Uncertainties in Snowpack Simulations—Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point‐Scale Energy Balance Snow Model Performance

Abstract

In this study, we assess the impact of forcing data errors, model structure, and parameter choices on 1‐D snow simulations simultaneously within a global variance‐based sensitivity analysis framework. This approach allows inclusion of interaction effects, drawing a more representative picture of the resulting sensitivities. We utilize all combinations of a multiphysics snowpack model to mirror the influence of model structure. Uncertainty ranges of model parameters and input data are extracted from the literature. We evaluate a suite of 230,000 model realizations at the snow monitoring station Kühtai (Tyrol, Austria, 1,920 m above sea level) against snow water equivalent observations. The results show throughout the course of 25 winter seasons (1991–2015) and different model performance criteria a large influence of forcing data uncertainty and its interactions on the model performance. Mean interannual total sensitivity indices are in the general order of parameter choice < model structure < forcing error, with precipitation, air temperature, and the radiative forcings controlling the variance during the accumulation period and air temperature and longwave irradiance controlling the variance during the ablation period, respectively. Model skill is highly sensitive to the snowpack liquid water transport scheme throughout the whole winter period and to albedo representation during the ablation period. We found a sufficiently long evaluation period (>10 years) is required for robust averaging. A considerable interaction effect was revealed, indicating that an improvement in the knowledge (i.e., reduction of uncertainty) of one factor alone might not necessarily improve model results.

Regional Snow Parameters Estimation for Large‐Domain Hydrological Applications in the Western United States

“…We evaluated the snow model parameter sensitivity for the full winter season from 2007 to 2013. As suggested by Günther et al (2019) through model sensitivity analyses conducted for a snow monitoring station in the Northern Calcareous Alps (Tyrol, Austria), snow albedo parameters exhibited higher sensitivity during the melt season than the accumulation season. In this regard, future sensitivity evaluations could be performed separately for the accumulation and melt period to better understand the parameter behaviors and their implications for snow processes.…”

Towards the assimilation of satellite reflectance into semi-distributed ensemble snowpack simulations

“…The avalanche forecasting services of some countries use a chain composed of meteorological forcings, coming from either a Numerical Weather Prediction model (NWP) or observations, and a detailed multilayer snowpack model such as Crocus (Vionnet et al, 2012) or SNOWPACK (Lehning et al, 2002). Both meteorological forcings and snowpack modelling induce errors and uncertainties in the simulations (Essery et al, 2013;Vernay et al, 2015;Raleigh et al, 2015;Günther et al, 2019). These errors are considerably limiting the use of snowpack models by avalanche hazard forecasters (Morin et al, 2018).…”

Improving Medium‐Range Forecasts of Rain‐on‐Snow Events in Prealpine Areas

“…The ground heat flux, which was not considered in this study, has been shown to provide less than 1% of total energy input and is thus of minor importance during RoS events (Würzer et al, ). Finally, it should be noted that the application of physically based snow energy balance models is found to be sensitive to the parameter choice, the model structure, and the forcing error, even when applied at the point scale (Günther et al, ). An application of such approaches at the catchment scale is further hampered by the high temporal and spatial variability of input variables and the lack of corresponding measurements at this scale.…”