Structure Identification for High-Dimensional Data in the Vicinity of Bear Lake

This report focuses on seven water quality measurements taken at 43 different depths on the Bear Lake, Utah-Idaho for the months of June - November in the years 2018 - 2023. These measurements create a high-dimensional dataset on which we apply state-of-the-art machine learning (ML) techniques to look for low-dimensional structure in the data. A similar effort was made for weather measurements taken near the lake. Our analysis reveals that water quality measurements tend to cluster (i.e., group together) by year, while weather measurements tend to cluster by time of the year. This in mind, we explore potential drivers of this strong year to year clustering in the water quality data. This includes an exploration of land use change as well as an exploration of water inflows/outflows. We find that measured inflows and outflows from the lake explain about 50% of the variability in the position of each year within the low dimensional representations of the platform data. With only six years to compare, it is difficult to know whether or not this phenomenon is due to chance, but the discovery motivates further exploration of the lasting impact of inflow/outflows on Bear Lake water quality measurements.