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

Assimilation of snow covered area into a hydrologic model

Accurate knowledge of water content in seasonal snow can be helpful for water resource management. In this study, a distributed temperature index snow model based on temperature and precipitation as forcing data, is used to estimate snow storage in the Jollie catchment approximately 20km east of the main divide of the central Southern Alps, New Zealand. The main objective is to apply a frequently used assimilation method, the ensemble Kalman square root filter, to assimilate remotely sensed snow covered area into the model and evaluate the impacts of this approach on simulations of snow water equivalent. A 250m resolution remotely sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS), specifically tuned to the study location was used. Temperature and precipitation were given on a 0.055 latitude/longitude grid. Precipitation was perturbed as input into the model, generating 100 ensemble members, which represented model error. Only observations of snow covered area that had less that 25% cloud cover classification were used in the assimilation precess. The error in the snow covered area observations was assumed to be 0.1 and grow linearly with cloud cover fraction up to 1 for a totally cloud covered pixel. As the model was not calibrated, two withholding experiments were conducted, in which observations withheld from the assimilation process were compared to the results. Two model states were updated in the assimilation, the total snow accumulation state variable and the total snow melt state variable. The results of this study indicate that the model underestimates snow storage at the end of winter and/or does not detect snow fall events during the ablation period. The assimilation method only affected simulated snow covered area and snow storage during the ablation period. That corresponded to higher correlation between modelled snow cover area and the updated state variables. Withholding experiments show good agreement between observations and simulated snow covered area. This study successfully applied the ensemble Kalman square root filter and showed its applicability for New Zealand conditions