Landslides are ubiquitous geomorphological phenomena with potentially catastrophic consequences. In several countries, landslide mortality can be higher than that of any other natural hazard. Predicting landslides is a difficult task that is of both scientific interest and societal relevance that may help save lives and protect individual properties and collective resources. The session focuses on innovative methods and techniques to predict landslide occurrence, including the location, time, size, destructiveness of individual and multiple slope failures. All landslide types are considered, from fast rockfalls to rapid debris flows, from slow slides to very rapid rock avalanches. All geographical scales are considered, from the local to the global scale. Of interest are contributions investigating theoretical aspects of natural hazard prediction, with emphasis on landslide forecasting, including conceptual, mathematical, physical, statistical, numerical and computational problems, and applied contributions demonstrating, with examples, the possibility or the lack of a possibility to predict individual or multiple landslides, or specific landslide characteristics. Of particular interest are contributions aimed at: the evaluation of the quality of landslide forecasts; the comparison of the performance of different forecasting models; the use of landslide forecasts in operational systems; and investigations of the potential for the exploitation of new or emerging technologies e.g., monitoring, computational, Earth observation technologies, in order to improve our ability to predict landslides. We anticipate that the most relevant contributions will be collected in the special issue of an international journal.
EGU Session NH3.7
Welcome to the Session NH3.7 on Space and Time Forecast of Landslides
The chat session will proceed by maintaining the original order provided by the session program. However, only the presentations with actually uploaded material will be listed in the session chat list.
Authors will be introduced In groups of 3 and will have 1-2 minutes each, in sequence, to briefly introduce their work by copy-pasting some brief sentences that summarise the research
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Chat time: Wednesday, 6 May 2020, 14:00–15:45
Most statistically-based landslide susceptibility maps are supposed to portray the relative likelihood of an area to be affected by future landslides. Literature indicates that vital modelling decisions, such as the selection of explanatory variables, are frequently based on quantitative criteria (e.g. predictive performance). The results obtained by apparently well-performing statistical models are also used to infer the causes of slope instability and to identify landslide “safe” terrain. It seems that comparably few studies pay particular attention to background information associated with the available landslide data. This research hypothesizes that inappropriate modelling decisions and wrong conclusions are likely to follow whenever the origin of the underlying landslide data is ignored. The aims were to (i) analyze the South Tyrolean landslide inventory in the context of its origin in order to (ii) highlight potential pitfalls of performance driven procedures and to (iii) develop a predictive model that takes landslide background information into account. The available landslide data (1928 slide-type movements) of the province of South Tyrol (~7400 km²) consists of positionally accurate points that depict the scarp location of events that induced interventions by e.g. the road service or the geological office. An initial exploratory statistical analysis revealed general relationships between landslide presence/absence data and frequently used explanatory variables. Subsequent modelling was based on a Generalized Additive Mixed Effects Model that allowed accounting for (non-linear) fixed effects and additional “nuisance” variables (random intercepts). The evaluation of the models (diverse variable combinations) focused on modelled relationships, variable importance, spatial and non-spatial predictive performance and the final prediction surfaces. The results highlighted that the best performing models did not reflect the “actual” landslide susceptibility situation. A critical interpretation led to the conclusion that the models simultaneously reflected both, effects likely related to slope instability (e.g. low likelihood of flat and very steep terrain) and effects rather associated with the provincial landslide intervention strategy (e.g. few interventions at high altitudes, increasing number of interventions with decreasing distance to infrastructure). Attempts to separate the nuisance related to “intervention effects” from the actual landslide effects using mixed effects modelling proved to be challenging, also due to omnipresent spatial interrelations among the explanatory variables and the fact that some variables concurrently represent effects related to landslide predisposition and effects associated with the intervention strategy (e.g. altitude). We developed a well-performing predictive landslide intervention index that is in line with the actual data origin and allows identifying areas where future interventions are more or less likely to take place. The efficiency of past interventions (e.g. stabilization of slopes) was demonstrated during recent storm events, because previously stabilized slopes were not affected by new landslides. This also showed that the correct interpretation of the final map requires a simultaneous visualization of both, the spatially predicted index (from low to high) and the available landslide inventory (low likelihood due to past interventions). The results confirm that wrong conclusions can be drawn from excellently performing statistical models whenever qualitative background information is disregarded.
How to cite: Steger, S., Mair, V., Kofler, C., Schneiderbauer, S., and Zebisch, M.: The necessity to consider the landslide data origin in statistically-based spatial predictive modelling – A landslide intervention index for South Tyrol (Italy), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3440, https://doi.org/10.5194/egusphere-egu2020-3440, 2020.
In general, the most reliable parameters to forecast the occurrence of a landslide are kinematics parameters, such as displacement, velocity and acceleration, since they represent the direct indicator of the stability conditions of a slope. Despite recent advancement in satellite interferometry, the highest temporal resolution, necessary to set up an effective early warning system, are still achievable from ground-based instrumentation.
Within this framework a few methods to forecast the time of failure of landslides at slope-scale have been developed in the last decades and, in many instances, they have been successfully used to prevent casualties and economic losses.
Common applications include public safety situations and open-pit mines, for which accurate warnings are crucial to protect workers and at the same time avoid unnecessary interruptions of the extraction activities.
In this work, a review of the most relevant kinematics-based forecasting methods is presented. Some examples are shown to illustrate the respective advantages, limitations and range of applicability of each method. Future challenges, trends and opportunities provided by technological innovations and scientific advances, also in related fields such as Material Science and Applied Mathematics, are also presented.
How to cite: Intrieri, E., Carlà, T., Gigli, G., and Casagli, N.: Forecasting landslide at slope-scale: past achievements, present challenges and future perspectives, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11291, https://doi.org/10.5194/egusphere-egu2020-11291, 2020.
Landslide early warning systems (LEWS) can be categorized into two groups: territorial and local systems. Territorial landslide early warning systems (Te-LEWS) deal with the occurrence of several landslides in wide areas: at municipal/regional/national scale. The aim for such systems is to forecast the increased probability of landslides occurrence in a given warning zone. Nowadays, there are around 30 Te-LEWS operational worldwide. The performance evaluation of such systems is often overlooked, and a standardized procedure is still missing. Often, a contingency matrix 2x2, usually employed for rainfall thresholds validation purposes, is used. Recently an original method has been proposed by Calvello and Piciullo, 2016: the EDuMaP.
This paper describes the new excel user-friendly tool for the application of the method. Moreover, a description of different indicators used for the performance evaluation of different Te-LEWS is provided. Subsequently, the most useful ones have been selected and implemented into the tool. The EDuMaP tool has been used for the performance evaluation of the SMART warning model operating in Piemonte region, Italy. The analysis highlights the warning zones with the highest performance and the ones that need thresholds refinement. The SMART performance has been evaluated with both the EDuMaP and a 2x2 contingency table for comparison purposes. The result highlights that the latter approach can lead to an imprecise and not detailed analysis, because it cannot differentiate among the levels of warning and the variable number of landslides that may occur in a time interval. Moreover, a comparison of the performance of different Te-LEWS with the SMART model has been carried out highlighting critical issues and positive aspects. Finally, the weakness aspects and the future developments of the SMART warning model are described.
This paper has been conceived in the context of the research-based innovation project Klima 2050 - "Risk reduction through climate adaptation of buildings and infrastructure" http://www.klima2050.no/.
How to cite: Cepeda, J., Luca, P., Davide, T., Gaetano, P., and Michele, C.: Comparison of the performance of different Territorial Landslide Early Warning Systems, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21648, https://doi.org/10.5194/egusphere-egu2020-21648, 2020.
Prediction of rainfall-induced landslides is often entrusted to the definition of empirical thresholds (usually expressed in terms of rainfall intensity and duration), linking the precipitation to the triggering of landslides. However, rainfall intensity-duration thresholds do not exploit the knowledge of the hydrological processes developing in the slope, so they tend to generate false and missed alarms. Rainfall-induced shallow landslides usually occur in initially unsaturated soil covers following an increase of pore water pressure, due to the increase of soil moisture, caused by large and persistent rainfall. Clearly, it should be possible to use soil moisture for landslide prediction. Recently, Bogaard & Greco (2018) proposed the cause-trigger conceptual framework to develop hydro-meteorological thresholds that combine the antecedent causal factors and the actual trigger connected with landslide initiation. In fact, in some regions where rainfall-induced shallow landslides are particularly dangerous and pose a serious risk to people and infrastructures, the antecedent saturation is the predisposing factor, while the actual landslide triggering is associated with the hydrologic response to the recent and incoming precipitation. In fact, numerous studies already tried to introduce, directly or with models, the effects of antecedent soil moisture content in the empirical thresholds for improving landslide forecasting. Soil moisture can be measured locally, by a range of on-site measurement techniques, or remotely, from satellites or airborne. On-site measurements have proved promising in improving the performance of thresholds for landslide early warning. On-site data are accurate but sparse, so there is an increasing interest on the possible use of remotely sensed data. And in fact, recent research has shown that they can provide useful information for landslide prediction at regional scale, despite their coarse resolution and inherent uncertainty.
However, while remote sensing techniques provide near-surface (5cm depth) soil moisture estimate, the depth involved in shallow landslide is typically 1-2m below the surface. This depth, overlapping with the root penetration zone, is influenced by antecedent precipitation, soil texture, vegetation and, so, it is very difficult to find a clear relationship with near-surface soil moisture. Many studies have been conducted to provide root-zone soil moisture through physically-based approaches and data driven methods, data assimilation schemes, and satellite information.
In this study, the question if soil moisture information derived from current or future satellite products can improve landslide hazard prediction, and to what extent, is investigated. Hereto, real-world landslide and hydrology information, from two sites of Southern Italy characterized by frequent shallow landslides (Peloritani mountains, in Sicily, and Partenio mountains, in Campania), is analyzed. To get datasets long enough to carry out statistical analyses, synthetic time series of rainfall and soil cover response have been generated, with the application of a stochastic rainfall model and a physically based infiltration model, for both the sites. Near-surface and root-zone soil moisture have been tested, accounting also for effects of uncertainty and of coarse spatial and temporal resolution of measurements. The obtained results show that, in all cases, soil moisture information allows building hydro-meteorological thresholds for landslide prediction, significantly outperforming the currently adopted purely meteorological thresholds.
How to cite: Marino, P., Greco, R., Peres, D. J., and Bogaard, T. A.: Hydro-meteorological thresholds based on synthetic dataset for improved prediction of rainfall-induced shallow landslides., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10479, https://doi.org/10.5194/egusphere-egu2020-10479, 2020.
In many landslide studies, the possibility to predict future behaviour is still a major concern. To date, early-warning systems have mostly relied on the availability of detailed, high-frequency data from sensors installed in situ. Methods deducing reliable failure predictions have been largely applied at local scale, where in situ monitoring systems can be installed.
The same purpose could not be chased through spaceborne monitoring applications, as these could not yield information acquired in sufficiently systematic fashion: the low data sampling frequency of most of the satellite systems hampered the possibility to retrieve the necessary details of tertiary creep characterized by accelerating deformation. So far, the lack of systematic information on ground displacement acquired at regional scale was another serious limit hampering the application of failure prediction methods at wide scale. Such limitations can be partially solved through the exploitation of new generation spaceborne platforms.
The launch of Sentinel-1 mission opened a new opportunity for InSAR monitoring applications thanks to the increased acquisition frequency, the regularity of acquisitions and the policy on data access. We demonstrate the potential of satellite Interferometric Synthetic Aperture Radar (InSAR) to identify precursors to catastrophic slope failures.
Here we present three sets of Sentinel-1 constellation images processed by means of multi-interferometric analysis. We detect clear trends of accelerating displacement prior to the catastrophic failure of three large slopes of very different nature: an open-pit mine slope, a natural rock slope in alpine terrain, and a tailings dam embankment. We determine that these events could have been located several days or weeks in advance. The results highlight that satellite InSAR may now be used to support decision making and enhance predictive ability for this type of hazard.
How to cite: Raspini, F., Carlà, T., Intrieri, E., Bardi, F., Farina, P., Ferretti, A., Colombo, D., Novali, F., and Casagli, N.: Perspectives on the prediction of catastrophic slope failures from satellite InSAR, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13158, https://doi.org/10.5194/egusphere-egu2020-13158, 2020.
In the Alps, shallow landslides repeatedly pose a risk to infrastructure and residential areas. For example, dozens of shallow landslides led to the destruction of several houses, killed one person and led to the evacuation of more than 50 houses, multiple road closure for several days in Austria in Nov. 2019. To analyse and predict the risk posed by shallow landslide, a wide range of scientific methods and tools for modelling disposition and runout exists, both for local and regional scale analyses. Most of these tools, however, do not take the protective effect, i.e. root reinforcement, of vegetation into account. Therefore, we developed SlideforMap (SfM), a probabilistic model that allows for a regional assessment of the disposition of shallow landslides while considering the effect of different scenarios of forest cover and management and of rainfall intensity.
SfM uses a probabilistic approach by attributing landslide surface areas, randomly selected from a gamma shaped distribution published by Malamud (2004), to random coordinates within a given study area. For each generated landslide, SfM calculates a factor of safety using the limit equilibrium infinite slope approach. Thereby, the relevant soil parameters, i.e. angle of internal friction, soil cohesion and soil depth, are defined by normal distributions based on mean and standard deviation values representative for the study area. Hydrology is implemented using a stationary flow approach and the topographical wetness index. Root reinforcement is computed based on root distribution and root strength derived from single tree detection data and the root bundle model of Schwarz et al. (2013). Finally, the fraction of unstable landslides to the number of generated slides per raster cells is calculated and used as an index for landslide onset susceptibility. Inputs for the model are a Digital Terrain Model, a topographical wetness index and a file containing positions and sizes of trees.
Validation of SfM has been done by calculating the AUC (Metz, 1978) for three test areas with a reliable landslide inventory in Switzerland. These test areas are in mountainous areas ranging 0.5 – 7.5 km2 with varying mean slope gradients (18 - 28°). The density of inventoried historical landslides varied from 0.4 – 59 slides/km2. This resulted in AUC values between 0.64 and 0.86. Our study showed that the approach used in SfM can reproduce shallow landslide onset susceptibility on a regional scale observed in reality.
SfM was developed to quantify the stabilizing effect of vegetation at regional scale and localize potential areas where the protective effect of forests can be improved. A first version of the model will be released in 2020 by the ecorisQ association (www.ecorisq.org).
How to cite: van Zadelhoff, F., Dorren, L., and Schwarz, M.: SlideforMap – a regional scale probabilistic model for shallow landslide onset analysis and protection forest management, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19633, https://doi.org/10.5194/egusphere-egu2020-19633, 2020.
Estimation of landslide susceptibility in mountainous areas is a prerequisite for risk assessment and contingency planning. The susceptibility to landslide is modelled based on thematic layers of information such as geomorphology, hydrology, or geology, where detailed characteristics of the area are depicted. The growing use of machine learning techniques to identify complex relationships among a high number of variables decreased the time required to distinguish areas prone to landslides and increased the reliability of the results. However, numerous countries lack detailed thematic databases to feed in the models. As a consequence, susceptibility assessment often relies heavily on geomorphic parameters derived from Digital Elevation Models. Simple parameters such as slope, aspect and curvature, calculated under a moving window of 3x3-pixels are mostly used. Furthermore, advanced morphometric indices such as topographic position index or surface roughness are increasingly used as additional input parameters. These indices are computed under a bigger window of observation usually defined by the researcher and the goal of the study. While these indices proved to be useful in capturing the overall morphology of an entire slope profile or regional processes, little is known on how the selection of the moving window size is relevant and affects the output landslide susceptibility model.
In order to address this question, we analysed how the predicting capabilities and reliability of landslide susceptibility models were impacted by the morphometric indices and their window of observation. For this purpose, we estimate the landslide susceptibility of an area located in Tajikistan (SW Tien Shan) using a Random Forest algorithm and different input datasets. Predicting factors include commonly used 3x3-pixel morphometrics, environmental, geological and climatic variables as well as advanced morphometric indices to be tested (surface roughness, local relief, topographic position index, elevation relief ratio and surface index). Two approaches were selected to address the moving window size. First, we chose a common window of observation for all the morphometric indices based on the study area valley’s characteristics. Second, we defined an optimal moving window(s) for each morphometric index based on the importance ranking of models that include moving windows from a range of 300 to 15000 m for each index. A total of 20 models were iteratively created, started by including all the moving windows from all the indices. Predicting capabilities were evaluated by the receiver operator curve (ROC) and Precision-Recall (PR). Additionally, a measure of reliability is proposed using the standard deviation of 50 iterations. The selection of different moving windows using the feature importance resulted in better-predicting capabilities models than assigning an optimal for all. On the other hand, using a single different moving window per morphometric index (eg. most important ranked by random forest) decreases the evaluating metrics (a drop of PR from 0.88 to 0.85). Landslide susceptibility models can thus be improved by selecting a variety of meaningful (physically and methodological) windows of observation for each morphometric index. A 3x3-pixel moving window is not recommended because it is too small to capture the morphometric signature of landslides.
How to cite: Barbosa, N., Andreani, L., and Gloaguen, R.: Improving landslide susceptibility models using morphometric indices: Influence of the observation window in the reliability of the results. , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14571, https://doi.org/10.5194/egusphere-egu2020-14571, 2020.
Until now, slope stability models include the effects of the vegetation by adding a fixed value of apparent root cohesion as an estimate of root strength. However, some studies have demonstrated that root reinforcement depends on poorly constrained factors such as the heterogeneous distribution of roots in the soil and their tensional and compressional strength behavior.
SOSlope (Self-Organized Slope) is a hydro-mechanical model that computes the factor of safety on a hillslope discretized into a two-dimensional array of blocks connected by bonds to simulate the interactions of root-soil systems (Cohen and Schwarz, 2017). SOSlope estimates slope stability considering the presence of vegetation as a function of parameters such as species, tree density and diameter at breast height. In particular, bonds between adjacent blocks represent mechanical forces acting across the blocks due to roots and soil, in tension or compression, depending on the relative position of blocks. It is a strain-step discrete element model that reproduces the self-organized redistribution of forces on a slope during a rainfall-triggered shallow landslide. The innovative aspect of this model is a complete evaluation of the effects of roots on slope stability calculated using the Root Bundle Model with Weibull survival function (RBMw, Schwarz et al, 2013).
In this case study, SOSlope was used to reconstruct a critical shallow landslide triggering and to observe how the factor of safety changes depending on the presence, or not, of vegetation. The study area is located in the north-eastern part of the Oltrepò Pavese (Pavia, Italy), and is characterized by a high density of past landslides as reported in the database of Italian landslide inventories (IFFI). In the past, the common land use was vineyards, abandoned in the 1980s. Presently, the vegetation consists of grasses and shrubs moving to a thinned forest of young Robinia pseudoacacia L.
On 27 and 28 April 2009 a shallow landslide triggered after an intense and prolonged rainfall event (160 mm accumulated in 62 h with a maximum intensity of 22.6 mm/h). A large number of shallow landslides occurred in the surrounding area with about 29 landslides per km2 (1600 landslides in 240 km2). Five years later, on 28 February - 2 March 2014, 15 meters from a monitoring station and close to the previously affected area, another superficial landslide was triggered after 30 days of rain with a total precipitation of 105.5 mm (68.9 mm in 42 h recorded by the rain gauge of the monitoring station). In addition to the significance of this large landslide, this case study was scientifically important because it wasthe first documented case of a natural shallow landslide induced by rainfall since the 1950s (Bordoni et al, 2015).
The results of SOSlope simulations show good agreement with the real event of 28 February - 2 March 2014, and emphasize the important role of tree roots in the variation of the factor of safety. In this specific case, adding trees results in a reduction of about 39% of the dimensions of the unstable area.
How to cite: Schwarz, M., Murgia, I., Giadrossich, F., Bordoni, M., Meisina, C., Bischetti, G. B., Capra, G. F., and Cohen, D.: Factor of safety analysis with and without vegetation using the SOSlope model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21264, https://doi.org/10.5194/egusphere-egu2020-21264, 2020.
Landslide detection is an essential component of landslide risk assessment and hazard mitigation. It can be used to produce landslide inventories which are considered as one of the fundamental auxiliary data for regional landslide susceptibility analysis. In order to achieve high landslide interpretation accuracy, visual interpretation is frequently used, but suffers in time efficiency and labour demand. Hence, an automatic landslide detection method utilizing deep learning techniques is implemented in this work to conduct high-accuracy and fast landslide interpretation. As the ground characteristics and terrain features can precisely capture the three-dimensional space form of landslides, high-resolution digital terrain model (DTM) is taken as the data source for landslide detection. A case study in Hong Kong, China is conducted to validate the applicability of deep learning techniques in landslide detection. The case study takes multiple data layers derived from the DTM (e.g., elevation, slope gradient, aspect, etc.) and a local landslide inventory named enhanced natural terrain landslide inventory (ENTLI) as its data sources, and integrates them into a database for learning. Then, a deep learning technique (e.g., convolutional neural network) is used to train models on the database and perform landslide detection. Results of the case study show great performance and capacity of the applied deep learning techniques, which provides valuable references for advancing landslide detection.
How to cite: Wang, H. and Zhang, L.: DTM-based landslide detection using deep learning: A case study in Hong Kong , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4090, https://doi.org/10.5194/egusphere-egu2020-4090, 2020.
Keywords: Landslides, FraneItalia, cluster analysis, spatio-temporal point process
In Italy landslides pose a significant and widespread risk, resulting in a large number of casualties and huge economic losses. Landslide inventories are critical to support investigations of where and when landslides have happened and may occur in the future, i.e. to establish reliable correlations between triggering factors and landslide occurrences. To deal with this issue, statistical methods originally developed for spatio-temporal stochastic point processes can be useful for identifying correlations between events in space and time and detecting a significant excess of cases within large landslide datasets.
In the present study, the authors propose an approach to analyze and visualize spatio-temporal clusters of landslides occurred in Italy in the period 2010-2017, considering the weather warning zones as territorial units. Besides, a regional analysis was conducted in Campania region considering the municipalities as territorial units. Data on landslide occurrences derived from the FraneItalia catalog, an inventory retrieved from online Italian news. The database contains 8931 landslides, grouped in 4231 single events and 938 areal events (records referring to multiple landslides triggered by the same cause in the same geographic area). Analyses were performed both annually, considering each year individually, and globally, considering the entire frame period. We applied the spatio-temporal scan statistics permutation model (STPSS, integrated in SaTScanTM software), which allowed detecting clusters’ location and estimating their statistical significance. STPSS is based on cylindrical moving windows which scan the area across the space and in time counting the number of observed and expected occurrences and computing the likelihood ratio. The statistical inference (p-value) is evaluated by Monte Carlo sampling and finally the most likely clusters in the real and randomly generated datasets are compared.
Although more detailed analyses are required for the determination of cause-effect relationships among landslides and other variables, some relations with the local topographic and meteorological conditions can already be argued. At national scale, spatio-temporal clusters of landslides are mainly recurrent in two zones: the area enclosing Liguria Region – Northern Tuscany at north-west and the area between Abruzzo and Molise regions at centre-east. During the year, landslide clusters are particularly abundant between October and March. as most of the events in the FraneItalia catalog are rainfall-induced, strongly influenced by seasonal rainfall patterns. Concerning the regional analysis, most of the clusters are located in the Lattari mountains, the Pizzo d’Alvano massif and the Picentini mountains, areas highly susceptible to landslide occurrence due to geomorphological factors.
In conclusion, the application of spatio-temporal cluster analysis at various scale allowed the identification of frame periods with greater landslide activity. The question of whether this increase in activity depends climate conditions or topographic factors is still open and request further investigations.
Calvello, M., Pecoraro, G. FraneItalia: a catalog of recent Italian landslides. Geoenvironmental Disasters. 5: 13 (2018)
Tonini, M. & Cama, M. Spatio-temporal pattern distribution of landslides causing damage in Switzerland. Landslides 16 (2019)
How to cite: Tonini, M., Romailler, K., Pecoraro, G., and Calvello, M.: Spatio-temporal cluster analyses of landslides in Italy at national and regional scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7393, https://doi.org/10.5194/egusphere-egu2020-7393, 2020.
In 1998, intense rainfall events hit the Pohang state, south west of Korea, which results in highest number of landslides registered in this area (generally the area has a relatively short history of landslide inventorying). The current inventory was digitized using Aerial photographs (lack of photogeological stereoscopic analysis of the aerial images) and coupled with basic field verification (due to limit funding available). Leaving the applied susceptibility maps models performed, using this inventory, with high degree of uncertainty. Currently a research initiative carried to audit the landslide inventory using freely available aerial photographs and the time tuning function in Google earth for aerial archives. We notice some slopes area covered with deformed forest types that is similar in texture to that seen in digitized locations of landslides inventory. Due to long retune period of similar rainfall event, and with an assumption that the available landslides inventory might not complete. A certain hypothesis of additional investigation including field work to audit the landslides incidents is highly needed. In the current research, we assumed that, some dormant slopes caused by the 1998 event can be reactivated with the current extreme (uncontrolled) uses of slopes by human activities (constructions of real estate’s projects). To that end, a methodology of three main stages were proposed.
Stage one; Dormant susceptibility map (DSM) coupled with landslide susceptibility map will be produced. Machine learning supervised classification of eXtreme Gradient Boosting algorithms and Ensemble Random Forest, that run on tree-based classification assumption considering only active and dormant landslides as well as stable ground. Stage two; field work needs to be designed by geological and geotechnical experts to collect the doubtful locations by guidance of DSM and consider the new locations as dormant inventory. However, the areas of low dormant susceptibility (or mutual zones with Landslide susceptibility) will be recommended for advanced filed work and soil sampling test to complete the landslides identification of such highly urbanized area. Stage three; knowing the contour depths of diluvial and alluvial deposits can be useful for extracting areas that are more prone to landslides. Especially in the case of a rigid bedrock beneath the diluvial crust. Therefore, reconstructing the Quaternary formation thickness using boreholes repository and then represent the entire study area using CoKriging surface interpolation technique with elevation model. The current research results will provide us a better understanding of landcover stability conditions and their spatial prediction features.
How to cite: Althuwaynee, O. F., Hwang, I.-T., Park, H., Kim, S.-W., and Aydda, A.: Dormant and Active Landslides Classification Using Machine Learning Algorithms Coupled With Geological Field Inspection: Pohang Case Study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6749, https://doi.org/10.5194/egusphere-egu2020-6749, 2020.
Studies of landslide evolution that improve the knowledge of ground movements are essential to understand the mechanism of deformation for landslide-prone territories to mitigate the associated risk. The large Qingpo landslide, with a volume of about 200,0000 m3, is located in a mega ancient landslide (with a width of 1300 m and a height difference about 400 meters), and a pylon is just located on the boundary of Qingpo landslide. How to accurately judge the historical evolution process, current evolution stage and the future evolution trend of the large landslides is very important for landslide and pylon monitoring and early warning. In this study, on the basis of a detailed on-site investigation, a total of 114 Sentinel-1A Images over five years with Level-1 Single Look Complex (SLC) mode and Interferometric Wide (IW) acquisition mode were downloaded from Copernicus Open Access Hub and were preprocessed by time series InSAR model, which allow us to produce deformation time series and mean deformation velocity maps. An automatic monitoring and warning scheme was designed, 10 sets of ground-based sensors, containing self-adapting crack meter, rain gauge, strain gauge and dip meter were installed, followed by real-time monitoring within one month. Ultimately, the temporal and spatial evolution characteristics of the landslide were comprehensively analyzed through on-site deformation investigation, long-term deformation monitoring by InSAR and ground-based real-time monitoring. The applicability of long-term remote sensing monitoring and real-time monitoring methods and how to use them together have also been verified. This study may can also provide a typical case for the comprehensive use of multi-source data.
How to cite: Zhao, W., Xie, M., and Ju, N.: Landslide mapping, monitoring and early warning by using optical remote sensing, InSAR and ground-based sensors: case study of the Qingpo landslide (Wenchuan, China), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3770, https://doi.org/10.5194/egusphere-egu2020-3770, 2020.
In the Three Gorges Reservoir area of China, landslides have caused considerable losses of lives, environmental and social economy during the last decade. Hence, landslide susceptibility mapping is an urgent task that could help local decision makers in disaster risk assessment and management. This study aims at generating a regional landslide susceptibility map for the Wanzhou District in the Three Gorges Reservoir (China), based on random forest (RT) and cluster algorithms. Specifically, our objectives mainly include: (i) comparing the performances among different machine learning approaches, and (ii) validating the accuracy of a novel susceptibility reclassification method which used cluster algorithm. First, nine GIS-based thematic maps presenting landslide causal factors were prepared, including elevation, slope angle, aspect, lithology, land use, topographic wetness index (TWI), distance to rivers, distance to roads, and distance to geological structures. Total 441 landslides in a landslide inventory map were divided into two subsets: 75% landslides were used as training data, and 25% landslides were validation data. To establish the hybrid intelligent method, random forest was employed to calculate the landslide occurrence probability at every raster cell whereas the cluster algorithm was used to perform landslide susceptibility zonation. The analysis results of receiver operating characteristic (ROC) curve pointed out the prediction performance of random forest was 92.8%, better than that obtained from popular artificial neural network (ANN) (81.9%) and support vector machine (84.7%) models. Meanwhile, compared with traditional GIS-based reclassification methods, in the susceptibility zonation map obtained from cluster algorithm, more historical landslides distributed in the high susceptibility zones. Hence, the proposed approach is a promising tool for spatial prediction of landslides at the study area.
How to cite: Guo, Z., Yin, K., Chen, L., and Zhou, C.: Spatial prediction of landslides for the Wanzhou District (China) applying a hybrid intelligent method based on random forest and cluster algorithms, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-522, https://doi.org/10.5194/egusphere-egu2020-522, 2020.
Monitoring is essential to the prevention and control of geological hazards, yet conventional monitoring is often conducted for local geological hazards, and the relation between monitored results and geological hazards remains poorly understood. In this study, regional load deformation field model was constructed based on data from Continuously Operating Reference Stations (CORS). The relation between load-induced changes and geological hazards, as the Regular Characteristics (RCS), are obtained by comparing the geological hazards with the impact of the total load change in the whole region. Geological hazards are more prone to occurring when there are one or more RCS, especially abnormal dynamic environment appear at the same time, such as solid high tide, heavy rainfall, and so on. The RCS included the ground geodesy height change rate increasing, the ground gravity change rate decreasing, the ground vertical deviation diverging, the ground geodesy height gradient growing larger, and the ground gravity gradient growing larger. It was found that the comprehensive observations of CORS and gravity stations can effectively monitor the RCS of the load-induced changes. The results of this study provide more insights associated with the geological hazards monitoring and analysis methods as well as effective support for geological hazard forecasting.
How to cite: Wang, W., Zhang, C., Hu, M., and Yang, Q.: Monitoring and analysis of geological hazards based on loading impact change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2256, https://doi.org/10.5194/egusphere-egu2020-2256, 2020.
Geological maps convey different and multifaceted information including lithology, age, tectonism and so on. This complex information is not fully exploited in landslide susceptibility (LS) studies, as a single parameter is usually derived from the geological map of the study area (e.g. the area is divided into lithological or lithostratigraphic or geological units). The aim of this work is testing different approaches to extract significant information from geological maps, creating different parameterizations, and analyzing the sensitivity of a LS model to these variations.
Our test site is a 3100 km2 wide area in Tuscany (Italy) characterized by a very complex geological setting. A 1:10000 scale geological map subdivides the area into 194 different lithostratigraphic units. This map was reclassified according to different criteria, creating 6 different parameters derived from the same geological map: lithology (6 lithological classes), age of deposition (the area was subdivided into 6 chronological units), paleogeography (6 units were differentiated on the basis of their environment of formation), genesis of the bedrock (5 classes accounted for the mechanism of formation of the outcropping rock/terrain), broad tectonic domain (the mapped elements were grouped into 5 broad structural units accounting for their tectonic history), detailed tectonic domain (same as before but with a more detailed subdivision into 10 classes).
Some of these parameters have already been used in LS studies, others have been used here for the first time; however, all of them have some connections with landslide predisposition. These parameters were used (one by one and altogether) to run seven times a landslide susceptibility model based on the widely used random forest machine learning algorithm. The model configurations and resulting maps were evaluated in terms of AUC(Area Under Curve) and OOBE(out of bag error): while the former expresses the forecasting effectiveness of each configuration, the latter expresses, among a single configuration, the importance of each input parameter.
We discovered that the results are very sensitive to the approach used to consider geology in the susceptibility assessment, with AUC values ranging from 63.5% (using chronological units) to 70.0% (using genetic units) and 75.2% (using all the geology-derived parameters simultaneously). These results are in line with OOBE statistics, which showed a similar relative importance of the geologically-driven parameters.
These outcomes can to assist future landslide susceptibility studies for different reasons:
(i)at least in our study area, lithology, which is commonly used in LS, did not provide the best results;
(ii)as geological maps provide multifaceted information, a single classification approach cannot fully grasp this complexity; therefore, the best results can be obtained using different geology-based parameters simultaneously, because each of them can account for specific features connected to landslide predisposition (to our knowledge, a similar approach has never been attempted before in LS literature).
(iii)When using thematic maps to feed LS models, it is important to fully understand the nature and the meaning of the information provided by the geology-related maps: results are very sensitive to this kind of information and the interpretation of the results should take it into account.
How to cite: Luti, T., Segoni, S., Tamburini, B., Pappafico, G., and Catani, F.: An attempt to increase the geological information in landslide susceptibility mapping and sensitivity to different geological parameters, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8320, https://doi.org/10.5194/egusphere-egu2020-8320, 2020.
Where landslide hazard mitigation is impossible, Early Warning Systems are a valuable alternative to reduce landslide risk. To this extent nowcasting and Early Warning Systems for landslide hazard have been implemented mostly at local scale. Unfortunately, such systems are often difficult to implement at regional scale or in remote areas due to dependency on local sensors. However, in recent years various studies have demonstrated the effective application of Machine Learning for deformation forecasting of slow-moving, deep-seated landslides. Machine Learning, combined with satellite Remote Sensing products offers new opportunities for both local and regional monitoring of deep-seated landslides and associated processes.
Working from the key variables of the landslide process we selected the available satellite Remote Sensing products, the necessary assumptions for a satellite only application and evaluated the potential benefit of local information. In the absence of continuous, satellite deformation measurements, nowcasting of the system state will provide a short term deformation prediction. We demonstrate the opportunities of Machine Learning on multi-sensor monitored Austrian landslide and anticipate on the integration in an Early Warning System. Furthermore, we highlight the risks and opportunities arising from the limited physics constraints in Machine Learning.
How to cite: van Natijne, A., Lindenbergh, R., and Bogaard, T.: Machine Learning: potential for local and regional deep-seated landslide nowcasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19515, https://doi.org/10.5194/egusphere-egu2020-19515, 2020.
Real Time Disaster Information Transfer and Emergency Operation Systems Established for Remote Mountainous Communities in Southwestern Taiwan
Kuang-Jung TSAI 1, Tsai-Tsung Tsai 2,Yie-Ruey CHEN 3, Ming-Hsi Lee4,Jia-Xuan Li 5
1Department of Land Management and Development, Chang Jung Christian University, Tainan , Taiwan
2 Department of DPRC, National Chengkuang University ,Tainan,Taiwan
3 Department of Land Management and Development, Chang Jung Christian University, Tainan , Taiwan
4 Department of Soil and Water Conservation, National Pingtung University of Science and Technology, Pingtung ,Taiwan Corresponding
5 Department of Land Management and Development, Chang Jung Christian University, Tainan , Taiwan
According to the report (1990) proposed by Intergovernmental Panel on Climate Change (IPCC) indicated that Extreme Climate Change has a detrimental effect on the environmental ecology, cultural system, human society and national economic development all over the world since 1950. Taiwan is located at Pacific-rim area and belongs to the sub-tropic to tropic weather characteristics. Recently, extreme heavy rainfall resulted from climate change to induce serious sediment related disasters, such as large-scale landslide and debris flow, are critical in Taiwan. There are almost 24% of total remoted mountainous communities were located within Chiayi, Tainan, Kaohsiung and Pingtung counties/cities with the amount of 50 remote communities where is almost 24% of high potential risk area occupied by remote mountainous communities in Taiwan. Most of these communities were frequently attacked by typhoons likes Morakot (2009), which brought the accumulated rainfall more than 2450 mm within continuous 72 hours. This extreme rainfall has triggered off a crisis of compound disasters to destroy the environment systems, agricultural productions, human lifes, properties and public facilities. Within there mountainous communities more than 608 landslides with total area of 968.2ha were induced by these disasters which were based on the field investigations. In order to decrease the risk of sediment related disasters attack these remoted mountainous areas, the adaption strategy of environmental conservation, new technology of filed investigations, hazard mitigation system, environmental vulnerability analysis and disaster risk assessment should be executed as soon as possible. According to the historical record (2007-2018) from soil & water conservation Bureau indicated that most of the remote mountainous communities located at southwestern Taiwan attacked by these compound disasters are significant. Meanwhile, study on the mechanism and behavior of compounded disasters induced by extremely heavy rainfall become an important issue which was seriously concerned by Taiwan government. An establishment of real time disaster information transfer and emergency operation systems would be positively concerned and recognized as an important issue by this research. Hopefully, all results can be expected to promote and enhance the disaster prevention capability for the remoted mountainous communities in southern Taiwan.
Keywords：climate change, extreme rainfall, sediment related disasters, adaption strategy
How to cite: Tsai, K.-J., Chen, Y.-R., Tsai, T. T., Lee, M.-H., and Li, J.-X.: Real Time Disaster Information Transfer and Emergency Operation Systems Established for Remote Mountainous Communities in Southwestern Taiwan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1730, https://doi.org/10.5194/egusphere-egu2020-1730, 2020.
A wireless tracer real-time monitoring system was developed and verified to be suitable for the real-time remote dynamic monitoring of typhoon- and flood-related scour at riverbeds and human-made structures (such as bridge abutments, spur dikes, and embedments). This study focused on the use of a wireless tracer to aid the real-time dynamic monitoring of natural disasters, including slope landslides, thus devising a real-time warning system for sediment disaster prevention and response. We selected Dajin Bridge, which is situated at Taiwan’s Zhoukou River, as the research site for deploying the monitoring system. Monitoring stations for detecting changes in the river’s course were established at both a downstream meander of the Dajin Bridge and a nearby revetment. Specifically, scour monitoring columns were separately buried at these two locations. Each column was equipped with five wireless tracers, and 16 coding sand jars were used to facilitate vertical installation of wireless tracers. Real-time monitoring stations for tracking slope changes were constructed using two methods. In both methods, an upright column was used to install the tracers, and a shielding net cover was additionally used in the second method to expand its monitoring range. After several heavy rain events, no slides or landslides were detected by the landslide stations; an on-site investigation corroborated this observation. As for the detection of the change in the river’s course, three wireless tracers were flushed away. Nonetheless, because the scour depth posed no immediate threat to river bank safety, additional safety measures were not required. The remaining wireless tracers were also adequate for the safety monitoring of river banks, bridges, and other structures within the research area. The aforementioned results demonstrate the effectiveness of the devised remote real-time monitoring system for detecting environmental changes. The system can thus provide real-time remote safety information on changes in slope and a river’s course for residents in mountainous areas.
How to cite: Yang, H. C., Su, C. C., and Chen, Y. C.: Research of Landslide Environment Monitoring Technology for Villages at Fluvial Terraces of the Gaoping River Basin of Taiwan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1750, https://doi.org/10.5194/egusphere-egu2020-1750, 2020.
Soil depth plays critical role in prediction studies reflecting hydrologic mechanism such as shallow landslide and debris flow although there are many parameters. Thus, many researchers are studying the estimation of soil depth distribution using various methods such as a kriging and artificial neural networks (ANNs) since it is not easy to get a detailed soil depth distribution in field. The aims of this study are 1) to estimate detailed spatial distribution of soil depth (various methods such as ANNs, Kriging, s- and z-model, and c-model) and, 2) to apply them for assessment of shallow landslide instability and debris flow. To do this, soil depth of 760 points using knocking pole test method and elevation datasets using GPS-RTK were collected at Mt Jiri, South Korea. To analysis the accuracy of each estimated soil depth distribution, the lowest root mean square error (RMSE), mean absolute error (MAE) and the highest values of the coefficient of determination (R2) were applied and, ANNs method showed reasonable result better than did others. In the effect of shallow landslide instability and debris flow assessment with the each soil depth distribution results, soil depth distribution using an ANNs method also showed high simulated model performance by modified success ratio (MSR). These results indicated that ANNs can be one of the methods to estimate the soil depth distribution for improvement of accuracy of shallow landslide instability mapping and debris flow assessment.
How to cite: Kim, M., Kim, J., Oh, H.-J., and Kim, J.: Estimation of spatial soil depth and its application for shallow landslides and debris flow assessment: case study at Mt. Jiri, S. Korea , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4354, https://doi.org/10.5194/egusphere-egu2020-4354, 2020.
There are almost 24% of total remoted mountainous communities located in Chiayi, Tainan, Kaohsiung and Pingtung counties/cities of southern Taiwan. During recent years, the extreme rainfall events brought huge amounts of rainfall and triggered severe environmental disasters such as landslides, debris flows, flooding and sediment disasters in southern Taiwan. The maximum rainfall of typhoon Morakot in August 2009 was approaching 3,000 mm during 4 days in mountainous area of Chiayi city. There are 359 landslides occurred nearby the remoted mountainous communities in the study area during the typhoon event. The landslide area was over 900 ha.
The potential assessments of environmental disasters for 38 remoted mountainous communities nearby the riverbank were analyzed. The landslide areas nearby the 38 communities in last 10 years (2007-2016) were identified. The numerical models (HEC-RAS, CCHE-2D and FLO-2D) were used to simulate the flooding level, scouring and deposition of river bed and the influence area of debris-flow occurrence under different return periods (25, 50 and 100 years). The results show that there are 5, 4 and 14 high potential communities of landslide, flooding and debris flow disasters, respectively. The results proposed by this study can provide the disaster risk management of administrative decisions to lessen the impacts of environmental disasters for remoted mountainous communities nearby the riverbank in southern Taiwan under climate change.
How to cite: Lee, M.-H., Chiang, K.-F., and Tsai, K.-J.: The Potential Assessment of Environmental Disasters for Remoted Mountainous Communities nearby the Riverbank in Southern Taiwan under Climate Change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4935, https://doi.org/10.5194/egusphere-egu2020-4935, 2020.
Taiwan is located in the Pacific volcanic seismic zone and frequently suffers from landslides and debris flow caused by typhoons. On average, there are four typhoons which may cause tremendous disasters such as massive landslides in Taiwan mainly from July to September every year. The aim of this study is to evaluate the development of large-scale landslide area under various cumulative rainfalls. The study area of this study is Liouquei, Kaohsiung in southern Taiwan. Firstly, the relationship of rainfall and groundwater level were built. The equation of change of groundwater level and rainfall is h=38.2R, R2=0.83. Then, 10m digital elevation model (10m-dem) was used to evaluate elevation, slope, aspect and etc. Finally, geology and 10m-dem were used to build Scoops3D model of Liouquei area.
Scoops3D, which is released by the United States geological survey (USGS), evaluates slope stability throughout a digital landscape represented by a digital elevation model (DEM). The program uses a three-dimensional (3D) method of columns limit-equilibrium analysis to assess the stability of many potential landslides (typically millions) within a user-defined size range. We simulated the potential landslide area under a cumulative rainfall in 24 hours from 800mm~1600mm. The results show that landslide area contributed 65%~76% of the entire potential large-scale landslide area.
How to cite: Chiang, J.-L. and Kuo, C.-M.: Evaluation of rainfall-induced large-scale landslide potential using Scoops3D, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6397, https://doi.org/10.5194/egusphere-egu2020-6397, 2020.
The landslide prediction analyzes the various landslides related factors and their correlations physically or mathematically. Many researches used statistical methods to consider the relationships between landslide occurrence location and related factors such as topography, and geology. Existing statistical methods produces errors due to the variety and uncertainty of the input data. Recently, machine learning techniques using artificial intelligence and big data is proposed to improve the accuracy and efficiency of landslide prediction and management. Landslide is caused by the nonlinear relationships of potential related factors and the effects of triggered factors such as meteorological or man-made damage. This study proposes a better performance of the prediction results by using machine learning model that is suitable for considering the nonlinear correlation of related factors.
Generally, landslides occur in very small numbers in widely study areas. In order to construct a predictive model using machine learning, the information about the landslide occurrence location and the non-landslide occurrence location must be used. However, all the study area data is used, the landslide prediction results are not reliable because they are mainly affected by the information about the non-landslides. Therefore, to minimize over-fitting or under-fitting due to data imbalance, the appropriate sampling rate of landslide and non-landslide data should be considered.
In this study, landslide prediction was performed using a machine learning models Random Forest (RF) and Multi-Layer Perceptron (MLP). RF builds multiple decision trees and merges them together to get a more accurate and stable prediction. RF model can be obtained variable importance which variables have the most predictive power. This value is used to identify the characteristics of related factors and to select the related factors to be used for landslide predicts. MLP is feedforward neural network with one or more layers between input and output layer. This model consists of at least three layers of nodes and each node is a neuron that uses a nonlinear activation function. So, it can distinguish data that is not linearly separable. Use this model to analyze nonlinear correlation landslide data, taking into account the importance of the factors and the sampling rate, and to verify the results.
This study aims to compare the results (susceptibility index) according to the change of sampling data rate using Random Forest and Multi-Layer Perceptron and to verify the model performance.
Acknowledgement: This research was supported by the MSIT (Ministry of Science, ICT), Korea, under the High-Potential Individuals Global Training Program (2019-0-01561) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation).
How to cite: Lee, J., Park, H., Lee, D., and Lim, S.: Landslide Susceptibility Assessment Considering Imbalanced Data: Comparison of Random Forest and Multi-Layer Perceptron, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12265, https://doi.org/10.5194/egusphere-egu2020-12265, 2020.
Shallow landslide susceptibility modelling at regional scale may be performed using both a physically based and statistical approach. For the same area, these two approaches can have inconsistent results, mainly because the two methods are conceptually different. Physically based models are based on the infinite slope model and consists on the computation cell by cell of a safety factor comparing between driving and resisting forces. The assumption that landslides occur in slopes that are characterized by predisposing factors similar to those in which landslides have occurred in the past, is the concept behind the statistical models. The aim of this work is to compare the two approach and investigate the differences between the two models. The study area is located in northern Tuscany, central Italy, in which an extensive field survey highlighted that about 60% of landslides involve bedrock. For this reason, we developed a physically based susceptibility analysis taking into account both the surficial layer (slope deposit, SD) and the underlying layer (BR), characterized by weathered and fractured bedrock. This model is compared to the statistically based one, which take into account topographic and geologic predisposing factor as well as bedrock geo-mechanical properties, such Geological Strength Index (GSI), Schmidt hammer rebound values (Rv) and Joint density (Jv). The accuracy of the models is evaluated using a multi-temporal landslide inventory, in which involving bedrock landslides are distinct from slope deposits landslides. Within this general framework results are discussed regarding the model’s predictive capacity and spatial agreement.
How to cite: D'Addario, E., Disperati, L., Zêzere, J. L., De Melo, R., and Oliveira, S.: Shallow landslides involving weathered and fractured bedrock: a comparative susceptibility analysis between deterministic and statistical models , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18614, https://doi.org/10.5194/egusphere-egu2020-18614, 2020.
Physically based models based on the combination of hydrological and slope stability models are important tools in spatial and temporal prediction of landslides, since they can be used for hazard mapping as an aid for land planning. In many applications, hydrological models are combined with very simple infinite slope stability analysis, given that multi-dimensional analysis is more computationally demanding. Only a few studies have attempted to apply such algorithms to the catchment scale. Thus, there is a need for more studies on this issue, also to understand the real advantages of applying multi-dimensional slope stability analysis in comparison with the one-dimensional.
This study aims to compare the performance of two different forecasting models, namely the infinite slope and the three-dimensional stability analysis by SCOOPS3D (Software to analyze three-dimensional slope stability throughout a digital landscape), a very efficient model proposed by USGS to be applied to the catchment scale, which has seldom been applied so far in the literature. In particular, TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-stability Model) is used for hydrological analysis. Then the resulting pressure head field is used first as input to the infinite slope stability model embedded into TRIGRS program itself and then as input to SCOOPS3D. To calibrate the terrain stability-related parameters of either piece of software, a multi-objective optimization is proposed in this work to maximize the model predictability performance, in an attempt to optimize ROC performance statistics, i.e. to maximize the true positive rate while simultaneously minimizing the false positive rate.
The approach was applied to a real case study, a catchment in the Oltrepò Pavese (northern Italy), in which the areas of triggered landslides were accurately monitored during an extreme rainfall on 27-28 April, 2009, featuring 160 mm in 48 h. Compared to other works in the scientific literature, in which only a generic point of location of landslides was known, the present work benefits from the availability of a detailed landslide inventory containing observed landslide shapes.
The results point out the significantly better performance of SCOOPS3D, in comparison with the infinite slope stability. Though SCOOPS3D seems to overestimate landslide prone areas, the 3D method is more realistic than the 1D method as far as the slip surface definition is concerned. Therefore, the proposed methodology, lying in the use of SCOOPS 3D with optimized parameters, can be a helpful tool for providing multiple landslide hazard maps for planning.
How to cite: Palazzolo, N., Peres, D. J., Bordoni, M., Meisina, C., Creaco, E., and Cancelliere, A.: Comparison of the performance of spatial landslide prediction with TRIGRS1D and SCOOPS3D models and parameter optimization: application to the Oltrepò Pavese, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19584, https://doi.org/10.5194/egusphere-egu2020-19584, 2020.
Seasonal snow cover occupies around 33 % of the earth’s surface and draws the underlying landscape to serious natural hazards under climate change. The frequency of shallow landslides in seasonal cold regions is increasing, i.e., in the French Alps, Umbria in Italy, and Hokkaido in Japan. Further, tectonically active seasonally cold areas are more susceptible to spring landslides if an earthquake occurs during pre-winter. Hazard assessment and risk mitigation of snowmelt-induced landslides in such a scenario requires physically-based prediction models. However, studies focusing on the impacts of future snowmelt on shallow landslides are scarce. To comprehend these, the complex interactions between the atmosphere, hydrological, and geomechanical systems within a catchment under future climate need detailed studies. Present methods for snowmelt induced soil slope instability analysis are single-slope based and applied for individual cases. The challenge remain is to simulate the interactions between the atmosphere, hydrological, and geomechanical systems by coupling micro and macro-scale processes within a catchment for regional-scale future forecasts. In this perspective, we developed a novel spatially distributed, a physically-based numerical approach to compute slope stability within a basin, explicitly considering the atmosphere-ground, hydrology, and mechanical interactions on a day to day time step. Using this model, we envisaged future snowmelt-induced landslides under increased and decreased melt rates and post-earthquake settings. We obtained the probability density curves of these future landslides and found that under slower snowmelt rates, the occurrence probability of individual landslides remains the same, whereas, under rapid and increased snowmelt rates, the size-distribution of the landslides increase one magnitude and doubles if rapid snowmelt follows an earthquake.
How to cite: Siva Subramanian, S., Fan, X., Yunus, Ali. P., van Asch, T., Xu, Q., and Huang, R.: Envisaging post-earthquake snowmelt-induced shallow landslides under climate change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12199, https://doi.org/10.5194/egusphere-egu2020-12199, 2020.
Deep-seated landslide is one of most catastrophic and disastrous geohazards. Probing the spatial extent and basal sliding interface of the deep-seated landslide is not only particularly critical for understanding landslide size (i.e., volume and collapsed area), but also crucial for landslide hazard assessment. The conventional investigations such as the borehole drilling and seismic profiles are usually challenging for investigating landslide body comprehensively in space due to the expensive cost and the limitations of geophysical exploration. Recent studies of ambient seismic noise monitoring have provided an additional tool to monitor the subsurface medium in a non-invasive and relatively inexpensive way, which advances the investigating landslide geological structure. Here, we applied the ambient seismic noise monitoring technique to deep-seated landslide at Fanfan, Ilan area in northeastern Taiwan. The multiple geophysical, geotechnical and geodetic approaches including active multi-channel analysis of surface wave (MASW), real-time kinematic (RTK) measurement, campaign GPS, borehole time-domain reflectometer (TDR) and groundwater level (GWL) gauge are adopted during our monitoring period. A series of relation analysis found that the variations of frequency-dependent seismic velocity changes (dv/v), TDR sliding behavior, time series of groundwater level associated to two heavy rainfall episodes concurrently. With the available shear-wave velocity model (VS) derived from MASW, the depth range sensitive to different frequency band for surface wave can be certainly determined. Clear 3-5 Hz dv/v measurement at seismic station of V01 collocated with GWL gauge can be found with the largest reduction of ~ 1%, coinciding with 1 m GWL increasing. Models with different thickness layer (H), basal depth (d), Vs perturbation (dVs) were exercised, and a good fit between predicted spectral dv/v and the frequency-dependent dv/v measurements at seismic station V02 with H = 0.5 m, d = 21 m and dVs = 0.5. TDR measurement showed the obvious sliding signals is consistent with the shear zones identified by borehole log with the depth ranging from 48 to 50 m. These results demonstrate that multidisciplinary perspectives are needed to increase a better understanding of landslide structure. Consequently, a model linking variations of dv/v and TDR measurements is proposed to better understand sliding characteristics, which could potentially toward failure prediction of deep-seated landslide.
How to cite: Chao, W.-A., Lin, C.-H., Yang, C.-M., Kang, K.-H., Kuo, Y.-T., Nugi, J., Chung, M.-C., Lin, C.-P., and Tai, T.-L.: Seismologically understanding the basal sliding depth and groundwater level for deep-seated landslide, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8862, https://doi.org/10.5194/egusphere-egu2020-8862, 2020.
In landslide studies, comparing the outcomes obtained by different models is a very robust test for their predictive capability and quantitative indexes are often used to assess which model provides the best predictions. The literature about landslide susceptibility is rich of works where two or more susceptibility models are validated in terms of AUC (area under ROC curve), then the AUC values are compared and the model that provided the highest AUC value is considered the best one.
The main purpose of this work is to expand this classical approach, which is too simplistic as it neglects any geomorphological consideration, and to propose a new approach that shifts the comparison at the pixel scale, linking the local-scale differences encountered with specific features of the study area. The proposed advanced comparison approach can be summarized with the following steps:
- The susceptibility maps obtained by different models are compared on a pixel-by-pixel basis to define pixels affected by underestimation (UE) and overestimation (OE) of susceptibility values.
- If present, systematic spatial patterns of UE and OE are identified.
- The patterns are cross-checked with all the explanatory variables used in the susceptibility assessments.
- The lithological and morphological features of the study area that are typically associated to underestimations and overestimations of susceptibility are identified and quantitatively characterized.
- The quantitative information provided by the previous steps is used to provide a geomorphological interpretation of the differences in the susceptibility values provided by the models, thus adding a more robust element to judge which of them should be used in hazard management, and how.
As a case of study, we used four susceptibility maps already defined with random forest (RF), index of entropy (IOE), frequency ratio (FR), and certainty factor (CF) in Wanzhou County (China). A classical validation procedure showed that RF provided the best outcomes, with a 0.801 AUC. After applying the advanced comparison procedure, we obtained deeper insights on the susceptibility models, explaining e.g. why and where RF performed significantly better than the other models and identifying systematic errors that could be associated to distinctive geomorphological features of the test site. Indeed, we discovered that RF is more able to exploit the very complex parameterization of the problem, with 13 parameters, sometimes interrelated each-other, with a total of 80 classes. Moreover, we found that the other models produced systematic errors in correspondence with some lithological units and in fluvial terraces. The area is characterized by 5 orders of relict fluvial terraces, clearly defined only in some small stretches, and the results obtained showed that landsliding has probably been one of the predominant geomorphological process responsible for their depletion.
How to cite: Segoni, S., Xiao, T., Chen, L., Yin, K., and Casagli, N.: An advanced method to validate and compare susceptibility maps by investigating local-scale differences and highlighting the role of geomorphological features, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8456, https://doi.org/10.5194/egusphere-egu2020-8456, 2020.
This study develops the spatial prediction and hazard assessment models of hillslope type debris flow (HDF) to enhance the prevention and early warning of the HDF disaster to the villages. Induced by serious earthquakes and extreme weather conditions, HDF occurred frequently on the villages-side slopeland in Eastern Taiwan. The small scale HDF are often prone into high damage, because those slopeland is adjacent to the village. Based on this, to develop the spatial prediction and hazard assessment models of HDF is improving the safety of the residents.
This study uses the slope unit concept to establish the proper topographic unit for the spatial analysis. Fisher’s discriminant method is applied to develop the HDF spatial prediction model which consisted in 7 factors achieved from the slope units of metamorphic geology area in Eastern Taiwan. 27 HDF and 27 landslide events were adopted to develop the spatial prediction model, the model as following:
In above equation, y is the discriminant function, X1 is the Average width of watershed, X2 is the Average gradient of the initiation region, X3 is the form factor of the initiation region, X4 is average width of the initiation region, X5 is the Depression ratio of the initiation segment1, X6 is the depression ratio of the transport segment DRT, X7 is the Gradient ratio of the initiation region. If the discriminant function y is greater than 0, a HDF is identified, otherwise a shallow landslide slope is identified. The results showed the overall correct estimation ratio is 88.2% and 85% verification ratio have been achieved in this study.
The prediction model was then applied to 8 villages in study area, and the results show that 15 HDF have been caught in a total of 19 HDF in 8 village. The capture rate is about 79% and the overall capture rate of HDF and landslide unit is also 85%. In overall, the results show a good applicability of the prediction model in the metamorphic rock.
The project further draw up the hazard assessment model and comparing the result to the real HDF events which investigated by the field survey in 8 villages. Results showed that the potential of real HDF events were mostly classified in medium and high potential levels. Among them, there are 15 HDFs classified in medium and high potential in 19 HDFs. Concluding the results of the potential analysis, the result show a good application tendency in this research.
How to cite: Chen, T.-C., Cheng, C.-Y., Su, C., and Yin*, H. Y.: Spatial Prediction and Hazard Assessment Models of Hillslope Debris Flows at Village-Side Hillslope in Eastern Taiwan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12901, https://doi.org/10.5194/egusphere-egu2020-12901, 2020.
This research is concerned with the prediction accuracy and applicability of statistical landslide susceptibility model to the areas with dense landslide distribution caused by extreme rainfall events and how to draw the annual landslide susceptibility maps after the extreme rainfall events. The landslide induced by 2009 Typhoon Morakot, i.e. an extreme rainfall event, in the Chishan river watershed is dense distributed. We compare the annual landslide inventories in the following 5 years after 2009 Typhoon Morakot and finds the similarity of landslide distribution.
The landslide distributions from 2008 to 2014 are concentrated in the midstream and upstream watersheds. The landslide counts and area in 2009 are 3.4 times and 7.4 times larger than those in 2008 due to 2009 Typhoon Morakot. The landslide counts and area in 2014 are only 69.8% and 53.4 % of those in 2009. The landslide area from 2010 to 2014 shows that the landslide area in the following years after 2009 Typhoon Morakot gradually decreases if without any heavy rainfall event with more accumulated rainfall than that during 2009 Typhoon Morakot.
The landslide ratio in the upstream watershed in 2008 is 1.37%, and that from 2009 to 2014 are over 3.51%. The landslide ratio in the upstream watershed in 2014 is 1.17 times larger than that in 2009. On average, the landslide inventory from 2010 to 2014 in the upstream watershed is composed of 60.1 % old landslide originated from 2009 Typhoon Morakot and 39.9 % new landslide.
The landslide ratio in the midstream watershed reaches peak (9.19%) in 2009 and decreases gradually to 2.56 % in 2014. The landslide ratio in 2014 in the midstream watershed is only 27.9% of that in 2009, and that means around 72.1 % of landslide area in 2009 in the midstream watershed has recovered. On average, the landslide inventory from 2010 to 2014 in the midstream watershed is composed of 76.1 % old landslide originated from 2009 Typhoon Morakot and 23.9 % new landslide.
The research uses the landslide area in 2009 and 2014 in the same subareas to calculate the expanding or contracting ratio of landslide area. The contracting ratio of riverbank and non-riverbank landslide area in the midstream watershed are 0.760 and 0.788, while that in the downstream watershed are 0.732 and 0.789. The expanding ratio of riverbank and non-riverbank landslide area in the upstream watershed are 1.04 and 1.02.
The annual landslide susceptibility in each subarea in the Chishan river watershed in a specific year from 2010 to 2014 is the production of landslide susceptibility in 2009 and the contraction or expanding ratio to the Nth power, and the N number is how many years between 2009 and the specific year. We adopt the above-mentioned equation and the landslide susceptibility model based on the landslide inventory after 2009 Typhoon Morakot to draw the annual landslide susceptibility maps in 2010 to 2014. The mean correct ratio value of landslide susceptibility model in 2009 is 70.9%, and that from 2010 to 2014 are 62.5% to 73.8%.
How to cite: Wu, C.: Drawing the landslide susceptibility maps based on long term evolution of extreme rainfall-induced landslide , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8361, https://doi.org/10.5194/egusphere-egu2020-8361, 2020.
Landslide forecasting and early warning at regional scale are difficult task and they are usually accomplished by the mean of statistical approaches aimed to define rainfall thresholds and landslide susceptibility maps.
Landslide susceptibility maps are based on the analysis of predisposing factors to assess the spatial probability of landslide occurrence, while rainfall thresholds are based on the correlation, valid on a wide area, between landslide occurrence and triggering factors, which usually are a couple of rainfall parameters, such as rainfall duration and intensity.
Susceptibility maps are static map that can be used for the spatial prediction of the most landslide prone areas, nut cannot be used to predict the temporal occurrence of a landslide triggering.
Rainfall thresholds can be used for temporal prediction, but with a coarse spatial resolution (usually some hundreds or thousands of km2), and the reference areas could contains both plains and hillslopes, so the alerts could involves both areas, even if landslides are improbable in river plains; this means that rainfall thresholds are not very suitable to identify the most probable triggering sites.
Rainfall thresholds and susceptibility maps can be therefore conveniently combined into dynamic hazard matrixes to obtain spatio-temporal forecasts of landslide hazard.
To combine these inputs, they are combined in a purposely-built hazard matrix, where each parameter is classified into 3 classes: landslide susceptibility map has been classified in S1 (low susceptibility), S2 (medium susceptibility) and S3 (high susceptibility), while rainfall rate has been classified in the classes R1, R2 and R3, by the definition of 2 rainfall thresholds.
The combination of the aforementioned classes allowed to define a matrix with 5 hazard classes, from H0 (null hazard) to H4 (high hazard), which was calibrated so that there was not any landslide in the H0 class and that the 90% of the landslide were in H2-H4 classes.
The result of this procedure is a dynamic hazard map, where the hazard, which is calculated for each pixel, can change over the time, based on rainfall rate variations.
For operational purposes, such a map cannot be used, since the pixel based resolution is too fine to be used during an emergency or to plan any activity in the planification phase, so the results have been aggregated at municipality scale, which is more easily readable for the end-users as local administrators and decision makers.
In this way it is possible to overcome the issues due to the stillness of susceptibility maps and to the coarse spatial resolution of rainfall thresholds, also avoiding results which could be hardly understandable outside of the scientific community.
This procedure was tested in a test site located in Northern Tuscany (Italy) and the work showed the possibility of obtaining results which are balanced between the scientific soundness and the needs of end-users like mayors, local administrators and civil protection personnel.
How to cite: Rosi, A., Segoni, S., Tofani, V., and Catani, F.: Spatio-temporal landslide forecasting based on combination of rainfall thresholds and landslide susceptibility maps: a test in the Northern Apennines (Italy)., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9124, https://doi.org/10.5194/egusphere-egu2020-9124, 2020.
Landslides are common geological hazards that not only affect the normal road traffic but also pose a great threat and damage to human lives and properties. This study aims to conduct such a hazard risk mapping using Random Forest Classification (RFC) approach taking Ruijin County in Jiangxi, China as an example. Multi-source data namely terrain (DEM, slope and aspect), precipitation, the normalized difference vegetation index (NDVI) representing vegetation condition and abundance, strata and their lithology, distance to roads, distance to rivers, distance to faults, thickness of weathering crust, soil type and texture, etc., were employed for this study. The non-numeric data such as geological strata, soil units, faults, were spatialized and assigned values in terms of their susceptibility to landslide. Similarly, linear features such as roads, rivers and faults were buffered with distances of 0-30, 30-60, 60-90 and 90-120 m and each buffer zone was assigned a susceptibility value of landslide, e.g., zones 0-30, 30-60, 60-90 and 90-120 of road buffers were assigned respectively 10, 7, 4, and 1, meaning that the closer to the road, the higher risk of landslide. In total, 16 hazard factor layers were derived and converted into raster. 156 landslide hazards that have truly taken places (points) and been verified in field were used to create a training set (TS, 70% of total landslides) and a validation set (VS, 30%) by buffering-based rasterization procedure. A number of polygons were defined in places where landslide is unlikely to occur, e.g., water bodies, zero-slope plain, and urban areas. These polygons were added to the TS as non-risk area. Then, RFC was conducted to model the probability of landslide risk using these 16 factor layers as predictors and TS for training. The obtained RF model was applied back to the 16 factor layers to predict the probability of landslide risk at each pixel in the whole county. The prediction map was checked against the VS and found that the Overall Accuracy and Kappa Coefficient are respectively 92.18% and 0.8432, and the landslide-prone areas are mainly distributed on two sides of the roads. The results reveal that extremely high-risk zones with a probability of more than 0.9 take up 76.70 km2 in the county, and the distance to roads is the most important factor followed by precipitation among all factors causing landslides as road construction and housing development cut off slopes leading to instability of the weathered crust; and heavy rainfalls trigger the instability. Our study shows that the RFC prediction has high accuracy and in good consistency with field observation.
How to cite: Xiaoting, Z., Wu, W., Lin, Z., Zhang, G., Chen, R., Yong, S., Zhiling, W., Tao, L., Penghui, O., Wenchao, H., Yang, Z., Lifeng, X., Huang, X., Qin, Y., Peng, S., and Chongjian, S.: Landslide hazard risk mapping based on Random Forest Classification in Ruijin, Jiangxi, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8278, https://doi.org/10.5194/egusphere-egu2020-8278, 2020.
Strong earthquakes, especially on mountain slopes, generate unconsolidated deposits of regolith, prone to remobilization by aftershocks and rainstorms. Assessing the hazard they pose and what controls their remobilizations in the years following the mainshock has not yet been attempted, primarily because of the lack of multitemporal landslide inventories. By exploiting a multitemporal inventory (2005–2018) covering the epicentral region of the 2008 Wenchuan earthquake and a set of predictor variables (seismic, topographic, and hydrological), we perform statistical tests to understand the evolution of controlling factors for debris remobilization in time. Our analyses, supported by a random-forest susceptibility assessment model, reveal a prediction capability of seismic-related variables depleting with time, as opposed to hydro-topographic parameters gaining importance and becoming predominant within a decade. Results may have important implications on the way conventional susceptibility/hazard assessment models should be employed in areas where coseismic landslides are the main sediment production mechanism on slopes.
How to cite: Yunus, A. P., Fan, X., Scaringi, G., and Catani, F.: Post-earthquake hazard assessments might become rapidly ineffective under rapidly-evolving landslide controls, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10545, https://doi.org/10.5194/egusphere-egu2020-10545, 2020.
Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties, and the environment. Investigators have for long attempted to estimate landslide hazard to determine where, when, and how destructive landslides are expected to be in an area. This information is useful to design landslide mitigation strategies, and to reduce landslide risk and societal and economic losses. In the geomorphology literature, most attempts at predicting the occurrence of populations of landslides rely on the observation that landslides are the result of multiple interacting, conditioning and triggering factors. Here, we propose a novel Bayesian modelling framework for the prediction of space-time landslide occurrences of the slide type caused by weather triggers. We consider log-Gaussian cox processes, assuming that individual landslides stem from a point process described by an unknown intensity function. We tested our prediction framework in the Collazzone area, Umbria, Central Italy, for which a detailed multi-temporal landslide inventory spanning 1941-2014 is available together with lithological and bedding data. We tested five models of increasing complexity. Our most complex model includes fixed effects and latent spatio-temporal effects, thus largely fulfilling the common definition of landslide hazard in the literature. We quantified the spatio-temporal predictive skill of our model and found that it performed better than simpler alternatives. We then developed a novel classification strategy and prepared an intensity-susceptibility landslide map, providing more information than traditional susceptibility zonations for land planning and management. We expect our novel approach to lead to better projections of future landslides, and to improve our collective understanding of the evolution of landscapes dominated by mass-wasting processes under geophysical and weather triggers.
How to cite: Lombardo, L., Opitz, T., Ardizzone, F., Huser, R., and Guzzetti, F.: Space-Time Landslide Predictive Modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6487, https://doi.org/10.5194/egusphere-egu2020-6487, 2020.
Soil water content is often a landslide’s trigger factor, in particular the shallow ones. Although there is no simple relationship between the water content into the soil and the hydraulic conditions of the slopes at the depths at which the landslides develop, the knowledge of the actual soil moisture is fundamental for the study of landslides, thus, it should be monitored.
The LAMP (LAndslide Monitoring and Predicting) system is employed in the INTERREG-ALCOTRA project called AD-VITAM. LAMP (Bovolenta et al., 2016) was yet formulated for the analysis and forecasting of landslides triggered by rain. It adopts a physically based Integrated Hydrological Geotechnical (IHG) model (Passalacqua et al., 2016) and is implemented in GIS. In this Project, the IHG model is fed by data measured using a Wireless Sensor Network (WSN), this formed by low-cost and self-sufficient sensors. The WSN may gather rainfall, temperature, surface’s displacement data (these by mass-market GNSS receivers in RTK) and, in this case, soil water content (by capacitive sensors).
The WaterScout SM100 capacitive sensors were lab-analyzed then, recognized as satisfactory, installed on-site together with their related equipment. These sensors connect to a “Sensor Pup”, which has four available channels; therefore, four sensors are installed at each node, at different depths from ground-level, in order to achieve a vertical soil-moisture profile and the rate of infiltration.
The selection of the most suitable spots for the water content soil-sensors’ installations depends on the presence of shallow soil layers and of the radio signal emission-reception’s too.
The sensors may be set up both in vertical or horizontal direction. In general, the vertical installation is preferable. This implies the creation of small adjacent vertical holes, each one reaching a different depth, where the sensors are singularly pushed. Alternatively, the horizontal one may be adopted, by the opening of a small trench where the sensors are manually inserted at different depths, along a quasi-vertical vertical line. The full contact between the soil and the sensors is always verified, immediately after the installation, using a directly connected FieldScout reader to any single sensor. Furthermore, it is necessary to protect the emerging cables and to avoid preferential ways for water infiltration along the wiring lines.
The monitoring networks, installed at the two Italian sites of Mendatica and Ceriana, are currently providing informations in real-time. The data acquired at five nodes, distributed at each of these two sites (40 sensors in total), are currently relayed on a specific web-portal by a GSM connected Retriever-Modem, marking the evolutions of soil moisture profiles at depths between 10 and 85 cm from ground level: these continuous data allow the analysis of the infiltration and evapotranspiration phenomena. Moreover, a correlation between the soil moisture contents and the local displacements is made possible. Finally, a specific calibration of the SM100 sensors’ in relation to the on-site soil types is in progress.
How to cite: Passalacqua, R., Bovolenta, R., Federici, B., and Iacopino, A.: Soil water contents and displacements monitoring, integrated into a Hydrological-Geotechnical Model for the evaluation of large-scale susceptibility to landslides triggered by rainfalls, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5014, https://doi.org/10.5194/egusphere-egu2020-5014, 2020.