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@INPROCEEDINGS{Guan:1030407,
      author       = {Guan, Buliao and Naz, Bibi and Strebel, Lukas and
                      Hendricks-Franssen, Harrie-Jan and Lannoy, Gabrielle De and
                      Suresh, Simran},
      title        = {{A}ssessing uncertainties in snow-related variables using
                      ensemble-based simulations of {CLM}5 over {E}uropean
                      {S}ites},
      reportid     = {FZJ-2024-05280},
      year         = {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.},
      month         = {Jan},
      date          = {2024-01-31},
      organization  = {The 4th International Conference on
                       Snow Hydrology, Grenoble (France), 31
                       Jan 2024 - 2 Feb 2024},
      subtyp        = {Plenary/Keynote},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / DFG project 450058266 - SFB 1502: Regionaler
                      Klimawandel: Die Rolle von Landnutzung und Wassermanagement
                      (450058266)},
      pid          = {G:(DE-HGF)POF4-2173 / G:(GEPRIS)450058266},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1030407},
}