Landslide Susceptibility Based on Extreme Rainfall-Induced Landslide Inventories and the Following Landslide Evolution

Wu, Chao‐Ming

doi:10.3390/w11122609

Cited by 18 publications

(15 citation statements)

References 24 publications

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“…The large earthquake-induced landslides considered were the landslide cases after the 2005 Kashmir Earthquake (Mw = 7.6) in Pakistan [9] and the 2008 Wenchuan Earthquake (Mw = 7.9) in China [7,8,16]. The extreme rainfall-induced landslides used in the study were landslides occurring in the Chishan River Watershed [10], Ailiao River Watershed [19], and Taimali River Watershed [19] after 2009 Typhoon Morakot, and the TRW before and after 2009 Typhoon Morakot.…”

Section: Methodsmentioning

confidence: 99%

“…The analysis of landslide recovery or landslide evolution had been widely used in the long-term observation of landslide distribution after large earthquake events, including the 1999 Chichi earthquake [5], 2005 Kashimir earthquake [6], 2008 Wenchuan earthquake [7,8], and 2015 Gorkha earthquake [9]. Landslide evolution in the years following a large earthquake or extreme rainfall event in a watershed with dense landslide cases is the key determinant of watershed management and the mitigation of secondary geohazards [10]. Some artificial factors, including land use [11,12] and road development [13], were the significant factors for the evolution and reoccurrence of landslide.…”

Section: Introductionmentioning

confidence: 99%

“…Some artificial factors, including land use [11,12] and road development [13], were the significant factors for the evolution and reoccurrence of landslide. Some studies have been conducted on the characteristics of landslide evolution, including landslide activity [6,7], the spatiotemporal distributions of landslides [8,14], landslide recovery characteristics [15], and landslide evolution trends [10,16], with regard to severe landslides induced by large earthquakes or extreme rainfall. However, few studies have explored landslide evolution 2 of 19 by examining spatiotemporal hotspots.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Landslide Stability and Vegetation Recovery: Case Studies in the Tsengwen Reservoir Watershed in Taiwan

2021

Water

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The sediment yield from numerous landslides triggered in Taiwan’s mountainous regions by 2009 Typhoon Morakot have had substantial long-term impacts on the evolution of rivers. This study evaluated the long-term evolution of landslides induced by 2001 Typhoon Nari and 2009 Typhoon Morakot in the Tsengwen Reservoir Watershed by using multiannual landslide inventories and rainfall records for the 2001–2017 period. The landslide activity, vegetation recovery time, and the landslide spatiotemporal hotspot analyses were used in the study. Severe landslides most commonly occurred on 35–45° slopes at elevations of 1400–2000 m located within 500 m of the rivers. The average vegetation recovery time was 2.29 years, and landslides with vegetation recovery times exceeding 10 years were most frequently retrogressive landslide, riverbank landslides in sinuous reaches, and the core area of large landslides. The annual landslide area decline ratios after 2009 Typhoon Morakot in Southern Taiwan was 4.75% to 7.45%, and the time of landslide recovery in the Tsengwen reservoir watershed was predicted to be 28.48 years. Oscillating hotspots and coldspots occupied 95.8% of spatiotemporal patterns in the watershed area. The results indicate that landslides moved from hillslopes to rivers in the 2001–2017 period because the enormous amount of sediment deposited in rivers resulted in the change of river geomorphology and the riverbank landslides.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the Landslide Stability and Vegetation Recovery: Case Studies in the Tsengwen Reservoir Watershed in Taiwan

2021

Water

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“…Almost all extreme climate occasions are related to water-based situations. For instance, heavy rainfall events have become more frequent and concentrated, leading to disasters such as landslides and debris flow in many watersheds locating at the upstream area, which have seriously affected the safety of the people [26][27][28]. In August 2009, Typhoon Morakot brought record-breaking heavy rainfall to the upstream watershed in southwestern and southeastern Taiwan.…”

Section: Introductionmentioning

confidence: 99%

Cluster and Redundancy Analyses of Taiwan Upstream Watersheds Based on Monthly 30 Years AVHRR NDVI3g Data

Tsai

Wong

2021

Atmosphere

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The study uses 30 years of the third generation of Advanced Very-High-Resolution Radiometer (AVHRR) NDVI3g monthly data from 1982 to 2012 to identify the natural clusters and important driving factors of the upstream watersheds in Taiwan through hierarchical cluster analysis (HCA) and redundancy analysis (RDA), respectively. Subsequently, as a result of HCA, six clusters were identified based on the 30 years of monthly NDVI data, delineating unique NDVI characteristics of the upstream watersheds. Additionally, based on the RDA results, environmental factors, including precipitation, temperature, slope, and aspect, can explain approximately 52% of the NDVI variance over the entire time series. Among environmental factors, nine factors were identified significantly through RDA analysis for explaining NDVI variance: average slope, temperature, flat slope, northeast-facing slope, rainfall, east-facing slope, southeast-facing slope, west-facing slope, and northwest-facing slope, which reflect an intimate connection between climatic and orthographic factors with vegetation. Furthermore, the rainfall and temperature represent different variations in all scenarios and seasons. With consideration of the characteristics of the clusters and significant environmental factors, corresponding climate change adaptation strategies are proposed for each cluster under climate change scenarios. Thus, the results provide insight to assess the natural clustering of the upstream watersheds in Taiwan, benefitting future sustainable watershed management.

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“…Spatiotemporal landslide susceptibility mapping (LSM) is thus studied as an excellent method of susceptibility assessment and provides information on potential landslides under the impact of heavy rainfall [15,16].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan

Wang

Han

et al. 2021

Water

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Landslide represents an increasing menace causing huge casualties and economic losses, and rainfall is a predominant factor inducing landslides. Landslide susceptibility assessment (LSA) is a commonly used and effective method to prevent landslide risk, however, the LSA does not analyze the impact of the rainfall on landslides which is significant and non-negligible. Therefore, the spatiotemporal LSA considering the inducing effect of rainfall is proposed to improve accuracy and applicability. In this study, the influencing factors are selected using the chi-square test, out-of-bag error and multicollinearity test. The spatial LSA are thus obtained using the random forest (RF) model, deep belief networks model and support vector machine, and compared using receiver operating characteristic curve and seed cell area index to determine the optimal assessment result. According to the heavy rainfall characteristics in the study area, the rainfall period is divided into four stages, and the effective rainfall model is employed to generate the rainfall impact (RI) maps of the four stages. The spatiotemporal LSAs are obtained by coupling the optimal spatial LSA and various RI maps and verified using the landslide warning map. The results demonstrate that the optimal spatiotemporal LSA is obtained using the spatial LSA of the RF model and temporal LSA of the rainfall data in the peak stage. It can predict the area where rainfall-induced landslides are likely to occur and prevent landslide risk.

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Landslide Susceptibility Based on Extreme Rainfall-Induced Landslide Inventories and the Following Landslide Evolution

Cited by 18 publications

References 24 publications

Evaluating the Landslide Stability and Vegetation Recovery: Case Studies in the Tsengwen Reservoir Watershed in Taiwan

Evaluating the Landslide Stability and Vegetation Recovery: Case Studies in the Tsengwen Reservoir Watershed in Taiwan

Cluster and Redundancy Analyses of Taiwan Upstream Watersheds Based on Monthly 30 Years AVHRR NDVI3g Data

Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan

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