Articles | Volume 20, issue 4
https://doi.org/10.5194/hess-20-1483-2016
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

Abstract. Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e. correlation tests) and distributional properties (i.e. tests for equality of probability distributions). Outputs from seven downscaling methods – bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), the climate imprint delta method (CI), and bias corrected CI (BCCI) – are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3-day peak flow and 7-day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational data sets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational data set. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7-day low-flow events, regardless of reanalysis or observational data set. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis data sets complicated the evaluation of downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled reanalyses and gridded observations was useful for diagnosing the reliability of the various historical data sets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios.

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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.
{"cc_re__cc_re__cc_by_3_0": ["/a></nobr></li>\n                        </ul>\n                    </div>\n        </div>\n    </div>\n    \n        \n        <div class=\"ms-type row no-gutters d-none d-lg-flex mb-1 mt-0 align-items-center\">\n            <div class=\"col\">\n                <div class=\"row no-gutters align-items-center\">\n                    <div class=\"col-auto\">\n                                Research article\n                    </div>\n                        <div class=\"col\">\n                            &nbsp;|&nbsp;<a target=\"_blank\" href=\"https://creativecommons.org/licenses/by/3.0/\" rel=\"license\" class=\"licence-icon-svg\"><img src=\"https://contentmanager.copernicus.org/2175966/ssl\"></a>\n                        </div>\n                </div>\n            </div>\n            <div class=\"col-auto text-right\">19 Apr 2016</div>\n        </div>\n        <div class=\"ms-type row no-gutters d-lg-none mb-1 align-items-center\">\n            <div class=\"col-12\">\n                Research article | \n                    <a target=\"_blank\" href=\"https://creativecommons.org/licenses/by/3.0/\" rel=\"license\" class=\"licence-icon-svg \"><img", "nbsp;<a target=\"_blank\" href=\"https://creativecommons.org/licenses/by/3.0/\" rel=\"license\" class=\"licence-icon-svg\"><img src=\"https://contentmanager.copernicus.org/2175966/ssl\"></a>\n                        </div>\n                </div>\n            </div>\n            <div class=\"col-auto text-right\">19 Apr 2016</div>\n        </div>\n        <div class=\"ms-type row no-gutters d-lg-none mb-1 align-items-center\">\n            <div class=\"col-12\">\n                Research article | \n                    <a target=\"_blank\" href=\"https://creativecommons.org/licenses/by/3.0/\" rel=\"license\" class=\"licence-icon-svg \"><img src=\"https://contentmanager.copernicus.org/2175966/ssl\"></a>&nbsp;|&nbsp;<span>19 Apr 2016</span>\n            </div>\n        </div>\n    \n    <h1>Hydrologic extremes \u2013 an intercomparison of multiple gridded statistical  downscaling methods </h1>\n        <div class=\"mb-3 authors-with-affiliations\">\n            <nobr><span class=\"hover-cursor-pointer journal-contentLinkColor hover-underline\" data-toggle=\"modal\" \n                    data-target=\"#author332762\">Arelia T. Werner<a href=\"mailto:"], "cc_re__cc_re__cc_by_4_0": ["g\" alt=\"\" style=\"width: 1784px; height: 330px;\" /></a></span>\r\n</div>\n\n                    </div>\n\n                </div>\n\n            </div>\n\n        </div>\n\n    </div>\n\n    <div class=\"links pb-4 pt-4\">\n\n        <div class=\"container\">\n\n            <div class=\"row align-items-center\">\n\n                <div class=\"col-12 col-xl-auto mt-3\">\n\n                    <div class=\"row align-items-start align-items-lg-center\">\n\n                        <div class=\"d-none d-md-block col-md-auto pr-2 pt-1\"><a href=\"https://creativecommons.org/licenses/by/4.0/\" target=\"_blank\"><img src=\"https://www.hydrology-and-earth-system-sciences.net/ci_refresh_graphic_cc_by_footer_logo.jpg\" cofileid=\"14271\" alt=\"\"></a></div>\n\n                        <div class=\"col-12 col-md mb-3 mb-md-0 pl-md-0 text-center text-md-left\">All site content, except where otherwise noted, is licensed under the <a href=\"https://creativecommons.org/licenses/by/4.0/\" target=\"_blank\">Creative Commons Attribution 4.0 License</a>.</div>\n\n                        <div class=\"d-md-none col-12 text-center\"><a ", "w align-items-start align-items-lg-center\">\n\n                        <div class=\"d-none d-md-block col-md-auto pr-2 pt-1\"><a href=\"https://creativecommons.org/licenses/by/4.0/\" target=\"_blank\"><img src=\"https://www.hydrology-and-earth-system-sciences.net/ci_refresh_graphic_cc_by_footer_logo.jpg\" cofileid=\"14271\" alt=\"\"></a></div>\n\n                        <div class=\"col-12 col-md mb-3 mb-md-0 pl-md-0 text-center text-md-left\">All site content, except where otherwise noted, is licensed under the <a href=\"https://creativecommons.org/licenses/by/4.0/\" target=\"_blank\">Creative Commons Attribution 4.0 License</a>.</div>\n\n                        <div class=\"d-md-none col-12 text-center\"><a href=\"https://creativecommons.org/licenses/by/4.0/\" target=\"_blank\"><img src=\"https://www.hydrology-and-earth-system-sciences.net/ci_refresh_graphic_cc_by_footer_logo.jpg\" cofileid=\"14271\" alt=\"\"></a></div>\n\n                    </div>\n\n                </div>\n\n                <div class=\"col-12 text-center text-md-left col-lg-auto mt-3\">\n\n                    <div class=\"row al", "k\"><img src=\"https://www.hydrology-and-earth-system-sciences.net/ci_refresh_graphic_cc_by_footer_logo.jpg\" cofileid=\"14271\" alt=\"\"></a></div>\n\n                        <div class=\"col-12 col-md mb-3 mb-md-0 pl-md-0 text-center text-md-left\">All site content, except where otherwise noted, is licensed under the <a href=\"https://creativecommons.org/licenses/by/4.0/\" target=\"_blank\">Creative Commons Attribution 4.0 License</a>.</div>\n\n                        <div class=\"d-md-none col-12 text-center\"><a href=\"https://creativecommons.org/licenses/by/4.0/\" target=\"_blank\"><img src=\"https://www.hydrology-and-earth-system-sciences.net/ci_refresh_graphic_cc_by_footer_logo.jpg\" cofileid=\"14271\" alt=\"\"></a></div>\n\n                    </div>\n\n                </div>\n\n                <div class=\"col-12 text-center text-md-left col-lg-auto mt-3\">\n\n                    <div class=\"row align-items-center\">\n\n                        <div class=\"col d-md-none px-0\"></div>\n\n                        <div class=\"col-auto pr-1\"><a href=\"https://www.hydrology-and-earth-system-science"], "copyright__copyright__copyright": ["ort summary\nSeven 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.", "ort summary\nSeven 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.", "ort summary\nSeven 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.", "ort summary\nSeven 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."]}