Articles | Volume 20, issue 3
https://doi.org/10.5194/hess-20-1031-2016
https://doi.org/10.5194/hess-20-1031-2016
Research article
 | 
08 Mar 2016
Research article |  | 08 Mar 2016

Sensitivity analysis of runoff modeling to statistical downscaling models in the western Mediterranean

Benjamin Grouillet, Denis Ruelland, Pradeebane Vaittinada Ayar, and Mathieu Vrac

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Cited articles

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Benke, K. K., Lowell, K. E., and Hamilton, A. J.: Parameter uncertainty, sensitivity analysis and prediction error in a water-balance hydrological model, Math. Comp. Model., 47, 1134–1149, https://doi.org/10.1016/j.mcm.2007.05.017, 2008.
Brigode, P., Oudin, L., and Perrin, C.: Hydrological model parameter instability: A source of additional uncertainty in estimating the hydrological impacts of climate change?, J. Hydrol., 476, 410–425, https://doi.org/10.1016/j.jhydrol.2012.11.012, 2013.
Buishand, T. A., Shabalova, M. V., and Brandsma, T.: On the choice of the temporal aggregation level for statistical downscaling of precipitation, J. Clim., 17, 1816–1827, https://doi.org/10.1175/1520-0442(2004)017<1816:OTCOTT>2.0.CO;2, 2004.
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This original paper provides a guideline to select statistical downscaling methods (SDMs) in climate change impact studies (CCIS) to minimize uncertainty from downscaling. Three SDMs were applied to NCEP reanalysis and 2 GCM data values. We then analyzed the sensitivity of the hydrological model to the various downscaled data via 5 hydrological indicators representing the main features of the hydrograph. Our results enable selection of the appropriate SDMs to be used to build climate scenarios.