Exploring the world of water through science, code, and diverse perspectives—this is a platform to decipher natural phenomena from data.
About This Site
DeepFlows is a site run by a researcher specializing in groundwater science and geochemistry, dedicated to explaining the science and documenting the practice of data analysis. Rainwater seeps underground, flows slowly through aquifers, and eventually mixes with seawater along the coast. We decipher these "invisible movements of water," one step at a time, using numerical simulation and code.
This is not about memorizing textbook facts. From groundwater flow analysis with MODFLOW, to modeling water–rock chemical reactions with PHREEQC, to data visualization with Python, we explain the very methods used in real research—including the dead ends and the trial-and-error that rarely make it into published papers. The content is written for students learning hydrogeology, engineers in the water-resource and environmental fields, and anyone curious about numerical analysis and data science.
The figures and code on this site are built with an AI assistant (Claude Code). Scripts that compute molecular geometry and draw the figures, cross-checks of every number against its primary source, read-throughs of finished drafts—much of the hands-on work is delegated. What is not: deciding what to write, how to explain it, and whether a number can be trusted. Those remain entirely the author's. The rule that nothing goes in unless it can be traced to a source has not changed either. How to work alongside AI is itself one of this site's subjects.