SuperflexPy is an open-source Python framework for constructing conceptual hydrological models for lumped and semi-distributed applications.
SuperflexPy builds on our 10 year experience with the development and application of Superflex, including collaborations with colleagues at the Eawag (Switzerland), TU-Delft (Netherlands), LIST (Luxembourg), University of Adelaide (Australia), and others. The SuperflexPy framework offers a brand new implementation of Superflex, allowing the modeler to build fully customized, spatially-distributed hydrological models.
Thanks to its object-oriented architecture, SuperflexPy can be easily extended to meet your modelling requirements, including the creation of new components with customized internal structure, in just a few lines of Python code.
Constructing a hydrological model is straightforward with SuperflexPy:
- inputs and outputs are handled directly by the modeler using common Python libraries (e.g. Numpy or Pandas). The modeller can use hence data files of their own design, without the need to pre- and/or post- process data into text formats prescribed by the framework itself;
- the framework components are declared and initialized through a Python script;
- the framework components are implemented as classes with built-in functionalities for handling parameters and states, routing fluxes, and solving the model equations (e.g. describing reservoirs, lag functions, etc.);
- the numerical implementation is separated from the conceptual model, allowing the use of different numerical methods for solving the model equations;
- the framework can be run at multiple levels of complexity, from a single-bucket model to a model that represents an entire river network;
- the framework is available as an open source Python package from Github;
- the framework can be easily interfaced with other Python modules for calibration and uncertainty analysis.
The core team consists of:
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Using SuperflexPy requires a general knowledge of Python and Numpy. Other Python libraries may be needed for pre- and post- processing of the data.
In line with the Python terminology, we will use the word define when referring to the definition of a class, and initialize when referring to the creation of an instance of a class, i.e. an object.