NH4.2 | Utilizing both physical and data-driven models to assess geophysical hazards and risks with the aim of mitigating disasters
Utilizing both physical and data-driven models to assess geophysical hazards and risks with the aim of mitigating disasters
Convener: Antonella Peresan | Co-conveners: Margaret Glasscoe, Bandana KarECSECS, Elisa Varini, Guy J.-P. Schumann, Katalin Gribovszki, Katerina OrfanogiannakiECSECS
| Fri, 19 Apr, 08:30–10:15 (CEST)
Room 1.31/32
Posters on site
| Attendance Fri, 19 Apr, 10:45–12:30 (CEST) | Display Fri, 19 Apr, 08:30–12:30
Hall X4
Posters virtual
| Attendance Fri, 19 Apr, 14:00–15:45 (CEST) | Display Fri, 19 Apr, 08:30–18:00
vHall X4
Orals |
Fri, 08:30
Fri, 10:45
Fri, 14:00
The mitigation of disasters associated with geophysical hazards encompasses several components, including the identification, evaluation and reduction of related risks. Each component comprises multiple facets: a) The analysis of hazards, including its physical characteristics and its effects on built and natural environment and social systems. b) The assessment of vulnerability, exposure to hazards and resilience. c) Long-term preparedness and response following an event (e.g. earthquake, landslide, tsunami). Given the diverse nature of geophysical disaster mitigation, a variety of hazard and risk models have been developed at different time scales utilizing varius methodologies and datasets ( such as imagery, census data, geospatial datasets of built and natural environment, etc.) to provide timely and reliable information for effective disaster mitigation.

This session aims to address both theoretical and implementation challenges, along with considerations related to communication and science policy, focusing on various aspects of risk research and assessment, as well as their application in mitigating disasters. The session will encompass:
The development of physical/statistical models for risk, exposure and vulnerability assessment across various temporal and spatial scales.
1. The assessment of model accuracy against observations (from EO observations to non-traditional seismological data).
2. Time-dependent situational awareness information to assist with early warning and alerting for effective emergency management
3. Analyzing earthquake-induced cascading effects such as landslides and tsunamis, and conducting multi-risk assessments.
The interdisciplinary session encourages the exchange of knowledge and the sharing of good practices acquired through various methodologies. In doing so, it offers opportunities to enhance our understanding of disaster risk in all its facets including vulnerability, capacity, exposure of individuals and assets, hazard attributes and the environment. At the same time, it points out current deficiencies and indicate the way towards future research directions.

Orals: Fri, 19 Apr | Room 1.31/32

Chairpersons: Antonella Peresan, Bandana Kar, Katalin Gribovszki
Part 1 - Seismic hazard and risk assessment
On-site presentation
Jun-Yang Xu, Yung-Wei Hsu, and Kuo-Jen Chang

Taiwan situated on an active orogenic between the Eurasian plate and the Philippine Sea plate with an annual contraction rate of >8 cm, possesses therefore high seismicity and frequent geological hazards. Furthermore, many of the seismogenic faults pass across dense population areas and cause severe damages. In September 2022, several large earthquakes occurred in Longitudinal Valley, caused more than 100 casualties and damage roads, bridges and houses. However, exception of a few apparent surface cracks, the location, total length and actual deformation of the faults remain incomplete in most areas. In order to better constrain deformation and faulting behavior and potential threats, we focus on the Longitudinal Valley fault and Central Range fault in eastern Taiwan. To better estimate ground deformation around the active fault, large-area high-resolution geoinformatic datasets before and after the earthquake are critical. In this study, we use DMC aerial images, taken in April and September 2022, to produce DTM and orthomosaic images. Based on paired orthoimages before and after the earthquake, the particle image velocimetry (PIV) method was used to calculate the horizontal ground deformation. Vertical displacements near the fault were estimated from digital terrain models (DTM) of differences (DoD) pre- and post-earthquake. The results showed that the maximum horizontal displacement was greater than 2-3 meters and was been verified on field.

How to cite: Xu, J.-Y., Hsu, Y.-W., and Chang, K.-J.: Analysis of active fault morphotectonic behavior and seismic deformation during the September 2022 earthquakes in eastern Taiwan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3911, https://doi.org/10.5194/egusphere-egu24-3911, 2024.

On-site presentation
Anastasia Nekrasova, Vladimir Kossobokov, and Ekaterina Podolskaia

Seismic hazard assessment (SHA) and evaluation of seismic risks (SRs) require an adequate understanding of the actual distribution of earthquakes over magnitude, space, and time ranges. The standard probabilistic seismic hazard analysis (PSHA) has never been subjected to unbiased scrutiny before publication of the final maps, which are misleading to unacceptable human and economic losses; this has been proven on many occasions, including the most recent cases – 6 February 2023 (Turkey), 8 September 2023 (Morocco), and 1 January 2024 (Japan).  Neo-deterministic seismic hazard assessment (NDSHA) methodologies have been developed to improve the reliability and accuracy of reproducible seismic hazard maps. In the last decade, the application of NDSHA in many regions of the world has confirmed the availability of reliable and effective input for mitigating earthquake risks (Panza et al., 2021). NDSHA results have passed intensive testing by historical evidence and realistic modelling of scenario earthquakes.

We used two agents of the NDSHA synergy, i.e. Unified Scaling Law for Earthquakes (USLE) and anisotropic propagation of seismic effect, to evaluate SRs for the Lake Baikal regional railroad system on the basis of seismic hazard maps of maximum macroseismic intensity expected in 50 years with 10%, 5%, and 1% chance of exceedance. Specifically, we employed the regional layers from the widely used crowdsourced dataset, Open Street Map, which features global coverage. These layers include infrastructure elements such as tracks, bridges, and tunnels.

We have extended our analysis of seismic risk assessment for the Lake Baikal Region railway system presented earlier (Nekrasova et al., 2024) and compare our results with the SR evaluations based on the General Seismic Zonation 2016 and Global Earthquake Model 2018 final hazard maps at identical levels of probability of exceedance.

A comparison of PSHA and NDSHA approaches in application to the Lake Baikal railway system disclose significant overestimation of the reconstruction costs for expected state of extreme damage (Hazus state standard ds5 - Complete) due to earthquakes, if GSZ2016 or GEM2018 and not USLE modelling is used. In particular, the significant discrepancy in the area of expected ground shaking of macroseismic intensity VIII or higher that may damage the railroad tracks, bridges, and tunnels leads to a dramatic difference in the seismic risk values measured in arbitrary units of currency.

Our results are presented for academic purposes only. Evidently, more adequate though significantly more complex procedures involving more complicated procedures of convolution of seismic hazard, exposures, and their vulnerability are required when addressing realistic and practical assessment of seismic risks. Such assessments should involve experts in seismology, earthquake engineering, social sciences, and economics.


Nekrasova A, Kossobokov V, Podolskaia E (2024) Regional seismic risk assessment based on the Unified Scaling Law for Earthquakes: The Lake Baikal railway system. Soil Dynamics and Earthquake Engineering 177, 108402. https://doi.org/10.1016/j.soildyn.2023.108402

Panza G, Kossobokov V, De Vivo B, Laor E (Eds) (2021) Earthquakes and Sustainable Infrastructure: neo-deterministic (NDSHA) approach guarantees prevention rather than cure. Elsevier. Paperback ISBN: 9780128235034, eBook ISBN: 9780128235416, xxv, 672 p. https://doi.org/10.1016/C2020-0-00052-6

How to cite: Nekrasova, A., Kossobokov, V., and Podolskaia, E.: A comparison of the seismic risk assessment for the Lake Baikal railway system based on standard probabilistic and neo-deterministic approaches, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9166, https://doi.org/10.5194/egusphere-egu24-9166, 2024.

On-site presentation
Zehra Cagnan

The continuous functionality of critical infrastructure such as oil, gas, and water pipelines, as well as tunnels and bridges following an earthquake is important for the effective management of response actions and influences the seismic resilience of communities. Failure of such infrastructure may result in injuries and human fatalities, environmental pollution as well as significant direct and indirect economic losses. One of the most catastrophic earthquake-induced actions is the permanent fault displacements observed when fault ruptures propagate up to the ground surface in case of large magnitude earthquakes. Any structure but mostly pipelines, tunnels and bridges crossing active faults are prone to such permanent fault displacements as they develop excessive deformations as a result. The earthquake resistant design of pipelines, tunnels and bridges is based on reliable estimation of such excessive deformations.

6 February 2023 Kahramanmaras Turkiye Earthquake with Moment Magnitude 7.8 (United States Geological Survey, USGS) ruptured 3 consecutive segments of the East Anatolian Fault Zone: namely Amanos, Pazarcik and Erkenek segments. East Anatolian Fault Zone is one of the most active strike-slip faults in the world. The total observed surface rupture as a result of the Kahramanmaraş Earthquake exceeded 300km with measured permanent displacements reaching 5m near Kahramanmaras and diminishing to 0.5m towards South and North. In this study, by carrying out probabilistic fault displacement hazard assessment for these segments of the East Anatolian Fault Zone expected permanent fault displacements were computed as a function of return period. Influence of multi-segment rupture, selected earthquake recurrence model, adopted maximum magnitude value are studied on the computed permanent displacement results. Computed displacements are critically compared against the observed permanent displacements. Pipeline Systems and Liquid Storage Tanks Earthquake Code of Turkey (2021) and EN1998-4:2006 suggest the use of empirical fault rupture length-permanent fault displacement relationships in design of pipelines crossing active faults. prEN 1998-4:2022 however suggests an alternative methodology that allows approximate calculation of permanent fault displacements corresponding to any given return period based on fault mechanism, fault rupture length and fault productivity. These code-based estimates are compared with measured fault displacements and conducted probabilistic fault displacement hazard results.

How to cite: Cagnan, Z.: Comparison of probabilistic fault displacement hazard assessment results with observed permanent ground displacements in the aftermath of the 6 February 2023 Kahramanmaras Turkiye Earthquake., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2911, https://doi.org/10.5194/egusphere-egu24-2911, 2024.

On-site presentation
Alik Ismail-Zadeh

Scientific discoveries on earthquake occurrences integrated into hazard, vulnerability and exposure studies help in disaster risk assessment and its understanding. Research on the lithosphere dynamics, extreme seismic occurrences, and earthquake forecasting as well as seismic hazards assessments and early warning have significantly advanced during the last decades. Numerical earthquake simulations coupled with a seismic hazard analysis provide a better assessment of potential ground shaking due to future large earthquakes. Hazard analysis, modeling, and forecasting help in development of preventive measures aimed to reduce impacts of extreme events. Meanwhile, we cannot counteract disasters without the nexus between the scientific knowledge on seismic hazards, preparedness to large earthquakes and public awareness about disaster risks. Integrating natural, engineering, social and behavioral sciences and practices with policymaking should significantly improve measures to reduce disaster risks. To this end, a fundamental change in scientific approaches to disaster risk reduction is needed by shifting the current emphasis on individual hazard assessment (e.g., seismic hazard assessment only), which is dominant in the geoscientific community, to a transdisciplinary system analysis with action-oriented research on disaster risk co-produced with multiple stakeholders. This will allow for acquisition of policy-relevant knowledge and its immediate application to evidence-based policy and decisionmaking for risk reduction.

How to cite: Ismail-Zadeh, A.: Why earthquakes cause disasters and how to counteract it?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11974, https://doi.org/10.5194/egusphere-egu24-11974, 2024.

Part 2 - Assessing the impact of earthquakes and other natural hazards: from modelling to earth observations
On-site presentation
Subash Ghimire and Philippe Guéguen

Assessing or forecasting seismic damage to buildings is crucial for earthquake disaster management. Several classical damage assessment methods are available for seismic damage assessment by combining hazard, exposure, and vulnerability. However, during emergencies, collecting all the necessary data for seismic damage assessment may not be feasible due to time and resource constraints, as this information may not be readily available.

In this context, machine learning methods can offer a paradigm shift by reasonably assessing damage by relying on readily available data cost-effectively. In this study, we aim to study the damage prediction efficacy of machine learning models for regional scale damage assessment. Machine learning models were trained and tested on the post-earthquake building damage database.

Results show that the readily available building features such as the number of stories, age, floor area, and height can result in a reasonable assessment of damage at a large scale, mainly when using a traffic-light-based (green, yellow, and red) damage classification framework.

The machine learning models trained on past earthquake building damage portfolios can reasonably estimate damage during the future earthquake for a different region with similar building portfolios.

How to cite: Ghimire, S. and Guéguen, P.: Testing machine learning models for rapid building damage assessment at regional scale., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19972, https://doi.org/10.5194/egusphere-egu24-19972, 2024.

On-site presentation
Anna Kaiser, Jen Andrews, Bill Fry, Nick Horspool, Biljana Lukovic, Chris Massey, Emily Warren-Smith, Calum Chamberlain, Tatiana Goded, Elisabetta D'Anastasio, Chris Zweck, and Florent Aden

The New Zealand R-CET Endeavour programme has been developing a suite of tools to characterize the earthquake source and its shaking in near real-time.  A key goal is to provide pathways to improve rapid earthquake impact forecasts.  

Small-to-moderate earthquakes can be reasonably represented by a ‘point source’.  This allows us to generate first shaking maps (GNS Shaking Layers; Horspool et al. 2023) automatically and robustly based on basic earthquake solutions (magnitude and hypocentre). These maps are now routinely available to the public within 10 – 20 minutes: https://www.geonet.org.nz/about/earthquake/shakinglayers#:~:text=What%20is%20Shaking%20Layers%3F,intensity%20anywhere%20in%20the%20country.

For very large earthquakes (M6.5+), a ‘point-source’ is a poor representation of the earthquake, which can rupture tens to hundreds of kilometres of the earth.  First shaking models based on ‘point sources’ could severely underestimate shaking in areas further from the epicentre, but close to the fault rupture. Rapid 3D characterization of the rupture area, even if approximate, has the potential to significantly improve shaking estimates, and allow meaningful first impact forecasts to be generated.

Here we present an overview of rapid source characterization tools implemented for New Zealand under the R-CET programme. These tools include FinDer (Andrews et al. 2023), w-phase (Fry et al. 2022), EQCorrScan (Chamberlain et al. 2017; Warren-Smith & Chamberlain 2022), G-FAST and others . We show examples of tool outputs for large (M6.5+) historical earthquakes in New Zealand and examine their potential to improve rapid shaking models, loss estimates and landslide forecasts. Our results show the importance of including rapid source characterization as a key component of our earthquake response systems, to underpin quality scientific advice for emergency responders.

How to cite: Kaiser, A., Andrews, J., Fry, B., Horspool, N., Lukovic, B., Massey, C., Warren-Smith, E., Chamberlain, C., Goded, T., D'Anastasio, E., Zweck, C., and Aden, F.: Using rapid source characterisation to improve ShakeMaps and impact forecasts for large earthquakes (M6.5+) in New Zealand, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20937, https://doi.org/10.5194/egusphere-egu24-20937, 2024.

On-site presentation
Massimiliano Alvioli, Antonella Peresan, Valerio Poggi, Chiara Scaini, Alberto Tamaro, and Fausto Guzzetti

Different approaches exist to describe the seismic triggering of rockfalls. Statistical approaches rely on the analysis of local terrain properties and their empirical correlation with observed rockfalls. Conversely, deterministic, or physically based approaches, rely on the modeling of individual trajectories of boulders set in motion by seismic shaking. They require different data and allow various interpretations and applications of their results. Here, we present a new method for earthquake-triggered rockfall scenario assessment making use of ground shaking estimates. Its key inputs are the locations of likely initiation points of rockfall trajectories, namely, rockfall sources, obtained by statistical analysis of digital topography [1,2,3].

In the approach proposed here [4], ground shaking maps corresponding to a specific earthquake are used to suppress the probability of activation of sources at locations with low ground shaking, while enhancing that in areas of high shaking close to the epicenter. Rockfall trajectories are calculated from the probabilistic source map by three-dimensional kinematic modeling using the software STONE [5,6]. Here, we apply the method to the 1976 MI = 6.5 Friuli earthquake, for which an accurate inventory of seismically-triggered rockfalls exists [7].

We suggest that using peak ground acceleration as a modulating parameter to suppress/enhance rockfall source probability the model reasonably reproduces observations. Calibration of the method is peculiar of the area, but it is expected to be valid for future earthquake-induced rockfalls in the same area, for similar seismic events. The method was previously applied at different scales and with different assumptions across Italy [8], in particular at national scale using maps of maximum expected ground shaking with different return times [9].

Results of this work [4] allow for a preliminary impact evaluation before field observations become available. We suggest that the framework may be suitable for rapid rockfall impact assessment as soon as ground-shaking estimates (from empirical ShakeMap [10] or from physical models of wave’s propagation) are available after a seismic event.


[1] Alvioli et al., Engineering Geology (2021). https://doi.org/10.1016/j.enggeo.2021.106301

[2] Alvioli et al., Geomatics, Natural Hazards and Risk (2022). https://doi.org/10.1080/19475705.2022.2131472

[3] Pokharel et al., Bulletin of Engineering Geology and the Environment (2023). https://doi.org/10.1007/s10064-023-03174-8

[4] Alvioli et al., Landslides (2023). https://doi.org/10.1007/s10346-023-02127-2

[5] Guzzetti et al., Computers & Geosciences (2002). https://doi.org/10.1016/S0098-3004(02)00025-0

[6] Valagussa et al., Engineering Geology (2014). https://doi.org/10.1016/j.enggeo.2014.07.009

[7] Govi, Bulletin Int. Assoc. Engineering Geology (1977). https://doi.org/10.1007/BF02592650

[8] https://frasi-project.irpi.cnr.it

[9] Alvioli et al., Geomorphology (2023). https://doi.org/10.1016/j.geomorph.2023.108652

[10] Worden et al., (2020). ShakeMap 4 Manual, USGS. https://doi.org/10.5066/F7D21VPQ

How to cite: Alvioli, M., Peresan, A., Poggi, V., Scaini, C., Tamaro, A., and Guzzetti, F.: A scenario-based approach for immediate post-earthquake rockfall impact assessment and case study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10711, https://doi.org/10.5194/egusphere-egu24-10711, 2024.

On-site presentation
Valerio Tramutoli, Mohammad Kazemi Garajeh, Annibale Guariglia, Parivash Paridad, Raffaele Santangelo, and Valeria Satriano

Natural disasters, in recent years, are increasinglyaffecting critical infrastructures and, particularly, electric and gas supply pipelineswhich play a vital role in modern society. In order to enable effectivedisaster response, ensure the safety of affected populations, and facilitatethe recovery and rebuilding process following sudden-onset disasters, it is crucialto have accurate and timely information on infrastructure damage. This studyimplements an advanced multi-temporal technique to detect even small landslideaffecting electrical poles.  Land-coverinformation obtained by the Multispectral Instrument (MSI) sensor aboard theCopernicus Sentinel-2 platforms are used to timely (within days/weeks) identifysuch events. To this end, long-term satellite data are processed in the GoogleEarth Engine (GEE) environment to preliminarily characterize unperturbed soilconditions before to implement the RST (Robust Satellite Techniques) foranomalous soil conditions identification. Our findings reveal that severalelectrical poles had been affected by landslides in Sicily from 2016-2023. Theresults of this study also confirmed the efficiency of an RST-likeapproach  for landslide detection.

How to cite: Tramutoli, V., Kazemi Garajeh, M., Guariglia, A., Paridad, P., Santangelo, R., and Satriano, V.: A Robust Satellite Technique for monitoring landslides impact on electrical infrastructures, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19587, https://doi.org/10.5194/egusphere-egu24-19587, 2024.

On-site presentation
Petros Mouzourides, Chrysoula Papathanasiou, Eleftheriou Andreas, Giorgos Nikolaidis, Marios Vlachos, Valantis Tsiakos, Georgios Tsimiklis, Chrysanthos Savvides, Emily Vasiliadou, Angelos Amditis, and Marina K-A Neophytou

As global climate change continues to exacerbate global warming and global warming moves into the future, desert ecosystems are expected to face a heightened vulnerability to its impacts, including rising temperatures, sea level rise and variations in intensity and frequency of precipitation. These conditions directly affect the structural integrity of desert ecosystems and their ability itself to function as ecosystems. The CiROCCO Project aims to address this critical issue by integrating a network of cost-effective sensing nodes with advanced remote and in-situ data fusion techniques1. This initiative intends to cover under-sampled desert areas and those profoundly impacted by Desert Dust Storms (DDS). The project focuses on four pilot areas, namely Egypt, Cyprus, Serbia, and Spain. Within the context of the Cyprus pilot study, CiROCCO aims to examine the nexus between the urban environment in the Municipality of Idalion and the consequences of DDS on air quality2 and public health3. As part of the project, an Early Warning System (EWS) for Air Quality will be put in place, with weather prediction models playing a significant role in the development of the EWS. The evaluation of the WRF-Chem model forms an integral part of the preliminary phase for establishing the Cyprus pilot. This assessment utilizes PM10 near-ground concentration obtained from a background monitoring station in Cyprus2, spanning the period from 2021 to 2023, predating the installation of the CIROCCO sensing node network. Future reevaluation of the WRF-Chem model aims to quantify the improvement in prediction accuracy resulting from the assimilation of model forecasts with in-situ datasets and earth observations facilitated by the CIROCCO infrastructure. Regulatory authorities plan to adopt the EWS, providing public access via a dedicated website and mobile app. The project contributes insights applicable to similar regions in Cyprus and Eastern Europe.


This research work is part of the CiROCCO Project. CiROCCO Project is funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or REA. Neither the European Union nor the granting authority can be held responsible for them.


1Papathanasiou C., Mouzourides P., Vlachos M., Neophytou M.K-A, Tsiakos V., Tsimiklis G., Amditis A. (2023). Enhancing in-situ environmental observation to support desert dust storm events monitoring, 10th Int. Conference on Civil Protection & New Technologies, SafeGreece2023, 25-27 September, Athens, Greece

2Kinni, P., Kouis, P., Dimitriou, H., Yarza, S., Papatheodorou, S.I., Kampriani, E., Charalambous, M., Middleton, N., Novack, V., Galanakis, E. and Yiallouros, P.K., (2021). Health effects of desert dust storm events in the south-eastern Mediterranean: perceptions and practices of local stakeholders. Eastern Mediterranean Health Journal, 27(11), pp.1092-1101.

3Achilleos, S., Mouzourides, P., Kalivitis, N., Katra I., Kloog, I., Kouis, P., Middleton, N., Mihalopoulos, N., Neophytou, M., Panayiotou, A., Papatheodorou, S., Savvides, C., Tymvios, F., Vasiliadou, E., Yiallouros, P. and Koutrakis, P. (2020).  Spatio-temporal variability of desert dust storms in Eastern Mediterranean (Crete, Cyprus, Israel) between 2006 and 2017 using a uniform methodology, Science of the Total Environment, 714, 136693

How to cite: Mouzourides, P., Papathanasiou, C., Andreas, E., Nikolaidis, G., Vlachos, M., Tsiakos, V., Tsimiklis, G., Savvides, C., Vasiliadou, E., Amditis, A., and Neophytou, M. K.-A.: Integratıng earth observatıons for enhancıng human health and ınfrastructure resılıence durıng desert dust storms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19654, https://doi.org/10.5194/egusphere-egu24-19654, 2024.

Virtual presentation
Shivam Priyadarshi, Somnath Bera, and Pritha Ghosh

The Indian Himalayas are facing rapid construction of critical infrastructure (CI) which is significant for the functioning of mountainous society. At the same time, these infrastructures are disrupted frequently during monsoon season particularly due to the onset of debris flows. It causes soaring economic losses and cutting off essential services like food, water, and health during disasters. In the background, mapping detailed CI and assessing their risk to debris flows is essential. However, one of the challenges of estimating debris flow is that it not only damages infrastructure at its source area but far beyond wherever it travels as run-out. The literature shows the risk of CI under such a run-out scenario is limited. Therefore, the study proposes a spatial framework for assessing CI risk to debris flow hazard scenarios. The frame is constituted in two parts, the first one focuses on developing a debris flow hazard model by integrating source and their runout areas. The second part concentrates on a systematic mapping of exposed CI and its risk-hotspot zonation. In the debris flow hazard modelling, an inventory database of sources and runouts are generated covering the year 2005 to 2022. The conditioning factors cover topographic, hydrological, geological, and environmental variables that are generated from multiple data sources such as DEM (ALOS PALSAR), Google Earth images, and high-resolution satellite images (Planet Scope). Next, the susceptibility of the debris flow source area is estimated by using Stacking Random Forest Model. Further, the runout area is simulated using the Flow R model in which susceptible debris flow source areas are considered as input. We generate two debris flow scenarios; one is considered normal rainfall-induced debris flows and another is a worst-case scenario that is developed considering extreme rainfall-induced debris flows. The models are validated using a confusion matrix and further, applied to CI risk analysis. In the second part of the paper, the detail of twelve category of CI is identified and mapped using GIS. These CI is treated as hard assets such as transportation, electricity, water lines, telecommunication, hospitals, schools, waste management, dam, recreation areas, hotels, helipads, and evacuation shelters. The spatial data of critical infrastructure are collected from multiple sources data such as Open Street Map, My Maps, Google Earth Images, Toposheet and various published reports. Then, the density of each CI at each village is generated and it is overlayed with the debris flow hazard scenarios for estimating risk. Finally, the hotspot of CI risk is analyzed using Global Moran's I method. The modelling framework is applied in the Sikkim Himalayas which is the one of sensitive debris flow regions of the world. We find a positive linearity with remoteness and debris flow hazard. However, a non-linearity exists with remoteness and CI risk. The findings and output map of the study can be used for financing and policy-making towards disaster-resilient infrastructure development.

Keywords: Critical infrastructure; Debris flow; Runout; Geo AI; Stacking Random Forest; Global Moran's I

How to cite: Priyadarshi, S., Bera, S., and Ghosh, P.: Hotspot analysis of critical infrastructure risk to debris flow hazard scenarios using Geo AI approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-882, https://doi.org/10.5194/egusphere-egu24-882, 2024.

Posters on site: Fri, 19 Apr, 10:45–12:30 | Hall X4

Display time: Fri, 19 Apr 08:30–Fri, 19 Apr 12:30
Chairpersons: Guy J.-P. Schumann, Katerina Orfanogiannaki, Margaret Glasscoe
Poster Part 1 - Earthquake and cascading hazards assessment
Elisa Varini, Amel Benali, Abdollah Jalilian, Sara Idrissou, and Antonella Peresan

This work has a twofold objective: to analyze for the first time the background seismicity of Northern Algeria and its vicinity and to apply a variety of statistical methods to identify its spatiotemporal features (Benali et al., Axioms, 2023).

The earthquake catalog of Northern Algeria and its vicinity includes events from 1950 to 2021 within the region between latitudes 32° and 38° and longitudes -2° and 10°. Based on the investigation of the frequency-magnitude for the overall catalog, the magnitude threshold for completeness is set at 3.7, so that the analyzed dataset includes 1561 earthquakes. The considered area comprises the largest and the most damaging earthquakes ever recorded in the Mediterranean region: the M7.3 earthquake at El Asnam in 1980, the M6.9 earthquake at Boumerdes in 2003, and the M6.7 earthquake at El Asnam in 1954.

The dataset is declustered by applying different methods, namely: the Gardner and Knopoff, Gruenthal, Uhrhammer, Reasenberg, Nearest Neighbour, and Stochastic Declustering methods. Each method identifies a different declustered catalog, that is a different subset of the earthquake catalog that represents the background seismicity, which is usually expected to be a realisation of a homogeneous Poisson process over time. A variety of statistical methods is applied to assess whether the background seismicity identified by each declustering method has the spatiotemporal properties typical of such a Poisson process. The main statistical tools of the analysis are the coefficient of variation, the Allan factor, the Markov-modulated Poisson process (also named switched Poisson process with multiple states), the Morisita index, and the L-function.

Summing up our findings, all declustering methods reduce the time of correlation structures in the background seismicity at small timescales, but temporal correlation still remains at intermediate and higher timescale ranges. In particular, Gruenthal and Stochastic Declustering methods turn out the most effective in removing the time clustering structures from this catalog. The spatial clustering structure is also significantly reduced, but it is not eliminated from the declustered catalogs, due to the natural clustering of seismicity along active fault systems.

How to cite: Varini, E., Benali, A., Jalilian, A., Idrissou, S., and Peresan, A.: Spatiotemporal analysis of the background seismicity in Northern Algeria, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5603, https://doi.org/10.5194/egusphere-egu24-5603, 2024.

Anastasiia Agaian, Anastasia Nekrasova, and Shamil Bogoutdinov

The Topological Filtering Algorithm, Discrete Perfect Set (DPS) by Agayan et al. (2018), was developed within the framework of discrete mathematical analysis, which involves fuzzy models of discrete analogs of fundamental concepts of classical mathematical analysis. It is designed to select clusters of discrete observations according to a given criterion (classification of discrete observations belonging to one of the clusters) (Gordon, 1981). In this study, we applied the iterative S-DPS modification of the DPS algorithm (Agayan et al., 2022) for the sequential extraction of linear structures from an initial array of point objects. Specifically, we considered the catalogue data from the Baikal Division of the Geophysical Survey, Federal Research Center of the Russian Academy of Sciences, as the initial set of point objects. In each iteration, the densifications identified by S-DPS can be interpreted as discontinuous disturbances of various ranks. With each iteration, weaker yet significant concentrations are discerned. The identification of these discontinuous disturbances as linear structures, and their interpretation were conducted using an expert, non-automated approach.

Derived from expert analysis of a few sequential iterations of the S-DPS algorithm, this facilitated the identification of potential seismogenic structures for two selected territories in the Lake Baikal region. For both territories, the obtained structures were compared with the mapped active faults in the Lake Baikal region (Active Faults of Eurasia Database, Zelenin et al., 2022).

The iterative application of the S-DPS algorithm, combined with expert linear structures analysis, provides a nuanced approach to understanding the complexities of seismogenic features and their potential seismic implications. This methodology offers significant potential for analysing regional seismicity, aiming to discover new or adjust already mapped seismogenic structures.


Agayan SM, Bogoutdinov SR, Dzeboev, BA, Dzeranov BV, Kamaev DA, Osipov MO DPS Clustering: New Results. Appl. Sci. 2022, 12, 9335. https://doi.org/10.3390/app12189335

Zelenin E, Bachmanov D, Garipova S, Trifonov V, and Kozhurin A: The Active Faults of Eurasia Database (AFEAD): the ontology and design behind the continental-scale dataset, Earth Syst. Sci. Data, 2022, 14, 4489–4503, https://doi.org/10.5194/essd-14-4489-2022.

How to cite: Agaian, A., Nekrasova, A., and Bogoutdinov, S.: Application of topological filtering (DPS) algorithm for identifying linear seismogenic structures in the Lake Baikal region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4914, https://doi.org/10.5194/egusphere-egu24-4914, 2024.

Daya Shanker and Mohamed Rafih AP

Seismic hazard assessment involves quantitatively estimating ground shaking at site. There are two main approaches for estimating seismic hazard, Deterministic seismic hazard assessment (DSHA) and Probabilistic seismic hazard assessment (PSHA). In the present investigation DSHA is used, it relies on the worst-case scenario earthquake. The state of Kerala (9°N - 13°N and 75°E - 78°E) which lies in the seismic zone III, having a zone factor 0.16, has been considered to estimate seismic hazard in terms of PGA. Based on analyses of seismotectonics of the area, 26 seismogenic sources were identified and used for the PGA estimation. The peak horizontal accelerations (Ah) 0.2344g and peak vertical accelerations (Av) 0.1395g were computed for the city of Calicut. For other cities, Thiruvananthapuram, Kollam, Palakkad, Kochi, and Thrissur horizontal (Ah) and vertical accelerations (Av) were also estimated. However, the highest value was found to be at Calicut followed by Palakkad. In case of Palakkad, the values were influenced by cluster of faults located there while, at Calicut was caused by the Fault – 1 (source) which is in the Arabian Sea near the coast of the city. It has been observed that the seismic hazard assessment of Kerala advocate the PGA (Ah) falls in the range of 0.02g to 0.47g and PGA (Av) varies from 0.01g to 0.33g. These accelerations seem to be more realistic since they are based on consideration of many seismotectonic sources in the region that may rupture causing earthquakes. The ratio of Av/Ah was found to be in the range of 0.70 to 0.38. The prepared contour maps for the region show that larger peak ground accelerations are present in the region where there is a higher density of larger faults and vice versa. The findings highlight the need for further studies and enhanced preparedness measures in Kerala, aiming to mitigate potential seismic risks and ensure the safety of its residents in this seismically active region.

How to cite: Shanker, D. and Rafih AP, M.: Seismic Hazard in terms of Peak Ground Acceleration (PGA) for coast of Calicut, State of Kerala (INDIA), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-437, https://doi.org/10.5194/egusphere-egu24-437, 2024.

Katalin Gribovszki, Péter Mónus, Chuan-Chou Shen, Daniele Pinti, Bassam Ghaleb, Ernő Prácser, Marketa Lednická, Lili Czirok, Zoltan Jerg, Attila Novák, Tamás Bazsó, Gábor Brolly, and Sándor Szalai

To verify seismic hazard maps by independent observations that serves long-term information should be necessary. It requires studying vulnerable dripstones, since they survived all earthquakes that have occurred over thousands of years, depending on the age of them. Examination of an intact vulnerable stalagmite (IVSTM) in Ördöglik part of Domica cave (Slovakia) has been done. This IVSTM is suitable for estimating the upper limit of horizontal ground acceleration (HGA) generated by prehistoric earthquakes. This research is the continuation of our previous examination of IVSTMs in Baradla and Domica cave system, north-east Hungary.

               The density, the Young’s modulus and the tensile failure stress of the samples originating from broken speleothems have been measured in geo-mechanical laboratories, whereas the dimensions and natural frequency of the IVSTM was determined by different types of in situ observations. The value of HGA resulting in failure and the natural frequency of the IVSTM were assessed by theoretical calculations. The ages of the samples taken from a column next to the investigated IVSTM at different heights have been determined by MC-ICPMS analysis. The measured ages fall between about 7.6 and 2.4 kyr. The critical HGAvalues as a function of time going back into the past determined from the stalagmite that we investigated are presented. Our results show that all values of probabilistic seismic hazard maps, SHARE Map (Giardini et al. 2014) and PSHA Map (Tóth et al. 2006) at the location of Ördöglik part of Domica cave, are above the critical horizontal ground acceleration (CHGA) curve calculated by using the dimensions, geo-mechanical and elastic parameters of IVSTM, and the values of CHGA caves are lower than 0.05g since 2.7 kyr (0.05g was estimated by Szeidovitz et al. (2008) using another vulnerable stalagmite 4 km far from Ördöglik, in the Baradla cave.) All these means that the seismic hazard is overestimated at the territory of Ördöglik, Domica cave.

               This result have to be taken into account when calculating the seismic potential of faults near to Ördöglik part of Domica cave (e.g. Darnó and Rozsnyó lines).

How to cite: Gribovszki, K., Mónus, P., Shen, C.-C., Pinti, D., Ghaleb, B., Prácser, E., Lednická, M., Czirok, L., Jerg, Z., Novák, A., Bazsó, T., Brolly, G., and Szalai, S.: Constraints on long-term seismic hazard from an intact, vulnerable stalagmite for the surroundings of Ördöglik (Čertova diera) part of Domica cave, Slovakia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12516, https://doi.org/10.5194/egusphere-egu24-12516, 2024.

Antonella Peresan and Hany M. Hassan

Significant earthquake-induced tsunamis in Northern Adriatic are rare and only a few historical events were reported in the literature, with sources mostly located along with central and southern parts of the Adriatic coasts. Recently, a tsunami alert system has been established for the whole Mediterranean area; however, a detailed description of the potential impact of tsunami waves on coastal areas is still missing for several sites. This study aims at modelling the hazard associated with possible tsunamis, generated by offshore earthquakes, with the purpose of contributing to tsunami risk assessment for selected urban areas located along the Northeastern Adriatic coasts. Tsunami modelling is performed by the NAMI DANCE software, which allows accounting for seismic source properties, variable bathymetry, and non-linear effects in wave’s propagation.

Preliminary hazard scenarios at the shoreline are developed for the coastal areas of Northeastern Italy and at selected cities (namely Trieste, Monfalcone, Lignano and Grado). A wide set of potential earthquake-induced tsunamis, located in three distance ranges (namely at Adriatic-wide, regional and local scales), are considered for the modelling; sources are defined according to available literature, which includes catalogues of historical tsunami and existing active faults databases. Accordingly, a preliminary set of tsunami-related parameters and maps are obtained (e.g. arrival times, maximum wave amplitude, synthetic mareograms), relevant towards planning emergency and mitigation actions at the selected sites.

In addition, a fully formalized operational procedure for time-dependent seismic hazard scenarios has been developed during the last two decades, which integrates earthquake forecasting information from pattern recognition analysis (CN algorithm), with the realistic modeling of seismic waves propagation by the neo-deterministic approach (NDSHA). For offshore large earthquakes, this integrated approach can be naturally extended to the definition of time-dependent tsunami scenarios, based on physical models of tsunami waves propagation. We review the outcomes from its applicaton for the recent events that occurred in the Adriatic region (namely: Durres, Albania 2019; Pesaro, Italy 2022), which support the possibility of developing time-dependent tsunamis scenarios, by integrating earthquake forecasts with the physical modelling of tsunami waves propagation.

How to cite: Peresan, A. and Hassan, H. M.: Quantifying Tsunami Hazard for the Northeastern Adriatic Coasts: A Multi-Scenario Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3744, https://doi.org/10.5194/egusphere-egu24-3744, 2024.

Hui Luo, Wei Hu, and Li Zhou


Strong earthquakes with greater magnitudes and longer durations can trigger numerous landslides. Several mechanisms have been proposed to explain the triggering process, such as seismic waves providing additional driving force, which increases the shear stress on the sliding plane. Earthquakes may also increase pore pressure, which reduces the effective normal stress on the sliding plane. However, the mechanisms that apply to both wet and dry conditions are not fully understood. Herein, we purposed to study the seismic response and triggering mechanisms through vibration experiments on granular materials.


We used a ring shear apparatus to study the mechanical behavior of granular materials under cyclic loading. 0.2-0.4 mm glass spheres (SiO2) were placed in a ring shear box, and constant normal stress and constant shear stress were applied. Then, sinusoidal cyclic shear stress with different numbers of cycles were applied respectively. The frequency of cyclic loading was 1 Hz. We used dynamic triaxial-bender tests to monitor the evolution of the shear modulus of samples under cyclic loading. The confining pressure was 300 kPa and deviatoric stress was 380 kPa, and sinusoidal cyclic dynamic loads with amplitudes of 45 kPa, a frequency of 1 Hz and a cycle number of 200 were applied. All of the experiments were under dry conditions (room humidity).


The dynamic ring shear experiments show that the co-vibration and post-vibration shear displacements increased with an increase in the number of cycles, and the instability of granular materials can be triggered by a larger number of cycles while the shear stress returned to the initial value after the vibration ended. The stepwise increase curves of co-vibration shear displacement show platform segments and upward segments, the platform segments corresponded to trough segments of cyclic shear stress, and the upward segments to crest segments of cyclic shear stress. The shear displacement value of each upward segment increased with the increase in the number of vibration cycles (Fig. 1). The dynamic triaxial-bender test result shows that the specimen shear modulus decreased with the increase of the number of vibration cycles, while the density decreased slightly and the axial strain increased, roughly follow a logarithmic law during vibration (Fig. 2).


Our dynamic ring shear experimental observations suggested that it is easier to trigger landslides with longer durations of vibration, and the triggering is closely related to the weakening of shear resistance. The results suggest that the reduction of shear modulus is an important mechanism for triggering failure of earthquake-triggered landslides. We infer that the earthquake-induced decrease in shear modulus was caused by structure changes, particle rearrangement, and slipping at the scale of micro-asperities.

How to cite: Luo, H., Hu, W., and Zhou, L.: Unraveling the Dynamic Weakening Mechanism of Earthquake-Triggered Landslides through Vibration-Induced Frictional Sliding Instability Experiments on Granular Materials, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2516, https://doi.org/10.5194/egusphere-egu24-2516, 2024.

Poster Part 2 - New data and satellite observations for monitoring and assessment of risks
Laura Giaccio, Roberta Ravanelli, Valeria Belloni, and Mattia Crespi

The potential role of satellite geodesy techniques for seismic hazard assessment, with particular focus on GNSS and InSAR, has been widely investigated in the last decades. These technologies can detect differences in ground velocity of less than one millimeter per year, and could therefore be suitable to highlight the accumulation of tectonic strain. While conventional strain field estimation is performed from a two-dimensional planimetric point of view, a novel approach was introduced by incorporating the independent a-priori tectonic knowledge of the study area to pre-select the directions along which strain accumulation signs should be searched [1, 2]. This method was applied to the earthquakes of Amatrice (2016) and Emilia (2012), analyzing the ground velocity estimated from GNSS station data along two transects of interest. Despite the promising results obtained, the spatial density of GNSS stations was too low to provide a detailed description of the velocity profile along the transects. In this sense, the combination  of GNSS and InSAR techniques could greatly improve these analyses. The recent European Ground Motion Service (EGMS) [3] constitutes an ideal dataset to pursue this objective. In the present work, we evaluated the suitability of EGMS data for seismic hazard assessment. To achieve this, we defined transects covering known high seismic hazard regions of Italy, following the scheme outlined in [2], but greatly improving both the spatial resolution along the transects and their inter-distance, leveraging the high spatial density of InSAR measurement points. We evaluated the velocity profile along the transects using the data provided by the EGMS service, and compared the results obtained with velocities measured from GNSS station data, both projecting GNSS data along the satellite line of sight and retrieving displacements eastward and upward considering SAR acquisitions from  ascending and descending orbits. Through this comparison we assessed whether the accuracy, the revisiting time and the covered temporal window of EGMS data are sufficient to ensure a correct velocity estimation, and took a first step in the direction of a future integration. Preliminary results obtained in the Irpinia region (Italy) suggested a good performance of EGMS data for the detailed description of the velocity profile and an excellent agreement with GNSS station data.



[1] Panza, G. F., Peresan, A., Sansò, F., Crespi, M., Mazzoni, A., & Nascetti, A. (2018). How geodesy can contribute to the understanding and prediction of earthquakes. Rendiconti Lincei. Scienze Fisiche e Naturali29, 81-93.

[2] Crespi, M., Kossobokov, V., Panza, G. F., & Peresan, A. (2020). Space-time precursory features within ground velocities and seismicity in North-Central Italy. Pure and Applied Geophysics177(1), 369-386.

[3] European Ground Motion Service. https://egms.land.copernicus.eu

How to cite: Giaccio, L., Ravanelli, R., Belloni, V., and Crespi, M.: Monitoring seismic hazard with satellite geodesy in Italy: first steps for the integration of GNSS and European Ground Motion Service data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11144, https://doi.org/10.5194/egusphere-egu24-11144, 2024.

Shoujia Ren, Yaozhong Pan, Chuanwu Zhao, Yuan Gao, Gelilan Ma, Hanyi Wu, Yu Zhu, and Zhengyang Zhang

Earthquake is one of the most divesting natural events that threaten human life during history. After the earthquake, having information about the damaged or anomaly artificial surface area can be a great help in the relief and reconstruction for disaster managers. It is very important that these measures should be taken immediately after the earthquake because any negligence could be more criminal losses. In this study, we developed a method for near real-time, general and robustly identify anomaly artificial surfaces using 3DTF and ASAI. This method was designed to identify the impervious surface areas using single-temporal imagery without pre-disaster data. The features of the contrast, Gabor, and Con-Gabor features were used to construct 3DTF, which distinguish forest, bare land, shadows with artificial surface. And then it was then used with the K-means classifier to map the artificial surfaces area. Based on the different textures of normal and anomaly artificial surfaces, we constructed the ASAI using entropy and homogeneity, and used the index to detect anomalies in mapped artificial surface areas. The performance of the detecting anomalies method was developed at three different sites in Turkey Earthquake and the mapped results of artificial surface showed that the overall accuracies at sites A-C, and C were > 93%. Using The mapped artificial surface area and ASAI identified anomaly artificial surface. The results showed the overall accuracies were 93.76%, 91.4% and 90.07%. Given the promising and accurate outcomes of this study, further developments remain warranted to determine the robustness of the anomaly artificial surface detecting method in areas with complex artificial surface distribution.

How to cite: Ren, S., Pan, Y., Zhao, C., Gao, Y., Ma, G., Wu, H., Zhu, Y., and Zhang, Z.: A novel artificial surface anomaly index (ASAI) based on post-disaster texture features using single-temporal and high-resolution imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1228, https://doi.org/10.5194/egusphere-egu24-1228, 2024.

Chiara Scaini, Alberto Tamaro, and Antonella Peresan

The Central Asia region encompasses a wide variety of climatic areas and geological settings. It is therefore prone to multiple hazards which can affect different parts of the region, including trans-boundary areas. Earthquakes, in particular, are a known threat for the region, and caused substantial damages and financial losses in the past. Here, we develop the first high-resolution regionally-consistent exposure database for Central Asia, encompassing multiple asset types including residential and non-residential buildings and transportation. The dataset was assembled using both global and regional-scale data and country-based information provided by local experts and authorities (e.g. building census, national statistics). This information includes also reconstruction costs, which are usually difficult to retrieve at country level, and images for different building typologies, which could serve as input for the development of image-based classification systems. Country-based data was collected interacting with local experts, and was supported by 5 workshops, one for each country, which fostered data sharing and knowledge transfer. Finally, the residential buildings exposure layer was used as a basis to estimate the projected exposure in 2080 under different Shared Socioeconomic Pathways. The lessons learned while developing the exposure database for Central Asia will be discussed in order to provide insights for developing similar datasets in other areas.

How to cite: Scaini, C., Tamaro, A., and Peresan, A.: Lessons learned developing a regionally-consistent exposure database for Central Asia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1822, https://doi.org/10.5194/egusphere-egu24-1822, 2024.

Mohamad Rashidi Md.Razli, Mohamad Faruq Syahmi Md Aripin, Muhammad Afiq Ariff Mohd Hellmy, Muhammad Faris Qusyairi Hamat, Zakaria Mohamad, Abd Rasid Jaapar, Azizan Ali, and Mohamed Syahrizal Zakaria

Gunung Jerai, or Mount Jerai, is in northern Malaysia, a tropical forest where the intrusion of the granitic batholith and metasedimentary rocks controls the topographical condition. Due to the geological aesthetic and the socio-economic history, Gunung Jerai was gazetted as a National Geopark named Jerai Geopark. The geological disaster at Gunung Jerai on 18th August 2021 significantly impacted approximately 1000 families in the region, driven by a maximum cumulative rainfall of around 200mm per 3 hours. This study aims to determine the geological and geomorphological characteristics of the debris flow event on 18th August 2021, which affects mainly three catchments: Seri Perigi Catchment, Batu Hampar Catchment, and Titi Hayun Catchment by using geospatial technique. Three main remote sensing techniques were utilised to achieve the aim of the study, namely IfSAR, LiDAR, and photogrammetry. Aerial photogrammetry was conducted using fixed-wing UAVs with mounted camera sensors covering the catchment areas to visualise the current terrain condition after the debris flow event, which is utilised for the individual landslide inventory, debris flow path, formation of the natural temporary dam, and deposition of the debris flow materials. LiDAR was also employed separately using multi-rotor UAV to conduct detailed terrestrial mapping of selected major landslides to determine landslide classification such as landslide type, dimensions, activity, distribution, and causal factors. At the regional scale, DEM from IfSAR and field geological mapping is utilised for demarcation and delineation of geomorphological features and to generate morphometric data, including slope curvature, slope gradient, slope aspect, flow direction, and flow accumulation, which is derived from the DEM. This information is crucial for disaster management and mitigation efforts, aiding in better preparedness and response. These abstract aims to explain how each geospatial data is utilised and optimised to characterise the debris flow disaster event.


How to cite: Md.Razli, M. R., Md Aripin, M. F. S., Mohd Hellmy, M. A. A., Hamat, M. F. Q., Mohamad, Z., Jaapar, A. R., Ali, A., and Zakaria, M. S.: Utilizing Various Geospatial Data for Debris Flow Geological Hazard Characterization and Mapping at Jerai Geopark, Malaysia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7234, https://doi.org/10.5194/egusphere-egu24-7234, 2024.

Yung-Wei Hsu, Jun-Yang Xu, and Kuo-Jen Chang

            Since the Chi-Chi earthquake in 1999 and the subsequent Typhoon Morakot in 2009, mass movements such as landslides have become a prominent focus of study. Particularly noteworthy are significant disasters like the Shiaolin Village debris flow and the Yushuishi debris flow in the Southern Cross-Island Highway, which have had a substantial impact on the environment and people's livelihoods. Consequently, the issue of sediment-related disasters has continued to gain attention and expand in recent years. However, estimating the volume of colluvium debris on the slope within the watershed, as well as understanding the transport and deposition of materials in the river channels, poses a challenging issue. This challenge arises from the complexity of geological factors and causes, the prolonged duration of these processes, and the difficulty of implementing engineering solutions. Therefore, effective estimation of the volume, transport, and deposition of sediment, especially in high-risk areas, along with the assessment of potential disaster risks, can be achieved with minimal human resources. This approach can provide effective early warning and reduce the impact of disasters, preventing them before they occur.

           The vigorous development of geospatial information technology has not only yielded positive results in land monitoring but has also gradually extended to other application fields. Hazard monitoring is one of its crucial applications. Geospatial information can be obtained through surveying and mapping technology, and through multi-temporal geospatial data, the production, migration, and accumulation of debris deposits can be quantitatively evaluated in a reasonable time and space within the catchment scale.

         For these purposes, this study focuses on the Laonongshi catchment, which has experienced past disasters and still retains a substantial amount of residual colluvium on its slopes. This study is dedicated in multi-temporal aerial photogrammetry and dataset generation. By combining surface geological investigations with various existing remote sensing images.Detailed DTMs and Orthomosaic images were established, since pre-Typhoon Morakot in 2009, and post events, including 2009, 2013, 2015, and 2018 with 2 meter resolution. The result reveals significant changes in the river channel and numerous reactivation of landslide debris accumulation.

How to cite: Hsu, Y.-W., Xu, J.-Y., and Chang, K.-J.: Assessment of landslide, Sediment Production and Disaster Prevention in in mountainous watersheds in Taiwan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3915, https://doi.org/10.5194/egusphere-egu24-3915, 2024.

Tobias Weiß and Mathias Bochow

Marine debris is a severe environmental problem. It originates from many sources and causes a wide spectrum of environmental, economic, safety, health and cultural impacts. Millions of tonnes enter the oceans every year and tackling the issue is gaining momentum at all levels.

Our project presents an approach to detect and track floating marine debris such as lost fishing nets, debris aggregations or patches based on high-resolution remote sensing time series, machine learning, and ocean current modeling. The goal is to obtain more reliable data regarding quantity, position, material properties and sources of litter as well as to address the lack of understanding of floating debris behavior in the open sea due to the limited monitoring capabilities (Garaba and Dierssen, 2018). In order to achieve this goal, a convolutional neural network was trained with a hand-labeled dataset of objects floating on the sea surface extracted from Planet satellite imagery, to learn the spatial characteristics of these objects. A software pipeline has been developed to automatically retrieve, process and analyze large amounts of satellite images and enable continuous monitoring. As identifying floating objects in the marine environment from space remains a challenging and difficult task and ground truthing is near to impossible, we further apply a mechanism to find matching objects on time series of satellite images using an ocean current model as well as different image processing techniques.

How to cite: Weiß, T. and Bochow, M.: TRACE: An approach to detect, track and monitor large floating marine litter in our seas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12503, https://doi.org/10.5194/egusphere-egu24-12503, 2024.

Deep Learning-Based Monitoring of Artisanal Mining to Tackle Environmental Degradation: A SERVIR West Africa Case Study in Ghana
(withdrawn after no-show)
Kidia Gelaye, Emmanuel Asare, Mary Amponsah, Paul Bartel, Pierre CS Traore, Jacob Abramowitz, Emil Cherrington, and Foster Mensah

Posters virtual: Fri, 19 Apr, 14:00–15:45 | vHall X4

Display time: Fri, 19 Apr 08:30–Fri, 19 Apr 18:00
Chairpersons: Bandana Kar, Elisa Varini
Tural Babayev and Gulam Babayev

This research aims to distribute b-value estimation spatially on the Mingachevir water reservoir area, a seismically active zone situated in Azerbaijani part of Kur depression. There are several researches proposing a relation between seismicity and b-value (Babayev G. et. al., 2020, Babayev T. et. al., 2023b, Telesca et. al., 2020). It is shown that, b-value is lower in the high-intensity zones, and higher in the low-intensity zones (Babayev T. et. al., 2023b). b-Value is an important parameter of a linear relation that is called Gutenberg-Richter law. Gutenberg-Richter law represents the earthquakes distribution with respect to the magnitude (Gutenberg and Richter, 1942). The relation below (1) presents Gutenberg-Richter law:

(1) log N = a - bM or N = 10a - bM,

where N is the number of earthquakes greater and equal than M, the magnitude of the earthquakes, a and b are the real constants.

 b-Value is the slope of this linear relation, in terms of mathematics. It is a parameter reflecting the relative size distribution of earthquakes (Babayev T. et. al., 2023a). Several ways were improved to estimate b-value (El-Isa, Eaton, 2014). We applied linear least-square fitting method to logarithmically binned data since it is preferred due to its instructivity (El-Isa, Eaton, 2014; Milojevic, 2010). In order to obtain spatial distribution of b-value the microzonation method of Babayev G. et. al., 2020 was applied. By use of this method, the area was divided to the grids of 10 km * 10 km. The seismic events in the Mingachevir catalog were clustered to each grid and b-value was estimated for each grid with the events. The microzonation of the study area was done on ArcGIS 10.7 (ESRI, 2018) software and the mapping of b-value spatial variation was done on SURFER software. According to the result of the research, we observe that mainly, in the study area the b-value has a low quantity, which is proposed to be related with higher seismic activity (Babayev G. et. al., 2020 and Babayev T. et. al., 2023b). Still, it varies through the study area and in the northern part of Mingachevir water reservoir, that is close to Southern Greater Caucasus and in the south-eastern part of the reservoir, that is close to Yevlakh region b-value is around 1. b-variations are impacted by tectonic stress (El-Isa, 2013). Wiemer and Wyss, 1997; Wiemer et. al., 1998; Zuniga and Wyss, 2001; Gerstenberger et. al., 2001; Wiemer and Wyss, 2002; Schorlemmer, 2004; Schorlemmer et. al., 2005; Wyss et. al., 2004; Wyss and Matsumura, 2006; Wyss and Stefansson, 2006 propose b-decrease with the increase of the tectonic stress. The studied area is a depression zone under compression between Lesser and Greater Caucasus mountains. The Kur depression forms a reverse fault or subduction zone by moving under the Greater Caucasus at a rate of 8 mm/year with the stress applied by Lesser Caucasus (Aktug et. al., 2009, 2013; Reilinger et. al., 2006). Therefore, lower b-values and high number of seismic events allow us to consider the relation among b-value, seismicity and tectonic factors.

How to cite: Babayev, T. and Babayev, G.: Spatial distribution of b-value on the Mingachevir water reservoir area for seismicity analysis applying the microzonation method of Babayev G. et. al., 2020., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1040, https://doi.org/10.5194/egusphere-egu24-1040, 2024.

Margaret Glasscoe, Bandana Kar, Guy Schumann, Marina Mendoza, Doug Bausch, Jun Wang, and Greg Hampe

Flooding is one of the most frequent and costliest extreme weather events. The Model of Models (MoM) generates integrated products using ensembled hydrologic models and flood outputs derived from Earth observations. MoM provides global flood early warning and near-real time flood severity estimation. MoM results are shared via the Pacific Disaster Center’s (PDC) DisasterAWARE® multi-hazard alerting platform to the global community. Currently, DisasterAWARE incorporates Model of Models (MoM) outputs as flood “incidents,” visually depicting potential floods in the context of population and infrastructure that may become affected. Automated procedures categorize MoM outputs as DisasterAWARE “hazards,” allowing for their dissemination to users along with other flood products that assess potential impacts.

How to cite: Glasscoe, M., Kar, B., Schumann, G., Mendoza, M., Bausch, D., Wang, J., and Hampe, G.: Global Flood Alerting with an Ensemble of Models and Remotely Sensed Observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20837, https://doi.org/10.5194/egusphere-egu24-20837, 2024.

Binita Kc, Dave Meyer, Mahabal Hegde, Jennifer Wei, and Mohammad Khayat

NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC), a Distributed Active Archive Center (DAAC), is dedicated to archiving remotely-sensed and model observations from multiple disciplines. These datasets, stemming from NASA's Earth-observing satellites, field measurement programs, and collaborations with international partners such as the European Space Agency (ESA) ― including datasets from the TROPospheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor satellite and Radio Occultation data from Sentinel-6, are all publicly accessible. Operating as a multidisciplinary DAAC, GES DISC equips users with the ability to integrate datasets across multiple disciplines while ensuring data quality and provenance.

GES DISC offers comprehensive services, enabling users to map and monitor extreme weather and climate hazards, including tropical cyclones and floods, through high spatial and temporal resolution datasets in near real-time. Giovanni, a visualization and analytical tool designed for users with limited expertise, has been widely utilized in risk and post-disaster assessments, and natural hazards research. Additionally, API services such as Data Rods, offering a long-term time series of climate datasets, aid in monitoring the vulnerability of critical infrastructures to hydro-meteorological conditions.

In response to the increasing data volumes in the Earth System Observatory (ESO) era, GES DISC is currently migrating data and services to the Earthdata Cloud. This cloud migration not only advances hazard and disaster studies, but also empowers users to fully leverage the benefits of the cloud, such as improved accessibility, cost-efficiency, scalability, and collaboration.

How to cite: Kc, B., Meyer, D., Hegde, M., Wei, J., and Khayat, M.: Empowering Hazard and Disaster Informatics: Data Services at NASA GES DISC in the Earth System Observatory Era, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13355, https://doi.org/10.5194/egusphere-egu24-13355, 2024.