Bibliography#

[ABAP22]

Paul C. Astagneau, François Bourgin, Vazken Andréassian, and Charles Perrin. Catchment response to intense rainfall: evaluating modelling hypotheses. Hydrological Processes, 36(8):e14676, 2022. doi:10.1002/hyp.14676.

[Bot12]

Léon Bottou. Stochastic Gradient Descent Tricks, pages 421–436. Springer Berlin Heidelberg, 2012. doi:10.1007/978-3-642-35289-8_25.

[CMM98]

Ven Te Chow, David R. Maidment, and Larry W. Mays. Applied Hydrology. McGraw-Hill Series in Water Resources and Environmental Engineering, 1998.

[DRGD18]

A. Douinot, H. Roux, P.-A. Garambois, and D. Dartus. Using a multi-hypothesis framework to improve the understanding of flow dynamics during flash floods. Hydrology and Earth System Sciences, 22(10):5317–5340, 2018. doi:10.5194/hess-22-5317-2018.

[DW21]

Marcos Duarte and Renato Naville Watanabe. Notes on scientific computing for biomechanics and motor control. 2021. doi:10.5281/zenodo.4599319.

[DHS11]

John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of machine learning research, 2011. URL: http://jmlr.org/papers/v12/duchi11a.html.

[EAL+15]

I. Emmanuel, H. Andrieu, E. Leblois, N. Janey, and O. Payrastre. Influence of rainfall spatial variability on rainfall–runoff modelling: benefit of a simulation approach? Journal of Hydrology, 531:337–348, 2015. Hydrologic Applications of Weather Radar. doi:10.1016/j.jhydrol.2015.04.058.

[FA20]

Nathalie Folton and Patrick Arnaud. Indicateurs sur la ressource en eau estimés par une modélisation pluie-débit regionalisée : la base de donnees web loieau. La Houille Blanche, 3:22 – 29, 2020. doi:10.1051/lhb/2020034.

[GLG+17]

Federico Garavaglia, Matthieu Le Lay, Frèderic Gottardi, Rèmy Garçon, Joèl Gailhard, Emmanuel Paquet, and Thibault Mathevet. Impact of model structure on flow simulation and hydrological realism: from a lumped to a semi-distributed approach. Hydrology & Earth System Sciences, 2017. doi:10.5194/hess-21-3937-2017.

[Gra13]

Alex Graves. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013. doi:10.48550/arXiv.1308.0850.

[HOLeary93]

Per Christian Hansen and Dianne Prost O’Leary. The use of the l-curve in the regularization of discrete ill-posed problems. SIAM Journal on Scientific Computing, 14(6):1487–1503, 1993. doi:10.1137/0914086.

[HP13]

Laurent Hascoet and Valérie Pascual. The tapenade automatic differentiation tool: principles, model, and specification. ACM Transactions on Mathematical Software (TOMS), 39(3):1–43, 2013. doi:10.1145/2450153.2450158.

[JAJG+20]

Maxime Jay-Allemand, Pierre Javelle, Igor Gejadze, Patrick Arnaud, Pierre-Olivier Malaterre, Jean-Alain Fine, and Didier Organde. On the potential of variational calibration for a fully distributed hydrological model: application on a mediterranean catchment. Hydrology and Earth System Sciences, pages 1–24, 2020. doi:10.5194/hess-24-5519-2020.

[Ker20]

James R Kermode. F90wrap: an automated tool for constructing deep python interfaces to modern fortran codes. J. Phys. Condens. Matter, 2020. doi:10.1088/1361-648X/ab82d2.

[KB14]

Diederik P Kingma and Jimmy Ba. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. doi:10.48550/arXiv.1412.6980.

[Klemevs86]

Vit Klemeš. Operational testing of hydrological simulation models. Hydrological Sciences Journal, 31(1):13–24, 1986. doi:10.1080/02626668609491024.

[LM08]

N. Le Moine. Le bassin versant de surface vu par le souterrain : une voie d'amélioration des performances et du réalisme des modles pluie-débit ? PhD thesis, Université Pierre et Marie Curie (Paris 6), 2008. Thèse de doctorat dirigée par Andréassian, Vazken Hydrologie Paris 6 2008. URL: http://www.theses.fr/2008PA066468.

[LLWB94]

Xu Liang, Dennis P. Lettenmaier, Eric F. Wood, and Stephen J. Burges. A simple hydrologically based model of land surface water and energy fluxes for general circulation models. Journal of Geophysical Research: Atmospheres, 99(D7):14415–14428, 1994. doi:10.1029/94JD00483.

[LH79]

V. Lyne and M. Hollick. Stochastic time-variable rainfall-runoff modelling. In Institute of Engineers Australia National Conference, volume 79, 89–93. Institute of Engineers Australia Barton, Australia, 1979.

[Mic89]

Claude Michel. Hydrologie appliquée aux petits bassins ruraux. Hydrology handbook (in French), Cemagref, Antony, France, 1989.

[MPA03]

Claude Michel, Charles Perrin, and Vazken Andrèassian. The exponential store: a correct formulation for rainfall—runoff modelling. Hydrological Sciences Journal, 48(1):109–124, 2003. doi:10.1623/hysj.48.1.109.43484.

[Mon24]

Jerome Monnier. Data Assimilation - Inverse Problems, Assimilation, Control, Learning. INSA Toulouse, 2024. URL: https://www.math.univ-toulouse.fr/~jmonnie/Enseignement/CourseVDA.pdf.

[NM90]

Rory J Nathan and Thomas A McMahon. Evaluation of automated techniques for base flow and recession analyses. Water resources research, 26(7):1465–1473, 1990. doi:10.1029/WR026i007p01465.

[NM65]

J. A. Nelder and R. Mead. A simplex method for function minimization. The Computer Journal, 7(4):308–313, 01 1965. doi:10.1093/comjnl/7.4.308.

[PMA03]

Charles Perrin, Claude Michel, and Vazken Andrèassian. Improvement of a parsimonious model for streamflow simulation. Journal of hydrology, 279(1-4):275–289, 2003. doi:10.1016/S0022-1694(03)00225-7.

[Pow64]

M. J. D. Powell. An efficient method for finding the minimum of a function of several variables without calculating derivatives. The Computer Journal, 7(2):155–162, 01 1964. doi:10.1093/comjnl/7.2.155.

[PPLeMoine+11]

Raji Pushpalatha, Charles Perrin, Nicolas Le Moine, Thibault Mathevet, and Vazken Andréassian. A downward structural sensitivity analysis of hydrological models to improve low-flow simulation. Journal of Hydrology, 411(1):66–76, 2011. doi:10.1016/j.jhydrol.2011.09.034.

[STP18]

Léonard Santos, Guillaume Thirel, and Charles Perrin. Continuous state-space representation of a bucket-type rainfall-runoff model: a case study with the gr4 model using state-space gr4 (version 1.0). Geoscientific Model Development, 11(4):1591–1605, 2018. doi:10.5194/gmd-11-1591-2018.

[Tod96]

E. Todini. The arno rainfall—runoff model. Journal of Hydrology, 175(1):339–382, 1996. doi:10.1016/S0022-1694(96)80016-3.

[VGO+20]

Pauli Virtanen, Ralf Gommers, Travis E Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. Scipy 1.0: fundamental algorithms for scientific computing in python. Nature methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.

[WM15]

IK Westerberg and Hilary K McMillan. Uncertainty in hydrological signatures. Hydrology and Earth System Sciences, 19(9):3951–3968, 2015. doi:10.5194/hess-19-3951-2015.

[ZBLN97]

Ciyou Zhu, Richard H. Byrd, Peihuang Lu, and Jorge Nocedal. Algorithm 778: l-bfgs-b: fortran subroutines for large-scale bound-constrained optimization. ACM Trans. Math. Softw., 23(4):550–560, 1997. doi:10.1145/279232.279236.

[ZBV+11]

D. Zoccatelli, M. Borga, A. Viglione, G. B. Chirico, and G. Blöschl. Spatial moments of catchment rainfall: rainfall spatial organisation, basin morphology, and flood response. Hydrology and Earth System Sciences, 15(12):3767–3783, 2011. doi:10.5194/hess-15-3767-2011.