DOI: 10.5061/dryad.wpzgmsbp9
title: The role of terrestrial productivity in regulating aquatic dissolved organic carbon concentrations in boreal catchments

description: 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 (&lt;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.
description: 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<sup>°</sup>14′ N, 19<sup>°</sup>46′E) (Fig
 S1). In Krycklan, C2[S-forest] is covered by forest with the size of 0.14
 km<sup>2</sup>; C4[S-mire] of 0.19 km<sup>2</sup>
 is covered by 40.4% of wetlands, and the remainder is forest; C6[M-mix] of
 1.3 km<sup>2 </sup>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 <sup>◦</sup>C, and July 14.7 <sup>◦</sup>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<sup>°</sup>44′ N,
 29<sup>°</sup>27′E) approximately 120 km north of the Arctic
 Circle close to the northern timberline (Fig S1). NT [L-mix] covers about
 40 km<sup>2</sup> 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 <sup>◦</sup>C. The mean temperature in
 January is -11.4 <sup>◦</sup>C, and in July 13.1
 <sup>◦</sup>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. <i>Prediction of DOC based on
 real-time spectral absorbance </i> 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
 '<i>tsclean’</i> function of package <i>‘forecast’
 </i>(Hyndman & Khandakar, 2008) in R (R Core Team,
 2019). <i>4.Water
 discharge</i> 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. <i>Carbon
 fluxes</i> 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
 (<i>Pinus sylvestris</i> 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
 (https://deims.org/b471311f-e819-4f6f-bbae-1ac86cd9777f). 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 <i>et
 al.</i> (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 (<i>Pinus sylvestris</i> L.), and the
 soil is a deep deposit of sand and fine sand. The ground vegetation is
 dominated by blueberries (<i>Vaccinium myrtillus </i>L.) and
 lingonberries (<i>Vaccinium vitis-idaea </i>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 (<i>Sphagnum balticum</i>, <i>Sphagnum
 majus</i> and <i>Sphagnum Lindbergii</i>) dominating the
 bottom layer. The field layer is dominated by the cottongrass
 (<i>Eriophorum vaginatum </i>L.) and cranberry
 (<i>Vaccinium oxycoccos </i>L.), bog-rosemary
 (<i>Andromeda polifolia </i>L.), deergrass
 (<i>Trichophorum cespitosum </i>L.). Sedges (<i>Carex
 spp.</i>) 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 (https://github.com/icos-etc/ONEFlux), including spike detection, data flagging, and friction velocity filtering (Papale et al., 2006). ONEFlux processing pipeline is described in more detail in Pastorello <i>et al.</i> (2020).