Impacts of using state-of-the-art multivariate bias correction methods on hydrological modeling over North America

Authors: GUO, QIANG - Wuhan University; CHEN, JIE - Wuhan University; Zhang, Xunchang; XU, CHONG-YU - University Of Oslo; CHEN, HUA - Wuhan University

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/17/2020
Publication Date: 4/21/2020
Citation: Guo, Q., Chen, J., Zhang, X.J., Xu, C., Chen, H. 2020. Impacts of using state-of-the-art multivariate bias correction methods on hydrological modeling over North America. Water Resources Research. 56(5):e2019WR026659. https://doi.org/10.1029/2019WR026659.
DOI: https://doi.org/10.1029/2019WR026659

Interpretive Summary: Bias-correction techniques are widely used to bridge the gap between climate model outputs and input requirements of hydrological and agricultural models to assess the climate change impacts on water resources. In addition to univariate bias-correction method, several multivariate bias-correction methods were proposed recently, which can not only correct the biases of individual climate variables, but also can properly adjust the biased inter-variable correlations simulated by climate models. Due to the diversities of climate regimes and climate model bias, hydrological simulation for the watersheds under various climate conditions may show different sensitivities to the correction of inter-variable correlations. Therefore, it is of great importance to investigate 1) whether the correction of inter-variable correlations has impacts on hydrological modeling, and 2) how these impacts vary with watersheds under different climate conditions. To achieve these goals, this study evaluates the behaviors and spatial variability of multiple state-of-the-art multivariate bias correction methods in hydrological modeling over 2840 watersheds distributed in different climate regimes in North America. The results show that, compared to the use of univariate bias correction method, applying multivariate methods can significantly improve the simulation of snow proportion, snowmelt, evaporation, monthly mean streamflow, high and low flow, and time variables for most watersheds. In addition, multivariate methods show more advantages in hydrological simulation for watersheds with arid and warm temperate climates mainly located in southern regions of North America, while they present limited benefits for snow-characterized watersheds located in northern regions. Overall, this study demonstrates the importance of using multivariate bias correction methods instead of univariate methods in hydrological climate change impact studies, especially for watersheds in arid and warm temperate climates. This work would be useful to climatologists and hydrologists to evaluate the impact of climate changes on water resources.

Technical 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 method, several multivariate bias correction methods were proposed recently, which can not only correct the biases in marginal distributions of individual climate variables, but also can properly adjust the biased inter-variable correlations simulated by climate models. Due to the diversities of climate regimes and climate model bias, hydrological simulation for the watersheds under various climate conditions may show different sensitivities to the correction of inter-variable correlations. Therefore, it is of great importance to investigate 1) whether the correction of inter-variable correlations has impacts on hydrological modeling, and 2) how these impacts vary with watersheds under different climate conditions. To achieve these goals, this study evaluates the behaviors and spatial variability of multiple state-of-the-art multivariate bias correction methods in hydrological modeling over 2840 watersheds distributed in different climate regimes in North America. The results show that, compared to the use of univariate bias correction method, applying multivariate methods can significantly improve the simulation of snow proportion, snowmelt, evaporation, monthly mean streamflow, high and low flow, and time variables for most watersheds. In addition, multivariate methods show more advantages in hydrological simulation for watersheds with arid and warm temperate climates mainly located in southern regions of North America, while they present limited benefits for snow-characterized watersheds located in northern regions. Overall, this study demonstrates the importance of using multivariate bias correction methods instead of univariate methods in hydrological climate change impact studies, especially for watersheds in arid and warm temperate climates.