What can we learn from comparing glacio-hydrological models?

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Stoll, E.; Hanzer, F.; Oesterle, F.; Nemec, J.; Schöber, J. et al.: What can we learn from comparing glacio-hydrological models? In: Atmosphere 11 (2020), Nr. 9, 981. DOI: https://doi.org/10.3390/atmos11090981

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/10620

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Abstract: 
Glacio-hydrological models combine both glacier and catchment hydrology modeling and are used to assess the hydrological response of high-mountain glacierized catchments to climate change. To capture the uncertainties from these model combinations, it is essential to compare the outcomes of several model entities forced with the same climate projections. For the first time, we compare the results of two completely independent glacio-hydrological models: (i) HQsim-GEM and (ii) AMUNDSEN. In contrast to prevailing studies, we use distinct glacier models and glacier initialization times. At first glance, the results achieved for future glacier states and hydrological characteristics in the Rofenache catchment in ötztal Alps (Austria) appear to be similar and consistent, but a closer look reveals clear differences. What can be learned from this study is that low-complexity models can achieve higher accuracy in the calibration period. This is advantageous especially when data availability is weak, and priority is given to efficient computation time. Furthermore, the time and method of glacier initialization play an important role due to different data requirements. In essence, it is not possible to make conclusions about the model performance outside of the calibration period or more specifically in the future. Hence, similar to climate modeling, we suggest considering different modeling approaches when assessing future catchment discharge or glacier evolution. Especially when transferring the results to stakeholders, it is vital to transparently communicate the bandwidth of future states that come with all model results. © 2020 by the authors.
License of this version: CC BY 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2020
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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pos. country downloads
total perc.
1 image of flag of Germany Germany 45 53.57%
2 image of flag of United States United States 18 21.43%
3 image of flag of China China 10 11.90%
4 image of flag of Netherlands Netherlands 3 3.57%
5 image of flag of Austria Austria 3 3.57%
6 image of flag of Taiwan Taiwan 1 1.19%
7 image of flag of Peru Peru 1 1.19%
8 image of flag of Mexico Mexico 1 1.19%
9 image of flag of Europe Europe 1 1.19%
10 image of flag of Switzerland Switzerland 1 1.19%

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