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Observed intensification of temporally compound dry-wet transitions and serial combinations across East Asia
Jinwook Lee1, Eunsaem Cho2, Changhyun Jun3
1Department of Civil, Environmental and Construction Engineering, University of Hawaii at Manoa, Honolulu, HI, 96822, USA.
Abstract:
Rapid transitions between dry and wet spells, referred to as hydroclimatic swing events (HSEs), pose significant threats to human health, infrastructure, and ecosystems, particularly across East Asia. These risks are further amplified when HSEs are preceded by additional dry or wet spells, forming serially combined events; however, such complex cases have received limited attention in previous research. This study investigates emerging trends of HSEs and serially combined HSEs through a multivariate analysis of the ERA5-Land dataset from 1951 to 2020. A nonparametric index merging multiple proxies for dry and wet spells, termed the standardized dry-wet index (SDWI), is introduced to capture both individual and temporally compound events. The Mann-Kendall test and likelihood multiplication factor are applied to quantify trends and dependences between extreme dry and wet conditions. Results indicate that over 80 % of East Asia has experienced a rising risk of HSEs from 1970 onward, with the eastern region showing high vulnerability to consecutive HSEs. Northern East Asia is more exposed to dry-to-wet (D2W) transitions, with statistically significant increases compared to wet-to-dry (W2D) transitions. Hotspots for various types of serially combined HSEs have also been identified mostly covering northeastern East Asia. This study also highlights the critical role of climatology in generating HSEs across different climate zones. Comparative analysis on the dependence of HSE occurrences between past and current decades suggests that the increasing frequency of HSEs and their combinations is driven by stronger dependencies between events. These findings imply that individual events contributing to HSEs are highly correlated due to shared climatic drivers, rather than being influenced by random variability.
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