Hydrology and Earth System Sciences (Apr 2023)

Dynamically coupling system dynamics and SWAT+ models using Tinamït: application of modular tools for coupled human–water system models

  • J. Z. Harms,
  • J. J. Malard-Adam,
  • J. J. Malard-Adam,
  • J. F. Adamowski,
  • A. Sharma,
  • A. Nkwasa

DOI
https://doi.org/10.5194/hess-27-1683-2023
Journal volume & issue
Vol. 27
pp. 1683 – 1693

Abstract

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Participatory water resource management requires modeling techniques that are accurate and flexible yet stakeholder-friendly. While different modeling frameworks offer advantages and disadvantages, system dynamics (SDs) models have seen sustained use as a stakeholder-friendly approach for participatory water resource modeling. Physically based models (e.g., SWAT+) have seen sustained use to model the hydrological components of water systems. Proposed as a way to combine the relative strengths of both modeling paradigms, model coupling allows researchers to, for example, build participatory SD models with stakeholders, while delegating the hydrological components of the overall model to an external hydrological model. Recently developed to facilitate model coupling, the Tinamït Python package presents an extensible, outward-facing application programming interface (API). It allows for the development of extensions (wrappers) that expand compatibility with different physically based models. However, no watershed hydrological model has yet been connected to this API. In the present study, a socket and JavaScript Object Notation (JSON)-based communication protocol was developed with the goal of facilitating the coupling of models written in languages such as Fortran. This novel protocol served to develop a Tinamït-compatible wrapper for the hydrological model SWAT+, allowing it to be coupled to human–water SD models. The novel coupling protocol was then applied to a case study of Tanzania's Usa river catchment. This approach provides the modeler with the benefits of both physically based and SD models, thereby allowing the detection of potentially far-reaching effects of policy-makers' decisions.