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Title: Regional Snow Parameters Estimation for Large-Domain Hydrological Applications in the Western United States

Abstract

In snow-dominated regions, a critical source of uncertainty in hydrologic prediction and forecasting is the magnitude and distribution of snow water equivalent (SWE). With ensemble simulations, we demonstrate that SWE variability across the mountain ranges of the western United States (represented by 246 Snow Telemetry stations) can largely be captured at the daily time scale by a simple mass and energy-balance snow model with four physically reasonable parameters—three snow albedo parameters and one snow temperature threshold for precipitation partitioning. The model skill is lower in the maritime Pacific Northwest where SWE variability is more sensitive to errors associated with simulated energy balance (e.g., downward radiation fluxes) and the temperature-only precipitation partitioning approach. Poor model skill in high-altitude, windy locations in the Northern Rockies can be attributed to precipitation undercatch and underrepresented wind processes. For the purpose of large-domain hydrologic applications, regional snow parameters were developed for eight ecoregions characterized by a distinct hydroclimatic regime across the western United States. Results indicate that regionally coherent snow parameterizations are able to capture daily variations in SWE at most Snow Telemetry stations, suggesting that areas with a similar hydroclimate share a similar snow regime. While the three albedo parameters show limited spatial variabilitymore » across all regions, the regional snow temperature threshold (Ts) shows marked spatial variation correlated with relative humidity; the Ts values increase from 0.2 °C in the higher-humidity Pacific Northwest to 4.0 °C in the colder, lower-humidity Rocky Mountains.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1];  [1]; ORCiD logo [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Univ. of Washington, Seattle, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1532548
Report Number(s):
PNNL-SA-134653
Journal ID: ISSN 2169-897X
Grant/Contract Number:  
AC05-76RL01830; RC‐2546
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Geophysical Research: Atmospheres
Additional Journal Information:
Journal Volume: 124; Journal Issue: 10; Journal ID: ISSN 2169-897X
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; snow modeling skill, DHSVM, Western United States, SNOTEL, predictive uncertainty, parameter sensitivity and transferability

Citation Formats

Sun, Ning, Yan, Hongxiang, Wigmosta, Mark S., Leung, L. Ruby, Skaggs, Richard, and Hou, Zhangshuan. Regional Snow Parameters Estimation for Large-Domain Hydrological Applications in the Western United States. United States: N. p., 2019. Web. doi:10.1029/2018JD030140.
Sun, Ning, Yan, Hongxiang, Wigmosta, Mark S., Leung, L. Ruby, Skaggs, Richard, & Hou, Zhangshuan. Regional Snow Parameters Estimation for Large-Domain Hydrological Applications in the Western United States. United States. doi:10.1029/2018JD030140.
Sun, Ning, Yan, Hongxiang, Wigmosta, Mark S., Leung, L. Ruby, Skaggs, Richard, and Hou, Zhangshuan. Tue . "Regional Snow Parameters Estimation for Large-Domain Hydrological Applications in the Western United States". United States. doi:10.1029/2018JD030140.
@article{osti_1532548,
title = {Regional Snow Parameters Estimation for Large-Domain Hydrological Applications in the Western United States},
author = {Sun, Ning and Yan, Hongxiang and Wigmosta, Mark S. and Leung, L. Ruby and Skaggs, Richard and Hou, Zhangshuan},
abstractNote = {In snow-dominated regions, a critical source of uncertainty in hydrologic prediction and forecasting is the magnitude and distribution of snow water equivalent (SWE). With ensemble simulations, we demonstrate that SWE variability across the mountain ranges of the western United States (represented by 246 Snow Telemetry stations) can largely be captured at the daily time scale by a simple mass and energy-balance snow model with four physically reasonable parameters—three snow albedo parameters and one snow temperature threshold for precipitation partitioning. The model skill is lower in the maritime Pacific Northwest where SWE variability is more sensitive to errors associated with simulated energy balance (e.g., downward radiation fluxes) and the temperature-only precipitation partitioning approach. Poor model skill in high-altitude, windy locations in the Northern Rockies can be attributed to precipitation undercatch and underrepresented wind processes. For the purpose of large-domain hydrologic applications, regional snow parameters were developed for eight ecoregions characterized by a distinct hydroclimatic regime across the western United States. Results indicate that regionally coherent snow parameterizations are able to capture daily variations in SWE at most Snow Telemetry stations, suggesting that areas with a similar hydroclimate share a similar snow regime. While the three albedo parameters show limited spatial variability across all regions, the regional snow temperature threshold (Ts) shows marked spatial variation correlated with relative humidity; the Ts values increase from 0.2 °C in the higher-humidity Pacific Northwest to 4.0 °C in the colder, lower-humidity Rocky Mountains.},
doi = {10.1029/2018JD030140},
journal = {Journal of Geophysical Research: Atmospheres},
number = 10,
volume = 124,
place = {United States},
year = {2019},
month = {5}
}

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