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2017

a lot of damage and hence changes in

these events and their causes have been
drawing considerable attention. This

study investigated EPEs resulting from
western North Pacific (WNP) tropical

cyclones (TCs) and their potential link to El
Nino-Southern-Oscillation (ENSO), using

TC track data, daily precipitation data
from 2313 stations for 1951-2014,

and the NCAR-NCEP reanalysis dataset. Two types of EPEs were considered: EPEs within
500 km from the TC center, and those caused by mesoscale and synoptic systems, referred to as

predecessor rain events (PREs), beyond 1000 km from the TC center. Results indicated significant
impacts of TCs on EPEs along the coastal areas, and discernable effects in inland areas of China.

However, the effect of TCs on EPEs tended to be modulated by ENSO. During neutral yea
rs, inland areas of China are more affected by TC-induced extremeprecipitation than during

El Nino or La Nina years, with the highest density of TC tracks and larger-than average
numbers of tropical storms, typhoons, and landfalling TCs. During the El Nino phase, the central

and eastern equatorial Pacific was characterized by higher sea surface temperature (SST), greater
low-level vorticity (1000 hPa) and upper-level divergence (250 hPa), and stronger prevailing

westerlies, which combined to trigger the movement of mean genesis to the eastern and
southeastern WNP, resulting in fewer TCs passing through the Chinese territory.

论文链接：https://doi.org/10.1175/JCLI-D-17-0474.1

22.Timing of floods in southeastern China: Seasonal properties and potential causes
作 者 : Zhang Qiang*；Gu Xihui*； Singh Vijay P.； Shi Peijun ； Luo Ming
JOURNAL OF HYDROLOGY 卷 : 552 页 : 732-744 出版年 : SEP 2017

摘 要：Flood hazards and flood risks in southeastern China have been causing increasing
concerns due to dense population and highly-developed economy. This study attempted to

address changes
of seasonality, timing of peak floods and variability of occurrence date of peak floods using,

circular statistical methods and the modified Mann-Kendall trend detection method. The causes
of peak flood changes were also investigated. Results indicated that: (1) floods were subject to

more seasonality and temporal clustering when compared to precipitation extremes. However,

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