National Landslide Research Articles

An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland

AbstractAt the time of its development, GeoSure was created using expert knowledge based on a thorough understanding of the engineering geology of the rocks and soils of Great Britain. The ability to use a data-driven methodology to develop a national-scale landslide susceptibility was not possible due to the relatively small size of the landslide inventory at the time. In the intervening 20 years, the National Landslide Database has grown from around 6000 points to over 18,000 records today and continues to be added to. With the availability of this additional inventory, new data-driven solutions could be utilised. Here, we tested a Bernoulli likelihood model to estimate the probability of debris flow occurrence and a log-Gaussian Cox process model to estimate the rate of debris flow occurrence per slope unit. Scotland was selected as the test site for a preliminary experiment, which could potentially be extended to the whole British landscape in the future. Inference techniques for both of these models are applied within a Bayesian framework. The Bayesian framework can work with the two models as additive structures, which allows for the incorporation of spatial and covariate information in a flexible way. The framework also provides uncertainty estimates with model outcomes. We also explored consideration on how to communicate uncertainty estimates together with model predictions in a way that would ensure an integrated framework for master planners to use with ease, even if administrators do not have a specific statistical background. Interestingly, the spatial predictive patterns obtained do not stray away from those of the previous GeoSure methodology, but rigorous numerical modelling now offers objectivity and a much richer predictive description.

Assessment of Internal and External Factors Causing Slow-Moving Landslides

There have been limited studies on slow-moving landslides in South Korea despite their frequent occurrence. Moreover, a national slow-moving landslide hazard information system (SMLHIS) is needed. Herein, we conducted an overlap analysis of 15 slow-moving landslide areas with clear occurrence timings with national landslide hazard maps (LHMs) using the geographic information system data. Additionally, internal and external factors causing slow-moving landslides were analyzed. The results of the overlap analysis showed that slow-moving landslide areas occurred in low-hazard and excluded non-hazard areas on the LHM. The study of internal factors revealed that slow-moving landslides occurred mainly in the Gyeongsang supergroup, which has sedimentary rock type and sandy loam. The analysis of external factors, e.g., rainfall, showed that slow-moving landslides occurred during intensive rainfall, with continuous and 15-day antecedent rainfall exceeding 100 and 200 mm, respectively. The longer the continuous rainfall duration before a slow-moving landslide; the greater the rainfall on the day of the landslide; the greater the maximum hourly rainfall; the greater the 3-, 5-, and 7-day antecedent rainfalls; and the greater the rainfall intensity during the landslide, the greater the size of the slow-moving landslide. This study provides information for developing a national SMLHIS, presenting novel perspectives for slow-moving landslide research.

Assessing damage data availability in national landslide databases for SFDRR reporting: a case study of Kuala Lumpur as a local-level application

Assessing damage data availability in national landslide databases for SFDRR reporting: a case study of Kuala Lumpur as a local-level application

A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps

Empirical evidence shows that climate, deforestation and informal housing (i.e. unregulated construction practices typical of fast-growing developing countries) can increase landslide occurrence. However, these environmental changes have not been considered jointly and in a dynamic way in regional or national landslide susceptibility assessments. This gap might be due to a lack of models that can represent large areas (>100km2) in a computationally efficient way, while simultaneously considering the effect of rainfall infiltration, vegetation and housing. We therefore suggest a new method that uses a hillslope-scale mechanistic model to generate regional susceptibility maps under changing climate and informal urbanisation, which also accounts for existing uncertainties. An application in the Caribbean shows that the landslide susceptibility estimated with the new method and associated with a past rainfall-intensive hurricane identifies ~67.5 % of the landslides observed after that event. We subsequently demonstrate that the hypothetical expansion of informal housing (including deforestation) increases landslide susceptibility more (+20 %) than intensified rainstorms due to climate change (+6 %). However, their combined effect leads to a much greater landslide occurrence (up to +40 %) than if the two drivers were considered independently. Results demonstrate the importance of including both land cover and climate change in landslide susceptibility assessments. Furthermore, by modelling mechanistically the overlooked dynamics between urban growth and climate change, our methodology can provide quantitative information of the main landslide drivers (e.g. quantifying the relative impact of deforestation vs informal urbanisation) and locations where these drivers are or might become most detrimental for slope stability. Such information is often missing in data-scarce developing countries but is key for supporting national long-term environmental planning, for targeting financial efforts, as well as for fostering national or international investments for landslide mitigation.

A national landslide inventory for Denmark

Abstract. Landslides are a frequent natural hazard occurring globally in regions with steep topography. Additionally, landslides play an important role in landscape evolution by transporting sediment downslope. Landslide inventory mapping is a common technique to assess the spatial distribution and extent of landslides in an area of interest. High-resolution digital elevation models (DEMs) have proven to be useful databases to map landslides in large areas across different land covers and topography. So far, Denmark had no national landslide inventory. Here, we create the first comprehensive national landslide inventory for Denmark derived from a 40 cm resolution DEM from 2015 supported by several 12.5 cm resolution orthophotos. The landslide inventory is created based on a manual expert-based mapping approach, and we implemented a quality control mechanism to assess the completeness of the inventory. Overall, we mapped 3202 landslide polygons in Denmark with a level of completeness of 87 %. The complete landslide inventory is freely available for download at https://doi.org/10.6084/m9.figshare.16965439.v2 (Svennevig and Luetzenburg, 2021) or as a web map (https://data.geus.dk/landskred/, last access: 6 June 2022) for further investigations.

Applicability Evaluation of Landslide Vulnerability Criteria for Decision on Landcreep-Vulnerable Areas in South Korea

Landcreep, which is a natural hazard, frequently occurs in South Korea. However, despite many differences between general landslides and landcreep, landcreep is still treated as a kind of landslide. A bigger problem in this reality is that no verification has been made on whether the national landslide vulnerability criteria can be applied to the decision on landcreep-vulnerable areas. Therefore, this study was conducted to examine the applicability of the landslide vulnerability criteria for the decision on landcreep-vulnerable areas. For verification, first, as a result of a correlation analysis that extracted seven types of geomorphological environment criteria that are used in deciding landslide-vulnerable areas from 57 landcreep areas, a positive correlation was shown only in the slope type and the parent rock. Second, as a result of the evaluation of the landcreep area by applying the landslide vulnerability criteria, it was analyzed that 61.4% were areas with low or no possibility of the occurrence of landslides. Third, on the basis of the overlapping analysis of the landslide hazard map and landcreep areas, 67.6% were in Level 3 or lower, except for Levels 1 and 2, which had high hazards, and 21.5% were landcreep areas that were not included in the hazard levels. Applying the landslide vulnerability criteria for deciding on landcreep-vulnerable areas is not appropriate, and it is urgent to prepare landcreep vulnerability criteria.

Participatory Landslide Inventory (PLI): An Online Tool for the Development of a Landslide Inventory

A landslide inventory is a detailed register of the spatial distribution, geometry, and attributes of landslides and is essential for landslide hazard analysis, risk management, regional planning, and land use management and development, especially in landslide-prone regions. However, the development of a national landslide inventory is time-consuming and costly. Accordingly, most developing countries, including Sri Lanka, have basic landslide databases, which identify the location, date, and time of occurrence on a point map. This study, therefore, aimed to introduce a new method to report landslide information via a mobile application based on a participatory approach with information recorded in a web portal called the participatory landslide inventory (PLI). Twenty-one landslide site locations and their attributes were captured on the PLI web portal using the PLI mobile app. The system administrator then demarcated the landslide boundaries and performed geometrical calculations for each landslide to complete the inventory. Finally, the landslide information was published through the PLI web portal. The PLI is an effective and efficient tool for developing a landslide inventory economically.

Application of InSAR satellite method for mapping of active landslides in Bulgaria – opportunities and perspectives

Abstract. Landslides are geological phenomena that are spread on Bulgarian territory mainly along the northern Black Sea coast and on the right banks of the Danube in the western part of the country. Mitigation of the negative effects of these destructive geological phenomena is the compilation of inventory maps of their distribution and registers with the main characteristics of the individual landslides. Conventional methods for making such maps are time-consuming and resource-intensive. Modern satellite, air and ground-based remote sensing technologies facilitate the production of landslide maps, reducing the time and resources required to compile and systematically update them. In this paper, we demonstrate the applicability of Differential Sentinel-1A satellite SAR interferometry (DInSAR) to assess the movement activity and use the information for further updating the national landslide inventories in Bulgaria. We perform several analyses based on multi-temporal InSAR techniques of Sentinel-1A data over selected areas prone to landslides. The use of new opportunities for free access to satellite images, which can be applied in conjunction with other methods, greatly facilitates the processes of inventory, mapping and study of landslides.

Application of Machine Learning Algorithm in China National Geohazard Susceptibility Assessment

Landslide susceptibility is an important activity in landslide hazard assessment. With the advancement of artificial intelligence, the machine learning algorithm is applied in the landslide susceptibility assessment, has recently gained immense attention due to the advantages of obtaining the insights of landslide events and conditioning parameters based on data mining, which is important when tackling the challenge of mapping landslide prone areas in regional scale due to the complex nonlinear correlations among landslides and parameters and uncertainties associated during parameters reclassification. Therefore, the machine learning algorithm has become a standard approach for modeling landslide susceptibility over large regions. In this study, the random forest method is applied to produce the China national landslide susceptibility mapping based on the national landslide database containing more than 300 thousand landslide events. Thirty different categories of conditioning parameters related to the development, triggering, and potentially vulnerable elements of the landslide were collected using a scale of approximately 1:1,000,000. Through the data mining process, lithology, faults, topography, soil erosion, precipitation, and human activities were found to be the top six important contribution factors to landslide susceptibility. The mapping results show the areas of four degrees of susceptibility from high to low are 101, 191, 337, and 331 thousand square kilometers, respectively. The receiver operating curve (ROC) and area under curve (AUC) value was calculated to 0.81, indicating that results are well satisfying and could guide landslide mitigation on the national level.

The development and application of China national landslide database and information system

Landslide caused significant damage to people and economical properties. Establishing a comprehensive landslide database is a fundamental task to support landslide mitigation. However, numerous challenges such as non-standardized data collection, inefficient data maintenance, discontinuous data update, and insufficient data service have become the major problems to establish a reliable, dynamic, and comprehensive landslide database. After two decades of development, more than 322,000 landslide events have been collected through professional field surveys, routine field inspections, and public reporting into the China national landslide database. Also, an information system, which was primarily built for data dynamic management, field data acquisition and integration, classified data storage, efficient data processing, real-time statistics, and information services have been achieved. In this paper, the China national landslide database and information system, including the development progress, database architecture, data services, and future developments, are introduced in details. The key considerations and experiences obtained through the development of China national landslide database and information system such as the establishment of massive landslide inventory data acquisition standardization process, comprehensive mechanism of data quality control and dynamic maintenance, services of landslide information system could be helpful for any other countries or regions that also greatly suffering from landslides, and contribute to the improvement of landslide risk reduction in the world.

Landslide susceptibility mapping using MT-InSAR and AHP enabled GIS-based multi-criteria decision analysis

Landslide susceptibility maps (LSMs) are generally prepared by integrating multiple prominent thematic layers, including DEM derived products (elevation, slope, and aspect), and other parameters such as lithology, geomorphology, LULC, etc. These parameters can be assigned optimum weights using the analytic hierarchy process (AHP) method, followed by a GIS-based weighted overlay analysis. In recent years, multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been rigorously explored, for land deformation detection and monitoring, by extracting highly stable measurement pixels using tens of SAR acquisitions simultaneously. In this research work, a GIS-based multi-criteria decision analysis to prepare LSMs is proposed, with MT-InSAR derived displacement estimates used as a critical input parameter. An LSM is generated by processing 20 ERS-1/2 and Envisat ASAR images, acquired over ∼120 sq. km wide river basin, located in Uttarakhand, India. The generated LSM is found to be congruent with the susceptible maps made available by the Geological Survey of India (GSI) under the National Landslide Susceptibility Mapping (NLSM) program. Preliminary results indicate that the majority of the unstable zones along the Alaknanda River are correctly identified. The approach is further implemented to generate an updated susceptibility map using 60 scenes of freely available Sentinel-1A dataset, followed by validation through actual field survey. This resulted in the generation of an updated susceptibility map, which helped in the identification of 44.5% new landslide susceptible zones (LSZs). Furthermore, the status of previously identified zones is also quantified. The performance of the proposed approach suggests its usability in generating and updating near-real-time LSMs.

Assessment and Applicability Analysis of Dynamic Landslide Hazard Using Matrix Approach

The models to predict when and where a landslide occurs were constructed with different purposes and scopes, but tended to be complementary to each other. The objective of this study was to convert the static landslide hazard into the dynamic landslide hazard and combine a spatiotemporal hazard matrix to assess it, in order to enhance the usability of the early warning system for landslide and the landslide hazard map developed by the Korea Forest Service. We applied various matrix evaluation models to develop scenarios and assess the 2017 landslide occurred in Cheonan, Korea. The results of this study showed that a simple combination of matrices facilitated quantitative evaluation of the dynamic landslide hazard and its accuracy was higher than the static landslide hazard map. In addition, the dynamic landslide hazard can be assessed for the near future by integrating the 1-hour prediction data provided by the Korea Forest Service. The results of this study can help maximize the utilization of the existing national landslide forecast system and landslide hazard map, which should enhance the efficiency of managing landslide-prone zones during the rainy season.

National-scale landslide susceptibility map of Romania in a European methodological framework

Landslide susceptibility maps are useful tools for risk analysis and assessment with practical implications because they provide relevant information for territorial planning, land use sustainable management or even forecast and early warning systems. Achievement of accurate assessments of landslide susceptibility for large regions (i.e. including national territories) is still a challenge, mainly because of the lack of proper landslide inventory and monitoring data. Romania represents one of the most landslide-affected countries in Europe. The current study presents an approach for drawing the landslide susceptibility map at a national scale for the Romanian territory, in agreement with the European methodological framework promoted for small-scale evaluations of landslide susceptibility. The methodological approach was adapted to the specific mophostructural, climatic and landuse conditions of the country, as well as to the quantity and quality of the available data, in order to achieve a susceptibility zonation for slides and flows for the national territory. It follows a mixed statistical-heuristic approach based on a Spatial Multi-Criteria Evaluation (SMCE) procedure integrating both landslide information and expert knowledge. The national landslide susceptibility map outlines large areas ranked as having high and very high susceptibility throughout the Subcarpathian chain, the Moldavian and Transylvanian Plateaux and the Getic Piedmont. The prediction performance was examined quantitatively and qualitatively, by making use of regional geomorphical knowledge. The evaluations suggest that, despite uncertainties inherent at this analysis scale, spatially-differentiated models are able to better capture landslide conditioning frameworks and reproduce inter- and, especially, intraregional variability of landslide distribution as compared to a previous version of the national susceptibility map. The study proves that combining statistical and heuristic approaches, calibrated and later on validated for distinct homogeneous morpho-litho-structural units allows to increase the prediction capacity of the national-scale model. The results are useful to public authorities at national, regional, county and municipality levels, providing knowledge for the enhancement of disaster prevention and response plans.

Landslides in the Andes: Forests can provide cost-effective landslide regulation services

Landslides cause billions of dollars (USD) in damage and hundreds of life losses every year in mountainous areas globally, and these effects are exacerbated by climate change and increased human occupation of vulnerable areas. In many mountainous regions forests deliver slope stability, helping to prevent landslides. However, forests are progressively converted into other land uses in many mountainous regions. In this study, we focus on the Colombian Andes, the most populated and deadly landslide-prone part of Colombia. We aim to determine the difference in frequency of landslides from forested and non-forested areas, and subsequently, quantify the potential costs and benefits of protecting forest and of restoring forest from agricultural lands. To that end, we combine economic data with geographical information related to public and private infrastructure, land use, and landslide susceptibility. Analyzing the national landslide database of Colombia, we established that landslides are almost six times (581%) more likely to occur on non-forested lands than on forested lands. From an economic perspective, by preventing landslides, forests provide a net benefit through the provision of slope stability services. Our most conservative estimates indicate it is 16 times more cost-effective to promote forest corridors, via conservation or reforestation along roads by paying farmers and cattle herders their opportunity costs, than for the public to pay the expected value of landslide damage. Our analysis provides strong evidence that vegetated hillsides can provide a cost-effective ecosystem service approach to mitigate economic losses due to landslides in one of the world's most landslide prone areas.

Landslide Mapping and Monitoring Using Persistent Scatterer Interferometry (PSI) Technique in the French Alps

Continuous geodetic measurements in landslide prone regions are necessary to avoid disasters and better understand the spatiotemporal and kinematic evolution of landslides. The detection and characterization of landslides in high alpine environments remains a challenge associated with difficult accessibility, extensive coverage, limitations of available techniques, and the complex nature of landslide process. Recent studies using space-based observations and especially Persistent Scatterer Interferometry (PSI) techniques with the integration of in-situ monitoring instrumentation are providing vital information for an actual landslide monitoring. In the present study, the Stanford Method for Persistent Scatterers InSAR package (StaMPS) is employed to process the series of Sentinel 1-A and 1-B Synthetic Aperture Radar (SAR) images acquired between 2015 and 2019 along ascending and descending orbits for the selected area in the French Alps. We applied the proposed approach, based on extraction of Active Deformation Areas (ADA), to automatically detect and assess the state of activity and the intensity of the suspected slow-moving landslides in the study area. We illustrated the potential of Sentinel-1 data with the aim of detecting regions of relatively low motion rates that be can attributed to activate landslide and updated pre-existing national landslide inventory maps on a regional scale in terms of slow moving landslides. Our results are compared to pre-existing landslide inventories. More than 100 unknown slow-moving landslides, their spatial pattern, deformation rate, state of activity, as well as orientation are successfully identified over an area of 4000 km2 located in the French Alps. We also address the current limitations due the nature of PSI and geometric characteristic of InSAR data for measuring slope movements in mountainous environments like Alps.

Assessing the potential of soil moisture measurements for regional landslide early warning

In mountainous terrain, rainfall-induced landslides pose a serious risk to people and infrastructure. Regional landslide early warning systems (LEWS) have proven to be a cost-efficient tool to inform the public about the imminent landslide danger. While most operational LEWS are based on rainfall exceedance thresholds only, recent studies have demonstrated an improvement of the forecast quality after the inclusion of soil hydrological information. In this study, the potential of in situ soil moisture measurements for regional landslide early warning is assessed. For the first time, a comprehensive soil moisture measurement database was compiled for Switzerland and compared with a national landslide database (Swiss flood and landslide damage database, WSL). The time series were homogenized and normalized to represent saturation values. From ensembles of sensors, the mean and standard deviation saturation were calculated and infiltration events were delimited, characterized, and classified as landslide-triggering or non-triggering based on the occurrence of landslides within a specified forecast distance. A logistic regression function was applied to model the landslide activity based on the infiltration event characteristics and several models were analysed and compared with receiver operating characteristics (ROC). A strong distance dependence becomes apparent showing a forecast goodness decrease with increasing distance between water content measurement site and landslide, and a better forecast goodness for long-lasting as opposed to short-duration precipitation events. While most variability can be explained by the two event properties antecedent saturation and change of saturation during an infiltration event, event properties that describe antecedent conditions are more important for long-lasting as opposed to short-duration precipitation events that can be better explained by properties describing event dynamics. Overall, the analysis demonstrated that in situ soil moisture data effectively contains specific information useful for landslide early warning.

Spatial and temporal patterns and the socioeconomic impacts of landslides in the tropical and mountainous Colombian Andes.

Landslides are a natural hazard that presents a major threat to human life and infrastructure. Although they are a very common phenomenon in Colombia, there is a lack of analysis that entails national and comprehensive spatial, temporal, and socioeconomic evaluations of such events based on historical records. This study provides a detailed assessment of the spatial and temporal patterns and the socioeconomic impacts associated with landslides that occurred in the country between 1900 and 2018. Two national landslide databases were consulted and this information was complemented by local and regional landslide catalogues. A total of 30,730 landslides were recorded in the 118-year period. Rainfall is the most common trigger of landslides, responsible for 92 per cent of those registered, but most fatalities (68 per cent) are due to landslides caused by volcanic activity and earthquakes. An 'fN curve' revealed a very high frequency of small and moderate fatal landslides in the time frame.

Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping

Remote sensing and geographic information systems (GIS) are widely used for landslide susceptibility mapping (LSM) to support planning authorities to plan, prepare and mitigate the consequences of future hazards. In this study, we compared the traditional per-pixel models of data-driven frequency ratio (FR) and expert-based multi-criteria assessment, i.e. analytical hierarchical process (AHP), with an object-based model that uses homogenous regions (‘geon’). The geon approach allows for transforming continuous spatial information into discrete objects. We used ten landslide conditioning factors for the four models to produce landslide susceptibility maps: elevation, slope angle, slope aspect, rainfall, lithology, geology, land use, distance to roads, distance to drainage, and distance to faults. Existing national landslide inventory data were divided into training (70%) and validation data (30%). The spatial correlation between landslide locations and the conditioning factors were identified using GIS-based statistical models. Receiver operating characteristics (ROC) and the relative landslide density index (R-index) were used to validate the resulting susceptibility maps. The area under the curve (AUC) was used to obtain the following values from ROC for the per-pixel based FR approach (0.894) and the AHP (0.886) compared with the object-based geon FR approach (0.905) and the geon AHP (0.896). The object-based geon aggregation yielded a higher accuracy than both per-pixel based weightings (FR and AHP). We proved that the object-based geon approach creates meaningful regional units that are beneficial for regional planning and hazard mitigation.

МАГНІТНІ ДОСЛІДЖЕННЯ ПРИРОДНИХ І ТЕХНОГЕННИХ ПРОЦЕСІВ ОБ'ЄКТІВ КРИТИЧНОЇ ІНФРАСТРУКТУРИ НА ПРИКЛАДІ ДІЛЯНКИ "ГЛИНКА"

The results of magnetic soil and underlying rocks studies at the area near Lake Glinka are considered. This study was performed as a part of the landslide investigation of the urban environment of the Kyiv agglomeration. The aim was to detect the natural and man-made processes affecting critical infrastructure. Under field condition we measured the volume magnetic susceptibility of soil κ (10-3 SI), and sampling was performed. Under laboratory conditions we measured and then calculated the mass-specific magnetic susceptibility χ (10-8 m3/kg), and its frequency dependence χFD (%). The soil of the study area is gray forest (Greyic Phaeozems Albic in WRB) with signs of urban soil. Magnetic studies were conducted at two points on the high landslide bank of the Lake Glinka, as well as on the opposite low bank. On the low bank, we organised measurements and sampling along the micro-catena and at the vertical soil section to study the soil genetic horizons. Magnetic polygons were constructed based on cosmography downloaded by ArcGIS. The classification of the polygons is made by the attracting the magnetic susceptibility and its frequency dependence. We identified the polygon with the highest values of magnetic susceptibility (χ = 54 × 10-8 m3/kg) and the lowest value of frequency dependence (χFD = 3.6 %). This is the soil with man-made pollution. Two polygons with similar values of magnetic parameters were identified: χ = 25-35 × 10-8 m3/kg, χFD = 8-10 %. Such values are related to the natural soils. Three polygons (two on the the low bank and one on the high landslide bank of Glinka) have average intensity values of magnetic parameters: χ=35-40 × 10-8 m3/kg, χFD = 6-7 %. The vertical distribution of soil magnetic parameters in genetic horizons was observed and the redistribution of magnetic matherial was detected. Such kind of the redistribution indicates the erosion processes. The magnetic properties of soils are important for the joint interpretation with electric tomography, GPR measurements, and GIS analysis of the national landslide database of Ukraine. The results are useful for the development of the algorithm for the environmental monitoring of the critical infrastructure at the urban area.

Application of hydrological modelling for temporal prediction of rainfall-induced shallow landslides

In some rainfall-triggered landslides, intensity-duration thresholds can have limited prediction ability; therefore, investigation of alternative approaches that can be used for temporal prediction of rainfall-induced landslides is needed. This paper presents a methodology for predicting rainfall-induced shallow landslides based on a lumped conceptual hydrological model. The production storage level during the rainfall event and the rainfall sum during the event are used for landslide prediction. Based on these two hydro-meteorological variables a threshold is defined that could be used for rainfall-induced landslides prediction as part of an early warning system. The presented methodology is tested using the meso-scale Selška Sora River catchment case study in western Slovenia where 20 active landslides from the Slovenian National Landslide Database are used to calibrate and evaluate the methodology performance. The results are compared to three different (i.e. local, regional, and global) intensity-duration thresholds. The results of the presented approach are superior in terms of several goodness-of-fit criteria compared to tested local and global ID thresholds. Because only daily rainfall, evapotranspiration, and discharge data are needed to calibrate the selected hydrological model and only daily rainfall and evapotranspiration to run the model, the presented approach could also be useful for data-scarce areas where detailed physically based landslide prediction models that require many data cannot be constructed. Moreover, we have also derived the probabilistic version of the proposed threshold for triggering of shallow landslides using copula functions.