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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 19, issue 7
Hydrol. Earth Syst. Sci., 19, 3153–3179, 2015
https://doi.org/10.5194/hess-19-3153-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 19, 3153–3179, 2015
https://doi.org/10.5194/hess-19-3153-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 20 Jul 2015

Research article | 20 Jul 2015

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

M. S. Raleigh et al.

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Cited articles

Archer, G. E. B., Saltelli, A., and Sobol, I. M.: Sensitivity measures,anova-like Techniques and the use of bootstrap, J. Stat. Comput. Simul., 58, 99–120, https://doi.org/10.1080/00949659708811825, 1997.
Bales, R. C., Molotch, N. P., Painter, T. H., Dettinger, M. D., Rice, R., and Dozier, J.: Mountain hydrology of the western United States, Water Resour. Res., 42, W08432, https://doi.org/10.1029/2005WR004387, 2006.
Barnett, T. P., Pierce, D. W., Hidalgo, H. G., Bonfils, C., Santer, B. D., Das, T., Bala, G., Wood, A. W., Nozawa, T., Mirin, A. A., Cayan, D. R., and Dettinger, M. D.: Human-induced changes in the hydrology of the western United States, Science, 319, 1080–1083, https://doi.org/10.1126/science.1152538, 2008.
Baroni, G. and Tarantola, S.: A General Probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study, Environ. Model. Softw., 51, 26–34, https://doi.org/10.1016/j.envsoft.2013.09.022, 2014.
Bastola, S., Murphy, C., and Sweeney, J.: The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments, Adv. Water Resour., 34, 562–576, https://doi.org/10.1016/j.advwatres.2011.01.008, 2011.
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A sensitivity analysis is used to examine how error characteristics (type, distributions, and magnitudes) in meteorological forcing data impact outputs from a physics-based snow model in four climates. Bias and error magnitudes were key factors in model sensitivity and precipitation bias often dominated. However, the relative importance of forcings depended somewhat on the selected model output. Forcing uncertainty was comparable to model structural uncertainty as found in other studies.
A sensitivity analysis is used to examine how error characteristics (type, distributions, and...
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