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The role of terrestrial productivity in regulating aquatic dissolved organic carbon concentrations in boreal catchments


Zhu, Xudan et al. (2022), The role of terrestrial productivity in regulating aquatic dissolved organic carbon concentrations in boreal catchments, Dryad, Dataset,


The past decades have witnessed an increase in dissolved organic carbon (DOC) concentrations in the catchments of the Northern Hemisphere. Increases in terrestrial productivity may be a reason for the increases in DOC concentration. The aim of this study is to investigate the impacts of increased terrestrial productivity and changed hydrology following climate change on DOC concentrations. We tested and quantified the effects of gross primary production (GPP), ecosystem respiration (RE) and discharge on DOC concentrations in boreal catchments over three years. As catchment characteristics can regulate the extent of rising DOC concentrations caused by the regional or global environmental changes, we selected four catchments with different sizes (small, medium and large) and landscapes (forest, mire and forest-mire mixed). We applied multiple models: Wavelet coherence analysis detected the delay-effects of terrestrial productivity and discharge on aquatic DOC variations of boreal catchments; thereafter, the distributed-lag linear models (DLMs) quantified the contributions of each factor on DOC variations. Our results showed that the combined impacts of terrestrial productivity and discharge explained 62% of aquatic DOC variations on average across all sites, whereas discharge, GPP and RE accounted for 26%, 22% and 3%, respectively. GPP dominated the DOC variations in small catchments (<1 km2), but in large catchments, DOC variations were mainly dependent on discharge. The direction of the relation between GPP and discharge on DOC varied. Increasing RE always made a positive contribution to DOC concentration. This study demonstrated that terrestrial greening and changing hydrology caused by climate change did affect the DOC export from terrestrial to aquatic ecosystems, which improves the mechanistic understanding of surface water DOC regulation in boreal catchments under climate change.


1. Study site

Four catchments with different sizes (small, medium and large) and landscapes (forest, mire and forest-mire mixed) were studied. Hereafter, following the catchment named ‘S’, ‘M’ and ‘L’ state the catchment sizes while ‘forest’, ‘mire’ and ‘mix’ show the land cover types. Three sub-catchments locate in Krycklan, about 50 km northwest of the city of Umeå in northern Sweden (64°14′ N, 19°46′E) (Fig S1). In Krycklan, C2[S-forest] is covered by forest with the size of 0.14 km2; C4[S-mire] of 0.19 km2 is covered by 40.4% of wetlands, and the remainder is forest; C6[M-mix] of 1.3 km2 is constituted by 72.8% of forest, 24.1% of wetland and 3.1% of lakes (Table 1). The climate is characterised as a cold temperate humid type with persistent snow cover during the winter season. The 30-year mean annual temperature (1981-2010) is 1.8 C, January -9.5 C, and July 14.7 C. The mean annual precipitation is 614 mm, mean annual mean runoff is 311 mm, giving an annual average evapotranspiration of 303 mm (Laudon et al., 2013). The 40-year average duration of winter snow cover is 167 days, but this has been decreasing over time (Laudon et al., 2021). Yli-Nuortti (NT [L-mix]) is a catchment nearby Nuorttiaapa measuring station and located in Värriö, Finland (67°44′ N, 29°27′E) approximately 120 km north of the Arctic Circle close to the northern timberline (Fig S1). NT [L-mix] covers about 40 km2 with 25% of peatlands, and 5% of the area is covered by alpine vegetation on the top of the fells while the rest of the catchment is dominated by pine forests on glacial tills (Table 1). There are no lakes above the measurement station. According to the statistics of the Finnish Meteorological Institute (1981-2012), the mean annual air temperature is -0.5 C. The mean temperature in January is -11.4 C, and in July 13.1 C. The mean annual precipitation is 601 mm. The average number of days with snow cover is 205-225 days (Pohjonen et al., 2008).

2. Sampling and laboratory DOC measurement

High-density polyethene bottles were used for collecting water samples. In Finland (NT [L-mix]), we sampled monthly in winter and fall, fortnightly in spring and every week in summer (2018-2020). In Sweden (C2[S-forest], C4[S-mire] and C6[M-mix]), water samples were collected monthly during winter, every two weeks during summer and fall, and every third day during the spring flood (2016-2018). Water samples were filtered immediately after sampling by a filtration system made of glass using Whatman GF/F Glass Microfiber Filters (pore size 0.45 μm), which had been rinsed by the sample water before filtration. All samples were frozen until further DOC analysis.

In Finland, DOC concentrations were determined by thermal oxidation coupled with infrared detection (Multi N/C 2100, Analytik Jena, Germany) following acidification with phosphoric acid. In Sweden, DOC concentrations were measured with Shimadzu TOC-5000 using catalytic combustion (Laudon et al., 2004).

3. Prediction of DOC based on real-time spectral absorbance

To monitor the real-time spectral absorbance, in-situ portable multi-parameter UV–Vis probes (spectro:lyser, S:CAN Messtechnik GmbH, Austria) were installed in Yli-Nuortti river on June 12, 2018, and in the Krycklan catchments on May 9, 2016. The spectro:lyser measures absorbance across the wavelengths from 220 to 732.5 nm at 2.5 nm intervals with a path length of 35 mm. The benefits of in-situ UV–Vis probe is to make high-frequency aquatic monitoring possible, especially during short-duration events or in remote areas (Avagyan et al., 2014; Rode et al., 2016; Zhu et al., 2020).

Principal component regression (PCR) was used to model the relationship between DOC concentration and absorbance. In the PCR model, absorbance values from 250 nm to 732.5 nm at 2.5 nm intervals (194 variables) were the independent variables. The dependent variables were the DOC concentrations measured in the lab from water samples collected in the respective days. The observations were split into a training and testing data set. The training set contained 75% of observations that were randomly selected from all samples (C2[S-forest], C4[S-mire], C6[M-mix]and NT [L-mix]), and the testing set contained the remaining 25% of observations. The PCR analyses were conducted with the 'pls' package (Mevik et al., 2019) in R (R Core Team, 2019). After the PCR model was built, hourly real-time spectral absorbances were used as input to predict hourly DOC concentrations. The hourly predicted DOC concentrations were aggregated into daily data for further analysis. The outlier values were automatically detected and corrected using the 'tsclean’ function of package ‘forecast’ (Hyndman & Khandakar, 2008) in R (R Core Team, 2019).

4.Water discharge

In Finland, water discharge was determined based on the continuous water depth measurements carried out by pressure sensors measuring the hydrostatic pressure (Levelogger, Solinst, Georgetown, Canada) in the bottom of the river, which was corrected by barometric pressure measurements (Barologger, Solinst, Georgetown, Canada). The water depth measurements were converted to flow rates using channel cross-section, water depth and manual flow rate measurements (Flow Tracker Handheld ADV, SonTek, CA, USA) carried out at sampling locations.

In Sweden, water discharge was computed hourly from water level measurements (using pressure transducers connected to Campbell Scientific dataloggers, USA or duplicate WT-HR water height data loggers, Trutrack Inc., New Zealand). Rating curves were derived based on discharge measurements using salt dilution or time-volume methods (Laudon et al., 2011).

5. Carbon fluxes

There are three measuring stations nearby our study sites where the C exchange between the terrestrial ecosystem and the atmosphere is continuously recorded by the EC technology (Medlyn et al., 2005). The EC data included GPP, RE and NEP. We assumed that NEP=-NEE (Black et al., 2007), and the value for RE and GPP was taken from day-time measurements (Aubinet et al., 2012).

In Finland, the Värriö measuring station SMEAR I (67°45′ N, 29°36′ E, 390m asl) is close to NT [L-mix]. Most of the area is dominated by 60-year-old Scots pine (Pinus sylvestris L.) forests, in addition to which there are also large wetlands and deep gorges in the surroundings(Vehkamäki et al., 2004, pp. 1998–2002)(Vehkamäki et al., 2004, pp. 1998–2002)(Vehkamäki et al., 2004, pp. 1998–2002). Flux data from SMEAR I was applied to NT [L-mix]. The flux data were collected from the Dynamic Ecological Information Management System ( The processing pipeline differed from the two Swedish sites due to polar day (24 hours sunlight) during the growing season. More details about the whole process for data quality control are presented in Kulmala et al. (2019).

In Sweden, the Rosinedalsheden station (64°10′N, 19° 45′E, 145m asl) is located in a forest stand that consists of naturally regenerated 80-year-old Scots pine (Pinus sylvestris L.), and the soil is a deep deposit of sand and fine sand. The ground vegetation is dominated by blueberries (Vaccinium myrtillus L.) and lingonberries (Vaccinium vitis-idaea L.). Degerö station (64°11′N, 19°33′E, 270m asl) is situated on a highland between two major rivers, Umeälven and Vindelälven. The site is a nutrient-poor minerogenic mire dominated by flat mire lawn plant communities with bog mosses (Sphagnum balticum, Sphagnum majus and Sphagnum Lindbergii) dominating the bottom layer. The field layer is dominated by the cottongrass (Eriophorum vaginatum L.) and cranberry (Vaccinium oxycoccos L.), bog-rosemary (Andromeda polifolia L.), deergrass (Trichophorum cespitosum L.). Sedges (Carex spp.) occur more sparsely. C fluxes data from Rosinedalsheden were applied to C2[S-forest], and C6[M-mix] and C fluxes data from Degerö was used in C4[S-mire] (Table S1). C fluxes data from the two EC towers were obtained from the ICOS data portal (Drought 2018 Team & ICOS Ecosystem Thematic Centre, 2020). The data had been subjected to standardised quality control using the ONEFlux processing pipeline (, including spike detection, data flagging, and friction velocity filtering (Papale et al., 2006). ONEFlux processing pipeline is described in more detail in Pastorello et al. (2020).


Kone project , Award: 201906598

Academy of Finland, Award: 326818

Academy of Finland, Award: 323997

Academy of Finland, Award: 337550

European Union’s Horizon 2020, Award: 734317

European Commission-Horizon 2020, Award: 689443

Academy of Finland, Award: 337549

Academy of Finland, Award: 304460