Articles | Volume 19, issue 7
https://doi.org/10.5194/hess-19-3153-2015
https://doi.org/10.5194/hess-19-3153-2015
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, J. D. Lundquist, and M. P. Clark

Related authors

Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data
Cécile B. Ménard, Richard Essery, Alan Barr, Paul Bartlett, Jeff Derry, Marie Dumont, Charles Fierz, Hyungjun Kim, Anna Kontu, Yves Lejeune, Danny Marks, Masashi Niwano, Mark Raleigh, Libo Wang, and Nander Wever
Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019,https://doi.org/10.5194/essd-11-865-2019, 2019
Short summary
ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018,https://doi.org/10.5194/gmd-11-5027-2018, 2018
Short summary

Related subject area

Subject: Hydrometeorology | Techniques and Approaches: Uncertainty analysis
Quantifying spatiotemporal and elevational precipitation gauge network uncertainty in the Canadian Rockies
André Bertoncini and John W. Pomeroy
Hydrol. Earth Syst. Sci., 29, 983–1000, https://doi.org/10.5194/hess-29-983-2025,https://doi.org/10.5194/hess-29-983-2025, 2025
Short summary
Toward merging MOPEX and CAMELS hydrometeorological datasets: compatibility and statistical comparison
Katharine Owen Sink and Tom Brikowski
EGUsphere, https://doi.org/10.5194/egusphere-2024-4182,https://doi.org/10.5194/egusphere-2024-4182, 2025
Short summary
Comparison of BARRA and ERA5 in Replicating Mean and Extreme Precipitation over Australia
Kevin K. W. Cheung, Fei Ji, Nidhi Nishant, Jin Teng, James Bennett, and De Li Liu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-286,https://doi.org/10.5194/hess-2024-286, 2024
Revised manuscript accepted for HESS
Short summary
Comparison of high-resolution climate reanalysis datasets for hydro-climatic impact studies
Raul R. Wood, Joren Janzing, Amber van Hamel, Jonas Götte, Dominik L. Schumacher, and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2905,https://doi.org/10.5194/egusphere-2024-2905, 2024
Short summary
Diurnal Variability of Global Precipitation: Insights from Hourly Satellite and Reanalysis Datasets
Rajani Kumar Pradhan, Yannis Markonis, Francesco Marra, Efthymios I. Nikolopoulos, Simon Michael Papalexiou, and Vincenzo Levizzani
EGUsphere, https://doi.org/10.5194/egusphere-2024-1626,https://doi.org/10.5194/egusphere-2024-1626, 2024
Short summary

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.
Download
Short summary
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.
Share