The role of the sediment budget in understanding debris flow susceptibility

Abstract

This study proposes a sediment-budget model to predict the temporal variation of debris volume stored in a debrisflow prone watershed. The sediment-budget is dominated by shallow landslides and debris outflow. The basin topography and the debris volume stored in the source area of the debris-flow prone watershed help evaluating its debris-flow susceptibility. The susceptibility model is applied to the Tungshih area of central western Taiwan. The importance of the debris volume in predicting debris-flow susceptibility is reflected in the standardized coefficients of the proposed statistical discriminant model. The high prediction rate (0·874) for the occurrence of debris flows justifies the capability of the proposed susceptibility models to predict the occurrence of debris flows. This model is then used to evaluate the temporal evolution of the debris-flow susceptibility index. The analysis results show that the numbers of watershed which are classified as a debris-flow group correspond well to storage of sediment at different time periods. These numbers are 10 before the occurrence of Chi-Chi earthquake, 13 after the occurrence of Chi-Chi earthquake, 16 after the occurrence of landslides induced by Typhoon Mindulle (Typhoon M), and 14 after the occurrence of debris flows induced by Typhoon M. It indicates that the occurrence of 7·6 Chi-Chi earthquake had significant impact on the debris flow occurrence during subsequent typhoons. Figure 1. Location (red square marked in the index map) and geology of the Tungshih study site (Lee, 2000). The index map shows the geologic provinces of Taiwan (Ho, 1975).Figure 2. Selected 44 potential debris-flow torrents (PDFTs) in the Tungshih quardrangle. PDFTs are marked in yellow; the drainage basin (black polygon) and code number for each PDFT are shown. Red indicates landslides triggered by Typhoon Mindulle. Note: A ls (C), A ls (T), and A ls (M), shallow landslide area triggered by the Chi-Chi earthquake (C) and Typhoons T and M (T, M); V stor (M B ), debris storage volume (field investigated) between Typhoons T and M; L, channel length; S, channel gradient; A, catchment area; F, form factor; V SB (T) and V SB (M), storage volume for debris during Typhoons T and M.Figure 7. (a) The debris volume stored in the catchment of 44 selected PDFTs [log(V stor )]; and (b) susceptibility index I of PDFTs; which vary with the six different periods (P1-P6). The middle line of each period represents the mean log(V stor ) and I. The upper/lower lines indicate the range of plus/minus one standard deviation of log(V stor ) and I. The variation in the debris storage index log(V stor ) is influenced by the shallow landslides (P2, P3, and, P5) and debris outflow (P4, and P6) in the PDFTs. The squares in (b) represent the values of I greater than zero.

Characteristics of rainfall triggering of debris flows in the Chenyulan watershed, Taiwan

“…3. Our results are consistent with those obtained by Dong et al (2009). Both severe rainfall and earthquakes would change the condition of RI, in terms of both the magnitude and number of debris flow events.…”

“…The variation of critical RI is related to debris supply within the watershed. Dong et al (2009) developed a susceptibility index (SI) that accounted for debris supply to estimate the susceptibility to debris flow. They reported that the SI increased following the CCE and after TT (before the occurrence of debris flow), because the volume of debris contributed by shallow landslides increased; and that SI decreased when the volume of debris was reduced by debris outflow during TT and TMi.…”

Debris Flow Susceptibility Assessment in the Wudongde Dam Area, China Based on Rock Engineering System and Fuzzy C-Means Algorithm

“…Most of these models have been created using regional debris flow inventories derived from remotely sensed data. Statistical analyses, including logistic regression [12][13][14][15], discriminant analysis [16,17], and Bayes learning [18], are deemed to be suitable for susceptibility assessment in large and complex areas [19][20][21]. Using Bayes learning and logistic regression to predict debris flows in southwest Sichuan, China, Xu et al [22] pointed out that both methods have disadvantages: Bayes requires some variable assumptions that are difficult to be completely met in practice, whereas logistic regression needs large samples for the iterative calculation to obtain stable model parameters.…”

Catastrophic debris flows triggered by a 14 August 2010 rainfall at the epicenter of the Wenchuan earthquake

“…The impact of the Chi-Chi earthquake (Taiwan, 1999) on subsequent rainfall-induced debris flows has been studied by various authors (Lin et al 2006;Chen and Petley 2005). Dong et al (2009) used a discriminant model, which takes account of the debris budget, to study the influence of earthquakes on the subsequent occurrence of debris flows. Their study showed quantitatively that the occurrence of the 1999 Chi-Chi earthquake had significant impact on the debris flow occurrence during subsequent typhoons.…”