Thematic Modules: Environment

Hython Wflow_SBM Hydrological Model

Hydrological model data processing
Updated 13/02/2024


A trained surrogate model that reproduces the physical representation of hydrological processes of the model Wflow, by learning complex physical aspects using spatially distributed data.

The developed model integrated into openEO as a user interface for researchers who can test the performance of a drought early warning system using a trained model and EO data. They can also contribute to further enhancing the trained model. The model is also helpful for public authorities in the field of agriculture and river basin management to identify areas potentially affected by hydrological drought.

Release Notes

The current version of Hython Python package is currently underway, and it is being under development to examine seasonal forecast of hydrological drought for the alps at river basin scale. The current Hython version is only supporting LSTM. In the current version of the Hython package, a surrogate model of the hydrological model is developed by linking dynamic and static data parameters for two target variables.

Future Plans

In updated versions our plan to include following features:

  • Train surrogate model other catchments of Alps.
  • Calibrate parameters of the hydrological (surrogate) model.
  • More preprocessing options: normalization, handling NaNs. Hyperparameter Optimization, Data augmentation, Visualization, Parallel and Distributed ML tasks, Explainable AI.
  • Add more timeseries models like GRU, ARIMA.


Target Audience
  • Researchers
  • Local/Regional public authorities in the field of agriculture
  • hydrology and river basin management authorities
  • Journalist for environmental topics with little expertise about technical data

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