index.json

[{"uri":"https://dasshydro.github.io/","title":"DassHydro homepage","tags":[],"description":"","content":"DassHydro a computational software modeling rainfall to discharge \u0026amp; inundation dynamics (rainfall-runoff hydraulics). MathHydroNum the multidisciplinary research project-team focusing on the numerical modeling of water fluxes, and developing DassHydro. "},{"uri":"https://dasshydro.github.io/codes_presentation/pres_shared_tech/","title":"Shared technologies for all codes","tags":[],"description":"The DassFlow and SMASH codes are built on shared technologies.","content":"Codes presentation DassFlow is a code simulating river dynamics either based on the 1D Saint-Venant system (1D SW), DassFlow1D code, or on the 2D Shallow Water system (2D SW). DassFlow 2D code enables 2D zooms on 1D-like networks with a single solver. SMASH is a code simulating hydrology (spatially distributed model) at watershed scale. DassHydro is a code integrating SMASH and DassFlow, which are weakly coupled in the way SMASH - DassFlow 2D.\nCodes languages and architecture The three codes ( DassFlow2D , DassFlow1D and SMASH ) have been built up following the same spirit and technologies.\nThe computational kernel codes are written in Fortran 90 with parallel capabilities for DassFlow2D and SMASH (useless feature for DassFlow1D).\nThe Fortran kernel codes are wrapped in Python using f90wrap [Kermode, 2020].\nThis enables to use numerous Python libraries on top of the physics-based computations such as pre or post-processing of the computed fields or data, but also Machine Learning, wavelet basis etc.\nUQ libraries such as OpenTurns can be easily jointly employed too.\nLinear systems are solved using MUMPS or NumPy libraries.\nEach code version includes a forward code, the corresponding adjoint code generated by performing the automatic differentiation tool Tapenade [Hascoet and Pascual, 2013].\nEach code version includes various optimization algorithms and estimation approaches (e.g. Bayesian analysis).\nThe typical optimization algorithms are (non exhaustive list):\nLimited-memory Broyden-Fletcher-Goldfarb-Shanno Bounded (L-BFGS-B): Quasi-Newton methods for bounded optimization [Zhu et al., 1994]. Adaptive Moment Estimation (ADAM): Adaptive learning rates with momentum for fast convergence [Kingma and Ba, 2014]. The complete optimization processes provide parameters identification, reduced uncertainties, calibrated model(s).\nVDA formulations are based on priors: optimization starting points and covariances operators (probabilistic priors).\nClassical and original covariances operators can be considered.\nSeveral regularization strategies can be considered.\nHybrid physics based modeling with Machine Learning techniques.\nDifferentiable codes interfaced in Python can be easily combined with external Machine Learning librairies and algorithms (data flux in forward mode, gradient composition for inverse mode).\nReduced order 2D flows model based hybrid POD-NN (Proper Orthogonal Decomposition - Neural Networks) enabling extremly fast simulations.\nEach code is interfaced with few pre and post-processors enabling to tackle large scale real-world datasets: GIS libraries, mesh generators, visualization tools.\nKeywords: Python-wrapped computational codes; Fortran computational kernels (utilizing MPI for parallelization); adjoint codes; optimization libraries; pre and post-processing Python interfaces; mesh procedures for models coupling. "},{"uri":"https://dasshydro.github.io/download/dl_dassflow_smash/","title":"Download DassFlow & SMASH","tags":[],"description":"Get the different versions of DassFlow and SMASH on GitHub.","content":"Codes for DassFlow and SMASH are available on GitHub. Please refer to the README.txt files for more information on how to get these codes.\nDassFlow1D Download DassFlow1D DassFlow2D Download DassFlow2D SMASH Download SMASH "},{"uri":"https://dasshydro.github.io/examples/ex_dassflow2d/ex_real-world_cases_dassflow2d/","title":"Real-world cases","tags":[],"description":"This page presents applications of DassFlow2D on real cases.","content":"Title From Pujol-Garambois-Monnier, GMD 2022 with datasets from SPC. Coupled hydrological - 1D/2D hydraulic models with data assimilation: Adour basin case (south-western France) (L)) with a zoom on Bayonne city (water depth h in m) (R).\nYou can download the different codes on this page.\n"},{"uri":"https://dasshydro.github.io/examples/ex_dasshydro/","title":"DassHydro test cases","tags":[],"description":"Test cases for DassHydro (coupling of SMASH and DassFlow2D).","content":" You can download the different codes on this page.\n"},{"uri":"https://dasshydro.github.io/the_team/team_objectives/","title":"Objectives","tags":[],"description":"MathHydroNum, a French multidisciplinary research team focusing on the modeling and numerical estimations of water fluxes.","content":"MathHydroNum denotes a French multidisciplinary research project-team offering expertise in numerical hydrology and the software platform DassHydro (Data Assimilation in Hydrology) dedicated to numerical modeling of surface hydrodynamics and hydrology.\nThe team brings together expertise in computational sciences, hydrology, hydraulics, mathematical models and methods, inverse problems, statistical inference, sensitivity analysis, data assimilation, reduced models, and hybrid physically informed AI models.\nThe objectives of MathHydroNum are to design new generation models, numerical methods, algorithms, and computational softwares that address scientific challenges in the following areas:\nNumerical simulations of floods and inundations. Spatial hydrology, both at the global scale and regional scales, based on spatial observations (CNES, ESA, NASA etc missions), First article, at the source of DassHydro Physics-Informed Learning Methods for Continental Waters and Marine Risks.pdf (144 KB) Current research areas Multi-scale flexible hydrological-hydraulic modeling. Multi-source data assimilation. Model reduction in hydraulics and hydrology. Hybrid physically-informed neural networks. DassHydro a computational software modeling rainfall to discharge \u0026amp; inundation dynamics (rainfall-runoff hydraulics).\nHybrid deterministic stochastic data assimilation algorithms. Algorithmic products Numerical models for flood propagation at the watershed scale. River discharge estimation at both planetary and regional scales. Rivers bathymetry profiles estimation. Hydrology-hydraulics models coupling. Non-linear rheology parameters estimation. Computational software: DassHydro DassHydro includes DassFlow and SMASH softwares.\nThese codes rely on shared technologies : MPI Fortran kernels, Python wrapping, data assimilation processes, ML processes.\nThese codes are available on GitHub.\nDassFlow The various developments of DassFlow have been co-funded by INSA Toulouse since 2008, by CNRS in the 2010s, by INSA Strasbourg in 2015-2020, by CNES since 2014, by INRAe Aix since 2020, and by the French agency ANR (Muffins project, 2022-25).\nSMASH The various developments of SMASH have been co-funded by INRAe and the national flood forecasting center SCHAPI-DGPR since 2017, by the French agency ANR (Muffins project, 2022-25) and European agency DG-ECHO.\nANITI = Artificial and Natural Intelligence Toulouse Institute\nANR = French National Research Agency\nCNES = French National Center for Space Studies\nCNRS = French National Center for Scientific Research\nDG-ECHO = European Civil Protection and Humanitarian Aid Operations\nINRAe = French National Research Institute for Agriculture, Food and Environment\nINSA = French National Institute of Applied Sciences\n"},{"uri":"https://dasshydro.github.io/the_team/","title":"Project-team MathHydroNum","tags":[],"description":"","content":" Objectives MathHydroNum, a French multidisciplinary research team focusing on the modeling and numerical estimations of water fluxes.\nMembers Get to know the persons behind MathHydroNum.\n"},{"uri":"https://dasshydro.github.io/codes_presentation/pres_dasshydro/","title":"DassHydro","tags":[],"description":"Sequential coupling of SMASH and DassFlow codes: from rainfall to discharge & inundation dynamics (rainfall-runoff hydraulics).","content":"\nWhat is DassHydro ? DassHydro denotes the name of the code sequentially coupling SMASH and DassFlow2D .\nDassHydro enables complete simulations from rainfall to discharge and inundation dynamics (rainfall-runoff hydraulics).\nAs a consequence, DassHydro provides a quite complete computational code dedicated to hydrology (floods, inundations).\nDassHydro inherits from all capabilities of DassFlow2D and SMASH, in particular of the Data Assimilation capabilities.\nTest cases and real-world applications of DassHydro\n"},{"uri":"https://dasshydro.github.io/codes_presentation/","title":"Codes presentation","tags":[],"description":"","content":" Shared technologies for all codes The DassFlow and SMASH codes are built on shared technologies.\nDassHydro Sequential coupling of SMASH and DassFlow codes: from rainfall to discharge \u0026amp; inundation dynamics (rainfall-runoff hydraulics).\nDassFlow 1D \u0026amp; 2D DassFlow (Data Assimilation for Free Surface Flows), a set of computational codes aiming at modeling free surface geophysical flows with data assimilation capabilities.\nSMASH SMASH (Spatially distributed Modelling and Assimilation for Hydrology) is an open-source Python-Fortran library, which development is lead by INRAE Aix-en-Provence, and that provides a variety of user-friendly routines for spatially distributed hydrological modeling and multisource data-assimilation.\nHIVDI algorithm (SWOT Mission) Algorithm dedicated to spatial hydrology using NASA-CNES et al. satellite data.\n"},{"uri":"https://dasshydro.github.io/download/dl_hivdi/","title":"Download the HiVDI algorithm","tags":[],"description":"HIVDI has not been officially published yet, but users can have access to the code via a simple form.","content":"HiVDI is an open-source algorithm. If you want access to the code, just fill in the form below and we will get back to you as soon as possible.\nAbout the academic institution Institution Name: * Institution Complementary Information (scientific leader, team name): * Institution URL: * Institution Address: About you Full Name: * GitHub Username: * Email: * Position: * [Enter your position] Complementary information, remark or specific needs: * Required field I have read and agree to the GNU AFFERO GENERAL PUBLIC LICENSE terms and conditions. I also agree to cite the authors in the event of a publication. * I hereby confirm that all the information above is true and correct. * "},{"uri":"https://dasshydro.github.io/examples/ex_dassflow2d/ex_validation_cases_dassflow2d/","title":"Validation cases","tags":[],"description":"This page presents validation tests of DassFlow2D computational codes.","content":"Finite Volumes schemes accuracy From: [Couderc, F., Madec, R., Monnier, J., \u0026amp; Vila, J. P. (2013). Dassfow V2.0: numerical schemes, user and developer guides. Research report university of Toulouse]. See also: [Monnier, J., Couderc, F., Dartus, D., Larnier, K., Madec, R., \u0026amp; Vila, J. P. (2016). Inverse algorithms for 2D shallow water equations in presence of wet dry fronts: Application to flood plain dynamics. Advances in Water Resources, 97, 11-24.] 1. Regularised dam break problem with friction and non-trivial topography. 2. With wet-dry front dynamics. 3. Falling Gaussian (exact solution from [MacDonald’s et al. JHE 1997]). DassFlow2d documentation is available on GitHub\nYou can download the different codes on this page.\n"},{"uri":"https://dasshydro.github.io/examples/ex_dassflow2d/","title":"DassFlow2D test cases","tags":[],"description":"Test cases for DassFlow2D.","content":" Real-world cases This page presents applications of DassFlow2D on real cases.\nValidation cases This page presents validation tests of DassFlow2D computational codes.\n"},{"uri":"https://dasshydro.github.io/the_team/team_members/","title":"Members","tags":[],"description":"Get to know the persons behind MathHydroNum.","content":"Founders and lead INSA Toulouse - IMT INRAE Aix (UMR RECOVER) Jérôme Monnier (Professor) Webpage Pierre-André Garambois (Researcher) Webpage Collaborators INSA Toulouse - IMT INRAE Aix (UMR RECOVER) Robin Bouclier (Professor) Webpage Benjamin Renard (Researcher) Olivier Roustant (Professor) Webpage François Colleoni (R\u0026amp;D Engineer) Mustapha Allabou (Ph.D. Student 2021-24) Truyen Huynh (Ph.D. student 2022-25) Hugo Boulenc (Ph.D. Student 2022-25) HydroMatters HydroSciences Montpellier Kévin Larnier (R\u0026amp;D Engineer) Webpage Léo Pujol (postdoctoral researcher) Webpage Adrien Paris (R\u0026amp;D Engineer) Stéphane Calmant (Scientific Director - Emeritus IRD) Engees-Icube SERTIT-Icube Guilhem Dellinger (Associate professor) Webpage Alessandro Caretto (R\u0026amp;D engineer) IMFT - INPT International Helene Roux (Professor) Webpage Shangzhi Chen (Assistant Professor, Anhui University of Science and Technology in China) Webpage Co-funders "},{"uri":"https://dasshydro.github.io/codes_presentation/pres_dassflow/","title":"DassFlow 1D & 2D","tags":[],"description":"DassFlow (Data Assimilation for Free Surface Flows), a set of computational codes aiming at modeling free surface geophysical flows with data assimilation capabilities.","content":"\nFine scale 1D hydraulic model with hydrological inflows of the Negro River in the Amazon basin built from multi-satellite and in situ data. From [Pujol et al. 2020, JoH] What is DassFlow ? DassFlow (Data Assimilation for Free Surface Flows) denotes a set of computational codes aiming at modeling free surface geophysical flows with data assimilation capabilities. These flows can be water-rivers flows (Newtonian rheology) but also ice-glaciers flows (power-laws rheology) or lavas, muds flows1 etc (based on Herschel-Bulskley rheology laws). The dynamics flow models rely, mainly but not only, on the Shallow Water systems (depth-integrated equations, long-wave assumption).\nTwo code versions are available:\nDassFlow1D : networks of 1D river flow models.\nDassFlow2D : accurate and robust 2D flows with wet-dry front dynamics; affordable over river networks with a 1D-like 2D approach (see Fig.); spatialized modeling of hydrological source terms and boundary conditions.\nThe basic technologies of both codes are described in the Shared technologies for all codes page.\nDassFlow1D What does it include ? The 1D Shallow Water system (Saint-Venant’s equations) in variables (S,Q)(x,t). The Diffusive Wave equation (the standard one and an original two-scales one). Multi-branches (rivers network). Data Assimilation capabilities (variational or not) from multi-source / multi-nature data. The numerical schemes are: the classical Preissmann FD scheme or potentially FV schemes. Documentations, benchmarks (analytical, toys, real data). Global Sensitivity Analyses / UQ are quite easy to implement by interfacing with e.g. OpenTurns library. An additional original flow model dedicated to spatial hydrology is available: a low-Froude algebraic model. Key applications 1D river flows, potentially network. \u0026ldquo;Effective cross-sections\u0026rdquo; inference. Assimilation of multi-source data. Spatial hydrology: see the HiVDI algorithm , which enables to estimate rivers discharge at global scale from SWOT measurements. Test cases and real-world applications of HiVDI (based on DassFlow1D)\nDassFlow2D What does it include ? The 2D Shallow Water system in variables (h,qx,qy) solved by accurate FV schemes (1st order or actual 2nd order). Robust treatment of wet-dry front dynamics. A capability to locally degenerate the 2D model into a 1D-like model (therefore much less CPU-time consuming). This enables to build up e.g. multiple branches 1D-like models with 2D zooms at the branches junction, or in highest-interest areas, or in inundations areas. Various Boundary Conditions enable to tackle real-world problems. The GR4 hydrological model (developed by L. Santos and G. Thirel from INRAe, HYCAR Research Unit) is a lumped hydrological model implemented in a semi-distributed manner in DassFLow2D. This enables to perform hydrological calibration via hydraulic information feedback in a sequential coupling GR4 - Shallow Water 2D.\nTo consider a fully distributed hydrological model, please use SMASH . The corresponding adjoint code and a complete 4D-var optimization process (see the Shared technologies page). Parallel computations (MPI Fortran). Assimilation of multi-source data. Documentations. Benchmarks (analytical, toys, real data cases). An easy use of Python libraries such as deep learning, signal processing and statistics libraries since the Fortran computational kernel code is wrapped in Python (see the Shared technologies page). A Reduced Order Model (ROMs) of DassFlow2D is in progress. Key applications Rivers network dynamics with local zooms, e.g. in inundation areas (integrated 1D-2D flow solvers). Flood plain dynamics with wet-dry fronts. Lava / mud flows (Herschel-Bulckley rheology). Full 2D computations of Garonne river (Toulouse-Castelsarrasin portion, southern France). 87O K cells (MPI computations both for the direct and the adjoint codes). Datasets provided by D. Dartus et al. IMFT, regional flood forecasting center (SPC Toulouse). Solver from [J. Monnier, F. Couderc, D. Dartus, K. Larnier, R. Madec, J.P. Vila. AWR 2016.] Mixed 1D-like / 2D computations of the Adour river southwestern France. Datasets provided by the regional flood forecasting center (SPC GAD). Image from [Pujol-Garambois-Monnier, GMD 2022]. Test cases and real-world applications of DassFlow2D\nGreetings to the great former contributors of DassFlow! - Leo Pujol, PhD ICUBE Strasbourg-IMT-CNES. (Amazing update! 2D-1D integrated version, hydrology coupling, wrapping, applications) (2018-2022).\n- Thibault Malou, PhD IMT-INSA CLS corp. (covariance matrices, diffusive wave equations adapted tp spatial observations) (2019-2021).\n- David Kibe, PhD U. Christchurch NZ - INSA IMT (Herschel-Buckley rheology term (2021)).\n- Shenuyan MA and Ngo Truyen Huynh, INSA students, wrapping of the 2D version (2020-21).\n- J. Zhu, postdoc INSA-IMT (CNES funds), DassFlow-Py including deep learning (2018).\nJiamin is R\u0026D Eng. in optimal control, I.A. in a private company.\n- Jonas Verley INSA-IMT (CNES funds) (2017).\n- Pierre Brisset, Eng. INSA-IMT (CNES funds), DassFlow 1D, 1st main architect (2014-16).\nPierre is Res. eng. in computational sciences (HPC, inverse methods) in a private company.\n- Ronan Madec, Res. Eng. IMT Toulouse and ANR AMAC. Adjoint MPI codes, DassFlow2D \u0026 3D FE version (2010-13).\nRonan is Res. eng. in computational sciences (HPC, inverse methods) in a private company.\n- F. Couderc, Res. Eng. CNRS IMT \u0026 INSA Toulouse. MPI features in DassFlow 2D, Perl scripts (2010-12).\n- J.-P. Vila, Prof. INSA Toulouse (2010-12). Finite Volume schemes order 2 (MUSCL) for 2D SWE (2010-12).\n- N. Martin, IMT \u0026 INSA Toulouse (PhD 2013). Adjoint code / gradient accuracy, FE solvers (2010-13).\nNathan became R\u0026D Eng. in computational sciences (FE software) in a private company.\n- J. Marin, INRIA Grenoble (Res. engineer 2007). 2nd main architect, DassFlow2D (2006-08).\nJoel is Res. eng. in computational sciences in a private company.\n- I. Gejadze, INRIA, lab. LJK (postdoc 2006). coupled version 1D-2D.\nIgor is Senior Researcher at IRSTEA Montpellier (SIC2 hydraulic model).\n- X. Lai, INRIA, lab. LJK (postdoc 2005) 1st real test cases with image data (version non maintained).\nXijun is researcher at NIGLAS, Nanking, China.\n- M. Honnorat, PhD INP Grenoble \u0026 INRIA, lab. LJK (PhD 2007). 1st main architect, DassFlow2D (2004-06).\nMarc is Res. eng. in computational sciences (sometimes as a private consultant).\nOther versions DassFlow-RheoPy: DassFlow-RheoPy denotes purely Python codes for various shallow flows models: multi-regimes non-Newtonian fluids (power-law, Bingham, Herschel-Bulkley rheologies). One-equation models (lubrication type). Two-equation models (Shallow-Water type). Plug type model (SSA in glaciology). Finite Elements schemes (FEniCS library). Continuous adjoint equations. Applications: Glaciers dynamics (e.g. in ice-sheets), volcano lavas.\nFor examples in glaciology: J. Monnier, J. Zhu. \u0026ldquo;Physically-constrained data-driven inversions to infer the bed topography beneath glaciers flows. Application to East Antarctica\u0026rdquo;. Computational Geosciences, vol. 25, pp 1793-1819, 2021. DOI DassFlow-3d: Stokes like systems-power law rheology, low inertia, FE schemes. DassFlow is developed under the GNU AFFERO GENERAL PUBLIC LICENSE.\nFor more information, please contact the PIs.\ns Some references for DassFlow are available on this page.\nThese features are yet to be implemented in future releases of DassFlow.\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n"},{"uri":"https://dasshydro.github.io/examples/ex_smash/","title":"SMASH test cases","tags":[],"description":"Test cases for SMASH.","content":" You can download the different codes on this page.\n"},{"uri":"https://dasshydro.github.io/examples/","title":"Test Cases","tags":[],"description":"","content":" DassHydro test cases Test cases for DassHydro (coupling of SMASH and DassFlow2D).\nDassFlow2D test cases Test cases for DassFlow2D.\nSMASH test cases Test cases for SMASH.\nHiVDI-DassFlow1D test cases Test cases for HiVDI (based on DassFlow1D).\n"},{"uri":"https://dasshydro.github.io/doc/","title":"Documentations","tags":[],"description":"","content":" DassFlow1D DassFlow1D documentation DassFlow2D Up-to-date DassFlow2D documentation generated with SPHINX: DassFlow2D documentation Former documentation detailing the finite volume schemes accuracy:\nsee parts \u0026ldquo;7. Numerical Schemes\u0026rdquo; and \u0026ldquo;8. Validation\u0026rdquo;.\nSMASH Up-to-date SMASH documentation generated with SPHINX: SMASH documentation HiVDI No documentation is currently available. All details on the algorithm are presented in the research articles. HiVDI article HiVDI_Article_Larnier_Monnier.pdf (1 MB) However, a python script is available on the test page .\n"},{"uri":"https://dasshydro.github.io/codes_presentation/pres_smash/","title":"SMASH","tags":[],"description":"SMASH (Spatially distributed Modelling and Assimilation for Hydrology) is an open-source Python-Fortran library, which development is lead by INRAE Aix-en-Provence, and that provides a variety of user-friendly routines for spatially distributed hydrological modeling and multisource data-assimilation.","content":"\nWhat is SMASH? SMASH development is lead by INRAE Aix-en-Provence (cf. documentation Documentation )\nSMASH Spatially distributed Modeling and Assimilation for Hydrology denotes a computational code aiming at simulating discharge hydrographs and hydrological states at any spatial location within a basin and reproduce the hydrological response of contrasted catchments. It contains several hydrological operators and flexible model structures with data assimilation and sensitivity analysis algorithms. The code is designed both for operational forecasting of floods and low flows, and can take advantage of spatially distributed meteorological forcings, physiographic data and hydrometric observations.\nThe code is available on GitHub and its SPHINX documentation on the Documentation page.\nThe basic technologies of the code are described in the Shared technologies for all codes page.\nWhat does it include? SMASH is a modular platform that contains conceptual representations and numerical approximations of dominant hydrological processes while aiming to maintain a relative parsimony. It also contains several algorithms for pre and post-processing, signal analysis, model optimization and data assimilation over large datasets. It originally enables to use variational data assimilation and hybrid methods based on statistical/machine learning approaches. SMASH enables to work at multiple spatio-temporal resolutions, with heterogeneous data in nature and sampling.\nMultiple flow operators can be combined to describe spatialized hydrological processes at pixel scale, including runoff production, and flow routing towards catchment outlet. These operators are based on effective and widely used conceptualizations and models used in the hydrology community.\nGR-like (cf. [Perrin et al. 2003], Brief history of GR models). VIC-like (cf. [Liang et al. 1994], VIC overview). Lateral flow routing with conceptual models or Kinematic wave. Several optimization algorithms can be considered: see Shared technologies for all codes . Different estimation methods have been investigated, for example:\nBayesian-like parameter estimation algorithm [Huynh et al. in prep] following [Chelil et al. 2022]. Variational Data Assimilation (VDA) [Jay-Allemand et al. 2020] and Hybrid Variational Data Assimilation Parameter regionalization (HVDA-PR) [Huynh et al. 2023a]. Bayesian-Guided Multivariate Regression [Huynh et al. 2023b], Bayesian estimation tools that can be used alone or with HD algorithms. Rainfall moments and Hydrological signatures can be computed at multiple scales ; signatures can be used in optimization [Huynh et al. 2022]. LSTM algorithms applicable to model inputs-outputs [Huynh et al. in prep] after [Hashemi et al. 2021]. Key applications Floods forecasting over the French territory (underly the VigiCrues Flash platform). Regionalization from worldwide databases. Test cases and real-world applications of SMASH\n"},{"uri":"https://dasshydro.github.io/codes_presentation/pres_hivdi/","title":"HIVDI algorithm (SWOT Mission)","tags":[],"description":"Algorithm dedicated to spatial hydrology using NASA-CNES et al. satellite data.","content":" Description of the solved inverse problem. Images extracted from [Larnier-Monnier, Compt. GeoSci. 2023]. What is HiVDI ? HiVDI = Hybrid Hierarchical Variational Discharge Inference\nThe HiVDI algorithm aims at estimating rivers discharge and rivers bathymetry from measurements of the SWOT instrument (NASA-CNES et al. mission).\nIn a nutshell, the algorithm relies on DassFlow1D code , Deep Learning and a Bayesian analysis, see figure below (details available in [Larnier-Monnier 2023]). The algorithm is here available upon simple request.\nSWOT measurements SWOT instrument is an altimeter providing Water Surface Elevation (WSE) and river width measurements at ~250 m scale (with an accuracy +/-10cm for WSE). Each overpass provides two swaths of 60 km width each at ~10-20 days frequency depending on the location on Earth.\nHere a report of historical thoughts of founders. Figure. (L) SWOT mission (launched nov. 2022, NASA-CNES et al.). (R) Number of reaches (~10 km long) observed by SWOT instrument: the SWORD database from [Alteneau-Pavelsky et al. (2021)]. (SWOT Science Team). HiVDI results Test cases and real-world applications of HiVDI\nInitial - \u0026ldquo;historical\u0026rdquo; thoughts Below a few short notes on the SWOT blog too:\nApril 2016: On Variational Data Assimilation of SWOT-like data in 1D and 2D river flow models. Nov. 2013: On Variational sensitivities, data assimilation and inversion for small scale river flows. Dec. 2018: posters at AGU meeting HiVDI_poster_river_discharge_and_bathymetry.pdf (1 MB) HiVDI_poster_ungauged_braided_river.pdf (4 MB) "},{"uri":"https://dasshydro.github.io/download/","title":"Download","tags":[],"description":"","content":" Download DassFlow \u0026amp; SMASH Get the different versions of DassFlow and SMASH on GitHub.\nDownload the HiVDI algorithm HIVDI has not been officially published yet, but users can have access to the code via a simple form.\n"},{"uri":"https://dasshydro.github.io/tools/","title":"Tools","tags":[],"description":"","content":" External tools Embedded tools "},{"uri":"https://dasshydro.github.io/tools/external_tools/","title":"External tools","tags":[],"description":"","content":""},{"uri":"https://dasshydro.github.io/tools/embedded_tools/","title":"Embedded tools","tags":[],"description":"","content":""},{"uri":"https://dasshydro.github.io/shortcuts/references/","title":"A few references","tags":[],"description":"","content":"This pages contains a non exhaustive list of references for the different codes: DassFlow , SMASH , and DassHydro .\nReferences for DassFlow References introducing the approaches, methods, algorithms (and not those focusing on new applications, new flows, databases) Know-hows on VDA \u0026amp; fundamentals of DassFlow\u0026rsquo;s algorithms: basics of inverse problems, optimal control, gradient-based methods, gradient computations, adjoints (equations, codes), codes assessements, covariances operators, regularizations terms, link with BLUE - Kalman filters etc :\nJ. Monnier, \u0026ldquo;Variationnal Data Assimilation\u0026rdquo;, Open Online Course, INSA - University of Toulouse. consult the course. Discharge estimation from altimetry data: HiVDI algorithm (based on 1D and 0.5D flow models). River discharge estimations, bathymetry estimations, identifiability maps (1D SW model = Saint-Venant\u0026rsquo;s equations):\nK. Larnier, J. Monnier. \u0026ldquo;Learning river features from altimetry\u0026rdquo;. K. Larnier, J. Monnier, P.-A. Garambois, J. Verley. \u0026ldquo;Estimation of river discharges from altimetry\u0026rdquo;. Inv. Pb Sc. Eng.(IPSE), 2020. P. Brisset, J. Monnier, P.-A. Garambois, H. Roux. \u0026ldquo;On the assimilation of altimetry data in 1D Saint-Venant river models\u0026rdquo;. Adv. Water Ress. (AWR) Vol. 19, 2018. See also a poster for HiVDI and an application .\nThe complete hydraulic 2d/1d -hydrology chain few advanced applications\nL. Pujol, P.-A. Garambois, J. Monnier, K. Larnier et al., submitted, 2022. A few advanced applications (including complex flows analysis)\nL. Pujol, P.-A. Garambois, P. Finaud-Guyot, J. Monnier, K. Larnier, R. Mose, S. Biancamaria, H. Yesou, D. Moreira, A. Paris, S. Calmant. \u0026ldquo;Estimation of Multiple Inflows and Effective Channel by Assimilation of Multi-satellite Hydraulic Signatures: The Ungauged Anabranching Negro River\u0026rdquo;. J. of Hydrology (JoH), 2020. P.-A. Garambois, K. Larnier, J. Monnier, P. Finaud-Guyot, J. Verley, A. Montazem, S. Calmant. \u0026ldquo;Variational inference of effective channel and ungauged anabranching river discharge from multi-satellite water heights of different spatial sparsity\u0026rdquo;. J. of Hydrology (JoH), 2019. Tuozzolo, S., Lind, G., Overstreet, B., Mangano, J., Fonstad, M., Hagemann, M., Frasson, R., Larnier, K., Garambois, P.-A., Monnier, J., Durand, M. \u0026ldquo;Estimating river discharge with swath altimetry: A proof of concept using AirSWOT observations\u0026rdquo;. Geophys. Res. Lett. (GRL), 2019. 2D flows \u0026amp; VDA inversions\nFlood plain \u0026amp; FV schemes (including 2nd order VF schemes, wet-dry front dynamics, variational sensitivities):\nJ. Monnier, F. Couderc, D. Dartus, K. Larnier, R. Madec, J.-P. Vila. \u0026ldquo;Inverse computational algorithms for 2D shallow water equations in presence of wet dry fronts. Application to flood plain dynamics\u0026rdquo;. Adv. Water Res. 2016. Assimilation of a flood plain image into 2D shallow water model:\nR. Hostache, X. Lai, J. Monnier, C. Puech. J. Hydrology (JoH) (2010). X. Lai, J. Monnier J. Hydrology (2009) and M. Honnorat, X. Lai, J. Monnier, FX Le Dimet, Computational Methods for Water Ressources (2006) -Pearl river, unpublished elsewhere-. Assimilation of Lagrangian surface data:\nM. Honnorat, J. Monnier, FX Le Dimet, Comput. Visu. Sc. (2009). M. Honnorat, J. Monnier, N. Riviere, E. Huot, FX Le Dimet, Comput. Visu. Sc. (2010). Bed topography estimations beneath glaciers flows (mix of physical-based models and statistical-learning methods):\nJ. Monnier, J. Zhu. \u0026ldquo;Estimations of bed topography elevation inland East Antarctica\u0026rdquo;. J. Monnier, J. Zhu. \u0026ldquo;Inference of the bottom topography in anisothermal mildly-sheared shallow ice flows\u0026rdquo;. Comput. Meth. Applied Meth. Eng.\u0026quot;. CMAME 2019. Code version developped in Python.\nCoupling 1D-2D SW models:\nJ. Marin, J. Monnier, Math. Comput. Simul. (2009), E. Fernandez-Nieto, J. Marin, J. Monnier, Comput. Fluid. (2010). I. Gejadze, J. Monnier, Comp. Meth. Appl. Mech. Engnr. (2007). Save memory and tune your gradient accuracy if using algorithmic - automatic differentiation:\nN. Martin, J. Monnier, The Cryosphere (2013) and N.Martin\u0026rsquo;s PhD thesis 2013. Inference of rheometry \u0026amp; conditions at bottom in power-law fluid flows (low inertial flows e.g. glacier, lava flows):\nN. Martin, J. Monnier, Europ. J. Mech. B/Fluids (2014). Variational sensitivities for Stokes power-law flows and advanced four fields FE schemes:\nN. Martin, J. Monnier, SIAM JSC (2013) and N.Martin\u0026rsquo;s PhD thesis 2013. References for SMASH Bayesian-like parameter estimation algorithm: [Huynh et al. in prep] following [Chelil et al. 2022]. Variational Data Assimilation (VDA): [Jay-Allemand et al. 2020] Hybrid Variational Data Assimilation, Parameter regionalization (HVDA-PR): [Huynh et al. 2023a]. Bayesian-Guided Multivariate Regression and Bayesian estimation tools that can be used alone or with HD algorithms: [Huynh et al. 2023b] Rainfall moments and Hydrological signatures computable at multiple scales ; signatures usable in optimization: [Huynh et al. 2022]. LSTM algorithms applicable to model inputs-outputs: [Huynh et al. in prep] after [Hashemi et al. 2021]. References for DassHydro Coming soon\u0026hellip;.\n"},{"uri":"https://dasshydro.github.io/shortcuts/how_to_cite/","title":"How to cite","tags":[],"description":"","content":"If using one of the codes, please cite both the code website reference (see bibtex code below) and corresponding research articles. See references:\nFor DassFlow For SMASH DassFlow1D @misc{dassflow-1d, title = {DassFlow (Data Assimilation for Free Surface Flows) computational software}, author = {K. Larnier, J. Monnier et al.}, note = {INSA, Math. Institute of Toulouse (IMT), CS Group, INRae}, url = {https://mathhydronum.insa-toulouse.fr/}, year={2023}, } Download DassFlow2D @misc{dassflow-2d, title={DassFlow (Data Assimilation for Free Surface Flows) computational software}, author={L. Pujol, P.-A. Garambois, K. Larnier, J. Monnier, L. Villeneuve et al.}, note = {Math. Institute of Toulouse (IMT), INRAe, INSA, Univ. Strasbourg}, url = {https://update}, year={2023}, } Download SMASH Download DassHydro @misc{dasshydro, title={DassHydro (Data Assimilation for Hydrology) computational software}, author={P.-A. Garambois, F. Colleoni, T. Huynh, J. Monnier, L. Pujol, L. Villeneuve et al.}, note = {INRAe, INSA, Univ. Strasbourg et al.}, url = {https://update}, year={2023}, } Download HIVDI @article{hivdi2023, title={Hybrid Neural Network - Variational Data Assimilation algorithm to infer river discharges from SWOT-like data}, author={Larnier, Kevin and Monnier, Jerome}, journal={Computational Geosciences}, pages={}, year={2023}, publisher={Springer} } Download "},{"uri":"https://dasshydro.github.io/shortcuts/faq/","title":"Frequently Asked Questions","tags":[],"description":"","content":"Are DassFlow and SMASH freewares open-source? Yes, they are freely distributed under GNU AFFERO GENERAL PUBLIC LICENSE.\nIf you use or adapt one of these codes, please cite:\nSee How to Cite and References .\nIs it free for commercial use? No. Commercial use is allowed with an agreement in due form only.\nHow to cite the use of the code? You can find up-to-date bibtex code for each code in How to Cite .\nHow to obtain any source code ? Please consult the Download page for DassFlow and HiVDI.\nHiVDI is yet to be released. Once it is, one will be able to request the source code with a simple form.\nWhich OS is required ? Our codes are distributed with a Makefile that should work on most Linux systems.\nFor more information about the prerequisites, refer to the Documentations .\nWhat free pre and post-processor softwares can be used ? Mesh generators: Gmsh, Telemac mesh generator and others like Elmer library mesh generator, Emc2 (Inria). Visualizers: Pyvista, vtk format (eg Paraview) and Tecplot format. "},{"uri":"https://dasshydro.github.io/download/dl_success/","title":"Form submission successfull","tags":[],"description":"","content":" Thank you for your interest into our HiVDI algorithm. An email of your request has been sent to @email1 and @email2. A response will be given to you in the shortest delays.\nYou can have a look at the presentation of the HiVDI algorithm , or check some test cases and real-world applications .\n"},{"uri":"https://dasshydro.github.io/examples/ex_hivdi_dassflow1d/","title":"HiVDI-DassFlow1D test cases","tags":[],"description":"Test cases for HiVDI (based on DassFlow1D).","content":"Below a few historical results presented in [Larnier-Monnier, Compt. GeoSci. 2023]. You can download the HiVDI algorithm on this page.\n"},{"uri":"https://dasshydro.github.io/shortcuts/","title":"","tags":[],"description":"","content":" A few references This pages contains a non exhaustive list of references for the different codes: DassFlow , SMASH , and DassHydro . References for DassFlow References introducing the approaches, methods, algorithms (and not those focusing on new applications, new flows, databases) Know-hows on VDA \u0026amp; fundamentals of DassFlow\u0026rsquo;s algorithms: basics of inverse problems, optimal control, gradient-based methods, gradient computations, adjoints (equations, codes), codes assessements, covariances operators, regularizations terms, link with BLUE - Kalman filters etc :\nHow to cite If using one of the codes, please cite both the code website reference (see bibtex code below) and corresponding research articles. See references: For DassFlow For SMASH DassFlow1D @misc{dassflow-1d, title = {DassFlow (Data Assimilation for Free Surface Flows) computational software}, author = {K. Larnier, J. Monnier et al.}, note = {INSA, Math. Institute of Toulouse (IMT), CS Group, INRae}, url = {https://mathhydronum.insa-toulouse.fr/}, year={2023}, } Download DassFlow2D @misc{dassflow-2d, title={DassFlow (Data Assimilation for Free Surface Flows) computational software}, author={L.\nFrequently Asked Questions Are DassFlow and SMASH freewares open-source? Yes, they are freely distributed under GNU AFFERO GENERAL PUBLIC LICENSE. If you use or adapt one of these codes, please cite: See How to Cite and References . Is it free for commercial use? No. Commercial use is allowed with an agreement in due form only. How to cite the use of the code? You can find up-to-date bibtex code for each code in How to Cite .\n"},{"uri":"https://dasshydro.github.io/categories/","title":"Categories","tags":[],"description":"","content":""},{"uri":"https://dasshydro.github.io/tags/","title":"Tags","tags":[],"description":"","content":""}]