Rainfall-triggered Landslides Research Articles

The Key Parameters Involved in a Rainfall-Triggered Landslide

In addition to the intensity of precipitation, the different hydraulic and mechanical properties of a soil hill can also be responsible for landslides. More specifically, the same rainfall amount can trigger or not trigger a mass movement depending on its characteristics. This issue represents a great geotechnical hazard in mountainous regions such as Brazil, Italy, South Africa, Japan and Hong Kong. The understanding of each of the key factors involved in a rainfall-triggered landslide can be deepened and also quantified. Thus, this research defines, through a numerical model, which parameters are the key factors involved in slope stability. A homogeneous and unsaturated hill was studied. Its different hydraulic and mechanical properties were varied. Geometry and mechanical parameters were shown to exert the greatest influence on stability. Hydraulic parameters, for the same amount of rain, showed a lower influence. The fitting parameters of the soil–water characteristic curve of the materials had a low impact on stability when compared to other parameters assessed. Our conclusions can help future laboratory and field studies to focus more on the accuracy and confidentiality of the key parameters. The results are also important as they give direction to studies related to precipitation threshold definition.

Developmental characteristics of rainfall-induced landslides from 1999 to 2016 in Wenzhou City of China

Many landslides are triggered by excess precipitation. In the eastern part of China, landslides caused by extreme rainfall from typhoons in the monsoon season are the main geomorphological process with catastrophic impacts on society and the environment. In this study, Wenzhou City in eastern China was taken as the study area, and we compiled a detailed inventory of rainfall-triggered landslides between 1999 and 2016. The developmental characteristics of these landslides were determined with an emphasis on temporo-spatial distribution. The results showed that most of the landslides were located in the mountainous area of the western part of Wenzhou City. Landslides triggered by typhoon rainfall were commonly concentrated in a short period from July to September, when more than 70% of the landslides occurred. The landslides in this region were mainly of the debris-flow type, most of which were on a small scale, but had severe effects because of large elevation differences and long runout distances. Because the precipitation in typhoon events was commonly extreme, the initiation area of most landslides coincided with the region of highest hourly precipitation. Our results can provide reference data and guidelines for developing an early warning system for landslides and risk reduction in the study area.

The influence of land use and land cover change on landslide susceptibility in the Lower Mekong River Basin

The Lower Mekong River Basin in Southeast Asia experiences frequent rainfall-triggered landslides especially during the monsoon season. In this study, the influence of land use and land cover change and other causative factors on landslide susceptibility is evaluated in the Lower Mekong Basin. Frequency ratio analysis is performed to quantify the relationship between LULC change and susceptibility. Detailed landslide inventory maps are used for analysis with yearly LULC maps. The LULC change is used as a contributing variable in a logistic regression-based susceptibility model with other variables including distance to roads, slope, aspect, forest loss, and soil properties. The Receiver Operating Characteristic (ROC) curve and Area Under the Curve are estimated for the model trained by each landslide inventory. The models show good performance, with AUC values ranging from 0.697 to 0.958 and an average AUC equal to 0.820. Both the Frequency Ratio analysis and the Logistic Regression models indicate LULC change from agricultural land to forest has a positive correlation with landslide occurrence. The most significant factors in the models are found to be distance to roads, slope, and aspect. A better understanding of the effects of LULC on landslide susceptibility can be useful for local land and disaster management and for the implementation of LULC as a factor in future susceptibility models. Using datasets that are unique to the Lower Mekong region, this study provides additional insights into the relationship between causative factors and landslide activity to better inform regional and global landslide susceptibility modeling.

Landslide rainfall threshold for landslide warning in Northern Thailand

Northern Thailand is a hotspot for landslides. Rainfall-triggered landslides in this region have caused much suffering and many fatalities. In this work, a landslide-triggering rainfall threshold for Northern Thailand is proposed based on rainfall data relating to 48 triggering rainfall events that caused 59 landslides in the study area. To account for different mechanism of landslide formation, the threshold was portioned into two parts for different duration of rainfall events. A split point of 3 days was chosen as a separator for portioning the threshold to be 1) a threshold for rainfall events of duration no longer than 3 days and 2) a threshold for rainfall events of duration longer than 3 days. The threshold also required a suitable variable for antecedent rainfalls which was found to be cumulative rainfall over 25-day period (CR 25) of 140 mm. Therefore, the thresholds combining cumulative rainfall with rainfall event - duration (CED) were established by incorporating the CR25 of 140 mm into the traditional ED threshold. This is the first attempt to incorporate the difference mechanism of landslide formation by dividing the CED threshold to two portions for difference rainfall duration. The introduced threshold shows positive sign of the prediction, particularly in term of false alarm rate, false alarm ratio, and critical success index. The introduced threshold will be useful for landslide warning system in the study area.

Evaluation of potential changes in landslide susceptibility and landslide occurrence frequency in China under climate change

Climate change can alter the frequency and intensity of extreme rainfall across the globe, leading to changes in hazards posed by rainfall-induced landslides. In recent decades, China suffered great human and economic losses due to rainfall-induced landslides. However, how the landslide hazard situation will evolve in the future is still unclear, also because of sparse comprehensive evaluations of potential changes in landslide susceptibility and landslide occurrence frequency under climate change. This study builds upon observed and modelled rainfall data from 24 bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs), a statistical landslide susceptibility model, and empirical rainfall thresholds for landslide initiation, to evaluate changes in landslide susceptibility and landslide occurrence frequency at national-scale. Based on four Shared Socioeconomic Pathways (SSP) scenarios, changes in the rainfall regime are projected and used to evaluate subsequent alterations in landslide susceptibility and in the frequency of rainfall events exceeding empirical rainfall thresholds. In general, the results indicate that the extend of landslide susceptible terrain and the frequency of landslide-triggering rainfall will increase under climate change. Nevertheless, a closer inspection provides a spatially heterogeneous picture on how these landslide occurrence indicators may evolve across China. Until the late 21st century (2080–2099) and depending on the SSP scenarios, the mean annual precipitation is projected to increase by 13.4 % to 28.6 %, inducing an 1.3 % to 2.7 % increase in the modelled areal extent of moderately to very highly susceptible terrain. Different SSP scenarios were associated with an increase in the frequency of landslide-triggering rainfall events by 10.3 % to 19.8 % with respect to historical baseline. Spatially, the southeastern Tibetan Plateau and the Tianshan Mountains in Northwestern Basins are projected to experience the largest increase in landslide susceptibility and frequency of landslide-triggering rainfall, especially under the high emission scenarios. Adaptation and mitigation methods should be prioritized for these future landslide hotspots. This work provides a better understanding of potential impacts of climate change on landslide hazard across China and represents a first step towards national-scale quantitative landslide exposure and risk assessment under climate change.

An investigation of the combined effect of rainfall and road cut on landsliding

The reduction of soil suction and consequent loss of shear strength due to infiltration is known to trigger shallow landslides during periods of concentrated rainfall. In the mountainous terrain of Nepal, the risk of shallow rainfall-induced landsliding is further exacerbated by non-engineered hillslope excavation for local roads construction. To better understand the combined effect of rainfall and road cutting on landsliding, a detailed investigation of a shallow landslide was conducted, on a site with a steep road cut that failed due to rainfall infiltration in July 2018. An integrated investigation approach was adopted, combining field and laboratory testing and field monitoring with a series of coupled hydro-mechanical analyses with the finite element code PLAXIS 2D. The field and laboratory tests were performed to characterise the subsoil condition and determine the soil parameters for the hydro-mechanical analyses. The field monitoring program was set up to obtain real-time measurements of rainfall and volumetric water content of the soil. The monitored data was used to calibrate the numerical model and assess the reliability of its predictions. Results of the numerical back-analysis suggest that the investigated landslide was triggered by rainfall infiltration causing a gradual reduction of soil suction at shallow depths of ≤1.7 m and the presence of the steep road cut promoted slope failure by allowing larger displacements to occur in the hillslope. Without the road cut, the slope was found to have ~35% greater initial factor of safety and under the landslide-triggering rainfall, the undisturbed slope was found to remain stable with ~170% greater factor of safety than that in the slope with the road cut. This indicates that the presence of a road cut increases the likelihood of landslide during rainfall and lowers the minimum level of rainfall needed for landslide initiation. Hence, rainfall-induced roadside slope failures could become more frequent and extensive if roads continue to be built by informal slope excavation, without adopting suitable interventions, some examples of which are presented in this study.

Analysis of Maximum-Rainfall-Infiltration-Induced Slope Stability Using the Transient Rainfall Infiltration and Grid-based Regional Slope-stability Model in Cililin, West Java, Indonesia

DOI:10.17014/ijog.9.2.263-278Landslide is one type of geological disasters that frequently occurs during the rainy season. Rainfall infiltration can cause soil saturation that increases the positive pore water pressure, disturbing the slope stability. Therefore, knowledge of future landslide-triggering rainfall is required for mitigation efforts and reducing the risk of landslide hazards. This paper presents slope-stability modeling in the Cililin area using the well-established infinite slope model called the transient rainfall infiltration and grid-based regional slope-stability (TRIGRS). The modeling used the rainfall data obtained from the statistical analysis of the maximum daily rainfall by using the Gumbel distribution. The present study applied six scenarios in the modeling. Scenario I is the initial condition without rainfall, showing the slope stability influenced by topography, slope, and soil characteristics. TRIGRS modeling involves rainfall infiltration in scenarios II, III, IV, V, and VI. The maximum rainfalls used in the modeling are 66, 76, 101, 120, and 132 mm/d, showing that rainfall infiltration affected the slope stability. The result indicates that rainfall triggered an increase and expansion of the area distributions critical to the slope stability.

Global Landslide Forecasting System for Hazard Assessment and Situational Awareness

Landslides triggered by extreme rainfall can be devastating, resulting in loss of life, property, and infrastructure. Landslide forecasting systems provide an opportunity to build awareness of potential hazards and ultimately take preemptive measures. There is currently a dearth of forecasting systems that provide regional or global coverage, but these systems can offer important situational awareness in data-sparse, ungauged, or large-scale catchments. A near global, primarily satellite-based system called the Landslide Hazard Assessment for Situational Awareness (LHASA) provides near real-time estimates of potential landslide hazard and exposure around the world. In this work, a precipitation forecast module is introduced into LHASA to complement the existing LHASA framework and provide an estimate of landslide hazard up to 3 days in advance at 1 km resolution. The model-based Goddard Earth Observing System-Forward Processing (GEOS-FP) precipitation forecast product is used as the forcing input for the model in place of the satellite-based Integrated Multi-satellitE Retrievals for Global Precipitation Mission product. Soil moisture and snow depth from the GEOS-FP assimilated product are also incorporated. The study period January 2020–January 2021 is used to test the model performance against the LHASA near real-time estimates at multiple spatiotemporal scales. Validation of the model is carried out using a collection of rainfall-triggered landslide inventories from around the world as case studies to demonstrate the potential utility and limitations of this system. The rescaling of the GEOS-FP precipitation product is a critical step in incorporating the forecasted precipitation data within LHASA-Forecast (LHASA-F). Combining different streams of forecasted data within the LHASA-F framework shows promise, particularly for larger events at the 1- and 2-days lead time for events. Results indicate that for the case studies evaluated, the LHASA-F is generally able to resolve major landslide events triggered by extreme rainfall, such as from tropical cyclones. The analysis shows that landslide forecast outputs may be represented differently depending on the user’s needs. This framework serves as a first milestone in providing a global predictive view of landslide hazard.

The Role of Citrus Groves in Rainfall-Triggered Landslide Hazards in Uwajima, Japan

Landslides often cause deaths and severe economic losses. In general, forests play an important role in reducing landslide probability because of the stabilizing effect of the tree roots. Although fruit groves consist of trees, which are similar to forests, practical land management, such as the frequent trampling of fields by laborers and compression of the terrain, may cause such land to become prone to landslides compared with forests. Fruit groves are widely distributed in hilly regions, but few studies have examined their role in landslide initiation. This study aims at filling this gap evaluating the predisposing and triggering conditions for rainfall-triggering landslides in part of Uwajima City, Japan. A large number of landslides occurred due to a heavy rainfall event in July 2018, where citrus groves occupied about 50% of the study area. In this study, we combined geodata with a regression model to assess the landslide hazard of fruit groves in hilly regions. We developed maps for five conditioning factors: slope gradient, slope aspect, normalized difference vegetation index (NDVI), land use, and geology. Based on these five maps and a landslide inventory map, we found that the landslide area density in citrus groves was larger than in forests for the categories of slope gradient, slope aspect, NDVI, and geology. Ten logistic regression models along with different rainfall indices (i.e., 1-h, 3-h, 12-h, 24-h maximum rainfall and total rainfall) and different land use (forests or citrus groves) in addition to the other four conditioning factors were produced. The result revealed that “citrus grove” was a significant factor with a positive coefficient for all models, whereas “forest” was a negative coefficient. These results suggest that citrus groves have a higher probability of landslide initiation than forests in this study area. Similar studies targeting different sites with various types of fruit groves and several rainfall events are crucial to generalize the analysis of landslide hazard in fruit groves.

The Effects of Different Geological Conditions on Landslide-Triggering Rainfall Conditions in South Korea

How landslide-triggering rainfall conditions vary with geology is unclear. The effects of three different geological conditions (gneiss, GN; granite, GR; sedimentary rock, SR) on variations in intensity–duration (I-D) conditions and rainfall characteristics responsible for initiating shallow landslides were examined using data from 476 landslides in South Korea from 1963 to 2018 and detailed statistical analyses. Results from quantile regression and one-way analysis of variance analyses clearly showed that impermeable SR slopes result in smaller critical rainfall than permeable GN and GR slopes do, indicating a relatively high occurrence exceedance probability and susceptibility to landslides in SR slope. These findings suggest that geological conditions, particularly the relatively high susceptibility of SR slopes, should be considered when establishing rainfall information-based landslide warning criteria for South Korea. Our findings can contribute to the assessment of landslide susceptibility and probability based on geological conditions; however, they should be further investigated through in situ observations.

Wadenow: A Matlab Toolbox for Early Forecasting of the Velocity Trend of a Rainfall-Triggered Landslide by Means of Continuous Wavelet Transform and Deep Learning

A procedure aimed at forecasting the velocity trend of a landslide for a period of some hours to one or two days is proposed here together with its MATLAB implementation. The method is based on continuous wavelet transform (CWT) and convolutional neural network (CNN) applied to rainfall and velocity time series provided by a real-time monitoring system. It is aimed at recognizing the conditions that induce a strong increase, or even a significant decrease, in the average velocity of the unstable slope. For each evaluation time, the rainfall and velocity scalograms related to the previous days (e.g., two weeks) are computed by means of CWT. A CNN recognizes the velocity trend defined in the training stage corresponds to these scalograms. In this way, forecasts about the start, persistence, and end of a critical event can be provided to the decision makers. An application of the toolbox to a landslide (Perarolo di Cadore landslide, Eastern Alps, Italy) is also briefly described to show how the parameters can be chosen in a real case and the corresponding performance.

A Landslide Numerical Factor Derived from CHIRPS for Shallow Rainfall Triggered Landslides in Colombia

Despite great advances in remote sensing technologies, accurate satellite information is sometimes challenged in tropical regions where dense vegetation prevents the instruments from retrieving reliable readings. In this work, we introduce a satellite-based landslide rainfall threshold for the country of Colombia by studying 4 years of rainfall measurements from The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) for 346 rainfall-triggered landslide events (the dataset). We isolate the two successive rainy/dry periods leading to each landslide to create variables that simulate the dynamics of antecedent wetness and dryness. We test the performance of the derived variables (Rainfall Period 1 (PR1), Rainfall Sum 1 (RS1), Rainfall Period 2 (PR2), Rainfall Sum 2 (RS2), and Dry Period (DT)) in a logistic regression that includes three (3) static parameters (Soil Type (ST), Landcover (LC), and Slope angle). Results from the logistic model describe the influence of each variable in landslide occurrence with an accuracy of 73%. Subsequently, we use these dynamic variables to model a landslide threshold that, in the absence of satellite antecedent soil moisture data, helps describe the interactions between the dynamic variables and the slope angle. We name it the Landslide Triggering Factor—LTF. Subsequently, with a training dataset (65%) and one for testing (35%) we evaluate the LTF threshold performance and compare it to the well-known event duration (E-D) threshold. Results demonstrate that The LTF performs better than the E-D threshold for the training and testing datasets at 71% and 81% respectively.

Geoinformatic Analysis of Rainfall-Triggered Landslides in Crete (Greece) Based on Spatial Detection and Hazard Mapping

Among several natural and anthropogenic conditioning factors that control slope instability, heavy rainfall is a key factor in terms of triggering landslide events. In the Mediterranean region, Crete suffers the frequent occurrence of heavy rainstorms that act as a triggering mechanism for landslides. The Mediterranean island of Crete suffers from frequent occurrences of heavy rainstorms, which often trigger landslides. Therefore, the spatial and temporal study of recent storm/landslide events and the projection of potential future events is crucial for long-term sustainable land use in Crete and Mediterranean landscapes with similar geomorphological settings, especially with climate change likely to produce bigger and more frequent storms in this region. Geoinformatic technologies, mainly represented by remote sensing (RS) and Geographic Information Systems (GIS), can be valuable tools towards the analysis of such events. Considering an administrative unit of Crete (municipality of Rethymnon) for investigation, the present study focused on using RS and GIS-based approaches to: (i) detect landslides triggered by heavy rainstorms during February 2019; (ii) determine the interaction between the triggering factor of rainfall and other conditioning factors; and (iii) estimate the spatial component of a hazard map by spatially indicating the possibility for rainfall-triggered landslides when similar rainstorms take place in the future. Both landslide detection and hazard mapping outputs were validated by field surveys and empirical analysis, respectively. Based on the validation results, geoinformatic technologies can provide an ideal methodological framework for the acquisition of landslide-related knowledge, being particularly beneficial to land-use planning and decision making, as well as the organization of emergency actions by local authorities.

A Framework to Project Future Rainfall Scenarios: An Application to Shallow Landslide-Triggering Summer Rainfall in Wanzhou County China

Fatal landslides are a widespread geohazard that have affected millions of people and have claimed the lives of thousands around the globe. A change in climate has significantly increased the frequency and magnitude of rainfall, which affect the susceptibility of slopes to shallow landslides. This paper presents a methodological framework to assess the future changes in extreme and seasonal rainfall magnitudes with climate model projections. This framework was applied to project summer rainfall over Wanzhou County, China, using an ensemble of four regional climate models (RCMs) from the East Asian domain of the Coordinated Downscaling Experiment (CORDEX) under the Phase 5 Coupled Intercomparison Modeling Project (CMIP5). The results find that extreme daily rainfall was projected to decrease in the mid-21st century, with an uncertainty measured by a coefficient of variation between 5% and 25%. The mean seasonal rainfall is projected to increase in the mid-21st century up to a factor of 1.4, and up to a factor of 1.8 in the late-21st century. The variation in the mid-21st century ranged from 10% to 35%, and from 30% to 50% in the late-21st century. This case study delivered a proof-of-concept for a methodological framework to derive shallow landslide-triggering rainfall scenarios under climate change conditions. The resulting spatially distributed climate change factors (CCFs) can be used to incorporate future rainfall scenarios in slope susceptibility models and climate impact assessments.

Changes in the factors contributing to the reduction of landslide fatalities between 1945 and 2019 in Japan

Landslides are natural hazards that cause severe damage and human losses. Japan has succeeded in reducing the number of landslide fatalities and is one of the few countries with long-term databases of landslide fatalities. In this study, we identified the factors that contributed to the decrease in fatalities associated with rainfall-triggered landslides in Japan between 1945 and 2019. We examined trends in landslide fatalities and six factors for Periods I, II, III, IV, and V—each period spans 15 years of the study period—and for Periods I–II, II–III, III–IV, and IV–V. We examined the trends in the number of landslides (NL) and in the ratio between the number of fatalities (NF) and the number of landslides (NF/NL), and considered fatalities as the product of the number of landslides and the probability of fatalities. The number of fatalities decreased continuously between Periods I and IV; the rate of the decrease declined over time. During Period I–II, NF/NL decreased, whereas NL remained unchanged. Decreases in the average number of household members, changes in building structure, and increases in the number of people evacuated may have contributed to the decrease in NF/NL. During Periods II–III and III–IV, NL also decreased. During Period II–III, the area of mature forests increased slowly. During Period III–IV, the implementation of structural measures (i.e., hard measures) was aggressively pursued. The factors that contributed to the decrease in landslide fatalities changed with time, suggesting that measures for reducing landslide fatalities changed according to the degree of maturity of the nation. Furthermore, we identified increases in rainfall and NL in Period V, which might indicate a future increase in landslide fatalities.

Accounting for satellite rainfall uncertainty in rainfall-triggered landslide forecasting

With an increasing spatial/temporal resolution and accuracy of satellite rainfall products, their potential in landslide forecasting has gradually been recognized. Despite this, few studies have evaluated the effect of satellite rainfall uncertainty on landslide forecasting. In this study, a specific procedure is developed to account for the satellite rainfall uncertainty in rainfall-triggered landslide forecasting. We select the Emilia-Romagna region in northern Italy as the study area, and the NASA GPM-based IMERG Early Run product as the representative of satellite rainfall estimates. An error model for satellite rainfall is first constructed based on the distribution of GR|SR (gauge rainfall | satellite rainfall), which consists of a deterministic distortion component and a stochastic component. With the error model, the satellite rainfall uncertainty is characterized by generating the ensemble of probable “true rainfall” for each rainfall condition responsible for landslides. Then the generated rainfall ensembles are applied to the definition of rainfall thresholds using the bootstrap technique. Finally, the landslide forecasting performance of rainfall thresholds is evaluated with the criteria of hit rate, false alarm rate and the Euclidean distance to the perfect point. Results show that the simulated uncertainty band of the IMERG product encompasses most of the rain gauge measurements. The IMERG uncertainty also propagates to the rainfall thresholds, especially when rainfall events have long durations, the cumulated event rainfall that is likely to trigger landslides has a larger variation range. Besides, the landslide forecasting performance for thresholds that account for IMERG uncertainty varies a lot in terms of false alarm rate from 0.50 to 0.75, demonstrating that it is necessary to take into account the uncertainty associated with the IMERG Early Run product when using it to construct rainfall thresholds for landslide forecasting.

Creep of clayey soil induced by elevated pore-water pressure: Implication for forecasting the time of failure of rainfall-triggered landslides

Many models have been proposed to forecast the time of failure of landslides, among them the Voight model (Ω¨=AΩ̇α) has been widely used. Values for controlling parameters of α and A, defined by accelerating movement, are representative of kinematic behavior prior to material failure. Nevertheless, due to the unknowns in the key parameters (α and A), failure-time forecast using this model remains elusive. To examine the nature of these two parameters and their physical background, we conducted ring-shear tests on clayey soil to reproduce tertiary creep through two different approaches: a pore-water pressure-controlled approach to simulate the initiation process of landslides resulting from elevated pore-water pressure during rainfall, and a shear-stress-controlled approach often used in studies of the creep behavior of clayey soils. By examining the creeping behavior observed in the pore-water pressure-controlled tests, we found a 3-stage log-linear relationship between velocity and acceleration. The sample showed drastic dilation with short shear displacement in the first stage, and the dilation decreased in the subsequent two stages with further progress of shear displacement. The smaller value of α in the first stage increased to another value which was larger in the third stage. This variation might result from changes in the dominant shear mechanisms, i.e., in the first stage the observed creeping displacement results from pervasive grain realignment within a relatively wide shear zone, while in the following two stages, it may arise from more localized grain slip within a narrowing domain of shear zone along the sliding surface. In contrast, no evident variations in α value or sample height were found in the shear-stress-controlled tests. The effects of increasing rate of pore-water pressure or shear stress on the value of α were not pronounced in our tests. These results provide further information for understanding the progressive failure process and the Voight model of failure-time forecast. The failure-time forecast of rainfall-triggered landslides could be fundamentally difficult because of a changeable α value with the intricate volumetric variation and a diverse range of operating shear mechanisms involved.

Probabilistic model calibration of spatial variability for a physically-based landslide susceptibility model

ABSTRACT Physically-based landslide susceptibility models have been widely used to predict rainfall-triggered landslides. Calibration of these models is mainly conducted by tuning various inputs, but spatially varying soil properties are not considered. In this study, an efficient probabilistic model calibration method is proposed, which integrates discrete cosine transform (DCT) representation of spatial variability with Bayesian parameter estimation to characterise the spatial variation of soil properties in a physically-based landslide susceptibility model. The efficacy of DCT inversion in representing fields with binary information about landslide occurrence and non-occurrence is investigated. To illustrate the capability and feasibility of the proposed method, a hypothetical example and a case study of a 2014 regional rainfall-triggered landslide event in Qinglian Town, Chongqing, China are presented. The results demonstrate that the location, size and shape of landslide have little influence on the DCT inversion. Considering spatial variation in model calibration can significantly improve model prediction performance for regional rainfall-induced landslides. The topography of the landslide initiation area may affect the influence of the model calibration if the field observations do not agree with the fundamental physics underlying the stability equation used in the model.

The triggering threshold estimation of Majiagou landslide based on data mining

The deformation of the reservoir landslide is mainly governed by the combined action of rainfall and the fluctuation of the reservoir water level. The determination of the threshold of triggering factors is of great significant in the stability analysis and evaluation of the potential landslide. Existing empirical threshold model is mainly based on statistical analysis to fit the explicit function between triggering factors and displacement, which is widely used in rainfall-triggered landslides. However, for reservoir landslides, the relationship among rainfall intensity, reservoir water level, fluctuation rate and landslide displacement is highly nonlinear, which hindered the application of existing empirical threshold model. To tackle the scientific challenge, a novel data mining-based threshold estimation method is proposed in this study. The Majiagou landslide, located in the Three Gorges Reservoir (TGR) region, is selected as the study stie. Firstly, the Distributed Fiber Optical Sensing (DFOS) technology has been adopted to record the rainfall, reservoir water level fluctuations, and deformation information for two years in real time; Then, the evolution pattern of Majiagou landslide was analyzed in depth; Finally, the cluster analysis and decision tree algorithm are used to determine the threshold value of the rainfall and the fluctuation of the reservoir water level. Among which, 80% of the data set is used for training model, and the remaining 20% is used for validation. The study here provides a new and effective method to estimate the triggering threshold and contribute to the prediction and early warning of reservoir landslides.

Shallow landslides and vegetation at the catchment scale: A perspective

Shallow, rainfall-triggered landslides are an important catchment process that affect the rate and calibre of sediment within river networks and create a significant hazard, particularly when shallow landslides transform into rapidly moving debris flows. Forests and trees modify the magnitude and rate of shallow landsliding and have been used by land managers for centuries to mitigate their effects. We understand that at the tree and slope scale root reinforcement provides a significant role in stabilising slopes, but at the catchment scale root reinforcement models only partially explain where shallow landslides are likely to occur due to the complexity of subsurface material properties and hydrology. The challenge of scaling from slopes to catchments (from 1-D to 2-D) reflects the scale gap between geomorphic process understanding and modelling, and temporal evolution of material properties. Hence, our understanding does not, as yet, provide the necessary tools to allow vegetation to be targeted most effectively for landslide reduction. This paper aims to provide a perspective on the science underpinning the challenges land and catchment managers face in trying to reduce shallow landslide hazard, manage catchment sediment budgets, and develop tools for catchment targeting of vegetation. We use our understanding of rainfall-triggered shallow landslides in New Zealand and how vegetation has been used as a tool to reduce their incidence to demonstrate key points.