D. P. Ames and J. S. Horsburgh, HydroDesktop: Web services-based software for hydrologic data discovery, download, visualization, and analysis, Environmental Modelling & Software, vol.37, pp.146-156, 2012.
DOI : 10.1016/j.envsoft.2012.03.013

M. J. Asher and B. F. Croke, A review of surrogate models and their application to groundwater modeling, Water Resources Research, vol.63, issue.1, pp.5957-5973, 2015.
DOI : 10.1016/j.advwatres.2013.10.014

P. Baldi and P. Sadowski, Searching for exotic particles in high-energy physics with deep learning, Nature Communications, vol.ACAT, p.4308, 2014.
DOI : 10.1103/PhysRevLett.102.152001

Y. Bengio and O. Delalleau, On the Expressive Power of Deep Architectures, Algorithmic Learning Theory, pp.18-36, 2011.

K. Beven, A manifesto for the equifinality thesis, Journal of Hydrology, vol.320, issue.1-2, pp.18-36, 2006.
DOI : 10.1016/j.jhydrol.2005.07.007

J. Bodin and P. Ackerer, Predictive modelling of hydraulic head responses to dipole flow experiments in a fractured/karstified limestone aquifer: Insights from a comparison of five modelling approaches to real-field experiments, Journal of Hydrology, vol.454, issue.455, pp.82-100, 2012.
DOI : 10.1016/j.jhydrol.2012.05.069
URL : https://hal.archives-ouvertes.fr/hal-00724362

A. Castelletti and S. Galelli, A general framework for Dynamic Emulation Modelling in environmental problems, Environmental Modelling & Software, vol.34, pp.5-18, 2012.
DOI : 10.1016/j.envsoft.2012.01.002

C. Certes and G. De-marsily, Application of the pilot point method to the identification of aquifer transmissivities, Advances in Water Resources, vol.14, issue.5, pp.284-300, 1991.
DOI : 10.1016/0309-1708(91)90040-U

M. P. Clark and B. Nijssen, A unified approach for process-based hydrologic modeling: 1. Modeling concept, Water Resources Research, vol.10, issue.D3, pp.2498-2514, 2015.
DOI : 10.5194/hess-10-981-2006

Y. K. Demissie and A. J. Valocchi, Integrating a calibrated groundwater flow model with error-correcting data-driven models to improve predictions, Journal of Hydrology, vol.364, issue.3-4, pp.257-271, 2009.
DOI : 10.1016/j.jhydrol.2008.11.007

M. Denil and P. Agrawal, Learning to Perform Physics Experiments via Deep Reinforcement Learning, 2016.

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

T. P. Ferre, Revisiting the Relationship Between Data, Models, and Decision-Making, Groundwater, vol.43, issue.5, 2017.
DOI : 10.1029/2008WR006803

M. Gallagher and J. Doherty, Parameter estimation and uncertainty analysis for a watershed model. Environmental Modelling & Software 22 no, pp.1000-1020, 2007.

H. V. Gupta and M. P. Clark, Towards a comprehensive assessment of model structural adequacy, Water Resources Research, vol.44, issue.6, 2012.
DOI : 10.1111/j.1745-6584.2006.00203.x

M. A. Gusyev and H. M. Haitjema, Use of Nested Flow Models and Interpolation Techniques for Science-Based Management of the Sheyenne National Grassland, North Dakota, USA, Ground Water, vol.391, issue.3-4, pp.414-420, 2013.
DOI : 10.1016/j.jhydrol.2010.07.024

T. Hastie and R. Tibshirani, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2003.

G. E. Hinton and R. R. Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks, Science, vol.313, issue.5786, pp.504-507, 2006.
DOI : 10.1126/science.1127647

M. Hirn and N. Poilvert, Quantum energy regression using scattering transforms, 2016.

M. Hrachowitz and O. Fovet, Process consistency in models: The importance of system signatures, expert knowledge, and process complexity, Water Resources Research, vol.44, issue.2, pp.7445-7469, 2014.
DOI : 10.1029/2007WR006716
URL : https://hal.archives-ouvertes.fr/hal-01209255

K. L. Hsu and H. V. Gupta, Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis, Water Resources Research, vol.181, issue.3, 2002.
DOI : 10.1016/0022-1694(95)02918-4

K. L. Hsu and H. V. Gupta, Artificial Neural Network Modeling of the Rainfall-Runoff Process, Water Resources Research, vol.17, issue.4, pp.2517-2530, 1995.
DOI : 10.1016/0893-6080(90)90004-5

C. P. Kikuchi and T. P. Ferré, On the optimal design of experiments for conceptual and predictive discrimination of hydrologic system models, Water Resources Research, vol.41, issue.5, pp.4454-4481, 2015.
DOI : 10.1029/2005/WR003936

J. W. Kirchner, Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology, Water Resources Research, vol.17, issue.3, 2006.
DOI : 10.1002/hyp.1328

A. Krizhevsky and I. Sutskever, ImageNet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012.
DOI : 10.1162/neco.2009.10-08-881
URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf

P. Kumar, Hydrocomplexity: Addressing water security and emergent environmental risks, Water Resources Research, vol.13, issue.7, pp.5827-5838, 2015.
DOI : 10.5194/hess-13-1273-2009
URL : http://onlinelibrary.wiley.com/doi/10.1002/2015WR017342/pdf

Y. Lecun and Y. Bengio, Deep learning, Nature, vol.9, issue.7553, pp.436-444, 2015.
DOI : 10.1007/s10994-013-5335-x

H. W. Lin and M. Tegmark, Why Does Deep and Cheap Learning Work So Well?, Journal of Statistical Physics, vol.13, issue.6, 2016.
DOI : 10.1007/BF02165411
URL : http://arxiv.org/pdf/1608.08225

S. Mallat, Understanding deep convolutional networks, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.374, issue.2065, p.16, 2016.
DOI : 10.1109/CVPR.2015.7298904
URL : http://rsta.royalsocietypublishing.org/content/roypta/374/2065/20150203.full.pdf

M. Mathieu and C. Couprie, Deep multi-scale video prediction beyond mean square error, ArXiv e-prints, 2015.

H. N. Mhaskar and T. Poggio, Deep vs. shallow networks: An approximation theory perspective, Analysis and Applications, vol.23, issue.5, pp.829-848, 2016.
DOI : 10.1016/S0042-6989(97)00008-4

D. C. Montgomery, Design and Analysis of Experiments, 2006.

G. S. Nearing and H. V. Gupta, The quantity and quality of information in hydrologic models, Water Resources Research, vol.14, issue.12, pp.524-538, 2015.
DOI : 10.5194/hess-14-2545-2010

A. Radford and L. Metz, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015.

S. Razavi and B. A. Tolson, Review of surrogate modeling in water resources, Water Resources Research, vol.220, issue.5, 2012.
DOI : 10.1016/j.ecolmodel.2008.11.021

W. Schilders, Introduction to Model Order Reduction In Model Order Reduction: Theory, Research Aspects and Applications, 2008.

M. Schmidt and H. Lipson, Distilling Free-Form Natural Laws from Experimental Data, Science, vol.418, issue.5923, pp.81-85, 2009.
DOI : 10.1038/35065725
URL : http://creativemachines.cornell.edu/sites/default/files/Science09_Schmidt.pdf

J. Seibert and J. J. Mcdonnell, On the dialog between experimentalist and modeler in catchment hydrology: Use of soft data for multicriteria model calibration, Water Resources Research, vol.14, issue.11, pp.23-24, 2002.
DOI : 10.1002/1099-1085(20001015)14:14<2457::AID-HYP106>3.0.CO;2-I

D. Silver and A. Huang, Mastering the game of Go with deep neural networks and tree search, Nature, vol.34, issue.7587, pp.484-489, 2016.
DOI : 10.3233/ICG-2011-34302

G. Sposito, Scale Dependence and Scale Invariance in Hydrology, p.12, 1998.
DOI : 10.1017/CBO9780511551864

D. Stauffer and A. Aharony, Introduction to percolation theory, second edition, 1992.

F. Szidarovszky and E. A. Coppola, A Hybrid Artificial Neural Network-Numerical Model for Ground Water Problems, Ground Water, vol.43, issue.4, pp.590-600, 2007.
DOI : 10.1007/978-1-4899-2750-7

Y. Tao and X. Gao, A Deep Neural Network Modeling Framework to Reduce Bias in Satellite Precipitation Products, Journal of Hydrometeorology, vol.17, issue.3, pp.931-945, 2016.
DOI : 10.1175/JHM-D-15-0075.1

Z. Thomas and P. Rousseau-gueutin, Constitution of a catchment virtual observatory for sharing flow and transport models outputs, Journal of Hydrology, vol.543, pp.59-66, 2016.
DOI : 10.1016/j.jhydrol.2016.04.067
URL : https://hal.archives-ouvertes.fr/insu-01312759

P. A. Troch and G. A. Carrillo, Dealing with Landscape Heterogeneity in Watershed Hydrology: A Review of Recent Progress toward New Hydrological Theory, Geography Compass, vol.10, issue.6, pp.375-392, 2009.
DOI : 10.1017/CBO9780511551864

X. Yu and C. Duffy, Cyber-Innovated Watershed Research at the Shale Hills Critical Zone Observatory, IEEE Systems Journal, vol.10, issue.3, pp.1239-1250, 2016.
DOI : 10.1109/JSYST.2015.2484219

D. A. Zimmerman and G. D. Marsily, A comparison of seven geostatistically based inverse approaches to estimate transmissivities for modeling advective transport by groundwater flow, Water Resources Research, vol.22, issue.1, pp.1373-1413, 1998.
DOI : 10.1029/WR022i002p00095