# Integrated modelling of sediment and organic carbon fluxes in a large catchment: quantifying riverine contributions to the Mediterranean Sea

Clément Fabre, Yoann Copard, Sabine Sauvage, Olivier Radakovitch, Hervé Piegay, José Miguel Sánchez-Pérez, Mathieu Fressard, Olivier Boutron

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## ► To cite this version:

Clément Fabre, Yoann Copard, Sabine Sauvage, Olivier Radakovitch, Hervé Piegay, et al.. Integrated modelling of sediment and organic carbon fluxes in a large catchment: quantifying riverine contributions to the Mediterranean Sea. CATENA, 2026, 264, pp.109794. 10.1016/j.catena.2026.109794. hal-05457336

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
1 Integrated Modelling of Sediment and

2 Organic Carbon Fluxes in a large

3 catchment: Quantifying Riverine

4 Contributions to the Mediterranean Sea

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6 Clément Fabre¹,²,*, Yoann Copard³, Sabine Sauvage², Olivier Radakovitch⁴, Hervé Piegay⁵,

7 José Miguel Sánchez-Pérez², Mathieu Fressard⁶, Olivier Boutron¹,*

8 ¹Tour du Valat, Research Institute for the Conservation of Mediterranean Wetlands, 13200,

9 Arles, France

10 ²Université de Toulouse, Toulouse INP, CNRS, IRD, CRBE, Toulouse, France

11 ³Univ Rouen Normandie, Université Caen Normandie, CNRS, Normandie Univ, M2C UMR

12 6143, F-76000, Rouen, France

13 ⁴Autorité de sûreté nucléaire et de radioprotection (ASNR), PSE-ENV/STAAR/LRTA, F-13115,

14 Saint-Paul-lez-Durance, France

15 ⁵UMR 5600 EVS, CNRS, ENS Lyon, University of Lyon, Lyon, France

16 ⁶Normandie Univ, UNICAEN, CNRS, IDEES, 14000 CAEN, France

17 Corresponding authors: [clement.fabre@utoulouse.fr](mailto:clement.fabre@utoulouse.fr); [boutron@tourduvalat.org](mailto:boutron@tourduvalat.org)

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# 1 Abstract

Within the Land Ocean Aquatic Continuum, the export of dissolved and particulate organic carbon (DOC, POC) is crucial for various processes in aquatic ecosystems and a primary source of greenhouse gas emissions in hydrosystems. Quantifying DOC and POC fluxes at high spatiotemporal resolution is complex because numerous sources and processes influence their dynamics, including climate, geological, and anthropogenic activities. Such quantifications at the catchment scale can be performed using hydrological models that consider OC processes. This study employed the Soil and Water Assessment Tool - Carbon (SWAT-C) to simulate OC dynamics in the large Mediterranean catchment of the Rhône River from 2002 to 2020. We represent hydrological and sediment fluxes at the catchment scale by accounting for sub-catchment diversity and dam operations. The average resulting exports reach 5.52 Mt y⁻¹, 78.8 kt y⁻¹, and 95.1 kt y⁻¹ with 54.4%, 71.5%, and 44.3% exported during the highest discharges for sediment, POC, and DOC, respectively. The contributions of the southern sub-basins to POC and DOC fluxes are significant, while the northern sub-basins do not show significant patterns. Regarding OC quality, the proportions of labile/refractory OC at the Rhône outlet are 85/15 and 5/95 for POC and DOC, respectively. Global changes could alter flows, such as the labile/refractory OC proportions, which should be quantified using the tool developed in this article to anticipate their impacts on biogeochemical dynamics in the Mediterranean Sea. Finally, our study demonstrates the potential and limitations of SWAT-C for modelling OC dynamics in a large Mediterranean catchment under multiple anthropogenic pressures.

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**Keywords:** POC; DOC; Watershed modelling; SWAT-C; Rhône; Riverine exports; Spatial and temporal patterns

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# 2 Introduction

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Sediment and organic carbon (OC) delivered by large catchments play crucial roles in the dynamics of freshwater and coastal ecosystems (Alvarez-Cobelas et al., 2012; Hauer et al., 2018). However, multiple processes affect river dynamics (Bauer & Bianchi, 2011; Bianchi & Bauer, 2011; Worrall et al., 2018). As such, multiple human-made barriers, such as dams, affect sediment and OC connectivity and cycling, especially in cascade dams (Walling, 2012; Belletti et al., 2020; Wohl et al., 2017; Maavara et al., 2017, 2020; Shi & Qin, 2023). Riverine OC is classified into two forms based on its size: dissolved (DOC) and particulate OC (POC), with an equivalent proportion of both forms in the fluxes exported to the oceans (Spitzy & Ittekot, 1991; Li et al., 2017). Riverine POC and DOC originate from several sources, from
autochthonous production to the mobilisation of fresh terrestrial organic matter through erosion and leaching of continental surfaces, including soils, surficial formations, and rocks (Hope et al., 1994). In addition, there are anthropogenic, petrogenic, and black-carbon sources, sometimes with significant proportions in transported fluxes (Raymond & Bauer, 2001; Copard et al., 2018; Coppola et al., 2022). Moreover, riverine OC is also divided into two categories based on degradability: labile and refractory OC, referring to a biodegradable fraction readily assimilated by microorganisms and a more stable fraction resistant to degradation. The temporal and spatial evolution of the mobilisation of the different sources, as well as the proportions of labile and refractory OC at large catchment scales, is crucial in understanding the contribution of OC from large rivers to the oceans and for the C cycle (Cole et al., 2007; Hossler & Bauer, 2012; Drake et al., 2017; Soares et al., 2019; Berggren & Al-Kharusi, 2020; Wen et al., 2021).

Sediment and OC exported to the oceans play an even more important role in almost closed seas, such as the Mediterranean Sea (MS). The main Mediterranean rivers deliver important amounts of sediment and OC to the MS. However, most Mediterranean rivers have suffered a significant reduction in their flow during the 20th century (MWO, 2018). Several factors, including a drop in precipitation, an increase in temperature, river management such as damming, and increased water withdrawals for irrigation, industries and domestic purposes, can explain this decrease (Parrinello, 2022).

The drainage area delivering freshwater to the MS covers about 4,135,000 km². Except for the Nile River basin, the Rhône (97,800 km²) is the largest catchment in the Mediterranean drainage basin. Along the Mediterranean rim, various studies have been conducted at different spatial scales to quantify water (Ludwig et al., 2010), sediment (Delmas et al., 2012), and OC (Sadaoui et al., 2018) exports from several rivers. Regarding water flows to the MS, a large portion comes from the Rhône and the Po rivers (Table 1), whereas exports from the Nile River are largely limited by the Aswan reservoir.

<table><caption>Table 1: Water, sediment and OC exports from the main Mediterranean rivers. Water exports come from Ludwig et al. (2010). Other references with values of sediment and OC exports for several of these catchments: Beusen et al. 2005; Milliman & Syvitski, 1992; Meybeck & Ragu, 1995; Cozzi et al., 2018; Sadaoui et al., 2018.</caption><thead><tr><th rowspan="2">Stream</th><th rowspan="2">Water (km³ y⁻¹)</th><th colspan="2">Pre-damming</th><th colspan="3">Post-damming</th><th rowspan="2">References</th></tr><tr><th>Sediment (Mt y⁻¹)</th><th>POC (kt y⁻¹)</th><th>Sediment (Mt y⁻¹)</th><th>POC (kt y⁻¹)</th><th>DOC (kt y⁻¹)</th></tr></thead><tbody><tr><td>Rhône</td><td>56.5</td><td>22-56</td><td>306</td><td>1.5-9.9</td><td>52-402</td><td>70-178</td><td>Cauwet et al., 1990; Sempéré et al. 2000; Panagiotopoulos et al., 2012; Higueras et al., 2014; Copard et al., 2018; Poulier et al., 2019</td></tr><tr><td>Nile</td><td>15</td><td>104-134</td><td>2,009</td><td>2</td><td></td><td>298.1</td><td>Degens et al. 1991; Abu & Ella, 1993</td></tr><tr><td>Ebro</td><td>10.2</td><td>14-25</td><td>42.8</td><td>0.07-0.2</td><td>6.6</td><td>26.5</td><td>Guillen & Palanques, 1992; Gomez-Gutierrez et al., 2006</td></tr><tr><td>Po</td><td>56.2</td><td>8.0-35.3</td><td>250</td><td>5.5</td><td>47.8-149</td><td>106-125</td><td>Pettine et al., 1985; 1998</td></tr></tbody></table>
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Suspended sediment delivery was divided by two in the main catchments of the Mediterranean drainage basin, with a flux going from 480-997 Mt y⁻¹ to 284-648 Mt y⁻¹ before and after the construction of more than 3,000 dams during the twentieth century (Poulos & Collins, 2002; Sadaoui et al., 2018). The Rhône exports decreased by 60%, dropping from 22-56 Mt y⁻¹ at the end of the nineteenth century (Poulos & Collins, 2002; Milliman & Farnsworth, 2013) to 1.5-9.9 Mt y⁻¹. This reduction could be explained by the construction of more than 30 dams since 1934 (BRLi, 2022), some of which are known to retain sediment over the long term. Still, the Rhône is the main sediment supplier to the Western Mediterranean Sea (Poulos, 2019). Moreover, since the damming of the Nile, the Rhône has provided among the largest amounts of sediment to the Mediterranean Sea (Table 1; Milliman & Farnsworth, 2013; Cozzi et al., 2018).

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Regarding OC, the total OC fluxes from Mediterranean rivers were estimated at 2,400-9,960 kt y⁻¹ (Sempéré et al., 2000) with a contribution of 4,810-6,650 kt y⁻¹ for POC and 4,400-5,500 kt y⁻¹ for DOC (Ludwig et al., 1996; Li et al., 2017). Sadaoui et al. (2018) attempted to model pre- and post-damming POC fluxes to the MS. They estimated a POC export of 7,790 kt y⁻¹ before damming, while this amount decreased to 5,450 kt y⁻¹ of POC after (Sadaoui et al., 2018), meaning that approximately 30% of the POC is retained in dams, with notable spatial variations. Indeed, the most significant decreases are in the South Levantine (where the Nile flows), by -63%, and in the Northwestern MS (where the Ebro and Rhône flow), by -49%. Multiple estimates of POC and DOC exports from the Rhône catchment were performed after damming. POC and DOC fluxes exported by the Rhône range from 52-402 kt y⁻¹ and 70-178 kt y⁻¹, respectively (Table 1). The Rhône POC fluxes are also the largest exported to the MS, while DOC fluxes are roughly of the same order of magnitude as other major rivers. Thus, since the period of damming in the Mediterranean basin, the Rhône has constituted a significant source of sediments and OC for the whole of the MS and especially for its northwestern part.

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As one of the leading suppliers of water, sediment, and OC to the MS, it is essential to understand and quantify OC dynamics in the Rhône and the downstream exported fluxes. Hydrological and biogeochemical modelling can help answer this question. Several models have already tackled water and sediment dynamics in the Rhône (Branger et al., 2016, 2023; Vázquez-Tarrío et al., 2019). However, no catchment-scaled modelling of OC fluxes exists on the Rhône. Modelling can be a useful tool for understanding the mobilisation of different sediment and OC sources in complex basins like the Rhône, by capturing its strong spatial heterogeneity, with areas exhibiting markedly contrasting hydrological functions.
Therefore, this study aims to 1) better understand OC dynamics in the Rhône catchment, 2) estimate OC exports from the Rhône River to the MS with a monthly time-step approach during the 2002-2020 period, 3) highlight the spatial and temporal variations of sediment and OC fluxes delivered by the Rhône to the MS, and 4) evaluate the capabilities of SWAT-C in reproducing large catchment OC dynamics.

## 3 Materials and methods

### 3.1 Study area

The Rhône catchment covers 97,800 km² over Switzerland and France and flows to the MS (Figure 1). The Swiss and French parts of the Rhône catchment are separated by the Lake of Geneva. In this study, we considered only the French part of the catchment, which accounted for 90% of the total catchment area.

<figure><img src="image_3.png" /><figcaption>Figure 1: The Rhône catchment. The main tributaries, sampling stations, and reservoir dams are described. The names of the dams that appear in the text are mentioned. NDC stands for Notre-Dame-de-Commiers. The "other subcatchments" delineation contains the sub-basins for which data are not available in large quantities as well as the Rhône corridor.</figcaption></figure>

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The Rhône watershed comprises multiple sub-catchments with varying climatic and geological conditions, resulting in diverse water and sediment deliveries. The Saône River is the main tributary of the Rhône River in terms of water discharge (Appendix 1). With an oceanic pluvial regime, it provides higher water releases in winter. Many other sub-catchments, such as the Isère, the second main tributary of the Rhône River, drain the Alps, leading to higher water discharges during spring and summer due to snowmelt (Bodereau et al., 2022).

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Many anthropogenic pressures alter the hydrology of this large catchment. Multiple reservoir dams store large amounts of water and sediments in the upstream regions, particularly in the Alps (Figure 1). Regarding specific flows, the main contributors are basins with steeper slopes that drain the mountain systems of the Rhône (Figure 1 and Appendix 1).

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## 3.2 Model choice

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Multiple modelling efforts were performed in the Rhône catchment (Appendix 2). Several exercises focused on exports to the MS (Pastor et al., 2011; Boudet et al., 2017). Concerning catchment hydrology, Etchevers et al. (2001) simulated past and future Rhône hydrology using the CROCUS-ISBA-MODCOU distributed model. Similarly, modelling efforts were made to represent the hydrology of the Rhône main sub-catchments, e.g., the Saône (Thiéry and Amraoui, 2001) or the Swiss part of the Rhône (Rahman et al., 2013; Fatichi et al., 2015). Recently, the semi-distributed model J2000-Rhône has been used to simulate the Rhône water balance and to estimate current and future water availability and demands at the catchment scale (BRLi, 2022).

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Regarding sediment fluxes, a 1D hydrological model (Mage) coupled with a suspended sediment model (ADIS-TS) was developed for the Rhône corridor to assess sediment dynamics in the mainstream, yielding convincing results; however, a simplified treatment of contributions from sub-basins was employed (Kieffer et al., 2024). Another modelling effort using the CASCADE tool assessed sediment connectivity between the Rhône mainstream and its main sub-catchments (Fabre et al., 2024). It highlighted the difficulties in modelling the various sediment dynamics of the heterogeneous sub-catchments of the Rhône.

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Modelling of OC dynamics was not conducted in the Rhône catchment, except for empirical studies (Diodato et al., 2024) or European/global-scale models (Fabre et al., 2020; Gommet et al., 2022). As this study intends to assess sediment and OC dynamics, selecting a model capable of simulating water, sediment, and OC processes is necessary. Therefore, we selected SWAT-C (Zhang et al., 2013), an adapted version of the Soil and Water Assessment Tool (SWAT; Arnold et al., 1998).

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166 **3.3 The SWAT-C Model**

167 SWAT is a hydro-agro-climatological model developed by the USDA Agricultural
168 Research Service and Texas A&M AgriLife Research. It is a semi-distributed model that has
169 already demonstrated its effectiveness in representing water and nutrient dynamics across
170 multiple catchment scales under various climatic and soil conditions (Krysanova and White,
171 2015). Multiple studies assessed water, nitrate, and sediment dynamics in various European
172 catchments using the SWAT model (Abbaspour et al., 2015; Vigiak et al., 2017; Cakir et al.,
173 2021). However, SWAT has not yet been explicitly applied to the Rhône, except in the Swiss
174 part (Rahman et al., 2013).

175 **3.3.1 Modelling of hydrological fluxes**

176 SWAT calculates water stocks and fluxes within Hydrological Response Units (HRUs),
177 which are homogeneous units of land use, soil, and slope classes. Hydrological fluxes,
178 including contributions via surface, subsurface, and groundwater fluxes to the nearest
179 watercourse, are deducted. Irrigation was activated in SWAT for specific crops (general crops,
180 vineyards, hay fields, rice paddies, and orchards) in the Saône, Isère, Drôme, Ardèche,
181 Durance, and Gardon sub-basins (BRLi, 2022). Water was withdrawn from the closest stream
182 to the cultivated crops to facilitate calculations. Then, SWAT automates the irrigation
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184 **3.3.2 Modelling of sediment fluxes**

185 Hillslope erosion is estimated using the Modified Universal Soil Loss Equation (Williams,
186 1975). Regarding in-stream sediment transport, the channel transport capacities are calculated
187 using several stream power models: the Simplified Bagnold model (default), the Kodoatie
188 model (silt-gravel), the Molinas and Wu model (sand), and the Yang model (sand-gravel).
189 Based on the variability in median values (D50) of the grain-size distribution of bed material
190 for each main contributor (Appendix 1), we decided to use the Kodoatie model. Details on this
191 model are in Appendix 3.

192 **3.3.3 Modelling of dams dynamics**

193 SWAT models dam operations in several ways. To automate the operation, reservoir
194 areas and volumes were extracted from the French Committee for Dams and Reservoirs
195 database (Table 1). Then, SWAT automated the management of dam outflows based on a
196 daily average flow rate we provided (Appendix 4). Moreover, multiple dams are known to
197 disrupt sediment connectivity, including those on the Ain River (Vouglans), the Durance River
198 (Serre-Ponçon), the Drac River (Notre-Dame-de-Commiers), and the Verdon River (Greoux;
Figure 1; Fabre et al., 2024). Low sediment concentrations in these reservoirs were forced in the model to limit sediment exports (Appendix 4).

### 3.3.4 Modelling of OC dynamics

The original SWAT simulates water and sediment dynamics but is limited in its simulation of OC dynamics. SWAT-C is a revised version of the SWAT model, developed to more effectively assess carbon transfers in soil and watercourses (Zhang et al., 2013; Du et al., 2019, 2020; Qi et al., 2020). It models multiple processes involved in organic carbon cycling and influencing the transport and transformation of labile and refractory POC and DOC (Figure 2). Since 2013, the model has undergone improvements in various aspects, including the impact of OC on greenhouse gas emissions and the evolution of OC in the watercourse (Du et al., 2019; Qi et al., 2020; Liang et al., 2023a, b; Luo et al., 2023). Its ability to model carbon stocks and fluxes at the watershed scale has been evaluated (Liang et al., 2022; Mukundan et al., 2023; Tijjani et al., 2023). To date, it has already demonstrated its capabilities to model POC and DOC fluxes in several catchments of various sizes (200-10,000 km²; Du et al., 2019; 2023; Qi et al., 2020; Dangol et al., 2023; Mukundan et al., 2023; Wu et al., 2023). Furthermore, some studies have assessed the impact of climate or land-use change on the evolution of OC cycling (Loiselle et al., 2020; Kim et al., 2025; Lee et al., 2025).

Various parameters control the processes shown in Figure 2 (e.g. nutrient or light limitation, decomposition rates). The equations controlling the dynamics of POC and DOC in SWAT-C are detailed in Appendices 5 and 6.
<figure><img src="image_4.png" /><figcaption>Figure 2: Main processes integrated within the SWAT-C model to simulate OC processes in terrestrial and aquatic ecosystems. Adapted from Qi et al. (2020) and https://sites.google.com/view/swat-carbon/.</figcaption></figure>

## 3.4 HRUs delineation and In-stream data used for calibration/validation

Table 2 summarises the datasets used to set up the SWAT model and those used for calibration and validation. The delimitation of basins, sub-basins and HRUs in QSWAT3 (version 1.6.3) is detailed in Appendices 7 and 8.

<table><caption>Table 2: Datasets used as SWAT inputs or observed data for calibration.</caption><thead><tr><th>Data Type</th><th>Resolution</th><th>Source</th></tr></thead><tbody><tr><td colspan="3"><strong>SWAT inputs</strong></td></tr><tr><td>Digital Elevation Model (DEM)</td><td>25m</td><td>BD Alti® (https://geoservices.ign.fr/bdalti)</td></tr><tr><td>Land use</td><td>100m</td><td>Corine Land Cover 2012 (https://land.copernicus.eu/en/products/corine-land-cover)</td></tr><tr><td>Soils</td><td>~1km</td><td>Harmonised World Soil Database v 2.0 (HWSD) (https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v20/en/)</td></tr><tr><td>Meteorological data</td><td>8*8km; daily</td><td>SAFRAN - Météo France (https://donneespubliques.meteofrance.fr/): 1,465 virtual stations</td></tr><tr><td>Reservoir dams</td><td></td><td>French Committee for Dams and Reservoirs (https://www.barrages-cfbr.eu/)</td></tr><tr><td colspan="3"><strong>Observed data for calibration</strong></td></tr><tr><td>River discharge</td><td>Daily</td><td>France: HydroPortail (https://hydro.eaufrance.fr/) Switzerland: Federal Office for the Environment (https://www.bafu.admin.ch/bafu/en/home/topics/water/state/data/obtaining-monitoring-data-on-the-topic-of-water/hydrological-data-service-for-watercourses-and-lakes.html)</td></tr></tbody></table>
<table><tr><td rowspan="2">Suspended sediment, POC, and DOC</td><td>High frequency (at least 1 sample a day & 1 sample every 4-6 hours during floods; sediment and POC)</td><td>OSR framework (https://bdoh.irstea.fr/OBSERVATOIRE-DES-SEDIMENTS-DU-RHÔNE/) and Mediterranean Ocean Observing System on Environment (MOOSE; https://www.moose-network.fr/rivers-inputs/)</td></tr><tr><td>Low frequency (~1 sample a month; sediment and DOC)</td><td>NAIADES (https://naiades.eaufrance.fr/)</td></tr></table>

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The Rhône Sediment Observatory (OSR) program conducts high-frequency analyses of sediment and POC at 22 locations along the Rhône River (Lagadec et al., 2012; Lepage et al., 2021; Thollet et al., 2024). Based on their monitoring, we selected 13 stations: one at the Rhône outlet (Arles) and 12 near the outlets of the main tributaries of the Rhône. The remaining stations are either further upstream tributaries whose outlets are already included in the 13 stations or outlets of small tributaries that contribute little to TSS and OC fluxes. Similarly, national institutions sample sediment and DOC concentrations approximately once a month at multiple sites in France (NAIADES database). Data from the NAIADES stations closest to the various outlets constitute the DOC dataset, which enriches the sediment dataset. Monthly time step discharges are available at many gauging sites in France. More than 100 gauging stations are located within the Rhône catchment. Again, we selected 13 gauging stations close to the monitoring sites. The locations of the monitoring and gauging sites are shown in Figure 1. For reservoir dams, the volumes and surface areas required to simulate their functioning in SWAT-C were collected from the French Committee for Dams and Reservoirs database and are available in Appendix 4.

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## 3.5 Model calibration and validation

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### 3.5.1 Upstream-downstream calibration and uncertainty analysis

We performed an independent upstream-downstream calibration, focusing first on each main sub-catchment that delivers water and nutrients to the Rhône River (Figure 1). Then, we calibrated the other sub-catchments and the Rhône corridor using available data at the Rhône River outlet. The simulation was performed from 2002 to 2020, with a two-year warm-up period from 2000 to 2001. For hydrology, POC, and DOC, the calibration period was set from 2002 to 2011, and the validation period from 2012 to 2020. Regarding sediment, because most monitoring stations in sub-catchments were implemented in 2014, we extended the calibration period to 2017 and reduced the validation period from 2018 to 2020.

First, the hydrological calibration was carried out manually to select sensitive parameters and represent well-specific water balance components, such as snow cover or evapotranspiration. Once the components of the hydrological cycle were within the correct range, calibration was automatically performed with R-SWAT (Nguyen et al., 2022) for each

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major sub-basin flowing into the Rhône (Figure 1). Two iterations were performed. The first iteration of 25 simulations constrained the parameter interval. The second iteration of 100 simulations was used to select the best value for all parameters in the ranges outlined by the manual calibration and the first iteration. This first automatic calibration concerned only HRUs-or subbasin-scaled parameters. These parameters were calibrated to the same value across the entire drained basin upstream of the measurement station, without accounting for spatial variability within it. Snow and groundwater-related parameters were the most sensitive (Appendix 9). For each major sub-basin, the best simulation was retained, allowing the parameters to be set for that area. Then, the same iterations were performed to calibrate the parameters for the remaining areas at the Rhône basin scale. The same method was then applied to calibrate the sediment. For the latter, sediment routing parameters emerged as the most sensitive. Concerning OC parameters, as riverine OC-related parameters cannot be spatialized (e.g., can be calibrated only at the watershed scale), the model was evaluated based solely on observed POC and DOC at the outlet. Thus, a manual calibration followed by two iterations of 25 and 100 simulations was performed on catchment-scale hydrology and organic carbon parameters.

After identifying preferred values for all parameters, 200 final iterations were conducted for uncertainty analysis, using ±10% intervals for the catchment-scaled parameters (in bold in Appendix 8). This step treats the parameters within the large sub-watersheds as fixed and performs calibration only in the "other sub-watersheds" zone, as defined in Figure 1. This uncertainty analysis helped to define the 95% prediction uncertainties (95PPU) intervals using the Sequential Uncertainty Fitting (SUFI-2) algorithm (Abbaspour et al., 2004).

### 3.5.2 Indices of performance

Four indices were used to assess model performance: the Nash–Sutcliffe efficiency (NSE), the coefficient of determination ($R^2$), the percent of bias (PBIAS), and the Kling–Gupta efficiency (KGE; Moriasi et al., 2007, 2015; Gupta et al., 2009).

NSE ranges from -∞ to 1. A value of 1 indicates that the model perfectly matches the observations, while 0 indicates that it performs no better than the observed mean. Negative values mean the observed mean is a better predictor than the model. $R^2$ shows how closely the simulated data follow the observed data. It ranges from 0 to 1, and higher values indicate a better fit. PBIAS measures whether the model tends to underestimate or overestimate the observations. The ideal value is 0. A positive PBIAS indicates underestimation, while a negative PBIAS indicates overestimation. KGE evaluates model simulations by combining correlation, bias, and variability. It provides a more balanced assessment than metrics focused on a single aspect of model performance. KGE ranges from -∞ to 1, with 1 indicating a perfect
agreement between simulated and observed data. According to Knoben et al. (2019), KGE > -0.41 indicates that the model performs better than using the observed mean as a predictor, whereas good performance is usually considered for KGE > 0.5. However, there are no clearly defined thresholds for KGE. Furthermore, these thresholds differ depending on whether one is considering hydrology or biogeochemistry. Therefore, the threshold of -0.41 was maintained across all variables in this study. Two additional indices are used for automatic calibration: the p-factor, corresponding to the percentage of data within the 95% prediction uncertainty (95PPU), and the r-factor, calculated as the mean width of the 95PPU divided by the standard deviation of the variable under consideration (Abbaspour et al., 2004, 2015). Table 3 presents the expected values of the indices for the monthly time-step modelling to be considered satisfactory.

<table><caption>Table 3: Values of satisfactory indices considered in this study based on Abbaspour et al. (2004, 2015), Gupta et al. (2009), Moriasi et al. (2015), and Knoben et al. (2019).</caption><thead><tr><th></th><th>NSE</th><th>R<sup>2</sup></th><th>PBIAS</th><th>KGE</th><th>p-factor</th><th>r-factor</th></tr></thead><tbody><tr><td>Hydrology</td><td>&gt; 0.50</td><td>&gt; 0.60</td><td>≤ ±15%</td><td>&gt; -0.41</td><td>&gt; 0.7</td><td>&lt; 1.5</td></tr><tr><td>Sediment</td><td>&gt; 0.45</td><td>&gt; 0.40</td><td>≤ ±20%</td><td>&gt; -0.41</td><td></td><td></td></tr><tr><td>Nutrients</td><td>&gt; 0.35</td><td>&gt; 0.30</td><td>≤ ±30%</td><td>&gt; -0.41</td><td></td><td></td></tr></tbody></table>

## 4 Results

### 4.1 Sediment and OC fluxes in the Rhône catchment

The calibrated parameters for this model are provided in Appendix 9. The water and hydrological balance were first evaluated. Appendix 10 presents the model's spatial performance for discharge at the different gauging stations during the 2002-2020 period. Most of the main sub-catchments returned satisfactory-to-good performance. The resulting performance at the catchment outlet (Beaucaire) is very good, with NSE values of 0.75 for calibration and validation. Appendix 11 shows the monthly variations of observed and simulated discharge. The model provides a good representation of high- and low-flow periods at the outlet, even though some peaks are overestimated by SWAT-C.

Regarding sediment and OC, monthly simulations at the outlet match observed variations (Figure 3). For sub-catchments, the sediment dynamics are also satisfactory, with acceptable KGE values across the study sites (Table 4). Nevertheless, the other performance indices are mixed. The results are mixed regarding DOC: some sub-basins show satisfactory performance indices, while others do not.

At the Rhône outlet, the annual fluxes are well predicted, but the periodicity of high and low fluxes is not well reproduced, mainly with overestimations of low fluxes and underestimations of high peaks. It should be noted that the best simulation overestimates
sediment fluxes and underestimates POC and DOC fluxes as indicated by the PBIAS. In specific cases, the model represents sediment fluxes well but not OC dynamics, as in 2011 (Figure 3).

Table 5 shows the simulated sediment and OC fluxes exported by the main sub-catchments and the Rhône. The Rhône delivers $5.52 \pm 1.81$ Mt y$^{-1}$, $78.8 \pm 51.0$ kt y$^{-1}$, and $95.1 \pm 22.0$ kt y$^{-1}$ with 54.4%, 71.5%, and 44.3% exported during the highest discharges for sediment, POC, and DOC, respectively. The primary contributors of sediment and OC are the Durance, followed by the Isère for sediment, the Fier and the Isère for POC, and the Ain and the Isère for DOC.

<figure><img src="image_5.png" /><figcaption>Figure 3: Observed and simulated monthly fluxes of sediment (top), POC (centre), and DOC (bottom; log-scale) at the outlet of the Rhône catchment. Simulated fluxes are extracted at Beaucaire, while observed fluxes result from discharges at Beaucaire and sediment, POC, and DOC concentrations at Arles (see Figure 1 for station locations). The orange area represents the 95% prediction uncertainty (95PPU). The p-factors 0, 0.25, and 0.69 for sediment, POC, and DOC, respectively. The r-factors are 0, 0.13, and 5.66 for sediment, POC, and DOC, respectively.</figcaption></figure>

<table><caption>Table 4: Statistical performance for sediment and OC fluxes. Satisfactory values are shadowed in blue. n represents the number of months where observations are available for sediment, POC, and DOC. Streams are ordered according to their arrival at the Rhône from upstream to downstream. The calibration period is 2002-2011, and the validation period is 2012-2020, except for sediment, where the calibration and the validation periods are 2002-2017 and 2018-2020, respectively.</caption><thead><tr><th rowspan="2">Stream</th><th rowspan="2">Variable</th><th colspan="5">Calibration</th><th colspan="5">Validation</th></tr><tr><th>n</th><th>NSE</th><th>R<sup>2</sup></th><th>PBIAS</th><th>KGE</th><th>n</th><th>NSE</th><th>R<sup>2</sup></th><th>PBIAS</th><th>KGE</th></tr></thead><tbody><tr><td rowspan="2">Arve</td><td>Sediment</td><td>56</td><td>0.37</td><td>0.79</td><td>-9.51</td><td>0.54</td><td>36</td><td>-0.04</td><td>0.32</td><td>-5.09</td><td>0.53</td></tr><tr><td>DOC</td><td>14</td><td>-0.33</td><td>0.88</td><td>-4.50</td><td>0.23</td><td>52</td><td>0.07</td><td>0.38</td><td>34.51</td><td>-0.10</td></tr><tr><td rowspan="2">Fier</td><td>Sediment</td><td>33</td><td>0.37</td><td>0.90</td><td>-79.16</td><td>0.36</td><td>32</td><td>-1.49</td><td>0.49</td><td>-192.2</td><td>-1.12</td></tr><tr><td>DOC</td><td>90</td><td>0.18</td><td>0.48</td><td>9.58</td><td>0.08</td><td>30</td><td>-0.33</td><td>0.78</td><td>94.03</td><td>-0.38</td></tr><tr><td rowspan="2">Guiers</td><td>Sediment</td><td>4</td><td>0.61</td><td>1</td><td>-12.86</td><td>0.59</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>DOC</td><td>10</td><td>0.00</td><td>0.97</td><td>34.57</td><td>0.01</td><td>34</td><td>-0.11</td><td>0.69</td><td>29.13</td><td>0.26</td></tr><tr><td>Bourbre</td><td>Sediment</td><td>15</td><td>0.62</td><td>0.96</td><td>7.45</td><td>0.39</td><td>0</td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><tbody><tr><td></td><td>DOC</td><td>42</td><td>-0.38</td><td>0.86</td><td>57.50</td><td>-0.02</td><td>76</td><td>-0.15</td><td>0.67</td><td>62.55</td><td>-0.13</td></tr><tr><td rowspan="2">Ain</td><td>Sediment</td><td>7</td><td>0.49</td><td>0.99</td><td>-55.82</td><td>0.49</td><td>0</td><td></td><td></td><td></td><td></td></tr><tr><td>DOC</td><td>78</td><td>-24.40</td><td>0.13</td><td>-200.1</td><td>-9.84</td><td>76</td><td>-5.84</td><td>0.02</td><td>-103.1</td><td>-3.01</td></tr><tr><td rowspan="2">Saône</td><td>Sediment</td><td>33</td><td>0.14</td><td>0.83</td><td>-5.87</td><td>0.53</td><td>35</td><td>0.28</td><td>0.43</td><td>9.27</td><td>0.42</td></tr><tr><td>DOC</td><td>93</td><td>-0.16</td><td>0.52</td><td>63.06</td><td>-0.21</td><td>76</td><td>-0.04</td><td>0.53</td><td>58.89</td><td>-0.18</td></tr><tr><td rowspan="2">Gier</td><td>Sediment</td><td>43</td><td>0.41</td><td>0.78</td><td>-24.67</td><td>0.15</td><td>36</td><td>-14.40</td><td>0.39</td><td>-343.96</td><td>-8.33</td></tr><tr><td>DOC</td><td>91</td><td>0.23</td><td>0.50</td><td>18.32</td><td>0.12</td><td>77</td><td>0.25</td><td>0.43</td><td>-11.31</td><td>0.29</td></tr><tr><td rowspan="2">Isère</td><td>Sediment</td><td>85</td><td>0.08</td><td>0.67</td><td>-4.47</td><td>0.09</td><td>31</td><td>0.58</td><td>0.66</td><td>-52.00</td><td>0.29</td></tr><tr><td>DOC</td><td>42</td><td>-4.32</td><td>0.90</td><td>-91.36</td><td>-3.94</td><td>30</td><td>-1.87</td><td>0.87</td><td>-83.14</td><td>-2.36</td></tr><tr><td rowspan="2">Drôme</td><td>Sediment</td><td>0</td><td></td><td></td><td></td><td></td><td>15</td><td>0.69</td><td>0.82</td><td>-0.55</td><td>0.16</td></tr><tr><td>DOC</td><td>80</td><td>-1.69</td><td>0.78</td><td>-134.58</td><td>-1.80</td><td>78</td><td>-0.40</td><td>0.39</td><td>-127.94</td><td>-0.32</td></tr><tr><td rowspan="2">Ardèche</td><td>Sediment</td><td>12</td><td>0.67</td><td>0.99</td><td>-28.29</td><td>0.52</td><td>35</td><td>0.24</td><td>0.70</td><td>-76.71</td><td>0.24</td></tr><tr><td>DOC</td><td>80</td><td>0.24</td><td>0.83</td><td>-21.29</td><td>0.52</td><td>45</td><td>0.36</td><td>0.74</td><td>-7.70</td><td>0.26</td></tr><tr><td rowspan="2">Durance</td><td>Sediment</td><td>35</td><td>0.54</td><td>0.91</td><td>-69.39</td><td>0.08</td><td>27</td><td>0.62</td><td>0.74</td><td>-19.77</td><td>0.17</td></tr><tr><td>DOC</td><td>18</td><td>-97.64</td><td>0.98</td><td>-736.14</td><td>-261.4</td><td>13</td><td>-180.88</td><td>0.98</td><td>-735.8</td><td>-703.6</td></tr><tr><td rowspan="2">Gardon</td><td>Sediment</td><td>0</td><td></td><td></td><td></td><td></td><td>29</td><td>0.36</td><td>0.46</td><td>-12.45</td><td>0.20</td></tr><tr><td>DOC</td><td>12</td><td>0.33</td><td>1</td><td>-91.38</td><td>-9.02</td><td>33</td><td>-4.68</td><td>0.83</td><td>-169.90</td><td>-4.62</td></tr><tr><td rowspan="3">Rhône</td><td>Sediment</td><td>135</td><td>0.45</td><td>0.71</td><td>-31.77</td><td>0.20</td><td>14</td><td>0.35</td><td>0.80</td><td>-19.36</td><td>0.18</td></tr><tr><td>POC</td><td>84</td><td>0.22</td><td>0.72</td><td>31.02</td><td>0.56</td><td>36</td><td>0.34</td><td>0.74</td><td>33.32</td><td>0.11</td></tr><tr><td>DOC</td><td>84</td><td>-0.89</td><td>0.67</td><td>5.29</td><td>-1.19</td><td>35</td><td>0.00</td><td>0.70</td><td>34.89</td><td>0.57</td></tr></tbody></table>
<table><caption>Table 5: Simulated sediment and OC fluxes exported by the main sub-catchments of the Rhône River and the whole watershed. Streams are ordered according to their arrival at the Rhône from upstream to downstream. The percentages represent the proportion of the fluxes exported by the highest discharges (above the 75th percentile)</caption><thead><tr><th>Stream</th><th>Arve</th><th>Fier</th><th>Guiers</th><th>Bourbre</th><th>Ain</th><th>Saône</th><th>Gier</th><th>Isère</th><th>Drôme</th><th>Ardèche</th><th>Durance</th><th>Gardon</th><th>Rhône</th></tr></thead><tbody><tr><td>Sediment (Mt y<sup>-1</sup>)</td><td>0.50 ± 0.10</td><td>0.19 ± 0.06</td><td>0.02 ± 0.01</td><td>0.01 ± 0.01</td><td>0.14 ± 0.05</td><td>0.34 ± 0.16</td><td>0.02 ± 0.01</td><td>1.70 ± 0.62</td><td>0.39 ± 0.18</td><td>0.07 ± 0.04</td><td>4.37 ± 1.69</td><td>0.08 ± 0.04</td><td>5.52 ± 1.81</td></tr><tr><td>% during highest discharges</td><td>54.7%</td><td>56.6%</td><td>53.8%</td><td>72.7%</td><td>51.4%</td><td>72.5%</td><td>72.2%</td><td>54.9%</td><td>71.2%</td><td>78.6%</td><td>66.3%</td><td>79.8%</td><td>54.4%</td></tr><tr><td>POC (kt y<sup>-1</sup>)</td><td>18.12 ± 4.55</td><td>45.03 ± 11.04</td><td>3.06 ± 1.05</td><td>7.97 ± 5.32</td><td>11.04 ± 3.51</td><td>19.03 ± 8.93</td><td>9.35 ± 4.45</td><td>26.34 ± 15.96</td><td>11.16 ± 6.84</td><td>3.08 ± 2.68</td><td>68.14 ± 47.69</td><td>11.49 ± 8.25</td><td>78.78 ± 51.03</td></tr><tr><td>% during highest discharges</td><td>58.1%</td><td>62.6%</td><td>55.4%</td><td>63.4%</td><td>51.5%</td><td>63.9%</td><td>85.1%</td><td>67.3%</td><td>81.3%</td><td>91.4%</td><td>88.9%</td><td>78.9%</td><td>71.5%</td></tr><tr><td>LPOC (kt y<sup>-1</sup>)</td><td>15.6 ± 4.42</td><td>41.55 ± 10.52</td><td>2.89 ± 0.99</td><td>7.37 ± 5.03</td><td>9.41 ± 3.12</td><td>14.78 ± 7.66</td><td>7.71 ± 3.82</td><td>21.32 ± 12.63</td><td>9.42 ± 5.87</td><td>2.56 ± 2.25</td><td>62.69 ± 44.64</td><td>10.24 ± 7.65</td><td>67.26 ± 44.11</td></tr><tr><td>RPOC (kt y<sup>-1</sup>)</td><td>2.52 ± 0.42</td><td>3.48 ± 0.91</td><td>0.18 ± 0.07</td><td>0.6 ± 0.41</td><td>1.63 ± 0.45</td><td>4.25 ± 1.28</td><td>1.63 ± 1.04</td><td>5.02 ± 5.44</td><td>1.74 ± 1.31</td><td>0.52 ± 0.44</td><td>5.45 ± 3.48</td><td>1.26 ± 0.87</td><td>11.51 ± 11.52</td></tr><tr><td>DOC (kt y<sup>-1</sup>)</td><td>2.10 ± 0.36</td><td>2.31 ± 0.45</td><td>0.6 ± 0.11</td><td>0.25 ± 0.08</td><td>17.96 ± 7.21</td><td>12.19 ± 1.76</td><td>0.28 ± 0.07</td><td>14.93 ± 3.23</td><td>1.41 ± 0.40</td><td>3.29 ± 1.21</td><td>33.53 ± 16.7</td><td>2.05 ± 0.78</td><td>95.13 ± 21.96</td></tr><tr><td>% during highest discharges</td><td>46.3%</td><td>48.2%</td><td>44.2%</td><td>45.7%</td><td>20.5%</td><td>38.1%</td><td>55.8%</td><td>45.7%</td><td>54.5%</td><td>64.2%</td><td>81.2%</td><td>63%</td><td>44.3%</td></tr><tr><td>LDOC (kt y<sup>-1</sup>)</td><td>0.03 ± 0.004</td><td>0.07 ± 0.01</td><td>0.03 ± 0.01</td><td>0.06 ± 0.03</td><td>1.11 ± 0.21</td><td>1.22 ± 0.32</td><td>0.04 ± 0.01</td><td>0.74 ± 0.24</td><td>0.04 ± 0.01</td><td>0.26 ± 0.20</td><td>1.53 ± 0.90</td><td>0.40 ± 0.26</td><td>4.36 ± 1.96</td></tr><tr><td>RDOC (kt y<sup>-1</sup>)</td><td>2.07 ± 0.36</td><td>2.24 ± 0.45</td><td>0.57 ± 0.10</td><td>0.19 ± 0.06</td><td>16.85 ± 7.19</td><td>10.98 ± 1.49</td><td>0.24 ± 0.06</td><td>14.19 ± 3.06</td><td>1.37 ± 0.39</td><td>3.02 ± 1.04</td><td>32 ± 15.88</td><td>1.65 ± 0.55</td><td>90.76 ± 20.28</td></tr></tbody></table>
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## 4.2 Temporal trends of sediment and OC fluxes to the outlet

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### 4.2.1 Interannual evolution

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Figure 4 shows the evolution of sediment, POC, and DOC fluxes over the study period (2002-2020). Temporal Mann-Kendall tests were conducted to highlight increasing or decreasing tendencies. Only a significant increase in observed DOC fluxes at the outlet was observed (p=0.02), with data covering a shorter period (2007-2016). Appendix 15 shows this trend for each season. Again, no significant increase or decrease is observed.

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<figure><img src="image_6.png" /><figcaption>Figure 4: Temporal trends of simulated (grey bars) and observed (white bars) sediment, POC, and DOC exports to the outlet. Temporal Mann-Kendall p-values are shown. The blue and red lines indicate no significant trend and a significant trend, respectively.</figcaption></figure>

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### 4.2.2 Monthly patterns

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Figure 5 shows the monthly evolution of sediment fluxes, POC and DOC over the study period (2002-2020). The largest fluxes at the outlet occur in November, but the high-flux period lasts until June, while the July-October period shows the least flux. It is interesting to note that, although transported by sediments, POC does not follow the same trend, showing a significant difference between November and December and the rest of the period, during which high sediment exports occur. Similarly, the August and September fluxes, although equivalent for sediments, are very different for the POC. For the DOC, November also stands out, but the signal remains relatively stable over the rest of the months.

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<figure><img src="image_7.png" /><figcaption>Figure 5: Monthly patterns of sediment, POC, and DOC fluxes exported at the Rhône outlet over the 2002-2020 period. Error bars show the standard errors.</figcaption></figure>

In the same way, Appendices 12, 13, and 14 show these patterns for all the major sub-watersheds presented in Figure 1. The southern tributaries are mainly responsible for the peak in exports in November and December, with a marked difference compared to other months, while the more upstream tributaries have more stable exports, which explain the period of high flows from January to June.

## 4.3 Spatial evolution of OC fluxes

### 4.3.1 POC and DOC deliveries to streams

The contribution of the sub-basins to the POC and DOC fluxes is not equivalent (Figure 6). Specific POC deliveries to streams are higher in the main southern sub-catchments (Ardèche, Gardon, and Durance) and in some Saône sub-basins. For DOC, the contribution of the southern basins is also significant, with the Ardèche and Gardon rivers in the southwest (but rather upstream of these sub-basins), while the Saône is not a major contributor. The remaining inputs mainly come from the Alps, with significant specific deliveries to streams from sub-basins of the Arve and the upper Isère. The same patterns emerge when looking at the
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data by season (Appendix 16). It should be noted that the maximum contribution of POC occurs between June and August, while it is between March and May for DOC.

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Regarding the contribution of the different land uses in the Rhône catchment, Appendix 17 shows a slightly larger contribution from forest areas to DOC fluxes, while crops dominate the contribution to POC fluxes. Thus, unlike the POC, the DOC flows seem to be explained more by the slopes of the HRUs rather than by land use (Appendix 18).

<figure><img src="image_8.png" /></figure>

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Figure 6: Subbasin POC (left) and DOC (right) fluxes (kt ha⁻¹ y⁻¹) to streams for the 2002-2020 period. The main watersheds shown in Figure 1 are outlined in black.

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### 4.3.2 Spatial evolution in the Rhône corridor

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By focusing on the Rhône corridor, Figure 7 details the evolution of labile and refractory POC and DOC from Lake Geneva to the outlet. The two upstream reservoir dams significantly influence OC dynamics. As mentioned in the previous section, the Durance, Isère, and Saône are the main tributaries that contribute to POC and DOC fluxes. Little DOC passes through the Rhône corridor before the contributions from the Ain. Afterwards, the labile DOC dominates the transported fluxes. SWAT-C returns a dominant labile part in POC, while a significant flux of refractory POC only appears after the Ain and the Saône contributions.
<figure><img src="image_9.png" /></figure>

Figure 7: Spatial evolution of simulated labile and refractory POC and DOC fluxes in the Rhône corridor. Vertical bars show the confluence between one of the main tributaries and the Rhône River (see Figure 1 for tributaries' locations). Arrows designate the reservoir dams of the Rhône corridor.

# 5 Discussion

## 5.1 Sediment and OC dynamics in the Rhône catchment

### 5.1.1 Agreement with previous studies

Regarding water balance, the annual precipitation of 1,085 mm (including 135 mm y⁻¹ as snow) falls within the range reported in previous studies (1,000-1,300 mm y⁻¹; Habets et al., 1999; Etchevers et al., 2001). In the same way, the evapotranspiration of 499 mm y⁻¹ agrees with the IMPACT2C predictions of ca. 500 mm y⁻¹ in the region (Greuell, 2015).

Regarding sediment and OC fluxes exported by the large sub-catchments and at the Rhône outlet, Figure 8 compares our estimates with past research. Our fluxes are in the same proportions for sediment, POC, and DOC at the catchment outlet, for the Isère, and for the Saône. Nevertheless, sediment and POC exported by the Durance are significantly higher than those estimated by Copard et al. (2018), especially by looking at the ratio between the Rhône and the Durance exports.

The Durance River is subject to numerous anthropogenic pressures (dams, agricultural and industrial withdrawals and releases). The hydrology of this sub-basin is least well simulated by SWAT-C (Table 2), likely due to poor representation of these activities. In fact, SWAT-C overestimates water fluxes (high PBIAS). Thus, the sediment and POC fluxes resulting from this overestimation are also higher than the observations. As the main supplier of sediment to the Rhône, more precise work on this sub-basin would improve the spatial
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representation of water to improve further the simulation of sediment and POC fluxes at the catchment scale.

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Furthermore, the annual flows appear consistent, but the monthly flows do not align with the observed flows. The hydrology appears consistent, but, although the model dynamics are correct, it underestimates high and overestimates low sediment and OC fluxes, respectively (Figure 3). Using an upstream-downstream approach, we sought to limit uncertainties in inputs from the main Rhône tributaries. However, the difficulties observed in simulating the fluxes of certain tributaries known to be major contributors to sediment fluxes, such as the Durance or the Isère (Poulier et al., 2019; Fabre et al., 2024), may explain the high uncertainties observed in the simulated fluxes at the Rhône outlet.

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Moreover, the POC and DOC fluxes estimated by SWAT-C on the main sub-catchments of the Rhône may be highly uncertain due to the catchment-scaled parameterisation of riverine OC processes in SWAT-C. To obtain a representative OC balance for the Rhône basin, these parameters were calibrated against data from the outlet. However, regionalising the parameters would undoubtedly allow us to refine our conclusions about the roles of the different tributaries in the exported fluxes.

<figure><img src="image_10.png" /></figure>

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<caption>Figure 8: Comparison of simulated sediment, POC, and DOC fluxes with previous studies. Other studies covered most of the years simulated in our paper, except for Nemery et al. (2013), Panagiotopoulos et al. (2012), and Semperé et al. (2000).</caption>

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### 5.1.2 Deliveries of labile and refractory OC to the MS

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We estimated sediment and TOC exports to the MS to be 5.52 ± 1.81 Mt y⁻¹, 173.9 ± 73.0 kt y⁻¹. All Mediterranean rivers deliver 2,400-9,960 kt y⁻¹ of TOC (Sempere et al, 2000). Thus, the TOC fluxes from the Rhône estimated in our study represent between 1.7 and 7.2% of exported fluxes. However, as mentioned in the previous section, the POC and DOC fluxes estimated by Sempéré et al. (2000) are slightly higher than in our study, placing the influence of the Rhône at 3-13% of the fluxes exported to MS.
Within this TOC flux, Sempéré et al. (2000) estimated that between 396-1,620 kt y⁻¹ is labile. For the Rhône, they found contributions of 32.4 and 19.2 kt y⁻¹ for labile POC and DOC, and 110.4 and 156.0 kt y⁻¹ for refractory POC and DOC, respectively.

Thus, the labile/refractory proportions are 23/77 and 11/89 for POC and DOC, respectively. The refractory portion is largely predominant. In our study, these proportions are reversed for the POC (85/15) but are of the same order of magnitude for the DOC (5/95). This difference is due to certain parameters calibrated in the model to best represent the total fluxes of POC and DOC exported by the basin, but it does not represent the distribution between the labile and refractory fractions of POC. Indeed, the LPOC dissolution rate factor (klp), which changes LPOC to LDOC, is one order of magnitude lower than the default value. In the same way, the decay rate of LPOC to RPOC factor (klrp) was not sensitive but may play an important role in the equilibrium between labile and refractory proportions of POC. It would be worthwhile to recalibrate the parameters specific to the POC transformation processes to better represent this balance without significantly altering the fluxes exported to the MS. By better representing this balance, it will then be possible to assess the impact of changes in the watershed (climate or land-use changes) on the dynamics of POC and DOC exported to the MS.

As mentioned, the labile/refractory POC and DOC proportions estimated by SWAT-C and their evolution along the Rhône corridor (Figure 7) are uncertain. The labile/refractory signals from the different tributaries are known to be variable, while SWAT-C does not allow this variability to be represented in its current state (Bodereau et al., 2022; 2024; Copard et al., 2018). Our results show that the closer we get to the outlet, the greater the proportion of refractory DOC, aligning with the estimates of Semperé et al. (2000) at the Rhône outlet. This spatial evolution could be explained by rapid consumption of labile DOC by the trophic chain (Li et al., 2021), while labile DOC production, e.g., by POC dissolution, is not ensuring sufficient renewal, leaving only refractory DOC circulating in the Rhône corridor. However, in the long term, this refractory DOC could become bioreactive through photochemical degradation if the water residence time is increased (Soares et al., 2019). Thus, dam management on the Rhône River in the face of global changes could play a crucial role in balancing labile and refractory OC.

Labile POC remains high, but its consumption is slower than that of labile DOC. Therefore, a significant part can circulate without being intercepted by freshwater ecosystems and can be delivered to the Gulf of Lion. Nevertheless, Copard et al. (2018) estimated a portion of refractory POC (petrogenic POC) as 26 % of their estimated total exported flux to the MS. In our study, the proportion of refractory POC exported by this sub-basin is only 7%, probably due to an over-representation of labile POC. Thus, the labile/refractory proportions exported
491 by the different sub-basins of the Rhône, shown in Figure 7, must be interpreted with caution
492 and validated with laboratory estimates.

493 **5.1.3 Temporal deliveries to the MS**

494 We have shown that the largest sediment and OC fluxes from the Rhône to the
495 Mediterranean Sea occur in November. Comparing this seasonality with that of other major
496 Mediterranean basins, it appears that these basins complement each other in their sediment
497 and nutrient inputs. Indeed, the Po and Ebro rivers release their largest flows in May and
498 February, respectively (Pettine et al., 1998; Gomez-Gutierrez et al., 2006). It should be noted
499 that the fluxes modelled by SWAT-C are subject to significant uncertainties (Figure 3). This
500 seasonality could be even more pronounced given the large contrast in exported flows between
501 low and high-water levels.

502 Furthermore, smaller streams play a crucial role in supplying nutrients to the MS.
503 Higueras et al. (2014) demonstrated that POC levels within TSS are significantly higher in
504 smaller watersheds around the Rhône River than in the Rhône River itself. It could be due to
505 greater eutrophication caused by longer drought periods. Thus, even with lower total sediment
506 fluxes, the additional POC delivered by these streams could be substantial for the MS.

507 **5.1.4 Catchment-scale evolution**

508 Our study shows that the Durance and the Isère are the main contributors of sediments
509 to the Rhône (Table 5), which agrees with numerous studies (Sadaoui et al., 2016; Poulier et
510 al., 2019; Fabre et al., 2024). However, the difference between the Durance and the Isère
511 contributions highlighted by our study is not a consensus. According to previous studies, these
512 two large sub-basins contribute equally to the fluxes delivered to the Rhône. This difference
513 can be explained by an overestimate of the Durance contribution (PBIAS of -70.87% and -
514 20.24%; Table 3). For the POC, our study mentions the important role of the Fier River.
515 However, this sub-basin is relatively small and does not exhibit significant sediment fluxes. It
516 would be interesting to better understand why this sub-basin emits this signal. For the DOC,
517 after the Durance and the Isère, the two northern basins contribute the most (the Saône and
518 the Ain). This is consistent with the study by Gommet et al. (2022), which shows greater DOC
519 leaching in the northern part of the basin than in the southern part, likely due to less steep
520 slopes and more evenly distributed, less intense rainfall episodes.

<table><caption>Table 6: Contribution from each main sub-catchment to the Rhône (%) compared to the total flux entering the Rhône.</caption><thead><tr><th></th><th colspan="2">Sediment</th><th></th><th></th><th></th></tr><tr><th>Stream</th><th>This study</th><th>Sadaoui et al. (2016)</th><th>Poulier et al. (2019)</th><th>POC</th><th>DOC</th></tr></thead><tbody><tr><td>Arve</td><td>6.3</td><td></td><td></td><td>7.8</td><td>2.3</td></tr><tr><td>Fier</td><td>2.5</td><td></td><td></td><td>19.3</td><td>2.5</td></tr></tbody></table>
<table><tr><td>Guiers</td><td>0.3</td><td></td><td></td><td>1.3</td><td>0.7</td></tr><tr><td>Bourbre</td><td>0.2</td><td></td><td></td><td>3.4</td><td>0.3</td></tr><tr><td>Ain</td><td>1.8</td><td></td><td></td><td>4.7</td><td>19.8</td></tr><tr><td>Saône</td><td>4.4</td><td>12.7</td><td>5</td><td>8.1</td><td>13.4</td></tr><tr><td>Gier</td><td>0.2</td><td></td><td>&lt;0.5</td><td>4.0</td><td>0.3</td></tr><tr><td>Isère</td><td>21.7</td><td>19.4</td><td>25</td><td>11.3</td><td>16.4</td></tr><tr><td>Drôme</td><td>5.0</td><td></td><td></td><td>4.8</td><td>1.5</td></tr><tr><td>Ardèche</td><td>0.9</td><td>3.1</td><td></td><td>1.3</td><td>3.6</td></tr><tr><td>Durance</td><td>55.7</td><td>18.4</td><td>24</td><td>29.1</td><td>36.9</td></tr><tr><td>Gardon</td><td>1.0</td><td></td><td></td><td>4.9</td><td>2.3</td></tr></table>

523

524

## 5.2 SWAT-C potential and model limits

525

526

527

528

529

530

531

532

SWAT-C enables the monitoring of carbon fluxes in all their forms at the watershed scale by combining SWAT with modules that simulate terrestrial and aquatic OC dynamics (Du et al., 2019; Qi et al., 2020). Using this model, this paper estimates hydrological, sediment, and OC fluxes at the Rhône scale. It is a significant step forward in understanding biogeochemical cycles in highly disturbed basins, such as the Rhône. Hydrology and sediment dynamics are well represented by the model at the outlet. Nevertheless, spatial issues remain, especially in small sub-catchments. Many uncertainties related to the conceptualisation of SWAT-C and the input data we provided are detailed in the following sections.

533

### 5.2.1 Spatial discretisation of parameters

534

535

536

537

538

539

540

541

542

543

544

545

A regionalisation of hydrological and sediment-transport parameters was carried out using the literature and data available at the outlets of the main tributaries of the Rhône River, using the Rhône Sediment Observatory (Figure 1). This regionalisation could have been more effective in several respects. First, the stations of the Rhône Sediment Observatory were selected to focus only on the outlets of the main sub-basins of the Rhône (Figure 1). Other stations within these large sub-basins could have allowed for a more comprehensive regionalisation of the processes. Similarly, the hydrological parameters, particularly those related to the distribution of water between the surface, subsurface, and groundwater, have only one value for these large sub-basins (Appendix 11). It would have been preferable to regionalise these processes with existing knowledge of soils and hydrogeology in these areas. This is particularly the case in alpine sub-basins, where areas with low slope and very steep slopes mix, leading to markedly different hydrological functions.

546

547

548

549

550

551

Moreover, regarding OC parameters, SWAT-C allows only basin-scale calibration for those related to in-stream OC transport. This is a critical limitation when calibrating the carbon cycle in large basins. The diversity of climate types and anthropogenic pressures, the functioning of aquatic ecosystems, and the various hydromorphological conditions (e.g., number of river islands and meanders) affect the properties of available OC (Bodereau et al., 2022; 2024). Such diversity may result in a variety of in-stream processes kinetics. The
possibility of calibrating parameters at the scale of large sub-catchments will be a significant step forward in better representing and understanding POC and DOC fluxes at the Rhône scale.

### 5.2.2 Dams control

The dams included in this modelling effort, distributed across different areas of the Rhône, have simplified management. This choice reduces the amount of input data required and accelerates calculation time. However, SWAT can simulate dam releases in various ways. Better consideration of dam operation by providing observed release flows, if available, should improve the representation of catchment hydrological and biogeochemical dynamics.

### 5.2.3 Input data

#### 5.2.3.1 Climatology and time step

The quality of meteorological data is crucial for accurately representing the processes in a hydrological model. SAFRAN daily data provide a discretisation that represents the spatial variability necessary for large-scale studies. However, several studies have shown significant biases in air temperature in the high mountain areas of south-eastern France and in precipitation amounts across all French mountains (Quintana-Seguí et al., 2008). These deviations can be responsible for significant differences between observed and simulated fluxes in the mountainous sub-catchments such as the Isère or the Durance. Furthermore, alpine rivers are highly reactive, with flood peaks occurring over very short periods. Poor consideration of precipitation in these areas could lead to poor estimates of hydrological and, therefore, biogeochemical fluxes, as most transported particles are transported during these floods (Copard et al., 2018).

#### 5.2.3.2 Soil properties: Erodibility factor and OC content

The intrinsic sensitivity of soils to erosion and their OC content are fundamental inputs to the model, conditioning both erosion rates and OC sources. This study uses soil distribution from HWSD (FAO and IIASA, 2023). This database is directly available, is harmonised for large-scale catchments, and facilitates calculations. However, its resolution is low (~1km), resulting in a limited representation of the soil diversity at the Rhône scale. Plus, the spatialization of riverine OC-related parameters is not yet possible in SWAT-C. Most of the spatial variability is driven by heterogeneity in erosion-related parameters and OC content in soils. Alternative European-scaled (https://esdac.jrc.ec.europa.eu/) or national-scaled (https://www.gissol.fr/) soil databases could be explored in future studies to conduct a sensitivity analysis on the influence of soil data sources on OC dynamics modelling.
585 **5.2.3.3 Land use impacts on erosion and OC stocks**

586 It should be noted that our model simulating OC dynamics over the period 2002-2020
587 uses only Corine Land Cover 2012 as input data, without land-use renewal. Therefore,
588 changes in OC stocks in soils due to land use changes are not represented in the simulation.
589 But land-use changes affect erosion rates and sediment and OC fluxes to aquatic ecosystems
590 (Poeplau and Don, 2012). Reductions in sediment and OC inputs could significantly impact
591 ocean dynamics, particularly in the small and almost enclosed seas, such as the MS.
592 Considering the evolution of land use with tools like the SWAT-Landuse Update Tool (Moriasi
593 et al., 2019) and its repercussions on erosion rates and OC stocks calculations seems
594 necessary to improve the long-term sediment and OC dynamics in the Rhône.

595 **5.2.4 Determination of uncertainties**

596 Calibration in this study was performed from upstream to downstream, first calibrating
597 the large sub-basins and setting the parameters relative to the best simulation in that area.
598 Then, the 95 PPU intervals defined in this study are independent for streamflow, sediment,
599 and organic carbon, and only for parameters at the watershed scale to simplify calculations
600 and accelerate processing. It would have been preferable to propagate uncertainties while
601 maintaining the variability of each parameter regardless of the scale and the variable.

602 **6 Conclusion**

603 This work is a first attempt to use the SWAT-C model to quantify OC dynamics in a large
604 Mediterranean watershed, the Rhône River. Despite numerous uncertainties in the
605 regionalisation of the processes, we estimated annual exports of 5.52 ± 1.81 Mt y⁻¹, 78.8 ±
606 51.0 kt y⁻¹, and 95.1 ± 22.0 kt y⁻¹ with 54.4%, 71.5%, and 44.3% exported during the highest
607 discharges for sediment, POC, and DOC, respectively, over the period 2002-2020. The spatial
608 and temporal distribution of fluxes within the catchment area could be explored, with significant
609 fluxes exported in November due to sediment and OC deliveries from sub-basins draining the
610 Alps. The distribution between labile and refractory OC could also be approximated. Despite
611 numerous uncertainties, it appears that the exported POC is primarily labile, while the exported
612 DOC is primarily refractory. This work allows us to consider further research on these dynamics
613 in a watershed subject to global changes, but whose contributions are essential for the
614 Mediterranean Sea.

615 Moreover, this paper highlights the limitations of SWAT-C in representing POC and DOC
616 fluxes at a large catchment scale, draining sub-basins with very different hydrological
617 functioning. SWAT-C allows the assessment of all forms of carbon at the watershed scale, but,
618 in its current state, its conceptualisation strongly limits its deployment in large, highly
619 heterogeneous watersheds such as the Rhône. Many uncertainties remain, particularly in 620 quantifying OC deliveries from the main sub-catchments and in understanding interactions 621 between labile/refractory POC and DOC, requiring further research to advance our 622 understanding of the carbon cycle at these scales.

623 **7 Acknowledgements**

624 Part of the observed data used to validate the model were obtained from the MOOSE 625 program (Mediterranean Oceanic Observing System for the Environment), sponsored by the 626 french organisms Alliance Allenvi and CNRS-INSU with the support of the "Agence de l'Eau 627 Rhône-Méditerranée et Corse" and with the technical assistance of the Mediterranean Institute 628 of Oceanology and from the Rhône Sediment Observatory (OSR) program, a multi-partner 629 research program funded through the Plan Rhône of the European Regional Development 630 Fund (ERDF), Agence de l'eau Rhône Méditerranée Corse, CNR, EDF, and three regional 631 councils (Region Auvergne-Rhône-Alpes, PACA, and Occitanie).

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920
921 Appendices

<table><thead><tr><th></th><th>Mean Discharge (m³ s⁻¹)</th><th>Area (km²)</th><th>Specific discharge (L s⁻¹ km⁻²)</th><th>Average grain size (µm)</th></tr></thead><tbody><tr><td>Arve¹</td><td>67</td><td>1,664</td><td>40</td><td>2,169</td></tr><tr><td>Fier¹</td><td>37</td><td>1,379</td><td>27</td><td>1,916</td></tr><tr><td>Guiers¹</td><td>19</td><td>585</td><td>32</td><td>1,618</td></tr><tr><td>Ain¹</td><td>117</td><td>3,630</td><td>32</td><td>517</td></tr><tr><td>Bourbre¹</td><td>8</td><td>750</td><td>11</td><td>1,016</td></tr><tr><td>Saone²</td><td>416</td><td>29,950</td><td>14</td><td>71</td></tr><tr><td>Gier²</td><td>3</td><td>417</td><td>7</td><td>1,343</td></tr><tr><td>Isère²</td><td>309</td><td>11,890</td><td>26</td><td>1,695</td></tr><tr><td>Drôme¹</td><td>16</td><td>1,150</td><td>14</td><td>2,889</td></tr><tr><td>Ardèche²</td><td>65</td><td>2,258</td><td>29</td><td>1,583</td></tr><tr><td>Durance²</td><td>68</td><td>14,225</td><td>5</td><td>1,457</td></tr><tr><td>Gardon³</td><td>32.7</td><td>1,999</td><td>16</td><td>934</td></tr><tr><td>Rhône²</td><td>1,630</td><td>97,800</td><td>17</td><td>226</td></tr></tbody></table>

922 Appendix 1: Hydrologic characteristics of the Rhône River and its main tributaries. Subcatchments are ordered according to their arrival to the Rhône from upstream to downstream. Data come from ¹BRLi (2022), ²Poulier et al. (2019), and ³French National Gauging Stations Network (https://hydro.eaufrance.fr/). Average bed grain size distribution for each main subcatchment of the Rhône River comes from Snelder et al. (2011).

926

<table><thead><tr><th></th><th>Model</th><th>Scale</th><th>Type</th><th>Spatial</th><th>Historical range</th><th>Climate change range</th><th>References</th></tr></thead><tbody><tr><td rowspan="6">Hydrology</td><td>CROCUS-ISBA-MODCOU</td><td>Full catchment</td><td>Physically-based</td><td>Distributed</td><td>1981-1994</td><td>2054-2064</td><td>Etchevers et al. (2001; 2002)</td></tr><tr><td>J-2000 Rhône</td><td>Full catchment</td><td>Physically-based</td><td>Distributed</td><td></td><td></td><td>Branger et al. (2016; 2023); BRLi (2022)</td></tr><tr><td>MAGE</td><td>Rhône corridor</td><td>Physically-based</td><td>1D</td><td></td><td></td><td>Kieffer et al. (2024)</td></tr><tr><td>MARTHE</td><td>Saône</td><td>Physically-based</td><td>3D</td><td>1981-1995</td><td></td><td>Thiéry and Amraoui (2001)</td></tr><tr><td>SWAT</td><td>Swiss Rhône</td><td>Physically-based</td><td>Semi-distributed</td><td></td><td></td><td>Rahman et al. (2013)</td></tr><tr><td>TETIS</td><td>Upper Rhône</td><td>Physically-based</td><td>Distributed</td><td>1980-2010</td><td>2070-2100</td><td>Ruiz-Villanueva et al. (2015)</td></tr><tr><td></td><td>Topkapi-ETH</td><td>Swiss Rhône</td><td>Physically-based</td><td>Distributed</td><td>1991-2010</td><td>2011-2050</td><td>Fatichi et al. (2015)</td></tr><tr><td rowspan="4">Sediment</td><td>ADIS-TS</td><td>Rhône corridor</td><td>Physically-based</td><td>1D</td><td>1984-2012 flushes</td><td></td><td>Andries et al. (2012); Dugué et al. (2015); Guertault et al. (2016)</td></tr><tr><td>ARMAX</td><td>Swiss Rhône</td><td>Empirical</td><td></td><td>1981-2020</td><td></td><td>Vaheddoost et al. (2022)</td></tr><tr><td>CASCADE</td><td>Full catchment</td><td>Physically-based</td><td>Semi-distributed</td><td></td><td></td><td>Schmitt et al. (2016); Fabre et al. (2024)</td></tr><tr><td>MAGE</td><td>Local river section</td><td>Physically-based</td><td>1D</td><td></td><td></td><td>Vázquez-Tarrío et al. (2019)</td></tr><tr><td rowspan="2">OC</td><td>IREM</td><td>Full catchment</td><td>Empirical</td><td></td><td>1858-2022</td><td></td><td>Diodato et al. (2024)</td></tr><tr><td>ORCHILEAK</td><td>European catchments</td><td>Physically-based</td><td>Distributed</td><td>1961-2006</td><td></td><td>Gommet et al. (2022)</td></tr></tbody></table>

927 Appendix 2: Description of the multiple models used in the Rhône catchment.
929 The Kodoatie model (Kodoatie, 1999) estimates maximum sediment concentration in
930 stream ($conc_{sed,ch.mx}$) based on hydrogeomorphology variables:

931
$$conc_{sed,ch.mx} = \left( \frac{a v_{ch}^{b} y^{c} S^{d}}{Q_{in}} \right) \cdot \left( \frac{W + W_{btm}}{2} \right)$$

932 with $v_{ch}$ the mean flow velocity (m/s), $y$ the mean flow depth (m), $S$ the bed slope (m/m),
933 $Q_{in}$ the volume of water entering the reach in the day (m³), $W$ the width of the channel at the
934 water level, and $W_{btm}$ the bottom width of the channel (m). The parameters $a, b, c$, and $d$ are
935 regression coefficients based on the grain size distribution of bed material. To estimate these
936 parameters, SWAT requires D50 values of the grain size distribution of bed material for each
937 reach. Data from Smelder et al. (2011) representing the average grain size distribution in
938 French riverbeds were used and an average value was extracted and assigned to each major
939 sub-catchment (Appendix 1). Although the mean value and D50 are not correlated, we assume
940 the two values are close enough. We recalibrated the value in sub-catchments where the
941 differences between simulations and observations were too large. Details on the equation and
942 ranges of the parameters $a, b, c$, and $d$ can be found in Neitsch et al. (2011).
<table><thead><tr><th>Stream</th><th>Name</th><th>Volume (10<sup>4</sup> m<sup>3</sup>)</th><th>Surface area (ha)</th><th>Av. water release (m<sup>3</sup> s<sup>-1</sup>)</th><th>Sediment concentration released (mg L<sup>-1</sup>)</th></tr></thead><tbody><tr><td>Durance</td><td>Serre-Ponçon</td><td>127,200</td><td>2900</td><td>73.5</td><td>10</td></tr><tr><td>Verdon</td><td>Sainte Croix</td><td>76,690</td><td>2180</td><td>30</td><td></td></tr><tr><td>Ain</td><td>Vouglans</td><td>59,240</td><td>1650</td><td>15</td><td>10</td></tr><tr><td>Drac</td><td>Monteynard</td><td>30,900</td><td>660</td><td>338</td><td></td></tr><tr><td>Isere</td><td>Chevril</td><td>23,000</td><td>274</td><td>5</td><td></td></tr><tr><td>Doron</td><td>Roselend</td><td>18,500</td><td>320</td><td>5</td><td></td></tr><tr><td>Verdon</td><td>Castillon</td><td>14,900</td><td>500</td><td>30</td><td></td></tr><tr><td>Eau d'Olle</td><td>Grand'Maison</td><td>13,700</td><td>219</td><td>5</td><td></td></tr><tr><td>Drac</td><td>Sautet</td><td>10,770</td><td>348</td><td>84</td><td></td></tr><tr><td>Verdon</td><td>Greoux</td><td>7,855</td><td>329</td><td>34</td><td>10</td></tr><tr><td>Rhône</td><td>Genissiat</td><td>7,200</td><td>400</td><td>350</td><td></td></tr><tr><td>Romanche</td><td>Chambon</td><td>5,080</td><td>125</td><td>24</td><td></td></tr><tr><td>Ain</td><td>Coiselet</td><td>3,600</td><td>380</td><td>16.9</td><td></td></tr><tr><td>Altier</td><td>Villefort</td><td>3,570</td><td>136</td><td>3</td><td></td></tr><tr><td>Drac</td><td>Notre-Dame de Commiers</td><td>3,400</td><td>165</td><td>95</td><td>10</td></tr><tr><td>Drac</td><td>Saint Pierre Cognet</td><td>2,753</td><td>125</td><td>145</td><td></td></tr><tr><td>Verdon</td><td>Quinson</td><td>1,950</td><td>191</td><td>30</td><td></td></tr><tr><td>Ain</td><td>Allement</td><td>1,900</td><td>225</td><td>16.3</td><td></td></tr><tr><td>Ceze</td><td>Sénéchas</td><td>1,620</td><td>25</td><td>4</td><td></td></tr><tr><td>Durance</td><td>Escale</td><td>1,570</td><td>200</td><td>7</td><td></td></tr><tr><td>Eau d'Olle</td><td>Verney</td><td>1,550</td><td>75</td><td>7</td><td></td></tr><tr><td>Ain</td><td>Cize-Bolozon</td><td>1,470</td><td>260</td><td>15</td><td></td></tr><tr><td>Chassezac</td><td>Puylaurent</td><td>1,280</td><td>60</td><td>5</td><td></td></tr><tr><td>Rhône</td><td>Savieres</td><td>760</td><td>160</td><td>500</td><td></td></tr><tr><td>Rhône</td><td>Seyssel</td><td>760</td><td>160</td><td>500</td><td></td></tr><tr><td>Borne</td><td>Roujanel</td><td>667</td><td>41</td><td>5</td><td></td></tr><tr><td>Durance</td><td>Saint Lazare</td><td>619</td><td>118</td><td>15</td><td></td></tr><tr><td>Oignin</td><td>Charmine</td><td>472</td><td>77</td><td>6</td><td></td></tr><tr><td>Chassezac</td><td>Malarce</td><td>372</td><td>31</td><td>5</td><td></td></tr><tr><td>Laye</td><td>Laye</td><td>350</td><td>30</td><td>1</td><td></td></tr><tr><td>Eyrieux</td><td>Collanges</td><td>330</td><td>47</td><td>8</td><td></td></tr><tr><td>Doubs</td><td>Vaufrey</td><td>320</td><td>70</td><td>48</td><td></td></tr><tr><td>Gier</td><td>Soulages</td><td>260</td><td>17</td><td>1</td><td></td></tr><tr><td>Ain</td><td>Saut Mortier</td><td>180</td><td>37</td><td>15</td><td></td></tr><tr><td>Gardon d'Ales</td><td>Sainte Cecile d'Andorge</td><td>160</td><td>75</td><td>2</td><td></td></tr><tr><td>Chassezac</td><td>Raschas</td><td>155.4</td><td>25</td><td>5</td><td></td></tr><tr><td>Doubs</td><td>Grosbois</td><td>150</td><td>9</td><td>48</td><td></td></tr><tr><td>Bourne</td><td>Choranche</td><td>105</td><td>12</td><td>10</td><td></td></tr><tr><td>Ain</td><td>Blye-Charézier</td><td>100</td><td>56</td><td>35</td><td></td></tr><tr><td>Gardon d'Ales</td><td>Cambous</td><td>100</td><td>19</td><td>1</td><td></td></tr><tr><td>Guil</td><td>Maison du Roy</td><td>30</td><td>3.5</td><td>5</td><td></td></tr></tbody></table>

943 Appendix 4: Characteristics of the various reservoir dams of the Rhône catchment integrated into the modelling.

944 Data mainly come from the French Committee for Dams and Reservoirs (https://www.barrages-cfbr.eu/-Metropole-.html). Four dams (Vouglans, Serre-Ponçon, Greoux, and Notre-Dame de Commiers are known to stop sediment connectivity. Low sediment concentration releases were used to calibrate the parameters RES_SED and RES_NSED (Initial and normal sediment concentrations in the reservoir) for the concerned dams.

945

946

947

948
949 Appendix 5: Details on the SWAT-C equations and parameters. Adapted from Zhang (2018), Du et al. (2019) and
950 Qi et al. (2020). All acronyms used in this paragraph are explained in Appendix 6: Summary of the variables and
951 parameters used to describe POC and DOC processes into SWAT-C. Based on Du et al. (2019) and Qi et al.
952 (2020). Appendix 6.

953 • Soil processes

954 ○ Soil organic matter and erosion

955 The SWAT-C model (Zhang, 2018; Zhang et al., 2013) is based on the Century model
956 framework (Parton et al., 1994). Soil organic matter (SOM) and plant residues are represented
957 through five distinct pools, each characterised by turnover times ranging from a few months to
958 several thousand years. Plant residues are categorised into recalcitrant structural litter (e.g.,
959 lignin and cell walls) and easily degradable metabolic litter (e.g., proteins and sugars). The
960 model also includes a microbial biomass carbon pool (SOL_BMC), representing soil microbes
961 and their by-products, with turnover times spanning from months to a few years. Humus is
962 modelled in two separate pools: slow and passive. The slow humus pool receives carbon and
963 nitrogen through the decomposition of structural litter, metabolic litter, and microbial biomass,
964 with typical turnover times ranging from 20 to 50 years. The most recalcitrant forms of SOM
965 are found in the passive humus pool, which consists of SOM that is physically and chemically
966 stabilised—often bound to clay particles—and characterised by much longer turnover times,
967 typically between 400 and 2000 years (Parton et al., 1994). Beyond the intrinsic properties of
968 each substrate, the decomposition and transformation of structural litter, metabolic litter,
969 passive humus, slow humus, and microbial biomass are also regulated by abiotic factors such
970 as soil temperature, moisture content, tillage practices, oxygen availability, and soil texture
971 (Parton et al., 1994; Gijsman et al., 2002; Izaurralde et al., 2006).

972 SWAT-C models soil erosion using the Modified Universal Soil Loss Equation (MUSLE;
973 Williams and Berndt, 1977):

$$
974 \quad sed = 11.8 \cdot (Q_{surf} \cdot q_{peak} \cdot \text{area}_{hru})^{0.56} \cdot K_{USLE} \cdot C_{USLE} \cdot P_{USLE} \cdot LS_{USLE} \cdot CFRG
$$

975 The loss of soil organic carbon (SOC) due to erosion is calculated by multiplying the
976 amount of eroded soil by the SOC concentration in mineral soils and an enrichment ratio:

977
$sedC = sed \cdot conc_{soc,soil} \cdot ER$

978 Since finer particles are lighter and more easily transported than coarser ones, sediment
979 delivered to streams typically contains a higher proportion of clay-sized particles. As SOM is
980 predominantly bound to these colloidal clay particles, the sediment load tends to have a higher
981 concentration of organic carbon compared to the surface soil layer. In SWAT-C, this
982 enrichment is accounted for using the following empirical formula to estimate the enrichment
983 ratio:
$$ER = 0.78 \cdot conc_{sed,surf}^{-0.2468}$$

○ **DOC in soil layers**

The production of DOC in the various soil layers of each HRU is simulated based on an allocation coefficient of microbial biomass into soil solution (ABL) and the size of the SOL_BMC pool, where ABL is determined as in Izaurralde et al. (2006):

$$ABL = 1 - e^{-\frac{w_{mobile}}{sol_{awc}} + k_{OC \cdot soc \cdot con} \cdot 10^{-4}}$$

$$DOC_{ly} = ABL \cdot SOL_BMC$$

For simulating DOC transport through the soil profile, DOC can be removed from a soil layer via surface runoff, lateral flow, and percolation. Du et al. (2019) adapted the solute transport algorithms from SWAT (Neitsch et al., 2011) to model the movement of DOC within soil layers and its transfer to both surface and subsurface water bodies. To determine the amount of DOC transported with soil water, the concentration of DOC in the mobile water of each soil layer is first calculated. This concentration is then multiplied by the volume of water moving through each pathway to estimate the total mass of DOC lost from the layer. The volume of mobile water in a layer is defined as the total water lost through surface runoff, lateral flow, or percolation, and the concentration of DOC in this mobile water is computed as follows:

$$DOC_{con,mobile,ly} = \frac{DOC_{ly} \cdot \left(1 - e^{-\frac{w_{mobile}}{(1-\theta_e) \cdot SAT_{ly}}}\right)}{w_{mobile}}$$

DOC can be transported from topsoil layer by surface runoff and lateral runoff. The amount of DOC removed from soil layers is calculated as a fraction of the concentration of DOC in the mobile water for the topsoil layer using a DOC percolation coefficient ($\beta_{DOC}$):

$$DOC_{surq} = \beta_{DOC} \cdot DOC_{con,mobile,ly} \cdot Q_{surf}$$

$$DOC_{lat,surf} = \beta_{DOC} \cdot DOC_{con,mobile,ly} \cdot Q_{lat,surf}$$

$$DOC_{lat,ly} = DOC_{con,mobile,ly} \cdot Q_{lat,ly}$$

DOC entrained by leaching or percolation to lower layers is determined as:

$$DOC_{perc,ly} = DOC_{con,mobile,ly} \cdot Q_{perc,ly}$$

• **Groundwater processes**

The mass balance framework originally developed for simulating nitrate dynamics in the shallow aquifer (Neitsch et al., 2011) was adapted to model DOC processes in groundwater,
as both substances are dissolved and follow similar pathways within the aquifer system (Du et al., 2019). DOC enters the shallow aquifer via percolation from the soil column and can leave it to the main channel as well. An exponential decay function is applied to estimate the amount of DOC recharged into the shallow aquifer. The DOC input into the aquifer from recharge on a given day is calculated using the following approach:

$$DOC_{rchrg,i} = 1 - e^{\frac{-1}{\delta_{gw}}} \cdot DOC_{perc} + e^{\frac{-1}{\delta_{gw}}} \cdot DOC_{rchrg,i-1}$$

DOC present in the shallow aquifer can follow several pathways: it may remain stored within the aquifer, be transported with groundwater flow to the main channel, percolate further into the deep aquifer with recharge, or move upward into the soil zone in response to water deficits. The amount of DOC transported by groundwater to the main channel is calculated as follows:

$$DOC_{gwq} = \frac{(DOC_{sh,i} + DOC_{rchrg,i}) \cdot Q_{gw}}{aq_{sh,i} + Q_{gw} + w_{revap} + w_{rchrg,dp}}$$

In the shallow aquifer, DOC can be lost through biological and chemical reactions. To represent the combined effects of these processes, a first-order kinetic approach is applied (Sun et al., 2018). The remaining DOC after these reactions is calculated as follows:

$$DOC_{sh,i} = DOC_{sh,i-1} \cdot e^{k_{DOC,sh}}$$

Where the rate constant $k_{DOC,sh}$ is given as:

$$k_{DOC,sh} = \frac{0.693}{\frac{t_1}{2,DOC,sh}}$$

## • In-stream processes

Du et al. (2019) and Qi et al. (2020) developed, within SWAT-C, riverine DOC and POC modules to represent the fate of terrestrial DOC and POC as it enters and moves through river systems, ultimately estimating DOC and POC fluxes at the watershed scale.

The riverine modules receive POC and DOC inputs from upstream reaches and local surface runoff (additionally, subsurface and groundwater flows for DOC). It then transports POC and DOC to downstream reaches, accounting for transport dynamics and in-stream biogeochemical reactions. The underlying algorithms for these processes are based on POC and DOC kinetics described in the CE-QUAL-W2 (Cole and Wells, 2006) and QUAL2K (Chapra, 2003) models. Within these modules, riverine POC and DOC are divided into refractory (RPOC and RDOC) and labile (LPOC and LDOC) fractions, and the temporal changes in their concentrations in the water column are calculated accordingly.
Additionally, a bottom algae simulation module was incorporated into the in-stream water quality processes of SWAT to more accurately capture the interactions between various carbon forms and algal dynamics.

### ○ POC processes

#### ● LPOC kinetics

The mortality of floating and benthic algae contributes to the LPOC pool, whereas settling to bed sediments results in a loss. Processes that reduce LPOC stocks include its dissolution into labile dissolved organic carbon (LDOC), decay into inorganic carbon (IC), and hydrolysis into RPOC. The kinetic changes in LPOC are calculated as follows:

$$ \frac{dLPOC}{dt} = F_{LPOCp} \cdot k_{dp} \cdot r_{ca} \cdot A_p + \frac{1}{h} F_{LPOCb} \cdot k_{db} \cdot r_{cb} \cdot A_b - LPOC \left( k_{LPOC} + k_{LPOC,dec} + k_{LRPOC} + \frac{V_{LPOC}}{h} \right) $$

#### ● RPOC kinetics

RPOC is produced through the mortality of floating and benthic algae and is lost through settling to the bed sediment layer. Additional transformation processes affecting RPOC include its formation via LPOC hydrolysis, its dissolution into labile dissolved organic carbon (LDOC), and its decay into inorganic carbon (IC). The kinetic changes in RPOC are calculated as follows:

$$ \frac{dRPOC}{dt} = F_{RPOCp} \cdot k_{dp} \cdot r_{ca} \cdot A_p + \frac{1}{h} F_{RPOCb} \cdot k_{db} \cdot r_{cb} \cdot A_b + k_{LRPOC} \cdot LPOC - RPOC \left( k_{RPOC} + k_{RPOC,dec} + \frac{V_{RPOC}}{h} \right) $$

### ○ DOC processes

#### ● LDOC kinetics

LDOC levels increase due to the mortality of floating and benthic algae and decrease through consumption by the nitrate denitrification process. Processes contributing to LDOC accumulation include the dissolution of POC, while those reducing LDOC concentrations involve its decay into RDOC and its mineralization into inorganic carbon. The temporal changes in LDOC concentration are computed as follows:

$$ \frac{dLDOC}{dt} = F_{LDOCP} \cdot k_{dp} \cdot r_{ca} \cdot A_p + \frac{1}{h} F_{LDOCb} \cdot k_{db} \cdot r_{cb} \cdot A_b + k_{LPOC} \cdot LPOC + k_{RPOC} \cdot RPOC - LDOC (F_{OXC} \cdot k_{LDOC} + k_{LRPOC}) - r_{cn} \cdot (1 - F_{Oxdn}) \cdot k_{dnit} \cdot NO3 $$

#### ● RDOC kinetics

RDOC concentration increases because of the mortality of floating and benthic algae. It is further augmented by the decay of LDOC into RDOC, while mineralization of RDOC into IC leads to a decrease in its concentration. The overall processes governing RDOC dynamics are described as follows:
<table><tr><td>F<sub>RDOCp</sub></td><td>Fraction of algal mortality into RDOC (0–1.0)</td></tr><tr><td>F<sub>RPOCb</sub></td><td>fraction of benthic algal mortality into RPOC (0–1.0)</td></tr><tr><td>F<sub>RPOCp</sub></td><td>Fraction of algal mortality into RPOC (0–1.0)</td></tr><tr><td>h</td><td>Water depth (m)</td></tr><tr><td>k<sub>ab</sub></td><td>Bottom algae mortality rate (d<sup>-1</sup>)</td></tr><tr><td>k<sub>dnit</sub></td><td>denitrification rate (d<sup>-1</sup>)</td></tr><tr><td>k<sub>dp</sub></td><td>Algae mortality rate (d<sup>-1</sup>)</td></tr><tr><td>k<sub>LDOC</sub></td><td>mineralization rate of LDOC to IC (d<sup>-1</sup>)</td></tr><tr><td>k<sub>LPOC</sub></td><td>Dissolution rate of LPOC to LDOC (d<sup>-1</sup>)</td></tr><tr><td>k<sub>LPOC,dec</sub></td><td>Decay rate of LPOC to IC (d<sup>-1</sup>)</td></tr><tr><td>k<sub>LRDOC</sub></td><td>decay rate of LDOC to RDOC (d<sup>-1</sup>)</td></tr><tr><td>k<sub>LRPOC</sub></td><td>Decay rate of LPOC to RPOC (d<sup>-1</sup>)</td></tr><tr><td>k<sub>RDOC</sub></td><td>mineralization rate of RDOC to IC (d<sup>-1</sup>)</td></tr><tr><td>k<sub>RPOC</sub></td><td>Dissolution rate of RPOC to LDOC (d<sup>-1</sup>)</td></tr><tr><td>k<sub>RPOC,dec</sub></td><td>decay rate of RPOC to IC (d<sup>-1</sup>)</td></tr><tr><td>LDOC</td><td>Labile dissolved organic C concentration (mgC L<sup>-1</sup>)</td></tr><tr><td>LPOC</td><td>Labile particular organic C concentration (mgC L<sup>-1</sup>)</td></tr><tr><td>NO3</td><td>nitrate concentration in water column (mg-N L<sup>-1</sup>)</td></tr><tr><td>r<sub>ca</sub></td><td>Carbon to algae biomass ratio (mgC mg-D<sup>-1</sup>)</td></tr><tr><td>r<sub>cb</sub></td><td>Carbon to benthic algae biomass ratio (mgC mg-D<sup>-1</sup>)</td></tr><tr><td>r<sub>cn</sub></td><td>ratio of carbon to nitrogen</td></tr><tr><td>RDOC</td><td>Refractory dissolved organic C concentration (mgC L<sup>-1</sup>)</td></tr><tr><td>RPOC</td><td>Refractory particular organic C concentration (mg C L<sup>-1</sup>)</td></tr><tr><td>V<sub>LPOC</sub></td><td>LPOC settling velocity (m d<sup>-1</sup>)</td></tr><tr><td>V<sub>RPOC</sub></td><td>RPOC settling velocity (m d<sup>-1</sup>)</td></tr></table>

1081 Appendix 6: Summary of the variables and parameters used to describe POC and DOC processes into SWAT-C.

1082 Based on Du et al. (2019) and Qi et al. (2020).

1083

1084 Appendix 7: Basin, sub-basins and HRUs delineation

1085 The QSWAT3 (version: 1.6.3) plugin has been used in the QGIS 3.22 interface to compile SWAT input files. As mentioned above, the Swiss part was not considered in the study, and monthly discharges at Geneva were provided as inputs. The outlet was defined before the Rhône enters the delta (at Beaucaire), where the mean annual discharge is about 1,630 m³ s⁻¹ (Figure 1 & Appendix 1).

1090 The original global soil map categories (FAO and IIASA, 2023) were grouped into 36 homogeneous classes by averaging their properties to simplify HRU delineation. Out of these classes, 16 covered the Rhône watershed. The catchment has been discretised into 1,066 sub-basins, divided into 5,694 HRUs, based on 25 land use classes, 16 soil classes, and three slope classes (0-1, 1-2, and >2%). Agricultural lands and urban areas primarily cover the Rhône catchment's lowlands, while hills and mountains are dominated by forests and barren land (Appendix 8a). Cambisols and Luvisols represent the main classes across the entire watershed (Appendix 8b).
<figure><img src="image_11.png" /></figure>

1098

1099 Appendix 8: Land uses (a) soil classes (b), and (c) slopes (in degrees) across the Rhône catchment. For visibility,

1100 multiple crops, forests, grasslands, and urban types were grouped. Soil classification is based on the World

1101 Reference Base (WRB).
<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">Parameter</th><th rowspan="2">Range or Default</th><th rowspan="2">Meaning</th><th colspan="16">Calibrated values</th></tr><tr><th>Full basin</th><th>Arve</th><th>Fier</th><th>Guiers</th><th>Bourbre</th><th>Ain</th><th>Saône</th><th>Gier</th><th>Isère</th><th>Drôme</th><th>Ardèche</th><th>Durance</th><th>Gardon</th><th>Other</th></tr></thead><tbody><tr><td rowspan="12">Hydrology</td><td rowspan="10">Snow</td><td>SFTMP.bsn</td><td>-5-5</td><td>Snowfall temperature (°C)</td><td>1.34</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SMTMP.bsn</td><td>-5-5</td><td>Snowmelt temperature (°C)</td><td>1.66</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SMFMX.bsn</td><td>0-20</td><td>Max. melt factor (mm H₂O/°C-day)</td><td>13.54</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SMFMN.bsn</td><td>0-20</td><td>Min. melt factor (mm H₂O/°C-day)</td><td>15.77</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>TIMP.bsn</td><td>0-1</td><td>Snow pack temperature lag factor</td><td>0.037</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SNOCOVMX.bsn</td><td>0-500</td><td>Snow water content for 100% cover (mm H₂O)</td><td>161.5</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SNO50COV.bsn</td><td>0-1</td><td>Fraction of SNOCOVMX for 50% cover (mm H₂O)</td><td>0.114</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>IPET.bsn</td><td>0-3</td><td>PET method (2: Hargreaves)</td><td>2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ESCO.bsn</td><td>0-1</td><td>Soil evaporation compensation factor</td><td>0.8</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ESCO.hru</td><td>0-1</td><td>Soil evaporation compensation factor</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Groundwater</td><td>GWQMN.gw</td><td></td><td>Depths of water in aquifer for return flow and revaporation to occur (mm H₂O)</td><td></td><td>1,192</td><td>1,100</td><td>700</td><td>1,197</td><td>1,183</td><td>1,239</td><td>1,093</td><td>900</td><td>3,000</td><td>1,038</td><td>1,500</td><td>1,262</td></tr><tr><td>REVAPMN.gw</td><td></td><td></td><td></td><td>527</td><td>709</td><td>600</td><td>523</td><td>655</td><td>554</td><td>538</td><td>561</td><td>590</td><td>590</td><td>1,000</td><td>728</td></tr><tr><td>GW_REVAP.gw</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.4</td></tr><tr><td rowspan="4">Soils</td><td>SOL_AWC.sol</td><td></td><td>Available water capacity (mm H₂O/mm soil)</td><td></td><td>+18.4%</td><td>+13.2%</td><td>+7.5%</td><td>+22.2%</td><td>+22%</td><td>+2%</td><td>+23.9%</td><td>+20.9%</td><td></td><td>+22%</td><td></td><td></td><td>+21.8%</td></tr><tr><td>K_SAT.sol</td><td></td><td>Sediment routing method (2: Kodoatie)</td><td></td><td></td><td></td><td>-30%</td><td>-30%</td><td></td><td></td><td></td><td></td><td>-60%</td><td></td><td></td><td></td><td></td></tr><tr><td>Surface runoff</td><td>SURLAG.hru</td><td>0-20</td><td>Surface runoff lag coefficient</td><td>0.091</td><td>0.113</td><td>0.222</td><td>0.121</td><td>0.207</td><td>0.209</td><td>0.173</td><td>0.142</td><td>0.102</td><td>0.07</td><td>0.067</td><td>0.104</td><td>0.098</td></tr><tr><td>Management</td><td>CN2.mgt</td><td>35-98</td><td>SCS runoff curve number for moisture condition II</td><td>-25%</td><td>-25%</td><td>-25%</td><td>-25%</td><td></td><td>+8%</td><td>-15%</td><td>-25%</td><td>-25%</td><td>+3%</td><td>-25%</td><td>-25%</td><td>-25%</td></tr><tr><td rowspan="12">Sediment - OC</td><td rowspan="6">Sediment</td><td>.mgt</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>USLE_K.sol</td><td></td><td>USLE equation soil erodibility factor</td><td></td><td>-80%</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CH_EQN.rte</td><td>0-3</td><td>Sediment routing method (2: Kodoatie)</td><td>2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CH_BED_D50.rte</td><td>0-10,000</td><td>D50 Median particle size diameter of channel bed sediment (µm)</td><td>2,169*</td><td>2,100*</td><td>1,618*</td><td>517*</td><td>1,016*</td><td>71*</td><td>1,343*</td><td>3,000</td><td>1,583*</td><td>2,889*</td><td>1,457*</td><td>934*</td><td>226*</td></tr><tr><td>CH_N2.rte</td><td>0-0.150</td><td>Manning's n value for main channel</td><td>0.010</td><td></td><td>0.190</td><td>0.040</td><td></td><td>0.090</td><td>0.090</td><td>0.090</td><td>0.040</td><td>0.800</td><td>0.030</td><td>0.070</td><td>0.090</td></tr><tr><td>er_POC para.tes</td><td>0-10</td><td>POC enrichment ratio</td><td>24.2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="5">OC dynamics</td><td>peroc DIC para.tes</td><td>0-1</td><td>DIC percolation coefficient</td><td>0.77</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>peroc DOC para.tes</td><td>0-1</td><td>DOC percolation coefficient</td><td>0.01</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>hlife_doc para.tes</td><td>0-100</td><td>DOC half-life (days) in groundwater</td><td>48</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>klp.wcp</td><td>0.075</td><td>LPOC dissolution rate (/day)</td><td>0.006</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>krp.wcp</td><td>0.0025</td><td>RPOC dissolution rate (/day)</td><td>0.0005</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>krd.wcp</td><td>0.006</td><td>RDOC mineralization rate (/day)</td><td>0.004</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Appendix 9: Parameters used to calibrate water, sediment, and OC dynamics. Sub-catchments are ranked based on their confluence with the Rhône from upstream to downstream. Snow, evapotranspiration, and organic carbon related parameters are catchment-scale parameters. Parameters in bold were considered for the uncertainty analysis, and the value shown is that of the best simulation to optimise the NSE index for hydrology and the KGE index for sediment and OC. Parameters meaning for hydrology and sediment can be found in the SWAT documentation (Neitsch et al., 2011) and the SWAT-C documentation (https://sites.google.com/view/swat-carbon/). *D50 values based on Snelder et al. (2011).
<table><thead><tr><th rowspan="2">Stream</th><th colspan="5">Calibration</th><th colspan="5">Validation</th></tr><tr><th>n</th><th>NSE</th><th>R<sup>2</sup></th><th>PBIAS</th><th>KGE</th><th>n</th><th>NSE</th><th>R<sup>2</sup></th><th>PBIAS</th><th>KGE</th></tr></thead><tbody><tr><td>Arve</td><td>100</td><td>0.59</td><td>0.65</td><td>4.1</td><td>0.79</td><td>90</td><td>0.58</td><td>0.65</td><td>3.35</td><td>0.76</td></tr><tr><td>Fier</td><td>100</td><td>0.82</td><td>0.87</td><td>-13.97</td><td>0.84</td><td>40</td><td>0.89</td><td>0.97</td><td>-7.88</td><td>0.94</td></tr><tr><td>Guiers</td><td>0</td><td></td><td></td><td></td><td></td><td>53.9</td><td>0</td><td>0.53</td><td>-6.19</td><td>0.18</td></tr><tr><td>Bourbre</td><td>98.7</td><td>0.23</td><td>0.71</td><td>-27.01</td><td>0.05</td><td>89.2</td><td>0.43</td><td>0.77</td><td>-29.94</td><td>0.4</td></tr><tr><td>Ain</td><td>99.8</td><td>0.72</td><td>0.75</td><td>-3.43</td><td>0.48</td><td>89.5</td><td>0.75</td><td>0.78</td><td>-5.95</td><td>0.5</td></tr><tr><td>Saône</td><td>89</td><td>0.51</td><td>0.54</td><td>-9.13</td><td>0.53</td><td>85.4</td><td>0.54</td><td>0.6</td><td>-17.18</td><td>0.64</td></tr><tr><td>Gier</td><td>99.5</td><td>0.51</td><td>0.77</td><td>-44.3</td><td>0.44</td><td>89.9</td><td>0.62</td><td>0.78</td><td>-49.62</td><td>0.46</td></tr><tr><td>Isère</td><td>99.6</td><td>0.4</td><td>0.68</td><td>6.22</td><td>0.25</td><td>60</td><td>0.48</td><td>0.82</td><td>7.95</td><td>0.29</td></tr><tr><td>Drôme</td><td>97.9</td><td>0.43</td><td>0.7</td><td>-27.53</td><td>0.34</td><td>89.5</td><td>0.77</td><td>0.82</td><td>-12.49</td><td>0.8</td></tr><tr><td>Ardèche</td><td>99.2</td><td>0.89</td><td>0.9</td><td>-9.05</td><td>0.89</td><td>78.2</td><td>0.91</td><td>0.93</td><td>-12.45</td><td>0.81</td></tr><tr><td>Durance</td><td>14.9</td><td>-4.07</td><td>0.95</td><td>-143.46</td><td>-0.39</td><td>31.6</td><td>0.28</td><td>0.78</td><td>-25.84</td><td>0.65</td></tr><tr><td>Gardon</td><td>7.1</td><td>-1.04</td><td>0.94</td><td>-41.1</td><td>0.21</td><td>59.5</td><td>0.74</td><td>0.87</td><td>-18.89</td><td>0.67</td></tr><tr><td>Rhône</td><td>99.9</td><td>0.78</td><td>0.82</td><td>-8.47</td><td>0.87</td><td>90</td><td>0.79</td><td>0.88</td><td>-13.90</td><td>0.85</td></tr></tbody></table>

Appendix 10: Statistical performance for hydrology. Satisfactory values are shadowed in blue. n represents the % of coverage when observations are available for the whole period of study. Streams are ordered according to their arrival at the Rhône from upstream to downstream. The calibration period is 2002-2011, and the validation period is 2012-2020.

1102

1103

1104

1105

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

Appendix 11: Observed and simulated monthly discharge on the 2002-2020 period at the outlet of the Rhône catchment (Beaucaire). The orange area represents the 95% prediction uncertainty (95PPU). P-factor=0.02 and R-factor=0.03. The grey line represents the 75th percentile.

1106

1107

1108

1109
<figure><img src="image_13.png" /><figcaption>Appendix 12: Monthly patterns of sediment fluxes exported by the different sub-catchments and the Rhône basin over the 2002-2020 period. Error bars show the standard errors.</figcaption></figure>

1110

1111

1112
<figure><img src="image_14.png" /><figcaption>Appendix 13: Monthly patterns of POC fluxes exported by the different sub-catchments and the Rhône basin over the 2002-2020 period. Error bars show the standard errors.</figcaption></figure>

1113

1114

1115
<figure><img src="image_15.png" /><figcaption>Appendix 14: Monthly patterns of DOC fluxes exported by the different sub-catchments and the Rhône basin over the 2002-2020 period. Error bars show the standard errors.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>Appendix 15: Seasonal temporal trends of simulated sediment, POC, and DOC exports to the outlet. Temporal Mann-Kendall p-values are shown. The blue and red lines indicate no significant and significant trends, respectively.</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Appendix 16: Seasonal contribution of subbasins to POC (top) and DOC (bottom) fluxes in $kt \ ha^{-1} \ season^{-1}$.</figcaption></figure>

1119

1120

1121

1122

1123

1124
<figure><img src="image_18.png" /><figcaption>Appendix 17: Contribution of the different HRUs to the DOC (top) and POC (bottom) fluxes according to their type of land use.</figcaption></figure>

1125

1126

1127

1128
<figure><img src="image_19.png" /><figcaption>Appendix 18: Contribution of the different HRUs to the DOC (top) and POC (bottom) fluxes compared to the average HRU slope.</figcaption></figure>

1129

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1131

1132

1133

Additional references

1134

Andries, E., Camenen, B., Faure, J.B., Le Coz, J., Launay, M., 2012. Modélisation hydro-sédimentaire du Rhône à l'aide des modèles numériques Mage et Adis-TS (PhD Thesis).

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Branger, F., Bonneau, J., Pellerin, N., Coz, J.L., 2023. Modélisation prospective des transferts hydrologiques et des debits : Résultats des scénarios simulés avec le modèle J2000-Rhône.

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Branger, F., Gouttevin, I., Tilmant, F., Cipriani, T., Barachet, C., Montginoul, M., Le Gros, C., Sauquet, E., Braud, I., Leblois, E., 2016. Modélisation hydrologique distribuée du Rhône (PhD Thesis). irstea.

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