Articles | Volume 17, issue 3
https://doi.org/10.5194/essd-17-1265-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-1265-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Gridded rainfall erosivity (2014–2022) in mainland China using 1 min precipitation data from densely distributed weather stations
Yueli Chen
CORRESPONDING AUTHOR
State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
Yun Xie
CORRESPONDING AUTHOR
Department of Geographic Science, Faculty of Arts and Sciences, Beijing Normal University at Zhuhai, Zhuhai, 519087, China
Xingwu Duan
Institute of International Rivers and Eco-Security, Yunnan University, Kunming, 650091, China
Minghu Ding
State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
Related authors
Enwei Zhang, Yueli Chen, Shengzhao Wei, Chenli Liu, Hongna Wang, Bowen Deng, Honghong Lin, Xue Yang, Yawen Li, and Xingwu Duan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-215, https://doi.org/10.5194/essd-2025-215, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
We produced the first soil and water conservation terrace measures dataset with a fine classification system on Google Earth Engine platform. This dataset included terrace data and soil and water conservation measure factor values, covering the period from 2000 to 2020. The terraces are categorized into level terrace, slope terrace, zig terrace, and slope-separated terrace. The results showed that the average overall accuracy of the terrace was 91.90 % and the average F1 score was 76.75 %.
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
Short summary
Short summary
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.
Ruiqi Nan, Biao Tian, Xingfeng Ling, Weijun Sun, Yixi Zhao, Dongqi Zhang, Chuanjin Li, Xin Wang, Jie Tang, Bo Yao, and Minghu Ding
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-282, https://doi.org/10.5194/essd-2025-282, 2025
Preprint under review for ESSD
Short summary
Short summary
This study presents the first dataset of 11 fluorinated greenhouse gases observed in 2021 at Zhongshan Station, Antarctica. Most gas levels increased and were higher than at two other Antarctic stations. Their sources were linked to industrial activities such as refrigeration and electronics. Although limited to one year, the data provide important background information for detecting future changes in the Antarctic atmosphere.
Enwei Zhang, Yueli Chen, Shengzhao Wei, Chenli Liu, Hongna Wang, Bowen Deng, Honghong Lin, Xue Yang, Yawen Li, and Xingwu Duan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-215, https://doi.org/10.5194/essd-2025-215, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
We produced the first soil and water conservation terrace measures dataset with a fine classification system on Google Earth Engine platform. This dataset included terrace data and soil and water conservation measure factor values, covering the period from 2000 to 2020. The terraces are categorized into level terrace, slope terrace, zig terrace, and slope-separated terrace. The results showed that the average overall accuracy of the terrace was 91.90 % and the average F1 score was 76.75 %.
Fanyu Zhao, Di Long, Chenqi Fang, Yiming Wang, and Xingwu Duan
EGUsphere, https://doi.org/10.5194/egusphere-2025-652, https://doi.org/10.5194/egusphere-2025-652, 2025
Short summary
Short summary
The heterogeneous surge behaviors in Karakoram reveal critical knowledge gaps in the underlying mechanism, urging detailed investigations. We integrate multisource remote sensing (satellite altimetry, DEMs, optical/SAR imagery) to holistically characterize surge phases of a Karakoram glacier, quantifying flow velocity, surface elevation, terminus position, and lake level variations. This integrated approach underscores the value of multi-sensor synergies in deciphering complex surge mechanisms.
Lijing Chen, Lei Zhang, Yong She, Zhaoliang Zeng, Yu Zheng, Biao Tian, Wenqian Zhang, Zhaohui Liu, Huizheng Che, and Minghu Ding
Atmos. Chem. Phys., 25, 727–739, https://doi.org/10.5194/acp-25-727-2025, https://doi.org/10.5194/acp-25-727-2025, 2025
Short summary
Short summary
Aerosol optical depth (AOD) at Zhongshan Station varies seasonally, with lower values in summer and higher values in winter. Winter and spring AOD increases due to reduced fine-mode particles, while summer and autumn increases are linked to particle growth. Diurnal AOD variation correlates positively with temperature but negatively with wind speed and humidity. Backward trajectories show that aerosols on high-AOD (low-AOD) days primarily originate from the ocean (interior Antarctica).
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
Short summary
Short summary
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.
Tianming Ma, Zhuang Jiang, Minghu Ding, Pengzhen He, Yuansheng Li, Wenqian Zhang, and Lei Geng
The Cryosphere, 18, 4547–4565, https://doi.org/10.5194/tc-18-4547-2024, https://doi.org/10.5194/tc-18-4547-2024, 2024
Short summary
Short summary
We constructed a box model to evaluate the isotope effects of atmosphere–snow water vapor exchange at Dome A, Antarctica. The results show clear and invisible diurnal changes in surface snow isotopes under summer and winter conditions, respectively. The model also predicts that the annual net effects of atmosphere–snow water vapor exchange would be overall enrichments in snow isotopes since the effects in summer appear to be greater than those in winter at the study site.
Yuanyuan Xiao, Shuiqing Yin, Bofu Yu, Conghui Fan, Wenting Wang, and Yun Xie
Hydrol. Earth Syst. Sci., 27, 4563–4577, https://doi.org/10.5194/hess-27-4563-2023, https://doi.org/10.5194/hess-27-4563-2023, 2023
Short summary
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.
Minghu Ding, Xiaowei Zou, Qizhen Sun, Diyi Yang, Wenqian Zhang, Lingen Bian, Changgui Lu, Ian Allison, Petra Heil, and Cunde Xiao
Earth Syst. Sci. Data, 14, 5019–5035, https://doi.org/10.5194/essd-14-5019-2022, https://doi.org/10.5194/essd-14-5019-2022, 2022
Short summary
Short summary
The PANDA automatic weather station (AWS) network consists of 11 stations deployed along a transect from the coast (Zhongshan Station) to the summit of the East Antarctic Ice Sheet (Dome A). It covers the different climatic and topographic units of East Antarctica. All stations record hourly air temperature, relative humidity, air pressure, wind speed and direction at two or three heights. The PANDA AWS dataset commences from 1989 and is planned to be publicly available into the future.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Minghu Ding, Tong Zhang, Diyi Yang, Ian Allison, Tingfeng Dou, and Cunde Xiao
The Cryosphere, 15, 4201–4206, https://doi.org/10.5194/tc-15-4201-2021, https://doi.org/10.5194/tc-15-4201-2021, 2021
Short summary
Short summary
Measurement of snow heat conductivity is essential to establish the energy balance between the atmosphere and firn, but it is still not clear in Antarctica. Here, we used data from three automatic weather stations located in different types of climate and evaluated nine schemes that were used to calculate the effective heat diffusivity of snow. The best solution was proposed. However, no conductivity–density relationship was optimal at all sites, and the performance of each varied with depth.
Tingfeng Dou, Cunde Xiao, Jiping Liu, Qiang Wang, Shifeng Pan, Jie Su, Xiaojun Yuan, Minghu Ding, Feng Zhang, Kai Xue, Peter A. Bieniek, and Hajo Eicken
The Cryosphere, 15, 883–895, https://doi.org/10.5194/tc-15-883-2021, https://doi.org/10.5194/tc-15-883-2021, 2021
Short summary
Short summary
Rain-on-snow (ROS) events can accelerate the surface ablation of sea ice, greatly influencing the ice–albedo feedback. We found that spring ROS events have shifted to earlier dates over the Arctic Ocean in recent decades, which is correlated with sea ice melt onset in the Pacific sector and most Eurasian marginal seas. There has been a clear transition from solid to liquid precipitation, leading to a reduction in spring snow depth on sea ice by more than −0.5 cm per decade since the 1980s.
Minghu Ding, Biao Tian, Michael C. B. Ashley, Davide Putero, Zhenxi Zhu, Lifan Wang, Shihai Yang, Chuanjin Li, and Cunde Xiao
Earth Syst. Sci. Data, 12, 3529–3544, https://doi.org/10.5194/essd-12-3529-2020, https://doi.org/10.5194/essd-12-3529-2020, 2020
Short summary
Short summary
Dome A, is one of the harshest environments on Earth.To evaluate the characteristics of near-surface O3, continuous observations were carried out in 2016. The results showed different patterns between coastal and inland Antarctic areas that were characterized by high concentrations in cold seasons and at night. Short-range transport accounted for the O3 enhancement events (OEEs) during summer at DA, rather than efficient local production, which is consistent with previous studies.
Cited articles
Agnese, C., Bagarello, V., Corrao, C., D'Agostino, L., and D'Asaro, F.: Influence of the rainfall measurement interval on the erosivity determinations in the Mediterranean area, J. Hydrol., 329, 39–48, https://doi.org/10.1016/j.jhydrol.2006.02.002, 2006.
Angulo-Martínez, M. and Beguería, S.: Estimating rainfall erosivity from daily precipitation records: A comparison among methods using data from the Ebro Basin (NE Spain), J. Hydrol., 379, 111–121, https://doi.org/10.1016/j.jhydrol.2009.09.051, 2009.
Ayat, H., Evans, J., Sherwood, S. C., and Soderholm, J.: Intensification of subhourly heavy rainfall, Science, 378, 655–659, https://doi.org/10.1126/science.abn8657, 2022.
Borrelli, P., Robinson, D.A., Panagos, P., Lugato, E., Yang, J. E., Alewell, C., Wuepper, D., Montanarella, L., and Ballabio, C.: Land use and climate change impacts on global soil erosion by water (2015–2070), P. Natl. Acad. Sci. USA, 117, 21994–22001, https://doi.org/10.1073/pnas.2001403117, 2020.
Brown, L. and Foster, G.: Storm erosivity using idealized intensity distributions, T. ASAE, 30, 0379–0386, https://doi.org/10.13031/2013.31957, 1987.
Carter, C. E., Greer, J. D., Braud, H. J., and Floyd, J. M.: Raindrop characteristics in south central United States, T. ASAE 17, 1033–1037, https://doi.org/10.13031/2013.37021, 1974.
Chen, Y.: A new gridded dataset of rainfall erosivity (1950–2020) in the Tibetan Plateau, National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Terre.tpdc.271833, 2021.
Chen, Y.: The rainfall erosivity in mainland China (2014–2022), National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Terre.tpdc.301206, 2024.
Chen, Y., Duan, X., Ding, M., Qi, W., Wei, T., Li, J., and Xie, Y.: New gridded dataset of rainfall erosivity (1950–2020) on the Tibetan Plateau, Earth Syst. Sci. Data, 14, 2681–2695, https://doi.org/10.5194/essd-14-2681-2022, 2022.
Chen, Y., Ding, M., Zhang, G., Wang, Y., and Li, J.: Evaluation of ERA5 Reanalysis Precipitation Data in the Yarlung Zangbo River Basin of the Tibetan Plateau, J. Hydrometeorol., 24, 1491–1507, https://doi.org/10.1175/JHM-D-22-0229.1, 2023a.
Chen, Y., Ding, M., Zhang, G., Duan, X., and Wang, C.: The possible role of fused precipitation data in detecting the spatial-temporal pattern of rainfall erosivity over the Tibetan Plateau, China, CATENA, 228, 107114, https://doi.org/10.1016/j.catena.2023.107114, 2023b.
Chen, Y. L., Wei, T., Li, J. D., Xin, Y. F., and Ding, M. H.: Future changes in global rainfall erosivity: Insights from the precipitation changes, J. Hydrol., 638, 131435, https://doi.org/10.1016/j.jhydrol.2024.131435, 2024.
Dai, Q., Zhu, J., Lv, G., Kalin, L., Yao, Y., Zhang, J., and Han, D.: Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale, Sci. Adv., 9, eadg5551, https://doi.org/10.1126/sciadv.adg5551, 2023.
Davison, P., Hutchins, M. G., Anthony, S. G., Betson, M., Johnson, C., and Lord, E. I.: The relationship between potentially erosive storm energy and daily rainfall quantity in England and Wales, Sci. Total. Environ., 344, 15–25, https://doi.org/10.1016/j.scitotenv.2005.02.002, 2005.
Diodato, N., Ljungqvist, F. C., and Bellocchi, G.: Historical predictability of rainfall erosivity: a reconstruction for monitoring extremes over Northern Italy (1500–2019), NPJ Clim. Atmos. Sci., 3, 46, https://doi.org/10.1038/s41612-020-00144-9, 2020.
FAO and ITPS: Status of the world's soil resources (SWSR) – main report, Food and agriculture Organization of the United Nations and Intergovernmental Technical Panel on soils, Rome, Italy, ISBN 978-92-5-109004-6, 2015.
Freitas, E., Coelho, V., Xuan, Y., Melo, D., Gadelha, A., Santos, E., Galvão, C., Filho, G., Barbosa, L., Huffman, G., Petersen, W., and Almeida, C.: The performance of the IMERG satellite-based product in identifying sub-daily rainfall events and their properties, J. Hydrol., 589, 125128, https://doi.org/10.1016/j.jhydrol.2020.125128, 2020.
Gupta, S., Borrelli, P., Panagos, P., and Alewell, C.: An advanced global soil erodibility (K) assessment including the effects of saturated hydraulic conductivity, Sci. Total. Environ., 908, 168249, https://doi.org/10.1016/j.scitotenv.2023.168249, 2024.
Hersbach, H., Bell, B., Berrisford, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers, D., Simmons, A., Soci, C., and Dee, D.: Global reanalysis: Goodbye ERA-Interim, hello ERA5, ECMWF Newsletter No. 159, https://doi.org/10.21957/vf291hehd7, 2019.
IPCC: Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems, Cambridge University Press, Cambridge, UK and New York, NY, USA, 896 pp., https://doi.org/10.1017/9781009157988, 2019.
Kinnell, P. I. A.: Rainfall intensity-kinetic energy relationships for soil loss prediction, Soil Sci. Soc. Am. J., 45, 153–155, https://doi.org/10.2136/sssaj1981.03615995004500010033x, 1981.
Laws, J. O.: Measurements of fall-velocity of water-drops and raindrops, EOS T. Am. Geophys. Un., 22, 709–721, https://doi.org/10.1029/TR022i003p00709, 1941.
Laws, J. O. and Parsons, D. A.: The relation of raindrop size to intensity, EOS T. Am. Geophys. Un., 24, 452–460, https://doi.org/10.1029/TR024i002p00452, 1943.
Lim, Y. S., Kim, J. K., Kim, J. W., Park, B. I., and Kim, M. S.: Analysis of the Relationship between the Kinetic Energy and Intensity of Rainfall in Daejeon, Korea, Quaternary Int., 384, 107–117, https://doi.org/10.1016/j.quaint.2015.03.021, 2015.
Liu, B., Xie, Y., Li, Z., Liang, Y., Zhang, W., Fu, S., Yin, S., Wei, X., Zhang, K., Wang, Z., Liu, Y., Zhao, Y., and Guo, Q.: The assessment of soil loss by water erosion in China, Int. Soil Water Conserv., 8, 430, https://doi.org/10.1016/j.iswcr.2020.07.002, 2020.
McGuire, L. A., Ebel, B. A., Rengers, F. K., Vieira, D. C. S., and Nyman, P.: Fire effects on geomorphic processes, Nat. Rev. Earth Environ., 5, 486–503, https://doi.org/10.1038/s43017-024-00557-7, 2024.
Mineo, C., Ridolfi, E., Moccia, B., Russo, F., and Napolitano, F.: Assessment of Rainfall Kinetic-Energy-Intensity Relationships, Water, 11, 1994, https://doi.org/10.3390/w11101994, 2019.
Nearing, M. A., Yin, S., Borrelli, P., and Polyakov, V. O.: Rainfall erosivity: A historical review, CATENA, 157, 357–362, https://doi.org/10.1016/j.catena.2017.06.004, 2017.
Panagos, P., Borrelli, P., Meusburger, K., Yu, B., Klik, A., Lim, K. J., Yang, J. E, Ni, J., Miao, C., Chattopadhyay, N., Sadeghi, S. H., Hazbavi, Z., Zabihi, M., Larionov, G. A., Krasnov, S. F., Garobets, A., Levi, Y., Erpul, G., Birkel, C., Hoyos, N., Naipal, V., Oliveira, P. T. S., Bonilla, C. A., Meddi, M., Nel, W., Dashti, H., Boni, M., Diodato, N., Van, O. K., Nearing, M. A., and Ballabio, C.: Global rainfall erosivity assessment based on high-temporal resolution rainfall records, Sci. Rep., 7, 4175, https://doi.org/10.1038/s41598-017-04282-8, 2017.
Renard, K. G. and Freimund, J. R.: Using monthly precipitation data to estimate the R-factor in the revised USLE, J. Hydrol., 157, 287–306, https://doi.org/10.1016/0022-1694(94)90110-4, 1994.
Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., and Yoder, D. C.: Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE), USDA Agricultural Handbook No. 733, USDA, Washington, DC, 384 pp., ISBN 0-16-048938-5, 1997.
Richardson, C. W., Foster, G. R., and Wright, D. A.: Estimation of Erosion Index from Daily Rainfall Amount, T. ASAE, 26, 153–156, https://doi.org/10.13031/2013.33893, 1983.
Tilg, A., Vejen, F., Hasager, C. B., and Nielsen, M.: Rainfall Kinetic Energy in Denmark: Relationship with Drop Size, Wind Speed, and Rain Rate, J. Hydrometeorol., 21, 1621–1637, https://doi.org/10.1175/JHM-D-19-0251.1, 2020.
Uijlenhoet, R. and Stricker, J. N. M.: A consistent rainfall parameterization based on the exponential raindrop size distribution, J. Hydrol., 218, 101–127, https://doi.org/10.1016/S0022-1694(99)00032-3, 1999.
USDA-Agricultural Research Service: Science documentation: Revised Universal Soil Loss Equation Version 2 (RUSLE2), USDA-Agricultural Researcher Service, Washington, D.C., 2013.
Wischmeier, W. H.: A rainfall erosion index for a universal soil loss equation, Soil Sci. Soc. Am. Proc., 23, 246–249, https://doi.org/10.2136/sssaj1959.03615995002300030027x, 1959.
Wischmeier, W. H. and Smith, D. D.: Predicting rainfall-erosion losses from cropland east of the Rocky Mountains: Guide for selection of practices for soil and water conservation, Agriculture Handbook No. 282, U.S. Department of Agriculture, Washington, D.C., 1965.
Wischmeier, W. H. and Smith, D. D.: Predicting Rainfall Erosion Losses: A Guide to Conservation Planning, Agriculture Handbook No. 537, U.S. Department of Agriculture, Washington, D.C., 1978.
Wu, S., Guo, Z., Askar, A., Li, X., Hu, Y., Li, H., and Saria, A.E.: Dynamic land cover and ecosystem service changes in global coastal deltas under future climate scenarios, Ocean Coast. Manage., 258, 107384, https://doi.org/10.1016/j.ocecoaman.2024.107384, 2024.
Xie, Y., Liu, B. Y., and Zhang, W. B.: Study on standard of erosive rainfall, J. Soil Water Conserv., 14, 6–11, https://doi.org/10.3321/j.issn:1009-2242.2000.04.002, 2000 (in Chinese).
Xie, Y., Yin, S., Liu, B., Nearing, M., and Zhao, Y.: Models for estimating daily rainfall erosivity in China, J. Hydrol., 535, 547–558, https://doi.org/10.1016/j.jhydrol.2016.02.020, 2016.
Yin, S., Xie, Y., and Wang, C.: Calculation of rainfall erosivity by using hourly rainfall data, Geogr. Res., 26, 541–547, https://www.dlyj.ac.cn/CN/10.11821/yj2007030015 (last access: 25 March 2025), 2007 (in Chinese).
Yin, S., Xie, Y., Liu, B., and Nearing, M. A.: Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions, Hydrol. Earth Syst. Sci., 19, 4113–4126, https://doi.org/10.5194/hess-19-4113-2015, 2015.
Yu, B. and Rosewell, J. C.: A Robust Estimator of the R-factor for the Universal Soil Loss Equation, T. ASABE, 39, 559–561, https://doi.org/10.13031/2013.27535, 1996.
Yue, T., Yin, S., Xie, Y., Yu, B., and Liu, B.: Rainfall erosivity mapping over mainland China based on high-density hourly rainfall records, Earth Syst. Sci. Data, 14, 665–682, https://doi.org/10.5194/essd-14-665-2022, 2022.
Short summary
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.
Rainfall erosivity maps are crucial for identifying key areas of water erosion. Due to the...
Altmetrics
Final-revised paper
Preprint