A Low-Cost, Open-Source Snow Sensing Station Design for Increasing the Spatial Distribution of Snow Observations

Accurate snow monitoring is critical for understanding hydrological processes and managing water resources. However, traditional snow sensing networks in the United States, such as the United States Department of Agriculture’s (USDA) SNOwpack TELemetry (SNOTEL) system, are costly and limited in spatial coverage. This study presents the design and deployment of a lower-cost, open-source snow sensing station aimed at improving the accessibility and affordability of snow hydrology monitoring. The system integrates research-grade environmental sensors with an Arduino-based Mayfly datalogger, providing high temporal resolution measurements of snow depth, radiation fluxes, air and soil temperatures, and soil moisture. Designed for adaptability, the station supports multiple sensor types, various power configurations—including solar and battery-only setups—multiple telemetry options, and capability for diverse deployment environments, including forested and open terrain. A multi-site case study at Tony Grove Ranger Station in northern Utah, USA demonstrated the station’s performance across different physiographic conditions. Results show that the system significantly reduces costs while increasing the spatial resolution of data, offering a scalable solution for enhancing snow monitoring networks. This study contributes an open-source hardware and software design that facilitates replication and adaptation by other researchers, supporting advancements in snow hydrology research.