Aquatic ecosystem modelling has been a challenging task for scientists partly due to limited tools/software to support. This study attempted to develop a Python toolkit (PyAEM) that was able to support multiple procedures in aquatic ecosystem modelling. PyAEM included improved algorithms for sensitivity analysis, parameter optimization, data assimilation and model visualization. In PyAEM, one-at-a-time and variance-based methods were implemented to identify sensitive parameters in target models. Genetic algorithm was improved for global optimization of model parameters. Ensemble Kalman Filter was implemented for assimilating multi-source measured data to improve model performance. An aquatic ecosystem model viewer was specifically developed for model visualization with the advantages of multi-scenario comparison, model fit evaluation, multi-mesh support and time series data extraction. An application overview of PyAEM in watershed and lake modelling is given, and revealed that PyAEM can be easily adapted to support the implementation of a case for aquatic ecosystem modelling.

对于科学家来说，水生生态系统建模一直是一项艰巨的任务，部分原因是支持的工具/软件有限。这项研究试图开发一个Python工具箱（PyAEM），该工具箱能够支持水生生态系统建模中的多个程序。PyAEM包括用于灵敏度分析，参数优化，数据同化和模型可视化的改进算法。在PyAEM中，实施了一次基于变量的方法来识别目标模型中的敏感参数。改进了遗传算法，实现了模型参数的全局优化。Ensemble Kalman滤波器用于吸收多源测量数据以改善模型性能。专门为模型可视化开发了水生生态系统模型查看器，它具有多场景比较，模型拟合评估，多网格支持和时间序列数据提取。给出了PyAEM在流域和湖泊建模中的应用概述，并揭示了PyAEM可以轻松地进行调整以支持水生生态系统建模案例的实施。