Articles | Volume 15, issue 15
https://doi.org/10.5194/gmd-15-6085-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6085-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multi-dimensional hydrological–hydraulic model with variational data assimilation for river networks and floodplains
Léo Pujol
CORRESPONDING AUTHOR
Laboratoire des sciences de l'ingenieur, de l'informatique
et de l'imagerie (ICUBE), Fluid Mechanics Team, CNRS, Université de
Strasbourg, France
Pierre-André Garambois
INRAE (Irstea), Aix Marseille Université, RECOVER, Aix-en-Provence, France
Jérôme Monnier
Institut de Mathematiques de Toulouse (IMT), Toulouse, France
INSA Toulouse, Toulouse, France
Related authors
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Ngo Nghi Truyen Huynh, Pierre-André Garambois, Benjamin Renard, François Colleoni, Jérôme Monnier, and Hélène Roux
Hydrol. Earth Syst. Sci., 29, 3589–3613, https://doi.org/10.5194/hess-29-3589-2025, https://doi.org/10.5194/hess-29-3589-2025, 2025
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Understanding and modeling flash-flood-prone areas remains challenging due to limited data and scale-relevant hydrological theory. While machine learning shows promise, its integration with process-based models is difficult. We present an approach incorporating machine learning into a high-resolution hydrological model to correct internal fluxes and transfer parameters between watersheds. Results show improved accuracy, advancing the development of learnable and interpretable process-based models.
Ngo Nghi Truyen Huynh, Pierre-André Garambois, François Colleoni, and Jérôme Monnier
EGUsphere, https://doi.org/10.5194/egusphere-2025-2797, https://doi.org/10.5194/egusphere-2025-2797, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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To better understand hydrological processes and improve flood simulation, combining artificial intelligence (AI) with process-based models is a promising direction. We introduce a hybrid physics-AI approach that seamlessly integrates neural networks into a distributed hydrological model to refine water flow dynamics within an implicit numerical scheme. The hybrid models demonstrate strong performance and interpretable results, leading to reliable streamflow simulations for flood modeling.
Juliette Godet, Pierre Nicolle, Nabil Hocini, Eric Gaume, Philippe Davy, Frederic Pons, Pierre Javelle, Pierre-André Garambois, Dimitri Lague, and Olivier Payrastre
Earth Syst. Sci. Data, 17, 2963–2983, https://doi.org/10.5194/essd-17-2963-2025, https://doi.org/10.5194/essd-17-2963-2025, 2025
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This paper describes a dataset that includes input, output, and validation data for the simulation of flash flood hazards and three specific flash flood events in the French Mediterranean region. This dataset is particularly valuable as flood mapping methods often lack sufficient benchmark data. Additionally, we demonstrate how the hydraulic method we used, named Floodos, produces highly satisfactory results.
François Colleoni, Ngo Nghi Truyen Huynh, Pierre-André Garambois, Maxime Jay-Allemand, Didier Organde, Benjamin Renard, Thomas De Fournas, Apolline El Baz, Julie Demargne, and Pierre Javelle
EGUsphere, https://doi.org/10.5194/egusphere-2025-690, https://doi.org/10.5194/egusphere-2025-690, 2025
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We present smash, an open-source framework for high-resolution hydrological modeling and data assimilation. It combines process-based models with neural networks for regionalization, enabling accurate simulations from catchment to country scale. With an efficient, differentiable solver, smash supports large-scale calibration and parallel computing. Tested on open datasets, it shows strong performance in river flow prediction, making it a valuable tool for research and operational use.
Maxime Jay-Allemand, Julie Demargne, Pierre-André Garambois, Pierre Javelle, Igor Gejadze, François Colleoni, Didier Organde, Patrick Arnaud, and Catherine Fouchier
Proc. IAHS, 385, 281–290, https://doi.org/10.5194/piahs-385-281-2024, https://doi.org/10.5194/piahs-385-281-2024, 2024
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This work targets the improvement of a hydrologic model used for flash flood warnings. A gridded model is used to spatially describe the hydrological processes. We develop a method to estimate the best model setup based on scarce river flow observations. It uses a complex algorithm combined with geographical descriptors to generate gridded parameters that better capture catchment characteristics. Results are promising, improving the discharge estimations where no observations are available.
Guillaume Evin, Matthieu Le Lay, Catherine Fouchier, David Penot, Francois Colleoni, Alexandre Mas, Pierre-André Garambois, and Olivier Laurantin
Hydrol. Earth Syst. Sci., 28, 261–281, https://doi.org/10.5194/hess-28-261-2024, https://doi.org/10.5194/hess-28-261-2024, 2024
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Hydrological modelling of mountainous catchments is challenging for many reasons, the main one being the temporal and spatial representation of precipitation forcings. This study presents an evaluation of the hydrological modelling of 55 small mountainous catchments of the northern French Alps, focusing on the influence of the type of precipitation reanalyses used as inputs. These evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events.
Reyhaneh Hashemi, Pierre Brigode, Pierre-André Garambois, and Pierre Javelle
Hydrol. Earth Syst. Sci., 26, 5793–5816, https://doi.org/10.5194/hess-26-5793-2022, https://doi.org/10.5194/hess-26-5793-2022, 2022
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Hydrologists have long dreamed of a tool that could adequately predict runoff in catchments. Data-driven long short-term memory (LSTM) models appear very promising to the hydrology community in this respect. Here, we have sought to benefit from traditional practices in hydrology to improve the effectiveness of LSTM models. We discovered that one LSTM parameter has a hydrologic interpretation and that there is a need to increase the data and to tune two parameters, thereby improving predictions.
François Colleoni, Pierre-André Garambois, Pierre Javelle, Maxime Jay-Allemand, and Patrick Arnaud
EGUsphere, https://doi.org/10.5194/egusphere-2022-506, https://doi.org/10.5194/egusphere-2022-506, 2022
Preprint archived
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This contribution presents the first evaluation of Variational Data Assimilation successfully applied over a large sample to the spatially distributed calibration of a newly taylored grid-based parsimonious model structure and corresponding adjoint. High performances are obtained in spatio-temporal validation and at flood time scales, especially for mediterranenan and oceanic catchments. Regional sensitivity analysis revealed the importance of the non conservative and production components.
Abubakar Haruna, Pierre-Andre Garambois, Helene Roux, Pierre Javelle, and Maxime Jay-Allemand
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-414, https://doi.org/10.5194/hess-2021-414, 2021
Manuscript not accepted for further review
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We compared three hydrological models in a flash flood modelling framework. We first identified the sensitive parameters of each model, then compared their performances in terms of outlet discharge and soil moisture simulation. We found out that resulting from the differences in their complexities/process representation, performance depends on the aspect/measure used. The study then highlights and proposed some future investigations/modifications to improve the models.
Kevin Larnier and Jerome Monnier
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2020-32, https://doi.org/10.5194/npg-2020-32, 2020
Revised manuscript not accepted
Short summary
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A hybrid A.I. algorithm (data-driven and physically-informed) to estimate river discharges observed by the altimetry satellite SWOT (launch 2021).
Cited articles
Allen, M., Antwi-Agyei, P., Aragon-Durand, F., Babiker, M., Bertoldi, P., Bind,
M., Brown, S., Buckeridge, M., Camilloni, I., Cartwright, A., Masson-Delmotte, V., Zhai, P., Pörtner, H.-O.,
Roberts, D., Skea, J., Shukla, P. R., Pirani, A., Moufouma-Okia, W.,
Péan, C., Pidcock, R., Connors, S., Matthews, J. B. R., Chen, Y., Zhou, X.,
Gomis, M. I., Lonnoy, E., Maycock, T., Tignor, M., and Waterterfield, T.
(Eds.):
Technical Summary: Global warming of 1.5 ∘C, An IPCC Special Report on the
impacts of global warming of 1.5 ∘C above pre-industrial levels and related
global greenhouse gas emission pathways, in the context of strengthening the
global response to the threat of climate change, sustainable development, and
efforts to eradicate poverty, http://pure.iiasa.ac.at/15716 (last access: 27 June 2022),
2019. a
Amara, M., Capatina-Papaghiuc, D., and Trujillo, D.: Hydrodynamical modelling
and multidimensional approximation of estuarian river flows, Comput. Vis. Sci., 6, 39–46,
https://doi.org/10.1007/s00791-003-0106-z, 2004. a
Asch, M., Bocquet, M., and Nodet, M.: Data assimilation: methods, algorithms,
and applications, Fundamentals of Algorithms, SIAM,
https://hal.inria.fr/hal-01402885 (last access: 27 June 2022), 2016. a
Audusse, E. and Bristeau, M.-O.: A well-balanced positivity preserving
“second-order” scheme for shallow water flows on unstructured meshes, J.
Comput. Phys., 206, 311–333, https://doi.org/10.1016/j.jcp.2004.12.016, 2005. a
Audusse, E., Bouchut, F., Bristeau, M.-O., Klein, R., and Perthame, B.: A Fast
and Stable Well-Balanced Scheme with Hydrostatic Reconstruction for Shallow
Water Flows, SIAM J. Sci. Comput., 25, 2050–2065,
https://doi.org/10.1137/S1064827503431090, 2004. a
Barth, T.: Numerical Methods for conservative Laws on Structured and
Unstructured Meshes, Tech. rep., VKI Lecture Series, 2003. a
Barthélémy, S., Ricci, S., Morel, T., Goutal, N., Le Pape, E., and
Zaoui, F.: On operational flood forecasting system involving 1D 2D
coupled hydraulic model and data assimilation, J. Hydrol., 562,
623–634, https://doi.org/10.1016/j.jhydrol.2018.05.007,
2018. a
Bates, P., Trigg, M., Neal, J., and Dabrowa, A.: LISFLOOD-FP, User manual,
School of Geographical Sciences, University of Bristol, Bristol, UK, https://vdocument.in/lisflood-fp-user-manual-university-of-bristol (last access: 1 August 2022), 2013. a
Bates, P. D., Horritt, M. S., and Fewtrell, T. J.: A simple inertial
formulation of the shallow water equations for efficient two-dimensional
flood inundation modelling, J. Hydrol., 387, 33–45,
https://doi.org/10.1016/j.jhydrol.2010.03.027, 2010. a
Bertalanffy, L. v.: General Systems Theory, 1968. a
Beven, K.: Prophecy, reality and uncertainty in distributed hydrological
modelling, Adv. Water Resour., 16, 41–51, 1993. a
Beven, K. J.: Uniqueness of place and process representations in hydrological modelling, Hydrol. Earth Syst. Sci., 4, 203–213, https://doi.org/10.5194/hess-4-203-2000, 2000. a
Biancamaria, S., Lettenmaier, D., and Pavelsky, T.: The SWOT Mission and Its
Capabilities for Land Hydrology, Surv. Geophys., 37, 307–337,
https://doi.org/10.1007/s10712-015-9346-y, 2016. a
Biancamaria, S., Frappart, F., Leleu, A.-S., Marieu, V., Blumstein, D.,
Desjonquères, J.-D., Boy, F., Sottolichio, A., and Valle-Levinson, A.:
Satellite radar altimetry water elevations performance over a 200 m wide
river: Evaluation over the Garonne River, Adv. Space Res., 59,
128–146, https://doi.org/10.1016/j.asr.2016.10.008, 2017. a
Brêda, J., Paiva, R., Bravo, J., Passaia, O., and Moreira, D.: Assimilation
of satellite altimetry data for effective river bathymetry, Water Resour.
Res., 55, 7441–7463,
https://doi.org/10.1029/2018WR024010, 2019. a
Brunner, G. W.: HEC-RAS River Analysis System, Hydraulic Reference Manual,
Version 1.0., Tech. rep., Hydrologic Engineering Center Davis, CA, https://apps.dtic.mil/sti/pdfs/ADA311952.pdf (last access: 27 June 2022) 1995. a
Buffard, T. and Clain, S.: Monoslope and Multislope MUSCL Methods for
unstructured meshes, J. Comput. Phys., 229, 3745–3776,
https://doi.org/10.1016/j.jcp.2010.01.026, 2010. a
Chévrier, P. and Galley, H.: A Van Leer finite volume scheme for the Euler
equations on unstructured meshes, ESAIM-Math. Model. Num., 27, 183–201, 1993. a
Chow, V.: Open-channel Hydraulics, McGraw-Hill civil engineering series, McGraw-Hill, New-York, USA, ISBN 9780070859067, 1959. a
Collischonn, W., Allasia, D., Da Silva, B. C., and Tucci, C. E.: The MGB-IPH
model for large-scale rainfall-runoff modelling, Hydrolog. Sci.
J., 52, 878–895, https://doi.org/10.1623/hysj.52.5.878,
2007. a
Coron, L., Thirel, G., Delaigue, O., Perrin, C., and Andréassian, V.: The
suite of lumped GR hydrological models in an R package, Environ.
Modell. Softw., 94, 166–171,
https://doi.org/10.1016/j.envsoft.2017.05.002, 2017. a, b
Couderc, F., Madec, R., Monnier, J., and Vila, J.-P.: DassFow-Shallow,
Variational Data Assimilation for Shallow-Water Models: Numerical Schemes,
User and Developer Guides, University of Toulouse, CNRS,
IMT, INSA, ANR, Research report,
https://hal.archives-ouvertes.fr/hal-01120285 (last access: 27 June 2022), 2013. a, b, c, d, e
Cunge, J. A., Holly, F., M., and Verwey, A.: Practical Aspects of Computational
River Hydraulics, Pitam Publishing, ISBN 978-0273084426, 1980. a
Davy, P., Croissant, T., and Lague, D.: A precipiton method to calculate river
hydrodynamics, with applications to flood prediction, landscape evolution
models, and braiding instabilities, J. Geophys. Res.-Earth, 122, 1491–1512,
https://doi.org/10.1002/2016JF004156, 2017. a, b
Delestre, O., Darboux, F., James, F., Lucas, C., Laguerre, C., and Cordier, S.:
FullSWOF: Full Shallow-Water equations for Overland Flow, J. Open Source Softw., 2, 448, https://doi.org/10.21105/joss.00448, 2017. a
Finaud-Guyot, P., Garambois, P.-A., Chen, S., Dellinger, G., Ghenaim, A., and
Terfous, A.: 1D 2D porosity model for urban flood modeling: case of a dense
street networks, E3S Web Conf., 40, 06010,
https://doi.org/10.1051/e3sconf/20184006010, 2018. a
Fleischmann, A. S., Paiva, R. C. D.,
Collischonn, W., Siqueira, V. A., Paris, A., Moreira, D.
M., Papa, F., Bitar, A. A., Parrens, M., Aires, F., and
Garambois, P. A.: Trade-offs between
1-D and 2-D regional river hydrodynamic models, Water Resour. Res., 56,
e2019WR026812, https://doi.org/10.1029/2019WR026812, 2020. a, b, c
Galland, J.-C., Goutal, N., and Hervouet, J.-M.: TELEMAC: A new numerical model
for solving shallow water equations, Adv. Water Resour., 14,
138–148, 1991. a
Garambois, P., Calmant, S., Roux, H., Paris, A., Monnier, J., Finaud-Guyot, P.,
Montazem, A., and da Silva, J.: Hydraulic visibility: Using satellite
altimetry to parameterize a hydraulic model of an ungauged reach of a braided
river, Hydrol. Process., 31, 756–767,
https://doi.org/10.1002/hyp.11033, 2017. a, b
Garambois, P.-A. and Monnier, J.: Inference of effective river properties from
remotely sensed observations of water surface, Adv. Water Resour.,
79, 103–120,
https://doi.org/10.1016/j.advwatres.2015.02.007, 2015. a, b
Garambois, P.-A., Larnier, K., Monnier, J., Finaud-Guyot, P., Verley, J.,
Montazem, A., and Calmant, S.: Variational estimation of effective channel
and ungauged anabranching river discharge from multi-satellite water heights
of different spatial sparsity, J. Hydrol., 581, 124409,
https://doi.org/10.1016/j.jhydrol.2019.124409, 2020. a, b, c, d
Garandeau, L., Belleudy, A., Javelle, P., Organde, D., Janet, B., Demargne, J.,
De Saint-Aubin, C., and Fouchier, C.: Vigicrues Flash, un service
automatique d'avertissement pour les crues rapides, De la prévision
des crues à la gestion de crise, Société Hydrotechnique
de France, Avignon, France, 11,
https://hal.inrae.fr/hal-02608801 (last access: 27 June 2022), 2018. a
Gejadze, I. Y. and Monnier, J.: On a 2D zoom for the 1D shallow water
model: Coupling and data assimilation, Comput. Method. Appl. M., 196, 4628–4643,
https://doi.org/10.1016/j.cma.2007.05.026, 2007. a
Gervasio, P., Lions, J.-L., and Quarteroni, A.: Heterogeneous coupling by
virtual control methods, Numer. Math., 90, 241–264,
https://doi.org/10.1007/s002110100303, 2001. a
Goutal, N. and Maurel, F.: A finite volume solver for 1D shallow-water
equations applied to an actual river, Int. J. Numer. Meth. Fl., 38, 1–19,
https://doi.org/10.1002/fld.201, 2002. a
Grimaldi, S., Li, Y., Walker, J., and Pauwels, V.: Effective representation of
river geometry in hydraulic flood forecast models, Water Resour. Res.,
54, 1031–1057, https://doi.org/10.1002/2017WR021765, 2018. a, b
Gudmundsson, G. H.: Transmission of basal variability to a glacier surface,
J. Geophys. Res.-Sol. Ea., 108, B5,
https://doi.org/10.1029/2002JB002107, 2003. a
Guinot, V.: Wave propagation in fluids: models and numerical techniques, 2nd edn., vol. 49, edited by: ISTE Ltd., ISBN 978-9812707789, 2010. a
Guinot, V., Delenne, C., Rousseau, A., and Boutron, O.: Flux closures and
source term models for shallow water models with depth-dependent integral
porosity, Adv. Water Resour., 122, 1–26,
https://doi.org/10.1016/j.advwatres.2018.09.014, 2018. a, b, c
Hascoet, L. and Pascual, V.: The Tapenade automatic differentiation tool:
principles, model, and specification, ACM T. Math.
Software, 39, 1–43, https://doi.org/10.1145/2450153.2450158, 2013. a, b, c, d
Hocini, N., Payrastre, O., Bourgin, F., Gaume, E., Davy, P., Lague, D., Poinsignon, L., and Pons, F.: Performance of automated methods for flash flood inundation mapping: a comparison of a digital terrain model (DTM) filling and two hydrodynamic methods, Hydrol. Earth Syst. Sci., 25, 2979–2995, https://doi.org/10.5194/hess-25-2979-2021, 2021. a, b, c
Hostache, R., Lai, X., Monnier, J., and Puech, C.: Assimilation of
spatially distributed water levels into a shallow-water flood model. Part
II: Use of a remote sensing image of Mosel River, J. Hydrol., 390, 257–268,
https://doi.org/10.1016/j.jhydrol.2010.07.003, 2010. a
Hunter, N. M., Bates, P. D., Neelz, S.,
Pender, G., Villanueva, I., Wright, N. G., Liang, D.,
Falconer, R. A., Lin, B., Waller, S., Crossley, A. J., and
Mason, D. C.: Benchmarking 2D
hydraulic models for urban flooding, in: Proceedings of the Institution of
Civil Engineers-Water Management, Thomas Telford Ltd, 161, 13–30,
https://doi.org/10.1680/wama.2008.161.1.13, 2008. a
Iturbide, M., Gutiérrez, J. M., Alves, L. M., Bedia, J., Cerezo-Mota, R., Cimadevilla, E., Cofiño, A. S., Di Luca, A., Faria, S. H., Gorodetskaya, I. V., Hauser, M., Herrera, S., Hennessy, K., Hewitt, H. T., Jones, R. G., Krakovska, S., Manzanas, R., Martínez-Castro, D., Narisma, G. T., Nurhati, I. S., Pinto, I., Seneviratne, S. I., van den Hurk, B., and Vera, C. S.: An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets, Earth Syst. Sci. Data, 12, 2959–2970, https://doi.org/10.5194/essd-12-2959-2020, 2020. a
Jay-Allemand, M., Javelle, P., Gejadze, I., Arnaud, P., Malaterre, P.-O., Fine, J.-A., and Organde, D.: On the potential of variational calibration for a fully distributed hydrological model: application on a Mediterranean catchment, Hydrol. Earth Syst. Sci., 24, 5519–5538, https://doi.org/10.5194/hess-24-5519-2020, 2020. a, b
Kirstetter, G., Delestre, O., Lagrée, P.-Y., Popinet, S., and Josserand, C.: B-flood 1.0: an open-source Saint-Venant model for flash-flood simulation using adaptive refinement, Geosci. Model Dev., 14, 7117–7132, https://doi.org/10.5194/gmd-14-7117-2021, 2021. a
Lai, X. and Monnier, J.: Assimilation of spatially distributed water levels
into a shallow-water flood model. Part I: Mathematical method and test case,
J. Hydrol., 377, 1–11,
https://doi.org/10.1016/j.jhydrol.2009.07.058, 2009. a
Le Lay, M.: Modélisation hydrologique dans un contexte de variabilité
hydro-climatique. Une approche comparative pour l'étude du cycle
hydrologique à méso-échelle au Bénin, PhD thesis, Institut
National Polytechnique de Grenoble (INPG),
https://tel.archives-ouvertes.fr/tel-00116912 (last access: 27 June 2022), 2006. a
Leopold, L. B. and Maddock, T.: The hydraulic geometry of stream channels and
some physiographic implications, US Government Printing Office, 252, https://doi.org/10.3133/pp252,
1953. a
Lorenc, A. C., Ballard, S. P., Bell, R. S., Ingleby,
N. B., Andrews, P. L. F., Barker, D. M., Bray, J. R., Clayton, A. M.,
Dalby, T., Li, D., Payne, T. J., and Saunders, F. W.: The Met. Office global
three-dimensional variational data assimilation scheme, Q. J. Roy. Meteor. Soc., 126, 2991–3012, 2000. a
Malou, T. and Monnier, J.: Double-scale diffusive wave model dedicated to spatial river observation and associated covariance kernel for variational data assimilation , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10355, https://doi.org/10.5194/egusphere-egu21-10355, 2021. a
Malou, T. and Monnier, J.: Covariance kernels investigation from diffusive wave
equations for data assimilation in hydrology, Inverse Probl.,
https://doi.org/10.1088/1361-6420/ac509d, accepted, 2022. a
Malou, T., Garambois, P.-A., Paris, A., Monnier, J., and Larnier, K.:
Generation and analysis of stage-fall-discharge laws from coupled
hydrological-hydraulic river network model integrating sparse multi-satellite
data, J. Hydrol., 603, 126993,
https://doi.org/10.1016/j.jhydrol.2021.126993, 2021. a, b, c
Marin, J. and Monnier, J.: Superposition of local zoom models and simultaneous
calibration for 1D–2D shallow water flows, Math. Comput. Simulat., 80, 547–560,
https://doi.org/10.1016/j.matcom.2009.09.001, 2009. a
Martin, N. and Monnier, J.: Inverse rheometry and basal properties inference
for pseudoplastic geophysical flows, Eur. J. Mech. B-Fluid.,
50, 110–126,
https://doi.org/10.1016/j.euromechflu.2014.11.011, 2015. a
Miglio, E., Perotto, S., and Saleri, F.: Model coupling techniques for
free-surface flow problems: Part I, Nonlinear Anal.-Theor., 63, e1885–e1896,
https://doi.org/10.1016/j.na.2005.03.083, 2005a. a
Miglio, E., Perotto, S., and Saleri, F.: Model coupling techniques for
free-surface flow problems: Part II, Nonlinear Anal.-Theor., 63, e1897–e1908,
https://doi.org/10.1016/j.na.2005.03.085, 2005b. a
Milly, P.: Climate, soil water storage, and the average annual water balance,
Water Resour. Res., 30, 2143–2156,
https://doi.org/10.1029/94WR00586, 1994. a
Monnier, J.: Variational data assimilation: from optimal control to large scale
data assimilation, Open Online Course, INSA Toulouse,
https://www.math.univ-toulouse.fr/~jmonnie/Enseignement/CourseVDA.pdf (last access: 27 June 2022),
2014. a
Monnier, J.: Variational Data Assimilation and Model Learning, https://hal.archives-ouvertes.fr/hal-03040047 (last access: 1 August 2022), 2021. a
Monnier, J., Couderc, F., Dartus, D., Larnier, K., Madec, R., and Vila, J.-P.:
Inverse algorithms for 2D shallow water equations in presence of wet dry
fronts: Application to flood plain dynamics, Adv. Water Resour., 97,
11–24, https://doi.org/10.1016/j.advwatres.2016.07.005,
2016. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Montazem, A., Garambois, P.-A., Calmant, S., Finaud-Guyot, P., Monnier, J.,
Moreira, D., Minear, J., and Biancamaria, S.: Wavelet-Based River
Segmentation Using Hydraulic Control-Preserving Water Surface Elevation
Profile Properties, Geophys. Res. Lett., 46, 6534–6543,
https://doi.org/10.1029/2019GL082986, 2019. a, b, c
Nguyen, P., Thorstensen, A., Sorooshian, S., Hsu, K., AghaKouchak, A., Sanders,
B., Koren, V., Cui, Z., and Smith, M.: A high resolution coupled
hydrologic-hydraulic model (HiResFlood-UCI) for flash flood modeling,
J. Hydrol., 541, 401–420,
https://doi.org/10.1016/j.jhydrol.2015.10.047, 2016. a, b, c
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andréassian, V., Anctil,
F., and Loumagne, C.: Which potential evapotranspiration input for a lumped
rainfall-runoff model?: Part 2 Towards a simple and efficient potential
evapotranspiration model for rainfall-runoff modelling, J. Hydrol.,
303, 290–306, https://doi.org/10.1016/j.jhydrol.2004.08.026,
2005. a
Özgen, I., Zhao, J.-h., Liang, D.-f., and Hinkelmann, R.: Wave propagation
speeds and source term influences in single and integral porosity shallow
water equations, Water Sci. Eng., 10, 275–286,
https://doi.org/10.1016/j.wse.2017.12.003, 2017. a
Pontes, P., Fan, F., Fleischmann, A., Paiva, R., Buarque, D., Siqueira, V.,
Jardim, P., Sorribas, M., and Collischonn, W.: MGB-IPH model for
hydrological and hydraulic simulation of large floodplain river systems
coupled with open source GIS, Environ. Modell. Softw., 94,
1–20, https://doi.org/10.1016/j.envsoft.2017.03.029, 2017. a
Pujol, L., Garambois, P.-A., Finaud-Guyot, P., Monnier, J., Larnier, K.,
Mosé, R., Biancamaria, S., Yesou, H., Moreira, D., Paris, A., and
Calmant, S.: Estimation of multiple inflows and effective channel by
assimilation of multi-satellite hydraulic signatures: The ungauged
anabranching Negro river, J. Hydrol., 591, 125331,
https://doi.org/10.1016/j.jhydrol.2020.125331, 2020. a, b, c, d, e, f, g, h, i
Pujol, L., Garambois, P.-A., and Monnier, J.: DassFlow2D-V3 code and cases, Zenodo [code], https://doi.org/10.5281/zenodo.6342723, 2022. a, b
Rodríguez, E., Durand, M., and Frasson, R. P. d. M.: Observing rivers with
varying spatial scales, Water Resour. Res., 56, 9,
https://doi.org/10.1029/2019WR026476, 2020. a
Sanders, B. F., Schubert, J. E., and Detwiler, R. L.: ParBreZo: A parallel,
unstructured grid, Godunov-type, shallow-water code for high-resolution flood
inundation modeling at the regional scale, Adv. Water Resour., 33,
1456–1467, https://doi.org/10.1016/j.advwatres.2010.07.007,
2010.
a, b
Schuite, J., Flipo, N., Massei, N., Rivière, A., and Baratelli, F.:
Improving the Spectral Analysis of Hydrological Signals to Efficiently
Constrain Watershed Properties, Water Resour. Res., 55, 4043–4065,
https://doi.org/10.1029/2018WR024579, 2019. a
Schumann, G. J.-P. and Domeneghetti, A.: Exploiting the proliferation of
current and future satellite observations of rivers, Hydrol. Process.,
30, 2891–2896, https://doi.org/10.1002/hyp.10825, 2016. a
Steinstraesser, J. G. C., Delenne, C., Finaud-Guyot, P., Guinot, V., Casapia,
J. K., and Rousseau, A.: SW2D-LEMON: a new software for upscaled shallow
water modeling, in: Simhydro 2021 – 6th International Conference Models for
complex and global water issues-Practices and expectations, Sophia Antipolis, 16–18 June 2021,
https://hal.inria.fr/hal-03224050/ (last access: 27 June 2022), 2021. a
Uhe, P., Mitchell, D., Bates, P. D., Addor, N., Neal, J., and Beck, H. E.: Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0, Geosci. Model Dev., 14, 4865–4890, https://doi.org/10.5194/gmd-14-4865-2021, 2021. a, b
Vila, J.-P.: Simplified Godunov schemes for 2×2 systems of conservation laws,
SIAM J. Numer. Anal., 23, 1173–1192, https://doi.org/10.1137/0723079, 1986. a
Vila, J.-P. and Villedieu, P.: Convergence of an explicit finite volume scheme
for first order symmetric systems, Numer. Math., 94, 573–602,
https://doi.org/10.1007/s00211-002-0396-y,
2003. a
Zhu, C., Byrd, R. H., Lu, P., and Nocedal, J.: Algorithm 778: L-BFGS-B:
Fortran subroutines for large-scale bound-constrained optimization, ACM
T. Math. Software, 23, 550–560,
https://doi.org/10.1145/279232.279236, 1997. a, b, c
Short summary
This contribution presents a new numerical model for representing hydraulic–hydrological quantities at the basin scale. It allows modeling large areas at a low computational cost, with fine zooms where needed. It allows the integration of local and satellite measurements, via data assimilation methods, to improve the model's match to observations. Using this capability, good matches to in situ observations are obtained on a model of the complex Adour river network with fine zooms on floodplains.
This contribution presents a new numerical model for representing hydraulic–hydrological...