Abstract
AbstractIn 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.
Highlights
The water mass stored in the seasonal snowpack and the timing and intensity of the melt water release is crucial for water availability in many regions of the world and has wide implications for water resources management, impact studies, and risk assessments concerning drought and flood potential
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
Over the whole analyzed period model skill variance was governed in the order of parameter choice < model structure < input data error
Summary
The water mass stored in the seasonal snowpack and the timing and intensity of the melt water release is crucial for water availability in many regions of the world and has wide implications for water resources management, impact studies, and risk assessments concerning drought and flood potential. Snowmelt models able to simulate individual physical processes rather than models mapping melt rates merely to a temperature input are advantageous for many applications. In complex situations such as rain on snow events, on climatic extremes, and for avalanche forecasting, physically based snowpack models are indispensable tools. Unlike in type 3 models, snow layers in physically based models of medium complexity (model type 2; e.g., Marks et al, 1998) do not mimic real-world layering; rather, they are numerical constructs required to simulate vertical mass and energy fluxes in the snowpack
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