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

This study reports on a blending approach using snowpack measurements from wireless-sensor network, gauge precipitation, and atmospheric-moisture data to estimate mountain precipitation amount phase. We applied the in California's American River basin, dense network consisting of over 130 sensor nodes distributed across upper, more snow-dominated part basin (≥1,500 m elevation). Analysis 60 events water years 2014–2017 showed that provides estimates orographic enhancement reduce uncertainty apparent snow undercatch by limited gauges. also infers total based during rain-on-snow events. The yielded median upper-basin gradients (OPGs) 0.57 km−1, smaller than also-positive lower-basin (<1,500 m) medians OPGs gauges gauge-based gridded set 1.23 1.00 respectively. However, 73% events, both product negative upper inconsistent with typically positive values network. Upper-basin were (p-values < 0.03) heavy related atmospheric rivers Sierra barrier jets milder revealing challenges for reliably measure large moisture transport strong winds. In headwater areas, is recommended as being accurate decision support, providing critical rain-versus-snow amounts complementing precipitation-gauge data.