Orals: Thu, 1 May | Room N2
Cities near volcanoes expose dense concentrations of people, buildings, and infrastructure to volcanic hazards. Identifying urban centres exposed to volcanic hazards at a global scale supports local risk assessments, better land-use planning, and hazard mitigation. Previous approaches dominantly relied on city centroids to assess population exposure and proximity to volcanoes, overlooking the spatial variability of population distribution within city margins. In this research, firstly, we propose a novel framework to rank 1,106 cities globally in terms of volcanic hazard exposure using population count, distances to 596 Holocene volcanoes, and the number of nearby volcanoes. Notably, 50% of people living within 100 km of a volcano reside in cities. Bandung, Indonesia, ranks highest overall, with over 8 million people exposed within 30 km of up to 12 volcanoes. Regional rankings highlight Jakarta (~38 million), Tokyo (~30 million), and Manila (~24 million) having the largest populations within 100 km of a volcano. Finally, we show average trends in city population expansion towards volcanoes since 1975 and projected to 2070. We use case studies to show directions of expansions towards or away from hazardous areas, to emphasise how potential local drivers may influence hazard exposure. For some countries, such as El Salvador, Japan, or the Philippines, where >70% of land in each country is exposed to volcanic hazards, there are limits on the availability of safer areas for expansion. By understanding how urban environments are expanding towards volcanoes, we can better inform adaptive strategies to volcanic risks.
How to cite: Meredith, E. S., Teng, R. X. N., Jenkins, S. F., Hayes, J. L., Biass, S., Tennant, E., and Handley, H.: Global trends of city exposure to volcanic hazards, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6959, https://doi.org/10.5194/egusphere-egu25-6959, 2025.
We document a hybrid infrastructural/nature based restoration of an artificial backshore dune in Big Wave Bay (Tai Long Wan), a small embayed sandy beach system in Hong Kong. The 2018 Super Typhoon Mangkhut destroyed the dune, believed to have been built by a coastal village community more than 80 years ago and had withstood all intervening storms. The destruction had itself illustrated how extreme events may alter landforms at a catchment scale, and the vulnerabilities of coastal communities that lies within the catchment. Subsequently, the Hong Kong government made a decision to 'hold-the-line' and rebuild defence in-situ, believed to have driven by space constraints, engineering philosophy, and/or public perception. The result may not be the most storm-proof, but it could be seen as the best outcome based on compromises, and could represent the most probable responses towards extreme events in urban coastal communities. Both hard engineering (in the form of concrete footslabs) and nature based approach (in the form of coastal shrub planting) were installed in 2021, and had shown different trajectory of change in the subsequent years. Although the defence mechanisms had not been tested in an extreme event, comparative strengths of the solutions could be surmised by their integration with the natural processes of the beach system. The overall cost effectiveness of this 'hold-the-line' strategy in Big Wave Bay is estimated, using potential land loss from sea level rise fed into a simple socio-economic model to predict potential economic loss. The result could shed light on quantifying the social costs for adaptation strategies in urban coastal communities in response to climate change.
How to cite: Chiu, H. C., Leung, E. Y. L., and Wong, A. T. L.: Testing the effectiveness of hybrid infrastructural/nature based sand dune restoration as defence for an urban coastal community in Hong Kong , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9065, https://doi.org/10.5194/egusphere-egu25-9065, 2025.
As the negative impacts of natural hazards continue to escalate around the world due to increasing populations, climate change, and rapid urbanisation (among other factors and processes), there is an urgent requirement to develop structured and operational approaches towards multi-hazard risk-informed decision making on urban planning and design. This is a particularly pressing issue for low-to-middle income countries in the Global South, which are set to be impacted ever more disproportionately during future natural-hazard events if the “business as usual” urban-development approach continues unabated. The urban poor of these countries will suffer most under current, risk-insensitive development trajectories.
To address this crucial challenge, we introduce the Tomorrow’s Cities Decision Support Environment (TCDSE). The TCDSE facilitates a participatory, people-centred approach to risk-informed decision making, using state-of-the-art procedures for physics-based hazard and engineering impact modelling, integrating physical and social vulnerability in a unified framework, and expressing the consequences of future disasters across an array of stakeholder-weighted impact metrics that facilitate democratisation of the risk concept. Operation of the TCDSE leads to a risk-sensitive future urban scenario (consisting of an urban plan and a set of pertinent policies) owned not only by the planning authorities, municipalities, the government or the private sector, but also by the communities who will live in these future cities. It therefore represents a significant advancement in the state of the art towards inclusive, people-centred disaster risk reduction, as advocated by global policies and world-leading international agencies like the United Nations, the International Federation of Red Cross, and the World Bank.
This talk will cover the successful deployment of the TCDSE across a range of rapidly expanding urban areas in the Global South that lack formal planning and are increasingly exposed to multi-hazard occurrences (e.g., Nablus in Palestine, Cox’s Bazaar in Bangladesh, and Kathmandu in Nepal). The promising potential of the TCDSE to help minimise future urban risk creation in these contexts will be highlighted.
How to cite: Cremen, G., Comelli, T., Galasso, C., Gentile, R., Guragain, R., Hope, M., Manandhar, V., Mentese, E., Pelling, M., and Sinclair, H.: Creating and implementing a decision support environment for risk-sensitive, pro-poor urban planning and development of Tomorrow’s Cities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9317, https://doi.org/10.5194/egusphere-egu25-9317, 2025.
The rapid and typically uncontrolled growth of many African cities leads to a plethora of problems, including the formation and expansion of large urban gullies (UGs). These UGs often result in the destruction of homes and infrastructure, displacement of people, and loss of life. In many ways, the formation mechanisms of UGs are similar to those of gullies in other environments. Yet, urban land cover and tropical rainfall conditions, as well as their location in densely populated areas typically make them much more severe. Furthermore, the problems associated with UGs are likely to worsen in the near future as a result of continued urban expansion and climate change. However, this newly emerging geo-hydrological hazard received hitherto very little research attention. Several studies report on the occurrence and impacts of UGs but they remain limited to specific local case studies. A clear understanding of the patterns, impacts and driving factors of UGs at larger scales is currently lacking. To address this gap, we aim to better understand the spatial patterns and UG susceptibility at the scale of Africa.
Through the visual analysis of satellite imagery, we documented more than 4,000 cases of UG occurrence, significantly affecting 12 countries across Africa. These UGs are mainly spread over (sub-)tropical areas with D.R. Congo, Angola, Republic of Congo, Nigeria, and Mozambique being the most impacted countries. Using this database, we trained a random forest model that accurately simulates UG occurrence in (peri-)urban areas across Africa, with AUC greater than 0.9. Our results demonstrate that a combination of topography, rainfall characteristics, soil type, and variables describing the urban context (e.g. built-up area, road density) can explain variations in susceptibility to UG occurrence within and across cities. This dataset and model represent critical initial steps toward understanding, mitigating and preventing the risks of UGs in Africa, both now and in the future.
How to cite: Dujardin, E., Lutete Landu, E., Ilombe Mawe, G., Poesen, J., Dewitte, O., and Vanmaercke, M.: Understanding urban gully occurrence in Africa: A continent-wide model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8975, https://doi.org/10.5194/egusphere-egu25-8975, 2025.
Slow-moving landslides are very slow or extremely slow landslides that often affect urbanized slopes, involving a wide range of soil and rock materials. Often they can exhibit sudden changes in velocity related to local environmental changes, passing through slow (within 1 m/year) to rapid (more than 1 m/s) displacement. In built environments, the kinematic behaviour of these slope instabilities can lead to significant damage and even fatalities. Therefore, in active slow landslides, the prediction of movement acceleration is a crucial issue in the frame of landslide hazard and risk assessment for the design of warning systems and potential damage management. It is of great importance to investigate the factors that can drive velocity changes within unstable landslide bodies.
In this contribution, we focused on the role of hydrological preparatory and triggering factors (e.g., rainfall and groundwater level variations) on the unstable mass mobility. It is known that deep-seated slow-moving landslides are driven by pore-water pressure fluctuations that can result from infiltrating precipitation and/or snowmelt. However, the relationship between precipitation, hydrological responses and movement is not straightforward, primarily due to the complexity of the processes governing the recharge of groundwater in response to the rainfall regime, which can be influenced by many factors, both external (e.g., temperature, evapotranspiration, vegetation cover) and internal (e.g., layering, cracks, fissures). Therefore, including hydrological processes and their variability in landslide modelling is of paramount importance.
Here we present preliminary insights on the application of a simple physically-based model for quantifying groundwater fluctuations in response to discrete precipitation time-series in two reactivated slow-moving mass movements located in Liguria region (NW Italy): the Fontane landslide, in the Northern Apennines (eastern Liguria, Genoa Province) and the Mendatica landslide, in the Ligurian Alps (western Liguria, Imperia Province). Both landslides are rainfall-induced and affect small villages which have suffered damage in the past. The research activities are carried out in the framework of the RETURN (multi-Risk sciEnce for resilienT commUnities undeR a changiNg climate) project funded by the Italian MUR and the European Union Next-GenerationEU.
Long-term hydro-geotechnical monitoring data series (e.g., groundwater table levels) available for the two selected landslides and meteorological data (e.g., rainfall and temperature) from nearby measuring stations were collected and analyzed for two significant periods in order to grasp the seasonal fluctuations of the water table and the response to rainfall events. During the modelling, each period was split into two sub-periods: one, for the calibration phase, in which meteo-hydro-geotechnical data were used to estimate the parameters needed for the simulated water table to best approximate the measured one; the second, for the validation phase, in which the goodness of the model is verified. The outcomes of this study may represent an initial basis for gaining insights about the processes that influence groundwater table variations and defining models for the simulation/prediction of quantitative scenarios related to the hydrologic preparatory processes that influence the kinematic behaviour of the two selected slow-moving landslides. Indeed, the results of the groundwater model may be used as input data for predicting the landslide displacements.
How to cite: Bondanza, M., Cevasco, A., Armadillo, E., and Pepe, G.: Modelling groundwater level fluctuations in rainfall-driven urbanized slow-moving landslides: first insights from case studies in the Liguria Region (NW Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12893, https://doi.org/10.5194/egusphere-egu25-12893, 2025.
The Metropolitan Region of Recife (RM-Recife) is one of Brazil's most affected areas by landslides, with the municipalities in the region frequently ranking among the most impacted by fatalities caused by these events. In light of this, it is essential to use methodologies that determine susceptibility and risk, particularly in urban areas undergoing constant changes due to inadequate human activities. The study covers a sub-basin with an occupied slope, covering an area of 104,824.81 m²and containing 513 buildings in the Dois Unidos neighborhood, North Zone of Recife, Pernambuco. The region faces challenges such as irregular settlements and territorial fragmentation, which increase its vulnerability to natural disasters. The study aims to estimate geological risk in two stages. The first involves using Unmanned Aerial Vehicles (UAVs) to map buildings on urban slopes susceptible to landslides. Digital Terrain Models (DTM), Digital Surface Models (DSM), Digital Elevation Models (DEM), and orthophotos were generated to conduct the cadastral survey. IBGE data were used to assess the population exposed to risk. Subsequently, the data were overlaid on the susceptibility map generated using the TRIGRS model. For this purpose, geological-geotechnical investigations were conducted both in the field and in the laboratory, encompassing the Standard Penetration Test (SPT), sample collection, and the determination of soil hydraulic conductivity and strength. The runoff of rainwater is considered the changes in the drainage network imposed by buildings and obstacles from human occupation. The modeling scenario considered the intense rainfall of May 2022, which caused landslides and flooding in RM-Recife. During this event, a rain gauge near the study area recorded 342 mm of rain over 96 hours. Several Landslides occurred, putting the lives of residents and the buildings at risk. Overlaying cadastral information, census data, and the susceptibility map made it possible to identify the distribution of geological risk in the sub-basin. The analyses contribute to the implementation of preventive and mitigation strategies and provide support for improving risk and disaster management. The Results are part of a CNPq project coordinated by GEGEP/UFPE, with CEMADEN and international cooperation with IRPI-CNR, aiming to enhance TRIGRS to incorporate relevant human actions into the analyses.
How to cite: Quental Coutinho, R., de Morais, B. D., da Silva, B. Q., Pereira Neto, D., and de Moraes, M. A. E.: Geological Risk Estimation in Urban Hillslopes: Building Cadastral Mapping Using UAVs and Landslide Susceptibility Modeling with TRIGRS , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21334, https://doi.org/10.5194/egusphere-egu25-21334, 2025.
Rainfall-induced landslides are becoming a growing concern for disaster management due to the increasing frequency of high-intensity rainfall events. Identifying the space and temporal occurrence of such phenomena is paramount to ensure the development of reliable early warning systems and to effectively reduce the element exposed at risk. Conducting this analysis at the regional scale is a significant challenge due to the spatial variability of hydrological, geomorphological and geotechnical properties. Physically-based landslide models aim to identify potentially unstable areas during heavy rainfall by calculating the factor of safety (FS) across a spatial grid, integrating hydrological and geotechnical models.
Fully automated integration of such models into a Landslide Early Warning System (LEWS) is, however, still challenging due to complexities in real-time data acquisition, variability in model parameters, computational demands, and the need for accurate real-time forecasting. The proposed methodology uses meteorological forecasts, provided through meteorological Application Programming Interfaces (APIs), in addition to topographic and soil data to predict FS with an hourly resolution. These are visualized dynamically in real time on the ‘NGI Live’ data platform developed by the Norwegian Geotechnical Institute (NGI). Values of FS for each grid are uploaded to a cloud database as geotiff files and can be visualized in the form of maps in NGI Live. These prediction models, which are running at regular intervals to pull updated weather data from forecast APIs, are the model runners. Static input data for the models are kept in cloud storage, while API keys and other sensitive information are kept secure in a cloud secret store. The NGI Live dashboard offers a gateway to on-demand access to state-of-the-art predictions and historical data, and provide support for physics-informed decision-making relevant to disaster risk reduction and asset management.
This work is the result of collaboration between NGI and Universidade Federal do Rio de Janeiro, Brazil, through the project NATRISK (337241), ”Enhancing risk management & resilience to natural hazards in India, Brazil, & Norway through collaborative education, research, & innovation”, supported by the Research Council of Norway.
How to cite: Piciullo, L., Abraham, M. T., Liu, Z., Robinson, H., Drøsdal, I. N., Campos Maio, E., Nahas Ribeiro, W., and Barreto de Mendonça, M.: The automation of a physically-based slope stability model for real-time landslide forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16012, https://doi.org/10.5194/egusphere-egu25-16012, 2025.
Physically-based models used to assess slope stability are typically based on simplifying assumptions, such as the absence of anthropogenic structures, which are, in reality, often present on slopes. However, in many urbanized areas, the presence of buildings, roads, and other infrastructures can significantly affect slope stability, both in terms of load and alteration of water flow dynamics. Built structures modify surface water flow, hindering proper infiltration and increasing the occurrence of landslides. Furthermore, an additional factor to consider is the management of water that accumulates on buildings. The malfunction or damage of the sewer system, or in some cases, the absence of an appropriate drainage system, can further influence slope stability [1]. Moreover, the added weight of these structures, particularly when not properly founded on resistant layers, increases the forces acting on the slope, further compromising its stability. Despite this, most slope stability models do not account for the effects of these infrastructures, limiting their applicability in urbanized, sloped areas [2].
This study aims to address this gap by examining how urbanized areas influence slope stability, with a particular focus on small constructions such as houses and buildings located on steep terrains. The research explores the role of these structures in altering rainfall runoff and the ground's drainage capacity—both crucial factors for assessing slope stability. We propose an application of the physically-based model TRIGRS [3], which simulates changes in safety factors due to water infiltration, to analyse the effect of the presence of buildings on surface water flow and infiltration, and to identify areas most prone to shallow landslide triggering in built-up areas.
We applied the model on Ischia Island, a densely populated location Southern Italy, prone to different types of landslides [4]. A procedure was developed to consider the effects of the buildings on water runoff. To simulate the spatial distribution of rainfall, flow directions were modified to account for the presence of buildings, preventing excess water—unable to be absorbed by the ground—from accumulating where the buildings are located. Instead, water falling directly on the buildings is collected at a specific point at the boundary of the structure, simulating its discharge onto the ground.
The results demonstrate that this procedure effectively captures the effects of the building on water runoff, showing a significant increase in slope instability where water discharged from buildings accumulates. We expect the workflow outlined here to be most effective in areas with informal housing [5], in which additional factors such as weight of the buildings and water leaks may play a relevant role.
References
[1] Mendes, R. M. et al. (2018). Geotech. Geol. Eng. 36, 599. https://doi.org/10.1007/s10706-017-0303-z
[2] Bozzolan, E. et al. (2022). Sci. Tot. Env. 858, 159412. http://dx.doi.org/10.1016/j.scitotenv.2022.159412
[3] Alvioli, M., Baum, R. L. (2016). Env. Mod. Softw. 81, 122. https://doi.org/10.1016/j.envsoft.2016.04.002
[4] del Prete, S., Mele, R., (2006). Rend. Soc. Geol. It., 2, 29-47. https://api.semanticscholar.org/CorpusID:133382086
[5] Alvioli, M., et al. (2022). Geomatics, Natural Hazards and Risk, 13, 2712-2736. https://doi.org/10.1080/19475705.2022.2131472
[6] Bozzolan, E. et al. (2020). Natural Hazards and Earth System Sciences, 20, 3161-3177. https://doi.org/10.5194/nhess-20-3161-2020
How to cite: Ferriero, F., Marzocchi, W., and Guzzetti, F.: Evaluating the Influence of Urbanization on Slope Stability: A Case Study on Ischia Island, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11661, https://doi.org/10.5194/egusphere-egu25-11661, 2025.
Landslides are a growing threat to transport infrastructures, exacerbated by rapid urbanization, climate change, and artificially altered hydrological conditions. This study presents a systematic approach to delineating drainage basins potentially affecting linear communication networks, to enhance the resilience of such transport corridors. Knowledge of the drainage basins at intersection points with linear features provides essential data for assessing risks related to geohazards, such as rapid flow-like landslides, as well as flash floods. This example demonstrates the method on the road network of Italy, considering hydrological and geomorphological parameters calculated at 25 m spatial resolution. The software implementation of the methodology, developed within the open source environment GRASS GIS [1], is readily applicable to different areas, using similar input data.
This study used the European digital elevation model EU-DEM, and the official ANAS (the Italian agency for road management) vector graph, describing 29,500 km of roads. The method consists of the following steps, inspired by a similar application to the national railway network in Ref. [2]:
(i) We use the r.watershed hydrological model, based on a least-cost path method [3], to delineate a dense stream network with corresponding basins with minimum upslope contributing area of 25,000 m2 at the stream initiation point.
(ii) Next, we considered a buffer of width d = 300 m on both sides of the road segments to select intersections between streams and roads; here, d is a parameter of the method. This approach helps mitigating possible inaccuracies of input data, and includes situations where a stream segment flows on one hydrographic side of the main stream, and the road sits on opposite side. The procedure selects a conservative set of 66,018 intersection points.
(iii) Finally, we delineated watershed draining to each of the intersection points, using the software r.water.outlet [4], applied to each point with a data-parallel procedure. The result of this procedure is a polygonal vector layer containing all the watersheds associated to each interaction point.
Characterizing each watershed with morphometric indicators (area, slope, topographic wetness, and others) enables us to identify the road segments vulnerable to hydrological and geomorphological hazards, including flooding, erosion, and slope instability. This is in difference with the approach based on slope units [5], which are suited for slope-bound phenomena such as, for example, rockfalls [6], or for the determination of the likelihood of occurrence of landslide initiation points [7]. Watersheds of different sizes, relevant to phenomena with different reach distances and rapidity, can be selected in a parametric way. Preliminary results demonstrate the potential of the method to prioritize monitoring and maintenance of critical road segments.
References
[1] Neteler et al., Env. Mod. Softw. 31 (2012) https://doi.org/10.1016/j.envsoft.2011.11.014
[2] Marchesini et al., Eng. Geol. 332 (2024) https://doi.org/10.1016/j.enggeo.2024.107474
[3] Metz et al., Hydrol. Earth Syst. Sci. 15 (2011) https://doi.org/10.5194/hess-15-667-2011
[4] Ehlschlaeger, https://grass.osgeo.org/grass-stable/manuals/r.water.outlet.html
[5] Alvioli et al., Geomorphology 358 (2020) https://doi.org/10.1016/j.geomorph.2020.107124
[6] Alvioli et al., Eng. Geol. 293 (2021) https://doi.org/10.1016/j.enggeo.2021.106301
[7] Loche et al., Earth-Sci. Rev. 232 (2022) https://doi.org/10.1016/j.earscirev.2022.104125
How to cite: Alvioli, M., Esposito, G., Fiorucci, F., and Marchesini, I.: A parametric approach to delineating watersheds draining on linear infrastructures and assess their vulnerability to floods and landslides, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15358, https://doi.org/10.5194/egusphere-egu25-15358, 2025.
Mexico City, located in a lacustrine basin on highly heterogeneous terrain, presents a complex and unique scenario for studying sinkhole formation. Unlike karst regions, where these phenomena are typically associated with natural rock dissolution processes, in Mexico City they are linked to a specific interaction of geological, hydrological, and anthropogenic factors. Between 2017 and 2020, over 500 sinkholes were recorded, significantly impacting infrastructure and public safety. This context is particularly significant due to the high population density, extensive urbanization, and historical overuse of water resources, which aggravate land subsidence and soil collapse incidents. Previous studies, such as those by Ramírez Serrato et al. (2024) and García Cruzado et al. (2023), have explored the relationship between variables like subsurface composition, groundwater extraction, and infrastructure vulnerability. Ramírez Serrato and collaborators (2024) performed a statistical analysis to identify the degree of association of the conditioning factors to the presence of subsidence in the city through a Chi-square test and a regression analysis, with which they were able to perform a geographically weighted regression (GWR) model for mapping susceptibility in urban areas. While García Cruzado and collaborators (2023) analyzed the influence of different conditioning factors of the phenomenon for susceptibility mapping using the Weights of Evidence method, with which they were able to analyze the contribution of the main factors to the formation of the phenomenon, offering in both works valuable tools for the assessing the risk related to sinkholes. The objective of this study is to propose a conceptual model that characterizes the dynamics of the criteria involved in sinkhole formation in Mexico City. It integrates data from the Mexico City Risk Atlas along with the aforementioned analytical results. The study presents a model that organizes and visualizes the interaction between geological and anthropogenic factors, emphasizing the influence of water extraction, soil type, and urban pressures. This research aims not only to advance the understanding of the causes and dynamics of sinkholes but also to provide a useful tool for urban planning and risk mitigation, with the potential to safeguard Mexico City's infrastructure and population from this growing hazard.
Ramírez-Serrato, N. L., García-Cruzado, S. A., Herrera, G. S., Yépez-Rincón, F. D., & Villarreal, S. (2024). Assessing the relationship between contributing factors and sinkhole occurrence in Mexico City. Geomatics Natural Hazards And Risk, 15(1). https://doi.org/10.1080/19475705.2023.2296377
García Cruzado, S., Ramírez Serrato, N., and Herrera Zamarrón, G.: Mapping of Mexico City's susceptibility to sinkhole formation using the weights of evidence method, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10935, https://doi.org/10.5194/egusphere-egu23-10935, 2023.
How to cite: García-Cruzado, S. A., Ramírez-Serrato, N. L., Herrera, G., Hernandez-Hernandez, M. A., Yépez-Rincón, F. D., Villarreal, S., and Olea-Olea, S.: Mexico City Sinkhole Formation: Development of a Conceptual Model in a Non-Karst Environment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7819, https://doi.org/10.5194/egusphere-egu25-7819, 2025.
Posters on site: Thu, 1 May, 14:00–15:45 | Hall X3
This study quantitatively examines the correlation between land use/cover change (LUCC) and geohazards, specifically focusing on Lin'an District in Zhejiang Province, China. A multifaceted methodology encompassing the Patch-generating Land Use Simulation (PLUS) model, time series analysis, wavelet transform, and cross-lagged panel analysis was employed to scrutinize the distribution of land cover/land use and its nexus with geohazards.
The investigation began with applying the PLUS model to forecast land cover/land use distribution, integrating the Land Expansion Analysis Strategy and a Cellular Automata model based on Multiple Random Seeds to simulate spatial distribution and land cover/land use changes. Time series curves of land cover/land use and the Normalized Difference Vegetation Index (NDVI) for geohazard points were constructed. Wavelet transform techniques were then applied to uncover the underlying trends and periodicities within the geohazard and land cover/land use data. Correlation studies between various factors were conducted, and cross-lagged panel analysis was utilized to investigate the lag correlations between NDVI and land cover/land use types at geohazard points across different years.
The study has discovered some findings related to the significant temporal correlation between land use/cover changes and the occurrence of geohazards. For instance, changes in land use/cover typically precede geohazard events by 1-3 years, and the impact of geohazards on land use/cover is most pronounced within 1-2 years after the event. These findings indicate the complex interplay between land use/cover changes and geohazards. The conclusions drawn from this study, based on time series analysis and quantification of lag effects, provide theoretical underpinnings for understanding the intricate relationship between land cover/land use changes and geohazards and underscore their reciprocal interactions.
How to cite: Xia, J. and Chen, L.: Exploring the Temporal Linkages between Land Use/Cover Dynamics and Geohazards in Lin'an, Zhejiang, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2207, https://doi.org/10.5194/egusphere-egu25-2207, 2025.
Broad mountainous areas worldwide experience rainfall-induced slope movements, exacerbated by climate changes, causing heavy damages and fatalities. Often in a single geomorphological context, the same rainstorm can trigger many slope instabilities characterized by different degrees of mobility presenting reach angles varying from 10° and 50°.
This is the case of a wide area around Naples (South Italy) where shallow young pyroclastic granular covers initially in unsaturated conditions are frequently involved in fast slope movements showing a very different behaviour whose prediction, together with the consequent delimitation of the exposed areas at risk, is a fundamental step towards the individuation of mitigation strategies.
This study presents a series of long-term investigations conducted both in situ and in the laboratory to identify the parameters influencing the mobility of these landslides. Data collection at various sample sites consisted of suction and water content monitoring over time, also during intense rainfall events. Laboratory investigations involved hydro-mechanical characterization of these materials to examine soil behavior under both partially and fully saturated conditions and physical modelling to verify that a process of static liquefaction can establish in these deposits.
By synthesizing the knowledge gained from past and recent investigations on pyroclastic covers involved in catastrophic flowslides and debris avalanches during the last three decades, the main factors governing their response at the onset of failure and their subsequent mobility were identified and a physically-based flowchart has been developed. The proposed flowchart, basing on geomorphological and geotechnical data, can be used, under the simplified hypothesis, to make a preliminary prediction of the landslide's evolution and to enhance knowledge of the potential areas at risk.
How to cite: Brunzo, A., Damiano, E., de Cristofaro, M., and Olivares, L.: A simplified approach for assessing the evolution of rainfall-induced landslides in sandy soils, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6017, https://doi.org/10.5194/egusphere-egu25-6017, 2025.
Landslide susceptibility is the likelihood for a particular location to experience a landslide, based on terrain attributes and past landslide occurrences. The recent literature exhibits different approaches for the spatial zonation of landslide susceptibility. At the opposite sides of the spectrum of possible approaches lie physically based and statistically based methods. Physically based approaches calculate slope stability using well-defined equations, specific of the peculiar landslide type; here, we considered rockfalls [1,2]. Statistically based and machine learning approaches establish correlations between several topographic and environmental data and landslide presence [3].
Information on past landslides is useful for both methods, to calibrate model parameters and assess model performance. However, they differ significantly in their input requirements and methodological framework. In this study, we compare two state-of-the art susceptibility zonations, and their predictions at the location of different infrastructure in the whole of Italy, obtained by a physically based method [4] and with a slope unit-based statistical method [5].
To compare the two results, beyond classification performance, one has to figure out ways to cast the output maps of the two models in a similar format. Simulations with the 3D rockfall model produce raster maps, with a trajectory count for each grid cell, while the statistical result is a polygonal map [6]. We compared the two susceptibility zonations on the whole of Italy, first, and then we considered the predictions of the two results restricted to urban areas, railways, and road network.
The main difficulty lays in choosing an aggregation function for each polygonal or linear feature, to homogenize the two results. We performed either an average, for slope unit polygons, and empirical cumulative density functions (ECDFs), for linear features and urban areas. For the latter, we considered functional urban areas, or commuting zones, a standard choice to describe urban boundaries. Once average or ECDF values were obtained, for each polygon/linear segment, and for each version of susceptibility maps, we classified both results with an equal interval scheme. We acknowledge that the choice of aggregation functions and classification schemes are crucial for the final comparison, but we maintain that out choices are simple and objective.
The results indicate that the maps based on the considered models are drastically different. The observed disparities stem from the distinct conceptual frameworks and data dependencies of the two methods. While the physically based method can easily capture the mechanics of rockfall initiation, it requires input potentially limiting its use to data-rich locations. In contrast, the statistically based method is more flexible, and suitable for to regional-scale mapping. However, reconciling the two maps still looks challenging, and these preliminary results suggest complementary use of both methods.
[1] Guzzetti et al., Comp. Geosci. 28 (2002) https://doi.org/10.1016/S0098-3004(02)00025-0
[2] Sarkar et al., Nat. Haz.120 (2024) https://doi.org/10.1007/s11069-024-06821-9
[3] Alvioli et al., Earth-Sci. Rev. 258 (2024) https://doi.org/10.1016/j.earscirev.2024.104927
[4] Alvioli et al., Eng. Geol. 293 (2021) https://doi.org/10.1016/j.enggeo.2021.106301
[5] Loche et al., Earth-Sci. Rev. 232 (2022) https://doi.org/10.1016/j.earscirev.2022.104125
[6] Alvioli et al., Geomorphology (2023) https://doi.org/10.1016/j.geomorph.2023.108652
How to cite: Sarkar, N. and Alvioli, M.: Physically based and statistically based rockfall susceptibility along communication routes and in urban areas in Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8583, https://doi.org/10.5194/egusphere-egu25-8583, 2025.
Landslide occurrences are influenced by various spatial and climatic factors, some predictable to an extent while others remain uncertain. Physically-based models like TRIGRS are crucial for assessing slope stability and rainfall thresholds. In this study, we evaluated rainfall intensity (RI) and duration (RD) for landslide prediction in Guangdong's northern region, focusing on areas with historical high-intensity rainfall and landslides. Our study encompassed four rainfall intensities (1 mm to 50 mm) and 32 durations (1 to 72 hours), considering diverse hillslope gradients and geological formations (sedimentary and igneous rocks). Increasing RI correlated with decreasing RD until a threshold for slope failure was reached, defining spatial thresholds across varied rainfall simulations. Geological formations exhibited varying threshold intensities for slope failure, with igneous rock demonstrating greater resistance due to its granite and sandstone composition. Multiple calculations of the factor of safety for different intensities of rainfall events permitted the fitting of power-law equations to the critical intensity and rainfall durations for different grid cells. Simulation results indicated igneous rock failure after 4.3 hours of 50 mm/h rainfall, while sedimentary rock failure in low-strength areas within 2 to 3 hours with the same rainfall intensity at different locations. Validation with landslide data yielded accuracies of 67.42% for sedimentary rock, 68.13% for both sedimentary and igneous rock, and 63.51% for igneous rock alone using TRIGRS. This analysis highlights the geological role in slope failure and aids in future rainfall-based threshold evaluations for early landslide warnings.
How to cite: Ali, M. Z. and Chen, K.: Rainfall Threshold Analysis for Various Geological Formations in Northeastern Guangdong, China: A Physically-Based Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8937, https://doi.org/10.5194/egusphere-egu25-8937, 2025.
Based on multi-temporal mapping from air-photointerpretation, this contribution shows that the urban expansion of two Italian villages in the second half of the last century was substantially driven by the proximity to the pre-existing historic center, regardless of the presence of landslides (Zumpano et al., 2020). This is due both to a better accessibility to pre-existing services and sub-services, and to the lack of adequate knowledge - or the general underestimation - of the landslide hazard conditions adjacent to historic centres. In both vilages, this circumstance led to building on portions of pre-existing landslides - evidently not known, or considered stabilized - which were subsequently reactivated, posing serious risk conditions. These areas were investigated by deriving multi-temporal DEMs from the historical aerial photos (Santangelo et al., 2022) previously interpreted and using the Geomorphodiversity Index (GmI) (Burnelli et al., 2023) as a proxy for the morphometric modifications introduced by progressive urbanisation. Results demonstrate that in both cases investigated, the anthropic modifications of naturally achieved equilibrium conditions - measured by high differences in GmI before and after urbanization - were the most probable cause predisposing the partial reactivations of dormant landslides. This opens at the possibility of using GmI variability as a measure of the onset of potential geomorphological critical issues associated with new urbanizations, and possibly, computing the expected GmI variabilty already at the design phase, benefiting an adequate territorial planning. Overall, this study suggests caution in the urbanization of areas exposed to landslide hazards, even if landslides are considered dormant, and related hazard is only potential.
References:
Zumpano, V., Ardizzone, F., Bucci, F., Cardinali, M., Fiorucci, F., Parise, M., Pisano L., Reichenbach, P., Santaloia, F., Santangelo, M., Wasowski, J., Lollino, P. (2020). The relation of spatio-temporal distribution of landslides to urban development (a case study from the Apulia region, Southern Italy). Journal of Maps, 17(4), 133–140. https://doi.org/10.1080/17445647.2020.1746417
Burnelli, M., Melelli, L., Alvioli, M. (2023). Land surface diversity: a geomorphodiversity index of Italy. Earth Surface Processes and Landforms., 48(15), 3025–3040. Available from: https://doi.org/10.1002/esp.5679
Santangelo, M., Zhang, L., Rupnik, E., Deseilligny, M. P., and Cardinali, M. (2022). Landslide evolution pattern revealed by multi-temporal DSMS obtained from historical aerial images, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 1085–1092, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1085-2022, 2022
How to cite: Bucci, F., Cardinali, M., Santangelo, M., Fiorucci, F., and Alvioli, M.: Temporal evolution and interactions of landslides and urban areas revealed by air-photointerpretation and morphometric analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13083, https://doi.org/10.5194/egusphere-egu25-13083, 2025.
Active tectonics and high precipitation in Central Nepal Belts cause frequent rockfalls. This has caused severe impacts on communities and infrastructure, especially road networks. The major roads in Central Nepal, particularly the Pasang Lhamu Highway (PLH) and Galchhi-Rasuwagadhi Highway (GRH) have faced significant challenges due to rockfalls triggered by the 2015 Gorkha earthquake and seasonal high rainfall. These rockfalls obstructed transportation, and impeded road development and environmental management. Despite existing landslide susceptibility studies, limited research has focused specifically on rockfall susceptibility in the area.
This study addresses this gap by employing a physically based model, STONE [1], to assess rockfall susceptibility along these highways in the Rasuwa district. The model analyses individual rock blocks originating from user-defined locations, following independent paths influenced solely by gravity, and it is suited for assessing rockfall susceptibility along linear infrastructure [2]. To run the model, we used a 12.5 m resolution ALOS PALSAR digital elevation model and field investigation to prepare a rockfall source inventory. The second relevant input of the model is a map of locations for possible rockfall sources. Following Refs. [3], we obtained a probabilistic map of sources considering slope angle, relief, and vector ruggedness to establish numerical morphometric thresholds calibrated with observed rockfalls, and generalized the findings to unsurveyed sections across the whole study area. Next, we employed the STONE model to simulate three-dimensional rockfall trajectories and generate a rockfall susceptibility map.
The resulting map shows classified road segments into five susceptibility levels [4], with a susceptibility index ranging from 1 (low) to 5 (very high). Results highlighted high-susceptibility areas in Ramche, Dandagaun, and Syaprubesi, highlighting the segments of both highways most vulnerable to rockfall. As no rockfall protection strategies were adopted in these areas, which has affected road management and degraded the surrounding environment, results of this study would help to prioritize the sections of linear infrastructure that requires detailed rockfall studies and safety measures.
References
[1] Guzzetti et al., Comp. Geosci. (2002) https://doi.org/10.1016/S0098-3004(02)00025-0
[2] Alvioli et al., Eng. Geol. (2021) https://doi.org/10.1016/j.enggeo.2021.106301
[3] Alvioli et al, Geom. Nat. Haz. Risk (2022) https://doi.org/10.1080/19475705.2022.2131472
[4] Pokharel et al., Bull. Eng. Geol. Env. (2023) https://doi.org/10.1007/s10064-023-03174-8
How to cite: Pokharel, B., Lim, S., Nidhi Bhattarai, T., and Alvioli, M.: Implementing a physically based model to assess rockfall susceptibility in central Nepal, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20950, https://doi.org/10.5194/egusphere-egu25-20950, 2025.
This paper presents surface monitoring of a failure slope, which was located along the Halong-Vandon highway. Terrestrial laser scanning was adopted to scan the slope surface twice at the time that failure occurred and one year later. Whole slope surface could be scanned completely. Results show that the subsided area was about 5600 m2, at which ground settlement took place seriously at the center line of the area and slightly near boundaries. The slope surface settled down about 1 m at the first time scanning. Then, the development of ground settlement became slow and the maximum settlement increment was about 0.5m at the second time of scanning. Besides, the finite element method was adopted to model the slope surface settlement and compare with that was recorded at the first time of scanning.
How to cite: Do, T., Chau Lan, N., and The Viet, T.: Surface monitoring of a failure slope by terrestrial laser scanning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20299, https://doi.org/10.5194/egusphere-egu25-20299, 2025.
Assessing climate and, more specifically, flood vulnerability in rapidly urbanizing regions remains a challenge due to the complexity of diverse socio-economic, demographic, and spatial factors. This case study of Mumbai integrates household-level survey data (n = 1106) with morphological information to capture the multi-dimensional nature of vulnerability at the intra-urban scale. Focusing on flood-prone neighborhoods in Mumbai, we analyze household survey data (e.g., education, employment, income security, household assets) to identify distinct ‘archetypes’ of vulnerability.
We implement an advanced, unsupervised machine learning approach to generate distinct and heterogenous socio-economic profiles by grouping households across multiple variables (e.g., education, employment status, household assets) rather than relying on static thresholds. We further incorporate statistical association measures to robustly examine relationships between clusters and key vulnerability outcomes and indicators (e.g., perceived flood severity, loss of workdays, and health impacts).
To examine the influence of urban development on flood-related hazards, we complement the socio-economic clustering with a geospatial analysis that connects local urbanization conditions to the identified vulnerability profiles. First, we analyze household-reported impacts from flooding and perceived causes (e.g., blocked drainage channels, lack of maintenance) for each cluster to understand specific pathways by which urbanization exacerbates or alleviates flood risk. Second, we integrate these survey-based findings with geospatial data of topography (e.g., household location in the watershed) and urban form (e.g., open, or compact types) to assess the extent to which household location and built form shape or modify local flood vulnerability.
Our findings provide a data-driven baseline for capturing vulnerability that goes beyond singular proxies such as income. However, low data availability and quality—particularly in Global South contexts—can limit the replicability of this approach, and the high socio-spatial diversity within cities like Mumbai may not always be captured by coarser spatial data. Moreover, it remains unclear how well these findings hold over time, as vulnerability patterns may shift rapidly in evolving urban areas. Despite these caveats, by simultaneously assessing a range of household-level and urban form variables, this approach produces vulnerability profiles that can inform spatial prediction models and serve as inputs for spatial simulations of urbanization. The resulting flood vulnerability maps help to identify areas in need of interventions and offer a reproducible template for other flood-prone settings in the Global South.
How to cite: Mirbach, C., Santos, A. P., and Garschagen, M.: Identifying multi-dimensional vulnerability profiles in flood-prone urban environments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17538, https://doi.org/10.5194/egusphere-egu25-17538, 2025.
Urban surface runoff is intricately linked to the spatiotemporal distribution of rainfall and surface water flow dynamics. Therefore, when conducting simulations of urban surface runoff, it is essential to account for the hydrological and physiographic conditions within the study area. This research analyzed the terrain and landforms of the study area, arranged computational cells, and selected appropriate flow equations to simulate surface water movement. Using the concept of quasi-two-dimensional flow, a flood simulation core model was established and applied to simulate rainfall-runoff processes within metropolitan areas. Adjacent grid cells were connected using the continuity equation and suitable flow laws derived from quasi-two-dimensional flow theory to assess water levels and flow rates between cells.
A physiographic drainage-inundation model (PhD model) employed in this study utilized unstructured cells constructed based on physiographic conditions. The cells were designed and calibrated in accordance with current land use, spatial planning functional zones, or post-implementation urban planning zoning. The model encompassed five major river basins in Tainan City (Bazhang River, Jishui River, Zengwen River, Yanshui River, and Erren River), covering a total area of approximately 2,446.62 square kilometers and divided into 30,500 computational cells.
The analysis is based on a geomorphic scenario using current land use for runoff analysis, incorporating scenarios with 10-year return period rainfall, a quantitative torrential rainfall event (350mm/24hr). Both scenarios utilized the 10-year return period tidal levels along Tainan’s coastal areas as downstream boundary conditions. The results identified flooding hotspots near the Xiaying Interchange, Shinshih Interchange, and Rende District.
To assess the impact of future development areas on Tainan's flood risks, the study adjusted the CN (Curve Number) values of corresponding cells in PhD model to simulate flooding under the 10-year return period rainfall scenario. The findings revealed that future developments would exacerbate flood risks in Tainan, with significant increases in flooding depths observed in areas near Shinshih, Gueiren, and Rende Districts. The maximum increase reached up to 0.15 meters.
Finally, the study explored integrating runoff allocation plans into spatial planning to enhance urban flood resilience. Using the Zengwun-chi Drainage Plan as a case study, the simulation assessed the flood mitigation effects of implementing runoff detention and storage measures. Results indicated that areas with larger flood storage capacities exhibited more substantial flood reduction effects, with maximum reductions in flooding depth reaching 0.13 meters, while areas with smaller capacities showed limited effects.
In conclusion, this study established a reliable physiographic drainage-inundation model and simulated the impacts of various rainfall scenarios and future developments on flood risks in Tainan City. The findings serve as a valuable reference for governmental authorities to evaluate potential disasters associated with regional development and formulate mitigation strategies during urban planning processes.
How to cite: Wu, M.-H. and Lo, W.-C.: The Impact of Land Development on Runoff and the Analysis of Runoff Adaptation Resilience: A Case Study of Tainan City, Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17665, https://doi.org/10.5194/egusphere-egu25-17665, 2025.
This study addresses the challenges of urban flooding induced by climate change by proposing a refined flood risk assessment methodology to provide scientific support for the formulation of flood adaptation strategies. Focusing on the unique disaster characteristics of Taichung City, the research integrates AR5 and AR6 rainfall scenario data provided by Taiwan’s National Science and Technology Center for Disaster Reduction. Utilizing the physiographic drainage-inundation model (PhD model), the study simulates flood depth and distribution characteristics under varying rainfall intensities, complemented by historical data and local intelligence for model calibration. This approach enables precise identification of high-risk areas and systematically characterizes flood process, offering a quantitative foundation for planning flood control infrastructure and adaptation strategies. The results address the lack of quantitative data in current urban flood risk assessments and establish a reference framework for scientific risk evaluation under extreme climate scenarios.
For extended applications, the study explores the potential of integrating flood risk information with artificial intelligence (AI) technology, specifically through the development of an intelligent water level recognition model. This model leverages existing CCTV systems for water level monitoring, employing simulated imagery for training and validation. It demonstrates potential for real-time water level monitoring and flood early warning capabilities. While further optimization and field testing are necessary, this approach holds promise for enhancing disaster mitigation and emergency response efficiency, providing valuable insights for addressing future challenges posed by extreme climate conditions.
How to cite: Chen, P.-T. and Li, C.-Y.: Development and Application of an Urban Flood Risk Assessment Method under Climate Change with an Exploration of AI-Assisted Applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17692, https://doi.org/10.5194/egusphere-egu25-17692, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot 3
EGU25-21040 | Posters virtual | VPS13
Effects of Drainage Network on the Identification of Landslide-Susceptible Areas Using the TRIGRS ModelWed, 30 Apr, 14:00–15:45 (CEST) | vP3.18
Gravitational mass movements are recurrent events in Brazil, usually triggered by intense rainfall. When such rainfall events occur in urban areas, particularly on slopes, they often result in disasters, causing loss of human lives, social impacts, and economic damage. Thus, mapping and monitoring landslide susceptible areas are extremely important, as well as the implementation of a system capable of predicting their occurrence in advance. In this context, this study aims to assess the efficiency of the TRIGRS numerical model as a component of a prediction system for landslides on urban slopes. As a first step, the influence of the drainage network, which is altered due to urbanization on slopes, will be analyzed in relation to the safety factor, moisture profile, and pore pressure. The drainage network was calculated using a digital terrain model derived from LIDAR data. The TRIGRS model was applied to a small watershed located in the municipality of Campos do Jordão, São Paulo, Brazil. During the 72 hours analyzed period, two heavy rainfall events stroke the area and landslides were registered. The registered landslides show the model efficiency on the identification of the most susceptible areas, because they happened in areas identified by TRIGRS as extremely susceptible to landslides. The combined geotechnical and geophysical methodology for soil characterization and the use of more realistic drainage network feeding the TRIGRS has shown to be useful urban planning and early warning systems. This study is part of Brazilian Council for Scientific and Technological Development (CNPq) Project coordinated by GEGEP/UFPE, with the participation of Cemaden, and in collaboration under development with the National Research Council of Italy (CNR). It aims to implement a methodological procedure.
How to cite: Ernesto de Moraes, M. A., M. Mendes, R., Bortolozo, C. A., Metodiev, D., das Dores S. Medeiros, M., R. M. Andrade, M., S. G. Mendes, T., and Q. Coutinho, R.: Effects of Drainage Network on the Identification of Landslide-Susceptible Areas Using the TRIGRS Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21040, https://doi.org/10.5194/egusphere-egu25-21040, 2025.
EGU25-19948 | Posters virtual | VPS13
Entrainment-driven changes in runout deposition of debris flows at small scaleWed, 30 Apr, 14:00–15:45 (CEST) | vP3.19
Entrainment plays a vital role in shaping debris flow deposits, influencing their morphology and dynamics. Our study utilized a small-scale flume experiment to investigate the effects of water content (w/c), sediment composition, and bed morphology on granular flow behavior. Sixteen experiments were conducted with varying w/c levels (20–50%) and erodible bed configurations, analyzing deposit morphology in terms of width, thickness, and runout length. The results revealed distinct morphological patterns across different w/c levels. At low w/c levels (20–24%), deposits formed broad, shorter lobes with minimal scouring, resulting in cone-shaped structures. Moderate w/c (~28%) increased flow mobility, leading to thicker deposits near the flume bed due to reduced entrainment. At higher w/c levels (30–50%), deposits shifted farther downstream, characterized by greater entrainment volumes and extended runout distances. While higher w/c reduced deposit thickness, it significantly increased deposit width, highlighting the combined effects of w/c and entrainment. The study identified a clear relationship between entrainment and flow mobility, with greater entrainment volumes producing wider and flatter deposits. Water content was found to be the primary factor influencing deposit thickness, emphasizing its critical role in sediment transport dynamics. The deposits were poorly sorted and exhibited a bedding structure similar to natural debris flows, validating the experimental approach. This research presents an effective and scalable method for studying granular flow behavior over erodible beds, offering valuable insights into sediment transport processes and bridging mesoscale experiments with practical applications in natural hazard mitigation and geotechnical engineering.
How to cite: Satyam, N., Pandey, N. K., and Basumatary, B.: Entrainment-driven changes in runout deposition of debris flows at small scale , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19948, https://doi.org/10.5194/egusphere-egu25-19948, 2025.
EGU25-3939 | ECS | Posters virtual | VPS13
Innovative Geocell-Based Slope Stabilization for Sustainable Protection: A Case Study of a Radio Tower Site in Kodagu, IndiaWed, 30 Apr, 14:00–15:45 (CEST) | vP3.20
This study addresses slope stability challenges at the All-India Radio Telecommunication Tower site in Kodagu, Coorg, Karnataka, India. The hillock supporting the tower exhibited signs of instability following the monsoon of 2022, prompting the need for effective reclamation strategies to prevent future landslides. A detailed spatial analysis was conducted using open-source Digital Elevation Models (DEM) and the Scoop 3D spatial Limit Equilibrium Method (LEM) tool to identify critical regions susceptible to failure. To ensure robust and sustainable slope stabilization, geocell retaining walls were selected as an innovative solution. This technique promotes biotechnical stabilization by integrating structural reinforcement with natural vegetation, aligning with sustainability principles. The three-dimensional geometry of the proposed solution was modelled, and Finite Element Method (FEM) simulations were performed using PLAXIS 3D to evaluate the design’s performance under static and pseudo-static conditions, both with and without reinforcement. The analysis revealed that the geocell-based retaining system significantly enhances the slope's stability, achieving a Factor of Safety improvement of more than 10%. This solution not only addresses immediate stability concerns but also aligns with the United Nations Sustainable Development Goals (SDG) 9 and 11, emphasizing resilient infrastructure and sustainable urban development. The study concludes by recommending the implementation of this hybrid geocell retaining system to effectively mitigate future landslides and protect the telecommunication tower site.
How to cite: Menon, V. and Kolathayar, S.: Innovative Geocell-Based Slope Stabilization for Sustainable Protection: A Case Study of a Radio Tower Site in Kodagu, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3939, https://doi.org/10.5194/egusphere-egu25-3939, 2025.
