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      Spatial-Temporal Variation of Drought in China from 1982 to 2010 Based on a modified Temperature Vegetation Drought Index (mTVDI).

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          Abstract

          Droughts cause huge losses of society and environment, therefore it is important to study the spatial-temporal pattern of drought. The traditional remote sensing drought indices (AVI, VCI and TCI) only consider the single factor representing the soil moisture (surface temperature or NDVI). The comprehensive remote sensing drought indices (VSWI and TVDI) can estimate the soil moisture more accurately, but they are not suitable for large scale region especially with great elevation variation. In this study, a modified Temperature Vegetation Drought Index (mTVDI) was constructed based on the correction of elevation and dry edge. Compared with the traditional drought indices, mTVDI had a better relationship with soil moisture in all selected months (R = -0.376, -0.406, -0.459, and -0.265, p < 0.05). mTVDI was used to analyze the spatial-temporal patterns of drought in China from 1982 to 2010. The results showed that droughts appeared more frequently in Northwest China and the southwest of Tibet while drought centers of North and Southwest China appeared in Huanghuaihai Plain and Yunnan-Guizhou Plateau respectively. The frequency of drought was increasing as a whole while the frequency of severe drought increased significantly by 4.86% and slight drought increased slowly during 1982 to 2010. The results are useful for the understanding of drought and policy making of climate change.

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          Most cited references44

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                Author and article information

                Journal
                Sci Rep
                Scientific reports
                Springer Science and Business Media LLC
                2045-2322
                2045-2322
                December 12 2017
                : 7
                : 1
                Affiliations
                [1 ] School of Geographic and Oceanographic Sciences, Nanjing University, 163 Xianlin Ave, Qixia District, Nanjing, 210023, China. zhaosh@nju.edu.cn.
                [2 ] Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, 163 Xianlin Ave, Qixia District, Nanjing, 210023, China. zhaosh@nju.edu.cn.
                [3 ] Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China. zhaosh@nju.edu.cn.
                [4 ] School of Geographic and Oceanographic Sciences, Nanjing University, 163 Xianlin Ave, Qixia District, Nanjing, 210023, China.
                [5 ] Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, 163 Xianlin Ave, Qixia District, Nanjing, 210023, China.
                [6 ] Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China.
                [7 ] School of Geographic and Oceanographic Sciences, Nanjing University, 163 Xianlin Ave, Qixia District, Nanjing, 210023, China. hekexun@126.com.
                [8 ] China Automotive Technology & Research Center, 68 East Xianfeng Road, Dongli District, Tianjin, 300300, China. hekexun@126.com.
                [9 ] Norwegian Institute of Bioeconomy Research (NIBIO), Postboks 115, 1431, Ås, Norway.
                [10 ] CEES, Department of Biosciences, University of Oslo, Blindern, 0152, Oslo, Norway.
                [11 ] Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
                Article
                10.1038/s41598-017-17810-3
                10.1038/s41598-017-17810-3
                5727209
                29234101
                ca709eef-ccdd-4646-bbcb-ee1c30ffee9c
                History

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