Articles | Volume 20, issue 4
Hydrol. Earth Syst. Sci., 20, 1483–1508, 2016
https://doi.org/10.5194/hess-20-1483-2016
Hydrol. Earth Syst. Sci., 20, 1483–1508, 2016
https://doi.org/10.5194/hess-20-1483-2016

Research article 19 Apr 2016

Research article | 19 Apr 2016

Hydrologic extremes – an intercomparison of multiple gridded statistical downscaling methods

Arelia T. Werner and Alex J. Cannon

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Cited articles

Abatzoglou, J. T. and Brown, T. J.: A comparison of statistical downscaling methods suited for wildfire applications, Int. J. Climatol., 32, 772–780, 2012.
Ahmed, K. F., Wang, G., Silander, J., Wilson, A. M., Allen, J. M., Horton, R., and Anyah, R.: Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast, Global Planet. Change, 100, 320–332, 2013.
Benestad, B. E., Hanssen-Bauer, I., and Chen, D.: Chapter 8: Reducing Uncertainties, in: Emperical-Statistical Downscaling, World Scientific, Singapore, 2008.
Bennett, K. E., Werner, A. T., and Schnorbus, M.: Uncertainties in Hydrologic and Climate Change Impact Analyses in Headwater Basins of British Columbia, J. Climate, 25, 5711–5730, 2012.
Bürger, G., Schulla, J., and Werner, A. T.: Estimates of future flow, including extremes, of the Columbia River headwaters, Water Resour. Res., 47, W10520, https://doi.org/10.1029/2010WR009716, 2011.
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Short summary
Seven gridded statistical downscaling methods are tested for strength in simulating climate and hydrologic extremes. A recently developed technique, which is a post-processed version of bias corrected constructed analogues where the final bias correction is based on the bias corrected climate imprint method, is shown to be an especially strong method for hydrologic extremes versus other more commonly applied methods, including the popular bias corrected spatial disaggregation method.