Spatio-Temporal Analysis of Precipitation Patterns in Xinjiang Using TRMM Data and Spatial Interpolation Methods: A Comparative Study-Reference-Cited by-同舟云学术

Spatio-Temporal Analysis of Precipitation Patterns in Xinjiang Using TRMM Data and Spatial Interpolation Methods: A Comparative Study

Published:2024-01-22 Issue: Volume: Page:
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Author:

Zhang Minghui,Xu Juncai^ORCID,Zhang Xiaoping

Abstract

In the context of global warming, changes in precipitation patterns and the increase in extreme weather events have had a serious impact on regional development. In order to grasp the temporal and spatial distribution characteristics and trend changes of precipitation in Xinjiang, this paper uses TRMM3B43v7 data to interpolate with radial basis function method, inverse distance weighting method, ordinary kriging method and ANUSPLIN interpolation method, and uses evaluation indicators to determine the best interpolation method. The results show that the applicability of TRMM data in Xinjiang is good, but it is overestimated, and the average monthly scale is 1.30mm higher. Precipitation in Xinjiang is mainly concentrated in the north of the Tianshan Mountains, and less in the south. From 1998 to 2019, the precipitation trend in Xinjiang showed an increasing trend, with more than 63.64% of the total area of Xinjiang showing an increasing trend, and the western region showed a significant increase, while the eastern region showed a slight decreasing trend.

Publisher

Qeios Ltd

Link

https://www.qeios.com/read/OTSWRU.2/pdf

Reference24 articles.

1. Zhou, Qianjin. Interpretation of the IPCC AR6 Report: Changes in Extreme Weather and Climate Events[J/OL]. Climate Change Research, 2021, 17(6)[2023-12-28]. https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFQ&dbname=CJFDLAST2022&filename=QHBH202106011&v=.

2. Jiang., et al. Interpretation of the IPCC AR6 report: changes in the water cycle[J/OL]. Climate Change Research, 2021, 17(6)[2023-12-28]. https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFQ&dbname=CJFDLAST2022&filename=QHBH202106009&v=.

3. SUN Q, MIAO C, DUAN Q, ET AL. A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons[J/OL]. Reviews of Geophysics, 2018, 56(1): 79-107. DOI:10.1002/2017RG000574.

4. Wang Zhongjing, Shi Yujia, Zhang Teng. TRMM remote sensing precipitation underestimates or overestimates precipitation in Chinese mainland? [J/OL]. Advances in Earth Science, 2021, 36(6)[2023-12-28]. https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFQ&dbname=CJFDLAST2022&filename=DXJZ202106005&v=.

5. Shen., Fusion revision and spatial downscaling of satellite precipitation data in Chinese mainland[J/OL]. Journal of Surveying and Mapping, 2023, 52(11)[2023-12-30]. https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFQ&dbname=CJFDAUTO&filename=CHXB202311019&v=.