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Multi‐site bias correction of climate model outputs for hydro‐meteorological impact studies: An application over a watershed in China
Author(s) -
Su Tianhua,
Chen Jie,
Can Alex J.,
Xie Ping,
Guo Qiang
Publication year - 2020
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.13750
Subject(s) - environmental science , watershed , precipitation , climate change , spatial dependence , climate model , climatology , meteorology , statistics , computer science , mathematics , geology , geography , oceanography , machine learning
Bias correction methods remove systematic differences in the distributional properties of climate model outputs with respect to observations, often as a means of pre‐processing model outputs for use in hydrological impact studies. Traditionally, bias correction is applied at each weather station individually, neglecting the dependence that exists between different sites, which could negatively affect simulations from a distributed hydrological model. In this study, three multi‐variate bias correction (MBC) methods—initially proposed to correct the inter‐variable correlation or multi‐variate dependence of climate model outputs—are used to correct biases in distributional properties and spatial dependence at multiple weather stations. To reveal the benefits of correcting spatial dependence, two distribution‐based single‐site bias correction methods are used for comparison. The effects of multi‐site correction on hydro‐meteorological extremes are assessed by driving a distributed hydrological model and then evaluating the model performance in terms of several meteorological and hydrological extreme indices. The results show that the multi‐site bias correction methods perform well in reducing biases in spatial correlation measures of raw global climate model outputs. In addition, the multi‐site methods consistently reproduce watershed‐averaged meteorological variables better than single‐site methods, especially for extreme values. In terms of representing hydrological extremes, the multi‐site methods generally perform better than the single‐site methods, although the benefits vary according to the hydrological index. However, when applying the multi‐site methods, the original temporal sequence of precipitation occurrence may be altered to some extent. Overall, all multi‐site bias correction methods are able to reproduce the spatial correlation of observed meteorological variables over multiple stations, which leads to better hydrological simulations, especially for extremes. This study emphasizes the necessity of considering spatial dependence when applying bias correction to ccc outputs and hydrological impact studies.