Role of temporal resolution on meteorological inputs for process-based snow modeling

Authors: SOHRABI, M - University Of Idaho; TONINA, D - University Of Idaho; BENJANKAR, R - Southern Illinois University; KUMAR, M - Duke University; Kormos, Patrick; Marks, Daniel

Submitted to: Hydrological Processes
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/8/2018
Publication Date: 7/18/2018
Citation: Sohrabi, M.M., Tonina, D., Benjankar, R., Kumar, M., Kormos, P.R., Marks, D. 2018. Role of temporal resolution on meteorological inputs for process-based snow modeling. Hydrological Processes. 32(19):2976-2989. https://doi.org/10.1002/hyp.13242.
DOI: https://doi.org/10.1002/hyp.13242

Interpretive Summary: This paper describes how data from a watershed-scale instrument deployed over the 5311 km2 American River (AMR) basin above Sacramento, California in the Sierra Nevada improves our understanding of how precipitation and snow are distributed over this important mountain basin. Combining snow data from the high elevation cluster sites with low elevation rain gauge data provides an improved estimate of total storm precipitation input to the AMR, and the high elevation snow data improve operational estimates of snow water storage, snowmelt timing and amount.

Technical Abstract: A spatially distributed wireless-sensor network, installed across the 2154 km2 portion of the 5311 km2 American River basin above 1500 m elevation, provided spatial measurements of temperature, relative humidity and snow depth. 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 showed significant heterogeneity in rain versus snow precipitation that was not apparent in more-limited operational data. Using daily dew-point temperature and the amount of snow accumulation at each node to estimate the fraction of rain versus snow resulted in an underestimate of total precipitation below the 0 oC dew-point elevation, which averaged 1730 m across 10 precipitation events. Blending lower-elevation rain-gauge data with higher-elevation sensor-node data for each event provided precipitation estimates that 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. The distributed, representative sensor-network measurements also improve upon operational estimates of snowpack water storage, snowmelt amount and snowmelt timing across the basin.