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001030407 005__ 20240905204428.0
001030407 037__ $$aFZJ-2024-05280
001030407 041__ $$aEnglish
001030407 1001_ $$0P:(DE-Juel1)195753$$aGuan, Buliao$$b0$$eFirst author$$ufzj
001030407 1112_ $$aThe 4th International Conference on Snow Hydrology$$cGrenoble$$d2024-01-31 - 2024-02-02$$gSnowHydrology2024$$wFrance
001030407 245__ $$aAssessing uncertainties in snow-related variables using ensemble-based simulations of CLM5 over European Sites
001030407 260__ $$c2024
001030407 3367_ $$033$$2EndNote$$aConference Paper
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001030407 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1725525662_16118$$xPlenary/Keynote
001030407 520__ $$aSnow plays a pivotal role in the hydrological cycle, profoundly affecting surface energy and water balances, and exerting significant influence on floods and droughts in snow-dominated regions. The land surface model such as CLM5 (Community Land Model version 5) offers a valuable tool to study snow processes and their impact on water resources. However, due to uncertainties in meteorological forcing or model parameters, model simulations remain uncertain. To quantify this uncertainty, ensemble-based simulations of CLM5 were performed across 20 sites in French Alpine region to assess the impact of forcing data errors and parameter choices.  We applied perturbations to ERA5 and various snow-related parameters, particularly those associated with snow cover fraction (SCF), snow water equivalent (SWE), and snow depth (SD) using uncertainty ranges of model parameters and input data from the literature. We evaluate 100 model realization against SD, SCF and SWE observations, with a focus on assessing model performance using statistical metrics such as correlation, RMSE and ensemble spread skill. Our results shows that model performance is most sensitive to input data errors than to model parameters. Selection of best ensemble members also shows that model errors reduced significantly compared to full ensemble range. In future, this ensemble framework will be combined with an ensemble data assimilation algorithm to reduce these uncertainties in snowpack simulations.
001030407 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
001030407 536__ $$0G:(GEPRIS)450058266$$aDFG project 450058266 - SFB 1502: Regionaler Klimawandel: Die Rolle von Landnutzung und Wassermanagement (450058266)$$c450058266$$x1
001030407 65027 $$0V:(DE-MLZ)SciArea-140$$2V:(DE-HGF)$$aGeosciences$$x0
001030407 7001_ $$0P:(DE-Juel1)169794$$aNaz, Bibi$$b1$$ufzj
001030407 7001_ $$0P:(DE-Juel1)177778$$aStrebel, Lukas$$b2$$ufzj
001030407 7001_ $$0P:(DE-Juel1)138662$$aHendricks-Franssen, Harrie-Jan$$b3$$ufzj
001030407 7001_ $$0P:(DE-HGF)0$$aLannoy, Gabrielle De$$b4
001030407 7001_ $$0P:(DE-HGF)0$$aSuresh, Simran$$b5
001030407 8564_ $$uhttps://snowhydro2024.sciencesconf.org/
001030407 909CO $$ooai:juser.fz-juelich.de:1030407$$pVDB
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001030407 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)177778$$aForschungszentrum Jülich$$b2$$kFZJ
001030407 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138662$$aForschungszentrum Jülich$$b3$$kFZJ
001030407 9131_ $$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$$vFür eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten$$x0
001030407 9141_ $$y2024
001030407 920__ $$lyes
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