Displays

End of 2019 was particularly damaging in some Central and Eastern African countries due to the heavy rain which triggered numerous mass movements. Extremely heavy rainfall were recorded in Pokot South and Sigor Sub counties located in West Pokot County (Kenya) on 23 and 24 November 2019. An official from the West Pokot county government said 53 people died after devastating rains caused huge landslides in this County while several roads in the valley have been affected and at least 5 bridges were reported as destroyed. Indeed Kenya has seen several villages heavily affected by landslides after floods and torrential rain. These movements were detected from a combination of high-resolution Sentinel 2 images and very high-resolution Pléiades-1 images acquired before and after the landslide catastrophe with the engagement of the UNOSAT’s rapid mapping service which activated the International space charter mechanism. In the following days, a series of analysis of the affected zones from very high-resolution optical data were delivered in the following days to UNOSAT and the emergency response authorities in Kenya. This study explains the mechanism of the rapid mapping activation and the use of the Disaster Charter mechanism to help to detect the extent of the phenomena and impacted infrastructure by providing a rapid mapping related analysis, conducted at UNOSAT with satellite data provided by space agencies involved in the International Space Charter. Science-driven landslide inventories were created with the ALADIM change detection algorithm integrated on the ESA GeoHazards Exploitaton Platform. Over the studied region of 400 km², nearly 6000 landslides were detected, corresponding to an affected area of ca. 18 km². Then, the triggering factors of this disaster were analysed understanding how changing raining conditions is affecting the area while it was not considered as landslides-prone. This research aims to state if this particular event is considered as abnormal according to rainfall trends and landslide occurrence looking at long time series and/or human practices play a major role in triggering this type of catastrophe.

How to cite: Schlögel, R., Belabbes, S., Dell Oro, L., Déprez, A., and Malet, J.-P.: Disastrous landslides under changing forcing factors triggered end 2019 in West Kenya, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19153, https://doi.org/10.5194/egusphere-egu2020-19153, 2020.

There exist several relationships between landslide surface areas and volumes. Most of them are based on simple geometric shape such as half ellipsoid or surface prisms. Using a new inventory of 66 small and shallow landslides was created using high resolution digital elevation model in the canton of Vaud (Switzerland). The volume calculation was based on horizontal surface areas (A) and the maximum vertical depth, using both paraboloid-elliptic or half ellipsoid. The relationship between surface area – maximum vertical depth (z_max) is deduced using principal component analysis (PCA) in a log-log space, which leads to a power-law (Jaboyedoff et al., 2020). The distribution of the distances of the 66 couple of values to this line is close to a log-normal distribution. This allows to calculate the probability to overpass a volume using both paraboloid-elliptic and half ellipsoid to calculate volumes based on maximum depth z_max.

The trend of relationship is very similar to the one obtained by Guzzetti et al. (2009), but the confidence level narrower. In our case a volume can be 8 times larger between the two boundaries of the centred 95% confidence level, and for the Guzzetti et al. (2009) it reaches 38 times based on their confidence level.

This approach demonstrates that there are large uncertainties on the volume estimations. But if it is applied to coherent inventories, it can provide good approximations. As the landslide runout distance depends on the volume involved, such approach is promising for improving landslides hazard.

References

Guzzetti F., Ardizzone F., Cardinali M., Rossi M., Valigi, D. 2009. Landslide volumes and landslide mobilization rates in Umbria, central Italy. Earth and Planetary Science Letters, 279(3-4), 222-229.

Meier, C., Jaboyedoff, M., Derron, M.-H., Gerber, C., 2020. A method to assess the probability of thickness and volume estimates of small and shallow initial landslide ruptures based on surface area. Landslides. //doi.org/10.1007/s10346-020-01347-0

How to cite: Jaboyedoff, M., Meier, C., Marc-Henri, D., and Gerber, C.: Assessing the probability of volume estimates initial landslide ruptures based on surface area, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11822, https://doi.org/10.5194/egusphere-egu2020-11822, 2020.

Complex cascades of landslide processes in changing high-mountain areas have the potential to result in disasters with major loss of life and disruption of critical infrastructures. Simulation tools have been developed to anticipate and, consequently, more effectively manage, landslide hazards and risks. However, the detailed prediction of future events remains a major challenge particularly for complex cascading events. In the previous years, we have successfully back-calculated a set of well-documented historic landslide cascades with the mass flow simulation tool r.avaflow, deriving sets of optimized parameters. In the present contribution, we use the findings from these back-calculations to propose two approaches for predictive simulations with an updated version of r.avaflow, based on the multi-phase mass flow model by Pudasaini and Mergili (2019):

(i) Using the minima and maxima of the parameter sets summarized from the back-calculations to simulate areas of certain impact and areas of possible impact, and ranges of possible travel times and kinetic energies. The limitation of this method is that parameters often depend on the process magnitude and have to be spatially differentiated for zones of similar topography and process type, meaning that the process type has to be prescribed.

(ii) Deducing from the guiding parameter set a function that relates the key model parameters (particularly, friction parameters) to a suitable dynamic flow parameter (we suggest the kinetic energy). This approach has the advantage that the definition of zones becomes obsolete. However, much more research is necessary to constrain the proposed function.

We apply both approaches to the well-documented 2002 Kolka-Karmadon event in the Russian Caucasus, where an initial fall of ice and rock entrained almost an entire glacier, triggering a high-energy ice-rock avalanche followed by a distal mud flow. Both of the simulations (i) and (ii) yield empirically mostly adequate results in terms of impact areas, volumes, hydrographs, and flow velocities, leading to the preliminary conclusion that they represent a major step forward in our ability to predict high-mountain process chains. However, some aspects are not fully reproduced by (i), whereas others are not fully reproduced by (ii), calling for further research.

How to cite: Mergili, M. and Pudasaini, S. P.: Towards the predictive simulation of complex high-mountain landslide cascades, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2308, https://doi.org/10.5194/egusphere-egu2020-2308, 2020.

Landslides, like any natural hazardous process, do not generate risk until some type of interaction with human settlements or activities occur. Consequently, the landslide susceptibility assessment and the quantification of the exposure and potential losses of structures and infrastructures is crucial for informing emergency and spatial planning in areas prone to geomorphological hazards. Thus, the major aim of this work is to identify the current and future buildings’ exposure to landslides, in order to get useful information to support decision planners.

The current spatial distribution of buildings and future exposure trends will be assessed based on the Portuguese Census data, which will be used as ancillary information to derive the number and type of buildings at the basic census polygonal unit. The future buildings’ exposure will be determined from a cellular automata simulation model.

Four types of buildings are considered: E1 – light structures of wood or metal; E2 – buildings of adobe, rammed earth or loose stone walls; E3 - buildings with brick or stone masonry walls; and E4 - buildings of masonry walls confined with reinforced concrete. The evaluation of buildings’ exposure is made for two landslide event scenarios: one for shallow soil slips (with rupture surfaces depth < 1.5 m) and another one for deep-seated slides (with rupture surfaces depth > 1.5 m). The landslide susceptibility/hazard hotspots for both scenarios are assessed in a study area corresponding to four municipalities located in the northern sector of the Lisbon Metropolitan Area, Portugal. The landslide susceptibility models are based on a bivariate statistical method (Information Value) and on a dataset of eight independent variables assumed as predisposing factors for the occurrence of landslides: lithology, slope, curvature, aspect, slope/contribution area ratio, topographic position index (TPI), soil type and land use. The validation procedures include the computation of ROC curves and the calculation of AUROC. Landslide susceptibility and buildings’ exposure are presented as probabilities at the basic census unit scale. Results combine the probability of occurrence of a landslide with the probability of having a building of a certain type potentially affected by a landslide, for the two landslide event scenarios.

Finally, potential losses on buildings are assessed from exposure and damage on buildings caused by landslides in the past.

Acknowledgements:

This work was financed by national funds through FCT—Portuguese Foundation for Science and Technology, I.P., under the framework of the project BeSafeSlide—Landslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change (PTDC/GES-AMB/30052/2017) and by the Research Unit UIDB/00295/2020. Pedro Pinto Santos is funded by FCT through the project with the reference CEEIND/00268/2017.

How to cite: Oliveira, S. C., Melo, R., Alves, C., Rocha, J., Garcia, R. A. C., Tavares, A., Zêzere, J. L., Pereira, S., Santos, P. P., Morgado, P., and Costa, N.: Assessment of buildings exposure and potential losses to landslides based on census data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18953, https://doi.org/10.5194/egusphere-egu2020-18953, 2020.

Landslides cause every year worldwide severe damages to the population. A quantitative knowledge of the impact of landsliding phenomena on the society is fundamental for a proper and accurate assessment of the risk posed by such natural hazards. In this work, a novel approach is proposed to evaluate the spatial and temporal distribution of societal landslide risk from historical, sparse, point information on fatal landslides and their direct human consequence.s (Rossi et al., Accepted). The approach was tested in Italy, using a detailed catalogue listing 5571 fatalities caused by 1017 landslides at 958 sites across Italy, in the 155-year period 1861 – 2015. The model adopting a Zipf distribution to evaluate societal landslide risk for the whole of Italy, and for seven physiographic and 20 administrative subdivisions of Italy. The model is able to provide estimates of the frequency (and the probability) of fatal landslides, based on the parameters, namely (i) the largest magnitude landslide F, (ii) the number of fatal events E, and (iii) the scaling exponent of the Zipf distribution s, which controls the relative proportion of low vs. large magnitude landslides. Different grid spacings, g and circular kernel sizes, r were tested finally adopting g = 10 km and r = 55 km. Using such geometrical model configuration, the values of the F, E and s parameters were derived for each grid cells revealing the complexity of landslide risk in Italy, which cannot be described properly with a single set of such parameters. Based on such modeling configuration. This model configuration allowed to estimate different risk scenarios for landslides of increasing magnitudes, which were validated checking the anticipated return period of the fatal events against information on 130 fatal landslides between 1000 and 1860, and eleven fatal landslides between January 2016 and August 2018. Despite incompleteness in the old part of the record for the low magnitude landslides, and the short length and limited number of events in the recent period 2016 – 2018, the anticipated return periods are in good agreement with the occurrence of fatal landslides in both validation periods. Despite the known difficulty in modelling sparse datasets, the proposed approach was able to provide a coherent and realistic representation and new insight on the spatial and temporal variations of societal landslide risk in Italy.

How to cite: Rossi, M., Guzzetti, F., Salvati, P., Donnini, M., Napolitano, E., and Bianchi, C.: Modelling societal landslide risk in Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13661, https://doi.org/10.5194/egusphere-egu2020-13661, 2020.

Deadly landslides are becoming more frequent and disruptive with increasingly severe weather events associated with a changing climate. These changes are putting coastal slopes under stress due to extreme climatic events that contribute to their instability by two processes: due to intense rainfall and also to important storm waves that rise to unusual heights in coastal areas affected by atmospheric depressions. This study was conducted in 83 coastal locations to investigate the climate-induced extreme rainfall, population density and detect the role of severity of hurricane/cyclone where deadly landslides reported worldwide since 1995 to 2018. The global landslide database was used to locate and analyze sea cliffs already under stress where deadly landslides are reported. The analysis was conducted using R (ver 3.5.1) and ArcGIS (ver 10.4.1) software. Population distribution, the severity of hurricanes/ cyclones and extreme rainfall proved the strongest predictor of deadly landslides in coastal areas particularly in the Caribbean and Southeast Asian countries. This research will help improve resilience and forecast future erosion and hot spots for cliff retreat and will contribute not only to our understanding of landslide processes associated with extreme weather events but will also enlighten decision-makers and help them manage the coastal changes in the near future.

How to cite: Haque, U., da Silva, P. F., and Soltani, A.: Deadly coastal landslides and increased risk due to severe climatic events, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20960, https://doi.org/10.5194/egusphere-egu2020-20960, 2020.

A large number of scientific contributions (e.g. BAFU 2017, Speicher 2017, Phillips et al. 2017, Ravanel et al. 2017, Haque et al. 2016) have suggested that many recent rock slope failures in the European Alps have been triggered by climate warming. For example, Huggel et al. 2012 and Fischer et al. 2012 could show that rock fall frequencies above 2000 masl increased significantly since 1990 at regional (Swiss Alps and adjacent areas) and local (Mont Blanc) scale, based on 52 events larger than 1000 m³ (PERMOS data base) covering the period 1900-2010. This increase in frequency could be correlated with a significant departure of mean annual temperature from the 1960–1990 average, based on a dataset describing conditions in Switzerland. Paranunzio et al. 2016 systematically studied the climatic conditions and anomalies occurring before 41 rock fall events in the Italian Alps with volumes of several hundred to several million m³. They show that positive and negative temperature anomalies triggered the majority of analysed rock fall events in a complex manner, but that melting of permafrost was clearly not the only rock fall trigger.

However, there have been no studies which systematically investigate changes in the frequency of rock fall events based on complete inventories covering a large range of rock fall volumes. To fill this gap, we have generated a new database for rapid rock slope failures in the Swiss Alps covering events larger than 100’000 m³ (Bühler 2019, BSc Thesis ETH 2019). This catalogue covers the period between 1700 and 2019 and includes 86 events with reliably estimated volume, date and location of occurrence, and pre-disposing factors (such as slope orientation, permafrost occurrence and geological setting). Volume-cumulative frequency plots of the events demonstrate completeness of the catalogue for all size classes, and significant changes in the ratios between large and small events through time.

An enhanced frequency of the volume class of 10⁵ m³ (100’000-999’000 m³) is observed starting from 1940, predominantly occurring in permafrost areas and elevations ranging between 2800 and 3200 masl. This increasing frequency signal with time disappears for increasing volumes beyond a magnitude of about 400’000 m³ and is clearly absent for very large rock slope failure of millions to tens of millions of m³.

The volume dependence of climate sensitivity can be physically explained, as larger volume slope failures tend to have deeper failure surfaces. Typical failure depth for multi-million m³ slope failures in crystalline rocks are up to a few 100 meters, and beyond the depth of Alpine permafrost. Direct impacts of surface temperature changes on permafrost are mainly manifested through a minor thickening of the active layer, typically ranging between 1 and 10 meters, but indirect effects at the depth range of decameters (i.e. the depth of failure surfaces for events of the 10⁵ m³ class) have been assessed and demonstrated in a large number of studies.

How to cite: Loew, S., Buehler, N., and Aaron, J.: Does climate change influence the frequency of large rock slope failures?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7361, https://doi.org/10.5194/egusphere-egu2020-7361, 2020.

The Tazones Lighthouse landslide is an active mass movement affecting a stretch of the Cantabrian Coast (N Spain), characterized by the presence of almost vertical rocky cliffs developed on Jurassic rocks. The area is being monitored since 2018 when irreversible structural damages appeared in a building located in the surroundings of the lighthouse because of the fast evolution of the landslide.

On June 2018, the first 24 topographic marks were installed by the COSINES Project researchers and 10 more were set up on December of that year, after the appearance of new cracks. Since then, monthly monitoring campaigns have been carried out by total station to gauge the displacement of the 34 mentioned marks and 4 additional control points. One of the control marks was lost, between January and February 2019, due to the fast evolution of the movement. Monitoring has been complemented by the elaboration of detailed digital terrain models through drone flights carried out in November 2018 and November 2019. In addition, precipitation data registered on the rainfall gauges of the surroundings have been collected.

This contribution presents the recent fast evolution of the Tazones Lighthouse landslide, affecting an area about 70.000 m² and characterized by relevant horizontal and vertical displacements. Since the beginning of the 3D monitoring, the 50% of the marks have moved more than 1 meter and 34% of them have moved more than 2 meters, one of them exceeding 14 meters of displacement.

The detailed digital terrain models have allowed quantifying the volume of mobilized mass over a year from the main head of the movement, located 110 meters above sea level. Moreover, the comparison of these data with precipitation records has led to relate the evolution of the displacement with the rainfall, being able to establish a very good correlation between precipitation distribution and movement acceleration.

How to cite: Domínguez-Cuesta, M. J., González-Pumariega, P., Valenzuela, P., López-Fernández, C., Herrera, F., Mora, M., Meléndez, M., Marigil, M. Á., Espadas, C., Cuervas-Mons, J., Pando, L., and Jiménez-Sánchez, M.: The fast evolution of the Tazones Lighthouse landslide (N Spain): multidisciplinary 3D monitoring between 2018 and 2019, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10175, https://doi.org/10.5194/egusphere-egu2020-10175, 2020.

The riverbank landslide is considered as the major sediment supply in the watershed. It mostly due to the river flows erode the foot of the riverbank, which makes the slope unstable. This study focused on the watershed susceptibility analysis of the riverbank landslide in the Chenyulan watershed. The Logistic regression method was used to establish the landslide susceptibility model not only with the topography, geological and anthropic factors, but also with the hydraulic factors including the hydraulic Sinuosity index, channel gradient, and concave-or-convex bank. The study areas were classified into four regions, according to the river-bed slope and confluence of rivers. The effects of the hydraulic factors on the model results were investigated. In the upstream region with mild topographic slope, the landslides were found to be dominated by the topography factors. The area under the curve (AUC) value of the model was 74.2%. In the upstream region with steep topographic slope, the steep hillslopes and the channel erosion of the concave bank produced a high weight of concave-or-convex bank in the model. The developed model exhibited an increased AUC value of 77.2%. In the downstream region, the lateral erosion of the channel increased the weights of hydraulic sinuosity index and channel gradient in the model. The developed model exhibited high area under the curve (AUC) value of 89.2%. The hydraulic factors increased the predictive performance of the model considerably.

Keyword: Riverbank, Hydraulic factors, Logistic regression

How to cite: Hong, X.-Z., Chen, P.-A., and Chan, H.-C.: Investigate the Influence of Hydraulic Factors on Landslide Susceptibility of Riverbank for the Chenyulan watershed, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6598, https://doi.org/10.5194/egusphere-egu2020-6598, 2020.

Landslide domains are a useful tool for characterising and subdividing a region into homogenous units reflecting the style of landsliding, which is controlled by the environmental characteristics (e.g. geology, relief). Landslide domains can provide a framework for the application of landslide knowledge obtained from a data-rich area across areas within the domain that are less data rich but have similar environmental characteristics. We have constructed landslide domains for East Sikkim using a landslide inventory, geology, relief and expert-based knowledge of landslide processes in the region. First, we catalogued landslide processes in East Sikkim using peer-reviewed literature, supplementing this with the mapping of over 450 translational landslides and debris flows in Google Earth through visual analysis utilising process knowledge from the catalogue. Several dozens of old landslides were mapped with stereographic analysis of four Cartosat-1 stereo pairs (90 km²) captured in March and December of 2011. Morphometric maps were constructed from Aster GDEM. Finally, the driving environmental characteristics for each process have been determined via statistical analyses to inform expert-driven construction of the landslide domains. We find that landslide domains explain landslide processes in East Sikkim well, but they may be limited by the amount of data that is available. The construction of landslide domains is flexible and can be applied to many different areas. Future work includes the testing of large-scale regions and inclusion into susceptibility models, where we hope that they will facilitate the construction of more accurate and representative susceptibility maps.

How to cite: Heijenk, R., Malamud, B., Dashwood, C., Wood, J., Arnhardt, C., and Reeves, H.: Characterising landslide processes using limited data: case study on East Sikkim, India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18520, https://doi.org/10.5194/egusphere-egu2020-18520, 2020.

Variables related to terrain morphology are widely used and have proven particularly effective in landslides detection as well as susceptibility modelling. Altitude has often been found as one of the main predictors in landslide modelling, although it does not have clear conceptual or empirical justification as predisposing factor. As most other land-surface variables are derived from it, altitude might be just a surrogate for more meaningful predictors in a statistically-based landslide modeling. For instance, altitude might replace curvature simply because convexities tend to occur in upper parts of a landscape, while concavities are associated with lower altitudes. Our work intends to examine the hypothesis that altitude points out issues in sampling design when appears as a main predictor in landslide modeling. The tests were conducted in two study areas, one in the Buzău County, Romania and the other in the Shizuoka Prefecture, Japan, with landslide inventories available. Two sampling designs were tested in each study area: random sampling over the entire study area (random point allocation within each landslide scarp polygon and the same number of points randomly created outside landslide scarp area, as absence data), and stratified random sampling based on lithological strata. Following stratified random sampling based on lithological homogeneity, three study areas in Buzau and two in Japan resulted.  Variable importance analysis and prediction of landslide scarp were conducted with Random Forest (RF) on databases with presence/absence of landslide scarp and associated values of 14 terrain variables. The results of variable importance analysis showed that variable hierarchy changed significantly when using lithological stratified sampling. In the random sampling scenario, altitude showed as the second most important landslide predictor in both study areas. In four out of five cases, the lithologically stratified random sampling led to decrease of altitude importance as landslide predictor, in two cases altitude even being one of least important variables. The results of model performance metrics showed that in four out of five cases the lithologically stratified random sampling significantly improved the prediction. In both areas in Japan, all four metrics show improvement of lithological stratified sampling over random sampling, by 6 and 4 % for AUC, 3% for OOB, 3 and 5 % for OA, and 6 and 10 % for Kappa, respectively. We conclude that landslide modeling is sensitive to lithological homogeneity and the presence of altitude as an important predictor could indicate a bias in the sampling design.

How to cite: Dornik, A., Drăguț, L., Oguchi, T., Hayakawa, Y., and Micu, M.: Altitude as an indicator of biased sampling design in landslide prediction, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11304, https://doi.org/10.5194/egusphere-egu2020-11304, 2020.

Storm events that trigger hundreds to thousands of shallow landslides in New Zealand’s hill country are associated with significant costs in terms of damage to land and infrastructure, agricultural losses and impacts on freshwater environments. To reduce the impacts of these landslide events, we require finer-resolution landslide susceptibility and hazard information to support improved targeting of mitigation measures that increase landscape resilience to storm impacts. The acquisition of landslide data for susceptibility and hazard assessments is a significant challenge given the typical size of affected areas and the number of landslides generated. This often prevents comprehensive mapping of storm-impacted areas, restricting the development of event-based landslide inventories due to the time and costs involved. Moreover, individual landslide source areas (scars) are typically small (approximately 50-100 m² in median scar size). As a result, we require high-resolution imagery to enable 1) accurate detection of individual landslide features and 2) separation of landside scar and debris deposits for use in landslide susceptibility and hazard modelling.

Here, we compare manual and semi-automated methods for acquiring event-based landslide data and test sensitivity of three statistically-based landslide susceptibility models (logistic regression, neural network and random forest) to data acquisition method. Mapping focused on two high-magnitude storm events with maximum estimated recurrence intervals of 20 and 250 years using before and after high-resolution (<0.5 m) satellite or aerial imagery for the 175 and 178 km² study areas located on the North Island of New Zealand. Separate landslide inventories were prepared based on 1) manual mapping of all landslide initiation points and 2) semi-automated object-based image analysis (OBIA) mapping of landslide scar polygons within each study area.

We compare predictive performance between landslide inventories for the three models and their spatial predictions of landslide susceptibility. Our results highlight the challenges associated with semi-automated landslide detection over large areas where Producer’s and User’s accuracies ranged 57-76 and 50-61%, respectively, based on the number of OBIA-mapped landslide scars intersecting with a random sample of manually-mapped scars. Despite these levels of mapping accuracy, the mean area under receiver operating characteristic (ROC) curves was reduced on average by only 10% based on k-fold cross-validation using OBIA-mapped landslide scars compared to manual inventories. This suggests that landslide susceptibility analyses may be relatively insensitive to moderate classification error in semi-automated mapping when using large landslide inventories (here >7000 scars per study area) with high spatial densities. With growing demand for regional to national-scale quantitative information on landslide susceptibility and hazard that requires event-based data collection spanning a range in storm magnitudes, we see potential for semi-automated methods to complement manual methods of landslide data acquisition. This represents a balance between the amount of landslide data acquired, mapping accuracy, acquisition cost, and the resulting quality of landslide susceptibility and hazard assessments.

How to cite: Smith, H., Spiekermann, R., Betts, H., and Neverman, A.: Comparing methods of landslide data acquisition for landslide susceptibility and hazard assessments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2105, https://doi.org/10.5194/egusphere-egu2020-2105, 2020.

The landslide development laws vary in different landslide-prone areas, hence the susceptibility models often perform in varied ways in different regions. Due to the periodic regulation of reservoir water level, a large number of landslides occur in the Three Gorges Reservoir area (TGRA). These landslides seriously threaten the safety of local residents and their property. It is crucial to find the model that can generate a landslide susceptibility map with higher accuracy in the TGRA. The main objective of this study was to explore the preference of machine learning models for landslide susceptibility mapping in the TGRA.

The Wushan segment of TGRA was selected as a case study, which is located in the middle reaches of the TGRA, the southwest of China. In this study, 165 landslides were identified and 14 landslide causal factors were constructed from different data sources at first, including altitude, slope, aspect, curvature, plan curvature, profile curvature, stream power index, topographic wetness index (TWI), terrain roughness index, lithology, bedding structure, distance to faults, distance to rivers, and distance to gully. Subsequently, multicollinearity analysis and information gain ratio model were applied to select landslide causal factors. After removing five factors (altitude, TWI, profile curvature, plan curvature, curvature), the landslide susceptibility mapping using the calculated results of four models, which were support vector machines (SVM), artificial neural networks, classification and regression tree, and logistic regression. Finally, the accuracy of the four models was evaluated and compared using the accuracy statistic methods and the receiver operating characteristic (ROC). The results of accuracy analysis showed that the SVM model performed the best. At the same time, the SVM performance behavior for susceptibility modelling in other areas were collected. In these regions, the accuracy of SVM was always larger than 0.8. We could see that SVM performed acceptably in different regions, and thus it can be used as a recommended model in TGRA and other landslide-prone regions.

In this study area, a total of 62% of the landslides were within 300 m from the Yangtze River, and the distance to rivers was the most important factor. The impoundment of the TGRA impacted the landslide development in three aspects: (1) the long-term immersion of reservoir water gradually reducing the strength of rock (soil) at the saturated zone (mostly near the Yangtze river), reducing the resistance force of landslide; (2) the strong dynamic action of water enhancing the lateral erosion on the bank slope, changing the slope shape, and thus reducing the slope stability; (3) the periodic fluctuation of the reservoir water making the self-weight, static, and dynamic water pressure of the landslide change, which could increase the resistance force or reduce the sliding force of the landslide and even cause overall instability and damage. Hence, in order to reduce the losses caused by landslides in TGRA, we should pay more attention to the early warning of reservoir bank landslides.

How to cite: Yu, L., Wang, Y., and Zhang, Y.: Landslide Susceptibility Mapping Combining Information Gain Ratio and Support Vector Machines in the Three Gorges Reservoir Area, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12379, https://doi.org/10.5194/egusphere-egu2020-12379, 2020.

Himalayan Terrain is highly susceptible to landslide events triggered by frequent earthquakes and heavy rainfall. In the recent past, cloud burst events are on rising, causing massive loss of life and property, mainly attributed to climate change and extensive anthropogenic activities in the mountain region as experienced in case of 2013 Kedarnath Tragedy. The study aimed to identify the potential landslide hazard zone in Mandakini valley by utilizing different types of data including Survey of India toposheet, geological (lithological and structural) maps, IRS-1D, LISS IV multispectral and PAN satellite sensor data and field observations. Relevant 18 thematic layers pertaining to the causative factors for landslide occurrences, such as slope, aspect, relative relief, lithology, tectonic structures, lineaments, LULC, NDVI, distance to drainage, drainage density and anthropogenic factors like distance to road, have been generated using remote sensing images, field survey, ancillary data and GIS techniques.  A detailed landslide susceptibility map was produced using a logistic regression method with datasets developed in GIS. which has further been categorized into four landslide susceptibility zones from high to very low. Finally, the receiver operating characteristic (ROC) curve was used to evaluate the accuracy of the logistic regression analysis model. ROC curve analysis showing an accuracy of 87.3 % for an independent set of test samples. The result also showed a strong agreement between the distribution of existing landslides and predicted landslide susceptibility zones. Consequently, this study could serve as an effective guide for further land-use planning and for the implementation of development.

How to cite: Das, S.: GIS-based Landslide Susceptibility Mapping Using Logistic Regression Analysis: A Case study in the Kadernath valley, Central Himalaya. , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8042, https://doi.org/10.5194/egusphere-egu2020-8042, 2020.

Landslides are an important component in the formation of the slope sediment flux of the sediment discharges of mountain rivers. In this regard, assessing the likelihood of their formation is an important task. The territory of the Russian ski resort Krasnaya Polyana (the Mzymta River basin) is subject to active landslide processes, including due to increased anthropogenic activity during the preparations for the 2014 Winter Olympics. The resort continues to develop actively after the Winter Olympics. The construction of new facilities continues recently including on potentially landslide slopes. When designing objects, the engineering and geological substantiation of the project is carried out. However, modeling of the landslide risk is not performed at all. We undertaken such a simulation on an area of ​​1,500 square kilometers, including the villages of Krasnaya Polyana and Estosadok and the resort of Rosa Khutor. The study area covers the Mzymta River Valley, the Laura River and their small tributaries, as well as the slopes of the Aibga and Psekhako Ranges. For forecasting, we used the landslide distribution scheme (part of the geomorphological map of the territory with landslide destruction walls) created by one of the co-authors in 2008. Various “classic” morphometric variables (calculated by SAGA GIS) were applied for prediction. In total, 66 different variables were used, both standard for such forecasting (bias, aspect, flow accumulation), and less commonly used (topographic openness, etc. ). In addition, the spectral characteristics of the terrain were used: the result of DEM decomposition into a two-dimensional Fourier series on a moving window. These variables characterize the topographic pattern within a sliding window of different sizes minus the linear trend of elevation. The prediction of the danger of a landslide was made for three variants: only by “classical” variables, only by spectral variables and by all variables combined. Due to the small amount of input data, the accuracy of the obtained models was estimated by cross-checking without dividing the data into training and test samples. The final accuracy in the first case was 64%, in the second case - 69%, in the third case - 73%. The spectral characteristics of the terrain can enhance the predicted potential of landslide susceptibility models using DEM.

This study was funded by the Russian Science Foundation, project no. 19-17-00181.

How to cite: Shvarev, S., Golosov, V., and Kharchenko, S.: Landslide susceptibility prediction by supervised Kohonen network on classic and spectral geomorphometric variables (case study of the Krasnaya Polyana resort, Russia), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20234, https://doi.org/10.5194/egusphere-egu2020-20234, 2020.

Areas non susceptible to landslides are regions where landslides are not expected, or where susceptibility is negligible. Thus they can provide new insights into landslide hazard assessment and land use management and can be targeted as areas for urban planning and dwelling. Non-susceptible landslide areas can be determined with substantially less information as compared with landslide susceptibility. Previous works in Italy and the Mediterranean region and in the USA showed that only morphometric information is needed to distinguish non-susceptible landslide areas. We used 90-m digital terrain data (SRTM DEM V4.1) to calculate global slope and relief maps, and applied globally the quantile non-linear (QNL) model previously obtained in Italy. We define the output map a global landslide non-susceptibility map (GLNSM).  The QNL model is a relationship between terrain relief and slope based on an Italian landslide inventory dataset with high completeness and accuracy. Results indicate that 82.89% of the landmasses are non-susceptible areas across the globe, which is more than the percentage of non-mountainous areas (73.6% based on GEO-GNOME). We further considered GLNSM in relation to global climate, elevation, geology, land use, precipitation and seismicity classifications. High percentage (more than 85.0%) of non-susceptible areas are detected in the tropical and arid, flat (low than 500 m), sedimentary, artificial and high vegetated, less rainy (less than 400 mm per year) and seismicity inactive (less than 0.4) regions. Our results of GLNSM was also validated with some well-represented regional landslide inventory datasets, for which we used four national (Austria, China, Ireland and USA) datasets and nine regional (Arizona, Missouri, Oregon, Utah and Washington in USA, Guangdong and Yunnan in China, and Koshi river region in Nepal) datasets. Applicability of GLNSM reveals that 0.7% of non-susceptible areas are covered by artificial structures, about three times of that in susceptible areas (i.e., not non-susceptible areas), while population density of non-susceptible areas are about twice of that in susceptible areas. About 90.5% of population resides in the non-susceptible areas.

How to cite: Jia, G., Alvioli, M., Gariano, S., Guzzetti, F., Tang, Q., and Marchesini, I.: A Global Landslide Non-Susceptibility Map: variation and applicability, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15239, https://doi.org/10.5194/egusphere-egu2020-15239, 2020.

Landslides are one of the geomorphological hazards that cause significant human, economic and natural losses worldwide and in Turkey as well. In general, landslides triggered by natural factors such as earthquakes, heavy rainfall and snow melting, or human activities cause a large number of casualties. Knowing the precise number of deaths caused by landslides and their spatial and temporal distributions will facilitate a better understanding of the losses and damages, and further to prevent and minimize the damages caused by this type of disasters. In this respect, reliable historical inventories, including past landslide events, are crucial in understanding the future landslide hazards and risks. In this study a new data set of landslides that caused loss of life in the 90-year period from 1929 and 2019 has been compiled, providing new insight into the impact of landslides for Turkey, which is Europe's topmost deadly country.

The new archive inventory indicates that in the 90-year period a total of 1343 people lost their lives across the region in 389 landslide events in Turkey between 1929 and 2019. The distribution of the fatal landslides is highly varied and concentrated in two distinct zones along the Eastern Black Sea Region and Istanbul mega-city. Our analysis suggests that on a country scale the mapped factors that best explain the observed distribution are topographic relief and gradient, annual precipitation and population density. Temporal trend analysis reveals a significant rise in the number of deadly landslides and hotspots across the studied period was observed. The detailed analysis showed that the control factors of landslides caused by different triggering mechanisms (i.e., natural and anthropogenic) also vary. The landslides of natural trigger origin are concentrated in areas with high topographic relief and slope values, whereas those triggered by anthropogenic factors are concentrated in areas with low topographic relief and slope values. While the slope values were 10.5^° in the areas where all the fatal landslides occurred, these values were 14.5^° for natural landslides and 8^° for anthropogenic landslides. In the areas where landslides triggered by natural factors, the average topographic relief is approximately 600 meters higher than the landslides of anthropogenic origin. Moreover, we observed that fatal landslides have not triggered during the seasonal rainy period, but rather caused by sudden and heavy torrential rainfall during the summer period when the average annual rainfall is low. Fatal landslides triggered by natural factors are concentrated in the Eastern Black Sea section and occur on the Upper Cretaceous and Lower-Middle Eocene volcanics classified as median volcanic rocks with an average thickness of ten meters. The landslides on these lithological units are shallow landslides, which occur mainly a few meters above the regolithic zone, where chemical weathering is severe in this area. Fatal landslides of anthropogenic origin occur in urban and metropolitan centers where human activity is high due to infrastructure and construction works, and they are predominantly corresponding with areas where the topographic relief difference is low.

How to cite: Fidan, S. and Gorum, T.: Temporal trends and controlling factors of fatal landslides in Turkey, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-890, https://doi.org/10.5194/egusphere-egu2020-890, 2020.

The project Inform@Risk, a collaboration of German and Colombian Universities and Institutes funded by the German government, aims to install a landslide early warning system in the informal settlements in Medellín, Colombia. In the recent past the city has suffered from multiple landslides, some of them with up to 500 casualties. The informal settlements in the steep slopes at the city borders grow rapidly, which destabilizes the ground and complicates the installation and operation of an early warning system. Therefore, key goal of the project is to include the community in the process of the development of the early warning system.

Medellín is embedded in the Aburrá Valley in the Cordillera Central of the Andes. The region around the city consists of different triassic and cretaceous metamorphic rocks and magmatic batholites and plutonites. Especially the north-eastern slope is prone to landslides, as it is very steep and made up of a deep cover of soil over highly weathered dunite rock.

During the first field trip, carried out in August 2019, former landslide areas were located, and ERT-measurements were conducted at the study site Bello Oriente in the northeast of Medellín. After a first evaluation of the findings, the soil cover seems to be over 50 m high in the middle of the slope, which indicates a deep-seated landslide, that might have been moving downhill very slowly for thousands of years. The more dangerous landslides however, which are much faster, are the shallow ones on the surface. These landslides can appear on top of each other and are distributed across the whole study area but are most concentrated between and above the last houses of the barrio. During a second field campaign in 2020, the ERT-profiles will be calibrated and complemented by drillings and the hazard map will be completed accordingly.

How to cite: Breuninger, T., Gamperl, M., and Thuro, K.: Hazard assessment of landslide-prone areas on highly weathered dunite rock in Bello Oriente, Medellín, Colombia (Project Inform@Risk), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5582, https://doi.org/10.5194/egusphere-egu2020-5582, 2020.

The Abruzzo Region (Central Italy) is largely affected by landslide phenomena, widespread from the mountainous to the coastal areas. The area is located in the central-eastern part of the Italian peninsula and it is framed in a complex geological and geomorphological framework, closely connected to the combination of endogenous (morphotectonics) and exogenous processes (slope, fluvial, karst and glacial processes). Landslide phenomena are linked to the interaction of geological, geomorphological, and climatic factors (instability factors) in response to trigger mechanisms, mostly represented by heavy rainfall events, seismicity, or human action. This work illustrates the results of multidisciplinary analyses carried out in the Abruzzo area in recent years, in different physiographic and geomorphological-structural contexts (chain, foothills, fluvial, and coastal areas). These analyses are based on the combination of classic and advanced methods, including morphometric analysis of the topography and hydrography, detailed geological and geomorphological field mapping, geostructural analysis, photogeological analysis, supported by stability analysis and 2D/3D numerical modeling. Five case studies are representative of the main active geomorphological processes affecting different environments and morphostructural domains, with reference to the predisposing and/or triggering factors. The main landslide cases analyzed and discussed in this work consist of: debris flow and rockfalls in a mountain area, widely altered by wildfire events (Montagna del Morrone case); complex landslides systems in the foothills, characterized by a very rough topography documenting the activity of long-term landslide processes (Ponzano and San Martino sulla Marrucina cases); sliding and complex landslides (topples and rockfalls) in fluvial and coastal areas, following a heavy snow precipitation event and a moderate seismic sequence (Castelnuovo di Campli case) and induced by episodic and localized cliff recession processes combined with wave-cut and gravity-induced slope processes (Abruzzo rock coast cases). The work outlines the importance of combining geological and geomorphological approaches with integrated detailed analysis of field and laboratory data to characterize morphology, bedrock features, structural features and jointing, superficial continental deposits, and landforms distribution. This allows supporting large-scale analyzes to evaluate hazard and risk posed by different types of landslides with different magnitudes in different environments. This work could represent an effective integrated approach in geomorphological studies for landslide hazard modeling at different spatial scales, readily available to interested stakeholders.  Furthermore, it could provide a scientific basis for the implementation of sustainable territorial planning, emergency management, and loss-reduction measures.

How to cite: Paglia, G., Carabella, C., Epifani, C., Esposito, G., Fazzini, M., Mancinelli, V., and Miccadei, E.: Landslide hazard in the Abruzzo area (Central Italy): case studies of different types of landslides in different environments and morphostructural domains., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15993, https://doi.org/10.5194/egusphere-egu2020-15993, 2020.

Landslide hazard is becoming serious environmental constraints for the developmental activities in the highlands of Ethiopia. With the current infrastructure development, urbanization, rural development, and with the present landslide management system, it is predictable that the frequency and magnitude of landslide and losses due to such hazards would continue to increase. In the present study landslide hazard zone mapping were carried out in and around Gidole Town in Southern Ethiopia. The main objective of the study was to map landslide hazard zone using Information Value Bi-variant statistical model.  For landslide hazard zonation of the study area six causative factors namely; aspect, slope angle, elevation, Lithology, Normalized Deference Vegetation Index (NDVI) and land-use and land-cover were considered. The landslide inventory mapping for the present study area was carried out through field observations and Google Earth image interpretation. Later, Information value was calculated based on the influence of causative factors on past landslide. The distribution of landslide over each causative factor maps was obtained and analyzed. Weights for the class with in these causative factor maps was obtained using information value model. Distribution of landslide in the study area was largely governed by aspect of southwest facing, slope angel of 30-45^o, elevation of 1815–2150m, NDVI of 0.27−0.37, Lithology of colluvial deposit and land-use and land-cover of agricultural land. The landslide hazard zonation map shows that 78.38km² (36.3%) area fall within very low hazard (VLH) zone, 72.85km² (34.2%) of the area fall within low hazard (LH) zone, 12.78 km² (6.6%), 32.72 km² (15.4%) and 15.89 km² (7.5%) of the area falls into very high hazard (VHH), high hazard (HH) and moderate hazard (MH), respectively. Further, validation of LHZ map with past landslide inventory data shows that 92.3% of the existing landslides fall in very high hazard (VHH) and high hazard (VHH) zone. Thus, it can safely be concluded that the hazard zones delineated in the present study validates with the past landslide data and the potential zone depicted can reasonably be applied for the safe planning of the area.

Key words: Landslide, Gidole, Landslide hazard zone, Information Value model

How to cite: Walle, F. M., Suryabhagavan, K. V., Raghuvanshi, T., and Lewi, E.: Landslide hazard zone mapping using Information Value model: the case of Gidole Landslide, Southern Ethiopia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1719, https://doi.org/10.5194/egusphere-egu2020-1719, 2020.

Kathmandu-Kyirong highway, forced to bear recurrent mass failures, is currently the most important Sino-Nepal land route that built in difficult terrain of high tectonic belt, and extreme weather and climate system. Besides, young and weak geological settings and ongoing developmental activities further increase the frequency of landslide hazard. This route is also under the plan of upgrading not only as a high grade highway but also a strategic transboundary railway line. We mapped mass failure events of fifteen years (2004 to 2018) using multi-temporal high resolution satellite images and field investigation to analyze characteristics of landslides. Key informant interview (40), focus group discussion (5) and questionnaire survey (296) were done amongst residents of 8 on-road towns for the societal impact assessment. Four thousands five hundred and seventy seven landslides (31.68 km²) were mapped within the transportation corridor of 1682.5 km². Mass failures of continuous activation for different time periods were of 6.3 km² area. The density of landslides is high in late Paleozoic and pre-Cambrian lithological formations. Landslide occurrences were increased with incremental slope and relative relief.  In steep slopes rock falls were dominant. Southern slopes that receive more solar radiation and rainfall have more mass failures.  Most of the landslide events occurred in grassland, bushes and barren land. The runout that reached to the river system was 0.5% of the total failures. Stream proximity has shown reverse relation with land sliding, whereas distance from road has positive relation. It happened because most of the roads are in urban and sub-urban areas of flat landscape with few connections to mountainous belt. The epicenter proximity has also shown negative relationship with slope failures. Pre and post-quake events were increased with annual normal rainfall amount up to 3,000mm. Then slope failure started decreasing. In case of co-seismic landslides of 2015 Gorkha Nepal earthquake, the rainfall has negative influence because the earthquake event itself had occurred before the monsoon begun. Six major sectors – mobility of people, transportation of goods, health and education facilities, price hike and shortage of goods, tourism or other business loss, and   agriculture production and market access were identified as the most influenced sectors when road blocked by mass failures. Effect on agriculture production and market access is major in Ramche, Grang-Mulkharka and Mahadevbensi and reduction of tourists flow is dominant in Dhunche and Syabrubensi towns. Responders considered the constantly road blocking landslides in the past, co-seismic mass failures of 2015 tremor and landslides of 2018 while evaluating the impacts. Because of annual cycle of concentrated landslide incidences, town dwellers have developed a coping mechanism for road blockage time which includes operation of one way vehicles to avoid damaged area, carry goods by foot from nearby markets, mentally prepare themselves for daily mobility by walking, keep stock of goods, and proper savings for buying expensive stuffs in local shops during road blockage.

How to cite: Dhakal, S., Cui, P., Su, L., Rijal, C. P., Ghatri Chhetri, B., and Regmi, A.: Landslide Characteristics and Societal Impacts of Roadside Towns along Sino-Nepal Transportation Corridor: A Case of Kathmandu-Kyirong Highway, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12372, https://doi.org/10.5194/egusphere-egu2020-12372, 2020.

This study developed a novel landslide risk assessment framework to analyze landslide risk in Mt. Umyeon, Korea. The proposed framework included four main procedures: (1) Landslide hazard analysis using an ensemble statistical and physical model, (2) Analysis of physical vulnerability from vulnerability curve, (3) Analysis of physical vulnerability from semi-quantitative approach, (4) Risk index calculation from the results of previous steps using a proposed equation. The results of each step were compared to real landslide events occurred in July 2011 at Mt. Umyeon, Korea to confirm the reliability of the proposed risk assessment framework. The risk maps also were compared to real landslide event and showed that the proposed framework was successful in assessment of landslide risk at Mt. Umyeon, Korea. The new concept in landslide risk assessment of this study provides reliable decision-making in landslide risk assessment and management.

How to cite: Nguyen, B.-Q.-V., Lee, S.-R., and Kim, Y.-T.: A Novel Framework for Landslide Risk Assessment in Mt. Umyeon, Korea , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1983, https://doi.org/10.5194/egusphere-egu2020-1983, 2020.

The Tibetan plateau (QTP) has the highest average elevation in the world. As the third pole in the world, it has the largest cryosphere system at low and mid latitudes. It is a sensitive area of climate change, and the climate change is more significant. Global climate change has led to higher temperatures and increased rainfall on the Tibetan Plateau. This will lead to changes in the frequency and pattern of geological disasters. This spatiotemporal change and its influencing factors are not clear, so we collected a total of 898 geological disasters in the QTP from 1905 to 2015. Then we process the data to obtain various meteorological indicators of the QTP and combine them with the changes in the distribution of disaster points. Furthermore, the distribution pattern of the disaster points with the spatiotemporal changes of slope, altitude, precipitation and temperature is obtained. Statistics on the disaster data corresponding to each meteorological index are then made. Through the analysis of the distribution map and the statistical results of the data, the correlation between the occurrence of geological disasters and each element is obtained. The disaster points are superimposed with multiple influencing factors, and the influence of multiple factors on the distribution of geological hazards is discussed. The results showed that geological disasters have gradually expanded from the traditional high-incidence area of southern and eastern edges to the interior. The frequency of disasters in high altitude areas is increasing, and gradually extended from the rainy season to the non-rainy season.

How to cite: Jia, Y.: The evolution trend of geological disasters over spatial and temporal in the context of global warming —— taking the Qinghai-Tibet Plateau as an example , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4377, https://doi.org/10.5194/egusphere-egu2020-4377, 2020.

A gravimetric study was conducted on a site located at Tlemcen, a city of western Algeria, where it was intended to build residential buildings. During the excavation for the preparation of foundations, cavities were discovered. This is confirmed by visual inspection. The study area is geologically composed of dolomitic limestone jurrassique, characterized by strong pérméabilité caused by cracking and karstic formation.

The geophysical method more appropriate in this case is the microgravimetric. The gravimetric campaign which lasted 15 days is composed of more than 1000 stations measures and was realized on several zones at the site with a step of 2.5 m. All these stations have been identified topographically.

            The Bouguer anomaly map presents a short-wavelength gravity low which reaches a minimum value up of - 33.190 mGal. A qualitative analysis of this map showed that the relative gravity lows is related to the mass deficit. Some of the anomalies detected by microgravimetric are well correlated with cavities observed on the surface. Mass deficits have been assimilated as underground cavities and that can present a danger to the stability of buildings.

The 3D modelling has been realized using software based on the algorithm of Talwani (Talwani & all 1960), it has allowed us to determinate location and dimensions of the cavities detected.

Key words: Cavities, karst, Microgravimetic, Anomalies, Talwani, Tlemcen, Algeria.

How to cite: Khaldaoui, F., Abtout, A. A., Bouguerra, H., and Hedjazi, I.: Microgravity Methods to Characterize the karst structures in north-western Algeria, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6110, https://doi.org/10.5194/egusphere-egu2020-6110, 2020.

Rainfall-Induced Landslides (RIL) are one of the most important natural hazards due to their damage to populated areas, critical infrastructure, and roads. Therefore, their deep understanding is critical for decision-makers. The Southern Andes (~ 41.1ºS, 72.5ºW) has undergone recurring RIL processes in recent years, which have affected interurban connectivity with strong social impacts. The objective of this study is to understand the atmospheric conditions that could trigger RIL at the Southern Andes. We propose a correction of high-resolution atmospheric simulations based on the Weather and Research Forecast (WRF) model. Our results were corrected by meteorological in-situ stations using geostatistical techniques. We identify precursor signals at different pressure heights that could be used to the future in an early warning system. Our proposed methodology will support the generation of public policies in the context of climate change scenarios in catchments with low-dense instrumentation and low uncertainty. Hence, our database will provide new hydrometeorological perspectives in RIL studies. To the future, these results will allow the development of an early warning system applicable in the central-southern zone of Chile.

How to cite: Manque Roa, N., Fustos-Toribio, I., and Somos-Valenzuela, M.: Trigger atmospheric conditions for RIL in the Southern Andes., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12204, https://doi.org/10.5194/egusphere-egu2020-12204, 2020.

Earthquake and antecedent drought (drought for short) play important roles in triggering landslides, which change the formation condition of landslides by affecting topography, loose solid materials and water content. However, in most cases, landslide early warnings rely on rainfall thresholds or(/and) soil moisture conditions, without considering the effects of earthquake and drought. In this study, an analysis has been carried out on the latest version of Parametric Catalogue of Italian Earthquakes (Italian acronym CPTI15) and standardized precipitation index (SPI) (to represent droughts) and the landslide events in a northern Italian region in the past 116-years period 1901-2016. Based on the quantitative analysis on the relationship in time between landslides, earthquake activities and drought events, the interacting relationship between landslides, seismic activities and droughts were explored. It has been found that the impacts from earthquakes and droughts on landslides do exist. The impacts from earthquakes in the study area was less significant comparing with other regions (such as Wenchuan, China), and droughts play a complementarily minor role on landslides. Finally, a method is proposed for predicting the landslides based on early earthquake and drought monitoring and used on some cases. We expect this study can provide useful information for combining earthquake and drought in the landslide early warnings.

How to cite: Zhu, X., Dai, Q., Zhuo, L., Han, D., and Zhang, S.: Effects of earthquakes and antecedent droughts on landslide initiation in Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9274, https://doi.org/10.5194/egusphere-egu2020-9274, 2020.

The Prêcheur river is located to the West of Montagne Pelée, in the Northern part of Martinique island. For several decades it has produced numerous lahars that directly threaten the Prêcheur village, at the mouth of the river. In recent years, the most important lahars have been correlated to massive collapses of the Samperre cliff, 9 km upstream from the sea, that create a reservoir of loose material at the bottom of the cliff. In 2010, a lahar started from this reservoir, destroyed a bridge and inundated part of the Prêcheur village. A new major period of collapses of the Samperre cliff started on 2 January 2018, involving more than 4 × 10⁶ m³ of material. In the following days, intense rainfalls triggered several lahars that reached the Prêcheur village but remained confined in the river bed. Since then, lahars and collapses have continued to occur, even though their frequency has decreased with time and their intensity is smaller compared to the onset of the crisis. One single lahar overflowed the river bed on 22 February 2018 without significant impact on infrastructures.

In this study, we test different possible scenarios to model the first and most important phase of the collapse of the Samperre cliff, that occurred in early January 2018, with the shallow-water model SHALTOP. We constrain the collapse geometry with photogrammetric 3D models and LIDAR topographic surveys, acquired in 2010 and in late January 2018. We also consider an intermediate volume to take into account a possible retreat of the cliff face between 2010 and 2018. The modeled traveled distances are compared to field observations. Finally, we use geomorphological and geological observations to identify potentially unstable structures within the cliff, and model the associated collapses.

These simulations provide insights on the possible geometry (extent and depth) of the debris reservoir at the bottom of the cliff, after a major collapse episode. This is of prior importance in order to estimate the location and volume of future lahars. In order to investigate their dynamics, we model the major 2010 lahar, for which the initial debris reservoir volume is known (about 2 Mm³). We first simulate the progressive remobilization of the reservoir by water with the r.avaflow numerical code. In a second test, we impose instead a constant flow discharge upstream until the same volume has been released. We test different parameters to identify which ones have the most significant influence on the lahar travel time, from its initiation until it reaches the Prêcheur village.

How to cite: Peruzzetto, M., Levy, C., Thiery, Y., Grandjean, G., Mangeney, A., Mergili, M., Legendre, Y., Nachbaur, A., Saurel, J.-M., Lejeune, A.-M., Dewez, T., Vittecoq, B., Clouard, V., Komorowski, J.-C., Le Friant, A., and Lemarchand, A.: Modeling of major cliff destabilizations and subsequent lahars in the Prêcheur catchment, Martinique., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10134, https://doi.org/10.5194/egusphere-egu2020-10134, 2020.