Land use and ecosystem properties explain the composition and stability of dissolved organic matter across Northern Hemisphere headwater catchments

Land use and ecosystem properties explain the composition and stability of dissolved organic matter across Northern Hemisphere headwater catchments | 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 Article Land use and ecosystem properties explain the composition and stability of dissolved organic matter across Northern Hemisphere headwater catchments Philipp Maurischat, Sara E. Anthony, Alba Camacho-Santamans, Lukas Hallberg, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9351570/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract We analysed the chemical composition of dissolved organic matter (DOM) and the decomposition rates of cotton-strips in several headwater streams of the Northern Hemisphere. We conducted a coordinated distributed experiment to investigate concentration of dissolved organic carbon (DOC), DOM spectroscopic and fluorescence signatures including PARAFAC-resolved excitation emission matrices (EEMs), organic matter decomposition rates (OM stability), dissolved concentrations of CO 2 -C and CH 4 -C, and physio-chemical water quality parameters. We aimed to uncover relationships between land use classes (LULC), DOM composition and OM stability, in streams across different latitudes of Europe and the United States of America. Our data showed that physio-chemical water quality strongly reflects land use classes, and that in both agricultural and urban streams terrestrial DOM was lower compared to forested streams, indicating ecological disconnection with the riparian zone and higher photooxidation of chromophoric DOM (CDOM). However, on pan-continental scales, DOM characteristics instead can be pinned on abiotic and biotic controls, arising from a multitude of biogeographical factors within catchments, that dominate compositional differences. This leads to persistent chemodiversity in DOM composition under different LULC classes. Our results underscore the importance of coordinated distributed experiments to unravel local catchment dynamics to draw conclusions for global change assessments on DOM composition. We show that DOM is strongly affected by land use on the local scale, but geospatial and ecosystem properties superimpose these differences on larger scales. Earth and environmental sciences/Biogeochemistry Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology PARAFAC-Fluorescence Cotton strip assay Early Career Researchers (ECR) Agricultural and urban streams Coordinated distributed experiment (CDE) Chemogeography Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Headwaters are highly dynamic ecosystems and are key to understanding patterns of stream dissolved organic matter (DOM) processing and transport (Ledesma et al., 2018; Vidon et al., 2019; Reidy et al., 2025). Given the intimate link to terrestrial ecosystems, headwaters connect energy and nutrient fluxes of terrestrial and aquatic ecosystems (Bieroza et al. 2024; Maurischat et al., 2022). Consequently, headwaters are sensitive to changes in surrounding terrestrial ecosystems, land use influences and other anthropogenic perturbations (Fovet et al., 2020; Giling et al., 2014; Graeber et al., 2015). Compared to larger rivers, headwater streams receive less scientific attention despite having the longest channel network worldwide (Wohl, 2017; Bieroza et al. 2024; Wynants et al. 2025) and the strongest connection with terrestrial ecosystems (Caillon and Schelker, 2020; Coch et al., 2019; Kawahigashi et al., 2004). DOM contains essential elements such as carbon, nitrogen, and phosphorus and is among the most mobile and heterogeneous constituents in global elemental cycles (Kalbitz et al., 2000). Stoichiometric shifts driven by urbanisation and agriculture (Gücker et al., 2016) can alter the composition and stability of DOM in stream ecosystems (Graeber et al., 2021; Ferreira et al., 2015; Tiegs et al. 2019). Furthermore, chemical diversity (chemodiversity) in headwater streams is larger compared to higher order rivers (Mosher et al., 2015), leading to DOM compositional differences (Berggren et al., 2022; Dittmar et al., 2021). Biotic and abiotic factors vary between ecosystems and greatly influence the stability of DOM (Plamper et al., 2023). For example, chromophoric compounds in DOM (CDOM) can be quickly photo-oxidised (Stubbins et al. 2010), while labile compounds are typically consumed by heterotrophs and through this control the biodegradability of DOM and carbon dioxide (CO 2 ) production (Begum et al. 2023; Xu and Guo 2018; Cole and Caraco 2001). Agricultural land use led to a structural homogenisation of landscapes globally (Jongman 2002). This transformation has directly affected the chemistry of stream water, the composition of DOM, and organic matter degradation. The type of land use classes (LULC) is one of the key determinants for DOM composition in streams (Herzsprung et al., 2017; Kothawala et al., 2015; Schürings et al., 2024; Yates et al., 2023), leading to intense differences in DOM composition in agricultural and urban sites compared to forested watersheds (Ebeling et al., 2021; Raymond et al., 2008; Wilson and Xenopoulos, 2009I). Research in urban and agricultural systems documents stoichiometric shifts toward lower C ratios in DOM, higher concentration of inorganic nutrients, and a higher proportion of aquatic derived organic matter due to decreased terrestrial inputs (Gücker et al., 2016; Lee et al., 2021; Pisani et al., 2020). These changes suggest reduced hydrologic connectivity between aquatic and terrestrial ecosystems via the riparian zone in agricultural and urban headwaters compared to more pristine forested catchments (Pisani et al. 2020; Wagner et al. 2008). This change towards a more protein-rich DOM composition alters the intrinsic factors of DOM fate (Berggren et al. 2022) as it can can boost organic matter (OM) decomposition and increase gaseous carbon fluxes (CO₂-C and methane (CH₄-C)) to the atmosphere (Graeber et al. 2015). Although LULC is an important explanatory variable for DOM composition in streams, climate and hydrological controls are known to shape the exchange of water and matter in riparian zones to a great extent (Laudon et al., 2011; Ryan et al., 2024; Werner et al., 2019). Further extrinsic environmental controls influence riverine DOM cycling and composition across ecoregions, including mean annual precipitation, which can regulate DOM inputs of terrestrial-borne organic matter (Catalán et al., 2018; Kaplan and Cory, 2016; Kothawala et al., 2021; Roth et al., 2014). Additional factors such as soil type, catchment size, and relief were also shown to have large effects on DOM composition (Charamba et al., 2024; Orlova et al., 2024; Yates et al., 2023). Furthermore, the exchange of carbon greenhouse gases with the atmosphere is influenced by the cycling of terrestrial carbon in the stream and other factors such as groundwater inflow (Herreid et al., 2021; Hotchkiss et al., 2015). The multitude of geographical factors of a catchment, known as geodiversity (Fuß et al., 2024), ultimately control and shape the compositional variability of DOM, also known as chemodiversity (Kellerman et al., 2014). The compositional differences and geographic signatures of DOM on temporal and spatial scales, shaped by ecosystem properties are referred to chemogeography (Mosher et al., 2015; Hu et al. 2025). Many studies that investigate catchment scale LULC effects on DOM composition isolate these effects by focussing on geographically proximate catchments, masking additional geodiversity controls beyond LULC. This has led to a restricted understanding of DOM chemodiversity trends in streams (Tanentzap and Fonvielle, 2024; van Vliet et al., 2016; Gerhard et al., 2023). As a result, the predictability of riverine DOM composition and its sensitivity to future perturbations remain insufficient. To address this knowledge-gap coordinated distributed experiments (CDEs), which are comprised of a series of internally standardised methods and materials, conducted synchronously by teams across different locations, can be pursued (Fraser et al., 2013; Yu et al., 2021). Through this, CDEs can produce diverse datasets and are especially suited to overcome limitations of single case studies (Yahdjian et al., 2021). The main objective of this study was to explore differences in DOM composition as related to geographies of the Northern Hemisphere by investigating headwater streams along a gradient of LULC classes and geographical conditions. Subsequently, our objective was to explore the impacts of LULC classes both within and between the regions and to test whether LULC would explain the majority of DOM compositional differences or if they are responsive to ecosystem properties on the level of countries or regions besides land use. Generally, we expect forested catchments to exhibit a more terrestrial DOM composition compared to agricultural and urban land use at the local scale. We performed a CDE with case studies in North America and Europe to measure stream physio-chemical conditions, screening temperature, pH, electrical conductivity and nutrients: Nitrate-N (NO 3 -N), Ammonium-N (NH 4 -N), soluble reactive phosphorus (SRP), and dissolved organic carbon (DOC). Furthermore, dissolved CO 2 -C and CH 4 -C in the aqueous phase were measured. DOM chemodiversity was investigated by measuring ultraviolet absorbance (UV 254) and specific ultraviolet absorbance normalised by the concentration of DOC (SUVA 254 ), molecular weight indicators of DOM were calculated (UV E2: E3 and UV spectral slope). Fluorescence DOM was investigated by peak-picking and indexing on excitation-emission matrices (EEMs) informing about relative abundance of proteinaceous material (F Index, Freshness Index) and humic compounds (Humification Index). We further conducted parallel-factor decomposition (PARAFAC) of EEMs, allowing to assign fluorescence components. OM degradation was determined by using a cotton strip incubation approach. 2. Materials and Methods 2.1 Study sites, CDE sampling protocol and watershed information To examine to what degree LULC classes and ecosystem properties influence water quality, DOM composition, and OM stability, each CDE team selected three headwater streams located in different watersheds of similar size. Selected watersheds featured different LULC classes with ideally dominant cover of either urban, agriculture (cropland), or forest land use. This resulted in the selection of 31 sites across Europe (including Sweden, Germany and Spain) (Fig. 1 ) and the midwestern United States (Fig. 2 ). The sites were sampled in summer 2022, with two sampling dates 30 ± 5 days apart. In each stream, a 20 m reach was selected and divided into 3 sections with equal distance. Each team used a predetermined standardised experimental sampling plan to ensure consistency across teams (Fig. S1 ). Interpolated temperature and precipitation means were generated for the different watersheds from ClimateCharts.net (Zepner et al., 2021). The 2019 National Land Cover Database (USGS) and the 2018 CORINE land cover datasets were used to obtain LULC data for the USA and Europe, respectively (Dewitz, 2024; EEA, 2019). LULC classes were harmonised between the two datasets to represent agriculture, forest, and urban land use. Soil information for each catchment was extracted from the Harmonized World Soil Database (HWSD version 2.0, 2023). 2.2 In-situ measurements: physio-chemical properties Physio-chemical variables including water temperature, pH, electrical conductivity, and dissolved oxygen concentration were measured directly in the water column in the three sections of each reach on each sampling date. 2.3 Ex-situ laboratory measurements 2.3.1 Dissolved nutrients, DOC and elemental ratios For water chemistry characterisation of NO 3 -N, NH 4 -N, SRP and DOC, water samples were taken from 10 cm below the surface and filtered through 0.45 µm polyethersulfone (PES) membrane filters. Samples were stored in clean, pre-rinsed plastic bottles and cooled or frozen until analysis in laboratories at each CDE team’s institution following standard methods (Tab.S2). Dissolved inorganic nitrogen (DIN) was calculated as the sum of NO 3 -N and NH 4 -N. C:N:P ratios, as a representation of relative nutrient concentration (Turner et al., 2003), were calculated from DOC, DIN and SRP concentrations and normalised to the Godwin-Cotner ratio (68:14:1) (Godwin and Cotner, 2018) representing the mean nutrient ratio in the biomass of heterotrophic freshwater bacteria. We use this to assess whether single or co-limitation of nutrient uptake is plausible. The Godwin-Cotner normalised ratios were plotted as relative elemental concentrations in a ternary plot following Smith et al. (2017) and Jarvie et al. (2018) to assess nutrient stoichiometry, elemental relationships and to predict thresholds of substrate limitation and co-limitation. 2.3.1 UV/VIS and CDOM indices, fluorescence DOM: EEM measurement and peak assignment CDOM and DOM fluorescence was determined at the department of Soil and Environment of the Swedish University of Agricultural Sciences, Sweden with an Aqualog (Horiba, Japan) equipped with a 150 W Xenon arc lamp using a 1 cm pathlength Suprasil® cuvette in temperature-controlled conditions (20°C). Scans were blank corrected. Absorbance at 254 nm (A254) is used as an indicator of bulk CDOM. Specific ultraviolet absorbance (SUVA 254 ), as a proxy of DOM aromaticity, was calculated by normalising A254 readings with DOC concentration (Weishaar et al., 2003). Spectral slopes were calculated based on the ranges of 275–295 nm and 350–400 nm (Helms et al. 2008), and E2:E3 was calculated based on the ratio of two absorbance frequencies (A250:A365) (Peuravuori and Pihlaja 1997). Spectral slopes and E2:E3 both represent a DOC concentration independent inverse indicator relative molecular weight, with a lower ratio indicating higher relative molecular weight. Fluorescence spectral scans were recorded using EEMs at excitation wavelengths between 240 and 600 nm and emission wavelengths between 242 and 620 nm, at 1 s integration time and 2 nm scan width. Ultra-pure water blanks were scanned prior to analysis and the blank signal was subtracted from sample EEM scans to correct for Raman scattering. Raman peak intensities in blank samples were recorded to normalise sample EEM scans and account for the variation in Raman intensities over time. Sample EEM scans were corrected for inner-filter effect (McKnight et al., 2001) and signal intensities of first and second order Rayleigh scattering were removed using a masking filter. Instrument-specific bias caused by optical components was automatically corrected in the Aqualog software. PARAFAC (see section 2.4 for details), peak-picking and FDOM indexing was conducted to investigate the composition of fluorescence DOM. Allochthonous humic peaks (C, A, M) and autochthonous peaks (T, B) were assigned following Coble (2007). Indices were calculated including: Fluorescence index (F) as a measure of terrestrial and microbial contributions to the DOM pool, with a lower index (~ 1.2) suggesting terrestrial material and higher (~ 1.8) indices suggesting microbial, autochthonous production (Gabor et al., 2014; McKnight et al. 2001), Freshness index indicating biogeochemical cycling of DOM (Wilson and Xenopoulos 2009) and Humification index (HIX) suggesting contributions of humic substances in stream DOM (Ohno 2002). 2.3.2 Greenhouse gas measurements: CO 2 -C, CH 4 -C concentrations Two samples were collected in each reach for CO 2 -C and CH 4 -C analyses, using the headspace equilibration method (Hope et al., 2004). Briefly, 30 mL of stream water was drawn into a 60 mL syringe along with 30 mL of ambient air. The syringes were then shaken vigorously for 60 seconds after which 17 ml of gas was injected into a sealed pre-evacuated vial creating overpressure. In addition, atmospheric gas samples were taken from each reach of the stream to correct for GHG concentration in the ambient air. Levels of GHG (in ppm) were transformed into dissolved concentrations using Henry’s law and solubility equations for CO 2 -C and CH 4 -C, with additional adjustment for water temperature and atmospheric pressure (Anthony et al. 2012). The concentrations of dissolved greenhouse gases (GHGs) were measured by individual CDE teams using established methods and instrumentation (Tab.S3). 2.3.3 Organic matter decomposition rate by cotton strips During the first sampling at each site, two sets of cotton strips (n = 3) were installed closely to the bed of each stream. Cotton strips were prepared according to Tiegs et al. (2013) using unprimed 12-oz. heavy-weight cotton fabric (Fredrix Style #548, Lawrenceville, GA, USA). The strips were left in the stream for 30 ± 5 days. When collected, they were immediately rinsed with 80% ethanol to interrupt further microbial activity and dried at 60°C until constant weight was reached. The decomposition rate for each cotton strip followed the method presented by Tiegs et al. (2013) and was calculated from the loss of tensile strength of the incubated material, which serves as a measure of microbial cellulose degradation. This evaluation was compared to a control of nonincubated cotton strips. Laboratory measurements of tensile strength loss were carried out in the Department of Evolutionary Biology, Ecology and Environmental Science at the University of Barcelona, Spain, using a dynamometer (Mark-10, M5 series) coupled to a motorised test bench (ESM303, Mark-10) with a constant traction speed of 2 cm min − 1 . The results were normalised by the mean water temperature to eliminate temperature effects on cellulose decomposition and are expressed as the percentage of tensile loss per incubation day (degree day) which we hereafter refer to as OM degradation rate. 2.4 Data analysis Statistical testing for LULC classes and countries was performed with the Kruskal-Wallis test (Ostertagova et al., 2014). Subsequent group comparisons were conducted with Dunn’s test using a Bonferroni post hoc correction (Sedgwick, 2012). Significance was accepted on the α = 0.05 level; In the text we report significant pairwise group comparisons, and the respective Z score and p values can be found in the supplementary materials (Tab.S5). Statistical group testing was carried out in R Studio (R Studio Team, 2024) with the R base (R Core Team, 2023) and the ’FSA’ package (Ogle et al. 2015). Ternary plots were created using the ‘ternary’ package (Smith, 2017). For dataset decomposition, non-metric multidimensional scaling (NMDS) was performed with mean-centred and scaled data. Distance indices and dimension number were tested iteratively. The level of stress was monitored as quality control. The R package ‘vegan’ (Oksanen et al., 2020) was used for NMDS. PARAFAC was performed using the PLS_Toolbox 9.3 (Eigenvector Research, Manson, Washington, USA) in MATLAB R2023a (The MathWorks, Natick, USA). The model was iteratively fitted using a calibration sample set (n = 180) and a validation sample set (n = 21) (Halberg et al. 2023). Baseline noise was removed by subtracting an ultrapure water blank. Non-negativity constraints were imposed on modes 1 (sample scores) and 2 (excitation loadings). Individual sample raw data, reconstructed data, and residuals were screened for model probability. Core consistency of the model was 71% and the variation between components for the calibration and validation sample set showed only limited deviation. To further verify the plausibility of PARAFAC components and model quality, the components were tested against identified components using the OpenFluor database (Murphy et al., 2014; detailed information is provided in the supplements Tab.S4). Spearman rank correlations between the PARAFAC components sample scores were tested. 3. Results 3.1 LULC distribution in studied catchments The watershed-wide LULC classes were dominated by agriculture in the USA (61%), Sweden (60%), and Germany (60%), while this LULC class was absent in Spain (Fig. 3 a). 3.2 Physio-chemical water conditions, nutrients, DOC and elemental ratios Water temperatures were significantly lower in Germany and Sweden compared to the USA and Spain. Water pH was significantly lower at Swedish sites compared to more alkaline US sites, while German and Spanish streams showed intermediate values with near neutral pH. Electrical conductivity differed significantly between countries, and sampling sites in Spain and Sweden showed lower values compared to the headwaters of USA and Germany. The dissolved oxygen concentrations showed comparable values for all countries' headwater streams (Table 1). Across all countries, NO 3 -N concentrations were between 10 to 20 times higher than those of NH 4 -N. German headwaters showed significantly higher NO 3 -N concentrations compared to all other countries (Fig. 3 b), while the NH 4 -N concentrations did not show significant differences between countries. The concentration of SRP differed between countries; German sites showed the highest values followed by Swedish, US, and Spanish sites (Fig. 3 c). The concentration of DOC was different between countries, wherein US and Swedish headwaters showed significantly higher DOC concentrations compared to Spanish and German sites (Table 1, Fig. 3 c). Considering LULC classes, headwater catchments with urban land use showed significantly higher temperatures compared to forest sites, while agricultural sites had intermediate values. Near-neutral to slightly alkaline pH prevailed for all LULC classes, but forested watersheds had significantly lower pH compared to agricultural sites. Forest sites showed lowest electrical conductivity values, followed by agricultural headwaters, and highest values at urban sites. Dissolved oxygen concentration was significantly lower at urban sites compared to agricultural sites, while forested watersheds showed intermediate values. Agricultural sites showed significantly higher NO 3 -N concentrations compared to forests, while urban LULC had intermediate concentrations (Fig. 4a). For NH 4 -N, urban sites showed significantly higher concentrations compared to forest and agricultural sites (Table 1). Figure 4: Ternary plot with Godwin-Cotner ratio (Godwin and Cotner 2018; C:N:P = 68:14:1) normalised molar ratio relative elemental ratios a) with zones of elemental limitation for b) land use land cover class (LULC) and c) country. The dark circle depicts the zone of normalised Godwin-Cotner ratio, i.e. 33.3% DOC, 33.3% NH 4 -N + NO 3 -N, 33.3% SRP The Godwin-Cotner normalised ratios of DOC:DIN:SRP (Fig. 4b/c) showed that the majority of the streams indicated stoichiometric limitation. Nutrient colimitation of both SRP and DIN was the most observed scenario in our study, followed by either SRP or DIN limitation, while DOC limitation was restricted to few cases. At the country level (Fig. 4b), USA, Sweden and Spain showed similar patterns of limitation and colimitation, with DIN and SRP being the most abundant restriction, either as colimitation or a single limiting nutrient. German headwaters, had more sites with no visible limitation or limitation of just one element. Notably, DOC limitation occurred only in German streams, indicating enrichment with both DIN and SRP. For LULC classes (Fig. 4c), Godwin-Cotner normalised ratios showed that colimitation was only visible for DIN and SRP and was in general the most observed scenario in forests and urban sites. Stream water in agricultural dominated watersheds was more often limited only in SRP or DIN. In general, DOC limitation was the least observable scenario for all LULC classes. Table 1: Stream water chemistry by country and dominant land-use land cover of the catchments. Shown are arithmetic means and standard deviation (SD). NA=not measured by individual CDE participant. Superscript letters ( abcd ) between mean values denote statistically significant differences on the ɑ=0.01 level, and comparisons are within group only. *UV spectral slope is based on the ranges of 275-295 nm and 350-400 nm. SRP=soluble reactive phosphorous as PO 4 -P. ** Temperature corrected data of cotton strip tensile strength loss. Variable USA Sweden Germany Spain Agriculture Forest Urban Mean SD (±) Mean SD (±) Mean SD (±) Mean SD (±) Mean SD (±) Mean SD (±) Mean SD (±) Water Temperature [°C] 21.5 a 3.8 15.4 b 2.9 13.6 b 3.9 19.3 a 2.7 17.5 ab 5.1 15.5 a 3.3 21.2 b 3.7 pH 8.14 a 0.59 7.51 b 0.56 7.70 ab 0.76 7.76 ab 0.32 7.94 a 0.71 7.52 b 0.47 7.76 ab 0.51 EC [µS cm − 1 ] 974 a 351 258 b 278 12,381 a 27,321 354 b 379 685 a 329 211 b 207 32838 c 37341 NH 4 -N [µg + L −1 ] 103.1 a 179.1 50.2 a 23.8 58.4 a 56.9 NA NA 52.1 a 48.2 41.5 a 33.2 214.5 b 243.7 NO 3 -N [µg − L − 1 ] 1312 a 1875 640 a 842 2406 b 2052 939 a 1901 1610 a 1996 926 b 1587 896 ab 459 SRP [µg L − 1 ] 44.7 ab 67.9 62.9 bc 57.6 169.1 c 235.5 19.3 a 43.5 59.5 a 68.3 48.3 a 69.6 243.2 a 327.2 Dissolved Oxygen [mg L − 1 ] 8.1 a 2.4 6.8 a 2.8 5.3 a 3.9 7.4 a 1.6 7.8 a 3.0 6.5 ab 2.9 3.7 b 1.8 DOC [mg L − 1 ] 9.9 a 5.8 15.1 a 11.3 3.5 b 2.4 2.8 b 1.7 7.5 a 9.4 10.5 a 9.0 10.5 a 7.1 SUVA254 [L mg C-1 m-1] 1.27 a 0.72 3.34 b 1.26 4.88 b 2.60 3.59 b 1.25 3.51 ab 2.52 3.70 a 1.39 2.13 b 1.27 CO 2 -C [mM] 1.03 a 0.38 4.95 b 3.39 0.72 a 0.19 1.11 a 1.42 1.73 a 2.19 3.34 a 3.59 1.59 a 1.26 CH 4 -C [µM] 13.03 a 14.02 214.12 b 803 3.51 a 2.69 5.91 a 8.86 116.4 a 621 27.6 a 37.5 10.7 a 9.3 UV 254 (A254) [relative absorbance] 0.11 a 0.04 0.45 b 0.22 0.14 a 0.08 0.08 a 0.04 0.19 a 0.17 0.33 a 0.27 0.15 a 0.04 UV E2:E3 [ratio] 10.84 a 12.70 10.02 a 6.19 6.19 a 3.30 13.01 a 11.54 8.32 a 7.29 10.17 a 8.77 10.95 a 15.23 UV spectral slope [nm − 1 ]* 1.90 a 0.13 1.97 a 0.35 1.98 a 0.26 1.98 a 0.21 1.88 a 0.24 2.00 a 0.28 2.18 b 0.10 F index 1.63 a 0.05 1.54 b 0.04 1.63 a 0.05 1.57 b 0.14 1.61 a 0.06 1.52 b 0.08 1.64 a 0.03 Freshness index 0.74 a 0.06 0.66 b 0.06 0.74 a 0.09 0.71 ab 0.12 0.71 a 0.07 0.67 b 0.08 0.80 a 0.09 Humification index 0.88 ab 0.03 0.91 a 0.02 0.88 b 0.04 0.72 c 0.13 0.88 a 0.06 0.83 a 0.11 0.86 a 0.02 PARAFAC C1 (humic) 28.5 a 12.2 98.4 b 54.0 35.4 a 27.8 26.6 a 32.8 54.7 a 49.4 53.3 a 48.9 68.3 a 49.3 PARAFAC C2 (protein) 27.0 a 11.2 101.4 b 57.4 33.8 a 25.8 26.2 a 34.9 54.8 a 51.9 53.4 a 51.3 69.4 a 52.0 PARAFAC C3 (protein) 7.2 a 3.9 22.3 b 16.7 10.4 a 9.8 8.3 a 9.9 13.5 a 13.5 13.1 ab 13.3 16.9 b 13.6 OM degradation rate [%]** 0.13 a 0.02 0.19 ab 0.04 0.29 b 0.09 0.16 ab 0.02 0.21 a 0.09 0.18 a 0.04 0.15 a 0.01 3.3 UV absorbance, Fluorescence and PARAFAC components A254 on the country-level, was up to five times higher in Swedish streams compared to USA, Germany and Spain. The USA streams exhibited significantly lower SUVA 254 compared to all other countries (Table 1, Fig. 5 a, Tab.S5). German streams showed a lower E2:E3 ratio compared to other countries, suggesting higher relative molecular weight of DOM (Fig. 5 b). Fluorescence indices (F, Freshness and HIX) indicated similar significant differences among countries (Fig. 5 c), wherein Germany and USA showed higher values of the F and Freshness index, which are indicative of a higher share of microbial DOM. HIX denoting terrestrial-borne OM was significantly higher in Sweden compared to the other countries. In USA streams, HIX showed moderately high values and was significantly different from Spanish streams (Fig. 5 c). When assessing LULC classes, A254 showed a trend of higher values for forest streams compared to agricultural and urban headwaters (Table 1). For SUVA 254 , forested and agricultural catchments exhibited values up to 50% higher compared to urban catchments (Fig. 5 a, Tab.S5). The E2:E3 ratio of agricultural streams tended to be lower, suggesting higher relative molecular weight, compared to forests and urban sites (Fig. 5 b). For UV spectral slopes, significantly higher values were present in urban catchments suggesting lower molecular weight compared to forest and agricultural sites (Table 1). The F and Freshness indices differed between LULC classes (Fig. 5 c), with significantly higher values in agricultural and urban streams compared to forests (Table 1, Tab.S5). PARAFAC EEM decomposition yielded a three-component result. Iterative trials with more components (4–7) led to non-meaningful loadings, impaired split-half quality and low model core consistency. Component 1 (C1) had an excitation spectrum at 270 nm with an emission at 465 nm. Component 2 (C2) showed a slight excitation mode shift between 245 and 320 nm and emission at 435 nm. Component 3 (C3) had a more pronounced excitation mode shift between an early (240 nm) and a late (300 nm) phase with an emission at 340 nm (Fig.S2). Following a validation with OpenFluor (Murphy et al., 2014), C1 resembles a low salinity terrestrial derived, humic-like component. C2 is likely produced by microbes and has stages of temperature-dependent decay. C3 was identified as essential proteinaceous material. The scores of C1 and C2 were correlated (Spearman correlation, R = 0.995, p < 0.001). We observed higher relative concentrations of aquatic-protein FDOM (~ 55% mean value) compared to terrestrial-humic FDOM (~ 45%) for all headwater streams (Fig. 6 a). When examined by country, there were no differences in FDOM concentrations of aquatic-protein and terrestrial-humic components (Fig. 6 a/b). The concentration of all three PARAFAC components were significantly higher in Swedish streams compared to streams from other countries (Table 1, Tab.S5). Urban streams had higher relative concentrations for the two aquatic-protein FDOM components compared to agriculture and forest catchments (Fig. 6 a). In addition, the concentration of proteinaceous FDOM component C3 was significantly higher in urban compared to agricultural catchments (Table 1). 3.4 CO 2 -C and CH 4 -C concentrations CO 2 -C and CH 4 -C concentrations differed between countries, with significantly higher values in Swedish headwater streams compared to all other countries (Table 1). However, CO 2 -C and CH 4 -C concentrations did not differ between LULC classes (Table 1). 3.5 Ordination and decomposition of combined variables Dimension 1 of the NMDS ordination showed a distinction of Swedish streams from the other investigated countries (Fig. 7 a), explained by terrestrial-humic PARAFAC component (C1), DOC concentration, and CO 2 -C concentrations. Streams in the USA, Germany, and Spain were clustered in the positive direction of Dimension 1. When assessing LULC classes (Fig. 7 b), agricultural streams clustered around the origin of the ordination, while forest samples were more orientated towards the negative or positive directions of both dimensions, indicating stronger chemical homogenisation of agricultural streams. Urban samples extended toward the negative direction of dimension 2, and these samples showed the lowest scattering in the ordination space and a negative relationship to SUVA 254 and the UV E2:E3 ratio, which were positively correlated with dimension 2. The freshness and F indices, as well as HIX and the aquatic-protein PARAFAC component C3 were negatively correlated with dimension 2. When assessing LULC classes only at the country level, there was clear clustering visible, indicating impacts of land use on water chemistry and DOM regionally (Fig.S3). 3.6 OM degradation rate The OM degradation rate was highest in Germany, followed by Sweden, Spain, and USA (Table 1, Fig. 8 b). German streams showed almost twice the degradation rate compared to the USA. The degradation rates among LULC classes differed, with higher values in agricultural streams, followed by forested streams, and urban streams. A positive correlation of OM degradation rate was found with UV E2:E3, indicating that lower relative molecular weight was weakly positively correlated with OM degradation (ρ = 0.48, R² = 0.49;), this trend prevailed for all countries (Fig. 8 a). When qualitatively comparing OM degradation rates with Godwin-Cotner ratio normalised stoichiometry of DOC:DIN:SRP, degradation rates were higher under conditions of no limitations and under conditions of only single nutrient limitations. Trends of lower degradation rates were observed under colimitation of DIN and SRP (Fig. 8 b). 4. Discussion 4.1 Impacts of LULC on water quality, DOM composition and GHGs In our study land use had strong impacts on water quality parameters, especially pH, temperature, electrical conductivity, as well DIN concentrations, while DOC concentration was not influenced by LULC classes. This differs from some previous studies showing that DOC concentration was affected by LULC classes in individual streams and across stream networks (Gücker et al., 2016; Giling et al. 2014). In our study, the diverse nature of geographical contexts likely masks effects of LULC classes on DOC concentration as we hypothesized. Likewise, DOM indicators in our study were only partly responsive to differences in land use. SUVA 254 suggested less CDOM in urban streams and more CDOM in forested catchments. The occurrence of CDOM is strongly driven by the input of terrestrial derived OM (Helms et al., 2014; Hernes et al., 2013, 2009). Headwater streams are usually more affected by terrestrial inputs, visible through a stronger CDOM signal compared to lower reaches (Maurischat et al., 2022; Mosher et al., 2015). We also observed higher values of Freshness and F indices in urban and agricultural streams compared to forested study sites. Both indices suggest more recently produced organic matter of autochthonous microbial origin (Gabor et al., 2014), in the case of agricultural sites this can also be strongly related to fertiliser-influenced runoff (Moni and Hayes, 2026). Higher UV spectral slopes in urban catchments are indicative of a lower molecular weight of DOM, which likely results from phototransformation of CDOM in exposed urban streams (Helms et al., 2008) and reduced inputs of terrestrial derived material. Parr et al. (2015) found that urban catchments have less humic OM and, instead, more microbially produced FDOM. Our data for UV spectral slopes corroborate this finding and suggest a decline in allochthonous CDOM and a simultaneous increase in autochthonous FDOM. This was likely driven by an increase in ecological disturbance and a decrease in the morphological interaction between streams and river banks in both agricultural and urban systems. Riparian interaction is hampered in many urban and agricultural catchments, where streams are more strongly regulated and channelized (Booth et al., 2016). This leads to a disconnection of the riparian zone, affecting water quality and DOM composition (Pisani et al., 2020; Reidy et al., 2025). In this study GHG concentrations (CO 2 -C and CH 4 -C) did not differ between LULC classes. However, GHG fluxes from headwaters are highly variable across time and space, dependent on pH, groundwater exchange and water temperature (Lauerwald et al., 2013; Raymond et al., 2013). This was likely the reason why we were unable to connect GHG concentrations and LULC classes for this large spatial scope, as was also pointed out by Vidon et al. (2018). In summary, our results show that land use classes most strongly affect physiochemical water conditions and DIN concentrations. LULC classes explained a compositional shift in DOM from terrestrially derived, CDOM-rich forest streams to more microbially derived DOM in urban and agricultural catchments, while DOC concentrations, CO 2 -C, and CH 4 -C did not show a systematic LULC response. 4.2 Chemogeography superimposes LULC driven DOM composition Our investigation showed that agricultural and urban systems drove changes in DOM composition, particularly on the level of land use within a certain study region. However, chemogeographic biogeochemical conditions exerted a strong control on DOM composition in our study, along with local biotic and abiotic controls as well as changing environmental conditions fueled by anthropogenic activities. For example, in boreal and temperate ecosystems, environmental change in coniferous forests and peatlands leads to freshwater browning (Kaal et al., 2022). Visible by an increase in DOC concentration and simultaneous decrease in pH with a darker coloration of water, commonly associated with an increase of CDOM (Blanchet et al., 2022; Brüsecke et al., 2023). In our study, Swedish headwaters had the highest A254 readings, the highest contribution of PARAFAC resolved FDOM components, as well as highest the HIX and highest DOC concentrations. In contrast, the freshness index (indicative of microbial produced FDOM) was lowest in Sweden. These results mark the high abundance of terrestrial derived DOM in Swedish headwaters, which are a leading effect of freshwater browning and can assert positive feedbacks on GHG emissions visible by highest CO 2 -C and CH 4 -C concentrations in Swedish stream water (Kritzberg et al., 2020). The described DOM characteristics are strongly related to the boreal ecosystems in Sweden with acid-rich litter from coniferous forests and forested peatlands connected to humid climate (Weyhenmeyer et al., 2016). The lower pH in investigated Swedish streams supports the origin of DOM from acid-rich environments. The influence of freshwater browning, overruled differences within LULC classes, this demonstrates the effect of site-specific biogeochemical ecosystem processes. The majority of German streams fail to meet a good ecological status related to the EU Water Framework Directive due to a wide range of detrimental influences by agricultural activities, urban inputs, and industry (Schürings et al., 2024; Arle et al., 2016). In our study, German headwaters exhibited the highest concentrations of NO 3 -N and SRP regardless of the LULC class. The observed DOM composition reflects high CDOM contributions (SUVA 254 ) but lower relative molecular weight of DOM and low HIX in FDOM. The high CDOM was likely driven by terrestrial inputs from intensive human activities via run-off from soils or agricultural drainages (Boardman and Vandaele, 2023; Rotenhagen et al., 2025; Rotenhagen et al., 2026). The lower relative molecular weight of DOM and HIX were likely connected to the ubiquitous nutrient pollution, driving a higher bioavailability of DOM (Graeber et al., 2012; Wilson and Xenopoulos, 2009). One agricultural catchment of Germany was strongly influenced by drained peatlands (11 KoSDE with 45% hydromorphic soils, Tab.S1). Here, DOM had an even higher CDOM contribution and high HIX index. This corresponds to other studies showing influences of peat degradation on DOM composition of streams, with a strong humification of OM originating from effluents of degraded peatlands (Charamba et al., 2024; Yates et al., 2023; Kalbitz and Geyer, 2002). These examples show the response of DOM composition to ecosystem properties beyond influences of LULC classes. Headwater streams in the US have reported high concentrations of DOC (Biddanda and Cotner, 2002; Larson et al., 2020), which our findings corroborate. DOM in our investigated US headwater streams was low in CDOM. Our results align with those of a previous study where low aromaticity of Midwestern streams was determined (Vidon et al., 2008). We also observed elevated microbial-borne FDOM (high Freshness and F indices) in these streams, pointing towards a dominance of autochthonous production of DOM. This can be explained by a low inputs from riparian vegetation and stable organic-mineral complexes in surrounding soils due to high pH and sufficient occurrence of polyvalent base cations. In the water column, an increase in algae growth due to high dissolved nutrient concentrations can boost the production of autochthonous DOM, as suggested for agricultural streams of Illinois and Indiana (Royer and David, 2005; Tan et al. 2016) and corroborated by our results. In our NMDS analysis Spanish headwater streams formed a larger cluster with samples from US and German headwaters. However, our data show that DOM of Spanish streams has a specific composition, characterised by medium to high CDOM (SUVA 254 ) indicating enrichment of terrestrial-borne DOM but low DOC concentration. This is likely related to the shallow quarzitic soils with low OC content, typical for Mediterranean mountainous catchments (Ortega et al., 2016). Spanish DOM also exhibited the highest relative molecular weight (E2:E3), indicating terrestrial input from leaf litter as an important source (Wondzell and Ward, 2022). Tree litter input can affect DOM composition during drought conditions, which were observed during sampling, and is reported to drive a chromophore-rich DOM signature, as shown for the Llobregat River basin of north-east Spain (Casas-Ruiz et al., 2016; Marin-Garcia and Tauler, 2020). In addition to a strong terrestrial DOM signal, Spanish streams had the lowest electrical conductivity values and low concentrations of NO 3 -N, regardless of LULC classes. Previous studies found a strong hydrological control of DOC concentration and DOM composition in the Mediterranean (Acuña et al., 2007; Casas-Ruiz et al., 2017, 2016; Ylla et al., 2010). During summer drought conditions, readily available N sources are quickly removed from the water column by microbial activity, which can explain the observed low concentrations of NO 3 –N, as this can force heterotrophs to use less favourable substrates, such as complexer hydrocarbons (Ylla et al., 2010). Overall, when examined country by country, our results suggest that ecosystem properties resulting from various geographical factors in the northern hemisphere mask the effects of country-level LULC classes. Ultimately, geodiversity leads to chemogeographic signatures and determines the composition of DOM. 4.3 OM degradation rate under the influence of element stoichiometry and chemodiversity Our data indicate a trend of increased OM degradation rates in agricultural and forested catchments compared to urban headwaters. Total DIN concentrations in our investigated urban systems were lowest. This may explain the decreased OM degradation rates here, as N availability is a major control for OM decomposition (Imberger et al., 2010). Our results differ from other studies where higher NH 4 -N loads in urban streams, which could boost OM degradation were reported (Paul et al., 2006; Tiegs et al., 2013). The lower concentration of dissolved oxygen at our urban stream study sites also indicates hampered conditions for aerobic decomposition (Liu et al., 2022; Tonin and Hepp, 2011). Significant differences in OM degradation were observed at the country level, with the highest OM degradation rates in Germany followed by Sweden, Spain, and USA. German streams showed only a few occasions of DIN:SRP colimitation, but DOC limitation in some cases. Following the macronutrient-access hypothesis, this indicates an increased demand for carbon in these streams (Graeber et al., 2021), driving the degradation rate of cellulose cotton strips and leading to higher OM degradation rates in German streams. The Swedish headwaters were low in both DIN and SRP but had high DOC concentrations that can explain the moderate OM degradation rates we observed. Linked to this, the reported effects of freshwater browning in Sweden are often accompanied by nutrient increases and phytoplankton growth (Strandberg et al., 2023): this can boost OM degradation rates in stream systems and may explain these results in Sweden. The US and Spanish headwater streams were both co-limited in DIN and SRP, which can explain the low OM degradation rates found in our study. Cotton strips are a pure carbohydrate and are potentially persistent under DIN:SRP colimitation as was shown for different C:N ratios of leaf-litter in temperate freshwater streams (Cereghetti et al., 2025). This emphasises the importance of nutrient stoichiometry as a mechanistic control for in-stream decomposition processes (Graeber et al., 2021; Tiegs et al., 2019) and can serve as an explanation for the increased degradation of OM in nutrient polluted headwaters, such as German streams (Bieroza et al., 2024) and lower rates in less intensively farmed areas, such as the Spanish study sites. Besides the stoichiometry of nutrients, both the US and Spanish catchments are under conditions of hot summers, as our climatic data suggest. This can constrain primary productivity during summer droughts, by a build-up of heat-induced leaf litter fall with high C:N ratio (Acuña et al., 2007), and low available oxygen in the water column (Vazquez et al., 2011). In connection with this, our data indicate that the degradation rates of OM were weakly positively correlated with the UV E2:E3 ratio, which inversely represents the relative molecular weight of DOM (Peuravuori and Pihlaja, 1997). While lower molecular weight components are generally recognised as substrates for microbial activity (Xu and Guo, 2018), these compounds can also be generated from photooxidation of larger compounds (Lou and Xie, 2006). Photooxidation is known to stress decomposer communities (Sturt et al., 2011) and this might hamper the decomposition of cotton strips, therefore being relevant for observations of low OM degradation rates (Ashberry et al., 2021). OM degradation was generally higher in agricultural and forested sites compared to urban headwaters. This indicates that nutrient stoichiometry, especially DIN and SRP availability, oxygen conditions, climatic conditions, riparian disconnection, and photochemical processing control decomposition pathways throughout our broad spatial survey. Only to some extent can these variables be pinned on LULC classes, stressing the importance of geographic factors and chemodiversity. 5. Conclusion Our results demonstrate that for our case study in the northern hemisphere, headwater streams show a diverse DOM composition that can be explained by chemogeographic ecosystem properties. While we did find evidence of strong ordination connected to LULC classes at the country scale, the effects of urban and agricultural land use were strongest for physio-chemical water conditions and nutrient concentrations, underlining that elemental nutrient ratio imbalances, induced by land use changes overrule chemogeographic baselines, making nutrient ratio imbalances a powerful indicator for human-modification. However, these changes translated only to a lesser degree in SOM composition and were mediated by differences in the interaction of the riparian zone. Our study indicates that chemogeographic baselines of DOM composition compared over a large geographic distance prevail despite LULC class changes. Our results underline that the interplay between LULC classes and environmental chemogeographic signatures govern headwater stream biogeochemistry and are decisive for DOM chemodiversity. Overall, our CDE study shows that human impacts on the composition of DOM do not remain consistent over time and do not occur in the same way across all locations, this suggest that DOM composition is not always a powerful indicator for human disturbance. This is essential to understanding how ecosystems cope with climatic change and environmental stress, highlighting the importance of local geographic conditions and natural variation in shaping ecological responses. Efforts to predict and mitigate the consequences of climatic perturbations and land use change along fluvial systems will need to integrate both global-scale drivers and local-scale abiotic and biotic variability. Declarations Funding Declaration Magdalena Bieroza, the course organizer, would like to acknowledge the funding from Graduate School Focus on Soils and Water. No further funding was received for this study. Author Contribution The main author (PM) appears first and the project lead (MZB) is mentioned last. All other authors appear in alphatbetical order.PM: Additional analysis, methodology, writing – editingSEA:Additional analysis, writing – editingACS: Additional analysis, writing – editingLH: Additional analysis, writing – editingCJ: Additional analysis, writingANP: Additional analysis, writing – editingEDS: Additional analysis, writing – editingEMTC: Additional analysis, writing – editingAESV: Additional analysis, writing – editingMW: Additional analysis, methodology, writing – editingAZ: Additional analysis, writing – editingMZB: Design of experiment, additional analysis, methodology, writing – editing Acknowledgement This paper is a joint effort from Early Career Researchers enrolled in a doctoral course "Aquatic systems through the lens of organic matter stability and fate – a coordinated distributed experiment" held at Swedish University of Agricultural Sciences May-October 2022. We would like to express our sincere gratitude to the teachers during the course: Kevin Bishop, Stefan Bertilsson, Claudia von Brömssen, Chuxian Li, Maliheh Mehrshad, Mike Peacock, Anna Szekely, and Marcus Wallin for their dedicated teaching and guidance. Their expertise, time, and commitment provided an invaluable foundation for the work presented in this paper. We would like to thank Xavier Peñarroya for submitting data and giving advice with the writing of the manuscript. We greatly appreciate the work of Nina Schulze and Rhodelyn Saban who assisted with the field work. Data Availability Data generated and analyzed during this study are available from the corresponding author upon reasonable request. References Acuña, V., Giorgi, A., Muñoz, I., Sabater, F., Sabater, S., 2007. Meteorological and riparian influences on organic matter dynamics in a forested Mediterranean stream. Journal of the North American Benthological Society 26, 54–69. https://doi.org/10.1899/0887-3593(2007)26%255B54:MARIOO%255D2.0.CO;2 Anthony, S.E., Prahl, F.G., Peterson, T.D., 2012. Methane dynamics in the Willamette River, Oregon. Limnology & Oceanography 57, 1517–1530. https://doi.org/10.4319/lo.2012.57.5.1517 Arle, J., Mohaupt, V., Kirst, I., 2016. Monitoring of Surface Waters in Germany under the Water Framework Directive—A Review of Approaches, Methods and Results. Water 8, 217. https://doi.org/10.3390/w8060217 Ashberry, E.L., Rier, S.T., Halvorson, H.M., Kuehn, K.A., 2021. Algal-driven priming of cellulose decomposition along a phosphorus gradient in stream mesocosms. Freshwater Science 40, 580–592. https://doi.org/10.1086/717127 Begum, M.S., Park, J.-H., Yang, L., Shin, K.H., Hur, J., 2023. Optical and molecular indices of dissolved organic matter for estimating biodegradability and resulting carbon dioxide production in inland waters: A review. Water Res 228, 119362. https://doi.org/10.1016/j.watres.2022.119362 Berggren, M., Guillemette, F., Bieroza, M., Buffam, I., Deininger, A., Hawkes, J.A., Kothawala, D.N., LaBrie, R., Lapierre, J.-F., Murphy, K.R., Al-Kharusi, E.S., Rulli, M.P.D., Hensgens, G., Younes, H., Wünsch, U.J., 2022. Unified understanding of intrinsic and extrinsic controls of dissolved organic carbon reactivity in aquatic ecosystems. Ecology 103, e3763. https://doi.org/10.1002/ecy.3763 Biddanda, B.A., Cotner, J.B., 2002. Love Handles in Aquatic Ecosystems: The Role of Dissolved Organic Carbon Drawdown, Resuspended Sediments, and Terrigenous Inputs in the Carbon Balance of Lake Michigan. Ecosystems 5, 431–445. https://doi.org/10.1007/s10021-002-0163-z Bieroza, M., Hallberg, L., Livsey, J., Prischl, L.-A., Wynants, M., 2024. Recognizing Agricultural Headwaters as Critical Ecosystems. Environ. Sci. Technol. 58, 4852–4858. https://doi.org/10.1021/acs.est.3c10165 Blanchet, C.C., Arzel, C., Davranche, A., Kahilainen, K.K., Secondi, J., Taipale, S., Lindberg, H., Loehr, J., Manninen-Johansen, S., Sundell, J., Maanan, M., Nummi, P., 2022. Ecology and extent of freshwater browning - What we know and what should be studied next in the context of global change. Science of The Total Environment 812, 152420. https://doi.org/10.1016/j.scitotenv.2021.152420 Boardman, J., Vandaele, K., 2023. Soil erosion and runoff: The need to rethink mitigation strategies for sustainable agricultural landscapes in western Europe. Soil Use and Management 39, 673–685. https://doi.org/10.1111/sum.12898 Booth, D.B., Roy, A.H., Smith, B., Capps, K.A., 2016. Global perspectives on the urban stream syndrome. Freshwater Science 35, 412–420. https://doi.org/10.1086/684940 Brüsecke, J., Muotka, T., Huttunen, K.-L., Litjo, S., Lepo, W.-P., Jyväsjärvi, J., 2023. Drainage-induced browning causes both loss and change of benthic biodiversity in headwater streams. Limnology and Oceanography Letters 8, 620–627. https://doi.org/10.1002/lol2.10302 Caillon, F., Schelker, J., 2020. Dynamic transfer of soil bacteria and dissolved organic carbon into small streams during hydrological events. Aquat Sci 82, 964. https://doi.org/10.1007/s00027-020-0714-4 Casas-Ruiz, J.P., Catalán, N., Gómez-Gener, L., von Schiller, D., Obrador, B., Kothawala, D.N., López, P., Sabater, S., Marcé, R., 2017. A tale of pipes and reactors: Controls on the in-stream dynamics of dissolved organic matter in rivers. Limnology and Oceanography 62, S85–S94. https://doi.org/10.1002/lno.10471 Casas-Ruiz, J.P., Tittel, J., von Schiller, D., Catalán, N., Obrador, B., Gómez-Gener, L., Zwirnmann, E., Sabater, S., Marcé, R., 2016. Drought-induced discontinuities in the source and degradation of dissolved organic matter in a Mediterranean river. Biogeochemistry 127, 125–139. https://doi.org/10.1007/s10533-015-0173-5 Catalán, N., Casas-Ruiz, J.P., Arce, M.I., Abril, M., Bravo, A.G., Campo, R. del, Estévez, E., Freixa, A., Giménez-Grau, P., González-Ferreras, A.M., Gómez-Gener, L., Lupon, A., Martínez, A., Palacin-Lizarbe, C., Poblador, S., Rasines-Ladero, R., Reyes, M., Rodríguez-Castillo, T., Rodríguez-Lozano, P., Sanpera-Calbet, I., Tornero, I., Pastor, A., 2018. Behind the Scenes: Mechanisms Regulating Climatic Patterns of Dissolved Organic Carbon Uptake in Headwater Streams. Global Biogeochemical Cycles 32, 1528–1541. https://doi.org/10.1029/2018GB005919 Cereghetti, E., Bossart, R., Bruder, A., Krähenbühl, A., Wolf, F., Altermatt, F., 2025. The year of a leaf: Tracking the fate of leaf litter and its nutrients during aquatic decomposition and consumption. Ecology 106, e4520. https://doi.org/10.1002/ecy.4520 Charamba, L.V.C., Houska, T., Kaiser, K., Knorr, K.-H., Krüger, S., Krause, T., Chen, H., Krám, P., Hruška, J., Kalbitz, K., 2024. Tracing sources of dissolved organic matter along the terrestrial-aquatic continuum in the Ore Mountains, Germany. Science of The Total Environment 173807. https://doi.org/10.1016/j.scitotenv.2024.173807 Coble, P.G., 2007. Marine Optical Biogeochemistry: The Chemistry of Ocean Color. Chem. Rev. 107, 402–418. https://doi.org/10.1021/cr050350+ Coch, C., Juhls, B., Lamoureux, S.F., Lafrenière, M.J., Fritz, M., Heim, B., Lantuit, H., 2019. Comparisons of dissolved organic matter and its optical characteristics in small low and high Arctic catchments. Biogeosciences 16, 4535–4553. https://doi.org/10.5194/bg-16-4535-2019 Cole, J.J., Caraco, N.F., 2001. Carbon in catchments: connecting terrestrial carbon losses with aquatic metabolism. Mar. Freshwater Res. 52, 101. https://doi.org/10.1071/MF00084 Dewitz, J., 2024. National Land Cover Database (NLCD) 2019 Products (ver. 3.0, February 2024). https://doi.org/10.5066/P9KZCM54 Dittmar, T., Lennartz, S.T., Buck-Wiese, H., Hansell, D.A., Santinelli, C., Vanni, C., Blasius, B., Hehemann, J.-H., 2021. Enigmatic persistence of dissolved organic matter in the ocean. Nat Rev Earth Environ 2, 570–583. https://doi.org/10.1038/s43017-021-00183-7 Ebeling, P., Kumar, R., Weber, M., Knoll, L., Fleckenstein, J.H., Musolff, A., 2021. Archetypes and Controls of Riverine Nutrient Export Across German Catchments. Water Resources Research 57, e2020WR028134. https://doi.org/10.1029/2020WR028134 EEA, 2018. European Environment Agency under the framework of the Copernicus programme - [email protected] . Ferreira, V., Castagneyrol, B., Koricheva, J., Gulis, V., Chauvet, E., Graça, M.A.S., 2015. A meta-analysis of the effects of nutrient enrichment on litter decomposition in streams. Biological Reviews 90, 669–688. https://doi.org/10.1111/brv.12125 Fovet, O., Cooper, D.M., Jones, D.L., Jones, T.G., Evans, C.D., 2020. Dynamics of dissolved organic matter in headwaters: comparison of headwater streams with contrasting DOM and nutrient composition. Aquat Sci 82, 29. https://doi.org/10.1007/s00027-020-0704-6 Fraser, L.H., Henry, H.A., Carlyle, C.N., White, S.R., Beierkuhnlein, C., Cahill, J.F., Casper, B.B., Cleland, E., Collins, S.L., Dukes, J.S., Knapp, A.K., Lind, E., Long, R., Luo, Y., Reich, P.B., Smith, M.D., Sternberg, M., Turkington, R., 2013. Coordinated distributed experiments: an emerging tool for testing global hypotheses in ecology and environmental science. Frontiers in Ecol & Environ 11, 147–155. https://doi.org/10.1890/110279 Fuß, T., Thuile Bistarelli, L., Walther, F., Vitecek, S., Talluto, L., Singer, G., 2024. Geodiversity of a European river network controls algal biodiversity and function. Commun Earth Environ 5, 1–13. https://doi.org/10.1038/s43247-024-01478-5 Gabor, R.S., Baker, A., McKnight, D.M., Miller, M.P., 2014. Fluorescence Indices and Their Interpretation, in: Coble, P.G., Lead, J., Baker, A., Reynolds, D.M., Spencer, R.G.M. (Eds.), Aquatic Organic Matter Fluorescence. Cambridge University Press, pp. 303–338. https://doi.org/10.1017/CBO9781139045452.015 Gerhard, M., Koussoroplis, A.-M., Raatz, M., Pansch, C., Fey, S.B., Vajedsamiei, J., Calderó-Pascual, M., Cunillera-Montcusí, D., Juvigny-Khenafou, N.P.D., Polazzo, F., Thomas, P.K., Symons, C.C., Beklioğlu, M., Berger, S.A., Chefaoui, R.M., Ger, K.A., Langenheder, S., Nejstgaard, J.C., Ptacnik, R., Striebel, M., 2023. Environmental variability in aquatic ecosystems: Avenues for future multifactorial experiments. Limnology and Oceanography Letters 8, 247–266. https://doi.org/10.1002/lol2.10286 Giling, D.P., Grace, M.R., Thomson, J.R., Mac Nally, R., Thompson, R.M., 2014. Effect of Native Vegetation Loss on Stream Ecosystem Processes: Dissolved Organic Matter Composition and Export in Agricultural Landscapes. Ecosystems 17, 82–95. https://doi.org/10.1007/s10021-013-9708-6 Godwin, C.M., Cotner, J.B., 2018. What intrinsic and extrinsic factors explain the stoichiometric diversity of aquatic heterotrophic bacteria? The ISME Journal 12, 598–609. https://doi.org/10.1038/ismej.2017.195 Graeber, D., Boëchat, I.G., Encina-Montoya, F., Esse, C., Gelbrecht, J., Goyenola, G., Gücker, B., Heinz, M., Kronvang, B., Meerhoff, M., Nimptsch, J., Pusch, M.T., Silva, R.C.S., Von Schiller, D., Zwirnmann, E., 2015. Global effects of agriculture on fluvial dissolved organic matter. Sci Rep 5, 16328. https://doi.org/10.1038/srep16328 Graeber, D., Gelbrecht, J., Pusch, M.T., Anlanger, C., von Schiller, D., 2012. Agriculture has changed the amount and composition of dissolved organic matter in Central European headwater streams. Science of The Total Environment 438, 435–446. https://doi.org/10.1016/j.scitotenv.2012.08.087 Graeber, D., Tenzin, Y., Stutter, M., Weigelhofer, G., Shatwell, T., Von Tümpling, W., Tittel, J., Wachholz, A., Borchardt, D., 2021. Bioavailable DOC: reactive nutrient ratios control heterotrophic nutrient assimilation—An experimental proof of the macronutrient-access hypothesis. Biogeochemistry 155, 1–20. https://doi.org/10.1007/s10533-021-00809-4 Gücker, B., Silva, R.C.S., Graeber, D., Monteiro, J.A.F., Boëchat, I.G., 2016. Urbanization and agriculture increase exports and differentially alter elemental stoichiometry of dissolved organic matter (DOM) from tropical catchments. Science of The Total Environment 550, 785–792. https://doi.org/10.1016/j.scitotenv.2016.01.158 Halberg, H.F.F., Bevilacqua, M., Rinnan, Å., 2023. Is core consistency a too conservative diagnostic? Journal of Chemometrics 37, e3483. https://doi.org/10.1002/cem.3483 Harmonized World Soil Database, 2023. HWSD version 2.0. FAO; International Institute for Applied Systems Analysis (IIASA); https://doi.org/10.4060/cc3823en Helms, J.R., Stubbins, A., Ritchie, J.D., Minor, E.C., Kieber, D.J., Mopper, K., 2008. Absorption spectral slopes and slope ratios as indicators of molecular weight, source, and photobleaching of chromophoric dissolved organic matter. Limnology and Oceanography 53, 955–969. https://doi.org/10.4319/lo.2008.53.3.0955 Hernes, P.J., Bergamaschi, B.A., Eckard, R.S., Spencer, R.G.M., 2009. Fluorescence-based proxies for lignin in freshwater dissolved organic matter. Journal of Geophysical Research: Biogeosciences 114. https://doi.org/10.1029/2009JG000938 Hernes, P.J., Spencer, R.G.M., Dyda, R.Y., Pellerin, B.A., Bachand, P.A.M., Bergamaschi, B.A., 2013. DOM composition in an agricultural watershed: Assessing patterns and variability in the context of spatial scales. Geochimica et Cosmochimica Acta 121, 599–610. https://doi.org/10.1016/j.gca.2013.07.039 Herreid, A.M., Wymore, A.S., Varner, R.K., Potter, J.D., McDowell, W.H., 2021. Divergent Controls on Stream Greenhouse Gas Concentrations Across a Land-Use Gradient. Ecosystems 24, 1299–1316. https://doi.org/10.1007/s10021-020-00584-7 Herzsprung, P., Osterloh, K., Tümpling, W., Harir, M., Hertkorn, N., Schmitt-Kopplin, P., Meissner, R., Bernsdorf, S., Friese, K., 2017. Differences in DOM of rewetted and natural peatlands - Results from high-field FT-ICR-MS and bulk optical parameters. The Science of the total environment 586, 770–781. https://doi.org/10.1016/j.scitotenv.2017.02.054 Hope, D., Palmer, S.M., Billett, M.F., Dawson, J.J.C., 2004. Variations in dissolved CO2 and CH4 in a first-order stream and catchment: an investigation of soil–stream linkages. Hydrological Processes 18, 3255–3275. https://doi.org/10.1002/hyp.5657 Hotchkiss, E.R., Hall Jr, R.O., Sponseller, R.A., Butman, D., Klaminder, J., Laudon, H., Rosvall, M., Karlsson, J., 2015. Sources of and processes controlling CO2 emissions change with the size of streams and rivers. Nature Geosci 8, 696–699. https://doi.org/10.1038/ngeo2507 Hu, A., Stegen, J., Tanentzap, A.J., Wang, J., 2025. The Emergence and Promise of Functional Chemogeography of Organic Matter. Global Change Biology 31, e70435. https://doi.org/10.1111/gcb.70435 Imberger, S.J., Thompson, R.M., Grace, M.R., 2010. Searching for effective indicators of ecosystem function in urban streams: assessing cellulose decomposition potential. Freshwater Biology 55, 2089–2106. https://doi.org/10.1111/j.1365-2427.2010.02469.x Jarvie, H.P., Smith, D.R., Norton, L.R., Edwards, F.K., Bowes, M.J., King, S.M., Scarlett, P., Davies, S., Dils, R.M., Bachiller-Jareno, N., 2018. Phosphorus and nitrogen limitation and impairment of headwater streams relative to rivers in Great Britain: A national perspective on eutrophication. Science of The Total Environment 621, 849–862. https://doi.org/10.1016/j.scitotenv.2017.11.128 Jongman, R.H.G., 2002. Homogenisation and fragmentation of the European landscape: ecological consequences and solutions. Landscape and Urban Planning, Fragmentation and Land Use Planning: Analysis and beyond? 58, 211–221. https://doi.org/10.1016/S0169-2046(01)00222-5 Kaal, J., Pérez-Rodríguez, M., Biester, H., 2022. Molecular Probing of DOM Indicates a Key Role of Spruce-Derived Lignin in the DOM and Metal Cycles of a Headwater Catchment: Can Spruce Forest Dieback Exacerbate Future Trends in the Browning of Central European Surface Waters? Environ. Sci. Technol. 56, 2747–2759. https://doi.org/10.1021/acs.est.1c04719 Kalbitz, K., Geyer, S., 2002. Different effects of peat degradation on dissolved organic carbon and nitrogen. Organic Geochemistry 33, 319–326. https://doi.org/10.1016/S0146-6380(01)00163-2 Kalbitz, K., Solinger, S., Park, J.-H., Michalzik, B., Matzner, E., 2000. CONTROLS ON THE DYNAMICS OF DISSOLVED ORGANIC MATTER IN SOILS: A REVIEW. Soil Science 165, 277–304. https://doi.org/10.1097/00010694-200004000-00001 Kaplan, L.A., Cory, R.M., 2016. Dissolved Organic Matter in Stream Ecosystems, in: Stream Ecosystems in a Changing Environment. Elsevier, pp. 241–320. https://doi.org/10.1016/B978-0-12-405890-3.00006-3 Kawahigashi, M., Kaiser, K., Kalbitz, K., Rodionov, A., Guggenberger, G., 2004. Dissolved organic matter in small streams along a gradient from discontinuous to continuous permafrost. Global Change Biol 10, 1576–1586. https://doi.org/10.1111/j.1365-2486.2004.00827.x Kellerman, A.M., Dittmar, T., Kothawala, D.N., Tranvik, L.J., 2014. Chemodiversity of dissolved organic matter in lakes driven by climate and hydrology. Nat Commun 5, 3804. https://doi.org/10.1038/ncomms4804 Kothawala, D.N., Ji, X., Laudon, H., Ågren, A.M., Futter, M.N., Köhler, S.J., Tranvik, L.J., 2015. The relative influence of land cover, hydrology, and in-stream processing on the composition of dissolved organic matter in boreal streams. J. Geophys. Res. Biogeosci. 120, 1491–1505. https://doi.org/10.1002/2015JG002946 Kothawala, D.N., Kellerman, A.M., Catalán, N., Tranvik, L.J., 2021. Organic Matter Degradation across Ecosystem Boundaries: The Need for a Unified Conceptualization. Trends in ecology & evolution 36, 113–122. https://doi.org/10.1016/j.tree.2020.10.006 Kritzberg, E.S., Hasselquist, E.M., Škerlep, M., Löfgren, S., Olsson, O., Stadmark, J., Valinia, S., Hansson, L.-A., Laudon, H., 2020. Browning of freshwaters: Consequences to ecosystem services, underlying drivers, and potential mitigation measures. AMBIO: A Journal of the Human Environment 49, 375–390. https://doi.org/10.1007/s13280-019-01227-5 Larson, J.H., James, W.F., Fitzpatrick, F.A., Frost, P.C., Evans, M.A., Reneau, P.C., Xenopoulos, M.A., 2020. Phosphorus, nitrogen and dissolved organic carbon fluxes from sediments in freshwater rivermouths entering Green Bay (Lake Michigan; USA). Biogeochemistry 147, 179–197. https://doi.org/10.1007/s10533-020-00635-0 Laudon, H., Berggren, M., Ågren, A., Buffam, I., Bishop, K., Grabs, T., Jansson, M., Köhler, S., 2011. Patterns and Dynamics of Dissolved Organic Carbon (DOC) in Boreal Streams: The Role of Processes, Connectivity, and Scaling. Ecosystems 14, 880–893. https://doi.org/10.1007/s10021-011-9452-8 Lauerwald, R., Hartmann, J., Moosdorf, N., Kempe, S., Raymond, P.A., 2013. What controls the spatial patterns of the riverine carbonate system? — A case study for North America. Chemical Geology 337–338, 114–127. https://doi.org/10.1016/j.chemgeo.2012.11.011 Ledesma, J.L.J., Futter, M.N., Blackburn, M., Lidman, F., Grabs, T., Sponseller, R.A., Laudon, H., Bishop, K.H., Köhler, S.J., 2018. Towards an Improved Conceptualization of Riparian Zones in Boreal Forest Headwaters. Ecosystems 21, 297–315. https://doi.org/10.1007/s10021-017-0149-5 Lee, S.-C., Shin, Y., Jeon, Y.-J., Lee, E.-J., Eom, J.-S., Kim, B., Oh, N.-H., 2021. Optical properties and 14C ages of stream DOM from agricultural and forest watersheds during storms. Environmental Pollution 272, 116412. https://doi.org/10.1016/j.envpol.2020.116412 Liu, S., He, G., Fang, H., Xu, S., Bai, S., 2022. Effects of dissolved oxygen on the decomposers and decomposition of plant litter in lake ecosystem. Journal of Cleaner Production 372, 133837. https://doi.org/10.1016/j.jclepro.2022.133837 Lou, T., Xie, H., 2006. Photochemical alteration of the molecular weight of dissolved organic matter. Chemosphere, Environmental Chemistry 65, 2333–2342. https://doi.org/10.1016/j.chemosphere.2006.05.001 Marín-García, M., Tauler, R., 2020. Chemometrics characterization of The Llobregat river dissolved organic matter. Chemometrics and Intelligent Laboratory Systems 201, 104018. https://doi.org/10.1016/j.chemolab.2020.104018 Maurischat, P., Lehnert, L., Zerres, V.H.D., Tran, T.V., Kalbitz, K., Rinnan, Å., Li, X.G., Dorji, T., Guggenberger, G., 2022. The glacial-terrestrial-fluvial pathway: A multiparametrical analysis of spatiotemporal dissolved organic matter variation in three catchments of Lake Nam Co, Tibetan Plateau. The Science of the total environment 156542. https://doi.org/10.1016/j.scitotenv.2022.156542 McKnight, D.M., Boyer, E.W., Westerhoff, P.K., Doran, P.T., Kulbe, T., Andersen, D.T., 2001. Spectrofluorometric characterization of dissolved organic matter for indication of precursor organic material and aromaticity. Limnology and Oceanography 46, 38–48. https://doi.org/10.4319/lo.2001.46.1.0038 Moni, M.E.H., Hayes, M., 2026. Unlocking Soil Health and Surface Water Quality Management: A Review on Fluorescent Dissolved Organic Matter (fDOM) in Agricultural Systems. J. Agric. Food Chem. https://doi.org/10.1021/acs.jafc.5c13863 Mosher, J.J., Kaplan, L.A., Podgorski, D.C., McKenna, A.M., Marshall, A.G., 2015. Longitudinal shifts in dissolved organic matter chemogeography and chemodiversity within headwater streams: a river continuum reprise. Biogeochemistry 124, 371–385. https://doi.org/10.1007/s10533-015-0103-6 Murphy, K.R., Stedmon, C.A., Wenig, P., Bro, R., 2014. OpenFluor– an online spectral library of auto-fluorescence by organic compounds in the environment. Anal. Methods 6, 658–661. https://doi.org/10.1039/C3AY41935E Ogle, D.H., Doll, J.C., Wheeler, A.P., Dinno, A., 2015. FSA: Simple Fisheries Stock Assessment Methods. https://doi.org/10.32614/CRAN.package.FSA Ohno, T., 2002. Fluorescence Inner-Filtering Correction for Determining the Humification Index of Dissolved Organic Matter. Environ. Sci. Technol. 36, 742–746. https://doi.org/10.1021/es0155276 Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Peter R. Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Henry, M., Stevens, H., Szoecs, E., Wagner, H., 2020. vegan: Community Ecology Package. Orlova, J., Amiri, F., Bourgeois, A.K., Buttle, J.M., Cherlet, E., Cuss, C.W., Devito, K.J., Emelko, M.B., Floyd, W.C., Foster, D.E., Hutchins, R.H.S., Jamieson, R., Johnson, M.S., McSorley, H.J., Silins, U., Tank, S.E., Thompson, L.M., Webster, K.L., Williams, C.H.S., Olefeldt, D., 2024. Composition of Stream Dissolved Organic Matter Across Canadian Forested Ecozones Varies in Three Dimensions Linked to Landscape and Climate. Water Resources Research 60, e2023WR035196. https://doi.org/10.1029/2023WR035196 Ortega, E., Lozano, F.J., Martínez, F.J., Bienes, R., Gallardo, J.F., Asensio, C., 2016. Soils of the Mediterranean Areas, in: Gallardo, J.F. (Ed.), The Soils of Spain. Springer International Publishing, Cham, pp. 163–187. https://doi.org/10.1007/978-3-319-20541-0_5 Ostertagova, E., Ostertag, O., Kováč, J., 2014. Methodology and Application of the Kruskal-Wallis Test. Applied Mechanics and Materials 611, 115–120. Parr, T.B., Cronan, C.S., Ohno, T., Findlay, S.E.G., Smith, S.M.C., Simon, K.S., 2015. Urbanization changes the composition and bioavailability of dissolved organic matter in headwater streams. Limnology and Oceanography 60, 885–900. https://doi.org/10.1002/lno.10060 Paul, M.J., Meyer, J.L., Couch, C.A., 2006. Leaf breakdown in streams differing in catchment land use. Freshwater Biology 51, 1684–1695. https://doi.org/10.1111/j.1365-2427.2006.01612.x Peuravuori, J., Pihlaja, K., 1997. Molecular size distribution and spectroscopic properties of aquatic humic substances. Analytica Chimica Acta 337, 133–149. https://doi.org/10.1016/S0003-2670(96)00412-6 Pisani, O., Bosch, D.D., Coffin, A.W., Endale, D.M., Liebert, D., Strickland, T.C., 2020. Riparian land cover and hydrology influence stream dissolved organic matter composition in an agricultural watershed. Science of The Total Environment 717, 137165. https://doi.org/10.1016/j.scitotenv.2020.137165 Plamper, P., Lechtenfeld, O.J., Herzsprung, P., Groß, A., 2023. A Temporal Graph Model to Predict Chemical Transformations in Complex Dissolved Organic Matter. Environ. Sci. Technol. 57, 18116–18126. https://doi.org/10.1021/acs.est.3c00351 R Core Team, 2023. R Statistical Software. R Studio Team, 2024. RStudio: Integrated Development for R. RStudio. Raymond, P.A., Hartmann, J., Lauerwald, R., Sobek, S., McDonald, C., Hoover, M., Butman, D., Striegl, R., Mayorga, E., Humborg, C., Kortelainen, P., Dürr, H., Meybeck, M., Ciais, P., Guth, P., 2013. Global carbon dioxide emissions from inland waters. Nature 503, 355–359. https://doi.org/10.1038/nature12760 Raymond, P.A., Oh, N.-H., Turner, R.E., Broussard, W., 2008. Anthropogenically enhanced fluxes of water and carbon from the Mississippi River. Nature 451, 449–452. https://doi.org/10.1038/nature06505 Reidy, M., Berggren, M., Lupon, A., Laudon, H., Sponseller, R.A., 2025. Riparian Zone Heterogeneity Influences the Amount and Fate of Biodegradable Dissolved Organic Carbon at the Land-Water Interface. JGR Biogeosciences 130. https://doi.org/10.1029/2024jg008471 Rotenhagen, A.-L., Giani, L., Maurischat, P., 2025a. Phosphate mobility in soils and P-influxes via ditches and drainages in landscape areas characteristic of the coastal region of Lower Saxony. HyWa. Rotenhagen, A.-L., Massmann, G., Maurischat, P., Giani, L., 2025b. N aus Dränen. in preparation. Roth, V.-N., Dittmar, T., Gaupp, R., Gleixner, G., 2014. Ecosystem-Specific Composition of Dissolved Organic Matter. Vadose Zone Journal 13, 1–10. https://doi.org/10.2136/vzj2013.09.0162 Royer, T.V., David, M.B., 2005. Export of dissolved organic carbon from agricultural streams in Illinois, USA. Aquat. Sci. 67, 465–471. https://doi.org/10.1007/s00027-005-0781-6 Ryan, K.A., Garayburu-Caruso, V.A., Crump, B.C., Bambakidis, T., Raymond, P.A., Liu, S., Stegen, J.C., 2024. Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds. https://doi.org/10.22541/essoar.171164898.83027743/v1 Schürings, C., Globevnik, L., Lemm, J.U., Psomas, A., Snoj, L., Hering, D., Birk, S., 2024. River ecological status is shaped by agricultural land use intensity across Europe. Water Research 251, 121136. https://doi.org/10.1016/j.watres.2024.121136 Sedgwick, P., 2012. Multiple significance tests: the Bonferroni correction. BMJ 344, e509. https://doi.org/10.1136/bmj.e509 Smith, M.R., 2017. Ternary: Create Ternary and Holdridge Plots. https://doi.org/10.32614/CRAN.package.Ternary Strandberg, U., Hiltunen, M., Creed, I.F., Arts, M.T., Kankaala, P., 2023. Browning-induced changes in trophic functioning of planktonic food webs in temperate and boreal lakes: insights from fatty acids. Oecologia 201, 183–197. https://doi.org/10.1007/s00442-022-05301-w Stubbins, A., Spencer, R.G.M., Chen, H., Hatcher, P.G., Mopper, K., Hernes, P.J., Mwamba, V.L., Mangangu, A.M., Wabakanghanzi, J.N., Six, J., 2010. Illuminated darkness: Molecular signatures of Congo River dissolved organic matter and its photochemical alteration as revealed by ultrahigh precision mass spectrometry. Limnology and Oceanography 55, 1467–1477. https://doi.org/10.4319/lo.2010.55.4.1467 Sturt, M.M., Jansen, M. a. K., Harrison, S.S.C., 2011. Invertebrate grazing and riparian shade as controllers of nuisance algae in a eutrophic river. Freshwater Biology 56, 2580–2593. https://doi.org/10.1111/j.1365-2427.2011.02684.x Tan, J., Cherkauer, K.A., Chaubey, I., 2016. Developing a Comprehensive Spectral-Biogeochemical Database of Midwestern Rivers for Water Quality Retrieval Using Remote Sensing Data: A Case Study of the Wabash River and Its Tributary, Indiana. Remote Sensing 8, 517. https://doi.org/10.3390/rs8060517 Tanentzap, A.J., Fonvielle, J.A., 2024. Chemodiversity in freshwater health. Science 383, 1412–1414. https://doi.org/10.1126/science.adg8658 Tiegs, S.D., Clapcott, J.E., Griffiths, N.A., Boulton, A.J., 2013. A standardized cotton-strip assay for measuring organic-matter decomposition in streams. Ecological Indicators 32, 131–139. https://doi.org/10.1016/j.ecolind.2013.03.013 Tiegs, S.D., Costello, D.M., Isken, M.W., Woodward, G., McIntyre, P.B., Gessner, M.O., Chauvet, E., Griffiths, N.A., Flecker, A.S., Acuña, V., Albariño, R., Allen, D.C., Alonso, C., Andino, P., Arango, C., Aroviita, J., Barbosa, M.V.M., Barmuta, L.A., Baxter, C.V., Bell, T.D.C., Bellinger, B., Boyero, L., Brown, L.E., Bruder, A., Bruesewitz, D.A., Burdon, F.J., Callisto, M., Canhoto, C., Capps, K.A., Castillo, M.M., Clapcott, J., Colas, F., Colón-Gaud, C., Cornut, J., Crespo-Pérez, V., Cross, W.F., Culp, J.M., Danger, M., Dangles, O., de Eyto, E., Derry, A.M., Villanueva, V.D., Douglas, M.M., Elosegi, A., Encalada, A.C., Entrekin, S., Espinosa, R., Ethaiya, D., Ferreira, V., Ferriol, C., Flanagan, K.M., Fleituch, T., Follstad Shah, J.J., Frainer, A., Friberg, N., Frost, P.C., Garcia, E.A., García Lago, L., García Soto, P.E., Ghate, S., Giling, D.P., Gilmer, A., Gonçalves, J.F., Gonzales, R.K., Graça, M.A.S., Grace, M., Grossart, H.-P., Guérold, F., Gulis, V., Hepp, L.U., Higgins, S., Hishi, T., Huddart, J., Hudson, J., Imberger, S., Iñiguez-Armijos, C., Iwata, T., Janetski, D.J., Jennings, E., Kirkwood, A.E., Koning, A.A., Kosten, S., Kuehn, K.A., Laudon, H., Leavitt, P.R., Lemes da Silva, A.L., Leroux, S.J., LeRoy, C.J., Lisi, P.J., MacKenzie, R., Marcarelli, A.M., Masese, F.O., McKie, B.G., Oliveira Medeiros, A., Meissner, K., Miliša, M., Mishra, S., Miyake, Y., Moerke, A., Mombrikotb, S., Mooney, R., Moulton, T., Muotka, T., Negishi, J.N., Neres-Lima, V., Nieminen, M.L., Nimptsch, J., Ondruch, J., Paavola, R., Pardo, I., Patrick, C.J., Peeters, E.T.H.M., Pozo, J., Pringle, C., Prussian, A., Quenta, E., Quesada, A., Reid, B., Richardson, J.S., Rigosi, A., Rincón, J., Rîşnoveanu, G., Robinson, C.T., Rodríguez-Gallego, L., Royer, T.V., Rusak, J.A., Santamans, A.C., Selmeczy, G.B., Simiyu, G., Skuja, A., Smykla, J., Sridhar, K.R., Sponseller, R., Stoler, A., Swan, C.M., Szlag, D., Teixeira-de Mello, F., Tonkin, J.D., Uusheimo, S., Veach, A.M., Vilbaste, S., Vought, L.B.M., Wang, C.-P., Webster, J.R., Wilson, P.B., Woelfl, S., Xenopoulos, M.A., Yates, A.G., Yoshimura, C., Yule, C.M., Zhang, Y.X., Zwart, J.A., 2019. Global patterns and drivers of ecosystem functioning in rivers and riparian zones. Science Advances 5, eaav0486. https://doi.org/10.1126/sciadv.aav0486 Tonin, A.M., Hepp, L.U., 2011. Effects of nitrate enrichment on leaf litter decomposition. Acta 23, 86–94. https://doi.org/10.4322/actalb.2011.022 Turner, R.E., Rabalais, N.N., Justic’, D., Dortch, Q., 2003. Global patterns of dissolved N, P and Si in large rivers. Biogeochemistry 64, 297–317. https://doi.org/10.1023/A:1024960007569 van Vliet, J., Magliocca, N.R., Büchner, B., Cook, E., Rey Benayas, J.M., Ellis, E.C., Heinimann, A., Keys, E., Lee, T.M., Liu, J., Mertz, O., Meyfroidt, P., Moritz, M., Poeplau, C., Robinson, B.E., Seppelt, R., Seto, K.C., Verburg, P.H., 2016. Meta-studies in land use science: Current coverage and prospects. Ambio 45, 15–28. https://doi.org/10.1007/s13280-015-0699-8 Vazquez, E., Amalfitano, S., Fazi, S., Butturini, A., 2011. Dissolved organic matter composition in a fragmented Mediterranean fluvial system under severe drought conditions. Biogeochemistry 102, 59–72. https://doi.org/10.1007/s10533-010-9421-x Vidon, P., Wagner, L.E., Soyeux, E., 2008. Changes in the character of DOC in streams during storms in two Midwestern watersheds with contrasting land uses. Biogeochemistry 88, 257–270. https://doi.org/10.1007/s10533-008-9207-6 Vidon, P.G., Welsh, M.K., Hassanzadeh, Y.T., 2019. Twenty Years of Riparian Zone Research (1997–2017): Where to Next? J of Env Quality 48, 248–260. https://doi.org/10.2134/jeq2018.01.0009 Wagner, L.E., Vidon, P., Tedesco, L.P., Gray, M., 2008. Stream nitrate and DOC dynamics during three spring storms across land uses in glaciated landscapes of the Midwest. Journal of Hydrology 362, 177–190. https://doi.org/10.1016/j.jhydrol.2008.08.013 Weishaar, J.L., Aiken, G.R., Bergamaschi, B.A., Fram, M.S., Fujii, R., Mopper, K., 2003. Evaluation of specific ultraviolet absorbance as an indicator of the chemical composition and reactivity of dissolved organic carbon. Environmental science & technology 37, 4702–4708. https://doi.org/10.1021/es030360x Werner, B.J., Musolff, A., Lechtenfeld, O.J., de Rooij, G.H., Oosterwoud, M.R., Fleckenstein, J.H., 2019. High-frequency measurements explain quantity and quality of dissolved organic carbon mobilization in a headwater catchment. Biogeosciences 16, 4497–4516. https://doi.org/10.5194/bg-16-4497-2019 Weyhenmeyer, G.A., Müller, R.A., Norman, M., Tranvik, L.J., 2016. Sensitivity of freshwaters to browning in response to future climate change. Climatic Change 134, 225–239. https://doi.org/10.1007/s10584-015-1514-z Wilson, H.F., Xenopoulos, M.A., 2009. Effects of agricultural land use on the composition of fluvial dissolved organic matter. Nature Geosci 2, 37–41. https://doi.org/10.1038/ngeo391 Wohl, E., 2017. The significance of small streams. Front. Earth Sci. 11, 447–456. https://doi.org/10.1007/s11707-017-0647-y Wondzell, S.M., Ward, A.S., 2022. The Channel Source Hypothesis: Empirical Evidence for In-Channel Sourcing of Dissolved Organic Carbon to Explain Hysteresis in a Headwater Mountain Stream. Hydrol. Process. https://doi.org/10.1002/hyp.14570 Wynants, M., Hallberg, L., Prischl, L.-A., Livsey, J., Bieroza, M., 2025. Trends and purposes of European river monitoring and restoration. Environmental Science & Policy 170, 104130. https://doi.org/10.1016/j.envsci.2025.104130 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. https://doi.org/10.1016/j.watres.2018.02.016 Yahdjian, L., Sala, O.E., PiÑEiro-Guerra, J.M., Knapp, A.K., Collins, S.L., Phillips, R.P., Smith, M.D., 2021. Why Coordinated Distributed Experiments Should Go Global. BioScience 71, 918–927. https://doi.org/10.1093/biosci/biab033 Yates, C.A., Johnes, P.J., Brailsford, F.L., Evans, C.D., Evershed, R.P., Glanville, H.C., Jones, D.L., Lloyd, C.E.M., Marshall, M.R., Owen, A.T., 2023. Determining patterns in the composition of dissolved organic matter in fresh waters according to land use and management. Biogeochemistry 164, 143–162. https://doi.org/10.1007/s10533-022-00964-2 Ylla, I., Sanpera-Calbet, I., Vázquez, E., Romaní, A.M., Muñoz, I., Butturini, A., Sabater, S., 2010. Organic matter availability during pre- and post-drought periods in a Mediterranean stream, in: Stevenson, R.J., Sabater, S. (Eds.), Global Change and River Ecosystems—Implications for Structure, Function and EcosystemServices. Springer Netherlands, Dordrecht, pp. 217–232. https://doi.org/10.1007/978-94-007-0608-8_15 Yu, G., Piao, S., Zhang, Y., Liu, L., Peng, J., Niu, S., 2021. Moving toward a new era of ecosystem science. Geography and Sustainability 2, 151–162. https://doi.org/10.1016/j.geosus.2021.06.004 Zepner, L., Karrasch, P., Wiemann, F., Bernard, L., 2021. ClimateCharts.net – an interactive climate analysis web platform. International Journal of Digital Earth 14, 338–356. https://doi.org/10.1080/17538947.2020.1829112 Additional Declarations No competing interests reported. Supplementary Files 19022026CDEDraftSupplements.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 08 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor invited by journal 13 Apr, 2026 Editor assigned by journal 08 Apr, 2026 Submission checks completed at journal 08 Apr, 2026 First submitted to journal 08 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9351570","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":625023732,"identity":"af094d31-2e94-4a75-9de7-5398e63c2a5f","order_by":0,"name":"Philipp Maurischat","email":"data:image/png;base64,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","orcid":"","institution":"Carl von Ossietzky Universität Oldenburg","correspondingAuthor":true,"prefix":"","firstName":"Philipp","middleName":"","lastName":"Maurischat","suffix":""},{"id":625023736,"identity":"3d61ba43-4eb1-4484-ba59-8cd30afd4581","order_by":1,"name":"Sara E. Anthony","email":"","orcid":"","institution":"University of Rostock","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"E.","lastName":"Anthony","suffix":""},{"id":625023739,"identity":"bf5c4399-9dc9-4d8c-a799-156f93b78405","order_by":2,"name":"Alba Camacho-Santamans","email":"","orcid":"","institution":"University of Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Alba","middleName":"","lastName":"Camacho-Santamans","suffix":""},{"id":625023746,"identity":"adc0b771-a5cf-4049-b22c-245c2cf04b51","order_by":3,"name":"Lukas Hallberg","email":"","orcid":"","institution":"École Polytechnique Fédérale de Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Lukas","middleName":"","lastName":"Hallberg","suffix":""},{"id":625023752,"identity":"5cb48b2d-ee62-4941-b4f7-411d19cadce5","order_by":4,"name":"Carolina Jativa","email":"","orcid":"","institution":"Spanish National Research Council","correspondingAuthor":false,"prefix":"","firstName":"Carolina","middleName":"","lastName":"Jativa","suffix":""},{"id":625023756,"identity":"ec4dcffe-f1bf-4698-8997-5188b8a68aff","order_by":5,"name":"Abagael N. Pruitt","email":"","orcid":"","institution":"University of Notre Dame","correspondingAuthor":false,"prefix":"","firstName":"Abagael","middleName":"N.","lastName":"Pruitt","suffix":""},{"id":625023764,"identity":"3a10149b-5d2e-468c-951b-f49856bb15f6","order_by":6,"name":"Elise D. Snyder","email":"","orcid":"","institution":"University of Notre Dame","correspondingAuthor":false,"prefix":"","firstName":"Elise","middleName":"D.","lastName":"Snyder","suffix":""},{"id":625023771,"identity":"0194032c-4ff7-41e4-883f-402e733b516b","order_by":7,"name":"Emma M. Thrift-Cahall","email":"","orcid":"","institution":"University of Notre Dame","correspondingAuthor":false,"prefix":"","firstName":"Emma","middleName":"M.","lastName":"Thrift-Cahall","suffix":""},{"id":625023774,"identity":"4aff4c39-4a11-44b5-8822-01c9f1bcadda","order_by":8,"name":"Anna E.S. Vincent","email":"","orcid":"","institution":"University of Notre Dame","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"E.S.","lastName":"Vincent","suffix":""},{"id":625023775,"identity":"76a5e976-878b-46c9-9ba3-0349fa149a3d","order_by":9,"name":"Maarten Wynants","email":"","orcid":"","institution":"Griffith University","correspondingAuthor":false,"prefix":"","firstName":"Maarten","middleName":"","lastName":"Wynants","suffix":""},{"id":625023776,"identity":"ea09297b-c250-48eb-8983-811ab38e31f4","order_by":10,"name":"Alberto Zannella","email":"","orcid":"","institution":"Swedish University of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Alberto","middleName":"","lastName":"Zannella","suffix":""},{"id":625023777,"identity":"8d9dc326-af04-4804-bd11-a2b28bb0f00a","order_by":11,"name":"Magdalena Zofia Bieroza","email":"","orcid":"","institution":"Swedish University of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Magdalena","middleName":"Zofia","lastName":"Bieroza","suffix":""}],"badges":[],"createdAt":"2026-04-08 04:54:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9351570/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9351570/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107707645,"identity":"9a62a426-35e3-4fb7-b50b-d0c1814f056c","added_by":"auto","created_at":"2026-04-24 09:20:49","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3427645,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStudy sites in Europe. Scale factor is given in italics for each overview map. Land use land cover information are aggregated based on the Corine Land Cover 2018 /Copernicus (EEA, 2020). Abbreviations on each panel reference the assigned site code for the study.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9351570/v1/1519ebfa9190e1695ce90ce1.jpeg"},{"id":107669078,"identity":"b23fd1aa-eeda-4c8f-b6d5-103fbcd40a85","added_by":"auto","created_at":"2026-04-23 20:09:43","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1417322,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eUS study sites with individual sampling points. Scale factor is given in italics for each overview map. Land use land cover data are aggregated from the National Land Cover Data Set (Dewitz, 2024). Abbreviations on each panel reference the assigned site code for the study.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9351570/v1/2fdc8952546b3eb096b35e9d.jpeg"},{"id":107706353,"identity":"00326c19-696e-445a-bc1b-55d630f97626","added_by":"auto","created_at":"2026-04-24 09:17:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":321073,"visible":true,"origin":"","legend":"\u003cp\u003ea) pie charts of relative distribution of land-use land cover in watersheds per country, missing percentages relate to other land-use types. Boxplots for b) Nitrate NO\u003csub\u003e3\u003c/sub\u003e-N concentration and c) concentration of soluble reactive phosphorus (SRP) and d) concentration of dissolved organic carbon (DOC). Outliers are denoted by points defined with 1.5 times the IQR, mean values are marked by grey rhombuses. Letters (abc) denote statistical significant differences within groups.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9351570/v1/8ce3af6064daf354bc16d411.png"},{"id":107669071,"identity":"86ddf97b-0417-4677-acfd-250db61ed81e","added_by":"auto","created_at":"2026-04-23 20:09:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1004106,"visible":true,"origin":"","legend":"\u003cp\u003eTernary plot with Godwin-Cotner ratio (Godwin and Cotner 2018; C:N:P = 68:14:1) normalised molar ratio relative elemental ratios a) with zones of elemental limitation for b) land use land cover class (LULC) and c) country. The dark circle depicts the zone of normalised Godwin-Cotner ratio, i.e. 33.3% DOC, 33.3% NH\u003csub\u003e4\u003c/sub\u003e-N + NO\u003csub\u003e3\u003c/sub\u003e-N, 33.3% SRP\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9351570/v1/d2fb54309ecc44806207c428.png"},{"id":107669075,"identity":"165491d1-7044-4f4a-baa2-860ba4aa98c5","added_by":"auto","created_at":"2026-04-23 20:09:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":333901,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBoxplots for a) Specific ultraviolet absorbance normalised by DOC concentration (SUVA\u003c/em\u003e\u003csub\u003e\u003cem\u003e254\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e), b) UV E2:E3, c) Fluorescence index (F), and d) Humification index (HIX). Mean values are indicated by rhombuses. Resolved for the dominant LULC class per catchment and country. Italic letters (abc) denote statistically significant differences on the alpha = 0.01 level.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9351570/v1/eb75160e5a19323dcb5e0954.png"},{"id":107708024,"identity":"f9c32644-859f-4d7a-8dd1-31df06f4444d","added_by":"auto","created_at":"2026-04-24 09:21:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":198465,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBoxplots for PARAFAC component group concentration. Mean values are indicated by rhombuses. Statistical testing results are denoted for the alpha = 0.01 level by italic lettering (a,b,c). Colour overlay represents countries and LULC, respectively. A) Aquatic-protein PARAFAC component groups \u003c/em\u003eare\u003cem\u003e summed from component C2 and C3, b) the terrestrial-humic \u003c/em\u003egroup \u003cem\u003econsists of component C1.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9351570/v1/6b037da7aba7524dc9f83ec7.png"},{"id":107669081,"identity":"30eabee4-0219-46d2-88d2-46a5566ea465","added_by":"auto","created_at":"2026-04-23 20:09:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":416267,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ea) Non-metric dimensional scaling (NMDS) for the dataset with a country colour overlay. Included variable loadings were linear trend fitted onto the NMDS plane, collinear variables were excluded. Ellipses are calculated on assumed normal distribution for 95 % probability. b) Embedded \u003c/em\u003epane\u003cem\u003e with Land-use land cover (LULC) overlay; Note that no explained variance can be given for NMDS.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9351570/v1/8aebfc9127438bcff08472ca.png"},{"id":107669074,"identity":"9ab4e268-6e17-46ea-82ec-03f7daadc42a","added_by":"auto","created_at":"2026-04-23 20:09:42","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":590710,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ea) Scatterplot of organic matter degradation rate (as degree-day corrected tensile loss) for countries and LULC classes plotted against UV absorbance E2:E3 spectral slope ratio as an inverse measure of CDOM molecular weight. Linear regression models are depicted with a blue line and 95 % confidence intervals are depicted with grey shading, confidence intervals and ledger lines are printed to guide the eyes only. b) Ternary plot with Godwin-Cotner Ratio (68:14:1) normalised for C:N:P elemental concentrations given as relative values. The loss of cotton strip tensile strength is presented in percent for mean temperature corrected data. Point size and point colour increase in size and hue with increasing tensile loss. Note that country overlay in pane b is depicted by symbol not colour.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9351570/v1/3fbc45693af5b450d26cac6f.png"},{"id":107710217,"identity":"0f515e2c-e589-4dcc-a9ac-0b16283f557e","added_by":"auto","created_at":"2026-04-24 09:40:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7269773,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9351570/v1/6f2e30df-6faa-4b1e-a53d-e9effa5b888d.pdf"},{"id":107669076,"identity":"51d800fd-0a6d-43ea-b9f9-544dcd74d0e3","added_by":"auto","created_at":"2026-04-23 20:09:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1282103,"visible":true,"origin":"","legend":"","description":"","filename":"19022026CDEDraftSupplements.docx","url":"https://assets-eu.researchsquare.com/files/rs-9351570/v1/10acae985ec1add1e0d30feb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Land use and ecosystem properties explain the composition and stability of dissolved organic matter across Northern Hemisphere headwater catchments ","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHeadwaters are highly dynamic ecosystems and are key to understanding patterns of stream dissolved organic matter (DOM) processing and transport (Ledesma et al., 2018; Vidon et al., 2019; Reidy et al., 2025). Given the intimate link to terrestrial ecosystems, headwaters connect energy and nutrient fluxes of terrestrial and aquatic ecosystems (Bieroza et al. 2024; Maurischat et al., 2022). Consequently, headwaters are sensitive to changes in surrounding terrestrial ecosystems, land use influences and other anthropogenic perturbations (Fovet et al., 2020; Giling et al., 2014; Graeber et al., 2015). Compared to larger rivers, headwater streams receive less scientific attention despite having the longest channel network worldwide (Wohl, 2017; Bieroza et al. 2024; Wynants et al. 2025) and the strongest connection with terrestrial ecosystems (Caillon and Schelker, 2020; Coch et al., 2019; Kawahigashi et al., 2004). DOM contains essential elements such as carbon, nitrogen, and phosphorus and is among the most mobile and heterogeneous constituents in global elemental cycles (Kalbitz et al., 2000). Stoichiometric shifts driven by urbanisation and agriculture (G\u0026uuml;cker et al., 2016) can alter the composition and stability of DOM in stream ecosystems (Graeber et al., 2021; Ferreira et al., 2015; Tiegs et al. 2019). Furthermore, chemical diversity (chemodiversity) in headwater streams is larger compared to higher order rivers (Mosher et al., 2015), leading to DOM compositional differences (Berggren et al., 2022; Dittmar et al., 2021). Biotic and abiotic factors vary between ecosystems and greatly influence the stability of DOM (Plamper et al., 2023). For example, chromophoric compounds in DOM (CDOM) can be quickly photo-oxidised (Stubbins et al. 2010), while labile compounds are typically consumed by heterotrophs and through this control the biodegradability of DOM and carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) production (Begum et al. 2023; Xu and Guo 2018; Cole and Caraco 2001).\u003c/p\u003e \u003cp\u003eAgricultural land use led to a structural homogenisation of landscapes globally (Jongman 2002). This transformation has directly affected the chemistry of stream water, the composition of DOM, and organic matter degradation. The type of land use classes (LULC) is one of the key determinants for DOM composition in streams (Herzsprung et al., 2017; Kothawala et al., 2015; Sch\u0026uuml;rings et al., 2024; Yates et al., 2023), leading to intense differences in DOM composition in agricultural and urban sites compared to forested watersheds (Ebeling et al., 2021; Raymond et al., 2008; Wilson and Xenopoulos, 2009I). Research in urban and agricultural systems documents stoichiometric shifts toward lower C ratios in DOM, higher concentration of inorganic nutrients, and a higher proportion of aquatic derived organic matter due to decreased terrestrial inputs (G\u0026uuml;cker et al., 2016; Lee et al., 2021; Pisani et al., 2020). These changes suggest reduced hydrologic connectivity between aquatic and terrestrial ecosystems via the riparian zone in agricultural and urban headwaters compared to more pristine forested catchments (Pisani et al. 2020; Wagner et al. 2008). This change towards a more protein-rich DOM composition alters the intrinsic factors of DOM fate (Berggren et al. 2022) as it can can boost organic matter (OM) decomposition and increase gaseous carbon fluxes (CO₂-C and methane (CH₄-C)) to the atmosphere (Graeber et al. 2015).\u003c/p\u003e \u003cp\u003eAlthough LULC is an important explanatory variable for DOM composition in streams, climate and hydrological controls are known to shape the exchange of water and matter in riparian zones to a great extent (Laudon et al., 2011; Ryan et al., 2024; Werner et al., 2019). Further extrinsic environmental controls influence riverine DOM cycling and composition across ecoregions, including mean annual precipitation, which can regulate DOM inputs of terrestrial-borne organic matter (Catal\u0026aacute;n et al., 2018; Kaplan and Cory, 2016; Kothawala et al., 2021; Roth et al., 2014). Additional factors such as soil type, catchment size, and relief were also shown to have large effects on DOM composition (Charamba et al., 2024; Orlova et al., 2024; Yates et al., 2023). Furthermore, the exchange of carbon greenhouse gases with the atmosphere is influenced by the cycling of terrestrial carbon in the stream and other factors such as groundwater inflow (Herreid et al., 2021; Hotchkiss et al., 2015). The multitude of geographical factors of a catchment, known as geodiversity (Fu\u0026szlig; et al., 2024), ultimately control and shape the compositional variability of DOM, also known as chemodiversity (Kellerman et al., 2014). The compositional differences and geographic signatures of DOM on temporal and spatial scales, shaped by ecosystem properties are referred to chemogeography (Mosher et al., 2015; Hu et al. 2025).\u003c/p\u003e \u003cp\u003eMany studies that investigate catchment scale LULC effects on DOM composition isolate these effects by focussing on geographically proximate catchments, masking additional geodiversity controls beyond LULC. This has led to a restricted understanding of DOM chemodiversity trends in streams (Tanentzap and Fonvielle, 2024; van Vliet et al., 2016; Gerhard et al., 2023). As a result, the predictability of riverine DOM composition and its sensitivity to future perturbations remain insufficient. To address this knowledge-gap coordinated distributed experiments (CDEs), which are comprised of a series of internally standardised methods and materials, conducted synchronously by teams across different locations, can be pursued (Fraser et al., 2013; Yu et al., 2021). Through this, CDEs can produce diverse datasets and are especially suited to overcome limitations of single case studies (Yahdjian et al., 2021).\u003c/p\u003e \u003cp\u003eThe main objective of this study was to explore differences in DOM composition as related to geographies of the Northern Hemisphere by investigating headwater streams along a gradient of LULC classes and geographical conditions. Subsequently, our objective was to explore the impacts of LULC classes both within and between the regions and to test whether LULC would explain the majority of DOM compositional differences or if they are responsive to ecosystem properties on the level of countries or regions besides land use. Generally, we expect forested catchments to exhibit a more terrestrial DOM composition compared to agricultural and urban land use at the local scale. We performed a CDE with case studies in North America and Europe to measure stream physio-chemical conditions, screening temperature, pH, electrical conductivity and nutrients: Nitrate-N (NO\u003csub\u003e3\u003c/sub\u003e-N), Ammonium-N (NH\u003csub\u003e4\u003c/sub\u003e-N), soluble reactive phosphorus (SRP), and dissolved organic carbon (DOC). Furthermore, dissolved CO\u003csub\u003e2\u003c/sub\u003e-C and CH\u003csub\u003e4\u003c/sub\u003e-C in the aqueous phase were measured. DOM chemodiversity was investigated by measuring ultraviolet absorbance (UV 254) and specific ultraviolet absorbance normalised by the concentration of DOC (SUVA\u003csub\u003e254\u003c/sub\u003e), molecular weight indicators of DOM were calculated (UV E2: E3 and UV spectral slope). Fluorescence DOM was investigated by peak-picking and indexing on excitation-emission matrices (EEMs) informing about relative abundance of proteinaceous material (F Index, Freshness Index) and humic compounds (Humification Index). We further conducted parallel-factor decomposition (PARAFAC) of EEMs, allowing to assign fluorescence components. OM degradation was determined by using a cotton strip incubation approach.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study sites, CDE sampling protocol and watershed information\u003c/h2\u003e \u003cp\u003eTo examine to what degree LULC classes and ecosystem properties influence water quality, DOM composition, and OM stability, each CDE team selected three headwater streams located in different watersheds of similar size. Selected watersheds featured different LULC classes with ideally dominant cover of either urban, agriculture (cropland), or forest land use. This resulted in the selection of 31 sites across Europe (including Sweden, Germany and Spain) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and the midwestern United States (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The sites were sampled in summer 2022, with two sampling dates 30\u0026thinsp;\u0026plusmn;\u0026thinsp;5 days apart. In each stream, a 20 m reach was selected and divided into 3 sections with equal distance. Each team used a predetermined standardised experimental sampling plan to ensure consistency across teams (Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInterpolated temperature and precipitation means were generated for the different watersheds from ClimateCharts.net (Zepner et al., 2021). The 2019 National Land Cover Database (USGS) and the 2018 CORINE land cover datasets were used to obtain LULC data for the USA and Europe, respectively (Dewitz, 2024; EEA, 2019). LULC classes were harmonised between the two datasets to represent agriculture, forest, and urban land use. Soil information for each catchment was extracted from the Harmonized World Soil Database (HWSD version 2.0, 2023).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 In-situ measurements: physio-chemical properties\u003c/h2\u003e \u003cp\u003ePhysio-chemical variables including water temperature, pH, electrical conductivity, and dissolved oxygen concentration were measured directly in the water column in the three sections of each reach on each sampling date.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Ex-situ laboratory measurements\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Dissolved nutrients, DOC and elemental ratios\u003c/h2\u003e \u003cp\u003eFor water chemistry characterisation of NO\u003csub\u003e3\u003c/sub\u003e-N, NH\u003csub\u003e4\u003c/sub\u003e-N, SRP and DOC, water samples were taken from 10 cm below the surface and filtered through 0.45 \u0026micro;m polyethersulfone (PES) membrane filters. Samples were stored in clean, pre-rinsed plastic bottles and cooled or frozen until analysis in laboratories at each CDE team\u0026rsquo;s institution following standard methods (Tab.S2). Dissolved inorganic nitrogen (DIN) was calculated as the sum of NO\u003csub\u003e3\u003c/sub\u003e-N and NH\u003csub\u003e4\u003c/sub\u003e-N.\u003c/p\u003e \u003cp\u003eC:N:P ratios, as a representation of relative nutrient concentration (Turner et al., 2003), were calculated from DOC, DIN and SRP concentrations and normalised to the Godwin-Cotner ratio (68:14:1) (Godwin and Cotner, 2018) representing the mean nutrient ratio in the biomass of heterotrophic freshwater bacteria. We use this to assess whether single or co-limitation of nutrient uptake is plausible. The Godwin-Cotner normalised ratios were plotted as relative elemental concentrations in a ternary plot following Smith et al. (2017) and Jarvie et al. (2018) to assess nutrient stoichiometry, elemental relationships and to predict thresholds of substrate limitation and co-limitation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 UV/VIS and CDOM indices, fluorescence DOM: EEM measurement and peak assignment\u003c/h2\u003e \u003cp\u003eCDOM and DOM fluorescence was determined at the department of Soil and Environment of the Swedish University of Agricultural Sciences, Sweden with an Aqualog (Horiba, Japan) equipped with a 150 W Xenon arc lamp using a 1 cm pathlength Suprasil\u0026reg; cuvette in temperature-controlled conditions (20\u0026deg;C). Scans were blank corrected. Absorbance at 254 nm (A254) is used as an indicator of bulk CDOM. Specific ultraviolet absorbance (SUVA\u003csub\u003e254\u003c/sub\u003e), as a proxy of DOM aromaticity, was calculated by normalising A254 readings with DOC concentration (Weishaar et al., 2003). Spectral slopes were calculated based on the ranges of 275\u0026ndash;295 nm and 350\u0026ndash;400 nm (Helms et al. 2008), and E2:E3 was calculated based on the ratio of two absorbance frequencies (A250:A365) (Peuravuori and Pihlaja 1997). Spectral slopes and E2:E3 both represent a DOC concentration independent inverse indicator relative molecular weight, with a lower ratio indicating higher relative molecular weight. Fluorescence spectral scans were recorded using EEMs at excitation wavelengths between 240 and 600 nm and emission wavelengths between 242 and 620 nm, at 1 s integration time and 2 nm scan width. Ultra-pure water blanks were scanned prior to analysis and the blank signal was subtracted from sample EEM scans to correct for Raman scattering. Raman peak intensities in blank samples were recorded to normalise sample EEM scans and account for the variation in Raman intensities over time. Sample EEM scans were corrected for inner-filter effect (McKnight et al., 2001) and signal intensities of first and second order Rayleigh scattering were removed using a masking filter. Instrument-specific bias caused by optical components was automatically corrected in the Aqualog software.\u003c/p\u003e \u003cp\u003ePARAFAC (see section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e for details), peak-picking and FDOM indexing was conducted to investigate the composition of fluorescence DOM. Allochthonous humic peaks (C, A, M) and autochthonous peaks (T, B) were assigned following Coble (2007). Indices were calculated including: Fluorescence index (F) as a measure of terrestrial and microbial contributions to the DOM pool, with a lower index (~\u0026thinsp;1.2) suggesting terrestrial material and higher (~\u0026thinsp;1.8) indices suggesting microbial, autochthonous production (Gabor et al., 2014; McKnight et al. 2001), Freshness index indicating biogeochemical cycling of DOM (Wilson and Xenopoulos 2009) and Humification index (HIX) suggesting contributions of humic substances in stream DOM (Ohno 2002).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Greenhouse gas measurements: CO\u003csub\u003e2\u003c/sub\u003e-C, CH\u003csub\u003e4\u003c/sub\u003e-C concentrations\u003c/h2\u003e \u003cp\u003eTwo samples were collected in each reach for CO\u003csub\u003e2\u003c/sub\u003e-C and CH\u003csub\u003e4\u003c/sub\u003e-C analyses, using the headspace equilibration method (Hope et al., 2004). Briefly, 30 mL of stream water was drawn into a 60 mL syringe along with 30 mL of ambient air. The syringes were then shaken vigorously for 60 seconds after which 17 ml of gas was injected into a sealed pre-evacuated vial creating overpressure. In addition, atmospheric gas samples were taken from each reach of the stream to correct for GHG concentration in the ambient air. Levels of GHG (in ppm) were transformed into dissolved concentrations using Henry\u0026rsquo;s law and solubility equations for CO\u003csub\u003e2\u003c/sub\u003e-C and CH\u003csub\u003e4\u003c/sub\u003e-C, with additional adjustment for water temperature and atmospheric pressure (Anthony et al. 2012). The concentrations of dissolved greenhouse gases (GHGs) were measured by individual CDE teams using established methods and instrumentation (Tab.S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Organic matter decomposition rate by cotton strips\u003c/h2\u003e \u003cp\u003eDuring the first sampling at each site, two sets of cotton strips (n\u0026thinsp;=\u0026thinsp;3) were installed closely to the bed of each stream. Cotton strips were prepared according to Tiegs et al. (2013) using unprimed 12-oz. heavy-weight cotton fabric (Fredrix Style #548, Lawrenceville, GA, USA). The strips were left in the stream for 30\u0026thinsp;\u0026plusmn;\u0026thinsp;5 days. When collected, they were immediately rinsed with 80% ethanol to interrupt further microbial activity and dried at 60\u0026deg;C until constant weight was reached. The decomposition rate for each cotton strip followed the method presented by Tiegs et al. (2013) and was calculated from the loss of tensile strength of the incubated material, which serves as a measure of microbial cellulose degradation. This evaluation was compared to a control of nonincubated cotton strips. Laboratory measurements of tensile strength loss were carried out in the Department of Evolutionary Biology, Ecology and Environmental Science at the University of Barcelona, Spain, using a dynamometer (Mark-10, M5 series) coupled to a motorised test bench (ESM303, Mark-10) with a constant traction speed of 2 cm min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The results were normalised by the mean water temperature to eliminate temperature effects on cellulose decomposition and are expressed as the percentage of tensile loss per incubation day (degree day) which we hereafter refer to as OM degradation rate.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cp\u003eStatistical testing for LULC classes and countries was performed with the Kruskal-Wallis test (Ostertagova et al., 2014). Subsequent group comparisons were conducted with Dunn\u0026rsquo;s test using a Bonferroni post hoc correction (Sedgwick, 2012). Significance was accepted on the α\u0026thinsp;=\u0026thinsp;0.05 level; In the text we report significant pairwise group comparisons, and the respective Z score and p values can be found in the supplementary materials (Tab.S5). Statistical group testing was carried out in R Studio (R Studio Team, 2024) with the R base (R Core Team, 2023) and the \u0026rsquo;FSA\u0026rsquo; package (Ogle et al. 2015).\u003c/p\u003e \u003cp\u003eTernary plots were created using the \u0026lsquo;ternary\u0026rsquo; package (Smith, 2017). For dataset decomposition, non-metric multidimensional scaling (NMDS) was performed with mean-centred and scaled data. Distance indices and dimension number were tested iteratively. The level of stress was monitored as quality control. The R package \u0026lsquo;vegan\u0026rsquo; (Oksanen et al., 2020) was used for NMDS.\u003c/p\u003e \u003cp\u003ePARAFAC was performed using the PLS_Toolbox 9.3 (Eigenvector Research, Manson, Washington, USA) in MATLAB R2023a (The MathWorks, Natick, USA). The model was iteratively fitted using a calibration sample set (n\u0026thinsp;=\u0026thinsp;180) and a validation sample set (n\u0026thinsp;=\u0026thinsp;21) (Halberg et al. 2023). Baseline noise was removed by subtracting an ultrapure water blank. Non-negativity constraints were imposed on modes 1 (sample scores) and 2 (excitation loadings). Individual sample raw data, reconstructed data, and residuals were screened for model probability. Core consistency of the model was 71% and the variation between components for the calibration and validation sample set showed only limited deviation. To further verify the plausibility of PARAFAC components and model quality, the components were tested against identified components using the OpenFluor database (Murphy et al., 2014; detailed information is provided in the supplements Tab.S4). Spearman rank correlations between the PARAFAC components sample scores were tested.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 LULC distribution in studied catchments\u003c/h2\u003e \u003cp\u003eThe watershed-wide LULC classes were dominated by agriculture in the USA (61%), Sweden (60%), and Germany (60%), while this LULC class was absent in Spain (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Physio-chemical water conditions, nutrients, DOC and elemental ratios\u003c/h2\u003e \u003cp\u003eWater temperatures were significantly lower in Germany and Sweden compared to the USA and Spain. Water pH was significantly lower at Swedish sites compared to more alkaline US sites, while German and Spanish streams showed intermediate values with near neutral pH. Electrical conductivity differed significantly between countries, and sampling sites in Spain and Sweden showed lower values compared to the headwaters of USA and Germany. The dissolved oxygen concentrations showed comparable values for all countries' headwater streams (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eAcross all countries, NO\u003csub\u003e3\u003c/sub\u003e-N concentrations were between 10 to 20 times higher than those of NH\u003csub\u003e4\u003c/sub\u003e-N. German headwaters showed significantly higher NO\u003csub\u003e3\u003c/sub\u003e-N concentrations compared to all other countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), while the NH\u003csub\u003e4\u003c/sub\u003e-N concentrations did not show significant differences between countries. The concentration of SRP differed between countries; German sites showed the highest values followed by Swedish, US, and Spanish sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). The concentration of DOC was different between countries, wherein US and Swedish headwaters showed significantly higher DOC concentrations compared to Spanish and German sites (Table\u0026nbsp;1, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eConsidering LULC classes, headwater catchments with urban land use showed significantly higher temperatures compared to forest sites, while agricultural sites had intermediate values. Near-neutral to slightly alkaline pH prevailed for all LULC classes, but forested watersheds had significantly lower pH compared to agricultural sites. Forest sites showed lowest electrical conductivity values, followed by agricultural headwaters, and highest values at urban sites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDissolved oxygen concentration was significantly lower at urban sites compared to agricultural sites, while forested watersheds showed intermediate values. Agricultural sites showed significantly higher NO\u003csub\u003e3\u003c/sub\u003e-N concentrations compared to forests, while urban LULC had intermediate concentrations (Fig.\u0026nbsp;4a). For NH\u003csub\u003e4\u003c/sub\u003e-N, urban sites showed significantly higher concentrations compared to forest and agricultural sites (Table\u0026nbsp;1). \u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 4: Ternary plot with Godwin-Cotner ratio (Godwin and Cotner 2018; C:N:P\u0026thinsp;=\u0026thinsp;68:14:1) normalised molar ratio relative elemental ratios a) with zones of elemental limitation for b) land use land cover class (LULC) and c) country. The dark circle depicts the zone of normalised Godwin-Cotner ratio, i.e. 33.3% DOC, 33.3% NH\u003c/em\u003e \u003csub\u003e \u003cem\u003e4\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e-N\u0026thinsp;+\u0026thinsp;NO\u003c/em\u003e \u003csub\u003e \u003cem\u003e3\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e-N, 33.3% SRP\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe Godwin-Cotner normalised ratios of DOC:DIN:SRP (Fig.\u0026nbsp;4b/c) showed that the majority of the streams indicated stoichiometric limitation. Nutrient colimitation of both SRP and DIN was the most observed scenario in our study, followed by either SRP or DIN limitation, while DOC limitation was restricted to few cases.\u003c/p\u003e \u003cp\u003eAt the country level (Fig.\u0026nbsp;4b), USA, Sweden and Spain showed similar patterns of limitation and colimitation, with DIN and SRP being the most abundant restriction, either as colimitation or a single limiting nutrient. German headwaters, had more sites with no visible limitation or limitation of just one element. Notably, DOC limitation occurred only in German streams, indicating enrichment with both DIN and SRP.\u003c/p\u003e \u003cp\u003eFor LULC classes (Fig.\u0026nbsp;4c), Godwin-Cotner normalised ratios showed that colimitation was only visible for DIN and SRP and was in general the most observed scenario in forests and urban sites. Stream water in agricultural dominated watersheds was more often limited only in SRP or DIN. In general, DOC limitation was the least observable scenario for all LULC classes.\u003c/p\u003e \u003cp\u003e \u003cp\u003eTable 1: Stream water chemistry by country and dominant land-use land cover of the catchments. Shown are arithmetic means and standard deviation (SD). NA=not measured by individual CDE participant. Superscript letters (\u003cem\u003eabcd\u003c/em\u003e) between mean values denote statistically significant differences on the ɑ=0.01 level, and comparisons are within group only. *UV spectral slope is based on the ranges of 275-295 nm and 350-400 nm. SRP=soluble reactive phosphorous as PO\u003csub\u003e4\u003c/sub\u003e-P. ** Temperature corrected data of cotton strip tensile strength loss.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"16\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSD (\u0026plusmn;)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSD (\u0026plusmn;)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSD (\u0026plusmn;)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eSD (\u0026plusmn;)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eSD (\u0026plusmn;)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003eSD (\u0026plusmn;)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cem\u003eSD (\u0026plusmn;)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater Temperature [\u0026deg;C]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.5\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.4\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.6\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.3\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17.5\u003csup\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e15.5\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e21.2\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.14\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.51\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.70\u003csup\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.76\u003csup\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.94\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.52\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e7.76\u003csup\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC [\u0026micro;S cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e974\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e258\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12,381\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27,321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e354\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e685\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e211\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e32838\u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e37341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e-N [\u0026micro;g\u003csup\u003e+\u003c/sup\u003eL\u003csup\u003e\u0026minus;1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103.1\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.2\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.4\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e52.1\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e41.5\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e33.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e214.5\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e243.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e-N [\u0026micro;g \u003csup\u003e\u0026minus;\u003c/sup\u003e L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1312\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e640\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2406\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e939\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1610\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e926\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e896\u003csup\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRP [\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.7\u003csup\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.9\u003csup\u003e\u003cem\u003ebc\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e169.1\u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e235.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.3\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e59.5\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e68.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e48.3\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e69.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e243.2\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e327.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDissolved Oxygen\u003c/p\u003e \u003cp\u003e[mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.8\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.3\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.4\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.8\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.5\u003csup\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.7\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDOC [mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.9\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.1\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.5\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.8\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.5\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10.5\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e10.5\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSUVA254\u003c/p\u003e \u003cp\u003e[L mg C-1 m-1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.34\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.88\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.59\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.51\u003csup\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.70\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.13\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e-C [mM]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.95\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.11\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.73\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.34\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.59\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e-C [\u0026micro;M]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.03\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e214.12\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.51\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.91\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e116.4\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e27.6\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e10.7\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUV 254 (A254)\u003c/p\u003e \u003cp\u003e[relative absorbance]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.19\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.33\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.15\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUV E2:E3 [ratio]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.84\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.02\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.19\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.01\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.32\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10.17\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e10.95\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e15.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUV spectral slope [nm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e]*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.97\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.98\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.98\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.88\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.00\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.18\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.54\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.63\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.57\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.61\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.52\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.64\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreshness index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.71\u003csup\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.71\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.67\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.80\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumification index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003csup\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.72\u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.88\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.83\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.86\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePARAFAC C1 (humic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.5\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.4 \u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.4\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.6\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e54.7 \u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e49.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e53.3 \u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e48.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e68.3 \u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePARAFAC C2 (protein)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.0\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101.4 \u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.8\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.2\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e54.8 \u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e53.4 \u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e69.4 \u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e52.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePARAFAC C3 (protein)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.2\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.3 \u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.4\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.3\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.5 \u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e13.1 \u003csup\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e16.9 \u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOM degradation rate [%]**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003csup\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.16\u003csup\u003e\u003cem\u003eab\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.21\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.18\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.15\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 UV absorbance, Fluorescence and PARAFAC components\u003c/h2\u003e \u003cp\u003eA254 on the country-level, was up to five times higher in Swedish streams compared to USA, Germany and Spain. The USA streams exhibited significantly lower SUVA\u003csub\u003e254\u003c/sub\u003e compared to all other countries (Table\u0026nbsp;1, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, Tab.S5). German streams showed a lower E2:E3 ratio compared to other countries, suggesting higher relative molecular weight of DOM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Fluorescence indices (F, Freshness and HIX) indicated similar significant differences among countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), wherein Germany and USA showed higher values of the F and Freshness index, which are indicative of a higher share of microbial DOM. HIX denoting terrestrial-borne OM was significantly higher in Sweden compared to the other countries. In USA streams, HIX showed moderately high values and was significantly different from Spanish streams (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eWhen assessing LULC classes, A254 showed a trend of higher values for forest streams compared to agricultural and urban headwaters (Table\u0026nbsp;1). For SUVA\u003csub\u003e254\u003c/sub\u003e, forested and agricultural catchments exhibited values up to 50% higher compared to urban catchments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, Tab.S5). The E2:E3 ratio of agricultural streams tended to be lower, suggesting higher relative molecular weight, compared to forests and urban sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). For UV spectral slopes, significantly higher values were present in urban catchments suggesting lower molecular weight compared to forest and agricultural sites (Table\u0026nbsp;1). The F and Freshness indices differed between LULC classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), with significantly higher values in agricultural and urban streams compared to forests (Table\u0026nbsp;1, Tab.S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePARAFAC EEM decomposition yielded a three-component result. Iterative trials with more components (4\u0026ndash;7) led to non-meaningful loadings, impaired split-half quality and low model core consistency. Component 1 (C1) had an excitation spectrum at 270 nm with an emission at 465 nm. Component 2 (C2) showed a slight excitation mode shift between 245 and 320 nm and emission at 435 nm. Component 3 (C3) had a more pronounced excitation mode shift between an early (240 nm) and a late (300 nm) phase with an emission at 340 nm (Fig.S2). Following a validation with OpenFluor (Murphy et al., 2014), C1 resembles a low salinity terrestrial derived, humic-like component. C2 is likely produced by microbes and has stages of temperature-dependent decay. C3 was identified as essential proteinaceous material. The scores of C1 and C2 were correlated (Spearman correlation, R\u0026thinsp;=\u0026thinsp;0.995, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe observed higher relative concentrations of aquatic-protein FDOM (~\u0026thinsp;55% mean value) compared to terrestrial-humic FDOM (~\u0026thinsp;45%) for all headwater streams (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). When examined by country, there were no differences in FDOM concentrations of aquatic-protein and terrestrial-humic components (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea/b). The concentration of all three PARAFAC components were significantly higher in Swedish streams compared to streams from other countries (Table\u0026nbsp;1, Tab.S5). Urban streams had higher relative concentrations for the two aquatic-protein FDOM components compared to agriculture and forest catchments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). In addition, the concentration of proteinaceous FDOM component C3 was significantly higher in urban compared to agricultural catchments (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 CO\u003csub\u003e2\u003c/sub\u003e-C and CH\u003csub\u003e4\u003c/sub\u003e-C concentrations\u003c/h2\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e-C and CH\u003csub\u003e4\u003c/sub\u003e-C concentrations differed between countries, with significantly higher values in Swedish headwater streams compared to all other countries (Table\u0026nbsp;1). However, CO\u003csub\u003e2\u003c/sub\u003e-C and CH\u003csub\u003e4\u003c/sub\u003e-C concentrations did not differ between LULC classes (Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Ordination and decomposition of combined variables\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDimension 1 of the NMDS ordination showed a distinction of Swedish streams from the other investigated countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ea), explained by terrestrial-humic PARAFAC component (C1), DOC concentration, and CO\u003csub\u003e2\u003c/sub\u003e-C concentrations. Streams in the USA, Germany, and Spain were clustered in the positive direction of Dimension 1. When assessing LULC classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eb), agricultural streams clustered around the origin of the ordination, while forest samples were more orientated towards the negative or positive directions of both dimensions, indicating stronger chemical homogenisation of agricultural streams. Urban samples extended toward the negative direction of dimension 2, and these samples showed the lowest scattering in the ordination space and a negative relationship to SUVA\u003csub\u003e254\u003c/sub\u003e and the UV E2:E3 ratio, which were positively correlated with dimension 2. The freshness and F indices, as well as HIX and the aquatic-protein PARAFAC component C3 were negatively correlated with dimension 2. When assessing LULC classes only at the country level, there was clear clustering visible, indicating impacts of land use on water chemistry and DOM regionally (Fig.S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6 OM degradation rate\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe OM degradation rate was highest in Germany, followed by Sweden, Spain, and USA (Table\u0026nbsp;1, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). German streams showed almost twice the degradation rate compared to the USA. The degradation rates among LULC classes differed, with higher values in agricultural streams, followed by forested streams, and urban streams. A positive correlation of OM degradation rate was found with UV E2:E3, indicating that lower relative molecular weight was weakly positively correlated with OM degradation (ρ\u0026thinsp;=\u0026thinsp;0.48, R\u0026sup2; = 0.49;), this trend prevailed for all countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eWhen qualitatively comparing OM degradation rates with Godwin-Cotner ratio normalised stoichiometry of DOC:DIN:SRP, degradation rates were higher under conditions of no limitations and under conditions of only single nutrient limitations. Trends of lower degradation rates were observed under colimitation of DIN and SRP (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Impacts of LULC on water quality, DOM composition and GHGs\u003c/h2\u003e \u003cp\u003eIn our study land use had strong impacts on water quality parameters, especially pH, temperature, electrical conductivity, as well DIN concentrations, while DOC concentration was not influenced by LULC classes. This differs from some previous studies showing that DOC concentration was affected by LULC classes in individual streams and across stream networks (G\u0026uuml;cker et al., 2016; Giling et al. 2014). In our study, the diverse nature of geographical contexts likely masks effects of LULC classes on DOC concentration as we hypothesized. Likewise, DOM indicators in our study were only partly responsive to differences in land use. SUVA\u003csub\u003e254\u003c/sub\u003e suggested less CDOM in urban streams and more CDOM in forested catchments. The occurrence of CDOM is strongly driven by the input of terrestrial derived OM (Helms et al., 2014; Hernes et al., 2013, 2009). Headwater streams are usually more affected by terrestrial inputs, visible through a stronger CDOM signal compared to lower reaches (Maurischat et al., 2022; Mosher et al., 2015). We also observed higher values of Freshness and F indices in urban and agricultural streams compared to forested study sites. Both indices suggest more recently produced organic matter of autochthonous microbial origin (Gabor et al., 2014), in the case of agricultural sites this can also be strongly related to fertiliser-influenced runoff (Moni and Hayes, 2026).\u003c/p\u003e \u003cp\u003eHigher UV spectral slopes in urban catchments are indicative of a lower molecular weight of DOM, which likely results from phototransformation of CDOM in exposed urban streams (Helms et al., 2008) and reduced inputs of terrestrial derived material. Parr et al. (2015) found that urban catchments have less humic OM and, instead, more microbially produced FDOM. Our data for UV spectral slopes corroborate this finding and suggest a decline in allochthonous CDOM and a simultaneous increase in autochthonous FDOM. This was likely driven by an increase in ecological disturbance and a decrease in the morphological interaction between streams and river banks in both agricultural and urban systems. Riparian interaction is hampered in many urban and agricultural catchments, where streams are more strongly regulated and channelized (Booth et al., 2016). This leads to a disconnection of the riparian zone, affecting water quality and DOM composition (Pisani et al., 2020; Reidy et al., 2025).\u003c/p\u003e \u003cp\u003eIn this study GHG concentrations (CO\u003csub\u003e2\u003c/sub\u003e-C and CH\u003csub\u003e4\u003c/sub\u003e-C) did not differ between LULC classes. However, GHG fluxes from headwaters are highly variable across time and space, dependent on pH, groundwater exchange and water temperature (Lauerwald et al., 2013; Raymond et al., 2013). This was likely the reason why we were unable to connect GHG concentrations and LULC classes for this large spatial scope, as was also pointed out by Vidon et al. (2018). In summary, our results show that land use classes most strongly affect physiochemical water conditions and DIN concentrations. LULC classes explained a compositional shift in DOM from terrestrially derived, CDOM-rich forest streams to more microbially derived DOM in urban and agricultural catchments, while DOC concentrations, CO\u003csub\u003e2\u003c/sub\u003e-C, and CH\u003csub\u003e4\u003c/sub\u003e-C did not show a systematic LULC response.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Chemogeography superimposes LULC driven DOM composition\u003c/h2\u003e \u003cp\u003eOur investigation showed that agricultural and urban systems drove changes in DOM composition, particularly on the level of land use within a certain study region. However, chemogeographic biogeochemical conditions exerted a strong control on DOM composition in our study, along with local biotic and abiotic controls as well as changing environmental conditions fueled by anthropogenic activities. For example, in boreal and temperate ecosystems, environmental change in coniferous forests and peatlands leads to freshwater browning (Kaal et al., 2022). Visible by an increase in DOC concentration and simultaneous decrease in pH with a darker coloration of water, commonly associated with an increase of CDOM (Blanchet et al., 2022; Br\u0026uuml;secke et al., 2023). In our study, Swedish headwaters had the highest A254 readings, the highest contribution of PARAFAC resolved FDOM components, as well as highest the HIX and highest DOC concentrations. In contrast, the freshness index (indicative of microbial produced FDOM) was lowest in Sweden. These results mark the high abundance of terrestrial derived DOM in Swedish headwaters, which are a leading effect of freshwater browning and can assert positive feedbacks on GHG emissions visible by highest CO\u003csub\u003e2\u003c/sub\u003e-C and CH\u003csub\u003e4\u003c/sub\u003e-C concentrations in Swedish stream water (Kritzberg et al., 2020). The described DOM characteristics are strongly related to the boreal ecosystems in Sweden with acid-rich litter from coniferous forests and forested peatlands connected to humid climate (Weyhenmeyer et al., 2016). The lower pH in investigated Swedish streams supports the origin of DOM from acid-rich environments. The influence of freshwater browning, overruled differences within LULC classes, this demonstrates the effect of site-specific biogeochemical ecosystem processes.\u003c/p\u003e \u003cp\u003eThe majority of German streams fail to meet a good ecological status related to the EU Water Framework Directive due to a wide range of detrimental influences by agricultural activities, urban inputs, and industry (Sch\u0026uuml;rings et al., 2024; Arle et al., 2016). In our study, German headwaters exhibited the highest concentrations of NO\u003csub\u003e3\u003c/sub\u003e-N and SRP regardless of the LULC class. The observed DOM composition reflects high CDOM contributions (SUVA\u003csub\u003e254\u003c/sub\u003e) but lower relative molecular weight of DOM and low HIX in FDOM. The high CDOM was likely driven by terrestrial inputs from intensive human activities via run-off from soils or agricultural drainages (Boardman and Vandaele, 2023; Rotenhagen et al., 2025; Rotenhagen et al., 2026). The lower relative molecular weight of DOM and HIX were likely connected to the ubiquitous nutrient pollution, driving a higher bioavailability of DOM (Graeber et al., 2012; Wilson and Xenopoulos, 2009). One agricultural catchment of Germany was strongly influenced by drained peatlands (11 KoSDE with 45% hydromorphic soils, Tab.S1). Here, DOM had an even higher CDOM contribution and high HIX index. This corresponds to other studies showing influences of peat degradation on DOM composition of streams, with a strong humification of OM originating from effluents of degraded peatlands (Charamba et al., 2024; Yates et al., 2023; Kalbitz and Geyer, 2002). These examples show the response of DOM composition to ecosystem properties beyond influences of LULC classes.\u003c/p\u003e \u003cp\u003eHeadwater streams in the US have reported high concentrations of DOC (Biddanda and Cotner, 2002; Larson et al., 2020), which our findings corroborate. DOM in our investigated US headwater streams was low in CDOM. Our results align with those of a previous study where low aromaticity of Midwestern streams was determined (Vidon et al., 2008). We also observed elevated microbial-borne FDOM (high Freshness and F indices) in these streams, pointing towards a dominance of autochthonous production of DOM. This can be explained by a low inputs from riparian vegetation and stable organic-mineral complexes in surrounding soils due to high pH and sufficient occurrence of polyvalent base cations. In the water column, an increase in algae growth due to high dissolved nutrient concentrations can boost the production of autochthonous DOM, as suggested for agricultural streams of Illinois and Indiana (Royer and David, 2005; Tan et al. 2016) and corroborated by our results.\u003c/p\u003e \u003cp\u003eIn our NMDS analysis Spanish headwater streams formed a larger cluster with samples from US and German headwaters. However, our data show that DOM of Spanish streams has a specific composition, characterised by medium to high CDOM (SUVA\u003csub\u003e254\u003c/sub\u003e) indicating enrichment of terrestrial-borne DOM but low DOC concentration. This is likely related to the shallow quarzitic soils with low OC content, typical for Mediterranean mountainous catchments (Ortega et al., 2016). Spanish DOM also exhibited the highest relative molecular weight (E2:E3), indicating terrestrial input from leaf litter as an important source (Wondzell and Ward, 2022). Tree litter input can affect DOM composition during drought conditions, which were observed during sampling, and is reported to drive a chromophore-rich DOM signature, as shown for the Llobregat River basin of north-east Spain (Casas-Ruiz et al., 2016; Marin-Garcia and Tauler, 2020). In addition to a strong terrestrial DOM signal, Spanish streams had the lowest electrical conductivity values and low concentrations of NO\u003csub\u003e3\u003c/sub\u003e-N, regardless of LULC classes. Previous studies found a strong hydrological control of DOC concentration and DOM composition in the Mediterranean (Acu\u0026ntilde;a et al., 2007; Casas-Ruiz et al., 2017, 2016; Ylla et al., 2010). During summer drought conditions, readily available N sources are quickly removed from the water column by microbial activity, which can explain the observed low concentrations of NO\u003csub\u003e3\u003c/sub\u003e\u0026ndash;N, as this can force heterotrophs to use less favourable substrates, such as complexer hydrocarbons (Ylla et al., 2010).\u003c/p\u003e \u003cp\u003eOverall, when examined country by country, our results suggest that ecosystem properties resulting from various geographical factors in the northern hemisphere mask the effects of country-level LULC classes. Ultimately, geodiversity leads to chemogeographic signatures and determines the composition of DOM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3 OM degradation rate under the influence of element stoichiometry and chemodiversity\u003c/h2\u003e \u003cp\u003eOur data indicate a trend of increased OM degradation rates in agricultural and forested catchments compared to urban headwaters. Total DIN concentrations in our investigated urban systems were lowest. This may explain the decreased OM degradation rates here, as N availability is a major control for OM decomposition (Imberger et al., 2010). Our results differ from other studies where higher NH\u003csub\u003e4\u003c/sub\u003e-N loads in urban streams, which could boost OM degradation were reported (Paul et al., 2006; Tiegs et al., 2013). The lower concentration of dissolved oxygen at our urban stream study sites also indicates hampered conditions for aerobic decomposition (Liu et al., 2022; Tonin and Hepp, 2011).\u003c/p\u003e \u003cp\u003eSignificant differences in OM degradation were observed at the country level, with the highest OM degradation rates in Germany followed by Sweden, Spain, and USA. German streams showed only a few occasions of DIN:SRP colimitation, but DOC limitation in some cases. Following the macronutrient-access hypothesis, this indicates an increased demand for carbon in these streams (Graeber et al., 2021), driving the degradation rate of cellulose cotton strips and leading to higher OM degradation rates in German streams. The Swedish headwaters were low in both DIN and SRP but had high DOC concentrations that can explain the moderate OM degradation rates we observed. Linked to this, the reported effects of freshwater browning in Sweden are often accompanied by nutrient increases and phytoplankton growth (Strandberg et al., 2023): this can boost OM degradation rates in stream systems and may explain these results in Sweden. The US and Spanish headwater streams were both co-limited in DIN and SRP, which can explain the low OM degradation rates found in our study. Cotton strips are a pure carbohydrate and are potentially persistent under DIN:SRP colimitation as was shown for different C:N ratios of leaf-litter in temperate freshwater streams (Cereghetti et al., 2025). This emphasises the importance of nutrient stoichiometry as a mechanistic control for in-stream decomposition processes (Graeber et al., 2021; Tiegs et al., 2019) and can serve as an explanation for the increased degradation of OM in nutrient polluted headwaters, such as German streams (Bieroza et al., 2024) and lower rates in less intensively farmed areas, such as the Spanish study sites.\u003c/p\u003e \u003cp\u003eBesides the stoichiometry of nutrients, both the US and Spanish catchments are under conditions of hot summers, as our climatic data suggest. This can constrain primary productivity during summer droughts, by a build-up of heat-induced leaf litter fall with high C:N ratio (Acu\u0026ntilde;a et al., 2007), and low available oxygen in the water column (Vazquez et al., 2011). In connection with this, our data indicate that the degradation rates of OM were weakly positively correlated with the UV E2:E3 ratio, which inversely represents the relative molecular weight of DOM (Peuravuori and Pihlaja, 1997). While lower molecular weight components are generally recognised as substrates for microbial activity (Xu and Guo, 2018), these compounds can also be generated from photooxidation of larger compounds (Lou and Xie, 2006). Photooxidation is known to stress decomposer communities (Sturt et al., 2011) and this might hamper the decomposition of cotton strips, therefore being relevant for observations of low OM degradation rates (Ashberry et al., 2021). OM degradation was generally higher in agricultural and forested sites compared to urban headwaters. This indicates that nutrient stoichiometry, especially DIN and SRP availability, oxygen conditions, climatic conditions, riparian disconnection, and photochemical processing control decomposition pathways throughout our broad spatial survey. Only to some extent can these variables be pinned on LULC classes, stressing the importance of geographic factors and chemodiversity.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur results demonstrate that for our case study in the northern hemisphere, headwater streams show a diverse DOM composition that can be explained by chemogeographic ecosystem properties. While we did find evidence of strong ordination connected to LULC classes at the country scale, the effects of urban and agricultural land use were strongest for physio-chemical water conditions and nutrient concentrations, underlining that elemental nutrient ratio imbalances, induced by land use changes overrule chemogeographic baselines, making nutrient ratio imbalances a powerful indicator for human-modification. However, these changes translated only to a lesser degree in SOM composition and were mediated by differences in the interaction of the riparian zone. Our study indicates that chemogeographic baselines of DOM composition compared over a large geographic distance prevail despite LULC class changes. Our results underline that the interplay between LULC classes and environmental chemogeographic signatures govern headwater stream biogeochemistry and are decisive for DOM chemodiversity. Overall, our CDE study shows that human impacts on the composition of DOM do not remain consistent over time and do not occur in the same way across all locations, this suggest that DOM composition is not always a powerful indicator for human disturbance. This is essential to understanding how ecosystems cope with climatic change and environmental stress, highlighting the importance of local geographic conditions and natural variation in shaping ecological responses. Efforts to predict and mitigate the consequences of climatic perturbations and land use change along fluvial systems will need to integrate both global-scale drivers and local-scale abiotic and biotic variability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eDeclaration\u003c/p\u003e \u003cp\u003eMagdalena Bieroza, the course organizer, would like to acknowledge the funding from Graduate School Focus on Soils and Water. No further funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe main author (PM) appears first and the project lead (MZB) is mentioned last. All other authors appear in alphatbetical order.PM: Additional analysis, methodology, writing \u0026ndash; editingSEA:Additional analysis, writing \u0026ndash; editingACS: Additional analysis, writing \u0026ndash; editingLH: Additional analysis, writing \u0026ndash; editingCJ: Additional analysis, writingANP: Additional analysis, writing \u0026ndash; editingEDS: Additional analysis, writing \u0026ndash; editingEMTC: Additional analysis, writing \u0026ndash; editingAESV: Additional analysis, writing \u0026ndash; editingMW: Additional analysis, methodology, writing \u0026ndash; editingAZ: Additional analysis, writing \u0026ndash; editingMZB: Design of experiment, additional analysis, methodology, writing \u0026ndash; editing\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis paper is a joint effort from Early Career Researchers enrolled in a doctoral course \"Aquatic systems through the lens of organic matter stability and fate \u0026ndash; a coordinated distributed experiment\" held at Swedish University of Agricultural Sciences May-October 2022. We would like to express our sincere gratitude to the teachers during the course: Kevin Bishop, Stefan Bertilsson, Claudia von Br\u0026ouml;mssen, Chuxian Li, Maliheh Mehrshad, Mike Peacock, Anna Szekely, and Marcus Wallin for their dedicated teaching and guidance. Their expertise, time, and commitment provided an invaluable foundation for the work presented in this paper. We would like to thank Xavier Pe\u0026ntilde;arroya for submitting data and giving advice with the writing of the manuscript. We greatly appreciate the work of Nina Schulze and Rhodelyn Saban who assisted with the field work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData generated and analyzed during this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcu\u0026ntilde;a, V., Giorgi, A., Mu\u0026ntilde;oz, I., Sabater, F., Sabater, S., 2007. Meteorological and riparian influences on organic matter dynamics in a forested Mediterranean stream. Journal of the North American Benthological Society 26, 54\u0026ndash;69. https://doi.org/10.1899/0887-3593(2007)26%255B54:MARIOO%255D2.0.CO;2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnthony, S.E., Prahl, F.G., Peterson, T.D., 2012. Methane dynamics in the Willamette River, Oregon. Limnology \u0026amp; Oceanography 57, 1517\u0026ndash;1530. https://doi.org/10.4319/lo.2012.57.5.1517\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArle, J., Mohaupt, V., Kirst, I., 2016. Monitoring of Surface Waters in Germany under the Water Framework Directive\u0026mdash;A Review of Approaches, Methods and Results. Water 8, 217. https://doi.org/10.3390/w8060217\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshberry, E.L., Rier, S.T., Halvorson, H.M., Kuehn, K.A., 2021. Algal-driven priming of cellulose decomposition along a phosphorus gradient in stream mesocosms. Freshwater Science 40, 580\u0026ndash;592. https://doi.org/10.1086/717127\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBegum, M.S., Park, J.-H., Yang, L., Shin, K.H., Hur, J., 2023. Optical and molecular indices of dissolved organic matter for estimating biodegradability and resulting carbon dioxide production in inland waters: A review. Water Res 228, 119362. https://doi.org/10.1016/j.watres.2022.119362\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerggren, M., Guillemette, F., Bieroza, M., Buffam, I., Deininger, A., Hawkes, J.A., Kothawala, D.N., LaBrie, R., Lapierre, J.-F., Murphy, K.R., Al-Kharusi, E.S., Rulli, M.P.D., Hensgens, G., Younes, H., W\u0026uuml;nsch, U.J., 2022. Unified understanding of intrinsic and extrinsic controls of dissolved organic carbon reactivity in aquatic ecosystems. Ecology 103, e3763. https://doi.org/10.1002/ecy.3763\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiddanda, B.A., Cotner, J.B., 2002. Love Handles in Aquatic Ecosystems: The Role of Dissolved Organic Carbon Drawdown, Resuspended Sediments, and Terrigenous Inputs in the Carbon Balance of Lake Michigan. Ecosystems 5, 431\u0026ndash;445. https://doi.org/10.1007/s10021-002-0163-z\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBieroza, M., Hallberg, L., Livsey, J., Prischl, L.-A., Wynants, M., 2024. Recognizing Agricultural Headwaters as Critical Ecosystems. Environ. Sci. Technol. 58, 4852\u0026ndash;4858. https://doi.org/10.1021/acs.est.3c10165\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlanchet, C.C., Arzel, C., Davranche, A., Kahilainen, K.K., Secondi, J., Taipale, S., Lindberg, H., Loehr, J., Manninen-Johansen, S., Sundell, J., Maanan, M., Nummi, P., 2022. Ecology and extent of freshwater browning - What we know and what should be studied next in the context of global change. Science of The Total Environment 812, 152420. https://doi.org/10.1016/j.scitotenv.2021.152420\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoardman, J., Vandaele, K., 2023. Soil erosion and runoff: The need to rethink mitigation strategies for sustainable agricultural landscapes in western Europe. Soil Use and Management 39, 673\u0026ndash;685. https://doi.org/10.1111/sum.12898\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBooth, D.B., Roy, A.H., Smith, B., Capps, K.A., 2016. Global perspectives on the urban stream syndrome. Freshwater Science 35, 412\u0026ndash;420. https://doi.org/10.1086/684940\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBr\u0026uuml;secke, J., Muotka, T., Huttunen, K.-L., Litjo, S., Lepo, W.-P., Jyv\u0026auml;sj\u0026auml;rvi, J., 2023. Drainage-induced browning causes both loss and change of benthic biodiversity in headwater streams. Limnology and Oceanography Letters 8, 620\u0026ndash;627. https://doi.org/10.1002/lol2.10302\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaillon, F., Schelker, J., 2020. Dynamic transfer of soil bacteria and dissolved organic carbon into small streams during hydrological events. Aquat Sci 82, 964. https://doi.org/10.1007/s00027-020-0714-4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasas-Ruiz, J.P., Catal\u0026aacute;n, N., G\u0026oacute;mez-Gener, L., von Schiller, D., Obrador, B., Kothawala, D.N., L\u0026oacute;pez, P., Sabater, S., Marc\u0026eacute;, R., 2017. A tale of pipes and reactors: Controls on the in-stream dynamics of dissolved organic matter in rivers. Limnology and Oceanography 62, S85\u0026ndash;S94. https://doi.org/10.1002/lno.10471\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasas-Ruiz, J.P., Tittel, J., von Schiller, D., Catal\u0026aacute;n, N., Obrador, B., G\u0026oacute;mez-Gener, L., Zwirnmann, E., Sabater, S., Marc\u0026eacute;, R., 2016. Drought-induced discontinuities in the source and degradation of dissolved organic matter in a Mediterranean river. Biogeochemistry 127, 125\u0026ndash;139. https://doi.org/10.1007/s10533-015-0173-5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCatal\u0026aacute;n, N., Casas-Ruiz, J.P., Arce, M.I., Abril, M., Bravo, A.G., Campo, R. del, Est\u0026eacute;vez, E., Freixa, A., Gim\u0026eacute;nez-Grau, P., Gonz\u0026aacute;lez-Ferreras, A.M., G\u0026oacute;mez-Gener, L., Lupon, A., Mart\u0026iacute;nez, A., Palacin-Lizarbe, C., Poblador, S., Rasines-Ladero, R., Reyes, M., Rodr\u0026iacute;guez-Castillo, T., Rodr\u0026iacute;guez-Lozano, P., Sanpera-Calbet, I., Tornero, I., Pastor, A., 2018. Behind the Scenes: Mechanisms Regulating Climatic Patterns of Dissolved Organic Carbon Uptake in Headwater Streams. Global Biogeochemical Cycles 32, 1528\u0026ndash;1541. https://doi.org/10.1029/2018GB005919\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCereghetti, E., Bossart, R., Bruder, A., Kr\u0026auml;henb\u0026uuml;hl, A., Wolf, F., Altermatt, F., 2025. The year of a leaf: Tracking the fate of leaf litter and its nutrients during aquatic decomposition and consumption. Ecology 106, e4520. https://doi.org/10.1002/ecy.4520\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharamba, L.V.C., Houska, T., Kaiser, K., Knorr, K.-H., Kr\u0026uuml;ger, S., Krause, T., Chen, H., Kr\u0026aacute;m, P., Hruška, J., Kalbitz, K., 2024. Tracing sources of dissolved organic matter along the terrestrial-aquatic continuum in the Ore Mountains, Germany. Science of The Total Environment 173807. https://doi.org/10.1016/j.scitotenv.2024.173807\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoble, P.G., 2007. Marine Optical Biogeochemistry: The Chemistry of Ocean Color. Chem. Rev. 107, 402\u0026ndash;418. https://doi.org/10.1021/cr050350+\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoch, C., Juhls, B., Lamoureux, S.F., Lafreni\u0026egrave;re, M.J., Fritz, M., Heim, B., Lantuit, H., 2019. Comparisons of dissolved organic matter and its optical characteristics in small low and high Arctic catchments. Biogeosciences 16, 4535\u0026ndash;4553. https://doi.org/10.5194/bg-16-4535-2019\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCole, J.J., Caraco, N.F., 2001. Carbon in catchments: connecting terrestrial carbon losses with aquatic metabolism. Mar. Freshwater Res. 52, 101. https://doi.org/10.1071/MF00084\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDewitz, J., 2024. National Land Cover Database (NLCD) 2019 Products (ver. 3.0, February 2024). https://doi.org/10.5066/P9KZCM54\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDittmar, T., Lennartz, S.T., Buck-Wiese, H., Hansell, D.A., Santinelli, C., Vanni, C., Blasius, B., Hehemann, J.-H., 2021. Enigmatic persistence of dissolved organic matter in the ocean. Nat Rev Earth Environ 2, 570\u0026ndash;583. https://doi.org/10.1038/s43017-021-00183-7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbeling, P., Kumar, R., Weber, M., Knoll, L., Fleckenstein, J.H., Musolff, A., 2021. Archetypes and Controls of Riverine Nutrient Export Across German Catchments. Water Resources Research 57, e2020WR028134. https://doi.org/10.1029/2020WR028134\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEEA, 2018. European Environment Agency under the framework of the Copernicus programme - [email protected].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira, V., Castagneyrol, B., Koricheva, J., Gulis, V., Chauvet, E., Gra\u0026ccedil;a, M.A.S., 2015. A meta-analysis of the effects of nutrient enrichment on litter decomposition in streams. Biological Reviews 90, 669\u0026ndash;688. https://doi.org/10.1111/brv.12125\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFovet, O., Cooper, D.M., Jones, D.L., Jones, T.G., Evans, C.D., 2020. Dynamics of dissolved organic matter in headwaters: comparison of headwater streams with contrasting DOM and nutrient composition. Aquat Sci 82, 29. https://doi.org/10.1007/s00027-020-0704-6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFraser, L.H., Henry, H.A., Carlyle, C.N., White, S.R., Beierkuhnlein, C., Cahill, J.F., Casper, B.B., Cleland, E., Collins, S.L., Dukes, J.S., Knapp, A.K., Lind, E., Long, R., Luo, Y., Reich, P.B., Smith, M.D., Sternberg, M., Turkington, R., 2013. Coordinated distributed experiments: an emerging tool for testing global hypotheses in ecology and environmental science. Frontiers in Ecol \u0026amp; Environ 11, 147\u0026ndash;155. https://doi.org/10.1890/110279\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu\u0026szlig;, T., Thuile Bistarelli, L., Walther, F., Vitecek, S., Talluto, L., Singer, G., 2024. Geodiversity of a European river network controls algal biodiversity and function. Commun Earth Environ 5, 1\u0026ndash;13. https://doi.org/10.1038/s43247-024-01478-5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGabor, R.S., Baker, A., McKnight, D.M., Miller, M.P., 2014. Fluorescence Indices and Their Interpretation, in: Coble, P.G., Lead, J., Baker, A., Reynolds, D.M., Spencer, R.G.M. (Eds.), Aquatic Organic Matter Fluorescence. Cambridge University Press, pp. 303\u0026ndash;338. https://doi.org/10.1017/CBO9781139045452.015\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerhard, M., Koussoroplis, A.-M., Raatz, M., Pansch, C., Fey, S.B., Vajedsamiei, J., Calder\u0026oacute;-Pascual, M., Cunillera-Montcus\u0026iacute;, D., Juvigny-Khenafou, N.P.D., Polazzo, F., Thomas, P.K., Symons, C.C., Beklioğlu, M., Berger, S.A., Chefaoui, R.M., Ger, K.A., Langenheder, S., Nejstgaard, J.C., Ptacnik, R., Striebel, M., 2023. Environmental variability in aquatic ecosystems: Avenues for future multifactorial experiments. Limnology and Oceanography Letters 8, 247\u0026ndash;266. https://doi.org/10.1002/lol2.10286\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiling, D.P., Grace, M.R., Thomson, J.R., Mac Nally, R., Thompson, R.M., 2014. Effect of Native Vegetation Loss on Stream Ecosystem Processes: Dissolved Organic Matter Composition and Export in Agricultural Landscapes. Ecosystems 17, 82\u0026ndash;95. https://doi.org/10.1007/s10021-013-9708-6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGodwin, C.M., Cotner, J.B., 2018. What intrinsic and extrinsic factors explain the stoichiometric diversity of aquatic heterotrophic bacteria? The ISME Journal 12, 598\u0026ndash;609. https://doi.org/10.1038/ismej.2017.195\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraeber, D., Bo\u0026euml;chat, I.G., Encina-Montoya, F., Esse, C., Gelbrecht, J., Goyenola, G., G\u0026uuml;cker, B., Heinz, M., Kronvang, B., Meerhoff, M., Nimptsch, J., Pusch, M.T., Silva, R.C.S., Von Schiller, D., Zwirnmann, E., 2015. Global effects of agriculture on fluvial dissolved organic matter. Sci Rep 5, 16328. https://doi.org/10.1038/srep16328\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraeber, D., Gelbrecht, J., Pusch, M.T., Anlanger, C., von Schiller, D., 2012. Agriculture has changed the amount and composition of dissolved organic matter in Central European headwater streams. Science of The Total Environment 438, 435\u0026ndash;446. https://doi.org/10.1016/j.scitotenv.2012.08.087\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraeber, D., Tenzin, Y., Stutter, M., Weigelhofer, G., Shatwell, T., Von T\u0026uuml;mpling, W., Tittel, J., Wachholz, A., Borchardt, D., 2021. Bioavailable DOC: reactive nutrient ratios control heterotrophic nutrient assimilation\u0026mdash;An experimental proof of the macronutrient-access hypothesis. Biogeochemistry 155, 1\u0026ndash;20. https://doi.org/10.1007/s10533-021-00809-4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026uuml;cker, B., Silva, R.C.S., Graeber, D., Monteiro, J.A.F., Bo\u0026euml;chat, I.G., 2016. Urbanization and agriculture increase exports and differentially alter elemental stoichiometry of dissolved organic matter (DOM) from tropical catchments. Science of The Total Environment 550, 785\u0026ndash;792. https://doi.org/10.1016/j.scitotenv.2016.01.158\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalberg, H.F.F., Bevilacqua, M., Rinnan, \u0026Aring;., 2023. Is core consistency a too conservative diagnostic? Journal of Chemometrics 37, e3483. https://doi.org/10.1002/cem.3483\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarmonized World Soil Database, 2023. HWSD version 2.0. FAO; International Institute for Applied Systems Analysis (IIASA); https://doi.org/10.4060/cc3823en\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelms, J.R., Stubbins, A., Ritchie, J.D., Minor, E.C., Kieber, D.J., Mopper, K., 2008. Absorption spectral slopes and slope ratios as indicators of molecular weight, source, and photobleaching of chromophoric dissolved organic matter. Limnology and Oceanography 53, 955\u0026ndash;969. https://doi.org/10.4319/lo.2008.53.3.0955\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHernes, P.J., Bergamaschi, B.A., Eckard, R.S., Spencer, R.G.M., 2009. Fluorescence-based proxies for lignin in freshwater dissolved organic matter. Journal of Geophysical Research: Biogeosciences 114. https://doi.org/10.1029/2009JG000938\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHernes, P.J., Spencer, R.G.M., Dyda, R.Y., Pellerin, B.A., Bachand, P.A.M., Bergamaschi, B.A., 2013. DOM composition in an agricultural watershed: Assessing patterns and variability in the context of spatial scales. Geochimica et Cosmochimica Acta 121, 599\u0026ndash;610. https://doi.org/10.1016/j.gca.2013.07.039\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerreid, A.M., Wymore, A.S., Varner, R.K., Potter, J.D., McDowell, W.H., 2021. Divergent Controls on Stream Greenhouse Gas Concentrations Across a Land-Use Gradient. Ecosystems 24, 1299\u0026ndash;1316. https://doi.org/10.1007/s10021-020-00584-7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerzsprung, P., Osterloh, K., T\u0026uuml;mpling, W., Harir, M., Hertkorn, N., Schmitt-Kopplin, P., Meissner, R., Bernsdorf, S., Friese, K., 2017. Differences in DOM of rewetted and natural peatlands - Results from high-field FT-ICR-MS and bulk optical parameters. The Science of the total environment 586, 770\u0026ndash;781. https://doi.org/10.1016/j.scitotenv.2017.02.054\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHope, D., Palmer, S.M., Billett, M.F., Dawson, J.J.C., 2004. Variations in dissolved CO2 and CH4 in a first-order stream and catchment: an investigation of soil\u0026ndash;stream linkages. Hydrological Processes 18, 3255\u0026ndash;3275. https://doi.org/10.1002/hyp.5657\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHotchkiss, E.R., Hall Jr, R.O., Sponseller, R.A., Butman, D., Klaminder, J., Laudon, H., Rosvall, M., Karlsson, J., 2015. Sources of and processes controlling CO2 emissions change with the size of streams and rivers. Nature Geosci 8, 696\u0026ndash;699. https://doi.org/10.1038/ngeo2507\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, A., Stegen, J., Tanentzap, A.J., Wang, J., 2025. The Emergence and Promise of Functional Chemogeography of Organic Matter. Global Change Biology 31, e70435. https://doi.org/10.1111/gcb.70435\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImberger, S.J., Thompson, R.M., Grace, M.R., 2010. Searching for effective indicators of ecosystem function in urban streams: assessing cellulose decomposition potential. Freshwater Biology 55, 2089\u0026ndash;2106. https://doi.org/10.1111/j.1365-2427.2010.02469.x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJarvie, H.P., Smith, D.R., Norton, L.R., Edwards, F.K., Bowes, M.J., King, S.M., Scarlett, P., Davies, S., Dils, R.M., Bachiller-Jareno, N., 2018. Phosphorus and nitrogen limitation and impairment of headwater streams relative to rivers in Great Britain: A national perspective on eutrophication. Science of The Total Environment 621, 849\u0026ndash;862. https://doi.org/10.1016/j.scitotenv.2017.11.128\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJongman, R.H.G., 2002. Homogenisation and fragmentation of the European landscape: ecological consequences and solutions. Landscape and Urban Planning, Fragmentation and Land Use Planning: Analysis and beyond? 58, 211\u0026ndash;221. https://doi.org/10.1016/S0169-2046(01)00222-5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaal, J., P\u0026eacute;rez-Rodr\u0026iacute;guez, M., Biester, H., 2022. Molecular Probing of DOM Indicates a Key Role of Spruce-Derived Lignin in the DOM and Metal Cycles of a Headwater Catchment: Can Spruce Forest Dieback Exacerbate Future Trends in the Browning of Central European Surface Waters? Environ. Sci. Technol. 56, 2747\u0026ndash;2759. https://doi.org/10.1021/acs.est.1c04719\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalbitz, K., Geyer, S., 2002. Different effects of peat degradation on dissolved organic carbon and nitrogen. Organic Geochemistry 33, 319\u0026ndash;326. https://doi.org/10.1016/S0146-6380(01)00163-2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalbitz, K., Solinger, S., Park, J.-H., Michalzik, B., Matzner, E., 2000. CONTROLS ON THE DYNAMICS OF DISSOLVED ORGANIC MATTER IN SOILS: A REVIEW. Soil Science 165, 277\u0026ndash;304. https://doi.org/10.1097/00010694-200004000-00001\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaplan, L.A., Cory, R.M., 2016. Dissolved Organic Matter in Stream Ecosystems, in: Stream Ecosystems in a Changing Environment. Elsevier, pp. 241\u0026ndash;320. https://doi.org/10.1016/B978-0-12-405890-3.00006-3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawahigashi, M., Kaiser, K., Kalbitz, K., Rodionov, A., Guggenberger, G., 2004. Dissolved organic matter in small streams along a gradient from discontinuous to continuous permafrost. Global Change Biol 10, 1576\u0026ndash;1586. https://doi.org/10.1111/j.1365-2486.2004.00827.x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKellerman, A.M., Dittmar, T., Kothawala, D.N., Tranvik, L.J., 2014. Chemodiversity of dissolved organic matter in lakes driven by climate and hydrology. Nat Commun 5, 3804. https://doi.org/10.1038/ncomms4804\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKothawala, D.N., Ji, X., Laudon, H., \u0026Aring;gren, A.M., Futter, M.N., K\u0026ouml;hler, S.J., Tranvik, L.J., 2015. The relative influence of land cover, hydrology, and in-stream processing on the composition of dissolved organic matter in boreal streams. J. Geophys. Res. Biogeosci. 120, 1491\u0026ndash;1505. https://doi.org/10.1002/2015JG002946\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKothawala, D.N., Kellerman, A.M., Catal\u0026aacute;n, N., Tranvik, L.J., 2021. Organic Matter Degradation across Ecosystem Boundaries: The Need for a Unified Conceptualization. Trends in ecology \u0026amp; evolution 36, 113\u0026ndash;122. https://doi.org/10.1016/j.tree.2020.10.006\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKritzberg, E.S., Hasselquist, E.M., Škerlep, M., L\u0026ouml;fgren, S., Olsson, O., Stadmark, J., Valinia, S., Hansson, L.-A., Laudon, H., 2020. Browning of freshwaters: Consequences to ecosystem services, underlying drivers, and potential mitigation measures. AMBIO: A Journal of the Human Environment 49, 375\u0026ndash;390. https://doi.org/10.1007/s13280-019-01227-5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarson, J.H., James, W.F., Fitzpatrick, F.A., Frost, P.C., Evans, M.A., Reneau, P.C., Xenopoulos, M.A., 2020. Phosphorus, nitrogen and dissolved organic carbon fluxes from sediments in freshwater rivermouths entering Green Bay (Lake Michigan; USA). Biogeochemistry 147, 179\u0026ndash;197. https://doi.org/10.1007/s10533-020-00635-0\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaudon, H., Berggren, M., \u0026Aring;gren, A., Buffam, I., Bishop, K., Grabs, T., Jansson, M., K\u0026ouml;hler, S., 2011. Patterns and Dynamics of Dissolved Organic Carbon (DOC) in Boreal Streams: The Role of Processes, Connectivity, and Scaling. Ecosystems 14, 880\u0026ndash;893. https://doi.org/10.1007/s10021-011-9452-8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLauerwald, R., Hartmann, J., Moosdorf, N., Kempe, S., Raymond, P.A., 2013. What controls the spatial patterns of the riverine carbonate system? \u0026mdash; A case study for North America. Chemical Geology 337\u0026ndash;338, 114\u0026ndash;127. https://doi.org/10.1016/j.chemgeo.2012.11.011\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLedesma, J.L.J., Futter, M.N., Blackburn, M., Lidman, F., Grabs, T., Sponseller, R.A., Laudon, H., Bishop, K.H., K\u0026ouml;hler, S.J., 2018. Towards an Improved Conceptualization of Riparian Zones in Boreal Forest Headwaters. Ecosystems 21, 297\u0026ndash;315. https://doi.org/10.1007/s10021-017-0149-5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, S.-C., Shin, Y., Jeon, Y.-J., Lee, E.-J., Eom, J.-S., Kim, B., Oh, N.-H., 2021. Optical properties and 14C ages of stream DOM from agricultural and forest watersheds during storms. Environmental Pollution 272, 116412. https://doi.org/10.1016/j.envpol.2020.116412\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, S., He, G., Fang, H., Xu, S., Bai, S., 2022. Effects of dissolved oxygen on the decomposers and decomposition of plant litter in lake ecosystem. Journal of Cleaner Production 372, 133837. https://doi.org/10.1016/j.jclepro.2022.133837\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLou, T., Xie, H., 2006. Photochemical alteration of the molecular weight of dissolved organic matter. Chemosphere, Environmental Chemistry 65, 2333\u0026ndash;2342. https://doi.org/10.1016/j.chemosphere.2006.05.001\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMar\u0026iacute;n-Garc\u0026iacute;a, M., Tauler, R., 2020. Chemometrics characterization of The Llobregat river dissolved organic matter. Chemometrics and Intelligent Laboratory Systems 201, 104018. https://doi.org/10.1016/j.chemolab.2020.104018\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaurischat, P., Lehnert, L., Zerres, V.H.D., Tran, T.V., Kalbitz, K., Rinnan, \u0026Aring;., Li, X.G., Dorji, T., Guggenberger, G., 2022. The glacial-terrestrial-fluvial pathway: A multiparametrical analysis of spatiotemporal dissolved organic matter variation in three catchments of Lake Nam Co, Tibetan Plateau. The Science of the total environment 156542. https://doi.org/10.1016/j.scitotenv.2022.156542\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKnight, D.M., Boyer, E.W., Westerhoff, P.K., Doran, P.T., Kulbe, T., Andersen, D.T., 2001. Spectrofluorometric characterization of dissolved organic matter for indication of precursor organic material and aromaticity. Limnology and Oceanography 46, 38\u0026ndash;48. https://doi.org/10.4319/lo.2001.46.1.0038\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoni, M.E.H., Hayes, M., 2026. Unlocking Soil Health and Surface Water Quality Management: A Review on Fluorescent Dissolved Organic Matter (fDOM) in Agricultural Systems. J. Agric. Food Chem. https://doi.org/10.1021/acs.jafc.5c13863\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMosher, J.J., Kaplan, L.A., Podgorski, D.C., McKenna, A.M., Marshall, A.G., 2015. Longitudinal shifts in dissolved organic matter chemogeography and chemodiversity within headwater streams: a river continuum reprise. Biogeochemistry 124, 371\u0026ndash;385. https://doi.org/10.1007/s10533-015-0103-6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy, K.R., Stedmon, C.A., Wenig, P., Bro, R., 2014. OpenFluor\u0026ndash; an online spectral library of auto-fluorescence by organic compounds in the environment. Anal. Methods 6, 658\u0026ndash;661. https://doi.org/10.1039/C3AY41935E\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgle, D.H., Doll, J.C., Wheeler, A.P., Dinno, A., 2015. FSA: Simple Fisheries Stock Assessment Methods. https://doi.org/10.32614/CRAN.package.FSA\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOhno, T., 2002. Fluorescence Inner-Filtering Correction for Determining the Humification Index of Dissolved Organic Matter. Environ. Sci. Technol. 36, 742\u0026ndash;746. https://doi.org/10.1021/es0155276\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Peter R. Minchin, P.R., O\u0026rsquo;Hara, R.B., Simpson, G.L., Solymos, P., Henry, M., Stevens, H., Szoecs, E., Wagner, H., 2020. vegan: Community Ecology Package.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrlova, J., Amiri, F., Bourgeois, A.K., Buttle, J.M., Cherlet, E., Cuss, C.W., Devito, K.J., Emelko, M.B., Floyd, W.C., Foster, D.E., Hutchins, R.H.S., Jamieson, R., Johnson, M.S., McSorley, H.J., Silins, U., Tank, S.E., Thompson, L.M., Webster, K.L., Williams, C.H.S., Olefeldt, D., 2024. Composition of Stream Dissolved Organic Matter Across Canadian Forested Ecozones Varies in Three Dimensions Linked to Landscape and Climate. Water Resources Research 60, e2023WR035196. https://doi.org/10.1029/2023WR035196\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtega, E., Lozano, F.J., Mart\u0026iacute;nez, F.J., Bienes, R., Gallardo, J.F., Asensio, C., 2016. Soils of the Mediterranean Areas, in: Gallardo, J.F. (Ed.), The Soils of Spain. Springer International Publishing, Cham, pp. 163\u0026ndash;187. https://doi.org/10.1007/978-3-319-20541-0_5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOstertagova, E., Ostertag, O., Kov\u0026aacute;č, J., 2014. Methodology and Application of the Kruskal-Wallis Test. Applied Mechanics and Materials 611, 115\u0026ndash;120.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParr, T.B., Cronan, C.S., Ohno, T., Findlay, S.E.G., Smith, S.M.C., Simon, K.S., 2015. Urbanization changes the composition and bioavailability of dissolved organic matter in headwater streams. Limnology and Oceanography 60, 885\u0026ndash;900. https://doi.org/10.1002/lno.10060\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul, M.J., Meyer, J.L., Couch, C.A., 2006. Leaf breakdown in streams differing in catchment land use. Freshwater Biology 51, 1684\u0026ndash;1695. https://doi.org/10.1111/j.1365-2427.2006.01612.x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeuravuori, J., Pihlaja, K., 1997. Molecular size distribution and spectroscopic properties of aquatic humic substances. Analytica Chimica Acta 337, 133\u0026ndash;149. https://doi.org/10.1016/S0003-2670(96)00412-6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePisani, O., Bosch, D.D., Coffin, A.W., Endale, D.M., Liebert, D., Strickland, T.C., 2020. Riparian land cover and hydrology influence stream dissolved organic matter composition in an agricultural watershed. Science of The Total Environment 717, 137165. https://doi.org/10.1016/j.scitotenv.2020.137165\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlamper, P., Lechtenfeld, O.J., Herzsprung, P., Gro\u0026szlig;, A., 2023. A Temporal Graph Model to Predict Chemical Transformations in Complex Dissolved Organic Matter. Environ. Sci. Technol. 57, 18116\u0026ndash;18126. https://doi.org/10.1021/acs.est.3c00351\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team, 2023. R Statistical Software.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Studio Team, 2024. RStudio: Integrated Development for R. RStudio.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaymond, P.A., Hartmann, J., Lauerwald, R., Sobek, S., McDonald, C., Hoover, M., Butman, D., Striegl, R., Mayorga, E., Humborg, C., Kortelainen, P., D\u0026uuml;rr, H., Meybeck, M., Ciais, P., Guth, P., 2013. Global carbon dioxide emissions from inland waters. Nature 503, 355\u0026ndash;359. https://doi.org/10.1038/nature12760\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaymond, P.A., Oh, N.-H., Turner, R.E., Broussard, W., 2008. Anthropogenically enhanced fluxes of water and carbon from the Mississippi River. Nature 451, 449\u0026ndash;452. https://doi.org/10.1038/nature06505\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReidy, M., Berggren, M., Lupon, A., Laudon, H., Sponseller, R.A., 2025. Riparian Zone Heterogeneity Influences the Amount and Fate of Biodegradable Dissolved Organic Carbon at the Land-Water Interface. JGR Biogeosciences 130. https://doi.org/10.1029/2024jg008471\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRotenhagen, A.-L., Giani, L., Maurischat, P., 2025a. Phosphate mobility in soils and P-influxes via ditches and drainages in landscape areas characteristic of the coastal region of Lower Saxony. HyWa.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRotenhagen, A.-L., Massmann, G., Maurischat, P., Giani, L., 2025b. N aus Dr\u0026auml;nen. in preparation.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoth, V.-N., Dittmar, T., Gaupp, R., Gleixner, G., 2014. Ecosystem-Specific Composition of Dissolved Organic Matter. Vadose Zone Journal 13, 1\u0026ndash;10. https://doi.org/10.2136/vzj2013.09.0162\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoyer, T.V., David, M.B., 2005. Export of dissolved organic carbon from agricultural streams in Illinois, USA. Aquat. Sci. 67, 465\u0026ndash;471. https://doi.org/10.1007/s00027-005-0781-6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyan, K.A., Garayburu-Caruso, V.A., Crump, B.C., Bambakidis, T., Raymond, P.A., Liu, S., Stegen, J.C., 2024. Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damk\u0026ouml;hler numbers in nested watersheds. https://doi.org/10.22541/essoar.171164898.83027743/v1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSch\u0026uuml;rings, C., Globevnik, L., Lemm, J.U., Psomas, A., Snoj, L., Hering, D., Birk, S., 2024. River ecological status is shaped by agricultural land use intensity across Europe. Water Research 251, 121136. https://doi.org/10.1016/j.watres.2024.121136\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSedgwick, P., 2012. Multiple significance tests: the Bonferroni correction. BMJ 344, e509. https://doi.org/10.1136/bmj.e509\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith, M.R., 2017. Ternary: Create Ternary and Holdridge Plots. https://doi.org/10.32614/CRAN.package.Ternary\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrandberg, U., Hiltunen, M., Creed, I.F., Arts, M.T., Kankaala, P., 2023. Browning-induced changes in trophic functioning of planktonic food webs in temperate and boreal lakes: insights from fatty acids. Oecologia 201, 183\u0026ndash;197. https://doi.org/10.1007/s00442-022-05301-w\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStubbins, A., Spencer, R.G.M., Chen, H., Hatcher, P.G., Mopper, K., Hernes, P.J., Mwamba, V.L., Mangangu, A.M., Wabakanghanzi, J.N., Six, J., 2010. Illuminated darkness: Molecular signatures of Congo River dissolved organic matter and its photochemical alteration as revealed by ultrahigh precision mass spectrometry. Limnology and Oceanography 55, 1467\u0026ndash;1477. https://doi.org/10.4319/lo.2010.55.4.1467\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSturt, M.M., Jansen, M. a. K., Harrison, S.S.C., 2011. Invertebrate grazing and riparian shade as controllers of nuisance algae in a eutrophic river. Freshwater Biology 56, 2580\u0026ndash;2593. https://doi.org/10.1111/j.1365-2427.2011.02684.x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan, J., Cherkauer, K.A., Chaubey, I., 2016. Developing a Comprehensive Spectral-Biogeochemical Database of Midwestern Rivers for Water Quality Retrieval Using Remote Sensing Data: A Case Study of the Wabash River and Its Tributary, Indiana. Remote Sensing 8, 517. https://doi.org/10.3390/rs8060517\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanentzap, A.J., Fonvielle, J.A., 2024. Chemodiversity in freshwater health. Science 383, 1412\u0026ndash;1414. https://doi.org/10.1126/science.adg8658\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiegs, S.D., Clapcott, J.E., Griffiths, N.A., Boulton, A.J., 2013. A standardized cotton-strip assay for measuring organic-matter decomposition in streams. Ecological Indicators 32, 131\u0026ndash;139. https://doi.org/10.1016/j.ecolind.2013.03.013\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiegs, S.D., Costello, D.M., Isken, M.W., Woodward, G., McIntyre, P.B., Gessner, M.O., Chauvet, E., Griffiths, N.A., Flecker, A.S., Acu\u0026ntilde;a, V., Albari\u0026ntilde;o, R., Allen, D.C., Alonso, C., Andino, P., Arango, C., Aroviita, J., Barbosa, M.V.M., Barmuta, L.A., Baxter, C.V., Bell, T.D.C., Bellinger, B., Boyero, L., Brown, L.E., Bruder, A., Bruesewitz, D.A., Burdon, F.J., Callisto, M., Canhoto, C., Capps, K.A., Castillo, M.M., Clapcott, J., Colas, F., Col\u0026oacute;n-Gaud, C., Cornut, J., Crespo-P\u0026eacute;rez, V., Cross, W.F., Culp, J.M., Danger, M., Dangles, O., de Eyto, E., Derry, A.M., Villanueva, V.D., Douglas, M.M., Elosegi, A., Encalada, A.C., Entrekin, S., Espinosa, R., Ethaiya, D., Ferreira, V., Ferriol, C., Flanagan, K.M., Fleituch, T., Follstad Shah, J.J., Frainer, A., Friberg, N., Frost, P.C., Garcia, E.A., Garc\u0026iacute;a Lago, L., Garc\u0026iacute;a Soto, P.E., Ghate, S., Giling, D.P., Gilmer, A., Gon\u0026ccedil;alves, J.F., Gonzales, R.K., Gra\u0026ccedil;a, M.A.S., Grace, M., Grossart, H.-P., Gu\u0026eacute;rold, F., Gulis, V., Hepp, L.U., Higgins, S., Hishi, T., Huddart, J., Hudson, J., Imberger, S., I\u0026ntilde;iguez-Armijos, C., Iwata, T., Janetski, D.J., Jennings, E., Kirkwood, A.E., Koning, A.A., Kosten, S., Kuehn, K.A., Laudon, H., Leavitt, P.R., Lemes da Silva, A.L., Leroux, S.J., LeRoy, C.J., Lisi, P.J., MacKenzie, R., Marcarelli, A.M., Masese, F.O., McKie, B.G., Oliveira Medeiros, A., Meissner, K., Miliša, M., Mishra, S., Miyake, Y., Moerke, A., Mombrikotb, S., Mooney, R., Moulton, T., Muotka, T., Negishi, J.N., Neres-Lima, V., Nieminen, M.L., Nimptsch, J., Ondruch, J., Paavola, R., Pardo, I., Patrick, C.J., Peeters, E.T.H.M., Pozo, J., Pringle, C., Prussian, A., Quenta, E., Quesada, A., Reid, B., Richardson, J.S., Rigosi, A., Rinc\u0026oacute;n, J., R\u0026icirc;şnoveanu, G., Robinson, C.T., Rodr\u0026iacute;guez-Gallego, L., Royer, T.V., Rusak, J.A., Santamans, A.C., Selmeczy, G.B., Simiyu, G., Skuja, A., Smykla, J., Sridhar, K.R., Sponseller, R., Stoler, A., Swan, C.M., Szlag, D., Teixeira-de Mello, F., Tonkin, J.D., Uusheimo, S., Veach, A.M., Vilbaste, S., Vought, L.B.M., Wang, C.-P., Webster, J.R., Wilson, P.B., Woelfl, S., Xenopoulos, M.A., Yates, A.G., Yoshimura, C., Yule, C.M., Zhang, Y.X., Zwart, J.A., 2019. Global patterns and drivers of ecosystem functioning in rivers and riparian zones. Science Advances 5, eaav0486. https://doi.org/10.1126/sciadv.aav0486\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTonin, A.M., Hepp, L.U., 2011. Effects of nitrate enrichment on leaf litter decomposition. Acta 23, 86\u0026ndash;94. https://doi.org/10.4322/actalb.2011.022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner, R.E., Rabalais, N.N., Justic\u0026rsquo;, D., Dortch, Q., 2003. Global patterns of dissolved N, P and Si in large rivers. Biogeochemistry 64, 297\u0026ndash;317. https://doi.org/10.1023/A:1024960007569\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Vliet, J., Magliocca, N.R., B\u0026uuml;chner, B., Cook, E., Rey Benayas, J.M., Ellis, E.C., Heinimann, A., Keys, E., Lee, T.M., Liu, J., Mertz, O., Meyfroidt, P., Moritz, M., Poeplau, C., Robinson, B.E., Seppelt, R., Seto, K.C., Verburg, P.H., 2016. Meta-studies in land use science: Current coverage and prospects. Ambio 45, 15\u0026ndash;28. https://doi.org/10.1007/s13280-015-0699-8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVazquez, E., Amalfitano, S., Fazi, S., Butturini, A., 2011. Dissolved organic matter composition in a fragmented Mediterranean fluvial system under severe drought conditions. Biogeochemistry 102, 59\u0026ndash;72. https://doi.org/10.1007/s10533-010-9421-x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVidon, P., Wagner, L.E., Soyeux, E., 2008. Changes in the character of DOC in streams during storms in two Midwestern watersheds with contrasting land uses. Biogeochemistry 88, 257\u0026ndash;270. https://doi.org/10.1007/s10533-008-9207-6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVidon, P.G., Welsh, M.K., Hassanzadeh, Y.T., 2019. Twenty Years of Riparian Zone Research (1997\u0026ndash;2017): Where to Next? J of Env Quality 48, 248\u0026ndash;260. https://doi.org/10.2134/jeq2018.01.0009\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWagner, L.E., Vidon, P., Tedesco, L.P., Gray, M., 2008. Stream nitrate and DOC dynamics during three spring storms across land uses in glaciated landscapes of the Midwest. Journal of Hydrology 362, 177\u0026ndash;190. https://doi.org/10.1016/j.jhydrol.2008.08.013\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeishaar, J.L., Aiken, G.R., Bergamaschi, B.A., Fram, M.S., Fujii, R., Mopper, K., 2003. Evaluation of specific ultraviolet absorbance as an indicator of the chemical composition and reactivity of dissolved organic carbon. Environmental science \u0026amp; technology 37, 4702\u0026ndash;4708. https://doi.org/10.1021/es030360x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWerner, B.J., Musolff, A., Lechtenfeld, O.J., de Rooij, G.H., Oosterwoud, M.R., Fleckenstein, J.H., 2019. High-frequency measurements explain quantity and quality of dissolved organic carbon mobilization in a headwater catchment. Biogeosciences 16, 4497\u0026ndash;4516. https://doi.org/10.5194/bg-16-4497-2019\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeyhenmeyer, G.A., M\u0026uuml;ller, R.A., Norman, M., Tranvik, L.J., 2016. Sensitivity of freshwaters to browning in response to future climate change. Climatic Change 134, 225\u0026ndash;239. https://doi.org/10.1007/s10584-015-1514-z\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson, H.F., Xenopoulos, M.A., 2009. Effects of agricultural land use on the composition of fluvial dissolved organic matter. Nature Geosci 2, 37\u0026ndash;41. https://doi.org/10.1038/ngeo391\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWohl, E., 2017. The significance of small streams. Front. Earth Sci. 11, 447\u0026ndash;456. https://doi.org/10.1007/s11707-017-0647-y\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWondzell, S.M., Ward, A.S., 2022. The Channel Source Hypothesis: Empirical Evidence for In-Channel Sourcing of Dissolved Organic Carbon to Explain Hysteresis in a Headwater Mountain Stream. Hydrol. Process. https://doi.org/10.1002/hyp.14570\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWynants, M., Hallberg, L., Prischl, L.-A., Livsey, J., Bieroza, M., 2025. Trends and purposes of European river monitoring and restoration. Environmental Science \u0026amp; Policy 170, 104130. https://doi.org/10.1016/j.envsci.2025.104130\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, 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\u0026ndash;194. https://doi.org/10.1016/j.watres.2018.02.016\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYahdjian, L., Sala, O.E., Pi\u0026Ntilde;Eiro-Guerra, J.M., Knapp, A.K., Collins, S.L., Phillips, R.P., Smith, M.D., 2021. Why Coordinated Distributed Experiments Should Go Global. BioScience 71, 918\u0026ndash;927. https://doi.org/10.1093/biosci/biab033\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYates, C.A., Johnes, P.J., Brailsford, F.L., Evans, C.D., Evershed, R.P., Glanville, H.C., Jones, D.L., Lloyd, C.E.M., Marshall, M.R., Owen, A.T., 2023. Determining patterns in the composition of dissolved organic matter in fresh waters according to land use and management. Biogeochemistry 164, 143\u0026ndash;162. https://doi.org/10.1007/s10533-022-00964-2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYlla, I., Sanpera-Calbet, I., V\u0026aacute;zquez, E., Roman\u0026iacute;, A.M., Mu\u0026ntilde;oz, I., Butturini, A., Sabater, S., 2010. Organic matter availability during pre- and post-drought periods in a Mediterranean stream, in: Stevenson, R.J., Sabater, S. (Eds.), Global Change and River Ecosystems\u0026mdash;Implications for Structure, Function and EcosystemServices. Springer Netherlands, Dordrecht, pp. 217\u0026ndash;232. https://doi.org/10.1007/978-94-007-0608-8_15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu, G., Piao, S., Zhang, Y., Liu, L., Peng, J., Niu, S., 2021. Moving toward a new era of ecosystem science. Geography and Sustainability 2, 151\u0026ndash;162. https://doi.org/10.1016/j.geosus.2021.06.004\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZepner, L., Karrasch, P., Wiemann, F., Bernard, L., 2021. ClimateCharts.net \u0026ndash; an interactive climate analysis web platform. International Journal of Digital Earth 14, 338\u0026ndash;356. https://doi.org/10.1080/17538947.2020.1829112\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PARAFAC-Fluorescence, Cotton strip assay, Early Career Researchers (ECR), Agricultural and urban streams, Coordinated distributed experiment (CDE), Chemogeography","lastPublishedDoi":"10.21203/rs.3.rs-9351570/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9351570/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe analysed the chemical composition of dissolved organic matter (DOM) and the decomposition rates of cotton-strips in several headwater streams of the Northern Hemisphere. We conducted a coordinated distributed experiment to investigate concentration of dissolved organic carbon (DOC), DOM spectroscopic and fluorescence signatures including PARAFAC-resolved excitation emission matrices (EEMs), organic matter decomposition rates (OM stability), dissolved concentrations of CO\u003csub\u003e2\u003c/sub\u003e-C and CH\u003csub\u003e4\u003c/sub\u003e-C, and physio-chemical water quality parameters. We aimed to uncover relationships between land use classes (LULC), DOM composition and OM stability, in streams across different latitudes of Europe and the United States of America.\u003c/p\u003e \u003cp\u003eOur data showed that physio-chemical water quality strongly reflects land use classes, and that in both agricultural and urban streams terrestrial DOM was lower compared to forested streams, indicating ecological disconnection with the riparian zone and higher photooxidation of chromophoric DOM (CDOM). However, on pan-continental scales, DOM characteristics instead can be pinned on abiotic and biotic controls, arising from a multitude of biogeographical factors within catchments, that dominate compositional differences. This leads to persistent chemodiversity in DOM composition under different LULC classes. Our results underscore the importance of coordinated distributed experiments to unravel local catchment dynamics to draw conclusions for global change assessments on DOM composition. We show that DOM is strongly affected by land use on the local scale, but geospatial and ecosystem properties superimpose these differences on larger scales.\u003c/p\u003e","manuscriptTitle":"Land use and ecosystem properties explain the composition and stability of dissolved organic matter across Northern Hemisphere headwater catchments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 20:09:34","doi":"10.21203/rs.3.rs-9351570/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-08T17:56:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323646157560330251758216274124944224372","date":"2026-04-30T17:57:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T11:50:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-13T10:25:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-08T09:36:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-08T09:35:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-08T04:42:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d76cb21-08df-4e0b-8c44-2b9fcf4d140d","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-08T17:56:11+00:00","index":39,"fulltext":""},{"type":"reviewerAgreed","content":"323646157560330251758216274124944224372","date":"2026-04-30T17:57:04+00:00","index":37,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66528791,"name":"Earth and environmental sciences/Biogeochemistry"},{"id":66528792,"name":"Biological sciences/Ecology"},{"id":66528793,"name":"Earth and environmental sciences/Ecology"},{"id":66528794,"name":"Earth and environmental sciences/Environmental sciences"},{"id":66528795,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2026-04-23T20:09:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 20:09:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9351570","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9351570","identity":"rs-9351570","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}