Online Data Collection with Python and Jupyter: Making Stormwater Modeling Less of a Drain

Ellen Arrowsmith, Computational Hydraulics (CHI), ON, Canada


The plethora of available online data sources now enables water resources professionals to construct stormwater models from beginning to end more easily than ever, and without requiring extensive data collection in the field or hunting through paper records in physical archives. These data sources include digital elevation models (DEMs), watershed boundaries, land cover and zoning categorization, soil surveys, historical climatology data, flow/depth monitoring data, and intensity-duration-frequency (IDF) statistics to describe local rainfall characteristics. Jupyter Notebook is an interactive web-based platform used in data science projects, often by non-programmers. Notebooks allow scripters to view the output of code incrementally more easily than traditional development environments, which makes them a useful platform for beginners and seasoned modelers alike. Developing simple notebooks that interface with online data allows modelers to collect, prepare, and review the input parameters required to build or improve stormwater models more quickly and more thoroughly than ever before.

This presentation will provide an overview of the current data sources that are available to modelers (and model reviewers from regulatory agencies and local governments), as well as a live demonstration of how to use these resources to quickly build a stormwater model from scratch. The demonstration will be given within an interactive Jupyter Notebook, using a combination of code cells, code outputs (including graphs and summary tables), and documentation. The presentation will also include some scripting resources and demonstrations, including how to use ChatGPT to generate code. The resulting Jupyter Notebook will give an overview of the model building process from beginning to end, including data collection, compilation, and review, as well as automated summary and assessment of results.

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