Global Landslide Catalog Research Articles

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.

A data-driven evaluation of post-fire landslide susceptibility

Abstract. Wildfires change the hydrologic and geomorphic response of watersheds, which has been associated with cascades of additional hazards and management challenges. Among these post-wildfire events are shallow landslides and debris flows. This study evaluates post-wildfire mass movement trigger characteristics by comparing the precipitation preceding events at both burned and unburned locations. Landslide events are selected from the NASA Global Landslide Catalog (GLC). Since this catalog contains events from multiple regions worldwide, it allows a greater degree of inter-region comparison than many more localized catalogs. Fire and precipitation histories for each site are established using Moderate Resolution Imagine Spectroradiometer (MODIS) Burned Area and Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation data, respectively. Analysis of normalized 7 d accumulated precipitation for sites across all regions shows that, globally, mass movements at burned sites are preceded by less precipitation than mass movements without antecedent burn events. This supports the hypothesis that fire increases rainfall-driven mass movement hazards. An analysis of the seasonality of mass movements at burned and unburned locations shows that mass-movement-triggering storms in burned locations tend to exhibit different seasonality from rainfall-triggered mass movements in areas undisturbed by recent fire, with a variety of seasonal shifts ranging from approximately 6 months in the Pacific Northwest of North America to 1 week in the Himalayan region. Overall, this paper offers an exploration of regional differences in the characteristics of rainfall-triggered mass movements at burned and unburned sites over a broad spatial scale and encompassing a variety of climates and geographies.

Spatial and temporal landslide distributions using global and open landslide databases

Landslide databases are a potential tool for the analysis of landslide susceptibility, hazard, and risk. Additionally, the spatio-temporal distribution of landslides and their correlation with their triggering factors are inputs that facilitate the evaluation of landslide prediction models and the determination of thresholds necessary for early warning systems (EWS). This study presents an analysis of four widely known global databases—the International Disaster database (EM-DAT), the Disaster Inventory System (DesInventar), the Global Landslide Catalog (GLC), and the Global Fatal Landslide database (GFLD)—which contain relevant landslide information for different regions of the world. These databases were analysed and compared by means of the spatio-temporal distributions of their records. Subsequently, these databases were merged and depurated to obtain a more robust database, namely the Unified Global Landslide Database (UGLD), with 161 countries, 37,946 landslides, and 185,753 fatalities registered between 1903 and 2020. The merging process among the databases resulted in a small number of repeated landslides, indicating that the databases collect very different landslide information and complement each other. Finally, an update of the spatial and temporal analysis of landslides in the world was performed with the new database, in which patterns, trends, and the main triggers were presented and analysed. The results obtained from the analysis of the UGLD database show the American and Asian continents as the continents with the highest number of landslides and associated fatalities, showing a bimodal and unimodal annual temporal pattern, respectively. Regarding the most frequent triggers of landslides, rainfall, anthropogenic intervention, and earthquakes stand out.

Spatial and Temporal Analysis of Global Landslide Reporting Using a Decade of the Global Landslide Catalog

Rainfall-triggered landslides can result in devastating loss of life and property damage and are a growing concern from a local to global scale. NASA’s global landslide catalog (GLC) compiles a record of rainfall-triggered landslide events from media reports, academic articles, and existing databases at global scale. The database consists of all types of mass movement events that are triggered by rainfall and represents a minimum number of events occurring between 2007 and 2018. The GLC collection is no longer being compiled, and the dataset will not be updated past 2018. The research presented here evaluates global patterns in landslide reporting from events in the GLC. The evaluation includes an analysis of the spatial and temporal distribution of global landslide events and associated casualties and comparisons with other landslide inventories. This database has been used to estimate landslide hotspots, evaluate geographic patterns in landslides, and train and validate landslide models from local to global scales. The most notable landslide hotspots are in the Pacific Northwest of North America, High Mountain Asia, and the Philippines. Additionally, the relationship between country GDP and income status with landslide occurrence was determined to have a positive correlation between economic status and landslide reporting. The GLC also indicates a reporting bias towards English-speaking countries. The general goal of this research is to assess the decade of global landslide reports from the GLC and show how this database can be used for rainfall-triggered landslide research.

A Review of Spatial Variations of Multiple Natural Hazards and Risk Management Strategies in Pakistan

Natural hazards are dynamic and unpredictable events that are a continuous threat to global socio-economic development. Humans’ reactions to these catastrophes are influenced by their proximity to the hazards and their ability to anticipate, resist, cope with, and recover from their consequences. Due to climatic changes, the risk of multiple natural hazards is expected to increase in several regions of Pakistan. There is a pressing need to understand the spatial discrepancies of natural hazards due to climate change and identifying the regions that require special measures to increase resilience, achieve adaptation, and sustainable development goals. This paper synthesizes the related literature to understand spatial variations of natural hazards due to climate changes across Pakistan. The Emergency Events Database (EM-DAT), National Aeronautics and Space Administration Global Landslide Catalog (NASA-GLC), National Disaster Management Authority (NDMA), and Pakistan Meteorological Department (PMD) are utilized to analyze spatial discrepancies and vulnerabilities to natural hazards. This study unveils that Pakistan’s current risk analysis and management strategies seem to be obsolete compared to global trends. Because of spatial variations of hazards, most research work on hazard risk assessments and risk management focuses on a single hazard, neglecting the co-occurrence impact of different natural hazards. Very limited studies are included in comprehensive multi-hazard risk strategies. Therefore, in Pakistan, risk management would require integrated multi-hazard risk assessment approaches to detect, analyze, measure, and evaluate various natural hazards, their effects, and interconnections. Moreover, the Pakistan governmental institutes dealing with natural hazards should focus on pre-disaster mitigation and resilience techniques instead of investing only in post-disaster relief activities.

Estimating global landslide susceptibility and its uncertainty through ensemble modeling

Abstract. This study assesses global landslide susceptibility (LSS) at the coarse 36 km spatial resolution of global satellite soil moisture observations to prepare for a subsequent combination of a global LSS map with dynamic satellite-based soil moisture estimates for landslide modeling. Global LSS estimation contains uncertainty, arising from errors in the underlying data, the spatial mismatch between landslide events and predictor information, and large-scale LSS model generalizations. For a reliable uncertainty assessment, this study combines methods from the landslide community with common practices in meteorological modeling to create an ensemble of global LSS maps. The predictive LSS models are obtained from a mixed effects logistic regression, associating hydrologically triggered landslide data from the Global Landslide Catalog (GLC) with predictor variables describing the landscape. The latter are taken from the Catchment land surface modeling system (including input parameters of soil (hydrological) properties and resulting climatological statistics of water budget estimates), as well as geomorphological and lithological data. Road network density is introduced as a random effect to mitigate potential landslide inventory bias. We use a blocked random cross validation to assess the model uncertainty that propagates into the LSS maps. To account for other uncertainty sources, such as input uncertainty, we also perturb the predictor variables and obtain an ensemble of LSS maps. The perturbations are optimized so that the total predicted uncertainty fits the observed discrepancy between the ensemble average LSS and the landslide presence or absence from the GLC. We find that the most reliable total uncertainty estimates are obtained through the inclusion of a topography-dependent perturbation between 15 % and 20 % to the predictor variables. The areas with the largest LSS uncertainty coincide with moderate ensemble average LSS, because of the asymptotic nature of the LSS model. The spatial patterns of the average LSS agree well with previous global studies and yield areas under the receiver operating characteristic between 0.84 and 0.92 for independent regional to continental landslide inventories.

Data-Driven Landslide Nowcasting at the Global Scale

Landslides affect nearly every country in the world each year. To better understand this global hazard, the Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed previously. LHASA version 1 combines satellite precipitation estimates with a global landslide susceptibility map to produce a gridded map of potentially hazardous areas from 60° North-South every 3 h. LHASA version 1 categorizes the world’s land surface into three ratings: high, moderate, and low hazard with a single decision tree that first determines if the last seven days of rainfall were intense, then evaluates landslide susceptibility. LHASA version 2 has been developed with a data-driven approach. The global susceptibility map was replaced with a collection of explanatory variables, and two new dynamically varying quantities were added: snow and soil moisture. Along with antecedent rainfall, these variables modulated the response to current daily rainfall. In addition, the Global Landslide Catalog (GLC) was supplemented with several inventories of rainfall-triggered landslide events. These factors were incorporated into the machine-learning framework XGBoost, which was trained to predict the presence or absence of landslides over the period 2015–2018, with the years 2019–2020 reserved for model evaluation. As a result of these improvements, the new global landslide nowcast was twice as likely to predict the occurrence of historical landslides as LHASA version 1, given the same global false positive rate. Furthermore, the shift to probabilistic outputs allows users to directly manage the trade-off between false negatives and false positives, which should make the nowcast useful for a greater variety of geographic settings and applications. In a retrospective analysis, the trained model ran over a global domain for 5 years, and results for LHASA version 1 and version 2 were compared. Due to the importance of rainfall and faults in LHASA version 2, nowcasts would be issued more frequently in some tropical countries, such as Colombia and Papua New Guinea; at the same time, the new version placed less emphasis on arid regions and areas far from the Pacific Rim. LHASA version 2 provides a nearly real-time view of global landslide hazard for a variety of stakeholders.

Spatio-temporal variability of monsoon precipitation and their effect on precipitation triggered landslides in relation to relief in Himalayas

Orography of Himalaya governs the regional weather system and monsoon of Indian sub-continent. The intense persistent precipitation in the form of rainfall during monsoon season causes landslides which are the most frequent naturally occurring hazards in the Himalaya. This study attempts to investigate the spatio-temporal variability of precipitation and their effect on precipitation triggered landslides during monsoon season (June–September) in relation to relief in Himalayan region by utilizing satellite-derived precipitation products (GPM) over the span of 2000–2018 in addition to NASA Landslide Viewer, Global Landslide Catalog and Global Risk Data Platform data for landslides and ASTER DEM for elevation. The results exhibit that the Eastern Himalayas received the highest amount of precipitation of 2385 mm with intensity of 19.5 mm/day, followed by the Central Himalayas with 1860 mm and intensity of 17.5 mm/day and the least in the Western Himalayas with 1400 mm and intensity of 15 mm/day, respectively. The monsoon precipitation in the lower elevations (below 2600 m) are mostly responsible for causing a vast majority of the precipitation induced landslide events with a maximum of 68.66% in the Central Himalayas followed by the Western Himalayas at 62.23% and the least in the Eastern Himalayas at 41.16%. The overall accumulated precipitation and intensity during monsoon season and landslide distribution were strongly correlated with relief pattern. This comprehensive study signifies how relief regulated the occurrences of landslides in monsoon season and recommends vegetation cover and least interference of human-induced land use to alleviate the landslides.

Synoptic-scale precursors of landslides in the western Himalaya and Karakoram

In the Upper Indus Basin (UIB), precipitation associated with synoptic-scale circulations impinges on the complex and steep orography of the western Himalaya and Karakoram. Heavy rainfall often falls over the foothills, frequently triggering landslides there. This study explores the role of these synoptic-scale circulations – extratropical western disturbances (WDs) and tropical depressions (TDs) – in producing the conducive conditions necessary to trigger landslides, using data from the NASA Global Landslide Catalog and WD and TD track databases.During the winter (October to April), UIB landslides peak in February and occur at a rate of 0.05 day−1, 61% of which are associated with the passage of a WD. They are most common when a WD is located within a few hundred kilometres of 30°N, and significantly rarer if the WD is north of 40°N. WDs provide moist southwesterly flow from the Arabian Sea and Mediterranean Sea to the UIB, resulting in large-scale precipitation, but landslide probability is not related to WD intensity. Non-WD winter landslides are associated with small-scale orographic precipitation that we hypothesise is due to cloudbursts.During the summer (May to September), UIB landslides peak in August and occur at a rate of 0.11 day−1, 60% of which are associated with TD activity. Many of these TDs are found over central India, slightly south of the climatological monsoon trough, where they provide strong monsoonal southeasterlies to the UIB flowing along the Himalayas. Increased landslide frequency is also associated with TD activity over the southern Bay of Bengal (BoB), and it is hypothesised that this is related to monsoon break conditions. Landslide frequency is significantly correlated with TD intensity. Non-TD landslides are associated with a northwestward extension of the monsoon trough, providing southeasterly barrier flow to the UIB.Implications for forecasting and climate change are discussed.

Comparison between the Naïve Bayes and Hierarchical Clustering to Classify The Global Landslide Catalog for the Prediction of the Landslide.

Machine Learning has been used since long to identify the features of a given datasets that are important for the prediction. Landslides are complex events taking place in the various regions of the world. It is the movement of the debris, soil or rocks from an upper plane in downward direction. Identification of the features that are used for the Landslide involves consideration of various categories of parameters. Present paper studies about the performance comparison between a supervised algorithm Naïve Bayes and unsupervised algorithm Hierarchical Clustering. Naïve Bayes is a non parametric supervised algorithm that can be used for the forecasting purposes in the field of Agriculture, Economics, Aviation etc, whereas Hierarchical Clustering is used to partition the available instances of a dataset into optimal homogeneous groups on the basis of the similarities between the datapoints. The present paper draws a comparison between the accuracy of the Naïve Bayes and Hierarchical Clustering for the prediction of the Landslide dataset. The dataset used is the Global Landslide Catalog that has important parameters like date, location coordinates, country, trigger of the event, continent etc. Before the implementation of both the algorithms, reduction of the parameters is carried out using subset evaluation of the parameters and considering only the most important.

Global detection of rainfall-triggered landslide clusters

Abstract. An increasing awareness of the cost of landslides on the global economy and of the associated loss of human life has led to the development of various global landslide databases. However, these databases typically report landslide events instead of individual landslides, i.e., a group of landslides with a common trigger and reported by media, citizens and/or government officials as a single unit. The latter results in significant cataloging and reporting biases. To counteract these biases, this study aims to identify clusters of landslide events that were triggered by the same rainfall event. An algorithm is developed that finds a series of landslide events that (a) is continuous with no more than 2 d between individual events and where (b) precipitation at the location of an individual event correlates with precipitation of at least one other event. The developed algorithm is applied to the Global Landslide Catalog (GLC) maintained by NASA. The results show that more than 40 % of all landslide events are connected to at least one other event and that 14 % of all studied landslide events are actually part of a landslide cluster consisting of at least 10 events and up to 108 events in 1 d. Duration of the detected clusters also varies greatly from 1 to 24 d. Our study intends to enhance our understanding of landslide clustering and thus will assist in the development of improved, internationally streamlined mitigation strategies for rainfall-related landslide clusters.

Using citizen science to expand the global map of landslides: Introducing the Cooperative Open Online Landslide Repository (COOLR).

Robust inventories are vital for improving assessment of and response to deadly and costly landslide hazards. However, collecting landslide events in inventories is difficult at the global scale due to inconsistencies in or the absence of landslide reporting. Citizen science is a valuable opportunity for addressing some of these challenges. The new Cooperative Open Online Landslide Repository (COOLR) supplements data in a NASA-developed Global Landslide Catalog (GLC) with citizen science reports to build a more robust, publicly available global inventory. This manuscript introduces the COOLR project and its methods, evaluates the initial citizen science results from the first 13 months, and discusses future improvements to increase the global engagement with the project. The COOLR project (https://landslides.nasa.gov) contains Landslide Reporter, the first global citizen science project for landslides, and Landslide Viewer, a portal to visualize data from COOLR and other satellite and model products. From March 2018 to April 2019, 49 citizen scientists contributed 162 new landslide events to COOLR. These events spanned 37 countries in five continents. The initial results demonstrated that both expert and novice participants are contributing via Landslide Reporter. Citizen scientists are filling in data gaps through news sources in 11 different languages, in-person observations, and new landslide events occurring hundreds and thousands of kilometers away from any existing GLC data. The data is of sufficient accuracy to use in NASA susceptibility and hazard models. COOLR continues to expand as an open platform of landslide inventories with new data from citizen scientists, NASA scientists, and other landslide groups. Future work on the COOLR project will seek to increase participation and functionality of the platform as well as move towards collective post-disaster mapping.

Satellite-based assessment of rainfall-triggered landslide hazard for situational awareness.

Determining the time, location, and severity of natural disaster impacts is fundamental to formulating mitigation strategies, appropriate and timely responses, and robust recovery plans. A Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed to indicate potential landslide activity in near real-time. LHASA combines satellite-based precipitation estimates with a landslide susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss. Precipitation data from the Global Precipitation Measurement (GPM) mission are used to identify rainfall conditions from the past seven days. When rainfall is considered to be extreme and susceptibility values are moderate to very high, a "nowcast" is issued to indicate the times and places where landslides are more probable. When LHASA nowcasts were evaluated with a Global Landslide Catalog, the probability of detection (POD) ranged from 8 to 60%, depending on the evaluation period, precipitation product used, and the size of the spatial and temporal window considered around each landslide point. Applications of the LHASA system are also discussed, including how LHASA is used to estimate long-term trends in potential landslide activity at a nearly global scale and how it can be used as a tool to support disaster risk assessment. LHASA is intended to provide situational awareness of landslide hazards in near real-time, providing a flexible, open source framework that can be adapted to other spatial and temporal scales based on data availability.

A heuristic approach to global landslide susceptibility mapping.

Landslides can have significant and pervasive impacts to life and property around the world. Several attempts have been made to predict the geographic distribution of landslide activity at continental and global scales. These efforts shared common traits such as resolution, modeling approach, and explanatory variables. The lessons learned from prior research have been applied to build a new global susceptibility map from existing and previously unavailable data. Data on slope, faults, geology, forest loss, and road networks were combined using a heuristic fuzzy approach. The map was evaluated with a Global Landslide Catalog developed at the National Aeronautics and Space Administration, as well as several local landslide inventories. Comparisons to similar susceptibility maps suggest that the subjective methods commonly used at this scale are, for the most part, reproducible. However, comparisons of landslide susceptibility across spatial scales must take into account the susceptibility of the local subset relative to the larger study area. The new global landslide susceptibility map is intended for use in disaster planning, situational awareness, and for incorporation into global decision support systems.

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.

Spatial and temporal analysis of a global landslide catalog

Landslide inventories are critical to support investigations of where and when landslides have happened and may occur in the future; however, there is surprisingly little information on the historical occurrence of landslides at the global scale. This paper presents a new publicly available global landslide catalog (GLC), which is based on media reports, online databases, and other sources. This database is currently available at http://ojo-streamer.herokuapp.com/. The 5741 points in the GLC provide a foundation for evaluating spatial and temporal trends in landslide activity from 2007 to 2013. Globally, landslides were reported most frequently from July to September. Most events occurred in Asia, North America and Southeast Asia. In contrast, fewer than 5% of the fatalities were reported in North America, suggesting a significant amount of under-reporting in other regions as well as potential discrepancies between developing and developed regions. Reported landslide events were also compared to satellite-based precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) to evaluate the co-occurrence of extreme precipitation and landslide activity. Of the 3550 points considered in a subset of the GLC, approximately 60% of the reported landslides have daily precipitation exceeding the 95th percentile of precipitation calculated over a 14-year TRMM record for the same location. This study also investigated how the recurrence interval of extreme precipitation corresponded to some of the most catastrophic landslide events. In spite of several reporting and cataloging biases, spatial and temporal analysis of the GLC suggests that it is a valuable database for characterizing global patterns of landslide occurrence and evaluating relationships with extreme precipitation at regional and global scales.

Global Distribution of Extreme Precipitation and High-Impact Landslides in 2010 Relative to Previous Years

Abstract It is well known that extreme or prolonged rainfall is the dominant trigger of landslides worldwide. While research has evaluated the spatiotemporal distribution of extreme rainfall and landslides at local or regional scales using in situ data, few studies have mapped rainfall-triggered landslide distribution globally because of the dearth of landslide data and consistent precipitation information. This study uses a newly developed global landslide catalog (GLC) and a 13-yr satellite-based precipitation record from Tropical Rainfall Measuring Mission (TRMM) data. For the first time, these two unique products provide the foundation to quantitatively evaluate the co-occurrence of precipitation and rainfall-triggered landslides globally. Evaluation of the GLC indicates that 2010 had a large number of high-impact landslide events relative to previous years. This study considers how variations in extreme and prolonged satellite-based rainfall are related to the distribution of landslides over the same time scales for three active landslide areas: Central America, the Himalayan arc, and central eastern China. Several test statistics confirm that TRMM rainfall generally scales with the observed increase in landslide reports and fatal events for 2010 and previous years over each region. These findings suggest that the co-occurrence of satellite precipitation and landslide reports may serve as a valuable indicator for characterizing the spatiotemporal distribution of landslide-prone areas in order to establish a global rainfall-triggered landslide climatology. This study characterizes the variability of satellite precipitation data and reported landslide activity at the global scale in order to improve landslide cataloging and attempt to quantify landslide triggering at daily, monthly, and yearly time scales.

A global landslide catalog for hazard applications: method, results, and limitations

A systematic inventory of landslide events over the globe is valuable for estimating human and economic losses, quantifying the relationship between landslide occurrences and climate variations and for evaluating emerging global landslide prediction efforts based on remote sensing data. This study compiles a landslide catalog for rainfall-triggered events for several years, drawing upon news reports, scholarly articles, and other hazard databases to provide a landslide catalog at the global scale. While this database may only represent a subset of rainfall-triggered landslides globally, due to lack of reports, it presents a lower boundary on the number of events globally and provides initial insight into the spatiotemporal statistical trends in landslide distribution and impact. This article develops a methodology for landslide event compilation that can be used in evaluating global landslide forecasting initiatives and assessing patterns in landslide distribution and frequency worldwide.