Articles | Volume 27, issue 24
https://doi.org/10.5194/hess-27-4563-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-4563-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The most extreme rainfall erosivity event ever recorded in China up to 2022: the 7.20 storm in Henan Province
Yuanyuan Xiao
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
Shuiqing Yin
CORRESPONDING AUTHOR
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
Bofu Yu
Australian Rivers Institute, School of Engineering and Built Environment, Griffith University, Nathan, Queensland, QLD 4111, Australia
Conghui Fan
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
Wenting Wang
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
College of Arts and Sciences, Beijing Normal University at Zhuhai, Zhuhai, 519087, China
Yun Xie
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
College of Arts and Sciences, Beijing Normal University at Zhuhai, Zhuhai, 519087, China
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EGUsphere, https://doi.org/10.5194/egusphere-2025-3228, https://doi.org/10.5194/egusphere-2025-3228, 2025
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Intensity-Duration-Frequency (IDF) curves is important for designing infrastructure that can withstand floods. We compared traditional interpolation methods with machine learning to map these curves across mainland China. ML using widely available daily gridded data can estimate sub-daily intensity as accurately as methods needing rarer hourly site data. This study provides a valuable understanding for IDF in data-limited regions and generates a new IDF dataset for mainland China.
Yueli Chen, Yun Xie, Xingwu Duan, and Minghu Ding
Earth Syst. Sci. Data, 17, 1265–1274, https://doi.org/10.5194/essd-17-1265-2025, https://doi.org/10.5194/essd-17-1265-2025, 2025
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Rainfall erosivity maps are crucial for identifying key areas of water erosion. Due to the limited historical precipitation data, there are certain biases in rainfall erosivity estimates in China. This study develops a new rainfall erosivity map for mainland China using 1 min precipitation data from 60 129 weather stations, revealing that areas exceeding 4000 MJ mm ha−1 h−1yr−1 of annual rainfall erosivity are mainly concentrated in southern China and on the southern Tibetan Plateau.
Sha Zhou and Bofu Yu
EGUsphere, https://doi.org/10.5194/egusphere-2025-1124, https://doi.org/10.5194/egusphere-2025-1124, 2025
Preprint archived
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Accurate estimation of evapotranspiration is of considerable importance to hydrology, ecology, and agriculture. The complementary relationship between actual and apparent potential evapotranspiration provides a simple yet powerful framework for evapotranspiration estimation. We derive an alternative complementary relationship with a physically meaningful parameter. It has distinct advantages over existing ones in terms of its sound physical basis and practical applications without calibration.
Tian Zhao, Wanjuan Song, Xihan Mu, Yun Xie, Donghui Xie, and Guangjian Yan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-535, https://doi.org/10.5194/essd-2024-535, 2024
Revised manuscript under review for ESSD
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Our research aimed to provide reliable data for measuring fractional vegetation cover, essential for understanding climate patterns and ecological health. We used the MultiVI algorithm, which employs satellite images from various angles to enhance accuracy. Our method outperformed traditional statistical methods compared to field measurements, enabling precise large-scale mapping of vegetation cover for improved environmental monitoring and planning.
Yahui Che, Bofu Yu, and Katherine Bracco
Atmos. Chem. Phys., 24, 4105–4128, https://doi.org/10.5194/acp-24-4105-2024, https://doi.org/10.5194/acp-24-4105-2024, 2024
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Dust events occur more frequently during the Austral spring and summer in dust regions, including central Australia, the southwest of Western Australia, and the northern and southern regions of eastern Australia using remote sensing and reanalysis datasets. High-concentration dust is distributed around central Australia and in the downwind northern and southern Australia. Typically, around 50 % of the dust lifted settles on Australian land, with the remaining half being deposited in the ocean.
Yueli Chen, Xingwu Duan, Minghu Ding, Wei Qi, Ting Wei, Jianduo Li, and Yun Xie
Earth Syst. Sci. Data, 14, 2681–2695, https://doi.org/10.5194/essd-14-2681-2022, https://doi.org/10.5194/essd-14-2681-2022, 2022
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We reconstructed the first annual rainfall erosivity dataset for the Tibetan Plateau in China. The dataset covers 71 years in a 0.25° grid. The reanalysis precipitation data are employed in combination with the densely spaced in situ precipitation observations to generate the dataset. The dataset can supply fundamental data for quantifying the water erosion, and extend our knowledge of the rainfall-related hazard prediction on the Tibetan Plateau.
Tianyu Yue, Shuiqing Yin, Yun Xie, Bofu Yu, and Baoyuan Liu
Earth Syst. Sci. Data, 14, 665–682, https://doi.org/10.5194/essd-14-665-2022, https://doi.org/10.5194/essd-14-665-2022, 2022
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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
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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.
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
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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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Short summary
An exceptionally heavy rainfall event occurred on 20 July 2021 in central China (the 7.20 storm). The storm presents a rare opportunity to examine the extreme rainfall erosivity. The storm, with an average recurrence interval of at least 10 000 years, was the largest in terms of its rainfall erosivity on record over the past 70 years in China. The study suggests that extreme erosive events can occur anywhere in eastern China and are not necessarily concentrated in low latitudes.
An exceptionally heavy rainfall event occurred on 20 July 2021 in central China (the 7.20...