Including Parameter Uncertainty in an Intercomparison of Physically-Based Snow Models
Snow models that solve coupled energy and mass balances require model parameters to be set, just like their conceptual counterparts. Despite the physical basis of these models, appropriate choices of the parameter values entail a rather high degree of uncertainty as some of them are not directly mea...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-10-01
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| Series: | Frontiers in Earth Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2020.542599/full |
| _version_ | 1819159340795297792 |
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| author | Daniel Günther Florian Hanzer Michael Warscher Richard Essery Ulrich Strasser |
| author_facet | Daniel Günther Florian Hanzer Michael Warscher Richard Essery Ulrich Strasser |
| author_sort | Daniel Günther |
| collection | DOAJ |
| description | Snow models that solve coupled energy and mass balances require model parameters to be set, just like their conceptual counterparts. Despite the physical basis of these models, appropriate choices of the parameter values entail a rather high degree of uncertainty as some of them are not directly measurable, observations are lacking, or values are not adaptable from literature. In this study, we test whether it is possible to reach the same performance with energy balance snow models of varying complexity by means of parameter optimization. We utilize a multi-physics snow model which enables the exploration of a multitude of model structures and model complexities with respect to their performance against point-scale observations of snow water equivalent and snowpack runoff observations, and catchment-scale observations of snow cover fraction and spring water balance. We find that parameter uncertainty can compensate structural model deficiencies to a large degree, so that model structures cannot be reliably differentiated within a calibration period. Even with deliberately biased forcing data, comparable calibration performances can be achieved. Our results also show that parameter values need to be chosen very carefully, as no model structure guarantees acceptable simulation results with random (but still physically meaningful) parameters. |
| first_indexed | 2024-12-22T16:39:00Z |
| format | Article |
| id | doaj.art-259a511cb749425686311c90bd6bd887 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-12-22T16:39:00Z |
| publishDate | 2020-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-259a511cb749425686311c90bd6bd8872022-12-21T18:19:54ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632020-10-01810.3389/feart.2020.542599542599Including Parameter Uncertainty in an Intercomparison of Physically-Based Snow ModelsDaniel Günther0Florian Hanzer1Michael Warscher2Richard Essery3Ulrich Strasser4Department for Geography, University of Innsbruck, Innsbruck, AustriaDepartment for Geography, University of Innsbruck, Innsbruck, AustriaDepartment for Geography, University of Innsbruck, Innsbruck, AustriaSchool of GeoSciences, University of Edinburgh, Edinburgh, United KingdomDepartment for Geography, University of Innsbruck, Innsbruck, AustriaSnow models that solve coupled energy and mass balances require model parameters to be set, just like their conceptual counterparts. Despite the physical basis of these models, appropriate choices of the parameter values entail a rather high degree of uncertainty as some of them are not directly measurable, observations are lacking, or values are not adaptable from literature. In this study, we test whether it is possible to reach the same performance with energy balance snow models of varying complexity by means of parameter optimization. We utilize a multi-physics snow model which enables the exploration of a multitude of model structures and model complexities with respect to their performance against point-scale observations of snow water equivalent and snowpack runoff observations, and catchment-scale observations of snow cover fraction and spring water balance. We find that parameter uncertainty can compensate structural model deficiencies to a large degree, so that model structures cannot be reliably differentiated within a calibration period. Even with deliberately biased forcing data, comparable calibration performances can be achieved. Our results also show that parameter values need to be chosen very carefully, as no model structure guarantees acceptable simulation results with random (but still physically meaningful) parameters.https://www.frontiersin.org/articles/10.3389/feart.2020.542599/fullenergy balance snow modellingmulti-physics modelparameter uncertaintyparameter calibrationmodel complexity |
| spellingShingle | Daniel Günther Florian Hanzer Michael Warscher Richard Essery Ulrich Strasser Including Parameter Uncertainty in an Intercomparison of Physically-Based Snow Models Frontiers in Earth Science energy balance snow modelling multi-physics model parameter uncertainty parameter calibration model complexity |
| title | Including Parameter Uncertainty in an Intercomparison of Physically-Based Snow Models |
| title_full | Including Parameter Uncertainty in an Intercomparison of Physically-Based Snow Models |
| title_fullStr | Including Parameter Uncertainty in an Intercomparison of Physically-Based Snow Models |
| title_full_unstemmed | Including Parameter Uncertainty in an Intercomparison of Physically-Based Snow Models |
| title_short | Including Parameter Uncertainty in an Intercomparison of Physically-Based Snow Models |
| title_sort | including parameter uncertainty in an intercomparison of physically based snow models |
| topic | energy balance snow modelling multi-physics model parameter uncertainty parameter calibration model complexity |
| url | https://www.frontiersin.org/articles/10.3389/feart.2020.542599/full |
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