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• W4387124448 abstract "Abstract Correlation analysis is the common method to evaluate the relationship between two variables; however, it may sometimes cause spurious correlations. Specifically, in the field of hydrometeorology, with the impacts of climate change and human activities, correlation analysis is difficult to identify the true relationship between variables, and thus, causality analysis should be adopted instead. This study analyzed the causal relationship between meteorological drought and hydrological drought in different climatic regions of China by using convergent cross mapping (CCM). We improved the identification of CCM convergence by using the coefficient of variation and applied it in the field of large-scale hydrometeorology. The results of correlation analysis were compared, and the applicability of causality analysis was explored. The results revealed that: In Southeast China, the correlation and causality between meteorological drought and hydrological drought were both large. In Northeast China and central Qinghai–Tibet Plateau, the correlation between meteorological drought and hydrological drought was small, but the causality was large. In view of the spurious correlation, introducing causality analysis can better explain the relationship between meteorological drought and hydrological drought, especially in areas with snowmelt runoff. Overall, CCM can provide valuable causal information from common time series in the field of large-scale hydrometeorology and has a wide range of application values. However, causality analysis cannot explain the positive or negative relationship between variables. Therefore, when analyzing the relationship between variables, the advantages of the two methods should be given full play." @default.
• W4387124448 created "2023-09-29" @default.
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• W4387124448 date "2023-10-01" @default.
• W4387124448 modified "2023-10-09" @default.
• W4387124448 title "A new perspective for assessing hydro-meteorological drought relationships at large scale based on causality analysis" @default.
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• W4387124448 doi "https://doi.org/10.1088/1748-9326/acfe1e" @default.
• W4387124448 hasPublicationYear "2023" @default.
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