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

Abstract

[1] 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.

Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model

“…Considerable spatial subgrid variability in SWE and SD is caused, among others, by local precipitation patterns and by wind redistribution of snow, which again is dependent on the type of vegetation and topography (e.g. Armstrong and Brun, 2008;Clark et al, 2011). As an example of the observational uncertainty, the high-resolution SD measurements across the Hardangervidda mountain plateau (Ragulina et al, 2011;see Sect.…”

“…Secondly, there is the uncertainty connected to the rather substantial natural spatial variability in snow conditions within the model 1 × 1 km grid-cells (see e.g. Clark et al, 2011), and to how well the point-and snow course-based measurements can capture this variability. Finally, there is also the uncertainty connected to the snow observations, although this uncertainty is often relatively small when compared to the spatial variability, for example (except for density, which can sometimes be demanding to measure).…”

Analysis of Regional Difference in Altitude Dependence of Snow Depth Using High Resolve Numerical Experiments

“…Understanding the snow cover distribution is important to improve prediction of snow related disasters. The primary factors that affect snow cover distribution are related to the horizontal scale (Seyfried and Wilcox 1995;Clark et al 2011). The predominant elements of snow cover depend on the horizontal scale, surface roughness and presence of an individual tree or shrub on a point scale, effects of drifting and avalanching on a hillslope scale (0.001−0.1 km), freezing level and melt energy on a watershed scale (0.1−10 km), and horizontal precipitation gradients on a regional scale (10−1000 km).…”

Scaling, similarity, and the fourth paradigm for hydrology

“…Such controls of water table depth on runoff production and evapotranspiration on catchment scales represent just one hypothesis of similarity and scaling behavior -an example alternative hypothesis, used in the variable infiltration capacity (VIC) model (Liang et al, 1994), is the description of how subelement variability in soil moisture affects the development of saturated areas in a catchment and the partitioning of precipitation into surface runoff and infiltration (Moore and Clarke, 1981;Dümenil and Todini, 1992;Wood et al, 1992;Hagemann and Gates, 2003). Other scaling hypotheses are used for other physical processes, for example, how small-scale variability in snow affects large-scale snow melt (Luce et al, 1999;Liston, 2004;Clark et al, 2011a) and how energy fluxes for individual leaves scale up to the vegetation canopy (de Pury and Farquar, 1997;Wang and Leuning, 1998).…”