001048831 001__ 1048831
001048831 005__ 20251209202150.0
001048831 037__ $$aFZJ-2025-04939
001048831 082__ $$a690
001048831 1001_ $$0P:(DE-Juel1)195753$$aGuan, Buliao$$b0$$eFirst author
001048831 245__ $$aIdentifying uncertainties in snowpack variables using ensemble-based simulations of CLM5 over Alpine Sites
001048831 260__ $$aAmsterdam [u.a.]$$bElsevier$$c2025
001048831 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1765263810_604
001048831 3367_ $$2ORCID$$aWORKING_PAPER
001048831 3367_ $$028$$2EndNote$$aElectronic Article
001048831 3367_ $$2DRIVER$$apreprint
001048831 3367_ $$2BibTeX$$aARTICLE
001048831 3367_ $$2DataCite$$aOutput Types/Working Paper
001048831 520__ $$aSnow 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.
001048831 536__ $$0G:(DE-HGF)POF4-2152$$a2152 - Water resources and the environment (POF4-215)$$cPOF4-215$$fPOF IV$$x0
001048831 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x1
001048831 536__ $$0G:(GEPRIS)495864060$$aSFB 1502 C04 - Assimilation von Schnee-Satellitendaten und ihre Auswirkungen auf den hydrologischen Kreislauf und die atmosphärischen Flüsse (C04) (495864060)$$c495864060$$x2
001048831 7001_ $$0P:(DE-Juel1)169794$$aNaz, Bibi$$b1$$eCorresponding author
001048831 7001_ $$0P:(DE-Juel1)138662$$aHendricks-Franssen, Harrie-Jan$$b2
001048831 7001_ $$0P:(DE-Juel1)129549$$aVereecken, Harry$$b3
001048831 773__ $$0PERI:(DE-600)1473173-3$$tJournal of hydrology$$x0022-1694$$y2025
001048831 8564_ $$uhttps://juser.fz-juelich.de/record/1048831/files/Guan_et_al_2025_preprint.pdf$$yRestricted
001048831 909CO $$ooai:juser.fz-juelich.de:1048831$$pVDB
001048831 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)195753$$aForschungszentrum Jülich$$b0$$kFZJ
001048831 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)169794$$aForschungszentrum Jülich$$b1$$kFZJ
001048831 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138662$$aForschungszentrum Jülich$$b2$$kFZJ
001048831 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129549$$aForschungszentrum Jülich$$b3$$kFZJ
001048831 9131_ $$0G:(DE-HGF)POF4-215$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2152$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vTerrestrische Umwelt und Wasserressourcen: Dynamiken unter globalem Wandel und Klimawandel$$x0
001048831 9131_ $$0G:(DE-HGF)POF4-217$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2173$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vFür eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten$$x1
001048831 9141_ $$y2025
001048831 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2025-01-07$$wger
001048831 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bJ HYDROL : 2022$$d2025-01-07
001048831 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2025-01-07
001048831 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2025-01-07
001048831 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2025-01-07
001048831 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2025-01-07
001048831 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2025-01-07
001048831 915__ $$0StatID:(DE-HGF)1060$$2StatID$$aDBCoverage$$bCurrent Contents - Agriculture, Biology and Environmental Sciences$$d2025-01-07
001048831 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology$$d2025-01-07
001048831 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2025-01-07
001048831 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2025-01-07
001048831 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2025-01-07
001048831 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2025-01-07
001048831 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2025-01-07
001048831 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bJ HYDROL : 2022$$d2025-01-07
001048831 920__ $$lyes
001048831 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0
001048831 980__ $$apreprint
001048831 980__ $$aVDB
001048831 980__ $$aI:(DE-Juel1)IBG-3-20101118
001048831 980__ $$aUNRESTRICTED