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Linking typhoon tracks and spatial rainfall patterns for improving flood lead time predictions over a mesoscale mountainous watershed
被引:55
|作者:
Huang, Jr-Chuan
[2
]
Yu, Cheng-Ku
[1
]
Lee, Jun-Yi
[2
]
Cheng, Lin-Wen
[1
]
Lee, Tsung-Yu
[2
]
Kao, Shuh-Ji
[3
,4
]
机构:
[1] Chinese Culture Univ, Dept Atmospher Sci, Taipei 11114, Taiwan
[2] Natl Taiwan Univ, Dept Geog, Taipei 10764, Taiwan
[3] Acad Sinica, Res Ctr Environm Changes, Taipei 115, Taiwan
[4] Xiamen Univ, State Key Lab Marine Environm Sci, Xiamen, Peoples R China
关键词:
NEURAL-NETWORK;
MODEL;
PRECIPITATION;
HURRICANES;
RAINBANDS;
TAIWAN;
FOREST;
D O I:
10.1029/2011WR011508
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
Typhoon rainfall characteristics over a mesoscale mountainous watershed (drainage area of 620 km(2)) located in eastern Taiwan were analyzed to fill the gaps in our knowledge concerning the linkage between typhoon track, rainfall patterns, and flood peak time. This study used spatially high-resolution radar-derived rainfall estimates from 38 storm events (similar to 2800 h) to investigate this linkage. The effect of spatial rainfall patterns on the timing of flood peak for the selected events was examined with the aid of a diffusive wave model. The results show that the typhoon rainfall was spatially aggregated and that the relative variations in the rainfall became smaller at higher rainfall rates. The maximum hourly rainfall was approximately twice the areal mean rainfall. Three major rainfall types were identified statistically, and different typhoon tracks appeared to have preferable rainfall types. This finding is presumably due to the interaction of the typhoon circulation and precipitation with the mountainous landscape. Flood lead times were derived for the different rainfall types, and it was found that differences in their lead times could be as large as similar to 3 h over the studied mesoscale watershed. It is recommended that this empirical approach be incorporated into flood forecasting and warning systems.
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