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| Titel |
Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework |
| VerfasserIn |
M. S. Raleigh, J. D. Lundquist, M. P. Clark |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 7 ; Nr. 19, no. 7 (2015-07-20), S.3153-3179 |
| Datensatznummer |
250120765
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| Publikation (Nr.) |
 copernicus.org/hess-19-3153-2015.pdf |
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| Zusammenfassung |
| Physically based models provide insights into key hydrologic processes but
are associated with uncertainties due to deficiencies in forcing data, model
parameters, and model structure. Forcing uncertainty is enhanced in
snow-affected catchments, where weather stations are scarce and prone to
measurement errors, and meteorological variables exhibit high variability.
Hence, there is limited understanding of how forcing error characteristics
affect simulations of cold region hydrology and which error characteristics
are most important. Here we employ global sensitivity analysis to explore how
(1) different error types (i.e., bias, random errors), (2) different error
probability distributions, and (3) different error magnitudes influence
physically based simulations of four snow variables (snow water equivalent,
ablation rates, snow disappearance, and sublimation). We use the Sobol' global
sensitivity analysis, which is typically used for model parameters but
adapted here for testing model sensitivity to coexisting errors in all
forcings. We quantify the Utah Energy Balance model's sensitivity to forcing
errors with 1 840 000 Monte Carlo simulations across four sites and five
different scenarios. Model outputs were (1) consistently more sensitive to
forcing biases than random errors, (2) generally less sensitive to forcing
error distributions, and (3) critically sensitive to different forcings
depending on the relative magnitude of errors. For typical error magnitudes
found in areas with drifting snow, precipitation bias was the most important
factor for snow water equivalent, ablation rates, and snow disappearance
timing, but other forcings had a more dominant impact when precipitation
uncertainty was due solely to gauge undercatch. Additionally, the relative
importance of forcing errors depended on the model output of interest.
Sensitivity analysis can reveal which forcing error characteristics matter
most for hydrologic modeling. |
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