In the past decade, much work has been done on integrating different lake models using general frameworks to overcome model incompatibilities. However, a framework may not be flexible enough to support applications in different fields. To overcome this problem, we used Python to integrate three lake models into a Phytoplankton Prediction System for Lake Taihu (Taihu PPS). The system predicts the short-term (1–4 days) distribution of phytoplankton biomass in this large eutrophic lake in China. The object-oriented scripting language Python is used as the so-called ‘glue language’ (a programming language used for connecting software components). The distinguishing features of Python include rich extension libraries for spatial and temporal modelling, modular software architecture, free licensing and a high performance resulting in short execution time. These features facilitate efficient integration of the three models into Taihu PPS. Advanced tools (e.g. tools for statistics, 3D visualization and model calibration) could be developed in the future with the aid of the continuously updated Python libraries. Taihu PPS simulated phytoplankton biomass well and has already been applied to support decision making.
Huang, J., Gao, J., Hörmann, G., & Mooij, W. M. (2012). Integrating three lake models into a Phytoplankton Prediction System for Lake Taihu (Taihu PPS) with Python. Journal of Hydroinformatics, 14(2), 523-534. https://doi.org/10.2166/hydro.2011.020