# Reservoirs change pCO2 and water quality of downstream rivers: Evidence from three reservoirs in the Seine Basin

Xingcheng Yan, Vincent Thieu, Songjun Wu, Josette Garnier

► To cite this version:

Xingcheng Yan, Vincent Thieu, Songjun Wu, Josette Garnier. Reservoirs change pCO2 and water quality of downstream rivers: Evidence from three reservoirs in the Seine Basin. Water Research, 2022, 213, pp.118158. 10.1016/j.watres.2022.118158. hal-03841919

HAL Id: hal-03841919

https://hal.science/hal-03841919v1

Submitted on 25 Jul 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1 Reservoirs change pCO₂ and water quality of downstream rivers: evidence

2 from three reservoirs in the Seine Basin

3 Xingcheng Yan¹,*, Vincent Thieu¹, Songjun Wu²,³, Josette Garnier¹

4

5 1. Sorbonne Université, CNRS, EPHE, UMR 7619 METIS, 4 place Jussieu, Box 105,

6 75005, Paris, France

7 2. Department of Ecohydrology, Leibniz Institute of Freshwater Ecology and Inland

8 Fisheries, Müggelseedamm 310, 12587, Berlin, Germany

9 3. Department of Geography, Humboldt University of Berlin, Rudower Chaussee 16,

10 12489 Berlin, Germany

11

12

13

14

15 Correspondence

16 * Xingcheng Yan, Sorbonne Université, UMR 7619 METIS, 4 place Jussieu, Box

17 105, 75005, Paris, France

18 Email: xingcheng.yan@upmc.fr, vincent.thieu@upmc.fr, josette.garnier@upmc.fr

19

20

21

22
**Abstract:** The global increase in the construction of reservoirs has drawn attention given its documented hydrological and biogeochemical impacts on downstream rivers; however, the impact of reservoirs on downstream pCO₂ (partial pressure of carbon dioxide) is still poorly understood. To evaluate these impacts, the interactions between reservoirs and their corresponding upstream and downstream rivers were analyzed for three reservoirs in the Seine Basin based on monthly measurement during two hydrological years. The seasonal variations of water quality in the reservoirs were mainly driven by the entering water and the biogeochemical processes occurring in the reservoirs. Our results unravel the crucial role of reservoir in downstream water quality, which significantly increased DOC (dissolved organic carbon) and BDOC (biodegradable DOC) concentrations, while lowered DSi (dissolved silica) concentrations during emptying period (*p* < 0.01). Furthermore, the impacts of reservoirs on the annual fluxes of DOC, BDOC, and DSi were quantified and suggested that the three reservoirs respectively increased 20% and 23% of annual fluxes of DOC and BDOC, while decreased 33% of annual DSi fluxes in their downstream rivers. Additionally, the reservoirs significantly decreased downstream riverine pCO₂ (*p* < 0.01), and enhanced the gas transfer coefficient of CO₂ in downstream rivers by 1.3 times during the emptying period, which highlights the necessity to consider the potential impact of reservoirs on riverine CO₂ emissions. Overall, our results highlight the importance of combining biogeochemical and hydrological characteristics to understand the impacts of reservoirs on downstream rivers, and emphasize the need of similar studies under the current context of increasing reservoir constructions.
45

**Key words:** Reservoirs; Seine Basin; Downstream River; Water quality; Partial pressure of CO₂

46

47

# 1. Introduction

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

The increase in reservoirs has been well-reported since recent decades, due to its benefits and capacities in hydroelectric generation, irrigation, water supply, and downstream water flow regulation (Garnier et al., 1999; Lehner et al., 2011). However, concerns and ongoing debates still exist to date given their resulting socioenvironmental costs (Bertassoli et al., 2021; Harrison, 2019; Hermoso et al., 2019; Muller, 2019). For instance, reservoirs can significantly alter the flow regime (Vörösmarty et al., 1997), by shifting rivers from free-flowing systems to lentic and lotic systems of high discontinuity (Ward and Stanford, 1995). Such altered hydrological transport, together with the resulting changes in biogeochemical processes can further change the downstream water quality (Maavara et al., 2020b). Many studies have revealed the reservoir-derived changes in nutrient retention/elimination (Akbarzadeh et al., 2019; Garnier et al., 1999; Maavara et al., 2014, 2015b, 2017; Yan et al., 2021a), nutrient bioavailability (Chen et al., 2020), and stoichiometry (Maavara et al., 2020a; Wang, 2020; Yang et al., 2018), as well as in the molecular composition of dissolved organic matter in downstream rivers (He et al., 2020). However, most of the previous studies that investigated nutrient retentions in reservoirs were calculated by mass balance (e.g., Garnier et al., 1999; Yan et al., 2021) and modelling methods (e.g., mechanistic models for global estimation, see Akbarzadeh et al., 2019; Maavara et al.,
2014), and little attention has been paid to related processes (e.g., hydrological characteristics and biogeochemical processes) occurring in the reservoirs.

In addition to the hydrological and biogeochemical effects, GHG (greenhouse gas) emission from reservoirs is another concern, which is partially responsible for the climatic change (Giles, 2006). From a global perspective, GHG emissions from reservoirs were estimated as 773 Tg CO₂ (carbon dioxide) equivalents yr⁻¹ (330 Tg C yr⁻¹), suggesting reservoirs as critical sources of GHG (Deemer et al., 2016). CO₂, one of the crucial components of GHG, has been extensively studied around the world regarding its flux from reservoirs. Numerous studies have contributed to the estimation of global annual CO₂ emissions from reservoirs, with the reported different magnitudes, ranging from 36.8 to 272.7 Tg C yr⁻¹ (Deemer et al., 2016; Yan et al., 2021b). The high uncertainty of CO₂ emissions from global reservoirs indicates the necessity for future field measurements of CO₂ emissions in regional reservoirs, which are critical to understand the behaviour of CO₂ emissions from multiple reservoir settings and conditions (Bertassoli et al., 2021).

However, current studies of CO₂ emissions from reservoirs have mainly focused on CO₂ exchange at the water-air interface in the reservoirs (Soued and Prairie, 2020), while little attention has been paid to simultaneous CO₂ emissions and concentrations in their upstream and downstream rivers. Although it has been acknowledged that overall rivers are sources of CO₂ (Raymond et al., 2013), less is known about the potential impact of reservoirs on riverine CO₂ concentrations and emissions (Liu et al., 2016). A previous modeling approach suggested that the reservoirs in the Seine Basin
88 lowered the dissolved inorganic carbon and total alkalinity of downstream rivers
89 (Marescaux et al., 2020), two variables closely related to CO₂ concentration. However,
90 further research on CO₂ dynamics is still required to provide direct evidence to identify
91 the underlying mechanisms regarding the impact of these reservoirs on CO₂ in
92 downstream rivers.

93 The Marne, Aube, and Seine reservoirs in the Seine Basin (France) share the same
94 water management strategy for flood control and low-flow support (Garnier et al.,
95 2000, 1999), but differ from most reservoirs built on the stretches of river, as they were
96 diverted from their related rivers and connected with the rivers by artificial canals. The
97 annual shift in hydrological characteristics due to the emptying and filling operations
98 allows the co-existence of multiple biogeochemical processes in reservoirs, specifically
99 the development of phytoplankton in the reservoirs, which potentially drives the
100 changes in nutrient concentrations and pCO₂ (partial pressure of CO₂). By analyzing
101 the monthly observations in these three reservoirs, their upstream and downstream
102 rivers, we seek to identify and quantify the impact of three reservoirs on pCO₂ and
103 nutrient concentrations on downstream rivers.

104 **2. Materials and methods**

105 **2.1. Study sites**

106 Three reservoirs are located in the upstream of the Seine Basin, France (Fig. 1a), in
107 mixed-land-use catchments that are dominated by forest (40%–54%), agricultural land
108 (38%–48%), and fringed by grassland (5%–18%). The lithological characteristics of
the three reservoirs are clay (Garnier et al., 1999; Yan et al., 2021a). The Marne (storage capacity of 350 M m³), Aube (170 M m³), and Seine (208 M m³) reservoirs were built in diversion of the Marne, Aube, and Seine rivers in 1974, 1990, and 1966, respectively.

According to the upstream hydrological characteristics, the three reservoirs are filled from December to June of the following year (filling period: prevent flooding) and emptied from July to November (emptying period: support low water flows in downstream). The variation of volume in the reservoirs is directly related to water management objectives and is fully controlled by specific filling and emptying operations using artificial penstock canals (Garnier et al., 1999). More information on three reservoirs can be found on the Seine Grand Lacs website (SGL, http://seinegrandslacs.fr/).

## 2.2. Sampling strategy and measurements

Monthly field campaigns were conducted since 2019 for approximately two hydrological cycles of the reservoirs. A total of nine sampling sites were selected in the upstream and downstream rivers, and inside the three reservoirs (Fig. 1, Table S1). For each site, surface water samples were taken with a bucket from bridges or pontoons, and 5–L high-density polyethylene sampling bottles were filled. All samples were conditioned (e.g., filtration) and kept at < 10°C in the field. After returning to the laboratory, they were stored at 4°C or frozen until the analysis. In addition, pH, T (water temperature, °C), and DO (dissolved oxygen, mg L⁻¹) were measured on the field using a multiparameter instrument (YSI ® 6600 V2).

CO₂ sampling and measurements were realized on the field: 30 mL of water was
kept in four syringes (60 mL) with 30 mL of ambient air, and were shaken for 10 min. A
fifth syringe was filled with ambient air to measure the atmospheric pCO₂. The pCO₂ in
water was determined through a non-dispersive infrared gas analyzer (Licor, LI-820,
USA) using the syringe headspace equilibrium method in the field (Abril et al., 2015;
Marescaux et al., 2018a). The gas analyzer was calibrated with standard CO₂ (800 ppm)
and CO₂-free dinitrogen. The pCO₂ in water was then calculated based on the pCO₂
measured in the equilibrated headspace of the syringes and in the atmosphere
(Wanninkhof, 2014).

In the laboratory, filtered water samples (GF/F, 0.7 µm, grilled at 500 °C for 4 h) were collected in grilled glass flasks and acidified (0.1 mL H₂SO₄, 4 M in 30 mL of water), and were then analyzed with a TOC analyzer (Aurora 1030 TOC Analyzer, OI Analytical) for DOC (dissolved organic carbon, mg L⁻¹) (Marescaux et al., 2018a, 2018b). DOC and DOC concentrations after 45 days (DOC₄₅) of incubation at 20°C and in the dark were also analyzed, and BDOC (biodegradable DOC) concentrations were derived from the difference between the DOC and DOC₄₅ concentrations (Servais et al., 1995). Chlorophyll a concentrations (Chl-a, µg L⁻¹, algal cells retained on GF/C membrane filters) were determined spectrophotometrically after extraction using 90% acetone according to Lorenzen. (1967).

## 2.3. Calculations

### 2.3.1. Calculations of CO₂ flux and DO saturation

The diffusive fluxes of CO₂ in three reservoirs and rivers were calculated based on the boundary layer equation (Liss and Slater, 1974):
$$F = k \times M \times (C_w - C_{eq}) \qquad (1)$$

where F is the CO₂ flux at the water-air interface (mg m⁻² d⁻¹), k is the transfer velocity of CO₂ (m d⁻¹), M is the molar mass of CO₂, Cₗ is the CO₂ concentration in the water (µmol L⁻¹), Cₐq is the CO₂ concentration in the water that is in equilibrium with the atmospheric concentration at surface water temperature (µmol L⁻¹). Cₗ and Cₐq were calculated by the measured pCO₂ in water and atmosphere and β that was calculated according to Wanninkhof. (2014):

$$\ln \beta = -58.0931 + 90.5069(100/T) + 22.2940 \ln(T/100) \qquad (2)$$

where β is the Bunsen coefficient (mol L⁻¹ atm⁻¹) and T is the water temperature in Kelvin.

k was estimated from k₆₀₀ (Liss en Merlivat, 1986):

$$k = k_{600} \times (600/S_c)^n \qquad (3)$$

where n is 2/3 for U₁₀ ≤ 3.7 m s⁻¹ and 1/2 for U₁₀ > 3.7 m s⁻¹, and U₁₀ is the wind speed at an elevation of 10 m. In this study, we used the monthly average wind speed in the Champagne region, France (see supplementary material, SM, Fig. S1).

Sₐ is the Schmidt number of gas at surface water temperature calculated as (Wanninkhof, 2014):

$$S_c(T) = 1923.6 - 125.06T + 4.3773T^2 - 0.085681T^3 + 0.00070284T^4 \qquad (4)$$

In reservoirs, k₆₀₀ was calculated according to Cole and Caraco (1998), k₆₀₀ was presented in Fig. S1:

$$k_{600} = 2.07 + 0.215 \times U_{10}^{1.7} \qquad (5)$$

In rivers, k₆₀₀ (m d⁻¹) was calculated according to Raymond et al. (2012):
$$k_{600} = 2.02 + VS \times 2841 \qquad (6)$$

To calculate the water flow velocity (V in m s⁻¹), the mean depth (D, m) was calculated from Manning's formula (Billen et al., 1994):

$$D = [0.045 \times Q \times (W \times S^{0.5})^{-1}]^{0.6} \qquad (7)$$

$$V = Q / (D \times W) \qquad (8)$$

where S is the slope of the river, Q is the river water discharge (m³ s⁻¹), and W is the river width.

DO saturations (S<sub>DO</sub>) in the surface water were calculated:

$$S_{DO} = DO_w / DO_{sat} \times 100\% \qquad (9)$$

$$DO_{sat} = \exp(-139.34411 + 1.575701 \times 105 \times T^{-1} - 6.642308 \times 107 \times T^{-2} + 1.2438 \times 1010 \times T^{-3} - 8.621949 \times 1011 \times T^{-4}) \qquad (10)$$

where DO<sub>w</sub> is the measured DO concentration in water, DO<sub>sat</sub> is the saturated DO at water temperature T in Kelvin.

### 2.3.2. Quantifying the impact of reservoirs on downstream rivers

To qualitatively evaluate the impact of biogeochemical processes on water quality changes in the reservoir, the concentrations of water variables in reservoirs were calculated assuming they resulted only from the mixing with the entering water concentrations and without any alteration from biogeochemical processes, hereafter named “mixing values”. For this theoretical exercise, the daily concentrations of Chl-a, DSi, DOC, and BDOC were derived by a simple linear interpolation based on the monthly measurements. Their corresponding mixing values in reservoirs were calculated as follows:
$$C_i = C_{i-1} + (Q_{in-i} \times 3600 \times 24) \times (C_{up-i} - C_{i-1}) / Vol_i \quad (11)$$

where $C_i$ and $C_{up-i}$ represent the concentrations of water variable in the reservoir and the upstream rivers in day i, respectively. $C_{i-1}$ is the water variable concentrations in the reservoir at the previous day of i. $Q_{in-i}$ is the water discharges (m³ s⁻¹) entering reservoir in day i, and $Vol_i$ (m³) is reservoir volume in day i.

Additionally, to quantify the impact of reservoirs on downstream water quality, we calculated the annual fluxes of Chl-a, DSi, DOC, and BDOC in downstream rivers under the actual situation (with reservoir) and the hypothetical situation (without reservoir). In the actual situation, the annual fluxes of the mentioned variables in the downstream were calculated using discharge-weighted concentration method (Moatar and Meybeck, 2005; Yan et al., 2021):

$$AF = \frac{\sum_{i=1}^{n} (c_i \times Q_i)}{\sum_{i=1}^{n} Q_i} \times Q_m \quad (12)$$

where AF is the annual flux of related variable, $C_i$ is the measured instantaneous concentration value, $Q_i$ is the water discharges associated with $C_i$, $Q_m$ is the average water discharge during the annual water cycle (from December 2019 to November 2020), n represents the number of measured $C_i$.

In the hypothetical situation, the discharges in downstream rivers were revised according to the water entering and leaving the reservoirs. The revised discharges in the downstream rivers (Marne and Aube) were presented in the SM (Fig. S2). In a previous study of the Seine reservoirs (Yan et al., 2021b), we demonstrate that in the absence of reservoir releases, the water quality remains unchanged between upstream and downstream sites, ruling out any significant impacts of instream processes or
219 lateral inputs along the river sections between the upstream and downstream sampling
220 stations. Therefore, in the hypothetical situation of no reservoir, downstream water
221 quality was assumed to be similar to the upstream one. The annual fluxes of Chl-a,
222 DSi, DOC, and BDOC in downstream rivers were calculated by equation 12.

223 **2.4. Statistical analysis**

224 The Shapiro-Wilk method was used to test the normal distribution of the field data
225 before performing statistical analysis. The Wilcoxon test method was used to identify
226 the impact of the three reservoirs on water quality of downstream rivers during the
227 filling period and emptying period, including DOC, BDOC, DSi, and pCO2. The
228 Spearman's rank correlation coefficient was used to identify the relationship between
229 pCO2 and SDO and DSi during emptying period (in order to avoid the influence of the
230 upstream water entering the reservoirs). The criterion of p < 0.05 was used to determine
231 statistical significance. All of the aforementioned analysis was conducted using R,
232 version 3.5.1 (R Core Team, 2019).

233 **3. Result**

234 **3.1. Water quality in the reservoirs**

235 The seasonal variations of volumes and water quality in the three reservoirs
236 showed similar patterns, therefore, we only presented the Marne Reservoir as an
237 example (Fig. 2) (for Seine and Aube reservoirs please refer to Fig. S3 and S4). Chl-a
238 concentrations in upstream river showed slight variations, with values ranging from 2
239 µg L⁻¹ to 7 µg L⁻¹. In the reservoir, high Chl-a concentrations were found when the
240 reservoir volumes were the lowest (during November–December, Fig. 2b, S3b, and S4b), and were 4–14 times higher than those in the upstream river. Chl-a concentrations in the reservoir gradually declined from December to March, and sustained low concentrations from April to September (2-6 µg L⁻¹), and then increased from October to December. Comparing the observed Chl-a concentrations with the corresponding mixing values, the former were much higher than the latter during this period.

246 The DSi concentrations in the upstream rivers were generally higher than those in the reservoirs (Fig. 2, S3, and S4). DSi concentrations in the reservoirs gradually declined from December to August, and increased from September to December when water depth was low. In addition, the co-variation between observations and the mixing values of DSi concentrations was found from October to February. However, this was followed by continuously declining of DSi concentrations during April–July.

253 The dynamics of BDOC concentrations in reservoirs, which accounted approximately 25% of DOC, showed synchronous variations with DOC concentrations. DOC and BDOC concentrations in reservoirs decreased from December to March and were close to the mixing values; they gradually increased during April–November and were higher than the mixing values during April–November.

258 The seasonal variations of SDO and water temperature in the three reservoirs were generally similar (Fig. 3). Overall, high SDO (> 100%) was found in summer (June–July) at the end of the filling period when water level and temperature of reservoir were high, while low SDO (< 100%) was found in late autumn at the end of emptying
period when water level and temperature were low.

## 3.2. Seasonal dynamics of pCO₂ and CO₂ fluxes

pCO₂ in rivers were higher in summer and lower in winter, and were much higher (~2000 µatm) than that in the reservoirs (< 1000 µatm) (Fig. 4). The pCO₂ in the reservoirs showed general decline trends during the filling period despite high pCO₂ supplies by upstream rivers. The pCO₂ continued declining during the early emptying of the reservoirs in September and October, but increased during the rest of the emptying period. The pCO₂ in the downstream of Marne and Aube reservoirs were 1.5 times lower than that in upstream rivers during the emptying period, while they were relatively close during the filling period (Fig. 4). In the three reservoirs, we found that pCO₂ negatively correlated to S<sub>DO</sub> (n = 32, r = 0.61, p < 0.01), while positively correlated to DSi during emptying period (n = 32, r = -0.5, p < 0.01).

In terms of average CO₂ fluxes, all of three reservoirs were sources of CO₂ to the atmosphere during the study period (04/2019–11/2020), with emission rates of 115 ± 134 mg C m⁻² d⁻¹, 95± 124 mg C m⁻² d⁻¹, and 174 ± 305 mg C m⁻² d⁻¹ (mean ± SD) in the Marne, Aube, and Seine reservoirs, respectively. In contrast, the mean CO₂ fluxes in the upstream of Marne, Aube, and Seine rivers were 2377 ± 536 mg C m⁻² d⁻¹, 2426 ± 589 mg C m⁻² d⁻¹, and 3009 ± 574 mg C m⁻² d⁻¹ over the same time period, respectively. High CO₂ fluxes were observed in downstream of the Marne (1668 ± 652 mg C m⁻² d⁻¹) and Aube (1961 ± 757 mg C m⁻² d⁻¹) rivers in May/June, while relative lower values (40%~50%) were found during the emptying period (Fig. 4).
283 **3.3. Impact of reservoirs on their downstream rivers**

284 The comparison of water concentrations between upstream and downstream rivers
285 during the filling and emptying periods revealed that water released from three
286 reservoirs significantly changed downstream pCO₂ and water quality (DOC, BDOC,
287 DSi) (Fig. 5). During the filling period (almost without any release from the reservoirs),
288 no significant difference was found for the water quality and pCO₂ between the
289 upstream and downstream rivers. However, the three reservoirs significantly changed
290 downstream water quality and pCO₂ during emptying period (p < 0.01), i.e., increasing
291 DOC and BDOC concentrations, while decreasing DSi concentrations and pCO₂ of
292 downstream rivers.

293 Furthermore, the impacts of the reservoirs on the downstream rivers were
294 quantified by comparing the annual fluxes of Chl-a, DOC, BDOC, and DSi in
295 downstream river under the two situations, with and without reservoirs (see 2.3.2).
296 Results showed that the Marne and Aube reservoirs increased respectively by 22% and
297 19% the annual DOC fluxes, and by 34% and 12% the annual BDOC fluxes, while the
298 annual DSi fluxes in the downstream rivers was decreased by 35% and 31% (Table 1).
299 Two reservoirs slightly increased the annual Chl-a flux (7%) in their downstream
300 rivers.

301 In addition to pCO₂ and water quality, Marne and Aube reservoirs increased the
302 downstream discharge by 20 m³ s⁻¹ and 10 m³ s⁻¹ during emptying period, respectively.
303 Similarly, k₆₀₀ of the downstream rivers increase by 1.3 times during emptying period
304 (July–November).
305 **4. Discussion**

306 **4.1. Temporal changes of water parameters in the reservoirs**

307 The temporal changes of water quality in the three reservoirs were affected by the
308 biogeochemical processes in the reservoirs and the upstream discharge during the
309 filling period (December–June). The differences between observations of water
310 variables (Chl-a, DSi, DOC, and BDOC) and their corresponding mixing values
311 resulted from biogeochemical processes occurring in the reservoir (Fig. 2, S3, and S4).
312 In the three reservoirs, the observed Chl-a concentrations were much higher than their
313 corresponding mixing values, demonstrating the development of phytoplankton during
314 October–December in the reservoirs, which were favored by the weak limitations of
315 light and nutrient exchanges from sediment when water levels were shallow (Liu et al.,
316 2019). The gradual decline of Chl-a concentrations during December–March would
317 dominantly result from the entering water containing much lower Chl-a concentrations,
318 while other combined processes might not be excluded, e.g. phytoplankton primary
319 production (Garnier et al., 2000), mortality, sedimentation, as well as grazing by large
320 Cladocerans and Copepods, which has been reported in previous studies (Akopian et al.,
321 2002, 1999). The grazing pressure of zooplankton would sustain low Chl-a
322 concentrations in reservoir during March–October.

323 In addition to the mixing effect of the entering water, the dynamics of DOC, BDOC,
324 and DSi revealed the multiple processes occurring in the reservoirs. First, the
325 concentrations of DOC, BDOC, and DSi in reservoirs were mainly controlled by the
326 water entering reservoirs during December–March, due to the close values between
the observed concentrations and mixing values (Fig. 2). However, the observed concentrations of DSi were much lower than their mixing values during April–June, with a continuous decline trend, suggesting the influences of the phytoplankton development in the reservoirs (mainly diatoms). In contrast, the increasing trends of DSi concentrations in reservoirs during October–December suggested the dissolution of diatom frustules, as reported in previous study (Garnier et al., 1999; Maavara et al., 2015a). Differing from DSi trends, the observed concentrations of DOC and BDOC were higher than their mixing values during April–June. Such gradual increasing trends indicate a production of DOC and BDOC, potentially attributed to the lysis of phytoplankton, sloppy feeding by zooplankton, and mineralization of organic matter. Overall, the temporal changes of Chl-a, DSi, DOC, and BDOC concentrations provided sufficient evidence for identifying the dynamics of phytoplankton in the reservoirs.

## 4.2. Hydrological–biogeochemical–meteorological effects on pCO₂ dynamics

This study revealed that both hydrological and meteorological characteristics impact on pCO₂ in the three studied rivers, as shown by previous studies (Borges et al., 2018; Butman and Raymond, 2011; Marescaux et al., 2018a). The three rivers were supersaturated in CO₂ during all seasons, and thus act as CO₂ sources to the atmosphere, as found around the globe (Butman and Raymond, 2011; Raymond et al., 2013; Regnier et al., 2013). In this study, riverine pCO₂ peaks generally occurred in June/July (Fig. 4), and were probably due to the enhanced in situ respiration rate via the high water temperature (Butman and Raymond, 2011; Liu et al., 2017; Marescaux et al., 2018a; Ran et al., 2017). Moreover, reservoirs have not yet started to release their water at this
time period and the river flows were mostly driven by groundwater with high pCO₂ concentrations (Marescaux et al., 2020). Conversely, relatively low pCO₂ in rivers were found during late autumn and early winter, when river water was more sustained by surface runoff than groundwater (Macpherson, 2009; Marescaux et al., 2020). Such dynamic was reinforced by the seasonal changes in gas transfer velocity (k₆₀₀), with higher values in winter supporting more outgassing, and in contrast less pCO₂ evasion during summer (Fig. 6). In addition, mismatches of pCO₂ between upstream and downstream rivers were found, especially during the emptying period (July–November). These time periods exactly coincided with the time when water was released from the reservoirs increasing gas transfer exchanges (see also below), which further highlighted the potential importance of reservoir on the seasonal decrease of downstream pCO₂. Also, short-term decrease in downstream pCO₂ was observed during filling period in February and March (Fig. 4). This can happen in rainy winters when the reservoir filling-emptying operation is used to buffer any possible flooding in downstream. Overall, our result revealed that during the emptying periods, reservoirs have a significant impact on downstream river pCO₂ which superimposes on the effects of instream hydrological and biogeochemical processes.

For stagnant aquatic ecosystems, the dynamics of CO₂ concentrations are generally regulated by the balances between photosynthesis, respiration, and gas exchange rate (Cole et al., 2000; Duarte and Prairie, 2005; Gu et al., 2011; Marcé et al., 2010). DO saturation has been considered as the surrogate for surface lake net primary productivity (Marcé et al., 2015), and revealed to some extent, the balance between
371 photosynthesis and respiration. In the three reservoirs, DO saturation gradually
372 increased from the early beginning of the year to early summer (Fig. 3), corresponding
373 to the general decline trends of pCO2 in reservoirs, which would support that increasing
374 primary productivity consumed CO2 in these studied reservoirs. The low and even
375 undersaturated pCO2 (September-October) in the reservoirs probably resulted from the
376 assimilation by primary producers (e.g., phytoplankton which sharply increased at this
377 period, Fig. 2b). In late autumn, high values of pCO2 in reservoirs were observed and
378 were consistent with the undersaturated DO, increasing DSi, and decreasing BDOC
379 concentrations (Fig. 2, 3, and 4). All these variables typically revealed degradation of
380 organic matter and biogenic dissolution. Therefore, the seasonal patterns of pCO2 in the
381 studied rivers and reservoirs were regulated by hydrological, biogeochemical, and
382 meteorological effects, and the seasonal dynamics of downstream pCO2 were also
383 affected by the water released from the reservoirs.

384 **4.3. CO₂ fluxes in three reservoirs and up- and downstream rivers**

385 The calculation of CO₂ fluxes from surface water indicated that upstream and
386 downstream rivers are relatively important CO₂ sources to the atmosphere (1668 to
387 3000 mg CO₂-C m⁻² d⁻¹), while the amount of CO₂ emissions from the three reservoirs
388 were more moderate, and matches the average CO₂ fluxes in the temperate reservoirs at
389 the global scale (from 105 to 316 mg CO₂-C m⁻² d⁻¹) (Barros et al., 2011; Deemer et al.,
390 2016; Hertwich, 2013). It should be emphasized that spatial variations of CO₂
391 emissions from reservoirs have been reported in previous studies (e.g., Paranaíba et al.,
392 2018; Zhang et al., 2021), however, such spatial variations of CO₂ emissions were not
investigated here considering that these shallow reservoirs are only intermittently stratified and are homogeneous for a major part of their surface area (Garnier et al., 2000, for the Marne Reservoir). Despite still preliminarily, the annual CO₂ emissions from three reservoirs were for the first time, estimated at 4.2 Gg C yr⁻¹ from seasonal variations of the emissions at a single station extrapolated to the whole reservoir areas.

By comparison with the CO₂ emissions from the Seine drainage network (364 Gg C yr⁻¹) (Marescaux et al., 2020), the reservoir CO₂ emissions would account for about 1% of the contribution to the whole CO₂ emissions from the Seine River, which would remain low with documented spatial emissions. Compared with reservoirs, their related rivers showed much higher CO₂ fluxes to the atmosphere (Fig. 4), and were similar to the recent global estimation of CO₂ fluxes from streams and rivers at the global scale (2886 mg C m⁻² d⁻¹) (Li et al., 2021).

In addition to the CO₂ saturation in water, one of the other important factors controlling CO₂ exchange rates is the gas exchange coefficient, which was determined by k₆₀₀ and the Schmidt number (Drake et al., 2018; Raymond et al., 2013; Raymond and Cole, 2001; Wanninkhof, 2014). In rivers, despite of the other influencing factors, k₆₀₀ is proportional to water flow velocity (Alin et al., 2011; Raymond and Cole, 2001).

Therefore, wintertime k₆₀₀ values were higher than those in summer in the upstream rivers, while increasing k₆₀₀ values in downstream rivers were found due to the increase in water discharges during emptying period (Fig. 6). In the present study, the seasonal variation of CO₂ fluxes in the upstream rivers co-varied with k₆₀₀ and pCO₂, showing that both variables controlled CO₂ fluxes. Interestingly, increasing k₆₀₀ during emptying
period was offset by the significantly decreased in pCO₂, which resulted in an overall damped CO₂ flux (Fig. 4). In other words, pCO₂, rather than k₆₀₀, was the dominant driver of CO₂ fluxes in the downstream rivers, which is also the case for the reservoirs.

Therefore, the observed low pCO₂ in downstream rivers during emptying period strengthened the impact of reservoir on downstream CO₂ concentrations, and hence on the CO₂ emission rates. Our results further highlight the necessity to include reservoir-based effects into consideration when evaluating CO₂ emissions from rivers, especially in the context of a global increase in reservoir construction in the near future (Zarfl et al., 2015).

## 4.4. Overall impacts of the reservoirs on the downstream rivers

The result of this study revealed a variety of impacts on downstream rivers, including water discharges, water quality, pCO₂, and gas transfer coefficient (Fig. 5, 6; and S2). Water quality (DOC, BDOC, and DSi) in upstream and downstream showed no significant difference during filling period (Fig. 5), suggesting that the impact of other processes (e.g., lateral inputs, and internal biogeochemical processes) on water quality between upstream and downstream were not obvious. However, the three reservoirs significantly changed water quality in their downstream rivers during the emptying period (*p* < 0.01, Fig. 5). Our results confirm that reservoirs are “hot spots” for nutrient retention, and thus affect nutrient concentrations in their downstream rivers, as is widely recognized (Garnier et al., 1998; 1999, 2000; Maavara et al., 2020a, 2020b, 2017; Yan et al., 2021). This is because reservoirs usually show higher residence times than that in the rivers (Maavara et al., 2020b), and provide favorable conditions for
multiple biogeochemical processes, such as nutrient uptake and release induced by photosynthesis and respiration (Akbarzadeh et al., 2019; Frings et al., 2014; Garnier et al., 1999; 2000; Humborg et al., 2008), which are responsible for water quality changes. The coupling of biogeochemical processes and water management strategy of reservoirs (emptying from July to November, when the DSi concentrations were relatively low, while DOC and BDOC levels were high) plays the key role in affecting the water quality of downstream rivers.

In addition to nutrient concentrations, our results revealed that the reservoirs lowered downstream pCO₂ and such a finding is different from previous studies that have indicated that the water leaving deep and strongly stratified reservoirs from the bottom, might contain high concentrations of CO₂, leading to significant downstream emissions of CO₂ (Abril et al., 2005; Calamita et al., 2021; Winton et al., 2019). Interestingly, we indeed found that pCO₂ in the outflow at the bottom of Aube Reservoir, though being higher than that in the surface water, was still lower than that in the upstream river waters (Table S2). This is probably because the three reservoirs in our study were relative shallow (10-15 m at the deepest), leading to limited stratification in summer for a rather short period and thus well-mixed for most of the time (Garnier et al., 2000). Therefore, the importance of downstream emission patterns in the three reservoirs differed from the behaviour of deep and stratified one. Overall, our results enrich current knowledge on biogeochemical impacts of reservoirs on downstream rivers, especially in terms of CO₂ concentrations, besides of other water quality variables in the downstream rivers.
459 **5. Conclusion**

460 In this study, we evaluated the impacts of the three reservoirs (Marne, Aube, and
461 Seine) in the Seine Basin on their downstream rivers based on the monthly field
462 measurements during the period from 04/2019 to 11/2020. The temporal changes of the
463 concentrations of Chl-a, DSi, DOC, BDOC, and pCO2 in reservoirs resulted from
464 combined effects of entering waters and reservoir biogeochemical processes. We
465 indeed found that the reservoirs significantly changed downstream water quality during
466 the emptying period, i.e., lowering pCO2 in addition to DSi concentrations (by 33%),
467 and increasing DOC (by 20%) and BDOC (by 23%) concentrations. In addition, the
468 reservoirs changed the water discharges and increased gas transfer coefficient by 1.3
469 times of downstream rivers. The current study fills thus an important gap in the
470 understanding of the functioning of the three reservoirs and their impacts on the
471 downstream rivers during the emptying period. In terms of the CO2 emissions from the
472 surface water, these three reservoirs were moderate sources of CO2, with the average
473 emission rates ranging from 95 to 174 mg CO2-C m-2 d-1, while the rivers were
474 important CO2 sources, with the average emission rates ranging from 1668 to 3000
475 CO2-C m-2 d-1. CO2 fluxes in the upstream rivers were regulated by both the k600 and
476 pCO2, while they were dominantly driven by the pCO2 from the reservoirs in the
477 downstream rivers. Overall, our results emphasize that integrating hydrological and
478 biogeochemical functioning of reservoirs in the riverine drainage network could
479 provide new insights into the biogeochemical impacts of reservoirs on their
480 downstream rivers.
481 **Declaration of Competing Interest**

482 The authors declare that they have no known competing financial interest or
483 personal relationships that could have appeared to influence the work reported in this
484 paper.

485 **Acknowledgement**

486 Many thanks to Benjamin Mercier, Sébastien Bosc, Anunciacion Martinez Serrano,
487 and Romane Nespoulet for their assistance in the field with sampling and with chemical
488 analysis in the lab. Gilles Billen is greatly acknowledged for his interest in this study
489 and for the fruitful discussions. We also would like to thank the Seine Grands Lacs for
490 providing hydrological and related water quality data. Xingcheng Yan's PhD is funded
491 by CHINA SCHOLARSHIP COUNCIL (CSC, NO. 201806860053) and ADSB from
492 Sorbonne University. Vincent Thieu (Assistant Professor at Sorbonne University) and
493 Josette Garnier (Research Director at the CNRS) are the supervisors of the PhD thesis.
494 Songjun Wu's PhD is funded by CHINA SCHOLARSHIP COUNCIL (CSC, NO.
495 201906860020). Finally, Xingcheng Yan is pleased to thank Yang Su for her
496 encouragement during this study.

497 **Reference**

498 Abril, G., Bouillon, S., Darchambeau, F., Teodoru, C.R., Marwick, T.R., Tamooh, F.,
499 Ochieng Omengo, F., Geeraert, N., Deirmendjian, L., Polsenaere, P., Borges, A.
500 V., 2015. Technical note: Large overestimation of pCO₂ calculated from pH and
501 alkalinity in acidic, organic-rich freshwaters. Biogeosciences 12, 67–78.
502 https://doi.org/10.5194/bg-12-67-2015

503 Abril, G., Guérin, F., Richard, S., Delmas, R., Galy-Lacaux, C., Gosse, P., Tremblay,

504 A., Varfalvy, L., Dos Santos, M.A., Matvienko, B., 2005. Carbon dioxide and

505 methane emissions and the carbon budget of a 10-year old tropical reservoir (Petit

506 Saut, French Guiana). Global Biogeochem. Cycles 19, 1–16.

507 https://doi.org/10.1029/2005GB002457

508 Akbarzadeh, Z., Maavara, T., Slowinski, S., Van Cappellen, P., 2019. Effects of

509 Damming on River Nitrogen Fluxes: A Global Analysis. Global Biogeochem.

510 Cycles 33, 1339–1357. https://doi.org/10.1029/2019GB006222

511 Akopian, M., Garnier, J., Pourriot, R., 2002. Zooplankton in an aquatic continuum:

512 From the Marne River and its reservoir to the Seine Estuary. Comptes Rendus -

513 Biol. 325, 807–818. https://doi.org/10.1016/S1631-0691(02)01483-X

514 Akopian, M., Garnier, J., Pourriot, R., 1999. A large reservoir as a source of

515 zooplankton for the river: Structure of the populations and influence of fish

516 predation. J. Plankton Res. 21, 285–297. https://doi.org/10.1093/plankt/21.2.285

517 Alin, S.R., Rasera, M.D.F.F.L., Salimon, C.I., Richey, J.E., Holtgrieve, G.W., Krusche,

518 A. V., Snidvongs, A., 2011. Physical controls on carbon dioxide transfer velocity

519 and flux in low-gradient river systems and implications for regional carbon

520 budgets. J. Geophys. Res. Biogeosciences 116.

521 https://doi.org/10.1029/2010JG001398

522 Barros, N., Cole, J.J., Tranvik, L.J., Prairie, Y.T., Bastviken, D., Huszar, V.L.M., Del

523 Giorgio, P., Roland, F., 2011. Carbon emission from hydroelectric reservoirs
linked to reservoir age and latitude. Nat. Geosci. 4, 593–596.

https://doi.org/10.1038/ngeo1211

Bertassoli, D.J., Sawakuchi, H.O., Araújo, K.R. de, Camargo, M.G.P. de, Alem, V.A.T.,

Pereira, T.S., Krusche, A. V., Bastviken, D., Richey, J.E., Sawakuchi, A.O., 2021.

How green can Amazon hydropower be? Net carbon emission from the largest

hydropower plant in Amazonia. Sci. Adv. 7, eabe1470.

https://doi.org/10.1126/SCIADV.ABE1470

Billen G., Garnier J., Hanset P.H., 1994. Modelling phytoplankton development in

whole drainage networks: the Riverstrahler model applied to the Seine river

system. Hydrobiologia 289, 119–137.

Borges, A. V., Darchambeau, F., Lambert, T., Bouillon, S., Morana, C., Brouyère, S.,

Hakoun, V., Jurado, A., Tseng, H.C., Descy, J.P., Roland, F.A.E., 2018. Effects of

agricultural land use on fluvial carbon dioxide, methane and nitrous oxide

concentrations in a large European river, the Meuse (Belgium). Sci. Total Environ.

610–611, 342–355. https://doi.org/10.1016/j.scitotenv.2017.08.047

Butman, D., Raymond, P.A., 2011. Significant efflux of carbon dioxide from streams

and rivers in the United States. Nat. Geosci. 4, 839–842.

https://doi.org/10.1038/ngeo1294

Calamita, E., Siviglia, A., Gettel, G.M., Franca, M.J., Winton, R.S., Teodoru, C.R.,

Schmid, M., Wehrli, B., 2021. Unaccounted CO₂ leaks downstream of a large

tropical hydroelectric reservoir. Proc. Natl. Acad. Sci. 118.

https://doi.org/10.1073/PNAS.2026004118
546 Chen, Q., Shi, W., Huisman, J., Maberly, S.C., Zhang, J., Yu, J., Chen, Y., Tonina, D.,

547 Yi, Q., 2020. Hydropower reservoirs on the upper Mekong River modify nutrient

548 bioavailability downstream. Natl. Sci. Rev. https://doi.org/10.1093/nsr/nwaa026

549 Cole, J.J., Caraco, N.F., 1998. Atmospheric exchange of carbon dioxide in a low-wind

550 oligotrophic lake measured by the addition of SF6. Limnol. Oceanogr. 43, 647–

551 656. https://doi.org/10.4319/lo.1998.43.4.0647

552 Cole, J.J., Pace, M.L., Carpenter, S.R., Kitchell, J.F., 2000. Persistence of net

553 heterotrophy in lakes during nutrient addition and food web manipulations.

554 Limnol. Oceanogr. 45, 1718–1730. https://doi.org/10.4319/lo.2000.45.8.1718

555 Deemer, B.R., Harrison, J.A., Li, S., Beaulieu, J.J., Delsonro, T., Barros, N.,

556 Bezerra-Neto, J.F., Powers, S.M., Dos Santos, M.A., Vonk, J.A., 2016.

557 Greenhouse gas emissions from reservoir water surfaces: A new global synthesis.

558 Bioscience 66, 949–964. https://doi.org/10.1093/biosci/biw117

559 Drake, T.W., Raymond, P.A., Spencer, R.G.M., 2018. Terrestrial carbon inputs to

560 inland waters: A current synthesis of estimates and uncertainty. Limnol. Oceanogr.

561 Lett. 3, 132–142. https://doi.org/10.1002/lo12.10055

562 Duarte, C.M., Prairie, Y.T., 2005. Prevalence of heterotrophy and atmospheric CO₂

563 emissions from aquatic ecosystems. Ecosystems 8, 862–870.

564 https://doi.org/10.1007/s10021-005-0177-4

565 Frings, P.J., Clymans, W., Jeppesen, E., Lauridsen, T.L., Struyf, E., Conley, D.J., 2014.

566 Lack of steady-state in the global biogeochemical Si cycle: Emerging evidence

567 from lake Si sequestration. Biogeochemistry 117, 255–277.
568 https://doi.org/10.1007/s10533-013-9944-z

569 Garnier, J., Billen, G., Sanchez, N., Leporcq, B., 2000. Ecological functioning of the
570 Marne reservoir (upper Seine basin, France). Regul. Rivers Res. Manag. 16, 51-
571 71.

572 https://doi.org/10.1002/(sici)1099-1646(200001/02)16:1<51::aid-rrr571>3.3.co;2
573 -9

574 Garnier, J., Leporcq, B., Sanchez, N., Philippon, X., 1999. Biogeochemical
575 mass-balances (C, N, P, Si) in three large reservoirs of the Seine Basin (France).
576 Biogeochemistry 47, 119-146. https://doi.org/10.1023/A:1006101318417

577 Giles, J., 2006. Methane quashes green credentials of hydropower. Nature 444.
578 https://doi.org/10.1038/444524a

579 Gu, B., Schelske, C.L., Coveney, M.F., 2011. Low carbon dioxide partial pressure in a
580 productive subtropical lake. Aquat. Sci. 73, 317-330.
581 https://doi.org/10.1007/s00027-010-0179-y

582 Harrison, J.A., 2019. Dams: weigh pros and cons case by case. Nature 568, 171.
583 https://doi.org/10.1038/d41586-019-01137-2

584 He, D., Wang, K., Pang, Y., He, C., Li, P., Li, Y., Xiao, S., Shi, Q., Sun, Y., 2020.
585 Hydrological management constraints on the chemistry of dissolved organic
586 matter in the Three Gorges Reservoir. Water Res. 187, 116413.
587 https://doi.org/10.1016/j.watres.2020.116413

588 Hermoso, V., Clavero, M., Green, A.J., 2019. Don't let damage to wetlands cancel out
589 the benefits of hydropower. Nature 568, 171-171.
590 https://doi.org/10.1038/d41586-019-01140-7

591 Hertwich, E.G., 2013. Addressing biogenic greenhouse gas emissions from
592 hydropower in LCA. Environ. Sci. Technol. 47, 9604-9611.

593 https://doi.org/10.1021/es401820p

594 Humborg, C., Smedberg, E., Medina, M.R., Mörth, C.M., 2008. Changes in dissolved
595 silicate loads to the Baltic Sea - The effects of lakes and reservoirs. J. Mar. Syst. 73,
596 223-235. https://doi.org/10.1016/j.jmarsys.2007.10.014

597 Lehner, B., Liermann, C.R., Revenga, C., Vörömsmarty, C., Fekete, B., Crouzet, P.,
598 Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C.,
599 Rödel, R., Sindorf, N., Wisser, D., 2011. High-resolution mapping of the world's
600 reservoirs and dams for sustainable river-flow management. Front. Ecol. Environ.
601 9, 494-502. https://doi.org/10.1890/100125

602 Li, M., Peng, C., Zhang, K., Xu, L., Wang, J., Yang, Y., Li, P., Liu, Z., He, N., 2021.
603 Headwater stream ecosystem: an important source of greenhouse gases to the
604 atmosphere. Water Res. 190, 1167-1181. https://doi.org/10.1016/j.watres.2020.116738

606 Liss, P.S., Merlivat, L., 1986. Air-Sea Gas Exchange Rates: Introduction and Synthesis,
607 in: The Role of Air-Sea Exchange in Geochemical Cycling. Springer Netherlands,
608 Dordrecht, bll 113-127. https://doi.org/10.1007/978-94-009-4738-2_5

609 Liss, P.S., Slater, P.G., 1974. Flux of gases across the Air-Sea interface. Nature 247,
610 181-184. https://doi.org/10.1038/247181a0

611 Liu, J., Chen, Y., Li, M., Liu, B., Liu, X., Wu, Z., Cai, Y., Xu, J., Wang, J., 2019.
612 Water-level fluctuations are key for phytoplankton taxonomic communities and
613 functional groups in Poyang Lake. Ecol. Indic. 104, 470-478.
614 https://doi.org/10.1016/J.ECOLIND.2019.05.021

615 Liu, S., Lu, X.X., Xia, X., Yang, X., Ran, L., 2017. Hydrological and
616 geomorphological control on CO2 outgassing from low-gradient large rivers: An
617 example of the Yangtze River system. J. Hydrol. 550, 26-41.
618 https://doi.org/10.1016/j.jhydrol.2017.04.044

619 Liu, S., Lu, X.X., Xia, X., Zhang, S., Ran, L., Yang, X., Liu, T., 2016. Dynamic
620 biogeochemical controls on river pCO2 and recent changes under aggravating
621 river impoundment: An example of the subtropical Yangtze River. Global
622 Biogeochem. Cycles 30, 880-897. https://doi.org/10.1002/2016GB005388

623 Lorenzen, C.J., 1967. Determination of chlorophyll and pheo-pigments:
624 Spectrophotometric equations. Limnol. Oceanogr.
625 https://doi.org/10.4319/lo.1967.12.2.0343

626 Maavara, T., Akbarzadeh, Z., Van Cappellen, P., 2020a. Global Dam-Driven Changes
627 to Riverine N:P:Si Ratios Delivered to the Coastal Ocean. Geophys. Res. Lett. 47.
628 https://doi.org/10.1029/2020GL088288

629 Maavara, T., Chen, Q., Van Meter, K., Brown, L.E., Zhang, J., Ni, J., Zarfl, C., 2020b.
630 River dam impacts on biogeochemical cycling. Nat. Rev. Earth Environ. 1, 103-
631 116. https://doi.org/10.1038/s43017-019-0019-0

632 Maavara, T., Dürr, H.H., Van Cappellen, P., 2014. Worldwide retention of nutrient
633 silicon by river damming: From sparse data set to global estimate. Global
634 Biogeochem. Cycles 28, 842–855. https://doi.org/10.1002/2014GB004875

635 Maavara, T., Hood, J.L.A., North, R.L., Doig, L.E., Parsons, C.T., Johansson, J., Liber,

636 K., Hudson, J.J., Lucas, B.T., Vandergucht, D.M., Van Cappellen, P., 2015a.

637 Reactive silicon dynamics in a large prairie reservoir (Lake Diefenbaker,

638 Saskatchewan). J. Great Lakes Res. 41, 100–109.

639 https://doi.org/10.1016/j.jglr.2015.04.003

640 Maavara, T., Lauerwald, R., Regnier, P., Van Cappellen, P., 2017. Global perturbation

641 of organic carbon cycling by river damming. Nat. Commun. 8, 15347.

642 https://doi.org/10.1038/ncomms15347

643 Maavara, T., Parsons, C.T., Ridenour, C., Stojanovic, S., Dürr, H.H., Powley, H.R.,

644 Van Cappellen, P., 2015b. Global phosphorus retention by river damming. Proc.

645 Natl. Acad. Sci. U. S. A. 112, 15603–15608.

646 https://doi.org/10.1073/pnas.1511797112

647 Macpherson, G.L., 2009. CO₂ distribution in groundwater and the impact of

648 groundwater extraction on the global C cycle. Chem. Geol. 264, 328–336.

649 https://doi.org/10.1016/j.chemgeo.2009.03.018

650 Marcé, R., Moreno-Ostos, E., García-Barcina, J.M., Armengol, J., 2010. Tailoring dam

651 structures to water quality predictions in new reservoir projects: Assisting

652 decision-making using numerical modeling. J. Environ. Manage. 91, 1255–1267.

653 https://doi.org/10.1016/j.jenvman.2010.01.014

654 Marcé, R., Obrador, B., Morguí, J.-A., Lluís Riera, J., López, P., Armengol, J., 2015.

655 Carbonate weathering as a driver of CO₂ supersaturation in lakes. Nat. Geosci. 8,
656 107–111. https://doi.org/10.1038/ngeo2341

657 Marescaux, A., Thieu, V., Borges, A.V., Garnier, J., 2018a. Seasonal and spatial variability of the partial pressure of carbon dioxide in the human-impacted Seine River in France. Sci. Rep. 8. https://doi.org/10.1038/s41598-018-32332-2

660 Marescaux, A., Thieu, V., Garnier, J., 2018b. Carbon dioxide, methane and nitrous oxide emissions from the human-impacted Seine watershed in France. Sci. Total Environ. 643, 247–259. https://doi.org/10.1016/J.SCITOTENV.2018.06.151

663 Marescaux, A., Thieu, V., Gypens, N., Silvestre, M., Garnier, J., 2020. Modeling inorganic carbon dynamics in the seine river continuum in France. Hydrol. Earth Syst. Sci. 24, 2379–2398. https://doi.org/10.5194/hess-24-2379-2020

666 Moatar, F., Meybeck, M., 2005. Compared performances of different algorithms for estimating annual nutrient loads discharged by the eutrophic River Loire. Hydrol. Process. 19, 429–444. https://doi.org/10.1002/HYP.5541

669 Muller, M., 2019. Hydropower dams can help mitigate the global warming impact of wetlands. Nature 566, 315–317. https://doi.org/10.1038/d41586-019-00616-w

671 Paranaíba, J.R., Barros, N., Mendonça, R., Linkhorst, A., Isidorova, A., Roland, F., Almeida, R.M., Sobek, S., 2018. Spatially Resolved Measurements of CO₂ and CH₄ Concentration and Gas-Exchange Velocity Highly Influence Carbon-Emission Estimates of Reservoirs. Environ. Sci. Technol. 52, 607–615. https://doi.org/10.1021/ACS.EST.7B05138

676 R Core Team, 2019. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria [WWW Document]. URL
678 https://www.r-project.org/. (toegang verkry 11.23.21).

679 Ran, L., Lu, X.X., Liu, S., 2017. Dynamics of riverine CO₂ in the Yangtze River fluvial network and their implications for carbon evasion. Biogeosciences 14, 2183–2198.

681 https://doi.org/10.5194/bg-14-2183-2017

682 Raymond, P.A., Cole, J.J., 2001. Technical notes and comments: Gas exchange in rivers and estuaries: Choosing a gas transfer velocity. Estuaries 24, 312–317.

684 https://doi.org/10.2307/1352954

685 Raymond, P.A., Hartmann, J., Lauerwald, R., Sobek, S., McDonald, C., Hoover, M., Butman, D., Striegl, R., Mayorga, E., Humborg, C., Kortelainen, P., Dürr, H., Meybeck, M., Ciais, P., Guth, P., 2013. Global carbon dioxide emissions from inland waters. Nature 503, 355–359. https://doi.org/10.1038/nature12760

689 Raymond, P.A., Zappa, C.J., Butman, D., Bott, T.L., Potter, J., Mulholland, P., Laursen, A.E., McDowell, W.H., Newbold, D., 2012. Scaling the gas transfer velocity and hydraulic geometry in streams and small rivers. Limnol. Oceanogr. Fluids Environ.

692 2, 41–53. https://doi.org/10.1215/21573689-1597669

693 Regnier, P., Friedlingstein, P., Ciais, P., Mackenzie, F.T., Gruber, N., Janssens, I.A., Laruelle, G.G., Lauerwald, R., Luyssaert, S., Andersson, A.J., Arndt, S., Arnosti, C., Borges, A. V., Dale, A.W., Gallego-Sala, A., Goddéris, Y., Goossens, N., Hartmann, J., Heinze, C., Ilyina, T., Joos, F., LaRowe, D.E., Leifeld, J., Meysman, F.J.R., Munhoven, G., Raymond, P.A., Spahni, R., Suntharalingam, P., Thullner, M., 2013. Anthropogenic perturbation of the carbon fluxes from land to ocean. Nat. Geosci. 6, 597–607. https://doi.org/10.1038/ngeo1830
700 Servais, P., Barillier, A., Garnier, J., 1995. Determination of the biodegradable fraction
701 of dissolved and particulate organic carbon in waters. Ann. Limnol. - Int. J.
702 Limnol. 31, 75-80. https://doi.org/10.1051/limn/1995005

703 Soued, C., Prairie, Y.T., 2020. The carbon footprint of a Malaysian tropical reservoir:  
704 measured versus modelled estimates highlight the underestimated key role of  
705 downstream processes. Biogeosciences 17, 515-527.
706 https://doi.org/10.5194/bg-17-515-2020

707 Vörösmarty, C.J., Sharma, K.P., Fekete, B.M., Copeland, A.H., Holden, J., Marble, J.,
708 Lough, J.A., 1997. The storage and aging of continental runoff in large reservoir
709 systems of the world. Ambio 26, 210-219. https://doi.org/10.2307/4314590

710 Wang, F., 2020. Impact of a large sub-tropical reservoir on the cycling of nutrients in a
711 river. Water Res. 186, 116363. https://doi.org/10.1016/J.WATRES.2020.116363

712 Wanninkhof, R., 2014. Relationship between wind speed and gas exchange over the
713 ocean revisited. Limnol. Oceanogr. Methods 12, 351-362.
714 https://doi.org/10.4319/lom.2014.12.351

715 Ward, J. V., Stanford, J.A., 1995. The serial discontinuity concept: Extending the
716 model to floodplain rivers. Regul. Rivers Res. Manag. 10, 159-168.
717 https://doi.org/10.1002/rrr.3450100211

718 Yan, X., Thieu, V., Garnier, J., 2021a. Long-term assessment of nutrient budgets for the
719 four reservoirs of the Seine Basin (France). Sci. Total Environ. 778, 146412.
720 https://doi.org/10.1016/j.scitotenv.2021.146412

721 Yan, X., Thieu, V., Garnier, J., 2021b. Long-Term Evolution of Greenhouse Gas
722 Emissions From Global Reservoirs. Front. Environ. Sci. 9, 289.

723 https://doi.org/10.3389/FENVS.2021.705477/BIBTEX

724 Yang, D., Wang, S., Lu, W., Xiang, P., Yang, Y., Tan, D., Guo, M., Yeager, K.M.,

725 2018. Impoundment-induced nitrogen-phosphorus imbalance in cascade

726 reservoirs alleviated by input of anthropogenic nutrients. Inl. Waters 8, 196-206.

727 https://doi.org/10.1080/20442041.2018.1457853

728 Zarfl, C., Lumsdon, A.E., Berlekamp, J., Tydecks, L., Tockner, K., 2015. A global

729 boom in hydropower dam construction. Aquat. Sci. 77, 161-170.

730 https://doi.org/10.1007/s00027-014-0377-0

731 Zhang, Yifei, Lyu, M., Yang, P., Lai, D.Y.F., Tong, C., Zhao, G., Li, L., Zhang, Yuhan,

732 Yang, H., 2021. Spatial variations in CO₂ fluxes in a subtropical coastal reservoir

733 of Southeast China were related to urbanization and land-use types. J. Environ. Sci.

734 109, 206-218. https://doi.org/10.1016/J.JES.2021.04.003

735
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Geographical locations of the three reservoirs: (a) Seine Basin, (b) sketch map of sampling sites in three reservoirs. Detailed geographical information on the sampling sites is presented in SM (supplementary material, Table S1).</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Dynamics of Chl-a, DSi, DOC, and BDOC concentrations in the Marne upstream river (a, c, e, g) and in the reservoir (b, d, f, h). The shadow marked with light grey represents the changes in reservoir volumes. The blue line (mixing value) is the calculated concentrations (Chl-a, DSi, DOC, and BDOC) in the reservoir assuming they resulted from the mixing with the entering water (see 2.3.2).</figcaption></figure>
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 3. Seasonal variations of S<sub>DO</sub> (DO saturation) and water temperature in the three reservoirs.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Dynamics of pCO₂ (a, c, and e) and CO₂ fluxes (b, d, and f) in three reservoirs and their upstream and downstream rivers (Marne: a and b; Aube: c and d; Seine: e and f). The CO₂ flux in the downstream river of the Seine Reservoir was not calculated due to the lack of slope of river. The shaded light grey areas represent reservoir volume.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Comparison water quality and pCO₂ in upstream and downstream rivers of three reservoirs during the filling period and emptying period. \* indicates the difference is significant at the *p* < 0.05 level, while ns indicates that the difference was non-significant.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** The impact of reservoirs on downstream water discharges (a and b) and gas transfer coefficient of CO₂ (c and d).</figcaption></figure>
<table><caption>**Table 1.** Comparison of the annual fluxes of Chl-a, DOC, BDOC, and DSi in downstream river with and without the impact of reservoir.</caption><thead><tr><th rowspan="2">Site</th><th colspan="4">Without reservoir (hypothetical)</th><th colspan="4">With reservoir (reference situation)</th></tr><tr><th>Chl-a</th><th>DOC</th><th>BDOC</th><th>DSi</th><th>Chl-a</th><th>DOC</th><th>BDOC</th><th>DSi</th></tr><tr><th>Water parameter</th><th>t C yr<sup>-1</sup></th><th>t C yr<sup>-1</sup></th><th>t C yr<sup>-1</sup></th><th>t Si yr<sup>-1</sup></th><th>t C yr<sup>-1</sup></th><th>t C yr<sup>-1</sup></th><th>t C yr<sup>-1</sup></th><th>t Si yr<sup>-1</sup></th></tr></thead><tbody><tr><td>Unite</td><td></td><td></td><td></td><td></td><td>177</td><td>3101</td><td>642</td><td>1698</td></tr><tr><td>Marne downstream</td><td>163</td><td>2430</td><td>425</td><td>2286</td><td>(+8%) *</td><td>(+22%)</td><td>(+34%)</td><td>(-35%)</td></tr><tr><td>Aube downstream</td><td>61</td><td>1180</td><td>249</td><td>1195</td><td>(+6%)</td><td>(+19%)</td><td>(+12%)</td><td>(-31%)</td></tr></tbody></table>

<footnote>* Positive and negative values mean reservoir increase and decrease the annual flux of related water parameters in the downstream of Marne and Aube River, respectively. The conversion factor of Chl-a from µg L⁻¹ to mg C L⁻¹ is 0.035.</footnote>
<figure><img src="image_10.png" /></figure>

4.2 Gg C-CO₂ yr⁻¹

DOC

DSi
pCO₂

Biogeochemical
processes

Water quality changes

DOC

DSi
pCO₂

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

Reservoir

Reservoir effect on the
downstream river

Decline pCO₂

Increase kₗғas

Decline DSi fluxes (33%);

Increase DOC fluxes (20%)

<figure><img src="image_14.png" /></figure>

Downstream river