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

Abstract

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