Articles | Volume 371
https://doi.org/10.5194/piahs-371-109-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/piahs-371-109-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
How would peak rainfall intensity affect runoff predictions using conceptual water balance models?
B. Yu
CORRESPONDING AUTHOR
School of Engineering, Griffith University, Nathan 4111, Australia
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A gridded input dataset at a 10 km resolution of a weather generator, CLIGEN, was established for mainland China. Based on this, CLIGEN can generate a series of daily temperature, solar radiation, precipitation data, and rainfall intensity information. In each grid, the input file contains 13 groups of parameters. All parameters were first calculated based on long-term observations and then interpolated by universal kriging. The accuracy of the gridded input dataset has been fully assessed.
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This study quantified the bias of rainfall erosivity estimated from gridded precipitation data, and the results showed the grid-estimated mean annual rainfall erosivity were underestimated by 15–40 % in the eastern China. The scale difference between gridded data and gauge data was the main cause. In application, the empirical models established based on gauge data should not be used directly for gridded data, or a bias correction process needed to be considered for the model outputs.
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Tianyu Yue, Shuiqing Yin, Yun Xie, Bofu Yu, and Baoyuan Liu
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Short summary
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This paper provides new rainfall erosivity maps over mainland China based on hourly data from 2381 stations (available at https://doi.org/10.12275/bnu.clicia.rainfallerosivity.CN.001). The improvement from the previous work was also assessed. The improvement in the R-factor map occurred mainly in the western region, because of an increase in the number of stations and an increased temporal resolution from daily to hourly data.
Wenting Wang, Shuiqing Yin, Bofu Yu, and Shaodong Wang
Earth Syst. Sci. Data, 13, 2945–2962, https://doi.org/10.5194/essd-13-2945-2021, https://doi.org/10.5194/essd-13-2945-2021, 2021
Short summary
Short summary
A gridded input dataset at a 10 km resolution of a weather generator, CLIGEN, was established for mainland China. Based on this, CLIGEN can generate a series of daily temperature, solar radiation, precipitation data, and rainfall intensity information. In each grid, the input file contains 13 groups of parameters. All parameters were first calculated based on long-term observations and then interpolated by universal kriging. The accuracy of the gridded input dataset has been fully assessed.
Maoqing Wang, Shuiqing Yin, Tianyu Yue, Bofu Yu, and Wenting Wang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-633, https://doi.org/10.5194/hess-2020-633, 2020
Publication in HESS not foreseen
Short summary
Short summary
This study quantified the bias of rainfall erosivity estimated from gridded precipitation data, and the results showed the grid-estimated mean annual rainfall erosivity were underestimated by 15–40 % in the eastern China. The scale difference between gridded data and gauge data was the main cause. In application, the empirical models established based on gauge data should not be used directly for gridded data, or a bias correction process needed to be considered for the model outputs.
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Short summary
Hydrologic models use daily precipitation and potential evapotranspiration for streamflow estimation. The effect of an increase in rainfall intensity on the long-term water balance is not adequately accounted for in these hydrologic models. This study, using data from a forested watershed in France, shows that the effect of peak rainfall intensity on runoff prediction is insignificant for two models tested, and model performance is unlikely to improve when peak daily precipitation is included.
Hydrologic models use daily precipitation and potential evapotranspiration for streamflow...