Insights into mountain precipitation and snowpack from a basin‐scale wireless‐sensor network. Issue 8 (4th August 2017)

• Record Type: Journal Article
• Authors: Zhang, Z.; Glaser, S.; Bales, R.; Conklin, M.; Rice, R.; Marks, D.
• Is Part Of: Water resources research. Volume 53:Issue 8(2017)
• Page Start: 6626
• Page End: 6641
• Publication Date: 2017-08-04
• Subjects: wireless‐sensor network -- rain‐snow transition -- mountain precipitation -- mountain snow; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2016WR018825
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: A spatially distributed wireless‐sensor network, installed across the 2154 km 2 portion of the 5311 km 2 American River basin above 1500 m elevation, provided spatial measurements of temperature, relative humidity, and snow depth in the Sierra Nevada, California. The network consisted of 10 sensor clusters, each with 10 measurement nodes, distributed to capture the variability in topography and vegetation cover. The sensor network captured significant spatial heterogeneity in rain versus snow precipitation for water‐year 2014, variability that was not apparent in the more limited operational data. Using daily dew‐point temperature to track temporal elevational changes in the rain‐snow transition, the amount of snow accumulation at each node was used to estimate the fraction of rain versus snow. This resulted in an underestimate of total precipitation below the 0°C dew‐point elevation, which averaged 1730 m across 10 precipitation events, indicating that measuring snow does not capture total precipitation. We suggest blending lower elevation rain gauge data with higher‐elevation sensor‐node data for each event to estimate total precipitation. Blended estimates were on average 15–30% higher than using either set of measurements alone. Using data from the current operational snow‐pillow sites gives even lower estimates of basin‐wide precipitation. Given the increasing importance of liquid precipitation in a warming climate, a strategy that blends distributed measurements of both liquid and solid precipitation will provide more accurate basin‐wide precipitation estimates, plus spatial and temporal patters of snow accumulation and melt in a basin. Key Points: Distributed sensor data captures spatial heterogeneity in rain versus snow precipitation that is not apparent in more limited operational data Distributed dew‐point temperature measurements provide accurate estimates of snow‐rain transition elevation during storms Distributed, representative measurements can improve operational estimates of snowpack water storage and melt across the basin