Why this exists, how it works, and what is next.
Hey, KC Mike here. This tool summarizes USGS river data so our local floating crew can pick the safest, most fun river with confidence.
It prioritizes how close today’s flow is to the ideal CFS for each river, then sorts by distance. The goal is simple: fewer bad floats, more great weekends.
A Python job runs every few hours, pulls the last five days of flow readings, normalizes them, then updates the live table.
Each sparkline shows the last five daily readings; red and amber lines flag rivers that are rising or over threshold.
The system uses a local SQLite database for rivers, creeks, and boat ramps, and a few scripts to parse MDC data. AI tools helped with the data pipeline and visualization.
The interface was converted to Vue. In plain terms, Vue keeps the page responsive and fast by updating only the parts that change, instead of reloading the whole page. That means smoother updates and a more stable experience on mobile.
Individual pages were created for each monitoring location so users can focus on a single river, share a direct link, and get richer detail.
If you are curious about the text parsing behind the scenes, here’s the core regex pattern used for identifying river names:
pattern = r'\b(\w+)\s+(Creek|River)\b'