K. C. Abbaspour, R. Schulin, M. Van-genuchten, and . Th, Estimating unsaturated soil hydraulic parameters using ant colony optimization, Advances in Water Resources, vol.24, issue.8, pp.827-841, 2001.
DOI : 10.1016/S0309-1708(01)00018-5

E. A. Anderson, National Weather Service River Forecast System -Snow Accumulation and Ablation Model, 1973.

E. A. Anderson, Calibration of Conceptual Hydrologic Models for Use in River Forecasting, 2002.

T. H. Andres, Sampling methods and sensitivity analysis for large parameter sets, Journal of Statistical Computation and Simulation, vol.57, issue.1-4, pp.77-110, 1997.
DOI : 10.2307/1269548

T. H. Andres and C. H. Wayne, Using Iterated Fractional Factorial Design to Screen Parameters in Sensitivity analysis of a Probabilistic Risk Assessment Model, Proceedings of the Joint International Conference on Mathematical Methods and Supercomputing in Nuclear Applications, pp.328-337, 1993.

G. E. Archer, A. Saltelli, and I. M. Sobol-', Sensitivity measures,anova-like Techniques and the use of bootstrap, Journal of Statistical Computation and Simulation, vol.2, issue.2, pp.99-120, 1997.
DOI : 10.1142/S0129183195000204

K. Beven, Uniqueness of place and process representations in hydrological modeling, Hydrol. Earth Syst. Sci, vol.4203, issue.4, pp.203-214, 2000.

K. Beven and J. Freer, Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, Journal of Hydrology, vol.249, issue.1-4, pp.11-29, 2001.
DOI : 10.1016/S0022-1694(01)00421-8

G. E. Box, W. G. Hunter, and J. S. Hunter, Statistics for Experiments: An Introduction to Design, Data Analysis, and Modeling Building, 1978.

D. Boyle, H. Gupta, and S. Sorooshian, Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods, Water Resources Research, vol.11, issue.9, pp.3663-3674, 2000.
DOI : 10.1029/2000WR900207

P. Bratley and B. L. Fox, ALGORITHM 659: implementing Sobol's quasirandom sequence generator, ACM Transactions on Mathematical Software, vol.14, issue.1, pp.88-100, 1988.
DOI : 10.1145/42288.214372

R. J. Burnash, The NWS river forecast system-Catchment model, in: Computer Models of Watershed Hydrology, Water Resour. Publ., Highlands Ranch, CO, 1995.

K. Christiaens and J. Feyen, Use of sensitivity and uncertainty measures in distributed hydrological modeling with an application to the MIKE SHE model, Water Resources Research, vol.11, issue.5, pp.10-1029, 1169.
DOI : 10.1029/2001WR000478

E. Demaria, B. Nijssen, and T. Wagener, Monte Carlo sensitivity analysis of land surface parameters using the Variable Infiltration Capacity model, Journal of Geophysical Research, vol.18, issue.2, 2007.
DOI : 10.1029/2006JD007534

J. Doherty, Ground Water Model Calibration Using Pilot Points and Regularization, Ground Water, vol.32, issue.1, pp.170-177, 2003.
DOI : 10.1029/94WR02258

J. Doherty, PEST-Model Independent Parameter Estimation User Manual: 5th Edition, Watermark Numerical Computing, 2004.

J. Doherty and J. M. Johnston, METHODOLOGIES FOR CALIBRATION AND PREDICTIVE ANALYSIS OF A WATERSHED MODEL, Journal of the American Water Resources Association, vol.27, issue.9, pp.251-265, 2003.
DOI : 10.1016/S0022-1694(97)00107-8

Q. Duan, V. K. Gupta, and S. Sorooshian, Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resources Research, vol.27, issue.9, pp.1015-1031, 1992.
DOI : 10.1029/91WR02985

C. J. Duffy, A Two-State Integral-Balance Model for Soil Moisture and Groundwater Dynamics in Complex Terrain, Water Resources Research, vol.99, issue.B7, pp.2421-2434, 1996.
DOI : 10.1029/96WR01049

C. J. Duffy, Semi-discrete dynamical model for mountain-front recharge and water balance estimation, Rio Grande of southern Colorado and New Mexico, Water Science and Application Monograph, vol.37, issue.2, pp.236-255, 2004.
DOI : 10.1029/009WSA14

B. Efron and R. Tibshirani, An introduction to the bootstrap, 1993.
DOI : 10.1007/978-1-4899-4541-9

J. Fieberg and K. J. Jenkins, Assessing uncertainty in ecological systems using global sensitivity analyses: a case example of simulated wolf reintroduction effects on elk, Ecological Modelling, vol.187, issue.2-3, pp.259-280, 2005.
DOI : 10.1016/j.ecolmodel.2005.01.042

J. Freer, K. J. Beven, and B. Ambroise, Bayesian Estimation of Uncertainty in Runoff Prediction and the Value of Data: An Application of the GLUE Approach, Water Resources Research, vol.18, issue.11, pp.2161-2173, 1996.
DOI : 10.1029/95WR03723

H. Frey and S. Patil, Identification and Review of Sensitivity Analysis Methods, Risk Analysis, vol.58, issue.3, pp.553-578, 2002.
DOI : 10.1111/0272-4332.00039

J. Hall, S. Tarantola, P. D. Bates, and M. S. Horritt, Distributed sensitivity analysis of flood inumdation model calibration, J. Hydraulic Eng, vol.131, issue.117, pp.733-94292, 2005.

D. Hamby, A review of techniques for sensitivity analysis of environmental models, Environmental Modelling and Assessment, pp.135-154, 1994.

J. Helton and F. Davis, Illustration of Sampling-Based Methods for Uncertainty and Sensitivity Analysis, Risk Analysis, vol.48, issue.2, pp.622-691, 2002.
DOI : 10.1111/0272-4332.00041

J. Helton and F. Davis, Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems, Reliability Engineering & System Safety, vol.81, issue.1, pp.23-69, 2003.
DOI : 10.1016/S0951-8320(03)00058-9

A. Henderson-sellers, Z. Yang, and R. Dickinson, The Project for Intercomparison of Land-surface Parameterization Schemes, Bulletin of the American Meteorological Society, vol.74, issue.7, pp.1335-1349, 1993.
DOI : 10.1175/1520-0477(1993)074<1335:TPFIOL>2.0.CO;2

G. Hornberger and R. Spear, An approach to the preliminary analysis of environmental systems, J. Environ. Manage, vol.12, pp.7-18, 1981.

V. Koren, S. , R. Smith, M. Zhang, Z. Seo et al., Hydrology laboratory research modeling system (HL-RMS) of the US national weather service, Journal of Hydrology, vol.291, issue.3-4, pp.297-318, 2004.
DOI : 10.1016/j.jhydrol.2003.12.039

N. Kottegoda and R. Rosso, Statistics, Probability and Reliability for Civil and Environmental Engineering, 1997.

W. B. Langbein, Overview of Conference on Hydrologic Data Netwww .hydrol-earth-syst-sci, Hydrol. Earth Syst. Sci, vol.793, issue.11, pp.793-817, 2007.

Y. Tang, lumped model sensitivity analysis works, Water Resour. Res, vol.15, pp.1867-1871, 1979.

B. Lence and A. Takyi, Data requirements for seasonal discharge programs: An application of a regionalized sensitivity analysis, Water Resources Research, vol.19, issue.5, pp.1781-1789, 1992.
DOI : 10.1029/92WR00763

K. Levenberg, A method for the solution of certain non-linear problems in least squares, Quarterly of Applied Mathematics, vol.2, issue.2, pp.164-168, 1944.
DOI : 10.1090/qam/10666

X. Liang and J. Guo, Intercomparison of land-surface parameterization schemes: sensitivity of surface energy and water fluxes to model parameters, Journal of Hydrology, vol.279, issue.1-4, pp.182-209, 2003.
DOI : 10.1016/S0022-1694(03)00168-9

D. W. Marquardt, An Algorithm for Least-Squares Estimation of Nonlinear Parameters, Journal of the Society for Industrial and Applied Mathematics, vol.11, issue.2, pp.431-441, 1963.
DOI : 10.1137/0111030

M. Mckay, R. Beckman, and W. Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, vol.21, pp.239-245, 1979.

F. Misirli, H. V. Gupta, S. Sorooshian, and M. Thiemann, Bayesian recursive estimation of parameter and output uncertainty for watershed models, pp.113-124, 2003.
DOI : 10.1029/WS006p0113

A. Mokhtari and H. C. Frey, Sensitivity Analysis of a Two-Dimensional Probabilistic Risk Assessment Model Using Analysis of Variance, Risk Analysis, vol.72, issue.3, pp.1511-1529, 2005.
DOI : 10.1111/j.1539-6924.2005.00679.x

C. Moore and J. Doherty, Role of the calibration process in reducing model predictive error, Water Resources Research, vol.36, issue.8, pp.10-1029, 2005.
DOI : 10.1029/2004WR003501

F. Moreda, V. Koren, Z. Zhang, S. Reed, and M. Smith, Parameterization of distributed hydrological models: learning from the experiences of lumped modeling, Journal of Hydrology, vol.320, issue.1-2, pp.218-237, 2006.
DOI : 10.1016/j.jhydrol.2005.07.014

M. E. Moss, Space, time, and the third dimension (model error), Water Resources Research, vol.15, issue.6, pp.1797-1800, 1979.
DOI : 10.1029/WR015i006p01797

M. Muleta and J. W. Nicklow, Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model, Journal of Hydrology, vol.306, issue.1-4, pp.1-4, 2005.
DOI : 10.1016/j.jhydrol.2004.09.005

J. Neter, M. Kutner, C. Nachtsheim, and W. Wasserman, Applied Linear Statitical Models, 1996.

J. Oakley, O. Hagan, and A. , Probabilistic sensitivity analysis of complex models: a Bayesian approach, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.34, issue.3, pp.751-769, 2004.
DOI : 10.1214/ss/1009213004

O. Osidele and M. B. Beck, Identification of model structure for aquatic ecosystems using regionalized sensitivity analysis, Water Sci. Technol, vol.43, pp.271-278, 2001.

S. Panday and P. Huyakorn, A fully coupled physically-based spatially-distributed model for evaluating surface/subsurface flow, Advances in Water Resources, vol.27, issue.4, pp.361-382, 2004.
DOI : 10.1016/j.advwatres.2004.02.016

F. Pappenberger, I. Iorgulescu, and K. J. Beven, Sensitivity analysis based on regional splits and regression trees (SARS-RT), Environmental Modelling and Software, pp.976-990, 2005.

F. Pappenberger, I. Iorgulescu, and K. Beven, Sensitivity analysis based on regional splits and regression trees (SARS-RT), Environmental Modelling & Software, pp.976-990, 2006.

S. Patil and H. Frey, Comparison of sensitivity analysis methods based upon applications to a food safety risk model, Risk Analysis, vol.23, pp.135-154, 2004.

E. L. Peck, Catchment modeling and initial parameter estimation for the Natioanl Weather Service river forecast system, 1976.

M. Ratto, P. C. Young, R. Romanowicz2, F. Pappenberge, A. Saltelli1 et al., Uncertainty, sensitivity analysis and the role of data based mechanistic modeling in hydrology, Hydrol. Earth Syst. Sci, vol.33099, pp.3099-3146, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00298778

S. Reed, V. Koren, M. Smith, Z. Zhang, F. Moreda et al., Overall distributed model intercomparison project results, Journal of Hydrology, vol.298, issue.1-4, pp.27-60, 2004.
DOI : 10.1016/j.jhydrol.2004.03.031

A. Saltelli, Making best use of model evaluations to compute sensitivity indices, Computer Physics Communications, vol.145, issue.2, pp.280-297, 2002.
DOI : 10.1016/S0010-4655(02)00280-1

A. Saltelli, T. H. Andres, and T. Homma, Sensitivity analysis of model output. Performance of the iterated fractional factorial design method, Computational Statistics & Data Analysis, vol.20, issue.4, pp.387-407, 1995.
DOI : 10.1016/0167-9473(95)92843-M

A. Saltelli, S. Tarantola, C. , and K. P. , A quantitative modelindependent method for global sensitivity analysis of model output, Technometrics, pp.41-80, 1999.

A. Saltelli, S. Tarantola, C. , and F. , Sensitivity Analysis as an Ingredient of Modeling, Statistical Science, vol.15, pp.377-395, 2000.

A. Saltelli, A. Tarantola, F. Campolongo, and M. Ratto, Sensitivity Analysis in Practice-A Guide to Assessing Scientific Models, 2004.

A. Sieber and S. Uhlenbrook, Sensitivity analyses of a distributed catchment model to verify the model structure, Journal of Hydrology, vol.310, issue.1-4, pp.216-235, 2005.
DOI : 10.1016/j.jhydrol.2005.01.004

V. Singh and D. Woolhiser, Mathematical Modeling of Watershed Hydrology, Journal of Hydrologic Engineering, vol.7, issue.4, pp.270-292, 2002.
DOI : 10.1061/(ASCE)1084-0699(2002)7:4(270)

M. Smith, D. Seo, V. Koren, S. Reed, Z. Zhang et al., The distributed model intercomparison project (DMIP): motivation and experiment design, Journal of Hydrology, vol.298, issue.1-4, pp.4-26, 2004.
DOI : 10.1016/j.jhydrol.2004.03.040

I. Sobol-', Sensitivity estimates for nonlinear mathematical models, Math Model Comput. Exp, vol.1, pp.407-417, 1993.

I. Sobol-', Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Mathematics and Computers in Simulation, vol.55, issue.1-3, pp.271-280, 2001.
DOI : 10.1016/S0378-4754(00)00270-6

I. M. Sobol-', On the distribution of points in a cube and the approximate evaluation of integrals, USSR Computational Mathematics and Mathematical Physics, vol.7, issue.4, pp.86-112, 1967.
DOI : 10.1016/0041-5553(67)90144-9

I. M. Sobol-', A primer for the Monte Carlo method, 1994.

R. Spear, T. M. Grieb, and N. Shang, Parameter uncertainty and interaction in complex environmental models, Water Resources Research, vol.9, issue.2, pp.3159-3169, 1994.
DOI : 10.1029/94WR01732

Y. Tang, P. Reed, and J. Kollat, Parallelization strategies for rapid and robust evolutionary multiobjective optimization in water resources applications, Advances in Water Resources, vol.30, issue.3, pp.335-353, 2007.
DOI : 10.1016/j.advwatres.2006.06.006

Y. Tang, P. Reed, and T. Wagener, How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?, Hydrology and Earth System Sciences, vol.10, issue.2, pp.289-307, 2006.
DOI : 10.5194/hess-10-289-2006

URL : https://hal.archives-ouvertes.fr/hal-00298787

M. J. Tonkin and J. Doherty, A hybrid regularized inversion methodology for highly parameterized environmental models, Water Resources Research, vol.29, issue.6, pp.10-1029, 2005.
DOI : 10.1029/2005WR003995

V. Vandeberghe, W. Bauwens, and P. Vanrolleghem, Evaluation of uncertainty propagation into river water quality predictions to guide future monitoring campaigns, Environmental Modelling & Software, pp.725-732, 2007.

J. Vrugt, H. V. Gupta, L. A. Bastidas, W. Bouten, and S. Sorooshian, Effective and efficient algorithm for multiobjective optimization of hydrologic models, Water Resources Research, vol.3, issue.4, pp.10-1029, 1214.
DOI : 10.1029/2002WR001746

T. Wagener and J. Kollat, Numerical and visual evaluation of hydrological and environmental models using the Monte Carlo analysis toolbox, Environmental Modeling and Software, in press, 2007.

T. Wagener, D. Boyle, M. J. Lees, . Wheater, H. S. Gupta et al., A framework for development and application of hydrological models, Hydrology and Earth System Sciences, vol.5, issue.1, pp.13-26, 2001.
DOI : 10.5194/hess-5-13-2001

URL : https://hal.archives-ouvertes.fr/hal-00304565

T. Wagener, N. Mcintyre, M. J. Lees, H. S. Wheater, and H. V. Gupta, Towards reduced uncertainty in conceptual rainfall-runoff modelling: dynamic identifiability analysis, Hydrological Processes, vol.23, issue.2, pp.455-476, 2003.
DOI : 10.1002/hyp.1135

T. Wagener, H. S. Wheater, and H. V. Gupta, Rainfall-runoff modeling in gauged and ungauged catchments, 2004.

T. Wagener, Y. Liu, H. Gupta, E. Springer, D. Brookshire et al., Multi-resolution integrated assessment modeling for water resources management in arid and semi-arid regions, in: Regional hydrologic impacts of climate change -Impact assessment and decision making, pp.265-272, 2005.

P. C. Young, A general theory of modelling for badly defined dynamic systems, in: Modeling, Identification and Control in Environmental Systems, pp.103-135, 1978.