Characteristics of rainfall intensity, duration, and kinetic energy for landslide triggering in Taiwan

• DOI: 10.1016/j.enggeo.2017.10.006
• Publication Date: 2017-10-14T19:45:59Z
• Authors: Jui-Ming Chang; Hongey Chen; Ben Jong-Dao Jou; Nien-Chiao Tsou; Guan-Wei Lin
• Keywords: 13. Climate action; 0207 environmental engineering; 02 engineering and technology; 15. Life on land; 01 natural sciences; 0105 earth and related environmental sciences

Abstract Rainfall intensity-duration (I-D) characteristics are widely used to study landslide triggering. Recent studies also propose that rainfall kinetic energy (ek) can be used to quantify the rate of soil erosion induced by strong precipitation events. In the present study, the Joss-Waldvogel Disdrometers (JWD) was utilized to measure ek data, and the relationship between rainfall kinetic energy and rainfall intensity was determined as the regression results of ekN = 32.19 × (1–0.725e (− 0.029I)) for northern Taiwan and ekS = 32.23 × (1–0.643e(− 0.022I)) for southern Taiwan. Additionally, by examining data from 76 landslide events from the landslide inventory for the period of 2006–2012, the study established an empirical power law I-D relationship, which is crucial for determining the rainfall threshold needed in forecasting landslides. Two rainfall thresholds, i.e., functions of rainfall intensity (I) and rainfall duration (D), were established for study areas in northern and southern Taiwan to be IN = 13.81 D− 0.31 and IS = 66.44 D− 0.57 respectively. Landslides in northern Taiwan usually occurred when D was over 10 h and such events in southern Taiwan took place in two different D groups whose gap was between 16 h and 20 h. Two mechanisms were operative in triggering landslides in the two areas—water infiltration and high rainfall intensity. However, ek also proved to be an important rainfall parameter, and the present study further established that not only rainfall intensity-duration but also kinetic energy can provoke landslide triggering, thus allowing the compiling of more comprehensive information for more accurate landslide forecasting.