Representing spatial variability of snow water equivalent in hydrologic and land-surface models: A review

• Published in: Water resources research 2011-07, Vol.47 (7), p.n/a
• Main Authors: Clark, Martyn P., Hendrikx, Jordy, Slater, Andrew G., Kavetski, Dmitri, Anderson, Brian, Cullen, Nicolas J., Kerr, Tim, Örn Hreinsson, Einar, Woods, Ross A.
• Format: Article
• Language: English
• Subjects: Freshwater; Headwaters; Hydrology; Ice; modelling; Prairies; Predation; River basins; Scientific apparatus & instruments; Snow; Snow-water equivalent; spatial variability; Standard deviation; Temperature gradients; Topography; Variability; variogram
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1029/2011WR010745

This paper evaluates the use of field data on the spatial variability of snow water equivalent (SWE) to guide the design of distributed snow models. An extensive reanalysis of results from previous field studies in different snow environments around the world is presented, followed by an analysis of field data on spatial variability of snow collected in the headwaters of the Jollie River basin, a rugged mountain catchment in the Southern Alps of New Zealand. In addition, area‐averaged simulations of SWE based on different types of spatial discretization are evaluated. Spatial variability of SWE is shaped by a range of different processes that occur across a hierarchy of spatial scales. Spatial variability at the watershed‐scale is shaped by variability in near‐surface meteorological fields (e.g., elevation gradients in temperature) and, provided suitable meteorological data is available, can be explicitly resolved by spatial interpolation/extrapolation. On the other hand, spatial variability of SWE at the hillslope‐scale is governed by processes such as drifting, sloughing of snow off steep slopes, trapping of snow by shrubs, and the nonuniform unloading of snow by the forest canopy, which are more difficult to resolve explicitly. Subgrid probability distributions are often capable of representing the aggregate‐impact of unresolved processes at the hillslope‐scale, though they may not adequately capture the effects of elevation gradients. While the best modeling strategy is case‐specific, the analysis in this paper provides guidance on both the suitability of several common snow modeling approaches and on the choice of parameter values in subgrid probability distributions. Key Points Spatial variability is shaped by a mix of processes across a hierarchy of scales Probability distributions effectively represent variability at unresolved scales Explicitly resolving all processes requires an extremely fine horizontal grid