期刊
ENGINEERING GEOLOGY
卷 195, 期 -, 页码 335-346出版社
ELSEVIER
DOI: 10.1016/j.enggeo.2015.06.006
关键词
Debris flow; Prediction; Rainfall thresholds; Jiangjia Gully
资金
- National Natural Science Foundation of China [41202244]
- National Key Fundamental Research Program of China (973) [201403744703]
The Jiangjia Gully (JJG), Yunnan Province, China, is one of several debris flow gullies that characterize the topography of mountainous areas in China. Rainfall-triggered debris flows are frequent in the JJG. In order to model and predict these debris flow occurrences, we analyzed precipitation parameters (including intensity-duration thresholds and rainfall event-duration thresholds) that triggered past debris flow events in the JJG. Since the JJG is unique, any model used for predicting debris flows in this gully must be developed using data from only this gully. Furthermore, the effects of normalized precipitation and antecedent precipitation on debris flows in the JJG were considered. Debris flow data and precipitation data were compiled from 47 debris flow events in 1993, 1994, 1998-2001,2004 and 2006 in JJG. All of these debris flows were triggered following a precipitation event with a duration of 6 h or less, with most of the precipitation events exhibiting a duration of 3 to 6 h. Only three rainfall events with a duration of less than 1 h and average intensity between 1.0 and 42.0 mm/h produced debris flows. The 90%, 70% and 50% probability curves for debris flow events were then constructed. Intensity-duration (I-D) and rainfall event-duration (RE-D) thresholds were then used for debris flow modeling based on preliminary tests and comparisons. Antecedent precipitation was found not to be a significant factor in triggering debris flows in the JJG; however, intraday precipitation played a significant role. Normalized precipitation threshold curves from adjacent areas were not well-correlated with the patterns observed in the JJG. Determination of unique thresholds for each gully is necessary for developing an effective prediction system. Crown Copyright (C) 2015 Published by Elsevier B.V. All rights reserved.
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