Improvement of springtime streamflow prediction using a snow hydrology model aided with USDA SNOTEL and in-situ snowpit observations

• Published in: Hydrology Research Vol. 53; no. 12; pp. 1510 - 1528
• Main Authors: Kang, DoHyuk, Lee, Kyungtae, Kim, Edward
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.12.2022
• Subjects: Atmospheric models; Cold; Freshwater resources; Hydrologic models; Hydrologic observations; Hydrology; in-situ snowpit observation; Melting point; Melting points; Methods; Modelling; Physical properties; Predictions; Remote sensing; Simulation; snotel station; Snow; Snow hydrology; snow hydrology model; Snowmelt; snowmelt-dominant basin; Stream discharge; Stream flow; streamflow; Telemetry; Temperature; Temperature index; Watersheds; Weather; United States > US; Colorado
• ISSN: 0029-1277; 1998-9563; 2224-7955
• DOI: 10.2166/nh.2022.180

Abstract Estimating the streamflow driven by snowmelt in rugged mountain watersheds is difficult. Challenges are associated with the limited observations of hydrologic and meteorological datasets and inadequate implementation of the snow hydrology models. This study aims to improve streamflow prediction during the snowmelt season using a snow hydrology model aided by field observations. When the point-based weather forcing data and in-situ snowpit measurements exist in or near a small-scale (2–3 km2) watershed, the hydrologic model demonstrated an improved streamflow prediction during the snowmelt period. A snow hydrology model was applied to the Senator Beck Basin (SBB) in Colorado to improve the streamflow prediction. A temperature index method was implemented in the hydrological model to accommodate the snowmelt routine, which releases water as a multiplication factor for a grid temperature surplus above the melting point. The temperature index was adjusted using in-situ snowpit observations collected in the SBB by the NASA SnowEx Year-1 campaign in February 2017. Using the determined temperature index and weather forcing data from the nearby USDA snow observation telemetry station, the Nash-Sutcliffe Efficiency of the simulated streamflow was elucidated with a value of 0.88 against the observed streamflow during April 1–22, 2017.