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| Home > Publications database > Identifying uncertainties in snowpack variables using ensemble-based simulations of CLM5 over Alpine Sites > print |
| 001 | 1048831 | ||
| 005 | 20251209202150.0 | ||
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| 100 | 1 | _ | |a Guan, Buliao |0 P:(DE-Juel1)195753 |b 0 |e First author |
| 245 | _ | _ | |a Identifying uncertainties in snowpack variables using ensemble-based simulations of CLM5 over Alpine Sites |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2025 |b Elsevier |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1765263810_604 |2 PUB:(DE-HGF) |
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| 520 | _ | _ | |a 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. |
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| 536 | _ | _ | |a SFB 1502 C04 - Assimilation von Schnee-Satellitendaten und ihre Auswirkungen auf den hydrologischen Kreislauf und die atmosphärischen Flüsse (C04) (495864060) |0 G:(GEPRIS)495864060 |c 495864060 |x 2 |
| 700 | 1 | _ | |a Naz, Bibi |0 P:(DE-Juel1)169794 |b 1 |e Corresponding author |
| 700 | 1 | _ | |a Hendricks-Franssen, Harrie-Jan |0 P:(DE-Juel1)138662 |b 2 |
| 700 | 1 | _ | |a Vereecken, Harry |0 P:(DE-Juel1)129549 |b 3 |
| 773 | _ | _ | |y 2025 |0 PERI:(DE-600)1473173-3 |t Journal of hydrology |x 0022-1694 |
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