Role of extreme snowfall events in interannual variability of snowfall accumulation in the western United States. Issue 4 (2nd April 2014)

• Record Type: Journal Article
• Authors: Lute, A. C.; Abatzoglou, J. T.
• Is Part Of: Water resources research. Volume 50:Issue 4(2014:Apr.)
• Page Start: 2874
• Page End: 2888
• Publication Date: 2014-04-02
• Subjects: Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2013WR014465
• Languages: English
• ISSNs: 0043-1397

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>Water resources in the western United States are contingent on interannual variations in snowpack. Interannual snowpack variability has been attributed to large‐scale climate patterns including the El Niño‐Southern Oscillation (ENSO), however, the contribution of snowfall frequency and extreme snowfall events to this variability are less well quantified. Long‐term records from Snowpack Telemetry and Cooperative Observer Program stations in the 11 western states were used to investigate these relationships by considering the number of snowfall days and snowfall water equivalent (SFE) of extreme snowfall events. The top decile of snowfall events contributed 20–38% of annual SFE, depending on the region. An average of 65% and 69% of the interannual variability in annual SFE was explained by snowfall days and SFE of top decile snowfall events, respectively, with extreme events being a more significant predictor at most stations. The latitudinal dipole in SFE during ENSO phases results from changes in snowfall frequency and extreme events. In the Pacific Northwest, above normal SFE during La Niña winters was a product of both larger contributions from extremes and more snowfall days, while below normal SFE during El Niño winters was primarily associated with a substantial reduction in extremes. Conversely, annual SFE during ENSO phases in the mountains of Arizona was more closely linked to fluctuations in snowfall days than extremes. Results indicate the importance of extreme snowfall events in shaping interannual variability in water resources and suggest that improved predictive ability may inform better water resource management now and in the coming decades.</p> </abstract>