Evaluation and Bias Correction of S2S Precipitation for Hydrological Extremes

Wei Li State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China

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Jie Chen State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Hubei Provincial Key Laboratory of Water System Science for Sponge City Construction, Wuhan University, Wuhan, China

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Lu Li NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway

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Hua Chen State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China

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Bingyi Liu State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China

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Chong-Yu Xu State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Department of Geosciences, University of Oslo, Oslo, Norway

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Xiangquan Li State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China

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Abstract

Subseasonal to seasonal (S2S) weather forecasting has made significant advances and several products have been made available. However, to date few studies utilize these products to extend the hydrological forecast time range. This study evaluates S2S precipitation from eight model ensembles in the hydrological simulation of extreme events at the catchment scale. A superior bias correction method is used to correct the bias of S2S precipitation for hydrological forecasts, and the results are compared with direct bias correction of hydrological forecasts using raw precipitation forecasts as input. The study shows that the S2S models can skillfully forecast daily precipitation within a lead time of 11 days. The S2S precipitation data from the European Centre for Medium-Range Weather Forecasts (ECMWF), Korea Meteorological Administration (KMA), and United Kingdom’s Met Office (UKMO) models present lower mean error than that of other models and have higher correlation coefficients with observations. Precipitation data from the ECMWF, KMA, and UKMO models also perform better than that of other models in simulating multiple-day precipitation processes. The bias correction method effectively reduces the mean error of daily S2S precipitation for all models while also improving the correlation with observations. Moreover, this study found that the bias correction procedure can apply to either precipitation or streamflow simulations for improving the hydrological forecasts, even though the degree of improvement is dependent on the hydrological variables. Overall, S2S precipitation has a potential to be applied for hydrological forecasts, and a superior bias correction method can increase the forecasts’ reliability, although further studies are still needed to confirm its effect.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-19-0042.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Jie Chen, jiechen@whu.edu.cn; Lu Li, luli@norceresearch.no

Abstract

Subseasonal to seasonal (S2S) weather forecasting has made significant advances and several products have been made available. However, to date few studies utilize these products to extend the hydrological forecast time range. This study evaluates S2S precipitation from eight model ensembles in the hydrological simulation of extreme events at the catchment scale. A superior bias correction method is used to correct the bias of S2S precipitation for hydrological forecasts, and the results are compared with direct bias correction of hydrological forecasts using raw precipitation forecasts as input. The study shows that the S2S models can skillfully forecast daily precipitation within a lead time of 11 days. The S2S precipitation data from the European Centre for Medium-Range Weather Forecasts (ECMWF), Korea Meteorological Administration (KMA), and United Kingdom’s Met Office (UKMO) models present lower mean error than that of other models and have higher correlation coefficients with observations. Precipitation data from the ECMWF, KMA, and UKMO models also perform better than that of other models in simulating multiple-day precipitation processes. The bias correction method effectively reduces the mean error of daily S2S precipitation for all models while also improving the correlation with observations. Moreover, this study found that the bias correction procedure can apply to either precipitation or streamflow simulations for improving the hydrological forecasts, even though the degree of improvement is dependent on the hydrological variables. Overall, S2S precipitation has a potential to be applied for hydrological forecasts, and a superior bias correction method can increase the forecasts’ reliability, although further studies are still needed to confirm its effect.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-19-0042.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding authors: Jie Chen, jiechen@whu.edu.cn; Lu Li, luli@norceresearch.no

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