Home > Publications database > Assessing uncertainties in snow-related variables using ensemble-based simulations of CLM5 over European Sites > print

001     1030407
005     20240905204428.0
037 _ _ |a FZJ-2024-05280
041 _ _ |a English
100 1 _ |a Guan, Buliao
|0 P:(DE-Juel1)195753
|b 0
|e First author
|u fzj
111 2 _ |a The 4th International Conference on Snow Hydrology
|g SnowHydrology2024
|c Grenoble
|d 2024-01-31 - 2024-02-02
|w France
245 _ _ |a Assessing uncertainties in snow-related variables using ensemble-based simulations of CLM5 over European Sites
260 _ _ |c 2024
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a Other
|2 DataCite
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a LECTURE_SPEECH
|2 ORCID
336 7 _ |a Conference Presentation
|b conf
|m conf
|0 PUB:(DE-HGF)6
|s 1725525662_16118
|2 PUB:(DE-HGF)
|x Plenary/Keynote
520 _ _ |a 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.
536 _ _ |a 2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)
|0 G:(DE-HGF)POF4-2173
|c POF4-217
|f POF IV
|x 0
536 _ _ |a DFG project 450058266 - SFB 1502: Regionaler Klimawandel: Die Rolle von Landnutzung und Wassermanagement (450058266)
|0 G:(GEPRIS)450058266
|c 450058266
|x 1
650 2 7 |a Geosciences
|0 V:(DE-MLZ)SciArea-140
|2 V:(DE-HGF)
|x 0
700 1 _ |a Naz, Bibi
|0 P:(DE-Juel1)169794
|b 1
|u fzj
700 1 _ |a Strebel, Lukas
|0 P:(DE-Juel1)177778
|b 2
|u fzj
700 1 _ |a Hendricks-Franssen, Harrie-Jan
|0 P:(DE-Juel1)138662
|b 3
|u fzj
700 1 _ |a Lannoy, Gabrielle De
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Suresh, Simran
|0 P:(DE-HGF)0
|b 5
856 4 _ |u https://snowhydro2024.sciencesconf.org/
909 C O |o oai:juser.fz-juelich.de:1030407
|p VDB
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)195753
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)169794
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)177778
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)138662
913 1 _ |a DE-HGF
|b Forschungsbereich Erde und Umwelt
|l Erde im Wandel – Unsere Zukunft nachhaltig gestalten
|1 G:(DE-HGF)POF4-210
|0 G:(DE-HGF)POF4-217
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-200
|4 G:(DE-HGF)POF
|v Für eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten
|9 G:(DE-HGF)POF4-2173
|x 0
914 1 _ |y 2024
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IBG-3-20101118
|k IBG-3
|l Agrosphäre
|x 0
980 _ _ |a conf
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)IBG-3-20101118
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21