Abstract
Drought propagation analysis is of great significance to develop reliable drought-resistant schemes. In this study, based on the construction of time-variant meteorological and agricultural drought indicators SPIt and SSMIt through Generalized Additive Models for Location, Scale and Shape (GAMLSS) method, the static and dynamic drought propagation time were defined and determined by Five-element Subtraction Set Pair Potential (FSSPP) and Copula function methods. Then, the driving factors resulted in meteorological to agricultural drought propagation were recognized through Pearson Correlation Coefficient (PCC) indicator. And finally, the application results of the proposed approach in Anhui province, China indicated that, (1) the static propagation time of meteorological to agricultural drought process varied within 1 to 4 months, and differed slightly in different seasons. And drought propagation time in spring and summer was noticeably longer than that of autumn and winter. (2) the dynamic drought propagation time presented decreasing trend in winter, spring and summer but displayed slight increasing trend in autumn in Anhui province. On the whole, the research findings are beneficial for theoretical and practical research of drought propagation system.
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The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.
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Funding
The authors would like to thank the support of the National Natural Science Foundation of China (Grant No. U2240223, 52209012 and 52109009).
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Chengguo Wu: conceptualization, methodology, validation, formal analysis, writing-original draft, reparation, writing-review and editing; Yin Xu: conceptualization, writing-original draft, reparation; Juliang Jin: methodology, writing-original draft, preparation; Yuliang Zhou: data curation; Boyu Nie, Rui Li and Yi Cui: validation, funding acquisition; Fei Tong, Rui Li and Libing Zhang: investigation, data curation.
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Wu, C., Xu, Y., Jin, J. et al. Meteorological to Agricultural Drought Propagation Time Analysis and Driving Factors Recognition Considering Time-Variant Characteristics. Water Resour Manage 38, 991–1010 (2024). https://doi.org/10.1007/s11269-023-03705-8
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DOI: https://doi.org/10.1007/s11269-023-03705-8