Analyzing Effects of Beach Replenishment on Surf Breaks
From 2019 to 2021, I worked on a surf study with Nature Collective, an environmental NGO, to monitor the effects of 494,000 cubic yards of sand deposited on the shoreline of Encinitas and Solana Beach, CA. This was a novel study to monitor the change in surf breaks. This sand deposit was due to dredging The San Elijo Lagoon Restoration Project (SELRP). The California Coastal Commission, with encouragement from the Surfrider Foundation, requested a two-year study to determine any effects of the beach replenishment on local ocean waves. Surf breaks are an often overlooked resource, but provide many cultural and economic values to California and deserves protection.
My role in the project was to perform data analysis, and visualization. I first began with Nature Collective as an intern in high school and grew to be a co-author on the publication as I continued my work throughout college.
The study was conducted at nine surf spots, three as controls, and the rest as breaks near sand placement. Two types of data where collected, on shore monitoring and surf break mapping with GPS data. Me and the project lead, Timbo Stillinger, surfed at all the breaks with gps watches recording our location every second.
I first worked on analyzing this data in 2019, my senior year of high school, to determine an algorithm that sorted the GPS data points between paddling, take off points and wave riding. This was my first application of ArcGIS Pro and I manually sorted through the data to differentiate points that were only surfed waves and take off zones to validate the algorithm.
The rest of data visualization and analysis I did was primarily in 2021. This was when I learned how to code with Python and used packages such as Pandas, Matplotlib and Seaborn. I created figures of the take off locations at each break symbolized by their wave height and before and after sand placement. I calculated at each break the take off zone, peel angle, ride distance and ride time using the GPS data. Likewise visualized the number of surfers during surfability ratings of poor, fair, and good at each break during the study period and compared days observed with backwash.
This is a brief synopsis of the my role in the study and is not comprehensive of all the methods. Below is are demonstrations of the figures I created for the report. They include distribution histograms, box plots, polar plots, and spatial maps.
