yanhui qin

Mrs. yanhui qin


Research Focus & Metrics

Academic Career Length

11Years (2016-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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11 November 2017 in Journal of Geophysical Research: Atmospheres
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Journal Article
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YQyanhui qin
yanhui qin
Tonghua WuTonghua Wu
Tonghua Wu
LZLin Zhao
Lin Zhao
Xiaodong WuXiaodong Wu
Xiaodong Wu
RLRen Li
Ren Li
JHJunming Hao
Junming Hao

23 November 2017 in Hydrological Processes
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Journal Article
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YQyanhui qin
yanhui qin
Tonghua WuTonghua Wu
Tonghua Wu
XWXiaodong Wu
Xiaodong Wu
RLRen Li
Ren Li
CXChangwei Xie
Changwei Xie
WSWen Shang
Wen Shang

24 May 2017 in Theoretical and Applied Climatology
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Journal Article
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Tonghua WuTonghua Wu
Tonghua Wu
YQyanhui qin
yanhui qin
XWXiaodong Wu
Xiaodong Wu
RLRen Li
Ren Li
DZDefu Zou
Defu Zou
CXChangwei Xie
Changwei Xie

5 May 2016 in Environmental Earth Sciences
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Journal Article
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YQyanhui qin
yanhui qin
Tonghua WuTonghua Wu
Tonghua Wu
RLRen Li
Ren Li
WYWenjun Yu
Wenjun Yu
TWTianye Wang
Tianye Wang
LTLiming Tian
Liming Tian

22 February 2020 in Theoretical and Applied Climatology
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Journal Article
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YQyanhui qin
yanhui qin
WLWenfeng Liu
Wenfeng Liu
ZGZonghe Guo
Zonghe Guo
SXShanbin Xue
Shanbin Xue

27 September 2020 in International Journal of Climatology
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Journal Article
Open access
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YQyanhui qin
yanhui qin
Tonghua WuTonghua Wu
Tonghua Wu
PZPeng Zhang
Peng Zhang
WLWenfeng Liu
Wenfeng Liu
SXShanbin Xue
Shanbin Xue
ZGZonghe Guo
Zonghe Guo
The thawing and freezing conditions on the Qinghai‐Tibet Plateau (QTP) are considered effective indicators that are widely used in ecology, climate change, cold‐region engineering design, and permafrost mapping. The purpose of this study is to utilize reanalysis to detect spatial variations in freeze–thaw conditions across the QTP from 1981–2017. From a comparison of five recent reanalysis models (MERRA2: National Aeronautics and Space Administration Modern‐Era Reanalysis for Research and Applications, Version 2; ERA: European Center for Medium‐Range Weather Forecasts Interim Reanalysis; GLDAS: Global Land Data Assimilation System NOAH; CFS: National Centers for Environmental Prediction Climate Forecast System Reanalysis, Version 2; and CMFD: China Meteorological Forcing Dataset) against existing sparse observations of 2‐m surface air temperature (SAT), we find that NASA MERRA2 dataset is the most applicable to the QTP. Then, the MERRA2 SAT dataset was selected for this study and was corrected and validated by utilizing calibration models built from the observed data. The results revealed that the correlation coefficients between the corrected MERRA2 SAT and meteorological station observed data increased from 0.52 to 0.93, 0.49 to 0.90, 0.56 to 0.93, and 0.69 to 0.93 in spring, summer, autumn, and winter, respectively. The corrected parameters performed better in the southern and southeastern portions of the QTP. Finally, we utilize the corrected MERRA2 dataset to develop freeze–thaw indices and evaluate statistical trends. We find that relatively high air freezing indices are one of the important factors for the presence of permafrost in the central and northeastern portions of the QTP. Trends in the air thawing and freezing indices in the permafrost and seasonally frozen ground regions indicate that, from 1981 to 2017 in the permafrost regions, the warming was more significant in the summer than in the other seasons. However, the winter warming rates in permafrost and seasonally frozen ground regions are almost the same.