Wind Driven Stratification in a Freshwater Ecosystem
The Bay Was Calm. The Hardware Was Not.
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University of Washington, Ocean 351, Winter 2026
Sample code, finished sesnsor in PVC pipe, early circut example.
Before this project, much of my problem-solving experience came from leadership roles where I focused on group dynamics, communication, and building community. Transitioning into ocean technology challenged me to apply those same skills to circuits and sensors that did not have any feedback besides fail codes. I learned that the same patience, persistence, and systematic thinking I have developed while working with the public, students, and peers were applicable when troubleshooting sensors and code.
In my ocean technology coursework, I developed and deployed environmental sensors to investigate how wind influences thermal stratification in Portage Bay, Seattle Washington. This project required sensor hardware wiring, coding, and data analysis to answer my question. I used MicroPython, R Studio and ThinkSpeak to code ESP8266 microcontrollers and worked with sensors measuring temperature, salinity, light, wave height, air and water pressure. The process rarely was straightforward. We spent time debugging code, diagnosing hardware malfunctions, and troubleshooting failed sensors pushed me to develop technical problem-solving skills that textbook learning could not teach. Each obstacle was mechanical, not knowing if the issue was in the code, wiring, or a broken sensor. This taught me how to think through problems methodically to find a solution.
The culmination of this course was to create our own research question analyzing data from two arrays of seven temperature sensors placed at different depths in Portage Bay, Seattle Washington. My research question was, do higher wind speeds correlate with lower thermal stratification (a more homogenous mix) as a result of greater vertical mixing. To answer this question, I combined my sensor building and coding skills, to clean and synchronize real datasets that crossed daylight savings, created data visualizations in R Studio, applied statistical analysis, and mapped data collection locations with Google Earth Pro. The results showed that stronger winds were associated with increased thermal stratification, opposing my hypothesis, showing the role of wind-driven mixing in creating a less homogeneous water column by transport of warmer water masses. The final project reflects not only the scientific question I posed and answered, but also how I learned to troubleshoot code, mechanical sensors, and group dynamics.
ocean351.mp4cchin_351Final.pdf
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