Articles | Volume 30, issue 20
https://doi.org/10.5194/hess-30-6381-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-6381-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Oct 2026
Research article |
| 20 Oct 2026
| 20 Oct 2026
Climate change impacts on groundwater simulated using the AquiFR modelling platform
Laboratoire de Géologie, CNRS UMR 8538, École Normale Supérieure, PSL University, IPSL, Paris, France
CNRM, Université de Toulouse, Météo-France, CNRS UMR 3589, Toulouse, France
Jean-Pierre Vergnes
Water, Environment, Processes and Analyses Division, BRGM – French Geological Survey, Orléans, France
Simon Munier
CNRM, Université de Toulouse, Météo-France, CNRS UMR 3589, Toulouse, France
Florence Habets
Laboratoire de Géologie, CNRS UMR 8538, École Normale Supérieure, PSL University, IPSL, Paris, France
Related authors
Eric Sauquet, Guillaume Evin, Sonia Siauve, Ryma Aissat, Patrick Arnaud, Maud Bérel, Jérémie Bonneau, Flora Branger, Yvan Caballero, François Colléoni, Agnès Ducharne, Joël Gailhard, Florence Habets, Frédéric Hendrickx, Louis Héraut, Benoît Hingray, Peng Huang, Tristan Jaouen, Alexis Jeantet, Sandra Lanini, Matthieu Le Lay, Claire Magand, Louise Mimeau, Céline Monteil, Simon Munier, Charles Perrin, Olivier Robelin, Fabienne Rousset, Jean-Michel Soubeyroux, Laurent Strohmenger, Guillaume Thirel, Flore Tocquer, Yves Tramblay, Jean-Pierre Vergnes, and Jean-Philippe Vidal
Hydrol. Earth Syst. Sci., 30, 2277–2300, https://doi.org/10.5194/hess-30-2277-2026, https://doi.org/10.5194/hess-30-2277-2026, 2026
Short summary
Short summary
The Explore2 project has provided an unprecedented set of hydrological projections in terms of the number of hydrological models used and the spatial and temporal resolution. The results have been made available through various media. Under the high-emission scenario, the hydrological models mostly agree on the decrease in seasonal flows in the south of France, confirming its hotspot status, and on the decrease in summer flows throughout France, with the exception of the northern part of France.
Pierre Laluet, Luis Olivera-Guerra, Víctor Altés, Vincent Rivalland, Alexis Jeantet, Julien Tournebize, Omar Cenobio-Cruz, Anaïs Barella-Ortiz, Pere Quintana-Seguí, Josep Maria Villar, and Olivier Merlin
Hydrol. Earth Syst. Sci., 28, 3695–3716, https://doi.org/10.5194/hess-28-3695-2024, https://doi.org/10.5194/hess-28-3695-2024, 2024
Short summary
Short summary
Monitoring agricultural drainage flow in irrigated areas is key to water and soil management. In this paper, four simple drainage models are evaluated on two irrigated sub-basins where drainage flow is measured daily. The evaluation of their precision shows that they simulate drainage very well when calibrated with drainage data and that one of them is slightly better. The evaluation of their accuracy shows that only one model can provide rough drainage estimates without calibration data.
Malak Sadki, Gaëtan Noual, Simon Munier, Vanessa Pedinotti, Kaushlendra Verma, Clément Albergel, Sylvain Biancamaria, and Alice Andral
Hydrol. Earth Syst. Sci., 30, 6159–6187, https://doi.org/10.5194/hess-30-6159-2026, https://doi.org/10.5194/hess-30-6159-2026, 2026
Short summary
Short summary
his study explores how 20 years of European Space Agency Climate Change Initiative discharge products improve large-scale hydrological simulations through data assimilation. Tests in two contrasting models across Niger and Congo basins show that temporally dense discharge data reduces bias and improves internal variability. Although product quality, sampling, and basin processes control gain reliability, the findings support these datasets' value in refining models for climate studies.
Julien Lefebve, Sylvain Biancamaria, Alejandro Blazquez, Simon Munier, Laetitia Gal, Rômulo Augusto Jucá Oliveira, and Jordi Etchanchu
EGUsphere, https://doi.org/10.5194/egusphere-2026-4569, https://doi.org/10.5194/egusphere-2026-4569, 2026
Short summary
Short summary
Approaching the water budget closure of a basin gives confidence in our understanding of its water cycle. By analysing 82 major basins worldwide with the most up‑to‑date data, we demonstrate that the large spread in precipitation (44 %), evapotranspiration (33 %) and runoff (36 %) can produce compensations and lead to erroneous conclusions about water budget closure. In the absence of gauge records, calculating discharge from satellite observations rather than from models reduces these errors.
Adrien Manlay, Jean-Pierre Vergnes, Simon Munier, and Florence Habets
EGUsphere, https://doi.org/10.5194/egusphere-2026-1504, https://doi.org/10.5194/egusphere-2026-1504, 2026
Short summary
Short summary
Groundwater is a key resource for human activities, predicting its evolution months in advance poses a significant challenge. Forecasts can be erroneous due to model uncertainties. In this study, we developed a method that integrates observed groundwater levels into a numerical model to improve seasonal forecasts. By continuously updating the model with observations, we achieved more accurate water level simulations and predictions up to six months ahead.
Eric Sauquet, Guillaume Evin, Sonia Siauve, Ryma Aissat, Patrick Arnaud, Maud Bérel, Jérémie Bonneau, Flora Branger, Yvan Caballero, François Colléoni, Agnès Ducharne, Joël Gailhard, Florence Habets, Frédéric Hendrickx, Louis Héraut, Benoît Hingray, Peng Huang, Tristan Jaouen, Alexis Jeantet, Sandra Lanini, Matthieu Le Lay, Claire Magand, Louise Mimeau, Céline Monteil, Simon Munier, Charles Perrin, Olivier Robelin, Fabienne Rousset, Jean-Michel Soubeyroux, Laurent Strohmenger, Guillaume Thirel, Flore Tocquer, Yves Tramblay, Jean-Pierre Vergnes, and Jean-Philippe Vidal
Hydrol. Earth Syst. Sci., 30, 2277–2300, https://doi.org/10.5194/hess-30-2277-2026, https://doi.org/10.5194/hess-30-2277-2026, 2026
Short summary
Short summary
The Explore2 project has provided an unprecedented set of hydrological projections in terms of the number of hydrological models used and the spatial and temporal resolution. The results have been made available through various media. Under the high-emission scenario, the hydrological models mostly agree on the decrease in seasonal flows in the south of France, confirming its hotspot status, and on the decrease in summer flows throughout France, with the exception of the northern part of France.
Guillaume Evin, Benoit Hingray, Guillaume Thirel, Agnès Ducharne, Laurent Strohmenger, Lola Corre, Yves Tramblay, Jean-Philippe Vidal, Jérémie Bonneau, François Colleoni, Joël Gailhard, Florence Habets, Frédéric Hendrickx, Louis Héraut, Peng Huang, Matthieu Le Lay, Claire Magand, Paola Marson, Céline Monteil, Simon Munier, Alix Reverdy, Jean-Michel Soubeyroux, Yoann Robin, Jean-Pierre Vergnes, Mathieu Vrac, and Eric Sauquet
Hydrol. Earth Syst. Sci., 30, 1023–1051, https://doi.org/10.5194/hess-30-1023-2026, https://doi.org/10.5194/hess-30-1023-2026, 2026
Short summary
Short summary
Explore2 provides hydrological projections for 1,735 French catchments. Using QUALYPSO (Quasi-Ergodic Analysis of Climate Projections Using Data Augmentation), this study assesses uncertainties, including internal variability. By the end of the century, low flows are projected to decline in southern France under high emissions, while other indicators remain uncertain. Emission scenarios and regional climate models are key uncertainty sources. Internal variability is often as large as climate-driven changes.
Kaushlendra Verma and Simon Munier
EGUsphere, https://doi.org/10.5194/egusphere-2026-509, https://doi.org/10.5194/egusphere-2026-509, 2026
Short summary
Short summary
Satellite provide river observations as vector reaches, while large-scale hydrological models represent rivers on gridded routing networks. This structural mismatch limits direct data assimilation. We present a global, confidence-aware framework that assigns vector river reaches to routing pixels using geometric and hydrological consistency criteria. Results show that most routing pixels can be assigned with high confidence while preserving basin-scale drainage topology into hydrological models.
Pierre Laluet, Luis Olivera-Guerra, Víctor Altés, Vincent Rivalland, Alexis Jeantet, Julien Tournebize, Omar Cenobio-Cruz, Anaïs Barella-Ortiz, Pere Quintana-Seguí, Josep Maria Villar, and Olivier Merlin
Hydrol. Earth Syst. Sci., 28, 3695–3716, https://doi.org/10.5194/hess-28-3695-2024, https://doi.org/10.5194/hess-28-3695-2024, 2024
Short summary
Short summary
Monitoring agricultural drainage flow in irrigated areas is key to water and soil management. In this paper, four simple drainage models are evaluated on two irrigated sub-basins where drainage flow is measured daily. The evaluation of their precision shows that they simulate drainage very well when calibrated with drainage data and that one of them is slightly better. The evaluation of their accuracy shows that only one model can provide rough drainage estimates without calibration data.
Julia Pfeffer, Anny Cazenave, Alejandro Blazquez, Bertrand Decharme, Simon Munier, and Anne Barnoud
Hydrol. Earth Syst. Sci., 27, 3743–3768, https://doi.org/10.5194/hess-27-3743-2023, https://doi.org/10.5194/hess-27-3743-2023, 2023
Short summary
Short summary
The GRACE (Gravity Recovery And Climate Experiment) satellite mission enabled the quantification of water mass redistributions from 2002 to 2017. The analysis of GRACE satellite data shows here that slow changes in terrestrial water storage occurring over a few years to a decade are severely underestimated by global hydrological models. Several sources of errors may explain such biases, likely including the inaccurate representation of groundwater storage changes.
Laurent Strohmenger, Eric Sauquet, Claire Bernard, Jérémie Bonneau, Flora Branger, Amélie Bresson, Pierre Brigode, Rémy Buzier, Olivier Delaigue, Alexandre Devers, Guillaume Evin, Maïté Fournier, Shu-Chen Hsu, Sandra Lanini, Alban de Lavenne, Thibault Lemaitre-Basset, Claire Magand, Guilherme Mendoza Guimarães, Max Mentha, Simon Munier, Charles Perrin, Tristan Podechard, Léo Rouchy, Malak Sadki, Myriam Soutif-Bellenger, François Tilmant, Yves Tramblay, Anne-Lise Véron, Jean-Philippe Vidal, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 27, 3375–3391, https://doi.org/10.5194/hess-27-3375-2023, https://doi.org/10.5194/hess-27-3375-2023, 2023
Short summary
Short summary
We present the results of a large visual inspection campaign of 674 streamflow time series in France. The objective was to detect non-natural records resulting from instrument failure or anthropogenic influences, such as hydroelectric power generation or reservoir management. We conclude that the identification of flaws in flow time series is highly dependent on the objectives and skills of individual evaluators, and we raise the need for better practices for data cleaning.
Antoine Sobaga, Bertrand Decharme, Florence Habets, Christine Delire, Noële Enjelvin, Paul-Olivier Redon, Pierre Faure-Catteloin, and Patrick Le Moigne
Hydrol. Earth Syst. Sci., 27, 2437–2461, https://doi.org/10.5194/hess-27-2437-2023, https://doi.org/10.5194/hess-27-2437-2023, 2023
Short summary
Short summary
Seven instrumented lysimeters are used to assess the simulation of the soil water dynamic in one land surface model. Four water potential and hydraulic conductivity closed-form equations, including one mixed form, are evaluated. One form is more relevant for simulating drainage, especially during intense drainage events. The soil profile heterogeneity of one parameter of the closed-form equations is shown to be important.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
Short summary
Short summary
Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet
Geosci. Model Dev., 15, 8453–8471, https://doi.org/10.5194/gmd-15-8453-2022, https://doi.org/10.5194/gmd-15-8453-2022, 2022
Short summary
Short summary
Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.
Antoine Sobaga, Bertrand Decharme, Florence Habets, Christine Delire, Noële Enjelvin, Paul-Olivier Redon, Pierre Faure-Catteloin, and Patrick Le Moigne
EGUsphere, https://doi.org/10.5194/egusphere-2022-274, https://doi.org/10.5194/egusphere-2022-274, 2022
Preprint archived
Short summary
Short summary
Seven instrumented lysimeters are used to assess the simulation of the soil water dynamic in one land surface model. Three water potential and hydraulic conductivity closed-form equations including one mixed form are evaluated. The mixed form is more relevant to simulate drainage especially during intense drainage events. Soil profile heterogeneity of one parameter of the closed-form equations is shown to be important.
Simon Munier and Bertrand Decharme
Earth Syst. Sci. Data, 14, 2239–2258, https://doi.org/10.5194/essd-14-2239-2022, https://doi.org/10.5194/essd-14-2239-2022, 2022
Short summary
Short summary
This paper presents a new global-scale river network at 1/12°, generated automatically and assessed over the 69 largest basins of the world. A set of hydro-geomorphological parameters are derived at the same spatial resolution, including a description of river stretches (length, slope, width, roughness, bankfull depth), floodplains (roughness, sub-grid topography) and aquifers (transmissivity, porosity, sub-grid topography). The dataset may be useful for hydrology modelling or climate studies.
Cited articles
Baulenas, E., Versteeg, G., Terrado, M., Mindlin, J., and Bojovic, D.: Assembling the climate story: use of storyline approaches in climate-related science, Global Challenges, 7, 2200183, https://doi.org/10.1002/gch2.202200183, 2023. a
Baulon, L., Allier, D., Massei, N., Bessiere, H., Fournier, M., and Bault, V.: Influence de la variabilité basse-fréquence des niveaux piézométriques sur l'occurrence et l'amplitude des extrêmes, Géologues, Société Géologique de France https://hal.science/hal-03138860 (last access: 13 October 2026), 2020. a
Baulon, L., Massei, N., Allier, D., Fournier, M., and Bessiere, H.: Influence of low-frequency variability on high and low groundwater levels: example of aquifers in the Paris Basin, Hydrol. Earth Syst. Sci., 26, 2829–2854, https://doi.org/10.5194/hess-26-2829-2022, 2022. a, b
Birnbaum, Z. W. and Tingey, F. H.: One-Sided Confidence Contours for Probability Distribution Functions, Ann. Math. Stat., 22, 592–596, https://doi.org/10.1214/aoms/1177729550, 1951. a
Bouttier, F. and Marchal, H.: Probabilistic short-range forecasts of high-precipitation events: optimal decision thresholds and predictability limits, Nat. Hazards Earth Syst. Sci., 24, 2793–2816, https://doi.org/10.5194/nhess-24-2793-2024, 2024. a
Boé, J., Radojevic, M., Bonnet, R., and Dayon, G.: Scénarios sécheresse sur le bassin Seine-Normandie, Rapport technique, CECI, CERFACS-CNRS,, Toulouse, France, https://www.drieat.ile-de-france.developpement-durable.gouv.fr/IMG/pdf/rapport_boe_final.pdf (last access: 13 October 2026), 2018. a
Carroget, A., Perrin, C., Sauquet, E., Vidal, J.-P., Chazot, S., Rouchy, N., and Chauveau, M.: Explore 2070 : quelle utilisation d’un exercice prospectif sur les impacts des changements climatiques à l’échelle nationale pour définir des stratégies d’adaptation ?, Sciences Eaux & Territoires, Numéro 22, 4–11, https://doi.org/10.3917/set.022.0004, 2017. a, b, c
Chauveau, M., Chazot, S., Perrin, C., Bourgin, P.-Y., Sauquet, E., Vidal, J.-P., Rouchy, N., Martin, E., David, J., Norotte, T., Maugis, P., and De Lacaze, X.: Quels impacts des changements climatiques sur les eaux de surface en France à l’horizon 2070 ?, La Houille Blanche, 99, 5–15, https://doi.org/10.1051/lhb/2013027, 2013. a
Chery, L. and Cattan, A.: ADES : la banque de données nationale sur les eaux souterraines, La Houille Blanche, 89, 115–119, https://doi.org/10.1051/lhb/2003038, 2003. a
Costantini, M., Colin, J., and Decharme, B.: Projected Climate-Driven Changes of Water Table Depth in the World's Major Groundwater Basins, Earth's Future, 11, 1–16, https://doi.org/10.1029/2022EF003068, 2023. a
David, C. H., Habets, F., Maidment, D. R., and Yang, Z.-L.: RAPID applied to the SIM-France model, Hydrol. Process., 25, 3412–3425, https://doi.org/10.1002/hyp.8070, 2011. a
Dayon, G., Boé, J., Martin, E., and Gailhard, J.: Impacts of climate change on the hydrological cycle over France and associated uncertainties, Comptes Rendus, Géoscience, 350, 141–153, https://doi.org/10.1016/j.crte.2018.03.001, 2018. a, b, c, d
Decharme, B., Delire, C., Minvielle, M., Colin, J., Vergnes, J.-P., Alias, A., Saint-Martin, D., Séférian, R., Sénési, S., and Voldoire, A.: Recent Changes in the ISBA-CTRIP Land Surface System for Use in the CNRM-CM6 Climate Model and in Global Off-Line Hydrological Applications, J. Adv. Model. Earth Syst., 11, 1207–1252, https://doi.org/10.1029/2018MS001545, 2019. a, b
Driouech, F., Déqué, M., and Sánchez-Gómez, E.: Weather regimes – Moroccan precipitation link in a regional climate change simulation, Glob. Planet. Change, 72, 1–10, https://doi.org/10.1016/j.gloplacha.2010.03.004, 2010. a
Evin, G., Hingray, B., Blanchet, J., Eckert, N., Morin, S., and Verfaillie, D.: Partitioning Uncertainty Components of an Incomplete Ensemble of Climate Projections Using Data Augmentation, J. Clim., 32, 2423–2440, https://doi.org/10.1175/JCLI-D-18-0606.1, 2019. a, b
Evin, G., Reverdy, A., and Hingray, B.: Ensemble de projections Explore2 : changements moyens et incertitudes associés, Rapport final, IGE, INRAE, CNRS, Grenoble, France, https://doi.org/10.57745/KCOWZO, 2024. a, b, c
Evin, G., Hingray, B., Thirel, G., Ducharne, A., Strohmenger, L., Corre, L., Tramblay, Y., Vidal, J.-P., Bonneau, J., Colleoni, F., Gailhard, J., Habets, F., Hendrickx, F., Héraut, L., Huang, P., Le Lay, M., Magand, C., Marson, P., Monteil, C., Munier, S., Reverdy, A., Soubeyroux, J.-M., Robin, Y., Vergnes, J.-P., Vrac, M., and Sauquet, E.: Uncertainty sources in a large ensemble of hydrological projections: Regional Climate Models and Internal Variability matter, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-2727, 2025. a, b
Fereday, D., Chadwick, R., Knight, J., and Scaife, A. A.: Atmospheric Dynamics is the Largest Source of Uncertainty in Future Winter European Rainfall, J. Clim., 31, 963–977, https://doi.org/10.1175/JCLI-D-17-0048.1, 2018. a
Garner, G., Van Loon, A. F., Prudhomme, C., and Hannah, D. M.: Hydroclimatology of extreme river flows, Freshwater Biol., 60, 2461–2476, https://doi.org/10.1111/fwb.12667, 2015. a
Goubanova, K. and Li, L.: Extremes in temperature and precipitation around the Mediterranean basin in an ensemble of future climate scenario simulations, Glob. Planet. Change, 57, 27–42, https://doi.org/10.1016/j.gloplacha.2006.11.012, 2007. a
Guillaumot, L., Smilovic, M., Burek, P., de Bruijn, J., Greve, P., Kahil, T., and Wada, Y.: Coupling a large-scale hydrological model (CWatM v1.1) with a high-resolution groundwater flow model (MODFLOW 6) to assess the impact of irrigation at regional scale, Geosci. Model Dev., 15, 7099–7120, https://doi.org/10.5194/gmd-15-7099-2022, 2022. a
Guillaumot, L., Munier, S., Abhervé, R., Vergnes, J.-P., Jeantet, A., Le Moigne, P., and Habets, F.: Are regional groundwater models suitable for simulating wetlands, rivers and intermittence? The example of the French AquiFR platform, J. Hydrol., 644, 132019, https://doi.org/10.1016/j.jhydrol.2024.132019, 2024. a, b, c
Habets, F., Boé, J., Déqué, M., Ducharne, A., Gascoin, S., Hachour, A., Martin, E., Pagé, C., Sauquet, E., Terray, L., Thiéry, D., Oudin, L., and Viennot, P.: Impact of climate change on the hydrogeology of two basins in northern France, Climatic Change, 121, 771–785, https://doi.org/10.1007/s10584-013-0934-x, 2013. a, b, c, d
Habets, F., Amraoui, N., Thiéry, D., Vergnes, J.-P., Morel, T., Le Moigne, P., Munier, S., Leroux, D., de Dreuzy, J.-R., Longuevergne, L., Ackerer, P., Besson, F., Etchevers, P., Rousset, F., Willemet, J.-M., Viennot, P., and Gallois, N.: Plateforme de modélisation hydrogéologique nationale AQUI-FR, Rapport final de 2ème phase, OFB, Paris, France, https://hal.science/hal-04006877 (last access: 13 October 2026), 2021. a, b, c, d, e, f, g
Hawkins, E. and Sutton, R.: The Potential to Narrow Uncertainty in Regional Climate Predictions, Bull. Am. Meteorol. Soc., 90, 1095–1108, https://doi.org/10.1175/2009BAMS2607.1, 2009. a
Hingray, B. and Saïd, M.: Partitioning Internal Variability and Model Uncertainty Components in a Multimember Multimodel Ensemble of Climate Projections, J. Clim., 27, 6779–6798, https://doi.org/10.1175/JCLI-D-13-00629.1, 2014. a
Hingray, B., Blanchet, J., Evin, G., and Vidal, J.-P.: Uncertainty component estimates in transient climate projections, Clim. Dynam., 53, 2501–2516, https://doi.org/10.1007/s00382-019-04635-1, 2019. a
Højberg, A. L., Troldborg, L., Stisen, S., Christensen, B. B. S., and Henriksen, H. J.: Stakeholder driven update and improvement of a national water resources model, Environ. Model. Softw., 40, 202–213, https://doi.org/10.1016/j.envsoft.2012.09.010, 2013. a
IPCC: Climate Change 2014 : Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Final Report, Cambridge University Pres, Cambridge, United Kingdom and New York, NY, USA, Intergovernmental Panel on Climate Change (IPCC), ISBN 978-1-107-64165-5, 2014. a, b, c
IPCC: Climate Change 2022 : Impacts, Adaptation and Vulnerability: Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Final Report, Cambridge University Pres, Cambridge, United Kingdom and New York, NY, USA, publisher: Intergovernmental Panel on Climate Change (IPCC), https://doi.org/10.1017/9781009325844, 2022. a, b, c
Jacob, D., Teichmann, C., Sobolowski, S., Katragkou, E., Anders, I., Belda, M., Benestad, R., Boberg, F., Buonomo, E., Cardoso, R. M., Casanueva, A., Christensen, O. B., Christensen, J. H., Coppola, E., De Cruz, L., Davin, E. L., Dobler, A., Domínguez, M., Fealy, R., Fernandez, J., Gaertner, M. A., García-Díez, M., Giorgi, F., Gobiet, A., Goergen, K., Gómez-Navarro, J. J., Alemán, J. J. G., Gutiérrez, C., Gutiérrez, J. M., Güttler, I., Haensler, A., Halenka, T., Jerez, S., Jiménez-Guerrero, P., Jones, R. G., Keuler, K., Kjellström, E., Knist, S., Kotlarski, S., Maraun, D., van Meijgaard, E., Mercogliano, P., Montávez, J. P., Navarra, A., Nikulin, G., de Noblet-Ducoudré, N., Panitz, H.-J., Pfeifer, S., Piazza, M., Pichelli, E., Pietikäinen, J.-P., Prein, A. F., Preuschmann, S., Rechid, D., Rockel, B., Romera, R., Sánchez, E., Sieck, K., Soares, P. M. M., Somot, S., Srnec, L., Sørland, S. L., Termonia, P., Truhetz, H., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community, Reg. Environ. Change, 20, 51, https://doi.org/10.1007/s10113-020-01606-9, 2020. a, b
Jaouen, T., Benoit, L., Héraut, L., and Sauquet, E.: Will rivers become more intermittent in France? Learning from an extended set of hydrological projections, Hydrol. Earth Syst. Sci., 29, 3629–3671, https://doi.org/10.5194/hess-29-3629-2025, 2025. a
Jeantet, A., Thirel, G., Lemaitre-Basset, T., and Tournebize, J.: Uncertainty propagation in a modelling chain of climate change impact for a representative French drainage site, Hydrol. Sci. J., 68, 1426–1442, https://doi.org/10.1080/02626667.2023.2203322, 2023. a
Kendall, M. G.: Rank Correlation Methods, London (UK), charles griffin and co. edn., 1948. a
Kennel, C. F., Briggs, S., and Victor, D. G.: Making climate science more relevant, Science, 354, 421–422, https://doi.org/10.1126/science.aag3248, 2016. a
Knutti, R., Masson, D., and Gettelman, A.: Climate model genealogy: Generation CMIP5 and how we got there, Geophys. Res. Lett., 40, 1194–1199, https://doi.org/10.1002/grl.50256, 2013. a, b
Kundzewicz, Z. W., Kanae, S., Seneviratne, S. I., Handmer, J., Nicholls, N., Peduzzi, P., Mechler, R., Bouwer, L. M., Arnell, N., Mach, K., Muir-Wood, R., Brakenridge, G. R., Kron, W., Benito, G., Honda, Y., Takahashi, K., and Sherstyukov, B.: Flood risk and climate change: global and regional perspectives, Hydrol. Sci. J., 59, 1–28, https://doi.org/10.1080/02626667.2013.857411, 2014. a
Lafaysse, M., Hingray, B., Mezghani, A., Gailhard, J., and Terray, L.: Internal variability and model uncertainty components in future hydrometeorological projections: The Alpine Durance basin, Water Resour. Res., 50, 3317–3341, https://doi.org/10.1002/2013WR014897, 2014. a
Lanini, S., Robelin, O., Caballero, Y., and Sauquet, E.: Projections hydrologiques souterraines : recharge potentielle des aquifères, Rapport final, BRGM, INRAE, Orléans, France, https://doi.org/10.57745/ZQPNIE, 2024. a, b
Le Moigne, P., Besson, F., Martin, E., Boé, J., Boone, A., Decharme, B., Etchevers, P., Faroux, S., Habets, F., Lafaysse, M., Leroux, D., and Rousset-Regimbeau, F.: The latest improvements with SURFEX v8.0 of the Safran–Isba–Modcou hydrometeorological model for France, Geosci. Model Dev., 13, 3925–3946, https://doi.org/10.5194/gmd-13-3925-2020, 2020. a, b, c, d, e
Ledoux, E., Girard, G., de Marsily, G., Villeneuve, J. P., and Deschenes, J.: Spatially Distributed Modeling: Conceptual Approach, Coupling Surface Water And Groundwater, in: Unsaturated Flow in Hydrologic Modeling: Theory and Practice, edited by: Morel-Seytoux, H. J., NATO ASI Series, 435–454, Springer Netherlands, Dordrecht, ISBN 978-94-009-2352-2, https://doi.org/10.1007/978-94-009-2352-2_16, 1989. a
Lemaitre-Basset, T., Collet, L., Thirel, G., Parajka, J., Evin, G., and Hingray, B.: Climate change impact and uncertainty analysis on hydrological extremes in a French Mediterranean catchment, Hydrol. Sci. J., 66, 888–903, https://doi.org/10.1080/02626667.2021.1895437, 2021. a, b
Liang, X.-Z., Kunkel, K. E., Meehl, G. A., Jones, R. G., and Wang, J. X. L.: Regional climate models downscaling analysis of general circulation models present climate biases propagation into future change projections, Geophys. Res. Lett., 35, https://doi.org/10.1029/2007GL032849, 2008. a
Mann, H. B.: Nonparametric Tests Against Trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187, 1945. a
Maréchal, J.-C. and Rouillard, J.: Groundwater in France: Resources, Use and Management Issues, in: Sustainable Groundwater Management: A Comparative Analysis of French and Australian Policies and Implications to Other Countries, edited by Rinaudo, J.-D., Holley, C., Barnett, S., and Montginoul, M., Global Issues in Water Policy, 17–45, Springer International Publishing, Cham, ISBN 978-3-030-32766-8, https://doi.org/10.1007/978-3-030-32766-8_2, 2020. a
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013. a, b
Maurer, E. P., Hidalgo, H. G., Das, T., Dettinger, M. D., and Cayan, D. R.: The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California, Hydrol. Earth Syst. Sci., 14, 1125–1138, https://doi.org/10.5194/hess-14-1125-2010, 2010. a
Maxwell, R. M., Condon, L. E., and Kollet, S. J.: A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3, Geosci. Model Dev., 8, 923–937, https://doi.org/10.5194/gmd-8-923-2015, 2015. a
McKee, T. B., Doesken, N. J., and Kleist, J.: The relationship of drought frequency and duration to time scales, Vol. 17, No. 22, 179–183, California, 8th Conference on Applied Climatology, 17–22 January 1993, Anaheim, California, 1993. a
Meaurio, M., Zabaleta, A., Boithias, L., Epelde, A. M., Sauvage, S., Sánchez-Pérez, J.-M., Srinivasan, R., and Antiguedad, I.: Assessing the hydrological response from an ensemble of CMIP5 climate projections in the transition zone of the Atlantic region (Bay of Biscay), J. Hydrol., 548, 46–62, https://doi.org/10.1016/j.jhydrol.2017.02.029, 2017. a
Meehl, G., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J., Stouffer, R., and Taylor, K.: The WCRP CMIP3 multimodel dataset: A new era in climatic change research, Bull. Am. Meteorol. Soc., 88, 1383–1394, https://doi.org/10.1175/BAMS-88-9-1383, 2007. a
Michelangeli, P.-A., Vrac, M., and Loukos, H.: Probabilistic downscaling approaches: Application to wind cumulative distribution functions, Geophys. Res. Lett., 36, https://doi.org/10.1029/2009GL038401, 2009. a, b
Munier, S. and Decharme, B.: River network and hydro-geomorphological parameters at 1/12-degree resolution for global hydrological and climate studies, Earth Syst. Sci. Data, 14, 2239–2258, https://doi.org/10.5194/essd-14-2239-2022, 2022. a
Navarro-Racines, C., Tarapues, J., Thornton, P., Jarvis, A., and Ramirez-Villegas, J.: High-resolution and bias-corrected CMIP5 projections for climate change impact assessments, Sci. Data, 7, 7, https://doi.org/10.1038/s41597-019-0343-8, 2020. a
Noilhan, J. and Planton, S.: A Simple Parameterization of Land Surface Processes for Meteorological Models, Mon. Weather Rev., 117, 536–549, https://doi.org/10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2, 1989. a
Northrop, P. J. and Chandler, R. E.: Quantifying Sources of Uncertainty in Projections of Future Climate, J. Clim., 27, 8793–8808, https://doi.org/10.1175/JCLI-D-14-00265.1, 2014. a
Nuissier, O., Joly, B., Joly, A., Ducrocq, V., and Arbogast, P.: A statistical downscaling to identify the large-scale circulation patterns associated with heavy precipitation events over southern France, Q. J. Roy. Meteorol. Soc., 137, 1812–1827, https://doi.org/10.1002/qj.866, 2011. a, b
Pasquier, J.-L.: Les prélèvements d’eau douce en France : les grands usages en 2013 et leur évolution depuis 20 ans, Rapport final, Ministère de l'environnement, de l'énergie et de la mer, en charge des relations internationales sur le climat, La Defense, France, 2017. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a
Philippe, E., Habets, F., Ledoux, E., Goblet, P., Viennot, P., and Mary, B.: Improvement of the solute transfer in a conceptual unsaturated zone scheme: a case study of the Seine River basin, Hydrol. Process., 25, 752–765, https://doi.org/10.1002/hyp.7865, 2011. a
Phillips, N. A.: The general circulation of the atmosphere: A numerical experiment, Q. J. Roy. Meteorol. Soc., 82, 123–164, https://doi.org/10.1002/qj.49708235202, 1956. a
Potter, N. J., Chiew, F. H. S., Charles, S. P., Fu, G., Zheng, H., and Zhang, L.: Bias in dynamically downscaled rainfall characteristics for hydroclimatic projections, Hydrol. Earth Syst. Sci., 24, 2963–2979, https://doi.org/10.5194/hess-24-2963-2020, 2020. a
Prudhomme, C., Giuntoli, I., Robinson, E. L., Clark, D. B., Arnell, N. W., Dankers, R., Fekete, B. M., Franssen, W., Gerten, D., Gosling, S. N., Hagemann, S., Hannah, D. M., Kim, H., Masaki, Y., Satoh, Y., Stacke, T., Wada, Y., and Wisser, D.: Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment, P. Natl. Acad. Sci. USA, 111, 3262–3267, https://doi.org/10.1073/pnas.1222473110, 2014. a
Rajib, M. A., Sultana, S., Saha, M., and Rahman, M. M.: A multi-model ensembling approach for developing plausible countryscale climate change scenarios for future, Journal of Earth Science and Climatic Change, 5, 1–12, https://doi.org/10.4172/2157-7617.1000179, 2014. a
Randall, D. A. (Ed.): General Circulation Model Development: Past, Present, and Future, Vol. 70, International Geophysics Series, Elsevier Science, San Diego CA, Academic press edn., ISBN 978-0-08-050723-1, 2000. a
Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N., and Rafaj, P.: RCP8.5 – A scenario of comparatively high greenhouse gas emissions, Climatic Change, 109, 33, https://doi.org/10.1007/s10584-011-0149-y, 2011. a
Robin, Y., Corre, L., Marson, P., Bernus, S., Vrac, M., and Thao, S.: Explore 2 : Anticiper les évolutions climlatiques et hydrologiques en France. Projections Climatiques Régionalisées : Correction de Biais et Changements Futurs, Rapport final version 1.2, INRAE, OiEau, IPSL, Météo France, Gif-sur-Yvette, France, https://doi.org/10.57745/99X4CD, 2023. a
Roehrig, R., Beau, I., Saint-Martin, D., Alias, A., Decharme, B., Guérémy, J.-F., Voldoire, A., Abdel-Lathif, A. Y., Bazile, E., Belamari, S., Blein, S., Bouniol, D., Bouteloup, Y., Cattiaux, J., Chauvin, F., Chevallier, M., Colin, J., Douville, H., Marquet, P., Michou, M., Nabat, P., Oudar, T., Peyrillé, P., Piriou, J.-M., Salas y Mélia, D., Séférian, R., and Sénési, S.: The CNRM Global Atmosphere Model ARPEGE-Climat 6.3: Description and Evaluation, J. Adv. Model. Earth Syst., 12, e2020MS002075, https://doi.org/10.1029/2020MS002075, 2020. a
Saleh, F., Ducharne, A., Flipo, N., Oudin, L., and Ledoux, E.: Impact of river bed morphology on discharge and water levels simulated by a 1D Saint–Venant hydraulic model at regional scale, J. Hydrol., 476, 169–177, https://doi.org/10.1016/j.jhydrol.2012.10.027, 2013. a
Sansom, P. G., Stephenson, D. B., Ferro, C. A. T., Zappa, G., and Shaffrey, L.: Simple Uncertainty Frameworks for Selecting Weighting Schemes and Interpreting Multimodel Ensemble Climate Change Experiments, J. Clim., 26, 4017–4037, https://doi.org/10.1175/JCLI-D-12-00462.1, 2013. a
Sauquet, E., Strohmenger, L., Thirel, G., and Le Lay, M.: Quelles évolutions des régimes hydrologiques en France métropolitaine ?, Rapport final, INRAE, EDF, Lyon, France, https://doi.org/10.57745/TLUTKF, 2024. a, b
Sauquet, E., Evin, G., Siauve, S., Aissat, R., Arnaud, P., Bérel, M., Bonneau, J., Branger, F., Caballero, Y., Colléoni, F., Ducharne, A., Gailhard, J., Habets, F., Hendrickx, F., Héraut, L., Hingray, B., Huang, P., Jaouen, T., Jeantet, A., Lanini, S., Le Lay, M., Magand, C., Mimeau, L., Monteil, C., Munier, S., Perrin, C., Robelin, O., Rousset, F., Soubeyroux, J.-M., Strohmenger, L., Thirel, G., Tocquer, F., Tramblay, Y., Vergnes, J.-P., and Vidal, J.-P.: A large transient multi-scenario multi-model ensemble of future streamflow and groundwater projections in France, Hydrol. Earth Syst. Sci., 30, 2277–2300, https://doi.org/10.5194/hess-30-2277-2026, 2026. a, b, c, d, e
Shepherd, T. G.: Storyline approach to the construction of regional climate change information, P. Roy. Soc. A, 475, 20190013, https://doi.org/10.1098/rspa.2019.0013, 2019. a, b
Shepherd, T. G., Boyd, E., Calel, R. A., Chapman, S. C., Dessai, S., Dima-West, I. M., Fowler, H. J., James, R., Maraun, D., Martius, O., Senior, C. A., Sobel, A. H., Stainforth, D. A., Tett, S. F. B., Trenberth, K. E., van den Hurk, B. J. J. M., Watkins, N. W., Wilby, R. L., and Zenghelis, D. A.: Storylines: an alternative approach to representing uncertainty in physical aspects of climate change, Climatic Change, 151, 555–571, https://doi.org/10.1007/s10584-018-2317-9, 2018. a, b, c
Sobaga, A., Habets, F., Beaudoin, N., Léonard, J., and Decharme, B.: Decreasing trend of groundwater recharge with limited impact of intense precipitation: Evidence from long-term lysimeter data, J. Hydrol., 637, 131340, https://doi.org/10.1016/j.jhydrol.2024.131340, 2024. a
Soubeyroux, J.-M., Bernus, S., Corre, L., Drouin, A., Dubuisson, B., Etchevers, P., Gouget, V., Josse, P., Kerdoncuff, M., Samacoits, R., and Tocquet, F.: Les nouvelles projections climatiques de référence DRIAS 2020 pour la métropole, Rapport technique, Météo-France, Toulouse, 2021. a
Tapiador, F. J., Navarro, A., Moreno, R., Sánchez, J. L., and García-Ortega, E.: Regional climate models: 30 years of dynamical downscaling, Atmos. Res., 235, 104785, https://doi.org/10.1016/j.atmosres.2019.104785, 2020. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the Experiment Design, Bull. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a
Thiéry, D.: Code de calcul MARTHE – Modélisation 3D des écoulements dans les hydrosystèmes – Notice d’utilisation de la version 7.5, Rapport finale BRGM/RP-64554-FR, BRGM, Orléans, France, 2015. a
Thomson, A. M., Calvin, K. V., Smith, S. J., Kyle, G. P., Volke, A., Patel, P., Delgado-Arias, S., Bond-Lamberty, B., Wise, M. A., Clarke, L. E., and Edmonds, J. A.: RCP4.5: a pathway for stabilization of radiative forcing by 2100, Climatic Change, 109, 77, https://doi.org/10.1007/s10584-011-0151-4, 2011. a
Tramblay, Y. and Somot, S.: Future evolution of extreme precipitation in the Mediterranean, Climatic Change, 151, 289–302, https://doi.org/10.1007/s10584-018-2300-5, 2018. a
Tramblay, Y., Sauquet, E., Arnaud, P., Rousset, F., Soubeyroux, J.-M., Jeantet, A., Munier, S., and Vergnes, J.-P.: Scénarios d'extrêmes hydrologiques, Rapport final, IRD, INRAE, Météo-France, CNRM, BRGM, Montpellier, France, https://doi.org/10.57745/2XDJ5H, 2024. a, b
Tramblay, Y., Thirel, G., Strohmenger, L., Evin, G., Corre, L., Heraut, L., and Sauquet, E.: Evolution of flood generating processes under climate change in France, Hydrol. Earth Syst. Sci., 29, 7023–7039, https://doi.org/10.5194/hess-29-7023-2025, 2025. a, b, c, d
Van Loon, A. F.: Hydrological drought explained, WIREs Water, 2, 359–392, https://doi.org/10.1002/wat2.1085, 2015. a
van Vuuren, D. P., den Elzen, M. G. J., Lucas, P. L., Eickhout, B., Strengers, B. J., van Ruijven, B., Wonink, S., and van Houdt, R.: Stabilizing greenhouse gas concentrations at low levels: an assessment of reduction strategies and costs, Climatic Change, 81, 119–159, https://doi.org/10.1007/s10584-006-9172-9, 2007. a
Verfaillie, D., Déqué, M., Morin, S., and Lafaysse, M.: The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models, Geosci. Model Dev., 10, 4257–4283, https://doi.org/10.5194/gmd-10-4257-2017, 2017. a, b, c
Vergnes, J.-P., Roux, N., Habets, F., Ackerer, P., Amraoui, N., Besson, F., Caballero, Y., Courtois, Q., de Dreuzy, J.-R., Etchevers, P., Gallois, N., Leroux, D. J., Longuevergne, L., Le Moigne, P., Morel, T., Munier, S., Regimbeau, F., Thiéry, D., and Viennot, P.: The AquiFR hydrometeorological modelling platform as a tool for improving groundwater resource monitoring over France: evaluation over a 60-year period, Hydrol. Earth Syst. Sci., 24, 633–654, https://doi.org/10.5194/hess-24-633-2020, 2020. a, b, c, d, e
Vetter, T., Huang, S., Aich, V., Yang, T., Wang, X., Krysanova, V., and Hattermann, F.: Multi-model climate impact assessment and intercomparison for three large-scale river basins on three continents, Earth Syst. Dynam., 6, 17–43, https://doi.org/10.5194/esd-6-17-2015, 2015. a
Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M., and Soubeyroux, J.-M.: A 50-year high-resolution atmospheric reanalysis over France with the Safran system, Int. J. Climatol., 30, 1627–1644, https://doi.org/10.1002/joc.2003, 2010. a, b
Vidal, J.-P., Hingray, B., Magand, C., Sauquet, E., and Ducharne, A.: Hierarchy of climate and hydrological uncertainties in transient low-flow projections, Hydrol. Earth Syst. Sci., 20, 3651–3672, https://doi.org/10.5194/hess-20-3651-2016, 2016. a
Short summary
The AquiFR hydrogeological platform, forced by 36 climate projections, simulates future groundwater levels across France. Results reveal significant variability between climate and representative concentration pathways used as socio-economic scenarios, with a dominant signal of rising groundwater levels over most of the study area. Four storylines, representing worst-case scenarios, are selected to inform decision-makers and support the adoption of sustainable groundwater management policies.
The AquiFR hydrogeological platform, forced by 36 climate projections, simulates future...
