Roto-translational landslides are characterized by two movement types at different landslide parts, i.e., rotational movement at the headscarp and translational movement at the toe. They are widely distributed in clay formations with planar or subhorizontal layers, posing threats to human life and infrastructure. Due to the different shapes of the sliding surfaces, the kinematics of roto-translational landslides show complicated patterns with varying spatial and temporal distributions. Forecasting the rapid sliding of roto-translational landslides presents challenges, as they often manifest as unnoticed slowly movement. The sliding surfaces of the roto-translational landslides feature concave-upward shape at the landslide head and a planar shape at the landslide accumulation zone, leading to complex deformation mechanisms. Roto-translational landslides usually exhibit creep deformation along sliding surfaces, showing transverse cracks on the ground surfaces. However, the scarcity of experimental data has significantly hindered a deep understanding of their failure mechanisms. Our research probes into the rotational failure phenomena of landslides in stiff clay formations, utilizing geotechnical centrifuge modelling and laboratory creep tests. Our findings reveal that rotational failures in model slopes are exclusively triggered under conditions of an undrained boundary at the basal shear zone. The post-failure behaviour is characterized by a settlement at the slope crest and a pronounced bulge at the toe, resulting in complex rotational movements along the basal sliding surface. Moreover, our laboratory experiments illuminate the creep behaviour of shear-zone materials under undrained conditions. In particular, samples with a high initial water content under sustained loading are highly susceptible to a quick transition into tertiary creep, leading to accelerated failure. These experimental insights substantially advance our understanding of the rotational failure patterns observed in clay-based landslides.

How to cite: Peng, X., Kang, X., and Wu, W.: Centrifuge modelling of a roto-translational landslide in stiff clay formation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21656, https://doi.org/10.5194/egusphere-egu25-21656, 2025.

The fatal rock avalanche type landslide occurred in the northern part of the village Nergeeti (Imereti region) on February 7, 2024, which destroyed private houses, damaged a road, water supply, gas pipelines and different infrastructure objects, moreover, 9 persons lost their lives. The study area is located in the Khanistskali river valley and tectonically represents a frontal part of the Adjara-Trialeti fold-and-thrust Belt. Here, it is represented the data based on a detailed field investigation conducted to characterize the landslide body and identify its parameters (using a UAV). Slope is represented by the Middle Eocene (Zekari suite) volcanic and sedimentary rocks such as - tuffs, volcanic sandstones, volcanic breccias, and clays. These sediments are overlaid by the Quaternary diluvium-colluvium deposits. According to the local meteorological station, the total amount of precipitation during February 5-7 was 81 mm, which represents 46% of the entire month’s precipitation, generally. The AMSL of a main scarp and a base of the landslide body varies from 378 to 215 meters. Based on a DTM and field investigations, the total area of the landslide mass is 4.45 ha, while the height of a main scarp reaches up to 30 meters. The width in the upper part is 45-50 meters, while in the lower parts, it widens up to 140-160 meters. Moreover, nearby living 7 families were recommended to be moved to a low-risk area by the specialists of the Department of Geology of the National Environmental Agency. Event once again clearly shows the importance of integrating and advancing interdisciplinary methods in studying geohazards in a rapidly changing environment.

How to cite: Gaprindashvili, G., Gaprindashvili, M., Giorgadze, A., and Kurtsikidze, O.: Geologic and morphologic characteristics of Nergeeti landslide, Imereti, Georgia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5029, https://doi.org/10.5194/egusphere-egu25-5029, 2025.

The Akatani landslide, located on the Kii Peninsula of Japan, is a catastrophic deep-seated landslide triggered by intense rainfall during Typhoon Talas in 2011. The landslide mass travels a considerable distance, forming a landslide dam at the slope foot. Its instability is primarily attributed to the rapid reduction of shear strength in sandstone–mudstone (shale) materials and elevated pore water pressure. In this study, a fully three-dimensional physical model based on the Material Point Method (MPM) is applied for the first time to investigate the Akatani landslide. By employing the high-performance solver MaterialPointSolver.jl, an advanced numerical simulation is conducted, integrating geotechnical parameters from ring shear tests, pore pressure characteristics, and field-based geological and topographical data. The proposed model effectively replicates the rainfall-triggered reactivation of the landslide along pre-existing sliding surfaces identified through the Sloping Local Base Level (SLBL) ^{[1, 2]}. It captures the failure process, from initial instability to rapid downslope movement and channel blockage, under a coupled solid–fluid framework. Comparisons with field observations and previous LS-Rapid simulations demonstrate the high accuracy and applicability of this modeling approach. These findings provide essential insights for understanding the dynamic mechanisms of deep-seated rainfall-induced landslides, evaluating secondary disaster risks, and developing effective disaster mitigation strategies.

References

[1]. Chigira, M., Tsou, C. Y., Matsushi, Y., Hiraishi, N., & Matsuzawa, M. (2013). Topographic precursors and geological structures of deep-seated catastrophic landslides caused by Typhoon Talas. Geomorphology, 201, 479-493.

[2]. Jaboyedoff, M., Chigira, M., Arai, N., Derron, M. H., Rudaz, B., & Tsou, C. Y. (2019). Testing a failure surface prediction and deposit reconstruction method for a landslide cluster that occurred during Typhoon Talas (Japan). Earth Surface Dynamics, 7(2), 439-458.

How to cite: Huo, Z., Tang, X., Jaboyedoff, M., Podladchikov, Y., and Chigira, M.: High-Resolution 3D MPM Simulation of the 2011 Akatani Landslide, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9926, https://doi.org/10.5194/egusphere-egu25-9926, 2025.

Landslides affect millions of people annually in the mountainous regions of Latin America, resulting in significant economic, human and structural losses (Carrasco et al., 2011). The San José de Aloburo landslide, located in Imbabura-Ecuador, occurred in November 2021, significantly changing the landscape as well as the increase of the substantial damage to the locality. Vásquez et al. (2021) characterized it as a complex rotational landslide, highlighting its geomorphological and stratigraphical particularities. This study aims to integrate geophysical and geological approaches to further analyze the internal structure and physical properties of the materials involved in the landslide.

The methodology included the application of electrical resistivity tomography (ERT) profiles (Perrone, 2014), using low-cost equipment, suitable for the economic context of the region. It allowed to identify variations in the subsurface resistivity. Stratigraphic columns were constructed also to analyze the interlaying and composition of the displaced geological strata. In addition, a granulometric analysis was carried out on a representative sample to evaluate the particle size distribution.

The results reveal significant variations in resistivity associated with the distribution of the displaced materials and the presence of complex internal morphology. Likewise, the integration of geophysical and geological data allowed a more precise delineation of the rupture zone, the depth of displacement and the characteristics of the materials involved. These findings provide valuable information for understanding landslide processes in the region and monitoring this type of events with the additional advantage of being economically accessible.

Keywords: Landslides, Electrical Resistivity Tomography (ERT), Geophysical Integration

References:

Carrasco, J., et al. (2011). Impactos del cambio climático, adaptación y desarrollo en las regiones montañosas de América latina. Ministerio
de Relaciones Exteriores, Gobierno de Chile-Alianza para las Montañas-FAO-Banco Mundial.

Perrone, A., et al. (2014) Electrical resistivity tomography technique for landslide investigation: A review. Earth-Science Reviews, 135 , 65-82.

Vázquez, Y., et al. (2021). Informe técnico sobre el movimiento en masa ocurrido en san José de Aloburo (noviembre/2021), Pimampiro,

Imbabura. Escuela de Ciencias de la Tierra, Energía y Ambiente, Yachay Tech.

How to cite: Mayacela-Salazar, B., Torres-Ramirez, R., and Perez-Roa, R.: Landslide evaluation applying electrical tomography techniques: study case San José de Aloburo, Pimampiro,Imbabura, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20680, https://doi.org/10.5194/egusphere-egu25-20680, 2025.

Characterized by sudden occurrence, high velocity and long runout distance, flow-like landslides pose great threats to human communities. In essence, flow-like landslides can be regarded as the flow of granular materials under different topographic conditions, driven by external triggers or internal state changes. During the movement of landslides, the motion behavior transitions from a solid-like state to a fluid-like state, finally resulting in its extreme mobility. Based on the granular flow physics, we investigate the dynamic process of flow-like landslides from a rheological perspective, thereby exploring the motion transition from a solid-like state to a fluid-like state and its hypermobility feature. We utilize an elastic viscoplastic constitutive model to capture the changes in the motion behavior of landslides during their movement. This model accounts for both the elastic response of the material under low-strain conditions and the viscoplastic behavior under large strains, and incorporates both stress and strain rate dependencies, which help in describing the progressive transition from a solid-like deformation to a fluid- like flow. For practice, numerical analyses of column collapse are conducted using the Material Point Method (MPM), a numerical technique well-suited for simulating large deformations. Moreover, a typical flow-like landslide in China, the Luanshibao landslide, is well studied to investigate its long runout mechanism.

How to cite: Tang, X., He, S., Zhu, L., Zhang, H., Jaboyedoff, M., and Huo, Z.: Runout Mechanism of Flow-like Landslides Based on Granular Flow Physics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9062, https://doi.org/10.5194/egusphere-egu25-9062, 2025.

Predicting the dynamics of flood processes is paramount for effective disaster prevention and mitigation. Recently, Physics-Informed Neural Networks (PINNs) have been employed for flood dynamic prediction, demonstrating commendable performance in wave propagation forecasting. However, PINNs, which rely on traditional fully connected neural networks, exhibit certain limitations. Notably, their capacity for learning long-term wave propagation processes remains insufficient, and they struggle to generalize across diverse, previously untrained scenarios.In this study, we propose an innovative model that integrates a Convolutional Autoencoder (CAE) with a Long Short-Term Memory network (LSTM) to overcome these challenges. Drawing inspiration from the finite-difference method employed to solve the Shallow Water Equations (SWE), the CAE-LSTM model adeptly captures and predicts flow characteristics from both spatial and temporal dimensions. The CAE harnesses the power of convolutional neural networks to extract spatial features and generate compact latent representations, thereby reducing the complexity inherent in the physical system. Meanwhile, the LSTM captures the temporal dependencies within the latent feature space, enabling the prediction of the dynamic process based on time-series data.The efficacy of this model was validated through three classical two-dimensional dam-break scenarios. In the 60-second rolling prediction case, the accuracy of CAE-LSTM surpassed that of PINNs by approximately 60%, while its computational efficiency was enhanced by a factor of approximately 100. These results underscore the potential of CAE-LSTM to effectively capture the intricate dynamic behaviors of fluids, thereby offering a robust tool for predicting flood dynamics.

How to cite: Han, Z., Long, G., Li, C., Li, Y., Su, B., Xu, L., Wang, W., and Chen, G.: A Deep Learning-Based CAE-LSTM Model for Enhanced Long-Term Prediction of Flood Wave Propagation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3920, https://doi.org/10.5194/egusphere-egu25-3920, 2025.

On 1 November 2000, an intense rainfall event triggered a catastrophic debris flow in the Dacukeng Creek region of Ruifang Township in Taiwan, resulting in seven fatalities, one missing person, and extensive damage to residential structures and farmland. This disaster underscored the critical need for integrated debris flow mitigation strategies and rigorous engineering interventions within a comprehensive regional disaster prevention framework. In response, the present study developed a multifaceted approach combining high-resolution UAV-based terrain mapping, advanced numerical modeling, and immersive virtual reality (VR) simulations to quantitatively characterize debris flow dynamics and facilitate stakeholder engagement in risk assessment and mitigation planning. First, unmanned aerial vehicles (UAVs) were utilized to capture high-precision topographic data, which were processed with ContextCapture to generate a detailed 3D photogrammetric model. Next, FLO-2D simulations were employed to approximate debris flow rheology, analyzing flow depth, velocity, and inundation extents under various rainfall intensities. The resulting data were subsequently imported into Blender to create dynamic 3D visualizations illustrating potential flow pathways and associated hazards. Finally, a VR-based debris flow mitigation platform was constructed in Unity, featuring six degrees of freedom for user movement and interactivity. This platform enables engineers, policymakers, and community stakeholders to virtually navigate realistic hazard scenarios and evaluate the efficacy and cost-effectiveness of different structural and non-structural mitigation measures. By merging cutting-edge computational modeling with immersive visualization, the proposed framework allows for enhanced comprehension of debris flow mechanisms, fosters more productive communication among diverse stakeholders, and supports evidence-based policymaking. The real-time and interactive nature of the VR environment promotes deeper public engagement, improves collaborative planning, and ultimately strengthens regional resilience against debris flow hazards.

How to cite: Tsai, Y.-F., Gao, C., Wei, H.-Y., and Yang, M.-C.: Application of Virtual Reality in Debris Flow Control Engineering Planning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4951, https://doi.org/10.5194/egusphere-egu25-4951, 2025.

Glacial debris flows are prevalent across the southeastern Tibetan Plateau, driven by climate change-induced glacier retreat in this region. This retreat has facilitated an increased frequency of debris flow events, underscoring the need for a comprehensive understanding of their susceptibility to enhance hazard mitigation strategies. However, significant gaps remain in integrating climate change projections and glacier retreat dynamics into susceptibility assessments. This study presents a novel method for predicting the susceptibility of glacial debris flows under future climate change scenarios on the southeastern Tibetan Plateau. The proposed approach incorporates dynamic variables into susceptibility modeling, including annual precipitation, average annual temperature, projected glacier extents, and anticipated land cover changes. The analysis utilizes combined scenarios from Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs), specifically SSP1-2.6, SSP2-4.5, and SSP5-8.5, to evaluate the impacts of future climate conditions. Results indicate a notable increase in the number of glacier catchments with very high annual average temperatures from SSP1-2.6 to SSP5-8.5, particularly in the eastern portion of the study area, while annual precipitation exhibits minimal change. Land cover projections for 2030 suggest a shift from shrubland to bare land, signaling land degradation. Additionally, glacier retreat is evident, with a growing number of catchments projected to have a glacier area percentage below 0.05% by 2030. The susceptibility analysis reveals an increase in glacier catchments with high and very high susceptibility from SSP1-2.6 to SSP5-8.5. Notably, the number of catchments with very high susceptibility under SSP5-8.5 exceeds that of 2010 and closely resembles 2020 levels. These findings emphasize the escalating risks posed by climate change and glacier retreat, providing critical insights for developing adaptive hazard mitigation strategies in the region. 

How to cite: Zhao, F., Gong, W., Biachini, S., and Tang, Y.: Dynamic susceptibility assessment of glacial debris flows on the southeastern Tibetan Plateau under future climate change scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7529, https://doi.org/10.5194/egusphere-egu25-7529, 2025.

The Himalayan region, characterized by its rugged terrain, distinctive geography, and active tectonics, ranks among the most landslide-prone zones globally. Landslide susceptibility and hazard mapping are critical tools to mitigate future risks and devise effective management strategies. This study uses data-driven statistical approaches to evaluate co-seismic landslide susceptibility in District Hattian, NW Himalayas, Pakistan. A comprehensive co-seismic landslide inventory comprising 349, 393, and 735 landslide events from 2005, 2007, and 2012, respectively, was utilized to train, test and validate predictive models. 
Thirteen landslide causative factors (LCFs), including topographic, environmental, geologic, and anthropogenic variables, were analyzed to determine their influence on landslide occurrence. Three data-driven statistical models i.e., Weight of Evidence (WoE), Information Value (IV), and Frequency Ratio (FR) were employed to develop landslide susceptibility maps (LSMs). Model training used 70% of the landslide inventory, while 30% was reserved for validation. Model performance was evaluated using Receiver Operating Characteristic-Area Under Curve (ROC-AUC) metrics and predictive accuracy assessments. Among the models, the WoE approach outperformed well among the other models as ROC-AUC SRC scores of 84.4, 84.2, and 85.3 for 2005, 90.4, 86.4, and 87.2 for 2007, and 81.9, 86.7, and 85.9 for 2012 for WoE, FR, and IV models, respectively. PRC scores of the WoE, FR, and IV models were recorded as 85.7, 89.4, and 82.5 for 2005, 87.5, 77.5, and 80.4 for 2007, and 80.7, 88.3, and 87.7 for 2012. For the validation of long-term predictivity, efficiency models are checked by comparing the generated LSMs with newly recorded landslide events. The 2005 model was validated using 2007 data, the 2007 model with 2012 data, and the 2012 model with 2024 data. Results revealed a gradual decline in the predictive accuracy of the LSMs model of all three approaches over time; however, WoE consistently outperformed from the IV and FR models, maintaining robust predictive capabilities even after 12 years.
This study highlights that landslide-prone zones in District Hattian exhibit persistent mass movement activity and underscores the urgent need for proactive landslide management to minimize life loss and economic damage in this tectonically active region. The integration of advanced susceptibility modelling techniques with real-time validation offers a reliable framework for hazard assessment and risk mitigation. Policymakers and stakeholders are encouraged to implement targeted interventions, such as optimized land-use planning, the establishment of early warning systems, and increased community awareness programs, to enhance resilience against landslide hazards in the NW Himalayas.

How to cite: Riaz, M. T., Wani, S., Basharat, M., Riaz, M. T., and Manocha, A. R.: Evaluating the Efficiency and Predictive Accuracy of Temporal Susceptibility Models for Co-Seismic Landslides Using Real-Time Validation: A Case Study from the NW Himalayas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13226, https://doi.org/10.5194/egusphere-egu25-13226, 2025.

This short paper presents preliminary results ofstudy aimed at evaluating the effects of tree cutting as a predisposing factor of debris-flow triggering (Lepri et al., 2024). The study area (Nottoria, Perugia, Italy) was affected by debris flow events in 2012 and in 2015.

The material of the debris flow source area is classified as calcareous pebbles in a marly-clayey matrix, with very angular grains.

Woody beeches and oaks’ roots, with diameters varying between 0.5 and 2 mm were found in the retrieved soil samples.

In-situ investigations on the material involved in the debris flows, consisting of corkscrew tests, water content and suction monitoring, lidar drone is in progress, jointly with geotechnical laboratory experiments.

In this abstract we present the results of corkscrew tests.

The equipment presents a rotating arm at the end of which there is a load cell and a steel screw (Figure 1).

Figure 1. Corkscrew equipment.

The screw has a height H₄ = 125 mm, a diameter d_cs = 40 mm, a helix diameter = 6 mm and an helix pitch of 28 mm.

The peak strength was recorded using a 300 kg load cell (Steinberg systems – SBK-KW-300KG).

The corkscrew was driven into the ground by manual rotation, after which the load cell is connected, and the soil sample is pulled out by using a lever system. The load cell provides the pullout force T_max.

The shear stress along the lateral surface of the soil sample is then calculated following equation (1) provided by Meijer et al. (2018):

                                                                                         (1)

Corkscrew tests were performed at increasing depths (0–125, 125–250, 250–375 mm). Once the soil sample was extracted, the roots content was assessed and the water content and suction measured.

Figure 2 shows the location where corkscrew tests were performed, while the results are plotted in Figure 3 in terms of peak shear stress against the horizontal effective stress.

Figure 2. Corkscrew tests location

• a)    b)

Figure 3. a) Extracted rooted sample; b) Results from corkscrew tests: shear stress vs vertical effective stress

References

Lepri, A., Fraccica, A., Cencetti, C., and Cecconi, M. (2024a). A preliminary study on the possible effect of deforestation in debris flows deposits, EGU24-15726, Vienna, Austria, 14–19 Apr 2024.

Lepri A., Fraccica A., Cecconi M., Pane V. (2024b). Effetti del taglio di vegetazione sull'innesco di una colata detritica a Nottoria (PG): caratterizzazione geotecnica preliminare. Incontro Annuale dei Ricercatori di Geotecnica 2024- IARG 2024 - Gaeta, 4-6 Settembre 2024.

Meijer, G.J., Bengough, A.G., Knappett, J.A., Loades, K.W., Nicoll, B.C. (2018). In situ measurement of root-reinforcement using the corkscrew extraction method. Can. Geotech. J. 55 (10), 1372–1390. (https://doi.org/10.1139/cgj-2017-0344).

How to cite: Lepri, A.: Preliminary results of in situ corkscrew tests in coarse-grained debris with vegetation roots , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9090, https://doi.org/10.5194/egusphere-egu25-9090, 2025.

This short communication presents a new low-cost capacitive Soil Water Content (SWC) sensor, originally developed, whose application in situ in natural rooted soils could be of some interest for its impact in geotechnical engineering applications. It is very well known that in recent years, significant advancement has been made in laboratory and field testing for the understanding of the hydro-mechanical coupled behaviour of unsaturated soils. The complexity in characterizing such behaviour increases when the role of vegetation and the presence of organic matter is considered. The amount of literature on water content (SWC) measurements and related sensors is huge and involves several scientific fields. Among indirect methods to evaluate the SWC, time domain reflectometer (TDR), time domain transmissometer (TDT) and impedance sensors, such as resistive and capacitive, are the most common. Capacitive sensors are usually directly dependent on soil apparent dielectric constant K_a which increases with SWC. They have a little sensitivity compared to TDR/TDT, however, they find several applications due to their lower cost. Vegetation affects the hydrology and the effects of plant evapotranspiration may induce some changes in the water content and soil suction and therefore the soil water retention properties. The mutual interaction among roots and soils is very variable, depending on roots-type and soil type; the beneficial influence due to the reduction of water content/degree of saturation, due to the capacity of the plant system to absorb water from the surrounding soil and transfer it to the atmosphere through transpiration is also acknowledged in the literature. Therefore, quantifying root-induced modification in soil hydraulic properties, including SWRC, is vital to predict correctly the hydrology and, hence, for the analysis of slope stability of shallow soil covers. In this note, a new low-cost capacitive sensor, characterized by an interdigit layout and produced following a PCB process, is introduced (Figure 1).

The performance of this device are under evaluation with laboratory activities: several tests have been performed preparing samples of different-type granular materials at different SWC keeping constant the dry density: natural sandy soils, glass beads, and ground coffee mixtures were investigated. The electrical capacitance and conductance of the sensor were measured in the 10 – 100 kHz frequency range by using the HP 4275A LCR meter. Some results are shown in Figure 2. It is shown that the sensor response is affected by the measurement frequency. Moreover, a saturation behaviour is highlighted for both the capacitance and conductance at increasing SWC. The sensor impedance is affected also by the electrical conductivity of the medium surrounding the sensor, e.g. solid grains, water and organic materials, and for this reason the SWC estimation requires a correction to minimize the impact of water salinity. The experimental activity performed in the laboratory is a preliminary investigation aimed at identifying an analytical model of the electrical behaviour of the sensor. Once the model is defined, the sensor could be integrated with a portable system to be validated for in-situ applications.

How to cite: Papini, N.: A new low-cost and low-power capacitive sensor for soil water content measurements: preliminary analysis for possible application in rooted soils, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12296, https://doi.org/10.5194/egusphere-egu25-12296, 2025.

In recent years, the variety of satellite data that can be used for analysis in the event of a disaster has increased. At the same time, there is a need to process different satellite data using a unified analysis method, especially when extracting mudslide scars that have been newly exposed after a sediment disaster. Nonetheless, comparative studies focusing on spatial resolution, a potential factor affecting applicability and accuracy, have been lagging. Therefore, this study targeted the area surrounding Murakami City, Niigata Prefecture, which was the site of extensive sediment outflows due to heavy rainfall in August 2022. Specifically, the mudslide scar was estimated by calculating NDVI difference values (ΔNDVI) for four types of optical satellite data with different spatial resolutions. The data was extracted over a wide area and the effects of differences in spatial resolution on the applicability of the extraction method and the extraction rate were clarified. The relationship between precision and recall can be approximated by the quadratic equation y=ax²+bx+c, and there was a trade-off relationship between the two metrics; as the threshold value rose, precision increased while recall decreased. The optimal NDVI threshold for maximizing the F-measure ranged from 0.20 to 0.25. The medium-resolution satellite platforms Planet and Sentinel-2 had higher F-measure values, and the efficacy of NDVI extraction was not proportional to the fineness of the spatial resolution. The reason for this was that the area distribution of the mudslide scar in the target area was dominated by relatively small areas with a mode of 42 m² and a median of 253 m², which were considered to increase precision and recall. Consequently, selecting a spatial resolution that matches the area of the mudslide scar in the target area is considered to be effective.

How to cite: Akita, H.: Differences in applicability of mudslide scars estimation methods due to different spatial resolutions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1357, https://doi.org/10.5194/egusphere-egu25-1357, 2025.

Collection of data from landslides monitoring is crucial for a sustainable risk management. With this aim, the integrated monitoring systems combining in situ and remote sensing techniques provide a comprehensive understanding of landslide activity. One of the tasks of the Innovation Ecosystem "Tech4You - Technologies for Climate Change Adaptation and Quality of Life Improvement" focuses on analysing case studies to compare different landslide types, their associated monitoring networks and the displacements entity.

A key objective is to create a catalogue of displacements for typifying landslides. To achieve this goal, a comprehensive literature review was conducted. Only landslides with displacement data over time were considered. The catalogue records the landslide type, location, monitoring system, sensor type, installation year, monitoring period, and main dimensions.

A notable challenge in this research was the limited availability of raw displacement data. Many studies present monitoring results in graphical form, often as images, making numerical data extraction difficult. To overcome this, software tools and artificial intelligence (AI) methods have been employed to analyse graph images and extract numerical values. However, AI often encounters limitations in accurately interpreting and extracting numerical values from diverse graph formats. While AI offers rapid initial analyses, the use of dedicated software guarantees precision in data extraction. The combined workflow of inspection, validation, and software application ensures reliable outcomes, making the process more efficient than manual or traditional methods.

The catalogue now includes more than 60 classified landslides, and research on new case studies is always ongoing. For this reason, and to overcome the limitation of the reduce number of studies with associated data, this work serves as encouragement to increase the number of cases registered in the database.

A specialized digital tool will be developed to integrate in a general platform and utilize collected landslide displacement data. This platform aims to: i) support local and national public institutions, ii) facilitate widespread access to and utilization of the data for monitoring and mitigating landslide risk, and iii) assist in the identification and classification of landslides with characteristics similar to those catalogued in the database.

ACKNOWLEDGEMENTS

This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of Innovation Ecosystems', building 'Territorial R&D Leaders' (Directorial Decree n. 2021/3277) - project Tech4You – Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.

How to cite: Vennari, C., Coscarelli, R., and Gullà, G.:  Populating a catalogue with displacement vs. time data: a tool for typifing landslides kinematic and a support for sustainable risk management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8988, https://doi.org/10.5194/egusphere-egu25-8988, 2025.

Accurate spatial distribution of rainfall during extreme weather events is crucial for hydrological analysis and flood forecasting. Despite the availability of numerous neural network-based models for spatiotemporal rainfall interpolation, challenges remain due to the limited number of rain gauges and the presence of missing values in the recorded data. These limitations introduce significant uncertainties into existing models. This study focuses on the Ijzer Basin in Belgium, using 20 years of data collected at 15-minute intervals, including rainfall, humidity, and temperature measurements et. etc. By training several neural network models on these data, we aim to identify the most accurate model for rainfall interpolation. Results indicate that Long Short-Term Memory (LSTM) networks demonstrate superior performance compared to other models in capturing the spatial distribution of rainfall.

How to cite: Xiao, W.: Rainfall Interpolation Analysis in the Ijzer Basin Based on Neural Networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12497, https://doi.org/10.5194/egusphere-egu25-12497, 2025.

Barak River is one of the highly meandering rivers in India causing several problems to society during flooding events. In this study geomorphological changes of an urban meander loop, situated at the main city of Silchar Assam, India was carried out. Based on the adopted analysis, it was found that meander length, meander width, meander ratio, wavelength showed an increasing trend while sinuosity and radius of curvature shows a decreasing trend.  The land use and land cover were also analyzed of this urban meander loop and found that settlement increased gradually by 16.1798 % and waterbodies, dense vegetation and agricultural land decreased by 0.5732 %, 2.5832 % and 13.1558%, respectively. Autoregressive integrated moving average (ARIMA) model was employed for the prediction and the results recommended that shifting of channel in the urban meander loop fluctuated unexpectedly either to rightwards or leftwards. Observed and predicted values of showed a determination coefficient (R² = 0.8). The final step of the research was to generate the predicted values of channel shifting up to 2030.      

How to cite: Annayat, W., Samantaray, S., and Yaseen, Z. M.: Geomorphological transformation and prediction of urban meander loop: A case study of Barak River, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17185, https://doi.org/10.5194/egusphere-egu25-17185, 2025.

Being a natural calamity, flood poses serious threat to the livelihood of all living beings. Due to the adverse effects of climate change and anthropogenic activities, significant changes in the occurrence of extreme floods are happening day-by-day. An accurate flood susceptibility map plays a crucial role to adopt proper adaptation and mitigation strategies in protecting the vulnerable communities. This study performs a flood susceptibility mapping of the Jagatsinghpur district lying in the delta region of the Mahanadi River basin in the eastern part of India. This river basin has suffered from numerous recurring floods of variable extremities since the 1960s. A major concern arose when the frequency of extreme floods in this delta increased drastically post the 2000s. This study considered several key factors affecting flood occurrence like rainfall, topographic wetness index, land use/land cover, distance from river, elevation, slope and drainage density. The map layers of all these factors are integrated in the Geographic Information System (GIS) platform, wherein the Analytical Hierarchy Process (AHP) is used to develop and evaluate the flood susceptibility maps. The findings suggest that more than one-third of the study area falls into the low to high flood susceptibility zone. Nearly 40% of the area is under very low to low zone, and a small portion fell under the high to very high flood prone zone. The study serves as a preliminary study towards flood risk management and provides critical insights for the decision makers to develop appropriate disaster risk reduction strategies and strengthen the flood management policies.

How to cite: Khatun, A., Baruah, S., and Chatterjee, C.: Assessing Flood Susceptibility using Geospatial Techniques and Analytical Hierarchy Process in an Indian Catchment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12404, https://doi.org/10.5194/egusphere-egu25-12404, 2025.

In this study, the hydrodynamic behavior of a section of the Santa Catarina River in Nuevo León, Mexico, during Tropical Storm Alberto was investigated. A three-dimensional numerical simulation of river flow was performed using unsteady Reynolds-Averaged Navier-Stokes (RANS) equations coupled with the Volume of Fluid (VOF) method to model the water-air interface. The computational domain was constructed based on the specific area Digital Elevation Model (DEM), accurately capturing the river's morphology, with a structured mesh refined near the riverbed to resolve localized velocity gradients. The simulations focused on high-density water flows induced by extreme precipitation, analyzing key parameters, including velocity distribution, turbulence intensity, and effective viscosity, to evaluate the performance of turbulence models in replicating fluvial dynamics. Validation was achieved using velocity data derived from video footage of the storm, tracked via motion analysis techniques and compared against simulation outputs to ensure accuracy.

The comparative study included the Spalart-Allmaras, standard k-ε, realizable k-ε, and standard k-ω turbulence models. A sensitivity analysis and mesh independence verification ensured robust numerical predictions validated against field data obtained from video-derived velocity measurements.

Findings reveal distinct model performance under varying turbulence conditions. The realizable k-ε model captured peak effective viscosity (μ_eff) values of up to 820 kg/m·s at low turbulence intensities, demonstrating its suitability for flows with strong energy gradients and lower dissipation rates. Conversely, the standard k-ω model excelled under high turbulence intensity, effectively resolving dissipation dynamics and exhibiting μ_eff ​ values between 150–500 kg/m·s. These results highlight the capacity of these models to represent different aspects of riverine hydrodynamics, although neither achieved full optimization across all conditions.

Velocity profiles showed significant gradients near the riverbed, where high shear stress and energy dissipation dominated, reinforcing the importance of mesh refinement in capturing localized effects. Turbulence intensity exhibited a sharp decrease in shallow areas and near structural boundaries, directly influencing μ_eff ​ distributions.

While the evaluated turbulence models provided reliable frameworks for simulating complex fluvial flows, further refinements are needed. Incorporating advanced turbulence models, such as Reynolds Stress Models (RSM) or Large Eddy Simulations (LES), could enhance predictions, particularly for cases involving sediment transport and fluid-structure interactions.

This study contributes to the development of robust methodologies for river modeling under extreme conditions, with practical implications for flood management, hydraulic structure design, and sediment transport assessments. Future research should explore the performance of these models in simulating freshwater flows, assess their application under varying sediment concentrations, and investigate their capability to account for fluid-structure interactions related to bridge columns and other critical infrastructure.

How to cite: Bonasia, R., De la Cruz-Ávila, M., Barrios Piña, H. A., and Castillo Guerrero, F. J.: Three-Dimensional Numerical Modeling of a River Section under Extreme Discharge Conditions from a Tropical Storm: The Santa Catarina River Case Study, Mexico, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4693, https://doi.org/10.5194/egusphere-egu25-4693, 2025.

Nature-based solutions (NBS) employ natural processes to mitigate climatic risks and evolving environmental challenges, offering sustainable, cost-effective alternatives to traditional grey infrastructure. Traditional stone weirs are considered multifunctional and environmental friendly structures contributing to sustain ecosystems and protect communities from water-related hazards. This type of NBS has shown potential to mitigate flood impacts through controlled water flow and sedimentation retention by reducing both water velocity and erosion during peak flows, with main objective to enhance community resilience to climate change. During CARDIMED project a network of 120 traditional stone weirs will be developed and applied in Sifnos island (Greece) strategically placed across two main streams aimed at mitigating flood risks, recharge aquifers, enhancing biodiversity, and supporting small-scale agricultural water use, tailored to the unique arid ecosystems of the Greek islands.

This study aims to monitor the efficiency of stone weir NBS in order to quantify climate adaptation benefits, particularly in relation to stormwater regulation, with application area Sifnos island (Aegean sea, Greece). The analysis utilises an integrated monitoring approach which couples remote sensing observations with in-situ data collected through monitoring stations, off-the-shelf sensors, and crowdsourcing participatory campaigns. Earth Observation techniques based on Sentinel-2 are employed to derive relevant vegetation and water indices (i.e. NDVI, NDWI), enabling to assess of vegetation health, soil water availability, and land surface dynamics. These are expected to be complemented by high-resolution datasets from Copernicus Contributing Missions, such as WorldView and Pleiades imagery, to enhance spatial and temporal resolution at locations of interest. EO techniques are validated by in-situ data derived from monitoring systems installed at strategic locations which provide localized, real-time measurements of hydrological, meteorological, and ecological parameters under different climatic conditions. The proposed methodology has the potential to provide key information about the quantified impacts from the application of stone weirs but also an understanding about their scalability as sustainable solutions for enhancing climate resilience at regional scale.

Acknowledgement:

This research has been funded by European Union’s Horizon Europe research and innovation programme under CARDIMED project (Grant Agreement No. 101112731) (Climate Adaptation and Resilience Demonstrated in the MEDiterranean region).

How to cite: Michalis, P., Kossieris, S., Papachristos, E., Petrakos, K., Sakellarakis, F.-N., Tsimiklis, G., and Amditis, A.: Assessing the Potential of Traditional Stone Weirs in Stormwater Management Through Integrated EO, In-situ and Crowdsourcing Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1523, https://doi.org/10.5194/egusphere-egu25-1523, 2025.

Sweden, known for its abundant water resources, has recently experienced drought events with significant socio-economic and environmental impacts, revealing existing vulnerabilities in the society. Future climatic projections indicate changes in precipitation and temperature patterns, stressing the need for improved drought risk management. The vulnerability component of risk is often less studied than the hazard component, primarily due to its inherent complexity. Drought vulnerability is highly context-dependent, shaped by the interplay of social, ecological, and hydroclimatic factors. In the context of a changing climate, assessing drought vulnerability is becoming increasingly important. However, such assessments are scarce in Nordic regions.

To address this gap, this study quantifies vulnerability factors related to coping capacity, adaptive capacity, and susceptibility, and integrates them to map drought vulnerability hotspots across Sweden. Based on a stakeholder-validated set of vulnerability factors for water-dependent sectors (including agriculture, forestry, energy, water supply, and environmental management), municipal-level data sources were screened to identify and quantify relevant vulnerability indicators. A probabilistic approach was employed to assess the sensitivity of regional vulnerability patterns to the weighting of vulnerability factors. The resulting spatial distribution of relative vulnerability reflects the heterogeneous socio-hydrological systems across municipalities and highlights the importance of sustainable local economic adaptation to water availability in reducing sensitivity and mitigating drought impacts. Our vulnerability assessment provides valuable insights for local and regional planners, supporting the effective allocating of resources and the development of targeted drought mitigation strategies at municipal level. The findings underscoring the need for context-specific assessments to account for regional and sectoral differences in drought vulnerability. Furthermore, the results emphasize the complexity of drought risk and the challenges of integrating diverse vulnerability factors in diverse socio-hydrological contexts.

How to cite: Canedo Rosso, C., Stenfors, E., Teutschbein, C., and Nyberg, L.: Drought vulnerability assessment in Sweden, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20010, https://doi.org/10.5194/egusphere-egu25-20010, 2025.

Drought is an increasing hydroclimatic threat in the Mediterranean, profoundly impacting water resources and ecosystems. Andalusia (Spain) is highly vulnerable due to climatic variability and prolonged dry periods. Effective drought management requires methods to assess impacts on groundwater and surface water systems, which in turn threaten ecological and socio-economic resilience. While socio-economic impacts are more analysed, environmental effects are overlooked due to delayed onset or unclear links to drought. However, drought-induced degradation of natural resources and hydrology-linked ecosystem services can exacerbate challenges in agroforestry, livestock, and tourism. Examining the environmental dimensions of hydrological drought risk is therefore essential.

This research takes a first step in analysing the impacts of drought on water-related ecosystem services. It specifically investigates hydrological and hydrogeological anomalies and examines their spatial and temporal dynamics across varying levels of drought severity. This study defines hydrological anomalies by leveraging high-resolution, open-access data from Copernicus and other datasets available on Google Earth Engine. These include estimates of soil moisture, groundwater storage, terrestrial water storage, flows and evapotranspiration that can be obtained from GLDAS 2.2, FLDAS, CERRA-Land, etc. In situ measurements, such as piezometric and streamflow records, are also integrated to validate findings and provide a robust basis for analysis of the impacts on water systems. Machine learning algorithms are then used to model the complex linkages between the identified hydrological anomalies and the climatic conditions, measured with well-known drought indices like the Standardized Precipitation-Evapotranspiration Index (SPEI) at different scales.

A pilot study in an Andalusian sub-basin with minimal anthropogenic influence serves as a testbed for developing a scalable methodology to evaluate the impacts of short and long-term drought conditions on groundwater and surface water. In line with related relevant research, correlation analyses run for this pilot highlight strong associations between hydrological variables and drought indices. A rapid response of surface water systems to short-term droughts is observed, while groundwater displays delayed, yet significant changes linked to drought, reflecting its buffering capacity and resilience.

This research highlights the potential of tested datasets for assessing drought impacts on water systems and demonstrates the value of open-source hydrological data for improving drought risk assessment and predictive tools. However, the study also reveals limitations regarding spatial resolution, which constrain detailed-scale assessments. On the one hand, the follow-up research will expand the performed analysis to additional sub-basins across Andalusia to compare results. On the other hand, similar modelling methodologies will be applied to understand how the identified droughts and associated anomalies in surface and groundwater systems propagate, leading to a reduction in the provision of ecosystem services. This will include exploring ecological impacts such as failures to maintain ecological flows, declines in extension of wetlands, or anomalies in primary productivity and ecosystem functioning in natural areas.

How to cite: Serrano Acebedo, P., Limones Rodríguez, N., and Aguilar Alba, M.: Unveiling environmental dimensions of hydrological drought in Southern Spain using open-source data and machine learning techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13798, https://doi.org/10.5194/egusphere-egu25-13798, 2025.

Improved and affordable prediction techniques are required because the growing frequency of shallow landslides caused by shifting weather patterns poses severe dangers to ecosystems, infrastructure, and communities. Although comprehensive monitoring systems are available, their high costs and complexity often make them impractical in resource-constrained regions. This study aims to evaluate the predictive potential of volumetric water content (VWC) measurements for shallow landslides and leverage machine learning techniques to develop cost-effective prediction models. The study employed one-dimensional modified column tests to simulate various scenarios (e.g., soil densities, drainage conditions) using a one-meter-high acrylic column to measure VWC, pore water, and air pressure. Key findings include the identification of VWC-related parameters (e.g., steady-state VWC and its gradient) as effective predictors of slope failure. When integrated with ML models, these parameters demonstrate the potential for enhancing prediction accuracy. This study provides a pathway to developing cost-effective early warning systems for slope instability, offering a practical solution for improving safety, using volumetric water content measurements to protect infrastructure, and enhancing resilience in landslide-prone regions, mainly where comprehensive monitoring systems are infeasible.

How to cite: Avzalshoev, Z., Ahmad, W., and Ahmad, T.: Using volumetric water content measurements with the implementation of machine learning for monitoring shallow landslides induced by rainfall, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-851, https://doi.org/10.5194/egusphere-egu25-851, 2025.

Dynamic Flood and Erosion Modeling for the Sabarmati River Using RUSLE and GEE

Nensi A. Sachapara ^{a(0009-0000-9510-6198)}, Manan Patel ^{a(0009-0004-4712-3531)} , Hasti Dhameliya ^{b(0009-0003-8908-7906)}
Keval H Jodhani ^{c (0000-0002-3800-2402)}, Nitesh Gupta ^{c(0000-0003-0471-0133)} , Dhruvesh P. Patel ^{d (0000-0002-2074-7158)} Sudhir Kumar Singh ^{e  (0000-0001-8465-0649)}

^aUnder Graduate Student, Civil Engineering Department, Nirma University, Ahmedabad, 382481, Gujarat, India.  (nensisachapara16@gmail.com; mananrp07@gmail.com )

^bUnder Graduate Student, Biomedical Engineering Department, LD College of Engineering, Ahmedabad, 382481, Gujarat, India. (dhameliyahasti8@gmail.com)

^cAssistant Professor, Department of Civil Engineering, Institute of Technology, Nirma University, Ahmedabad, 382481, Gujarat, India. (jodhanikeval@gmail.com, niteshraz@gmail.com)

^dDepartment of Civil Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, 382007, Gujarat, India (dhruvesh1301@gmail.com)

⁶ K. Banerjee Centre of Atmospheric and Ocean Studies, IIDS, Nehru Science Centre, University of Allahabad, Prayagraj-211002, Uttar Pradesh, India (sudhirinjnu@gmail.com)

Abstract: Flooding and soil erosion are major environmental challenges impacting the Sabarmati River Basin (SRB), adversely affecting its ecology, agriculture, and infrastructure. This study employs the Google Earth Engine (GEE) platform to comprehensively analyze flood-prone areas and soil erosion using the Revised Universal Soil Loss Equation (RUSLE) model. High-resolution datasets from USGS Earth Explorer and GEE are integrated with remote sensing and geospatial technologies to assess the basin's vulnerabilities. Flood-prone regions were identified by analyzing historical rainfall (maximum annual rainfall of 1,667.15 mm in 2017), hydrological patterns, and topographic features. The RUSLE model quantified soil erosion, incorporating factors such as rainfall erosivity (R factor: 11,202.65–29,243.64 MJ mm ha⁻¹ h⁻¹ yr⁻¹), soil erodibility (K factor: 0.20–0.20004 t ha h ha⁻¹ MJ⁻¹ mm⁻¹), slope length and steepness (LS factor: 0–0.499), land cover (C factor: 0.327–1.078), and conservation practices (P factor: 1). Results indicate critical hotspots of soil erosion, with losses peaking at 1,232.33 t/ha/year in the northern SRB. Flood hazard mapping revealed that low-lying areas with recurrent flood events align with regions experiencing high rainfall and sediment transport. The overlap between high soil erosion and flood-prone zones highlights the need for integrated management strategies. These risks have significant socio-economic implications, including diminished agricultural productivity, infrastructure damage, and community displacement. This dual analysis underscores the efficacy of GEE for rapid environmental assessments, providing actionable insights for policymakers to prioritize interventions. The findings align with Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land), suggesting for adaptive strategies to mitigate flood and erosion risks and promoting sustainable resource management in vulnerable regions.

Keyword: RUSLE, GEE, Flood Hazard, SDG 13 & 15, Sabarmati Basin

How to cite: Sachapara, N., Patel, M., Dhameliya, H., Jodhani, K., Gupta, N., Patel, D., and Singh, S. K.: Dynamic Flood and Erosion Modeling for the Sabarmati River Using RUSLE and GEE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-664, https://doi.org/10.5194/egusphere-egu25-664, 2025.

Changing climate is enhancing the occurrence and intensity of natural disasters, profoundly impacting human lives, livelihoods, infrastructure, and economic growth. Modelling and prediction of deadly high-mountain slope failure hazards such as snow, ice, and rock avalanches have always been challenging. Current in-situ sensor-based approaches for slope failure predictions of hanging glaciers and rock faces are quite limited in their spatial continuity and extent and there is also a research gap on linking the pre-collapse slope movements with subsequent avalanche runouts. Earth observation datasets can offer a viable alternative for quantifying and monitoring pre-collapse dynamics at larger spatial scales. For the catastrophic 2021 rock-ice collapse in Chamoli, India, several studies had reported some anomalous movements weeks-to-months prior to the collapse. However, we need more analyses to understand how common such pre-collapse anomalous movements are before we can even start considering investigating them as potential precursors for effective avalanche predictions. To fill this research gap, using satellite remote sensing datasets and digital elevation models, we investigated several high-mountain slope failure events (e.g., Piz Scerscen in 2024, Piz Cengalo Bondo in 2017) of varying magnitudes and nature (i.e., rockfall, rock-ice avalanche, and ice avalanche) in different topographical and climate settings. While we were able to quantify pre-collapse dynamics for these events, we also observed variations in the occurrence and magnitude of anomalous movements prior to the events. These preliminary findings are encouraging and the future research and results from such analyses can bridge the knowledge gap on the detection and modelling capabilities, ultimately enhancing resilience to mountain hazards.

How to cite: Sam, L., Bhardwaj, A., and Temadri, P.: Quantifying pre-collapse dynamics of hanging rock-ice masses using remote sensing datasets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17719, https://doi.org/10.5194/egusphere-egu25-17719, 2025.

Rockfall modelling in Caminito del Rey (Málaga, Spain) represents a scientific and technical challenge due to the high geomorphological complexity of the environment, characterized by vertical cliffs, numerous overhangs, and complex geometries. In this context, within one of Malaga’s most visited tourist attractions (more than 300,000 people per year), a comprehensive study was required to address challenges across all phases, from the detailed characterization of the inventory to trajectory modelling. To address these difficulties, the most advanced technology currently available for remote data adquisitation (UAV, LIDAR and satellite) and three-dimensional modelling was used, along with the development and application of ad hoc methods and techniques specifically tailored to this study.

The high-precision georeferenced digital rockfall inventory had to tackle issues such as data heterogeneity, limitations in the available documentation, and errors related to mapping accuracy of the trail layout. On the other hand, the modelling process required a multiscale approach, examining all sections of Caminito del Rey with a focus on detailed scales for individual blocks. Custom input data were obtained for this purpose: (i) elevation models accounting for overhangs and both gorge walls; (ii) source areas for rockfalls derived using probabilistic approaches; (iii) block size estimation based on lithology type; and (iv) calibration and validation of the three coefficients maps in narrow and vertical sections (i.e., dynamic rolling friction, normal energy restitution, and tangential energy restitution) that simulate energy loss by a boulder when rolling and bouncing at impact points.

Reducing uncertainty in each input dataset is essential not only for improving the reliability and accuracy of analytical models but also for effectively establishing preventive measures. Furthermore, it plays a key role in identifying critical areas that require continuous monitoring. This abstract was supported by the KINGSTONE project, the Rockfall Susceptibility Study in Caminito del Rey (a collaboration among IGME-CSIC, the University of Granada, the University of Jaén, and the Caminito del Rey UTE), and the SARAI project (PID2020-116540RB-C21), funded by MCIN/AEI/10.13039/501100011033.

How to cite: Sarro, R., Galve, J. P., Martínez-Corbella, M., Fernández-Naranjo, F. J., Miranda-García, P. V., López-Vinielles, J., Jerez-Longres, P. S., Ruiz-Fuentes, A., Béjar-Pizarro, M., Guardiola-Albert, C., Azañón, J. M., and Mateos, R. M.: Challenges in rockfall modelling in active tourism gorges: The case study of Caminito del Rey (Malaga, Spain), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9241, https://doi.org/10.5194/egusphere-egu25-9241, 2025.

Dam safety is a primary concern for countries worldwide, as dam failure can have catastrophic consequences, including fatalities and losses to the economy, ecology, and environment. In recent decades, there has been growth in consensus that climate change has enhanced the risk to dams due to floods triggered by more frequent and intense extreme precipitation events. It necessitates reviewing the Probable Maximum Floods (PMFs) considered for planning and designing large dams and updating them for different speculated climate change scenarios to determine the projected future changes in dam break risk. Global initiatives, such as the Paris Agreement, are focused on developing strategies to limit the increase in global temperatures well below 2°C (preferably 1.5°C) above pre-industrial levels by 2050. However, significant discrepancies have been identified between the current global greenhouse gas emissions trajectory and the reductions needed in emissions to achieve the Paris Agreement's target. To bridge this gap, geoengineering climate intervention methods such as Stratospheric Aerosol Injection (SAI) and Solar Dimming (SD) have been proposed as potential solar radiation management (SRM) options to offset climate change effects. The latest Geoengineering Model Intercomparison Project (GeoMIP6) provides simulations from a suite of climate model experiments designed to assess the effect of potential SRM methods, including SAI and SD. To shed light on the effectiveness of geoengineering, this study assesses the impact of the current generation climate models (from Coupled Model Intercomparison Project Phase 6, CMIP6) and geoengineering models (from GeoMIP6) on Probable Maximum Precipitation (PMP) and the corresponding Probable Maximum Flood (PMF) at a typical large dam (Hemavathi) located in the Cauvery River basin in India. The current PMF of the dam is compared with future projections of the same derived corresponding to a CMIP6 high forcing scenario (SSP585) and two GeoMIP (G6sulphur and G6solar) scenarios. For both near and far future periods, the PMF hydrograph’s peak for the SSP585 scenario (GeoMIP6 scenarios) is significantly (marginally) greater than that of the current PMF of the dam. It indicates that geoengineering methods can offset climate change's impact on PMP and the corresponding PMF (depicting hydrological risk) at dams, which is of significance as worldwide many large dams have completed their design life.

How to cite: Goel, A. and Srinivas, V. V.: Impact of Geoengineering in Offsetting Climate Change-Induced Dam Break Risk, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-395, https://doi.org/10.5194/egusphere-egu25-395, 2025.

Within minutes of any major global earthquake, the U.S. Geological Survey (USGS) Ground Failure product (GFP) provides summary alert levels and spatial estimates of landslide and liquefaction hazard and population exposure. Since the GFP went live in September 2018, 187 events have had an elevated alert level (yellow, orange, or red), indicating limited to extensive hazard and exposure. These events include well-known ground-failure triggering earthquakes such as the 2023 Türkiye-Syria earthquake sequence, the 2021 Nippes, Haiti earthquake, as well as numerous other events. In many cases, the GFP proved to be valuable by estimating the potential extent of these hazards and their overlap with the local population.  In this presentation, we discuss how the product has performed since it was deployed and how it has been used for situational awareness, planning, and reconnaissance. Significant users of the GFP include scientists, the media, emergency responders, and the public. We also discuss operational considerations, such as how moving the GFP to the cloud has improved speed and reliability. We conclude with an overview of enhancements under development, such as model regionalization, road obstruction estimation, fatality estimation, ongoing hazard information, model updating, and integration into other USGS impact products, such as Prompt Assessment of Global Earthquakes for Response (PAGER) and ShakeCast.

How to cite: Allstadt, K. E., Thompson, E. M., Wald, D. J., Hunsinger, H. E., Haynie, K. L., Hearne, M., Bürgi, P. M., Ellison, S. M., Engler, D. T., Jaiswal, K. S., Marano, K., and Lin, K.: Performance and Future Directions of the USGS Near-real-time Earthquake-triggered Ground Failure Product, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13829, https://doi.org/10.5194/egusphere-egu25-13829, 2025.

Nature-based solutions (NBS) have gained increasing attention in flood management since the early 2000s as sustainable alternatives or complements to conventional flood defence strategies. Based on a systematic review of 1,080 published studies, we provide recommendations for implementing common NBS intervention types in flood management using the HEC-RAS modelling framework. The review considered published case studies ranging from small catchments of approximately 0.09 km² to large river basins exceeding 2,400 km².

The potential interventions explored include reforestation/afforestation, floodplain reconnection, wetland restoration, channel re-meandering, and the hybridization or removal of grey infrastructure. The recommendations detail how to adjust key parameters within HEC-RAS to effectively represent these interventions. For instance, increasing Manning's roughness coefficients can simulate the added vegetative roughness from reforestation. Likewise, modifying the digital elevation model allows for the representation of floodplain reconnection, benching, or channel modifications. By offering quantifiable methods and a clear linkage between interventions and hydraulic parameters, this work equips practitioners and researchers with the necessary tools to model flood mitigation strategies using NBS within HEC-RAS. To generalise the findings beyond HEC-RAS and make them applicable to other hydraulic modelling platforms, each intervention is linked to specific terms in the governing equations: conservation of mass and momentum equations, highlighting how parameters such as friction slope are affected.

How to cite: Sabeti, R., Rodding Kjeldsen, T., Chambers, M., Moftakhari, H., Stamataki, I., and Simmonds, S.: Assessing Nature-Based Solutions using the HEC-RAS modelling system: a review , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1168, https://doi.org/10.5194/egusphere-egu25-1168, 2025.