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@ARTICLE{Guan:1048831,
      author       = {Guan, Buliao and Naz, Bibi and Hendricks-Franssen,
                      Harrie-Jan and Vereecken, Harry},
      title        = {{I}dentifying uncertainties in snowpack variables using
                      ensemble-based simulations of {CLM}5 over {A}lpine {S}ites},
      journal      = {Journal of hydrology},
      issn         = {0022-1694},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2025-04939},
      year         = {2025},
      abstract     = {Snow plays a pivotal role in the hydrological cycle,
                      influencing surface energy and water balances in the
                      snow-dominated regions. Land surface models such as CLM5
                      (Community Land Model version 5) offer a valuable tool to
                      study snow processes and their impact on water resources,
                      but identifying sources of uncertainties is important as
                      meteorological forcings and parameters errors limit the
                      accuracy of snow-related land surface model prediction. To
                      identify sources of uncertainties in snow simulation, we
                      conducted ensemble-based simulations of CLM5 across 10 sites
                      in the French Alps for the period 1991-2022 using
                      meteorological inputs from bias corrected ECMWF Reanalysis
                      v5 (ERA5). Three ensemble experiments were performed to
                      isolate different sources of uncertainty by perturbing (1)
                      only snow-related parameters, (2) only atmospheric forcings,
                      and (3) both combined. Model performance was evaluated at
                      each site using 100 ensemble members against in-situ snow
                      depth (SD) and remotely sensed snow cover fraction (SCF)
                      observations using statistical metrics such as correlation
                      (R), Root Mean Square Error (RMSE) and ensemble spread skill
                      (SS). Our results show that observations are better captured
                      in the combined experiment, with forcing uncertainties
                      having a greater impact on model performance than parameter
                      uncertainties as reflected by relatively higher ensemble SS
                      values in both forcings and combined experiments (SD mean
                      SS: 0.133 for parameters, 0.222 for forcing, 0.292
                      combined). However, the low SS values indicate that the
                      ensemble spread does not fully capture the ensemble mean
                      error across all three experiments. Furthermore, the model
                      captures 30 observational variability more effectively
                      during the accumulation season than during the ablation
                      season, as reflected by higher SS values and lower RMSE in
                      the combined experiment (Snow depth ablation: mean SS:
                      0.299, and mean RMSE: 0.229m. Accumulation SS: 0.285, and
                      RMSE: 0.357m). In addition, CLM5 shows the lowest ensemble
                      skill for both snow depth (SD) and snow cover fraction (SCF)
                      for sites at high-elevation sites (>1800 m), while grassland
                      sites show better agreement with observations than forested
                      sites, likely due to the reduced complexity of snowpack
                      processes in the absence of canopy–snow interactions.
                      These findings are useful for improving uncertainty
                      characterization, reducing model errors, and support the
                      development of snowpack data assimilation in land surface
                      modeling.},
      cin          = {IBG-3},
      ddc          = {690},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2152 - Water resources and the environment (POF4-215) /
                      2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / SFB 1502 C04 - Assimilation von
                      Schnee-Satellitendaten und ihre Auswirkungen auf den
                      hydrologischen Kreislauf und die atmosphärischen Flüsse
                      (C04) (495864060)},
      pid          = {G:(DE-HGF)POF4-2152 / G:(DE-HGF)POF4-2173 /
                      G:(GEPRIS)495864060},
      typ          = {PUB:(DE-HGF)25},
      url          = {https://juser.fz-juelich.de/record/1048831},
}