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contributor authorChen, Jie
contributor authorSt-Denis, Blaise Gauvin
contributor authorBrissette, François P.
contributor authorLucas-Picher, Philippe
date accessioned2017-06-09T17:16:41Z
date available2017-06-09T17:16:41Z
date copyright2016/08/01
date issued2016
identifier issn1525-755X
identifier otherams-82290.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4225387
description abstractostprocessing of climate model outputs is usually performed to remove biases prior to performing climate change impact studies. The evaluation of the performance of bias correction methods is routinely done by comparing postprocessed outputs to observed data. However, such an approach does not take into account the inherent uncertainty linked to natural climate variability and may end up recommending unnecessary complex postprocessing methods. This study evaluates the performance of bias correction methods using natural variability as a baseline. This baseline implies that any bias between model simulations and observations is only significant if it is larger than the natural climate variability. Four bias correction methods are evaluated with respect to reproducing a set of climatic and hydrological statistics. When using natural variability as a baseline, complex bias correction methods still outperform the simplest ones for precipitation and temperature time series, although the differences are much smaller than in all previous studies. However, after driving a hydrological model using the bias-corrected precipitation and temperature, all bias correction methods perform similarly with respect to reproducing 46 hydrological metrics over two watersheds in different climatic zones. The sophisticated distribution mapping correction methods show little advantage over the simplest scaling method. The main conclusion is that simple bias correction methods appear to be just as good as other more complex methods for hydrological climate change impact studies. While sophisticated methods may appear more theoretically sound, this additional complexity appears to be unjustified in hydrological impact studies when taking into account the uncertainty linked to natural climate variability.
publisherAmerican Meteorological Society
titleUsing Natural Variability as a Baseline to Evaluate the Performance of Bias Correction Methods in Hydrological Climate Change Impact Studies
typeJournal Paper
journal volume17
journal issue8
journal titleJournal of Hydrometeorology
identifier doi10.1175/JHM-D-15-0099.1
journal fristpage2155
journal lastpage2174
treeJournal of Hydrometeorology:;2016:;Volume( 017 ):;issue: 008
contenttypeFulltext


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