DC2 catchment using an ARG100 tipping bucket rain gauge (Campbell, UK) and a shaded thermocouple Type T. Short-wave radiation (SR) was measured at 30 min intervals within DC2 about 150 m from the catchment outlet using a Huskeflux NR01 net radiometer. Atmospheric pressure was monitored at the meteorological station of Svartberget (located ca. 8.5 km from DC2) and data were downloaded from the ICOS carbon portal (https://www.icos-sweden.se/Svartberget). Stream CO₂ concentrations at C2 were measured (at 5 min resolution) using a Vaisala CARBOCAP GMP221 non-dispersive infra-red (NDIR) sensor (range 0–5 %), that was hermetically sealed and covered with a gas-penetrable membrane (Johnson et al., 2010; Leith et al., 2015; Campeau et al., 2018). The outlet of C2 is equipped with a V-notch weir in a heated dam house and with stage height recorded at 5 min resolution. As in DC2, discharge in C2 was determined according to a known stage height-discharge relationship based on volume-time measurements.

3.2. Water sampling

Manual sampling for water chemistry at the outlets of DC2 and C2 was conducted at biweekly intervals, as part of a regular monitoring program of KCS/TEA, for a total of 15 occasions. In addition, during the 3 weeks between September 20 and October 10, DC2 was manually sampled daily as part of an intensive sampling campaign in all the ditches within the TEA. For DC2, a total of 35 grab samples were taken during the study period with a mean sampling time at ca 10:45. At C2, the mean sampling time for the grab samples was ca 13:30. For analysis of dissolved CO₂, a headspace method was used where a 5 mL sample of bubble-free water was injected in a 22.5 ml glass vial sealed with a bromobutyl rubber septa (Wallin et al., 2010; Åberg and Wallin, 2014). The injection was made by using a sterile syringe which was flushed with stream water before sampling. The vial was pre-filled with 0.1 ml 85 % H₃PO₄ and N₂ at atmospheric pressure. The samples were stored dark and cold (4°C) for a maximum of 1 week prior to analysis. Samples were analyzed on a gas chromatograph equipped with a methanizer and flame ionization detector (GC-FID). Water samples for pH and EC analysis were collected in 50 mL polyethylene bottles tightened avoiding the formation of air bubbles. In the lab, pH and EC were measured with pH and conductivity electrodes (MP220, Mettler Toledo). Grab samples for DOC analysis were collected in 250 mL polyethylene bottles, filtered [0.45 μm mixed cellulose ester (MCE) syringe filters, Millipore^®] within 24 h and then acidified to remove inorganic carbon prior to analysis. Analysis was performed with a Shimadzu Total Organic Carbon Analyzer TOC-VCPH, following storage at 4°C for 2–3 days' periods (Leach et al., 2016; Campeau et al., 2018). Finally, filtered subsamples were stored at a temperature of −20°C for later analysis of nitrogen and phosphorus and their respective fractions (for more analytical details see Blackburn et al., 2017 and Mosquera et al., 2022).

3.3. Statistical analysis

All sensor data were visually inspected to examine their patterns and a 15 day moving average of CO₂ concentrations was calculated to better visualize the seasonal dynamics. Linear regression analysis was used to investigate the relationships between ditch CO₂ concentration or amplitude in diel CO₂ concentration and discharge, water temperature and daily accumulated SR. Linear regressions were considered significant if p < 0.05. Dynamics in ditch CO₂ concentration were explored and visualized on a diel (24 h) basis for the full study period, as well as for individual months, using box plots with a 30 min resolution. The response in CO₂ concentration to variable discharge was analyzed by constructing C-Q relationships [log daily median C (mg L⁻¹) vs. log specific discharge (mm d⁻¹)]. Such C-Q plots were created on a monthly basis in order to assess whether the hydrological control was changing throughout seasons. The values of the slopes obtained from C-Q regressions were interpreted as done in Rehn et al. (2023) and following Meybeck and Moatar (2012).

To distinguish the different controls on CO₂ dynamics, significant hydrological events were identified according to the method described in Lannergård et al. (2021). The method adopted for the event definition was based on the change in daily discharge (mm d⁻¹). The events started on (1) the rising limb of the hydrograph (the previous observation should be on the falling limb), (2) with an increase <3% from one observation to the next (x₁ = 0.03), (3) no threshold was set for excluding events during low flow conditions, meaning an event could start during the full study period (x₂ = 0). However, events with a magnitude lower than 0.4 mm h⁻¹ were omitted. To mark the end of an event, the decrease in discharge from one observation to the next was set to 20% (x₃ = −0.2) and the observed discharge was less than the baseflow decay function. The baseflow decay function is a baseline, starting at the 1st day of the event, with a starting value of the discharge during that day. It is then decreasing with 0.1% per day (further explained in Lannergård et al., 2021). The identification of the events was done in Python 3.9.

The hydrological control on ditch CO₂ concentrations was further explored using CO₂-Q hysteresis analysis and where the shape and direction of the hysteresis loops of each hydrological event were identified (Evans and Davies, 1998; Wallin et al., 2020). The shape of the hysteresis loops has been related to the timing of CO₂ and discharge responses depending on catchment characteristics and hydrological pathways (Evans and Davies, 1998). A clockwise shape indicates a system where CO₂ peaks before discharge, and could indicate a transport limited source of CO₂ but that eventually reaches a source limitation in the available catchment soil or stream bed CO₂ pool. An anti-clockwise pattern typically indicates a diluting effect on CO₂ suggesting a source limitation occurring already at low discharge increases (Wallin et al., 2020). A complex CO₂-Q loop, instead, indicates that the CO₂ pattern is not related to the changes in hydrology or that any relationship is interfered by additional controlling processes. Hysteresis indexes were calculated according to Lloyd et al. (2016) using a 20% increment of the discharge range for each event. For further analysis, the hysteresis indexes were combined with event characteristics (duration of event, season, Q_{max, mean, range, peakhour}, CO_{2mean, range, peakhour}, shape) and environmental conditions (water temperature, precipitation, EC_{mean, range}, SR_tot) (Supplementary Table 2). A principal component analysis (PCA) was used to explore the entire event data set (Supplementary Table 2) and to evaluate the different temporal controls on ditch CO₂ concentration dynamics.

Finally, statistical differences in chemical variables between the DC2 and C2 catchments were assessed using the non-parametric Wilcoxon test and were considered significant if p < 0.05. The software JMP Pro 15 (SAS Institute Inc., Cary, NC, USA) was used for all statistical calculations.

4. Results

4.1. Seasonal variation in hydrochemical variables and CO₂ concentrations

Mean air temperature and total precipitation registered at TEA for the full study period (8 May−28 October 2021) were 11.7°C and 571 mm, respectively (Figure 2A). Precipitation was distributed relatively evenly throughout the months of the study period (June–October, ~20%/month), with July and October being the months with the highest precipitation (118 mm each) and July 30 the day with the highest daily precipitation (57 mm). Mean ditch water temperature over the study period was 10°C and ranged from −0.4 to 19.7°C (Figure 2B). High diel variability in water temperature was evident, with daily temperature amplitudes being closely related to the daily mean SR (R² = 0.88, p < 0.0001). Mean and median daily discharge (Q) for the study period were 1.86 and 0.98 mm d⁻¹, ranging from 0 to 17 mm d⁻¹ (Figure 2C). The total number of dry days (i.e., days without any registered water flow over the V-notch weir) was 19 out of 174, or 11% of the study period. According to frequency analysis, 73% of the days had a daily mean discharge below the overall mean for the study period (1.86 mm d⁻¹), but the accumulated discharge during days with a discharge higher than the overall mean accounted for 71% of the total discharge (324 mm). Daily precipitation and discharge were positively related (R² = 0.53, p < 0.0001, Supplementary Figure 2) with an average response time between precipitation event and discharge peak of 2 h. The mean pH was 4.6 (n = 15) and the electrical conductivity was on average 35.5 μS cm⁻¹ (range: 22.9–66.0 μS cm⁻¹) and was positively related to variations in discharge (R² = 0.38, p < 0.0001).

FIGURE 2

Figure 2. Time series of (A) daily mean air temperature (line) and daily precipitation (bars) registered at TEA; (B) water temperature (blue) and shortwave incoming radiation, SR (red); (C) hourly discharge and electrical conductivity (EC); (D) dissolved CO₂ for the study period 8 May−28 October 2021 in DC2. Note the reverse axis for SR.

The mean CO₂ concentration at DC2 for the whole study period was 2.47 mg C L⁻¹ (IQR = 0.51 mg C L⁻¹) [corresponding to a partial pressure (pCO₂) of 2,848 μatm] and were ranging from 1.81 to 3.50 mg C L⁻¹ (pCO₂ range: 461–7,183 μatm) (Figure 2D). This should be compared with an estimated atmospheric equilibrium concentration of dissolved CO₂ of 0.23 mg C L⁻¹ (assuming an atmospheric CO₂ concentration of 417 ppm and an average water temperature of 10°C representing the entire study period). Ditch CO₂ concentrations displayed a clear seasonal pattern with higher CO₂ during summer than in spring and autumn. CO₂ increased from May until approximately the beginning of August and then started to decrease. The highest measured CO₂ concentrations occurred at midnight on 9 July. The CO₂ concentration displayed a bimodal distribution with frequency peaks at ~2.1 and ~2.7 mg C L⁻¹ (Supplementary Figure 3). The higher peak (n = 1,194) represents data collected during spring and late summer as well as on many days during the autumn period, whereas the lower peak (n = 797) was attributed to some days in early June and the summer period (July–August).

4.2. Light and temperature controls on variation in CO₂

Mean daily CO₂ concentration was positively related to daily mean water temperature for the full study period (R² = 0.75, p < 0.0001, Figure 3A). On a diel scale, CO₂ concentrations commonly displayed a cyclic pattern that were developed during early summer, with daily CO₂ amplitudes reaching maximum 1.07 mg C L⁻¹ in July, and then progressively decreasing to reach its minimum of 0.04 mg C L⁻¹ in October (Supplementary Figure 4). The amplitude of the diel CO₂ concentration was related to the daily accumulated shortwave radiation (R² = 0.24, p < 0.0001, Figure 3B) as well as to daily mean water temperature (R² = 0.59, p < 0.0001, Figure 3C). During the full period of study, daily CO₂ concentrations were higher during night- (22:00–7:00) than day hours (Supplementary Figure 5), with the highest and lowest concentrations within a diel cycle at around 1:00 and 14:00, respectively (mean values: 2.59 and 2.32 mg C L⁻¹). By separating the diel analysis by month (Figure 4), the highest monthly mean CO₂ concentration (3.18 mg C L⁻¹) was measured at 00:30 in July. July also showed the largest mean daily amplitude in CO₂ concentration (ΔCO₂ = 0.52 mg C L⁻¹), with the minimum values recorded at 13:30. Among the studied months (May–October), a shift in the hour of the day when CO₂ peaked was noticed. Daily maximum peak, [CO₂]_max, was recorded at around 23:30 during May and June, at 00:30 in July, 2:30 in August, 6:00 in September and finally occurred at 7:30 in October. Daily minimum peak, [CO₂]_min, was recorded at 12:00 in May but gradually shifted in time, from 13:00 in June, to 13:30 in July, 14:30 in August and, finally, 16:00 in September and October.

FIGURE 3

Figure 3. (A) Daily mean ditch concentration of CO₂ at DC2 as a function of daily mean water temperature; Diel amplitudes in CO₂ concentrations at DC2 as functions of (B) daily accumulated incoming short-wave radiation, SR and (C) water temperature.

FIGURE 4

Figure 4. Monthly distribution in ditch CO₂ concentrations at DC2 over the full study period presented on a diel (24 h) basis with each boxplot representing a 30 min period. The red line displays the mean diel CO₂ concentration pattern.

4.3. Hydrological control on variation in CO₂

Significant negative logCO₂-logQ relationships were found on a monthly basis from May to August with variable explanatory power (R² = 0.18–0.71) (Figure 5), with highest R² in May and lowest in June. The slope of the logCO₂-logQ relationships were classified as chemostatic for the entire study period, but progressively became less negative for every month from −0.09 in May to −0.02 in August. In contrast, during September and October no significant logCO₂-logQ relationships were identified suggesting low influence of variations in runoff on CO₂ at the monthly basis during autumn.

FIGURE 5

Figure 5. Log median CO₂ concentrations at DC2 as a function of Log median discharge for the different months of the study period.

4.4. Event based evaluation of controls on CO₂

Based on the event identification (see method section above), 19 hydrological events were identified during the full study period (Supplementary Figure 6) with different characteristics (Supplementary Table 2). The duration of each event varied between 2 and 10 days, with an average of 5.6 days. The events further covered a wide discharge range (between 0.04 and 1.8 mm h⁻¹, representing 92% of the monitored Q range). Three different shapes of CO₂-Q loops were identified by the calculated hysteresis indexes, (1) clockwise (CW) loop with positive indexes during the full event, (2), anticlockwise (AW) loop with negative indexes during the full event, (3) complex (complex) loop that contained both positive and negative indexes during the event (Supplementary Figure 7). Out of all the CO₂-Q loops, nine displayed CW shape, four displayed the AW shape and six displayed the complex “figure eight” shape (Figure 6). CW and complex hysteresis patterns occurred independent of season or size of the hydrological event. AW loops, on the other hand, were more common during the summer period and at medium discharge (0.07 < Q < 0.12 mm h⁻¹, Supplementary Figure 7).

FIGURE 6

Figure 6. Examples of the three different identified event types (CW, event no. 8, AW, event no. 9 and complex, event no. 13, see Supplementary Table 2). To the left, the three types of hysteresis loops with normalized CO₂ concentration (y-axis) and normalized discharge (x-axis). To the right, the time series of discharge (left y-axis) and CO₂ concentration (right y-axis) over time for each event (x-axis). Cold colors (blue/light blue) represent the beginning of the event and warm (red/orange) colors the end of the event.

Results of the PCA showed that the first two principal components (PC1 and PC2) accounted for 69% of the variation (Figure 7). The PCA showed a good assemblage of the events based on their seasonality (spring, summer, and autumn), but not according to the shape of the hysteresis loops. Summer events were generally positively associated with mean CO₂ and range of CO₂ variation, total SR, and mean water temperature. In contrast, these descriptive characteristics were generally negatively related to autumn events. Flow related characteristics (max, range and mean Q) were not related to CO₂ describing characteristics (or summer events). The total precipitation generating each event was closely related to the duration of the event, but also to the range in measured EC. The mean EC was closely associated to both the maximum (Q_Max) and range (Q_Range) in discharge generated at each event. Finally, no correlation was found between the CO₂ and Q peak hours. The shapes of the hysteresis loops were not clearly related to any of the descriptive characteristics during events.

FIGURE 7

Figure 7. Biplot of the principal component analysis (PCA) based on event-specific data (Supplementary Table 2). The arrows represent the event characteristics and markers in color represent the single events. Each event is presented by (1) season, with different marker colors (red: spring, green: summer, blue: autumn) and (2) shape of the hysteresis loop, by marker type, circles: CW, squares: AW and crosses (×): Complex.

4.5. Comparison with forested catchment

The CO₂ concentration time series from DC2 where further compared with a corresponding time-series collected simultaneously from the completely forested catchment (C2) to explore any differences in the observed CO₂ patterns between catchments with distinct land cover (Figure 8). It was evident from the 2-month comparison (May–June) that the two catchments differed in water chemistry (Table 1). DC2 and C2 displayed similar pH (4.6) but DC2 had generally higher EC than C2. Mean DOC concentrations in DC2 were twice the concentrations in C2. Nutrient levels were 4–14 times higher in DC2 than C2. Both the overall magnitude in CO₂ concentration and its associated diel dynamics were different between the two headwater catchments. The mean CO₂ concentration in DC2 for the 2 months was 2.32 mg C L⁻¹ (range: 1.81–3.27 mg C L⁻¹), to be compared with 1.51 mg C L⁻¹ (range: 1.22–2.86 mg C L⁻¹) for C2. On average, the amplitude of the diel cycle recorded at DC2 was 0.41 mg C L⁻¹, or four times as high as in C2 (0.10 mg C L⁻¹). All comparison between water chemistry variables at the two sites, except for pH, were significantly different (p < 0.05). The frequency distribution of CO₂ concentration for DC2 showed a left-skewed unimodal distribution peaking around 2.05–2.15 mg C L${}_{,}^{-1}$ accounting for 20% of the observations, while C2 has a left-skewed bimodal distribution (peak values at around 1.25–1.40 and 1.65 mg C L${}_{,}^{-1}$ representing 32% and 9% of the total, respectively) (Supplementary Figure 8). The LogCO₂ vs. LogQ relationship of C2 exhibited a stronger linear fit (R² = 0.82, p < 0.0001) and a steeper slope (i.e., −0.12) than DC2 (R² = 0.25, slope: −0.08, p < 0.0001) (Supplementary Figure 9). The water temperature time series of the two sites showed an overall similar seasonal pattern but with a much more pronounced diel water temperature amplitude (5.0°C) in DC2 than in C2 (2.0°C). It is to be noted that in C2 the initial discharge peak in May is attributed to the snowmelt that was still ongoing in the forested catchment for the two 1st weeks of the comparison. In contrast, for the clear-cut dominated DC2, the snow had already melted, and the discharge peak already passed prior to the comparing 2-month period. Despite the discharge peak induced by the snowmelt at C2, CO₂ stream concentrations were relatively stable.

FIGURE 8

Figure 8. Time series of CO₂, discharge, and water temperature for DC2 (red) and C2 (blue) during the period 8 May-5 July 2021 (2 months).

TABLE 1

Table 1. Water chemistry at the outlets of DC2 and C2 catchments manually collected during the period 8 May-5 July 2021 (n = 6).

5. Discussion

Headwater streams and ditches are known hotspots for atmospheric CO₂ emissions, and the hydrological export of CO₂ from catchment soils is commonly found as the main source in boreal regions (Striegl and Michmerhuizen, 1998; Rasilo et al., 2012; Riml et al., 2019). However, current large-scale estimates suffer from limited information regarding how these emissions are affected by human induced disturbances. Forestry is one such major disturbance, which is known to alter a wide range of hydrological and biogeochemical processes, but the effect on CO₂ concentration dynamics and associated emissions in connected drainage networks are largely unknown.

Here we observed a mean clear-cut ditch CO₂ concentration (2.47 mg C L⁻¹) that was relatively high compared to both what was observed in the comparing forested catchment (C2), but also compared to other high-resolution monitoring studies of forested headwaters found in the literature. For example, mean CO₂ concentration levels found in a study of streams draining different boreal and temperate forest ecosystems were generally lower (range of means: 0.73–2.13 mg C L⁻¹) than the mean of the current study (Dinsmore et al., 2013). Furthermore, expressed as partial pressure (pCO₂), the range found in our study (461–7,183 μatm) encompassed the full pCO₂ range found by Crawford et al. (2017) covering multiple ecosystem types from alpine tundra (434–536 μatm) to temperate forests (2,815–6,225 μatm). The overall seasonal CO₂ concentration pattern found, characterized by a summer peak in CO₂, is typically observed across different types of ecosystems (i.e., arctic tundra, boreal forest, temperate forest, temperate peatlands, and alpine regions) (Crawford et al., 2017). The observed seasonal CO₂ concentration pattern suggests a respiratory source further supported by the close relationship between mean daily CO₂ concentration and water temperature (Figure 3A). Respiration is strongly controlled by temperature (Del Giorgio and Williams, 2005; Yvon-Durocher et al., 2012), and microbial mineralization of soil organic material is known to increase after clear-cut harvest due to increased soil temperatures caused by the absence of shading trees (Liski et al., 1998; Schelker et al., 2013).

In addition to the observed overall seasonal patterns, the high-frequency measurements allowed us to capture ditch CO₂ concentration dynamics on short timescales (hourly or daily). For a majority of the study period, a clear diel signal was recorded with large day-to-night differences in CO₂ concentration (mean and medium Δ: 0.35 and 0.30 mg C L⁻¹, respectively corresponding to mean and median ΔpCO₂ of 1,105 and 885 μatm). These diel CO₂ cycles were particularly pronounced in amplitude during June and July (reaching up to 1.1 mg C L⁻¹ or 4,078 μatm) but became more constrained toward the autumn. The mean of observed daily CO₂ amplitudes was comparatively high in relation to other continuous CO₂ measurements in low-productive arctic and alpine streams exhibiting diel fluctuations. For example, Rocher-Ros et al. (2020) registered a mean summer amplitude of about 900 μatm in a Swedish arctic tundra stream, and Peter et al. (2014) found a mean diel CO₂ amplitude of about 370 μatm in an alpine stream, with amplitude values that peaked at 845 μatm in the summer during extended base flow. In contrast, much higher diel CO₂ amplitudes were found in a nutrient rich agricultural stream in Sweden (medium amplitude: 2.03 mg C L⁻¹, 2,974 μatm) (Wallin et al., 2020). We suggest that the pronounced diel cycles found in the ditch of the current study were driven by aquatic primary production consuming CO₂ during daytime, as the minimum concentrations were recorded during mid-day and with a gradual shift in timing toward the afternoon during autumn (Rocher-Ros et al., 2020, 2021; Gómez-Gener et al., 2021). The pronounced diel patterns we observed further suggests that the temporal control on ditch CO₂ has changed after clear-cut as a consequence of the elevated DOC and nutrient concentrations (compared to the forested catchment, C2) as well as due to increased light availability. Diel dynamics of the observed amplitude are typically observed in open canopy systems and is attributed to primary production largely driven by high light exposure (Crawford et al., 2017; Gómez-Gener et al., 2021).

Hydrology (i.e., variations in discharge) commonly plays an important role in regulating stream CO₂ dynamics across different environments although with site-specific CO₂-Q relationships (Dinsmore et al., 2013; Riml et al., 2019; Wallin et al., 2020). In our case study, the influence of variations in discharge on growing season ditch CO₂ concentrations was complex and not easy to disentangle from the metabolic diel dynamics. Median daily ditch CO₂ concentration was found negatively related to median daily discharge during spring and summer (May to August) but not during autumn (September to October) (Figure 5). However, the slopes of the monthly CO₂-Q relationships indicate a general “chemostatic” response in relation to variable discharge, implying a relatively low hydrological influence on ditch CO₂ concentrations. This suggests that (1) the terrestrial (or in-ditch) source for CO₂ is relatively stable in its hydrological connectivity, or (2) that non-hydrological processes counterbalance any variations in CO₂ caused by a variable discharge (Rehn et al., 2023). The response in CO₂ following individual hydrological events was in contrast highly variable. The CO₂-Q hysteresis plots were in many cases influenced by the diel CO₂ fluctuations leading to tangled hysteresis shapes, making it hard to extract information. Only one extreme hydrological event (i.e., event no. 8, Figure 6) showed an unequivocal and straightforward response in CO₂ concentrations from the analysis of the hysteresis loops. It is worth noting that this event registered both the highest incoming SR as well as the second highest total precipitation and had a runoff peak during day hours (Supplementary Table 2), when CO₂ is consumed due to high primary production rates. Other runoff events (some with comparable intensity) had a significant impact on the CO₂ level, but either had runoff peaks during night hours (e.g., events no. 4 and 13) or occurred in the autumn (e.g., events no. 16 and 18), when the SR is low. As a result, the simultaneous metabolic signals made the hysteresis plots complex. We suggest that the different hydrological responses on CO₂ are related to the timing of an event, during what season the event occurs, whether the event follows an extended dry period, or when during the day (day or night) the runoff peaks. The absence of a clear response in CO₂ concentration for most of the events, suggests that variations in runoff did not have a major control on ditch CO₂ dynamics and were instead overridden by the stronger light and temperature induced metabolic control operating at the diel timescale (Bernal et al., 2022).

The importance of in-situ metabolic processes controlling CO₂ dynamics was finally supported by the event based and control integrated PCA analysis (Figure 7). Both the magnitude in CO₂ concentration and range in diel CO₂ concentration amplitude were closely related to both daily total SR and mean water temperature. In contrast, the PCA displayed low influence on CO₂ by any of the hydrological metrics. The elevated in-situ control on ditch CO₂ following forest harvest was further evident when comparing continuous data collected from DC2 with the completely forested catchment C2 included in the KCS and located within 10 km from DC2. C2 is representative for the conditions at DC2 as they were prior to the forest harvest and is used as one of two forest control catchments within the experimental design of the TEA. From the comparative analysis between DC2 and C2 clear differences were observed, both in concentration magnitude and amplitude of the diel CO₂ cycles, but also in diel water temperature patterns. This suggests that the collective conditions after clear-cut, with elevated solar radiation exposure and increased DOC and nutrient concentrations alter the in-situ ditch ecosystem function, and by that enhancing the importance of temperature- and light-induced metabolic control on the CO₂ dynamics. Furthermore, the slope of the logCO₂-logQ relationship observed in C2 was more negative than in DC2 (−0.12 and −0.08, respectively) suggesting a higher runoff control on CO₂ concentration (Supplementary Figure 9). This comparative part of the study further supports our hypothesis that in-situ metabolism is a key driver of aquatic CO₂ dynamics in clear-cut catchments.

We acknowledge that the current study only represents a single ditch and observed patterns are likely site-specific. However, we believe our finding of an increased metabolic control on CO₂ dynamics in forest ditches and streams following clear-cut harvest should be valid across regions with similar climatic conditions and forest management. The increased short-term CO₂ concentration dynamics following forest harvest will also lead to altered emissions patterns. To what extent these altered patterns will influence total annual emissions is uncertain and will require detailed investigations. Klaus et al. (2018) found that despite significant increases of CO₂ in groundwater of clear-cut affected catchment soils, no change in GHG (including CO₂) fluxes in adjacent streams were detected within 3 years after the treatment. The authors explained the mismatch in patterns between ground- and stream water with that the trees left in the riparian zones most likely acted as an effective buffer zone mitigating stream GHG emissions. The findings of the current study suggest that increased aquatic productivity might play a role in consuming the elevated soil CO₂ export following forest harvest. Thus, inorganic C will be converted into organic forms shortly after being transported across the soil-water interface.

We conclude that CO₂ concentration dynamics in forest ditches affected by clear-cut harvest are driven by a complex interplay of light and hydrologically induced processes. Despite the common perception of forest ditches as nutrient-poor systems typically showing low metabolic rates, our findings suggest that metabolism, and primary production specifically, exerts significant control on short-term ditch CO₂ concentration dynamics. In contrast, variations in discharge displayed a comparatively less dominant influence on the variation in CO₂ concentrations. The high CO₂ concentration dynamics and the associated metabolic controls should be considered when scaling CO₂ emissions across boreal landscapes impacted by clear-cut forestry. To improve our understanding of these processes, we recommend that future studies combine measures of C export/emission with in-situ metabolism and that these are conducted over longer time scales (i.e., >single growing season). Overall, our results emphasize the need for more comprehensive and detailed investigations of the factors regulating CO₂ dynamics in forest ditches and their implications for the landscape-scale C budgets.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

AZ and MW designed the study. AZ carried out most of the fieldwork, analyzed the data, and wrote the first draft of the manuscript. MW, KE, EL, EM, and HL further provided scientific insight to the analysis and interpretation of the data. All authors commented on earlier versions of this paper.

Funding

The study was supported by funding from the Swedish Research Council Formas (2019:01105) and Oscar and Lili Lamm Foundation (DO2019-0010). The KCS infrastructure and long-term data collection have been funded by the Swedish Research Council VR (SITES, Grant No. 2021-00164), several individual Formas (e.g., 2018-00723), and VR grants, SKB, as well as by the Knut and Alice Wallenberg and Kempe Foundation (Grant 2018.0259). The TEA infrastructure was initiated and co-funded by the European Union GRIP on LIFE IP project (LIFE16IPE SE009 GRIP) led by the Swedish Forest Agency.

Acknowledgments

We would like to thank the land owner Holmen Skog. Matthias Peichl is acknowledged for sharing radiation data. The authors would also like to thank all the skilled and dedicated field and lab personnel within KCS/TEA which have enabled the current study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frwa.2023.1250068/full#supplementary-material

Footnotes

1. ^Laudon, H., Mosquera, V., Eklöf, K., Järveoja, J., Karimi, S., Krasnova, A., et al. (2023). Consequences of rewetting and ditch cleaning on hydrology, water quality and greenhouse gas balance in a drained northern landscape. (Manuscript submitted for publication).

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