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Impacts of Using State‐of‐the‐Art Multivariate Bias Correction Methods on Hydrological Modeling Over North America by Qiang Guo; Jie Chen; Xunchang John Zhang; Chong‐Yu Xu; Hua Chen is a Environmental Science article available to read on EtoBox.

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## Abstract Bias correction techniques are widely used to bridge the gap between climate model outputs and input requirements of hydrological models to assess the climate change impacts on hydrology. In addition to univariate bias correction methods, several multivariate bias correction methods were proposed recently, which can not only correct the biases in marginal distributions of individual climate variables but also properly adjust the biased intervariable correlations simulated by climate models. Due to the diversities of climate regime and climate model bias, hydrological simulation for watersheds under different climate conditions may show various sensitivities to the correction of intervariable correlations. Therefore, it is of great importance to investigate (1) whether the correction of intervariable correlations has impacts on the hydrological modeling and (2) how these impacts vary with watersheds under different climate conditions. To achieve these goals, this study evaluates behaviors and their spatial variability of multiple state‐of‐the‐art multivariate bias correction methods in hydrological modeling over 2,840 watersheds distributed in different climate regimes i

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Author

Qiang Guo; Jie Chen; Xunchang John Zhang; Chong‐Yu Xu; Hua Chen

Publisher

American Geophysical Union (AGU)

Published

2020

Language

EN

Field

Environmental Science (Physical Sciences)