Spatiotemporal Variations of Precipitation in China Using Surface Gauge Observations from 1961 to 2016
- Creators
- Su, Yunfei
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Zhao, Chuanfeng
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Wang, Yuan
- Ma, Zhanshan
Abstract
Long-term precipitation trend is a good indicator of climate and hydrological change. The data from 635 ground stations are used to quantify the temporal trends of precipitation with different intensity in China from 1961 to 2016. These sites are roughly uniformly distributed in the east or west regions of China, while fewer sites exist in the western region. The result shows that precipitation with a rate of <10 mm/day dominates in China, with a fraction of >70%. With a 95% confidence level, there is no significant temporal change of annually averaged precipitation in the whole of China. Seasonally, there are no significant temporal changes except for a robust decreasing trend in autumn. Spatially, significant differences in the temporal trends of precipitation are found among various regions. The increasing trend is the largest in Northwest China, and the decreasing trend is the largest in North China. The annually averaged number of precipitation days shows a decreasing trend in all regions except for Northwest China. Regarding precipitation type, the number of light precipitation days shows a robust decreasing trend for almost all regions, while other types show no significant change. Considering the high frequency, the temporal trends of light precipitation could highly explain the temporal variation of the total precipitation amount in China.
Additional Information
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Received: 16 February 2020; Accepted: 18 March 2020; Published: 20 March 2020. We acknowledge the data support from the Chinese Meteorology Administration. Data used in this study are available online from http://data.cma.cn/data/cdcdetail/dataCode/SURF_CLI_CHN_MUL_DAY_V3.0.html. This research was funded by the National Natural Science Foundation of China, grant number 41925022, 91837204, and 41575143; the National Key R&D Program on Monitoring, Early Warning and Prevention of Major Natural Disasters, grant number 2017YFC1501403; and the State Key Laboratory of Earth Surface Processes and Resources Ecology. Author Contributions: Conceptualization, Y.S. and C.Z.; methodology, Y.S.; software, Y.S.; validation, Y.S., C.Z.; formal analysis, Y.S., Z.M.; investigation, Y.S., C.Z.; resources, Z.M.; data curation, Z.M.; writing—original draft preparation, Y.S.; writing—review and editing, C.Z., Y.W.; visualization, Y.S.; supervision, C.Z.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript. The authors declare no conflict of interest.Attached Files
Published - atmosphere-11-00303.pdf
Supplemental Material - atmosphere-11-00303-s001.pdf
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Additional details
- Eprint ID
- 102023
- Resolver ID
- CaltechAUTHORS:20200320-121617124
- 41925022
- National Natural Science Foundation of China
- 91837204
- National Natural Science Foundation of China
- 41575143
- National Natural Science Foundation of China
- 2017YFC1501403
- Ministry of Science and Technology (Taipei)
- State Key Laboratory of Earth Surface Processes and Resources Ecology
- Created
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2020-03-20Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field