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

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.Martyn P. Clark, Jordy Hendrikx, Andrew G. Slater, Dmitri Kavetski, Brian Anderson, Nicolas J. Cullen, Tim Kerr, Einar Örn Hreinsson and Ross A. Wood

Persistence of topographic controls on the spatial distribution of snow depth in rugged mountain terrain

[1] We model the spatial distribution of snow depth across a wind-dominated alpine basin using a geostatistical approach with a complex variable mean. Snow depth surveys were conducted at maximum accumulation from 1997 through 2003 in the 2.3 km 2 Green Lakes Valley watershed in Colorado. We model snow depth as a random function that can be decomposed into a deterministic trend and a stochastic residual. Three snow depth trends were considered, differing in how they model the effect of terrain parameters on snow depth. The terrain parameters considered were elevation, slope, potential radiation, an index of wind sheltering, and an index of wind drifting. When nonlinear interactions between the terrain parameters were included and a multiyear data set was analyzed, all five terrain parameters were found to be statistically significant in predicting snow depth, yet only potential radiation and the index of wind sheltering were found to be statistically significant for all individual years. Of the five terrain parameters considered, the index of wind sheltering was found to have the greatest effect on predicted snow depth. The methodology presented in this paper allows for the characterization of the spatial correlation of model residuals for a variable mean model, incorporates the spatial correlation into the optimization of the deterministic trend, and produces smooth estimate maps that may extrapolate above and below measured values

Implications of observation-enhanced energy-balance snowmelt simulations for runoff modeling of Alpine catchments

Snow is an important component of the water balance of many mountain watersheds worldwide. In a warming climate, snowmelt modeling and consequent soil water input, is often challenged by complex conditions such as rain-on-snow situations. This is why detailed physics-based snow models are increasingly being used. These models however have much higher input data requirements, where in many cases accurate forcing fields are very difficult to provide.This study investigates whether the latest advances in the development of snow model framework actually translate into improved discharge simulations. To this end we integrated a distributed multi-layer energy-balance snow model with two recently developed methods of updating snow model mass and energy fluxes using snow observations to improve snow accumulation and depletion predictions. Surface water input from these simulations was used as input for subsequent streamflow modeling of 25 catchments in the Swiss Alps over four hydrological years.Our analysis clearly demonstrates the benefits of accurate snow simulations for hydrological modeling in Alpine catchments. Simulations that included the flux updates improved streamflow predictions, and offered best performance at high elevation, where snow most prominently affected watershed hydrology. These results were consistently achieved when analyzing model performance over entire hydrological years, over the snowmelt season only, and for individual events

Simulated Soil Water Storage Effects on Streamflow Generation in a Mountainous Snowmelt Environment, Idaho, USA

Although soil processes affect the timing and amount of streamflow generated from snowmelt, they are often overlooked in estimations of snowmelt-generated streamflow in the western USA. The use of a soil water balance modelling approach to incorporate the effects of soil processes, in particular soil water storage, on the timing and amount of snowmelt generated streamflow, was investigated. The study was conducted in the Reynolds Mountain East (RME) watershed, a 38 ha, snowmelt-dominated watershed in southwest Idaho. Snowmelt or rainfall inputs to the soil were determined using a well established snow accumulation and melt model (Isnobal). The soil water balance model was first evaluated at a point scale, using periodic soil water content measurements made over two years at 14 sites. In general, the simulated soil water profiles were in agreement with measurements (P \u3c 0·05) as further indicated by high R2 values (mostly \u3e 0·85), y-intercept values near 0, slopes near 1 and low average differences between measured and modelled values. In addition, observed soil water dynamics were generally consistent with critical model assumptions. Spatially distributed simulations over the watershed for the same two years indicate that streamflow initiation and cessation are closely linked to the overall watershed soil water storage capacity, which acts as a threshold. When soil water storage was below the threshold, streamflow was insensitive to snowmelt inputs, but once the threshold was crossed, the streamflow response was very rapid. At these times there was a relatively high degree of spatial continuity of satiated soils within the watershed. Incorporation of soil water storage effects may improve estimation of the timing and amount of streamflow generated from mountainous watersheds dominated by snowmelt

Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

Repeated light detection and ranging (lidar) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km2 lidar-derived data set of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically based energy-and-mass-balance snow model with satellite, airborne and automated ground-based observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the conterminous United States. Independent validation data are scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation data set with substantial geographic coverage. Within 12 distinctive 500 × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 lidar acquisitions. This supplied a data set for constraining the uncertainty of upscaled lidar estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled lidar snow depths were then compared to the SNODAS estimates over the entire study area for the dates of the lidar flights. The remotely sensed snow depths provided a more spatially continuous comparison data set and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between lidar observations and SNODAS estimates were most drastic, providing insight into the causal influences of natural processes on model uncertainty

Comparison of geostatistical approaches dealing with the distribution of snow

This paper deals with a stochastic simulation. Snow cover, representing a regionalized variable, was studied and used as an input parameter for a stochastic simulation. The first step included basic statistical analysis of individual parameters of snow, e.g. snow height. In the next step, an analysis of relationships between the snow and the geomorphological parameters (altitude, slope and aspect) was conducted. The most current methods of spatial interpolation and multifactor evaluation are based on weighted regression relationships. Primarily, the use of conditional stochastic simulation was tested in a variety of software. The main aim of this investigation is to compare selected interpolation methods with stochastic simulation, based on the development of the values and on the evaluation of the incidence of extreme events. The study shall provide users with recommendations for selecting the optimal interpolation method and its application to real data.Web of Science4461360