Stream water hydrochemistry as an indicator of carbon flow paths in Finnish peatland catchments during a spring snowmelt event

Science of the Total Environment 409 (2011) 4858–4867

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Stream water hydrochemistry as an indicator of carbon flow paths in Finnish peatland catchments during a spring snowmelt event Kerry J. Dinsmore a,⁎, Michael F. Billett a, Kirstie E. Dyson a, Frank Harvey a, Amanda M. Thomson a, Sirpa Piirainen b, Pirkko Kortelainen c a b c

Centre for Ecology & Hydrology, Bush Estate, Penicuik, Scotland, EH26 0QB, United Kingdom Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, 80101, Finland Finnish Environment Institute, P.O. Box 140, 00251 Helsinki, Finland

a r t i c l e

i n f o

Article history: Received 21 January 2011 Received in revised form 27 July 2011 Accepted 28 July 2011 Available online 31 August 2011 Keywords: Base cation Flow path Peatland Snowmelt Carbon Boreal

a b s t r a c t Extreme hydrological events are known to contribute significantly to total annual carbon export, the largest of which in Arctic and boreal catchments is spring snowmelt. Whilst previous work has quantified the export of carbon during snowmelt, the source of the carbon remains unclear. Here we use cation hydrochemistry to trace the primary flowpaths which govern the export of carbon during the snowmelt period; specifically we aim to examine the importance of snowpack meltwater to catchment carbon export. The study was carried out in two forested peatland (drained and undrained) catchments in Eastern Finland. Both catchments were characterised by base-poor stream water chemistry, with cation concentrations generally decreasing in response to increasing discharge. Streamflow during the snowmelt period was best described as a mixture of three sources: pre-event water, snowpack meltwater and a third dilute component we attribute to the upper snow layer which was chemically similar to recent precipitation. Over the study period, pre-event water contributed 32% and 43% of the total stream runoff in Välipuro (undrained) and Suopuro (drained), respectively. The results also suggest a greater near-surface throughflow component in Suopuro, the drained catchment, prior to snowmelt. CO2 and DOC concentrations correlated positively with cation concentrations in both catchments indicating a common, peat/groundwater flowpath. CH4 concentrations were significantly higher in the drained catchment and appeared to be transported in near-surface throughflow. Meltwater from the snowpack represented an important source of stream water CO2 in both catchments, contributing up to 49% of total downstream CO2 export during the study period. We conclude that the snowpack represents a potentially important, and often overlooked, transient carbon store in boreal snow-covered catchments. Crown Copyright © 2011 Published by Elsevier B.V. All rights reserved.

1. Introduction Peatlands represent a major terrestrial carbon store, the long-term stability of which is currently the focus of a significant research effort (e.g. Billett et al., 2010; Limpens et al., 2008). The importance of the aquatic pathway as a conduit for catchment carbon release is increasingly being recognised in budget studies (Billett et al., 2004; Dinsmore et al., 2010; Nilsson et al., 2008; Worrall et al., 2009). For example, surface waters associated with peatlands are repeatedly shown to be supersaturated in CO2 and CH4 with respect to the atmosphere, and hence represent a loss of carbon both via downstream export and evasion (degassing) to the atmosphere (Billett et al., 2004; Dinsmore et al., 2009, 2010; Hope et al., 2001; Rantakari et al., 2010; Richey et al., 2002). Peatland drainage waters are also associated with high concentrations of dissolved and particulate organic carbon (DOC and POC) (Aitkenhead et al., 1999; Billett

⁎ Corresponding author. Tel.: + 44 131 445 8583. E-mail address: [email protected] (K.J. Dinsmore).

et al., 2004). Due to the cool and acidic nature of streams draining many northern peatlands, in-stream productivity is often minimal and carbon is primarily allochthonous in origin (Billett et al., 2006; Dinsmore and Billett, 2008; Hope et al., 2004). The source of carbon, and hence the instream concentration, is therefore strongly dependent on water flow path. A significant proportion of annual aquatic (riverine) export from peatlands occurs during high-flow events such as stormflow or snowmelt. The spring snowmelt is the most extreme hydrological event annually in many northern catchments, and alone has been estimated to export 52–66% of total annual runoff in northern Sweden (Laudon et al., 2004) and ~ 50% of annual runoff from Finnish headwater streams (Kortelainen et al., 1997). Dyson et al. (2010) estimated that 37–45% of the total aquatic carbon exported in 2008 from the same two Finnish catchments used in this study, occurred during the snowmelt period. Aquatic carbon export during the snowmelt period may therefore represent an important component of terrestrial carbon budgets in northern peatlands. To understand how changes in precipitation patterns and snow coverage in the

0048-9697/$ – see front matter. Crown Copyright © 2011 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.scitotenv.2011.07.063

K.J. Dinsmore et al. / Science of the Total Environment 409 (2011) 4858–4867

future are likely to impact carbon export across the boreal region, we need to understand the primary water flowpaths during this snowmelt period. Previous studies disagree as to the primary source of stream discharge during snowmelt events (Carey and Quinton, 2004). Whilst some studies conclude the primary source is pre-event water displaced by the melting snow (Gibson et al., 1993; Metcalfe and Buttle, 2001), others suggest that increased stream discharge is almost entirely the meltwater itself (Cooper et al., 1991; McNamara et al., 1997). This variability between studies is likely to result from catchment specific differences, for example Laudon et al. (2007) showed that the proportion of peatland within a catchment significantly altered the primary source of stream discharge during melt events. These different conclusions potentially lead to very different magnitudes and forms of carbon export. The snowpack has previously been shown to contain significant concentrations of trapped CO2 (Dyson et al., 2010; Seok et al., 2009; Sommerfeld et al., 1991) and may therefore itself represent a transient carbon reservoir which contributes to aquatic fluxes during snowmelt. A significant proportion of boreal peatlands (54% of peatlands in Finland) have been drained for forestry (Peltola, 2009). Drainage ditches lower the water table and change the dominant flow path through the catchment, altering the sources of stream discharge and therefore potentially affecting the solute load. Lower water tables also change the thermal properties of the peat potentially altering frost depth and the timing of spring snowmelt (Latja and Kurimo, 1988). Hence northern peatlands are affected by both natural seasonal (freeze/thaw) and long-term man-made (e.g. drainage) changes, both of which are likely to influence hydrological flow paths and affect the export rate of carbon and other macronutrients. Understanding the effects of drainage is therefore important when deciding best-practise for future land management. Solute concentrations in surface waters are temporally dynamic during hydrological events, particularly during storm or snowmelt periods when soil water conditions vary along the course of the primary flow path (Ahearn et al., 2004; Dinsmore and Billett, 2008; Harriman et al., 1990). High surface water concentrations occur when the primary flow path links an area of solute production (or storage) to the stream channel. Assuming the solute is conservative and is not being actively removed from the stream, decreasing concentrations are the result of dilution as the primary flow path moves away from the zone of solute production. Stream water solutes such as Ca, Mg, Na and Si, produced primarily by chemical weathering, often follow a simple dilution model as stream discharge increases and flow paths become generally shallower (e.g. Billett and Cresser, 1996). However concentration–discharge relationships can be obscured by either external inputs (e.g. precipitation inputs of Na in oceanic regions) or biological/microbial regulation of solutes (e.g. root uptake of K). Other solutes such as Al and DOC (predominantly in complexed form in acidic soils), often show positive concentration–discharge relationships resulting from increased lateral flow through organic-rich surface horizons (Billett and Cresser, 1996). Two hydrochemical approaches are commonly used to separate stream water sources; natural or artificial tracers (e.g. Soulsby et al., 2004), or mixing models based on known source hydrochemistry (e.g. Laudon and Slaymaker, 1997). Here we use an end-member mixing model approach based on cation hydrochemistry to deconstruct the hydrographs of two contrasting Finnish peatland streams during the spring snowmelt event. Specifically the study aimed to answer the following questions: (1) what proportion of carbon was transported via ‘event’ (melting snowpack) and ‘pre-event’ (below ground) water sources, (2) does drainage influence the contribution of various water sources to total runoff and hence the carbon export from the catchments, and (3) can the export of different forms of aquatic carbon be individually attributed to specific hydrological pathways? This study is complemented by two additional papers: an analysis of the δ 13C and 14C composition of the exported C species (Billett et al.,

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in press), and a full quantification of carbon losses during the snowmelt period (Dyson et al., 2010). 2. Methods and materials 2.1. Site description Two adjacent study catchments, Välipuro and Suopuro, were selected from within a managed forest system in the commune of Sotkamo in North Karelia, Eastern Finland (63°52′N, 28°30′E) (Fig. 1). Both sites form part of the long term ‘Nurmes Study’ (Ahtiainen et al., 1988), launched in 1978, which aims to investigate the effects of silvicultural practices on the environment, in particular the effects on runoff and water quality. Both sites are old growth mixed coniferous forest dominated by Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.), overlaying an under-storey of dwarf shrubs (Vaccinium myrtillus L. and Vaccinium vitis-idaea L.) and feather mosses (Pleurozium schreberi (Brid.) and Hylocomium splendens (Hedw). B. S. G.). Hence the site was classified as Vaccinium-myrtillus type under the Finnish site classification system (Cajander, 1949; Piirainen et al., 2002). Välipuro (area 0.86 km2) is an undrained (control) catchment; in contrast, Suopuro (area 1.13 km 2) has had ~13% of the land area drained for forestry (1983). Both catchments were affected by small amounts of clearcutting between 1999 and 2001 (Ahtiainen et al., 1988; Dyson et al., 2010). Soils are primarily humic podzols and shallow histosols developed on shallow till material with underlying acidic bedrock of gneissose granite and granodiorite (Mannerkoski et al., 2005). The proportion of peatland (bog plus fen) in the Välipuro and Suopuro catchments is 56% and 70%, respectively. Peat depth varies from 2.5 m to 3.2 m (Ahtiainen et al., 1988; Latja and Kurimo, 1988). The catchments are both relatively flat and low-lying with altitudes ranging from 200 to 220 m a.s.l. (Ahtiainen et al., 1988). The main streams, which in both catchments have a gauging station and V-notch weir to continuously measure discharge, are monitored by the Finnish Forest Research Institute (METLA) and North Karelia Centre for Economic Development, Transport and the Environment. Water samples have been collected monthly since 1978 for analyses of various hydrochemical parameters. The streams are typically between 0.5– 1.5 m wide and 0.5–1.0 m deep and are characterised by low pH and low base cation concentrations (Table 1). The 2008 runoff values for Välipuro and Suopuro were 515 and 461 mm respectively, higher than the long-term (1978–2008) average of 354 (±63) and 372 (±74) mm. Maximum daily runoff values in 2008 (corresponding to the peak snowmelt) for Välipuro and Suopuro were 18.5 and 29.4 mm respectively, similar to the long-term average maximums of 15.3 (±4.6) and 29.4 (±12.4) mm. This part of Eastern Finland experiences a continental climate, with a range of ~26–27 °C between mean summer and winter temperatures. The long-term (1979–2006) average air temperature and precipitation are 1.5 °C and 612 mm, respectively, with ~ 40% of precipitation falling as snow (Dyson et al., 2010). Annual snow cover lasts an average of 5 to 6 months. 2.2. Methods We carried out measurements between 7th April and 1st June 2008. The main snowmelt event, as shown by elevated discharge levels, began on the 30th April and finished 7 days later on the 6th May. During the study period mean daily air temperatures ranged from −4 °C to 14 °C and the sites received a total of 65 mm of precipitation, ~ 45% of which occurred as snowfall (Fig. 2). A sensor based system was set up at both sites to continuously measure air temperature (Cambell Scientific Inc© CS108), soil temperature (Cambell Scientific Inc© CS108), water depth (Druck PDCR 1830), stream temperature and conductivity (Cambell Scientific

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N

Nurmes study area

Lieska

Hietäjarvi

Joensuu

Helsinki

Fig. 1. Location of the Välipuro (control) and Suopuro (drained) study catchments within Finland. The inset shows the catchment shape, stream position and approximate forestry/ open peatland cover (shaded areas represent open peatland, un-shaded areas represent tree cover). The Finnish map represents a schematic only and is not drawn to scale.

Inc© CS547A), stream water pH (Cambell Scientific Inc© CSIM11) and both soil and water CO2 concentrations using non-dispersive infrared (NDIR) sensors (Vaisala CARBOCAP® GMT220) (Johnson et al., 2010), at a 10-minute sampling frequency. In-stream sensors were fixed ~ 10 cm above the streambed. Water samples were collected every 1– 3 days for the determination of POC, DOC, dissolved inorganic carbon (DIC), base cations (Ca, Mg, Na, K), Si and Al. Headspace samples (see below) were also collected simultaneously to calculate the concentration of dissolved CO2 and CH4. Due to the different collection and analysis methods, CO2 and CH4 are considered independently throughout the study and are not included in references to DIC or DOC. Three bulked snow samples were collected from within the snow-pack covering a range of snow depths and analysed for Ca, Mg, Na, K, Si and Al concentrations using the same method described for water samples. Water samples for the determination of POC (defined as the fraction of organic carbon that will not pass through a 0.7 μm pore size filter), DOC and DIC were filtered through pre-ashed, pre-weighed Whatman GF/F filter papers. POC was then calculated using the losson-ignition method (Ball, 1964). The filtrate was stored in the dark at 4 °C and analysed for DOC and total carbon (TC) on a PPM LABTOC

analyser using ultraviolet oxidation and infra-red gas analysis within 3 weeks of collection; DIC was calculated as the difference between TC and DOC. The analyser has a detection limit of 1% of the calibrated range, which for this study was 0–50 mg L −1; samples outside the calibrated range were automatically diluted and reanalysed. Samples for base cation analyses were acidified with HCl for storage and analysed at the Analytical Chemistry Laboratory, Centre for Ecology & Hydrology, Lancaster, on a Perkin Elmer DV 4300 ICP-OES (detection limits Ca, Mg, Na, K, Si b 0.01 mg L −1, Al b 0.02 mg L −1; uncertainty: Ca b 0.17 mg L −1, Mg b 0.11 mg L −1, Na b 0.22 mg L −1, K b 0.27 mg L −1, Si b 0.09 mg L −1, Al b 0.06 mg L −1). Dissolved CO2 and CH4 concentrations were calculated using the widely cited headspace method (e.g. Billett et al., 2004; Dinsmore et al., 2009; Kling et al., 1991). A 40 mL water sample collected from a depth of 10 cm was equilibrated with 20 mL of ambient atmosphere at stream temperature by shaking vigorously underwater for 1 min. The equilibrated headspace was then transferred to a gas-tight syringe; a sample of ambient atmosphere was also collected in a separate gas-tight syringe. Headspace samples were analysed on an HP5890 Series II gas chromatograph (detection limits: CO2 b 199 ppmv, CH4 b 1.26 ppmv) with a methanizer and flame ionisation detector. Stream water dissolved

Table 1 Comparison of hydrochemistry of two study catchments. The symbols * and ** represent significant catchment differences (paired t-test) during the snowmelt period at p b 0.05 and p b 0.01, respectively. Values (± standard deviation) for the snowmelt period are based on n = 40. For 2008 n = 11 (data supplied by SYKE). Site

pH ** Conductivity (μS cm−1) ** Gran alkalinity (μmol L−1) Ca (mg L−1) ** Mg (mg L−1) * Na (mg L−1) ** K (mg L−1) ** Si (mg L−1) ** Al (mg L−1) ** nd = Not determined.

Snowmelt period

2008

Välipuro

Suopuro

Välipuro

Suopuro

4.24 ± 0.07 31.8 ± 5.26 nd 0.45 ± 0.15 0.24 ± 0.09 0.71 ± 0.14 0.30 ± 0.07 2.35 ± 0.76 0.16 ± 0.04

4.13 ± 0.14 17.0 ± 2.88 nd 0.57 ± 0.19 0.23 ± 0.07 0.64 ± 0.09 0.21 ± 0.05 1.84 ± 0.59 0.07 ± 0.01

4.26 ± 0.19 31.8 ± 9.18 − 67.0 ± 34.9 0.57 ± 0.20 0.35 ± 0.13 0.90 ± 0.29 0.23 ± 0.15 nd 0.20 ± 0.06

4.66 ± 0.18 17.1 ± 2.39 − 10.9 ± 17.4 0.68 ± 0.26 0.31 ± 0.13 0.60 ± 0.13 0.12 ± 0.09 nd 0.01 ± 0.03

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-4

-5

-8 1-Apr

Temperature (°C)

Precipitation (mm)

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-10 11-Apr

21-Apr

1-May

11-May

21-May

31-May

Date (2008) Rain

Snow

Air temp

Fig. 2. Total precipitation (both rain and snow) and mean daily temperature during the study period.

gas concentrations were calculated from headspace and ambient concentrations using Henry's law. Instantaneous discharge was calculated using water depth measurements and a rating curve (Välipuro r2 = 0.999; Suopuro r2 = 0.999) provided by the Finnish Environment Institute (SYKE) from V-notch weirs located approximately 5 m downstream of the two measurement sites. 2.3. Data analyses Data sets were tested for normality and adjusted where appropriate using a natural log transformation. Data sets were also tested for autocorrelation by regressing data against itself at time t −1; where autocorrelation was significant (p b 0.05) the first order residuals were used in subsequent statistical analyses. Linear regressions were used on transformed data sets to test for significant causal relationships between variables, and paired T-tests used to test for differences between the two study catchments. Where ± is given, it refers to one standard deviation unless stated otherwise. Analyses were carried out on Minitab® (version 15) statistical software. Hydrograph separations used a simple end-member mixing model (Eq. 1) where QStream refers to the total stream discharge, CStream the stream water concentration, Qn the discharge associated with a particular flow path and Cn the end-member concentration of that flow path. QStream CStream = Q1 C1 + Q2 C1 + … + Qn C1

ð1Þ

Four components were tested as end-members in the analysis including ‘event’, ‘pre-event’, ‘peat water’ and ‘precipitation’. ‘Event’ concentrations refer to the measured snowpack chemistry. Pre-event water concentrations were determined by analysis of pre-melt base flow concentrations in the streamwater (n = 4). Piezometers could not be installed as the soil was frozen during most of the study, therefore below ground flowpaths could not be separated with the available data. An additional below ground component was tested using published literature on peat water concentrations within the study area (Mannerkoski et al., 2005), this is referred to as the ‘peat component’. Concentrations of Ca, Mg, Na and K in precipitation were collected from the Hietajärvi station (63°10′N; 30°43′E), a similar catchment approximately 130 km away, and analysed monthly by the Finnish Meteorological Institute for inclusion in the Finnish national air quality information system (ILSE). This study utilises concentrations measured in April and May 2008 only. A literature comparison of Hietajärvi precipitation chemistry and that measured previously at the study site showed no significant difference; hence we are

confident that the precipitation chemistry measured at Hietajärvi is representative of our sites. Individual discharge values were calculated by solving the resultant simultaneous equations for all available cations using Excel Solver (constraints were set to minimise the difference between the sum of component discharges and total stream discharge, and to ensure individual component discharge values ≥0). A sensitivity analysis was carried out by changing component concentrations by ±b50% and assessing the effect on the resulting hydrograph separations. The conservative behaviour of Ca and Mg in the catchment was assessed by analysing the linearity in bivariate plots. 3. Results 3.1. Stream hydrochemistry Mean stream water pH during the snowmelt period was 4.24 ± 0.07 and 4.13 ± 0.14 at the outflows of Välipuro and Suopuro, respectively (Table 1); the differences were small but statistically significant. In contrast, monthly samples collected throughout 2008 (unpublished data, METLA) indicated a higher pH in Suopuro compared to Välipuro. Mean conductivity during the spring snowmelt period was similar to the annual 2008 values in both catchments. Conductivity trends followed a similar pattern in both catchments, decreasing in response to increased discharge; the response in Suopuro was less clear than in Välipuro and a recovery to pre-event levels after peak discharge was not observed. Ion concentrations in the Välipuro stream increased in the order Al b Mgb K b Cab Nab Si; concentrations in Suopuro were in the order Al b K b Mg b Cab Nab Si (Table 1). Concentrations during this study were in general significantly higher (pb 0.01) in Välipuro than Suopuro, the only exception being Ca concentration which was significantly higher in Suopuro (Table 1). Concentrations of Ca, Mg, Na, Si, and Al in Välipuro all showed a negative response to increased discharge during snowmelt; no significant discharge effect was seen for K (Table 2). Significant negative discharge relationships were only seen for Mg, Na and Si in Suopuro (the Ca concentration–discharge relationship was almost statistically significant, p = 0.07). Concentrations of K in both catchments (Fig. 3) show similar maxima at approximately the time of peak discharge, both on the snowmelt hydrograph and in response to a rainfall event in late May (Fig. 2); however these relationships with discharge were not statistically significant in either catchment. Statistically significant linear relationships (Välipuro: r 2 = 0.98, p b 0.01; Suopuro: r2 = 0.97, p b 0.01) were observed between Ca and Mg concentrations in both catchments over the course of the study. This

K.J. Dinsmore et al. / Science of the Total Environment 409 (2011) 4858–4867

Välipuro

Suopuro

b0.01 − 0.15* − 0.12* − 0.34** − 0.20** b0.01 − 0.70** − 0.24**

− 0.39** b0.01 − 0.07† − 0.24** − 0.16** b0.01 − 0.39** b0.01

suggests that the behaviour of both cations was similar and therefore likely to be conservative, justifying their use in hydrograph separation.

3.2. Hydrograph separation Initial two-component hydrograph separations for both catchments considered only event and pre-event water, solving simultaneous equations for all six measured chemical species. Event water was assumed to primarily represent snowmelt runoff, and pre-event

15

0.6

10

0.4 5

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0 17-Apr

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25

Na

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10

0.4 5

0.2 0.0 2-Apr

4.5 4.0 Si 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 2-Apr

2-May Date

17-May

25

15 10 5

2-May Date

17-May

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0 17-Apr

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1-Jun 25

K

0.5

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0.30

0 17-Apr

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0.0 2-Apr

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0 17-Apr

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0.0 2-Apr

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Concentration (mg L-1)

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0.0 2-Apr

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Ca

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H Conductivity Ca Mg Na K Si Al

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Al

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Variable

water a combination of groundwater and deep-peat throughflow from below the frozen surface layer. End-member concentrations for event and pre-event water, i.e. concentrations in the snowpack and pre-event base flow, are given in Table 3. No solution to the simultaneous equations could be found with the difference between total discharge and the component sum constrained to b10% of total discharge. Removing this constraint resulted in solutions for both catchments with a significant proportion (N30%) of the total stream water discharge remaining unaccounted for. The hydrograph separation was repeated with the inclusion of a third component. Precipitation chemistry and peat solution chemistry were both included to see if they improved the hydrograph separation. Precipitation and peat chemistry data were only available for Ca, Mg, K and Na; hence the 3-component separation models were solved using these 4 base cations (Table 3). The inclusion of the additional peat chemistry resulted in either near-zero or negative soil water components, and a large proportion of total discharge remaining unaccounted for. The sum of the discharge components using the 3-component model including precipitation chemistry accounted for N96% of total discharge over the sampling period in both catchments. The resultant ‘precipitation’, ‘pre-event’ and ‘event’ hydrograph separations for Välipuro and Suopuro are shown in Fig. 4. The remainder of our analysis is based upon this latter 3-component hydrograph separation. In Välipuro both the event and precipitation runoff components peaked at the same time, coinciding with a small increase in pre-event

Runoff (mm day-1)

Table 2 Results from regression analysis of streamwater chemical parameters against discharge (n = 36). Data sets were adjusted for normality (log transformation), and autocorrelation (1st order residuals) where appropriate. Significant p-values of b0.01, b0.05 are denoted by ** and *, respectively; † denotes a non-significant result with p b 0.10, no symbol indicates a non-significant result with p N 0.10.

0 17-Apr

2-May Date

Valipuro Concentrations

Suopuro Concentration

Valipuro Discharge

Suopuro Discharge

17-May

1-Jun

Fig. 3. Cation concentrations and discharge at time of sampling during the snowmelt period in the 2 study catchments.

Runoff (mm day-1)

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K.J. Dinsmore et al. / Science of the Total Environment 409 (2011) 4858–4867 Table 3 Component concentrations (mg L−1) used in mixing models. Error values represent standard deviation of the mean, no error terms were available soil peat solution chemistry. Precipitation data was provided by the Finnish Meteorological Institute, Finnish national air quality information system (ILSE), see methods section for a full description of the sources of chemistry data. Pre-event

Ca Mg Na K Si Al

Välipuro

Suopuro

0.67 ± 0.02 0.37 ± 0.01 0.87 ± 0.02 0.32 ± 0.03 3.30 ± 0.21 0.20 ± 0.01

0.89 ± 0.08 0.33 ± 0.03 0.72 ± 0.05 0.26 ± 0.03 2.66 ± 0.15 0.09 ± 0.01

Event

Peat

Precipitation

0.21 ± 0.08 0.05 ± 0.02 0.52 ± 0.16 0.49 ± 0.07 0.05 ± 0.02 0.01 ± N0.01

1.50 0.50 0.73 0.74 N/A N/A

0.23 ± 0.04 0.04 ± 0.02 0.11 ± 0.04 0.15 ± 0.10 N/A N/A

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Table 4 Results from regression analysis of different carbon species concentrations against discharge (n = 36). Data set were adjusted for normality (log transformation), and autocorrelation (1st order residuals) where appropriate. Significant p-values of b 0.01, b0.05 are denoted by ** and *, respectively. r2 Variable CO2–C CH4–C DOC DIC POC

Välipuro − 0.03 b 0.01 − 0.21** − 0.08* b 0.01

Suopuro b0.01 0.26** − 0.11* − 0.01 b0.01

3.3. Aquatic carbon concentrations

1-Jun

90 80 70 60 50 40 30 20 10 0 11-Jun

1-Jun

90 80 70 60 50 40 30 20 10 0 11-Jun

Runoff (mm day-1)

Valipuro 20 15 10 5 0 2-Apr

12-Apr 22-Apr 2-May 12-May 22-May

25 Runoff (mm day-1)

Suopuro 20 15 10 5 0 2-Apr

12-Apr 22-Apr 2-May 12-May 22-May Date (2008)

Q_total

Q_pre-event

Q_event

Q_precip.

Snow Depth (cm)

25

Snow Depth (cm)

water. In contrast, the pre-event water in Suopuro represented a much greater proportion of the total stream discharge compared to the event water. Peaks in event and precipitation runoff components in Suopuro did not coincide. Although the precipitation component peaked at the same time in both catchments, the event water peak in Suopuro occurred approximately two days after that in Välipuro. Although the precipitation component peaks did not coincide with any direct precipitation in either catchment, 45 mm of new snow accumulated in the catchments during April, immediately prior to the snowmelt event. A sensitivity analysis of the end-member cation concentrations suggested that although the contribution of event, pre-event and precipitation components changed with differing end-member concentrations, the relative importance of the components was stable until concentrations were changed by ~ 20% of their original values. In both catchments, the hydrograph separation was most sensitive to error in pre-event concentrations. No significant effect was observed on the timing of peak component discharges with end-member concentration changes within the range ±0–50%.

Concentration–discharge relationships of aquatic C species differed in the 2 study catchments (Table 4). Discharge was significantly correlated with DOC and DIC concentration in Välipuro, whereas CH4 and DOC were significantly correlated with discharge in Suopuro. Carbon concentrations in both catchments were also compared to base cation concentrations using Spearman's rank correlations (Table 5). Both CO2 and DOC were almost consistently positively correlated with all cation concentrations, the exceptions being in the Suopuro catchment where CO2 was unrelated to Na and Al, and DOC unrelated to K. CH4 concentrations correlated positively and significantly with Na and Al in Välipuro, whilst in Suopuro the correlation with Na was negative; no correlation was observed between CH4 and Al in Suopuro. In both catchments CO2 concentrations calculated from the two different methods (headspace and continuous NDIR sensor) deviated at the time of peak snowmelt when headspace measurements became consistently higher (Fig. 5). Headspace concentrations then dropped to realign with the continuous NDIR sensor measurements near the end of the receding limb of the snowmelt hydrograph. In Välipuro CO2 concentration decreased with increased discharge and returned to pre-event concentrations towards the end of the snowmelt event. CO2 concentration at the end of the study period was approximately equal to pre-event levels (Fig. 5). However, in Suopuro the concentration fell in a pattern similar to that seen in Välipuro but then remained low, post-event, until the end of the study period (Fig. 5). Significant differences therefore existed between the two catchments in the recovery of CO2 concentrations after the period of peak runoff. The magnitude and temporal patterns of CH4 concentrations were significantly different between the two study catchments (Fig. 6). In Välipuro, CH4 concentrations ranged from 0.2 μg L −1 to 1.6 μg L −1, except for one occasion when a high of 35.7 μg L −1 was recorded. In Suopuro concentrations were 1–2 orders of magnitude higher and Table 5 Results from Spearman's rank correlations between cation and carbon concentrations (n = 36). Bold results indicate differences between catchments, i.e. where correlations are significant and inverse or p b 0.05 in one catchment and p N 0.10 in the other.

CO2 CH4 DOC DIC POC

Snow depth

Fig. 4. Hydrograph separations for Välipuro and Suopuro catchments based on a 3component mixing model with ‘event’, ‘pre-event’ and ‘precipitation’ end-members.

Välipuro Suopuro Välipuro Suopuro Välipuro Suopuro Välipuro Suopuro Välipuro Suopuro

Ca

Mg

Na

K

Si

Al

0.59⁎⁎ 0.52⁎⁎ ns ns 0.92⁎⁎ 0.95⁎⁎

0.65⁎⁎ 0.52⁎⁎ ns ns 0.95⁎⁎ 0.95⁎⁎

0.73⁎⁎ ns 0.42⁎ − 0.39⁎ 0.79⁎⁎ 0.44⁎⁎

0.33⁎ 0.64⁎⁎ ns ns 0.39⁎

0.73⁎⁎ 0.59⁎⁎ ns ns 0.89⁎⁎ 0.96⁎⁎

0.69⁎⁎ ns 0.37⁎ ns 0.95⁎⁎ 0.75⁎⁎

ns ns ns − 0.39⁎

ns ns − 0.34⁎ − 0.40⁎

ns ns ns − 0.44⁎⁎

ns ns ns − 0.34⁎

ns ns ns ns

‘ns’ refers to correlations which were non-significant. ⁎⁎ p b 0.01. ⁎ p b 0.05.

ns ns ns ns ns

8

3

6

2

4

1

2

NDIR sensor

10 8 6 4 2 2-May Date

Headspace

17-May

1-Jun

0

Q_pre-event

Q_precip.

Peak of 35.7 µg C L-1

8 6

0.8

4 0.4

2

0.0

Runoff (mm day-1)

10

1.2

0 12

Suopuro-CH4

10

60

8

40

6 4

20

2

Runoff (mm day-1)

Concentration (µg C L-1) Concentration (µg C L-1)

12 Valipuro-CH4

0 2-May

17-May

1-Jun

Date

CH4

Q_pre-event

6 25

4

20

2

15

0 12 Suopuro-DOC

Q_precip.

10

23

8

18

6 4

13 8 2-Apr

2 0 17-Apr

2-May

17-May

1-Jun

Q_pre-event

Q_precip.

Q_event

Q_event

1.6

17-Apr

8

30

Date

ranged from 3 μg L −1 to 38 μg L −1. Aside from the peak already mentioned, concentrations in Välipuro remained relatively stable throughout the study until the end of the snowmelt period, when concentrations began to rise steadily. In Suopuro, however, concentrations dropped sharply pre-event, levelling off at ~ 8 μg L −1. The concentration then rose to a peak coinciding with peaks in both the pre-event and precipitation water components, returning to a stable concentration of ~ 5 μg L −1 where it remained for the rest of the study period. Similar to CO2, no recovery was seen in CH4 concentrations post-event in Suopuro. DOC concentrations (Fig. 7) decreased in both catchments in response to increased discharge, with the lowest concentrations coinciding with peak event water. Concentrations then rose slowly on the receding limb of the hydrograph reaching levels similar to pre-event concentrations by the end of the study period. No clear patterns were seen in either POC or DIC concentrations (data not shown). Although POC concentrations appeared to increase sharply near the beginning of the rising limb of the snowmelt event in both catchments, variability throughout the sampling period was high. DIC concentrations were also extremely variable, with a small decrease in Välipuro coinciding approximately with peak discharge;

0 2-Apr

10

35

DOC

Fig. 5. Temporal CO2 concentrations at Välipuro and Suopuro calculated using both the headspace spot sampling method and continuous NDIR sensor data (full carbon data presented in Dyson et al. 2010) alongside discharge at time of sampling and its modelled components.

80

28

12 Valipuro-DOC

Runoff (mm day-1)

12

4

40

Runoff (mm day-1)

8 7 Suopuro CO2 6 5 4 3 2 1 0 2-Apr 17-Apr

10

5

Runoff (mm day-1)

0

Valipuro CO2

Runoff (mm day-1)

Concentration (mg C L-1)

0

Concentration (mg C L-1)

12

6

Concentration (mg L-1)

K.J. Dinsmore et al. / Science of the Total Environment 409 (2011) 4858–4867

Concentration (mg L-1)

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Q_event

Fig. 6. Temporal CH4 concentrations at Välipuro and Suopuro (full carbon data presented in Dyson et al. 2010) alongside discharge at time of sampling and its modelled components.

Fig. 7. Temporal DOC concentrations at Välipuro and Suopuro (full carbon data presented in Dyson et al. 2010) alongside discharge at time of sampling and its modelled components.

however, no significant correlation was found between DIC and discharge. 4. Discussion Both catchments were characterised by base-poor stream water chemistry, with little evidence of carbonate weathering or sea salt inputs. Al, which was significantly higher in Välipuro compared to Suopuro, appeared to be primarily associated with organic matter as it followed a very similar temporal pattern, and was strongly positively correlated to DOC concentration (Table 5). 4.1. Hydrological flow paths The peaks in the precipitation component coincided approximately with the peak streamwater discharges in both catchments (Fig. 4) despite no direct rain or snowfall occurring at this time (Fig. 2). We can therefore only conclude that there is a dilute component, with chemistry similar to precipitation, contributing to stream water discharge which remains unidentified. Temporal variability in ‘event’ concentrations has been shown in previous studies with concentrations diluting over time (e.g. Williams and Melack, 1991, Stottlemyer and Toczydlowski, 1996). If the dilute component, rather than being a unique end-member, was a consequence of temporal variability in the snowmelt concentrations, the component peak would have occurred after the peak in ‘event’ water. In Suopuro the opposite was true and we can therefore eliminate this as the probable source of the unidentified dilute component. We therefore conclude that the most likely source of the low ionic strength component was recent snowfall which retained the chemistry of fresh precipitation. Daily average air temperatures were fluctuating above and below zero in early April (Fig. 2). Repeated thaw and refreeze events in the upper part of the snowpack may have created an impermeable barrier hydrologically separating new snowfall from the deeper snowpack. What has been classified as event water may therefore consist of meltwater from the packed snow below this impermeable layer. Based on the hydrograph separation and accounting for differences in catchment size, the precipitation component would represent 49 mm of precipitation in Välipuro and 40 mm in Suopuro. In the period from 7th April until the peak runoff in the precipitation component, both catchments received 45 mm of precipitation in the form of snow. Hence, based on the hypothesis above, the precipitation component would represent the snowfall covering the period one month prior to the melting event.

K.J. Dinsmore et al. / Science of the Total Environment 409 (2011) 4858–4867

The catchments differed in the contribution of snowmelt and preevent sources (often referred to as ‘new’ and ‘old’ water) to total stream discharge. In Välipuro new water contributed 42% or 67% of stream discharge (depending on whether we accept the above theory and include the precipitation component as part of the ‘snowmelt’) compared to 37% or 57% in Suopuro. Literature estimates of new water contributions during the snowmelt period range from 21% (Carey and Quinton, 2004) to 78% (Metcalfe and Buttle, 2001). Campbell et al. (1995) calculated new water contributions of 67% and N90% during early and peak snowmelt using O 18 compared to 25% and 55% for the same periods using dissolved silica concentrations in streams draining the Rocky Mountains. The authors suggested the difference resulted from water acquiring SiO2 during transportation through the shallow groundwater system. In this study, the use of multiple species as natural tracers minimises the error caused by interaction with the surrounding environment. Concentrations of all the measured cations, with the exception of Ca, were higher in Välipuro compared to Suopuro (Table 1), suggesting a greater groundwater influence on total runoff in the former. Si, a strong indicator of weathering inputs (e.g. Drever and Zobrist, 1992), showed the greatest proportional decrease of any of the species measured over the snowmelt period and provided an important indication of groundwater inputs independent of hydrograph separation (as it was not used in its derivation). Concentrations of Si were lower pre-event in Suopuro suggesting near-surface flow paths were more important in runoff. Billett et al. (in press) found that DOC exported from Suopuro had a younger radiocarbon age compared to Välipuro, again suggesting a greater influence of shallow soil horizons in the drained catchment, supporting the interpretation of the cation data. Previous research has indicated that the infiltration and throughflow capacity of frozen soil is dependent on the degree of saturation during freezing. Soils that freeze whilst highly water saturated can result in ‘concrete’ frost with very low permeability; the reverse case is granular frost that occurs in more freely drained unsaturated soils (Hardy et al., 2001). Although hydraulic conductivity was not measured in this study, the greater near-surface runoff contribution before snowmelt in Suopuro (the drained catchment) may be the result of a lower water table at the onset of the winter when the soil originally became frozen. Laudon et al. (2007) showed that the proportion of peatland within a catchment was an important factor explaining the groundwater contribution during snowmelt events. They suggest that due to the water holding capacity of peat and subsequent infiltration capacity, catchments with a larger peatland area are likely to have a smaller pre-event or ‘old water’ contribution. However, in this study we found the greatest ‘old water’ contribution in Suopuro which had a proportionally greater peatland area. The effect of drainage in this case appears to have had a greater effect on water flowpath than peatland coverage. Differences in infiltration capacity between the catchments may also explain both the occurrence of a pulse of pre-event water in Suopuro, and the delay between the peaks in the precipitation and event components seen in Suopuro, but not Välipuro (Fig. 4). Studies such as Carey and Quinton (2004) and Campbell et al. (1995) showed that when infiltration occurs, a significant proportion of streamflow is derived from pre-event water displaced by infiltrating snowmelt; Metcalfe and Buttle (2001) suggested that filling of surface stores was the primary cause of time lags between meltwater inputs and stream discharge responses. Our results therefore suggest that less infiltration occurred in the Välipuro catchment compared to Suopuro, the most likely explanation being the presence of drainage in the lower part of the Suopuro catchment. 4.2. Sources of aquatic carbon The strong correlations between aquatic CO2 and DOC and base cation concentrations indicate the source of both was associated with

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deeper flow paths (Table 5). Concentrations of CO2 were initially diluted by the input of meltwater from the surface snow layer. However, a late increase in surface water concentrations (headspace samples) in both catchments coincided with the peak in snowpack meltwater. The CO2 sensors were fixed in the stream prior to snowmelt whilst the headspace samples were collected from near the water surface, hence the separation in measured concentrations also coincided with the largest vertical separation between sampling points (Dyson et al., 2010). The discrepancy therefore appears to have resulted from incomplete mixing within the water column alongside near-surface inputs from the melting snowpack. Using the component discharge values from the hydrograph separation and assuming the surface snow CO2 concentrations were in equilibrium with the atmosphere, the mixing ratio equations were solved to calculate the contribution of pre-event and snowmelt runoff (combined event and precipitation components) to total downstream flux. The results showed that 49% of total downstream CO2 export in Välipuro was from the CO2 delivered in the snowmelt water and 51% from pre-event sources. In Suopuro the percentage from snowmelt and preevent sources were 38% and 62%, respectively. Excluding the uncertain precipitation component from the calculations reduces the snowmelt contribution in Välipuro and Suopuro to 46% and 35%, respectively. These are only estimates since they assume source concentrations remained constant throughout the study, which is unlikely to be the case due to both flushing and variability in below-ground contributions to the ‘preevent’ component. The CO2 input from snowmelt water occurred in both catchments despite the abovementioned differences in flow path, suggesting the CO2 was most likely stored in the snowpack itself rather than flushed from the soil profile. Dyson et al. (2010) found that CO2 concentrations in the snow reached N1800 ppmv, and studies such as Seok et al. (2009) and Sommerfeld et al. (1991) have demonstrated a strong vertical concentration gradient with near ambient concentrations at the snow surface. Hence snowpack emissions have been shown to represent an important part of the annual land-surface NEE balance (Sommerfeld et al., 1993). Here, using hydrograph separation, we demonstrate the potential importance of the snowpack to catchment carbon losses via the aquatic pathway and suggest that it is an important transient carbon store throughout the winter period. Dual isotope analysis (δ 13C and 14C) of the exported carbon suggested the primary source of both DOC and CO2 was C3 plants with the age of CO2 evaded (degassed) from the water surface ranging from ~AD 2002 pre-flood, to ~AD 1995 during the peak flood event and ~AD 2008 post-flood (Billett et al., in press). The slight 14C enrichment during the peak of the flood event, and the corresponding peak in snowpack melt water, suggests that the CO2 stored in the snowpack most likely originated from microbial decomposition of older soil organic matter during the winter (Billett et al., in press). This has important implications for studies which often assume carbon turnover in frozen soils is minimal. The temporal pattern of 14C enrichment was much clearer in the Välipuro catchment (undrained) where the hydrograph separation indicated snowmelt was the primary source of stream flow; in Suopuro (drained), where snowmelt appears to have infiltrated and displaced pre-event water, the pattern in 14C was more complex. Both studies therefore appear to point towards a more complex mix of water sources in Suopuro, the drained catchment. In both catchments there appeared to be a similar source for CO2 and DOC, but not CH4, the latter not showing clear correlations with base cations and a significantly higher concentration in Suopuro compared to Välipuro. A peak in CH4 concentration corresponding to both the precipitation peak and the pre-event water peak occurred in Suopuro, but not Välipuro. As the primary difference in flow pathways between the catchments was the presence of a shallow throughflow contribution in Suopuro, we suggest the aquatic CH4 was transported from a nearsurface terrestrial pool. This would suggest that methanogens were active even within the frozen soil; previous research has indicated that

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microbial activity occurs under snow at temperatures close to 0 °C (Sommerfeld et al., 1991). However, on the receding hydrograph limb in Suopuro concentrations remained stable and low, implying CH4 source limitation following flushing. In contrast, concentrations in Välipuro began to rise post-event indicating both an active microbial source and the re-establishment of soil-stream connectivity. The discharge related decrease in DOC concentrations reached a minimum coinciding with peak event water. Previous work has shown that bulk deposition to the ground surface in the North Karelian Kangasvaara catchment was substantially enriched in DOC (Piirainen et al., 2002), however no evidence of temporal changes in the source of DOC were evident from the δ 13C data (Billett et al., in press). Therefore although it is likely that DOC was present in the snowpack, the concentrations were low in comparison to pre-event sources. The recovery in concentrations after snowmelt is likely to represent a return to below-ground flow paths where DOC production occurred; again the recovery is less pronounced in Suopuro than Välipuro, possibly caused by the effect of drainage. 5. Conclusions Hydrograph separation of the spring 2008 snowmelt event in two forested peatland catchments in Eastern Finland suggests the existence of three distinct stream water sources: pre-event water, snowpack meltwater and a third dilute component we attribute to the top layer of recent snow characterised by the chemistry of local precipitation. In Suopuro (drained catchment), pre-event water contained both shallow soil throughflow and a deeper peat/groundwater component, whereas Välipuro (undrained catchment) appeared to consist primarily of deeper peat/groundwater. The pulse of pre-event water during the hydrograph peak and lag before snowpack meltwater reached the stream channel in Suopuro suggests infiltration was much greater in Suopuro compared to Välipuro. We suggest this may be a consequence of drainage in Suopuro and the permeability of the soil frost caused by the degree of soil saturation during freezing. Hence drainage may significantly alter soilstream connectivity even during the winter months when soils are frozen. The sources of aquatic carbon varied between the major carbon species. Few conclusions could be drawn about the sources of aquatic POC or DIC due to high temporal variability. Our results suggest commonality in CO2 and DOC sources, whereas CH4 concentrations displayed different temporal patterns and cation correlations. DOC was found to originate primarily from below-ground sources and it is suggested that aquatic CH4 was transported from near-surface sources that appear to have been active in Suopuro before soil thaw. Aquatic CO2 was transported from both below-ground sources and the melting snowpack, which contributed up to 49% of total aquatic CO2 export over the study period. The results indicate that not only do snowpack CO2 emissions represent a significant soil-atmosphere flux (Sommerfeld et al., 1993), but losses of CO2 dissolved in the snow may represent an important loss via the aquatic pathway during the spring snowmelt event. Acknowledgements We would like to acknowledge the support of the UK Natural Environment Research Council for providing financial support for this work, the Finnish Forest Research Institute (METLA) for logistical support and the provision of data, and also the Finnish Meteorological Institute for data provision. Appendix A. Supplementary data Supplementary data to this article can be found online at doi:10. 1016/j.scitotenv.2011.07.063.

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