Optical properties of dissolved organic matter along a salinity gradient from a boreal river estuary to open coastal waters.

Optical properties of dissolved organic matter along a salinity gradient from a boreal river estuary to open coastal waters. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optical properties of dissolved organic matter along a salinity gradient from a boreal river estuary to open coastal waters. Aleksandr Berezovski, Dag Olav Hessen, Hanne Halkjelsvik Børseth, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4497080/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the optical properties of coloured dissolved organic matter (CDOM) along a salinity gradient from the Glomma river to the outer Oslofjord. The research aims to determine the spectral, isotopic, and quantitative changes in CDOM across this gradient. Key findings indicate that total organic carbon (TOC) concentrations display notable seasonal variability, especially near shore, due to fluctuations in river discharge throughout the year, while remaining more constant in the outer sea environment​​. CDOM absorption, spectral slope and specific absorbance at 443 nm decreases linearly with increasing salinity. CDOM absorption exhibited significant variations, with low-salinity samples showing higher light absorption per unit of carbon, but little seasonal variations, hinting at the fact that CDOM had similar optical properties over the year of sampling. δ 13 C of TOC analysis revealed a strong positive correlation with salinity, indicating a linear transition from terrestrial to marine organic carbon sources. This method can be an effective way of tracking the fate of terrestrially-derived organic matter in estuarine systems, which is highly topical for coastal darkening research. Water darkening is an increasingly relevant problem affecting many coastal ecosystems, as it is exacerbated by the human activity and climate change. Colored dissolved organic matter δ13C of TOC salinity gradient CDOM absorption Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Landscapes, freshwaters and coastal areas are tightly linked in seaward catchments, and catchment properties and terrestrial processes will thus ultimately affect marine systems, e.g. in terms of export flux of substances and elements like dissolved organic carbon (DOC) (Regnier et al., 2022 ; Crapart et al., 2023 ). The current “browning”, i.e. increased load of coloured, terrestrial matter into freshwaters, is typically observed in boreal areas (Monteith et al., 2007 ; Finstad et al., 2016 ; Creed et al., 2018 ). This is expected to have an impact on coastal systems as well, often here denoted “coastal darkening” (Opdal et al., 2019 ; Frigstad et al., 2020 ; McGovern et al., 2020 ). Coastal darkening could be considered a form of optical deterioration of water quality, caused by coloured dissolved organic matter (CDOM) loading and resulting in increased light attenuation. The magnitude and ecological impact of coastal darkening is far from settled and will also have a pronounced spatial and temporal variability. The North Sea, Norwegian coastal waters and the Baltic Sea are all interconnected and influenced by the coloured organic matter coming from land. The quantity and quality of the organic matter entering marine waters is heavily affected both by catchment properties and climate. Typically, brown, DOC-rich water are prevalent in boreal system with coniferous forest and bogs as the main spatial determinants (Larsen et al., 2011 ; Finstad et al., 2016 ). These northern ecosystems are also disproportionately affected by climate warming (Ohmura, 2012 ; Myers-Smith et al., 2020 ), and many regions also experience changes in snow-cover and precipitation that may affect concentrations and runoff of DOC (de Wit et al., 2016). Boreal regions are undergoing accelerated greening (increased vegetation cover and thus also carbon fixation) as a result of afforestation, as well as higher temperatures and elevated runoff. Increased terrestrial net primary production translates into larger loads of organic matter entering the water systems, and is one of the likely causes for the observed browning (Larsen et al., 2011 ; Finstad et al., 2016 ; Kritzberg, 2017 ). Also, the reduced acid deposition promotes leaching and export of DOC from soils (Monteith et al. 2007 ). Dissolved organic matter (DOM) is the predominant form of organic carbon in aquatic systems (Hedges et al., 1997 ; Findlay and Parr, 2017 ). As it travels with water masses from catchments and between bodies of water, DOM undergoes microbial and photochemical degradation (Cory et al., 2014 ), meaning DOM cannot be considered a conservative substance. Also, exudates from phytoplankton and bacteria contribute to the total DOM pool and light attenuation, yet this autochthonous DOM lacks the highly brown humic compounds (Jaffé et al., 2004 ). Humic substances in allochthonous DOM strongly promote light attenuation in the shorter wavelengths, due to high absorption potential of double bonds in their molecular structure (Pérez San Martín et al., 2023). These double bond groups are known as chromophores, and they confer colour to humic compounds (Stevenson, 1994 ). CDOM is the component of DOM that primarily absorbs light. Increasing concentrations of CDOM can affect aquatic ecosystems by diminishing light, where the organic molecules become effectively a “competitor” to the light-harvesting pigments of phytoplankton. Thus, reducing photosynthetic activity in a water body, resulting in lower autotrophs production and biomass of phytoplankton or aquatic macrophytes and shift the compensation depth upward (Karlsson et al., 2009 ; Thrane et al., 2014 ). Reduced primary production will subsequently limit biomass of higher trophic levels (du Pontavice et al., 2021 ). Riverine exports will however also promote phytoplankton productivity in coastal systems, owing to the transport of nutrients like N, P, Si and Fe (Deininger et al., 2016 ). Thus, the net effect on the productivity of coastal waters is not settled and will depend both on the flux and fate of DOM vs nutrient elements. Phytoplankton blooms have been predicted to occur earlier as global warming progresses because of earlier and increased stratification (Desmit at al., 2019). Yet, for Norwegian coastal waters, peak spring phytoplankton bloom has been delayed by 22 days over the past century, which was attributed to coastal darkening (Opdal et al., 2019 ; Opdal et al., 2023 ). Mustaffa et al. ( 2020 ) studied the relationship between light intensity and phytoplankton growth rates in the Sognefjord at the Norwegian west coast, impacted by freshwater inputs from several rivers. They found that increased light intensity led to higher phytoplankton growth rates in the central and outer fjord, but with no effect on phytoplankton in the open ocean, where the impact of terrestrial DOM is marginal. Besides impact on autotrophs, darkening and spectral changes may also affect higher trophic levels by favouring tactile predators (e.g. jellyfish) over visual predators such as fish, potentially causing an ecosystem regime shift (Eiane et al., 1997; Aksnes et al., 2009 ). Increased inputs of DOC will also promote system heterotrophy by providing a substrate for heterotrophic bacteria while reducing primary production (Tranvik and Sieburth, 1989 ). Total organic carbon (TOC) is a sum of particulate organic carbon (POC) and DOC. DOC typically constitutes more than 90% of TOC in Norwegian coastal waters (Olsen et al., 2006 ). It is important to study TOC, when investigating light climate of aquatic environments, since organic carbon compounds are some of the most light attenuating elements in the water. To separate the sources of TOC along this transect from the river mouth to open waters, we used the δ 13 C isotope signature of TOC as a marker. This is often used to discriminate between allochthonous and autochthonous TOC sources, since δ 13 C signature of terrestrial C3 plant material is between − 23‰ and − 34‰, while for marine phytoplankton it typically ranges from − 18‰ and − 22‰ (Lee et al., 2020 ). These differences in δ 13 C signature occur due to differences in photosynthetic pathways and in CO 2 diffusion rates in air and water (O'Leary, 1988 ). δ 13 C TOC thus serves as a proxy for how much of the organic carbon dissolved in a water system is of terrestrial origin at the moment, unlike measuring δ 13 C in sediments that gives a record of historical carbon data deposition. This sort of analysis is rarely conducted, due to difficulties in methodology. However, as we show in this study, it can be effectively implemented for determining the source of organic carbon. The authors show that the use of stable isotope analysis of δ 13 C of TOC on a freshwater-marine gradient can be a valuable diagnostic tool for the origin of TOC. The main aim of this study was to identify whether and how terrestrially-derived DOM undergoes spectral, isotopic and quantitative changes along a spatial transect from land to open water. This in turn helped to assess the magnitude of darkening in the coastal waters. To achieve this we measured light absorption by CDOM along a salinity gradient from the mouth of the Glomma (largest river in Norway) to the outer Oslofjord in different seasons. We hypothesized that DOM concentrations would follow passive mixing, and hence water transparency should be negatively correlated to salinity. We also used spectroscopy and stable isotope analysis to infer the endmembers of terrestrial and marine TOC. We further hypothesized that there would be seasonal variations in CDOM absorbance with higher concentrations during high-runoff seasons. Materials And Methods 2.1 Study site and sampling methods The Glomma estuary and the transect in the outer Oslofjord in south-eastern Norway was chosen as a study site because of its location - it is a region where the DOC-rich water from the Glomma mixes with Baltic Sea water carried by the Skagerrak Coastal Current and seawater coming from the North Sea via Atlantic Current (Sætre, 2007 ). The Glomma river is the largest river in Norway with a mean daily discharge of 684 m 3 s − 1 (since 1964; NVE, 2023) and a total catchment area of 41970 km 2 (NVE Atlas, 2023). It drains large parts of alpine landscapes and boreal forest with bogs and wetlands, before entering the Oslofjord (Fig. 1 ). Flow regime in the sampling area is dependent on the Baltic outflow and most importantly on the discharge from the Glomma. Six sampling cruises took place between June 2020 and June 2021 (2020-06-04, 2020-09-08, 2020-11-26, 2021-02-23, 2021-03-25 and 2021-06-17). Three additional sampling cruises were conducted to obtain more data on DOM (2022-05-12, 2022-06-02 and 2022-06-29) (Børseth, 2022 ). A usual sampling route encompassed six sampling stations (marked OF2, OF1, Ø1, I1, L5 and L1 in Fig. 1 ). The same stations were visited on approximately the same times of the day (12:00 ± 2 hours) on all sampling days, meaning the differences of physicochemical properties of water due to the tide level were minimal (biggest tidal range between all six sampling dates was 40 cm (Kartverket, 2024 )). The stations represented a salinity gradient with the highest salinity at station OF2 and lowest at station L1. The salinities of each station show seasonal variation, yet at very different levels. For the open-sea site salinity ranged between 19 and 29, while the inner, riverine site had salinities ranging from 0–5. Salinity was recorded in PSU. The total transect length was 56 km. Conductivity, temperature and oxygen concentrations were recorded in situ using a CTD profiler (Sea-Bird Model SB9). 2.2 Absorbance measurements Water samples were collected at each sampling site using a Rosette sampler. Water samples were extracted from 3–4 m depths and brought to the laboratory where the absorbance by CDOM was measured on a double beam Shimadzu UV-2550 UV-VIS Spectrophotometer (Shimadzu scientific instruments, Columbia, Maryland, USA). The definition of “dissolved” organic matter is somewhat arbitrary, Xu and Guo ( 2017 ) describe DOM as any molecules that pass through a < 0.7 µm pore-sized filter. In this study, 0.7 µm pore GF/F Whatman glass fiber filters were used to separate the “dissolved” part of organic matter from larger detritus. Freshwater DOM in freshwater typically has molecular weights between 200 and 1250 Dalton (Asmala et al. 2021 ), which is many folds lighter than the molecular weight cutoff of 0.7 µm pore filters. Absorbance of the filtrate ( \({A}_{\lambda }\) ) was measured in 5 cm ( \(L\) ) quartz cuvettes over the 250–750 nm range with 1 nm increments and referenced to Milli-Q water. The resulting CDOM absorbance (log-10 scale of absorbance per cm of path length) was subsequently transformed into base-e absorption coefficients per m ( \({a}_{\lambda }\) , as seen in Eq. 1). All absorbance values were corrected for baseline offset by subtracting average absorbance between 740–750 nm (Green and Blough, 1994 ). 740–750 nm range was chosen because absorbance by CDOM is expected to be negligible here. \({a}_{\lambda }=ln\left(10\right) *\frac{{A}_{\lambda }}{L}\) (Eq. 1) 2.3 Organic carbon concentrations TOC concentrations were also analysed by high-temperature catalytic oxidation and non-dispersive infrared detection on a Shimadzu TOC-VCPH Analyzer (Shimadzu scientific instruments, Columbia, Maryland, USA). The water samples were heated to 680°C with platinum catalyst and subsequently converted into CO 2 gas and measured. POC concentrations were measured by filtering sample water through a 0.7 µm pore GF/F glass fiber filters and measured on a FlashEA 1112 Nitrogen and Carbon Analyzer (Thermo Fisher Scientific), where filters were combusted and the produced gas was analysed on elemental composition by gas chromatography. DOC concentrations were calculated by subtracting POC from TOC concentrations. 2.4 Stable isotope analysis δ 13 C of TOC (both dissolved plus particulate organic carbon) was measured with an isotope ratio mass spectrometry (IRMS). The reason for analysing TOC as a whole was not to prioritise organic molecules of certain sizes over the others and also to lower risk of contamination. Analysis of δ 13 C of TOC in marine water poses problems due to salt content. Salt combusted at high temperatures deteriorates quartz tubing inside the elemental analyzer as well as decreases the efficiency of combustion. Also, combusted salt may ‘compete’ with CO 2 molecules in the analyzer leading to incorrect readings (Lalonde et al., 2014 ). Because of these complications, data for TOC δ 13 C in salt water are rarely measured. Our δ 13 C analysis was performed by Jan Veizer Stable Isotope Laboratory (Ottawa, Canada). A predetermined quantity of water was deposited in a reaction chamber of an OI Analytical Aurora Model 1030W TOC Analyser, where it was mixed with hydrochloric acid to remove inorganic carbon. The water containing organic carbon was combusted several times and passed through chemical and Nafion traps to remove traces of water. CO 2 was trapped by GD-100 trap, where the peak was rinsed with ultrapure He gas before the analysis in IRMS (Lalonde et al., 2014 ). Organic carbon was converted into CO 2 gas as a result of combustion which is then passed into a Finnigan Mat DeltaPlusXP IRMS. Data was normalised using two different internal organic standards and the analytical precision was ± 0.2‰. 2.5 Modeling Salinity dilution curves were analysed by simple or additive regression models. For linear mixed models we used the lme4 package (Bates et al., 2014), while we used the mgcv package (Wood, 2017 ) for additive models. Hierarchical generalized additive models (HGAMs) were used to estimate smooth function relationships between dissolved substances and salinity, grouped by sampling date (Pedersen et al., 2019 ). HGAMs can represent non-conservative mixing as departures from linearity in the salinity relationship. CDOM absorption coefficients ( \({a}_{\lambda }\) ) as a function of wavelength was modeled as an exponential function with an offset (Eq. 2), as suggested by Stedmon et al. ( 2000 ). The model has 3 parameters: the absorption coefficient a 443 at the reference wavelength (443 nm), the spectral slope ( S ) and the background absorbance ( K ). The wavelengths were rescaled from nm to µm following Stedmon et al. ( 2000 ), such that the unit of S will be 1/µm. 443 nm (0.443 µm) was used as reference wavelength since it is around blue absorbance peak of chlorophyll a (Kirk, 2011 ), and is often used in remote sensing models of ocean colour. All CDOM spectra were fitted simultaneously to a non-linear model using the saemix package in R (Comets et al., 2017 ). \({a}_{\lambda }={a}_{443} {e}^{S\left(\frac{443-\lambda }{1000}\right)}+K\) (Eq. 2) CDOM absorption coefficient at 443 nm ( a 443 ) for every sample was estimated according to Stedmon et al. ( 2000 ) model. Specific absorption coefficient at 443 nm ( sa 443 ) was calculated as CDOM absorption coefficient at 443 nm ( a 443 ) divided by DOC concentration of the water sample ( \({sa}_{443}={a}_{443}/conc\left(DOC\right)\) ). Resulting units were m 2 g −1 . 443 nm wavelength was once again chosen as a reference wavelength. sa 443 is used to estimate DOC from remote sensing (Kutser et al., 2015 ). Results Water samples had a good spread of salinities ranging from 0.03 in the inner station (L1) to 29.50 PSU at the outer (OF2) (Fig. 2, x-axis). The six sampling cruises covered different seasons, reflecting that the discharge volumes from the Glomma river varied with season and precipitation. Cruises 2, 4, 5, 7 and 8 (Supplementary Table 2) took place during low discharge periods (< 600 m 3 /s), while cruises 1 and 9 saw medium discharge levels (< 1000 m 3 /s). Cruises 3 and 6 occurred after heavy discharge periods (< 1300 m 3 /s) (Supplementary Fig. 2). Salinity changed strongly between the sampling seasons in shallow water layers at every sampling station. 3.1 δ 13 C of TOC The δ 13 C of TOC data was predicted using HGAM as well as linear mixed effect models with or without random slopes, but all of them showed very small variance among the seasons. Therefore, a simple regression model without grouping by seasons was chosen as a preferable model to describe decreasing δ 13 C of TOC along the salinity gradient (Fig. 2). There was a strong positive linear correlation between δ 13 C and salinity ( F = 79.59, p < 0.00) and insignificant seasonal variations ( F = 2.18, p = 0.09). The boundaries for δ 13 C of freshwater and marine (0 and 35.2 PSU accordingly) endmembers were estimated from the regression model using the salinity range in Norwegian coastal waters reported by Aksnes ( 2015 ). If the TOC was of strictly freshwater or marine origin, the respective δ 13 C values would be constrained within these boundaries. This suggests that there is both marine and terrestrially derived carbon mixed in the water column at the freshwater sites. δ 13 C values of seawater from marine sites are higher and more tightly clustered together, when compared to more variable and lower δ 13 C values of freshwater samples. This effectively means that there are relatively more lighter carbon isotopes in the freshwater impacted inner estuary and that the proportion of carbon shifts to heavier carbon further out in the open sea. This strong and linear relationship implies that δ 13 C can serve as a good diagnostic tool for the fraction of terrestrial allochthonous vs marine, autochthonous DOM. Figure 2. Relationship between δ 13 C (‰) of TOC in water samples and salinity. Line represents simple regression. Red bars represent 95% confidence intervals of the freshwater and marine endmembers based linear regression model. Shapes of data points correspond to the dates of sampling and colours correspond to the sampling stations. 3.2 TOC concentration DOC was a major component of TOC (89% average; Supplementary Table 2). DOC/TOC ratios varied with seasons - June 2020 had the lowest mean DOC/TOC ratio of all the sampled seasons (71%; Supplementary Fig. 3), September saw higher average DOC/POC (84%), while November 2020 and February, March and June 2021 all had DOC constituting over 90% of TOC in the water column. We found no systematic change in dissolved fraction over salinity range: DOC/TOC fractions stayed mostly the same throughout the salinity gradient, except in June 2020, where a decline of DOC fraction at increased salinity was observed (Supplementary Fig. 3). HGAMs were used to fit TOC concentration over 0–35 salinity range grouped by sampling date (Fig. 3 ). AICs of HGAM and linear model were compared (8 and 64 accordingly), indicating non-linear relationship and thus a better fit from HGAMs. As a general trend, TOC concentrations decreased nearly linearly with increasing salinity in colder seasons (November-May and to a lesser extent September), while summer seasons showed indications of non-conservative mixing, especially in the freshwater end of the gradient, where TOC concentrations are more varied. The outer sampling sites were more uniform with less seasonal and site-by-site fluctuations of TOC concentrations. According to an ANOVA analysis, there were significant variations between TOC concentrations at different seasons ( F = 115.06, p < 0.00). A linear regression was used to relate TOC concentrations to mean discharge volume (time lag of 14 days) at the river mouth site throughout two years of sampling. The variations in the TOC concentration at the river mouth station was not related to river flow ( b = 74.70 ± 137.88, p = 0.61). 3.3 Spectral slopes and CDOM absorbance a 443 was linearly related to salinity in the Glomma estuary (Fig. 4 B). There were significant differences when comparing a 443 to salinity ( F = 27.57, p < 0.00), but without a significant variation between the seasons ( F = 1.98, p = 0.11). Spectral slope ( S ) followed a similar trend, but the decrease over salinity was even sharper ( F = 16.75, p < 0.00; Fig. 4 A). Both a 443 and S are good indicators of CDOM absorbance, both being variables in Eq. 2, where the higher values of these correspond to higher relative absorbance at wavelengths shorter than the reference ( \(\lambda\) 0 ). sa 443 values displayed a weak trend of decreased sa 443 at higher salinities (Fig. 4 C). However, there was a strong scatter in sa 443 across the salinity gradient, and the confidence intervals of the endmembers overlapped. This suggests that sa 443 does not differ significantly between freshwater and marine water samples ( F = 3.32, p = 0.08). Since S is variable across the salinity gradient, while sa 443 is not, it can be deduced that S changes the most at a pivot point somewhere below 443 nm. Discussion 4.1 DOM dynamics DOM undergoes qualitative and quantitative changes along the salinity gradient. The estuarine waters have higher DOM concentrations and thus also higher light absorption compared to more offshore waters. Seasonality played a major role in changes in TOC concentration especially in freshwater end of the gradient. The seasonal fluctuation declined in the outer sea, however still, with significant seasonal changes in TOC concentration (Fig. 3 ) and insignificant variations in absorption coefficients (Supplementary Fig. 1). Spring snowmelt, which occurs in May to June in this region (Poste et al., 2021 ), seems to add non-linearity to TOC mixing across the salinity gradient. All the summer samplings differ greatly in their observed TOC concentrations, however the common trend is that summer HGAMs show curvature, while samplings from colder seasons (including early May sampling) show more linear patterns of TOC mixing. Overall, TOC concentrations are higher at the winter seasons, which is consistent with previous studies (Poste et al., 2021 ). Element fluxes from Glomma are more evenly scattered throughout a year due to the combined effect of hydrology and agricultural runoff (Poste et al., 2021 ). TOC concentrations correlated weakly with discharge volumes in the days leading to the sampling days (Supplementary Fig. 2). For example, discharge volume during the November sampling was twofold higher when compared to February and March, however the TOC concentrations across the three seasons were similar (Fig. 3 ). In fact, TOC concentration at lower discharge seasons even exceeded November’s concentrations at the more saline part of the gradient. Conversely, the contrast between June 2020 and 2021 TOC concentrations shows that discharge volumes could indeed be used to predict TOC input in the area. Observations from June 2020 capture the beginning of summer flood, while those from June 2021 reflect the period after the major summer flood. This affected TOC concentrations in the way that there are generally lower TOC concentrations throughout the whole salinity gradient in the 2020 season. Overall, seasonal differences in TOC concentrations do not perfectly mirror seasonality in river discharge, in support of the findings from other boreal rivers (Lepistö et al., 2008 ; Schultze et al., 2022) and temperate estuaries (García-Martín et al., 2021 ). 4.2 Allochthonous/autochthonous organic carbon fraction δ 13 C of TOC describes the relative contribution of terrestrial organic matter in the Oslofjord fairly well (Fig. 2). Carbon isotope ratios changed linearly with salinity, consistent with the findings of Osburn and Stedmon ( 2011 ). Peterson et al. ( 1994 ) estimated the range for marine endmember in US estuaries to be between − 22‰ to -25‰, whereas the freshwater endmembers were between − 26‰ to -28‰. The majority of measured isotope ratios in this study lie well between these boundaries. The linear transition from terrestrially derived DOM in the inner estuary to marine DOM in the open fjord indicates a conservative mixing with salinity, as also confirmed by the linear response of CDOM absorption coefficients along the transect (Fig. 4 B). A study on isotopic composition of DOC in American estuaries reported the biggest seasonal variations in Altamaha estuary, with average carbon isotope signatures deviating between − 19.9‰ in summer/autumn to -22.3‰ in spring (Otero et al., 2003 ). However, we found minimal fluctuations in δ 13 C between all six sampling cruises, suggesting that the terrestrial and marine endmembers for DOM have consistent isotopic composition throughout the year (minimal and maximal average carbon isotopic composition are − 25.3‰ in June and − 24.1‰ in February). δ 13 C of TOC analysis of seawater is an underutilised method in scientific literature due to complications associated with salt combustion. Salts interfere with traditional IRMSs, meaning only a few labs are equipped to run such analysis, hence the results from a freshwater-marine gradient are rare (e.g. Sampedro-Avila et al., 2024 ). We think that showing a progressively smaller allochthonous signal throughout the salinity gradient (Fig. 2) is a visually demonstrative answer to the set hypothesis – CDOM absorbance reduces along the salinity gradient. While δ 13 C of TOC suspended in water column gives us good understanding of current isotopic composition of organic carbon in the area, application of δ 13 C analysis on sediment profiles may also give a proxy of the relative contribution of allochthonous vs autochthonous C over time. The main source of freshwater and terrestrial DOM to the surveyed transect is the Glomma river. The catchment of this largest Norwegian river is located in the boreal and heavily forested central-eastern Norway, providing a large export of terrestrial organic matter. Seasonal fluctuations in TOC concentrations were prevalent in fresher near-shore sites, reflecting seasonality and variations in discharge, while being more constant in the outer sea environments, where TOC had time to mix in the water column (Fig. 3 ). This is consistent with Schultze et al. (2022), who reported more seasonal variability in organic carbon in a boreal river compared to an open fjord where hardly any seasonal variations were encountered. 4.3 CDOM absorbance changes and along the salinity gradient There were also striking changes in DOM quality and optical properties along the salinity gradient as evident from the CDOM absorbance (Supplementary Fig. 1), where low-salinity samples absorbed more light per unit of carbon. Judging from spectral slope (Fig. 4 A), CDOM is following conservative mixing. Endmembers of spectral slope are significantly different, suggesting the shape of CDOM spectrum changes as it gets further from the shore. a 443 (Fig. 4 B) was less consistent, with many samples having disproportionately high absorption coefficients all throughout the salinity gradient. Nevertheless, the boundaries of endmember are far from overlapping, indicating that there is significant difference in how much light is absorbed by CDOM at shorter wavelengths in fresh versus marine waters, which is also supported by an ANOVA test. However, sa 443 (Fig. 4 C) values suggest that the variation in CDOM across the gradient is more quantitative than qualitative, as the endmember margins overlap. The fact that S changes, while sa 443 stays mostly constant indicates that the qualitative changes in the CDOM spectrum is mainly below 443 nm. Even though DOM is persistent in aquatic environments, it is being continuously removed through means of microbial degradation and photodegradation. Degradability of DOC is also dependent on the environment it is found in – ocean DOC is less degradable than inland water DOC (Catalan et al., 2016). Allochtonous DOM consists mainly of proteins (10%), carbohydrates (30–50%), lignin (15–25%) and other biomacromolecules (Nebbioso and Piccolo, 2013 ). While proteins and carbohydrates are susceptible to biodegradation, lignin is chemically stable and tends to stay in water for longer (Opsahl and Benner, 1997; Nebbioso and Piccolo, 2013 ; Baltar et al., 2021 ). Additionally, photochemically reactive molecules in DOM are degraded into lower molecular weight when exposed to sunlight (Ogawa and Tanoue, 2003 ; Bertilsson and Tranvik, 2000 ). Once DOM is at low molecular weight (< 1 kDa), it becomes resistant to microbial oxidation, which prevents it from further rapid degradation (Ogawa and Tanoue, 2003 ; Xu and Guo, 2018 ). At the same time, DOM needs to be small enough for transport across bacterial membranes to occur in the first place. Photodegradation also was shown to reduce the colour of DOM (Dempsey et al., 2020 ). This means that once DOM is out of groundwater and in open water systems, its attenuation potential are progressively lost. There was a gradual decrease in Secchi depth (a measure of water quality) recorded in the North and Baltic Seas over the past century (Dupont and Aksnes, 2013). CDOM enrichments was assigned as the most important contributor to the observed darkening, however there are also other factors influencing the clarity of water. Some elements (i.e. iron (Fe)) have synergistic effects with CDOM on light attenuation. This effect has been studied in the boreal headwaters and rivers, where Fe constitutes up to 25% of total light absorbance (Kritzberg and Ekström, 2012 ). However, this additive effect of Fe and CDOM is still understudied in marine environments and therefore requires more research. Another factor influencing water clarity is resuspension of bottom sediments driven by wind. Wilson and Heath ( 2019 ) report that change in wave climate in the North Sea over the past century may have played a big role in reduction of water clarity. 4.4 DOC mixing behavior along the land-ocean transect Flocculation potential of DOM changes with salinity and pH. DOM molecules become more stable when in seawater (Lasareva et al., 2019 ) and the optimal pH for flocculation is 6–7 (Asmala et al., 2014 ). Flocculation reduces DOM export from estuaries and promotes retention by sedimentation (Asmala et al., 2014 ). Our findings suggest generally a conservative DOC mixing (Supplementary Fig. 4) and minimal flocculation (Supplementary Table 2). Asmala et al. ( 2014 ) found that DOC tends to flocculate at lower salinities (0–7 PSU) into aggregates. Flocculation may therefore cause a transition of some DOC into POC as well as altering the spectral properties of DOC (Asmala et al., 2014 ). In our study most of the flocculation possibly occurred in the river upstream from the first sampled location. We did not find evidence for flocculation along the transect, with a possible exception for June 2020, where DOC/TOC fraction was relatively small to begin with and was decreasing in the more saline part of the transect. Otherwise, DOC/TOC fraction stays mostly the same across the whole gradient and absolute DOC concentrations perpetually decrease. This is similar to observed monotonous decrease of DOC with increase in salinity at other boreal estuaries (Hessen et al., 2010 ; Kleven et al., 2021; García-Martín et al., 2021 ; Schultze et al., 2022). 4.5 Broader ecological impacts. Loads of DOM coming from land to sea could lead to darkening of coastal areas. This impacts aquatic life in multiple ways - reduced transparency not only implies lower photosynthetic potential for phototrophs, but it also promotes heterotrophic bacteria that utilise this large supply of organic carbon. Visual predators are also affected by the change in optical light field, being disadvantaged by darker waters, while tactile predators are getting a competitive edge (Aksnes et al., 2004 ). There are thus potentially far-reaching and cascading ecosystem impacts of darkening, such as phenological shifts, destabilising whole ecosystems by mismatch between the spring bloom of primary producers and spawning seasons for consumers (Opdal et al., 2019 ; 2023 ). Since the darkening can be attributed to factors such as climate change and impacts of land-use change, forestry and afforestation, sediment resuspension, nutrient enrichment or reduced acid deposition, it points to the need for a holistic ecosystem connectivity perspective, where changes in coastal ecosystems literally is seen in light of the upstream impacts of climate, terrestrial and freshwater systems. Conclusion DOM optical characteristics of coastal waters are tightly linked to terrestrial DOM. The distance to the river mouth and to the shore are some of the most important factors affecting water clarity. The δ13C analysis of TOC indicates a linear transition from terrestrial to marine, marking it as an effective and accurate method of tracking the fate of terrestrially-derived organic matter in an estuary. Spectral slope, CDOM absorption and specific absorbance at 443 nm were used to describe qualitative changes in CDOM across the salinity transect. The observed seasonal and spatial patterns in TOC concentrations and CDOM absorbance provide insights into the biogeochemical processes governing carbon dynamics in estuarine systems. Understanding these dynamics is crucial for predicting the responses of estuarine ecosystems to changing environmental conditions and for managing these critical coastal zones. Declarations Authorship contribution statement Aleksandr Berezovski : Conceptualisation, Data curation, Formal analysis, Investigation, Visualisation, Writing - original draft. Dag O. Hessen : Conceptualisation, Supervision, Writing - review & editing. Hanne H. Børseth : Investigation. Tom Andersen : Conceptualisation, Supervision, Formal analysis, Writing - review & editing. Funding This work was supported by Norges Forskningsråd. Grant Number: TerraCoast 287490 Conflict of Interest The authors declare no conflict of interest. Data Availability All data generated or analyzed during this study are included in this published article [and its supplementary material]. References Finstad, A. G., Andersen, T., Larsen, S., Tominaga, K., Blumentrath, S., De Wit, H. A., Tømmervik, H. & Hessen, D. O. (2016). From greening to browning: Catchment vegetation development and reduced S-deposition promote organic carbon load on decadal time scales in Nordic lakes. Scientific Reports , 6 (1), 1-8. Aksnes, D. L. (2015). Sverdrup critical depth and the role of water clarity in Norwegian Coastal Water. ICES Journal of Marine Science , 72 (6), 2041-2050. Aksnes, D. L., Dupont, N., Staby, A., Fiksen, Ø., Kaartvedt, S., & Aure, J. (2009). Coastal water darkening and implications for mesopelagic regime shifts in Norwegian fjords. Marine Ecology Progress Series , 387 , 39-49. Aksnes, D. L., Nejstgaard, J., Sædberg, E., & Sørnes, T. (2004). Optical control of fish and zooplankton populations. Limnology and oceanography , 49 (1), 233-238. Asmala, E., Bowers, D. G., Autio, R., Kaartokallio, H., & Thomas, D. N. (2014). Qualitative changes of riverine dissolved organic matter at low salinities due to flocculation. Journal of Geophysical Research: Biogeosciences , 119 (10), 1919-1933. Asmala, E., Massicotte, P., & Carstensen, J. (2021). Identification of dissolved organic matter size components in freshwater and marine environments. Limnology and Oceanography , 66( 4), 1381-1393. Baltar, F., Alvarez-Salgado, X. A., Arístegui, J., Benner, R., Hansell, D. A., Herndl, G. J., & Lønborg, C. (2021). What is refractory organic matter in the ocean?. Frontiers in Marine Science , 327. Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software , 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 Bertilsson, S., & Tranvik, L. J. (2000). Photochemical transformation of dissolved organic matter in lakes. Limnology and Oceanography, 45(4), 753-762. Børseth, H. H. (2022). Organic matter and iron along a transect from the deep forest to outer Oslofjord The relationship between organic matter and iron and their optical properties in browning waters (Master's thesis). Catalán, N., Marcé, R., Kothawala, D. N., & Tranvik, L. J. (2016). Organic carbon decomposition rates controlled by water retention time across inland waters. Nature Geoscience, 9(7), Article 7. https://doi.org/10.1038/ngeo2720 Comets, E., Lavenu, A., & Lavielle, M. (2017). Parameter estimation in nonlinear mixed effect models using saemix, an R implementation of the SAEM algorithm. Journal of Statistical Software , 80 , 1-41. Cory, R. M., Ward, C. P., Crump, B. C., & Kling, G. W. (2014). Sunlight controls water column processing of carbon in arctic fresh waters. Science , 345 (6199), 925-928. Crapart, C., Finstad, A. G., Hessen, D. O., Vogt, R. D., & Andersen, T. (2023). Spatial predictors and temporal forecast of total organic carbon levels in boreal lakes. Science of the Total Environment , 161676. Creed, I. F., Bergström, A. K., Trick, C. G., Grimm, N. B., Hessen, D. O., Karlsson, J., Kidd, K.A., Kritzberg, E., McKnight, D.M., Freeman, E.C., Senar, O.E., … & Weyhenmeyer, G. A. (2018). Global change‐driven effects on dissolved organic matter composition: Implications for food webs of northern lakes. Global change biology , 24 (8), 3692-3714. Deininger, A., Faithfull, C. L., Lange, K., Bayer, T., Vidussi, F., & Liess, A. (2016). Simulated terrestrial runoff triggered a phytoplankton succession and changed seston stoichiometry in coastal lagoon mesocosms. Marine Environmental Research , 119 , 40-50. Dempsey, C. M., Brentrup, J. A., Magyan, S., Knoll, L. B., Swain, H. M., Gaiser, E. E. ... & Williamson, C. E. (2020). The relative importance of photodegradation and biodegradation of terrestrially derived dissolved organic carbon across four lakes of differing trophic status. Biogeosciences , 17 (24), 6327-6340. Desmit, X., Nohe, A., Borges, A. V., Prins, T., De Cauwer, K., Lagring, R., Van der Zande, D. & Sabbe, K. (2019). Changes in chlorophyll concentration and phenology in the North Sea in relation to de‐eutrophication and sea surface warming. Limnology and Oceanography , 65 (4), 828-847. du Pontavice, H., Gascuel, D., Reygondeau, G., Stock, C., & Cheung, W. W. L. (2021). Climate-induced decrease in biomass flow in marine food webs may severely affect predators and ecosystem production. Global Change Biology, 27(11), 2608–2622. https://doi.org/10.1111/gcb.15576 Dupont, N., & Aksnes, D. L. (2013). Centennial changes in water clarity of the Baltic Sea and the North Sea. Estuarine, Coastal and Shelf Science, 131, 282-289.Eiane, K., Aksnes, D. L., & Giske, J. (1997). The significance of optical properties in competition among visual and tactile planktivores: a theoretical study. Ecological Modelling , 98 (2-3), 123-136. Findlay, S. E. G., & Parr, T. B. (2017). Chapter 24—Dissolved Organic Matter. In G. A. Lamberti & F. R. Hauer (Eds.), Methods in Stream Ecology (Third Edition) (pp. 21–36). Academic Press. https://doi.org/10.1016/B978-0-12-813047-6.00002-4 Finstad, A. G., Andersen, T., Larsen, S., Tominaga, K., Blumentrath, S., De Wit, H. A., Tømmervik, H. & Hessen, D. O. (2016). From greening to browning: Catchment vegetation development and reduced S-deposition promote organic carbon load on decadal time scales in Nordic lakes. Scientific Reports , 6 (1), 1-8. Fogel, M. L., Cifuentes, L. A., Velinsky, D. J., & Sharp, J. H. (1992). Relationship of carbon availability in estuarine phytoplankton to isotopic composition. Marine Ecology Progress Series , 291-300. Frigstad, H., Harvey, E. T., Deininger, A., & Poste, A. (2020). Increased light attenuation in Norwegian coastal waters-A literature review. NIVA-rapport . García‐Martín, E. E., Sanders, R., Evans, C. D., Kitidis, V., Lapworth, D. J., Rees, A. P., ... & Mayor, D. J. (2021). Contrasting estuarine processing of dissolved organic matter derived from natural and human‐impacted landscapes. Global Biogeochemical Cycles , 35 (10), e2021GB007023. Green, S. A., & Blough, N. V. (1994). Optical absorption and fluorescence properties of chromophoric dissolved organic matter in natural waters. Limnology and Oceanography , 39 (8), 1903-1916. Hedges, J. I., Keil, R. G., & Benner, R. (1997). What happens to terrestrial organic matter in the ocean? Organic Geochemistry, 27(5), 195–212. https://doi.org/10.1016/S0146-6380(97)00066-1 Hessen, D. O., Carroll, J., Kjeldstad, B., Korosov, A. A., Pettersson, L. H., Pozdnyakov, D., & Sørensen, K. (2010). Input of organic carbon as determinant of nutrient fluxes, light climate and productivity in the Ob and Yenisey estuaries. Estuarine, Coastal and Shelf Science , 88 (1), 53-62. Jaffé, R., Boyer, J. N., Lu, X., Maie, N., Yang, C., Scully, N. M., & Mock, S. (2004). Source characterization of dissolved organic matter in a subtropical mangrove-dominated estuary by fluorescence analysis. Marine Chemistry, 84(3–4), 195–210. https://doi.org/10.1016/j.marchem.2003.08.001 Karlsson, J., Byström, P., Ask, J., Ask, P., Persson, L., & Jansson, M. (2009). Light limitation of nutrient-poor lake ecosystems. Nature , 460 (7254), 506-509. Kartverket (2024). Se havnivå, tidevann og vannstand. Accessed: 25.01.2024. https://www.kartverket.no/til-sjos/se-havniva/resultat?id=365324&location=Fredrikstad Kirk, J. T. (2011). Light and photosynthesis in aquatic ecosystems, 3rd edition. Cambridge university press. Kleven, A. (2021). Distribution of metals and organic material along a Norwegian river-ocean transect (Master's thesis, Norwegian University of Life Sciences, Ås). Kritzberg, E. S. (2017). Centennial‐long trends of lake browning show major effect of afforestation. Limnology and Oceanography Letters , 2 (4), 105-112. Kritzberg, E. S., and S. M. Ekström. (2012) Increasing iron concentrations in surface waters–a factor behind brownification?. Biogeosciences 9.4 : 1465-1478. Kutser, T., Alikas, K., Kothawala, D. N., & Köhler, S. J. (2015). Impact of iron associated to organic matter on remote sensing estimates of lake carbon content. Remote Sensing of Environment , 156 , 109-116. Lalonde, K., Middlestead, P., & Gélinas, Y. (2014). Automation of 13C/12C ratio measurement for freshwater and seawater DOC using high temperature combustion. Limnology and Oceanography: Methods , 12 (12), 816-829. Larsen, S., Andersen, T., & Hessen, D. O. (2011). Predicting organic carbon in lakes from climate drivers and catchment properties. Global Biogeochemical Cycles , 25 (3). Lasareva, E. V., Parfenova, A. M., Romankevich, E. A., Lobus, N. V., & Drozdova, A. N. (2019). Organic matter and mineral interactions modulate flocculation across Arctic river mixing zones. Journal of Geophysical Research: Biogeosciences , 124 (6), 1651-1664. Lee, S. A., Kim, T. H., & Kim, G. (2020). Tracing terrestrial versus marine sources of dissolved organic carbon in a coastal bay using stable carbon isotopes. Biogeosciences , 17 (1), 135-144. Lepistö, A., Kortelainen, P., & Mattsson, T. (2008). Increased organic C and N leaching in a northern boreal river basin in Finland. Global Biogeochemical Cycles , 22 (3). McGovern, M., Pavlov, A. K., Deininger, A., Granskog, M. A., Leu, E., Søreide, J. E., & Poste, A. E. (2020). Terrestrial inputs drive seasonality in organic matter and nutrient biogeochemistry in a high Arctic fjord system (Isfjorden, Svalbard). Frontiers in Marine Science , 7 , 747. Monteith, D. T., Stoddard, J. L., Evans, C. D., De Wit, H. A., Forsius, M., Høgåsen, T., Wilander, A., Skjelkvåle, B.L., Jeffries, D.S., Vuorenmaa, J. & Vesely, J. (2007). Dissolved organic carbon trends resulting from changes in atmospheric deposition chemistry. Nature , 450 (7169), 537-540. Mustaffa, N. I. H., Kallajoki, L., Hillebrand, H., Wurl, O., & Striebel, M. (2020). Sea surface phytoplankton community response to nutrient and light changes. Marine Biology , 167 (9), 1-15. Myers-Smith, I. H., Kerby, J. T., Phoenix, G. K., Bjerke, J. W., Epstein, H. E., Assmann, J. J., ... & Wipf, S. (2020). Complexity revealed in the greening of the Arctic. Nature Climate Change , 10 (2), 106-117. Nebbioso, A., & Piccolo, A. (2013). Molecular characterization of dissolved organic matter (DOM): a critical review. Analytical and bioanalytical chemistry , 405 (1), 109-124. Norwegian Water Resources and Energy Directorate (NVE) Atlas. (2023). Rainfall areas. Accessed: 07.06.2023. https://atlas.nve.no/html5Viewer/?viewer=nveatlas Norwegian Water Resources and Energy Directorate (NVE). (2023). Solbergfoss (2.605.0) Measurement Data. Accessed: 11.05.2023. https://sildre.nve.no/station/2.605.0 Ogawa, H., & Tanoue, E. (2003). Dissolved organic matter in oceanic waters. Journal of Oceanography , 59 (2), 129-147. Ohmura, A. (2012). Enhanced temperature variability in high-latitude climate change. Theoretical and Applied Climatology , 110 (4), 499-508. O'Leary, M. H. (1988). Carbon isotopes in photosynthesis. Bioscience, 38 (5), 328-336. Olsen, Y., Agustí, S., Andersen, T., Duarte, C. M., Gasol, J. M., Gismervik, I., ... & Vidal, M. (2006). A comparative study of responses in plankton food web structure and function in contrasting European coastal waters exposed to experimental nutrient addition. Limnology and oceanography , 51 , 488-503. Opdal, A. F., Andersen, T., Hessen, D. O., Lindemann, C., & Aksnes, D. L. (2023). Tracking freshwater browning and coastal water darkening from boreal forests to the Arctic Ocean. Limnology and Oceanography Letters . Opdal, A. F., Lindemann, C., & Aksnes, D. L. (2019). Centennial decline in North Sea water clarity causes strong delay in phytoplankton bloom timing. Global Change Biology , 25 (11), 3946-3953. Osburn, C. L., & Stedmon, C. A. (2011). Linking the chemical and optical properties of dissolved organic matter in the Baltic–North Sea transition zone to differentiate three allochthonous inputs. Marine chemistry , 126 (1-4), 281-294. Otero, E., Culp, R., Noakes, J. E., & Hodson, R. E. (2003). The distribution and δ13C of dissolved organic carbon and its humic fraction in estuaries of southeastern USA. Estuarine, Coastal and Shelf Science, 56(5-6), 1187-1194. Pedersen, E. J., Miller, D. L., Simpson, G. L., & Ross, N. (2019). Hierarchical generalized additive models in ecology: an introduction with mgcv. PeerJ , 7 , e6876. Pérez San Martín, A., Egea, F., Bustamante, M., & Curaqueo, G. (2023). Spectroscopy Techniques for Monitoring the Composting Process: A Review. Agronomy, 13, 2245. https://doi.org/10.3390/agronomy13092245 Peterson, B., Fry, B., Hullar, M., Saupe, S., & Wright, R. (1994). The distribution and stable carbon isotopic composition of dissolved organic carbon in estuaries. Estuaries , 17 , 111-121. Poste, A., Kaste, Ø., Frigstad, H., de Wit, H., Harvey, T., Valestrand, L., Deininger, A., Bryntesen, T., Delpech, L. & Christensen, G. (2021). The impact of the spring 2020 snowmelt floods on physicochemical conditions in three Norwegian river-fjord-coastal systems. NIVA-rapport . Regnier, P., Resplandy, L., Najjar, R. G., & Ciais, P. (2022). The land-to-ocean loops of the global carbon cycle. Nature , 603 (7901), 401-410. Sampedro-Avila, J. E., Lu, K. J., Xue, J. H., Liu, Z. F. & Maske, H. The chemical characteristics and mixing behaviors of particulate organic matter from small subtropical rivers in coastal Gulf of Mexico. Estuarine, Coastal and Shelf Science 299 (2024). Stedmon, C. A., Markager, S., & Kaas, H. (2000). Optical properties and signatures of chromophoric dissolved organic matter (CDOM) in Danish coastal waters. Estuarine, Coastal and Shelf Science , 51 (2), 267-278. Stevenson, F. J. (1994). Humus Chemistry: Genesis, Composition, Reactions. John Wiley & Sons. Sætre, R. (Ed.). (2007). The Norwegian coastal current: oceanography and climate. Akademika Pub. Thrane, J. E., Hessen, D. O., & Andersen, T. (2014). The absorption of light in lakes: negative impact of dissolved organic carbon on primary productivity. Ecosystems, 17 (6), 1040-1052. Tranvik, L. J., & Sieburth, J. M. (1989). Effects of flocculated humic matter on free and attached pelagic microorganisms. Limnology and oceanography , 34 (4), 688-699. Wilson, R. J., & Heath, M. R. (2019). Increasing turbidity in the North Sea during the 20th century due to changing wave climate. Ocean Science , 15 (6), 1615-1625. Wood, S.N. (2017). Generalized Additive Models: An Introduction with R, Second Edition (2nd ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781315370279 Xu, H., & Guo, L. (2017). Molecular size-dependent abundance and composition of dissolved organic matter in river, lake and sea waters. Water Research , 117 , 115-126. Xu, H., & Guo, L. (2018). Intriguing changes in molecular size and composition of dissolved organic matter induced by microbial degradation and self-assembly. Water research , 135 , 187-194. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4497080","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314760394,"identity":"e96fa236-1419-4412-b594-14a996e0f72d","order_by":0,"name":"Aleksandr Berezovski","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABJklEQVRIie2PMUvDQBTH7zi4LlezJqRJvkLKrUW/SkOgLgEHJ1HQcpAp6BpR8Cs4navlQbroLrSDMeDcUpQIQUwCTl7AbiL3G+7/ON6P9x5CGs1fpIdIE/jM/P5hePrcxE6XQhSK8JugWyiItnWXYggCw/Jk6dxdiWy1qkbBTQLx0Vu0O6CI5C9PPxUTaBiy7JVfL7PwMmWT4PZxGi8cGdaLUc4jxRhgHBCFIDUjTpgJ3DdwvLAkqRVGbYXigbGZlZ+NcrAhlQ/cu8DxoSVPOxUfGBn343YKIWgMLqoXw2sJncoQ6oX758DTQcZxcj9x/YeZsLGcM0rUt7hzUVjlOzipLQpUViPmJfv5+kMe7xk9kReq85UQ1r6/bW/A5TbdGo1G89/5ArV5YctGVGxBAAAAAElFTkSuQmCC","orcid":"","institution":"University of Oslo","correspondingAuthor":true,"prefix":"","firstName":"Aleksandr","middleName":"","lastName":"Berezovski","suffix":""},{"id":314760395,"identity":"9623979a-8a93-4d58-aedd-4f9dda0e02ee","order_by":1,"name":"Dag Olav Hessen","email":"","orcid":"","institution":"University of Oslo","correspondingAuthor":false,"prefix":"","firstName":"Dag","middleName":"Olav","lastName":"Hessen","suffix":""},{"id":314760396,"identity":"cfd98840-0b21-41bb-8856-842eeff6106f","order_by":2,"name":"Hanne Halkjelsvik Børseth","email":"","orcid":"","institution":"University of Oslo","correspondingAuthor":false,"prefix":"","firstName":"Hanne","middleName":"Halkjelsvik","lastName":"Børseth","suffix":""},{"id":314760397,"identity":"91e9f634-cb5d-4324-a0d8-e7b56946c5cd","order_by":3,"name":"Tom Andersen","email":"","orcid":"","institution":"University of Oslo","correspondingAuthor":false,"prefix":"","firstName":"Tom","middleName":"","lastName":"Andersen","suffix":""}],"badges":[],"createdAt":"2024-05-29 12:33:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4497080/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4497080/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58490106,"identity":"6783d910-d17b-49fd-9ad0-c5165140058b","added_by":"auto","created_at":"2024-06-17 10:26:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1006489,"visible":true,"origin":"","legend":"\u003cp\u003eSampling area with six sampling stations and the location of the sampling area on a map of Scandinavia (bottom right corner). The image was created in Stadiamaps.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4497080/v1/f173c62d1fc3d3caf4979e84.png"},{"id":58490629,"identity":"ee536908-8b08-4635-8d7e-f67f3bb6bb3e","added_by":"auto","created_at":"2024-06-17 10:34:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19563,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between δ\u003csup\u003e13\u003c/sup\u003eC (‰) of TOC in water samples and salinity. Line represents simple regression. Red bars represent 95% confidence intervals of the freshwater and marine endmembers based linear regression model. Shapes of data points correspond to the dates of sampling and colours correspond to the sampling stations.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4497080/v1/33af7c9ae7b081dd0c595361.png"},{"id":58490107,"identity":"1ada4f1b-5548-4b1e-ab64-22726a520d04","added_by":"auto","created_at":"2024-06-17 10:26:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43222,"visible":true,"origin":"","legend":"\u003cp\u003eTOC concentration (mg/L) plotted against salinity. Lines represent hierarchical generalized additive models grouped by sampling dates.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4497080/v1/472a45d13b7930cd0b6ad063.png"},{"id":58490105,"identity":"dd4c0d95-7913-4858-b9b5-6e5bda09df9f","added_by":"auto","created_at":"2024-06-17 10:26:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30153,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between salinity and saemix (Comets et al., 2017) model parameter estimates: A) spectral slope (\u003cem\u003eS\u003c/em\u003e; µm\u003csup\u003e-1\u003c/sup\u003e), B) absorption coefficient at the reference wavelength (\u003cem\u003ea\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e; m\u003csup\u003e-1\u003c/sup\u003e) and C) specific absorption coefficient (\u003cem\u003esa\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e; m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e) at reference wavelength. Red bars represent confidence intervals for freshwater and marine endmembers estimated by simple linear regression. Shapes of datapoints correspond to sampling dates.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4497080/v1/f46fc483d6bd9a249a0d5d7e.png"},{"id":65834842,"identity":"7c084a80-39ec-4540-a175-c9b4a2e966e6","added_by":"auto","created_at":"2024-10-03 10:23:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2202812,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4497080/v1/b4850be6-a4a2-45b8-915a-eb07edbe98b3.pdf"},{"id":58490103,"identity":"1e73a080-8bc8-4284-9f3e-7ec3bd8e8231","added_by":"auto","created_at":"2024-06-17 10:26:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":284013,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4497080/v1/00217c098055f88f9c373be2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optical properties of dissolved organic matter along a salinity gradient from a boreal river estuary to open coastal waters.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLandscapes, freshwaters and coastal areas are tightly linked in seaward catchments, and catchment properties and terrestrial processes will thus ultimately affect marine systems, e.g. in terms of export flux of substances and elements like dissolved organic carbon (DOC) (Regnier et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Crapart et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The current \u0026ldquo;browning\u0026rdquo;, i.e. increased load of coloured, terrestrial matter into freshwaters, is typically observed in boreal areas (Monteith et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Finstad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Creed et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This is expected to have an impact on coastal systems as well, often here denoted \u0026ldquo;coastal darkening\u0026rdquo; (Opdal et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Frigstad et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; McGovern et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Coastal darkening could be considered a form of optical deterioration of water quality, caused by coloured dissolved organic matter (CDOM) loading and resulting in increased light attenuation. The magnitude and ecological impact of coastal darkening is far from settled and will also have a pronounced spatial and temporal variability. The North Sea, Norwegian coastal waters and the Baltic Sea are all interconnected and influenced by the coloured organic matter coming from land. The quantity and quality of the organic matter entering marine waters is heavily affected both by catchment properties and climate. Typically, brown, DOC-rich water are prevalent in boreal system with coniferous forest and bogs as the main spatial determinants (Larsen et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Finstad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These northern ecosystems are also disproportionately affected by climate warming (Ohmura, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Myers-Smith et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and many regions also experience changes in snow-cover and precipitation that may affect concentrations and runoff of DOC (de Wit et al., 2016).\u003c/p\u003e \u003cp\u003eBoreal regions are undergoing accelerated greening (increased vegetation cover and thus also carbon fixation) as a result of afforestation, as well as higher temperatures and elevated runoff. Increased terrestrial net primary production translates into larger loads of organic matter entering the water systems, and is one of the likely causes for the observed browning (Larsen et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Finstad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kritzberg, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Also, the reduced acid deposition promotes leaching and export of DOC from soils (Monteith et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Dissolved organic matter (DOM) is the predominant form of organic carbon in aquatic systems (Hedges et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Findlay and Parr, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As it travels with water masses from catchments and between bodies of water, DOM undergoes microbial and photochemical degradation (Cory et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), meaning DOM cannot be considered a conservative substance. Also, exudates from phytoplankton and bacteria contribute to the total DOM pool and light attenuation, yet this autochthonous DOM lacks the highly brown humic compounds (Jaff\u0026eacute; et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Humic substances in allochthonous DOM strongly promote light attenuation in the shorter wavelengths, due to high absorption potential of double bonds in their molecular structure (P\u0026eacute;rez San Mart\u0026iacute;n et al., 2023). These double bond groups are known as chromophores, and they confer colour to humic compounds (Stevenson, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). CDOM is the component of DOM that primarily absorbs light. Increasing concentrations of CDOM can affect aquatic ecosystems by diminishing light, where the organic molecules become effectively a \u0026ldquo;competitor\u0026rdquo; to the light-harvesting pigments of phytoplankton. Thus, reducing photosynthetic activity in a water body, resulting in lower autotrophs production and biomass of phytoplankton or aquatic macrophytes and shift the compensation depth upward (Karlsson et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Thrane et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Reduced primary production will subsequently limit biomass of higher trophic levels (du Pontavice et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Riverine exports will however also promote phytoplankton productivity in coastal systems, owing to the transport of nutrients like N, P, Si and Fe (Deininger et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Thus, the net effect on the productivity of coastal waters is not settled and will depend both on the flux and fate of DOM vs nutrient elements.\u003c/p\u003e \u003cp\u003ePhytoplankton blooms have been predicted to occur earlier as global warming progresses because of earlier and increased stratification (Desmit at al., 2019). Yet, for Norwegian coastal waters, peak spring phytoplankton bloom has been delayed by 22 days over the past century, which was attributed to coastal darkening (Opdal et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Opdal et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Mustaffa et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) studied the relationship between light intensity and phytoplankton growth rates in the Sognefjord at the Norwegian west coast, impacted by freshwater inputs from several rivers. They found that increased light intensity led to higher phytoplankton growth rates in the central and outer fjord, but with no effect on phytoplankton in the open ocean, where the impact of terrestrial DOM is marginal. Besides impact on autotrophs, darkening and spectral changes may also affect higher trophic levels by favouring tactile predators (e.g. jellyfish) over visual predators such as fish, potentially causing an ecosystem regime shift (Eiane et al., 1997; Aksnes et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Increased inputs of DOC will also promote system heterotrophy by providing a substrate for heterotrophic bacteria while reducing primary production (Tranvik and Sieburth, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTotal organic carbon (TOC) is a sum of particulate organic carbon (POC) and DOC. DOC typically constitutes more than 90% of TOC in Norwegian coastal waters (Olsen et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). It is important to study TOC, when investigating light climate of aquatic environments, since organic carbon compounds are some of the most light attenuating elements in the water. To separate the sources of TOC along this transect from the river mouth to open waters, we used the δ\u003csup\u003e13\u003c/sup\u003eC isotope signature of TOC as a marker. This is often used to discriminate between allochthonous and autochthonous TOC sources, since δ\u003csup\u003e13\u003c/sup\u003eC signature of terrestrial C3 plant material is between \u0026minus;\u0026thinsp;23\u0026permil; and \u0026minus;\u0026thinsp;34\u0026permil;, while for marine phytoplankton it typically ranges from \u0026minus;\u0026thinsp;18\u0026permil; and \u0026minus;\u0026thinsp;22\u0026permil; (Lee et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These differences in δ\u003csup\u003e13\u003c/sup\u003eC signature occur due to differences in photosynthetic pathways and in CO\u003csub\u003e2\u003c/sub\u003e diffusion rates in air and water (O'Leary, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). δ\u003csup\u003e13\u003c/sup\u003eC TOC thus serves as a proxy for how much of the organic carbon dissolved in a water system is of terrestrial origin at the moment, unlike measuring δ\u003csup\u003e13\u003c/sup\u003eC in sediments that gives a record of historical carbon data deposition. This sort of analysis is rarely conducted, due to difficulties in methodology. However, as we show in this study, it can be effectively implemented for determining the source of organic carbon. The authors show that the use of stable isotope analysis of δ\u003csup\u003e13\u003c/sup\u003eC of TOC on a freshwater-marine gradient can be a valuable diagnostic tool for the origin of TOC.\u003c/p\u003e \u003cp\u003eThe main aim of this study was to identify whether and how terrestrially-derived DOM undergoes spectral, isotopic and quantitative changes along a spatial transect from land to open water. This in turn helped to assess the magnitude of darkening in the coastal waters. To achieve this we measured light absorption by CDOM along a salinity gradient from the mouth of the Glomma (largest river in Norway) to the outer Oslofjord in different seasons. We hypothesized that DOM concentrations would follow passive mixing, and hence water transparency should be negatively correlated to salinity. We also used spectroscopy and stable isotope analysis to infer the endmembers of terrestrial and marine TOC. We further hypothesized that there would be seasonal variations in CDOM absorbance with higher concentrations during high-runoff seasons.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study site and sampling methods\u003c/h2\u003e \u003cp\u003eThe Glomma estuary and the transect in the outer Oslofjord in south-eastern Norway was chosen as a study site because of its location - it is a region where the DOC-rich water from the Glomma mixes with Baltic Sea water carried by the Skagerrak Coastal Current and seawater coming from the North Sea via Atlantic Current (S\u0026aelig;tre, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The Glomma river is the largest river in Norway with a mean daily discharge of 684 m\u003csup\u003e3\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (since 1964; NVE, 2023) and a total catchment area of 41970 km\u003csup\u003e2\u003c/sup\u003e (NVE Atlas, 2023). It drains large parts of alpine landscapes and boreal forest with bogs and wetlands, before entering the Oslofjord (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Flow regime in the sampling area is dependent on the Baltic outflow and most importantly on the discharge from the Glomma.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSix sampling cruises took place between June 2020 and June 2021 (2020-06-04, 2020-09-08, 2020-11-26, 2021-02-23, 2021-03-25 and 2021-06-17). Three additional sampling cruises were conducted to obtain more data on DOM (2022-05-12, 2022-06-02 and 2022-06-29) (B\u0026oslash;rseth, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A usual sampling route encompassed six sampling stations (marked OF2, OF1, \u0026Oslash;1, I1, L5 and L1 in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The same stations were visited on approximately the same times of the day (12:00\u0026thinsp;\u0026plusmn;\u0026thinsp;2 hours) on all sampling days, meaning the differences of physicochemical properties of water due to the tide level were minimal (biggest tidal range between all six sampling dates was 40 cm (Kartverket, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)). The stations represented a salinity gradient with the highest salinity at station OF2 and lowest at station L1. The salinities of each station show seasonal variation, yet at very different levels. For the open-sea site salinity ranged between 19 and 29, while the inner, riverine site had salinities ranging from 0\u0026ndash;5. Salinity was recorded in PSU. The total transect length was 56 km. Conductivity, temperature and oxygen concentrations were recorded \u003cem\u003ein situ\u003c/em\u003e using a CTD profiler (Sea-Bird Model SB9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Absorbance measurements\u003c/h2\u003e \u003cp\u003eWater samples were collected at each sampling site using a Rosette sampler. Water samples were extracted from 3\u0026ndash;4 m depths and brought to the laboratory where the absorbance by CDOM was measured on a double beam Shimadzu UV-2550 UV-VIS Spectrophotometer (Shimadzu scientific instruments, Columbia, Maryland, USA). The definition of \u0026ldquo;dissolved\u0026rdquo; organic matter is somewhat arbitrary, Xu and Guo (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) describe DOM as any molecules that pass through a\u0026thinsp;\u0026lt;\u0026thinsp;0.7 \u0026micro;m pore-sized filter. In this study, 0.7 \u0026micro;m pore GF/F Whatman glass fiber filters were used to separate the \u0026ldquo;dissolved\u0026rdquo; part of organic matter from larger detritus. Freshwater DOM in freshwater typically has molecular weights between 200 and 1250 Dalton (Asmala et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which is many folds lighter than the molecular weight cutoff of 0.7 \u0026micro;m pore filters.\u003c/p\u003e \u003cp\u003eAbsorbance of the filtrate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({A}_{\\lambda }\\)\u003c/span\u003e\u003c/span\u003e) was measured in 5 cm (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(L\\)\u003c/span\u003e\u003c/span\u003e) quartz cuvettes over the 250\u0026ndash;750 nm range with 1 nm increments and referenced to Milli-Q water. The resulting CDOM absorbance (log-10 scale of absorbance per cm of path length) was subsequently transformed into base-e absorption coefficients per m (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{\\lambda }\\)\u003c/span\u003e\u003c/span\u003e, as seen in Eq.\u0026nbsp;1). All absorbance values were corrected for baseline offset by subtracting average absorbance between 740\u0026ndash;750 nm (Green and Blough, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). 740\u0026ndash;750 nm range was chosen because absorbance by CDOM is expected to be negligible here.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({a}_{\\lambda }=ln\\left(10\\right) *\\frac{{A}_{\\lambda }}{L}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;1)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Organic carbon concentrations\u003c/h2\u003e \u003cp\u003eTOC concentrations were also analysed by high-temperature catalytic oxidation and non-dispersive infrared detection on a Shimadzu TOC-VCPH Analyzer (Shimadzu scientific instruments, Columbia, Maryland, USA). The water samples were heated to 680\u0026deg;C with platinum catalyst and subsequently converted into CO\u003csub\u003e2\u003c/sub\u003e gas and measured.\u003c/p\u003e \u003cp\u003ePOC concentrations were measured by filtering sample water through a 0.7 \u0026micro;m pore GF/F glass fiber filters and measured on a FlashEA 1112 Nitrogen and Carbon Analyzer (Thermo Fisher Scientific), where filters were combusted and the produced gas was analysed on elemental composition by gas chromatography. DOC concentrations were calculated by subtracting POC from TOC concentrations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Stable isotope analysis\u003c/h2\u003e \u003cp\u003eδ\u003csup\u003e13\u003c/sup\u003eC of TOC (both dissolved plus particulate organic carbon) was measured with an isotope ratio mass spectrometry (IRMS). The reason for analysing TOC as a whole was not to prioritise organic molecules of certain sizes over the others and also to lower risk of contamination. Analysis of δ\u003csup\u003e13\u003c/sup\u003eC of TOC in marine water poses problems due to salt content. Salt combusted at high temperatures deteriorates quartz tubing inside the elemental analyzer as well as decreases the efficiency of combustion. Also, combusted salt may \u0026lsquo;compete\u0026rsquo; with CO\u003csub\u003e2\u003c/sub\u003e molecules in the analyzer leading to incorrect readings (Lalonde et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Because of these complications, data for TOC δ\u003csup\u003e13\u003c/sup\u003eC in salt water are rarely measured. Our δ\u003csup\u003e13\u003c/sup\u003eC analysis was performed by Jan Veizer Stable Isotope Laboratory (Ottawa, Canada). A predetermined quantity of water was deposited in a reaction chamber of an OI Analytical Aurora Model 1030W TOC Analyser, where it was mixed with hydrochloric acid to remove inorganic carbon. The water containing organic carbon was combusted several times and passed through chemical and Nafion traps to remove traces of water. CO\u003csub\u003e2\u003c/sub\u003e was trapped by GD-100 trap, where the peak was rinsed with ultrapure He gas before the analysis in IRMS (Lalonde et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Organic carbon was converted into CO\u003csub\u003e2\u003c/sub\u003e gas as a result of combustion which is then passed into a Finnigan Mat DeltaPlusXP IRMS. Data was normalised using two different internal organic standards and the analytical precision was \u0026plusmn;\u0026thinsp;0.2\u0026permil;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Modeling\u003c/h2\u003e \u003cp\u003eSalinity dilution curves were analysed by simple or additive regression models. For linear mixed models we used the lme4 package (Bates et al., 2014), while we used the mgcv package (Wood, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) for additive models. Hierarchical generalized additive models (HGAMs) were used to estimate smooth function relationships between dissolved substances and salinity, grouped by sampling date (Pedersen et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). HGAMs can represent non-conservative mixing as departures from linearity in the salinity relationship.\u003c/p\u003e \u003cp\u003eCDOM absorption coefficients (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{\\lambda }\\)\u003c/span\u003e\u003c/span\u003e) as a function of wavelength was modeled as an exponential function with an offset (Eq.\u0026nbsp;2), as suggested by Stedmon et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The model has 3 parameters: the absorption coefficient \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003e443\u003c/em\u003e\u003c/sub\u003e at the reference wavelength (443 nm), the spectral slope (\u003cem\u003eS\u003c/em\u003e) and the background absorbance (\u003cem\u003eK\u003c/em\u003e). The wavelengths were rescaled from nm to \u0026micro;m following Stedmon et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), such that the unit of \u003cem\u003eS\u003c/em\u003e will be 1/\u0026micro;m. 443 nm (0.443 \u0026micro;m) was used as reference wavelength since it is around blue absorbance peak of chlorophyll a (Kirk, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and is often used in remote sensing models of ocean colour. All CDOM spectra were fitted simultaneously to a non-linear model using the saemix package in R (Comets et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({a}_{\\lambda }={a}_{443} {e}^{S\\left(\\frac{443-\\lambda }{1000}\\right)}+K\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;2)\u003c/p\u003e \u003cp\u003eCDOM absorption coefficient at 443 nm (\u003cem\u003ea\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e) for every sample was estimated according to Stedmon et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) model. Specific absorption coefficient at 443 nm (\u003cem\u003esa\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e) was calculated as CDOM absorption coefficient at 443 nm (\u003cem\u003ea\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e) divided by DOC concentration of the water sample (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({sa}_{443}={a}_{443}/conc\\left(DOC\\right)\\)\u003c/span\u003e\u003c/span\u003e). Resulting units were m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e\u0026minus;1\u003c/sup\u003e. 443 nm wavelength was once again chosen as a reference wavelength. \u003cem\u003esa\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e is used to estimate DOC from remote sensing (Kutser et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWater samples had a good spread of salinities ranging from 0.03 in the inner station (L1) to 29.50 PSU at the outer (OF2) (Fig.\u0026nbsp;2, x-axis). The six sampling cruises covered different seasons, reflecting that the discharge volumes from the Glomma river varied with season and precipitation. Cruises 2, 4, 5, 7 and 8 (Supplementary Table\u0026nbsp;2) took place during low discharge periods (\u0026lt;\u0026thinsp;600 m\u003csup\u003e3\u003c/sup\u003e/s), while cruises 1 and 9 saw medium discharge levels (\u0026lt;\u0026thinsp;1000 m\u003csup\u003e3\u003c/sup\u003e/s). Cruises 3 and 6 occurred after heavy discharge periods (\u0026lt;\u0026thinsp;1300 m\u003csup\u003e3\u003c/sup\u003e/s) (Supplementary Fig.\u0026nbsp;2). Salinity changed strongly between the sampling seasons in shallow water layers at every sampling station.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 δ\u003csup\u003e13\u003c/sup\u003eC of TOC\u003c/h2\u003e \u003cp\u003eThe δ\u003csup\u003e13\u003c/sup\u003eC of TOC data was predicted using HGAM as well as linear mixed effect models with or without random slopes, but all of them showed very small variance among the seasons. Therefore, a simple regression model without grouping by seasons was chosen as a preferable model to describe decreasing δ\u003csup\u003e13\u003c/sup\u003eC of TOC along the salinity gradient (Fig.\u0026nbsp;2). There was a strong positive linear correlation between δ\u003csup\u003e13\u003c/sup\u003eC and salinity (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;79.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.00) and insignificant seasonal variations (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09). The boundaries for δ\u003csup\u003e13\u003c/sup\u003eC of freshwater and marine (0 and 35.2 PSU accordingly) endmembers were estimated from the regression model using the salinity range in Norwegian coastal waters reported by Aksnes (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). If the TOC was of strictly freshwater or marine origin, the respective δ\u003csup\u003e13\u003c/sup\u003eC values would be constrained within these boundaries. This suggests that there is both marine and terrestrially derived carbon mixed in the water column at the freshwater sites. δ\u003csup\u003e13\u003c/sup\u003eC values of seawater from marine sites are higher and more tightly clustered together, when compared to more variable and lower δ\u003csup\u003e13\u003c/sup\u003eC values of freshwater samples. This effectively means that there are relatively more lighter carbon isotopes in the freshwater impacted inner estuary and that the proportion of carbon shifts to heavier carbon further out in the open sea. This strong and linear relationship implies that δ\u003csup\u003e13\u003c/sup\u003eC can serve as a good diagnostic tool for the fraction of terrestrial allochthonous vs marine, autochthonous DOM.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eFigure 2. Relationship between δ\u003csup\u003e13\u003c/sup\u003eC (\u0026permil;) of TOC in water samples and salinity. Line represents simple regression. Red bars represent 95% confidence intervals of the freshwater and marine endmembers based linear regression model. Shapes of data points correspond to the dates of sampling and colours correspond to the sampling stations.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 TOC concentration\u003c/h2\u003e \u003cp\u003eDOC was a major component of TOC (89% average; Supplementary Table\u0026nbsp;2). DOC/TOC ratios varied with seasons - June 2020 had the lowest mean DOC/TOC ratio of all the sampled seasons (71%; Supplementary Fig.\u0026nbsp;3), September saw higher average DOC/POC (84%), while November 2020 and February, March and June 2021 all had DOC constituting over 90% of TOC in the water column. We found no systematic change in dissolved fraction over salinity range: DOC/TOC fractions stayed mostly the same throughout the salinity gradient, except in June 2020, where a decline of DOC fraction at increased salinity was observed (Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eHGAMs were used to fit TOC concentration over 0\u0026ndash;35 salinity range grouped by sampling date (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). AICs of HGAM and linear model were compared (8 and 64 accordingly), indicating non-linear relationship and thus a better fit from HGAMs. As a general trend, TOC concentrations decreased nearly linearly with increasing salinity in colder seasons (November-May and to a lesser extent September), while summer seasons showed indications of non-conservative mixing, especially in the freshwater end of the gradient, where TOC concentrations are more varied. The outer sampling sites were more uniform with less seasonal and site-by-site fluctuations of TOC concentrations. According to an ANOVA analysis, there were significant variations between TOC concentrations at different seasons (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;115.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.00). A linear regression was used to relate TOC concentrations to mean discharge volume (time lag of 14 days) at the river mouth site throughout two years of sampling. The variations in the TOC concentration at the river mouth station was not related to river flow (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;74.70\u0026thinsp;\u0026plusmn;\u0026thinsp;137.88, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.61).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Spectral slopes and CDOM absorbance\u003c/h2\u003e \u003cp\u003e \u003cem\u003ea\u003c/em\u003e \u003csub\u003e443\u003c/sub\u003e was linearly related to salinity in the Glomma estuary (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). There were significant differences when comparing \u003cem\u003ea\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e to salinity (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.00), but without a significant variation between the seasons (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.11). Spectral slope (\u003cem\u003eS\u003c/em\u003e) followed a similar trend, but the decrease over salinity was even sharper (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.00; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Both \u003cem\u003ea\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e and \u003cem\u003eS\u003c/em\u003e are good indicators of CDOM absorbance, both being variables in Eq.\u0026nbsp;2, where the higher values of these correspond to higher relative absorbance at wavelengths shorter than the reference (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\lambda\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e0\u003c/sub\u003e). \u003cem\u003esa\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e values displayed a weak trend of decreased \u003cem\u003esa\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e at higher salinities (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). However, there was a strong scatter in \u003cem\u003esa\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e across the salinity gradient, and the confidence intervals of the endmembers overlapped. This suggests that \u003cem\u003esa\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e does not differ significantly between freshwater and marine water samples (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08). Since \u003cem\u003eS\u003c/em\u003e is variable across the salinity gradient, while \u003cem\u003esa\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e is not, it can be deduced that \u003cem\u003eS\u003c/em\u003e changes the most at a pivot point somewhere below 443 nm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 DOM dynamics\u003c/h2\u003e \u003cp\u003eDOM undergoes qualitative and quantitative changes along the salinity gradient. The estuarine waters have higher DOM concentrations and thus also higher light absorption compared to more offshore waters. Seasonality played a major role in changes in TOC concentration especially in freshwater end of the gradient. The seasonal fluctuation declined in the outer sea, however still, with significant seasonal changes in TOC concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and insignificant variations in absorption coefficients (Supplementary Fig.\u0026nbsp;1). Spring snowmelt, which occurs in May to June in this region (Poste et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), seems to add non-linearity to TOC mixing across the salinity gradient. All the summer samplings differ greatly in their observed TOC concentrations, however the common trend is that summer HGAMs show curvature, while samplings from colder seasons (including early May sampling) show more linear patterns of TOC mixing. Overall, TOC concentrations are higher at the winter seasons, which is consistent with previous studies (Poste et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Element fluxes from Glomma are more evenly scattered throughout a year due to the combined effect of hydrology and agricultural runoff (Poste et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTOC concentrations correlated weakly with discharge volumes in the days leading to the sampling days (Supplementary Fig.\u0026nbsp;2). For example, discharge volume during the November sampling was twofold higher when compared to February and March, however the TOC concentrations across the three seasons were similar (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In fact, TOC concentration at lower discharge seasons even exceeded November\u0026rsquo;s concentrations at the more saline part of the gradient. Conversely, the contrast between June 2020 and 2021 TOC concentrations shows that discharge volumes could indeed be used to predict TOC input in the area. Observations from June 2020 capture the beginning of summer flood, while those from June 2021 reflect the period after the major summer flood. This affected TOC concentrations in the way that there are generally lower TOC concentrations throughout the whole salinity gradient in the 2020 season. Overall, seasonal differences in TOC concentrations do not perfectly mirror seasonality in river discharge, in support of the findings from other boreal rivers (Lepist\u0026ouml; et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Schultze et al., 2022) and temperate estuaries (Garc\u0026iacute;a-Mart\u0026iacute;n et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Allochthonous/autochthonous organic carbon fraction\u003c/h2\u003e \u003cp\u003eδ\u003csup\u003e13\u003c/sup\u003eC of TOC describes the relative contribution of terrestrial organic matter in the Oslofjord fairly well (Fig.\u0026nbsp;2). Carbon isotope ratios changed linearly with salinity, consistent with the findings of Osburn and Stedmon (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Peterson et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) estimated the range for marine endmember in US estuaries to be between \u0026minus;\u0026thinsp;22\u0026permil; to -25\u0026permil;, whereas the freshwater endmembers were between \u0026minus;\u0026thinsp;26\u0026permil; to -28\u0026permil;. The majority of measured isotope ratios in this study lie well between these boundaries. The linear transition from terrestrially derived DOM in the inner estuary to marine DOM in the open fjord indicates a conservative mixing with salinity, as also confirmed by the linear response of CDOM absorption coefficients along the transect (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). A study on isotopic composition of DOC in American estuaries reported the biggest seasonal variations in Altamaha estuary, with average carbon isotope signatures deviating between \u0026minus;\u0026thinsp;19.9\u0026permil; in summer/autumn to -22.3\u0026permil; in spring (Otero et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, we found minimal fluctuations in δ\u003csup\u003e13\u003c/sup\u003eC between all six sampling cruises, suggesting that the terrestrial and marine endmembers for DOM have consistent isotopic composition throughout the year (minimal and maximal average carbon isotopic composition are \u0026minus;\u0026thinsp;25.3\u0026permil; in June and \u0026minus;\u0026thinsp;24.1\u0026permil; in February). δ\u003csup\u003e13\u003c/sup\u003eC of TOC analysis of seawater is an underutilised method in scientific literature due to complications associated with salt combustion. Salts interfere with traditional IRMSs, meaning only a few labs are equipped to run such analysis, hence the results from a freshwater-marine gradient are rare (e.g. Sampedro-Avila et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We think that showing a progressively smaller allochthonous signal throughout the salinity gradient (Fig.\u0026nbsp;2) is a visually demonstrative answer to the set hypothesis \u0026ndash; CDOM absorbance reduces along the salinity gradient. While δ\u003csup\u003e13\u003c/sup\u003eC of TOC suspended in water column gives us good understanding of current isotopic composition of organic carbon in the area, application of δ\u003csup\u003e13\u003c/sup\u003eC analysis on sediment profiles may also give a proxy of the relative contribution of allochthonous vs autochthonous C over time.\u003c/p\u003e \u003cp\u003eThe main source of freshwater and terrestrial DOM to the surveyed transect is the Glomma river. The catchment of this largest Norwegian river is located in the boreal and heavily forested central-eastern Norway, providing a large export of terrestrial organic matter. Seasonal fluctuations in TOC concentrations were prevalent in fresher near-shore sites, reflecting seasonality and variations in discharge, while being more constant in the outer sea environments, where TOC had time to mix in the water column (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This is consistent with Schultze et al. (2022), who reported more seasonal variability in organic carbon in a boreal river compared to an open fjord where hardly any seasonal variations were encountered.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 CDOM absorbance changes and along the salinity gradient\u003c/h2\u003e \u003cp\u003eThere were also striking changes in DOM quality and optical properties along the salinity gradient as evident from the CDOM absorbance (Supplementary Fig.\u0026nbsp;1), where low-salinity samples absorbed more light per unit of carbon. Judging from spectral slope (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), CDOM is following conservative mixing. Endmembers of spectral slope are significantly different, suggesting the shape of CDOM spectrum changes as it gets further from the shore. \u003cem\u003ea\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) was less consistent, with many samples having disproportionately high absorption coefficients all throughout the salinity gradient. Nevertheless, the boundaries of endmember are far from overlapping, indicating that there is significant difference in how much light is absorbed by CDOM at shorter wavelengths in fresh versus marine waters, which is also supported by an ANOVA test. However, \u003cem\u003esa\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) values suggest that the variation in CDOM across the gradient is more quantitative than qualitative, as the endmember margins overlap. The fact that \u003cem\u003eS\u003c/em\u003e changes, while \u003cem\u003esa\u003c/em\u003e\u003csub\u003e443\u003c/sub\u003e stays mostly constant indicates that the qualitative changes in the CDOM spectrum is mainly below 443 nm.\u003c/p\u003e \u003cp\u003eEven though DOM is persistent in aquatic environments, it is being continuously removed through means of microbial degradation and photodegradation. Degradability of DOC is also dependent on the environment it is found in \u0026ndash; ocean DOC is less degradable than inland water DOC (Catalan et al., 2016). Allochtonous DOM consists mainly of proteins (10%), carbohydrates (30\u0026ndash;50%), lignin (15\u0026ndash;25%) and other biomacromolecules (Nebbioso and Piccolo, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). While proteins and carbohydrates are susceptible to biodegradation, lignin is chemically stable and tends to stay in water for longer (Opsahl and Benner, 1997; Nebbioso and Piccolo, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Baltar et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, photochemically reactive molecules in DOM are degraded into lower molecular weight when exposed to sunlight (Ogawa and Tanoue, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Bertilsson and Tranvik, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Once DOM is at low molecular weight (\u0026lt;\u0026thinsp;1 kDa), it becomes resistant to microbial oxidation, which prevents it from further rapid degradation (Ogawa and Tanoue, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Xu and Guo, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). At the same time, DOM needs to be small enough for transport across bacterial membranes to occur in the first place. Photodegradation also was shown to reduce the colour of DOM (Dempsey et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This means that once DOM is out of groundwater and in open water systems, its attenuation potential are progressively lost.\u003c/p\u003e \u003cp\u003eThere was a gradual decrease in Secchi depth (a measure of water quality) recorded in the North and Baltic Seas over the past century (Dupont and Aksnes, 2013). CDOM enrichments was assigned as the most important contributor to the observed darkening, however there are also other factors influencing the clarity of water. Some elements (i.e. iron (Fe)) have synergistic effects with CDOM on light attenuation. This effect has been studied in the boreal headwaters and rivers, where Fe constitutes up to 25% of total light absorbance (Kritzberg and Ekstr\u0026ouml;m, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, this additive effect of Fe and CDOM is still understudied in marine environments and therefore requires more research. Another factor influencing water clarity is resuspension of bottom sediments driven by wind. Wilson and Heath (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) report that change in wave climate in the North Sea over the past century may have played a big role in reduction of water clarity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4 DOC mixing behavior along the land-ocean transect\u003c/h2\u003e \u003cp\u003eFlocculation potential of DOM changes with salinity and pH. DOM molecules become more stable when in seawater (Lasareva et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the optimal pH for flocculation is 6\u0026ndash;7 (Asmala et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Flocculation reduces DOM export from estuaries and promotes retention by sedimentation (Asmala et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Our findings suggest generally a conservative DOC mixing (Supplementary Fig.\u0026nbsp;4) and minimal flocculation (Supplementary Table\u0026nbsp;2). Asmala et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that DOC tends to flocculate at lower salinities (0\u0026ndash;7 PSU) into aggregates. Flocculation may therefore cause a transition of some DOC into POC as well as altering the spectral properties of DOC (Asmala et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In our study most of the flocculation possibly occurred in the river upstream from the first sampled location. We did not find evidence for flocculation along the transect, with a possible exception for June 2020, where DOC/TOC fraction was relatively small to begin with and was decreasing in the more saline part of the transect. Otherwise, DOC/TOC fraction stays mostly the same across the whole gradient and absolute DOC concentrations perpetually decrease. This is similar to observed monotonous decrease of DOC with increase in salinity at other boreal estuaries (Hessen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kleven et al., 2021; Garc\u0026iacute;a-Mart\u0026iacute;n et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Schultze et al., 2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Broader ecological impacts.\u003c/h2\u003e \u003cp\u003eLoads of DOM coming from land to sea could lead to darkening of coastal areas. This impacts aquatic life in multiple ways - reduced transparency not only implies lower photosynthetic potential for phototrophs, but it also promotes heterotrophic bacteria that utilise this large supply of organic carbon. Visual predators are also affected by the change in optical light field, being disadvantaged by darker waters, while tactile predators are getting a competitive edge (Aksnes et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). There are thus potentially far-reaching and cascading ecosystem impacts of darkening, such as phenological shifts, destabilising whole ecosystems by mismatch between the spring bloom of primary producers and spawning seasons for consumers (Opdal et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Since the darkening can be attributed to factors such as climate change and impacts of land-use change, forestry and afforestation, sediment resuspension, nutrient enrichment or reduced acid deposition, it points to the need for a holistic ecosystem connectivity perspective, where changes in coastal ecosystems literally is seen in light of the upstream impacts of climate, terrestrial and freshwater systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDOM optical characteristics of coastal waters are tightly linked to terrestrial DOM. The distance to the river mouth and to the shore are some of the most important factors affecting water clarity. The \u0026delta;13C analysis of TOC indicates a linear transition from terrestrial to marine, marking it as an effective and accurate method of tracking the fate of terrestrially-derived organic matter in an estuary. Spectral slope, CDOM absorption and specific absorbance at 443 nm were used to describe qualitative changes in CDOM across the salinity transect. The observed seasonal and spatial patterns in TOC concentrations and CDOM absorbance provide insights into the biogeochemical processes governing carbon dynamics in estuarine systems. Understanding these dynamics is crucial for predicting the responses of estuarine ecosystems to changing environmental conditions and for managing these critical coastal zones.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eAuthorship contribution statement\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAleksandr Berezovski\u003c/strong\u003e: Conceptualisation, Data curation, Formal analysis, Investigation, Visualisation, Writing - original draft. \u003cstrong\u003eDag O. Hessen\u003c/strong\u003e: Conceptualisation, Supervision, Writing - review \u0026amp; editing. \u003cstrong\u003eHanne H. B\u0026oslash;rseth\u003c/strong\u003e: Investigation. \u003cstrong\u003eTom Andersen\u003c/strong\u003e: Conceptualisation, Supervision, Formal analysis, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e This work was supported by Norges Forskningsr\u0026aring;d. Grant Number: TerraCoast 287490\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConflict of Interest\u003c/u\u003e The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eData Availability\u0026nbsp;\u003c/u\u003eAll data generated or analyzed during this study are included in this published article [and its supplementary material].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFinstad, A. G., Andersen, T., Larsen, S., Tominaga, K., Blumentrath, S., De Wit, H. A., T\u0026oslash;mmervik, H. \u0026amp; Hessen, D. O. (2016). From greening to browning: Catchment vegetation development and reduced S-deposition promote organic carbon load on decadal time scales in Nordic lakes. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 1-8.\u003c/li\u003e\n\u003cli\u003eAksnes, D. L. (2015). Sverdrup critical depth and the role of water clarity in Norwegian Coastal Water. \u003cem\u003eICES Journal of Marine Science\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e(6), 2041-2050.\u003c/li\u003e\n\u003cli\u003eAksnes, D. L., Dupont, N., Staby, A., Fiksen, \u0026Oslash;., Kaartvedt, S., \u0026amp; Aure, J. (2009). Coastal water darkening and implications for mesopelagic regime shifts in Norwegian fjords. \u003cem\u003eMarine Ecology Progress Series\u003c/em\u003e, \u003cem\u003e387\u003c/em\u003e, 39-49.\u003c/li\u003e\n\u003cli\u003eAksnes, D. L., Nejstgaard, J., S\u0026aelig;dberg, E., \u0026amp; S\u0026oslash;rnes, T. (2004). Optical control of fish and zooplankton populations. \u003cem\u003eLimnology and oceanography\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(1), 233-238.\u003c/li\u003e\n\u003cli\u003eAsmala, E., Bowers, D. G., Autio, R., Kaartokallio, H., \u0026amp; Thomas, D. N. (2014). Qualitative changes of riverine dissolved organic matter at low salinities due to flocculation. \u003cem\u003eJournal of Geophysical Research: Biogeosciences\u003c/em\u003e, \u003cem\u003e119\u003c/em\u003e(10), 1919-1933.\u003c/li\u003e\n\u003cli\u003eAsmala, E., Massicotte, P., \u0026amp; Carstensen, J. (2021). Identification of dissolved organic matter size components in freshwater and marine environments. \u003cem\u003eLimnology and Oceanography\u003c/em\u003e,\u003cem\u003e 66(\u003c/em\u003e4), 1381-1393.\u003c/li\u003e\n\u003cli\u003eBaltar, F., Alvarez-Salgado, X. A., Ar\u0026iacute;stegui, J., Benner, R., Hansell, D. A., Herndl, G. J., \u0026amp; L\u0026oslash;nborg, C. (2021). What is refractory organic matter in the ocean?. \u003cem\u003eFrontiers in Marine Science\u003c/em\u003e, 327.\u003c/li\u003e\n\u003cli\u003eBates, D., M\u0026auml;chler, M., Bolker, B., \u0026amp; Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. \u003cem\u003eJournal of Statistical Software\u003c/em\u003e, 67(1), 1\u0026ndash;48. https://doi.org/10.18637/jss.v067.i01\u003c/li\u003e\n\u003cli\u003eBertilsson, S., \u0026amp; Tranvik, L. J. (2000). Photochemical transformation of dissolved organic matter in lakes. Limnology and Oceanography, 45(4), 753-762.\u003c/li\u003e\n\u003cli\u003eB\u0026oslash;rseth, H. H. (2022). Organic matter and iron along a transect from the deep forest to outer Oslofjord The relationship between organic matter and iron and their optical properties in browning waters (Master\u0026apos;s thesis).\u003c/li\u003e\n\u003cli\u003eCatal\u0026aacute;n, N., Marc\u0026eacute;, R., Kothawala, D. N., \u0026amp; Tranvik, L. J. (2016). Organic carbon decomposition rates controlled by water retention time across inland waters. Nature Geoscience, 9(7), Article 7. https://doi.org/10.1038/ngeo2720\u003c/li\u003e\n\u003cli\u003eComets, E., Lavenu, A., \u0026amp; Lavielle, M. (2017). Parameter estimation in nonlinear mixed effect models using saemix, an R implementation of the SAEM algorithm. \u003cem\u003eJournal of Statistical Software\u003c/em\u003e, \u003cem\u003e80\u003c/em\u003e, 1-41.\u003c/li\u003e\n\u003cli\u003eCory, R. M., Ward, C. P., Crump, B. C., \u0026amp; Kling, G. W. (2014). Sunlight controls water column processing of carbon in arctic fresh waters. \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e345\u003c/em\u003e(6199), 925-928.\u003c/li\u003e\n\u003cli\u003eCrapart, C., Finstad, A. G., Hessen, D. O., Vogt, R. D., \u0026amp; Andersen, T. (2023). Spatial predictors and temporal forecast of total organic carbon levels in boreal lakes. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, 161676.\u003c/li\u003e\n\u003cli\u003eCreed, I. F., Bergstr\u0026ouml;m, A. K., Trick, C. G., Grimm, N. B., Hessen, D. O., Karlsson, J., Kidd, K.A., Kritzberg, E., McKnight, D.M., Freeman, E.C., Senar, O.E., \u0026hellip; \u0026amp; Weyhenmeyer, G. A. (2018). Global change‐driven effects on dissolved organic matter composition: Implications for food webs of northern lakes. \u003cem\u003eGlobal change biology\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(8), 3692-3714.\u003c/li\u003e\n\u003cli\u003eDeininger, A., Faithfull, C. L., Lange, K., Bayer, T., Vidussi, F., \u0026amp; Liess, A. (2016). Simulated terrestrial runoff triggered a phytoplankton succession and changed seston stoichiometry in coastal lagoon mesocosms. \u003cem\u003eMarine Environmental Research\u003c/em\u003e, \u003cem\u003e119\u003c/em\u003e, 40-50.\u003c/li\u003e\n\u003cli\u003eDempsey, C. M., Brentrup, J. A., Magyan, S., Knoll, L. B., Swain, H. M., Gaiser, E. E. ... \u0026amp; Williamson, C. E. (2020). The relative importance of photodegradation and biodegradation of terrestrially derived dissolved organic carbon across four lakes of differing trophic status. \u003cem\u003eBiogeosciences\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(24), 6327-6340.\u003c/li\u003e\n\u003cli\u003eDesmit, X., Nohe, A., Borges, A. V., Prins, T., De Cauwer, K., Lagring, R., Van der Zande, D. \u0026amp; Sabbe, K. (2019). Changes in chlorophyll concentration and phenology in the North Sea in relation to de‐eutrophication and sea surface warming. \u003cem\u003eLimnology and Oceanography\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e(4), 828-847.\u003c/li\u003e\n\u003cli\u003edu Pontavice, H., Gascuel, D., Reygondeau, G., Stock, C., \u0026amp; Cheung, W. W. L. (2021). Climate-induced decrease in biomass flow in marine food webs may severely affect predators and ecosystem production. Global Change Biology, 27(11), 2608\u0026ndash;2622. https://doi.org/10.1111/gcb.15576\u003c/li\u003e\n\u003cli\u003eDupont, N., \u0026amp; Aksnes, D. L. (2013). Centennial changes in water clarity of the Baltic Sea and the North Sea. Estuarine, Coastal and Shelf Science, 131, 282-289.Eiane, K., Aksnes, D. L., \u0026amp; Giske, J. (1997). The significance of optical properties in competition among visual and tactile planktivores: a theoretical study. \u003cem\u003eEcological Modelling\u003c/em\u003e, \u003cem\u003e98\u003c/em\u003e(2-3), 123-136.\u003c/li\u003e\n\u003cli\u003eFindlay, S. E. G., \u0026amp; Parr, T. B. (2017). Chapter 24\u0026mdash;Dissolved Organic Matter. In G. A. Lamberti \u0026amp; F. R. Hauer (Eds.), Methods in Stream Ecology (Third Edition) (pp. 21\u0026ndash;36). Academic Press. https://doi.org/10.1016/B978-0-12-813047-6.00002-4\u003c/li\u003e\n\u003cli\u003eFinstad, A. G., Andersen, T., Larsen, S., Tominaga, K., Blumentrath, S., De Wit, H. A., T\u0026oslash;mmervik, H. \u0026amp; Hessen, D. O. (2016). From greening to browning: Catchment vegetation development and reduced S-deposition promote organic carbon load on decadal time scales in Nordic lakes. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 1-8.\u003c/li\u003e\n\u003cli\u003eFogel, M. L., Cifuentes, L. A., Velinsky, D. J., \u0026amp; Sharp, J. H. (1992). Relationship of carbon availability in estuarine phytoplankton to isotopic composition. \u003cem\u003eMarine Ecology Progress Series\u003c/em\u003e, 291-300.\u003c/li\u003e\n\u003cli\u003eFrigstad, H., Harvey, E. T., Deininger, A., \u0026amp; Poste, A. (2020). Increased light attenuation in Norwegian coastal waters-A literature review. \u003cem\u003eNIVA-rapport\u003c/em\u003e. \u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a‐Mart\u0026iacute;n, E. E., Sanders, R., Evans, C. D., Kitidis, V., Lapworth, D. J., Rees, A. P., ... \u0026amp; Mayor, D. J. (2021). Contrasting estuarine processing of dissolved organic matter derived from natural and human‐impacted landscapes. \u003cem\u003eGlobal Biogeochemical Cycles\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(10), e2021GB007023.\u003c/li\u003e\n\u003cli\u003eGreen, S. A., \u0026amp; Blough, N. V. (1994). Optical absorption and fluorescence properties of chromophoric dissolved organic matter in natural waters. \u003cem\u003eLimnology and Oceanography\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(8), 1903-1916.\u003c/li\u003e\n\u003cli\u003eHedges, J. I., Keil, R. G., \u0026amp; Benner, R. (1997). What happens to terrestrial organic matter in the ocean? Organic Geochemistry, 27(5), 195\u0026ndash;212. https://doi.org/10.1016/S0146-6380(97)00066-1\u003c/li\u003e\n\u003cli\u003eHessen, D. O., Carroll, J., Kjeldstad, B., Korosov, A. A., Pettersson, L. H., Pozdnyakov, D., \u0026amp; S\u0026oslash;rensen, K. (2010). Input of organic carbon as determinant of nutrient fluxes, light climate and productivity in the Ob and Yenisey estuaries. \u003cem\u003eEstuarine, Coastal and Shelf Science\u003c/em\u003e, \u003cem\u003e88\u003c/em\u003e(1), 53-62.\u003c/li\u003e\n\u003cli\u003eJaff\u0026eacute;, R., Boyer, J. N., Lu, X., Maie, N., Yang, C., Scully, N. M., \u0026amp; Mock, S. (2004). Source characterization of dissolved organic matter in a subtropical mangrove-dominated estuary by fluorescence analysis. Marine Chemistry, 84(3\u0026ndash;4), 195\u0026ndash;210. https://doi.org/10.1016/j.marchem.2003.08.001 \u003cbr\u003e \u003c/li\u003e\n\u003cli\u003eKarlsson, J., Bystr\u0026ouml;m, P., Ask, J., Ask, P., Persson, L., \u0026amp; Jansson, M. (2009). Light limitation of nutrient-poor lake ecosystems. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e460\u003c/em\u003e(7254), 506-509.\u003c/li\u003e\n\u003cli\u003eKartverket (2024). Se havniv\u0026aring;, tidevann og vannstand. Accessed: 25.01.2024. https://www.kartverket.no/til-sjos/se-havniva/resultat?id=365324\u0026amp;location=Fredrikstad\u003c/li\u003e\n\u003cli\u003eKirk, J. T. (2011). Light and photosynthesis in aquatic ecosystems, 3rd edition. Cambridge university press.\u003c/li\u003e\n\u003cli\u003eKleven, A. (2021). Distribution of metals and organic material along a Norwegian river-ocean transect (Master\u0026apos;s thesis, Norwegian University of Life Sciences, \u0026Aring;s).\u003c/li\u003e\n\u003cli\u003eKritzberg, E. S. (2017). Centennial‐long trends of lake browning show major effect of afforestation. \u003cem\u003eLimnology and Oceanography Letters\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(4), 105-112.\u003c/li\u003e\n\u003cli\u003eKritzberg, E. S., and S. M. Ekstr\u0026ouml;m. (2012) Increasing iron concentrations in surface waters\u0026ndash;a factor behind brownification?. \u003cem\u003eBiogeosciences 9.4\u003c/em\u003e: 1465-1478.\u003c/li\u003e\n\u003cli\u003eKutser, T., Alikas, K., Kothawala, D. N., \u0026amp; K\u0026ouml;hler, S. J. (2015). Impact of iron associated to organic matter on remote sensing estimates of lake carbon content. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, \u003cem\u003e156\u003c/em\u003e, 109-116.\u003c/li\u003e\n\u003cli\u003eLalonde, K., Middlestead, P., \u0026amp; G\u0026eacute;linas, Y. (2014). Automation of 13C/12C ratio measurement for freshwater and seawater DOC using high temperature combustion. \u003cem\u003eLimnology and Oceanography: Methods\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(12), 816-829.\u003c/li\u003e\n\u003cli\u003eLarsen, S., Andersen, T., \u0026amp; Hessen, D. O. (2011). Predicting organic carbon in lakes from climate drivers and catchment properties. \u003cem\u003eGlobal Biogeochemical Cycles\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(3).\u003c/li\u003e\n\u003cli\u003eLasareva, E. V., Parfenova, A. M., Romankevich, E. A., Lobus, N. V., \u0026amp; Drozdova, A. N. (2019). Organic matter and mineral interactions modulate flocculation across Arctic river mixing zones. \u003cem\u003eJournal of Geophysical Research: Biogeosciences\u003c/em\u003e, \u003cem\u003e124\u003c/em\u003e(6), 1651-1664.\u003c/li\u003e\n\u003cli\u003eLee, S. A., Kim, T. H., \u0026amp; Kim, G. (2020). Tracing terrestrial versus marine sources of dissolved organic carbon in a coastal bay using stable carbon isotopes. \u003cem\u003eBiogeosciences\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), 135-144.\u003c/li\u003e\n\u003cli\u003eLepist\u0026ouml;, A., Kortelainen, P., \u0026amp; Mattsson, T. (2008). Increased organic C and N leaching in a northern boreal river basin in Finland. \u003cem\u003eGlobal Biogeochemical Cycles\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(3).\u003c/li\u003e\n\u003cli\u003eMcGovern, M., Pavlov, A. K., Deininger, A., Granskog, M. A., Leu, E., S\u0026oslash;reide, J. E., \u0026amp; Poste, A. E. (2020). Terrestrial inputs drive seasonality in organic matter and nutrient biogeochemistry in a high Arctic fjord system (Isfjorden, Svalbard). \u003cem\u003eFrontiers in Marine Science\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 747.\u003c/li\u003e\n\u003cli\u003eMonteith, D. T., Stoddard, J. L., Evans, C. D., De Wit, H. A., Forsius, M., H\u0026oslash;g\u0026aring;sen, T., Wilander, A., Skjelkv\u0026aring;le, B.L., Jeffries, D.S., Vuorenmaa, J. \u0026amp; Vesely, J. (2007). Dissolved organic carbon trends resulting from changes in atmospheric deposition chemistry. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e450\u003c/em\u003e(7169), 537-540.\u003c/li\u003e\n\u003cli\u003eMustaffa, N. I. H., Kallajoki, L., Hillebrand, H., Wurl, O., \u0026amp; Striebel, M. (2020). Sea surface phytoplankton community response to nutrient and light changes. \u003cem\u003eMarine Biology\u003c/em\u003e, \u003cem\u003e167\u003c/em\u003e(9), 1-15.\u003c/li\u003e\n\u003cli\u003eMyers-Smith, I. H., Kerby, J. T., Phoenix, G. K., Bjerke, J. W., Epstein, H. E., Assmann, J. J., ... \u0026amp; Wipf, S. (2020). Complexity revealed in the greening of the Arctic. \u003cem\u003eNature Climate Change\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2), 106-117.\u003c/li\u003e\n\u003cli\u003eNebbioso, A., \u0026amp; Piccolo, A. (2013). Molecular characterization of dissolved organic matter (DOM): a critical review. \u003cem\u003eAnalytical and bioanalytical chemistry\u003c/em\u003e, \u003cem\u003e405\u003c/em\u003e(1), 109-124.\u003c/li\u003e\n\u003cli\u003eNorwegian Water Resources and Energy Directorate (NVE) Atlas. (2023). Rainfall areas.\u003cem\u003e \u003c/em\u003eAccessed: 07.06.2023. https://atlas.nve.no/html5Viewer/?viewer=nveatlas\u003c/li\u003e\n\u003cli\u003eNorwegian Water Resources and Energy Directorate (NVE). (2023).\u003cem\u003e \u003c/em\u003eSolbergfoss (2.605.0)\u003cem\u003e \u003c/em\u003eMeasurement Data.\u003cem\u003e \u003c/em\u003eAccessed: 11.05.2023. https://sildre.nve.no/station/2.605.0\u003c/li\u003e\n\u003cli\u003eOgawa, H., \u0026amp; Tanoue, E. (2003). Dissolved organic matter in oceanic waters. \u003cem\u003eJournal of Oceanography\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(2), 129-147.\u003c/li\u003e\n\u003cli\u003eOhmura, A. (2012). Enhanced temperature variability in high-latitude climate change. \u003cem\u003eTheoretical and Applied Climatology\u003c/em\u003e, \u003cem\u003e110\u003c/em\u003e(4), 499-508.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Leary, M. H. (1988). Carbon isotopes in photosynthesis. \u003cem\u003eBioscience, 38\u003c/em\u003e(5), 328-336.\u003c/li\u003e\n\u003cli\u003eOlsen, Y., Agust\u0026iacute;, S., Andersen, T., Duarte, C. M., Gasol, J. M., Gismervik, I., ... \u0026amp; Vidal, M. (2006). A comparative study of responses in plankton food web structure and function in contrasting European coastal waters exposed to experimental nutrient addition. \u003cem\u003eLimnology and oceanography\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e, 488-503.\u003c/li\u003e\n\u003cli\u003eOpdal, A. F., Andersen, T., Hessen, D. O., Lindemann, C., \u0026amp; Aksnes, D. L. (2023). Tracking freshwater browning and coastal water darkening from boreal forests to the Arctic Ocean. \u003cem\u003eLimnology and Oceanography Letters\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eOpdal, A. F., Lindemann, C., \u0026amp; Aksnes, D. L. (2019). Centennial decline in North Sea water clarity causes strong delay in phytoplankton bloom timing. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(11), 3946-3953.\u003c/li\u003e\n\u003cli\u003eOsburn, C. L., \u0026amp; Stedmon, C. A. (2011). Linking the chemical and optical properties of dissolved organic matter in the Baltic\u0026ndash;North Sea transition zone to differentiate three allochthonous inputs. \u003cem\u003eMarine chemistry\u003c/em\u003e, \u003cem\u003e126\u003c/em\u003e(1-4), 281-294.\u003c/li\u003e\n\u003cli\u003eOtero, E., Culp, R., Noakes, J. E., \u0026amp; Hodson, R. E. (2003). The distribution and \u0026delta;13C of dissolved organic carbon and its humic fraction in estuaries of southeastern USA. Estuarine, Coastal and Shelf Science, 56(5-6), 1187-1194.\u003c/li\u003e\n\u003cli\u003ePedersen, E. J., Miller, D. L., Simpson, G. L., \u0026amp; Ross, N. (2019). Hierarchical generalized additive models in ecology: an introduction with mgcv. \u003cem\u003ePeerJ\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, e6876.\u003c/li\u003e\n\u003cli\u003eP\u0026eacute;rez San Mart\u0026iacute;n, A., Egea, F., Bustamante, M., \u0026amp; Curaqueo, G. (2023). Spectroscopy Techniques for Monitoring the Composting Process: A Review. Agronomy, 13, 2245. https://doi.org/10.3390/agronomy13092245\u003c/li\u003e\n\u003cli\u003ePeterson, B., Fry, B., Hullar, M., Saupe, S., \u0026amp; Wright, R. (1994). The distribution and stable carbon isotopic composition of dissolved organic carbon in estuaries. \u003cem\u003eEstuaries\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e, 111-121.\u003c/li\u003e\n\u003cli\u003ePoste, A., Kaste, \u0026Oslash;., Frigstad, H., de Wit, H., Harvey, T., Valestrand, L., Deininger, A., Bryntesen, T., Delpech, L. \u0026amp; Christensen, G. (2021). The impact of the spring 2020 snowmelt floods on physicochemical conditions in three Norwegian river-fjord-coastal systems. \u003cem\u003eNIVA-rapport\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eRegnier, P., Resplandy, L., Najjar, R. G., \u0026amp; Ciais, P. (2022). The land-to-ocean loops of the global carbon cycle. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e603\u003c/em\u003e(7901), 401-410.\u003c/li\u003e\n\u003cli\u003eSampedro-Avila, J. E., Lu, K. J., Xue, J. H., Liu, Z. F. \u0026amp; Maske, H. The chemical characteristics and mixing behaviors of particulate organic matter from small subtropical rivers in coastal Gulf of Mexico. \u003cem\u003eEstuarine, Coastal and Shelf Science\u003c/em\u003e 299 (2024).\u003c/li\u003e\n\u003cli\u003eStedmon, C. A., Markager, S., \u0026amp; Kaas, H. (2000). Optical properties and signatures of chromophoric dissolved organic matter (CDOM) in Danish coastal waters. \u003cem\u003eEstuarine, Coastal and Shelf Science\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(2), 267-278.\u003c/li\u003e\n\u003cli\u003eStevenson, F. J. (1994). Humus Chemistry: Genesis, Composition, Reactions. John Wiley \u0026amp; Sons.\u003c/li\u003e\n\u003cli\u003eS\u0026aelig;tre, R. (Ed.). (2007). The Norwegian coastal current: oceanography and climate. Akademika Pub.\u003c/li\u003e\n\u003cli\u003eThrane, J. E., Hessen, D. O., \u0026amp; Andersen, T. (2014). The absorption of light in lakes: negative impact of dissolved organic carbon on primary productivity. \u003cem\u003eEcosystems, 17\u003c/em\u003e(6), 1040-1052.\u003c/li\u003e\n\u003cli\u003eTranvik, L. J., \u0026amp; Sieburth, J. M. (1989). Effects of flocculated humic matter on free and attached pelagic microorganisms. \u003cem\u003eLimnology and oceanography\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(4), 688-699.\u003c/li\u003e\n\u003cli\u003eWilson, R. J., \u0026amp; Heath, M. R. (2019). Increasing turbidity in the North Sea during the 20th century due to changing wave climate. \u003cem\u003eOcean Science\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(6), 1615-1625.\u003c/li\u003e\n\u003cli\u003eWood, S.N. (2017). Generalized Additive Models: An Introduction with R, Second Edition (2nd ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781315370279\u003c/li\u003e\n\u003cli\u003eXu, H., \u0026amp; Guo, L. (2017). Molecular size-dependent abundance and composition of dissolved organic matter in river, lake and sea waters. \u003cem\u003eWater Research\u003c/em\u003e, \u003cem\u003e117\u003c/em\u003e, 115-126.\u003c/li\u003e\n\u003cli\u003eXu, H., \u0026amp; Guo, L. (2018). Intriguing changes in molecular size and composition of dissolved organic matter induced by microbial degradation and self-assembly. \u003cem\u003eWater research\u003c/em\u003e, \u003cem\u003e135\u003c/em\u003e, 187-194.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Colored dissolved organic matter, δ13C of TOC, salinity gradient, CDOM absorption","lastPublishedDoi":"10.21203/rs.3.rs-4497080/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4497080/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the optical properties of coloured dissolved organic matter (CDOM) along a salinity gradient from the Glomma river to the outer Oslofjord. The research aims to determine the spectral, isotopic, and quantitative changes in CDOM across this gradient. Key findings indicate that total organic carbon (TOC) concentrations display notable seasonal variability, especially near shore, due to fluctuations in river discharge throughout the year, while remaining more constant in the outer sea environment​​. CDOM absorption, spectral slope and specific absorbance at 443 nm decreases linearly with increasing salinity. CDOM absorption exhibited significant variations, with low-salinity samples showing higher light absorption per unit of carbon, but little seasonal variations, hinting at the fact that CDOM had similar optical properties over the year of sampling. δ\u003csup\u003e13\u003c/sup\u003eC of TOC analysis revealed a strong positive correlation with salinity, indicating a linear transition from terrestrial to marine organic carbon sources. This method can be an effective way of tracking the fate of terrestrially-derived organic matter in estuarine systems, which is highly topical for coastal darkening research. Water darkening is an increasingly relevant problem affecting many coastal ecosystems, as it is exacerbated by the human activity and climate change.\u003c/p\u003e","manuscriptTitle":"Optical properties of dissolved organic matter along a salinity gradient from a boreal river estuary to open coastal waters.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-17 10:26:17","doi":"10.21203/rs.3.rs-4497080/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5cdf21e-f049-4c24-a77a-d954f208cc83","owner":[],"postedDate":"June 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-03T10:23:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-17 10:26:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4497080","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4497080","identity":"rs-4497080","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}