smash (Spatially distributed Modeling and ASsimilation for Hydrology) is a Python library, interfaced with an efficient Fortran computational engine, that provides user-friendly routines for both hydrological research and operational applications.

The platform enables the combination of vertical and lateral flow operators through either process-based conceptual models or hybrid physics-AI approaches incorporating Artificial Neural Networks (ANNs). It is designed to simulate discharge hydrographs and hydrological states at any spatial location within a basin, and to reproduce the hydrological responses of contrasting catchments by leveraging spatially distributed meteorological forcings, physiographic data, and hydrometric observations.

• Documentation: https://smash.recover.inrae.fr
• Source code: https://github.com/DassHydro/smash
• Contributing: https://smash.recover.inrae.fr/contributor_guide
• Citations and related papers: https://smash.recover.inrae.fr/citations
• Scientific references: https://smash.recover.inrae.fr/bibliography
• Bug reports: https://github.com/DassHydro/smash/issues

smash offers a range of advanced calibration techniques, including Variational Data Assimilation (VDA), Bayesian estimation for uncertainty quantification, and machine learning methods, all within a spatialized and differentiable modeling framework. This is enabled by a numerical adjoint model automatically generated using the Tapenade differentiation tool, which provides accurate gradients for high-dimensional, non-linear optimization and efficient model learning.

• Tapenade website: https://team.inria.fr/ecuador/en/tapenade
• Tapenade article: https://doi.org/10.1145/2450153.2450158
• Tapenade source code: https://gitlab.inria.fr/tapenade/tapenade.git

Whether you are managing water resources or conducting research in hydrological modeling, smash can provide an easy-to-use yet powerful solution to support your work. Refer to the Getting Started guide for installation instructions and an introduction to its features.

For smash software use, please cite:

Colleoni, F., Huynh, N. N. T., Garambois, P.-A., Jay-Allemand, M., Organde, D., Renard, B., De Fournas, T., El Baz, A., Demargne, J., and Javelle, P. (2025). SMASH v1.0: A Differentiable and Regionalizable High-Resolution Hydrological Modeling and Data Assimilation Framework. Geosci. Model Dev., 18, 2025, 7003–7034. https://doi.org/10.5194/gmd-18-7003-2025.

BibTeX entry:

@article{Colleoni2025smash,
    author  = {Colleoni, François and Huynh, Ngo Nghi Truyen and Garambois, Pierre-André and Jay-Allemand, Maxime and Organde, Didier and Renard, Benjamin and De Fournas, Thomas and El Baz, Apolline and Demargne, Julie and Javelle, Pierre},
    title   = {SMASH v1.0: a differentiable and regionalizable high-resolution hydrological modeling and data assimilation framework},
    journal = {Geoscientific Model Development},
    volume  = {18},
    year    = {2025},
    number  = {19},
    pages   = {7003--7034},
    doi     = {10.5194/gmd-18-7003-2025}
}

Please also cite the relevant references corresponding to the algorithms and methods used:

• Hybrid physics-AI framework for learning regionalization and refining internal water fluxes of algebraic or ordinary differential equations (ODEs)-based solvers:

  Huynh, N. N. T., Garambois, P.-A., Renard, B., Colleoni, F., Monnier, J., and Roux, H. (2025). A distributed hybrid physics–AI framework for learning corrections of internal hydrological fluxes and enhancing high-resolution regionalized flood modeling. Hydrol. Earth Syst. Sci., 29, 3589–3613. https://doi.org/10.5194/hess-29-3589-2025.

  Huynh, N. N. T., Garambois, P.-A., Colleoni, F., and Monnier, J. (2025). Hybrid Physics-AI and Neural ODE Approaches for Spatially Distributed Hydrological Modeling. EGUsphere, 2025, 1–24. https://doi.org/10.5194/egusphere-2025-2797.

• Hybrid Data Assimilation and Parameter Regionalization (HDA-PR) approach:

  Huynh, N. N. T., Garambois, P.-A., Colleoni, F., Renard, B., Roux, H., Demargne, J., Jay-Allemand, M., and Javelle, P. (2024). Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High-Resolution Hydrological Model: Application to the French Mediterranean Region. Water Resour. Res., 60, e2024WR037544. https://doi.org/10.1029/2024WR037544.

• Signatures, multi-criteria calibration, hydrograph segmentation algorithm:

  Huynh, N. N. T., Garambois, P.-A., Colleoni, F., and Javelle, P. (2023). Signatures-and-sensitivity-based multi-criteria variational calibration for distributed hydrological modeling applied to Mediterranean floods. J. Hydrol., 625, 129992. https://doi.org/10.1016/j.jhydrol.2023.129992.

• Fully distributed variational calibration:

  Jay-Allemand, M., Javelle, P., Gejadze, I., Arnaud, P., Malaterre, P.-O., Fine, J.-A., and Organde, D. (2020). 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.