Variability in rainfall threshold for debris flow after the Chi-Chi earthquake in central Taiwan, China

2009 ◽  
Vol 24 (2) ◽  
pp. 177-188 ◽  
Author(s):  
C.L. SHIEH ◽  
Y.S. CHEN ◽  
Y.J. TSAI ◽  
J.H. WU
Keyword(s):  
Debris Flow ◽  
Rainfall Threshold ◽  
Central Taiwan

Debris flow hazard mapping considering the effect of mitigation structure – a case study in Taiwan based on numerical simulation

2020 ◽  
Author(s):  
Chih-Hao Hsu ◽  
Chuan-Yi Huang ◽  
Ting-Chi Tsao ◽  
Hsiao-Yuan Yin ◽  
Hsiao-Yu Huang ◽  
...  
Keyword(s):  
Debris Flow ◽  
Water Conservation ◽  
Large Scale ◽  
Mass Movement ◽  
Hazard Mapping ◽  
Hazard Map ◽  
Inundation Depth ◽  
Central Taiwan ◽  
Debris Flow Hazard ◽  
Typhoon Herb

<p>This study added the dams and retain basin according to their dimensions measured with UAV onto the original 5m-resolition DEM to compare the effect of mitigation structures to debris flow hazard. The original and the modified DEMs were both applied to simulate the consequences by using RAMMS::Debris Flow (RApid Mass Movement Simulation) model.</p><p>Hazard map is the best tool to provide the information of debris flow hazard in Taiwan. It has an important role to play in evacuating the residents within the affected zone during typhoon season. For the reason, debris flow hazard maps become a useful tool for local government to execute the evacuation. As the mitigation structure is constructed, the intensity of debris flow hazard reduces.</p><p>The Nantou DF190 debris flow potential torrent is located in central Taiwan. In 1996 when Typhoon Herb stroke, 470,000 cubic-meter of debris were washed out and deposited in 91,200 square-meter area (Yu et al., 2006), and the event caused the destruction of 10 residential houses with 2 fatalities. After the event the Soil and Water Conservation Bureau constructed a 100-meter long sabo dam and sediment retain basin with capacity of 60,000 cubic-meters. In order to compare the difference of affected zone before and after the construction of mitigation structures, the study applies RAMMS to simulate the above-mentioned event.</p><p>The result shows when large-scale debris flow occurs, most of the sediments still overflow and deposit on the fan with shape similar to the 1996 Typhoon Herb event. However, the intensity has reduced significantly with 50% less in area, several meters less in inundation depth and 50% less in flow velocity approximately. The comparison shows the effect of mitigation structures and could provide valuable information for debris flow hazard mapping.</p><p>Key Words: Debris flow, RAMMS, Hazard map, Mitigation, Taiwan</p>

scholarly journals Assessment of Risk due to Debris Flow Events: A Case Study in Central Taiwan

2006 ◽  
Vol 39 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Gwo-Fong Lin ◽  
Lu-Hsien Chen ◽  
Jun-Nan Lai
Keyword(s):  
Debris Flow ◽  
Central Taiwan

Rainfall threshold calculation method for debris flow pre-warning in data-poor areas

2013 ◽  
Vol 24 (5) ◽  
pp. 854-862 ◽  
Author(s):  
Huali Pan ◽  
Jiangcheng Huang ◽  
Ren Wang ◽  
Guoqiang Ou
Keyword(s):  
Debris Flow ◽  
Calculation Method ◽  
Rainfall Threshold

scholarly journals Evaluation of Rainfall-Triggered Debris Flows under the Impact of Extreme Events: A Chenyulan Watershed Case Study, Taiwan

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2201
Author(s):  
Jinn-Chyi Chen ◽  
Wen-Shun Huang
Keyword(s):  
Debris Flow ◽  
Extreme Events ◽  
Debris Flows ◽  
Extreme Rainfall ◽  
Extreme Rainfall Event ◽  
Extreme Rainfall Events ◽  
Hourly Rainfall ◽  
Central Taiwan ◽  
The Impact

This study examined the conditions that lead to debris flows, and their association with the rainfall return period (T) and the probability of debris flow occurrence (P) in the Chenyulan watershed, central Taiwan. Several extreme events have occurred in the Chenyulan watershed in the past, including the Chi-Chi earthquake and extreme rainfall events. The T for three rainfall indexes (i.e., the maximum hourly rainfall depth (Im), the maximum 24-h rainfall amount (Rd), and RI (RI = Im× Rd)) were analyzed, and the T associated with the triggering of debris flows is presented. The P–T relationship can be determined using three indexes, Im, Rd, and RI; how it is affected and unaffected by extreme events was developed. Models for evaluating P using the three rainfall indexes were proposed and used to evaluate P between 2009 and 2020 (i.e., after the extreme rainfall event of Typhoon Morakot in 2009). The results of this study showed that the P‒T relationship, using the RI or Rd index, was reasonable for predicting the probability of debris flow occurrence.

scholarly journals Estimation of debris flow critical rainfall thresholds by a physically-based model

2012 ◽  
Vol 9 (11) ◽  
pp. 12797-12824 ◽  
Author(s):  
M. N. Papa ◽  
V. Medina ◽  
F. Ciervo ◽  
A. Bateman
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Threshold ◽  
Richards Equation ◽  
Reduced Form ◽  
Physically Based Model ◽  
Rainfall Characteristics ◽  
South Italy ◽  
Infinite Slope Stability Model ◽  
Physically Based

Abstract. Real time assessment of debris flow hazard is fundamental for setting up warning systems that can mitigate its risk. A convenient method to assess the possible occurrence of a debris flow is the comparison of measured and forecasted rainfall with rainfall threshold curves (RTC). Empirical derivation of the RTC from the analysis of rainfall characteristics of past events is not possible when the database of observed debris flows is poor or when the environment changes with time. For landslides triggered debris flows, the above limitations may be overcome through the methodology here presented, based on the derivation of RTC from a physically based model. The critical RTC are derived from mathematical and numerical simulations based on the infinite-slope stability model in which land instability is governed by the increase in groundwater pressure due to rainfall. The effect of rainfall infiltration on landside occurrence is modelled trough a reduced form of the Richards equation. The simulations are performed in a virtual basin, representative of the studied basin, taking into account the uncertainties linked with the definition of the characteristics of the soil. A large number of calculations are performed combining different values of the rainfall characteristics (intensity and duration of event rainfall and intensity of antecedent rainfall). For each combination of rainfall characteristics, the percentage of the basin that is unstable is computed. The obtained database is opportunely elaborated to derive RTC curves. The methodology is implemented and tested on a small basin of the Amalfi Coast (South Italy).

scholarly journals Early warning of debris flow using optimized self-organizing feature mapping network

2020 ◽  
Vol 20 (7) ◽  
pp. 2455-2470
Author(s):  
Xuedong Wang ◽  
Cui Wang ◽  
Chaobiao Zhang
Keyword(s):  
Neural Network ◽  
Debris Flow ◽  
Early Warning ◽  
Rainfall Threshold ◽  
Liaoning Province ◽  
Feature Mapping ◽  
Threshold Method ◽  
Flow Area ◽  
Competitive Neural Network ◽  
Self Organizing

Abstract Early warning of debris flow is one of the core contents of disaster prevention and mitigation work for debris flow disasters. There are few early warning methods based on the combination of rainfall threshold and geological environment conditions. In this paper, we presented an early warning method for debris flow based on the infinite irrelevance method (IIM) and self-organizing feature mapping (SOFM), and applied it to Liaoning Province, China. The proposed model consisted of three stages. Firstly, eight geological environmental conditions and two rainfall-inducing conditions were selected by analyzing the factors affecting the development of debris flow in the study area, and the rainfall threshold for debris flow outbreak was 150 mm. Secondly, the correlation between various factors was analyzed by IIM, which prevented the blindness of parameter selection and improved the prediction accuracy of the model. Finally, SOFM was employed to predict the test data. Experimental results showed that the IIM-SOFM model had a strong early warning ability. When 25 samples of low-frequency debris flow area were selected, the accuracy rate of the IIM-SOFM model with optimized network structure parameters was 100%, which it was obviously superior to the rainfall threshold method, BP neural network and competitive neural network. Consequently, it is feasible to use the IIM-SOFM model for early warning of debris flow, outperforming traditional machine learning methods.

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