Displays

One of the Sendai Framework 2015-2030 targets is to reduce the disruption of basic services, like educational facilities. Disaster education is actually considered to be an important factor to promote disaster risk reduction. The school resilience is not related to a specific hazard and vulnerability, but it takes into account many factors, including the people’s natural risk perception and awareness, along with their knowledge and capability of how to behave in an emergency. The scientific literature provides various notions of risk, risk perception, risk awareness, and risk preparedness. The literature on children’s natural risk perception is scarce and very recent compared with that about adults. Indeed, children’s perceptions about nature and environment are truly different from those of adults. The available research mainly concerns the implementation of earthquake emergency measures, while not much is available on flood-risk perception and even less on landslides. The relationship between risk perception, awareness, and preparedness is widely studied, but once again, there is no unambiguous or unique result that depends on the approach and the context.

We decided to refine the questionnaire that contributes to assess the school-resilience employed in the Geo-hazard Safety Classification method (GSC). We designed 7 different questionnaires, one for the adult personnel and six for the students, taking into account the peculiarity of each age. These questionnaires were thought through and designed to investigate three main awareness fundamentals: i) knowledge of the correct behaviours and procedure during a natural emergency that occurs at school; ii) perception of the natural risk of the area where the school is located; and iii) general knowledge of the correct behaviours during a natural emergency at school.

Three different analyses were carried out on the 5899 filled in questionnaires (820 by personnel and 5079 by students of each school stage) distributed in 27 schools of Tuscany Region (Central Italy): a) school by school; b) questionnaire typology (i.e., different school age); and c) topics (awareness fundamentals i, ii, and iii) and questionnaire typology (i.e., different school age). The results are coherent and show that a) young children’s knowledge is perfectly adequate to their age, b) as the age and responsibilities increase, the awareness and preparation do not increase proportionally, and c) the competences of the school personnel are not sufficient, probably caused by critical issues emerged (i.e., it is not clear where family reunification must take place) and because the wrong hazard perception leads to underestimating the importance of prevention actions and disaster education and. This last outcome turns out to be unexpected.

These questionnaires are a suitable, quick, easy and low-cost tool, even if considered separately from the GSC method. The school head-masters or the local and national educational offices actually could use them a) to evaluate the geo-hydrological and seismic risk knowledge and awareness of students, professors and school personnel; b) to project and design actions needed to improve the school-resilience; c) to verify the goodness of the activities developed at point b); and d) as an educational tool to improve the disaster education.

How to cite: Bandecchi, A. E., Pazzi, V., Morelli, S., Valori, L., and Casagli, N.: Evaluation of the natural risk perception, awareness, and preparedness at school by means of ad hoc questionnaires, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4916, https://doi.org/10.5194/egusphere-egu2020-4916, 2020.

During the last decades, the progresses in rock slope monitoring improved the reliability of the Early Warning Systems (EWS) all over the world. Among their features, EWS are designed to provide to the decision makers objective tools in order to support their decisions in activating emergency plans.

The choice to design an EW System, if only based on displacement or rainfall thresholds, may not be sufficient to support the decision-making process, when the monitored rockslide is threatening high value targets, both in terms of exposed human lives and potential economic losses.

As a matter of fact, the integrated monitoring systems usually installed on active rock slopes provide many different data about the behaviour of these phenomena. Most of these data are worth to be weighted in the decisional process, as they are relevant to confirm a specific event scenario.

In addition, experts and EWS managers are facing an increasing demand by the stakeholders and the population, to effectively communicate in a user-friendly way the decision-making process, as well as the uncertainty degree associated with each decisional step.

That is a necessity which becomes critical in the moment when the population and the stakeholders have no direct perception of a potential catastrophic event and the civil protection measures are preventively activated before the emergency.

The aim of this work is to present the Early Warning procedure elaborated by the regional Geological survey of the Aosta Valley Autonomous Region (Italy), which is based on the experience derived from the emergency management of the Mont de La Saxe rockslide in 2013. 

The new EW procedure has been successfully tested for the first time during the rockslide activation in spring 2014 and it has been refined and improved during the following years.

The potential collapse of the Mont de La Saxe rockslide threatens a part of the important touristic resort of Courmayeur and the E25 Motorway, one the most important national communication axes, connecting the industrial areas of the Northern Italy with France and Switzerland.

In such a sensitive situation, a not sufficiently motivated alert could have led to impacting civil protection measures like the evacuation of two villages and the traffic interruptions, damaging the Italian economy and the regional tourism.

Therefore, the EW managers have decided to strengthen the existing EW procedure, based on displacement thresholds, in order to achieve the maximum amount of confidence in the decisional process. The new procedure is based on a Bayesian inferential process, combining the available data provided by the monitoring system.

Thanks to this approach a quantitative degree of confidence can be assigned to each decisional step, increasing the warning levels up to the declaration of the emergency condition.

At the same time, the new EW procedure provides a transparent and replicable decisional process, where the confidence degree associated to the civil protection alert can be declared in the alert bulletins.

How to cite: Bertolo, D.: Improving the reliability of the decision-making process in a rockslide Early Warning procedure , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2013, https://doi.org/10.5194/egusphere-egu2020-2013, 2020.

Boulder movement can be observed not only in rock fall activity, but also in association with other landslide types such as rock slides, soil slides in colluvium originated from previous rock slides and debris flows.

Large boulders pose a direct threat to life and key infrastructure, causing destruction along their paths and amplifying landslide and flood hazards, as they move from the slopes to the river network. Despite the hazard they pose, boulders are generally not directly accounted for in hazard assessment methods, nor have they been targeted in dedicated early warning systems or used as a mean to detect landslide movement. The ability to monitor boulder movement in real time and to provide local stakeholders with timely alerts thus represents an important step forward.

Our study focuses on an area in the upper Bhote Koshi catchment northeast of Kathmandu, where the Araniko highway, a critical link between Nepal and China, is subjected to periodic landsliding and floods during the Monsoons and was heavily affected by coseismic landslides after the 2015 Gorkha earthquake. In the area, damage by boulders to properties, roads and other key infrastructure, such as hydropower plants, is observed every year.

In April 2019, we installed an innovative monitoring system to observe boulder movement occurring in different geomorphological settings on slopes, before reaching the river system. We embedded trackers in 23 boulders spread between a landslide body and two debris flow channels. The trackers are equipped with accelerometers and can detect, in real time, small angular changes in boulder positions as well as large forces acting on them. They are programmed to send regular data but, crucially, they can be triggered by movement and immediately transmit the data via a long-range wide area network gateway to a server.

Preliminary results show that 10 of the tagged boulders present patterns in the accelerometer data compatible with downslope movements. Of these, 6 lying within the landslide body show small angular changes, indicating a reactivation during the rainfall period and a movement consistent with the landslide mass. 4, located in a debris flow channel, show sharp changes in position, likely corresponding to larger free movements and rotations. The latter have not been found at their original location after the monsoon.

This study highlights that this innovative, cost-effective technology can be used to monitor boulders in prone sites and may set the basis for the development of an early warning system particularly in developing countries, where expensive mitigation strategies may be unfeasible.

How to cite: Dini, B., Bennett, G., Franco, A., Whitworth, M. R. Z., Senn, A., and Cook, K.: Monitoring boulder movement using the Internet of Things: towards a landslide early warning system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17392, https://doi.org/10.5194/egusphere-egu2020-17392, 2020.

Catalogues are the base to study the causes leading to slope failures and to define landslide hazard and early warning strategies at regional scales. Despite recent efforts, the knowledge on spatial and temporal landslide distribution is often very poor. Information on timing, location, magnitude and landslide dynamics, is generally available only when the events threat life or damage infrastructures, as well as when they are associated with catastrophic earthquakes or exceptional meteorological occurrences. Moreover, many landslide events are unreported because they occur in remote regions and thus do not have immediate impacts on human activities. This may strongly hinder the completeness of landslide catalogues, and thus the subsequent interpretation in terms of hazard assessment. Complete catalogues are crucial to study the relationships between local and regional landslide preconditioning factors, to recognize potential triggers, as well as to clearly identify the effect of climate forcing. In recent years two procedures are dominating the panorama of landslide event detection, i.e. remote sensing approaches and seismic data analyses. This is mainly due to the increased availability of such data at global scale, as well as to the applied open access data policies. Here we present a procedure to detect landslide events by jointly analyzing data acquired from regional broadband seismic networks and spaceborne radar imagery. As an exemplary case, we consider a series of events associated to the recent Piz Cengalo rock slope failure occurred on August, 2017 in the Swiss Alps, a region where we can now benefit from the high spatial density of the AlpArray seismic network and from the spatial and temporal resolution of Sentinel-1 radar imagery. The operational implementation of the herein proposed approach, in combination with the expected increase in availability of seismic and satellite data, can provide a new and efficient solution to build and/or expand landslide catalogues based on quantitative and homogeneous measurements, as well as to integrate landslide early warning systems at regional scales.

How to cite: Manconi, A. and Mondini, A. and the AlpArray Working Group: Using seismic networks and satellite radars to detect landslide events , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6693, https://doi.org/10.5194/egusphere-egu2020-6693, 2020.

A remote sensing-based system has been developed to characterize the potential for rainfall-triggered landslides across the globe in near real-time. The Landslide Hazard Assessment for Situational Awareness (LHASA) model uses a decision tree framework to combine a static susceptibility map derived from information on slope, rock characteristics, forest loss, distance to fault zones and distance to road networks with satellite precipitation estimates from the Global Precipitation Measurement (GPM) mission. Since 2016, the LHASA model has been providing near real-time and retrospective estimates of potential landslide activity. Results of this work are available at https://landslides.nasa.gov.

In order to advance LHASA’s capabilities to characterize landslide hazards and impacts dynamically, we have implemented a new approach that leverages machine learning, new parameters, and new inventories. LHASA 2.0 uses the XGBoost machine learning model to bring in dynamic variables as well as additional static variables to better represent landslide hazard globally. Global rainfall forecasts are also being evaluated to provide a 1-3 day forecast of potential landslide activity. Additional factors such as recent seismicity and burned areas are also being considered to represent the preconditioning or changing interactions with subsequent rainfall over affected areas. A series of parameters are being tested within this structure using NASA’s Global Landslide Catalog as well as many other event-based and multi-temporal inventories mapped by the project team or provided by project partners.

In addition to estimates of landslide hazard, LHASA Version 2 will incorporate dynamic estimates of exposure including population, roads and infrastructure to highlight the potential impacts that rainfall-triggered landslides. The ultimate goal of LHASA Version 2.0 is to approximate the relative probabilities of landslide hazard and exposure across different space and time scales to inform hazard assessment retrospectively over the past 20 years, in near real-time, and in the future. In addition to the hazard. This presentation will outline the new activities for LHASA Version 2.0 and present some next steps for this system.

How to cite: Kirschbaum, D., Stanley, T., Emberson, R., Amatya, P., Khan, S., and Tanyas, H.: Global Landslide Hazard Assessment for Situational Awareness (LHASA) Version 2: New Activities and Future Plans, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11012, https://doi.org/10.5194/egusphere-egu2020-11012, 2020.

Rainfall event properties like maximum intensity, total rainfall depth, or their representation in the form of intensity-duration (ID) or total rainfall-duration (ED) curves, are commonly used to determine the triggering rainfall (event) conditions required for landslide initiation. This rainfall data-driven prediction of landsliding can be extended by the inclusion of antecedent wetness conditions. Although useful for first order assessment of landslide triggering conditions in warning systems, this approach relies heavily on data quality and temporal resolution, which may affect the overall predictive model performance as well as its reliability.

In this work, we address three key aspects of rainfall thresholds when applied at large spatial scales: (a) the tradeoffs between higher and lower temporal resolution (hourly or daily) (b) the spatial variability associated with long term rainfall, and (c) the added value of antecedent rainfall as predictor. We explore all of these by utilizing a long-term landslide inventory, containing more than 2500 records in Switzerland and 3 gridded rainfall records: a long daily rainfall dataset and two derived hourly products, disaggregated using stations or radar hourly measurements.

We observe that while predictive performances improve slightly when utilizing high quality hourly record (using radar information), the length of the record decreases, as well as the number of landslides in the inventory, which affects the reliability of the thresholds. A tradeoff has to be found between using long records of less accurate daily rainfall data and landslide timing, and shorter records with highly accurate hourly rainfall data and landslide timing. Even daily rainfall data may give reasonable predictive performance if thresholds are estimated with a long landslide inventory. Good quality hourly rainfall data significantly improve performance, but historical records tend to be shorter or less accurate (e.g. fewer stations available) and landslides with known timing are fewer. Considering antecedent rainfall, we observe that it is generally higher prior to landslide-triggering events and this can partially explain the false alarms and misses of an intensity-duration threshold. Nevertheless, in our study antecedent rainfall shows less predictive power by itself than the rainfall event characteristics. Finally, we show that we can improve the performances of the rainfall thresholds by accounting for local climatology in which we define new thresholds by normalizing the event characteristics with a chosen quantile of the local rainfall distribution or using the mean annual precipitation.

How to cite: Leonarduzzi, E. and Molnar, P.: Can we get more out of rainfall thresholds? The temporal resolution tradeoff and the role of antecedent wetness and rainfall spatial variability, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19395, https://doi.org/10.5194/egusphere-egu2020-19395, 2020.

During the last two centuries, several debris flow events occurred in the upper part of the Zêzere valley, which is located in the Estrela mountain, in Central Portugal. These events were responsible for material damage as well as for the loss of lives. Given the susceptibility of this area to the occurrence of debris flows, a methodology for pedestrian evacuation modelling was implemented, in order to identify buildings at risk and pedestrian travel times to safety areas in a debris flow worst-case scenario. Starting from a dynamic run-out model, developed in previous works, the potential debris flow intensity was estimated (e.g. flow depth, velocity and run-out distance). Sequentially, the buildings potentially affected by the impact of debris flows, as well as the ones where the evacuation would take longer than the debris flows arrival, were identified. In addition, the potentially exposed population was estimated by applying a dasymetric distribution to each residential building. This population distribution took into account the identification of the older residents as the most exposed to debris flows, which is critical to develop reliable pedestrian evacuation travel time scenarios. The pedestrian evacuation modelling was performed using the Pedestrian Evacuation Analyst, a GIS tool developed by the United States Geological Survey. The evacuation modelling was based on an anisotropic approach, which considers the influence of slope direction on travel costs, thus its application is suitable in a mountainous area. The implemented methodology is a critical step towards the implementation of a reliable early warning system to debris flows that can be reproduced elsewhere.

Funding information: 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: Melo, R., Zêzere, J. L., Oliveira, S., Garcia, R., Oliveira, S., Pereira, S., Piedade, A., Santos, P., and van Asch, T.: Identifying buildings at risk and pedestrian travel times to safety areas in a debris flow worst-case scenario, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2403, https://doi.org/10.5194/egusphere-egu2020-2403, 2020.

A physically based model for shallow landslide triggering (HIRESSS – HIgh REsolution Soil Stability Simulator) was applied in a 100 km² test site in Central Italy (Urbino, Marche region). The objectives were assessing  the influence of additional cohesion provided by roots and testing the effectiveness of a geotechnical characterization performed in an another area, but on similar lithologies.

We performed two different simulations considering the rainfall event of January-February 2006, which triggered 14 landslides in the area. For both the simulations, rainfall data were fed into the model using the measurements at hourly time step of a nearby rain gauge station, while soil thickness was estimated using a state-of-the-art empirical model based on geomorphological parameters derived from curvature, slope gradient, lithology and relative position within the hillslope profile. Geotechnical input data were varied among the two simulations. In the first one, a few in-situ and laboratory tests were performed to characterize the main lithologies, while the remaining lithologies were characterized using literature data. In the second simulation, the main geotechnical and hydrological parameters (cohesion, internal friction angle, soil unit weight, hydraulic conductivity) were fed into the model using a geostatistical characterization performed on hundreds of measurements carried out in another Italian region, with similar lithologies. Furthermore, in the second simulation the additional cohesion provided by the plant roots was also taken into account.

The results obtained with the two simulations were validated considering the landslide dataset collected by field work and image interpretation shortly after the rainfall event studied. We discovered that the second simulation provided much more reliable results, with the areas surrounding the landslide locations characterized by much higher values of failure probability.

The outcome is very important to address future research in distributed slope stability modelling because it proved that: (i) additional root cohesion is an important factor that can be used to get more reliable results; (ii) when in need of characterizing the geotechnical parameters of the study area, instead of using just a few measurements performed therein, it is preferable to integrate also data coming from different areas but with similar lithologies if they were robustly characterized in geostatistical terms purposely for distributed slope stability studies.

How to cite: Masi, E. B., Stagnozzi, A., Stagnozzi, S., Tonelli, G., Veneri, F., Tofani, V., and Segoni, S.: Advanced distributed modelling of slope stability using root reinforcement and geostatistical parameterization of geotechnical soil properties, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13726, https://doi.org/10.5194/egusphere-egu2020-13726, 2020.

The identification of potentially critical events involving unstable slopes is a major aspect in the field of natural hazards risk mitigation and management. In this framework, Early Warning Systems (EWS) exploiting advanced technologies represent an efficient approach to decrease the risk generated by landslide phenomena, allowing to reduce the possibility of damages and losses of human lives. EWS effectiveness has increased significantly in recent years, thanks to relevant advances in sensing technologies and data processing. In particular, the introduction of innovative monitoring instrumentation featuring automatic procedures and increased performances in terms of sampling rate and accuracy has permitted to develop EWS characterised by a near-real time approach. Among the several aspects involved in the development of a reliable Early Warning System, one of the most important is the ability to minimize the dissemination of false alarms, which should be avoided or identified in advance. The approach proposed in this study represents a new procedure aimed to assess the hazard level posed by a potentially critical event, previously identified by analysing displacement monitoring data. The process is implemented in a near-real time EWS and defines a total of five different hazard levels, on the basis of the results provided by two different models, namely an accelerating trend identification criterion and a failure forecasting model based on the Inverse Velocity Method (IVM). In particular, the forecasting analysis is performed only if the dataset elaborated by the onset-of-acceleration model highlights a potentially critical behaviour, which represents a first alert level. Following levels are determined by different conditions imposed on three parameters featured by the failure forecasting model, i.e. dataset dimension, coefficient of determination R-squared, and number of sensors displaying an accelerating trend. As these criteria get fulfilled, it is assumed that the monitored phenomenon is gradually evolving towards a more critical condition, thus reaching an increasing alert level depending on the analysis results. According to this classification, it is possible to set up for each single threshold a dedicated warning message, which could be automatically issued to authorities responsible of monitoring activities, in order to provide an adequate dissemination of information concerning the ongoing event. Moreover, the proposed procedure allows to customize the alert approach, giving the possibility to issue warning messages only if a certain Level is reached during the analysis.

How to cite: Valletta, A., Carri, A., Savi, R., Cavalca, E., and Segalini, A.: Definition of a new multi-level early warning procedure for landslide risk management , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13777, https://doi.org/10.5194/egusphere-egu2020-13777, 2020.

Recent developments have led to an increased rural depopulation and migration into cities in Andean countries. This is especially the case in Colombia, where immigration from Venezuela has caused an increase in poverty in cities. In Medellín, the second largest Colombian city, this led to an accelerated growth of informal settlements in the steep slopes in the east and west of the city. Combined with the expected increase of heavy rainfall due to climate change, the landslide risk in this area is expected to increase further over the next decades. The risk is highest in the east of the city, where highly weathered dunites are exposed and the slope angle reaches 20-30° and more. In these regions, rotational slides have repeatedly occurred in the past, as detailed mapping has shown.

The project Inform@Risk tries to strengthen the resilience of these settlements against rainfall induced landslides, since relocation of the inhabitants at risk currently is not a feasible option. For this, an innovative low-cost EWS is being developed in the Barrio Bello Oriente in the east of the city. Since the exact location of a future landslide is unknown, the EWS requires a network of geosensors throughout the whole area at risk, whereby the network density is controlled by the landslide risk. This flexibility is achieved by combining horizontally installed CSM (Continuous Shear Monitor) cables with open-source wireless LoRa sensor nodes. The sensor nodes are developed on basis of an Arduino system and can be installed on infrastructure as well as in the ground. They all include a tilt sensor and additionally can be equipped with varying geotechnical and hydrogeological sensors, depending on the location and measuring target (e.g. piezometer, extensometer, inclinometer/tiltmeter).

The data produced by the geosensor network is processed by the Inform@Risk server and made available to the residents and municipal stake holders via an app and homepage. Based on meteorological, hydrological and geotechnical analyses the system can evaluate the current and make predictions of the future hazard situation. If necessary, a warning can be issued via app to the inhabitants.  Ultimately, the system should be replicable in other areas in the Andes and elsewhere in the world.

This work is funded by the German Ministry of Education and Research (BMBF).

How to cite: Gamperl, M., Singer, J., and Thuro, K.: Design of a low-cost Early Warning System (EWS) in informal settlements in Medellín, Colombia (Project Inform@Risk), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21305, https://doi.org/10.5194/egusphere-egu2020-21305, 2020.

Risk mitigation for rainfall-triggered shallow slides and debris flows at regional scale is challenging. Early warning systems are a helpful tool to depict the location and time of future landslide events so that emergency managers can act in advance. Recently, some of the regions that are recurrently affected by rainfall triggered landslides have developed operational landslide early warning systems (LEWS). However, there are still many territories where this phenomenon also represents an important hazard and lack this kind of risk mitigation strategy.

The main objective of this work is to study the feasibility to apply a regional scale LEWS, which was originally designed to work over Catalonia (Spain), to run in other regions. To do so we have set up the LEWS to Canton of Bern (Switzerland).

The LEWS combines susceptibility maps to determine landslide prone areas and in real time high-resolution radar rainfall observations and forecasts. The output is a qualitative warning level map with a resolution of 30 m.

Susceptibility maps have been derived using a simple fuzzy logic methodology that combines the terrain slope angle, and land use and land cover (LULC) information. The required input parameters have been obtained from regional, pan-European and global datasets.

Rainfall inputs have been retrieved from both regional weather radar networks, and the OPERA pan-European radar composite. A set of global rainfall intensity-duration data has been used to asses if a rainfall event has the potential of triggering a landslide event.

The LEWS has been run in the region of Catalonia and Canton of Bern for specific rainfall events that triggered important landslides. Finally, the LEWS performance in Catalonia has been assessed.

Results in Catalonia show that the LEWS performance strongly depends on the quality of both the susceptibility maps and rainfall data. However, in both regions the LEWS is generally able to issue warnings for most of the analysed landslide events.

How to cite: Palau, R. M., Berenguer, M., Hürlimann, M., Sempere-Torres, D., Berger, C., and Peter, A.: An early warning system for rainfall-triggered shallow slides and debris flows. Application in Catalonia, Spain and Canton of Bern, Switzerland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-479, https://doi.org/10.5194/egusphere-egu2020-479, 2020.

In Peru, heavy precipitations (PR) are the second natural phenomenon with the greatest number of people affected in recent decades. Landslides (known as “huaycos” in Peru) are mostly produced by PR and located overall on the Andes mountains. In this regard, to monitoring and inform in advance about the most susceptible regions to landslides, the National Service of Meteorology and Hydrology of Peru (SENAMHI) has launched the national system for monitoring of landslides produced by PR, called SILVIA (“Sistema de Monitoreo de Movimientos en Masa generados por Lluvias Intensas” in spanish).

The methodology couple PR thresholds (7 days of antecedent PR) from PISCO operational precipitation (a gridded daily precipitation product of SENAMHI) with the susceptibility map for landslide hazard produced by the Peruvian Geological, Mining and Metallurgical Institute (INGEMMET). Both inputs products are combined in a purposely-built hazard matrix to get a spatially and temporally variable for landslide hazard: while statistical PR thresholds are used to accomplish a temporal definition with very coarse spatial resolution, landslide susceptibility maps provide static spatial information about the probability of landslide occurrence at fine spatial resolution. The hazard matrix combines three susceptibility classes (S1, low susceptibility; S2 medium susceptibility; S3 high and very high susceptibility) and three PR rate classes (L1, L2, L3), defining three hazard classes, from P1 (low hazard) to P3 (high hazard).

SILVIA has been launched by SENAMHI (https://www.senamhi.gob.pe/?p=monitoreo-silvia) at a national scale. The implementation of SILVIA as a warning system has been improved using precipitation daily forecasting generating a daily-time forecast system to cope with streams activation in subbasins (“Activación de quebradas”, https://www.senamhi.gob.pe/?p=aviso-activacion-quebrada).

How to cite: Millan, C., Lavado, W., Vega, F., Felipe, O., Acuña, J., and Takahashi, K.: SILVIA: An operational system to monitoring landslides forced by heavy precipitations at national scale in Peru, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10970, https://doi.org/10.5194/egusphere-egu2020-10970, 2020.

Landslide thresholds determined empirically through the combined analysis of rainfall and landslide data are at the core of early warning systems. Given a set of rainfall and landslide data, several methods do exist to determine the threshold: methods based on triggering events only, methods based on the non-triggering events only, and methods based on both type of rainfall events. The first are the most commonly encountered in literature. Early work determined the threshold by drawing the lower envelope curve of the triggering events “by eye”. More recent work used more sophisticated statistical approaches in order to reduce the subjectivity. Among these methods, the so-called frequentist method has become prominent in the literature. These methods have been criticized because they do not account uncertainty, i.e. the fact that there is not a clear separation between rainfall characteristics of triggering and non-triggering events. Hence, methods based on the optimization of Receiver operating characteristic indices – count of true and false positives/negatives – have been proposed. One of the first methods proposed in this sense referred to the use of Bayesian a-posteriori probability, which is the same of using the so-called ROC Precision index. Others have used the True Skill Statistic. On the other hand, use of non-triggering events only has been discussed just by a few researchers, and the potentialities of this way to proceed have been scarcely explored.

The choice of the method is usually dictated by external factors, such as the availability of data and their reliability, but it should also take into account of the theoretical statistical properties of each method.

Given this context, in the present work we compare, through Monte Carlo simulations, the statistical properties of each of the above-mentioned methods. In particular, we attempt to provide the answer to the following questions: What is the minimum number of landslides that is needed to perform a reliable determination of thresholds? How robust is the method for drawing the threshold – i.e. their sensitivity to artifacts in the data, such as exchanges of triggering events with non-triggering events due to incompleteness of landslide archives? What are the performances of the methods in terms of the whole ROC confusion matrix?

The analysis is performed for various levels of uncertainty in the data, i.e. noise in the separation by triggering and non-triggering events. Results show that methods based on non-triggering events only may be convenient when few landslide data are available. Also, in the case of high uncertainty in the data, the performances of methods based on triggering events may be poor compared to those based on non-triggering events. Finally, the methods based on both triggering and non-triggering events are the most robust.

How to cite: Peres, D. J. and Cancelliere, A.: Which method should we use to draw empirical rainfall thresholds for landslide early warning?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14773, https://doi.org/10.5194/egusphere-egu2020-14773, 2020.

Rainfall Induced landslides are one among the major natural disasters which cause destruction to lives and properties across the world. Wayanad (Kerala, India) is a region characterized by highly destructive landslides during monsoons. During the recent past, in 2018 and 2019, substantial damage to lives, agricultural land and properties have occurred due to landslides in the region. To minimize the effect of such events, a Landslide Early Warning System (LEWS) should be developed for Wayanad at the earliest. Being the major triggering factor, it is essential to study the relationship between the rainfall parameters and occurrence of landslides. Understanding the historical rainfall parameters which resulted in landslides will help to identify the critical conditions which are potent to initiate landslides in future in the study area and can effectively contribute to a LEWS. As an initial step towards achieving this goal, a study was conducted to develop regional scale rainfall threshold for the region using Intensity and Duration conditions which resulted in landslides in the recent history (2010-2018) in Wayanad. A catalogue has been prepared for the study area, collecting details of landslides happened during 2010 - 2018. Analysis has been carried out using two different statistical approaches, Bayesian and Frequentist, using 123 landslide events considered for the analysis. It is observed that both the methods are complementary and the Bayesian threshold is comparable with the Frequentist threshold with 5% exceedance probability where an intensity of 0.97mmh^-1 can trigger landslides in the region when the duration of rainfall is 24h. Further studies can be conducted for the region using advanced methods also, to find the best suited approach to define a regional scale threshold and hence an effective LEWS.

How to cite: Abraham, M. T., Satyam, N., and Rosi, A.: Empirical Rainfall Thresholds for Occurrence of Landslides in Wayanad, India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-167, https://doi.org/10.5194/egusphere-egu2020-167, 2020.

In the last decades, rainfall thresholds for landslide occurrences were thoroughly investigated, producing several different test cases and relevant technical and scientific advances. However, a recent literature review on rainfall thresholds articles (Segoni et al., 2018), published in journals indexed in SCOPUS or ISI Web of knowledge databases in the period 2008-2016, highlighted significant advances and critical issues about this topic. Only in the 11% of the analysed papers (a total of 115) there were installed instruments for measuring physical parameters other than rainfall. The implication was that, in most cases, the occurrence of landslides was forecasted considering exclusively a rainfall correlation, completely neglecting soil characteristics.

A reanalysis dataset (ERA5-Land) providing a consistent view of the evolution of land variables over several decades at an enhanced resolution has been used to evaluate the soil water content. Reanalysis combines numerical model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. A comparison between in situ measurements with the results of the model has been carried out for two sites in Norway (Eidsvoll, Morsa catchmen) with 3 different vegetation types: grass, bush, tree. The results showed a good agreement between the modelled soil water content layer 2 and 3 (respectively representing 2 - 28 cm and 28 -100 cm depths) and, respectively, in-situ measurements at 30 and 50 cm depths.

Then, 15 Norwegian basins with moraine and peat covers and, previous landslide occurrences in the period 2009-2018, have been selected for correlations. Combinations of rainfall and soil water contents that triggered and not-triggered landslides have been analysed. Rainfall-soil water content thresholds have been defined for the selected basins highlighting the important role played by soil water content, together with rainfall, in triggering landslides. The use of the soil water content contributed to increase the performance of the thresholds and to reduce the uncertainties of landslide forecast.

This paper has been conceived in the context of the project "Klima 2050-Risk reduction through climate adaptation of buildings and infrastructure" http://www.klima2050.no/, and it is included into Work Package 3.3-Early warning systems.

How to cite: Piciullo, L. and Gilbert, G.: Definition of soil water content and rainfall thresholds for landslide occurrence, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16688, https://doi.org/10.5194/egusphere-egu2020-16688, 2020.

Landslides within Nepal result both from human interventions, intensive rainfall and tectonic activity. This work presents the steps taken towards the development of a Territorial landslide early warning system (Te-LEWSs) for predicting the relative probability of the occurrence of precipitation driven landslides in the west of Nepal. Since precipitation triggers may be dominated by intense short periods of rainfall focus is given to testing the use of relationships between high resolution local observed precipitation, satellite data and Numerical Weather Models output in the development of the forecasting model. Our results show the relative importance of these alongside the significance of human activity when the model is compared against observed data sets.

How to cite: Smith, P., Buytaert, W., Paul, J., and Allen, S.: Merging different resolution rainfall products to support landslide prediction over Nepal, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20820, https://doi.org/10.5194/egusphere-egu2020-20820, 2020.

Landslides across the globe are mostly triggered by extreme rainfall events affecting infrastructure, transportation and livelihoods. The risks are rarely quantified due to lack of data, analytical skills and limited modeling techniques. Knowledge of local to global scale landslide risks provides communities and national agencies the ability to adapt disaster management practices to mitigate and recover from these hazards. In order to minimize the risks and improve characterization of community resilience to landslides, it is vital to have reliable information about the factors triggering landslides such as rainfall, well ahead in time.

Forecasting potential landslide activity and impacts can be achieved through reliable precipitation forecast models. However, it is challenging because of the temporal and spatial variability of precipitation, an important factor in triggering landslides. Evaluation of the precipitation field, associated errors, and sampling uncertainties is integral for development of efficient and reliable landslide forecasting and early warning system.

This study develops a methodology to assess the viability of using a precipitation field provided by a global model and its potential integration in the landslide forecasting system. The study focuses on the comparison between the IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GEOS (NASA Goddard Earth Observing System)-Forecast product over contiguous United States (CONUS).  GEOS model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The framework is tested on the GEOS-Forecast Model initialized at 00 UTC using daily IMERG early product as reference using both categorical and continuous statistics. The categorical statistics includes the probability of detection (POD), success ratio (SR), critical success index (CSI), and the hit bias. Continuous statistics such as correlation, normalized standard deviation, and root-mean-square error are also evaluated. Overall, GEOS-Forecast precipitation field over the analysis period (~1 year) show underestimation with respect to IMERG early for the daily accumulated rainfall. However, the probability distribution function and cumulative distribution function of both show similar patterns. In terms of correlations, POD, SR, CSI, hit bias, the performance varies with respect to the rainfall threshold used.

How to cite: Khan, S., Kirschbaum, D. B., and Stanley, T.: Assessing the viability of using GEOS-Forecast Product for Landslides Forecasting−A step toward Early Warning Systems, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20222, https://doi.org/10.5194/egusphere-egu2020-20222, 2020.

The Norwegian mass movements database contains over 33,000 registered snow avalanche and landslide events from the past 500 years and is used as an input for The Norwegian Landslide Early Warning System (LEWS). However, the usability of the database is limited by factors including a spatial bias towards transport systems and incomplete or missing information on landslide characteristics (including precise date, time or location). This has serious consequences for the definition of triggering thresholds. Sentinel-2 optical satellite data, with its frequent return period in Norway (up to three days) and relatively high resolution (10 m), could provide an alternative source of data on landslide occurrence to supplement ground-based observations and improve the information in the database.

This study examined the potential for using Sentinel-2 data to detect landslides with two approaches, using (i) a national-, and (ii) a local-survey. Both used the change in the vegetation index (denoted dNDVI) between pre- and post-event images, to identify a loss of vegetation as an indicator of landslide occurrence. Firstly, 30 well-documented landslides with a minimum volume of 1000m³ were extracted from the national database. The selected landslides occurred across all Norway between 2015 to 2017. They were searched for in Sentinel 2 images to give insight into how factors including season, slope angle, aspect ratio, land cover, landslide size influenced landslide detection using the dNDVI-method. Secondly, the same approach was applied to the Jølster area in Western Norway, where an extreme short intense rainfall event in the summer of 2019 (30 July 2019) triggered multiple landslides. For Jølster, landslides were mapped and then verified by field and helicopter observations.

For the national survey, the season was found to have the greatest effect on detectability. For spring and summer events the percentage of landslides detected was 70-75%, while for winter and autumn this dropped to 14-20%. The main reasons for non-detection were clouds, shadows, snow, and lack of green vegetation. The average acquisition window for detected events was 43.3 days. The Jølster case study represented ideal conditions for using the dNDVI-method, with a five-day acquisition window (almost cloud-free images available from two days pre-, three days post-event), low shadow, and green summer vegetation. The mapping process produced an inventory of 99 events, giving a significant increase from the 14 events registered in the database.

The results indicate that the dNDVI-method has good potential for landslide detection in late-spring and summer in Norway, however, it is not recommended later in autumn and winter. We believe that the dNDVI-method provides an option for gaining more information on the size and location of landslides, which at the present, are only registered as points in the database. For the Jølster case, this method showed a great improvement with respect to the current practice, both in terms of an increased number of landslides and spatial distribution. This suggests good potential for improving inventories of landslides, necessary in landslide hazard analyses and definition of landslide thresholds.

How to cite: Lindsay, E., Rouault, C., Fjeld, M., and Nordal, S.: Potential of dNDVI-method for landslide detection in Norway, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4328, https://doi.org/10.5194/egusphere-egu2020-4328, 2020.

In January 2020, the Istituto di Ricerca per la Protezione Idrogeologica of the Italian National Research Council, the British Geological Survey, the Norwegian Water Resources and Energy Directorate, the Swiss Federal Institute for Forest, Snow and Landscape Research, and the University of Salerno - Italy have organised a 3-day workshop on regional Landslide Early Warning Systems (LEWS). The workshop, held in Perugia, Italy, follows a previous meeting held in Oslo, Norway, in October 2016. The main aims of the initiative are: to collect experiences from worldwide invited experts involved in the design, the development, the operation or the analysis of LEWS, and to exchange knowledge, experiences, challenges and best practices.

The first day of the workshop is dedicated to presentations from identified participants on specific topics relevant for the optimal design, implementation, and operation of global, national and regional LEWS. This is followed by a long discussion session, aimed at addressing many of the issues that are relevant for regional LEWS, including system performance, warning communication and involvement of the stakeholders. The second day is organized around four round tables on the following four topics: (i) data; (ii) landslide forecast models; (iii) warning models; (iv) scope, management structure, stakeholder involvement, and communication. The third day is focused on summarizing and formalizing the main issues discussed in an open document to be later shared with colleagues interested in LEWS.

The final purpose of the workshop is to establish and consolidate a community of experts in LEWS and to build relationships with other communities (e.g., meteorologists, climate scientists, communications scientists). This will help to level up the quality of both theory and practice, and to define standards in early warnings in order to provide timely advisories and to initiate emergency responses to landslides (particularly rainfall-induced) avoiding or reducing life and economic losses. The main outcomes of the workshop, the most debated issues, and the key recommendations included in the open document will be presented and shared.

How to cite: Calvello, M., Devoli, G., Freeborough, K., Gariano, S. L., Guzzetti, F., Reeves, H. J., Stähli, M., and LEWS2020 workshop participants, T.: LEWS2020 workshop on regional Landslide Early Warning Systems – experiences, progresses and needs, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9917, https://doi.org/10.5194/egusphere-egu2020-9917, 2020.

Soil thickness has a great importance in many processes such as slope stability, seismic local effects, landscape evolution, soil moisture distribution. It is a fundamental parameter in many environmental models. In local scale applications, direct or indirect measurements can be easily used to accurately measure soil thickness. Nevertheless, in large scale applications, it is often difficult to obtain a reliable distributed soil thickness map and existing methods have been applied only to test sites with shallow soil depth. In this research, we cope with this limitation showing a first attempt to test the applicability of some state-of-the-art soil thickness models in a test site characterized by a complex geological setting and soil thickness values extending from zero to forty meters. Two different approaches were used to derive distributed soil thickness maps: a modified version of the Geomorphologically Indexed Soil Thickness (GIST) model, purposely customized to better take into account the peculiar setting of the test site, and a regression performed with a machine learning algorithm, the Random Forest (RF), combined with the geomorphological parameters of GIST. The proposed models are implemented in a geographic information system environment on a pixel-by-pixel basis. Finally, validation quantifies errors of the two models and a comparison with geophysical data is carried out. The results showed that the GIST model is not able to fully grasp the high spatial variability of soil thickness of the study area: mean absolute error was is 10.68 m with 7.94 m standard deviation, and the frequency distribution of residuals showed a proneness to underestimation. In contrast, RF returned a better performance (mean absolute error is 3.52 m with 2.92 m standard deviation), and the derived map could be considered to be used in further analyses to feed models that require a distributed soil thickness map as a spatially distributed input parameter.

How to cite: Xiao, T., Yin, K., Yang, B., and Liang, X.: Geomorphology-based methods of generating soil thickness map in a section of Wanzhou County, Three Gorges reservoir, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1771, https://doi.org/10.5194/egusphere-egu2020-1771, 2020.

The power grid is one of the most important lifeline projects in modern society. However, the complex transmission network was fixed to the ground through millions of transmission towers, which were inevitably being affected by various types of geohazard. The failure of local facilities may further cause large-scale power outages, leading serious social impact and economic loss. From this perspective, this study aims to develop a territorial landslide early warning system (Te-LEWS) for the high-voltage transmission line coverage area by introducing a novel method, combining landslide inventory and susceptibility maps, rainfall thresholds and real time rainfall forecast, transmission tower vulnerability analysis and GIS-based dynamic alert system. To this objective, the power grid system consisting of over 130,000 high-voltage transmission towers and covering an area of 7 provinces in China was selected as study objective to conducted susceptibility mapping with different classification methods (information value, random forest and support vector). The rainfall threshold of each county was calculated through analyzing a 7 consecutive day rainfall data for the major historical landslide event. Instead of an ordinary landslide risk assessment practice within the transmission line coverage area, this study mainly focuses on the landslide risk over transmission towers and tries to generate an risk assessment result over a specific risk bearing element with linear distribution characteristic, in this case the electricity transmission lines. With real-time predicted rainfall value as input variable, a dynamic landslide warning system was established on a pixel basis, to identify the transmission towers that are potentially vulnerable to landslide disasters. The performance of the proposed Te-LEWS system were validated through the historical rainfall data and the landslides events from 2015-2019, to gain a comprehensive evaluation on its warning accuracy. Results suggest that the system has a high warning success rate and the false alarm was significantly reduced. In such case, the proposed To-LEWS would greatly support the grid authorities in reducing disaster risks and retrieving huge economic loss. The study shed a new light on the risk analysis method of a specific linear distributed risk bearing element towards geohazard, to demonstrate its potential over wide areas, an application to a huge area in China was shown and discussed.

Keywords: Landslides; GIS; early warning system; disaster risk reduction; high-voltage transmission line

How to cite: Liu, S., Yin, K., Zhang, Y., Xiao, T., and Lin, W.: Application of a dynamic terrestrial landslide early warning system in a wide high-voltage transmission line coverage area, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2093, https://doi.org/10.5194/egusphere-egu2020-2093, 2020.

The Three Gorges Reservoir area (TGRA) is one of the most landslide-prone areas in China. Landslide prediction is important for the mitigating of geohazards and it is also an essential component for developing landslide early warning systems. In the TGRA, the preparatory, triggering and controlling factors of landslides are very diverse. The local geological conditions and variations in the controlling factors result in pulsed movements of landslides, the so-called “step-like” deformation of landslides. Most of the existing predictive models are based on a single algorithm including static models and dynamic models. This study proposes an Ensemble model combined with a static model and a dynamic model which combined the advantages of the two models for landslide displacement prediction.

Based on displacement monitoring data of the Shengjibao landslide in the Three Gorges Reservoir area(TGRA), which is not a typical “step-like” landslide but with the “step-like” characteristic in its displacement-monitoring curve, long short-term memory neural networks (LSTM) model, support vector regression (SVR) model and an Ensemble model based on LSTM model and SVR model were proposed to predict its displacement. Moving average methods (MAM), were used to decompose the cumulative displacement into two parts: trend and periodic terms. The single-factor LSTM model and the single factor SVR model were proposed to predict the trend terms of displacement. Multi-factors LSTM model and multi-factors SVR model were proposed to predict the periodic terms of displacement. Precipitation, reservoir water level, and previous displacement are considered as the candidate factors for the multi-factors LSTM model and the multi-factors SVR model predictions. Meanwhile, an Ensemble model combined with the LSTM model and the SVR model was also proposed to predict the decompositions of displacement.

The results show that the LSTM model and the SVR model display good performance, the Ensemble model outperforms the other models, and the prediction accuracy can be improved by considering advantages from different models.

How to cite: Jiang, H., Yin, K., and Glade, T.: Displacement prediction of Shengjibao landslide based on an ensemble model in Three Gorges Reservoir Area, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9478, https://doi.org/10.5194/egusphere-egu2020-9478, 2020.

Landslides along river, lake, reservoir and ocean shorelines may trigger impulse waves when they slide into the water body with a high velocity. This secondary process can extremely expand the area threatened by the landslide beyond its primary impact zone. Since the impoundment of the Three Gorges Reservoir in 2003, several landslides have caused huge property damage and several casualties due to an insufficient understanding of and reaction to impulse waves as a secondary process in landslide disaster risk management. This contribution aims to provide an integrative approach for risk perception and mitigation of a local landslide considering impulse waves as a secondary disaster risk.

Jiuxianping landslide is located in the middle part of the Three Gorges Reservoir in China. Featuring a large thick layer of rock slope, the elevation of the landslide ranges from 95 to 385 m a.s.l., and the volume is approximately 5.7×10⁷ m³. The trailing edge of the landslide appeared as a more than 100 meters transverse tensile crack with an opening width of at least 25 cm in 2008, leading to damaged housing. The landslide stability is strongly influenced by rainfall and the reservoir water level. More than 300 people still live at the landslide site and there is a shipyard in operation at its toe.

As a new perspective to detect secondary disasters, the areas with the highest risk and probability of damage under different conditions were estimated using an auto search function in GeoStudio and the Morgenstern-Price method. Then, we simulated the landslide runout as well as wave generation and propagation using Tsunami Squares to predict the risk intensity and impact area of the generated impulse waves. Lastly, we evaluated the warning levels for different scenarios and proposed the area restricted for navigation at corresponding warning levels. Our case study demonstrates the necessity and the importance of considering secondary disaster risks such as impulse waves in landslide early warning system.

How to cite: Zhang, Y., Yin, Y., Evers, F., and Liang, X.: Landslide Early Warning Systems Considering Impulse Waves – a Case Study of the Jiuxianping Landslide in the Three Gorges Reservoir Area, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9959, https://doi.org/10.5194/egusphere-egu2020-9959, 2020.

More than 2500 landslides in the Three Gorge Reservoir (TGR) region are affected by reservoir impoundment and seasonal water fluctuation since 2003, accounting for huge property loss and remaining a great threat to the local residents. In order to detect these landslides with potential threat, a series of early warning systems (EWS) at different scales of analysis were installed in this area, and have gained significant consequences in issuing alarm information. However, some catastrophic landslide, e.g., Hongyangzi landslide and Gongjiafang landslide, indicating that landslides in the TGR area should be considered as disaster chains, as the landslide-induced waves may have more serious influences. Therefore, it is necessary to carry out a prospective risk perception about landslide-induced wave based on the existing early warning. This paper aims to assess the risk of impulsive wave in the TGR with a quantitative method, and a new perspective about risk mitigation is proposed with the purpose of controlling the size of the surge. The risk assessment method mentioned is applied to Ganjingzi landslide, a typical colluvial landslide in the Wu Gorge, activating by reservoir impoundment and fluctuation. The EWS installed indicates that the landslide undergoes a retrogressive evolution, and local failure of the strong-deformation area will decrease the stability of the landslide and induce the movement. By preforming Tsunami Squares method, potential waves generate by Ganjingzi landslide with different failure situations are simulated. The quantitative risk analysis is carried out with the consideration of both sailing and moored vessels in the Yangtze River. The result reveals that impulse wave induced by the strong-deformation area causes the maximum economic loss, of about 0.59 million USD. Moreover, a new risk mitigation measure is designed to lower the speed of landslide intrusion into the reservoir. Compared with the traditional control measure that only use anti-sliding piles (about 21.4 million USD), reducing the load around the trailing edge of landslide and settling anti-sliding piles in the strong-deformation area (about 3.5 million USD) is more economical and effective. Overall, the proposed method for risk assessment and mitigation may provide a basis for the risk management of geological hazards and early warning in the other reservoir areas with similar geological conditions and environmental backgrounds.

How to cite: Liang, X., Yin, K., Chen, L., Du, J., Xiao, T., and Zhang, Y.: Early warning and risk perception of landslide hazard chain in the Wu Gorge, Yangtze River, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9725, https://doi.org/10.5194/egusphere-egu2020-9725, 2020.

In general the determination of landslide rainfall threshold is mainly based on the empirical statistics of historical landslide disaster and rainfall. However, which often results in unsound prediction accuracy of regional rainfall-type landslide due to neglecting the difference effect of rainfall on diverse types of landslide disaster. In order to obtain accurate critical threshold of rainfall inducing landslides, based on the influence of rainfall on landslide mechanism and hydrological, in this paper a precise geological model is established and the soil water, ground water level and slope position shift of the landslides are monitored in real-time. By coupling the simulation results with the relationship between rainfall process and slope deformation, the regulation of slope failure induced by rainfall is discussed. The results indicate that a cumulative rainfall of 150 mm can make the landslide fully saturated, and generate the overall landslide instability along the soil-rock interface. Moreover, when the cumulative rainfall reaches 90 millimeter and lasts for more than 3 days, the displacement of bedding rock landslide exceeds 10 cm. This may because of the deterioration of the mechanical properties and the increase of the pore water pressure caused by the rainfall infiltration. The prediction criteria for landslide instability established from mechanism analysis can provide a theoretical basis for accurate prediction of rain-sensitive landslides.

How to cite: Lin, W., Yin, K., Li, Y., and Li, Y.: Rainfall-type landslide prediction based on landslide mechanism, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15745, https://doi.org/10.5194/egusphere-egu2020-15745, 2020.

The Ultra High Voltage (UHV) power grids in China are playing an important role of large-region power supply, contain long-distance interconnected channels that have to span a variety of different geomorphic units. However, geological disasters around transmission lines can threaten the reliability of UHV system. Landslides, one of the most common geological disasters in China, can affect the stability of transmission towers by shearing their foundations or involving them to move overall. Once a power tower is destroyed catastrophically, it may lead to widespread power outages, which can result in serious social adverse effects and huge economic losses. This paper presents a multi-technology early warning system for monitoring landslide deformation and observing transmission tower stability. In this system, there are three categories of monitoring information, including landslide displacement, external hydrological conditions and the stability of tower, integrated that are critical to predicting slope stability. To implement this system, a variety of techniques are employed. Firstly, advanced aviation technologies, such as Interferometric Synthetic Aperture Rader (InSAR) and unmanned aerial vehicle (UAV) are used to monitor the overall deformation of the landslide. Absolute surface displacement, subsurface displacement and relative displacement of cracks are recorded by the Global Navigation Satellite System (GNSS), deep inclinometer cooperating with optical fiber sensors and surface crack meters respectively. Second, the two main factors influencing landslide deformation, rainfall and underground water level, are observed by rain gauge and pressure gauge respectively. Third, in order to evaluate the stability of tower, earth pressure sensors are installed on the four foots of the tower foundation and pylon inclinometer is installed on the tower body. This system has been applied to the Doupozi landslide, where a tower of 500KV Shen-wan UHV line is located. Compared with that of traditional methods, the recording process of the multi-technology system is automatic and continuous, which can save human resource cost. Besides, the integrated monitoring data obtained from this system can be used to analyze the interaction between geological disasters and power towers. The multi-technology early warning system is also suitable for risk mitigation of transmission lines, oil and gas pipelines, highways, railways and other linear projects in mountainous areas.

How to cite: Huang, C., Yin, K., and Liang, X.: Real-time monitoring and early warning of transmission tower foundations under landslide disasters, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20946, https://doi.org/10.5194/egusphere-egu2020-20946, 2020.

For the recent years, highway safety control under extreme natural hazards in China has been facing critical challenges because of the latest extreme climates. Highway is a typical linear project, and neither the traditional single landslide monitoring and early warning model entirely dependent on displacement data, nor the regional meteorological early warning model entirely dependent on rainfall intensity and duration are suitable for it. In order to develop an efficient early warning system for highway safety, the authors have developed an early warning method based on both monitoring data obtained by GNSS and Crack meter, and meteorological data obtained by Radar. This early-warning system is not each of the local landslide early warning systems (Lo-LEWSs) or the territorial landslide early warning systems (Te-LEWSs), but a new system combining both of them. In this system, the minimum warning element is defined as the slope unit which can connect a single slope to the regional ones. By mapping the regional meteorological warning results to each of the slope units, and extending the warning results of the single landslides to the similar slope units, we can realize the organic combination of the two warning methods. It is hopeful to improve the hazard prevention and safety control for highway facilities during critical natural hazards with the progress of this study.

How to cite: Xiao, R.: Development of early warning system for linear engineering - a case study on highway in Sichuan, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22475, https://doi.org/10.5194/egusphere-egu2020-22475, 2020.

This study is to analyze the evacuation behavior of residents living in the mountainous area and predict landslide disasters during heavy rain. 70% of Japan has are mountainous areas, and landslide disasters have occurred due to heavy rains caused by typhoons and heavy rainfall, etc. the annual average amount of damage caused by landslide disasters is 1000 in recent years. Also, landslide disaster warning information and evacuation information are important, it is difficult to predict landslide disasters, however, if we issued the evacuation advisory when the disasters already happened, there will be not enough time for the evacuation. In order to protect residents from such disasters, it is important to clarify "what information is effective for evacuation" and "when should those information be released?" Therefore, we conducted a survey on the residents in the mountainous areas which suffered from the heavy rain disaster in 2017 and analyzed the answers.

As a result, some residents evacuated before the evacuation information was issued. Because some landslide disasters occurred even before the first evacuation information was transmitted, and they felt danger. This result shows that the early information based on the prediction of the disasters is important in mountainous areas.

Therefore, we suggested a method for predicting landslide disasters, the method uses a rainfall and runoff tank model with high reproducibility and robustness of geological characteristics and uses the cumulative rainfall at the time of disaster occurrence as an index. As a result, this model predicted the occurrence of the landslide disaster 3 hours earlier by using forecasted rainfall. it is an effective method.

How to cite: yoo, H., koyama, N., and yamada, T.: A Study on Parameters of Rainfall Runoff Model and The Prediction Method of Landslide Disaster in Mountainous Area, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18893, https://doi.org/10.5194/egusphere-egu2020-18893, 2020.

The existing Landslide Early Warning System (LEWS) for Indonesia was developed using rainfall thresholds, which were derived from the relationship between rainfall inducing landslides and landslide events in the past. The system utilized the median values of 1-day and 3-day cumulative observed rainfall for determining the threshold and a relatively limited number of landslide events throughout Indonesia during the period of the system development. The system employed a single set of threshold values for all regions despite the possibility of differences in rainfall intensity characteristics for each region. For prediction, the system used rainfall data derived from satellite products and rainfall forecast data with a spatial resolution of 0.25° x 0.25°, which is not adequate for catchment-scale landslide analysis.

We attempt to improve the LEWS by applying a statistical approach based on rainfall intensity and duration for a longer time-series of data set. Instead of determining the thresholds for national scale, we focus on the Special Region of Yogyakarta and surrounding cities in Central Java which are prone to landslides but have high population density. In addition to that, we also perform preliminary exploration of the potential of the output of high-resolution numerical weather prediction in simulating the rainfall inducing the landslides for several historical landslide events. This study is part of a project called BILEWS, a Blueprint for an Indonesian Landslide Early Warning System, which aims to develop threshold for landslides and debris flows as the basis for early warning to be applied at several test sites in Java, using tailored rainfall data, combined with empirical and physically-based hydrological and landslide models, as well as historical landslide data.

How to cite: Satyaningsih, R., Sopaheluwakan, A., Nuryanto, D. E., Nuraini, T. A., Mulyana, A. R., Hidayat, R., Munir, M. D., and Jetten, V.: Exploration of the characteristics of landslide triggering rainfall using rain gauge and numerical weather prediction for Yogyakarta and Central Java, Indonesia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19449, https://doi.org/10.5194/egusphere-egu2020-19449, 2020.

Most of landslide and debris flow take place during rainy season (June-September) in Korea. It is well known that rainfall is one of the most significant triggering factors in Korea. The mountainous area is composed of about 70%, which is a terrain where slope disaster can occur frequently. In addition, there is a great exposure to slope disaster due to rapid urbanization. The main objective of this paper is to assess landslide physical vulnerability using susceptibility map with hazard level-based rainfalls for urban area in Busan, Korea. Firstly, we computed rainfall thresholds for different hazard levels by using a quantile-regression method based on 258 landslide occurrence data from 1999 to 2019. Secondly, the combined landslide susceptibility map was developed according to hazard level-based rainfalls using both physical-based model and statistical-based model. To assess the vulnerability, source area were extracted from landslide high potential area based on the combined susceptibility map. The extracted source area is used to evaluate the propagation of debris flow. Affected building of debris flow was calculated using propagation results of debris flow. Physical vulnerability assessment was carried out using the affected building of debris flow from the analysis of the propagation of debris flow. Finally, vulnerability index (0 to 1) were categorized and evaluated by the degree of damage of the building. The proposed techniques can sufficiently contribute to protect of human causalities, property loss and also diminish the risk from landslides.

How to cite: Lee, J.-S., Ha, Y.-S., Song, C.-H., Kang, H.-S., and Kim, Y.-T.: Landslide physical vulnerability assessment using susceptibility map with hazard level-based rainfalls: a case study to Busan, Korea, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13530, https://doi.org/10.5194/egusphere-egu2020-13530, 2020.