Regional Landslide Hazard Assessment Research Articles

Updated Predictive Models for Permanent Seismic Displacement of Slopes for Greece and Their Effect on Probabilistic Landslide Hazard Assessment

Earthquake-triggered landslides have been widely recognized as a catastrophic hazard in mountainous regions. They may lead to direct consequences, such as property losses and casualties, as well as indirect consequences, such as disruption of the operation of lifeline infrastructures and delays in emergency response actions after earthquakes. Regional landslide hazard assessment is a useful tool to identify areas that are vulnerable to earthquake-induced slope instabilities and design prioritization schemes towards more detailed site-specific slope stability analyses. A widely used method to assess the seismic performance of slopes is by calculating the permanent downslope sliding displacement that is expected during ground shaking. Nathan M. Newmark was the first to propose a method to estimate the permanent displacement of a rigid body sliding on an inclined plane in 1965. The expected permanent displacement for a slope using the sliding block method is implemented by either selecting a suite of representative earthquake ground motions and computing the mean and standard deviation of the displacement or by using analytical equations that correlate the permanent displacement with ground motion intensity measures, the slope’s yield acceleration and seismological characteristics. Increased interest has been observed in the development of such empirical models using strong motion databases over the last decades. It has been almost a decade since the development of the latest empirical model for the prediction of permanent ground displacement for Greece. Since then, a significant amount of strong motion data have been collected. In the present study, several nonlinear regression-based empirical models are developed for the prediction of the permanent seismic displacements of slopes, including various ground motion intensity measures. Moreover, single-hidden layer Artificial Neural Network (ANN) models are developed to demonstrate their capability of simplifying the construction of empirical models. Finally, implementation of the produced modes based on Probabilistic Landslide Hazard Assessment is undertaken, and their effect on the resulting hazard curves is demonstrated and discussed.

Landslide Hazard Assessment Method Considering the Deformation Factor: A Case Study of Zhouqu, Gansu Province, Northwest China

Landslides are geological disasters that can cause great damage to natural and social environments. Landslide hazard assessments are crucial for disaster prevention and mitigation. Conventional regional landslide hazard assessment results are static and do not take into account the dynamic changes in landslides; thus, areas with landslides that have been treated and stabilized are often still identified as high-risk areas. Therefore, a new hazard assessment method is proposed in this paper that combines the deformation rate results obtained by interferometric synthetic aperture radar (InSAR) with the results of conventional hazard assessments to obtain the hazard assessment level while considering the deformation factor of the study area, with Zhouqu, Gansu Province, selected as the case study. First, to obtain the latest landslide inventory map of Zhouqu, the hazard assessment results of the study area were obtained based on a neural network and statistical analysis, and an innovative combination of the deformation rate results of the steepest slope direction from the ascending and descending data were obtained by InSAR technology. Finally, the hazard assessment level considering the deformation factor of Zhouqu was obtained. The method proposed in this paper allows for a near-term hazard assessment of the study area, which in turn enables dynamic regional landslide hazard assessments and improves the efficiency of authorities when conducting high-risk-area identification and management.

A dynamic landslide hazard monitoring framework for the Lower Mekong Region

The Lower Mekong region is one of the most landslide-prone areas of the world. Despite the need for dynamic characterization of landslide hazard zones within the region, it is largely understudied for several reasons. Dynamic and integrated understanding of landslide processes requires landslide inventories across the region, which have not been available previously. Computational limitations also hamper regional landslide hazard assessment, including accessing and processing remotely sensed information. Finally, open-source software and modelling packages are required to address regional landslide hazard analysis. Leveraging an open-source data-driven global Landslide Hazard Assessment for Situational Awareness model framework, this study develops a region-specific dynamic landslide hazard system leveraging satellite-based Earth observation data to assess landslide hazards across the lower Mekong region. A set of landslide inventories were prepared from high-resolution optical imagery using advanced image-processing techniques. Several static and dynamic explanatory variables (i.e., rainfall, soil moisture, slope, relief, distance to roads, distance to faults, distance to rivers) were considered during the model development phase. An extreme gradient boosting decision tree model was trained for the monsoon period of 2015–2019 and the model was evaluated with independent inventory information for the 2020 monsoon period. The model performance demonstrated considerable skill using receiver operating characteristic curve statistics, with Area Under the Curve values exceeding 0.95. The model architecture was designed to use near-real-time data, and it can be implemented in a cloud computing environment (i.e., Google Cloud Platform) for the routine assessment of landslide hazards in the Lower Mekong region. This work was developed in collaboration with scientists at the Asian Disaster Preparedness Center as part of the NASA SERVIR Program’s Mekong hub. The goal of this work is to develop a suite of tools and services on accessible open-source platforms that support and enable stakeholder communities to better assess landslide hazard and exposure at local to regional scales for decision making and planning.

Regional Landslide Hazard Assessment Using Extreme Value Analysis and a Probabilistic Physically Based Approach

The accurate assessment of landslide hazards is important in order to reduce the casualties and damage caused by landslides. Landslide hazard assessment combines the evaluation of spatial and temporal probabilities. Although various statistical approaches have been used to estimate spatial probability, these methods only evaluate the statistical relationships between factors that have triggered landslides in the past rather than the slope failure process. Therefore, a physically based approach with probabilistic analysis was adopted here to estimate the spatial distribution of landslide probability. Meanwhile, few studies have addressed temporal probability because historical records of landslides are not available for most areas of the world. Therefore, an indirect approach based on rainfall frequency and using extreme value analysis and the Gumbel distribution is proposed and used in this study. In addition, to incorporate the nonstationary characteristics of rainfall data, an expanding window approach was used to evaluate changes in the mean annual maximum rainfall and the location and scale parameters of the Gumbel distribution. Using this approach, the temporal probabilities of future landslides were estimated and integrated with spatial probabilities to assess and map landslide hazards.

Developing Real-Time Nowcasting System for Regional Landslide Hazard Assessment under Extreme Rainfall Events

In this research, a real-time nowcasting system for regional landslide-hazard assessment under extreme-rainfall conditions was established by integrating a real-time rainfall data retrieving system, a landslide-susceptibility analysis program (TRISHAL), and a real-time display system to show the stability of regional slopes in real time and provide an alert index under rainstorm conditions for disaster prevention and mitigation. The regional hydrogeological parameters were calibrated using a reverse-optimization analysis based on an RGA (Real-coded Genetic Algorithm) of the optimization techniques and an improved version of the TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-Stability) model. The 2009 landslide event in the Xiaolin area of Taiwan, associated with Typhoon Morakot, was used to test the real-time regional landslide-susceptibility system. The system-testing results showed that the system configuration was feasible for practical applications concerning disaster prevention and mitigation.

A new tree-ring-based index for the expression of spatial landslide activity and the assessment of landslide hazards

Dendrogeomorphic methods enable a very accurate reconstruction of spatio-temporal landslide behaviour. Unfortunately, existing approaches of spatial reconstruction suffer from many imperfections (e.g. dependence on tree age or impact of forest management). This study developed, presented, and tested a new approach for the spatial reconstruction of landslide movements based on tree-ring data that eliminates the mentioned problems. The new spatial event-response index (SIt ), developed for GIS, follows the logic of the standard event-response index used for the expression of chronological landslide behaviour and considers the uneven spatial pattern of trees. The main advantage of the new approach is in the consideration of the position of disturbed trees but also of undisturbed ones. This principle prevents over-interpretation of results because it can effectively filter the possible effects of soil creep (nonslide) movements. The approach was tested on three landslides of different types using data from 306 tree-ring series. The results suggest good agreement of the new index with the standard chronological event-response index for all landslide types. This index can be used for hazard assessment of large landslide areas and possibly, if research extended to zones outside of the landslide, even for regional landslide hazard assessment.

Regional landslide hazard assessment through integrating susceptibility index and rainfall process

Due to the difference of the spatial and temporal distribution of rainfall and the complex diversity of the disaster-prone environment (topography, geological, fault, and lithology), it is difficult to assess the hazard of landslides at the regional scale quantitatively only considering rainfall condition. Based on detailed landslide inventory and rainfall data in the hilly area in Sichuan province, this study analyzed the effects of both rainfall process and environmental factors on the occurrence of landslides. Through analyzing environmental factors, a landslide susceptibility index (LSI) was calculated using multiple layer perceptron (MLP) model to reflect the regional landslide susceptibility. Further, the characteristics of rainfall process and landslides were examined quantitatively with statistical analysis. Finally, a probability model integrating LSI and rainfall process was constructed using logistical regression analysis to assess the landslide hazard. Validation showed satisfactory results, and the inclusion of LSI effectively improved the accuracy of the landslide hazard assessment: Compared with only considering the rainfall process factors, the accuracy of the landslide prediction model both considering the rainfall process and landslide susceptibility is improved by 3%. These results indicate that an integration of susceptibility index and rainfall process is essential in improving the timeliness and accuracy of regional landslide early warning.

Landslide inventory for hazard assessment in a data-poor context: a regional-scale approach in a tropical African environment

Landslide hazard remains poorly characterized on regional and global scales. In the tropics in particular, the lack of knowledge on landslide hazard is in sharp contrast with the high landslide susceptibility of the region. Moreover, landslide hazard in the tropics is expected to increase in the future in response to growing demographic pressure and climate and land use changes. With precipitation as the primary trigger for landslides in the tropics, there is a need for an accurate determination of rainfall thresholds for landslide triggering based on regional rainfall information as well as reliable data on landslide occurrences. Here, we present the landslide inventory for the central section of the western branch of the East African Rift (LIWEAR). Specific attention is given to the spatial and temporal accuracy, reliability, and geomorphological meaning of the data. The LIWEAR comprises 143 landslide events with known location and date over a span of 48 years from 1968 to 2016. Reported landslides are found to be dominantly related to the annual precipitation patterns and increasing demographic pressure. Field observations in combination with local collaborations revealed substantial biases in the LIWEAR related to landslide processes, landslide impact, and the remote context of the study area. In order to optimize data collection and minimize biases and uncertainties, we propose a three-phase, Search-Store-Validate, workflow as a framework for data collection in a data-poor context. The validated results indicate that the proposed methodology can lead to a reliable landslide inventory in a data-poor context, valuable for regional landslide hazard assessment at the considered temporal and spatial resolutions.

Invited perspectives: Hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: proposing hydro-meteorological thresholds

Abstract. Many shallow landslides and debris flows are precipitation initiated. Therefore, regional landslide hazard assessment is often based on empirically derived precipitation intensity-duration (ID) thresholds and landslide inventories. Generally, two features of precipitation events are plotted and labeled with (shallow) landslide occurrence or non-occurrence. Hereafter, a separation line or zone is drawn, mostly in logarithmic space. The practical background of ID is that often only meteorological information is available when analyzing (non-)occurrence of shallow landslides and, at the same time, it could be that precipitation information is a good proxy for both meteorological trigger and hydrological cause. Although applied in many case studies, this approach suffers from many false positives as well as limited physical process understanding. Some first steps towards a more hydrologically based approach have been proposed in the past, but these efforts received limited follow-up. Therefore, the objective of our paper is to (a) critically analyze the concept of precipitation ID thresholds for shallow landslides and debris flows from a hydro-meteorological point of view and (b) propose a trigger–cause conceptual framework for lumped regional hydro-meteorological hazard assessment based on published examples and associated discussion. We discuss the ID thresholds in relation to return periods of precipitation, soil physics, and slope and catchment water balance. With this paper, we aim to contribute to the development of a stronger conceptual model for regional landslide hazard assessment based on physical process understanding and empirical data.

Rainfall-induced Landslide Hazard Analysis Based on GIS in Yanbian Region, China

This paper mainly studies landslide hazard in Yanbian region，8 landslide factors of soil moisture content, hydrogeology, land use type, slope, slope aspect, fault, river and rainfall are analyzed in the area of Yanbian region. Using the analytic hierarchy process (AHP), the relative weights of 8 landslide factors were obtained, and the internal weights of each factor were determined by the data; the rainfall factors were then divided into three categories such as average annual rainfall, average flood season rainfall, and the maximum average rainfall. And the comparison of the rainfall factors was made. Finally based on the results of these weights to the remote sensing technology(RS) and geographic information system(GIS) technology combined to evaluate its area, we are mapping the risk assessment area for landslide hazard in Yanbian region. According to the map analysis, figure in the dangerous hierarchies result in accordance with the historical landslides is better. At the same time. landslides and hazard area of statistical data analysis showed that the AHP method combined with GIS technology is suitable for promoting the Yanbian regional landslide hazard assessment.

Regional landslide hazard assessment from seismically induced displacements in Monterrey Metropolitan area, Northeastern Mexico

The first regional seismic landslide hazard assessment for Northeastern Mexico is presented. Regional maps predicting the expected areal limits for potential earthquake-induced landslides in terms of coseismic displacements for the 2010 San Pedro earthquake (M 4.0) and two postulated M 5.5 and M 6.5 scenario earthquakes are shown. Permanent displacements were obtained from a global empirical regression based on a critical acceleration ratio (ac/PGA) estimation. Results for the M 4.0 San Pedro event show low cumulative displacements concentrated around the epicentral zone. For this event, occurrence of a few small landslides as disrupted slides and falls were likely. For M 5.5 and M 6.5 scenarios the coseismic displacements depict a high potential for widespread and complex slope failures mainly in the Sierra Madre Oriental hilly areas, although foothills inside the Monterrey Metropolitan Area would be affected. The worst scenario we have postulated is a M 6.5 earthquake during the hurricane season. Hypothetical full-saturated and strong seismic shaking conditions would trigger several rock and soil avalanches as well as other complex landslides. Related damage for this event would be very catastrophic to civil infrastructure and substantial injuries and loss of life are expected mainly in the southwestern portion of the Monterrey Metropolitan Area. Results constitute a useful tool in order to mitigate potential earthquake-induced damage related to regional slope failures.

Landslide hazard assessment and judgment of reliability: a geomechanical approach

Landslide hazard maps are often defined as reliable a posteriori, in accordance with the real landslides occurring from the time of the map production. However, to be useful for planning, a reliability judgment concerning the hazard mapping should be a priori, based on data uncertainty characterization, and must be driven by the knowledge of the slope instability mechanisms. The landslide hazard assessment, when based on the deterministic diagnosis of the processes, may really lead to really providing clues about how and why the slope could fail (landslide susceptibility) and, possibly, when (landslide hazard). Such deterministic assessment can be pursued only through the interpretation and the geo-hydro-mechanical modelling of the slope equilibrium. In practice, though, the landslide hazard assessment is still seldom dealt with slope modelling, in particular when it addresses intermediate to regional zoning. The paper firstly offers an overview of the key steps of a methodology called the multiscalar method for landslide mitigation, MMLM, which that is a methodological approach for the intermediate to regional landslide hazard assessment using the hydro-mechanical diagnoses of landsliding. The validation of the MMLM to the geologically complex outer sectors of the Southern Apennines (Daunia-Lucanian mountains; Italy) is also delineated, together with a practical approach to incorporate a reliability judgment in the landslide susceptibility/hazard mapping.

Is there Predictive Power in Hydrological Catchment Information for Regional Landslide Hazard Assessment?

Regional landslide hazard assessment is often carried out by means of empirical meteorological thresholds, which reliability is sometimes limited by the lack of information about the hydrological processes which lead to landslide triggering in slopes. Hence, in this paper the inclusion of hydrological information at catchment scale in the definition of landslide triggering thresholds is applied to a catchment in the northern Apennines (Italy). In particular, an hydro-meteorological threshold based on event precipitation and catchment specific storage (H-S threshold) is proposed. The performance of the proposed threshold is compared with the one of the usually adopted precipitation Intensity-Duration (I-D) threshold. Although most of the landslide recorded in the observed period (2002-2013) were triggered by short and intense precipitation events with little influence of the slope conditions prior the precipitation, the H-Sthreshold performs slightly better than the I-D threshold.

A Geomechanical Approach to Landslide Hazard Assessment: The Multiscalar Method for Landslide Mitigation

The landslide hazard assessment, when based on the deterministic diagnosis of the processes, can be pursued only through the interpretation and the geo-hydro-mechanical modelling of the slope equilibrium. In practice, though, landslide hazard assessment is still seldom dealt with slope modelling, in particular when it addresses vast areas, where either heuristic or statistical methods do not entail any geo-hydro-mechanical knowledge of slope features and stability. The Multiscalar Method for Landslide Mitigation (MMLM) is an original methodological approach for intermediate to regional landslide hazard assessment. It is based on the geo-hydro-mechanical knowledge achieved from the application of a stage-wise diagnostic methodology of the landslide mechanism at the slope scale. The paper discusses the main steps of the MMLM aiming at diagnoses of landslide hazard based on hydro-mechanics, for small scale hazard mapping (at the large area).

A dynamic landslide hazard assessment system for Central America and Hispaniola

Abstract. Landslides pose a serious threat to life and property in Central America and the Caribbean Islands. In order to allow regionally coordinated situational awareness and disaster response, an online decision support system was created. At its core is a new flexible framework for evaluating potential landslide activity in near real time: Landslide Hazard Assessment for Situational Awareness. This framework was implemented in Central America and the Caribbean by integrating a regional susceptibility map and satellite-based rainfall estimates into a binary decision tree, considering both daily and antecedent rainfall. Using a regionally distributed, percentile-based threshold approach, the model outputs a pixel-by-pixel nowcast in near real time at a resolution of 30 arcsec to identify areas of moderate and high landslide hazard. The daily and antecedent rainfall thresholds in the model are calibrated using a subset of the Global Landslide Catalog in Central America available for 2007–2013. The model was then evaluated with data for 2014. Results suggest reasonable model skill over Central America and poorer performance over Hispaniola due primarily to the limited availability of calibration and validation data. The landslide model framework presented here demonstrates the capability to utilize globally available satellite products for regional landslide hazard assessment. It also provides a flexible framework to interchange the individual model components and adjust or calibrate thresholds based on access to new data and calibration sources. The availability of free satellite-based near real-time rainfall data allows the creation of similar models for any study area with a spatiotemporal record of landslide events. This method may also incorporate other hydrological or atmospheric variables such as numerical weather forecasts or satellite-based soil moisture estimates within this decision tree approach for improved hazard analysis.

A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran

In recent years, Iran has experienced many landslides due to high tectonic activity, and a variety of geological and climatic conditions. This paper proposes a novel hybrid model based on step-wise weight assessment ratio analysis (SWARA) method and adaptive neuro-fuzzy inference system (ANFIS) to evaluate landslide susceptible areas using geographical information system (GIS). At first, based on an inventory map, landslide locations were randomly divided into two parts, 70% of which were used for generating the landslide hazard map and 30% of which were used for the validation of the model. Then, twelve landslide predisposing factors, such as lithology, slope angle, slope aspect, plan curvature, profile curvature, altitude, distance to streams, distance to faults, distance to roads, land use, seismicity, and rainfall were considered for the analysis. All the factors were then weighted by the SWARA method. Considering the nature of predisposing factors, they were split into two groups, factors with discrete data and factors with continuous data. For factors with discrete data, the SWARA method was used for final weight of each class, and for factors with continuous data, results related to the center of each class were obtained from the SWARA method. Subsequently, AFNIS was used to obtain weight of each value. All the values obtained from the model were then used to generate the landslide hazard map of the study area. Finally, the landslide hazard map was validated by receiver operating characteristics (ROC) using both success rate curve and prediction rate curve. 70% of observed landslides were used for the former while the remaining was used for the latter. The validation results showed that the area under the success rate curve and prediction rate curve (AUC) are 0.84 and 0.80 respectively. Additionally, the prediction performance of the SWARA method for landslide hazard mapping was investigated and the results were compared with those obtained from the proposed model. The comparison revealed that the developed model has better prediction ability for landslide hazard assessment. The results also indicated that the proposed model used in this study produced satisfactory and reliable landslide hazard map, which can be used for preliminary land use and infrastructure planning in Iran.

From a phenomenological to a geomechanical approach to landslide hazard analysis

The diagnosis of landsliding at the slope scale resulted from synergic geohydromechanical analyses of the slope factors, which should represent the first step to assess landslide hazard. According to the methodological approach discussed in the paper, the landslide hazard analysis should start from a phenomenological interpretation of the slope behaviour, including the definition of the slope factors, getting then to a quantitative prediction of the slope evolution with time. This quantitative evaluation should result from limit equilibrium analyses and numerical modelling, both of them performed considering the outcomes of the phenomenological reconstruction. Therefore, the understanding of the slope factors and of the landslide mechanism at the slope scale should drive the landslide hazard assessment, through analyses performed for different levels of diagnosis (phenomenological, analytical and numerical). Some landslides, representative for chain slopes in the Italian peninsula, are discussed in the paper in order to show the maturity of the geohydromechanical diagnosis of landslide hazard and, hence, to properly design the mitigation actions. A methodology for intermediate to regional landslide hazard assessment, based on geomechanical interpretations, is finally proposed.

Landslide hazard assessment using digital elevation models

Human beings are, in general, risk-averse and willing to go to great lengths to reduce failure consequences. However, if the underlying issues are not understood, effective action cannot properly be taken. A landslide hazard assessment framework capable of estimating regional probabilities of slope failure can be used to aid a vast number of communities currently living in landslide “danger zones”. Such a framework would provide a tool with which community resources can be optimized and ensure that appropriate preparedness and mitigation strategies are in place. Maximum slope angles, as estimated using digital elevation models (DEMs), are one of the most important indicators for landslide hazard assessment. This paper uses local averaging theory to determine how the resolution of DEMs affects regional landslide probability estimates. Emphasis is on a regional landslide hazard assessment, measured by the probability that one or more slopes of at least a critical minimum scale will fail within the region.