. Ducharne, All hydro-meteorological data were provided by two research programs that addressed the 708 potential impacts of climate change: the RexHySS project, Ducharne et 709 al. 2011) on the Seine and the Somme basins and the ICC-Hydroqual, p.711, 2009.

F. Anctil, C. Perrin, and V. Andréassian, Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models, Environmental Modelling & Software, vol.19, issue.4, pp.357-368, 2004.
DOI : 10.1016/S1364-8152(03)00135-X

N. W. Arnell, The effect of climate change on hydrological regimes in Europe: a continental perspective, Global Environmental Change, vol.9, issue.1, pp.5-23, 1999.
DOI : 10.1016/S0959-3780(98)00015-6

N. W. Arnell, Climate change and global water resources, Global Environmental Change, vol.9, issue.999, pp.31-49, 1999.
DOI : 10.1016/S0959-3780(99)00017-5

N. W. Arnell, Uncertainty in the relationship between climate forcing and hydrological response in UK catchments, Hydrology and Earth System Sciences, vol.15, issue.3, pp.897-912, 2011.
DOI : 10.5194/hess-15-897-2011

S. Bastola, C. Murphy, and J. Sweeney, The role of hydrological modelling uncertainties 732 in climate change impact assessments of Irish river catchments, Advances in Water 733 Resources, 2011.

K. J. Beven and M. J. Kirkby, A physically based, variable contributing area model of basin hydrology / Un mod??le ?? base physique de zone d'appel variable de l'hydrologie du bassin versant, Hydrological Sciences Bulletin, vol.4, issue.1, pp.43-69, 1080.
DOI : 10.1016/0022-1694(65)90084-3

K. Beven, Prophecy, reality and uncertainty in distributed hydrological modelling, Advances in Water Resources, vol.16, issue.1, pp.41-51, 1993.
DOI : 10.1016/0309-1708(93)90028-E

J. Boé, L. Terray, F. Habets, and E. Martin, A simple statistical?dynamical downscaling 740 scheme based on weather types and conditional resampling, D23106. doi, p.742, 1029.

M. J. Booij, Impact of climate change on river flooding assessed with different spatial 743 model resolutions, Journal of Hydrology, vol.303, 2005.

J. Chen, F. P. Brissette, A. Poulin, and R. Leconte, Overall uncertainty study of the 746 hydrological impacts of climate change for a Canadian watershed, Water Resources. 747 Research, vol.47, p.748, 1029.

L. Coron, V. Andréassian, C. Perrin, J. Lerat, J. Vaze et al., Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments, Water Resources Research, vol.181, issue.1-4, pp.751-761, 2011011721.
DOI : 10.1029/2011WR011721
URL : https://hal.archives-ouvertes.fr/hal-00763573

?. Donnelly, L. M. Makowecki, and R. D. Moore, Hierarchical testing of three rainfall???runoff models in small forested catchments, Journal of Hydrology, vol.219, issue.3-4, pp.136-152, 1999.
DOI : 10.1016/S0022-1694(99)00056-6

M. Franchini, J. Wendling, C. Obled, and E. Todini, Physical interpretation and sensitivity analysis of the TOPMODEL, Journal of Hydrology, vol.175, issue.1-4, pp.293-338, 1996.
DOI : 10.1016/S0022-1694(96)80015-1

T. Y. Gan and S. J. Burges, An assessment of a conceptual rainfall?runoff model " s ability 769 to represent the dynamics of small hypothetical catchments, 2: hydrologic responses 770 for normal and extreme rainfall, Water Resources Research, vol.26, pp.1605-1619, 1990.

E. Nilson, R. Lammersen, C. Perrin, D. Volken, and W. G. , Assessment of Climate 773 Change Impacts on Discharge in the Rhine River Basin: Results of the 774 RheinBlick2050 Project, CHR report, I?23 Climate change 2007: the physical science basis, pp.978-90, 2007.

A. J. Jakeman, T. H. Chen, D. A. Post, G. M. Hornberger, I. G. Littlewood et al., Assessing uncertainties in hydrological response to climate at large scale, pp.37-47, 1993.

T. Jiang, Y. D. Chen, C. Xu, X. Chen, X. Chen et al., Comparison of hydrological impacts of climate change simulated by six hydrological models in the Dongjiang Basin, South China, Journal of Hydrology, vol.336, issue.3-4, pp.316-333, 2007.
DOI : 10.1016/j.jhydrol.2007.01.010

V. Kleme?, Operational testing of hydrological simulation models. Hydrological 786, Sciences Journal, vol.31, issue.1, 1986.

L. Lay, M. Galle, S. Saulnier, G. M. Braud, and I. , Exploring the relationship between 788 hydroclimatic stationarity and rainfall?runoff model parameter stability: A case study 789 in West Africa, Water Resources Research, vol.43, pp.790-800, 2007.

R. Ludwig, I. May, R. Turcotte, L. Vescovi, M. Braun et al., The role of hydrological model complexity and uncertainty in climate change impact assessment, Advances in Geosciences, vol.21, pp.63-71, 2009.
DOI : 10.5194/adgeo-21-63-2009

J. Luo, E. Wang, S. Shen, H. Zheng, and H. Zhang, Effects of conditional parameterization 796 on performance of rainfall-runoff model regarding hydrologic non-stationarity. 797 Hydrological Processes, 2011.

L. S. Matott, J. E. Babendreier, and S. T. Purucker, Evaluating uncertainty in integrated 799 environmental models: A review of concepts and tools, Water Resources. Research, vol.800, p.45, 0801.

R. Merz, J. Parajka, and G. Blöschl, Time stability of catchment model parameters: Implications for climate impact analyses, Water Resources Research, vol.45, issue.10, p.804, 2011.
DOI : 10.1029/2010WR009505

C. Michel, C. Perrin, and V. Andreassian, The exponential store: a correct formulation for rainfall???runoff modelling, Hydrological Sciences Journal, vol.4, issue.1, 2003.
DOI : 10.2307/2401739

J. E. Nash and J. V. Sutcliffe, River flow forecasting through conceptual models. Part I?A 810 discussion of principles, Journal of Hydrology, vol.1070, issue.3, pp.282-290, 1970.

H. Niel, J. E. Paturel, and E. Servat, Study of parameter stability of a lumped hydrologic model in a context of climatic variability, Journal of Hydrology, vol.278, issue.1-4, pp.213-230, 2003.
DOI : 10.1016/S0022-1694(03)00158-6

L. Oudin, C. Perrin, T. Mathevet, V. Andréassian, and C. Michel, Impact of biased and 816 randomly corrupted inputs on the efficiency and the parameters of watershed models, Journal of Hydrology, vol.817, issue.320, pp.62-83, 2006.

L. Oudin, V. Andréassian, T. Mathevet, C. Perrin, and C. Michel, Dynamic averaging of 819 rainfall-runoff model simulations from complementary model parametrizations, p.821, 1029.

H. L. Penman, Natural Evaporation from Open Water, Bare Soil and Grass, 822 Proceedings of the Royal Society of London. Series A, Mathematical and Physical 823 Sciences, pp.120-145, 1948.
DOI : 10.1098/rspa.1948.0037

C. Perrin, C. Michel, and V. Andréassian, Improvement of a parsimonious model for 825 streamflow simulation, Journal of Hydrology, vol.27914, issue.03, pp.275-289, 2003.

C. Perrin, L. Oudin, V. Andreassian, ?. Rojas, C. Serna et al., 828 Impact of limited streamflow data on the efficiency and the parameters of rainfall- 829 runoff models, Hydrological Sciences Journal, vol.52, issue.1, 2007.

A. Poulin, F. Brissette, R. Leconte, R. Arsenault, and J. Malo, Uncertainty of 831 hydrological modelling in climate change impact studies in a Canadian, snow- 832 dominated river basin, Journal of Hydrology, vol.409, 2011.

C. Prudhomme and H. Davies, Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate, Climatic Change, vol.320, issue.1-2, pp.197-836, 2009.
DOI : 10.1007/s10584-008-9461-6

?. Quintana, P. Segui, P. Le-moigne, Y. Durand, E. Martin et al., Analysis of near?surface atmospheric 839 variables: Validation of the SAFRAN analysis over France, Journal of Applied 840 Meteorology and Climatology, vol.471, pp.92-107, 2008.

J. C. Refsgaard and J. Knudsen, Operational Validation and Intercomparison of Different Types of Hydrological Models, Water Resources Research, vol.26, issue.10, pp.2189-2202, 1996.
DOI : 10.1029/96WR00896

J. C. Refsgaard, J. P. Van-der-sluijs, J. Brown, and P. Van-der-keur, A framework for 845 dealing with uncertainty due to model structure error, Advances in Water Resources, vol.846, issue.29, 2006.

E. Rosero, Z. L. Yang, T. Wagener, L. Gulden, S. Yatheendradas et al., 848 Quantifying parameter sensitivity, interaction, and transferability in hydrologically 849 enhanced versions of the Noah land surface model over transition zones during the 850 warm season, Journal of Geophysical Research, vol.115, pp.10-1029, 2010.

J. Seibert, Reliability of model predictions outside calibration conditions, Nordic 852 Hydrology, pp.477-492, 2003.

G. Schoups and J. A. Vrugt, A formal likelihood function for parameter and predictive 854 inference of hydrologic models with correlated, heteroscedastic, and non-Gaussian 855 errors, Water Resources Research, vol.46, pp.201010-1029, 2010.

G. Seiller, F. Anctil, and C. Perrin, Multimodel evaluation of twenty lumped 857 hydrological models under contrasted climate conditions, Hydrology and Earth 858 System Sciences, pp.1171-1189, 2012.

R. Singh, T. Wagener, K. Van-werkhoven, M. E. Mann, and R. Crane, A trading-space-for- 860 time approach to probabilistic continuous streamflow predictions in a changing 861 climate ? accounting for changing watershed behavior, Hydrology and Earth System 862 Sciences, pp.3591-3603, 2011.

J. Teng, J. Vaze, F. H. Chiew, B. Wang, and J. M. Perraud, Estimating the Relative Uncertainties Sourced from GCMs and Hydrological Models in Modeling Climate Change Impact on Runoff, Journal of Hydrometeorology, vol.13, issue.1, pp.122-139, 2012.
DOI : 10.1175/JHM-D-11-058.1

J. Vaze, D. A. Post, F. H. Chiew, J. M. Perraud, N. R. Viney et al., Climate non? 868 stationarity ? Validity of calibrated rainfall?runoff models for use in climate change 869 studies, Journal of Hydrology, vol.394, pp.3-4, 2010.

J. Vaze, A. Davidson, J. Teng, and G. Podger, Impact of Climate Change on Water 871 Availability in the Macquarie-Castlereagh River Basin in Australia, Hydrological, vol.872, issue.2516, pp.2597-2612, 2011.

J. Vaze and J. Teng, Future Climate and Runoff Projections Across New South Wales, 2011.
DOI : 10.1002/hyp.7812

. Australia, Results and Practical Applications, Hydrological Processes, vol.25, issue.1, pp.18-35

J. Vidal, E. Martin, L. Franchistéguy, M. Baillon, and J. Soubeyroux, A 50- 877 year high-resolution atmospheric reanalysis over France with the Safran system, International Journal of Climatology, vol.878, issue.30, pp.1627-1644, 2003.

N. J. Vos, . De, T. H. Rientjes, and H. V. Gupta, Diagnostic evaluation of conceptual rainfall-runoff models using temporal clustering, Hydrological Processes, vol.204, issue.1, pp.2840-2850, 2010.
DOI : 10.1002/hyp.7698

J. A. Vrugt, T. Braak, C. Diks, C. Robinson, B. Hyman et al., Accelerating 883 Markov chain Monte Carlo simulation by differential evolution with self-adaptive 884 randomized subspace sampling, International Journal of Nonlinear Sciences and 885 Numerical Simulation, vol.10, issue.3, pp.273-290, 2009.

R. L. Wilby and I. Harris, A framework for assessing uncertainties in climate change 887 impacts: Low?flow scenarios for the River Thames, W02419. doi, p.42, 1029.

R. L. Wilby, Uncertainty in water resource model parameters used for climate change 890 impact assessment, Hydrological Processes, vol.19, issue.16, 2005.

P. O. Yapo, H. V. Gupta, and S. Sorooshian, Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data, Journal of Hydrology, vol.181, issue.1-4, pp.23-48, 1996.
DOI : 10.1016/0022-1694(95)02918-4

. Fig, Comparison of Aridity Index (AI) values for the different calibration and validation 909 sub-periods considered and for the three time slices, ) for the 89 catchments

. Fig, Distributions of the NSEsq values obtained by the two models illustrating (i) 921 calibration performance over the dry validation sub-periods (black boxplots) and (ii) 922 validation performance over the dry validation sub-periods using the other four calibration 923 sub-periods considered (wet, mean, dry, and whole record without the dry validation sub- 924 period illustrated, respectively, with blue, green, red and white boxplots) Results are shown 925 for GR4J (left) and TOPMO (right) The boxplots show the 0

. Fig, Comparison of the simulations of three streamflow characteristics (from top to bottom: 941 Q95, QMA and Q05) obtained on the present time slice (PT) and future time slices (MC and 942 EC) under projected climate conditions with the two hydrological models (left: GR4J; right: 943 TOPMO). The range bars represent, for each catchment, the range of estimated values with 944 the four optimal parameter sets corresponding to the four calibration periods

. Fig, Proportions of catchments showing (or not) hydrological trends between present (PT) 948 and future (MC and EC) time slices considering different calibration sub-periods for the two 949 hydrological models: white highlights a clear decrease

. Fig, Distribution of NSEsq values obtained by the two models illustrating (i) calibration 954 performance of the optimal parameter sets over the dry-validation subperiods (black 955 boxplots) and (ii) validation performance over the dry validation sub-periods using optimal 956 (white " " OPT " " boxplots) and posterior (grey " " POS " " boxplots) parameter sets identified on 957 the whole record periods without the dry validation sub-periods. Results are shown for GR4J 958 (left) and TOPMO (right) The boxplots show the 0

. Fig, Comparison of the simulations of three streamflow characteristics (from top to 974 bottom: Q95, QMA and Q05) obtained on the present time slice (PT) and future time slices 975 (MC and EC) under projected climate conditions with the two hydrological models (left: 976 GR4J; right: TOPMO). For each catchment, the range bars represent the range of