Precipitation and net ecosystem exchange are the ... - AGU Publications

PUBLICATIONS Journal of Geophysical Research: Biogeosciences RESEARCH ARTICLE 10.1002/2014JG002705 Special Section: Carbon and Nitrogen Fluxes at the Land-Ocean Interface Key Points: • Runoff, DOC flux, NEE, litterfall, precipitation, and temperature were studied • NEE, temperature, and DOC concentrations increased over the 15 year study period • Precipitation mostly determined DOC flux, but it was also affected by NEE

Correspondence to: J. Pumpanen, jukka.pumpanen@helsinki.fi

Citation: Pumpanen, J., et al. (2014), Precipitation and net ecosystem exchange are the most important drivers of DOC flux in upland boreal catchments, J. Geophys. Res. Biogeosci., 119, 1861–1878, doi:10.1002/2014JG002705. Received 6 MAY 2014 Accepted 21 AUG 2014 Accepted article online 26 AUG 2014 Published online 18 SEP 2014

Precipitation and net ecosystem exchange are the most important drivers of DOC flux in upland boreal catchments Jukka Pumpanen1, Aki Lindén1, Heli Miettinen2, Pasi Kolari3, Hannu Ilvesniemi4, Ivan Mammarella3, Pertti Hari1, Eero Nikinmaa1, Jussi Heinonsalo5, Jaana Bäck1, Anne Ojala2, Frank Berninger1, and Timo Vesala3 1

Department of Forest Sciences, University of Helsinki, Helsinki, Finland, 2Department of Environmental Sciences, University of Helsinki, Helsinki, Finland, 3Department of Physics, University of Helsinki, Helsinki, Finland, 4Finnish Forest Research Institute, Vantaa, Finland, 5Department of Food and Environmental Sciences, Helsinki, Finland

Abstract According to recent studies, dissolved organic carbon (DOC) concentrations in rivers throughout the boreal zone are increasing. However, the mechanistic explanation of this phenomenon is not yet well known. We studied how the short and long-term changes in precipitation, soil temperature, soil water content, and net ecosystem exchange (NEE) are reflected to DOC concentrations and runoff DOC fluxes in two small forested upland catchments in Southern Finland. We used continuous eddy covariance measurements above the forest and runoff flow measurements from the catchment areas conducted over a 15 year long time period to study the correlation between NEE, gross photosynthetic production, total ecosystem respiration, litter production, and runoff DOC. In addition, we looked for the most important environmental variables in explaining the interannual changes in runoff DOC by using multiple linear regression. Finally, we studied the temporal connection between runoff DOC concentrations, precipitation, soil water content, and NEE by using wavelet coherence analysis technique. Our results indicate that the DOC concentrations have increased over the last 15 years. The DOC flux was to a large extent determined by the amount of precipitation, but the previous year’s NEE and litter production had also a small but significant effect on runoff DOC fluxes.

1. Introduction Recently, it has been shown that in lakes and streams dissolved and total organic carbon (DOC and TOC) concentrations throughout the boreal zone are increasing [Worrall et al., 2004; Sarkkola et al., 2009; Couture et al., 2011]. There are several potential explanations to this trend. Land use changes such as forest management practices or drainage of peatlands are among the suggested explanations, which temporarily increase the release and transport of DOC to aquatic systems [Kortelainen and Saukkonen, 1998; Nieminen, 2004]. Another explanation is the decrease in the amount of atmospheric acid deposition and the consequent release of organic acids, which affects the soil organic matter (SOM) solubility by changing either the acidity of soils or the ionic strength of soil solutions or both [Monteith et al., 2007]. Changes in precipitation pattern and evapotranspiration are one of the most studied factors behind the DOC/TOC trends since autumn and winter precipitation is expected to increase at the northern latitudes [Intergovernmental Panel on Climate Change (IPCC), 2013]. This will increase the discharge of water from the mineral soils and peatlands [Carey et al., 2010]. In addition, longer nonfrozen time periods may accelerate the decomposition of SOM [Piao et al., 2008; Vesala et al., 2010] possibly enhancing DOC release from the soil. A positive correlation between increased precipitation and DOC concentration as well as temperature and DOC concentration in lake and river waters has indeed been found [Freeman et al., 2001; Evans et al., 2002; Clark et al., 2005; Worrall et al., 2004; Ellis and Freeman, 2007; Erlandsson et al., 2008]. A proportion of carbon fixed in terrestrial ecosystems in boreal biome is transferred through streams and rivers to lakes. The carbon is finally buried into lake sediments, released as CO2 to the atmosphere, or transported downstream [Huotari et al., 2011; Einola et al., 2011]. The amount of terrestrially fixed carbon lost to aquatic systems in either dissolved inorganic or dissolved organic (DOC) form is estimated to range between 10% and 24% of net ecosystem exchange (NEE) in upland forest ecosystems and mires, respectively [Huotari et al., 2011; Dinsmore et al., 2010]. The enhanced decomposition of SOM may significantly affect the

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transport of terrestrial carbon to the rivers, estuaries, and the coastal ocean, a process with a poorly known contribution to the global and regional carbon flux [Raymond and Bauer, 2001]. Increases in atmospheric CO2 concentration, accelerating in recent years [IPCC, 2013], influence global temperature and CO2 assimilation. The long-term increasing trend in atmospheric CO2 concentration and temperature may increase gross photosynthetic production (GPP) and carbon flow into the soil [Phillips et al., 2011]. Plants are an important carbohydrate source for soil food webs by emitting root exudates which sustain not only mycorrhizal fungal symbionts but also other groups of specialized microorganisms [Epron et al., 2011; Högberg et al., 2010; Högberg and Read, 2006; Pumpanen et al., 2009; Heinonsalo et al., 2010]. Up to 26% of the carbon recently assimilated by vegetation is allocated to belowground through root respiration and exudation [Pumpanen et al., 2009; Heinonsalo et al., 2010]. Most of the root exudates are probably used quickly and released as CO2 in respiration by soil microbes. However, part of the recent photosynthates may also end up as DOC in the soil water. The flux of carbon below ground may also stimulate the decomposition of soil organic matter (SOM) [Drake et al., 2011; Phillips et al., 2011], thus further increasing the amount of DOC in the soil water. Thus, we hypothesized that an increase in the net ecosystem exchange (NEE) is reflected in soil water DOC concentration and finally in DOC flux from the catchments. We expected that the annual NEE and litter production affect the runoff DOC fluxes. We also assumed that the vernal DOC concentrations and consequently DOC fluxes are affected by the amount of thawing snow, because the bulk of the runoff fluxes takes place during the snowmelt in the spring [Ilvesniemi et al., 2010]. Furthermore, we expected that the weather conditions of the previous year, e.g., drought in the preceding summer and soil temperature in the previous year could affect the DOC fluxes in the following year due to their effects on decomposition and photosynthesis. Finally, we studied whether the possible changes in soil water H+ ion concentrations are reflected to DOC concentrations, because the decrease in acidic deposition has been shown to increase DOC concentrations [Monteith et al., 2007]. We tested these hypotheses in two small upland boreal catchment areas in Southern Finland with a 15 year long continuous data set from 1998 to 2012 on precipitation, DOC concentrations and fluxes in the runoff, snow water storage, litter production as well as GPP, total ecosystem respiration (TER), and NEE of the ecosystem. The relative importance of precipitation, snow water storage, NEE, litter fall, soil water content and temperature, and runoff water H+ ion concentrations were studied with linear mixed-effects models. Finally, we examined the temporal correlations between the DOC effluxes and NEE as well as precipitation, soil temperature and soil water content using wavelet coherence analysis.

2. Materials and Methods 2.1. Study Site The study site was located at the Station for Measuring Ecosystem Atmosphere Relationships (SMEARII) in Southern Finland (61°51′N, 24°17′E, 180 m above sea level). The annual mean temperature of the area is +3.5° C February being the coldest month (mean 7.7°C) and July the warmest (mean +16.0°C). The annual precipitation averages 711 L m2 and the rainiest months are July (92 L m2) and August (85 L m2) [Pirinen et al., 2012]. The dominant tree population at the site is a 52 year old Scots pine (Pinus sylvestris L.). The pine seeds were sown in 1962 on burned, mechanically prepared soil. The study area has two small natural catchment areas formed on the granite bedrock by glacier during the last glaciation period which ended about 10 000 years ago in Finland (Figure 1). The two adjacent catchments 889 m2 (No 1) and 301 m2 (No 2) in size can be regarded as separate hydrological units. The soil covering the bedrock at an average depth of 0.50–0.70 m is Haplic podzol on glacial till [Food and Agriculture Organization-United Nations Educational, Scientific and Cultural Organization, 1990]. The average humus layer depth is 0.05 m. Some general properties of the soil are presented in Ilvesniemi et al. [2009]. The dominant species in the understorey vegetation are Vaccinium myrtillus L. and Vaccinium vitis-idaea L. The ground vegetation consists mainly of mosses Dicranum polysetum Sw., Hylocomium splendens (Hedw.) B.S.G., and Pleurozium schreberi (Brid.) Mitt. The average stand height in the catchments 1 and 2 in 2006 was 14.7 m and 12.9 m with an average diameter of 0.136 m and 0.127 m, respectively. The corresponding number of trees was 1880 and 1060 ha1. The tree stand in the smaller catchment (No 2) was thinned in 2002. According to Ilvesniemi et al. [2010], catchment 1 had higher needle biomass (0.637 g C m2) compared to catchment 2 (0.457 g C m2) before the thinning. In the thinning, about 26% of the tree stand basal area [Vesala et al., 2005] and 30.5% of the

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Figure 1. Schematic presentation of the two catchment areas at Hyytiälä measurement station.

needle area [Ilvesniemi et al., 2010] were removed, but the thinning did not result in changes in the net ecosystem carbon fluxes because the changes in photosynthesis and respiration compensated each other quickly after the thinning [Vesala et al., 2005]. The runoff from the catchment 2 was initially higher compared to the catchment 1 already before thinning. However, the throughfall increased in the thinned catchment, and the difference in the runoff between the thinned and nonthinned catchments was about 20% higher in the 4 years following the thinning compared to the 4 years preceding the thinning [Ilvesniemi et al., 2010]. 2.2. Precipitation, Throughfall, Snow Water Storage, and Runoff Measurements Precipitation was measured daily at the Finnish Meteorological Institute weather station 300 m west from the SMEAR II measurement station using a Tretjakov-type rain gauge equipped with a windshield. Precipitation was also collected above the canopy at 20 m height in a polyethylene funnel (0.20 m in diameter) installed on the top of the measuring tower [Ilvesniemi et al., 2009]. The water entering the funnel was stored in a collection bottle covered with aluminum foil. The bottles were washed on a regular basis after each forthnightly sample collection and rinsed with deionized water for 5 times. Throughfall was monitored at 1 m height above the soil surface with seven collectors (five in catchment 1 and two in catchment 2) consisting of two gently sloping stainless steel gutters (length of both wings 2 m and width 0.1 m with a total open area of 0.385 m2) and an automatic tipping bucket counter measuring the water running from the gutters. The water was collected into 20 L containers which were weighed, and a composite sample of all collectors was taken for DOC analysis forthnightly. The snowfall below the canopy was measured from seven snow collectors (0.50 m in diameter) between 1998 and 2000 and from two collectors in years 2001–2012. The melted water was weighed monthly throughout the winter. Snow water storage was calculated from snow depths and snow water equivalents measured on a weekly basis from seven measurement poles at the catchment area. The snow water equivalents were measured by taking a volumetric snow sample and melting the water. Daily snow water equivalents were estimated by using linear regression based on the last 7 days moving average in air temperature at 4.2 m height and snow water

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equivalent as in Ilvesniemi et al. [2009]. Daily snow water storage was calculated by interpolating daily snow depth values from weekly snow depth measurements and multiplying by the snow water equivalent. The runoff was measured from two concrete weirs casted on the bedrock. In both weirs, the water flow was continuously recorded by a flow meter (Schlumberger, Schlumberger Ltd., Houston, TX). Water samples (see below) were collected daily from both weirs for a week from the onset of each runoff episode and later on a weekly basis. 2.3. DOC and pH Measurements Water samples from precipitation, throughfall, and runoff were taken into sample bottles, stored in the dark and transported immediately to the laboratory. In the laboratory, the samples were filtered through a 0.45 μm membrane filter with a vacuum filtering system (Millipore, Millipore Corporation, Billerica, MA). The filtered samples were stored in the dark at 18°C until analysis. DOC concentrations were analyzed from the melted water samples with a carbon analyzer (Shimadzu TOC 5000A, Shimadzu Corporation, Kyoto, Japan) using calibration standards after every tenth sample. On the sampling day, also the pH value and electric conductivity were measured from unfiltered samples using a pH- and conductivity meter (ACCUMET 20, ThermoFischer Scientific, Waltham, MA). 2.4. DOC Flux Calculations For the amount of DOC in the annual runoff, the measured runoff and DOC concentration measurements were first interpolated so that the DOC concentration values of the missing days were estimated using linear interpolation from the water samplings carried out first at daily and later during the lower flow rate on weekly intervals. Next, the amount of DOC running through the weirs was calculated by multiplying the daily runoff (liters) with the corresponding DOC concentration (g L1) on a daily basis. Finally, the annual DOC flux was obtained by integrating the daily DOC fluxes over the whole year. In order to get a time series with regular intervals from the samples taken at daily to weekly intervals, we also calculated monthly averages from the daily values of DOC concentrations and fluxes, which were later used in seasonal Mann-Kendall trend tests. The throughfall DOC fluxes were calculated by multiplying the throughfall sample volumes with DOC concentrations measured from the same samples. In the winter, the snow fall samples collected below the tree canopy represented the throughfall in winter. The snow fall was collected from the same positions as the throughfall using polyethylene buckets 0.5 m in diameter and height. The snow in the buckets was collected at 1 month intervals, melted, weighted, and filtered similarly to the other water samples. The annual values were obtained by summing up weekly or monthly values to annual values. 2.5. Measurements of NEE, GPP, TER, and Litterfall The net ecosystem exchange (NEE) was measured using the eddy covariance (EC) technique [Aubinet et al., 2012]. In short, the measurement system, located 23.3 m above the ground, included an ultrasonic anemometer (Solent Research 1012R2, Gill Instruments Ltd, Lymington, Hampshire, England) for measuring three wind speed components and temperature and a closed-path infrared gas analyzer (LI-6262, LI-COR Biosciences, Lincoln, NE) which measured CO2 and H2O concentrations. The EC tower was located at about 50 m distance from the catchment areas in a homogeneous Scots pine forest, and the foot print area of the EC system was within approximately 200 m radius around the tower. The 30 min averaged NEE was partitioned into total ecosystem respiration (TER) and gross primary productivity (GPP) as described in Kolari et al. [2009] using half-hourly averaged incident photosynthetically active radiation measured above the canopy with quantum sensor (Li-Cor LI-190 SZ, Lincoln, NE) and EC measurements. TER was calculated from nighttime NEE measurements with an Arrhenius-type function [Lloyd and Taylor, 1994] using the soil organic layer temperature as the driving force. Soil temperature was recorded at 15 min intervals using silicon temperature sensors (Philips KTY81–110, Philips semiconductors, Eindhoven, the Netherlands) installed in the middle of soil horizons O (organic horizon) and A (eluvial horizon in the mineral soil) at five measuring instrument pits on the area at depths of 0.025 m and 0.05 m on average (Figure 1). The temperature dependence of the nighttime TER was applied to daytime and the 30 min daytime GPP (defined to be always positive) was calculated by subtracting the measured NEE (negative for the net uptake) from the estimated TER (always positive). In case of missing or rejected NEE, the 30 min GPP was calculated from a saturating light response parameterized with the accepted NEE data. The annual values for NEE, TER, and GPP were calculated by summing up the 30 min values over the whole year. For further details of the calculations, see Kolari et al. [2009]. Notice, that in the results, we use similar notation for the sign of the

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Journal of Geophysical Research: Biogeosciences Table 1. Linear Mixed-Effects Models Fitted Against Flow-Weighted Annual a Average DOC Concentration and Annual DOC Flux b

Model

Model Expression

1 2 3 4 5

CDOC = a P + b CAT CDOC = a P + b NEE + c CAT CDOC = a P + b NEE + c LITTER + d CAT CDOC = a P + b NEE + c LITTER + d SNOW + e CAT CDOC = a P + b NEE + c LITTER + d SNOW + e SWC + f CAT

6 7 8 9

CDOC = a P + b NEEPrev + c CAT CDOC = a P + b NEEPrev + c SNOW + d CAT CDOC = a P + b NEEPrev + c SNOW + d SWC + e CAT CDOC = a P + b NEEPrev + c SNOW + d SWC + e Hþ Conc + f CAT

10 11 12 13 14

CUMDOC = a P + b CAT CUMDOC = a P + b NEE + c CAT CUMDOC = a P + b NEE + c LITTER + d CAT CUMDOC = a P + b NEE + c LITTER + d SNOW + e CAT CUMDOC = a P + b NEE + c LITTER + d SNOW + e Hþ Conc + f CAT

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flux as in most of the EC studies, i.e., negative values mean that the site was a sink of carbon and positive values that the site was a source of carbon to the atmosphere. Notice also that here the notation for GPP and TER is opposite to NEE and thus, positive values in GPP mean photosynthesis and positive values in TER indicate magnitude of respiration.

Litterfall from the tree canopy was measured from 21 litter traps (0.5 m in diameter), which were placed systematically over the two catchment areas (Figure 1). The traps were funnels placed about 15 CUMDOC = a P + b NEEPrev + c CAT 0.60 m above the soil surface. The 16 CUMDOC = a P + b NEEPrev + c LITTER + d CAT litter was collected from the traps 17 CUMDOC = a P + b NEEPrev + c LITTER + d SNOW + e CAT þ monthly except for years 2001–2003 18 CUMDOC = a P + b NEEPrev + c LITTER + d SNOW + e HConc + f CAT when the collection interval was 3 a 1 CDOC = Flow-weighted annual average DOC concentration (g L ); 2 1 2 1 months. The litter was first air dried in CUMDOC = Annual DOC flux (g C m yr ); P = Precipitation (L m yr ); 2 1 paper bags, separated by hand into NEE = Net ecosystem exchange (g C m yr ); NEEPrev = Net ecosystem 2 1 exchange of the previous year (g C m yr ); LITTER = Litter fall of the predifferent cohorts (needles, leaves, 2 1 2 vious year (g DW m yr ); SNOW = Snow water storage in March (L m ); branches, bark, cones, and seeds) 3 3 SWC = Soil water content in the July of the previous year (m m ); + + 1 and oven dried at 60°C for 24 h. The HConc = H ion concentration (mol L ); and CAT = Catchment area (dimenmass of oven dried litter cohorts was sionless value, Catchment A = 1, Catchment B = 2). b The models marked with bold letters were tested with sensitivity analysis. measured immediately after drying and C concentrations of the cohorts were measured from samples ground with a ball mill (Retsch, Han, Germany) using elemental analyzer (varioMAX CN elemental analyzer, Elementar Analysensysteme GmbH, Hanau, Germany). 2.6. Statistical Analyses First, Kolmogorov-Smirnov’s test was used to determine whether the DOC and associated data collected at daily to weekly intervals over the years were normally distributed and whether the variances were equal and unchanged over the whole time period. The Kolmogorov-Smirnov’s tests were carried out by using Statistical Package for the Social Sciences Statistics 22.0 (IBM Corp., Armonk, NY). Since only the pH values measured from precipitation were normally distributed, the nonparametric seasonal Mann-Kendall trend test was used to reveal over the study years the temporal trend of runoff, precipitation, throughfall, and runoff DOC concentration and H+ ion concentration as well as snow water storage. The seasonal trends were studied from monthly averages using the Mann-Kendall trend test. The seasonal Mann-Kendall trend tests and Mann-Kendall trend tests were done by using R package “Kendall.” The temporal coherence between DOC concentrations and precipitation, NEE as well as temperature and water content in the soil were studied with wavelet coherence analysis using averaged value of each measurement interval [Grinsted et al., 2004]. This technique has been widely applied in geosciences [Torrence and Compo, 1998; Vargas et al., 2010, 2011] to quantify the spectral characteristics of time series that may be nonstationary and heteroscedastic. The hydrological variables investigated in this study, e.g., runoff and its DOC concentration and precipitation violate the stationary assumption required in the analysis of spectral properties and wavelet analysis is an alternative technique [Torrence and Compo, 1998]. The wavelet coherence analysis was done by using R package “mvcwt” [Keitt, 2008]. The annual values of precipitation, runoff, and DOC fluxes in runoff, NEE, GPP, TER, litterfall mass flux, and DOC fluxes in throughfall were tested using Kolmogorov-Smirnov’s test and since they were normally distributed, the use of parametric methods in statistical analysis was possible. Correlations between annual runoff, DOC fluxes in runoff, NEE, litterfall, DOC fluxes in throughfall and temperature sums were studied using Pearson correlation. The PUMPANEN ET AL.


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2


a

4

7.60 × 10 4 (4.82 × 10 )

0.047 182.2416 0.281 3 4.08 × 10 3 (1.57× 10 , P = 0.021) 7 9.71 × 10 6 (2.02 × 10 , P = 0.639)

1

4

8.0 × 10 4 (4.44 × 10 , P = 0.095)

0.193 184.337 0.128 3 6.70× 10 3 (2.12 × 10 , P = 0.0574) 7 1.26 × 10 6 (1.91 × 10 , P = 0.95) 6 9.1 × 10 6 (4.83 × 10 , P = 0.08)

2

4

9.20 × 10 4 (3.34 × 10 , P = 0.017)

5

1.71 × 10 6 (5.11 × 10 , P = 0.006)

0.548 193.5543 0.008 2 1.65 × 10 3 (3.26 × 10 , P < 0.001) 6 3.34 × 10 6 (1.77 × 10 , P = 0.083) 5 1.749 × 10 6 (4.40 × 10 , P = 0.002)

3

4

8.47 × 10 4 (3.19 × 10 , P = 0.022)

5

1.57 × 10 6 (4.92 × 10 , P = 0.009) 6 8.31 × 10 6 (5.34 × 10 , 0.148)

0.596 194.9382 0.008 3 1.43 × 10 3 (3.40 × 10 , P = 0.002) 6 2.52 × 10 6 (1.75 × 10 , P = 0.178) 5 1.60 × 10 6 (4.27 × 10 , P = 0.178)

4

4

7.76 × 10 , 4 (2.64 × 10 , P = 0.015)

5

1.72 × 10 3 (4.09 × 10 , P = 0.002) 5 1.052 × 10 6 (4.48 × 10 , P = 0.041) 2 1.09 × 10 3 (4.34 × 10 , P = 0.031)

0.727 201.2045 0.002 2 1.59 × 10 3 (2.86 × 10 , P < 0.001) 3 2.93 × 10 6 (1.45 × 10 , P = 0.071) 5 1.17 × 10 6 (3.91 × 10 , P = 0.014)

5

In models 6–9, the NEE of previous year was used as explaining factor instead of the NEE in current year (1–5).

Catchment area (stderr, P)

H ion concentration (stderr, P)

+

SWC in previous July (stderr, P)

Snow water content in March (stderr, P)

Litter of the previous year (stderr, P)

NEE of the previous year (stderr, P)

NEE (stderr, P)

Precipitation (stderr, P)

Adjusted r AIC Significance value Intercept (stderr, P)

Model

4

7.50 × 10 4 (4.30 × 10 , P = 0.105)

6

8.89 × 10 6 (4.18 × 10 , P = 0.053

0.239 185.3311 0.091 3 1.60 × 10 3 (1.83 × 10 , P = 0.398) 3 1.46 × 10 6 (1.82 × 10 , P = 0.437)

6

4

6.64 × 10 3 (3.99 × 10 , P = 0.122)

5

1.21 × 10 6 (6.57 × 10 , P = 0.092)

6

8.3 × 10 6 (3.85 × 10 , P = 0.052)

0.356 187.5343 0.052 5 6.04 × 10 3 (1.88 × 10 , P = 0.975) 6 2.08 × 10 6 (1.71 × 10 , P = 0.247)

7

a

Table 2a. Values of the Parameters of the Fitted Models 1–9 Using Annual Flow-Weighted Average DOC Concentrations as the Dependent Variable

4

6.13× 10 4 (3.47 × 10 , P = 0.105)

5

1.35 × 10 6 (5.75 × 10 , P = 0.039) 2 1.04 × 10 3 (4.67 × 10 , P = 0.049)

6

7.81 × 10 6 (3.36 × 10 , P = 0.04)

0.514 191.7969 0.019 3 2.47 × 10 3 (1.96 × 10 , P = 0.234) 6 1.49 × 10 6 (1.51 × 10 , P = 0.346)

8

5

2.02 × 10 6 (5.95 × 10 , P = 0.007) 2 1.24 × 10 3 (4.20 × 10 , P = 0.014) 2 6.07 × 10 2 (2.89 × 10 , P = 0.062) 4 5.84 × 10 4 (3.04 × 10 , P = 0.084)

5

1.23 × 10 6 (3.62 × 10 , P = 0.007)

0.629 196.0032 0.00918 3 2.82 × 10 3 (1.723 × 10 , P = 0.133) 6 2.47 × 10 6 (1.40 × 10 , P = 0.108)

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2


1

1.5 × 10 1 (1.2 × 10 , P = 0.245)

0.523 6.315848 0.0022 1 9.7 × 10 1 (4.03 × 10 , P = 0.030) 3 2.22 × 10 3 (5.18 × 10 , P < 0.001)

10

1

1.34 × 10 2 (9.00 × 10 , P = 0.160)

0.746 3.683789 0.0001 1 1.75 × 10 1 (4.3 × 10 , P = 0.690) 3 1.89 × 10 4 (3.89 × 10 , P < 0.001) 3 3.59 × 10 4 (9.81 × 10 , P = 0.003)

11

1

1.23 × 10 2 (8.98 × 10 , P = 0.194)

3

1.52 × 10 3 (1.34 × 10 , P = 0.290)

0.751 3.336901 0.0003 1.02 × 10°, 1 (8.76 × 10 , P = 0.266) 3 1.58 × 10 4 (4.75 × 10 , P = 0.006) 3 4.33 × 10 3 (1.18 × 10 , P = 0.003)

12

1

1.38 × 10 2 (9.03 × 10 , P = 0.156)

3

1.24 × 10 4 (1.39 × 10 , P = 0.392) 3 1.61 × 10 3 (1.51 × 10 , P = 0.310)

0.753 3.003468 0.0006 1 5.91 × 10 1 (9.61 × 10 , P = 0.551) 3 1.74 × 10 4 (4.95 × 10 , P = 0.005) 3 4.04 × 10 3 (1.21 × 10 , P = 0.007)

13

3

1.72 × 10 3 (1.22 × 10 , P = 0.191) 5 6.54 × 10 3 (1.52 × 10 , P = 0.966) 5 1.30 × 10 4 (5.99 × 10 , P = 0.055) 1 1.28 × 10 2 (7.82 × 10 , P = 0.133)

0.816 7.572088 0.0003 1 7.53 × 10 1 (8.35 × 10 , P = 0.388) 3 1.42 × 10 4 (4.53 × 10 , P = 0.011) 3 4.58 × 10 3 (1.07 × 10 , P = 0.002)

14

1

1.53 × 10 1 (1.06 × 10 , P = 0.172)

3

2.52 × 10 3 (1.03 × 10 , P = 0.029)

0.649 1.831455 0.0008 1.67 × 10°, 1 (4.5 × 10 , P = 0.003) 3 2.36 × 10 4 (4.48 × 10 , P < 0.001)

15

In the models 15–18, the NEE of the previous year was used as explaining factor instead of the NEE in current year in models 10–14.

a

Catchment area (stderr, P)

H ion concentration (stderr, P)

+

Snow water content in March (stderr,P)

Litter of the previous year (stderr, P)

NEE of the previous year (stderr, P)

NEE (stderr, P)

Precipitation (stderr, P)

Adjusted r AIC Significance value Intercept (stderr, P)

Model

a

Table 2b. Values of the Parameters of the Fitted Models 10–18 Using Annual DOC Flux as the Dependent Variable

1

1.67 × 10 2 (9.47 × 10 , P = 0.104)

3

3.14 × 10 4 (9.64 × 10 , P = 0.007) 3 2.62 × 10 3 (1.26 × 10 , P = 0.06)

0.720 1.400914 0.0005 2.90 × 10° 1 (7.1 × 10 , P = 0.002) 3 2.81 × 10 4 (4.5 × 10 , P < 0.001,

16

1

1.84 × 10 3 (8.92 × 10 , P = 0.064)

3

3.05 × 10 4 (9.03 × 10 , P = 0.006) 3 2.71 × 10 3 (1.18 × 10 , P = 0.04) 3 2.42 × 10 3 (1.47 × 10 , P = 0.128)

0.755 3.147493 0.0006 3.25 × 10° 1 (6.99 × 10 , P < 0.001) 3 2.95 × 10 4 (4.33 × 10 , P < 0.001)

17

3

4.08 × 10 3 (1.11 × 10 , P = 0.004) 3 3.23 × 10 3 (1.18 × 10 , P = 0.021) 3 3.72 × 10 3 (1.66 × 10 , P = 0.049) 5 1.21 × 10 4 (8.25 × 10 , P = 0.175) 1 1.91 × 10 2 (8.50 × 10 , P = 0.049)

0.778 4.438952 0.0008 3.52 × 10° 1 (6.91 × 10 , P < 0.001) 3 3.26 × 10 4 (4.62 × 10 , P < 0.001)

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correlation between runoff and the daily to weekly measured DOC concentration was studied with Spearman’s rank correlation. The effect of thinning on the annual DOC fluxes was studied with oneway analysis of variance where the annual DOC fluxes from both catchments were compared before and after thinning. The factors affecting interannual variation in DOC fluxes and DOC concentrations were studied by using linear mixed-effects models where the annual DOC fluxes (CumDOC) and annual flow-weighted mean DOC concentrations (CDOC) in both catchments were used as dependent variables (Table 1). The effects of annual precipitation, NEE of the current year, NEE of the previous year, litterfall of the previous year, snow water storage in March, temperature sum of the previous year, soil water content of the previous July, the H+ ion concentration in the runoff water, and the catchment on DOC concentrations and fluxes were used as independent variables. The effects of annual precipitation and catchment area were first tested by fitting a simple model: Y ¼ b0 þ b1 X 1 þ b2 X 2 2

Figure 2. (a) Annual precipitation and snow water storage (L m ) in 2 March before the spring thaw and (b) annual runoff (L m ) from the two catchments.

Figure 3. Annual temperature sum in O horizon on top of the mineral soil and in A horizon at 0.05 m depth in the mineral soil. The threshold temperature in the temperature sum calculation was 0°C.

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where Y is the annual DOC flux (g C m2) (or annual flow-weighted mean DOC concentration (g C L1), b0 is the intercept of the model, b1 is the regression coefficient for annual precipitation (X1) and b2 is the regression coefficient for catchment area (X2). Next, the number of explaining variables in the model was increased one by one. If the addition of a new variable did not increase the adjusted r2 of the previous model, it was discarded and the next variable was added to the model. This was continued until there was no more increase in the adjusted r2 of the fitted model. Also, the Akaike value (AIC) was calculated for each model. The models with passed these selection criteria are presented in Tables 2a and 2b. The Akaike and adjusted r2 values were calculated to four group of models separately (1–5, 6–9, 10–14, and 15–18), and the best model from each group was selected for sensitivity

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analysis of the variables. The models with the lowest Akaike value and the highest adjusted r2 value were regarded as best. The linear regression fitting was carried out by using R package “lme4.” The sensitivity analysis was carried out by changing one variable at a time by ±10% above and below the annual mean value of the variables while the other variables remained unchanged. The outcome of the model simulations was compared to that simulated with the annual means.

3. Results 3.1. Precipitation, Throughfall, and Temperature The average annual precipitation over the entire study period was 705 L m2, and there was a large variation in the amount of rain between the years. The driest year was 2009 when the annual precipitation was only 496 L m2, and the wettest year was 2012 with 906 Lm2 precipitation (Figure 2). The snow water storage was highest just before spring thaw in March– April ranging between 45 and 162 L m2. The largest snow water storages were measured in the beginning and end of 1 + Figure 4. (a) DOC concentrations (g C L ) and (b) H concentrations the study period, i.e., in 1999–2000 and 1 (mol L ) in the runoff flow collected from catchments 1 and 2 at 2011–2012 and the lowest in 2007. The SMEARII in 1998–2012. n = 649 in catchment 1 and n = 561 in catchment 2. annual average runoff was 175 L m2 and 2 268 L m in catchments 1 and 2, respectively, the lowest runoff occurring in 2009 (57 and 201 L m2 in catchments 1 and 2) and the highest in 2008 (302 and 470 L m2 in catchments 1 and 2) (Figure 2b). The monthly average DOC concentration in the precipitation was 3.24 × 103 g C L1 (standard deviation (SD) 0.0016) and there was a small but significant increase in it during the study period. The monthly average throughfall DOC concentration was 1.01 × 102 g C L1 (SD 8.5 × 103) and the throughfall DOC flux 2 × 101 g C m2 mo1 (SD 1.93 × 101), and there was no increasing or decreasing trend in either of them during the study period (throughfall DOC concentration P = 0.89 and DOC flux P = 0.11). The annual average DOC flux in precipitation was 2.32 g C m2 yr1 (SD 0.64) and in throughfall 4.35 g C m2 yr1 (SD 1.40). The monthly average H+ concentration in the precipitation was 1.9 × 105 mol L1 (SD 1.8 × 105) which corresponds to pH value of 4.72, and there was a small but significant decreasing trend in it over the study period (P < 0.001). The monthly average soil temperatures in A horizon showed an increasing trend (P < 0.001). The mean of the annual soil temperature sums with 0°C as a threshold value ranged from 1939°C days in A horizon to 2023°C days in the O horizon (Figure 3). 3.2. Runoff and DOC Concentration and Fluxes The monthly average DOC concentrations in the runoff over the whole study period were 3.84 × 103 (SD 1.56 × 103) and 3.44 × 103 (SD 2.04 × 103) g C L1 in catchments 1 and 2, respectively. The highest DOC concentrations were measured during the first week following the onset of the spring flow after which the concentrations gradually decreased to a base level of approximately 2 × 103 g C L1 in both catchments (Figure 4). The seasonal Mann-Kendall trend test revealed an increase over time for catchment 1 (P = 0.0164) and trend toward weak increase over time for catchment 2 (P = 0.062). The monthly average H+ ion

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concentration in the runoff water was 3.6 × 106 mol L1 (SD 1.3 × 106) and 3.6 × 106 mol L1 (SD 1.5 × 106) in catchments 1 and 2, respectively, which correspondences to pH value of 5.45. Also, the H+ ion concentration in the runoff water showed an increasing trend in both catchments (P < 0.001). The DOC concentrations at the end of the study period were on average 123% and 91% higher than at the start of the study period in catchments 1 and 2. The concentrations in April seemed to be increasing more during the last years of the study period compared to other months (Figure 5). However, the trend was nonsignificant at the P = 0.05 level in both catchments (Mann-Kendall trend test catchment 1 P = 0.059 and catchment 2 P = 0.435). The daily runoff DOC fluxes averaged over month had a clear seasonal pattern following the runoff fluxes (Figure 6). Highest DOC fluxes, 2 × 102 — 2.5 × 102 g C m2 d1, were observed in April and another smaller peak was seen in November during the autumn runoff. The annual fluxes ranged from the dry year minimum of 2.01 × 101 g C 1 Figure 5. Daily DOC concentrations (g C L ) in runoff calculated over m2 yr1 to the wet year maximum of monthly averages (a) in catchment 1 and (b) in catchment 2. The DOC 1.89 g C m2 yr1 (Figure 7). The annual concentrations in April highlighted with larger symbols seemed to be higher during the recent years; however, in neither of the catchments the DOC fluxes at the beginning of the study period were lower than at the end increasing trend was significant. of the study period. However, the interannual variation in DOC fluxes was large due to variation in the annual runoff; thus, the increasing trend over the whole period was not statistically significant (Catchment 1 P = 0.820, catchment 2 P = 0.330). The annual DOC fluxes differed between the wet and dry years, the dry years’ fluxes being on average only 41 and 45% (catchments 1 and 2, respectively) of those measured in years with high runoff. There were no systematic changes in the differences of the annual DOC fluxes between the studied catchments during the wet years before (1998–2001) and after (2006–2008) the thinning (Catchment 1 P = 0.495 and Catchment 2 P = 0.885). The DOC fluxes before thinning were on average 7.58 × 101 and 9.08 × 101 g C m2 yr1 in catchments 1 and 2, respectively, and after thinning 6.45 × 101 and 9.44 × 101 g C m2 yr1. 3.3. NEE, TER, and GPP The site was clearly a sink of carbon during all the years studied, with a clear trend in the monthly average NEE over the study period (P = 0.002). The average annual NEE over the whole study period was 234 g C m2 yr1, and it varied from 221 g C m2 yr1 in 1998 to 281 g C m2 yr1 in 2012 (Figure 8). The average annual litterfall over the study period was 149 g C m2 yr1 (SD 38) and did not show an increasing trend over the years (P = 0.151). 3.4. Correlations Between NEE, GPP, TER, Litter Production, and DOC Fluxes The annual DOC fluxes over the whole study period correlated with the annual runoff (Pearson correlation coefficient 0.876 (P < 0.001) and 0.734 (P = 0.002) in catchment 1 and 2, respectively) (Table 3, Figure 9). There was also a correlation between the canopy throughfall DOC flux and runoff DOC flux (catchment 1 Pearson PUMPANEN ET AL.


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correlation coefficient 0.583 (P = 0.029) and catchment 2 Pearson correlation coefficient 0.532 (P = 0.05)). The annual DOC fluxes were not significantly correlated with the annual NEE, GPP, or TER neither did the annual litterfall correlate with annual runoff DOC fluxes. Also, the correlation between the previous year’s litterfall and current year runoff DOC flux was not significant. There was a positive correlation between runoff and the daily to weekly measured DOC concentration; the Spearmann correlation coefficient in catchment 1 and 2 was 0.395 (P < 0.001) and 0.254 (P < 0.001), respectively. 3.5. Importance of the Environmental Factors Explaining the Annual Runoff DOC Fluxes and DOC Concentrations

2

1

Figure 6. Daily DOC fluxes in runoff (g C m day ) calculated over monthly averages (a) in catchment 1 and (b) in catchment 2. The DOC fluxes in April are highlighted with larger symbols.

2

Figure 7. Annual DOC fluxes in runoff (g C m 2 during dry years and during wet years.

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yr

1

) in catchment 1 and


The models explaining most of the annual variation in the flow-weighted average DOC concentration were models 5 and 9 (Table 2a) and for annual DOC flux the best models were models 14 and 18 (Table 2b). These models were also the best based on the Akaike information criteria among the four group of models. In the model selection, the temperature sum of the previous year was dropped out, because according to the adjusted r2-value, it did not improve the explanation power of the models. In general, the models predicting flowweighted average DOC fluxes were less sensitive to changes in variables than those models predicting the annual DOC fluxes (Figure 10). All models seemed to be sensitive to changes in NEE and those predicting the annual DOC fluxes were especially sensitive to precipitation. Thereafter followed litterfall of the previous year, snow water storage in March and H+ ion concentration in the runoff water. The ±10% change in the variables in models 5 and 9 changed the predicted CDOC values by ±0.54–4.74% and ±3.99–7.66%, respectively. In the models 14 and 18, the respective changes were ±0.12–19.83%. The model 5 was least sensitive to changes in precipitation

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and most sensitive to changes in soil water content in the July of the previous year. The model 9 was least sensitive to soil water content in the summer of the previous year and most sensitive to the NEE of the previous year. The model 14 was least sensitive to the snow water storage in March and most sensitive to current year precipitation. The model 18 was most sensitive to current year precipitation and least sensitive to litter fall of the previous year. The models 9, 14, and 18 were somewhat sensitive also to the H+ ion concentrations in the runoff water.

1

Figure 8. Annual GPP, TER, and NEE (g C m yr ) measured with eddy covariance technique. The negative values for NEE indicate carbon uptake and positive values for GPP and TER assimilation and respiration.

3.6. Wavelet Coherence Analysis

The wavelet coherence analysis indicated that there was a strong temporal synchrony between weekly average precipitation and runoff DOC concentration in both catchments over short 1 week 1 month time periods (Figure 11). There was also some temporal correlation between the weekly average precipitation and runoff DOC concentrations over longer, 6 month time periods in 1999, 2004, 2010, and 2012 in both catchments. The temporal correlations over longer 1–2 year time periods, were virtually nonexisting. There was also a strong temporal synchrony between weekly average soil temperature and weekly average DOC concentrations (Figure 11). The temporal correlation seemed to be very similar to that of weekly average precipitation and DOC the correlation being strongest over 1 week to 1 month time periods, intermediate over 6 month time periods and weakest over 1–2 year time periods. The temporal synchrony between weekly average soil moisture and runoff DOC concentrations was less obvious than that between runoff DOC concentrations and weekly average precipitation and weekly average temperature (Figure 11). There was no temporal synchrony between the weekly average NEE and DOC concentrations in either of the catchments.

4. Discussion We observed an increasing trend in the DOC concentrations in the runoff water in both catchment areas. There was no significant increasing or decreasing long-term trend in the DOC concentration or H+ ion Table 3. Pearson Correlation Coefficients Between Variables of Interest at the Annual Scale Time

NEE NEE of the previous year GPP TER Litter Litter of the previous year Can. TTF (canopy throughfall) DOC flux DOC flux Catch. 1 DOC flux Catch. 2 Runoff Catch. 1 Runoff Catch. 2.

NEE

NEE of the Previous Year

GPP

TER

Litter

Litter of the Previous Year

Can. TF DOC Flux

1 0.087 a 0.668 0.150 0.191 a 0.614 0.368

1 0.077 0.027 0.155 0.182 0.410

1 b 0.633 0.331 0.470 0.104

1 0.208 0.034 0.416

1 0.074 0.102

1 0.060

1

0.279 0.109 0.264 0.228

0.357 0.314 0.197 0.105

0.008 0.212 0.081 0.176

0.294 0.391 0.170 0.000

0.116 0.300 0.096 0.275

0.396 0.350 0.374 0.232

0.583 0.532 0.363 0.196

b

DOC Flux Catch 1

DOC Flux Catch 2

Runoff Catch 1

Runoff Catch. 2

1 a 0.880 a 0.876 a 0.719

1 a 0.800 a 0.734

1 a 0.929

1

a Statistical b

significance of P < 0.01. Significance of P < 0.05.

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concentration in precipitation and throughfall; this suggests that additional, more important driving forces must be the cause for the increasing DOC concentration in runoff water in Hyytiälä in addition to variability in the pH of precipitation that Monteith et al. [2007] found to be one explanation of increasing DOC concentrations in their system. The H+ ion concentration in the runoff water increased throughout the study period, simultaneously with the increasing DOC concentrations. The prescribed burning in 1962 has probably increased the soil pH, but only for a short time: the effects of prescribed burning on soil pH disappeared within 2 years in a recent prescribed burning 2 experiment close to the area of this Figure 9. Annual runoff (L m ) plotted against annual DOC fluxes 2 1 (g C m yr ) in 1998–2012. study [Kulmala et al. 2014]. The forest is still in the middle of its rotation period and accumulating carbon in the soil [Ilvesniemi et al., 2009]. The increase in H+ ion concentration in soil water probably results from the accumulation of coniferous and ericaceous leaf litter and moss on top of the soil leading to increasing amount humic and fulvic acids in the soil water [Buurman and Jongmans, 2005]. Most of the recent studies have shown that the lake and stream water dissolved/total organic carbon (DOC/TOC) concentrations throughout the boreal zone are increasing [Worrall et al., 2004; Sarkkola et al., 2009; Couture et al., 2011; Peltomaa and Ojala, 2012], and our observations are in line with these observations. However, the underlying mechanisms behind the increasing DOC concentrations may be different in different catchments. We found a weak positive correlation between the daily runoff and DOC concentration in both catchment areas. Also, the annual precipitation and snow water storage before the spring thaw is important for annual DOC fluxes, meaning that ultimately the water balance determines the interannual changes in the amount of DOC leaving the catchment. However, there was no increasing trend in snow water storage or monthly runoff over the 15 year long study period which could explain the increasing trend in DOC concentrations in the runoff water. Ultimately, the net primary productivity of the site, i.e., the production of decomposable material sets limits for the DOC fluxes leaving the catchment, although the carbon stores in boreal podzol soils are large. Despite precipitation being the most important factor in the sensitivity analysis of the linear mixed-effects models 14 and 18 predicting the annual DOC fluxes, also NEE and litterfall of the previous year, strongly affected the annual DOC fluxes (Table 2a.). This is probably related to a time delay in biological and transport processes in the soil. During the first year, the mass loss of needle litter in the decomposition is about 30% [Berg et al., 1984; Prescott et al., 2000]. Most of the carbon in the decomposition is released into the atmosphere as CO2, but a smaller proportion is converted to DOC [Cotrufo et al., 1994] which is transported in the soil water out of the catchment area. Part of the GPP of the vegetation is allocated belowground to sustain the mycorrhizal hyphae or is leached out of the root system which also may add some DOC in the soil water [Zhai et al., 2013]. The contribution of aquatic DOC export to the carbon balance of the total ecosystem was small (Figure 12). The average NEE over the whole study period was 240 g C m2 yr1, whereas the average DOC flux over the years was 0.75 and 0.93 g C m2 yr1 in catchments 1 and 2, respectively, and thus represented only 0.32–0.40% of the total annual NEE. Compared with terrestrial fluxes between vegetation and atmosphere the DOC flux in the runoff flow is minor. These are small values compared to other studies carried out in northern watersheds which have found that the runoff DOC losses were about 6% to >30% of NEE, with the highest value for a subarctic watershed where the NEE was low [Buffam et al., 2011; Jonsson et al., 2007; Christensen et al., 2007].

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The annual DOC fluxes in the Hyytiälä catchments (0.20–1.89 g C m2 yr1) were also smaller compared to 3 g C m2 yr1, which we observed in another catchment in Southern Finland [Rasilo et al., 2012; Rasilo et al. Concentrations and quality of DOC along the terrestrialaquatic continuum in a boreal forested catchment, submitted to Journal of Fresh Water Science, 2014]. The catchment was mainly occupied by old-growth forest and had a thick, 0.5–1 m, organic layer (histosol) on top of the mineral soil, whereas in this study, the organic layer was only 0.05–0.1 m thick. Moreover, the ground water level in the catchment in Rasilo et al. [2012] was much higher than in SMEARII catchment where there is practically no standing groundwater. In the SMEARII catchment, the water entering the soil surface percolates through mineral soil podzolic horizons before reaching the bedrock and most of the DOC is precipitated in the illuvial horizon (B horizon). The C horizon contains only a fraction of the carbon measured in the surface layers of the soil [Pumpanen et al., 2003]. Therefore, the amount of DOC released from SMEARII catchments is much smaller because DOM is effectively precipitated in the illuvial horizon in the podzolic soil [Ilvesniemi et al., 2009]. The catchment areas in SMEARII have been studied in detail including mapping of the soil depth and bedrock with soil penetrating radar at 1 × 1 m grid. The bedrock of the catchment areas is solid without major intrusions, and it has a top layer of clay that effectively seals the possible cracks in the bedrock. Thus, we can assume that once the water reaches the bedrock, it moves horizontally on the Figure 10. Results of the sensitivity analyses of the regression models (a) bedrock in the C horizon soil. The 5, (b) 9, (c) 14, and (d) 18. infiltration rate of the water in the soil is rather slow. The time lag between the start of runoff after the onset of autumn rains after a dry summer was 36 days [Ilvesniemi et al., 2010]. This indicates that, in the autumn, all of the runoff water moves through the soil profile and no surface flow takes place. In the spring, the contribution of surface flow is also very small in Hyytiälä since the soil never becomes totally frozen [Sevanto et al., 2006]. Even in the soil surface, the temperature remains around 0°C under the insulating snow cover meaning, that during the snow melt, the water percolates through the soil profile which probably explains the small DOC fluxes in the runoff flow in Hyytiälä also in the spring. In histosols, with low PUMPANEN ET AL.


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Figure 11. Wavelet coherence analysis between (a) precipitation, (b) soil temperature, (c) soil moisture, and (d) NEE and runoff DOC concentration. The colors of power values are from red (low values) to yellow (intermediate) to blue (high values). The yellowish parts in the figure indicate temporal coherence between the studied parameters. The Y axis indicates the length of the time window in the wavelet coherence analysis (in years).

hydraulic conductivity, water from the melting snow is transported horizontally on the soil surface to the ditches and streams [Holden and Burt, 2003], and high loads of DOC thus enter the aquatic systems since no chemical absorption of DOC in mineral soil takes place. The increasing trend in the DOC concentration of the runoff may be related to several factors. It could be a direct result of increasing NEE over the 15 study years. The carbon stocks in the tree biomass has more than doubled since the establishment of the measurement station in 1995 [Ilvesniemi et al., 2009], and the annual biomass increase has accelerated over the last years from 200 g C m2 yr1 in the 1990s to 300 g C m2 yr1 in 2008 [Ilvesniemi et al., 2009]. However, the litterfall from the tree canopy did not show any increasing trend over the years. Thus, the increasing trend in DOC concentrations could not be explained with increasing litter production. The fact, that there was no increase in annual litterfall over the years may be due to self-thinning of the trees, and thus, the CO2 assimilates would be used to the construction of stem and below ground biomass. A large proportion of the assimilated carbon is allocated belowground; 21% of Scots pine seedlings’ assimilation was allocated to roots and mycorrhiza, 11% was found in soil and 26% in root and rhizosphere respiration in a microcosm experiment with Pinus sylvestris seedlings [Pumpanen et al., 2009]. Epron et al. [2011] observed in a field experiment that 18–21% of assimilated carbon was allocated to belowground PUMPANEN ET AL.


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respiration, but the allocation patterns vary seasonally [Epron et al., 2011; Högberg et al., 2010]. We have also estimated the contribution of autotrophic and heterotrophic soil respiration using continuous chamber measurements and girdling in the Hyytiälä forest stand and observed that 33–58% of the annually respired CO2 from soil originated from recently assimilated CO2 (Pumpanen et al., Seasonal dynamics of autotrophic respiration in boreal forest soil estimated from continuous chamber measurements, submitted to Boreal Environment Research, 2014). Thus, if the belowground carbon has increased as a result of the increasing GPP and NEE, it probably also has increased the DOC in soil water and thus DOC in runoff. Another possible explanation for the increasing DOC concentrations in the Figure 12. Average carbon fluxes and stocks measured at the catchment runoff could be the increasing soil areas between 1998 and 2012. temperature. We observed an increasing trend in the annual temperature sum in the soil, but in the monthly average temperatures the trend was not significant. Particularly, the higher temperatures in the autumn and prolonged autumn period may increase the decomposition of SOM leading to increasing DOC in the soil, which is then flushed from the soil and thus from terrestrial to aquatic systems [Piao et al., 2008; Vesala et al., 2010]. Increase in soil temperature may also increase the GPP of vegetation by increasing the belowground carbon sink of photoassimilates [Pumpanen et al., 2012]. This mechanism could accelerate the flow of easily decomposable carbon to the soil, which has been suggested to stimulate the decomposition of old recalcitrant SOM pools through the so-called priming effect [Fontaine et al., 2007; Kuzyakov, 2010]. The temporal correlation between NEE and DOC concentration (and other biometeorological parameters) depends on the length of the time lag studied. In the wavelet coherence analysis we observed that there was no temporal synchrony between weekly average NEE and DOC concentrations, whereas the sensitivity analyses of the linear mixed-effects models indicated that, when integrated on an annual scale, NEE plays an important role in predicting annual runoff DOC concentrations or fluxes. The different results of these two analyses are probably related to the time lags in biological processes, e.g., decomposition of SOM. The wavelet coherence analysis was based on short-term averages of NEE over the forest canopy with the EC method, whereas the linear mixed-effects model analysis was based on annual flux values. Thus, the two methods are not directly comparable. The wavelet coherence analysis method indicated that the temporal synchrony between runoff DOC concentrations and precipitation as well as soil temperature depended on the length of the time period studied. The temporal synchrony was most obvious over short 1 week to 1 month time periods which is most likely related to the seasonal pattern in soil temperature and precipitation. There are two regular runoff peaks, one during the snowmelt and the other during the late autumn before the soil surface freezes over, and the runoff DOC also has higher concentrations during the onset of runoff flow. Also, the soil temperature in Hyytiälä follows a typical seasonal pattern, the highest temperatures measured in late summer and lowest in winter [Pumpanen et al., 2003]. There was also some temporal synchrony between soil water content and runoff DOC concentrations which is also related to the seasonal pattern in soil water content. The soil water contents are highest during the onset of runoff and lowest in July and August when the evapotranspiration in Hyytiälä is high [Ilvesniemi et al., 2010] resulting in temporal synchrony between soil water content and runoff DOC concentrations.

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5. Summary By using the 15 year long time series, we were able to cover large interannual variation in precipitation and runoff and estimate the long-term trends in runoff DOC fluxes and their correlation with other ecosystem carbon fluxes. Our results also indicate that the annual DOC fluxes and DOC concentration are most sensitive to the variation precipitation, but also NEE and litterfall of the previous year play an important role in DOC fluxes and concentrations. The annual DOC fluxes were small compared to other ecosystem carbon fluxes (0.32–0.40% of the annual NEE). However, our study revealed that the DOC concentration in the runoff water is increasing which could increase the annual DOC fluxes during wet years. Acknowledgments This study was supported by the Academy of Finland projects 218094 and 213093 as the Academy of Finland Finnish Centre of Excellence Program. We also thank for the staff of Hyytiälä Forestry Field Station, e.g., Sirkka Lietsala and Veijo Hiltunen for assisting in the field work. Academy of Finland project 218094 as well as ICOS 271878, ICOS-Finland 281255 and ICOS-ERIC 281250 funded by Academy of Finland and the Centre of Excellence programme 1118615; EU, through projects GHG-Europe and InGOS; NordForsk, through the Nordic Centre of Excellence (project DEFROST). The data used in this manuscript is available from the ICOS data base.

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