Water circulation within a catchment in both shallow and deep hydrological systems represents the most common factor controlling and triggering slope movements. Nevertheless, the integration of hydrological knowledge into landslide occurrence analysis, such as water storage, water-rock interactions, soil-bedrock exchange, preferential flows, and frost conditions, is still limited. Similarly, the incorporation of hydrological information into rainfall threshold development is still not fully developed or widely adopted. Researchers from all fields are warmly invited
to submit contributions ranging from field monitoring, modelling and novel data-driven approaches to advance the knowledge of processes leading to landslide occurrence.
Orals: Thu, 7 May, 10:45–12:30 | Room 1.31/32
The dynamics of water in hillslopes influence the processes that govern slope stability and the triggering of landslides, particularly under intense rainfall events. Understanding hydrological processes across multiple scales, from the watershed to the microscopic level of the soil, is essential for identifying the causes and triggers of slope instability. Hydraulic anisotropy, the presence of discontinuities, textural variability, and slope angle control the direction and intensity of water flows over time, generating both vertical and lateral flow components.
On steep slopes, water flow in the soil can be described by two distinct infiltration fronts: a transient front, which propagates perpendicular to the ground surface (associated with vertical flow), and a stationary front, which develops parallel to the slope and is governed by lateral flow. The predominance of transient front or stationary front depends on variables such as the initial depth of the water table, soil hydraulic conductivity, constant infiltration rate, and slope angle.
In this context, the present study evaluates the validity of the hypothesis of predominant one-dimensional flow in simulating infiltration on landslide-prone hillslopes, focusing on periods of intense rainfall, during which the short duration of events tends to limit the contribution of lateral flows. Simulations of volumetric soil moisture were performed using observed rainfall data and hydraulic parameters derived exclusively from pedotransfer functions. However such type of simulation has not satisfactorily reproduced the observed hydrological behavior (mean Spearman correlation coefficient ρ = -0.27). On the other side, when the hydraulic parameters have been adjusted based on soil moisture field in situ measurements, the calibrated simulations showed fairly acceptable and good agreement between both, simulated and observed soil moisture, depicting positive and statistically significant correlations at all monitored depths (mean Spearman correlation coefficient ρ = 0.80).
The results indicated a predominance of downward vertical flow during intense rainfall events, depicting that, despite the fact that hillslope hydrology is inherently multidimensional, one-dimensional model approach still can adequately represent soil moisture dynamics under transient conditions associated with rapid infiltration events. Furthermore, the results highlight the need for site-specific calibration of soil hydraulic parameters.
Overall, the findings highlight the importance of site-specific calibration of soil hydraulic parameters and reinforce the value of continuous soil moisture monitoring as an effective tool for identifying hillslope areas susceptible to shallow landslides.
How to cite: teixeira silva, D., Paulo Michel, G., Zanandrea, F., Ferreira Fernandes, N., Correa Rotunno Filho, O., Nonato Vieira Cereto, A., Moreira Cardoso, C., and coutinho Loureiro Mansur, R.: Are one-dimensional infiltration models suitable for simulating soil moisture in landslide-prone hillslopes? , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20673, https://doi.org/10.5194/egusphere-egu26-20673, 2026.
The Norwegian landslide forecasting and warning service provides daily regional predictions of shallow landslides (i.e. debris avalanches and debris flows) triggered by intense rainfall, snowmelt and high soil moisture conditions.
While landslide initiation is clearly linked to hydrological processes through infiltration and increasing soil-water pressure, no distinct signature from rainfall–runoff models has yet been identified for use at local scale alongside existing landslide forecasting models. Progress is limited because few landslides occur in catchments with calibrated hydrological models, leaving little basis for relating landslide triggers to simulated hydrological states.
To address this gap, the Norwegian Water Resources and Energy Directorate (NVE) has developed a system to parameterise the Distance Distribution Dynamics (DDD) rainfall-runoff model for ungauged basins. The DDD model use a parsimonious set of parameters that can be estimated from landscape and climatic characteristics. We configure the DDD model for landslide-affected catchments, using samples from the Norwegian landslide database (containing landslide type, location, time of occurrence and observation quality), and simulate time series of hydrological variables at 1 hour temporal resolution from 2014 onward.
The DDD model simulates hydrological variables such as soil moisture in saturated and unsaturated zones, snow parameters, flood values, and runoff. By examining these variables at the time of landslide events, we aim to identify hydrological signatures associated with landslide initiation. Preliminary results indicate that, subsurface saturation, high flows, and the incremental rate in subsurface saturation relative to the incremental rate in runoff, are key factors in triggering shallow landslides. Analysis of historical events across multiple regions supports dependencies between simulated hydrological states and landslide occurrence. Ultimately, integrating simulated hydrological states into operational forecasting could enhance landslide prediction and improve early warning systems.
How to cite: Devoli, G., Skaugen, T., Grønsten, H. A., Zelalem, M., Berthling, I., Iseni, A., and Colleuille, H.: Model-derived hydrological signatures of debris avalanches and debris flows for enhanced landslide prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5276, https://doi.org/10.5194/egusphere-egu26-5276, 2026.
Moisture-driven landslides (MDLs) are a recurrent natural hazard in the Northeastern Himalayas (NEH) during the southwest monsoon season, where steep terrain and prolonged wetness frequently trigger catastrophic slope failures, underscoring the need for a credible early warning systems. In our recent work (Monga & Ganguli, 2025), we propose the compounding role of triggering and antecedent moisture content at an optimal d-day time lag to derive regional and local scale Event–duration (E–D) threshold model for northeastern Himalayas (NEH); however, we have not explicitly quantify the role of subsurface soil saturation in modulating the landslide likelihood. Here, we present at-site analysis of over the 21 landslide-prone sites across the NEH, considering the compound interaction of d-day time lag antecedent moisture, triggering rainfall and sub-surface root-zone saturation (up to 200 cm depth), and develop a moisture-preconditioned ED threshold model for landslides in a Bayesian probabilistic framework coupled with non-crossing quantile regression. To this end, we analyze 764 rainfall-induced landslides over 13-year (2007–2019) across the NEH and consider at-site rainfall time series from gauge-based high-frequency daily observations. The site-specific antecedent moisture content shows a mid-to-long-term memory, spanning from 2–3-week, prior to slope failure, reflecting the need to consider preceding antecedent accumulated moisture content in developing the ED threshold model. The derived 3-d E-D thresholds, computed at the modest (20th percentile) hazard level, demonstrate significant spatial variability: approximately 30% (6/21) of the sites show the robust control of antecedent moisture content over triggering rainfall, with varying optimal time lags that range from 3 to 60 days in triggering landslides. Conversely, ~25% (5/21) of the sites are more responsive to intense, short-duration rainfall in triggering slope failure. Within the Bayesian probabilistic framework, incorporating root-zone saturation, alongside the compounding role of triggering rainfall and antecedent moisture content, systematically elevates the landslide likelihood. At Kalimpong, accounting for effective soil saturation (S) of 0.85, we find an increase in the skill score by a factor of two in derived E–D thresholds, indicating the new model outperforms our earlier model as well as the one proposed in the literature. Regionally, landslide likelihood peaks when high rainfall co-occurs with elevated sub-surface soil saturation, confirming a strong nexus between accumulated antecedent moisture content, subsurface soil saturation and short-duration record rainfall, in triggering slope failure. The derived insights aid in operational early warning systems, offering improved landslide forecast credibility in the NEH region with predominant space-time rainfall seasonality.
How to cite: Monga, D. and Ganguli, P.: Improving Skill of Rainfall Thresholds for Moisture-Driven Landslides by Integrating Root-Zone Soil Moisture at the Northeastern Himalayas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1906, https://doi.org/10.5194/egusphere-egu26-1906, 2026.
Shallow landslides pose a significant threat to people and infrastructure in mountainous regions and can occur abruptly on steep soil slopes. To assess their hazard potential, data-driven landslide susceptibility mapping aims to predict the spatial likelihood of such events. In recent decades, machine learning approaches and high-quality spatial information have continuously improved landslide susceptibility assessment. Nevertheless, discrepancies between predicted susceptibility and observed landslide occurrence seem to remain unavoidable. In simple terms, this mismatch between predicted and observed patterns can have two causes: 1) the information on covariates controlling landslide triggering is limiting (the predicted susceptibility is ‘wrong’) or 2) the observed time scale is too short to capture the failure of more areas with similar (correctly predicted) susceptibilities. To explore these two options, we first developed a landslide susceptibility map for Switzerland based on a wide range of spatial datasets and machine-learning methods. Next, we evaluated its performance against an independent inventory which contains detailed field information of 763 landslides. Information from soil profiles collected at the head scarps of these landslides allowed us to assess the specific conditions that lead to slope instabilities which large-scale spatial models are not capable of addressing. In a third step, we performed field investigations at selected past landslide sites and compared their subsurface structure (deduced from electrical resistivity tomography) with nearby locations that had not yet failed but exhibited similar predicted susceptibility values. These measurements revealed significant differences in the subsurface. Our approach highlights the critical role of subsurface complexity in controlling hydrological flow paths that ultimately govern slope failure. In particular, variations in soil texture, soil development, soil type and soil depth strongly influence the mechanical and hydrological conditions affecting slope stability. These findings provide new insights into the limitations of large-scale susceptibility mapping and emphasize the importance of subsurface hydrology in understanding shallow landslide initiation.
How to cite: Halter, T., Bast, A., Aaron, J., Lehmann, P., and Stähli, M.: What is wrong with landslide susceptibility mapping: Insights from data-driven analysis and field investigations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5658, https://doi.org/10.5194/egusphere-egu26-5658, 2026.
The Campania region (southern Italy) is characterized by a widespread hydrogeological risk due to the presence of many slopes covered by pyroclastic deposits derived from the activity of the Vesuvius and Campi Flegrei volcanic complexes. Indeed, numerous areas in the region are prone to rainfall-induced landslides—particularly shallow landslides and debris flows—which are often triggered by short-duration, high-intensity precipitation events. Over the years, the region has been affected by severe landslide events, causing loss of lives and economic damage. In Campania, a territorial landslide early warning system (Te-LEWS) has been operational since 2005 managed by the regional department for Civil Protection. For early warning purposes, the regional territory is divided into eight distinct warning zones according to the following factors: hydrography, morphology, climate, geology, land use and administrative boundaries. The predictions of a weather numerical model are used to evaluate the possible occurrence of rainfall-induced landslides within each warning zone. The daily assessment of the criticality is established by comparing the weather forecasts to a set of thresholds associated with rainfall precursors.
In the scientific literature it is widely recognized that rainfall primarily acts as a triggering mechanism, while hydrological variables (e.g., soil moisture) control slope predisposition to failure. Therefore, an evaluation that neglects antecedent hydrological conditions may result in a high number of false alarms, limiting the reliability and credibility of rainfall-only warning models. In recent years, a growing number of weather and hydrological reanalysis products have been produced at fine temporal and spatial resolutions, allowing the potential use of soil moisture data into operational Te-LEWS. This study proposes a hydrometeorological approach integrating meteorological and hydrological information and testing its performance in a landslide-susceptible area of the Campania region, southern Italy. A two-dimensional Bayesian analysis is employed to quantify the conditional probability of landslide occurrence and to derive multiple hydro-meteorological thresholds associated with increasing warning levels. The performances of the warning models are assessed by means of statistical indicators to identify the best-performing combination of hydro-meteorological thresholds. Finally, the potential added value of incorporating soil moisture into territorial landslide warning models is assessed by comparing the hydro-meteorological model developed in this study with the current regional warning system in a real-case scenario.
How to cite: Pecoraro, G., Calvello, M., and Zhang, S.: Adopting a hydrometeorological approach for territorial landslide early warning: insights and effectiveness evaluation from a case study in Campania (Italy), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10933, https://doi.org/10.5194/egusphere-egu26-10933, 2026.
Rainfall on frozen slopes represents a critical hydrological control on landslide and debris-flow initiation in cold and alpine environments, as frozen soil layers can strongly limit infiltration and favour surface runoff. Depending on the thermal and hydraulic state of the subsurface, precipitation may either infiltrate through partially unfrozen pathways or be rapidly converted into runoff, with important implications for erosion and slope destabilisation. Freeze-thaw dynamics and preferential flow through macropores can further complicate this partitioning by transiently modifying soil permeability and infiltration pathways, while their interaction with pre event soil moisture conditions remains poorly constrained under event-scale conditions.
We present nine large-scale rainfall experiments conducted on an inclined frozen soil body inside a controlled climate chamber. The experiments systematically varied initial volumetric water content and the presence or absence of an interconnected macropore network, while continuously monitoring soil temperature, liquid water content, subsurface drainage, and surface runoff. Our results show that hydrological responses of frozen slopes are primarily controlled by initial water content, with macropores exerting a secondary but highly non-linear influence. At low initial water content, infiltration was dominated by matrix flow despite frozen conditions, resulting in limited surface runoff. At intermediate water content, macropores enabled rapid bypass infiltration through the partially frozen profile, promoting early drainage and subsurface water transfer. At high initial water content, the frozen matrix became effectively impermeable and infiltration depended almost entirely on macropore flow. However, macropore functionality was transient: progressive refreezing and particle-assisted clogging reduced hydraulic connectivity during ongoing infiltration, causing a rapid shift from bypass infiltration to runoff-dominated conditions.
These results demonstrate that macropores in frozen slopes act as dynamic flow pathways whose hydraulic effectiveness depends on pre-event moisture conditions. While open macropores can enable subsurface infiltration under otherwise restrictive frozen conditions, progressive refreezing or clogging can reduce their functionality during infiltration events, causing a shift from infiltration-dominated responses toward surface runoff. The observed regime shifts highlight the need to explicitly represent transient preferential flow and refreezing processes in landslide hydrology and slope stability models, as they critically control hydrological preconditioning and the timing and magnitude of runoff-driven erosion and slope instability in seasonally frozen terrain.
How to cite: Bauer, J., Müller, S., Heinze, T., Khanahmadi Bafghi, H., and Baselt, I.: When Frozen Slopes Switch Regimes: Moisture-Controlled Runoff Generation and the Transient Role of Macropores, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9877, https://doi.org/10.5194/egusphere-egu26-9877, 2026.
In late July 2024, a prolonged spell of extreme monsoon rainfall led to progressive slope saturation in the upper Punnapuzha catchment, culminating in a catastrophic landslide and debris-flow disaster in Meppadi Grama Panchayat, Wayanad District, Kerala, India, which then resulted in widespread loss of life and severe geomorphic alteration of the Punnapuzha river corridor. Understanding the hydrological processes that governed initiation of slope failure, debris mobilization, and long runout is critical for improving landslide hazard assessment in steep, monsoon-dominated terrains. This study presents an integrated, event-based reconstruction of the disaster, focusing on the role of rainfall characteristics, catchment-scale hydrological response, and debris-flow dynamics. Rainfall analysis was carried out using data from several raingauge stations surrounding the landslide crown, with particular emphasis on spatial representativeness and consistency during extreme events. These rainauges recorded more than 570 mm of rainfall over 29–30 July 2024, indicating rapid slope saturation and exceptional hydrological loading. Catchment response was simulated using the SWAT+ hydrological model, calibrated and validated against observed discharge records. The model reproduces daily runoff dynamics reasonably well and provides insight into the antecedent moisture conditions and runoff generation that preceded slope failure. To capture terrain modification caused by the event, post-landslide LiDAR-derived elevation data (0.1 m resolution) were compared with pre-event satellite-based DEMs. This analysis reveals extensive aggradation, channel widening, and reorganization of flow paths along an approximately 8 km debris-flow corridor. Two-dimensional debris-flow simulations were then performed using the non-Newtonian module in HEC-RAS, adopting Bingham rheology to represent high-concentration sediment–water mixtures. Simulations on pre-event terrain show strong agreement with observed runout extent and deposition patterns, with maximum flow depths exceeding 40 m near the landslide crown and progressively decreasing downstream. The results demonstrate that the disaster was controlled not by rainfall magnitude alone, but by the combined effects of intense short-duration rainfall, rapid catchment response, and efficient debris routing along confined valley geometry. By explicitly linking rainfall variability, hydrological response, and debris-flow propagation, this study provides a process-based framework for interpreting extreme landslide events in tropical mountain regions and highlights the importance of integrating hydrological understanding into landslide hazard analysis.
How to cite: Harikumar, A., Thampi, S. G., Ramesh VV, S., Vinod, M. K., and Mohan, J.: Hydrological Controls on the 30 July 2024 Wayanad Debris-Flow Disaster: Rainfall Extremes, Catchment Response, and Runout Dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9604, https://doi.org/10.5194/egusphere-egu26-9604, 2026.
In recent years, the frequency and severity of extreme rainfall events have increased in the Himalayas, triggering landslides and debris flows often as a cascading hazard. Understanding the interactions between rainfall, initial soil moisture, and the triggering of landslides or debris flows is essential for mitigating risks to communities and infrastructure. However, the limited availability of observed hydrometeorological data poses serious challenges for accurate hazard assessment and early warning. To address these, numerical modelling-based reanalysis of rainfall-induced cascading hazards are chosen as a good choice. This study examines the influence of key hydrometeorological parameters, particularly rainfall and initial soil moisture, on the initiation and progression of shallow landslides and runoff-generated debris flows within a small mountainous catchment in the Himalayas, utilizing a basin-scale numerical modeling approach. We utilize multiple precipitation data sources, including reanalysis products, satellite-based retrievals, and outputs from numerical weather prediction models, to conduct a retrospective analysis of a historical cascading hazard event. This approach enables us to assess how these parameters impact the timing and severity of individual hazards, such as landslides and debris flows, within the cascading hazard chain. Our findings reveal distinct temporal patterns and triggering mechanisms for shallow landslides and runoff-generated debris flows, shedding light on their cascading behaviour in data-scarce, topographically complex regions. We also observe that initial soil moisture has a strong influence on hazard severity, and understanding its connection with rainfall is crucial for reliable hazard assessment.
How to cite: Dixit, S., Siva Subramanian, S., and Sen, S.: Exploring the role and connections between rainfall and soil moisture over cascading hazards in the Himalayas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10404, https://doi.org/10.5194/egusphere-egu26-10404, 2026.
Landslides represent a major threat to human safety and infrastructure, particularly in mountainous regions. Accurately predicting landslide susceptibility in a physically based deterministic manner requires an integrated, multidisciplinary approach that combines geology, geomorphology, and hydrology. In this work, a hydromechanical modeling framework is developed to forecast the initiation of large-scale shallow landslides by computing the local factor of safety (LFS) as a measure of slope instability. The framework couples (1) a finite element method (FEM) solver for hydromechanically coupled landslide processes implemented within a Java-based, object-oriented modeling environment, with (2) an external hydrologic model, allowing for detailed three dimensional simulations of slope response to transient rainfall events across extensive hillslope domains. The proposed framework is first validated using a benchmark test on a homogeneous hillslope with constant inclination and is subsequently applied to a real-world large-scale case study in the Braies Alpine Catchment, Alto Adige, Northern Italy. In the benchmark scenario, the model successfully reproduces shallow landslide triggering under prolonged rainfall, while in the real-case application it reliably captures the initiation of multiple landslides during an intense summer storm. These results highlight the framework’s robustness and accuracy in predicting landslide initiation in complex terrain, demonstrating its potential as a cost-effective tool for landslide hazard and risk assessment.
How to cite: Busti, R., Formetta, G., and Lu, N.: A Regional-Scale Framework for Landslide Prediction Combining Three-Dimensional Hydrological Modeling and the Local Field Factor of Safety, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7592, https://doi.org/10.5194/egusphere-egu26-7592, 2026.
Posters on site: Thu, 7 May, 16:15–18:00 | Hall X3
Recent studies have suggested that rainwater infiltrates not only the soil layer but also the underlying bedrock in small mountainous catchments, forming a bedrock aquifer. This bedrock groundwater subsequently discharges into the soil layer, potentially affecting the initiation of shallow landslides. To clarify this influence, it is essential to understand the runoff dynamics of bedrock springs in response to precipitation. However, direct observation of bedrock spring runoff remains challenging because bedrock springs are usually covered by thick soil layers. As a result, only a limited number of studies have investigated the runoff characteristics of individual bedrock springs, and the development of runoff models for bedrock springs is still insufficient. In this study, we conducted detailed field observations in a small mountainous catchment (6.87 ha) in Hokkaido, northern Japan. The study site is underlain by granite, and bedrock layer is exposed on both banks of the stream, where springs emerge from fractures in the bedrock. As of 2025, multiple bedrock spring outlets have been identified within the catchment. Soil temperature, bedrock spring water temperature, precipitation, and bedrock spring runoff were monitored. Soil temperature and bedrock spring water temperature were continuously recorded at hourly intervals. Precipitation was measured at hourly intervals using a tipping bucket rain gauge. Bedrock spring runoff was measured by constructing small dams immediately downstream of each spring outlet and directing all spring water into triangular weirs or tipping bucket discharge gauges. In addition, soil water, bedrock spring water, and rainwater were collected for water quality analysis. Soil temperature, bedrock spring water temperature, and water quality data were used to estimate the origin of the bedrock spring water. We applied the Pw1 model, a functional model based on antecedent precipitation, to reproduce bedrock spring runoff dynamics. This model was originally proposed by Kosugi et al. (2013) to reproduce groundwater level variations that cause deep-seated landslides, using antecedent precipitation with an arbitrary half-life time and positive constants. Model parameters were optimized to maximize the Nash–Sutcliffe efficiency (NSE). Finally, we discuss the relationship between the origin of bedrock spring water and the model parameters.
Reference
Kosugi, K., Fujimoto, M., Yamakawa, Y, Masaoka, N, Itokazu, T, Mizuyama, and T, Kinoshita, A. (2013): Functional models correlating antecedent precipitation indices to bedrock groundwater levels, Journal of the Japan Society of Erosion Control Engineering, Vol.66, No.4, p.21 - 32.
How to cite: Saito, H., Katsura, S., and Tanabe, R.: Runoff dynamics and functional modeling of bedrock springs in a small granitic mountainous catchment in Hokkaido, northern Japan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2913, https://doi.org/10.5194/egusphere-egu26-2913, 2026.
Rainfall-induced floods and landslides are globally prevalent natural hazards. Moreover, floods and landslides often occur in a cascading manner, posing significant risks and amplifying losses beyond each individual hazard event. Effective disaster preparedness and hazard management heavily rely on sufficient knowledge of flood-landslide cascading processes and accurate assessment of potential consequences. However, existing methods predominately analyse individual hazard event, and there is a notable lack of rapid, physically-based modeling approaches, particularly for regions where observations are limited. To address this challenge, we propose a novel framework to quantify flood and landslide risks by integrating remote sensing data with a high-performance hydrological-geotechnical model. The model is driven exclusively by remote sensing data (including meteorological forcings and ground properties) and forecasts flood and landslide processes based on physical principles. Moreover, this framework quantifies risk by synthesizing hazard intensity, population exposure, and regional socioeconomic conditions, while explicitly accounting for the compound interactions between these hazards. We evaluate this framework utilizing a heavy rainfall event of July 3–4, 2012 in the Yuehe River Basin, which triggered widespread floods, landslides, and debris flows. Our results demonstrate that the model effectively reproduces meteorological forcings and disaster processes, offering a new perspective for disaster risk assessment in data-scarce regions. The proposed framework could contribute to the development of effective mitigation strategies, enhancing regional resilience against cascading natural hazards.
How to cite: Chen, G., Chao, L., Jia, T., and Wang, S.: Coupling Remote Sensing and Hydrological-Geotechnical Modeling for Rapid Assessment of Cascading Flood-Landslide Risks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4339, https://doi.org/10.5194/egusphere-egu26-4339, 2026.
Landslide-dammed lakes are natural barriers formed by slope failures that can cause serious hazards downstream. Their stability depends on both dam shape and material and on the upstream hydrological conditions that control lake extension and water level. Changes in these conditions can increase lake level and activate hydraulic processes like seepage and overtopping, which can compromise the stability of dam. Understanding the interplay of upstream hydrology and stability is important to assess dam safety and downstream flood risk.
In early 2010, a rockslide in Attabad created a dam on the Hunza River in Pakistan, forming a lake that still exists today. In this study, lake surface area and volume were assessed using Landsat images and the Normalized Difference Water Index (NDWI), and pre-lake digital elevation model was used to estimate lake volume changes. Observations show seasonal fluctuations and consistency in lake volume over the years, influenced by spillway excavations and other hydrological processes.
A simplified geometry of dam body was defined based on literature data and images. Grain size distribution of dam materials typical of rockslides was also analyzed, and the Hazen formula was used to estimate hydraulic conductivity values. These were applied in GeoStudio SEEP/W to simulate nine scenarios with different combinations of clay and gravel permeability. Results show that total seepage (under current conditions) is moderate but strongly depends on material properties. Gravel-dominated zones have higher seepage, while clay-dominated zones have lower seepage. Some gravel areas could be prone to localized internal erosion or piping under high water levels.
We also analyze dam’s stability under different hydrological conditions. One approach is to evaluate seepage and structural response using current lake water level, which can help back-analyze and validate the mechanical properties of the dam materials. The second approach is to simulate future possible water levels to assess whether the dam remains stable under extreme conditions.
This study shows that combining remote sensing and hydrological modelling allows developing scenario-based analyses that can help understand how hydrology and dam material and shape control its stability. It provides a useful approach for monitoring and managing landslide-dammed lakes in areas with limited field data.
Keywords: Landslide dams, hydrological modeling, dam stability, scenario-based analysis, remote sensing
How to cite: Shazil, M. S., Damiano, E., and Greco, R.: Scenario-Based Assessment of Material and Hydrological Controls on Attabad Landslide Dam Stability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5859, https://doi.org/10.5194/egusphere-egu26-5859, 2026.
This study presents a methodological framework to assess climate change impacts on the hydrological conditions leading landslide occurrence. The approach is applied to a ~170 km² landslide-prone area in southern Italy, characterized by complex topography and rainfall-driven slope instability. Regional climate projections from CORDEX for the period 2006–2070, under moderate (RCP4.5) and high (RCP8.5) emission scenarios, were bias-corrected using observed rainfall data (2006–2023) and evaluated against a synthetic dataset representing present-day climatic conditions.
Aiming at event-scale detection of rainfall-triggered landslides throughout the study period, soil hydrological processes were simulated using physically based models and coupled with slope stability analyses that account for unsaturated soil behavior. Scenario-based statistical comparisons were carried out across three rainfall-homogeneous subregions. The analysis reveals a general trend toward drier conditions, in line with regional climate projections, together with enhanced rainfall variability at the subregional scale. Nevertheless, landslide occurrence is projected to increase significantly in climate change scenarios, with a more pronounced rise under RCP4.5 compared to RCP8.5.
This apparently counterintuitive response reflects contrasting changes in rainfall and landslide dynamics. Under RCP8.5, landslides are mainly triggered by more intense rainfall events, whereas under RCP4.5 they arise from the combined influence of wetter antecedent soil conditions and more intense early-peak rainfall. These results underscore the persistent and critical role of antecedent soil moisture in landslide initiation, even under rapidly evolving climate conditions.
How to cite: Roman Quintero, D. C., Greco, R., Bogaard, T., and van der Ent, R.: When Climate Change Affects Rainfall, Landslide Frequency Responds: An Assessment at the Subregional Scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5909, https://doi.org/10.5194/egusphere-egu26-5909, 2026.
Large mountainous areas of Campania (southern Italy) are frequently hit by rainfall-triggered shallow landslides, which often cause significant damage to buildings and infrastructures. Specifically, they involve steep slopes covered with unsaturated air-fall pyroclastic deposits, formed by alternating layers of ashes and pumices of variable thickness, lying upon heavily fractured limestone bedrock. The main triggering factor of these catastrophic events is the rainfall. Nonetheless, there are other causes linked to the hydrological conditions predisposing the slopes to failure (Roman Quintero et al., 2025), often associated with soil moisture conditions prior to the onset of rainfall (Greco et al., 2021). Predicting the occurrence of these landslides is highly challenging due to the significant spatial and temporal variability of the factors driving them. Thus, landslide hazard assessment needs attention and remains a critical task, especially in terms of reliably predicting the triggering location. In this work, a method for the preliminary assessment of landslide hazard in a sloping area covered by pyroclastic deposits is proposed, based on available historical precipitation records and considering only slope inclination and soil thickness as geomorphological controlling factors while assuming soil characteristics as homogeneous. The study area is located on the Cornito slope near the town of Cervinara, around 40 km northeast of the city of Naples, which belongs to the north-facing part of the Partenio Massif in the southern Apennines of Campania. Specifically, a small catchment of 0.4 km2 was investigated, where on 16 December 1999 a rain event of approximately 300mm in 48h triggered several landslides evolving in the form of fast debris flows. The largest one travelled nearly 2 km downslope toward the town of Cervinara, causing destruction and killing five people. The natural landforms of the catchment were considered using the Digital Elevation Model (DEM) with a resolution of 10 m grid cell, downloaded from the dataset TINITALY/01. This DEM was obtained by simple linear interpolation of contour lines digitized from the 1:25000 maps of the Istituto Geografico Militare (IGM) before the landslides of 1999 (Tarquini et al., 2007). Grid cells were grouped into fifteen classes of slope inclination and corresponding soil thickness, ranging from 33.5° to 47.5°, for simulating the hydrological processes of rainwater infiltration. Specifically, the 1D Richards’ equation model was run to simulate soil saturation profile at hourly resolution for each cell, considering the hourly rainfall recorded during the event of 1999. The model has been calibrated with both laboratory measurements (Roman Quintero et al., 2024) and field data collected during previous hydrological monitoring activities (Marino et., 2020). Then, based on the results obtained with the unsaturated flow model, the landslide hazard map is generated by looking at cells with a Factor of Safety, calculated under the infinite slope hypothesis, smaller than 1. The generated areal landslide hazard map was validated by comparison with the documented landslide inventory, showing agreement with the spatial distribution of reported landslides, especially with the location of the scarp of the largest one recorded, with an estimated mobilized volume of 30000 m3.
How to cite: Marino, P., Abdullah, A., Roman Quintero, D. C., Santonastaso, G. F., and Greco, R.: Areal landslide hazard assessment: case study of landslide-prone area covered by pyroclastic deposits , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6463, https://doi.org/10.5194/egusphere-egu26-6463, 2026.
The development of reliable tools for assessing rainfall-induced landslide hazard over large areas is often constrained by the limited availability of historical landslide inventories and high-quality rainfall data. This challenge is particularly evident in the pyroclastic soil deposits of Campania (southern Italy), where coarse-grained soils formed by air-fallen volcanic material exist in alternating layers. These deposits are frequently affected by rainfall-induced landslides, primarily triggered by intense rainfall, with antecedent soil moisture acting as a key preparatory factor.
In this study, the Partenio and Sarno Mountains, covering an area of approximately 500 km² and monitored by 23 rain gauges, were subdivided into three zones based on the probability distributions of rainfall event series. Events were separated using a minimum inter-event time of 24 hours with rainfall amounts lower than 2 mm. The zoning reflects the orographic control on rainstorms in the area and was defined using Kolmogorov-Smirnov tests. For each zone, the NRSP stochastic model of rainfall was calibrated based on observed rainfall data, and 500-year-long synthetic hourly rainfall time series were generated. These synthetic series were then used as input to a 1D model of the flow in the unsaturated soil deposit, to simulate the response to precipitation for a representative slope in each zone. The resulting time series of soil moisture and soil suction were employed to perform slope stability analyses, evaluating the factor of safety (FS) with the infinite slope model.
Using the synthetic dataset, empirical thresholds for landslide prediction were derived for each zone, including both meteorological thresholds (based on rainfall intensity and duration) and hydrometeorological thresholds (combining rainfall depth with antecedent root-zone soil moisture). The results indicate that hydrometeorological thresholds are more effective than meteorological thresholds when rainfall and slope properties are accurately known. Moreover, the inclusion of antecedent hydrological variables allows the identification of two distinctive landslide-triggering mechanisms typical of the initial and end phases of the rainy season.
To improve the reliability of the proposed approach, uncertainties associated with the spatial variability of geomorphological slope properties and hydrometeorological variables were explicitly considered. These uncertainties were modeled as normally distributed random errors, and the synthetic datasets of the representative slopes were accordingly perturbed. Accounting for uncertainty shows the robustness of the hydrometeorological thresholds, limiting both false alarms and missed events across all zones. This result was confirmed through validation against available landslide, rainfall, and root-zone soil moisture data for the period 1999-2025.
The proposed methodology provides a practical framework for incorporating uncertainty in hydrometeorological information into landslide hazard assessment over large areas. Furthermore, once the site-specific dominant hydrological processes and controlling variables are identified, the approach can be readily transferred to other regions affected by rainfall-induced landslides.
How to cite: Abdullah, A., Roman Quintero, D. C., Marino, P., and Greco, R.: Large-scale assessment of rainfall-induced landslides in pyroclastic soils of Campania (Italy): a synthetic hydrometeorological approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8173, https://doi.org/10.5194/egusphere-egu26-8173, 2026.
Understanding the relationship between rainfall and groundwater level response is crucial for elucidating landslide mechanisms and for planning structural mitigation measures, such as groundwater drainage works, for deep-seated, slow-moving landslides. It is well known that elevated groundwater levels induce landslide displacement; however, such displacement often causes fracturing and deformation of the landslide mass. These processes can alter the internal hydrogeological structure of the landslide, potentially changing the rainfall–groundwater level response relationship. Although some previous studies have reported differences in this relationship before and after large landslide displacements, its linkage to fracturing and deformation remains unclear.
In this study, we observed groundwater level dynamics at four observation wells (depth: 1.3–7.4 m) within a deep-seated, slow-moving landslide in Biratori, Hokkaido, northern Japan. The slip surface was estimated to be located at a depth of approximately 8 m. In November 2023, the landslide experienced approximately 4 m of displacement over a two-week period. The observation period was divided into intervals before and after this large displacement, and covariance analysis was applied to evaluate changes in the rainfall–groundwater level response relationship. For each analysis period, an antecedent precipitation index (API) was calculated from daily rainfall data. The half-life (days) and lag time (days) were optimized to maximize the correlation coefficient with the observed groundwater levels, and these optimized parameters were used in the covariance analysis. Preliminary results indicate statistically significant changes at three of the four observation wells. Furthermore, comparison with topographic changes derived from UAV-LiDAR measurements (10-cm resolution DEMs acquired on November 9 and 28, 2023), suggests that changes in half-life reflect variations in landslide-mass permeability caused by compression and tension, whereas decreases in lag time indicate the formation of new seepage pathways associated with increased fracturing. These findings suggest that the rainfall–groundwater level response relationship is not stable in actively moving landslide masses. Further analyses will examine and discuss its linkage to topographic changes in greater detail.
How to cite: Kobayashi, S., Katsura, S., Aoki, T., Kasai, M., and Hayakawa, Y.: Changes in rainfall–groundwater level response associated with large displacement of a deep-seated landslide in Japan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8752, https://doi.org/10.5194/egusphere-egu26-8752, 2026.
Hydrometeorological thresholds are central to many operational landslide early warning systems, yet they often remain coarse and weakly linked to slope physics. Two persistent limitations are (i) the dependence on landslide inventories that are incomplete and often poorly timed, and (ii) the assumption that a single regional threshold can represent heterogeneous and evolving slope stability conditions. This contribution presents a deformation-informed perspective to advance threshold-based landslide early warning. We show that slope deformation, measured as continuous time series, can act as an transition state variable that bridges hydrometeorological forcing and slope failure. By explicitly incorporating deformation, hydrometeorological thresholds can be better constrained as well as better used operationally. First, deformation observations can be used to supplement event information for threshold assessment. Automated extraction of “deformation events” from geodetic time series can complement landslide records as physically meaningful proxies, reducing the sensitivity of threshold estimation to inventory incompleteness and timing uncertainty, and improving the robustness of calibrated thresholds. Second, deformation can guide the spatiotemporal refinement of warning criteria. By quantifying how different slopes respond to rainfall over multiple time windows, deformation-derived indices can characterize slope-specific response patterns and stability states. This information enables a downscaling strategy in which regional hydrometeorological thresholds for landslide initiation are transformed into slope-specific, dynamically updated thresholds that better reflect local conditions and temporal changes in stability. In this way, deformation moves thresholds from static and regionally averaged triggers toward adaptive criteria that are more physically grounded and spatially actionable.
Overall, the proposed deformation-aware framework brings two complementary benefits in early warning: (1) strengthening landslide initiation threshold development through deformation-informed event characterization, and (2) enhancing threshold application through slope-specific, time-varying adaptation. This approach is sensor-agnostic (applicable to GNSS and InSAR) and compatible with different threshold formulations, offering a practical pathway to improve reliability and reduce uncertainty in landslide early warning across data-limited and highly heterogeneous regions.
How to cite: Feng, X., Lombardo, L., Du, J., Chai, B., and Bogaard, T.: Deformation-informed hydrometeorological thresholds for landslide early warning: inventory enhancement and spatiotemporal downscaling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15316, https://doi.org/10.5194/egusphere-egu26-15316, 2026.
Soil moisture plays a central role in slope hydrology by integrating atmospheric forcing and subsurface processes, thereby shaping antecedent wetness conditions relevant to landslide preconditioning. A key property governing this role is soil moisture memory, defined as the persistence of moisture anomalies following external perturbations. Despite its importance, the seasonal organization of soil moisture memory and its physical controls remain insufficiently constrained at regional scales.
This study focuses on understanding how soil moisture variability across Italy is organized by the interplay between external hydro-meteorological forcing and internal system persistence across seasons. Specifically, using high-resolution gridded reanalysis-based data within a causal discovery framework, the analysis examines the seasonal dominance of different drivers and the spatial and temporal variability of soil moisture persistence, with emphasis on large-scale background hydrological states.
The results indicate pronounced spatial and seasonal heterogeneity. Soil moisture variability is primarily governed by atmospheric water inputs over large portions of the domain, while cryospheric and energy-related processes become relevant under specific climatic and seasonal conditions. Crucially, soil moisture persistence exhibits systematic seasonal contrasts and is not uniformly associated with the apparent strength of external forcing. The joint behavior of forcing strength and memory instead organizes soil moisture dynamics into distinct seasonal regimes, reflecting different modes of system response shaped by land–atmosphere coupling and soil water loss processes. These findings support a physically consistent interpretation of antecedent wetness conditions relevant to landslide preconditioning.
How to cite: Liu, X. and De Michele, C.: Seasonal controls and memory of soil moisture variability across Italy: a process-oriented perspective relevant to landslide preconditioning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11992, https://doi.org/10.5194/egusphere-egu26-11992, 2026.
