# Carbon emissions from inland waters may be underestimated: Evidence from European river networks fragmented by drying

Naiara López-Rojo, Thibault Datry, Francisco Peñas, Gabriel Singer, Nicolas Lamouroux, José Barquín, Amaia Rodeles, Teresa Silverthorn, Romain Sarremejane, Rubén del Campo, et al.

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## To cite this version:

Naiara López-Rojo, Thibault Datry, Francisco Peñas, Gabriel Singer, Nicolas Lamouroux, et al. Carbon emissions from inland waters may be underestimated: Evidence from European river networks fragmented by drying. Limnology and Oceanography Letters, 2024, 10.1002/lol2.10408. hal-04660305

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Submitted on 3 Dec 2025

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## LETTER

# Carbon emissions from inland waters may be underestimated: Evidence from European river networks fragmented by drying

Naiara López-Rojo<sup>1,2*</sup> Thibault Datry<sup>1,2</sup> Francisco J. Peñas<sup>1,3</sup> Gabriel Singer<sup>1,4</sup> Nicolas Lamouroux<sup>1,2</sup> José Barquín<sup>1,3</sup>

Amaia A. Rodeles<sup>1,3</sup> Teresa Silverthorn<sup>1,2</sup> Romain Sarremejane<sup>1,2,5</sup> Rubén del Campo<sup>1,4</sup> Edurne Estévez<sup>1,4</sup> Louise Mimeau<sup>1,2</sup>

Frédéric Boyer<sup>1</sup>, Annika Künne<sup>1,6</sup>, Martin Dalvai Ragnoli<sup>1,4</sup>, Arnaud Foulquier<sup>1</sup>

<sup>1</sup>University of Grenoble Alpes, University of Savoie Mont Blanc, CNRS, LECA, Laboratoire d'Ecologie Alpine, Grenoble, France; <sup>2</sup>National Research Institute for Agriculture, Food and Environment (INRAE), RIVERLY, Lyon-Grenoble Auvergne-Rhône-Alpes Center, Villeurbanne, France; <sup>3</sup>IHCantabria-Instituto de Hidráulica Ambiental de la Universidad de Cantabria, Santander, Spain; <sup>4</sup>Department of Ecology, University of Innsbruck, Innsbruck, Austria; <sup>5</sup>School of Science and Technology, Nottingham Trent University, Nottingham, UK; <sup>6</sup>Department of Geoinformation Science, Friedrich Schiller University, Jena, Germany

### Scientific Significance Statement

More than 50% of total river length are non-perennial rivers and this proportion is increasing. However, current global estimates of C-emissions have generally excluded them. This study aims to understand the role of non-perennial rivers for global C-evasion from inland waters. We found that drivers of both CO₂ and CH₄ emissions from flowing water differed between perennial and non-perennial rivers, indicating a legacy effect of drying. When upscaled, annual C-emissions from dry riverbeds represented 14%-77% of total emissions, highlighting the urgent need of including non-perennial reaches in global C-emissions estimates.

### Abstract

River networks contribute disproportionately to the global carbon cycle. However, global estimates of carbon emissions from inland waters are based on perennial rivers, even though more than half of the world's river length is prone to drying. We quantified CO₂ and CH₄ emissions from flowing water and dry riverbeds across six European drying river networks (DRNs, 120 reaches) and three seasons and identified drivers of emissions using local and regional variables. Drivers of emissions from flowing water differed between perennial and non-perennial reaches, both CO₂ and CH₄ emissions were controlled partly by the annual drying severity, reflecting a drying legacy effect. Upscaled CO₂ emissions for the six DRNs at the annual scale revealed that dry riverbeds

<footnote>*Correspondence: naiara.lopez.rojo@gmail.com</footnote>

<footnote>Associate editor: Janne Karlsson</footnote>

<footnote>**Author Contribution Statement:** NLR, AF, FB, FJP, AAR, EE, and RdC performed the data collection in the field, with help from TS, RS, TD, and MD. LM and AK performed the hydrological models and NL provided additional resources and help for the data analysis. NLR performed the data compilation, centralized lab analyses and data analyses with notable help from AF. NLR led the writing of the manuscript with AF and notable contributions by TD. All authors commented on and contributed to revising draft versions. TD, AF, GS, and JB were responsible for the overall project conceptualization and hypotheses of the research.</footnote>

<footnote>**Data Availability Statement:** Data are available in the Zenodo repository (doi: 10.5281/zenodo.10491926).</footnote>

<footnote>Additional Supporting Information may be found in the online version of this article.</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>
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contributed up to 77% of the annual emissions, calling for an urgent need to include non-perennial rivers in global estimates of greenhouse gas emissions.

After being long considered as passive pipes transporting terrestrial carbon (C) to the ocean (Wohl et al. 2017), river networks are now recognized as important reactors contributing disproportionately to the global C cycle (Cole et al. 2007; Battin et al. 2023). Their dendritic structure allows an efficient drainage of the landscape, linking terrestrial and freshwater biomes to the sea (Battin et al. 2023). Thus, they receive substantial terrestrial inputs of organic and inorganic C (Hotchkiss et al. 2015), which are stored in the sediments, transported to the ocean, or emitted to the atmosphere as carbon dioxide (CO₂) and methane (CH₄) (Raymond et al. 2013; Rocher-Ros et al. 2023) after being used by heterotrophic organisms or subjected to abiotic processes such as photomineralization (Cory et al. 2010). Recent estimates indicate that between 48% and 76% of the influx of terrestrial C to inland waters is released back to the atmosphere (Drake et al. 2018; Kirschbaum et al. 2019), generating a global CO₂ evasion rate of 1.8–2.0 petagrams of C (PgC) per year from streams and rivers (Raymond et al. 2013; Liu et al. 2022).

However, these global estimates are considered to be underestimated because the spatiotemporal patterns of C dynamics in streams and rivers exhibit substantial variability. Moreover, current global estimates from inland waters are based exclusively on data from perennial reaches (PRs), whereas recent studies estimate that 51%–60% of the total global river network dries for at least 1 day of the year (Messager et al. 2021). And in the context of climate change, shifts from perennial to non-perennial flow are predicted in all regions for the next decades (Döll and Schmied 2012; Datry et al. 2023). Non-perennial reaches (NPRs) have been ignored on the rationale that recurrent drying periods rendered their contribution to C emissions negligible when they are flowing (Ran et al. 2015) and because they were perceived outside the domain of inland waters when they are dry. However, drying alters many C-related ecosystem functions (Acuña and Tockner 2010; Arce et al. 2018; Datry et al. 2018a) and NPRs can contribute considerably to CO₂ and CH₄ evasion even during dry phases (von Schiller et al. 2014; Gómez-Gener et al. 2015) and upon flow resumption (Datry et al. 2018b). We meanwhile know that the shifting habitat-mosaic of drying river networks (DRNs), including flowing water and dry riverbeds with or without isolated pools (Datry et al. 2014), alters riverine contributions to global C fluxes by disrupting river networks' longitudinal, vertical, and lateral connectivity (Datry et al. 2018a). The complex hydrological cycle of DRNs (Fig. 1a) may induce a unique stream functioning including during the flowing phase, due to the legacy effects of previous drying events (e.g., Datry et al. 2011; Mora-Gomez et al. 2020), which may require specific modeling for understanding local and regional scale patterns. This drying legacy has been previously shown for other ecosystem processes such as leaf litter decomposition, with lower rates observed in NPRs compared with PRs long after flow resumption

(Datry et al. 2011). In the case of greenhouse gases (GHG), it can potentially reduce or promote CO₂ or CH₄ emissions from local (river reach) to regional (river network) scale. However, network-scale estimates of GHG emissions from DRNs are scarce, and the few existing studies focus on Mediterranean rivers (Gómez-Gener et al. 2016). Due to the prevalence of NPRs, analyzing GHG dynamics across climatic zones is now required to understand and quantify the role of DRNs in the global C cycle.

The main objective of this study was to analyze the patterns and drivers of GHG emissions, based on field quantifications of CO₂ and CH₄. We predicted that (1) environmental variables both at local and regional scales will drive GHG emissions from PRs and NPRs, (2) but that main drivers will strongly differ between PRs and NPRs due to the effects of drying at both local and regional scales (Fig. 1b,c), and (3) at the network scale, dry riverbeds will contribute significantly to total carbon emissions. To test these predictions, we measured in-situ CO₂ and CH₄ emissions from flowing water and dry riverbeds in 120 reaches (including PRs and NPRs) from six European DRNs spanning four different climatic zones and six ecoregions across a hydrological year. We then explored the drivers of GHG emissions from flowing water and dry riverbeds and calculated the contribution of the two habitats.

## Methods

### Study area

This study is part of the European project DRYvER [Securing biodiversity, functional integrity, and ecosystem services in DRYing river networks (Datry et al. 2021)]. It was conducted in six DRNs across Europe (Supporting Information Data S1); in each DRN, 20 sampling reaches were selected, including the main stem and most tributaries with equivalent proportions of PRs and NPRs. Detailed methodology can be found in Supporting Information Data S2.

### GHG measurements

Emissions were measured across three sampling campaigns in 2021 coinciding with three seasons (spring, summer, and winter) to encompass most of the hydrological variability. For each DRN, sampling reach and campaign, CO₂ and CH₄ fluxes were measured in the different habitats present: flowing water, dry riverbeds and isolated pools. Fluxes were measured with a chamber operated in closed loop mode directly coupled to an infra-red gas analyzer. Relative gas concentrations in the chamber were recorded over 5 min. Gas fluxes (µgC-CO₂ m⁻² h⁻¹ or µgC-CH₄ m⁻² h⁻¹) were calculated according to the following formula (Dossa et al. 2015; Wilkinson et al. 2018),

$$ \text{Gasflux} = \frac{\frac{d[\text{gas}]}{d[t]} * M * P * V}{A * R * T} $$
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** (a) The hydrological cycle of DRNs owing to successive drying and rewetting events creating a mosaic of habitats (flowing water, dry riverbed, and isolated pools) at the network scale. (b) GHG emissions happen from flowing water and dry riverbed as main habitats (represented by blue and orange arrows, respectively) in PR and NPRs. Total reach emissions can result purely from flowing waters, from flowing water and dry riverbed during flow contraction or only from dry riverbeds when there is no surface water in NPRs. (c) Conceptual model showing hypothesized drivers of GHG emissions including local (reach scale, blue), regional (green) and the effect of drying and spatial fragmentation (orange). Land uses and geology impact drying regimes (e.g., promoting water abstraction and determining riverbed permeability). Drying acts at both the local (modulating local environmental conditions and determining the proportion of flowing water and dry riverbed) and regional scale (through river network fragmentation). All factors ultimately modulate CO₂ and CH₄ emissions.</figcaption></figure>

where *d*[gas]/*d*[t] is the variation in relative gas concentration (ppm) over time (h); *M* is the molar mass of *C* (g mol⁻¹); *P* is the atmospheric pressure (atm); *V* and *A* are the volume (L) and area (m²) of the chamber; *R* is the ideal gas constant (L atm mol⁻¹ K⁻¹) and *T* is the air temperature (K).

## Local and regional environmental drivers

At each sampling reach and campaign, we measured several environmental variables (air and water temperature and water dissolved oxygen, pH, connectivity, and nutrient content) and, for each habitat present we estimated sediment moisture, organic matter, C and nitrogen content. The six DRNs were delineated using the same digital elevation model, each sampling reach was assigned to the corresponding closest river segment and several variables related to topography, catchment land uses and climate were extracted.

## Hydrological modeling and local drying metrics

Daily discharges for all of 2021 for each river segment were simulated using a hybrid model. The hybrid model included spatially distributed hydrological models calibrated and validated with discharge data at local gauging stations and

RandomForest (RF) models trained with real flow or no-flow observations. The detailed methodology of the hydrological modeling can be found in Mimeau et al. (2024). We then calculated several metrics to characterize the local drying patterns: (1) flow prevalence (proportion of flowing days) for every river segment and (2) for NPRs: drying severity (mean duration of dry periods) for the sampling year (2021) and last 10 years before sampling. These hydrological metrics have been previously used to characterize the river flow regimes (Sauquet et al. 2021). To account for any potential short- or middle- or long-term drying legacy effects, we also calculated the cumulative number of dry days, the number of drying events and the drying severity for a period of 7, 30, and 90 d before each sampling date.

## Spatial structure and fragmentation by drying at the river-network scale

To take into account the spatial aspects, we calculated: (1) the hydrological distance to the closest water source and (2) the total river length draining to each sampling reach. To account for the effect of fragmentation by drying we measured,
for each sampling date, the length of flowing reaches (m) draining to each sampling reach.

## Statistical analyses

Significant differences in emissions depending on the flow regime (PRs and NPRs) were tested for flowing water and dry riverbed using linear mixed-effects models with CO₂ and CH₄ emissions (log-transformed) as response variables, flow regime as fixed factor and DRN nested within sampling campaign as random factor. The drivers of GHG emissions were assessed using RandomForest (RF) models including local and regional variables, variables accounting for the effect of fragmentation by drying and, for NPRs, drying metrics (Fig. 1c; Supporting Information Data S3). The models were run for the two dependent variables (CO₂ or CH₄ fluxes, log-transformed), for each habitat and flow regime (predictions 1 and 2). No models were run for isolated pools due to the small sample size. We also used the RF modeling approach to upscale, for each entire DRN, the annual dynamics of daily CO₂ emissions. To do so, we fitted a second set of RF models, based on the predictor variables available for all DRN segments, to calculate the daily CO₂ emission rates for each reach. We then calculated the daily dry and flowing surface area of each reach over the year 2021, using the modeled daily discharge and estimates of the corresponding flowing wetted width, obtained from hydraulic geometry relationships. Daily total CO₂ emissions for each DRN were then calculated as the sum of the contribution of dry riverbeds and flowing water (prediction 3) (detailed methods for the upscaling in Supporting Information Data S4).

## Results and discussion

CO₂ and CH₄ emissions (including together the emissions from both flowing water and dry riverbeds) were of similar magnitude in perennial and non-perennial reaches, emissions from dry riverbeds were similar in PRs and NPRs, but higher in PRs in flowing water (Fig. 2; Supporting Information Data S5). Emissions from isolated pools in NPRs were similar in magnitude to those from flowing waters, but the low presence of pools during the sampling campaigns prevented statistical analysis of their patterns and drivers. CO₂ emissions presented a seasonal pattern, with highest emission rates in the dry season (summer period), as has been found in other studies of GHG fluxes in freshwater ecosystems (Liu et al. 2022).

### GHG emissions in NPRs show a drying legacy effect after flow resumption

For CO₂ emissions from flowing water, the main drivers both for PRs and for NPRs were water pH and soil clay content in the draining catchment (Fig. 3a; Table 1). Across all DRNs, CO₂ emissions decreased when pH was higher than 7, reflecting effects on the chemical equilibrium between carbon dioxide, carbonic acid and carbonates. Biological activities such as the decomposition of terrestrial organic matter are a direct source for CO₂ (Battin et al. 2023), but they may also indirectly

promote CO₂ emissions via affecting carbonate equilibria [protonation of this CO₂ to carbonic acid and release of H⁺ (Thomas et al. 2022)]. The presence of soil containing clay surrounding the river reaches is known to facilitate the transfer of fine particles from the terrestrial to the aquatic environment by run-off during high flow events. Increasing amounts of fine sediments to streambeds provide extensive surface area for microbial colonization and promote the retention of moisture, organic matter, and nutrients (Gallo et al. 2014). The positive effect of soil clay content agrees with other studies highlighting the importance of the soil-stream connectivity when predicting the CO₂ partial pressure (pCO₂) (Rocher-Ros et al. 2019)—similar relationships were reported between the amount of total suspended solids and pCO₂ (Ran et al. 2015).

In PRs, discharge was the third most important predictor (Fig. 3a). pCO₂ and gas transfer velocity have been identified as fundamental variables of CO₂ emissions in rivers, with positive effect of river discharge on gas transfer velocity (Raymond et al. 2012; Long et al. 2015). Water pH was also within the most important drivers of CH₄ emissions from flowing water in PRs, together with elevation and karstic area in the draining catchment. The negative effect of pH on CH₄ fluxes (Fig. 3b), aligns with studies reporting that optimal values for methanogenesis lie between pH 4 and 7 (Dalal and Allen 2008). This can explain the negative relationship of emissions with karstic surface extent, both in PRs and NPRs, as karst soils typically present high pH values (Reuter et al. 2008). The negative relationship of CH₄ emissions with elevation, found also in a global study from perennial rivers (Rocher-Ros et al. 2023), may be due to the higher turbulence and water aeration of headwater streams that together with their coarser substrate and lower mean annual temperatures enhances aerobic conditions (Marx et al. 2017).

In NPRs, drying severity was one of the most important variables of both CO₂ and CH₄ emissions from flowing water (Table 1). Emissions were affected by previous drying events even long after flow resumption, indicating that the functioning of NPRs differ from PRs. Such “drying legacy” has been shown for other ecosystem processes such as leaf litter breakdown (Datry et al. 2011; Mora-Gomez et al. 2020) due to long-term effects of drying on biotic communities (Piano et al. 2019; Cooper et al. 2021). This highlights the need to model CO₂ and CH₄ emissions from flowing water independently for PRs and NPRs. As most river networks contain both reach types (Messager et al. 2021) and because the prevalence of drying is increasing due to global change (Datry et al. 2023), our results have major implications for the estimations of global C budgets. Average annual drying severity (i.e., mean duration of dry periods) enhanced CO₂ emissions from flowing water in NPRs (Fig. 3a), which is the first evidence that drying can promote CO₂ emissions in subsequent flowing phases even long after flow resumption. One of the underlying mechanisms, particularly in summer, is likely the accumulation of terrestrial and aquatic organic matter on
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Boxplot showing the first quartile, median, and the third quartile of CO₂ (a–c) and CH₄ (d–f) fluxes from the different habitats (flowing water, dry riverbed, and isolated pools) measured in PRs (blue) and NPRs (orange) across three sampling campaigns. Whiskers denote the minimum and maximum non-outlier data.</figcaption></figure>

dry riverbeds and riparian areas (Corti and Datry 2012; Barthélémy et al. 2022). Upon rewetting, the respiration owing to the decomposition of this material can generate hot-moments of heterotrophic biological activity, promoting CO₂ pulses (von Schiller et al. 2014; Datry et al. 2018b). Conversely, CH₄ emissions during the flowing phase were higher in reaches experiencing very short dry periods (lower drying severity) (Fig. 3b), which are more likely to allow humid and anoxic conditions to persist (potentially including the presence of isolated pools) promoting conditions benefiting methanogenic communities (Holgerson 2015). CH₄ generated in isolated pools during those short periods could be quickly outgassed after flow resumption. However, in our study, the isolated pools that remained during the dry periods were small (<50 cm depth) and rare (present only in 12 occasions taking into account all sampling dates–3 campaigns–and reaches–120). More research, with adapted sampling designs, is need to better understand the patterns and drivers of GHG emissions from isolated pools.

## The trade-off between enhanced microbial activity and water stress drives emissions from dry riverbeds

Across the six DRNs, the same drivers explained the emissions of CO₂ from dry riverbeds. CO₂ emissions were driven by climatic variables, specifically monthly precipitation and evapotranspiration, together with sediment temperature. However,

the drivers of CH₄ emissions differed between PRs and NPRs (Table 1). Previous studies of CO₂ emissions from dry riverbeds identified local variables such as sediment moisture, temperature, organic matter content and texture as main drivers (Gallo et al. 2014; von Schiller et al. 2014; Keller et al. 2020). While those studies compiled punctual, local CO₂ measurements, our study is the first to integrate the variability across seasons allowing examining directly the relative importance of local and regional drivers. Integrating the temporal scale, both in PRs and NPRs, CO₂ emissions from dry riverbeds were driven by seasonal climatic conditions. CO₂ emissions increased with monthly precipitation and evapotranspiration and were promoted by higher sediment temperature, probably due to enhanced microbial activity. However, precipitation values above 100 mm had a negative effect: excessive sediment moisture limits gas exchange with the atmosphere, promoting anoxic conditions and thus reducing aerobic respiration. This, together with the asymptotic pattern observed for evapotranspiration and sediment temperature (Fig. 3c), suggests that CO₂ emissions from dry riverbeds depend on the trade-off between the stimulation of microbial activity and the prevention of either excessive water stress or excessive moisture.

Similar mechanisms could drive CH₄ emissions, although the results should be taken with caution due to the lower performance of our models (Table 1). In PRs, evapotranspiration
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Partial plots of the main drivers of GHG emissions from flowing water (CO₂ and CH₄ in panels (a) and (b), respectively) and dry riverbeds (CO₂ and CH₄ in panels (c) and (d), respectively) for PRs (blue) and NPRs (orange).</figcaption></figure>

showed a bell-shaped pattern, with the highest emissions at 150-200 mm while in NPRs, emissions increased with increased sediment moisture up to levels around 30%, where the relationship turned asymptotic (Fig. 3d). On one side, higher evapotranspiration could promote excessive water loss and thus, water limitation. On the other side, higher moisture levels, even if promoting methanogenic activity maintaining anoxic habitats, could limit gas diffusivity. Both extremes (water limitation promoted in sediments with highest evapo-transpiration and excessive sediment moisture preventing gas diffusivity), decrease CH₄ emissions. In the case of high sediment moisture, anoxic conditions and reduced gas diffusivity could promote CH₄ production and accumulation, respectively, which can be emitted when flow resumes (supporting the relationship between short dry periods and CH₄ emissions from flowing water found here). This decoupling of CH₄ production and emission has been previously proposed for sediment respiration and C emissions (Gómez-Gener et al. 2015). Our findings align with the bell-shaped curve characterizing the relationship between moisture and aerobic respiration in soils (Wang et al. 2019; Manzoni et al. 2020) and corroborate trade-offs between temperature and water stress previously reported for methanogenic activity in soils (Fowler et al. 2009). This suggests that CO₂ and CH₄ emissions in soils and dry riverbeds share similar patterns and drivers.

## Estimates of carbon emissions from inland waters need to be reconsidered

The upscaling of the CO₂ emissions to the whole DRNs revealed that the annual contribution of dry riverbeds to total CO₂ emissions exceeded 50% in three of the six DRNs (from 14% up to 77%, Fig. 4). Even in the boreal and most perennial DRN (Lepsämänjoki, Finland), incorporating the contribution of dry riverbeds led to a 14% increase of annual emissions. This indicates that current estimates of global C emissions from streams and rivers (Lauerwald et al. 2013; Raymond et al. 2013) are substantially underestimated, as both surfaces of dry riverbeds of PRs and NPRs and dry periods of NPRs were considered as negligible. The unique nature of NPRs—not terrestrial, but not always aquatic (Arce et al. 2018)—complicates their inclusion in global C estimates and leads to the potential risk of double-counting emissions from these ecosystems when they are also included in terrestrial C budgets (e.g., within the footprint of eddy covariance towers) (Casas-Ruiz et al. 2023). Interdisciplinary efforts are thus needed to identify and integrate all ecosystems and landscape components, including NPRs and dry riverbeds into global assessments of C cycling (von Schiller et al. 2014; Datry et al. 2018b; Marcé et al. 2019).

This is the first large network scale study measuring CO₂ and CH₄ emissions from both flowing water and dry riverbeds covering a wide latitudinal range across European DRNs and
<table><caption>**Table 1.** Results of final RF models (only the top five predictors are shown, notice that in some cases, the final model included less than five variables) for CO₂ and CH₄ emissions from flowing water and dry riverbeds for PRs and NPRs. The variables used as predictors are described in Supporting Information Data S3. Inc. MSE corresponds to the increase in the mean squared error of the predictions after permutation. Inc. node purity is the average decrease in node impurity measured as the residual sum of squares. Both are used to assess the importance of predictors in RF models. For both measures, the higher the value, the more important the variable.</caption><thead><tr><th>Response variable</th><th>Variance explained (%)</th><th>Predictor variable</th><th>Inc. MSE (%)</th><th>Inc. node purity (%)</th></tr></thead><tbody><tr><td colspan="5"><strong>CO₂ emissions flowing water</strong></td></tr><tr><td rowspan="5">PRs</td><td rowspan="5">57</td><td>Water pH</td><td>64.78</td><td>6.63</td></tr><tr><td>Soil clay</td><td>40.76</td><td>3.41</td></tr><tr><td>Discharge</td><td>38.33</td><td>3.64</td></tr><tr><td>Reach slope</td><td>31.20</td><td>2.87</td></tr><tr><td>Dissolved carbon</td><td>31.11</td><td>2.25</td></tr><tr><td rowspan="5">NPRs</td><td rowspan="5">35</td><td>Soil clay</td><td>46.31</td><td>2.27</td></tr><tr><td>Water pH</td><td>34.84</td><td>2.45</td></tr><tr><td>Drying severity</td><td>30.83</td><td>2.49</td></tr><tr><td>Discharge</td><td>29.40</td><td>2.36</td></tr><tr><td>Sediment OM</td><td>28.41</td><td>2.29</td></tr><tr><td colspan="5"><strong>CO₂ emissions dry riverbeds</strong></td></tr><tr><td rowspan="6">PRs</td><td rowspan="6">50</td><td>Monthly evapotranspiration</td><td>48.09</td><td>3.11</td></tr><tr><td>Sediment temperature</td><td>38.32</td><td>2.65</td></tr><tr><td>Monthly precipitation</td><td>37.90</td><td>3.01</td></tr><tr><td>Air temperature</td><td>37.43</td><td>2.34</td></tr><tr><td>Climatic zone</td><td>35.71</td><td>1.48</td></tr><tr><td>Monthly evapotranspiration</td><td>65.91</td><td>4.57</td></tr><tr><td rowspan="5">NPRs</td><td rowspan="5">64</td><td>Sediment temperature</td><td>52.90</td><td>3.61</td></tr><tr><td>Monthly precipitation</td><td>51.60</td><td>3.08</td></tr><tr><td>Karstic area</td><td>46.71</td><td>1.60</td></tr><tr><td>Sediment OM</td><td>42.66</td><td>2.98</td></tr><tr><td>CH₄ emissions flowing water</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="6">PR</td><td rowspan="6">69</td><td>Elevation</td><td>49.43</td><td>4.98</td></tr><tr><td>Water pH</td><td>35.49</td><td>2.98</td></tr><tr><td>Karstic area</td><td>35.22</td><td>3.10</td></tr><tr><td>Discharge</td><td>34.78</td><td>1.61</td></tr><tr><td>Soil clay</td><td>32.63</td><td>2.62</td></tr><tr><td>Monthly evapotranspiration</td><td>36.72</td><td>0.41</td></tr><tr><td rowspan="5">NPR</td><td rowspan="5">39</td><td>Sediment C</td><td>27.77</td><td>0.50</td></tr><tr><td>Drying severity</td><td>24.06</td><td>0.34</td></tr><tr><td>Elevation</td><td>23.30</td><td>0.32</td></tr><tr><td>Sediment moisture</td><td>22.22</td><td>0.42</td></tr><tr><td>CH₄ emissions dry riverbeds</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="5">PR</td><td rowspan="5">32</td><td>Karstic area</td><td>21.32</td><td>0.16</td></tr><tr><td>Soil clay</td><td>21.17</td><td>0.16</td></tr><tr><td>Sediment pH</td><td>17.80</td><td>0.21</td></tr><tr><td>Monthly evapotranspiration</td><td>15.03</td><td>0.18</td></tr><tr><td>Sediment moisture</td><td>32.03</td><td>1.18</td></tr><tr><td rowspan="2">NPR</td><td rowspan="2">16</td><td>Sediment C</td><td>28.70</td><td>1.62</td></tr><tr><td></td><td></td><td></td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Annual patterns of total CO₂ emissions (Tg C-CO₂ per day) for each of the DRNs (total value including all river reaches), across 2021. Blue and orange areas represent the contribution of emissions from flowing water and dry riverbeds, respectively.</figcaption></figure>

integrating the temporal dynamic. The drivers of CO₂ and CH₄ emissions differed between PR and NPR reaches, indicating the “drying legacy” effect from NPR flowing water, even long after flow resumption, thus highlighting the need to model GHG emissions from PR and NPR reaches independently. By including the emissions from dry riverbeds during

flow contraction phase from both PRs and NPRs and flow cessation in NPRs, our study indicates that current estimates of C emissions from inland waters are underestimated. Previous studies compiling snapshot CO₂ emission measurements have estimated that the emissions from dry inland waters could represent around 10% of the global CO₂ emissions from
freshwater ecosystems (Marcé et al. 2019; Keller et al. 2020) and, that the CO₂ released from leaf litter accumulated in the riverbeds during a single rewetting event could represent up to 10% of these emissions (Datry et al. 2018b). Our study allows quantifying the influence of contracting and expanding flow patterns to the river network scale balance of C emissions. Ignoring the contribution of dry riverbeds could lead to underestimation of the annual C emission of rivers and streams by up to 77% (Fig. 4; Supporting Information Data S4) and thus calls for a more accurate upscaling of GHG emissions from streams and rivers.

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Acknowledgments

This project was financially supported by the DRYvER project, which has received funding from the European Unions Horizon 2020 research and innovation program under grant agreement no. 869226. We thank Cindy Arnoldi and Abdelkader Azougui for their help with methodological aspects, field work and/or laboratory analysis.

Submitted 14 November 2023

Revised 22 April 2024

Accepted 29 April 2024