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| Home > Publications database > Assessing uncertainties in snow-related variables using ensemble-based simulations of CLM5 over European Sites |
| Conference Presentation (Plenary/Keynote) | FZJ-2024-05280 |
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2024
Abstract: Snow plays a pivotal role in the hydrological cycle, profoundly affecting surface energy and water balances, and exerting significant influence on floods and droughts in snow-dominated regions. The land surface model such as CLM5 (Community Land Model version 5) offers a valuable tool to study snow processes and their impact on water resources. However, due to uncertainties in meteorological forcing or model parameters, model simulations remain uncertain. To quantify this uncertainty, ensemble-based simulations of CLM5 were performed across 20 sites in French Alpine region to assess the impact of forcing data errors and parameter choices. We applied perturbations to ERA5 and various snow-related parameters, particularly those associated with snow cover fraction (SCF), snow water equivalent (SWE), and snow depth (SD) using uncertainty ranges of model parameters and input data from the literature. We evaluate 100 model realization against SD, SCF and SWE observations, with a focus on assessing model performance using statistical metrics such as correlation, RMSE and ensemble spread skill. Our results shows that model performance is most sensitive to input data errors than to model parameters. Selection of best ensemble members also shows that model errors reduced significantly compared to full ensemble range. In future, this ensemble framework will be combined with an ensemble data assimilation algorithm to reduce these uncertainties in snowpack simulations.
Keyword(s): Geosciences (2nd)
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