Introduction

Inland waters are important sources of atmospheric carbon dioxide (CO2) partially offsetting the terrestrial carbon sink1,2. Streams and rivers therein represent major CO2 emitters3. Fluvial CO2 fluxes are primarily controlled by the gas exchange velocity at the water-air interface (k) and the gradient between the water and atmospheric partial pressures of CO2 (pCO2)4. Both parameters are highly variable in space and time5,6, causing uncertainty in the magnitude of regional and global fluvial CO2 emissions2.

The high spatiotemporal variability of k and water pCO2 can be attributed to a complex interplay of underlying controls. While k in streams is mostly driven by water turbulence created by variations in flow and stream morphology7, the water pCO2 is influenced by the degree of hydrological connectivity between the stream and the adjacent riparian soils8 as well as by in-stream processes (e.g., stream metabolism). The supply of CO2 from external sources, such as soil water or groundwater, into streams, varies with reach and season5,9. Furthermore, seasonal and diel changes in stream pCO2 are attributed to stream metabolism driven by temperature and solar radiation10,11,12,13. Ecosystem respiration, a source of CO2 in the stream, takes place throughout the whole day, and gross primary production, a sink of CO2, occurs only during daylight. Temperature and solar radiation also directly influence water pCO2, the former by changing the solubility of the gas and the latter due to photomineralization14. However, questions remain regarding the magnitude and relative drivers of seasonal and diel fluctuations of CO2 fluxes in streams.

Presently, most fluvial CO2 emission values are derived from k estimates based on water velocity and stream channel slope and on water pCO2 values indirectly calculated from alkalinity, pH, and temperature3. This approach fails to capture the high spatiotemporal variability observed for k and pCO2 and therefore can provide imprecise estimates of CO2 fluxes15,16. Direct field observations provide the means to improve estimates and understanding of the drivers behind spatiotemporal variability, and thus the dynamics of CO2 outgassing from running waters. However, besides mostly local studies that indirectly infer CO2 fluxes from pCO2 and k11,12,17,18, no direct measurements exist that compare day-time and night-time CO2 fluxes from streams on a larger spatial scale.

The aim of this study was to assess the magnitude and drivers of stream CO2 flux variations between day and night across European streams. We hypothesized that CO2 fluxes would differ between day and night due to diel variations in terrestrial inorganic carbon inputs, in situ metabolism, and temperature. As higher temperatures and solar radiation may drive differences in pCO2, we expected a higher difference between day-time and night-time fluxes with warmer temperatures and at lower latitudes. Hence, we measured day-time and night-time fluxes of CO2 at four different periods throughout one year from 34 streams (Strahler stream orders from 1 to 6) in 11 countries across Europe following a standardized procedure. CO2 fluxes were measured starting at midday (11 a.m. Greenwich Mean Time (GMT)) and midnight (11 p.m. GMT) with drifting flux chambers equipped with CO2 sensors as described in Bastviken et al.19. In the majority of the European streams, we found increased CO2 fluxes at the water–air interface in the night compared to the day with a median increase of 0.5 mmol m−2 h−1. Most of the observed CO2 flux variability was explained by changes in pCO2 from day to night with more pronounced changes at lower latitudes.

Results and discussion

Magnitude of CO2 flux variation from day to night

Midday CO2 fluxes at the water–air interface ranged from −2.7 (uptake) to 19.9 mmol m−2 h−1 (emission) (1.4 [0.5, 3.1]; median [interquartile range (IQR)]; n = 107) and midnight fluxes ranged from −0.3 to 25.6 mmol m−2 h−1 (2.1 [0.9, 3.7]; n = 107) (Fig. 1a; Supplementary Table S3). Our measured fluxes are comparable to other studies conducted in temperate and boreal streams that used chambers20,21 or empirical models12,22,23, although they were in the lower range of the numbers modeled in a study in the USA23 (Supplementary Fig. S2). The lower numbers might be due to the lack of tributary inflows, large woody debris, and strong hydraulic jumps in the selected stream sections (Supplementary Sampling manual).

Fig. 1: Day-to-night changes of CO2 fluxes at the water–air interface of the sampled European streams.
figure 1

Stream CO2 fluxes (in mmol CO2 m−2 h−1) at day-time (yellow) and night-time (blue) (a) and the calculated changes from night minus day (ΔCO2 flux) (b) for all data and separately for each sampling period. In the sampling periods comparisons in (a), CO2 fluxes for individual stream sites are indicated by red (day) and light blue (night) dots. The boxplots visualize the median of all stream sites (line), the first and third quartiles (hinges), the 1.5*inter-quartile ranges (whiskers), and the outliers outside the range of 1.5*inter-quartile ranges (black dots). On top of (a) are p values retrieved from paired comparisons of median CO2 fluxes tested by Wilcoxon signed-rank tests and the sample size (n). Significant p values with p < 0.05 are in bold with an asterisk. The differences in the CO2 fluxes (b) in mmol CO2 m−2 h−1 from day to night are for October: 0.5 [0.1, 1.2]; January: 0.5 [0.3, 0.9]; April: 1.1 [0.1, 2.3]; July: 0.3 [−0.2, 1.1] (median [IQR]).

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To assess stream CO2 flux variations between day and night, we computed the difference of night-time minus day-time fluxes for each stream and sampling period, where positive numbers indicate an increase from day to night and vice versa (Fig. 1b). Differences in CO2 fluxes amounted to 0.5 mmol m−2 h−1 [0.1, 1.4] (n = 107) across all sites and sampling periods, which is equivalent to a relative increase of 39% [4%, 100%] (n = 101; n reduced due to exclusion of relative comparisons to zero flux at day-time) (Fig. 2). Altogether, these results point towards a high relevance of night-time CO2 fluxes as reported earlier for single pre-alpine streams12, stream networks13,17 or rivers18, and in a recent compilation of diel CO2 data from 66 streams worldwide24. A rough annual extrapolation of fluxes from our study sites (Supplementary Methods) shows that the inclusion of night-time fluxes increases annual estimates of site-specific stream CO2 emissions by 16% [6%; 25%] (Supplementary Table S4). Hence, our measurements and the simplified extrapolation of our data emphasize the need to collect and integrate night-time CO2 flux data into sampling protocols as well as regional upscaling efforts.

Fig. 2: Relative changes in CO2 fluxes from day to night for all data together and for each sampling period.
figure 2

A positive value indicates an increase in CO2 fluxes during the night and vice versa (expressed as a %-change of the daytime values). Outliers (>1.5*IQR) were excluded for illustration purposes as the large relative variation in these fluxes was due to minor absolute variation in fluxes close to zero. The median relative changes were positive throughout all sampling periods, ranging from 32% [0.6%, 95%] in October, 38% [16%, 50%] in January, 60% [7%, 177%] in April, to 24% [−16%, 69%] in July (median [IQR]; n = 26, 21, 28, and 26, respectively).

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Looking into the individual comparisons, we found 83 increases in median CO2 fluxes from day to night with seven comparisons where the stream even switched from a sink to a source of CO2 to the atmosphere (Supplementary Table S3). However, we also found four comparisons where median CO2 fluxes at day and night were the same and 20 decreases in the night (Supplementary Table S3). These results and also other studies13,25,26 suggest that the direction and strength of diel pCO2 pattern can be largely variable across space and time.

Diel CO2 flux differences vary as a function of latitude and water temperature

The diel differences in CO2 fluxes were significantly negatively related to latitude (Table 1A), with substantial diel variation more likely at lower latitudes. Likewise, the interaction with latitude and the water temperature was significant (Table 1A), which might be explained by higher temperatures at lower latitudes during the sampling periods and higher solar radiation boosting in-stream primary production27. This dataset is derived from only 34 streams distributed across different climate zones in Europe. However, to our knowledge, it is currently the largest study of its kind, using flux chambers to measure CO2 fluxes, and compare those fluxes at day-time and night-time on such a spatial scale.

Table 1 Results of the linear mixed-effect models (LME).
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We found no significant differences in the magnitude of diel differences in CO2 fluxes related to water temperature (Table 1A) using a linear mixed-effect model (LME). However, comparing the CO2 fluxes at midday to midnight at the different sampling periods, we detected significant diel changes in CO2 fluxes in October, January, and April (Fig. 1a). Contrary to our expectation that higher differences can be expected at higher temperatures, we did not detect significant changes from day to night in July (Fig. 1a), during which period the lowest changes in absolute numbers were recorded (0.3 mmol m−2 h−1; Fig. 1b). The highest differences of CO2 fluxes from day to night were measured during April (1.1 mmol m−2 h−1), followed by January (0.5 mmol m−2 h−1) and October (0.5 mmol m−2 h−1). Lower day-night changes in July could be explained by increased riparian shading reducing photosynthesis28,29. For example, reduced in-stream photosynthesis in summer compared to spring has been shown for a subalpine stream network29 or a temperate forested headwater stream28. However, comparing the canopy cover of the streams and the differences in CO2 fluxes from day to night (Supplementary Fig. S3h) revealed no clear pattern. A probable alternate explanation is that CO2 production via photomineralization during the day counteracted a decrease via CO2 fixation by photosynthesis30 and diminished diel pCO2 and ultimately CO2 flux changes. This highlights the complex interplay between different light-dependent processes in streams influencing pCO2 on a diel scale.

The importance of year-round measurements is highlighted by the January data set containing the second-highest diel CO2 flux changes. European ice-free streams may be perceived “dormant” during these periods and representative CO2 flux estimates are thus often missing3. Our January data showed a magnitude of flux compared to the rest of the year across the European streams as well as high diel variability in CO2 fluxes (Fig. 1). This may be attributed in part to the latitudinal coverage of our study as we included streams from the boreal to the Mediterranean. For example, the water temperatures of the Spanish streams were still relatively high in winter with around 2.8–9.5 °C during the day whereas Swedish streams showed these temperatures in October and April. A study in the coterminous US looking into stream pCO2 variability also reports varying strengths of diel pCO2 variability, dependent on the investigated stream and time25. Hence, diel pCO2 and CO2 flux variability can be large in streams of the northern hemisphere, stressing the need to unravel the site-specific drivers of and mechanisms behind these diel changes.

Diel CO2 flux variability driven by changes in water pCO2

To understand the mechanisms behind the observed changes in CO2 fluxes from day to night, we first selected the two primary controls of CO2 fluxes at the water–air interface, i.e., the gas exchange velocity and water pCO2 and explored the influence of these parameters on absolute CO2 flux changes using an LME. The diel CO2 flux variability in European streams could be mostly attributed to changes in water pCO2 (Table 1B), whereas changes in the gas exchange velocity k appeared less important. In fact, we did not measure significant variations in k from day to night in our streams (Fig. 3; Supplementary Fig. S4h). Although diel variabilities of gas exchange velocities have been reported for CO2 and other gases31,32, the majority of the investigated streams in this study did not show those changes. The pCO2 as a major driver of diel CO2 flux variability was also identified by a global compilation of high-frequency CO2 measurements24. Consequently, if no major changes in physical drivers of gas exchanges occur that strongly affect the turbulence, such as heavy rain events, it is sufficient to focus on pCO2 for assessing diel flux changes at the water–air interface.

Fig. 3: Diel changes in CO2 fluxes (FCO2) and other physical and chemical parameters for October/January/April and July, respectively.
figure 3

The physical and chemical parameters comprise atmospheric CO2 (Air CO2), the differences of CO2 concentrations in the water minus the air (CO2 gradient), the water–air gas transfer velocity (k), the differences of temperatures in the water minus the air (Tw−Ta), the water temperature (WT), the oxygen concentration in the water (O2), pH in the water, the partial pressure of CO2 in the water (pCO2), and conductivity (Cond). The arrows indicate significant increases (↑) or significant decreases (↓) from day to night and the line indicates no significant change (―) tested by a Wilcoxon signed-rank test (see Supplementary Fig. S4 for more information). The differences between the sampling periods October/January/April and July, respectively, detected in this European study are highlighted in red.

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In a second step, we tested the influence of biogeochemical parameters that vary on a diel scale on water pCO2 day-to-night differences (Table 1C). This LME identified a link between the day-to-night changes in water pCO2 and water dissolved O2, with pCO2 generally increasing and O2 decreasing from day to night (Supplementary Fig. S4b, c). This potentially reflects a diel cycle of CO2 controlled by aquatic primary production and respiration (in-stream metabolism). Hence, even though in situ metabolism may play a minor role in determining the baseline pCO2 and flux in smaller streams (mostly controlled by terrestrial inputs23), our results suggest that metabolism can be an important driver of the diel fluctuations in CO2 fluxes. Indeed, increased water pCO2 during the night has been attributed to a decrease in CO2 fixation by primary producers13,18,24, although a recent study suggests that the adjacent groundwater can also show measurable but less pronounced diel pCO2 variations33. Previous research suggests that in situ mineralization of CO2 should play a larger role in CO2 dynamics in larger streams because they are less influenced by external CO2 sources23. Nevertheless, we did not find any trend in CO2 flux day-to-night differences with stream width or discharge as a proxy for size (Supplementary Fig. S3c, f) or with stream order (Supplementary Fig. S5) although other studies suggest change over a size gradient23,34. Furthermore, the LME testing hydromorphological and catchment variables on pCO2 day-to-night differences (Table 1D) did not reveal significant relationships with either of these drivers. This could either be due to the fact that we missed the best proxy that determines day-to-night differences in pCO2 in European streams or that there are no common drivers among the investigated streams. Large diel variability of CO2 patterns within one Swedish stream26 or among US headwater streams25 have been described, which complicates the identification of general drivers. Hence, further research is needed to decipher the diel variability of the sources and dynamics of pCO2 in streams and to understand the environmental, hydromorphological, and catchment drivers before their importance on a regional or global scale can be assessed.

In-stream metabolism with photosynthetic CO2 fixation diminishing pCO2 during the day may explain the increase in CO2 fluxes from day to night, but cannot explain why in some instances we measured a lower CO2 flux at night. Potential explanations for a lower night flux might include: (i) higher atmospheric CO2 concentrations due to the absence of terrestrial CO2 fixation during night and therefore a lower water–atmosphere pCO2 gradient, (ii) photomineralization of organic matter to CO2 counteracting the CO2 fixation by primary producers during day-time, and (iii) lower turbulence due to a decrease in stream discharge in the night. We found significant increases in atmospheric CO2 close to the investigated streams at night. However, this was usually accompanied by concomitant increases in water pCO2 and therefore did not translate into smaller CO2 gradients between the water–air interface (Fig. 3; Supplementary Fig. S4b, e, i). Production of CO2 due to photomineralization of dissolved organic carbon (DOC) could play a role in diel CO2 dynamics in streams with high amounts of colored terrestrial organic matter35. In the highly colored streams, diel CO2 patterns can additionally be influenced by DOC shading diminishing benthic primary production36. In October, we measured DOC concentrations in a subset of the investigated streams for another study37 where an agricultural stream in Sweden and peatland-dominated streams in Great Britain had high DOC concentrations (>10 mg L−1) whereas the median DOC was much lower with 2.6 mg L−1 37. Due to the limited data, we could not test the effect of DOC on pCO2 changes and we can neither confirm nor exclude that photomineralization might play a role for diel pCO2 and consequently CO2 flux variability in the studied streams. We did find, nonetheless, that the majority of the streams where CO2 fluxes were lower during the night also had a lower gas transfer velocity (k600), likely due to a slight decrease in stream discharge and therefore turbulence. Thus, while there was a general tendency of increased pCO2 from day to night (only 4 out of 20 decreases in CO2 fluxes from day to night showed a concomitant decrease in water pCO2), individual streams at single time points seemed to experience diel fluctuations in discharge as described elsewhere38. This can simultaneously reduce the gas exchange velocity of the stream and therefore cause lower night-time CO2 fluxes. In this study, we only measured stream discharge during the day, and therefore the importance of this mechanism remains to be confirmed.

Maximum CO2 flux differences might be even higher—limitations of the study design

For organizational reasons, the sampling scheme of this collaborative study was standardized to fixed times of measurements for the day and the night. All teams across Europe started their measurements at 11:00 (midday) and 23:00 GMT (midnight) during each sampling period, which has consequences for the magnitude of the observed diel variability of the CO2 fluxes. The largest diel differences in stream pCO2 have generally been detected at the end of the day compared to the end of the night12,18,39. In an agricultural Swedish stream, diel maximum and minimum CO2 concentrations were reached at 04:00 and 16:00 (GMT), respectively, during spring and early summer periods (late April to early July) where diel dynamics were most pronounced26. In these scenarios, sampling midday and midnight, as conducted in this study, would be close to those maxima and minima as they can be reached already earlier during the day (see Supplementary Fig. S6 in May). However, the maxima and minima of diel CO2 dynamics in streams can vary largely (see Supplementary Fig. S6 in October, April, July). In another example of German streams39, the times of minima and maxima differ between streams and times, and the fixed time points chosen in this study would miss the maximum differences that can be observed (see Supplementary Fig. S7 in August). Hence, our estimates could be conservative as we compared fixed time points at midday and midnight. In general, CO2 flux measurements in streams are highly sensitive towards the time of the day because diel minimum and maximum of pCO2 can vary largely from month to month but also from day to day. As we found that the diel variability of pCO2 was the major driver of diel CO2 fluxes, we recommend future studies that plan to measure CO2 fluxes directly with the chamber method, to additionally monitor the diel variability of pCO2 with loggers at a high temporal resolution. This approach will provide the opportunity to estimate if the measurements are done during peak times or not.

While our results provide a first insight into the drivers of day-night differences in CO2 fluxes, the high uncertainty in the models as well as the sometimes opposing patterns—increases and decreases from day to night in different streams and sampling periods—point towards different drivers varying on a temporal and spatial scale. We recommend that future study designs incorporate high-frequency CO2 data together with biogeochemical variables from the stream (e.g., O2) and the atmosphere (e.g., CO2 or temperature)40. Additionally, we recommend including radioactive or stable carbon isotope signatures to track potential sources of CO2 and their changes in streams41,42 to better assess terrestrial–aquatic linkages. Linking temporal patterns of fluvial CO2 fluxes with their drivers across large spatial scales is a path towards a more accurate understanding of their role in regional and global carbon cycles. Our results demonstrate that, in many streams across Europe, night-time CO2 fluxes exceed day-time, resulting in a potential underestimation of global CO2 emissions from inland waters if not considered. It is thus critical to account for the diel variability of fluvial CO2 fluxes for accurate daily and annual estimates of CO2 emissions from inland waters.

Methods

Sampling scheme

The project included 16 teams distributed across 11 European countries. Every team sampled one to three streams (Supplementary Table S1) every 3 months (October 2016/January 2017/April 2017/July 2017) within a time frame of 2 weeks throughout a whole year. These sampling periods roughly cover the seasons autumn/winter/spring/summer although, due to the large latitudinal coverage of the sampling sites, the seasons and their characteristics vary largely. In total, 34 stream sites (Supplementary Fig. S1) were visited each sampling period during the specified 2 weeks’ time frame except for 11 streams in January that were frozen during the sampling weeks (Supplementary Table S3).

CO2 fluxes were measured once every sampling period with drifting flux chambers equipped with CO2 sensors. This method has proven to be a reliable and least biased direct measurement of CO2 fluxes at the water–air interface in streams19,43. CO2 concentrations in the chamber headspace were logged every 30 s over a period of 5–10 min during each run, and CO2 fluxes were calculated based on the rate of change over time in pCO2 in the chamber headspace. At each stream, we measured CO2 fluxes with the flux chamber (five times), pCO2 in the atmosphere and water with the CO2 sensors in the flux chamber (details described in Supplementary Methods), pH, temperature, conductivity, and oxygen in the water with a multiprobe (Supplementary Table S2). These measurements were started at 11:00 and 23:00 (GMT) and lasted approximately two hours and are referred to as midday and midnight throughout this article. Stream width, depth, canopy cover, and discharge were determined during the day (see Supplementary Sampling manual for details). In addition, the following information was collected for each stream once during the study: stream order, climate zone, catchment area until the endpoint of the investigated stream site and the percentage of coverage of different land use classes in this catchment area, and predominant geology (Supplementary Table S1).

Calculations of CO2 fluxes and gas transfer velocity

Flux rates were obtained from the linear slopes of the pCO2 in the chamber headspace over time and flux was accepted if the coefficient of determination (R2) of the slope was at least 0.6544. An exception was made in cases where the slope was close to zero and the pCO2 in the atmosphere and water (measured at the same time) were at equilibrium. These fluxes were set to zero. Final flux rates F (mmol CO2 m−2 h−1) were calculated according to Eq. (1)45:

$$F=S* {10}^{-3}\frac{PV}{RTA}* 60* 60,$$
(1)

where S is the slope (ppm s−1), P is the pCO2 in the atmosphere (atm), V is the volume (mL) of the drifting chamber, R is the gas constant (82.0562 mL atm K−1 mol−1), T is the chamber air temperature (K), A is the bottom area of the chamber (m2), and the last term is the conversion from seconds to hours. In this study, we followed the sign convention whereby positive values indicate a CO2 flux from the stream to the atmosphere (source) and negative values indicate a flux from the atmosphere to the stream (sink). The magnitudes of variations between day-time and night-time measurements are additionally stated as percent increases, which were computed by dividing the difference between the values at night minus day by the value at day and expressing the result as a percent change from day to night.

We used F (Eq.(1)) to calculate the gas transfer velocity (k in cm h−1) by inverting the equation for Fick’s law of gas diffusion, according to Eq. (2):

$$k=\frac{F}{kH({{\rm{CO}}}_{{2}_{{\rm{water}}}}-{{\rm{CO}}}_{{2}_{{\rm{air}}}})}* 100,$$
(2)

where kH is Henry’s constant (in mol L−1 atm−1) adjusted for temperature46.

For comparison of transfer velocities between sites and sampling periods and with the literature, k (Eq. ( 2)) was standardized to k600 (Eq. (3)):

$${k}_{600}=k{\left(\frac{600}{\rm{{{Sc}}}}\right)}^{-0.5}$$
(3)

where k is the transfer velocity at in situ temperature (T), Sc is the Schmidt number for in situ temperature T, the Schmidt number for 20 °C in freshwater is 600, and representing a hydrodynamic rough water surface typical in streams the exponent of −0.5 was chosen47.

Statistical analyses

All statistical analyses were performed with median values of three to five floating chamber runs per day and night, respectively, using the statistical programming language R48 (version 3.5.1). Samplings that generated less than three values for either day or night due to an R2 of the slope <0.6544 were excluded from further analysis reducing the number from 136 to 107 day–night comparisons. For our statistical tests, the alpha level was set to α = 0.05. Significant differences between day-time and night-time measurements for each sampling period across all streams were tested with Wilcoxon signed-rank tests49 where median day-time and night-time values for each stream site were paired (Fig. 1a). The same tests were conducted for the other biogeochemical variables measured at midday and midnight (see Fig. 3; Supplementary Fig. S4).

With a first linear mixed-effect model (LME) we tested the latitudinal and water temperature effect on CO2 flux differences from day to night. A second LME was built to evaluate the two major drivers of CO2 flux differences from day to night: pCO2 and gas exchange velocity (k). A third LME was subsequently used to determine the biochemical factors potentially influencing the differences of the night-time minus day-time pCO2, which was identified as the only significant driver in the second LME. Finally, a fourth LME was built to evaluate the effect of catchment and hydromorphological parameters on the day-to-night differences of pCO2. For these tests, we used the “lmer” function of the R-package “lme4”50 with maximum-likelihood estimation. Fixed effects for the LME with biogeochemical parameters for pCO2 differences from day to night included absolute differences from day to night of oxygen concentration in the water, pH, conductivity, temperature gradient of atmosphere and water, and water temperature. Fixed effects for the LME with catchment and hydromorphological parameters included day length (i.e., sun hours from sunrise to sunset), stream wetted width, discharge, % forest of the catchment, and catchment area. These variables are mostly remotely available for streams. For the LMEs we included stream ID as a random effect allowing different intercepts for each stream to account for pseudoreplication (one data point per sampling period per stream) and z-scaled all fixed effects with the “scale” function before running the models. Statistical significances of fixed effects were assessed with likelihood ratio tests using the function “drop1”51. The respective LMEs were followed by a model validation, checking the residuals for normal distribution and homogeneity of variances52. A separation of the dataset to check if drivers between increases from day to night and decreases from day to night differ did not reveal acceptable models in terms of model validation (i.e., residuals were not normally distributed). Although our dataset provided a large spatial coverage on day–night differences in CO2 fluxes in European streams, it did not have the statistical power to test for significant drivers separately for increases and decreases.