Increasing the Spatial and Temporal Resolution of Snow Water Equivalent Estimates Using SNOTEL Data and Low‐Cost Sensing

Accurate estimation of snow water equivalent (SWE) is critical for understanding hydrologic processes in snow‐dominated watersheds. This study presents a method for improving the spatial and temporal resolution of SWE estimates using low‐cost snow depth sensors, manual snow sampling, and snow density data from nearby SNOwpack TELemetry (SNOTEL) stations. Snow depth measurements were collected from five low‐cost monitoring stations installed surrounding the Tony Grove Ranger Station (TGRS) SNOTEL station in northern Utah, USA, employing an Arduino Mayfly data logger and a MaxBotix MB7374 ultrasonic sensor. Linear regression models were developed to estimate snow density at each low‐cost station based on the SNOTEL data, enabling the computation of SWE time series with associated confidence intervals at low‐cost stations. Results show significant variability in snow accumulation across different physiographic settings, with notable differences driven by solar exposure, aspect, and land cover type. While SWE estimates at some stations closely matched the SNOTEL station, others exhibited substantial deviations, highlighting the importance of localized physiographic influences on snow distribution. This method demonstrates the potential for expanding lower cost snow monitoring networks for creating data that can improve hydrologic modeling accuracy and enhancing water resource management in watersheds with snow‐driven hydrology.