NH3.7

The LANDSLIP (LANDSLIde multi-hazard risk assessment, Preparedness and early warning in South Asia) research project commenced in 2016 with the aim of developing a prototype regional landslide forecasting and early warning system to help build resilience to hydrologically related landslides in two case study regions of India, the Nilgiris and Darjeeling. Here we present our pathway and reflections on the development of the LANDSLIP prototype LEWS (landslide early warning system) and its component parts, which includes a decision-support information dashboard and protype daily landslide forecast bulletin.

Central to the LEWS was a common and shared understanding of its conceptual framework. In other words, what were the components of the LEWS and how did they interact? To develop our LEWS conceptual framework we engaged a LANDSLIP interdisciplinary team which consisted of a range of researchers and practitioners from the British Geological Survey, Kings College London, Amrita University, Consiglio Nazionale delle Ricerche, Practical Action, UK Met Office, and Newcastle University. We developed the conceptual framework in collaboration with in-country partners (e.g. Save the Hills, Keystone, National Centre for Medium Range Weather Forecasting (NCMRWF) and District Management Authorities). As the nodal agency for landslides in India, the Geological Survey of India (GSI) partnered with the project and provided a focal point for the prototype LEWS.

The result of our final conceptual framework for the LEWS consisted of: (A) Dynamic forecast modelling data products, (B) semi-static landslide data layers feeding into (A), and (C) additional data sources. (A) to (C) then feed into (D) a LEWS information dashboard (data and physical models display). Finally, our conceptual framework included the communication flows, operating procedures and guidance documentation surrounding these communications. The aim of the conceptual framework was to help ensure that the prototype LEWS would create insight from the data and models and lead to behavioural change by recipients of the daily landslide forecast bulletins (i.e. District authorities).

The development of the LEWS conceptual framework occurred, not by design but out of necessity. At the start of the project, it was assumed all partners in the consortium had a shared vision for the LEWS. However, it quickly transpired that there were slightly different interpretations and nuances to this vision, which resulted in disparate working and a degree of disenfranchisement. By acknowledging this, and exploring it through a series of discussions and workshops, the consortium developed a shared and common conceptual framework for LANDSLIP’s prototype LEWS. This common framework helped guide the project and enabled all partners to realise how everyone contributed to the overall vision of the project. This session will cover some of the challenges, processes, outcomes and learning encountered through developing a conceptual framework for LANDSLIP’s prototype LEWS.

How to cite: Bee, E. and Malamud, B. and the LANDSLIP project partners: Developing the conceptual framework for a prototype government-led regional Landslide Early Warning System in India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4990, https://doi.org/10.5194/egusphere-egu22-4990, 2022.

Early Warning Systems are one of the most effective tools for reducing disaster risk, however the development of Landslide Early Warning Systems (LEWS) is complicated due to the random nature of landslide occurrence and the uncertainty in mapping the parameters that cause them. Local LEWS have been effective for known landslides, but regional scale LEWS based on rainfall thresholds have not been very effective up to now. In recent years physically-based multi-hazard models have been developed which allow to predict mass movement hazards at a local scale. However, it is still difficult to apply these in LEWS in a local scale due to the coarse resolution of rainfall estimates and the high computational modelling requirements for running such models real-time. On the other hand, machine learning approaches have been used to assess the relationship between the distribution of the landslide hazard and the catchment morphometric features.

This research applies a physically-based multi-hazard model combined with machine learning to forecast the mass movement impact, based on rainfall predictions in an area in Java, Indonesia. The landslide inventory was developed using a combination of local reporting data and machine learning techniques. The integrated physically-based multi hazard model OpenLISEM is used to create a database of hazard intensity maps under various rainfall scenarios. The resulting hazard intensity maps are subsequently used to subdivide the area in homogeneous zones for which warning levels are given. Machine learning is used to query the database and extract the most likely hazard intensity map based on the rainfall prediction. The intensity is then combined with exposure information of people, buildings, transportation infrastructure and agriculture to provide impact forecasts. The output of combining physically-based models with machine learning approaches has the potential to improve the prediction of landslide impact. The method also allows to make more specific local decisions related to the actions for various levels of warning (e.g. increased vigilance, removal of resources, evacuation of people). The method is currently under development as part of an Indonesian-Netherlands collaboration project to develop a blueprint to use tailored rainfall data, in combination with empirical and physically-based hydrological and landslide models, and historical landslide data for the development of thresholds for landslides and debris flows, as the basis for early warning at settlement level, applied to several test sites in Java.

How to cite: Subiyantoro, A., Westen, C. V., Bout, B. V. D., Victor, J., Muntohar, A., Mushthofa, A., Yuniawan, R. A., and Satyaningsih, R.: Development of a local impact-based Landslide Early Warning System using physically-based multi-hazards modelling and machine learning in Java, Indonesia., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6969, https://doi.org/10.5194/egusphere-egu22-6969, 2022.

As observed worldwide during the last decades, landslides are one of the deadliest natural hazards in mainland Portugal and Azores archipelago being responsible for significant direct and indirect societal and economic losses, justifying the implementation of a landslide early warning system at the regional scale.

The BeSafeSlide project aims to develop and implement a soft technology/low-cost prototype for precipitation-triggered landslide early warning system (LEWS) in Portugal. We plan it to allow be adaptable to a changing climate and a changing land use by working with different climate scenarios. Future changes on regional rainfall patterns due to climate change were evaluated in the LEWS for 2071-2100 period, considering two emission scenarios: RCP 4.5 and RCP 8.5. To evaluate future exposure trends and effects in risk analysis, simulations of changes in land use, by the end of the 21th century, will be carried out. The uncertainty of future projections will be addressed by developing a set of different scenarios.

The LEWS prototype for Portugal is sustained on different types of regional rainfall thresholds for landslide occurrence based on daily/hourly rainfall series available for each BeSafeSlide study area. The proposed prototype aims at integrating 3-day rainfall forecasts on rainfall thresholds monitoring and on dynamic physically based susceptibility models, to anticipate changes in hydrological conditions and consequently on the spatio-temporal occurrence of landslides. Special attention is given to two different types of rainfall-triggered landslide events, recognized as responsible for shallow and deep-seated landslides occurrence on natural slopes, which are permanently monitored within the regional early warning system in hotspot risk areas: (i) landslide events associated to intense, short-duration rainfall periods; and (ii) landslide events associated to long-lasting rainfall periods.

The LEWS main goals are to provide information to civil protection services to anticipate and manage people’s evacuation from landslide prone areas and to ensure the maintenance and operability of regional transport, energy and communications networks and the safeguarding of people´s lives. Although the LEWS is being developed within the framework of Portugal we expect to be applicable in different settings. The application of the LEWS will define warning communication procedures, assess response capacity of stakeholders and develop social capacity practices, to reduce vulnerability and mitigate risk, providing a reduction of affected people, economic losses and critical infrastructures/basic services disruptions.

Acknowledgments: This work was financed by national funds through FCT (Foundation for Science and Technology, I. P.), in 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 the Research Unit UIDB/00295/2020 and UIDP/00295/2020.

How to cite: Oliveira, S. C., Zêzere, J. L., Trigo, R. M., Marques, F., Tavares, A., Marques, R., Ramos, A. M., and Melo, R.: BeSafeSlide – A Landslide early warning soft technology prototype to improve community resilience and adaptation to environmental change , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10685, https://doi.org/10.5194/egusphere-egu22-10685, 2022.

Landslides are one of the most disastrous natural hazards that frequently occur in Indonesia. Since 2017, Balai Sabo has developed an Indonesia Landslide Early Warning System (ILEWS) by utilizing a single rainfall threshold for an entire nation. This condition might lead to inaccuracy of the landslide prediction. Therefore, this study aims to improve the accuracy of the system by updating the rainfall threshold. This study focused on Java Island, where most of the landslides in Indonesia occur. We analyzed 420 landslide events with the one-day and three-day cumulative rainfall for each landslide event. Rainfall data were obtained from the Global Precipitation Measurement (GPM), which is also used in the ILEWS. We propose four methods to derive the thresholds, 1^st is the existing threshold applied in the Balai Sabo-ILEWS, the 2^nd and the 3^rd use the average and minimum of rainfall that trigger landslides, respectively, and the 4^th uses the minimum values of rainfall that induce major landslides. We employed the Receiver Operating Characteristic (ROC) analysis to evaluate the predictability of the rainfall thresholds. The 4^th method shows the best result compared to the others, and this method provides a good prediction of landslide events with a low error value. The chosen threshold will be used as a new threshold in the Balai Sabo-ILEWS.

How to cite: Yuniawan, R. A., Rifai, A., Faris, F., Westen, C. V., Jetten, V., den Bout, B., Subiyantoro, A., Muntohar, A., Musthofa, A., Hidayat, R., Hidayah, A., Ridwan, B., Priangga, E., Satyaningsih, R., and Sutanto, S.: Revisited Rainfall Threshold In the Indonesia Landslide Early Warning System , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7863, https://doi.org/10.5194/egusphere-egu22-7863, 2022.

Landslide occurrences are governed by precondition factors and triggering factors. Hence, it is desirable to include physical parameters representing precondition factors in determining thresholds over which landslides are likely to occur. In the case of rainfall-triggered landslides, such parameters include soil properties and land cover information. However, high-resolution data required for a physical-based approach are rarely readily available for a large area, especially in developed countries. Therefore, in developing a landslide early warning system (LEWS) for a large area, rainfall thresholds are derived by optimizing the usage of rainfall datasets.

This study aims to derive rainfall thresholds from a meteorological perspective regarding rainfall event characteristics (e.g., cumulative rainfall, intensity, duration) that result in trigger the landslides in Progo Catchment in Java, Indonesia.  We explore various hourly rainfall datasets, including rain gauge measurements and satellite-based rainfall products (e.g., the Japan Aerospace Exploration Agency’s Global Satellite Mapping of Precipitation/GSMaP and the Climate Prediction Center/National Oceanic and Atmospheric Administration’s morphing technique/ CMORPH), to derive the thresholds. The effect of rainfall event characteristics is assessed by clustering the rainfall event types and preceding conditions associated with different triggering mechanisms leading to the landslide occurrences. The rainfall thresholds are then derived using the frequentist method for each group, hence “dynamic.” 

How to cite: Satyaningsih, R., Jetten, V., Ettema, J., Sopaheluwakan, A., Eko Nuryanto, D., Umer, Y., Astuti Nuraini, T., and Anggraeni, R.: Dynamic Rainfall Thresholds for Landslide Early Warning System in Progo Catchment, Java, Indonesia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12227, https://doi.org/10.5194/egusphere-egu22-12227, 2022.

The rain that falls weeks or months before the occurrence of landslides can play a major role in the failure process, therefore it is crucial to account for it in hazard assessments and warning systems. It is especially relevant in tropical areas, where the amount of water that falls during wet seasons can be very high. In the Philippines, rainfall and typhoon events trigger Multiple-Occurrence Regional Landslide Events (MORLEs, Crozier, 2005), which cause hundreds of fatalities and significant economic damage every year.

Satellite-based rainfall measurements (IMERG GPM) associated with three typhoons that triggered MORLEs in the area of Itogon (Benguet, Philippines) and water infiltrated into the soil during the previous months are analysed. Data from the three typhoons are compared with 560 high intensity rainfall events (from period 2000-2020) that did not trigger regional landslide events. Results show that landslides occurred when typhoon rainfall exceeds 300 mm and the water infiltrated was higher than 1000 mm in the previous months. For one specific landslide-triggering typhoon event, satellite-based soil moisture data (1 m top soil layer) are analysed and compared to other non-landslide triggering rainfalls. Results do not show a clear correlation of critical rainfall and soil moisture values that triggered landslides.

The findings of this work highlight that the antecedent rainfall, and in particular its infiltration below the top soil layer, plays a major role in the triggering process of landslides, especially in tropical regions.

 Crozier, M.J. Multiple-occurrence regional landslide events in New Zealand: Hazardmanagement issues. Landslides 2, 247–256 (2005). https://doi.org/10.1007/s10346-005-0019-7

How to cite: Abancó, C., Medina, V., Bennett, G. L., Matthews, A. J., and Hürlimann, M.: Analysis of landslide-triggering rainfalls in a typhoon-prone region of the Philippines, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7819, https://doi.org/10.5194/egusphere-egu22-7819, 2022.

In this work, we use a probabilistic approach for modelling rainfall thresholds (Caine 1980) triggering shallow landslides with a case study for the Alpes-Maritimes region (France).

In particular, the CTRL-T algorithm (Melillo and al. 2018) is tested to output critical rainfall thresholds, based on the accumulated rainfall – duration parameters (E-D), for different exceedance probabilities from respectively a landslide and two climate datasets. The first climate dataset stores high resolution gridded rainfall data (1km resolution, hourly) and the second climate dataset contains lower resolution gridded rainfall, snow, temperature and evapotranspiration data (8km resolution, daily); the first dataset provides the rainfall records directly used for defining the rainfall events and then for the threshold construction; the second one enables to assess the region’s climate via parameters imported in CTRL-T. The thresholds are then validated using a method designed by Gariano and al. (2015).

Several improvements are made to the method. First, potential evapotranspiration values approximated from temperatures and latitudes in one of the process’ steps are replaced by values from the second climate dataset, the result accounting best for the regional climate. Then, climate-specific duration values, used to split the raw rainfall records in events and sub-events, are computed for each mesh point. This second modification enables considering the heterogeneity of the Alpes-Maritimes climate.

Rainfall thresholds are eventually obtained for different exceedance probabilities, first from a set of probable conditions (MRC), then from a set of highly probable conditions (MPRC). The validation process strengthens the analysis as well as enables to identify best performing thresholds. This work represents novel scientific progress towards landslide reliable warning systems by (a) making a case study of probabilistic rainfall thresholds for Alpes-Maritimes, (b) using for the first time high-resolution rainfall data and (c) adapting the method to climatically heterogeneous zones.

How to cite: Barthelemy, S., Bernardie, S., and Grandjean, G.: Assessing rainfall triggering of shallow landslides with an automatic tool generating thresholds: a case study for the Alpes-Maritimes region, France, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5864, https://doi.org/10.5194/egusphere-egu22-5864, 2022.

El Salvador is a country that is geologically young in most of its territory, with steep slopes covered with unconsolidated volcanic sediments. It is frequently affected by extreme weather events and it also has the highest population density in Central America, which makes it very vulnerable to landslides. Therefore, predicting when landslides will occur it is necessary, and rainfall thresholds are a useful tool for that purpose. In this study, thresholds represented by cumulated rainfall (E, in mm) and duration (D, in hours) for shallow landslide initiation in El Salvador have been generated, with the objective of using them in the future in a national landslide early warning system. The thresholds have been delineated with the CTRL-T code (Melillo et al, 2018), which automatically reconstructs the rainfall conditions that triggered the landslides and determines thresholds at different non-exceedance probabilities. Rainfall data from an automatic rain gauge network and landslide data occurred in the period of 2004 to 2019 were used. A validation of the thresholds with the procedure introduced by Gariano et al (2015) has been conducted, using rainfall and landslide data for the year 2020. There are not previous ED thresholds at national level created for El Salvador, so a comparison with global and national thresholds from other countries was done.

References

Gariano S.L., Brunetti, M.T., Iovine, G., Melillo, M., Peruccacci, S., Terranova, O., Vennari, C., Guzzetti, F. (2015). Calibration and validation of rainfall thresholds for shallow landslide forecasting in Sicily, southern Italy. Geomorphology 228:653–665.

Melillo, M., Brunetti, M. T., Peruccacci, S., Gariano, S. L., Roccati, A., & Guzzetti, F. (2018). A tool for the automatic calculation of rainfall thresholds for landslide occurrence. Environmental Modelling & Software, 105:230-243.

How to cite: Reyes, M.: Rainfall thresholds for shallow landslides triggering in El Salvador, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6762, https://doi.org/10.5194/egusphere-egu22-6762, 2022.

Several territorial landslide early warning systems in different parts of the world are based on empirical rainfall thresholds for landslide triggering. The calculation of such thresholds, using rainfall measurements gathered from rain gauges, has been examined frequently, especially considering uncertainties, modeling complexity, spatial assumptions, and analytical tools. Installed rain gauge networks that are spatially clustered in crowded areas have different spatial and attribute settings based on landslide occurrence conditions, such as rainfall record accessibility, processing, and usability, as well as specific locational, morphological, and hydrological settings.

In this research work, we introduce an automatic tool called DEWS (Distance, Elevation, Watershed, and Slope unit) for rainfall-induced landslide spatial reference rain gauge selection. DEWS can be considered supplementary and complementary to the CTRL-T tool (Calculation of Thresholds for Rainfall-induced Landslides Tool) developed earlier, and works on a macro-to-micro scale of the spatial components of  CTRL-T rain gauge selections. The output information, i.e. the list of selected reference rain gauges, can be used as input for CTRL-T to calculate frequentist rainfall thresholds at different non-exceedance probabilities. The DEWS tool fills the gap of the current literature, where the selection of reference rain gauges is mostly based on the nearest distance location and on statistical or manual procedures, without considering the morphological and hydrological settings of the area in which landslides occurred.

The tool allows extracting rain gauges referring to landslide locations by employing four spatial filters: F1 (Distance), F2 (Elevation), F3 (Watershed), F4 (Slope unit), needing only a DEM, the coordinates of landslide and rain gauge locations and the parameters of the filter’s algorithms as inputs. More in detail, F1 selects rain gauges within a specified buffer distance from the landslide locations using the setting parameters and the coordinates of the landslides and rain gauges. Then, F2 uses the DEM to extract the elevation of the rain gauges and the landslides and then calculates the differences within each buffer circle; therefore, the filter keeps only the rain gauge with closest elevation values to each landslide (within F1 results) using the recommended/preferred/ or allowable elevation difference defined by the parameter’s settings. In F3, the rain gauges falling in the watershed that contains the landslide locations are extracted (within F1 and F2 results). F4, which is the smallest and most focused filter, uses a previously developed tool pack (within F1, F2, and F3 results) to extract the slope units associated with each landslide. Consequently, only the rain gauges falling within these slope units are selected.

DEWS was implemented in a free tool pack in QGIS software, with default parameter values for non-expert users. The tool pack is divided into three main blocks following the filter structure (F1 and F2 are kept together). The reliability of DEWS was tested at a territorial scale in South Korea, using 223 landslides and 328 rain gauges. As a second step, frequentist rainfall thresholds were calculated in the study area.

How to cite: Althuwaynee, O. F., Melillo, M., Gariano, S. L., Lambardo, L., Park, H.-J., Kim, S.-W., Hader, P., Mohajane, M., Quevedo, R. P., Catani, F., and Aydda, A.: DEWS: a QGIS tool pack for the automatic selection of reference rain gauges for landslide-triggering rainfall thresholds, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2774, https://doi.org/10.5194/egusphere-egu22-2774, 2022.

Rainfall intensity-duration landslide-triggering thresholds have been proposed as a possible component for the implementation of territorial landslide early warning systems. Given a set of rainfall and landslide data, three approaches can be distinguished to determine thresholds: (i) methods based on triggering events only, (ii) methods based on the non-triggering events only, and (iii) methods based on both type of rainfall events. The aim of the present research is to compare these three possible approaches based on statistical criteria: robustness, sampling variation, and performance. This comparison can provide an insight on which of the three approaches is more appropriate based on the dataset that happens to be available for the area of interest.

We address these aspects by setting up a virtual simulation framework combining a stochastic rainfall model with a hydrological and slope stability model, which allows to make repeated experiments and to simulate different uncertainty conditions.

Our analysis shows that methods based on triggering rainfall only can be the worst with respect to the three investigated statistical properties. Methods based on both triggering and non-triggering rainfall have the highest performances in terms of the ROC true skill statistic; they are also robust, but still require a quite large sample to sufficiently limit the sampling variation of the threshold parameters. On the other side, methods based on non-triggering rainfall only, which are mostly overlooked up, are characterized by good robustness and low sampling variation. It can also be shown that in realistic scenarios their performances can be acceptable and even higher than thresholds derived from triggering events only. Indeed, the use of triggering rainfall only, a common practice in the past literature, yields to thresholds with the worse statistical properties, except when there is a clear separation between triggering and non-triggering events.

Based on these results, it can be stated that methods based on non-triggering rainfall only deserve wider attention, as they have also the practical advantage that can be in principle used where limited information on landslide occurrence is available. The fact that relatively large samples (about 200 landslides events) are needed for a sufficiently precise estimation of threshold parameters when using triggering rainfall, provides a possible insight on the level of uncertainty of thresholds proposed in the past literature.

How to cite: Peres, D. J. and Cancelliere, A.: An analysis of robustness, sampling variation and performances of landslide triggering thresholds determined by different approaches, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3630, https://doi.org/10.5194/egusphere-egu22-3630, 2022.

The occurrence of rainfall-induced shallow landslides is frequently caused by an interplay of predisposing environmental factors and dynamic preparatory and triggering conditions. For large-area assessments and for regional early warning, event-based landslide inventories are often employed to establish critical rainfall thresholds using statistical procedures (e.g., non-exceedance probability curves). These approaches typically put the spotlight on rainfall conditions associated with known landslide occurrences. Not accounting for rainfall conditions that did not induce slope instability comes along with a variety of criticalities, such as the impossibility to discriminate landslide from non-landslide rainfall conditions or the difficulty to validate the results.

This contribution proposes a data-driven approach based on Generalized Additive Mixed Models (GAMM) to identify season-dependent shallow landslide rainfall conditions for the province of South Tyrol, Italy. The work builds upon high resolution gridded daily rainfall data and landslide observations for the period from 2000 to 2020. The workflow comprised an initial filtering of rainfall-induced landslides (presence data) and a rule-based stratified random sampling procedure to select non-landslide rainy days at the same locations (absence data). The time periods (time windows in days) to describe preparatory and triggering cumulative rainfall conditions were determined using an optimization procedure based on cross validation. In addition to modelling a yearly effect, a circular day-of-the-year variable was included in the model to consider additional seasonal influences. The underlying nested data structure (i.e., repeated measurements at each landslide location) was accounted for via a location-dependent random intercept. The resulting probability scores for the analysed variables were validated using space-time cross validation, visualized in the form of probability surface plots and complemented with quantitative thresholds (e.g., curves that optimally separate landslide presences and absences).

Validation of the model showed a high capability to distinguish the two groups (presence vs. absence observations). The results further indicate that the temporal prediction of shallow landslides in South Tyrol can be improved by accounting for systematic seasonal effects other than triggering and preparatory rainfall variables. This novel approach is flexible and will further be extended to derive space-time predictions. Strengths and limitations for regional landslide early warning will be discussed.

The research leading to these results are related to the Proslide project that received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige.

How to cite: Steger, S., Kohrs, R., Crespi, A., Moreno, M., Zellner, P. J., Goetz, J., Mair, V., Gariano, S. L., Brunetti, M. T., Melillo, M., Peruccacci, S., and Pittore, M.: A data-driven approach to establish prediction surfaces for rainfall-induced shallow landslides in South Tyrol, Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5902, https://doi.org/10.5194/egusphere-egu22-5902, 2022.

In this research the occurrence of landslides triggered by rainfall is investigated through the assessment of rainfall thresholds for three regions in Portugal: Lisbon, Oporto, and Coimbra. An historical landslide inventory is used, based on newspapers published between 1865 and 2010, associated with daily precipitation databases from three long-term meteorological stations. An empirical approach based on antecedent rainfall is applied to define the rainfall thresholds for landslides occurrence. The analysis is focused on each single rain gauge, for which the spatial representativeness is evaluated. The daily rainfall data is analysed using the Gumbel probability distribution for different durations. The critical rainfall combinations (cumulated rainfall duration) with the highest return period are associated with the landslide occurrence.

For the three regions, rainfall thresholds are defined from regression (linear and potential), extreme values (upper and lower) and probability (probability of a rainfall event resulting in a landslide event when the threshold is exceeded), and the results are assessed and calibrated using the receiver operating characteristic (ROC) metrics. The thresholds comparation reveal regional patterns in rainfall thresholds. The differences in regional critical rainfall conditions for landslide occurrence between regions are associated with geological, geomorphological, and climatic features.

This work is part of the project BeSafeSlide (BSS) - Landslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change [PTDC/GES-AMB/30052/2017]. JL Zêzere was supported by the RiskCoast project - Development of tools to prevent and manage geological risks on the coast linked to climate change, Interreg SUDOE [SOE3/P4/EO868]

How to cite: Vaz, T. and Zêzere, J. L.: Empirical rainfall thresholds for landslide activity based on long-term Portuguese meteorological stations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6389, https://doi.org/10.5194/egusphere-egu22-6389, 2022.

The prediction of debris flows is relevant because this type of natural hazard can pose a threat to humans and infrastructure. Debris-flow (and landslide) early warning systems often rely on rainfall intensity–duration (ID) thresholds. Multiple competing methods exist for the determination of such ID thresholds but have not been objectively and thoroughly compared at multiple scales, and a validation and uncertainty assessment is often missing in their formulation. As a consequence, updating, interpreting, generalizing and comparing rainfall thresholds is challenging. Here, we present the findings of Hirschberg et al. (2021), which focused on (i) uncertainties related to ID thresholds, (ii) differences in local compared to regional ID thresholds, and (iii) how prediction can potentially be improved using statistical learning algorithms. The findings are of interest for debris-flow (and landslide) early-warning developers.

We use a 17-year record of rainfall and 67 debris flows in a Swiss Alpine catchment (Illgraben) to determine ID thresholds and associated uncertainties as a function of record du- ration. This included comparing two methods for rainfall threshold definition based on linear regression and/or true-skill-statistic maximization. The main difference between these approaches and the well-known frequentist method is that non-triggering rainfall events were additionally considered for obtaining ID-threshold parameters. Depending on the method applied, the ID-threshold parameters and their uncertainties differed significantly. We found that 25 debris flows are sufficient to constrain uncertainties in ID-threshold parameters to ±30% for our study site. We further demonstrated the change in predictive performance of the two methods if a regional landslide data set with a regional rainfall product was used instead of a local one with local rainfall measurements. Hence, an important finding is that the ideal method for ID- threshold determination depends on the available landslide and rainfall data sets. Furthermore, for the local data set we tested if the ID-threshold performance can be increased by considering other rainfall properties (e.g. antecedent rainfall, maximum intensity) in a multivariate statistical learning algorithm based on decision trees (random forest). The highest predictive power was reached when the peak 30 min rainfall intensity was added to the ID variables, while no improvement was achieved by considering antecedent rainfall for debris-flow predictions in Illgraben. Although the increase in predictive performance with the random forest model over the classical ID threshold was small, such a framework could be valuable for future studies if more predictors are available from measured or modelled data.

How to cite: McArdell, B., Hirschberg, J., Badoux, A., Leonarduzzi, E., and Molnar, P.: Uncertainties in local and regional mass movement prediction using rainfall, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-990, https://doi.org/10.5194/egusphere-egu22-990, 2022.

Landslides are a natural hazard affecting alpine regions all over the world. They cause not only substantial economic damages, but also human casualties. The focus here is on rainfall induced shallow landslides, which happen following an increase in the pore water pressure in the soil. As the name suggests, this typically occurs after rainfall events, either prolonged in time or short but intense, and combining such rainfall data with landslide inventories allows the definition of landslide-triggering rainfall thresholds. Nevertheless, it is now widely accepted that antecedent conditions, i.e., the wetness of the soil prior to the (non) triggering rainfall, also plays an essential role. Not accounting for the soil condition prior to the rainfall event is the main limitation of rainfall thresholds, together with the fact that they do not consider spatial heterogeneities within the domain.

Here we take advantage of two long records of daily rainfall (MeteoSwiss) and landslides events (WSL) existing in Switzerland, as well as the hydrological estimates provided by two hydrological forecasting systems operational over Switzerland. We use these not only to confirm the importance of antecedent conditions, but also to explore how to best exploit them to improve upon classical rainfall thresholds to predict landslide occurrence.

We start by considering antecedent rainfall and demonstrate that it is helpful in reducing the misclassification associated with rainfall thresholds: missed landslide events are anticipated by high N-day antecedent rainfall, while false alarms by low N-day antecedent rainfall. Recognising the limit of this simple proxy of antecedent conditions, which cannot account for snowmelt or water redistribution, we proceed by considering the soil saturation provided by a) a European physically based hydrological forecasting system (TerrSysMP) and b) a Swiss conceptual hydrological model (PREVAH). The comparison between these two systems leads to the following main findings. First, the soil saturation estimates provided by PREVAH are more informative for landslides prediction, due to a much higher spatial resolution (Prevah 250m while TerrSysMP 12.5km). Second, if spatial heterogeneities in triggering conditions are considered by using the hydrological soil wetness estimates for the calculation of the Factor of Safety (infinite slope stability model), the separation between triggering and non-triggering conditions improves compared to just using saturation. Third, while the information content of antecedent conditions is evident, accounting for them in a regional warning system is not straightforward. In fact, we find a classical hydrometeorological threshold (with a measure of antecedent conditions on the x-axis and a measure of triggering rainfall on the y-axis) to be less successful than a pure rainfall threshold. Instead, we propose a sequential threshold, where first a soil saturation threshold is used to separate “wet” and “dry” conditions, and then 2 rainfall thresholds are utilised for the wet and dry antecedent conditions.

How to cite: Leonarduzzi, E., Maxwell, R., and Molnar, P.: Rainfall induced shallow landslides: rainfall thresholds and antecedent conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12899, https://doi.org/10.5194/egusphere-egu22-12899, 2022.

Rainfall-induced shallow landslides can provoke severe consequences to people, infrastructures, cultivations and environment. For these reasons, it is necessary assessing the spatial and temporal probability of occurrence of these phenomena in the most prone zones of a territory, for early warning system strategies and land planning. The most adopted method for the determination of triggering events are rainfall thresholds. Empirical thresholds consider only rainfall attributes, such as duration, intensity and cumulated amount, while physically-based thresholds take into consideration also soil attribute, representing the soil conditions at the beginning of an event, such as the soil saturation degree at the depth of the sliding surface. This work focused to develop hydro empirical and physically-based thresholds for the occurrence of shallow landslides, taking into account field rainfall observations and soil moisture data, retrieved by hydrometeorological monitoring stations datasets. Monitoring stations were placed in 2 test sites representative of the hilly area of northern Italian Apennines and provided hydrometeorological time series, collecting data every 10 minutes. Empirical thresholds showed a good capacity to detect True Positives (TP: 95%) but they resulted affected by a high percentage of False Positives (FP: 24%), while physically-based thresholds detected 100% of TP and only 7% of FP, confirming the importance of soil conditions at the beginning of the event. Physically-based thresholds are reconstructed through a data-driven technique, based on “random forest”, that allows to find the best pair of parameters chosen within rainfall cumulated amounts and mean soil moisture conditions between 1 and 7 days. The model is calibrated considering a time span of 11 years (2007-2018) and validated using data between 2019 and 2021. The methodological approach is testing in different catchments of Oltrepò Pavese hilly area (northern Italy), that is representative of Italian Apennines environment. This work was made in the frame of the ANDROMEDA project, funded by Fondazione Cariplo.

How to cite: Vivaldi, V., Bordoni, M., and Meisina, C.: Rainfall thresholds for shallow landslides occurrence in a prone area of Northern Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6380, https://doi.org/10.5194/egusphere-egu22-6380, 2022.

Landslides are one of the most widespread natural hazards on earth and has been a major problem in many countries, especially in developing countries. Rainfall induced shallow landslides are ubiquitous on steep terrains of Himalayas, India and are accountable for substantial damage to properties, loss of human lives and livestock. They are densely distributed across territories, very frequent in time and space, and occur without any significant premonitory signals. Due to the surge in occurrence of extreme precipitation events as a result of climate change, rainfall induced landslides have become more frequent in the Himalayas. Since the mountains are becoming increasingly inhabited because of the population expansion, the geohazards like landslides have become more destructible. The Himalayas is one of the most vulnerable areas in the world and is a region of crucial interests.  The Himalayas has been receiving surplus amount of rainfall and which is a trigger for devastating landslides along the steep terrains.  Prediction of rainfall induced landslides can help the policy makers and local administration to propose appropriate mitigation strategies for unstable and vulnerable terrains.

In the present study, a hydrological model is integrated with a dynamic physically based slope stability model for the grid-wise forecasting of the stability of the terrain in the central Himalayas. The model has been optimised and calibrated based on remotely sensed data and multi-temporal landslide inventory corresponding to various landslide inducing precipitation events. HYDRUS 1D platform is used for the hydrological modelling which includes the derivation of SHPs and subsurface soil moisture. The hydrological model with finer resolution SHPs and subsurface soil moisture is later integrated with Transient Rainfall Infiltration and Grid-based Regional Slope-stability (TRIGRS) model to compute the factor of safety of the terrain. The integrated model is validated for the study area with the previous occurrence of the rainfall induced landslides. The integrated model shows higher positive rate for landslide prediction as compared with the utilization of simple slope stability model.

Keywords: Himalayas, landslides, HYDRUS 1D, TRIGRS

How to cite: Thomas, J. and Gupta, M.: Prediction of rainfall-induced shallow landslides through integration of hydrological model with a slope stability model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-273, https://doi.org/10.5194/egusphere-egu22-273, 2022.

Prediction of rainfall-induced landslides is a complex task, due to the multitude of processes involved, heterogeneity of soil properties, spatial variability of rainfall and uncertainty in landslide inventories. Rainfall thresholds can provide a useful insight on the prediction of rainfall-induced landslides; however, they just describe a part of the problem, completely neglecting the hydrological conditions. Empirical thresholds, generally focus on the characteristics of precipitation, expressed in terms of intensity and duration (I-D threshold). Although an increasing number of studies is aiming at defining the link between precipitation characteristics and soil moisture data, few are describing the usefulness of soil moisture together with empirical thresholds for rainfall-induced landslide prediction. Soil moisture data are generally used in physically based models being a function of the characteristics of the soils therefore highly site-specific and obtainable with instrumental observations and/or in situ or laboratory analyzes.

In this study, a preliminary analysis on the use of soil moisture data for the definition of empirical rainfall thresholds is carried out. The newly released fifth-generation reanalysis product of the European Center for Medium Range Weather Forecasts (ECMWF), i.e., ERA5, provides soil moisture data even for those areas in which no measuring instruments are available. ERA5 data are available in the Climate Data Store on regular latitude-longitude grids at 0.1° x 0.1° resolution covering a period from 1950 to the present with hourly resolution. The goodness of the product has been verified comparing in situ available data with those obtained with ERA by statistical analysis including the Taylor diagram that links correlation coefficient, standard deviation and root mean squared difference between two analyzed series. Soil moisture data have been collected for several stations located in Norway and Italy.

Soil moisture data for Norway has been collected from stations in two different places near Oslo, while soil moisture data for Italy comes from the International Soil Moisture Network (ISMN), specifically, Calabria region stations have been used. Rainfall-soil moisture thresholds have been defined for two case studies and the performance of thresholds considering and neglecting the soil moisture has been evaluated.

How to cite: Distefano, P., Piciullo, L., Peres, D. J., Scandura, P., and Cancelliere, A.: Implementation of soil moisture data into landslide rainfall thresholds: two case studies in Italy and Norway, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3449, https://doi.org/10.5194/egusphere-egu22-3449, 2022.

After the 2008 Wenchuan earthquake (Mw 7.9), increased occurrence of rainfall-induced debris flows was initially observed in the earthquake-hit region (Sichuan, China). In the following years, the frequency of debris flows gradually reduced, indicating a progressive recovery of stability of debris deposits accumulated along slopes and in gullies after the earthquake. To assess these dynamically changing conditions, empirical thresholds have been identified to predict post-seismic debris flow occurrence with two approaches: a meteorological approach based only on precipitation characteristics, and a hydrometeorological approach that also considers the hydrologic conditions before the onset of rainfall. Both used the available record of precipitations and debris flows that occurred between 2008 and 2015 in several gullies, tributary of the upper Minjiang river course, in Wenchuan county. Hydrometeorological thresholds for debris flows were identified at the gully catchment scale, by assessing the water balance with a simplified lumped hydrological model, based on the Budyko framework. The parameters of the model were estimated based on the scarce available information about the water balance of the entire watershed of the upper Minjiang. Simulated catchment water storage was used as a proxy of the moisture state of the slopes. The results indicate that both meteorological and hydrometeorological thresholds allow catching the progressive recovery of stability of the debris deposits. Specifically, the assessment of water balance at the catchment scale highlights the role played by the hydrological processes affecting the slopes, leading to the definition of reliable thresholds, that resulted robust despite the uncertainty of the estimated parameters of the hydrological model. Therefore, the hydrometeorological approach appears suitable to define thresholds for early warning of debris flows at the catchment scale.

How to cite: Greco, R., Marino, P., and Fan, X.: Hydrometeorological thresholds based on catchment storage to predict changes in debris-flow susceptibility after the Wenchuan earthquake (Sichuan, China), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8081, https://doi.org/10.5194/egusphere-egu22-8081, 2022.

Norway's high-relief landscape is susceptible to gravity-driven natural hazards including snow avalanches, landslides, debris flows, and rockfalls. Rockfalls are the most numerous geohazard in Norway. There are currently over 35 000 rockfall events registered in Norway's national hazard database, accounting for nearly 50% of the total number of events for all hazard types. Rockfalls commonly impact the functioning of infrastructure assets such as roads and railways, and occasionally damage buildings and result in death.

The relationship between rockfall events and weather conditions is recognised but not straightforward. Several hydrometeorological variables are significant for rockfall triggering including precipitation, snow melt, freezing and thawing, temperature, insolation, and soil or rock moisture. The highest frequency of rockfall activity in Norway is observed in spring, a period of snowmelt and freeze-thaw cycling. Given the links to meteorological variables, rockfall frequency is expected to change with climate, altering the exposure of population and infrastructures to rockfalls.

Rockfall risk mitigation at regional scale is challenging. Early warning systems are a helpful tool to depict the time and location of future rockfall events so that emergency managers can act in advance. At present, most existing rockfall early warning systems (REWS) are based on the monitoring and analysis of seismic signals to determine the movement of boulders or the cracking of joints. Little previous research has been conducted to analyse the meteorological conditions that could trigger rockfalls. There is currently no REWS in Norway.

The main objective of this work is to investigate the feasibility of using hydrometeorological thresholds for regional scale rockfall warning. To do so rainfall, temperature, and soil moisture data from SeNorge.no, and the rockfall inventory contained in the Norwegian national hazard database have been analysed to find relations between the hydrometeorological conditions and the triggering of rockfalls in Norway.

How to cite: Palau, R. M., Gilbert, G. L., Solheim, A., Capobianco, V., and Gisnås, K.: Are hydrometeorological thresholds useful for regional-scale rockfall early warning systems? A preliminary analysis of the hydrometeorological conditions leading to rockfalls in Norway, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8464, https://doi.org/10.5194/egusphere-egu22-8464, 2022.

Landslide early warning systems based on rainfall intensity and duration thresholds neglect the role of antecedent rainfall events on the hydration state that defines the disposition of a steep slope to fail in forthcoming rainfall events. Water content, water potential and mechanical strength of the soil largely depend on the antecedent rainfall signature and the soil hydraulic properties. To investigate how soil moisture information can be used for LEWS, six soil moisture measuring stations have been installed in the Napf-Emmental region as part of an ongoing pilot study to develop a territorial LEWS in Switzerland. In order to estimate the spatial distribution of the initial water content and its effect on landslide frequency and magnitude, we combine water content patterns from these stations, topographic disposition and regional rainfall data. The calculated soil water content patterns are used as input for landslide triggering simulations using the hydromechanical model framework STEP-TRAMM. STEP-TRAMM calculates the load distribution between mechanically interacting soil columns that may result in progressive failure culminating in hazardous landslides. Using landslide inventory data for the pilot region, we calibrate and validate the landslide model and evaluate the role of uncertainty in initial water content pattern on landslide characteristics and rainfall thresholds. We found high correlations between the measured and simulated water content based on rainfall characteristics and topographic disposition (R² = 0.94), allowing a reasonable estimate of the spatial distribution of the initial water content which underlines the outcome of further landslide triggering simulations.

How to cite: Halter, T., Lehmann, P., Wicki, A., and Stähli, M.: Role of measured and simulated water content patterns for landslide early warning systems , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3806, https://doi.org/10.5194/egusphere-egu22-3806, 2022.

A combination of extreme environmental conditions such as high soil moisture content and heavy or prolonged precipitation contribute to landslide initiation in mountainous areas worldwide. On-site soil moisture monitoring equipment and rain gauge have been widely used to record these variables despite the sparse spatial coverage. Satellite‐based technologies provide estimates of rainfall and soil moisture over large spatial areas sufficient to be explored for landslide hazard assessment in data scarce regions. In this study, we used statistical metrics to compare the gauge based to the satellite precipitation products: TRMM42, CHIRPS, PERSIANN-CDR, GLDAS-2.1, CFSV2, GPM-IMERG, and ERA-5 and assess their performance. Similarly, high resolution satellite and hydrological model derived soil moisture was compared to the automated soil moisture observations at Rwanda weather station sites to assess the usefulness in empirical landslide hazard assessment thresholds in Rwanda. Based on statistical indicators, the NASA GPM based IMERG showed the highest skill to reproduce the main spatiotemporal precipitation patterns. Similarly, the satellite and hydrological model derived soil moisture broadly reproduce the in situ measured soil moisture. The landslide explanatory variables from IMERG satellite precipitation; event rainfall volume E and Duration D in bilinear thresholds framework reveal promising results with improved landslide prediction capabilities in terms of true positive alarms ~80-90% and low rate of false alarms ~14-16%. However, the incorporation of satellite and model derived antecedent soil moisture to the empirical landslide hydro-meteorological thresholds showed no significant improvement. This may be attributed to the probable long and no constant timescale of the defined landslide triggering events that could be shortened to further improve the landslide prediction and support the early warning system development in Rwanda.

How to cite: Uwihirwe, J., Riveros, A., Sadeghi Tehrani, F., Sperna Weiland, F., Hrachowitz, M., and A. Bogaard, T. A. B.: The potential of Satellite and model derived precipitation and soil moisture for estimation of landslide hazard thresholds in Rwanda, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9375, https://doi.org/10.5194/egusphere-egu22-9375, 2022.

Earthquake-induced landslides are among the most common seismic hazards in Indian Himalayan high terrains, claiming hundreds of lives and infrastructural losses. Uttarakhand state is in the Western Himalayas and comes under high seismic activity zones as per the seismic code of India. However, a detailed seismically induced landslide hazard assessment is unavailable for the region. Therefore, a parametric time probabilistic approach was used to evaluate the co-seismic landslide hazard in Uttarakhand. Characteristics of the seismicity affecting the area were considered to estimate the critical acceleration (A_c)_x(p,t) that slopes should have to limit the probability of exceedance of Newmark displacement value x within time t. Initially, occurrence probabilities for different degrees of seismic shaking for a time frame of 50 years were calculated in terms of Arias intensity. Then, the spatial distribution of the slope strength demand was mapped using the empirical relationship of the Newmark displacement with Arias intensity and critical acceleration. Newmark displacement of 2 and 10 cm were considered critical thresholds with a 10% probability of exceedance. The obtained results suggested that the significant part of the region along the Main Boundary Thrust (MBT) and Main Central thrust (MCT) have Arias Intensity value greater than 2 m/s. Higher Arias intensity values of approximately 4.5 m/s for soil slope conditions and 3 m/s for rock slope conditions were observed throughout the lesser Himalayan zone. In these areas, for the thresholds mentioned above, the exceedance probability in 50 years reaches 50% in the case of 0.32 m/s for soil slope conditions and 70% in the case of 0:11 m/s for rock slope conditions. By comparing the anticipated strength demand with the actual critical acceleration values computed from slope material parameters and slope angle, the resultant slope strength demand maps could offer the basis for determining if particular slopes have a considerable failure probability.

How to cite: Gupta, K. and Satyam, N.: Seismically induced Landslide hazard assessment based on the spatial distribution of the slope strength demand in the Western Himalayas, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7328, https://doi.org/10.5194/egusphere-egu22-7328, 2022.

Landslide susceptibility maps (LSMs) are inevitable parts of regional scale landslide forecasting models. The susceptibility maps can provide the spatial probability of occurrence of landslides and have crucial role in the development and planning activities of any region. With the wide availability of satellite-based data and advanced computational facilities, data driven LSMs are being developed for different regions across the world. Since a decade, machine learning (ML) algorithms have gained wide acceptance for developing LSMs and the performance of such maps depends highly on the quality of input data and the choice of ML algorithm. This study employs a k fold cross validation technique for evaluating the performance of five different ML models, viz., Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), K Nearest Neighbors (KNN) and Support Vector Machines (SVM), to develop LSMs, by varying the train to test ratio. The ratio is varied by changing the number folds used for k fold cross validation from 2 to 10, and its effect on each algorithm is assessed using Receiver Operating Characteristic (ROC) curves and accuracy values. The method is tested for Wayanad district, Kerala, India, which is highly affected by landslides during monsoon. The results show that RF algorithm performs better among all the five algorithms considered, and the maximum accuracy values were obtained with the value of k as 8, for all cases. The variation between the minimum and maximum accuracy values were found to be 0.6 %, 0.74 %, 1.71 %, 1.92 % and 1.83 % for NB, LR, KNN, RF and SVM respectively.

How to cite: Abraham, M. T., Satyam, N., and Pradhan, B.: Effect of data splitting and selection of machine learning algorithms for landslide susceptibility mapping, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6795, https://doi.org/10.5194/egusphere-egu22-6795, 2022.

Rainfall-induced shallow landslides and debris flows often cause casualties and significant damage to property. Territorial landslide early warning systems are recognized as an important countermeasure to avoid or reduce fatalities during rainfall events. A reliable warning model is a key component of these systems. Warning models operating over large areas usually relate the occurrence of landslides to rainfall monitoring data adopting appropriate thresholds (e.g., intensity-duration, cumulated rainfall-duration, hourly/daily rainfall indicators). The increasing availability of large sets of atmospheric and land monitoring data represents an opportunity to upgrade and improve existing landslide warning models. At the same time, appropriately treating such data may pose a significant challenge to analysts that are used to deal with much smaller amounts of data.

The objective of this preliminary study is to demonstrate that machine learning techniques can be effectively used to process monitoring data over large areas at regional scale, with the aim of defining and selecting the variables that best correlate with the initiation of shallow landslides and debris flows. The machine learning models have been tested in one of the warning zones defined by the regional civil protection agency for hydrogeological risk management in Campania (Italy). Two categories of data are used for the analyses: distributed monitoring data, and a landslide inventory. The monitoring variables are derived from the fifth generation of ECMWF atmospheric reanalysis (ERA5), available with a spatial resolution of about 31 km and a temporal resolution of 1 h (http://dx.doi.org/10.24381/cds.adbb2d47). Data on landslide events come from “FraneItalia”, a geo-referenced openly available catalogue of Italian landslides created consulting online news from 2010 onwards (http://dx.doi.org/10.17632/zygb8jygrw.2). Different machine learning models have been defined, trained, and tested to relate the occurrence of landslides in the case study area to multiple variables arising from different combinations of the adopted monitoring data, mainly rainfall and soil water content. The performance of these models is evaluated by means of standard contingencies and skill scores. The best performing variables are used to define an optimal multivariate threshold to be adopted in the landslide warning model. The results of the optimal model are also compared with the outcomes of an application of a more classical exceedance probability statistical methodology based on cumulated rainfall-duration thresholds.

How to cite: Calvello, M., Pecoraro, G., Esposito, M., Pota, M., Rianna, G., and Reder, A.: Using machine learning for defining distributed monitoring variables correlated to the occurrence of rainfall-induced shallow landslides and debris flows: a case study in Campania region, Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5272, https://doi.org/10.5194/egusphere-egu22-5272, 2022.

Anticipatory action requires models that can accurately predict the impact of both the primary hazard and its consecutive events. In the Philippines, typhoons trigger 90% of landslides, causing a lot of fatalities and damage to infrastructure and agriculture. The lack of information on past landslides hampers the development of accurate forecasting models of landslide occurrence and impact. An impact-based forecasting model for typhoons currently operational in the Philippines predicts impact due to the high wind speeds associated with typhoons and includes the possible impact due to landslides only via a static landslide susceptibility map. This study expands the impact-based forecasting model of 510, an initiative of the Netherlands Red Cross, with a dynamic landslide component via hybrid modeling for two typhoon events in the Bicol region in the Philippines.

A hydrometeorological model to forecast landslide occurrences was successfully created, even with the limited data on landslide occurrences and rainfall available. The newly established regional event duration threshold was applied on the case study events with an increased impact boundary of 300 km compared to the typhoon impact boundary of 100 km. The dynamic multi-hazard model showed an improved impact forecast - compared to the model considering solely static input of landslides - both in geographical impact extent and accuracy: the True Positives doubled, whereas the False Negatives reduced by half. A separate landslide forecasting model as an extension of the existing ML model provided additional benefits as the models can be decoupled to optimize the performance and reliability of both models. This study resulted in a prototype of an impact-based multi-hazard or consecutive event model for the Philippines and demonstrated the importance of considering the impact from consecutive hazards.

Keywords: Landslide, typhoon, consecutive hazards, impact-based forecasting, rainfall, machine learning, Philippines

How to cite: Free, R., van den Homberg, M., Sperna Weiland, F., Teklesadik, A., Melillo, M., and Bogaard, T.: Extending a ML impact-based forecasting model for typhoons in the Philippines with a rainfall threshold for consecutive landslide events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10474, https://doi.org/10.5194/egusphere-egu22-10474, 2022.

On August 8, 2017, a magnitude 7 earthquake struck Jiuzhaigou County, Aba Prefecture, Sichuan Province, inducing a large number of landslides. Evaluating the susceptibility to landslides induced by strong earthquakes can provide a scientific basis for disaster risk management and monitoring. However, different evaluation models can obtain different spatial distributions of landslide susceptibility, and thus, selecting the optimal model is the most effective way to improve the susceptibility evaluation. To select the most suitable evaluation model for a strong earthquake area (Jiuzhaigou), 12 influencing factors affecting the landslide occurrence, including slope, elevation, and aspect, were extracted, and different statistical analysis methods and machine learning models were used to calculate the susceptibility index. The results show that the deep neural network model had the highest accuracy (85.4%), followed by the random forest and support vector machine models (84.2% and 82.3%, respectively), while the logistic regression model and certainty factor models achieved accuracies of 80.8% and 76.2%, respectively. Accordingly, the deep neural network model can be considered a new tool to achieve the more accurate zonation of landslide susceptibility in meizoseismal regions.

How to cite: Wang, X., Wang, D., and Li, S.: Multi-model-based evaluation of landslide susceptibility in a meizoseismal area, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6895, https://doi.org/10.5194/egusphere-egu22-6895, 2022.

Distributed physically based slope stability models represent the most advanced and scientifically sound method to forecast landslide triggering conditions. However, their operational application in regional warning systems is still hindered by some limitations. Among these, the problem of a robust validation (a task that is time consuming and not standardized) and the difficulty to manage a model output that (especially in the most advanced applications) is constituted by a raster of small pixels expressing the probability of landslide triggering: to activate an operational response an evaluation is usually performed on the overall conditions of larger spatial units and not on a pixel basis.

To overcome these shortcomings, we developed a GIS tool that can be fed with the results of slope stability models (raster maps representing the probability of landslide occurrence) and landslide inventory maps. The tool automatically performs a long series of operations traditionally performed by GIS operators to validate their models: the raw instability maps are reaggregated from pixels to watershed; warning maps are drawn; they are compared with the landslide inventory; a contingency matrix (with true positives, true negatives, false positive, and false negatives) is built; the validation results are drawn in a map. The warning criterium is defined based on two threshold values:  the probability of failure above which a pixel should be considered stable and the percentage of unstable pixels that a watershed needs to consider the hazard level widespread enough to justify the issuing of an alert. The tool was named Double Threshold Validation Tool (DTVT) and after some tests in three different test sites it was verified that: (i) DTVT can be used to carry out a standardized validation procedure in a very shorter time than traditional methods (ii) a reiterated application of the tool (by varying the values of the thresholds) can be used to identify the best warning criterion for each test site (e.g. which double threshold maximizes correct predictions while minimizing missed alarms). It is important to stress that DTVT does not improve the results obtained with the slope stability model; instead, this newly proposed tool that can be used to shift form a triggering model to a warning model, the latter being aimed at identifying when larger spatial units need the activation of operational procedures.

How to cite: Segoni, S., Pappafico, G., Masi, E. B., Rossi, G., and Tofani, V.: DTVT: a GIS tool for the automatic validation of Physically Based Landslide Models and the identification of the optimal warning criterium, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1998, https://doi.org/10.5194/egusphere-egu22-1998, 2022.

An Idealized curve surface (ICS) with two constant curvatures was proposed in Tai et al. (2020) for mimicking the plausible landslide failure surface in numerical simulation.  For ease of illustrating the ICS, Ko et al. (2021) suggested the concept of a reference ellipse for constructing the associated ICS, i.e. the ellipse-ICS method. Hence, with respect to a landslide-prone area, the most appropriate ICS can be figured out by translating, rotating and side-tilting the reference ellipse.

In the present study, the criteria for the searching the most appropriate ICS consist of the terrain characteristics (cracks, scarps, erosion gullies) and the data of the gauging station (inclinometer and groundwater indicators), where the terrain characteristics indicate the plausible boundary of the failure area, the records of inclinometer help to identify the (local) depth of sliding surface. Since the inclinometer and groundwater indicators provide the local data only, the proposed ellipse-ICS method is employed as an efficient tool to construct the plausible ICS and to investigate the impacts of the groundwater distribution on the slope stability.

The ellipse-ICS method is therefore applied to two potential large-scale landslide areas in Taiwan, i.e., the T003 at Yanping Township in eastern Taiwan and the T002 at Fuxing District in northern Taiwan. The ICSs are identified with respect to the failure depths measured by inclinometer, where the safety factors are estimated. Together with the numerical approach given in Tai et al. (2019), the subsequent flow paths of post-failure can be estimated and may serve as useful information for hazard assessment.

Keywords:

ellipse-ICS, inclinometer, groundwater level, safety factors, flow paths

References

• Tai, Y. C., Heß, J., & Wang, Y. (2019). Modeling Two‐Phase Debris Flows with Grain‐Fluid Separation over Rugged Topography: Application to the 2009 Hsiaolin Event, Taiwan. Journal of Geophysical Research: Earth Surface, 124(2), 305-333.
• Tai, Y. C., Ko, C. J., Li, K. D., Wu, Y. C., Kuo, C. Y., Chen, R. F., & Lin, C. W. (2020). An idealized landslide failure surface and its impacts on the traveling paths. Frontiers in Earth Science, 8, 313.
• Ko, C. J., Wang, C. L., Wong, H. K., Lai, W. C., Kuo, C. Y. & Tai, Y. C. (2021). Landslide Scarp Assessments by Means of an Ellipse-Referenced Idealized Curved Surface. Frontiers in Earth Science, 9,862.

How to cite: Wong, H.-K., Wang, C.-L., Ma, C.-Y., and Tai, Y.-C.: Integration of the Terrain Characteristics and Data of Gaging Station for Mimicking the Plausible Surface of Slope Failures., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10918, https://doi.org/10.5194/egusphere-egu22-10918, 2022.

A complete assessment of slope stability is achieved by identifying and monitoring the subsurface properties and processes leading to slope failure conditions. Monitoring technologies need to be applied at appropriate scales and resolution, and with sufficient coverage, to be able to assess these conditions in local landslide early warning systems. A holistic understanding of the subsurface at the slope-scale is not always captured by some landslide monitoring approaches, such as remote sensing observations with limited depth penetration or sparse resolution, or point sensor measurements with highly localised information. Geophysical techniques have demonstrable capacity to link between the different scales, resolutions and coverage of these established landslide monitoring techniques. Here, we present a novel framework identifying the benefits and limitations of including geophysical imaging and monitoring techniques at different stages of local landslide early warning system strategies. These include the use of geophysical inputs to aid the design of local landslide early warning systems, monitor slopes at risk of failure, inform forecasting, and support decision making for stakeholders.

How to cite: Whiteley, J., Watlet, A., Kendall, J.-M., and Chambers, J.: Geophysical imaging for local landslide early warning systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13151, https://doi.org/10.5194/egusphere-egu22-13151, 2022.

Geohazard monitoring is a key component in an early warning system (EWS). The implementation of monitoring actions provides data for the acquisition of variables related to the landslide, its triggering and kinematic; additionally, it can provide insights of its evolution in time in order to plan mitigation actions, including alarms and warnings. The use of high-frequency systems can also provide such data in the shortest time thus optimizing the aforementioned actions. In the last decades, numerous surface monitoring systems were developed, with various features, providing punctual information, like GNSS or Robotized Total Stations, at high frequency or large-scale data, i.e. Remote Sensing, at lower temporal resolution. The choice of the best one is related to the goals to be fulfilled but, independently from the selected method, surface techniques monitor only a displacement resulting from the sum of all the deep-seated ground deformations. To properly detect the subsoil behavior of a landslide, the use of subsurface sensors is necessary. To couple the subsoil survey with high frequency monitoring a robotic inclinometric system was developed, and patented, by the Geohazard Monitoring Group (GMG) of CNR-IRPI. The instrumentation features the operational characteristics of the manual inclinometric measures (reliability, double readings 0/180˚…) but integrates the advantages of the robotization (accuracy, measurement frequency…), too. The robotized instrumentation also called “Automated Inclinometer System” (AIS) allows the automatic exploration of all the borehole length (up to 120 meters in the standard configuration) with a single probe. The AIS is remotely connected by a 4G modem so it is possible to define the acquisition parameters, download measured data and check the device functioning parameters. The instrumentation was deployed, at the beginning of December 2021, in a borehole located in Passiria Valley (northeastern Italy) to monitor a large and slow-moving landslide involving the whole mountain face; thanks to instrumentation modularity, the AIS is ready to measure after only 4÷5 hours of installation time. Concurrently with the main installation, a GNSS benchmark was positioned and surveyed to provide, with the next measurement campaigns, a crosscheck with the AIS results. After 10÷15 days of monitoring at 1 measurement/day the landslide’s sliding surface, its depth and deformation rate, were clearly identified, thus confirming the capability of the AIS to perform early detection of the landslide kinematic. This result is key information in the risk reduction chain as it shortens the time necessary to achieve the numerical parameters describing the landslide and, consequently, plain the following, mitigating, actions.

How to cite: Godone, D., Allasia, P., Baldo, M., Guenzi, D., and De Polo, F.: The use of robotized inclinometric system in Early Warning System. The case study of a large landslide monitoring., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7388, https://doi.org/10.5194/egusphere-egu22-7388, 2022.

Waste materials produced by mining activities (tailings) can be collected in artificial ponds delimited by earth embankments (tailings dams). In case of tailings dam failure, the consequences are often catastrophic for the surrounding communities and livelihoods as this rupture may release large amounts of tailings and mining wastewater that moves downstream. Furthermore, the mining by-products cause, in many cases, a devastating impact on the surrounding environments and ecosystem. As an increased trend of tailings dam failure has been observed in the last decade, there is an urgent demand from the industry as well as the civil society and the investor community to gain a broader understanding of the risks posed by tailings facilities. Furthermore, efficient techniques to monitor and predict the failure of tailings dams are also crucial.
 
This study investigates how the satellite remote sensing interferometric synthetic aperture radar (InSAR) technique can be used to monitor tailings dams and the applicability of the inverse velocity method to predict failures. InSAR data have been used to map surface displacement prior to dam failures in two case studies: the Feijao tailings dam in Brazil and the Cadia tailings dam in Australia. In the case of the Feijao dam, both the SBAS and PS techniques were applied to process displacement time-series from the satellite data. For the Cadia dam, data processing was carried out using the SqueeSAR algorithm.

The inverse velocity method uses surface displacement measurement points to predict a time of failure. For the Feijao dam InSAR dataset, the inverse velocity method was applicable to different periods presenting an evident increase in the displacement rate. However, it was difficult to retrieve any reliable indication of failure. Contrary to the Feijao dam, the results from the Cadia dam shows a significantly accelerating deformation with time, and by applying the inverse velocity method a predicted time of failure can be retrieved in good agreement with the actual failure.  

How to cite: Svendsen, I., Piciullo, L., Vöge, M., Montalti, R., and Intrieri, E.: Tailings dam monitoring and early warning with InSAR technique, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11011, https://doi.org/10.5194/egusphere-egu22-11011, 2022.

Abstract:

Karst aquifers belong to the fractured aquifer family. The Zaghouan region located in NE of Tunisia (North Africa) is characterized by a high degree of karstification due to the climate impact and the development of fracture network. Survey using electrical resistivity tomography (ERT) is deployed to provide a cost-effective characterization of the subsurface karst environments. A total of three ERT profiles with a length of 300 meters were evaluated in Zaghouan region.

The area represents an anticline of Jurassic limestone rocks, which is overlain by a thin clay layer. In this study, an ERT survey was conducted to examine the spatial distribution and shape of underground cavities in the karst area of Jebel Bent Saidan. In this study, geological, hydrogeological and electrical resistivity tomography (ERT) methods were applied to determine the geometry of the karst aquifer in the Zaghouan area (NE Tunisia). The area is characterized by fractured and karstic limestone aquifer of Jurassic. Three resistivity profiles were carried out along the study area (Jebel Bent Saidan). The correct resistivity data was interpreted using ZONDRes 2D software.  The results of the interpreted geo-electrical sections showed that the resistivity of the carbonate aquifer ranges from 350 to over 4000 Ωm. The thickness of the aquifer varies between 15 and 30 meters, while its depth from the surface is between 10 and 40 meters. The ERT not only provided accurate near-surface information, but was also very useful in establishing the geometry of the aquifer. It was also very useful in establishing the 3D geometry and position of several potential karst cavities and conduits. The results show the presence of two large isolated cavities at different depths. The low resistivity of karst cavities in the Jurassic carbonate of Jebel Bent Saidane was explained by the saturation of groundwater. The ERT imaging technique using to identify and characterize the discontinuities, faults and water investigation of the fractured and karstified limestone aquifers in the Bent Saidan Mounts. The conducted research demonstrated that the ERT method was an effective tool for imaging the subsurface in the karst terrain.

Keywords: Bent Saidan (NE Tunisia), karst aquifers, electrical resistivity tomography (ERT), cavities.

How to cite: Mhimdi, A., Gabtni, H., Ezzine, I., Hamzaoui, F., Ghanmi, M., and Bouhlila, R.: Electrical Resistivity Tomography (ERT) Applied to the assessment of Karst Carbonate Aquifers structure: Case Study from Zaghouan-Bent Saidan (NE Tunisia), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10251, https://doi.org/10.5194/egusphere-egu22-10251, 2022.