Precipitation Estimates and Orographic Gradients Using Snow, Temperature, and Humidity Measurements From a Wireless‐Sensor Network

Abstract

Mixed-phase precipitation in mountainous regions has large temporal and spatial variability (Buytaert et al., 2006;Costa-Cabral et al., 2013); and in comparatively warm environments like the Sierra Nevada, solid precipitation amounts (i.e., snowfall) are sensitive to small elevation-dependent temperature changes. Accurate estimates of precipitation partitioning into rain and snow are crucial as a primary input for hydrologic predictions and process studies (Henn et al., 2018;Morin et al., 2006;Sharma et al., 2012). This is because the amount and spatial pattern of mixed-phase winter precipitation in mountainous regions, where snowfall can account for a considerable portion of annual precipitation, play a crucial role in determining local-to-regional water supply and flood risk. For example, winter precipitation combined with snowpack storage in the Sierra Nevada provides the major water source for urban, irrigation, hydroelectric, and ecosystem uses in California (Bales et al., 2006;Pandey et al., 1999), where its Mediterranean climate leads to wet winters and dry summers. Besides the sparse availability (Avanzi et al., 2021), maintenance, and representativeness issues of precipitation gauges in high-elevation regions, the systematic bias introduced by wind-induced undercatch is also a considerable source of uncertainty (

A new approach to meteorological observations on remote polar glaciers using open-source internet of things technologies

“…Further details on the technology used were released later (Malek et al, 2019;Zhang et al, 2022). Cui et al (2022) used the WSN data to improve precipitation estimates and orographic effect, two long standing problems in mountain hydrology. Ioannou et al (2021) provide an extensive reflection on the diversity of technology and the complex set of choices one must make in designing a WSN system.…”