Bibliography#
Full list of references cited across the HydroModPy theory documentation.
The list is generated from docs/source/theory/references.bib via the
sphinxcontrib-bibtex extension. To add a new reference, append a BibTeX
entry to that file and cite it with :cite:`<key>` from any documentation
page.
R. Abhervé, C. Roques, A. Gauvain, L. Longuevergne, S. Louaisil, L. Aquilina, and J.-R. de Dreuzy. Calibration of groundwater seepage against the spatial distribution of the stream network to assess catchment-scale hydraulic properties. Hydrology and Earth System Sciences, 27(17):3221–3239, 2023. doi:10.5194/hess-27-3221-2023.
R. Abhervé, C. Roques, J.-R. de Dreuzy, T. Datry, P. Brunner, L. Longuevergne, and L. Aquilina. Improving calibration of groundwater flow models using headwater streamflow intermittence. Hydrological Processes, 2024. doi:10.1002/hyp.15167.
R. Abhervé, C. Roques, J.-R. de Dreuzy, T. Van Der Veen, L. Dumaine, E. Chatton, P. Brunner, L. Aquilina, and L. Servière. Projected climate change impacts on groundwater-surface water connectivity in a compartmentalized mountain headwater bedrock aquifer. Water Resources Research, 2025. doi:10.1029/2025WR040083.
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. Optuna: a next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2623–2631. 2019. doi:10.1145/3292500.3330701.
J. Bergstra and Y. Bengio. Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13:281–305, 2012. URL: https://www.jmlr.org/papers/v13/bergstra12a.html.
J. Boussinesq. Essai sur la théorie des eaux courantes. Mémoires présentés par divers savants à l'Académie des Sciences, 23:1–680, 1877.
J. Boussinesq. Recherches théoriques sur l'écoulement des nappes d'eau infiltrées dans le sol. Journal de Mathématiques Pures et Appliquées, 10:5–78, 1904.
W. Brutsaert. Hydrology: An Introduction. Cambridge University Press, Cambridge, 2005. doi:10.1017/CBO9780511808470.
J. A. Christen and C. Fox. Markov chain Monte Carlo using an approximation. Journal of Computational and Graphical Statistics, 14(4):795–810, 2005. doi:10.1198/106186005X76983.
J. Dupuit. Études théoriques et pratiques sur le mouvement des eaux dans les canaux découverts et à travers les terrains perméables. Dunod, Paris, 2nd edition, 1863.
M. G. Floriancic, R. Abhervé, C. Bouchez, J. J. Martinez, and C. Roques. Evidence of groundwater seepage and mixing at the vicinity of a knickpoint in a mountain stream. Geophysical Research Letters, 2024. doi:10.1029/2024GL111325.
N. Hansen. The CMA evolution strategy: a tutorial. 2016. URL: https://arxiv.org/abs/1604.00772, arXiv:1604.00772.
M. S. Hantush. Modification of the theory of leaky aquifers. Journal of Geophysical Research, 65(11):3713–3725, 1960. doi:10.1029/JZ065i011p03713.
A. W. Harbaugh. MODFLOW-2005, the U.S. Geological Survey modular ground-water model: the ground-water flow process. Techniques and Methods 6-A16, U.S. Geological Survey, 2005. doi:10.3133/tm6A16.
C. D. Langevin, J. D. Hughes, E. R. Banta, R. G. Niswonger, S. Panday, and A. M. Provost. Documentation for the MODFLOW 6 groundwater flow model. Techniques and Methods 6-A55, U.S. Geological Survey, 2017. doi:10.3133/tm6A55.
M. Le Mesnil, A. Gauvain, F. Gresselin, L. Aquilina, and J. de Dreuzy. Characterizing coastal aquifer heterogeneity from a single piezometer head chronicle. Journal of Hydrology, pages 131859, 2024. doi:10.1016/j.jhydrol.2024.131859.
E. Marti, S. Leray, and C. Roques. Catchment landforms predict groundwater-dependent wetland sensitivity to recharge changes. Hydrology and Earth System Sciences Discussions, 2024. doi:10.5194/HESS-2024-381.
J. A. Nelder and R. Mead. A simplex method for function minimization. The Computer Journal, 7(4):308–313, 1965. doi:10.1093/comjnl/7.4.308.
R. G. Niswonger, S. Panday, and M. Ibaraki. MODFLOW-NWT, a Newton formulation for MODFLOW-2005. Techniques and Methods 6-A37, U.S. Geological Survey, 2011. doi:10.3133/tm6A37.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011. URL: https://www.jmlr.org/papers/v12/pedregosa11a.html.
C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA, 2006. URL: https://gaussianprocess.org/gpml/.
C. V. Theis. The relation between the lowering of the piezometric surface and the rate and duration of discharge of a well using groundwater storage. Eos, Transactions American Geophysical Union, 16(2):519–524, 1935. doi:10.1029/TR016i002p00519.
P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, İ. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, and P. van Mulbregt. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17:261–272, 2020. doi:10.1038/s41592-019-0686-2.