Event-based Landslide Inventory Research Articles

Landslide susceptibility assessment using different rainfall event-based landslide inventories: advantages and limitations

AbstractThe present work aims to evaluate potential sources of uncertainty associated with rainfall-triggered event-based landslide inventories within the framework of landslide susceptibility assessment. Therefore, this study addresses the following questions: (i) How representative is an event-based landslide inventory map of the total landslide activity and distribution in a study area?; (ii) How reliable is an event-based landslide susceptibility map?; (iii) How appropriate is an event-based landslide inventory map for independently validating a landslide susceptibility map? To address these questions, two independent and contrasting rainfall event-based landslide inventories were used, together with a historical landslide inventory, to assess landslide susceptibility for different types of landslides in a study area located north of Lisbon, Portugal. The results revealed the following findings: (i) contrasting rainfall critical conditions for failure can trigger similar landslide types, although they may vary in size and be spatially constrained by different predisposing conditions, particularly lithology and soil type; (ii) landslide susceptibility models using event-based landslide inventories are not reliable in the study area, regardless of the landslide inventory map used for training and validation; and (iii) complementary sources of uncertainty results from using incomplete historical landslide inventories to assess landslide susceptibility and non-totally independent landslide inventories for modeling validation. The present study enhances the understanding of regional landslide susceptibility patterns based on contrasting rainfall-trigger conditions, providing valuable information to minimize exposure; to design regional landslide early warning systems for specific rainfall-trigger landslide events; and to improve the response and preparedness of civil protection services.

Assessing uncertainties in landslide susceptibility predictions in a changing environment (Styrian Basin, Austria)

Abstract. The assessment of uncertainties in landslide susceptibility modelling in a changing environment is an important, yet often neglected, task. In an Austrian case study, we investigated the uncertainty cascade in storylines of landslide susceptibility emerging from climate change and parametric landslide model uncertainty. In June 2009, extreme events of heavy thunderstorms occurred in the Styrian Basin, triggering thousands of landslides. Using a storyline approach, we discovered a generally lower landslide susceptibility for the pre-industrial climate, while for the future climate (2071–2100) a potential increase of 35 % in highly susceptible areas (storyline of much heavier rain) may be compensated for by much drier soils (−45 % areas highly susceptible to landsliding). However, the estimated uncertainties in predictions were generally high. While uncertainties related to within-event internal climate model variability were substantially lower than parametric uncertainties in the landslide susceptibility model (ratio of around 0.25), parametric uncertainties were of the same order as the climate scenario uncertainty for the higher warming levels (+3 and +4 K). We suggest that in future uncertainty assessments, an improved availability of event-based landslide inventories and high-resolution soil and precipitation data will help to reduce parametric uncertainties in landslide susceptibility models used to assess the impacts of climate change on landslide hazard and risk.

A Scientometric Analysis of Predicting Methods for Identifying the Environmental Risks Caused by Landslides

The effects of a landslide can represent a very big problem, including the death of people, damage to the land, environmental pollution and the loss of natural resources. Landslides are the most important medium for transferring sediments and polluting waterways by earth and organic materials. An excess of sediments reduces the quality of fish habitat and the potability of water. In order to understand landslides in depth, a thorough study was conducted using a scientometric analysis, as well as a thorough practical examination of landslide analysis and monitoring techniques. This review focused on methods used for landslide analysis, including physical models requiring easily prepared event-based landslide inventory, probabilistic methods which are useful for both shallow and earthquake-based landslides, and landslide monitoring performed by remote sensing techniques, which provide data helpful for prediction, monitoring and mapping. The fundamental principles of each method are described in terms of the method used, and its advantages, and limits. People and infrastructure are at danger from landslides caused by heavy rain, so this report highlights landslide-prone regions and considers the analysis methods for landslides used in these countries, with a view to identifying mitigation measures for coping with landslide risks in hilly areas. Furthermore, future landslide research possibilities, as well as possible modeling methods, are addressed. The report summarizes some landslide prediction and monitoring techniques used in landslide-prone countries which can help inform researchers seeking to protect the public from danger in landslide areas.

Performance Assessment of Event-Based Ensemble Landslide Susceptibility Models in Shihmen Watershed, Taiwan

While multi-year and event-based landslide inventories are both commonly used in landslide susceptibility analysis, most areas lack multi-year landslide inventories, and the analysis results obtained from the use of event-based landslide inventories are very sensitive to the choice of event. Based on 24 event-based landslide inventories for the Shihmen watershed from 1996 to 2015, this study established five event-based single landslide susceptibility models employing logistic regression, random forest, support vector machine, kernel logistic regression, and gradient boosting decision tree methods. The ensemble methods, involving calculating the mean of the susceptibility indexes (PM), median of the susceptibility indexes (PME), weighted mean of the susceptibility indexes (PMW), and committee average of binary susceptibility values (CA) of the five single models were then used to establish four event-based ensemble landslide susceptibility models. After establishing nine landslide susceptibility models, using each inventory from the 24 event-based landslide inventories or a multi-year landslide inventory, we identified the differences in landslide susceptibility maps attributable to the different landslide inventories and modeling methods, and used the area under the receiver operating characteristic curve to assess the accuracy of the models. The results indicated that an ensemble model based on a multi-year inventory can obtain excellent predictive accuracy. The predictive accuracy of multi-year landslide susceptibility models is found to be superior to that of event-based models. In addition, the higher predictive accuracy of ensemble landslide susceptibility models than that of single models implied that these ensemble methods were robust for enhancing the model’s predictive performance in the study area. When employing event-based landslide inventories in modeling, PM ensemble models offer the best predictive ability, according to the Kruskal–Wallis test results. Areas with a high mean susceptibility index and low standard deviation, identified using the 24 PM ensemble models based on different event-based landslide inventories, constitute places where landslide mitigation measures should be prioritized.

Constructing a complete landslide inventory dataset for the 2018 monsoon disaster in Kerala, India, for land use change analysis

Abstract. Event-based landslide inventories are important for analyzing the relationship between the intensity of the trigger (e.g., rainfall, earthquake) and the density of the landslides in a particular area as a basis for the estimation of the landslide probability and the conversion of susceptibility maps into hazard maps required for risk assessment. They are also crucial for the establishment of local rainfall thresholds that are the basis of early warning systems and for evaluating which land use and land cover changes are related to landslide occurrence. The completeness and accuracy of event-based landslide inventories are crucial aspects to derive reliable results or the above types of analyses. In this study, we generated a relatively complete landslide inventory for the 2018 monsoon landslide event in the state of Kerala, India, based on two inventories that were generated using different methods: one based on an object-based image analysis (OBIA) and the other on field surveys of damaging landslides. We used a collaborative mapping approach based on the visual interpretation of pre- and post-event high-resolution satellite images (HRSIs) available from Google Earth, adjusted the two inventories, and digitized landslides that were missed in the two inventories. The reconstructed landslide inventory database contains 4728 landslides consisting of 2477 landslides mapped by the OBIA method, 973 landslides mapped by field survey, 422 landslides mapped both by OBIA and field methods, and an additional 856 landslides mapped using the visual image (Google Earth) interpretation. The dataset is available at https://doi.org/10.17026/dans-x6c-y7x2 (van Westen, 2020). Also, the location of the landslides was adjusted, based on the image interpretation, and the initiation points were used to evaluate the land use and land cover changes as a causal factor for the 2018 monsoon landslides. A total of 45 % of the landslides that damaged buildings occurred due to cut-slope failures, while 34 % of those having an impact on roads were due to road cut-slope failures. The resulting landslide inventory is made available for further studies.

Comparison of Earthquake-Triggered Landslide Inventories: A Case Study of the 2015 Gorkha Earthquake, Nepal

Despite landslide inventories being compiled throughout the world every year at different scales, limited efforts have been made to critically compare them using various techniques or by different investigators. Event-based landslide inventories indicate the location, distribution, and detected boundaries of landslides caused by a single event, such as an earthquake or a rainstorm. Event-based landslide inventories are essential for landslide susceptibility mapping, hazard modeling, and further management of risk mitigation. In Nepal, there were several attempts to map landslides in detail after the Gorkha earthquake. Particularly after the main event on 25 April 2015, researchers around the world mapped the landslides induced by this earthquake. In this research, we compared four of these published inventories qualitatively and quantitatively using different techniques. Two principal methodologies, namely the cartographical degree of matching and frequency area distribution (FAD), were optimized and applied to evaluate inventory maps. We also showed the impact of using satellite imagery with different spatial resolutions on the landslide inventory generation by analyzing matches and mismatches between the inventories. The results of our work give an overview of the impact of methodology selection and outline the limitations and advantages of different remote sensing and mapping techniques for landslide inventorying.

Adopting the <i>I</i><sub>3</sub>–<i>R</i><sub>24</sub> rainfall index and landslide susceptibility for the establishment of an early warning model for rainfall-induced shallow landslides

Abstract. Rainfall-induced landslides number among the most devastating natural hazards in the world and early warning models are urgently needed to reduce losses and fatalities. Most landslide early warning systems are based on rainfall thresholds defined on the regional scale, regardless of the different landslide susceptibilities of various slopes. Here we divided slope units in southern Taiwan into three categories (high, moderate and low) according to their susceptibility. For each category, we established separate rainfall thresholds so as to provide differentiated thresholds for different degrees of susceptibility. Logistic regression (LR) analysis was performed to evaluate landslide susceptibility by using event-based landslide inventories and predisposing factors. Analysis of rainfall patterns of 941 landslide cases gathered from field investigation led to the recognition that 3 h mean rainfall intensity (I3) is a key rainfall index for rainfall of short duration but high intensity; in contrast, 24 h accumulated rainfall (R24) was recognized as a key rainfall index for rainfall of long duration but low intensity. Thus, the I3–R24 rainfall index was used to establish rainfall thresholds in this study. Finally, an early warning model is proposed by setting alert levels including yellow (advisory), orange (watch) and red (warning) according to a hazard matrix. These differentiated thresholds and alert levels can provide essential information for local governments to use in deciding whether to evacuate residents.

Probability of Road Interruption due to Landslides under Different Rainfall-Return Periods Using Remote Sensing Techniques

AbstractHeavy rainfall-induced landslides along mountain routes in Taiwan easily cause road interruption, especially under extreme rainfall conditions. Road interruption due to rainfall-induced landslides is a major problem for Taiwanese authorities. In this study, remote sensing data, including satellite images and aerial photos, were employed to interpret landslides, which were mapped using geographical information systems (GISs). The goal was an event-based landslide inventory. Causative and triggering factors for landslides were adopted in a logistic model to predict landslide occurrence. These factors included lithology, normalized difference vegetation index, slope gradient, slope roughness, maximum elevation, total curvature, total slope height, maximum hourly rainfall, and total accumulated rainfall. With the proposed landslide prediction model, the probability of road interruption due to rainfall-induced landslides was evaluated under different rainfall-return periods. The model was confirmed by ...

Exposure of inhabitants, buildings and infrastructure to landslides – a case of five Slovenian municipalities

In the frame of national research and innovation project MASPREM exposure maps of inhabitants, buildings and infrastructures to landslide occurrence were developed for five selected Slovenian municipalities. Maps represent an upgrade of the landslide susceptibility maps that were elaborated based on synthesis of analysis of event-based landslide inventory and field investigations. Exposure maps were developed for five municipalities: Bovec, La{ko, Slovenj Gradec, Trbovlje and Železniki. Exposure of inhabitants, construction and infrastructures to landslide occurrence was analysed using simple cross-analysis of landslide susceptibility maps at scale of 1:25,000 with locations of exposed elements. All analyses were conducted in the GIS with software tool ArcMap. Exposure maps, based on landslide susceptibility, were classified into six classes, with values ranging from one to six where class one represents areas with negligible exposure and class six areas with very high exposure to landslide occurrence. Exposure maps of selected municipalities provide the basis for further assessment of risk and consequentially better risk management.

Landslides triggered by the June 2013 extreme rainfall event in parts of Uttarakhand state, India

Extreme rainfall during 15–17 of June 2013 triggered numerous landslides and caused widespread damage and loss of life in the Bhagirathi and Alaknanda river valleys, with Kedarnath town bearing the brunt of the disaster. In this paper, we present a corroboration of observations gathered from very high resolution satellite data which indicated that two events in close succession caused damage to the Kedarnath town. While the flooding on June 16, 2013 was due to the reactivation of an old landslide that resulted in breaching of the river training wall, breaching of the moraine dammed Chorabari lake on June 17, 2013 caused further flooding and damage to the Kedarnath town. We also prepared a detailed landslide inventory using multi-temporal Resourcesat-2 images (5.8 m) acquired before and after the event in order to avoid issues related to cloud and shadow. A total of 6,013 landslides were mapped, out of which 3,472 landslides with 30.4 km2 area were classified as new ones, 1,137 landslides with 9.1 km2 area were classified as old ones and 1,401 landslides with 11.7 km2 area were classified as reactivated ones. The frequency-area statistics of the mapped landslides shows near completeness of this event-based landslide inventory. The debris brought down by rivers from these landslides, particularly from the glaciated deposits in upstream areas, caused severe river bank erosion in downstream areas.

Average landslide erosion rate at the watershed scale in southern Taiwan estimated from magnitude and frequency of rainfall

This study calculated the long-term average landslide erosion rate in the Kaoping River watershed in southern Taiwan and investigated the relative importance of extreme rainfall events on landslide erosion. The method followed three steps: first, calculating landslide volumes for 10 rainfall events from a multi-temporal, event-based landslide inventory; second, estimating the frequency of landslide-generating rainfall by using hydrologic frequency analyses; and third, combining the two sets of data to estimate the average landslide erosion rate. Results of the study showed that the average landslide erosion rate is 2.65–5.17mmyr−1, corresponding well to rates reported in other studies using other methods. The study also found that extreme-intensive rainfall events play a more important role on landslide erosion than frequent-moderate rainfall events. Extreme rainfall (maximum 24-h rainfall >600mm) contributes 64–79% of the average landslide erosion rate. Moreover, the natural variation of landslide erosion magnitudes is extremely large and can cause significant uncertainty in estimating the landslide erosion rate from total landslide volume. This study found ±1.2mmyr−1 of uncertainty based on simulation results involving a hypothetical 100-year landslide inventory. In summary, this study demonstrates the importance of extreme rainfall events on landslide erosion, and the method proposed in this study is capable of calculating a reliable estimate of average landslide erosion rate in areas with insufficient landslide records.

Modeling the spatial occurrence of shallow landslides triggered by typhoons

Many shallow landslides in Taiwan are triggered by typhoons (tropical cyclones) during the summer months. Each typhoon produces a different rainfall distribution, depending on its track and position and the atmospheric conditions. This study investigated whether the additional rainfall data in a landslide susceptibility model can improve its performance in predicting typhoon-triggered landslides, and whether information on past typhoon events, combined with an event-based landslide inventory, can help predict landslides triggered by a typhoon. To answer these questions, the study adopted a method that integrates rainfall data with the critical rainfall model (a landslide susceptibility model based on geoenvironmental factors) to derive a logistic regression model for predicting landslide occurrence. Results of a back analysis of landslides triggered by nine typhoons from 2001 to 2009 reveal that, by including rainfall data, the integrated method performs better than the critical rainfall model in the average overall accuracy rate (0.78 vs. 0.45) and the average modified success rate (0.75 vs. 0.68). Our preliminary results also suggest that it is possible to predict landslides triggered by a typhoon by using a catch-all model developed from all other typhoon events in an inventory, or a group model developed from other typhoon events of similar rainfall characteristics in an inventory. This study opens up a new research direction in analyzing rainfall-triggered landslides in Taiwan and elsewhere.

Regional-scale landslide activity and landslide susceptibility zonation in the Nepal Himalaya

Landslide susceptibility zonation mapping is a fundamental procedure for geo-disaster management in tropical and sub-tropical regions. Recently, various landslide susceptibility zonation models have been introduced in Nepal with diverse approaches of assessment. However, validation is still a problem. Additionally, the role of various predisposing causative parameters for landslide activity is still not well understood in the Nepal Himalaya. To address these issues of susceptibility zonation and landslide activity, about 4,000 km2 area of central Nepal was selected for regional-scale assessment of landslide activity and susceptibility zonation mapping. In total, 655 new landslides and 9,229 old landslides were identified with the study area with the help of satellite images, aerial photographs, field data and available reports. The old landslide inventory was “blind landslide database” and could not explain the particular rainfall event responsible for the particular landslide. But considering size of the landslide, blind landslide inventory was reclassified into two databases: short-duration high-intensity rainfall-induced landslide inventory and long-duration low-intensity rainfall-induced landslide inventory. These landslide inventory maps were considered as proxy maps of multiple rainfall event-based landslide inventories. Similarly, all 9,884 landslides were considered for the activity assessment of predisposing causative parameters. For the Nepal Himalaya, slope, slope aspect, geology and road construction activity (anthropogenic cause) were identified as most affective predisposing causative parameters for landslide activity. For susceptibility zonation, multivariate approach was considered and two proxy rainfall event-based landslide databases were used for the logistic regression modelling, while a relatively recent landslide database was used in validation. Two event-based susceptibility zonation maps were merged and rectified to prepare the final susceptibility zonation map and its prediction rate was found to be more than 82 %. From this work, it is concluded that rectification of susceptibility zonation map is very appropriate and reliable. The results of this research contribute to a significant improvement in landslide inventory preparation procedure, susceptibility zonation mapping approaches as well as role of various predisposing causative parameters for the landslide activity.

Rainfall-triggered shallow landslides at catchment scale: Threshold mechanics-based modeling for abruptness and localization

[1] Rainfall-induced shallow landslides may occur abruptly without distinct precursors and could span a wide range of soil mass released during a triggering event. We present a rainfall-induced landslide-triggering model for steep catchments with surfaces represented as an assembly of hydrologically and mechanically interconnected soil columns. The abruptness of failure was captured by defining local strength thresholds for mechanical bonds linking soil and bedrock and adjacent columns, whereby a failure of a single bond may initiate a chain reaction of subsequent failures, culminating in local mass release (a landslide). The catchment-scale hydromechanical landslide-triggering model (CHLT) was applied to results from two event-based landslide inventories triggered by two rainfall events in 2002 and 2005 in two nearby catchments located in the Prealps in Switzerland. Rainfall radar data, surface elevation and vegetation maps, and a soil production model for soil depth distribution were used for hydromechanical modeling of failure patterns for the two rainfall events at spatial and temporal resolutions of 2.5 m and 0.02 h, respectively. The CHLT model enabled systematic evaluation of the effects of soil type, mechanical reinforcement (soil cohesion and lateral root strength), and initial soil water content on landslide characteristics. We compared various landslide metrics and spatial distribution of simulated landslides in subcatchments with observed inventory data. Model parameters were optimized for the short but intense rainfall event in 2002, and the calibrated model was then applied for the 2005 rainfall, yielding reasonable predictions of landslide events and volumes and statistically reproducing localized landslide patterns similar to inventory data. The model provides a means for identifying local hot spots and offers insights into the dynamics of locally resolved landslide hazards in mountainous regions.

Spatial statistical modeling of shallow landslides—Validating predictions for different landslide inventories and rainfall events

Statistical models that exploit the correlation between landslide occurrence and geomorphic properties are often used to map the spatial occurrence of shallow landslides triggered by heavy rainfalls. In many landslide susceptibility studies, the true predictive power of the statistical model remains unknown because the predictions are not validated with independent data from other events or areas. This study validates statistical susceptibility predictions with independent test data. The spatial incidence of landslides, triggered by an extreme rainfall in a study area, was modeled by logistic regression. The fitted model was then used to generate susceptibility maps for another three study areas, for which event-based landslide inventories were also available. All the study areas lie in the northern foothills of the Swiss Alps. The landslides had been triggered by heavy rainfall either in 2002 or 2005. The validation was designed such that the first validation study area shared the geomorphology and the second the triggering rainfall event with the calibration study area. For the third validation study area, both geomorphology and rainfall were different. All explanatory variables were extracted for the logistic regression analysis from high-resolution digital elevation and surface models (2.5 m grid). The model fitted to the calibration data comprised four explanatory variables: (i) slope angle (effect of gravitational driving forces), (ii) vegetation type (grassland and forest; root reinforcement), (iii) planform curvature (convergent water flow paths), and (iv) contributing area (potential supply of water). The area under the Receiver Operating Characteristic (ROC) curve ( AUC) was used to quantify the predictive performance of the logistic regression model. The AUC values were computed for the susceptibility maps of the three validation study areas (validation AUC), the fitted susceptibility map of the calibration study area (apparent AUC: 0.80) and another susceptibility map obtained for the calibration study area by 20-fold cross-validation (cross-validation AUC: 0.74). The AUC values of the first and second validation study areas (0.72 and 0.69, respectively) and the cross-validation AUC matched fairly well, and all AUC values were distinctly smaller than the apparent AUC. Based on the apparent AUC one would have clearly overrated the predictive performance for the first two validation areas. Rather surprisingly, the AUC value of the third validation study area (0.82) was larger than the apparent AUC. A large part of the third validation study area consists of gentle slopes, and the regression model correctly predicted that no landslides occur in the flat parts. This increased the predictive performance of the model considerably. The predicted susceptibility maps were further validated by summing the predicted susceptibilities for the entire validation areas and by comparing the sums with the observed number of landslides. The sums exceeded the observed counts for all the validation areas. Hence, the logistic regression model generally over-estimated the risk of landslide occurrence. Obviously, a predictive model that is based on static geomorphic properties alone cannot take a full account of the complex and time dependent processes in the subsurface. However, such a model is still capable of distinguishing zones highly or less prone to shallow landslides.

Statistical approach to storm event-induced landslides susceptibility

Abstract. For the interpretation of the storm event-induced landslide distribution for an area, deterministic methods are frequently used, while a region's landslide susceptibility is commonly predicted via a statistical approach based upon multi-temporal landslide inventories and environmental factors. In this study we try to use an event-based landslide inventory, a set of environmental variables and a triggering factor to build a susceptibility model for a region which is solved using a multivariate statistical method. Data for shallow landslides triggered by the 2002 typhoon, Toraji, in central western Taiwan, are selected for training the susceptibility model. The maximum rainfall intensity of the storm event is found to be an effective triggering factor affecting the landslide distribution and this is used in the model. The model is built for the Kuohsing region and validated using data from the neighboring Tungshih area and a subsequent storm event – the 2004 typhoon, Mindulle, which affected both the Kuohsing and the Tungshih areas. The results show that we can accurately interpret the landslide distribution in the study area and predict the occurrence of landslides in the neighboring region in a subsequent typhoon event. The advantage of this statistical method is that neither hydrological data, strength data, failure depth, nor a long-period landslide inventory is needed as input.