Hydrologic extremes – an intercomparison of multiple gridded statistical downscaling methods

Abstract

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 eventscale 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.

Skill of Subseasonal Forecasts in Europe: Effect of Bias Correction and Downscaling Using Surface Observations

“…For temperature we applied an additive correction and for precipitation a multiplicative correction to avoid artificial generation of negative precipitation values. Many previous studies follow the same approach when applying bias correction techniques (e.g., Addor et al, ; Teutschbein & Seibert, ; Werner & Cannon, ).…”

Indices of Canada’s future climate for general and agricultural adaptation applications

“…The atlas uses monthly values of precipitation and surface mean temperatures simulated by 29 GCMs, which have spatial resolutions ranging from about 1°× 1°to over 2.5°× 2.5°(latitude × longitude). We downscale daily temperatures and precipitation amounts simulated by 24 of these GCMs (all models for which daily values were available at the time of analysis; see Table S3) to~10 km resolution over Canada using the BCCAQ method described by Murdock et al (2014) and Werner and Cannon (2016). We considered the RCP2.6, the RCP4.5, and the RCP8.5 emission scenarios (van Vuuren et al 2011) for the future projections.…”

“…The plots compare the quantiles of the gridded observational data (horizontal axis) with the quantiles of station observations (vertical axis, left column), raw GCM output (vertical axis, central column), and downscaled GCM output (vertical axis, right column) bias-correction/spatial disaggregation (BCSD; Wood et al 2004) were evaluated as a followup to the downscaling intercomparison by Bürger et al (2012Bürger et al ( , 2013. A method called BCCAQ, which combines downscaling by BCCA with bias correction by trend-preserving quantile mapping (Cannon et al, 2015), was found to produce the most robust projections according to a set of tests that measure day-to-day temporal sequencing, equality of distributions, and spatial co-variability (Murdock et al 2014;Werner and Cannon 2016). Evaluation was performed via temporal split-sample validation with multiple reanalyses standing in for GCMs (Werner and Cannon 2016), as well as a Bperfect model^setup (Dixon et al 2016) in which spatially degraded RCM projections stand in for GCMs to test robustness under future climate conditions (Murdock et al 2014).…”

“…A method called BCCAQ, which combines downscaling by BCCA with bias correction by trend-preserving quantile mapping (Cannon et al, 2015), was found to produce the most robust projections according to a set of tests that measure day-to-day temporal sequencing, equality of distributions, and spatial co-variability (Murdock et al 2014;Werner and Cannon 2016). Evaluation was performed via temporal split-sample validation with multiple reanalyses standing in for GCMs (Werner and Cannon 2016), as well as a Bperfect model^setup (Dixon et al 2016) in which spatially degraded RCM projections stand in for GCMs to test robustness under future climate conditions (Murdock et al 2014).…”

Inter-comparison of daily precipitation products for large-scale hydro-climatic applications over Canada

“…Dinku et al, 2008;Asadullah et al, 2008), North America (e.g. Tian et al, 2009;West et al, 2007), South America (e.g. Vila et al, 2009), and China (e.g.…”