Landslides are extensively distributed across the globe. About 17% of deaths due to natural hazards reported in the last decade are attributed to landslides. The spatial and temporal distribution of landslides are related to static and dynamic factors. The first group involves terrain aspects and land use, and the second group includes triggering factors, such as rainfall and earthquakes. The  61% of worldwide landslides recorded are triggered by rainfall. In Colombia, the percentage reaches 92%, becoming the main factor that triggers landslides. 

The Aburrá Valley is located in the central northern Andes, characterized by its complex topography and one of Colombia's most densely populated valleys, with 3.9 million inhabitants. In this study, the spatial and temporal relationship between landslides and rainfall in the Aburrá Valley is unraveled. Three kinds of rainfall information are used: pluviograph (145 stations), radar and satellite. Regarding the landslide database, 940 landslides were compiled between 1921 and 2023. The temporal analysis includes the understanding of different time scales: decadal, annual, daily, and hourly because several macroclimatic aspects affect the precipitation regime, such as El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Intertropical Convergence Zone (ICZ). This understanding leads to knowing on what scale there is clear evidence that regional precipitation changes can affect the occurrence of landslides. Regarding the spatial analysis, the radar and satellite information complements the data of punctual pluviographic stations.

The results show that the ENSO affects the development of rainfall regimes on all time scales. When the PDO and ENSO match, the effects of EL Niño and La Niña phases are exacerbated, resulting in lower and higher landslides, respectively. In general, Aburrá Valley exhibits a bimodal precipitation phase, where the annual cycle peak of landslides matches with the peaks of rainfall annual cycle; the Enso affects the cycle mentioned, showing that, especially in dry periods, the effects of Enso increase the rainfall difference and landslides register. The daily analysis demonstrates a peak shift between the two variables evaluated, showing that the landslides will need antecedent rainy days to trigger them. There is no clear relationship at the hourly scale because of the reduced number of hourly landslide events registered. Concerning the spatial variation, a hot-spot of landslide is located in the valley's east hill, where the rainfall events with more duration are placed. Another finding is that satellite information is highly correlated with on-site measurements when the antecedent precipitation is evaluated for more than 15 days. 

How to cite: Herrera, D. and Aristizábal, E.: Spatial and temporal distribution of precipitation and its relationship with landslides within the Aburrá Valley, northern Colombian Andes., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12621, https://doi.org/10.5194/egusphere-egu24-12621, 2024.

The occurrence of natural hazard events is heavily affected by climatic drivers and triggers. Changes in these climatic drivers are likely to bring about potentially severe consequences for mountain communities due to shifts in natural hazard occurrence patterns and characteristics. Although there is a well-established scientific consensus regarding cause-and-effect relationships from a physical science standpoint, substantiating these connections can pose challenges for specific hazard processes when viewed through an empirical and data-driven lens (Schlögl et al., 2021).

The Sixth Assessment Report (IPCC, 2021) highlighted changes in intensity/magnitude, frequency, duration, timing and spatial extent of a wide range of climate hazards. Following this line we investigated trends in torrential flooding in the Austrian Alps. Torrential flooding comprises a set of natural hazard processes, which originate in small and steep mountain headwater catchments and are characterized by highly variable discharge and sediment transport volumes.

We used a comprehensive data set data of damage-inducing torrential flooding events as collected by the Austrian Torrent and Avalanche Control Service (WLV). Assuming inventory completeness after 1945 (Heiser et al., 2019), our trend analyses with respect to the core climate change characteristics based on nearly 80 years of event data (and more than 11,000 events) yielded the following results:

• Intensity/magnitude: Using deposition volume as a proxy for event magnitude, a statistically significant decrease over time was detectable. However, caution is warranted due to a potential under-reporting bias for smaller events in earlier years and changes in data recording procedures.
• Frequency: Event frequency exhibited a significant positive trend, with breakpoints evident in the cumulative number of events. Changes in documentation standards in 2005 and high overdispersion in the dataset contributed to challenges in interpreting trends.
• Duration: Lack of data on precise event onset and end impeded reliable assessment of changes in event duration.
• Timing (seasonality): Analysis of date information revealed a peak in event occurrence during summer, particularly in July and August. No significant shifts in seasonality were confirmed, thereby challenging previous reports of changes in timing of maximum discharge or permafrost degradation.
• Spatial extent: Spatial extent analysis, based on deposition area, was limited to the period 2012-2022, hindering a reliable trend analysis. We acknowledged the lesser relevance of spatial extent for torrential flooding compared to changes in event magnitude.

Summarizing, reliably detecting generalizable trends in torrential flooding characteristics is challenged by issues such as incomplete data, data quality, and dynamically changing drivers. While issues related to documentation techniques can be addressed in future data collection efforts, dynamically changing drivers (including climate change, mitigation activities, exposure dynamics, and land use changes) pose ongoing challenges due to complex interactions and nonlinear effects. We underscore the importance of controlling for these effects when assessing trends in torrential flooding in a changing climate.

References

Heiser M., Hübl J. & Scheidl, C. (2019). Completeness analyses of the Austrian torrential event catalog. Landslides 16, 2115–2126 (2019). https://doi.org/10.1007/s10346-019-01218-3.

Schlögl M., Fuchs S., Scheidl C. & Heiser M. (2021). Trends in torrential flooding in the Austrian Alps: A combination of climate change, exposure dynamics, and mitigation measures. Climate Risk Management 32: 100294. https://doi.org/10.1016/j.crm.2021.100294.

How to cite: Schlögl, M., Heiser, M., Scheidl, C., and Fuchs, S.: Trends in torrential flooding in the Austrian Alps: Assessing different types of changes to a mountain hazard profile, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3987, https://doi.org/10.5194/egusphere-egu24-3987, 2024.

Landslides triggered by snowmelt, as one of the main hydrometeorological triggering factors, and their interaction with Atmospheric Rivers (ARs—long and narrow horizontal water vapor transport characterized by high water vapor content and strong low-level winds) and topographic conditions are not adequately elucidated. During the February–April 2022 period, extreme snowfalls in the Northern Anatolian Mountains, followed by a rapid snowmelt event, triggered more than 300 landslides. Accordingly, based on local and national news sources as well as public institution reports, an inventory was created by mapping 330 landslide events that occurred as a result of rapid snowmelt during this period. This landslide inventory compiled for the Northern Anatolia Region, one of the most susceptible regions in Europe, as well as Türkiye in terms of landslide events, provides a unique opportunity to understand the process dynamics underlying snowmelt-induced landslides. Revealing the combined and/or individual roles of meteorological weather events such as sudden temperature rises, heat waves, rain-on-snow events, and/or the foehn effect, associated with ARs or synoptic-scale weather events, in triggering these landslides is essential for better understanding possible such events in the near-future and to taking effective measures to mitigate socio-economic losses.

The spatio-temporal distribution of snowmelt, air temperature, and snow-water equivalent (SWE) variables at daily and monthly scales for the February to April 2022 period according to long-term climatology (1993–2022) as well as landslide events triggered by ARs were analyzed. Additionally, the impacts of altitude and slope steepness on the spatio-temporal distribution of landslide events were revealed. Over the study area during February–April 2022, both monthly SWE and snowmelt values had positive anomalies, while air temperature values showed positive anomalies only for February and April. The analysis of landslide events triggered by ARs based on a 5-day window for AR passages showed that ARs as a triggering factor were responsible for 62% of total landslide events. On the other hand, as time progressed during the period February–April 2022, an increase in the altitude and slope steepness values at which landslide events occurred gradually increased. In addition to a gradual escalation of landslide occurrences to higher altitudes with time, we observed that landslides are limited to around 800 m, which further suggested that this may be caused either by limited soil thickness cover above a certain altitude or by the air temperature below the thawing degree.

How to cite: Aslan, H., Gorum, T., Bozkurt, D., Sen, O. L., Ezber, Y., Akbas, A., and Fidan, S.: The interplay of Atmospheric Rivers and topography on snowmelt induced landslides in Northern Anatolian Mountains (Türkiye), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-455, https://doi.org/10.5194/egusphere-egu24-455, 2024.

How to cite: Demmel, S., Agostini, L., Garipova, S., Leonarduzzi, E., Schlunegger, F., and Molnar, P.: Climatic triggering of landslide sediment supply in the Alpine Rhine, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11270, https://doi.org/10.5194/egusphere-egu24-11270, 2024.

In mountainous areas, landslides initiation is strongly influenced by temperature and precipitation. Liking temperature and precipitation anomalies to landslides occurrence is among the viable methods to predict the future occurrence frequency of such hazards under climate change. Currently, most of the methods to detect such anomalies rely on in-situ measurements. This demands significant efforts for data retrieval and homogenization, which, together with the constraints on data sharing, pose important challenges to the replicability of the studies and to the comparison among different methods. Open access gridded datasets could overcome these limitations, but their ability to capture meteorological anomalies needs to be assessed. Here we address this issue.

By means of a consolidated statistical-based approach, we here exploit: a) half-hourly precipitation estimates from the Integrated Multi-Satellite Retrievals from GPM (IMERG), b) daily temperature observations from ENSEMBLES OBServation (E-OBS) and c) daily temperature and total precipitation  of global reanalysis ERA5 to demonstrate that open access gridded climate datasets can complement or even replace in-situ data in studies linking meteorological anomalies (defined as percentiles above 0.9 or below 0.1) with the occurrence of geomorphic hazards. We focus on a vast catalogue of 483 different geomorphic hazards (mainly landslides, rockfalls and debris flows) occurred along 2000-2020 over the Italian Alps.

Findings indicate that the statistical significance of the paired anomalies derived by observations and gridded datasets is often achieved. Mismatches are related to limited sample sizes. In general, E-OBS and IMERG demonstrate to provide information on temperature and precipitation anomalies, respectively, that is comparable or even better than the one provided by in-situ observations and ERA5 reanalyses. Additionally, our findings reveal that IMERG, by providing information directly on the initiation zone, can detect precipitation anomalies at the daily scale that in-situ measurements fail to detect, especially in the case of debris/mud flows events triggered by small-scale convective processes.

Overall, gridded datasets can help to improve our knowledge on the statistical relation between landslide initiation and meteorological anomalies, which in the future can be adopted to quantify changes in the occurrence probability of these events under a changing climate.

How to cite: Paranunzio, R. and Marra, F.: Open access gridded climate datasets can help to reveal meteorological anomalies’ role in landslides initiation , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3180, https://doi.org/10.5194/egusphere-egu24-3180, 2024.

Landslides are natural phenomena of different types, with high randomness and variability. They are triggered or influenced by multiple natural phenomena, among which rainfall plays by far the main role in most areas of the world. A relevant complication in the evaluation of landslide activity is global warming, in particular the related ongoing and expected changes in rainfall and temperature patterns. Indeed, due to larger atmospheric retention capability, an increase in the frequency and magnitude of intense rainfall events was already observed in many areas and more significant changes are expected due to climate change. Given the high spatial and temporal variability of the landslides, climate change can affect them in multiple ways and at different temporal and geographical scales.

One of the main approaches used to study the impact of climate change on landslides relies on the adoption of landslide models forced with climate projections generated by physically based, data-driven or hybrid simulation chains. Overall, these studies are based on a similar framework, with a climate modelling chain and a landslide model. The climate chain involves the choice of the Earth System models and concentration scenarios, of a downscaling technique for the production of local assessments, and, in most cases, a bias correction technique with the aim of removing the errors (assumed as systematic) in the assessment of the key climatic variables. The landslide models fed with these variables can be either physically-based (geotechnical, hydrological) or statistical (including empirical analyses or susceptibility assessment), and can operate at different spatial scales, from the slope to the regional/national scale.

In the scientific literature, most of the studies that aimed at evaluating the impact of expected climate and environmental changes to landslides considered case studies in the European continent.

We analyze fifty articles, book chapters and proceedings that proposed modelling approaches in the study of climate-change-landslide relationships, published between 1999 and 2023. We study the spatial scale, the investigated period (both the control and future periods), and the considered climate and landslide variables. Moreover, we study all the components of the climate modelling chain and the features of the landslide models.

We observe an increase in the number of basin-, regional- and national-scale works over the years. In addition, we observe that most of the works focusing on the slope scale are related to the hydro-geotechnical modelling of deep-seated landslides, while most of the basin-scale analyses consider shallow landslides and debris flows and use statistical analyses to model the landslide-climate relationships.

How to cite: Gariano, S. L. and Rianna, G.: Modelling approaches to evaluate the role of climate change in landslide activity in Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14725, https://doi.org/10.5194/egusphere-egu24-14725, 2024.

In recent years, with the frequent occurrence of extreme climate, rainfall-induced landslide (RIL) has become one of the main geological hazards that endanger human life and property safety. Understanding the relationship between meteorological factors and RIL is essential for promoting safety. Although the research community has been studying the spatial or temporal probability relationship between climate variability and RIL using quantitative and qualitative methods at different spatial or temporal scales for decades, the spatio-temporal probability of synchronous hazard prediction has rarely been studied. Here, we constructed a hybrid model of Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) and utilized the spatial feature extraction capability of the CNN model and the temporal feature extraction capability of the LSTM model to infer the causal relationship of RIL and simulate the risk of RIL from the meteorological data from 1980 to 2015. In this study, the Wanzhou District of Chongqing, China was used as the research area to train and test the model, in order to provide a new idea to synchronously predict the spatial probability and temporal probability of RIL. Our results reveal that the spatio-temporal probability prediction model has higher prediction accuracy than the single spatial probability or temporal probability prediction model, and it is more consistent with the actual occurrence of RIL. The predicted results of our model show that the occurrence of RIL is mainly affected by the geological environment, followed by the intensity and duration of rainfall in extreme climates. The inferred patterns show that precipitation extremes are associated with an increased risk of RIL. Therefore, in addition to understanding the geological factors that trigger the hazards themselves, a better understanding of climate-hazard linkages enhances the spatio-temporal modeling capacity for the risk of RIL. In the future, it can be used to analyze the world's risks of RIL caused by climate change.

How to cite: Zhao, Y. and Chen, L.: Spatio-temporal probability prediction of rainfall-induced landslides based on deep learning under climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-68, https://doi.org/10.5194/egusphere-egu24-68, 2024.

Anthropogenic activities, including the operation of reservoirs and infrastructure expansion, coupled with extreme climatic events are increasing landslide hazards worldwide, but information on the detailed impact of these factors on slope stability is often lacking. In-situ monitoring systems in these potential landslide-prone areas are often unavailable, challenging landslide hazard assessment. This study comprises a multi-scale and multi-sensor satellite remote sensing approach in combination with advanced statistical methods to investigate the life cycle of the catastrophic Hoseynabad-e Kalpush landslide failure that occurred in March-April 2019 in Semnan province of North Central Iran. The landslide occurred on the adjacent slope of a nearby reservoir built in early 2013 following an exceptional precipitation period in the spring of 2019. The failure resulted in the damage of more than 300 houses, of which 163 had to be evacuated due to the severity of the destruction.

In our remote sensing approach, we first derived the spatiotemporal evolution of the pre-, co- and post-failure landslide kinematic fields using Digital Image Correlation based on PlanetScope 3-m resolution data (November 2018 and May 2019) and Multi-temporal InSAR using ascending and descending orbits Envisat ASAR (July 2003 to September 2010) and Sentinel-1 (October 2014 to December 2021) acquisitions. Remote sensing results are then integrated with advanced statistical and clustering approaches to derive trends and seasonality in the time series of the analyzed remote sensing data before correlating the results with external triggering factors. Long-term monthly cumulative precipitation observations (2000-2022) were obtained from The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). The reservoir water level was derived by a GIS-based approach using Landsat-8 (April 2013 to August 2016), PlanetScope (August 2016 to December 2021) data and the Shuttle Radar Topography Mission (SRTM) 1 arc-second global digital elevation model.

Our results suggest that the impoundment of the recently built reservoir reactivated the previously relict landslide and triggered a retrogressive destabilization mechanism. During the pre-failure creeping, the landslide stability conditions permanently degraded. The combination of exceptional precipitation of 2019 and the sudden increment of pore-water pressure, was the final trigger of the landslide main failure in March of that same year in what is a typical deep-seated failure mechanism. In the aftermath, the landslide was still active, with trends in displacement rate comparable to the pre-failure phase, which decreased until its final stabilization in the second half of 2021. The outcomes of this study reveal the complex interactions between reservoir water level changes and extreme precipitation events in influencing landslide kinematics and elevating the hazard of landslide reactivation and failure. Thus, the investigation of the Hoseynabad-e Kalpush landslide case is also relevant for other settings where artificial reservoirs have been built adjacent to relict landslide-prone slopes and where no or only limited in-situ monitoring data are available.

How to cite: Vassileva, M., Motagh, M., Roessner, S., and Xia, Z.: A multi-sensor remote sensing approach for understanding slow-moving landslide reactivation: a case study from North Central Iran following reservoir impoundment and extreme precipitations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13222, https://doi.org/10.5194/egusphere-egu24-13222, 2024.

Temperature fluctuations within landslide shear zones can arise from interactions with deeper subsurface layers and the atmosphere crossing the landslide body. Shallow landslides, particularly those with depths less than 10 m, are notably susceptible to seasonal temperature variations and swift climatic shifts. The hydro-mechanical properties of clayey soils exhibit sensitivity to temperature alterations. Some investigations have proposed significant variations in residual shear strength, even within temperature ranges typical of shallow layers in temperate and warm regions.

This study examines the response of two pure clays (Ca-bentonite and kaolin) to shearing at temperatures up to approximately 55 °C, considering various normal stresses (50–150 kPa) and shear rates (0.018–44.5 mm/min). A temperature-control system integrated into a ring-shear device facilitated the experimentation.

Subsequently, we conducted experiments on an ideal slope to quantify the impact of ground temperature on the stability of clay slopes across seasons and prolonged warming. By accounting for the most substantial effects determined experimentally (residual shear strength changing by ±1.5 %/°C), we identified variations in the global factor of safety of approximately 20% for rotational slides at depths of approximately 6 m, solely due to seasonal heating-cooling cycles. A warming of 5 °C over decades would introduce an additional ±7% change in the stability condition.

While acknowledging the simplified geometry and boundary conditions in these results, and the exclusion of triggers, preconditions, and effects of other thermo-hydro-mechanical couplings, they establish an upper limit for the influence of temperature-dependent residual shear strength on the factor of safety. We emphasize that this influence should not be disregarded in slope stability and landslide hazard assessments for clay-rich soils, necessitating thorough experimental analyses and advanced modelling.

How to cite: Loche, M. and Scaringi, G.: Temperature-Dependent Shear Strength in Clay Slopes: Experimental Insights and Implications for Stability Assessment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5246, https://doi.org/10.5194/egusphere-egu24-5246, 2024.

The shear strength is a fundamental mechanical parameter that controls the occurrence and propagation of landslides. In pure clays, this parameter is temperature-dependent according to the clay’s mineral composition and hydro-mechanical boundary conditions. Landslide soils are typically heterogeneous mixtures with a variable content of clay minerals. Particularly for low-plasticity soils, the impact of changes in temperature on the mechanical response remains to be determined, and little can be said about possible macroscopic alterations of slope stability or landslide dynamics.

In this study, we conducted ring-shear tests using natural soils from the Melamchi catchment in central Nepal, which suffered widespread instabilities and rainfall-induced debris flows. We performed experiments under typical landslide stress levels (50-150 kPa) in water-saturated conditions and under a constant rate of shearing (0.1 mm/min). We controlled the temperature during testing and performed a heating-cooling cycle (20-50-20 °C) only after attaining the residual shear condition. We prepared multiple samples from the same soil by retaining its finest portion under different cutoff grain sizes (0.125-0.020 mm) to evaluate the fine fraction's role in the thermo-shear response.

As expected, we observed a decrease in shear strength with the clay fraction increasing. Samples with a coarser cutoff (and hence a lower clay fraction) did not exhibit any change in shear strength during the heating-cooling cycle. However, as the clay fraction increased, a heating-induced weakening emerged, corresponding to up to a 1° difference in friction angle in the samples with a 0.020 mm cutoff. In the specific case study, this weakening may be minor and will not affect evaluations of slope stability in simple limit equilibrium analyses, especially in the absence of explicitly accounting for spatial heterogeneities in soil properties and boundary conditions. Nevertheless, incorporating this effect into physically-based models, either entailing advanced soil constitutive models or equations for surface flows (both of which can include additional temperature-dependent parameters), may provide useful insights into the complexity of thermo-hydro-mechanical responses and their effects on landslides at the local and regional scales.

How to cite: Dhakal, O. P., Loche, M., and Scaringi, G.: Shear weakening with increasing temperature: effect of clay fraction in low plasticity soils from the Melamchi catchment in Nepal, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10778, https://doi.org/10.5194/egusphere-egu24-10778, 2024.