Rainfall-triggered Landslides Research Articles

Optimizing rainfall-triggered landslide thresholds for daily landslide hazard warning in the Three Gorges Reservoir area

Abstract. Rainfall is intrinsically linked to the occurrence of landslide catastrophes. Identifying the most suitable rainfall threshold model for an area is crucial for establishing effective daily landslide hazard warnings, which are essential for the precise prevention and management of local landslides. This study introduces a novel approach that utilizes multilayer perceptron (MLP) regression to calculate rainfall thresholds for 453 rainfall-induced landslides. This research represents the first attempt to integrate MLP and ordinary least squares methods for determining the optimal rainfall threshold model tailored to distinct subregions, categorized by topographical and climatic conditions. Additionally, an innovative application of a three-dimensional convolutional neural network (CNN-3D) model is introduced to enhance the accuracy of landslide susceptibility predictions. Finally, a comprehensive methodology is developed to integrate daily rainfall warning levels with landslide susceptibility predictions using a superposition matrix, thus offering daily landslide hazard warning results for the study area. The key findings of this study are as follows. (1) The optimal rainfall threshold models and calculation methods vary across different subregions, underscoring the necessity for tailored approaches. (2) The CNN-3D model substantially improves the accuracy of landslide susceptibility predictions. (3) The daily landslide hazard warnings were validated using anticipated rainfall data from 19 July 2020, thereby demonstrating the reliability of both the landslide hazard warning results and the rainfall threshold model. This study presents a substantial advancement in the precise prediction and management of landslide hazards by employing innovative modeling techniques.

Assessing rainfall-triggered landslides considering small-scale depth to bedrock uncertainty: A random field approach

Depth to bedrock (DTB) is a critical factor for rainfall–induced slope failures. However, the influence of uncertainties in these measurements, particularly at a small–scale, has not been fully understood. Numerical modeling was conducted to assess the impact of a variable bedrock topography on the stability of a real–world unsaturated slope. The simulations included a three–dimensional pore–water pressure estimation, derived from the numerical solution of the Richards equation, coupled with a slope stability assessment using numerical limit analysis. The study explored the potential of incorporating random fields (RFs) into an established DTB model to improve the understanding of rainfall–triggered landslides. The proposed methodology was applied to the analysis of a small watershed within the Papagaio River basin in Brazil, an area historically subjected to landslides triggered by rainfall events. Our main findings reveal that small variations in DTB can significantly impact the safety factor and probability of failure estimations. Furthermore, they influence the shape, location and failure volume associated to predicted landslides. The incorporation of RFs effectively addresses small–scale uncertainty in DTB, controls bedrock morphology, and enhances the assessment of probabilistic numerical modeling for landslide susceptibility. This study highlights the importance of accurate and comprehensive DTB characterization for assessing rainfall–induced landslides at local slope scale.

Environmental critical thresholds based on statistical analysis for modelling landslide susceptibility in Continental Basaltic Provinces

Abstract. The study aims to estimate the environmental critical thresholds using statistical approaches to understand the landslide conditioning factors that can trigger landslides in the Continental Basaltic Provinces a landslide-prone area, using as reference the landslides that occurred in an extreme rainfall event. The study area is a region that was the scene of an extreme hydrological event in January 2017, with an accumulated volume of rain of 163.9 mm in 8 hours, causing a widespread event of shallow planar landslides with more than 400 scars detected. Hydrological, anthropic, geological, geomorphological, and topographical features of this region were analyzed considering landslides and non-landslides samples set, and their influence in the event was carried out using the Frequency Ratio method, followed by Pearson's Linear Correlation Coefficient and Linear Regression. The results showed that this process helped us to understand environmental critical thresholds based on classes of conditioning factors that have a greater influence on rainfall-triggered landslide occurrences and, consequently, higher predictive capacity in the landslide susceptibility models with the same geoenvironmental parameters which is a valuable insight for risk management.

Landslide Hazard Prediction Based on UAV Remote Sensing and Discrete Element Model Simulation—Case from the Zhuangguoyu Landslide in Northern China

Rainfall-triggered landslides generally pose a high risk due to their sudden initiation, massive impact force, and energy. It is, therefore, necessary to perform accurate and timely hazard prediction for these landslides. Most studies have focused on the hazard assessment and verification of landslides that have occurred, which were essentially back-analyses rather than predictions. To overcome this drawback, a framework aimed at forecasting landslide hazards by combining UAV remote sensing and numerical simulation was proposed in this study. A slow-moving landslide identified by SBAS-InSAR in Tianjin city of northern China was taken as a case study to clarify its application. A UAV with laser scanning techniques was utilized to obtain high-resolution topography data. Then, extreme rainfall with a given return period was determined based on the Gumbel distribution. The Particle Flow Code (PFC), a discrete element model, was also applied to simulate the runout process after slope failure under rainfall and earthquake scenarios. The results showed that the extreme rainfall for three continuous days in the study area was 151.5 mm (P = 5%), 184.6 mm (P = 2%), and 209.3 mm (P = 1%), respectively. Both extreme rainfall and earthquake scenarios could induce slope failure, and the failure probabilities revealed by a seepage–mechanic interaction simulation in Geostudio reached 82.9% (earthquake scenario) and 92.5% (extreme rainfall). The landslide hazard under a given scenario was assessed by kinetic indicators during the PFC simulation. The landslide runout analysis indicated that the landslide had a velocity of max 23.4 m/s under rainfall scenarios, whereas this reached 19.8 m/s under earthquake scenarios. In addition, a comparison regarding particle displacement also showed that the landslide hazard under rainfall scenarios was worse than that under earthquake scenarios. The modeling strategy incorporated spatial and temporal probabilities and runout hazard analyses, even though landslide hazard mapping was not actually achieved. The present framework can predict the areas threatened by landslides under specific scenarios, and holds substantial scientific reference value for effective landslide prevention and control strategies.

A hybrid approach combining physics-based model with extreme value analysis for temporal probability of rainfall-triggered landslide

A hybrid approach combining physics-based model with extreme value analysis for temporal probability of rainfall-triggered landslide

Failure mechanism of a massive fault–controlled rainfall–triggered landslide in northern Pakistan

Failure mechanism of a massive fault–controlled rainfall–triggered landslide in northern Pakistan

Landslide Assessment Classification Using Deep Neural Networks Based on Climate and Geospatial Data

This study presents a method for classifying landslide triggers and sizes using climate and geospatial data. The landslide data were sourced from the Global Landslide Catalog (GLC), which identifies rainfall-triggered landslide events globally, regardless of size, impact, or location. Compiled from 2007 to 2018 at NASA Goddard Space Flight Center, the GLC includes various mass movements triggered by rainfall and other events. Climatic data for the 10 years preceding each landslide event, including variables such as rainfall amounts, humidity, pressure, and temperature, were integrated with the landslide data. This dataset was then used to classify landslide triggers and sizes using deep neural networks (DNNs) optimized through genetic algorithm (GA)-driven hyperparameter tuning. The optimized DNN models achieved accuracies of 0.67 and 0.82, respectively, in multiclass classification tasks. This research demonstrates the effectiveness of GA to enhance landslide disaster risk management.

Changes in vegetation composition and structure following landslide-induced disturbance in the Himalaya

The Himalaya cover 12% of India's landmass and are prone to approximately one-seventh of global rainfall-triggered landslides. Still, very few studies have examined the after-effects of landslides on native vegetation structure and composition. This study aims to fulfill this gap by analyzing the vegetation structure and composition of 10 landslide-impacted sites in Uttarakhand Himalaya along an elevational gradient between 1400–3500 m. The investigations revealed that physiognomically, the younger landslide-disturbed sites were dominated by herbaceous taxa while shrubs and trees dominated the older landslide-disturbed sites. Shannon–Wiener diversity values were highest at the old-low disturbed site compared with recent and young disturbed sites. Sorensen similarity index values indicated that the older landslide sites had the highest similarity in species composition of disturbed and undisturbed sites. The younger and recently disturbed landslide sites were highly dissimilar in species composition and structure as compared to the adjacent undisturbed sites. Notably, both the landslide-disturbed and undisturbed sites had a high percentage of native species (90%–95%). The fundamental understanding developed from this study can have potential applications in evolving management practices for ecological restoration of the degraded ecosystems in the Himalaya and other mountain ecosystems around the world.

Non-parametric fragility curves for probabilistic risk assessment of rainfall-triggered landslides

Fragility analysis of slopes under rainfall condition express the conditional probabilities of exceeding prescribed slope limit states under a range of rainfall intensities, which is the basic and pivotal procedure in rainfall-triggered landslide risk assessment. Classical fragility analysis methods of slopes always develop fragility curves with the assumption of lognormal distribution. The validation of assuming the shape of fragility curves as lognormal distribution remains a open question in practice. In addition, the existing fragility analysis of slopes under rainfall condition have not dealt with the effect of spatially-variable geo-material parameters. In the present study, a novel fragility analysis methodology is proposed for the construction of non-parametric fragility curves of slopes under rainfall condition, in which the spatially-variable soil properties are taken into account. The non-parametric fragility curves are developed by the calculated slope failure probabilities under various rainfall intensities on the basis of probability density evolution theory, without restricting fragility functions to some assumed distribution models. With the proposed method and a set of probabilistic slope rainfall seepage and stability analyses, non-parametric fragility curves, fragility surface using two rainfall intensity measures and time-dependent fragility curves of a slope subjected to various rainfall scenarios are obtained for rainfall-triggered landslides risk assessment.

Modelling the rainfall threshold for shallow landslides considering the landslide predisposing factors in Portugal

Rainfall-triggered landslides pose a significant threat to both infrastructure and human lives, making it crucial to comprehend the factors that contribute to their occurrence. Specifically, understanding the relationship between these factors and the amount of rain that is necessary for triggering such events is essential for effective prediction and mitigation strategies. To address this issue, our study proposes a statistical modelling approach using machine learning, specifically the Random Forest algorithm, to investigate the connection between landslide predisposing factors and the daily rainfall intensity threshold necessary for the initiation of shallow landslides in Portugal. By leveraging a comprehensive dataset comprising historical landslide events, associated critical rainfall, and ten distinct landslide predisposing factors, we developed several models and used cross-validation technique to evaluate their performance. Our findings demonstrate that the Random Forest model effectively captures a relationship among landslide predisposing factors, critical daily rainfall intensity, and landslide occurrences. The models exhibit a satisfactory accuracy in assessing the spatial variation of critical daily rainfall intensity based on the predisposing factors, with a mean absolute percentage error (MAPE) of around 17%. Furthermore, the models provide valuable insights into the relative importance of various predisposing factors in landslide triggering, highlighting the significance of each factor. It was found that it takes higher rainfall intensity to trigger shallow landslides in the north region of Portugal when considering critical rainfall events of 3 and 13 days. Slope aspect, slope angle, and clay content in the soil are among the main predisposing factors used for defining the spatial variation of the daily rainfall intensity threshold.

Rainfall-Triggered Landslides and Numerical Modeling of Subsequent Debris Flows at Kalli Village of Suntar Formation in the Lesser Himalayas in Nepal

Hazardous debris flows are common in the tectonically active young Himalayas. The present study is focused on the recurrent, almost seasonal, landslides and debris flows initiated from Kalli village in Achham District of Nepal, located in the Lesser Himalayas. Such geological hazards pose a significant threat to the neighboring communities. The field survey reveals vulnerable engineering geological conditions and adverse environmental factors in the study area. It is found that a typical complete debris transport process may consist of two stages depending on the rainfall intensity. In the first stage, debris flows mobilized from a landslide have low mobility and their runout distance is quite modest; in the second stage, with an increase in water content they are able to travel a longer distance. Numerical simulations based on a multi-phase flow model are conducted to analyze the characteristics of the debris flows in motion, including the debris deposition profiles and runout distances in both stages. Overall, the numerical results are reasonably consistent with relevant field observations. Future debris flows may likely occur again in this area due to the presence of large soil blocks separated by tension cracks, rampant in the field; numerical simulations predict that these potential debris flows may exhibit similar characteristics to past events.

A study of rainfall thresholds for landslides in Badung Regency using satellite-derived rainfall grid datasets

Integrating field rainfall data with satellite data improves data accuracy and overcomes rainfall data limitations for rain thresholds. Integration can involve field rainfall data, satellite rainfall data, or a different satellite dataset. Merging these rainfall data sources provides more spatial coverage of satellite data. To determine how well rainfall thresholds predict rainfall-triggered landslides, the threshold model must be validated. This study will evaluate satellite rainfall data before and after integration in developing a rainfall threshold model for landslide prediction in Badung Regency. To do so, the study used a cumulative rainfall threshold over 3, 7, 15, and 30 days and two rainfall satellite products (integrated merged multi-satellite retrievals (IMERG) and precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)). Median, first, and third quartiles were used to set thresholds. The area under the curve (AUC) was calculated to validate rainfall threshold outcomes using receiver operating characteristic (ROC) curves. Analysis showed that integrating satellite rainfall data into the rainfall threshold model for landslide prediction yields better results than other methods. An AUC value of 0.903 (90.3%) for the 30-day cumulative rainfall thresholds supports this claim. This model could be a good input for a landslide early warning system in Badung Regency.

Hydromechanical modeling of evolving post-wildfire regional-scale landslide susceptibility

Post-wildfire mass wasting is a major problem throughout many regions worldwide. Recent dramatic increases in global wildfire activities coupled with a shift in wildfire-prone elevation to higher altitudes raise the need to better predict post-fire rainfall-triggered landslides. Despite its importance, only a limited number of studies have investigated landslide susceptibility in areas hit by wildfires using hydromechanical models. However, most of these studies follow either qualitative or semi-quantitative approaches without explicitly considering the fire's effects on the impacted area's physical behavior. This study aims to develop and employ a physics-based framework to generate susceptibility maps of rainfall-triggered shallow landslides in areas disturbed by wildfire. A coupled hydromechanical model considering unsaturated flow and root reinforcement is integrated into an infinite slope stability model to simulate the triggering of shallow landslides from rainfall. The impact of fire is considered through its effects on soil and land cover properties, near-surface processes, and canopy interception. The developed model is then integrated into a geographic information system (GIS) to characterize the regional distribution of landslide potential and its variability considering topography, geology, land cover, and burn severity. The proposed framework was tested for a study site in Southern California. The site was burned in the San Gabriel Complex Fire in June 2016 and experienced widespread landsliding almost three years later following an extreme rainstorm in January 2019. The proposed framework could successfully model the location of observed shallow landslides. The model also revealed a significantly higher likelihood for slope failure in areas burned at moderate to high severities as opposed to unburned and low-burn severity areas. The findings of this study can be employed to predict the timing and general locations of rainfall-triggered shallow landslides following wildfires.

PyLandslide: A Python tool for landslide susceptibility mapping and uncertainty analysis

Mitigating the impacts of landslides and planning resilient infrastructure necessitates assessing the exposure to this hazard through, for example, susceptibility mapping involving the spatial integration of various contributing factors. Here, we introduce PyLandslide, an open-source Python tool that leverages machine learning and sensitivity analysis to quantify the weights of various contributing factors, estimate the associated uncertainties, and generate susceptibility maps. We apply PyLandslide to the case of rainfall-triggered landslides in Italy driven by historical precipitation data (1981–2023) and nine climate projections for the mid-century (2041–2050). Results highlight distance to roads as the most influential factor in determining landslide susceptibility in Italy, followed by slope. Our findings reveal an overall reduction in susceptibility in the mid-century compared to the historical period; however, the directional changes vary spatially. Uncertainty analysis should play a central role in decision-making on landslides, where weights are intricately linked to investments.

Brief report of fatal rainfall-triggered landslides from record-breaking 2023 storms in Auckland, New Zealand

The aim of this paper is to report on fatal landslides resulting from two extraordinary storm events in January and February 2023, in Auckland, New Zealand, including the characteristics of the terrain, the site geology, and the storms. The January 27 Auckland Anniversary storm was from an “atmospheric river,” dumping Summer’s worth of rain (265 mm) in one day. This was the highest 24-h total on record, estimated a 1 in 200-year event, with a peak 2-min rainfall of 4.2 mm, coinciding with Friday’s evening “rush hour.” This led to widespread landsliding throughout Auckland, including a fatal landslide in Parnell, central Auckland. Notably, this occurred only meters from a similarly destructive landslide in 1997. With January 2023 Auckland’s wettest January on record, and with the region still in a state of emergency, Tropical Cyclone Gabrielle crossed the region on February 13–14. This also caused widespread landsliding, including a fatal landslide at Muriwai on the west Auckland coast. Fatal landslides from the same escarpment had occurred in 1965, close to the February 13, 2023, fatal landslide. Taken together, both storm events caused > 140,000 landslides across the North Island, and flooding, making hundreds of people homeless, with damage estimates of > US$8.6 billion (3.4% of NZ’s GDP). For future disaster risk reduction, attention should be paid to revising legislation and local planning, which currently allows people to legally build (and re-build) houses in landslide-prone areas of New Zealand.

Quantifying effects of changes in forest age distribution on the landslide frequency in Japan

Landslides are destructive natural disasters that cause human and economic losses. Although many studies report the effects of forest age on landslide susceptibility, especially for shallow landslides, no studies have examined the effects at a national scale. We assumed that temporal variations in the annual number of rainfall-triggered landslides in Japan were determined by variations in rainfall and forest age distribution. By this assumption, this study aimed to quantify the decrease in the frequency of rainfall-induced landslides owing to the increasing maturity of forests in Japan. Data were collated from 21 studies covering 11 sites in three countries that reported a landslide susceptibility index (i.e., frequency ratio or landslide density) and the relation between forest age and the normalized landslide susceptibility index (NLSI) was modeled. Using this relation and the area for each forest age class, the change in landslide susceptibility at a national scale (NLSIJpn) was quantified during 1966–2017. The authors developed generalized linear models (GLMs) using the annual number of landslides as the response variable and the NLSIJpn and a rainfall index for each year as the explanatory variables. The number of rainfall-induced landslides was simulated in the GLMs in 15 scenarios with different forest age distributions and rainfall amounts. The number of landslides in young-age-dominated and middle-age-dominated forests was estimated to be 2.4 and 1.1 times, respectively, that in mature-age-dominated forests. The change in the number of landslides from young-age-dominated to mature-age-dominated forests was larger than that from an increase in the rainfall amount of 20%. We conclude that increasing the maturity of forests greatly reduces landslide frequency in Japan. In a changing climate with potentially threatening increases in rainfall, preserving mature forests is important to avoid amplifying landslide susceptibility on a national scale.

Investigating the Atmospheric Conditions Associated with Impactful Shallow Landslides in California (USA)

Abstract Shallow landslides are often triggered during rainfall events, which can increase subsurface soil water pressure and destabilize hillslopes. The likelihood of regional shallow landslide initiation is often assessed through a comparison of rainfall intensity and duration to pre-established thresholds. While informative for landslide warning, this exclusive focus on rainfall exceeding thresholds does not consider the meteorological conditions producing the rainfall. Here, we ask the question, are there common meteorological characteristics that lead to landslide-triggering precipitation? We develop a catalog of 18 post-1995 widespread, impactful shallow landslide events occurring within 13 storms across California, USA, where initiation time could be constrained to a ≤6-h window. We examine storm characteristics during the landslide initiation window using atmospheric reanalysis products, radar observations, and quantitative precipitation estimates. We find that, while there are some common atmospheric characteristics across landslide events, they can occur under a range of atmospheric conditions. For example, all Northern California landslide events assessed are associated with moderate to strong atmospheric rivers (ARs), while Southern California landslides feature non-AR to strong AR conditions. The storm events evaluated herein share many characteristics of hydrologically important storms in California that did not necessarily result in landslides; thus, atmospheric characteristics alone may not be sufficient to determine whether landslides will occur. However, documenting the characteristics of landslide-triggering storms defines the conditions under which landslides tend to occur, provides analog events that can be useful in forecast applications, helps define future research directions relating to atmospheric conditions and landslides, and supports interdisciplinary research efforts. Significance Statement Rainfall-triggered landslides pose a threat to communities situated in and around California’s steep terrain. Thresholds related to measured antecedent rainfall accumulation and anticipated rainfall intensity over various durations are typically used for predicting landslide occurrence. Here, we assess various atmospheric characteristics of 18 storms that triggered landslides to determine whether there are common characteristics that could provide insight into landslide occurrence beyond precipitation information. Our results indicate while there are some common characteristics across landslide events, the events can occur under a range of conditions. This finding and the documentation of these events are useful for communicating weather forecasts and potential hazards, help build an interdisciplinary understanding of landslide-triggering precipitation, and highlight future research needs.

Brief Report of Fatal Rainfall-Triggered Landslides from Record-Breaking 2023 Storms in Auckland, New Zealand

Brief Report of Fatal Rainfall-Triggered Landslides from Record-Breaking 2023 Storms in Auckland, New Zealand

Land Cover Trajectories and Their Impacts on Rainfall-Triggered Landslide Occurrence in a Cultivated Mountainous Region of Western Japan

Land cover changes in mountainous regions are potential precursors to landslide disasters. However, the effects of past long-term land cover changes on the characteristics of recent landslides remains underexplored. We studied land cover evolution over a 56-year period on Omishima Island in western Japan to examine the spatial relations of landslides in the July 2018 storm event based on rainfall, land cover trajectories, and topography. We generated land cover maps for 1962, 1981, and 2018 by aerial photo interpretation. We also identified 512 new landslides. Based on 47-year precipitation records, we estimated the return periods of 1- to 264-h rainfalls during the storm using the generalised extreme value (GEV) distributions. Return periods showed wide variation when the derived GEV distributions were applied to 1-km grid rainfall distributions. Despite such pronounced spatial variations in rainfall, we did not observe a clear correlation between rainfall intensity and landslide distribution. In contrast, land cover trajectories had a pronounced effect on landslide occurrence. Landslides were more concentrated on slopes that experienced land cover changes after 1962. A comparison of slopes on farmland developed between 1962 and 1981 (mainly citrus orchards) indicated that landslide density and area ratio were significantly lower on slopes that had reverted to forests than on those remaining as farmland. However, the values of the reforested slopes exceeded those of forests and farmlands that remained since before 1962. Our geospatial analysis revealed that even if the field had shifted to forests, the effects of reduced slope stability due to orchard development had remained for at least 37 years. This suggested that the impacts of converting forests to orchards last longer than harvesting in managed plantation forests.

Machine Learning and IoT-Based Approaches to Detect and Predict Rainfall-Triggered Landslides

Machine Learning and IoT-Based Approaches to Detect and Predict Rainfall-Triggered Landslides