UC Merced Undergraduate Research Journal

Flooding and irrigation uncertainty in the Upper Merced River watershed present serious challenges for water managers and farmers. This project investigates how snowmelt, precipitation, and dam operations interact to influence river overflow and water availability ,especially near Yosemite National Park,. By comparing a dry water year (2022) with a wet year (2023), the project combines remote sensing data, streamflow records, and dam release patterns to model potential flood risks and seasonal irrigation supply. High-resolution snow data from the Airborne Snow Observatory (ASO) [1], which uses LiDAR to measure snow water equivalent (SWE), revealed significant snowpack differences between years. ENVI software was used to visualize snowmelt rates using band math and custom color lenses, while precipitation records from the National Oceanic and Atmospheric Administration (NOAA) [3] and river flow data from the California Data Exchange Center (CDEC) [2] helped map hydrological trends. Dam operation reports from the Merced Irrigation District (MID) [4] were manually compiled into an operational timeline. Results showed that although 2023 had greater SWE, the melt was slower and better regulated by MID dams, reducing immediate flood risk. In contrast, 2022’s lower snowpack melted rapidly, overwhelming limited flow controls. These findings support the adoption of more adaptive irrigation planning and early-warning systems tied to snowmelt dynamics. While the model remains simplified, it demonstrates how open-source data and remote sensing tools can enhance regional water management, especially under intensifying climate variability.