A New Perspective on Drought Propagation: Causality. Issue 2 (27th January 2022)

• Record Type: Journal Article
• Authors: Shi, Haiyun; Zhao, Yiyang; Liu, Suning; Cai, Hejiang; Zhou, Zhaoqiang
• Is Part Of: Geophysical research letters. Volume 49:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-27
• Subjects: Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2021GL096758
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: The essence of propagation from meteorological to hydrological drought is the cause‐effect relationship between precipitation and runoff. This study challenged the reliability of applying linear or non‐linear correlation (i.e., closeness/similarity, a non‐directional scalar) to study drought propagation (i.e., causality, a directional vector). Meanwhile, in the field of hydrometeorology, causality analysis is burgeoning in model simulations, but still rare in analyzing the observations. Therefore, this study aims to provide a new perspective on drought propagation (i.e., causality) using convergent cross mapping (CCM) based on pure observations. Compared with the results in previous studies, the effectiveness of applying causality analysis in drought propagation study was proven, indicating that causality analysis would be more powerful than correlation analysis, especially for detecting drought propagation direction. Plain Language Summary: The cause‐effect relationship (causality) between meteorological and hydrological drought is clear, that is, precipitation deficit can lead to decrease in runoff. Naturally, a significant correlation between the behaviors of precipitation and runoff is expected, based on which the drought propagation time can be calculated. This study is inspired by the existence of spurious correlation, which implies that correlation is commonly confused with causality. Though the causality in drought propagation study is physically logical, spurious correlation accentuates the irrationality of applying correlation analysis wherever causality concerns. Hence, this study applied causality analysis to explore drought propagation, and the results were satisfying especially in detecting two main characteristics of drought propagation: direction and time. Nevertheless, this study not only aims to apply a new method in detecting drought propagation direction and time, but also to reassess the nature of drought propagation. We advocate to reject the overuse of correlation analysis and embraces the true causality analysis in the field of hydrometeorology. Key Points: Causality analysis, instead of correlation analysis, was introduced in drought propagation study for the first time Convergent cross mapping (CCM) was proven to be powerful in detecting drought propagation direction and time based on pure observations This study can fill the gap of using real‐world observations (rather than model simulations) in the causality analysis to a large extent