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Global Water Futures
Global Water Futures Observatories (GWFO)
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Global Institute for Water Security (GIWS)
International Network of Alpine Research Catchment Hydrology (INARCH 2)
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Uncertainties in Snowpack Simulations - Assessing the Impact of Model Structure
Section 1: Publication
Günther, D., Marke, T., Essery, R. and Strasser, U.
Uncertainties in Snowpack Simulations - Assessing the Impact of Model Structure, Parameter and Forcing Data Error on Point-Scale Energy-Balance Snow Model Performance
WRR, 55, 2779-2800
Günther, D., Marke, T., Essery, R. and Strasser, U. (2019): Uncertainties in Snowpack Simulations - Assessing the Impact of Model Structure, Parameter and Forcing Data Error on Point-Scale Energy-Balance Snow Model Performance, WRR, 55, 2779-2800,
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.
Plain Language Summary
Section 2: Additional Information
GWF: Global Water Futures
INARCH: International Network of Alpine Research Catchment Hydrology
GWF-AWF: Agricultural Water Futures
INARCH1: International Network of Alpine Research Catchment Hydrology (Phase 1)
Section 3: Download
T-2021-11-12-t1WIVI1BRsUi9HKpt1eAILVA Publication 1.0