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LHB

Hydroscience Journal

ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/tlhb21

# Future streamflow along the French part of the Meuse River – a closer look at uncertainties

G. Thirel, L. Collet, F. Rousset, O. Delaigue, D. François, J. Gailhard, M. Le Lay, C. Perrin, M. Reverdy, R. Samacoits, M. Terrier, J.-P. Vidal & J.-P. Wagner

**To cite this article:** G. Thirel, L. Collet, F. Rousset, O. Delaigue, D. François, J. Gailhard, M. Le Lay, C. Perrin, M. Reverdy, R. Samacoits, M. Terrier, J.-P. Vidal & J.-P. Wagner (2025) Future streamflow along the French part of the Meuse River – a closer look at uncertainties, LHB, 111:1, 2484192, DOI: 10.1080/27678490.2025.2484192

**To link to this article:** https://doi.org/10.1080/27678490.2025.2484192

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© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

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Published online: 29 Apr 2025.

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# Future streamflow along the French part of the Meuse River – a closer look at uncertainties

G. Thirel IDa,b, L. Collet IDa,c, F. Roussetd, O. Delaigue IDa, D. François Id,e, J. Gailhardf, M. Le Lay Id,f, C. Perrin Id,a, M. Reverdyg, R. Samacoits Id,d, M. Terrier Id,a,h, J.-P. Vidal Idg and J.-P. Wagneri

ªHYCAR, Université Paris-Saclay, INRAE, Antony, France; bNow at CESBIO, Univ Toulouse, CNES/IRD/CNRS/INRAE, Toulouse, France; cEDF R&D OSIRIS – Optimization, Simulation, Risks and Statistics Department, EDF, Palaiseau, France; dDirection de la Climatologie et des Services Climatiques, Météo-France, Toulouse, France; eLaboratoire LOTERR, Université de Lorraine, Metz, France; fEDF DTG – Water and Environment Department, EDF, St Martin le Vinoux, France; gRiverLy, INRAE, Villeurbanne, France; hNow at COVEA, Paris, France; iDREAL Grand-Est, Metz, France

## ABSTRACT

It is essential to assess the impact of future climate change on catchment hydrology in a rigorous manner, accounting for uncertainties. In this study, we assess the impact of climate change on the natural streamflow of the French part of the Meuse catchment. Climate projections from two Representative Concentration Pathways (RCPs) and five General Circulation Model/Regional Climate Model (GCM/RCM) couples were retrieved to feed four hydrological models run with several parameter sets to assess future streamflow. A variance analysis tool was employed to partition the sources of uncertainty. Although an increase in air temperature is expected in the future, particularly with the RCP 8.5, the projected trend in precipitation remains uncertain. A slight increase in annual precipitation, an increase in winter precipitation and an uncertain signal for summer precipitation are anticipated. These developments will result in an increase in winter streamflow and an uncertain signal for summer streamflow, although these evolutions may not be homogeneous along the Meuse course. Regarding uncertainties, internal variability represents the greatest uncertainty in the near future, while climate models account for the highest uncertainty in the mid and far futures. The results were found to be comparable to those of the Francewide Explore2 project.

# Futurs débits de la partie française de la rivière Meuse – un examen plus approfondi des incertitudes

## RÉSUMÉ

Il est essentiel d'évaluer l'impact du changement climatique futur sur l'hydrologie des bassins versants de manière rigoureuse, en tenant compte des incertitudes. Dans cette étude, nous évaluons l'impact du changement climatique sur le débit naturel de la partie française du bassin versant de la Meuse. Une chaîne de modélisation comprenant deux RCP, cinq couples GCM/RCM, quatre modèles hydrologiques et plusieurs jeux de paramètres a été utilisée pour évaluer l'évolution du débit futur. Un outil d'analyse de la variance a été utilisé pour répartir les sources d'incertitude de cette évaluation. Bien que l'on s'attende à une augmentation de la température de l'air à l'avenir, en particulier avec le RCP 8.5, la tendance projetée en matière de précipitations reste incertaine. On prévoit une légère augmentation des précipitations annuelles, avec une augmentation des précipitations hivernales et un signal incertain pour les précipitations estivales. Ces évolutions se traduiront par une augmentation des débits hivernaux et un signal incertain pour les débits estivaux, bien que ces évolutions pourraient ne pas être homogènes le long du cours de la Meuse. En ce qui concerne la répartition des incertitudes, la variabilité interne représente la plus grande incertitude dans le futur proche, tandis que les modèles climatiques représentent la plus grande incertitude dans les futurs moyen et lointain. Les résultats sont comparables à ceux du projet français Explore2.

## KEYWORDS

Climate change; hydrological modelling; uncertainty partitioning; Meuse River

## MOTS-CLÉS

Changement climatique ; modélisation hydrologique ; partition d'incertitudes ; rivière Meuse

# 1. Introduction

The Intergovernmental Panel of Experts on Climate Change (IPCC) demonstrated in its 2021 report (IPCC, 2021) that the global climate is undergoing significant alterations. Furthermore, in addition to an

increase in air temperature, there is a possibility that the frequency and intensity of climate extremes may also rise (IPCC, 2021). This will result in an increase in the risks associated with water resources, particularly in relation to floods and low flows, and will
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consequently place additional strain on the manage-
ment of these risks. It is therefore important to pro-
vide scientific information to water stakeholders.

In order to achieve this, it is essential to refine
climate and hydrological projections at a scale that is
consistent with the issues pertaining to water manage-
ment. Despite the efforts made over the past decade and
the improvements made to the methods used, the
downscaling of data from larger scales to the catchment
scale remains a challenge, and is affected by uncertain-
ties (Keller et al., 2022). In particular, an evaluation of
the impact of climate change at the catchment scale
typically requires the use of greenhouse gas emission
scenarios (here radiative concentration pathways,
RCPs), general circulation models (GCMs), regional
climate models (RCMs), downscaling and/or bias cor-
rection methods, and hydrological models. All the com-
ponents of this modelling chain are subject to
uncertainty and are frequently the subject of debate in
the literature (Jeantet et al., 2023; Johnson & Sharma,
2015; Senatore et al., 2022).

The Meuse catchment is an international catch-
ment that flows through France, Luxembourg,
Belgium, Germany, and the Netherlands. Given that
over seven million people rely on the Meuse River for
their drinking water supply and that there is an
increasing prevalence of water stress, it is imperative
to assess the impact of climate change on its hydrol-
ogy. The impact of climate change on water quality
and quantity has been demonstrated in this basin by
numerous authors (Booij, 2004; Chauveau et al., 2013;
Commeaux et al., 2010; Detrembleur et al., 2015;
Dewals et al., 2013; Drogue et al., 2010; François
et al., 2011; Grelier et al., 2016; Krueger et al., 2013;
Pyka et al., 2016; Romgens, 2013; Sjerps et al., 2017;
Van Vliet & Zwolsman, 2008; De Wit et al., 2007). In
the French part of the Meuse catchment, Chauveau
et al. (2013) demonstrated, within the context of the
Explore 2070 study, that the mean annual streamflow
could potentially decline by 15 to 20% of the annual
amount, while low flow values might decrease by
between 20 and 60%. However, the projected evolu-
tion of high flows remains uncertain. Drogue et al.
(2010) and the majority of the aforementioned studies
reached similar conclusions regarding the French part
of the Meuse, as well as the area located farther down-
stream. However, the recent results of the Explore2
project indicated a somewhat different signal for the
Meuse basin. The analysis of the projections produced
over the catchment revealed an uncertain evolution of
annual streamflow, a probable decrease of low flows
and a likely increase in high flows. For the three
streamflow ranges, the intensity of changes exhibited
considerable variability (Héraut et al., 2024).

It is notable that the aforementioned studies, which
were conducted with a specific focus on the Meuse
basin (i.e. all but Explore 2070 and Explore2), did not

undertake a comprehensive assessment of the poten-
tial sources of uncertainty that could arise throughout
the impact modelling chain. One or several crucial
steps of the modelling chain, such as climate models
or hydrological models, were regarded as determinis-
tic, entailing the exclusive consideration of a single
"realisation" or "occurrence" (i.e. a single option at
each step of the modelling chain). The only exception
to this lack of uncertainty analysis is the Explore2
project, which considered the majority of the afore-
mentioned (with the exception of hydrological model
parameterisation) in order to produce a countrywide
uncertainty analysis. In the present study, we present
the results of the CHIers-MEuse hydrological Regime
Evolution in the 21st century project (CHIMERE21).
In this project, we retrieved climate projections from
two RCPs and five GCM/RCM couples, which we bias
corrected, and we set up and calibrated four hydro-
logical models, which we employed to obtain hydro-
logical projections. We propose to undertake
a comprehensive assessment of the impact of climate
change on the natural streamflow of the French part of
the Meuse catchment. A variance analysis framework
was employed to partition and analyse the sources of
uncertainty derived from the two RCPs, five GCM/
RCM couples, four hydrological models and several
hydrological model parameter sets. In a second step,
we propose to discuss the results of CHIMERE21
comparatively to the results of the Explore2 project.
This comparison is important because CHIMERE21 is
the most recent project focusing on the Meuse catch-
ment, while Explore2 uses more up-to-date climate
projections but was conducted at a national scale,
without focusing on the Meuse catchment.

In general, climate change impact studies focus on
specific streamflow stations, aggregate results over
multiple stations (for example, through the use of
boxplots), or consider maps. While map analyses,
which represent the evolution of indicators at stations
across a given territory, provide valuable insight into
the spatial consistency of hydrological evolution, they
may not fully capture the upstream-downstream con-
sistency inherent to catchment hydrology. It is notable
that not all rivers or tributaries are represented on
these maps, which presents a challenge for those with-
out detailed knowledge of the catchments in question
when attempting to comprehend the upstream-down-
stream consistency. Furthermore, at the regional or
national scale, it is challenging to differentiate between
catchments. To address this limitation, the use of
stream profiles represents a valuable alternative. In
the context of this study, stream profiles are defined
as schematic representations of streamflow indicators
along the river course, with tributaries clearly deli-
neated on the scheme (Erhard-Cassegrain & Margat,
1983; Lambert, 1995). They serve to highlight
upstream-downstream dependencies with regard to
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streamflow indicators, as well as the evolution of streamflow in relation to the area of the drained catchments. Consequently, they can be employed for the definition of regulation streamflow (François & Sary, 1994), or the spatialisation of low flows (Corbonnois et al., 1999; Grelier et al., 2023). Recently, Delaigue et al. (2023) released a tool for visualising stream profiles for French rivers based on past observations. However, to the best of our knowledge, this representation has not been widely employed for analysing the impact of climate change. In this article, we will present stream profiles for the evolution of both high and low flows along the Meuse River.

We present in Section 2 the catchment area and dataset. Then, we present in Section 3 the modelling protocol and the uncertainty assessment framework we applied in this work. Results are presented in Section 4 and discussed in Section 5. Finally, we conclude the study in Section 6.

## 2. Catchment and data

### 2.1. The French Meuse catchment

The study area is the French part of the Meuse catchment, which encompasses the Meuse River from its source to the Chooz station, located at the border with Belgium. The French Meuse area is 10,120 km², including 2300 km² in Belgium and 140 km² in Luxembourg through the Chiers tributary that flows into the Meuse just upstream of Chooz. The study area represents 29% of the international Meuse catchment (i.e. to its outlet in the North Sea), and its streamflow accounts for 36% of low flows and 47% of high flows in the Netherlands (Drogue et al., 2010). The French section of the Meuse is characterised by low urbanisation, with the principal urban centre being Charleville Mézières, which has a population of 45,000.

The lowest recorded flows occur during the summer months, with the most extreme levels observed in 1976 (minimal monthly streamflow of 12.5 m³/s), 2011 (17.7 m³/s), 2019 (20 m³/s), 2018 (22.3 m³/s), 1971 (22.6 m³/s), and 2003 (22.9 m³/s) at the Chooz station. In this area, irrigation is not a significant practice, yet it can be challenging to meet the drinking water demand during the summer months. Furthermore, these periods of low flows may have an impact at Chooz (upstream area: 10,141 km²), where a nuclear power plant can be partially stopped when the 12-day average streamflow falls below 22 m³/s.

The largest floods in the downstream part of the catchment occurred in December 1993 (1440 m³/s at Chooz), January 1995 (1570 m³/s at Chooz), while they occurred in the upstream part of the catchment in April-May 1983 (217 m³/s at Carignan and 586 m³/s at SaintMihiel), December 2001-January 2002 (283 m³/s at Goncourt), and October 2006 (173 m³/s at

Goncourt). The water agency has identified four areas as territories with high flood risk, situated in close proximity to the cities of Neufchâteau (upper catchment), Verdun (centre part), Longwy (on the Chiers catchment) and from Sedan to Givet in the downstream part, extending down to the Belgian border.

### 2.2. Observed hydroclimatic data

The Système d'Analyse Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN) (Vidal et al., 2010) atmospheric reanalysis was employed to provide observed climatic variables, including air temperature, precipitation, wind speed, radiation and air humidity. SAFRAN was available over France on an 8 × 8 km² regular grid from 1958 to 2016 (Delaigue et al., 2022). Potential evapotranspiration was calculated using the Penman Monteith formulation (Allen et al., 1998).

Streamflow measurements were collected from the Banque HYDRO database (Leleu et al., 2014). A selection of 17 hydrological stations was identified based on three criteria: data availability from 1970 to 2016, data quality information provided by the data producers, and an attempt to cover the study area in a relatively homogeneous manner (Figure 1).

The assessment of water withdrawals was conducted across the catchment. It was demonstrated that withdrawals are relatively limited in the catchment (Terrier et al., 2018), which provides a rationale for not naturalising streamflow time series. However, an exception was made for the Chooz station time series, where the impact of a nuclear power plant situated a few kilometres upstream, namely the induced evaporation due to reactor cooling, was negated by the addition of the estimated evaporated amount to the measured streamflow at this station.

### 2.3. Climate projections

A total of five GCM/RCM couples were selected for this study (Table 1). The couples were selected from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) EuroCordex dataset (Taylor et al., 2012). In order to examine the impact of different climate scenarios, two RCPs were employed as forcing for each couple, namely RCP 4.5 and RCP 8.5. These projections were selected because of their availability at the time the work was carried out. The projection data were available for the period from 1976 to 2100. The projections were statistically downscaled and bias corrected (based on the SAFRAN reanalysis) using the ADAMONT method (Verfaillie et al., 2017). The final projection data were therefore available over the study area on the same regular grid as the SAFRAN atmospheric reanalysis. It should be noted that more recent climate projections, which we will also discuss later on,
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<figure><img src="image_14.png" /><figcaption>**Figure 1.** Map of the French Meuse, its main rivers and the gauging stations utilised in this study.</figcaption></figure>

<table><caption>**Table 1.** Overview of the couples of general circulation models and regional climate models utilised.</caption><thead><tr><th></th><th>GCM</th><th>RCM</th></tr></thead><tbody><tr><td>Couple 1</td><td>CNRM-CM5</td><td>ALADIN53</td></tr><tr><td>Couple 2</td><td>IPSL-CM5A</td><td>IPSL-INERIS-WRF</td></tr><tr><td>Couple 3</td><td>CNRM-CM5</td><td>RCA4</td></tr><tr><td>Couple 4</td><td>IPSL-CM5A</td><td>RCA4</td></tr><tr><td>Couple 5</td><td>MPI-ESM</td><td>RCA4</td></tr></tbody></table>

were produced within the recent Explore2 project (Marson et al., 2024; Sauquet et al., 2024) and are available in the (Donner accès aux scénarios climatiques Régionalisés français pour l'Impact et l'Adaptation de nos Sociétés et environnement) DRIAS climate portal (https://www.driasclimat.fr/).

Two types of analysis were conducted on the climate or hydrological projections. The first type involved comparisons of indicators of future periods, namely the near (2021–2050), mid (2046–2075), and far (2071–2100) futures, in relation to indicators of a reference period, 1976–2005. The second type involved comparisons on moving 30-year time periods.

# 3. Modelling protocol

## 3.1. Hydrological models

A number of factors contribute to the inherent uncertainty associated with hydrological modelling. They may originate from (i) input data (Engeland et al., 2016), (ii) model choice (Dayon et al., 2018), or (iii) model parameters (Liu et al., 2017). To produce streamflow projections and assess the uncertainties associated with the structure and parameterisation of hydrological models, four hydrological models were employed (Table 2). These models were selected because they have already been used in climate change studies in France, especially on the Meuse catchment, and because the partners of the projects have a strong expertise with these models, which they contribute to developing. Given (i) the diverse assumptions on which these models are based (equations, spatial resolution, calibration), (ii) their high performance to reproduce observed streamflow simulations, and (iii) the expertise of the model developers who were involved in this study for each model, it may be
<figure><img src="image_15.png" /><figcaption>**Table 2.** Hydrological models employed and their key characteristics.</figcaption></figure>

<table><thead><tr><th>Model</th><th>Type</th><th>Spatialisation</th><th>Local calibration</th><th>Number of parameters to calibrate</th><th>Reference</th></tr></thead><tbody><tr><td>GRSD</td><td>Conceptual</td><td>Semi-distributed</td><td>Yes</td><td>6</td><td>De Lavenne et al. (2019)</td></tr><tr><td>SIM2</td><td>Physically based</td><td>Distributed</td><td>No, but streamflow was bias corrected</td><td>-</td><td>Le Moigne et al. (2020)</td></tr><tr><td>PRESAGES</td><td>Conceptual</td><td>Lumped</td><td>Yes</td><td>7</td><td>Lang et al. (2006)</td></tr><tr><td>MORDOR-TS</td><td>Conceptual</td><td>Semi-distributed</td><td>Yes</td><td>11</td><td>Rouhier et al. (2017)</td></tr></tbody></table>

reasonably expected that this study will provide an accurate representation of past and future streamflow, as well as the related uncertainties.

The Génie Rural Semi-Distribué (GRSD) model, a semi-distributed version of the Génie Rural à 5 paramètres Journalier (GR5J) model, demonstrated high performance for streamflow simulation and temporal transferability across France (De Lavenne et al., 2019). It has been employed with climate change projections across the nearby Moselle catchment in a previous study (Thirel et al., 2019). The SAFRAN-ISBA-MODCOU2 (SIM2) model has been successfully applied with climate change projections (Chauveau et al., 2013) and is utilised for real-time flood forecasting (Coustau et al., 2015) across France. The PREvisions et Simulations pour l'Annonce et la Gestion des Etiages Sévères (PRESAGES) model was developed with the objective of forecasting low flows in the French Meuse and Rhine catchments (Lang, 2007; Lang et al., 2006). It was also tested as part of the prévision des étiages par des modèles hydrologiques, comparaison et évaluation (PREMHYCE) study (Nicolle et al., 2014) and the Chaine Intégrée pour la PRévision Hydrométéorologique des Étiages et des Sécheresses (CIPRHES) research project (Tilmant et al., 2023) in the context of low-flow forecasting. The MOdèle à Réservoirs de Détermination Objective du Ruissellement – Totalement Spatialisé (MORDOR-TS) model is the distributed version of the MORDOR hydrological model operated at Electricité de France (EDF) for about 30 years. It has been applied extensively in a variety of contexts (real-time forecasting, flood frequency analysis, and continuous monitoring of water resources; Evin et al., 2024; Garavaglia et al., 2017; Rouhier et al., 2017). The objective of all hydrological models is to simulate natural streamflow.

## 3.2. Hydrological model calibration

Each model used its own calibration algorithm and, where applicable, objective function to determine optimal parameter values. However, two specific aspects of model calibration were targeted. Firstly, the modellers were instructed to utilise two distinct objective

functions, one focusing on low flows and the other on the full range of streamflow. Secondly, the modellers were required to calibrate their model using the complete period of observed data, spanning from 1970 to 2016. Additionally, they were instructed to conduct calibration on two sub-periods: 1970–1993 and 1993–2016. This approach is based on the classical split-sample test methodology proposed by Klemeš (1986). The first constraint is a direct result of the fact that both low flows and the complete range of streamflow were targeted in this work. The second constraint allows for two key assessments: the temporal transferability of hydrological models and the provision of diverse parameter sets (in the present case, three times two, equating to six parameter sets for each model). This allows for a comprehensive examination of this source of uncertainty within the modelling framework. As the SIM2 model does not permit calibration but instead relies on an a posteriori quantile-quantile bias correction applied across the entire range of streamflow, only three versions of this model were employed, based on the application of the quantile-quantile correction over the three aforementioned periods.

All configurations of the four hydrological models were shown to exhibit a good level of performance and robustness (Thirel et al., 2021), with a moderate decline in performance based on the Kling-Gupta efficiency (Kling et al., 2012) over independent periods, an accurate reproduction of observed flow duration curves and of high- and low-flow indicators, as well as an accurate simulation of the severe 1976 and 2003 low-flow events. Subsequently, all versions of the model parameterisations were employed for the production of hydrological projections, with the low-flow calibration being used for the assessment of future changes in low flows.

## 3.3. The QUALYPSO framework

It is now widely accepted that an analysis of the uncertainty cascade is essential in any impact study. This is to characterise the modelling steps that generate the most uncertainty, particularly in hydrological projections (see e.g. Kundzewicz et al., 2008). In this study, the uncertainty quantification method, QUALYPSO, developed by Evin et al. (2019) and available in an R package (Evin, 2019), was selected.
<figure><img src="image_16.png" /></figure>

QUALYPSO is an analysis-of-variance approach (ANOVA, see Déqué et al., 2007; Sunyer et al., 2015) that quantifies and hierarchises uncertainties arising from an impact modelling chain. The QUALYPSO approach employs a continuous time series of projections (QE-ANOVA, Hingray & Saïd, 2014) and consists of separating the forced climate response from the internal variability using a trend model for a more precise assessment of uncertainty components (Hingray et al., 2019). It should be noted, however, that not all combinations of GCMs and RCMs were available (see Table 1), which introduces an additional and non-negligible source of uncertainty. However, through a Bayesian process, QUALYPSO is able to deal with such an incomplete matrix of experiments, which justifies our choice. QUALYPSO is able to disentangle the natural variability from the long-term climate change signal even when only one run of each climate model is available, as was the case for both the CHIMERE21 and Explore2 projects (Evin et al., 2019). Furthermore, QUALYPSO has already been successfully applied in climate studies (Hingray & Saïd, 2014) and hydrology studies (Lemaitre-Basset et al., 2021; Vidal et al., 2016; Visser-Quinn et al., 2019).

# 4. Results

## 4.1. Change in climate variables

Figure 2 shows that there is no clear correlation between changes in precipitation and air temperature in the climate projections for the French Meuse. The change in annual catchment air temperature shows an increasing intensity over time, with a range from +0.8 to +1.3°C in the near future, a range of +1.1 to +3.0°C in the mid future and a range of +1.8 to +4.1°C in the far future (Figure 2). As anticipated, the degree of uncertainty is greater in the far future and for RCP 8.5. The increase in temperature is consistent across all seasons (not shown here).

For all GCM/RCM couples, annual precipitation demonstrates an increase in comparison to the reference period. However, no discernible trend is evident between the different future time horizons (Figure 2). In the near future horizon, the increase in precipitation ranges from +3.1% to +27.2%, while in the mid future it ranges from +1.2% to +21.2%, and in the far future from +3.3% to +31.1%. The RCP 8.5 scenario results in a higher precipitation increase than the RCP 4.5 scenario, particularly in the far future. Consequently, the variability between the different climate projections regarding the change in annual precipitation remains high for all future time periods.

The annual variability can be attributed to a strong seasonal variability, as illustrated in Figure 3. It is noteworthy that, while the precipitation projections indicate a clear increase during winter, the outlook for summer is less certain, with potential for both increases and decreases. Nevertheless, the range of future changes remains considerable for each month.

## 4.2. Change in the interannual streamflow regimes

In order to provide an initial hydrological analysis, the interannual streamflow regimes are presented for the Meuse River at Chooz, as this station aggregates all upstream climate changes. Figure 4 illustrates the observed and projected regimes for the reference time period and for both the near and far futures. The observed streamflow regime of the Meuse can be characterised as predominantly pluvial, with higher streamflow in winter and lower streamflow in summer. While the hydrological models forced by SAFRAN yield to streamflow regimes that are similar to the observed one (not shown here), a comparison of observations and streamflow obtained with climate data on the reference period reveals that the use of climate projections results in an underestimation of winter

<figure><img src="image_17.png" /><figcaption>Figure 2. Change in annual air temperature (in °C) and annual precipitation (in %) over the entire French Meuse catchment for the five GCM/RCM couples from 1976–2005 to the near (2021–2050), mid (2046–2075), and far (2071–2100) future for RCPs 4.5 and 8.5.</figcaption></figure>
<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

**Figure 3.** Monthly precipitation over the French Meuse catchment for the far future (2071–2100) for the five GCM/RCM couples under RCP 4.5 (coloured dotted lines) and RCP 8.5 (coloured solid lines), and for the SAFRAN reanalysis (black dashed line).

<figure><img src="image_20.png" /></figure>

**Figure 4.** Interannual streamflow regimes (from 1 January to 31 December) for the hydrological projections of the Meuse at Chooz for all hydrological models and the five GCM/RCM couples. Upper panels (a, b), RCP 4.5; lower panels (c, d), RCP 8.5. On the left (a, c), the 2021–2050 period, observed streamflow and the 1976–2005 reference period; on the right (b, d), the 2071–2100 period, observed streamflow and the 1976–2005 reference period. Each line represents a projection. Observations (1976–2005 period) are plotted with a black dashed line in all plots.

streamflow. A comparison of the streamflow projections for the reference and future periods indicates that winter streamflow is expected to increase, while the trend in summer is more uncertain. For RCP 8.5, the increase in winter

streamflow appears to be higher in the far future than the near future, and shows higher increases than RCP 4.5 for all time horizons. In addition, we note that some clusters of hydrological projections appear in Figure 4. They originate from the climate
<figure><img src="image_21.png" /></figure>

projections, which means that the choice of a climate projection determines the hydrological regime

more than the hydrological model used or its objective function for calibration.

### 4.3. Change in streamflow along the Meuse

In order to investigate whether the effects of climate change may be similar across the entire catchment, we propose that stream profiles be employed along the Meuse River course. Figure 5 illustrates two indicators commonly employed in water management in France,

namely a high-flow indicator (the 95th percentile of daily streamflow, designated as Q95), and a low-flow indicator (the minimal 3-day average streamflow with a 10-year period, designated as VCN3-10). These indicators are presented for observations and the reference, near, and far future periods for RCP 8.5. Comparing the future periods to the reference period shows the impact of climate change on the streamflow indicators. Figure 5 illustrates a strong increase in the Q95 high-flow indicator across the entire Meuse catchment. This increase is more pronounced in the far future, and it is stronger in the downstream part of the catchment (as evidenced by the Sedan and Chooz

<figure><img src="image_22.png" /><figcaption>**Figure 5.** Stream profile of the Meuse Q95 (95th percentile of daily streamflow, top row) and VCN3-10 (minimal 3-day average streamflow with a 10-year period, bottom row) for the reference period and the near (left) and far (right) futures for RCP 8.5. The envelope represents the min and max of the indicator values for all GCM/RCM couples and all hydrological models, while the lines represent the average. The names of the stations along the main course of the Meuse River are shown in black, and the location of its tributaries are shown in blue. To compute these indicators, we used the parameter sets of the hydrological models that were obtained when calibration targeted the whole range of flows for Q95 or the low flows for VCN3-10.</figcaption></figure>
<figure><img src="image_23.png" /></figure>

stations) than in its upstream part (as illustrated by the Goncourt and Neufchâteau stations). The presence of the Ardennes massif downstream may be a contributing factor to this outcome. This result is noteworthy, as water managers have already identified this area as a region with a high risk of flooding.

Regarding the VCN3-10 low-flow indicator, the values exhibit a considerable degree of overlap with the values for the reference period regarding the near future at all stations, and the far future at downstream stations. Nevertheless, a decrease in this indicator can be seen for the upstream stations and for the far future. Interestingly, the steepest slopes of the stream profile are seen for limestone areas (between Neufchâteau and Chalaines) and the flattest slopes are seen for karstic areas (between Chalaines and St-Mihiel). Overall, the evolutions present a large uncertainty, as evidenced by the substantial overlap between the envelopes. Once more, the projected trend may raise concerns given that upstream areas are already the most prone to droughts and water shortage and lack the necessary water storage infrastructures to effectively mitigate the impact of droughts.

The change in hydrological indicators with RCP 4.5 was less pronounced (not shown). Furthermore, the change in mean streamflow was shown to increase in a similar way to Q95.

## 4.4. Uncertainty partitioning

As previously observed, the degree of uncertainty is considerable for certain indicators and time periods. The identification of uncertainty sources and their subsequent quantification can help water managers in their decision-making and improve the methods employed to assess the impact of climate change on hydrology. Figure 6 illustrates the uncertainty partitioning for the low-flow indicator (VCN3, the minimal 3-day average streamflow). It shows that the uncertainty partitioning follows a comparable pattern for all the stations in the Meuse River. Namely, the greatest proportion of uncertainty is attributable to climate internal variability at the beginning of the future period, while GCMs and RCMs show a rapid increase in the share of uncertainty. Although the uncertainty pertaining to hydrological model

<figure><img src="image_24.png" /><figcaption>**Figure 6.** Uncertainty partition obtained with the QUALYPSO method for a low-flow indicator (VCN3, the minimal 3-day average streamflow) for six stations along the Meuse catchment in France. “HMs” stands for hydrological model structures, “HM params” stands for hydrological model parameterisation, “Res. Var.” stands for the residual variability, and “Int. Var.” stands for the internal variability.</figcaption></figure>
<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /><figcaption>**Figure 7.** Uncertainty partition obtained with the QUALYPSO method for a high-flow indicator (Q95, the 95th streamflow quantile) for six stations along the Meuse catchment in France. “HMs” stands for hydrological model structures, “HM params” stands for hydrological model parameterisation, “Res. Var.” stands for the residual variability, and “Int. Var.” stands for the internal variability.</figcaption></figure>

structures and parameterisation exists, it remains minor in comparison to the uncertainty associated with other sources of uncertainty of the modelling chain. In addition, the uncertainty pertaining to RCPs is low.

The uncertainty partitioning for the high-flow indicator (Q95, the 95th streamflow quantile) is shown in Figure 7. Once more, the uncertainty partition yields highly comparable results for all stations. Similarly to low flows, the uncertainty partition for high flows shows that internal variability will be the primary driver in the near future, while GCMs and RCMs rapidly show a growing share of uncertainty. The main difference is the emergence of the RCPs share in the far future (red area), possibly indicating a higher uncertainty associated with the intensity of temperature rise. Once more, this result aligns with the findings of previous studies (Lemaitre-Basset et al., 2021; Vidal et al., 2016).

# 5. Discussion

## 5.1. On the choice of climate projections

In the present study, we used a set of five GCM/RCM couples as input data for four hydrological models. The number of climate projections used may be considered low to adequately identify the complete possible range of future climates. As is the case with all climate change impact studies, this choice was, however, constrained by the availability of climate projections at the time when the hydrological models were run (2018–2019). The use of a limited number of climate projections may result in a biased estimation of the range of changes in future climate. To address this potential bias, the climate projections we used were subsequently compared for the same river basin, as part of the CHIMERE21 project, to two different sets: (i) the climate projections used in a previous project, Explore 2070 (Chauveau et al., 2013) and (ii) a larger set of climate projections
<figure><img src="image_27.png" /></figure>

uploaded to the national climate projections repository, DRIAS, in 2020 (DRIAS-2020 dataset: https://www.drias-climat.fr/; Soubeyroux et al., 2021). As shown by Thirel et al. (2021), the climate projections presented in CHIMERE21 yield higher precipitation than those evaluated in the Explore 2070 project, and slightly higher precipitation than those of the DRIAS-2020 dataset. No significant difference was observed with regard to air temperature. This could potentially lead to modifications in the conclusions regarding future changes in low flows. However, it is noteworthy that the projections used in this study do not diverge much from the most recent assessment of the DRIAS-2020 dataset, which is reassuring.

Furthermore, a single bias correction method was applied to climate projections in this work. This means that this potential source of uncertainty was not addressed in our study. However, it is plausible that this source of uncertainty may be less than the others in France, as suggested by Evin et al. (2024).

## 5.2. On the comparison between CHIMERE21 and the more recent Explore2 projections

New France-wide hydrological projections were produced within the Explore2 project and made available in 2024. This dataset employed two bias correction methods, a set of 17 GCM/RCM couples across France, and up to nine hydrological models (see https://entrepot.recherche.data.gouv.fr/dataverse/explore2 for all the project reports and summary sheets). While hydrological projections were produced for several stations of the Meuse River basin, they were analysed on maps of France, with no specific focus on the Meuse River basin (Sauquet et al., 2024). Summary sheets were produced for several Meuse stations, but no Meuse-wide analysis was conducted as part of the

Explore2 project. Consequently, it was not feasible to evaluate the climate-related discrepancies between the two projects across all stations of the Meuse River and its tributaries. In comparison to CHIMERE21, Explore2 used an additional bias correction method (namely CDF-t), in addition to ADAMONT, and a greater number of climate projections and hydrological models. Comparing the interannual regimes for the Chooz station produced by the Explore2 project with those of the CHIMERE21 project (Figure 8) reveals an underestimation of winter streamflow in the results of Explore2 for the 1976–2005 reference period compared to observed streamflow, as was also the case for the CHIMERE21 results. This underestimation is even larger than the one of the CHIMERE21 projections. With regard to the impact of climate change, it is evident that the Explore2 projections result in a lower increase of winter streamflow than those of CHIMERE21. This can be attributed to the higher precipitation in the CHIMERE21 dataset. Furthermore, there is some discrepancy between the two projects with regard to low-flow periods, with Explore2 showing lower streamflow for this period. Additional results from the Chooz station in the Explore2 factsheet (see Appendix) indicate a consensus among projections regarding an increase in high flows (such as in CHIMERE21, but with a lower intensity) as well as agreement consensus among projections regarding a decrease in low flows (with a higher intensity than that in CHIMERE21). Overall, the findings of the CHIMERE21 project appear to be corroborated by the more recent outcomes of the Explore2 project, although the CHIMERE21 climate projections could be considered slightly wetter than those utilised in the Explore2 project.

Although the CHIMERE21 results may be perceived as “outdated” in comparison to those of the Explore2 project, we believe that they retain intrinsic

<figure><img src="image_28.png" /><figcaption>**Figure 8.** Interannual streamflow regimes (from 1 January to 31 December) for the hydrological projections of the Meuse at Chooz for CHIMERE21 (blue) and for Explore2 (red). (a) Interannual regime for the 1976–2005 reference period; (b) interannual regime for the 2070–2099 future period. Observations (1976–2005 period) are plotted with a black dashed line in both plots.</figcaption></figure>
<figure><img src="image_29.png" /></figure>

interest and value. Indeed, the projections produced by Explore2 were designed to align with the requirements of a national framework. Consequently, all methodological choices were done to fulfil a national goal. Namely, in Explore2, the preparation of hydrological models (including calibration) was conducted for the entirety of France in a unified manner, whereas in CHIMERE21 the calibration of models was tailored to this specific basin. This indicates that although the calibration of hydrological models was conducted for a number of stations across the Meuse River basin in Explore2, the verification of their performance was carried out on the basis of all French stations included in each model, with no adaptation made for the Meuse basin. In contrast, the CHIMERE21 hydrological models were assessed and tailored to all stations of the Meuse River basin independently (Thirel et al., 2021). Furthermore, the simulations of the SIM2 model, the only non-calibrated hydrological model, were bias corrected, whereas in Explore2 nearly half of the hydrological models were neither calibrated nor bias corrected. Collectively, this resulted in a reduction in uncertainty regarding hydrological modelling.

Furthermore, the Explore2 projections, which were produced in multiple stations across the Meuse River basin, were not subjected to a detailed analysis specific to the Meuse River basin. Instead, they were primarily examined through cartographical analyses encompassing the entire country. It is also notable that no stream profiles were analysed in previous projects. This analysis represents a novel contribution to the field, offering insights into how climate change impacts propagate from upstream to downstream within a river basin.

It is crucial to emphasise the importance of the selection of climate projections. Despite being based on a subset of the CMIP5 dataset and sharing a similar bias correction method and other methodological elements, our findings indicate discrepancies between the obtained hydrological projections. While these discrepancies do not result in divergent outcomes, they underscore the influence that different climate projections may exert and emphasise the necessity for meticulous preparation in this regard.

In conclusion, the two initiatives (Explore2 and CHIMERE2) are regarded as complementary to better understand the climate change impacts over the Meuse catchment and their associated uncertainties. For more detailed analyses and adaptation plans, however, we advise readers to download the projections from the Explore2 climate repository (https://www.drias-climat.fr/) and the Explore2 hydrological repository (https://www.drias-eau.fr/). This is particularly recommended if other catchments are of interest.

### 5.3. On the estimation of uncertainty

In this study, we employed the QUALYPSO framework, which is constrained by the relatively limited number of climate projections. Specifically, not all RCMs and GCMs are represented by at least two occurrences in the GCM/RCM combinations (see Table 1). This could result in an inaccurate assessment of the uncertainty associated with GCMs and RCMs, in comparison to other elements of the modelling chain.

It is noteworthy that in the area under investigation, uncertainty is not primarily influenced by hydrological modelling, which differs from the findings in other regions (e. g. the French Mediterranean and the French Alps, as documented by Lemaitre-Basset et al., 2021; Vidal et al., 2016). This is nevertheless consistent with the findings of Evin et al. (2024) using the Explore2 projections over France, as well as the results from Wang et al. (2020) in Canada and China, which demonstrates that the contribution of hydrological models to the overall uncertainty can be highly variable. For example, this contribution can be major for snow-dominated catchments (see Poulin et al., 2011, in Canada, or Bosshard et al., 2013, in Switzerland, especially during winter and spring), but can be minor compared to climate inputs or even the methods used to calculate flood frequency analyses (Lawrence, 2020).

In addition, the high uncertainty which is initially linked to the internal variability and then linked to GCMs and RCMs is not a novel result (see e.g. Collet et al., 2017; Lemaitre-Basset et al., 2021; Vidal et al., 2016). However, here the uncertainty associated with hydrological modelling and RCPs (or extreme value assessment in Collet et al., 2017) is shown to be relatively low in comparison to what was observed in these previous studies for low flows. This can be attributed to the fact that the hydrological regime of the Meuse River is pluvial, i.e. driven by rainfall, with a low contribution from underground water transfers or snow accumulation and melt processes. As a result, the uncertainty associated with precipitation projections, which is high, becomes more important in relative terms than the contribution of other hydrological processes to the overall uncertainty. For example, the studies by Vidal et al. (2016) and Lemaitre-Basset et al. (2021), which showed a greater proportion of uncertainty associated with hydrological modelling, were conducted in a snow-dominated catchment and in a Mediterranean catchment in France, respectively. The processes in these catchments are more influenced by temperature changes.

We hope that these results will provide guidance for future hydrological developments, enabling a more accurate assessment of the impact of climate change on hydrology. This will involve identifying the areas
<figure><img src="image_30.png" /></figure>

requiring improvement and the processes involved
(Evin et al., 2024).

Ultimately, our findings highlight the changes
across the entire Meuse River course. Additionally,
the uncertainty partition remains remarkably similar
across the different stations. This diagnostic was not
feasible in the Explore2 project.

5.4. Hydrological projections: a step towards
adaptation

In this study, as in the case of Explore2, the impact of
climate change was assessed on streamflow that was
considered natural river streamflow. Consequently,
this framework does not account for water withdrawal
and release of water for anthropogenic uses, nor for
future changes in these water uses, or water restric-
tions in periods of drought. Furthermore, this frame-
work does not allow for the assessment of the efficacy
of adaptation measures that could be employed on this
territory to enhance resilience to climate change. In
other words, this study examined the impact of cli-
mate change only on the hydrological cycle, assuming
a steady socio-economic context. Nevertheless, it can
serve as a robust foundation for the development of
adaptation plans, namely how the socio-economic
context could be modified to align with future hydro-
climatic contexts. This regional diagnostic represents
a crucial preliminary step towards addressing this
pressing issue, as evidenced by the work of Lemaitre-
Basset (2023) and Lemaitre-Basset et al. (2024) on the
nearby Moselle catchment. We believe that this study,
in conjunction with the most recent findings of
Explore2, will inform and guide local initiatives in
the future.

6. Conclusions

In this study, we assessed the impact of climate change
on the streamflow of the French part of the Meuse
catchment. A robust modelling chain was employed,
comprising two RCPs, five GCM-RCM couples, four
hydrological models and several parameter sets. While
an increase in air temperature is logically to be
expected, particularly in line with the RCP 8.5 sce-
nario, future changes in precipitation in the river basin
are less clear. It is anticipated that there will be a slight
increase in annual precipitation, with a notable rise in
winter precipitation. With regard to summer precipi-
tation, the change signal remains highly uncertain,
and therefore no conclusions can be drawn. In terms
of expected hydrological impacts, these climate pro-
jections will result in an increase in winter streamflow,
with an uncertain change signal for summer stream-
flow. A stream profile-based analysis allowed for the
assessment of the anticipated hydrological impacts
along the river course. This enabled the identification

of zones with a higher impact and the overlaying of
these with the known zones of higher vulnerability.
The analysis of variance (QUALYPSO tool) was
employed to partition the sources of uncertainty in
future streamflow. Our findings indicate that the
internal variability is the primary source of uncer-
tainty in the near future, whereas the variability within
GCMs and RCMs is the predominant contributor to
uncertainty in the mid and far futures. The hydrolo-
gical model calibration efforts undertaken in
CHIMERE21, in conjunction with the uncertainty
analysis results, indicate that hydrological uncertainty
did not contribute to the overall uncertainty in the
study. Furthermore, we conducted a comparative ana-
lysis with the more recent France-wide Explore2 pro-
ject. Our findings revealed that the CHIMERE21
projections employed in this study exhibited an over-
lap with the Explore2 projections, although the
CHIMERE21 projections can be considered slightly
wetter. This emphasises the importance of the selec-
tion of climate projections.

Acknowledgment

This project was made possible by the financial support of
the Rhin-Meuse French Water Agency (convention
17C08004). The climate data were provided by
Météo-France and the hydrological data were retrieved
from the HYDRO database, which is maintained by
SCHAPI. We express our gratitude to Fanny Sarrazin,
Maria-Helena Ramos and Vazken Andréassian from
INRAE, for their insightful comments on the preliminary
version of this manuscript.

Funding

This work was supported by the Agence de l'eau Rhin-
Meuse [17C08004; Agence de l'eau Rhin-Meuse].

ORCID

G. Thirel http://orcid.org/0000-0002-1444-1830

L. Collet http://orcid.org/0000-0003-2569-1509

O. Delaigue http://orcid.org/0000-0002-7668-8468

D. François http://orcid.org/0000-0001-7305-7719

M. Le Lay http://orcid.org/0000-0002-0865-7125

C. Perrin http://orcid.org/0000-0001-8552-1881

R. Samacoits http://orcid.org/0009-0008-2560-3306

M. Terrier http://orcid.org/0000-0001-6665-323X

J.-P. Vidal http://orcid.org/0000-0002-3748-6150

Data availability

The CHIMERE21 projection data are available on request
from the lead author. The Explore2 projection data are
available on the Drias portals. Observed hydrological and
meteorological data are available on request from the data
producers.
<figure><img src="image_31.png" /></figure>

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<figure><img src="image_33.png" /></figure>

# Appendix: Explore2 summary sheet for the Meuse at Chooz

Page 1 and page 2 of the Explore2 summary sheet for the Meuse at Chooz (Héraut et al., 2024).

## B720000001 - La Meuse à Chooz [Trou Du Diable]

**Région hydrographique : Meuse**

Superficie : 10120 km²

X = 827606 m (Lambert93)

Y = 7000529 m (Lambert93)

Nombre de projections sous RCP 8.5 : 99

Nombre de modèles hydrologiques : 4

### (a) Régime hydrologique (m³/s)

H0 : Période de référence 1976-2005

<figure><img src="image_34.png" /></figure>

Narratifs

<figure><img src="image_35.png" /></figure>

Réchauffement marqué et augmentation des précipitations

<figure><img src="image_36.png" /></figure>

Changements futurs relativement peu marqués

<figure><img src="image_37.png" /></figure>

Fort réchauffement et fort asseurement en été (et en annuel)

<figure><img src="image_38.png" /></figure>

Fort réchauffement et forts contrastes saisonniers en précipitations

SAFRAN

<figure><img src="image_39.png" /></figure>

Ensemble des projections

<figure><img src="image_40.png" /></figure>

<figure><img src="image_41.png" /></figure>

### (b) Régime hydrologique (m³/s)

H2 : Milieu de siècle 2041-2070

<figure><img src="image_42.png" /></figure>

### (c) Régime hydrologique (m³/s)

H3 : Fin de siècle 2070-2099

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

### (d) QJXA - Débit journalier maximal annuel

Changements

<figure><img src="image_45.png" /></figure>

Plus d'eau

<figure><img src="image_46.png" /></figure>

Moins d'eau

Évolution

Moyenne d'ensemble

Incertitude de modélisation

Variabilité naturelle

<figure><img src="image_47.png" /></figure>

Accord sur le signe de l'évolution

▲ Augmentation

○ Pas d'accord

▼ Diminution

### (e) QA - Moyenne annuelle du débit journalier

<figure><img src="image_48.png" /></figure>

Stripes

Les palettes de couleur sont communes aux quatre narratifs pour chaque variable.

<figure><img src="image_49.png" /></figure>

### (f) VCN10_été - Minimum estival de la moyenne sur 10 jours du débit journalier

<figure><img src="image_50.png" /></figure>
<figure><img src="image_51.png" /></figure>

(g) Changements en milieu de siècle 2041-2070 (H2) et en fin de siècle 2070-2099 (H3) par rapport à la période de référence 1976-2005

<figure><img src="image_52.png" /></figure>

(h) QJXA-10 - Crues de période de retour 10 ans

<figure><img src="image_53.png" /></figure>

(i) VCN10-5 - Étiages de période de retour 5 ans

<figure><img src="image_54.png" /></figure>

Avertissement : Ces résultats comportent des incertitudes. Ils sont donnés à titre indicatif. Il ne s'agit pas de prévisions mais d'indications d'évolutions possibles. Ces fiches sont volontairement synthétiques et une notice d'accompagnement fournit des informations pour la lecture et l'interprétation des graphiques de cette fiche.