Divergent drivers of the spatial variabilities in CO2, CH4, N2O, and N2 concentrations along the Rhine river and the Mittelland canal in Germany

Divergent drivers of the spatial variabilities in CO2, CH4, N2O, and N2 concentrations along the Rhine river and the Mittelland canal in Germany | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Divergent drivers of the spatial variabilities in CO 2 , CH 4 , N 2 O, and N 2 concentrations along the Rhine river and the Mittelland canal in Germany Ricky Mwangada Mwanake, Hannes Imhof, Ralf Kiese This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3722436/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Lotic ecosystems transversing mixed land-use landscapes are sources of GHGs to the atmosphere, but their emissions are uncertain due to longitudinal GHG heterogeneities. In this study, we quantified summer CO 2 , CH 4 , N 2 O, and N 2 concentrations, as well as several water quality parameters along the Rhine river and the Mittelland canal, two critical inland waterways in Germany. Our main objectives were to compare GHG concentrations along the two ecosystems and to determine the main driving factors responsible for their longitudinal heterogeneities. The results indicated that GHGs in the two ecosystems were up to three orders of magnitude oversaturated relative to equilibrium concentrations, particularly in the Mittelland canal, a hotspot for CH 4 and N 2 O concentrations. We also found significant longitudinal variabilities in % GHG saturations along the mainstems of both ecosystems (CV = 26 – 98 %), with the highest variability recorded for CH 4 concentrations in the Mittelland canal, suggesting that single GHG measurements along large lotic ecosystems are unrepresentative of entire reaches. However, these significant longitudinal GHG heterogeneities were driven by divergent drivers between the two lotic ecosystems. Within the Canal, longitudinal CO 2 and CH 4 hotspots were linked to external inflows of the GHGs from surrounding WWTPs. Contrastingly, harbors and in-situ biogeochemical processes such as methanogenesis and respiration explained CH 4 and CO 2 hotspots along the Rhine river. In contrast, N 2 O was strongly linked to N 2 concentrations, with a negative relationship in the Rhine river and a positive relationship in the Mittelland canal. Based on these N 2 relationships, we hypothesized that denitrification drove N 2 O hotspots in the Canal, while coupled N-fixation and nitrification accounted for N 2 O hotspots in the Rhine. This finding stresses the need to include N 2 concentration measurements in GHG sampling campaigns, as it has the potential to determine whether nitrogen is fixed through N-fixation or lost through denitrification. Greenhouse gases Harbors External inputs Metabolism rates N-fixation Denitrification Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Inland waters, comprising lotic (streams, rivers, and canals) and lentic (reservoirs, lakes, and ponds) ecosystems, are increasingly recognized as significant sources of greenhouse gases (GHG), contributing (~ 25%; 13.5 (9.9–20.1) Pg CO 2 -eq) of the global CO 2 -equivalent emissions (Lauerwald et al., 2023 ). In comparison, lotic ecosystems contribute disproportionately higher CO 2 -eq emissions (> 60%) than lentic ecosystems, linked to their close connectivity with terrestrial landscapes and their flowing and turbulent nature that favors gas evasion to the atmosphere (Raymond et al., 2013 ; Rocher-Ros et al., 2023 ; Yao et al., 2020 ). Because of the variable nature of the surrounding landscapes affecting catchment hydrology and differences in channel geomorphologies, the GHG concentrations of lotic ecosystems are highly heterogeneous in space (e.g., Ho et al., 2022 ; Mwanake et al., 2022 ; Park et al., 2023 ; Teodoru et al., 2015 ),and may introduce significant uncertainties to GHG emission estimates from inland waters if not precisely quantified. While research has demonstrated that headwaters, the inception points of river networks, contribute more than two-thirds of the global GHG emissions from riverine ecosystems (e.g., Li et al., 2021 ; Liu et al., 2022 ), anthropogenic landscapes along large rivers may modify or even reverse this trend. Previous studies have shown that nutrients and carbon inputs from human-influenced landscapes to large rivers favor GHG production processes such as nitrification, incomplete denitrification, methanogenesis, and respiration, creating GHG hotspots comparable to or higher than those from headwater streams (Borges et al., 2018 ; Mwanake et al., 2019 ; Park et al., 2018 ; Mwanake et al., 2023b ). For instance, Mwanake et al. ( 2023a ) reported higher or comparable GHG emissions from temperate rivers in Germany relative to small streams linked to inorganic N and labile carbon inputs from surrounding upstream cropland and urban areas. Similar findings were also found along the Seine River downstream of wastewater treatment plants in large metropolitan areas in France (Marescaux et al., 2018 ). Nevertheless, field studies incorporating multiple measurements of GHG concentrations along large river transects with varying channel morphologies and transversing cropland and urban-dominated landscapes are still limited (e.g., Begum et al., 2021 ; Leng et al., 2023 ; Park et al., 2023 ), making it challenging to understand critical drivers, magnitude and large-scale longitudinal heterogeneities of river GHG emissions. Drainage ditches and canals, whose GHG dynamics are often understudied compared to rivers, have also been shown to be potent GHG hotspots of mainly CH 4 emissions due to their low flow velocities that provide suitable anaerobic conditions for CH 4 production. It is estimated that these canals contribute ~ 3% of the global anthropogenic CH 4 emissions (Peacock et al., 2021 ), while they can also be hotspots of N 2 O emissions, especially when they receive N inputs from surrounding landscapes (Reay et al., 2003 ). Like rivers, different channel morphologies and land uses along canal systems may also result in significant spatial GHG heterogeneities. However, this remains uncertain, as most studies often take single samples along large canals to represent whole channel GHG magnitudes (Peacock et al., 2021 ). Apart from GHG hotspots directly linked to allochthonous nutrients and carbon inputs, increased water-column primary production, especially during warm periods of the year, may also alter longitudinal GHG trends along large lotic ecosystems with relatively low water flow conditions. Although the influence of primary producers on lotic GHG concentrations remains uncertain due to the scarcity of studies with paired measurements of GHG concentrations and river metabolism rates (Battin et al., 2023 ), several potential mechanisms can be hypothesized linked to their effects on nutrient and carbon cycling. The decomposition of primary producers could supply fresh and more labile autochthonous organic C, favoring GHG production processes through C and N cycling. At the same time, N-fixing cyanobacteria may contribute to excess mineral N, driving in-situ GHG production, particularly of N 2 O. Conventional sampling approaches, which primarily focus on single locations along large rivers and canals, have been extensively applied to determine the magnitudes of their reach-scale GHG emissions (e.g., Mwanake et al., 2022 ; Peacock et al., 2021 ). Such approaches fail to capture the intricate nuances of large-scale longitudinal GHG dynamics (e.g., Bussmann et al., 2022 ; Park et al., 2023 ; Teodoru et al., 2015 ), resulting in overestimation or underestimation of reach scale fluxes when the single measurements are upscaled over long distances. The lack of better spatial coverage in these large lotic ecosystems has been mainly driven by extensive resource requirements in making detailed longitudinal measurements along elongated reaches that can be hundreds of km long. One approach that can overcome resource barriers is using high precision and relatively cheap sensors that simultaneously measure water quality parameters such as dissolved oxygen, nutrients, and organic matter (OM) content (Bieroza et al., 2023 ). Combined with GHG concentration analysis, these sensors may constrict the critical drivers of longitudinal GHG discontinuities along lotic ecosystems, enhancing our capacity to model and manage these spatial GHG hotspots. This study tested a novel sampling approach along one of the largest European rivers (Rhine, Germany) and canal systems (Mittelland canal, Germany). Within the framework of a cruise mission, we explored a combined lotic reach of 632 km. Our approach encompassed longitudinal measurements of grab GHG samples (N 2 O, CO 2 , and CH 4 ) with onsite sensor measurements of multiple water physicochemical parameters during the temperate summer. In addition, we also took grab samples for N 2 concentration analysis along both lotic ecosystems, which is a proxy for major N pathways (denitrification or N 2 fixation) but remain currently understudied in large lotic ecosystems. Our main objectives were: 1) To compare GHG concentrations and % saturations from the Rhine river with the Mittelland canal system. 2) To quantify large-scale longitudinal GHG heterogeneities along the two lotic ecosystems and infer the key drivers of these longitudinal GHG trends. We hypothesized that the canal would be a hotspot for CH 4 and N 2 O % saturations, driven by more anoxic conditions, point source inputs of nutrients, and availability of labile carbon from autotrophic processes. We also hypothesized that changes in either morphological (e.g., the existence of harbors) or biogeochemical processes would explain most of the longitudinal variabilities along both lotic ecosystems mainstems. 2 Materials and methods 2.1 Study area The Rhine river is among the ten largest rivers in Europe, with a total length of 1233 km and a catchment size of 185500 km². The Rhine river has its sources in the Swiss Alps, enters Lake Constance on the eastern part, crosses Lake Constance via Konstanz, and leaves it on the southwestern side. From Basel onwards, the Rhine river flows towards the North Sea, and from here on, the Rhine is intensively used as an inland waterway and for hydropower production, traversing multiple landscapes with mixed land uses. This study was conducted on the German side of the Rhine river catchment, one of Germany's most important inland waterways, with a yearly transport volume of 6 million tons. The first sampling point along the Rhine river was located at the midsection of the river (653 km from the source), while our most downstream location was at Wesel, Germany (1023 km from the source) (Table 1 ; Fig. 1 ). Besides large industrial areas, the upstream land use at this downstream station in Wesel comprises 38% forest, 29% croplands, 9% urban, and 17% Grasslands (Fig. 1 ). The Mittelland canal traverses Germany from West to East. It is one of the central connections of the industrialized areas in the northern Ruhr area to the large harbors of the North Sea (e.g., Rotterdam, Antwerpen, Amsterdam). Moreover, via the Elbe Side canal, goods are shipped to Hamburg and the Eastern Sea. In addition to the Rhine and Neckar, the canal is one of Germany's most important inland waterways and has a yearly transport volume of 6 million tons. Six large locks are located between sites 16 to 23 in Friedrichsfeld, Hünxe, Dorsten, Flaesheim, Ahsen, and Datteln (Fig. 1 ). These locks control the water flow within the Canal by pumping water in and out, assisting the movement of large ships from west to east. Apart from its use as an inland waterway, the Mittelland canal is part of the water distribution network in Germany. Specifically, freshwater is transported from the Lippe, the lower Ruhr, and the Rhine rivers to the east, where industries and irrigation use it. According to an environmental report by the local environment agency, the canal is considered highly eutrophic due to point-source inputs of nutrients from several wastewater treatment plants and rivers along its longitudinal section (Landesumweltamt Nordrhein-Westfalen, 2002 ). 2.2 Sampling strategy Sampling was performed between 12.06.2023 and 06.07.2023 mostly between 9:00 am and 5:00 pm on the Science and Media Vessel ALDEBARAN run by the German Ocean Foundation (Table 1 ; Figure S1 ). This field campaign was part of a more extensive Rhine field expedition, which aimed at connecting actors from society, business, and politics concerning the traces of human influences on water quality and biodiversity along the Rhine. In this study, 23 lotic sites were sampled, comprising 16 river sites (including four harbors) along the Rhine river (386km stretch) and seven canal sites (including two harbors) along the Mittelland canal (246 km stretch) (Table 1 ; Fig. 1 ). 2.3 Water and gas sampling River and canal water was collected with a bucket from a depth of ~ 1m below the water surface and sampled for ammonium, GHG (CO 2 , N 2 O, and CH 4 ), and N 2 concentrations. For GHGs, triplicate samples were drawn from the bucket water using the headspace equilibration technique (Aho & Raymond, 2019 ). In brief, 80 ml of water was equilibrated with 20 ml of atmospheric air in a syringe after shaking for 2 minutes in the bucket to maintain in-situ temperatures. The headspace gas samples were transferred into 10 ml glass vials for GHG concentration analysis in the laboratory using an SRI gas chromatograph (8610C, Germany) with an electron capture detector (ECD) for N 2 O and a flame ionization detector (FID) with an upstream methanizer for simultaneous measurements of CH 4 and CO 2 concentrations. Dissolved GHG concentrations in the river and canal water were calculated from post-equilibration gas concentrations in the headspace after correcting for atmospheric (ambient) GHG concentrations (Aho & Raymond, 2019 ; Mwanake et al., 2022 ). Duplicate water samples for N 2 concentration measurements were collected from the bucket in gas-tight 12 ml exetainers (Labco, UK) without air bubbles and stored in a refrigerator until analysis. In the laboratory, measurements of dissolved N 2 were carried out on a membrane inlet mass spectrophotometer (MIMS; Bay instruments, USA) at close to in-situ temperatures (~ 20 ℃) following the procedure outlined inKana et al. ( 1994 ). In brief, the MIMS measurements involved continuous uptake of the water samples through a gas-permeable silicone membrane using a peristaltic pump and detecting N 2 (mass 28) on a quadrupole mass spectrophotometer (Pfeiffer vacuum PrismaPlus). We used N 2 :Ar current ratios to measure N 2 concentrations with high precision (< 0.05 CV%) (Kana et al., 1994 ; An et al., 2001). The MIMS setup included a liquid N 2 trap and a reduction furnace to minimize water vapor interference and other dissolved gases on the N 2 measurements (Kana et al., 1994 ). Lastly, samples for ammonium measurements were taken from the same volume of water in the bucket and temporarily stored in a falcon tube. Analysis of ammonium (NH 4 -N) was done immediately in the field using a pHPhotoFlex Turb (WTW Germany) and the ammonium cuvette test A6/25 (173700, WTW Germany). 2.4 Sensor measurements of water physicochemical properties Onsite measurements of water physicochemical properties such as NO 3 -N, total organic carbon (TOC), total suspended solids (TSS), dissolved organic carbon (DOC), Chlorophyll-a, UV254, and UV254f were done simultaneously with the grab samples using a calibrated optical sensor (S::Can spectro::lyser V3, Messtechnik GmbH, Vienna, Austria). The specific ultraviolet absorbance at 254 nm (SUVA 254, a measure of DOC quality) was calculated by dividing UV254 by DOC concentrations to estimate DOC aromaticity (e.g., Bodmer et al., 2016 ). Additional water parameters such as pH, conductivity, water temperature, atmospheric pressure, and dissolved oxygen were also monitored using a locally calibrated multiprobe (YSI ProDSS probe, USA). The S::Can and the YSI ProDSS probes were mounted at the back of the ship in a bucket with a volume of ~ 20L and an overflow pipe. Water inflow was maintained by a freshwater pump from ~ 1m depth with a water flow rate of ~ 12.7 L/min (Figure A1). 2.5 Continuous oxygen and water temperature data from Rhine monitoring stations We used continuous hourly data (12.06.23–30.06.23) of dissolved oxygen and water temperature from four monitoring stations along the Rhine river for the estimation of normalized gas transfer velocities (k 600 : m d − 1 ), ecosystem respiration (ER), and gross primary production (GPP) rates in g O 2 m − 2 d − 1 . The dataset was sourced from continuous water quality monitoring stations of the NRW State Agency for Nature, Environment and Consumer Protection (LANUV) for the stations at Bimmen and Bad Honnef and from the State Office for the Environment Rhineland-Palatinate (LFU RLP) for the stations at Mainz and Worms. The modeling of these parameters from the hourly DO measurements was done using a Bayesian model built in the "StreamMetabolizer" R package (Appling et al., 2018 ). Final daily estimates of k 600 , GPP, and ER rates for the four sites were calculated by averaging daily estimates of the duration of our sampling (22-day average). In addition to the GPP and ER rates, we also calculate net ecosystem production (NEP) as the difference between GPP and ER rates. Table 1 Descriptions of the sampling sites, including name, date of sampling, flow status, and coordinates of sampling sites. Channel Morphology Site ID Site Name Longitude Latitude Ecosystem Sampling date 1 Rhine, before Neckar 49.5079 8.4373 River 12.06.2023 Mainstem 2 Rhine, after Neckar 49.5138 8.4350 River 12.06.2023 Mainstem 3 Rhine 49.5685 8.4173 River 13.06.2023 Mainstem 4 Rhine 49.8095 8.3949 River 13.06.2023 Mainstem 5 Rhine before Main 49.9945 8.2912 River 13.06.2023 Mainstem 6 Mainz harbor 50.0002 8.2502 River 14.06.2023 Harbor 7 Rhine 50.0148 8.2596 River 14.06.2023 Mainstem 8 Bingen harbor 49.9703 7.9194 River 14.06.2023 Harbor 9 Rhine 50.0177 7.8393 River 15.06.2023 Mainstem 10 St Goar harbor 50.1540 7.7088 River 15.06.2023 Harbor 11 Rhine 50.3470 7.5977 River 16.06.2023 Mainstem 12 Rhine 50.4423 7.4000 River 17.06.2023 Mainstem 13 Rhine 51.0742 6.8592 River 20.06.2023 Mainstem 14 Rhine 51.2592 6.7168 River 21.06.2023 Mainstem 15 Wesel harbor 51.6618 6.5873 River 21.06.2023 Harbor 16 Rhine 51.6503 6.6025 River 23.06.2023 Mainstem 17 Mitelland canal 51.7850 7.4066 Canal 23.06.2023 Mainstem 18 Mitelland canal 52.0562 7.6894 Canal 26.06.2023 Mainstem 19 Bad Essen harbor 52.3223 8.3495 Canal 27.06.2023 Harbor 20 Mitelland canal 52.3018 8.9190 Canal 27.06.2023 Mainstem 21 Mitelland canal 52.3743 9.1819 Canal 28.06.2023 Mainstem 22 Hannover harbor 52.4056 9.7464 Canal 29.06.2023 Harbor 23 Mitelland canal 52.3571 9.8675 Canal 29.06.2023 Mainstem 2.6 Statistical analysis To determine the importance of lotic ecosystem type (river and canal) and the presence of harbors on the spatial variability of water physicochemical variables, N 2 , and GHG % saturations, analysis of variance from significant (p-value < 0.05) linear regression models followed by a Tukey post hoc analysis of least square means was performed. The performances of these models were assessed based on the r 2 of the regressions. Apart from the presence of harbors, we used bivariate linear regression analysis to infer key biogeochemical drivers of the longitudinal GHG dynamics along the Rhine river and the canal mainstems. The dependent variables in the regression models were CO 2 , CH 4, and N 2 O % saturations, which were transformed using the natural logarithm to meet the normality assumption. The independent variables in the models were water physicochemical variables and N 2 % saturations, which serve as direct or indirect indicators of GHG production or consumption processes. Additional predictor variables such as GPP, ER, NEP, and k 600 rates were used at four sites along the Rhine river where the data was available (see methods for details). 3 Results 3.1 Comparisons between the river and canal ecosystems 3.1.1 Water physicochemical properties Summertime water physicochemical properties along the longitudinal transects of the two lotic ecosystems varied up to an order of magnitude, with SUVA 254 (a measure of DOC quality), Chlorophyll-a, TOC, and TSS concentrations showing the highest variabilities, ranging from 4.34–35.35 L mg-m − 1 , 92.87–267.30 µg L − 1 , 3.56–55.05 mg L − 1 , and 4.64–273.74 mg L − 1 , respectively (Fig. 2 ). In contrast, water temperature and DOC concentrations had a narrower range of 19.26–28.20 ℃ and 1.94–5.09 mg L − 1 , respectively. Chlorophyll-a, DOC, SUVA 254 , TOC, and TSS were significantly higher in the Mittelland canal than in the Rhine river (Table 2 ; Fig. 2 ). NO 3 -N concentrations were an order of magnitude higher than NH 4 -N concentrations, ranging from 3.91– 13.27 mg L − 1 compared to 0.15–0.50 mg L − 1 for NH 4 -N. Comparing the two lotic ecosystems, NO 3 -N concentrations were 1.4 times higher in the Mittelland canal than in the Rhine river. In contrast to NO 3 -N, NH 4 -N concentrations were not significantly different between the canal and the river system (Table 2 ; Fig. 2 ). The presence of harbors within both ecosystem types had no significant effects on all water quality parameters except for NO 3 -N concentrations in the Mittelland canal, which were two times lower in the harbors than in the canal mainstem (Fig. 2 ). Table 2 Analysis of variance results from linear regression models predicting the effect of lotic ecosystem type (river vs. canal) and presence of harbors on water physicochemical properties, N 2 and GHG concentrations, and % saturations. ANOVA from linear regression models Independent variable Df F-statistic p-value r 2 Water temperature °C 3 n.s Chl-a µg L − 1 3 19.20 8.00E-06 0.72 SUVA 254 L mg-m − 1 3 7.34 2.00E-03 0.49 DOC mg L − 1 3 7.82 1.71E-03 0.51 TOC mg L − 1 3 19.50 6.92E-06 0.73 TSS mg L − 1 3 18.41 1.02E-05 0.71 NH 4 -N mg L − 1 3 n.s NO 3 -N mg L − 1 3 7.18 2.54E-03 0.48 N 2 µmol L − 1 3 n.s N 2 saturation (%) 3 n.s CO 2 µmol L − 1 3 n.s CO 2 saturation (%) 3 n.s CH 4 µmol L − 1 3 14.33 4.05E-05 0.65 CH 4 saturation (%) 3 14.76 3.35E-05 0.65 N 2 O µmol L − 1 3 n.s N 2 O saturation (%) 3 n.s 3.1.2 N 2 and GHG concentrations N 2 concentrations and their respective % saturations were either under or over-saturated compared to equilibrium concentrations, with ranges from 462.08–522.20 µmol L − 1 and 92–104%, respectively (Fig. 3 ). Neither ecosystem type nor the presence of harbors significantly affected N 2 concentrations and % saturations (Table 2 ). CH 4 concentrations and % saturations in the Rhine and the Mittelland canal showed the highest variability with values ranging up to two orders of magnitude (0.05–3.27 µmol L − 1 and 1848–114264%). In contrast to CH 4 , CO 2 and N 2 O were less spatially variable, ranging from 11.66–82.61µmol L − 1 and 107–684% for CO 2 , and 5.9–44.6 nmol L − 1 and 116–782% for N 2 O (Fig. 3 ). CH 4 concentrations and % saturations were 5–16 times higher in the Mittelland canal than in the Rhine mainstem. However, harbors along the Rhine created CH 4 hotspots, with CH 4 values at these harbors comparable to those quantified at the Mitteland canal (Table 2 ; Fig. 3 ). Even though ecosystem type and the presence of harbors had no significant effect on CO 2 and N 2 O concentrations and % saturations, the mean CO 2 values tended to be lower in the harbors than in the mainstems of the Mitteland canal and Rhine river, while the mean N 2 O concentrations and % saturations were ~ 2 times higher in the Mittelland canal than in the Rhine river (Table 2 ; Fig. 3 ). 3.2 GPP, ER, NEP, and k 600 along the Rhine At the four sites along the mainstem of the Rhine river, the daily estimated mean GPP, ER, and NEP rates for the duration of our study ranged from 1.59–7.33, -6.57 – -5.02, and − 4.31– 0.75 g CO 2 m − 2 d − 1 , respectively (Figure A2). The gas transfer velocity (k 600 ) also varied over the same order of magnitude, ranging from 1.47–2.21 m d − 1 (Figure A2). GPP, ER, and NEP showed tendencies of an increasing trend downstream, while N 2 concentrations and % saturations decreased, and the gas transfer velocity had no clear pattern (Figure A2). 3.3.1 Longitudinal GHG trends along the two lotic ecosystems not linked to harbors The presence of harbors resulted in discontinuities in the longitudinal GHG trends along the Rhine river, particularly for CO 2 and CH 4 concentrations and % saturations (Fig. 4 ). That said, the spatial heterogeneities of the GHG % saturations along the Rhine mainstem were still significant even when harbor sites were excluded, with coefficient of variations (CV) values of 26% for CO 2 , 58% for CH 4 , and 43% for N 2 O. These GHG trends also showed relationships with the distance from the Rhine's source (Fig. 4 ). CO 2 concentrations and % saturations along the Rhine tended to increase downstream, with the highest value found after the confluence with the Main River, similar to Chlorophyll-a, TOC, and TSS concentrations (Fig. S2). In contrast to CO 2 , CH 4 concentrations and % saturations decreased downstream, with the peak value found after the confluence with the Neckar River. River confluences and harbors less influenced the downstream trends of N 2 O and thus were more unidirectional, with concentrations and % saturations showing an increase with distance from the source (Fig. 4 ). Compared to the Rhine river, the variation in GHG % saturations along the Mittelland canals mainstem was up to 2.4 times higher, with CV values of 64% for CO 2 , 98% for CH 4 , and 68% for N 2 O (Fig. S3). Increases in N 2 O concentrations corresponded with increases in N 2 oversaturation, while peak CH 4 concentrations were found at a harbor site with the lowest NO 3 concentration (Fig. S3). 3.3.2 Biogeochemical controls on the longitudinal GHG trends along the Rhine river and the Mittelland canal mainstems We used bivariate linear regressions to reveal the most relevant biogeochemical drivers of the GHG concentration found along the Rhine and the Mitteland canal mainstems based on similar changes in in-situ water physicochemical variables and N 2 % saturations, as well as GPP, ER, and NEP rates at four sites along the Rhine river (Table 3 ). At the Rhine ecosystem, CO 2 % saturations were significantly (p-value < 0.05) positively correlated with ER rates and additionally marginally (p-value < 0.10) positively correlated with DOC concentration (Table 3 ). CH 4 % saturations were negatively correlated with water temperature and marginally negatively correlated with NO 3 -N concentrations (Table 3 ). In contrast to CO 2 and CH 4 , the increasing trend of N 2 O % saturations with distance from the source was significantly predicted by most of the water physicochemical properties and process rates that showed similar positive unidirectional trends (Fig. S2). N 2 O % saturations were positively correlated to Chlorophyll-a, TOC, TSS, and NH 4 -N concentrations and GPP and NEP rates and marginally positively correlated with ER rates (Table 3 ). In contrast, N 2 % saturation negatively predicted instream N 2 O % saturations (Table 3 ; Fig. S2). Contrary to the Rhine river, the longitudinal GHG variability in the Mittelland canal mainstem was much more difficult to predict, with CH 4 % saturations showing no significant correlation with water physicochemical properties (Table 3 ). In contrast to CH 4 , CO 2, and N 2 O % saturations were negatively related to NO 3 -N concentrations and marginally positively related to N 2 % saturations, respectively (Table 3 ; Fig. S3). Table 3 Summary results of bivariate linear regression models indicating the relationship of water physicochemical properties, N 2 % saturations, GPP, ER, and K 600 with GHG % saturations in the Rhine river and Mittelland canal mainstem (Harbors not included). The significance of the slopes is denoted by n.s,. ,*, **, *** representing p-values > 0.10, < 0.10, < 0.05, < 0.01, < 0,001, respectively. Ln CO 2 saturation (%) Ln CH 4 saturation (%) Ln N 2 O saturation (%) Rhine Canal Rhine Canal Rhine Canal Df Slope r 2 Df Slope r 2 Slope r 2 Slope r 2 Slope r 2 Slope r 2 Water temperature °C 10 n.s 3 n.s -0.34* 0.28 n.s n.s n.s SUVA 254 L mg-m − 1 8 n.s 3 n.s n.s n.s n.s n.s Chl-a µg L − 1 9 n.s 3 n.s n.s n.s 0.01* 0.35 n.s DOC mg L − 1 8 0.21. 0.39 3 n.s n.s n.s n.s n.s TOC mg L − 1 9 n.s 3 n.s n.s n.s 0.03** 0.52 n.s TSS mg L − 1 n.s 3 n.s n.s n.s 0.01* 0.45 n.s NH 4 -N mg L − 1 10 n.s 3 n.s n.s n.s 2.94** 0.48 n.s NO 3 -N mg L − 1 9 n.s 3 -0.14* 0.89 -0.19. 0.23 n.s n.s n.s N 2 saturation (%) 9 n.s 3 n.s n.s n.s -0.02*** 0.78 0.17. 0.77 k 600 (m d − 1 ) 3 n.s 3 n.s n.s GPP (g O 2 m − 2 d − 1 ) 3 n.s 3 n.s 0.20** 0.92 ER (g O 2 m − 2 d − 1 ) 3 -0.27* 0.71 3 n.s -0.58. 0.42 NEP (g O 2 m − 2 d − 1 ) 3 n.s 3 n.s 0.25** 0.94 4 Discussion In contrast to previous similar studies (e.g., Bussmann et al., 2022 ; Park et al., 2023 ), our study provides a spatially explicit dataset that includes measurements of all three biogenic GHGs, N 2 concentrations and several water quality parameters along two extensive European inland waterways. Our results showed that summertime GHG % saturations in the Rhine river and the Mittelland canal were up to 3 orders of magnitude oversaturated relative to equilibrium concentrations, contributing to the growing evidence that lotic ecosystems are significant sources of GHGs (Lauerwald et al., 2023 ; Peacock et al., 2021 ; Rocher-Ros et al., 2023 ). In agreement with our hypothesis, the Mittelland canal had higher N 2 O and CH 4 % saturations than the Rhine river, likely driven by higher nitrate and organic carbon concentrations fueling in-situ 2 O and CH 4 production processes or by the inflow of dissolved GHGs from wastewater treatment plant effluents (Landesumweltamt Nordrhein-Westfalen, 2002 ). In addition, GHG % saturations in the canal mainstem were also highly spatially variable compared to the Rhine, with > 64% CV values. The comparison of both lotic ecosystems shows that divergent drivers controlled longitudinal variabilities in GHG % saturations. Spatial variability along the Rhine river was linked to harbors and site-specific biogeochemical process rates, agreeing with findings from previous studies (Bussmann et al., 2022 ; Park et al., 2023 ). For example, harbors at the Rhine river led to local hotspots of CH 4 . At the same time, the downstream increasing trends of N 2 O and CO 2 were linked to either autotrophic or heterotrophic source processes inferred from in-situ measurements of N 2 concentrations as well as GPP, ER, and NEP rates (Table 3 ; Fig. S2). In contrast to the Rhine river, most of the longitudinal GHG trends in the canal were challenging to predict from similar changes in in-situ water physicochemical properties. This finding suggested that other factors not quantified in this study, such as point-pollution of GHG sources, may have significantly controlled longitudinal GHG heterogeneities along the Mittelland canal. Nevertheless, we did find that ~ 77% of the N 2 O spatial variability in the canal ecosystem was linked to N 2 oversaturation (e.g., Ritz et al., 2018 ; Beaulieu et al., 2011 ). This result implied that denitrification was an essential source of N 2 O in the canal, opposite to what we found in the Rhine river, where coupled N-fixation and nitrification inferred from N 2 undersaturation may have accounted for N 2 O hotspots (e.g., Mwanake et al., 2019 ; Wang et al., 2021 ). Taken together, our findings suggest that single measurements along large lotic ecosystems, which ignore the presence of harbors or local biogeochemical GHG hotspots, may result in significant uncertainties in GHG emission estimates; moreover, for canal ecosystems with higher and more spatially variable GHG concentrations than river ecosystems. This study was, however, conducted only during the summer season. Therefore, seasonal differences in discharge, temperature, and GHGs were not considered, which may additionally alter longitudinal GHG trends along the two lotic ecosystems. 4.1 CO 2 concentrations CO 2 concentrations quantified in this study (11.66–82.61µmol L − 1 ) are within the range of those quantified from other studies in temperate ecosystems, as well as the global estimates (Lauerwald et al., 2015 ; Raymond et al., 2013 ), but are much lower than those quantified from heavily polluted Asian rivers (Begum et al., 2021 ). Previous studies in riverine ecosystems have found instances of CO 2 undersaturation during the temperate summer due to autotrophic uptake (Gómez-Gener et al., 2021 ). However, this study's daytime CO 2 concentrations along the Rhine river were surprisingly oversaturated throughout the summer campaign, suggesting that CO 2 production via ecosystem respiration (ER) outweighed CO 2 consumption via gross primary production (GPP). This conclusion is supported by the net ecosystem production (NEP) estimates from 4 sites along the Rhine that were primarily negative, indicating a net CO 2 loss to the atmosphere (Fig. S2). Like the Rhine river, the Mittelland canal was also oversaturated with CO 2 , albeit slightly higher than the Rhine river. Judging by the higher carbon availability in the canal, one may assume that net insitu heterotrophic processes drove CO 2 oversaturation similar to what we inferred for the Rhine above. NEP estimates were unavailable for the Mittelland canal, making linking net heterotrophy to CO 2 oversaturation challenging. However, based on the previous environmental report that indicated several inflows of treated wastewater into the Mittelland canal (Landesumweltamt Nordrhein-Westfalen, 2002 ), we hypothesized that the CO 2 oversaturation in the canal is possibly mainly driven by external dissolved CO 2 supplies from these point-sources rather than internal production (e.g., Mwanake et al., 2023). Along the two large lotic ecosystems, longitudinal discontinuities in CO 2 concentrations were collectively linked to morphological, land use, and biogeochemical drivers, similar to several other studies (Begum et al., 2021 ; Deemer et al., 2016 ; Mwanake et al., 2023). Within the Rhine river, harbor areas resulted in lower CO 2 concentrations than the river mainstem, which we attributed to their morphology that favors low flow conditions, accumulation of organic matter, and anoxic conditions that limit aerobic respiration rates. This conclusion is further supported by the slightly higher DOC, TOC, and TSS concentrations in the harbors than in the mainstem of the Rhine (Fig. 2 ). Besides morphological controls, biogeochemical rates supported CO 2 hotspots along the Rhine’s mainstem. Previous fluvial studies have linked increased ecosystem respiration rates with labile organic carbon concentrations (Mulholland et al., 2001 ; Piatka et al., 2021 ; Stelzer et al., 2003 ). This study found a positive correlation between CO 2 and DOC, suggesting instream CO 2 production via favored ecosystem respiration rates under these conditions. Furthermore, we did find a positive correlation between ecosystem respiration and CO 2 , which accounted for up to 73 % of the downstream increasing trend that we found for CO 2 , strengthening this argument. In contrast to the Rhine river, the longiudinal trends in the Mittelland canal were unpredictable with our current driving factors, supporting our earlier claim that CO 2 oversaturation in this ecosystem is likely linked to unquantified point pollution sources. 4.2 CH 4 concentrations Like CO 2 , riverine CH 4 concentrations quantified here were mainly within the range of global estimates (Rocher-Ros et al., 2023 ). However, those quantified in the canal ecosystem were primarily at the higher end of the estimates, with CH 4 % saturations indicatingan oversaturation three orders of magnitude higher than equilibrium concentrations. These findings strengthen the idea that canal ecosystems are hotspots for CH 4 emissions, similar to what has been found in past studies(Peacock et al., 2021 ; Rocher-Ros et al., 2023 ). In the Rhine river, longitudinal variability of CH 4 concentrations was linked to both physical and biogeochemical drivers. Contrary to CO 2 , harbor areas were hotspots for CH 4 oversaturation, a finding agreeing with our earlier conclusion that low flow and high organic load conditions in these areas likely favor anoxic conditions suitable for methanogenesis. A similar longitudinal study along the Elbe River (Bussmann et al., 2022 ) found higher CH 4 oversaturation in harbor areas, alluding to similar controls of longer water residence time and high sediment and organic matter conditions favorable for methane production. Along the Rhine’s mainstem, CH 4 variability was attributed to variable production rates via methanogenesis. Several studies have shown that negative correlations between NO 3 -N and CH 4 imply methane production via methanogenesis, as NO 3 -N inhibits the processes as a terminal electron acceptor(TEA) (Baulch et al., 2011 ; Schade et al., 2016 ). Our study found a similar correlation, albeit marginally significant (p-value < 0.1), pointing out that methane production hotspots via methanogenesis along the Rhine may occur in areas with low TEA, such as NO 3 -N. Like CO 2 , CH 4 concentrations along the canal mainstem could not be linked to water physicochemical variables, supporting our earlier argument that GHG supersaturation along the canal is mainly sustained by external sources from wastewater treatment plants (e.g., Mwanake et al., 2023). 4.3 N 2 O and N 2 concentrations Like CO 2 and CH 4 , riverine N 2 O concentration values fell within the global range of past estimates from large rivers(Hu et al., 2016 ), with those from the Mitteland canal comparable to N 2 O concentration values from drainage ditches draining cropland landscapes ladened by high nitrate concentrations(Reay et al., 2003 ). The high nitrate conditions in the Mittelland canal relative to the Rhine river resulted in almost double the N 2 O concentrations in the latter ecosystem. Several studies in lotic ecosystems have linked high nitrate concentrations to elevated N 2 O production from incomplete denitrification (Andrews et al., 2021 ; Beaulieu et al., 2008 ; Mwanake et al., 2019 ). However, very few studies have connected N 2 O oversaturation with direct measures of denitrification (e.g., Beaulieu et al., 2011 ), with most of the above studies inferring to N 2 O sourced from denitrification based on positive correlations with NO 3 concentrations. Such inferences may be misleading, as N 2 O production from nitrification may also have a similar positive relationship with nitrate, the end product of nitrification. When looking at the drivers of the significant longitudinal heterogeneities (CV = 68%) in N 2 O concentrations along the Mittelland canal, we did find direct evidence of the critical role of denitrification in its production. This evidence was based on the marginally significant (p-value < 0.01) positive correlation between N 2 O and N 2 % saturations (Table 3 ; Fig. S3; r 2 = 0.77), as N 2 oversaturation is solely linked to the denitrification process and hence can be usd to quantify its rates (Chen et al., 2014 ; McCutchan et al., 2003 ; Ritz et al., 2018 ; Wang et al., 2018 ). In contrast to the canal ecosystem, longitudinal N 2 O hotspots downstream of the Rhine river were linked to processes other than denitrification. High N 2 fixation rates by autotrophic and heterotrophic diazotrophs, which supply fresh and bioavailable N, have been previously reported in freshwater environments (Geisler et al., 2022 ; Riemann et al., 2022 ). Within rivers, several studies have linked measures of N 2 undersaturation to significant rates of N 2 fixation by diazotrophs(Wang et al., 2021 ; Wu et al., 2013 ). This study found substantial downstream increases in N 2 undersaturation of up to 92%, which more than doubled instream N 2 O concentrations (Fig. 4 ; Fig. S2). Like N 2 O, NH 4 -N, Chlorophyll-a, TOC, TSS concentrations, and GPP and NEP rates also increased downstream of the Rhine (Fig. S2). These findings implied significant N 2 -fixation rates by cyanobacteria and heterotrophic diazotrophs, possibly providing the NH 4 -N required for N 2 O production via nitrification. Such significant N 2 fixation rates have been previously reported in similar highly urbanized rivers in Beijing, with an N 2 undersaturation of up to 88 %(Wang et al., 2021 ). We also found significant positive relationships of N 2 O with NH 4 -N, Chlorophll-a, TOC, GPP, and NEP rates and their antagonistic relationship with N 2 oversaturation, further supporting this argument (Table 3 ). Conclusion Our unique dataset, comprising CO 2 , CH 4 , N 2 O, and N 2 concentrations, multiple water physicochemical variables, and metabolism rate estimates, allowed us to compare the roles of biogeochemical processes, surrounding land use, and channel morphologies in controlling the longitudinal GHG heterogeneities along two large lotic ecosystems with a combined reach length of 632km. The findings of this study revealed that these drivers were divergent between the two lotic ecosystems characterized by different morphologies, stream velocities, and nutrient inputs. Within the Mittelland canal, longitudinal CO 2 and CH 4 hotspots were mainly linked to external inflows of the GHGs from surrounding WWTPs and less connected to in-situ biogeochemical processes. Contrastingly, harbors and in-situ biogeochemical processes such as methanogenesis and respiration explained most CH 4 and CO 2 hotspots along the Rhine river. Across both lotic ecosystems, N 2 O was strongly linked to N 2 concentrations, albeit with opposite relationships in the Rhine river and Mittelland canal, revealing source process information that would have been missed without quantifying the in-situ N 2 concentration. This finding stresses the need to include N 2 concentration measurements in GHG sampling campaigns, as it has the potential to determine whether nitrogen is fixed through N-fixation or lost through denitrification in an ecosystem, with severe consequences to both N and C cycling that results in the production of GHGs in lotic ecosystems. Overall, the longitudinal heterogeneities in GHG % saturations across both lotic ecosystems mainstems were highly significant (CV > 25%), suggesting that single measurements along large lotic ecosystems may result in significant uncertainties in GHG emission estimates(e.g., Bussmann et al., 2022 ). This finding is particularly relevant for large canal ecosystems in highly urbanized areas, which we showed have higher and more spatially variable GHG concentrations (CV > 90 %) than river ecosystems butremain underinvestigated. Declarations Funding Infrastructure for the research was provided by the TERENO Bavarian Alps/ Pre-Alps Observatory, funded by the Helmholtz Association through the joint program; Atmosphere and Climate (ATMO - PoF III) program of Karlsruhe Institute of Technology (KIT). Hannes Imhof was funded by the German Federal Ministry of Education and Research (BMBF) within the project Integrated Greenhouse Gas Monitoring for Germany (ITMS) – Module Sources & Sinks (Grant Number: 01LK2105D). Competing interest The authors have no relevant financial or non-financial interests to disclose. Author contribution All authors contributed to the conceptualization and design of the work. Funding acquisition was done by Ralf Kiese. Ricky. M. Mwanake and Hannes Imhof prepared the field materials and collected the field data: Ricky. M. Mwanake conducted the formal analysis and wrote the first manuscript draft. All authors contributed to revising and editing the final manuscript draft. Ethical Approval Not applicable Consent to Participate Not applicable Consent to Publish Not applicable Acknowledgments We would like to acknowledge the German Ocean Foundation for the possibility of being part of the Rhine Expedition 2023 and hosting our experiments. 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Dynamics of greenhouse gases (CO2, CH4, N2O) along the Zambezi River and major tributaries, and their importance in the riverine carbon budget. Biogeosciences , 12 (8), 2431–2453. https://doi.org/10.5194/bg-12-2431-2015 Wang, G., Wang, J., Xia, X., Zhang, L., Zhang, S., McDowell, W. H., & Hou, L. (2018). Nitrogen removal rates in a frigid high-altitude river estimated by measuring dissolved N2 and N2O. Science of The Total Environment , 645 , 318–328. https://doi.org/https://doi.org/10.1016/j.scitotenv.2018.07.090 Wang, G., Xia, X., Liu, S., Wang, J., & Zhang, S. (2021). Low diffusive nitrogen loss of urban inland waters with high nitrogen loading. Science of the Total Environment , 789 . https://doi.org/10.1016/j.scitotenv.2021.148023 Wu, J., Chen, N., Hong, H., Lu, T., Wang, L., & Chen, Z. (2013). Direct measurement of dissolved N2 and denitrification along a subtropical river-estuary gradient, China. Marine Pollution Bulletin , 66 (1), 125–134. https://doi.org/https://doi.org/10.1016/j.marpolbul.2012.10.020 Yao, Y., Tian, H., Shi, H., Pan, S., Xu, R., Pan, N., & Canadell, J. G. (2020). Increased global nitrous oxide emissions from streams and rivers in the Anthropocene. In Nature Climate Change (Vol. 10, Issue 2, pp. 138–142). Nature Research. https://doi.org/10.1038/s41558-019-0665-8 Supplementary Files Mwanakeimhofkiese181223SI.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revision 09 Feb, 2024 Reviewers agreed at journal 04 Jan, 2024 Reviewers invited by journal 04 Jan, 2024 Editor invited by journal 03 Jan, 2024 Editor assigned by journal 27 Dec, 2023 First submitted to journal 18 Dec, 2023 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3722436","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265463643,"identity":"4b6633f8-fc8e-4bc5-8f48-bff35f9fc777","order_by":0,"name":"Ricky Mwangada Mwanake","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDACZiCEMxkqbAzYQEwe4rWcSTNgYyOkhQFZC2PbYQMGQlrM25kfG/MwbEvsn9382OAD23ljPvkGxgdv23BrkTnMZpzMw3A7ccadY8aJM3humwEdxmw4F48WCWYe5sNALbkNNxKMD/NI3LYBamGT5iVGy/wb6Z8P/zE4B9LC/puQFpDDcjfcyDFOZkg4AHIYGzN+LWzGhnMMbtdvvJFTbNhzINmYjS2xWXLOOTxa+A8/lnhTcdtY7kb6Zomf/+wM5zcfPvjhTRluLSDAxGOAwmdswK8epOQHQSWjYBSMglEwogEAh41HntvVd2EAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1557-8345","institution":"KIT: Karlsruher Institut fur Technologie","correspondingAuthor":true,"prefix":"","firstName":"Ricky","middleName":"Mwangada","lastName":"Mwanake","suffix":""},{"id":265463644,"identity":"5301c4fc-eaf4-4ab3-90a7-f3f359cb1f1b","order_by":1,"name":"Hannes Imhof","email":"","orcid":"","institution":"KIT: Karlsruher Institut fur Technologie","correspondingAuthor":false,"prefix":"","firstName":"Hannes","middleName":"","lastName":"Imhof","suffix":""},{"id":265463645,"identity":"3f5c730b-08e2-47cc-b3b3-7f59e1e34a38","order_by":2,"name":"Ralf Kiese","email":"","orcid":"","institution":"KIT: Karlsruher Institut fur Technologie","correspondingAuthor":false,"prefix":"","firstName":"Ralf","middleName":"","lastName":"Kiese","suffix":""}],"badges":[],"createdAt":"2023-12-07 21:27:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3722436/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3722436/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49330784,"identity":"862ec9a4-b6b0-4c1d-99ff-86c51433a66f","added_by":"auto","created_at":"2024-01-08 19:10:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2892910,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the Rhine river and the Mittelland canal. The black dots represent the 23 sampled sites with their site number; the red dots symbolize the continuous water monitoring stations used to calculate metabolism data (Table 1). The background map is from Google Earth.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3722436/v1/c82839f4221a54ffc729e5d9.png"},{"id":49330785,"identity":"22ea572f-a342-4237-b6d2-29c4a902242e","added_by":"auto","created_at":"2024-01-08 19:10:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":116733,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots indicating the effect of lotic ecosystem type and presence of harbors on the spatial variability of water physicochemical properties. The letters on top of the boxplots indicate significant differences from Tukey post hoc analysis of least square means (Table 2).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3722436/v1/6b8016720c2325fa04cd2012.png"},{"id":49330786,"identity":"1809549f-75cc-40a0-8aca-ce4b395c6e71","added_by":"auto","created_at":"2024-01-08 19:10:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126208,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots indicating the effect of lotic ecosystem type and the presence of harbors on the spatial variability of N\u003csub\u003e2\u003c/sub\u003e and GHG concentrations and % saturations. The letters on top of the boxplots indicate significant differences from Tukey post hoc analysis of least square means (Table 2).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3722436/v1/6b665b91472b05daef8af9f7.png"},{"id":49330783,"identity":"ea0828cb-2969-4a66-a46e-6023c92b3730","added_by":"auto","created_at":"2024-01-08 19:10:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":91258,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal trends of CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4,\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO concentrations and saturations along the Rhine river. Blue-colored points indicate harbor sites, while black-colored points indicate sites along the mainstem.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3722436/v1/0764318230b106bb5f73fefd.png"},{"id":49331295,"identity":"eae561ad-4133-4ec9-bfe2-daface92ccc2","added_by":"auto","created_at":"2024-01-08 19:18:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3034183,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3722436/v1/23b37839-b084-4178-888d-d25ae4fd2fea.pdf"},{"id":49330787,"identity":"714ac4a8-2d54-4317-9dc7-5857d753d7f9","added_by":"auto","created_at":"2024-01-08 19:10:53","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1683906,"visible":true,"origin":"","legend":"","description":"","filename":"Mwanakeimhofkiese181223SI.docx","url":"https://assets-eu.researchsquare.com/files/rs-3722436/v1/25233488553ada9371cad38f.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eDivergent drivers of the spatial variabilities in CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e, N\u003csub\u003e2\u003c/sub\u003eO, and N\u003csub\u003e2\u003c/sub\u003e concentrations along the Rhine river and the Mittelland canal in Germany\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eInland waters, comprising lotic (streams, rivers, and canals) and lentic (reservoirs, lakes, and ponds) ecosystems, are increasingly recognized as significant sources of greenhouse gases (GHG), contributing (~\u0026thinsp;25%; 13.5 (9.9\u0026ndash;20.1) Pg CO\u003csub\u003e2\u003c/sub\u003e-eq) of the global CO\u003csub\u003e2\u003c/sub\u003e-equivalent emissions (Lauerwald et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In comparison, lotic ecosystems contribute disproportionately higher CO\u003csub\u003e2\u003c/sub\u003e-eq emissions (\u0026gt;\u0026thinsp;60%) than lentic ecosystems, linked to their close connectivity with terrestrial landscapes and their flowing and turbulent nature that favors gas evasion to the atmosphere (Raymond et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rocher-Ros et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yao et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Because of the variable nature of the surrounding landscapes affecting catchment hydrology and differences in channel geomorphologies, the GHG concentrations of lotic ecosystems are highly heterogeneous in space (e.g., Ho et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mwanake et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Teodoru et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e),and may introduce significant uncertainties to GHG emission estimates from inland waters if not precisely quantified.\u003c/p\u003e \u003cp\u003eWhile research has demonstrated that headwaters, the inception points of river networks, contribute more than two-thirds of the global GHG emissions from riverine ecosystems (e.g., Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), anthropogenic landscapes along large rivers may modify or even reverse this trend. Previous studies have shown that nutrients and carbon inputs from human-influenced landscapes to large rivers favor GHG production processes such as nitrification, incomplete denitrification, methanogenesis, and respiration, creating GHG hotspots comparable to or higher than those from headwater streams (Borges et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mwanake et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mwanake et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). For instance, Mwanake et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) reported higher or comparable GHG emissions from temperate rivers in Germany relative to small streams linked to inorganic N and labile carbon inputs from surrounding upstream cropland and urban areas. Similar findings were also found along the Seine River downstream of wastewater treatment plants in large metropolitan areas in France (Marescaux et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nevertheless, field studies incorporating multiple measurements of GHG concentrations along large river transects with varying channel morphologies and transversing cropland and urban-dominated landscapes are still limited (e.g., Begum et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Leng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), making it challenging to understand critical drivers, magnitude and large-scale longitudinal heterogeneities of river GHG emissions.\u003c/p\u003e \u003cp\u003eDrainage ditches and canals, whose GHG dynamics are often understudied compared to rivers, have also been shown to be potent GHG hotspots of mainly CH\u003csub\u003e4\u003c/sub\u003e emissions due to their low flow velocities that provide suitable anaerobic conditions for CH\u003csub\u003e4\u003c/sub\u003e production. It is estimated that these canals contribute\u0026thinsp;~\u0026thinsp;3% of the global anthropogenic CH\u003csub\u003e4\u003c/sub\u003e emissions (Peacock et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while they can also be hotspots of N\u003csub\u003e2\u003c/sub\u003eO emissions, especially when they receive N inputs from surrounding landscapes (Reay et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Like rivers, different channel morphologies and land uses along canal systems may also result in significant spatial GHG heterogeneities. However, this remains uncertain, as most studies often take single samples along large canals to represent whole channel GHG magnitudes (Peacock et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eApart from GHG hotspots directly linked to allochthonous nutrients and carbon inputs, increased water-column primary production, especially during warm periods of the year, may also alter longitudinal GHG trends along large lotic ecosystems with relatively low water flow conditions. Although the influence of primary producers on lotic GHG concentrations remains uncertain due to the scarcity of studies with paired measurements of GHG concentrations and river metabolism rates (Battin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), several potential mechanisms can be hypothesized linked to their effects on nutrient and carbon cycling. The decomposition of primary producers could supply fresh and more labile autochthonous organic C, favoring GHG production processes through C and N cycling. At the same time, N-fixing cyanobacteria may contribute to excess mineral N, driving in-situ GHG production, particularly of N\u003csub\u003e2\u003c/sub\u003eO.\u003c/p\u003e \u003cp\u003eConventional sampling approaches, which primarily focus on single locations along large rivers and canals, have been extensively applied to determine the magnitudes of their reach-scale GHG emissions (e.g., Mwanake et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Peacock et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such approaches fail to capture the intricate nuances of large-scale longitudinal GHG dynamics (e.g., Bussmann et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Teodoru et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), resulting in overestimation or underestimation of reach scale fluxes when the single measurements are upscaled over long distances. The lack of better spatial coverage in these large lotic ecosystems has been mainly driven by extensive resource requirements in making detailed longitudinal measurements along elongated reaches that can be hundreds of km long. One approach that can overcome resource barriers is using high precision and relatively cheap sensors that simultaneously measure water quality parameters such as dissolved oxygen, nutrients, and organic matter (OM) content (Bieroza et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Combined with GHG concentration analysis, these sensors may constrict the critical drivers of longitudinal GHG discontinuities along lotic ecosystems, enhancing our capacity to model and manage these spatial GHG hotspots.\u003c/p\u003e \u003cp\u003eThis study tested a novel sampling approach along one of the largest European rivers (Rhine, Germany) and canal systems (Mittelland canal, Germany). Within the framework of a cruise mission, we explored a combined lotic reach of 632 km. Our approach encompassed longitudinal measurements of grab GHG samples (N\u003csub\u003e2\u003c/sub\u003eO, CO\u003csub\u003e2\u003c/sub\u003e, and CH\u003csub\u003e4\u003c/sub\u003e) with onsite sensor measurements of multiple water physicochemical parameters during the temperate summer. In addition, we also took grab samples for N\u003csub\u003e2\u003c/sub\u003e concentration analysis along both lotic ecosystems, which is a proxy for major N pathways (denitrification or N\u003csub\u003e2\u003c/sub\u003e fixation) but remain currently understudied in large lotic ecosystems. Our main objectives were: 1) To compare GHG concentrations and % saturations from the Rhine river with the Mittelland canal system. 2) To quantify large-scale longitudinal GHG heterogeneities along the two lotic ecosystems and infer the key drivers of these longitudinal GHG trends. We hypothesized that the canal would be a hotspot for CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO % saturations, driven by more anoxic conditions, point source inputs of nutrients, and availability of labile carbon from autotrophic processes. We also hypothesized that changes in either morphological (e.g., the existence of harbors) or biogeochemical processes would explain most of the longitudinal variabilities along both lotic ecosystems mainstems.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe Rhine river is among the ten largest rivers in Europe, with a total length of 1233 km and a catchment size of 185500 km\u0026sup2;. The Rhine river has its sources in the Swiss Alps, enters Lake Constance on the eastern part, crosses Lake Constance via Konstanz, and leaves it on the southwestern side. From Basel onwards, the Rhine river flows towards the North Sea, and from here on, the Rhine is intensively used as an inland waterway and for hydropower production, traversing multiple landscapes with mixed land uses. This study was conducted on the German side of the Rhine river catchment, one of Germany's most important inland waterways, with a yearly transport volume of 6\u0026nbsp;million tons. The first sampling point along the Rhine river was located at the midsection of the river (653 km from the source), while our most downstream location was at Wesel, Germany (1023 km from the source) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Besides large industrial areas, the upstream land use at this downstream station in Wesel comprises 38% forest, 29% croplands, 9% urban, and 17% Grasslands (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Mittelland canal traverses Germany from West to East. It is one of the central connections of the industrialized areas in the northern Ruhr area to the large harbors of the North Sea (e.g., Rotterdam, Antwerpen, Amsterdam). Moreover, via the Elbe Side canal, goods are shipped to Hamburg and the Eastern Sea. In addition to the Rhine and Neckar, the canal is one of Germany's most important inland waterways and has a yearly transport volume of 6\u0026nbsp;million tons. Six large locks are located between sites 16 to 23 in Friedrichsfeld, H\u0026uuml;nxe, Dorsten, Flaesheim, Ahsen, and Datteln (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These locks control the water flow within the Canal by pumping water in and out, assisting the movement of large ships from west to east. Apart from its use as an inland waterway, the Mittelland canal is part of the water distribution network in Germany. Specifically, freshwater is transported from the Lippe, the lower Ruhr, and the Rhine rivers to the east, where industries and irrigation use it. According to an environmental report by the local environment agency, the canal is considered highly eutrophic due to point-source inputs of nutrients from several wastewater treatment plants and rivers along its longitudinal section (Landesumweltamt Nordrhein-Westfalen, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sampling strategy\u003c/h2\u003e \u003cp\u003eSampling was performed between 12.06.2023 and 06.07.2023 mostly between 9:00 am and 5:00 pm on the Science and Media Vessel ALDEBARAN run by the German Ocean Foundation (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This field campaign was part of a more extensive Rhine field expedition, which aimed at connecting actors from society, business, and politics concerning the traces of human influences on water quality and biodiversity along the Rhine. In this study, 23 lotic sites were sampled, comprising 16 river sites (including four harbors) along the Rhine river (386km stretch) and seven canal sites (including two harbors) along the Mittelland canal (246 km stretch) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Water and gas sampling\u003c/h2\u003e \u003cp\u003eRiver and canal water was collected with a bucket from a depth of ~\u0026thinsp;1m below the water surface and sampled for ammonium, GHG (CO\u003csub\u003e2\u003c/sub\u003e, N\u003csub\u003e2\u003c/sub\u003eO, and CH\u003csub\u003e4\u003c/sub\u003e), and N\u003csub\u003e2\u003c/sub\u003e concentrations. For GHGs, triplicate samples were drawn from the bucket water using the headspace equilibration technique (Aho \u0026amp; Raymond, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In brief, 80 ml of water was equilibrated with 20 ml of atmospheric air in a syringe after shaking for 2 minutes in the bucket to maintain in-situ temperatures. The headspace gas samples were transferred into 10 ml glass vials for GHG concentration analysis in the laboratory using an SRI gas chromatograph (8610C, Germany) with an electron capture detector (ECD) for N\u003csub\u003e2\u003c/sub\u003eO and a flame ionization detector (FID) with an upstream methanizer for simultaneous measurements of CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e concentrations. Dissolved GHG concentrations in the river and canal water were calculated from post-equilibration gas concentrations in the headspace after correcting for atmospheric (ambient) GHG concentrations (Aho \u0026amp; Raymond, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mwanake et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuplicate water samples for N\u003csub\u003e2\u003c/sub\u003e concentration measurements were collected from the bucket in gas-tight 12 ml exetainers (Labco, UK) without air bubbles and stored in a refrigerator until analysis. In the laboratory, measurements of dissolved N\u003csub\u003e2\u003c/sub\u003e were carried out on a membrane inlet mass spectrophotometer (MIMS; Bay instruments, USA) at close to in-situ temperatures (~\u0026thinsp;20 ℃) following the procedure outlined inKana et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). In brief, the MIMS measurements involved continuous uptake of the water samples through a gas-permeable silicone membrane using a peristaltic pump and detecting N\u003csub\u003e2\u003c/sub\u003e (mass 28) on a quadrupole mass spectrophotometer (Pfeiffer vacuum PrismaPlus). We used N\u003csub\u003e2\u003c/sub\u003e:Ar current ratios to measure N\u003csub\u003e2\u003c/sub\u003e concentrations with high precision (\u0026lt;\u0026thinsp;0.05 CV%) (Kana et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; An et al., 2001). The MIMS setup included a liquid N\u003csub\u003e2\u003c/sub\u003e trap and a reduction furnace to minimize water vapor interference and other dissolved gases on the N\u003csub\u003e2\u003c/sub\u003e measurements (Kana et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Lastly, samples for ammonium measurements were taken from the same volume of water in the bucket and temporarily stored in a falcon tube. Analysis of ammonium (NH\u003csub\u003e4\u003c/sub\u003e-N) was done immediately in the field using a pHPhotoFlex Turb (WTW Germany) and the ammonium cuvette test A6/25 (173700, WTW Germany).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sensor measurements of water physicochemical properties\u003c/h2\u003e \u003cp\u003eOnsite measurements of water physicochemical properties such as NO\u003csub\u003e3\u003c/sub\u003e-N, total organic carbon (TOC), total suspended solids (TSS), dissolved organic carbon (DOC), Chlorophyll-a, UV254, and UV254f were done simultaneously with the grab samples using a calibrated optical sensor (S::Can spectro::lyser V3, Messtechnik GmbH, Vienna, Austria). The specific ultraviolet absorbance at 254 nm (SUVA\u003csub\u003e254,\u003c/sub\u003e a measure of DOC quality) was calculated by dividing UV254 by DOC concentrations to estimate DOC aromaticity (e.g., Bodmer et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additional water parameters such as pH, conductivity, water temperature, atmospheric pressure, and dissolved oxygen were also monitored using a locally calibrated multiprobe (YSI ProDSS probe, USA). The S::Can and the YSI ProDSS probes were mounted at the back of the ship in a bucket with a volume of ~\u0026thinsp;20L and an overflow pipe. Water inflow was maintained by a freshwater pump from ~\u0026thinsp;1m depth with a water flow rate of ~\u0026thinsp;12.7 L/min (Figure A1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Continuous oxygen and water temperature data from Rhine monitoring stations\u003c/h2\u003e \u003cp\u003eWe used continuous hourly data (12.06.23\u0026ndash;30.06.23) of dissolved oxygen and water temperature from four monitoring stations along the Rhine river for the estimation of normalized gas transfer velocities (k\u003csub\u003e600\u003c/sub\u003e: m d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), ecosystem respiration (ER), and gross primary production (GPP) rates in g O\u003csub\u003e2\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The dataset was sourced from continuous water quality monitoring stations of the NRW State Agency for Nature, Environment and Consumer Protection (LANUV) for the stations at Bimmen and Bad Honnef and from the State Office for the Environment Rhineland-Palatinate (LFU RLP) for the stations at Mainz and Worms. The modeling of these parameters from the hourly DO measurements was done using a Bayesian model built in the \"StreamMetabolizer\" R package (Appling et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Final daily estimates of k\u003csub\u003e600\u003c/sub\u003e, GPP, and ER rates for the four sites were calculated by averaging daily estimates of the duration of our sampling (22-day average). In addition to the GPP and ER rates, we also calculate net ecosystem production (NEP) as the difference between GPP and ER rates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptions of the sampling sites, including name, date of sampling, flow status, and coordinates of sampling sites.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChannel Morphology\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEcosystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSampling date\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhine, before Neckar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.5079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.4373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhine, after Neckar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.5138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.4350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.5685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.4173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.8095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.3949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhine before Main\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.9945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.2912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMainz harbor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.2502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHarbor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.0148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.2596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBingen harbor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.9703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.9194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHarbor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.0177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.8393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSt Goar\u0026nbsp;harbor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.1540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.7088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHarbor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.3470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.5977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.4423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.0742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.8592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.2592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.7168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWesel harbor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.6618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.5873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHarbor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.6503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.6025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMitelland canal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.7850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.4066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCanal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMitelland canal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.0562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.6894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCanal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBad\u0026nbsp; Essen harbor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.3223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.3495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCanal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHarbor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMitelland canal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.3018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.9190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCanal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMitelland canal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.3743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.1819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCanal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHannover harbor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.4056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.7464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCanal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHarbor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMitelland canal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.3571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.8675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCanal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.06.2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMainstem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eTo determine the importance of lotic ecosystem type (river and canal) and the presence of harbors on the spatial variability of water physicochemical variables, N\u003csub\u003e2\u003c/sub\u003e, and GHG % saturations, analysis of variance from significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) linear regression models followed by a Tukey post hoc analysis of least square means was performed. The performances of these models were assessed based on the r\u003csup\u003e2\u003c/sup\u003e of the regressions.\u003c/p\u003e \u003cp\u003eApart from the presence of harbors, we used bivariate linear regression analysis to infer key biogeochemical drivers of the longitudinal GHG dynamics along the Rhine river and the canal mainstems. The dependent variables in the regression models were CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4,\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO % saturations, which were transformed using the natural logarithm to meet the normality assumption. The independent variables in the models were water physicochemical variables and N\u003csub\u003e2\u003c/sub\u003e% saturations, which serve as direct or indirect indicators of GHG production or consumption processes. Additional predictor variables such as GPP, ER, NEP, and k\u003csub\u003e600\u003c/sub\u003e rates were used at four sites along the Rhine river where the data was available (see methods for details).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Comparisons between the river and canal ecosystems\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Water physicochemical properties\u003c/h2\u003e \u003cp\u003eSummertime water physicochemical properties along the longitudinal transects of the two lotic ecosystems varied up to an order of magnitude, with SUVA\u003csub\u003e254\u003c/sub\u003e (a measure of DOC quality), Chlorophyll-a, TOC, and TSS concentrations showing the highest variabilities, ranging from 4.34\u0026ndash;35.35 L mg-m \u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 92.87\u0026ndash;267.30 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 3.56\u0026ndash;55.05 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 4.64\u0026ndash;273.74 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, water temperature and DOC concentrations had a narrower range of 19.26\u0026ndash;28.20 ℃ and 1.94\u0026ndash;5.09 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Chlorophyll-a, DOC, SUVA\u003csub\u003e254\u003c/sub\u003e, TOC, and TSS were significantly higher in the Mittelland canal than in the Rhine river (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). NO\u003csub\u003e3\u003c/sub\u003e-N concentrations were an order of magnitude higher than NH\u003csub\u003e4\u003c/sub\u003e-N concentrations, ranging from 3.91\u0026ndash; 13.27 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e compared to 0.15\u0026ndash;0.50 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for NH\u003csub\u003e4\u003c/sub\u003e-N. Comparing the two lotic ecosystems, NO\u003csub\u003e3\u003c/sub\u003e-N concentrations were 1.4 times higher in the Mittelland canal than in the Rhine river. In contrast to NO\u003csub\u003e3\u003c/sub\u003e-N, NH\u003csub\u003e4\u003c/sub\u003e-N concentrations were not significantly different between the canal and the river system (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The presence of harbors within both ecosystem types had no significant effects on all water quality parameters except for NO\u003csub\u003e3\u003c/sub\u003e-N concentrations in the Mittelland canal, which were two times lower in the harbors than in the canal mainstem (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of variance results from linear regression models predicting the effect of lotic ecosystem type (river vs. canal) and presence of harbors on water physicochemical properties, N\u003csub\u003e2\u003c/sub\u003e and GHG concentrations, and % saturations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eANOVA from linear regression models\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater temperature \u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChl-a \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.00E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUVA\u003csub\u003e254\u003c/sub\u003e L mg-m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOC mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.71E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOC mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.92E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSS mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e-N mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e-N mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.54E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003e \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003e saturation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e saturation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.05E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e saturation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.35E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003eO saturation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 N\u003csub\u003e2\u003c/sub\u003e and GHG concentrations\u003c/h2\u003e \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003e concentrations and their respective % saturations were either under or over-saturated compared to equilibrium concentrations, with ranges from 462.08\u0026ndash;522.20 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 92\u0026ndash;104%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Neither ecosystem type nor the presence of harbors significantly affected N\u003csub\u003e2\u003c/sub\u003e concentrations and % saturations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). CH\u003csub\u003e4\u003c/sub\u003e concentrations and % saturations in the Rhine and the Mittelland canal showed the highest variability with values ranging up to two orders of magnitude (0.05\u0026ndash;3.27 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 1848\u0026ndash;114264%). In contrast to CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO were less spatially variable, ranging from 11.66\u0026ndash;82.61\u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 107\u0026ndash;684% for CO\u003csub\u003e2\u003c/sub\u003e, and 5.9\u0026ndash;44.6 nmol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 116\u0026ndash;782% for N\u003csub\u003e2\u003c/sub\u003eO (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). CH\u003csub\u003e4\u003c/sub\u003e concentrations and % saturations were 5\u0026ndash;16 times higher in the Mittelland canal than in the Rhine mainstem. However, harbors along the Rhine created CH\u003csub\u003e4\u003c/sub\u003e hotspots, with CH\u003csub\u003e4\u003c/sub\u003e values at these harbors comparable to those quantified at the Mitteland canal (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Even though ecosystem type and the presence of harbors had no significant effect on CO\u003csub\u003e2\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO concentrations and % saturations, the mean CO\u003csub\u003e2\u003c/sub\u003e values tended to be lower in the harbors than in the mainstems of the Mitteland canal and Rhine river, while the mean N\u003csub\u003e2\u003c/sub\u003eO concentrations and % saturations were ~\u0026thinsp;2 times higher in the Mittelland canal than in the Rhine river (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 GPP, ER, NEP, and k\u003csub\u003e600\u003c/sub\u003e along the Rhine\u003c/h2\u003e \u003cp\u003eAt the four sites along the mainstem of the Rhine river, the daily estimated mean GPP, ER, and NEP rates for the duration of our study ranged from 1.59\u0026ndash;7.33, -6.57 \u0026ndash; -5.02, and \u0026minus;\u0026thinsp;4.31\u0026ndash; 0.75 g CO\u003csub\u003e2\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively (Figure A2). The gas transfer velocity (k\u003csub\u003e600\u003c/sub\u003e) also varied over the same order of magnitude, ranging from 1.47\u0026ndash;2.21 m d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Figure A2). GPP, ER, and NEP showed tendencies of an increasing trend downstream, while N\u003csub\u003e2\u003c/sub\u003e concentrations and % saturations decreased, and the gas transfer velocity had no clear pattern (Figure A2).\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Longitudinal GHG trends along the two lotic ecosystems not linked to harbors\u003c/h2\u003e \u003cp\u003eThe presence of harbors resulted in discontinuities in the longitudinal GHG trends along the Rhine river, particularly for CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e concentrations and % saturations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). That said, the spatial heterogeneities of the GHG % saturations along the Rhine mainstem were still significant even when harbor sites were excluded, with coefficient of variations (CV) values of 26% for CO\u003csub\u003e2\u003c/sub\u003e, 58% for CH\u003csub\u003e4\u003c/sub\u003e, and 43% for N\u003csub\u003e2\u003c/sub\u003eO. These GHG trends also showed relationships with the distance from the Rhine's source (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). CO\u003csub\u003e2\u003c/sub\u003e concentrations and % saturations along the Rhine tended to increase downstream, with the highest value found after the confluence with the Main River, similar to Chlorophyll-a, TOC, and TSS concentrations (Fig. S2). In contrast to CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e concentrations and % saturations decreased downstream, with the peak value found after the confluence with the Neckar River. River confluences and harbors less influenced the downstream trends of N\u003csub\u003e2\u003c/sub\u003eO and thus were more unidirectional, with concentrations and % saturations showing an increase with distance from the source (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Compared to the Rhine river, the variation in GHG % saturations along the Mittelland canals mainstem was up to 2.4 times higher, with CV values of 64% for CO\u003csub\u003e2\u003c/sub\u003e, 98% for CH\u003csub\u003e4\u003c/sub\u003e, and 68% for N\u003csub\u003e2\u003c/sub\u003eO (Fig. S3). Increases in N\u003csub\u003e2\u003c/sub\u003eO concentrations corresponded with increases in N\u003csub\u003e2\u003c/sub\u003e oversaturation, while peak CH\u003csub\u003e4\u003c/sub\u003e concentrations were found at a harbor site with the lowest NO\u003csub\u003e3\u003c/sub\u003e concentration (Fig. S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3.2 Biogeochemical controls on the longitudinal GHG trends along the Rhine river and the Mittelland canal mainstems\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe used bivariate linear regressions to reveal the most relevant biogeochemical drivers of the GHG concentration found along the Rhine and the Mitteland canal mainstems based on similar changes in in-situ water physicochemical variables and N\u003csub\u003e2\u003c/sub\u003e% saturations, as well as GPP, ER, and NEP rates at four sites along the Rhine river (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). At the Rhine ecosystem, CO\u003csub\u003e2\u003c/sub\u003e% saturations were significantly (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) positively correlated with ER rates and additionally marginally (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.10) positively correlated with DOC concentration (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). CH\u003csub\u003e4\u003c/sub\u003e% saturations were negatively correlated with water temperature and marginally negatively correlated with NO\u003csub\u003e3\u003c/sub\u003e-N concentrations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast to CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e, the increasing trend of N\u003csub\u003e2\u003c/sub\u003eO % saturations with distance from the source was significantly predicted by most of the water physicochemical properties and process rates that showed similar positive unidirectional trends (Fig. S2). N\u003csub\u003e2\u003c/sub\u003eO % saturations were positively correlated to Chlorophyll-a, TOC, TSS, and NH\u003csub\u003e4\u003c/sub\u003e-N concentrations and GPP and NEP rates and marginally positively correlated with ER rates (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, N\u003csub\u003e2\u003c/sub\u003e% saturation negatively predicted instream N\u003csub\u003e2\u003c/sub\u003eO % saturations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig. S2).\u003c/p\u003e \u003cp\u003eContrary to the Rhine river, the longitudinal GHG variability in the Mittelland canal mainstem was much more difficult to predict, with CH\u003csub\u003e4\u003c/sub\u003e% saturations showing no significant correlation with water physicochemical properties (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast to CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2,\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO % saturations were negatively related to NO\u003csub\u003e3\u003c/sub\u003e-N concentrations and marginally positively related to N\u003csub\u003e2\u003c/sub\u003e% saturations, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig. S3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary results of bivariate linear regression models indicating the relationship of water physicochemical properties, N\u003csub\u003e2\u003c/sub\u003e% saturations, GPP, ER, and K\u003csub\u003e600\u003c/sub\u003e with GHG % saturations in the Rhine river and Mittelland canal mainstem (Harbors not included). The significance of the slopes is denoted by n.s,. ,*, **, *** representing p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.10, \u0026lt; 0.10, \u0026lt;\u0026thinsp;0.05, \u0026lt;\u0026thinsp;0.01, \u0026lt; 0,001, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eLn CO\u003csub\u003e2\u003c/sub\u003e saturation (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003eLn CH\u003csub\u003e4\u003c/sub\u003e saturation (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e \u003cp\u003eLn N\u003csub\u003e2\u003c/sub\u003eO saturation (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRhine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eCanal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eRhine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eCanal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eRhine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003eCanal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater temperature \u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.34*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUVA\u003csub\u003e254\u003c/sub\u003e L mg-m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChl-a \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOC mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOC mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.03**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSS mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e-N mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.94**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e-N mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.14*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.19.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003csub\u003e2\u003c/sub\u003e saturation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.02***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.17.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ek\u003csub\u003e600\u003c/sub\u003e (m d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPP (g O\u003csub\u003e2\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.20**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER (g O\u003csub\u003e2\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.27*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.58.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEP (g O\u003csub\u003e2\u003c/sub\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003en.s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn contrast to previous similar studies (e.g., Bussmann et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), our study provides a spatially explicit dataset that includes measurements of all three biogenic GHGs, N\u003csub\u003e2\u003c/sub\u003e concentrations and several water quality parameters along two extensive European inland waterways. Our results showed that summertime GHG % saturations in the Rhine river and the Mittelland canal were up to 3 orders of magnitude oversaturated relative to equilibrium concentrations, contributing to the growing evidence that lotic ecosystems are significant sources of GHGs (Lauerwald et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Peacock et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rocher-Ros et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In agreement with our hypothesis, the Mittelland canal had higher N\u003csub\u003e2\u003c/sub\u003eO and CH\u003csub\u003e4\u003c/sub\u003e % saturations than the Rhine river, likely driven by higher nitrate and organic carbon concentrations fueling in-situ \u003csub\u003e2\u003c/sub\u003eO and CH\u003csub\u003e4\u003c/sub\u003e production processes or by the inflow of dissolved GHGs from wastewater treatment plant effluents (Landesumweltamt Nordrhein-Westfalen, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In addition, GHG % saturations in the canal mainstem were also highly spatially variable compared to the Rhine, with \u0026gt; 64% CV values.\u003c/p\u003e \u003cp\u003eThe comparison of both lotic ecosystems shows that divergent drivers controlled longitudinal variabilities in GHG % saturations. Spatial variability along the Rhine river was linked to harbors and site-specific biogeochemical process rates, agreeing with findings from previous studies (Bussmann et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For example, harbors at the Rhine river led to local hotspots of CH\u003csub\u003e4\u003c/sub\u003e. At the same time, the downstream increasing trends of N\u003csub\u003e2\u003c/sub\u003eO and CO\u003csub\u003e2\u003c/sub\u003e were linked to either autotrophic or heterotrophic source processes inferred from in-situ measurements of N\u003csub\u003e2\u003c/sub\u003e concentrations as well as GPP, ER, and NEP rates (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig. S2).\u003c/p\u003e \u003cp\u003eIn contrast to the Rhine river, most of the longitudinal GHG trends in the canal were challenging to predict from similar changes in in-situ water physicochemical properties. This finding suggested that other factors not quantified in this study, such as point-pollution of GHG sources, may have significantly controlled longitudinal GHG heterogeneities along the Mittelland canal. Nevertheless, we did find that ~ 77% of the N\u003csub\u003e2\u003c/sub\u003eO spatial variability in the canal ecosystem was linked to N\u003csub\u003e2\u003c/sub\u003e oversaturation (e.g., Ritz et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Beaulieu et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This result implied that denitrification was an essential source of N\u003csub\u003e2\u003c/sub\u003eO in the canal, opposite to what we found in the Rhine river, where coupled N-fixation and nitrification inferred from N\u003csub\u003e2\u003c/sub\u003e undersaturation may have accounted for N\u003csub\u003e2\u003c/sub\u003eO hotspots (e.g., Mwanake et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Taken together, our findings suggest that single measurements along large lotic ecosystems, which ignore the presence of harbors or local biogeochemical GHG hotspots, may result in significant uncertainties in GHG emission estimates; moreover, for canal ecosystems with higher and more spatially variable GHG concentrations than river ecosystems. This study was, however, conducted only during the summer season. Therefore, seasonal differences in discharge, temperature, and GHGs were not considered, which may additionally alter longitudinal GHG trends along the two lotic ecosystems.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 CO\u003csub\u003e2\u003c/sub\u003e concentrations\u003c/h2\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e concentrations quantified in this study (11.66–82.61µmol L\u003csup\u003e− 1\u003c/sup\u003e) are within the range of those quantified from other studies in temperate ecosystems, as well as the global estimates (Lauerwald et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Raymond et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), but are much lower than those quantified from heavily polluted Asian rivers (Begum et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Previous studies in riverine ecosystems have found instances of CO\u003csub\u003e2\u003c/sub\u003e undersaturation during the temperate summer due to autotrophic uptake (Gómez-Gener et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, this study's daytime CO\u003csub\u003e2\u003c/sub\u003e concentrations along the Rhine river were surprisingly oversaturated throughout the summer campaign, suggesting that CO\u003csub\u003e2\u003c/sub\u003e production via ecosystem respiration (ER) outweighed CO\u003csub\u003e2\u003c/sub\u003e consumption via gross primary production (GPP). This conclusion is supported by the net ecosystem production (NEP) estimates from 4 sites along the Rhine that were primarily negative, indicating a net CO\u003csub\u003e2\u003c/sub\u003e loss to the atmosphere (Fig. S2).\u003c/p\u003e \u003cp\u003eLike the Rhine river, the Mittelland canal was also oversaturated with CO\u003csub\u003e2\u003c/sub\u003e, albeit slightly higher than the Rhine river. Judging by the higher carbon availability in the canal, one may assume that net insitu heterotrophic processes drove CO\u003csub\u003e2\u003c/sub\u003e oversaturation similar to what we inferred for the Rhine above. NEP estimates were unavailable for the Mittelland canal, making linking net heterotrophy to CO\u003csub\u003e2\u003c/sub\u003e oversaturation challenging. However, based on the previous environmental report that indicated several inflows of treated wastewater into the Mittelland canal (Landesumweltamt Nordrhein-Westfalen, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), we hypothesized that the CO\u003csub\u003e2\u003c/sub\u003e oversaturation in the canal is possibly mainly driven by external dissolved CO\u003csub\u003e2\u003c/sub\u003e supplies from these point-sources rather than internal production (e.g., Mwanake et al., 2023).\u003c/p\u003e \u003cp\u003eAlong the two large lotic ecosystems, longitudinal discontinuities in CO\u003csub\u003e2\u003c/sub\u003e concentrations were collectively linked to morphological, land use, and biogeochemical drivers, similar to several other studies (Begum et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Deemer et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mwanake et al., 2023). Within the Rhine river, harbor areas resulted in lower CO\u003csub\u003e2\u003c/sub\u003e concentrations than the river mainstem, which we attributed to their morphology that favors low flow conditions, accumulation of organic matter, and anoxic conditions that limit aerobic respiration rates. This conclusion is further supported by the slightly higher DOC, TOC, and TSS concentrations in the harbors than in the mainstem of the Rhine (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Besides morphological controls, biogeochemical rates supported CO\u003csub\u003e2\u003c/sub\u003e hotspots along the Rhine’s mainstem. Previous fluvial studies have linked increased ecosystem respiration rates with labile organic carbon concentrations (Mulholland et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Piatka et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Stelzer et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This study found a positive correlation between CO\u003csub\u003e2\u003c/sub\u003e and DOC, suggesting instream CO\u003csub\u003e2\u003c/sub\u003e production via favored ecosystem respiration rates under these conditions. Furthermore, we did find a positive correlation between ecosystem respiration and CO\u003csub\u003e2\u003c/sub\u003e, which accounted for up to 73 % of the downstream increasing trend that we found for CO\u003csub\u003e2\u003c/sub\u003e, strengthening this argument. In contrast to the Rhine river, the longiudinal trends in the Mittelland canal were unpredictable with our current driving factors, supporting our earlier claim that CO\u003csub\u003e2\u003c/sub\u003e oversaturation in this ecosystem is likely linked to unquantified point pollution sources.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 CH\u003csub\u003e4\u003c/sub\u003e concentrations\u003c/h2\u003e \u003cp\u003eLike CO\u003csub\u003e2\u003c/sub\u003e, riverine CH\u003csub\u003e4\u003c/sub\u003e concentrations quantified here were mainly within the range of global estimates (Rocher-Ros et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, those quantified in the canal ecosystem were primarily at the higher end of the estimates, with CH\u003csub\u003e4\u003c/sub\u003e % saturations indicatingan oversaturation three orders of magnitude higher than equilibrium concentrations. These findings strengthen the idea that canal ecosystems are hotspots for CH\u003csub\u003e4\u003c/sub\u003e emissions, similar to what has been found in past studies(Peacock et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rocher-Ros et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the Rhine river, longitudinal variability of CH\u003csub\u003e4\u003c/sub\u003e concentrations was linked to both physical and biogeochemical drivers. Contrary to CO\u003csub\u003e2\u003c/sub\u003e, harbor areas were hotspots for CH\u003csub\u003e4\u003c/sub\u003e oversaturation, a finding agreeing with our earlier conclusion that low flow and high organic load conditions in these areas likely favor anoxic conditions suitable for methanogenesis. A similar longitudinal study along the Elbe River (Bussmann et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found higher CH\u003csub\u003e4\u003c/sub\u003e oversaturation in harbor areas, alluding to similar controls of longer water residence time and high sediment and organic matter conditions favorable for methane production. Along the Rhine’s mainstem, CH\u003csub\u003e4\u003c/sub\u003e variability was attributed to variable production rates via methanogenesis. Several studies have shown that negative correlations between NO\u003csub\u003e3\u003c/sub\u003e-N and CH\u003csub\u003e4\u003c/sub\u003e imply methane production via methanogenesis, as NO\u003csub\u003e3\u003c/sub\u003e-N inhibits the processes as a terminal electron acceptor(TEA) (Baulch et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Schade et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our study found a similar correlation, albeit marginally significant (p-value \u0026lt; 0.1), pointing out that methane production hotspots via methanogenesis along the Rhine may occur in areas with low TEA, such as NO\u003csub\u003e3\u003c/sub\u003e-N. Like CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e concentrations along the canal mainstem could not be linked to water physicochemical variables, supporting our earlier argument that GHG supersaturation along the canal is mainly sustained by external sources from wastewater treatment plants (e.g., Mwanake et al., 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 N\u003csub\u003e2\u003c/sub\u003eO and N\u003csub\u003e2\u003c/sub\u003e concentrations\u003c/h2\u003e \u003cp\u003eLike CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e, riverine N\u003csub\u003e2\u003c/sub\u003eO concentration values fell within the global range of past estimates from large rivers(Hu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with those from the Mitteland canal comparable to N\u003csub\u003e2\u003c/sub\u003eO concentration values from drainage ditches draining cropland landscapes ladened by high nitrate concentrations(Reay et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The high nitrate conditions in the Mittelland canal relative to the Rhine river resulted in almost double the N\u003csub\u003e2\u003c/sub\u003eO concentrations in the latter ecosystem. Several studies in lotic ecosystems have linked high nitrate concentrations to elevated N\u003csub\u003e2\u003c/sub\u003eO production from incomplete denitrification (Andrews et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Beaulieu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mwanake et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, very few studies have connected N\u003csub\u003e2\u003c/sub\u003eO oversaturation with direct measures of denitrification (e.g., Beaulieu et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), with most of the above studies inferring to N\u003csub\u003e2\u003c/sub\u003eO sourced from denitrification based on positive correlations with NO\u003csub\u003e3\u003c/sub\u003e concentrations. Such inferences may be misleading, as N\u003csub\u003e2\u003c/sub\u003eO production from nitrification may also have a similar positive relationship with nitrate, the end product of nitrification. When looking at the drivers of the significant longitudinal heterogeneities (CV = 68%) in N\u003csub\u003e2\u003c/sub\u003eO concentrations along the Mittelland canal, we did find direct evidence of the critical role of denitrification in its production. This evidence was based on the marginally significant (p-value \u0026lt; 0.01) positive correlation between N\u003csub\u003e2\u003c/sub\u003eO and N\u003csub\u003e2\u003c/sub\u003e % saturations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig. S3; r\u003csup\u003e2\u003c/sup\u003e = 0.77), as N\u003csub\u003e2\u003c/sub\u003e oversaturation is solely linked to the denitrification process and hence can be usd to quantify its rates (Chen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; McCutchan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ritz et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast to the canal ecosystem, longitudinal N\u003csub\u003e2\u003c/sub\u003eO hotspots downstream of the Rhine river were linked to processes other than denitrification. High N\u003csub\u003e2\u003c/sub\u003e fixation rates by autotrophic and heterotrophic diazotrophs, which supply fresh and bioavailable N, have been previously reported in freshwater environments (Geisler et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Riemann et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Within rivers, several studies have linked measures of N\u003csub\u003e2\u003c/sub\u003e undersaturation to significant rates of N\u003csub\u003e2\u003c/sub\u003e fixation by diazotrophs(Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This study found substantial downstream increases in N\u003csub\u003e2\u003c/sub\u003e undersaturation of up to 92%, which more than doubled instream N\u003csub\u003e2\u003c/sub\u003eO concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Fig. S2). Like N\u003csub\u003e2\u003c/sub\u003eO, NH\u003csub\u003e4\u003c/sub\u003e-N, Chlorophyll-a, TOC, TSS concentrations, and GPP and NEP rates also increased downstream of the Rhine (Fig. S2). These findings implied significant N\u003csub\u003e2\u003c/sub\u003e-fixation rates by cyanobacteria and heterotrophic diazotrophs, possibly providing the NH\u003csub\u003e4\u003c/sub\u003e-N required for N\u003csub\u003e2\u003c/sub\u003eO production via nitrification. Such significant N\u003csub\u003e2\u003c/sub\u003e fixation rates have been previously reported in similar highly urbanized rivers in Beijing, with an N\u003csub\u003e2\u003c/sub\u003e undersaturation of up to 88 %(Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We also found significant positive relationships of N\u003csub\u003e2\u003c/sub\u003eO with NH\u003csub\u003e4\u003c/sub\u003e-N, Chlorophll-a, TOC, GPP, and NEP rates and their antagonistic relationship with N\u003csub\u003e2\u003c/sub\u003e oversaturation, further supporting this argument (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur unique dataset, comprising CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e, N\u003csub\u003e2\u003c/sub\u003eO, and N\u003csub\u003e2\u003c/sub\u003e concentrations, multiple water physicochemical variables, and metabolism rate estimates, allowed us to compare the roles of biogeochemical processes, surrounding land use, and channel morphologies in controlling the longitudinal GHG heterogeneities along two large lotic ecosystems with a combined reach length of 632km. The findings of this study revealed that these drivers were divergent between the two lotic ecosystems characterized by different morphologies, stream velocities, and nutrient inputs. Within the Mittelland canal, longitudinal CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e hotspots were mainly linked to external inflows of the GHGs from surrounding WWTPs and less connected to in-situ biogeochemical processes. Contrastingly, harbors and in-situ biogeochemical processes such as methanogenesis and respiration explained most CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e hotspots along the Rhine river. Across both lotic ecosystems, N\u003csub\u003e2\u003c/sub\u003eO was strongly linked to N\u003csub\u003e2\u003c/sub\u003e concentrations, albeit with opposite relationships in the Rhine river and Mittelland canal, revealing source process information that would have been missed without quantifying the in-situ N\u003csub\u003e2\u003c/sub\u003e concentration. This finding stresses the need to include N\u003csub\u003e2\u003c/sub\u003e concentration measurements in GHG sampling campaigns, as it has the potential to determine whether nitrogen is fixed through N-fixation or lost through denitrification in an ecosystem, with severe consequences to both N and C cycling that results in the production of GHGs in lotic ecosystems.\u003c/p\u003e\u003cp\u003eOverall, the longitudinal heterogeneities in GHG % saturations across both lotic ecosystems mainstems were highly significant (CV \u0026gt; 25%), suggesting that single measurements along large lotic ecosystems may result in significant uncertainties in GHG emission estimates(e.g., Bussmann et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This finding is particularly relevant for large canal ecosystems in highly urbanized areas, which we showed have higher and more spatially variable GHG concentrations (CV \u0026gt; 90 %) than river ecosystems butremain underinvestigated.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInfrastructure for the research was provided by the TERENO Bavarian Alps/ Pre-Alps Observatory, funded by the Helmholtz Association through the joint program; Atmosphere and Climate (ATMO - PoF III) program of Karlsruhe Institute of Technology (KIT). Hannes Imhof was funded by the German Federal Ministry of Education and Research (BMBF) within the project Integrated Greenhouse Gas Monitoring for Germany (ITMS) \u0026ndash; Module Sources \u0026amp; Sinks (Grant Number: 01LK2105D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; All authors contributed to the conceptualization and design of the work. Funding acquisition was done by Ralf Kiese. Ricky. M. Mwanake and Hannes Imhof prepared the field materials and collected the field data: Ricky. M. Mwanake conducted the formal analysis and wrote the first manuscript draft. All authors contributed to revising and editing the final manuscript draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the German Ocean Foundation for the possibility of being part of the Rhine Expedition 2023 and hosting our experiments. Additionally, we thank the skipper Frank Schweikert and the staff of the Science \u0026amp; Media Vessel ALDEBARAN Sofie M\u0026ouml;hrle and Julia Kuhnle for their tremendous support during the sampling campaign. The same thanks go to Dr. Madlene Stange, Dr. Leighton Thomas, and Michel Posanski from the Leibniz Institute for the Analysis of Biodiversity Change. Furthermore, we thank the staff of the Flussgemeinschaft Rhein for establishing contact with the Landesamt f\u0026uuml;r Natur, Umwelt und Verbraucherschutz NRW, and the Landesamt f\u0026uuml;r Umwelt Rheinland-Pfalz, as well as the team of both agencies for the provisioning of hourly data four stations at the Rhine.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAho, K. S., \u0026amp; Raymond, P. A. (2019). Differential Response of Greenhouse Gas Evasion to Storms in Forested and Wetland Streams. \u003cem\u003eJournal of Geophysical Research: Biogeosciences\u003c/em\u003e, \u003cem\u003e124\u003c/em\u003e(3), 649\u0026ndash;662. https://doi.org/10.1029/2018JG004750\u003c/li\u003e\n\u003cli\u003eAndrews, L. F., Wadnerkar, P. D., White, S. A., Chen, X., Correa, R. E., Jeffrey, L. C., \u0026amp; Santos, I. 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Nature Research. https://doi.org/10.1038/s41558-019-0665-8\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Greenhouse gases, Harbors, External inputs, Metabolism rates, N-fixation, Denitrification","lastPublishedDoi":"10.21203/rs.3.rs-3722436/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3722436/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLotic ecosystems transversing mixed land-use landscapes are sources of GHGs to the atmosphere, but their emissions are uncertain due to longitudinal GHG heterogeneities. In this study, we quantified summer CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e, N\u003csub\u003e2\u003c/sub\u003eO, and N\u003csub\u003e2\u003c/sub\u003e concentrations, as well as several water quality parameters along the Rhine river and the Mittelland canal, two critical inland waterways in Germany. Our main objectives were to compare GHG concentrations along the two ecosystems and to determine the main driving factors responsible for their longitudinal heterogeneities. The results indicated that GHGs in the two ecosystems were up to three orders of magnitude oversaturated relative to equilibrium concentrations, particularly in the Mittelland canal, a hotspot for CH\u003csub\u003e4\u003c/sub\u003e and N\u003csub\u003e2\u003c/sub\u003eO concentrations. We also found significant longitudinal variabilities in % GHG saturations along the mainstems of both ecosystems (CV = 26 – 98 %), with the highest variability recorded for CH\u003csub\u003e4\u003c/sub\u003e concentrations in the Mittelland canal, suggesting that single GHG measurements along large lotic ecosystems are unrepresentative of entire reaches. However, these significant longitudinal GHG heterogeneities were driven by divergent drivers between the two lotic ecosystems. Within the Canal, longitudinal CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e hotspots were linked to external inflows of the GHGs from surrounding WWTPs. Contrastingly, harbors and in-situ biogeochemical processes such as methanogenesis and respiration explained CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e hotspots along the Rhine river. In contrast, N\u003csub\u003e2\u003c/sub\u003eO was strongly linked to N\u003csub\u003e2\u003c/sub\u003e concentrations, with a negative relationship in the Rhine river and a positive relationship in the Mittelland canal. Based on these N\u003csub\u003e2\u003c/sub\u003e relationships, we hypothesized that denitrification drove N\u003csub\u003e2\u003c/sub\u003eO hotspots in the Canal, while coupled N-fixation and nitrification accounted for N\u003csub\u003e2\u003c/sub\u003eO hotspots in the Rhine. This finding stresses the need to include N\u003csub\u003e2\u003c/sub\u003e concentration measurements in GHG sampling campaigns, as it has the potential to determine whether nitrogen is fixed through N-fixation or lost through denitrification.\u003c/p\u003e","manuscriptTitle":"Divergent drivers of the spatial variabilities in CO2, CH4, N2O, and N2 concentrations along the Rhine river and the Mittelland canal in Germany","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 19:10:48","doi":"10.21203/rs.3.rs-3722436/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2024-02-09T06:43:53+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-01-05T02:55:48+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-04T16:30:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2024-01-03T17:36:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-27T05:26:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2023-12-18T08:33:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"07ef8d8d-d88d-43e3-af05-94d63307aede","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-04-16T12:28:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-08 19:10:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3722436","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3722436","identity":"rs-3722436","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}