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.

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.

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.