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Monitoring drought dynamics in China using Optimized Meteorological Drought Index (OMDI) based on remote sensing data sets
Wei, Wei1; Zhang, Jing1; Zhou, Junju1; Zhou, Liang2,3; Xie, Binbin4; Li, Chuanhua1
2021-08-15
Source PublicationJOURNAL OF ENVIRONMENTAL MANAGEMENT
ISSN0301-4797
Volume292Pages:14
Corresponding AuthorZhang, Jing(18251907576@163.com)
AbstractTimely and accurate monitoring of the spatiotemporal changes in drought is very important for the reduction in the social losses caused by drought. The Optimized Meteorological Drought Index (OMDI), originally established in southwestern China, showed great potential for drought monitoring over large regions on a large scale. However, the applicability of the index requires further evaluation, especially when used throughout China, which has a different agricultural divisions, variable climatic conditions, complex terrain and diverse land cover. In addition, the OMDI model relies on training data to construct local parameters for the model. On a large scale, it is of great significance to use multisource remote sensing data sets to construct OMDI model parameters. In this paper, the constrained optimization method was used to establish weights for the MODIS-derived Vegetation Conditional Index (VCI), TRMM-derived Precipitation Condition Index (PCI), and GLDAS-derived Soil Moisture Condition Index (SMCI) and calculate the OMDI based on the Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and weather stations. The accuracy of the OMDI model was evaluated by using the correlation coefficient. Moreover, the spatiotemporal changes in drought were also analyzed through trend analysis, Mann-Kendall (MK) statistics and the Hurst index on the monthly and annual scales. The results showed that (1) the highest positive correlation between the OMDI and the SPI was SPI-1, which was higher than that for any other month interval, such as 3 months, 6 months, 9 months and 12 months of the SPI. The results indicated that the OMDI was suitable to monitor meteorological drought. (2) In the nine agricultural subareas in China, the degree of drought in the Yangtze River (DYR) area had the most severe evolution and change frequency. This region was very sensitive to drought in the past two decades. (3) The area with OMDI variation coefficient less than 0.1 accounted for 94%, indicating that the degree of drought fluctuates little; The linear tendency rate is 0.0004, and the area greater than 0 reaches 66.44%, indicating that the drought is developing in a lightning trend. (4) The Hurst index value is mostly higher than 0.5 (the area ratio is 56.31%), and the area of "Positive-Consistent" and "Negative- Opposite" accounted for 54.02%, indicating that more than half of China's area drought changes will show a trend of mitigation in the future.
KeywordOptimized meteorological drought index Evolution dynamics Remote sensing data sets Constrained optimization method China
DOI10.1016/j.jenvman.2021.112733
WOS KeywordSTANDARDIZED PRECIPITATION INDEX ; AGRICULTURAL DROUGHT ; INNER-MONGOLIA ; CLIMATE-CHANGE ; SOIL-MOISTURE ; VEGETATION ; MODIS ; TEMPERATURE ; PATTERNS ; CARBON
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41861040] ; National Natural Science Foundation of China[41761047] ; Natural Science Foundation of Gansu Province[1506RJZA129]
Funding OrganizationNational Natural Science Foundation of China ; Natural Science Foundation of Gansu Province
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000659406900001
PublisherACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/164135
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhang, Jing
Affiliation1.Northwest Normal Univ, Coll Geog & Environm Sci, Lanzhou 730070, Gansu, Peoples R China
2.Lanzhou Jiaotong Univ, Fac Geomat, Lanzhou 730070, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
4.Lanzhou City Univ, Sch Urban Econ & Tourism Culture, Lanzhou 730070, Gansu, Peoples R China
Recommended Citation
GB/T 7714
Wei, Wei,Zhang, Jing,Zhou, Junju,et al. Monitoring drought dynamics in China using Optimized Meteorological Drought Index (OMDI) based on remote sensing data sets[J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT,2021,292:14.
APA Wei, Wei,Zhang, Jing,Zhou, Junju,Zhou, Liang,Xie, Binbin,&Li, Chuanhua.(2021).Monitoring drought dynamics in China using Optimized Meteorological Drought Index (OMDI) based on remote sensing data sets.JOURNAL OF ENVIRONMENTAL MANAGEMENT,292,14.
MLA Wei, Wei,et al."Monitoring drought dynamics in China using Optimized Meteorological Drought Index (OMDI) based on remote sensing data sets".JOURNAL OF ENVIRONMENTAL MANAGEMENT 292(2021):14.
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