Author Topic: An open source Python framework for investigating conceptual models  (Read 125 times)

Pankaj Dey

  • Moderator
  • Team sea
  • *****
  • Karma: +113/-0
    • View Profile
  • Institute : Indian Institute of Science
  • Programming language : MATLAB, R
Catchment-scale hydrological models are widely used to represent and improve our understanding of hydrological processes, and to support operational water resources management. Conceptual models, where catchment dynamics are approximated using relatively simple storage and routing elements, offer an attractive compromise in terms of predictive accuracy, computational demands and amenability to interpretation. This paper introduces SuperflexPy, an open-source Python framework implementing the SUPERFLEX principles (Fenicia et al., 2011) for building conceptual hydrological models from generic components, with a high degree of control over all aspects of model specification. SuperflexPy can be used to build models of a wide range of spatial complexity, ranging from simple lumped models (e.g. a reservoir) to spatially distributed configurations (e.g. nested sub-catchments), with the ability to customize all individual model elements. SuperflexPy is a Python package, enabling modelers to exploit the full potential of the framework without the need for separate software installations, and making it easier to use and interface with existing Python code for model deployment. This paper presents the general architecture of SuperflexPy, and illustrates its usage to build conceptual models of varying degrees of complexity. The illustration includes the usage of existing SuperflexPy model elements, as well as their extension to implement new functionality. SuperflexPy is available as open-source code, and can be used by the hydrological community to investigate improved process representations, for model comparison, and for operational work. A comprehensive documentation is available online and provided as supplementary material to this paper.