NH10.6 | Tools and challenges in assessing compounding and multi-hazard risk in the evolving technological landscape
Tools and challenges in assessing compounding and multi-hazard risk in the evolving technological landscape
Convener: Funda Atun | Co-conveners: Silvia Torresan, Saman GhaffarianECSECS, Cees van Westen, Michele Calvello, Marleen de RuiterECSECS, Ivan Van BeverECSECS
Orals
| Fri, 19 Apr, 10:45–12:30 (CEST)
 
Room 0.15
Posters on site
| Attendance Fri, 19 Apr, 16:15–18:00 (CEST) | Display Fri, 19 Apr, 14:00–18:00
 
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, 10:45
Fri, 16:15
Fri, 14:00
The use of technology can affect the impact of multi-hazards both positively and negatively. This session addresses how technology could play a role in assessing multi-hazard risk and analysing risk changes across space and time, and how innovative tools can support the development of risk mitigation strategies. We will discuss the needs and ways to develop tools that enable systemic risk assessment across sectors and geographical settings. A range of tools are already used to assess individual hazards or possible climate adaptation scenarios, but a tool that enables a combined assessment of them all does not yet exist.
We consider three main standpoints that could enhance the tools in the constantly evolving technological landscape. First, climate scenarios need to be combined with land use and socio-demographic and economic trends that will impact exposure and vulnerability. Second, the timeframe of decision support tools should move from short to long-term by including long-term dynamic scenarios. Third, co-development needs to be considered as a new way to overcome uncertainties to involve various perspectives.
Keeping in mind these three standpoints, we would like to invite contributions that present:
• Tools that support the preparedness of first and second responders in the face of multi-hazard events and reduce the risks related to impacts on various sectors;
• Open-source software for multi-hazard/risk scenario generation, policy recommendations to enable decision-makers and practitioners to adopt a new approach;
• Applications of cutting-edge artificial intelligence (AI) and Machine Learning (ML) tools in the context of climate change, multi-hazard and multi-sector risk and resilience analytics.
• Decision-support systems (DSS) for disaster risk management considering multiple interacting natural hazards and cascading impacts that accounts for forecasted modifications in the hazard (e.g., climate change), vulnerability/resilience (e.g., aging structures and populations) and exposure (e.g., population decrease/increase);
• Multi-disciplinary best practices that focus on the transferability of the developed innovations to different territorial contexts and hazards;
• Novel risk assessment methods that are co-developed by various stakeholders for multi-hazard, multi-sector, and systemic risk management;
• Innovative tools to communicate risks to facilitate deeper learning in complex contexts and enable participants to learn, i.e. serious games.

Orals: Fri, 19 Apr | Room 0.15

The oral presentations are given in a hybrid format supported by a Zoom meeting featuring on-site and virtual presentations. The button to access the Zoom meeting appears just before the time block starts.
Chairpersons: Funda Atun, Silvia Torresan, Cees van Westen
10:45–10:50
10:50–11:00
|
EGU24-2689
|
NH10.6
|
ECS
|
On-site presentation
Mohammed Sarfaraz Gani Adnan, Christopher White, Eleonora Perugini, John Douglas, Enrico Tubaldi, Talfan Barnie, Esther Jensen, Matthew Roberts, Natalia Castillo, Marco Gaetani, Marcello Arosio, Frederiek Weiland, and Mario Martinelli

Multi-hazard events pose significant threats to human lives and assets, often exceeding the risks associated with single hazards due to simultaneous, cascading, or cumulative occurrences of multiple interacting natural hazards. The society and environment in various European regions, susceptible to many climatic extremes, are anticipated to be profoundly impacted in the coming decades due to the increasing frequency and severity of multi-hazard events linked to changing climatic conditions. While most natural hazard studies have predominantly focused on single hazards or multi-layer single hazards, the quantitative assessment of multi-hazard interactions remains in its early stage of development. Investigating diverse types of multi-hazard events is particularly challenging due to complex interactions between hazard drivers and the spatial and temporal heterogeneity of multiple hazard occurrences. This study aims to introduce an approach for investigating four distinct types of multi-hazard interactions: preconditioned and triggering, multivariate, temporally compounding, and spatially compounding events, under present-day and future climate change scenarios. The research is conducted as part of a Horizon Europe project MEDiate (Multi-hazard and risk informed system for Enhanced local and regional Disaster risk management), which seeks to "develop a decision-support system (DSS) for disaster risk management by considering multiple interacting natural hazards and cascading impacts." The framework is implemented on four interactive multi-hazard pairs—compounding coastal and riverine flooding, extreme heat and drought, extreme wind and precipitation, and extreme precipitation and landslides—in four European testbeds: Oslo (Norway), Nice (France), Essex (UK), and Múlaþing (Iceland), respectively. The proposed multi-hazard interaction framework aims to estimate the probability of occurrence of multiple hazards over various time and space scales, associated with the four types of multi-hazard events. The framework involves two key steps. First, it identifies extreme events for individual hazard indicators (e.g., peak river flow, surge, near-surface wind speed, precipitation, air temperature) at different time intervals (e.g., daily, monthly, quarterly) and locations within the testbed regions. Second, a nonparametric bivariate copula-based approach is employed to estimate joint return periods for various combinations of hazard indicators associated with different types of multi-hazard events. The analysis is conducted for both present-day conditions and the 2050 RCP 8.5 climate change scenario, by using several freely available regional and global observation and modelled datasets related to different indicators of multi-hazard events. The findings of this study illustrate the degree of statistical dependence between various combinations of interactive hazards in space and time, quantifying joint probabilities of multi-hazard events. Furthermore, it demonstrates how these probabilities are likely to change in the future due to the impacts of climate change. This research emphasises the importance of considering diverse scenarios of multi-hazard events in formulating future climate change adaptation responses. The findings of this study will inform the DSS being created in the MEDiate project by developing accurate multi-hazard scenarios to estimate the potential effects of different disaster risk mitigation and adaptation strategies. The results could also contribute valuable insights for developing multi-hazard risk management policies elsewhere globally, where susceptibility to multi-hazard events is increasing.

How to cite: Adnan, M. S. G., White, C., Perugini, E., Douglas, J., Tubaldi, E., Barnie, T., Jensen, E., Roberts, M., Castillo, N., Gaetani, M., Arosio, M., Weiland, F., and Martinelli, M.: A framework for investigating multi-hazard interactions to develop a decision-support system for disaster risk management , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2689, https://doi.org/10.5194/egusphere-egu24-2689, 2024.

11:00–11:10
|
EGU24-11170
|
NH10.6
|
ECS
|
On-site presentation
Till Wenzel, Philipp Marr, and Thomas Glade

Road infrastructure in mountain areas is essential for connecting local communities and for cross-regional mobility, such as fright transit and vacation traffic. The lack of redundancy and the high frequency of traffic indicate the critical importance of these roads. However, compounding and cascading geomorphic processes can have effects leading to the blockage or destruction of infrastructure. These geomorphic processes are amplified by anthropogenic activities such as slope undercutting, deforestation, and indirectly by ongoing climate change. It is therefore important to model the processes associated with these hazards that can cascade to cross-regional impacts. Here, we explore the possibility of using publicly available data, including topographic information and historical hazard data, as well as practitioner input, to produce appropriate assessments that delineate areas prone to geomorphic hazards along the Brenner Corridor connecting southern and northern Europe which is the most important infrastructure connection between northern and southern Europe.

To analyse the impact of geomorphic cascades, a comprehensive literature review of past hazardous events in the study area, and susceptibility maps will be prepared. Multi-hazard risk approaches to critical road infrastructure will be reviewed and, where applicable, evaluated for dynamic geomorphic hazard modelling. A conceptual framework combining both practitioner’s knowledge and data analysis with susceptibility assessments will be developed into impact chain models to combine qualitative expert input and quantitative data.

Preliminary results show that certain hazards that were not anticipated a few years ago may be changing in their processes, e.g. from avalanche hazard to landslide hazard, due to changing temporal precipitation patterns. One such example is a debris flow that blocked parts of the Brenner highway near the border between Italy and Austria, at the bottleneck of the corridor with highway, country road, and railroad, in a valley section only 70 m wide. The question remains whether such events are more likely to occur in the future in areas that have not yet been studied for these hazards. Further work will include whether, for example, land-use or climate-related changes can be incorporated into scenario impact modelling.

How to cite: Wenzel, T., Marr, P., and Glade, T.: Geomorphic Hazards and the Imperative of Multi-Hazard Assessment for Road Infrastructure in Mountain Areas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11170, https://doi.org/10.5194/egusphere-egu24-11170, 2024.

11:10–11:20
|
EGU24-22201
|
NH10.6
|
On-site presentation
Luke Bateson, Roxana Ciurean, Annie Winson, Kay Smith, and Erin Mills

Between January and June 2022, the UK experienced the driest weather in over 40 years. This culminated in July, when temperatures exceeded 40 degrees Celsius for the first time since records began. Unprecedented hot, dry conditions resulted in hazards and multi-hazard interactions that have not previously been experienced in the UK. This expression of high temperature induced multi-hazards along with more commonly seen hot weather induced hazards with longer residence times may lead to increased direct and indirect impacts on society and ecosystems as experienced in other parts of the world.

The accurate, timely, and efficient derivation of information and data products from EO data and technologies is instrumental in predicting, monitoring, assessing, and evaluating the occurrence of single natural hazard events and their potential impacts. What is not so well understood is the role of EO-derived environmental indicators in characterizing complex causal relationships and underlying mechanisms leading to cascading or compounding multi-hazard impacts. This may be demonstrated using time series analysis of a single indicator or derived from several time series of two or more indicators of interrelated hazard events such as droughts, heatwaves, subsidence, wildfires, flooding, and landslides.

In this study, we aim to advance the state-of-the-art by using long-term EO satellite data to identify thresholds, trends, and tipping points within time series of established environmental metrics which indicate the dynamic evolution of a multi-hazard event. This information will be complemented by in-situ observations and local, regional, and global models to identify environmental precursors and chains of effects that may be suggestive of multi-hazard event onset conditions. By utilizing several vulnerability and impact assessment models, such as impact chains, we will demonstrate the utility of EO techniques and datasets in enhancing multi-hazard risk assessment and management.

In this presentation, we briefly introduce the research context, questions, methodological approaches, preliminary results, and future direction of the UK Science Case as part of the High Impact Multi-hazards Science (EO4Multihazards) project funded by the European Space Agency (2023 – 2026).

How to cite: Bateson, L., Ciurean, R., Winson, A., Smith, K., and Mills, E.: The role of Earth Observation in advancing our understanding of high sustained temperature leading to dry conditions compound events: the UK Science Case, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22201, https://doi.org/10.5194/egusphere-egu24-22201, 2024.

11:20–11:30
|
EGU24-943
|
NH10.6
|
ECS
|
On-site presentation
Pavan Kumar Yeditha, Marcel Hürlimann, and Marc Berenguer

Compounding and cascading effects of rainfall-induced floods and landslides pose significant challenges and existing research has predominantly focused on individual hazards and the multi-hazard aspect has been understudied. To bridge the gap, the current study develops a multi-hazard approach by building upon validated flood and landslide hazard assessment methods.

The first steps in our approach involve determining the flood hazard in the river network and landslide warnings. With the landslide warnings denoting the initiation areas of the landslides, the warnings are combined with a morphological index to determine the potential sediment amount generated in subbasins and their connectivity to the drainage network. Finally, the multi-hazard aspect in the river network is obtained by integrating sediment transport and flood hazards. The results indicate the regions that have amplified hazards due to the combined effect of floods and sediment transport.

An initial version of the proposed framework was tested in the Tordera River basin, with a focus on the Gloria storm that occurred in January 2020. This storm caused widespread floods and landslides across Catalonia (NE Spain), resulting in substantial damage. Choosing this area allowed us to see how well the framework works in understanding multi-hazard aspects. The results highlight the areas with the highest occurrence of landslides and also the regions in the drainage network where significant changes occurred due to floods and sediment transport. The identified areas match with regions where noticeable alterations were observed during the Gloria storm. The preliminary results showcase the capability of the framework to effectively capture the amplified hazard, providing valuable insights into the compounding and cascading effects of rainfall-induced floods and landslides.

How to cite: Yeditha, P. K., Hürlimann, M., and Berenguer, M.: A multi-hazard framework integrating flood and landslide early warning systems: Application to the Tordera river basin (Catalonia, Spain), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-943, https://doi.org/10.5194/egusphere-egu24-943, 2024.

11:30–11:40
|
EGU24-9686
|
NH10.6
|
Highlight
|
On-site presentation
Abdelghani Meslem and Chen Huang

Intensive and extensive risks are growing at an unprecedented rate as reported by the Global Assessment Report on Disaster Risk Reduction. While disasters are claiming fewer lives annually, they are also costing more and increasing poverty and economic losses. Understanding and managing risk and resilience helps governments, institutions, businesses, and communities make better decisions in a world of uncertainty. Building on the existing risk and resilience management processes (e.g., national risk assessments, international risk management standards, literature on risk and resilience assessment), this study proposes a novel operational risk and resilience management process for emergency planning and civil contingency. The proposed framework is an iterative process consisting of interrelated phases:

  • Scope, context and criteria
  • Risk and Resilience Identification, analysis and evaluation
  • Risk mitigation and resilience strengthening
  • Monitoring and review
  • Communication and consultation
  • Recording and reporting
  • Initial and detailed assessment

The proposed operational framework will provide guidance for disaster management authorities to better understand and manage complex impact and systemic risk from a multi-hazard and disaster risk perspective.

How to cite: Meslem, A. and Huang, C.: A Novel Operational Risk and Resilience Management Process for Emergency Planning and Civil Contingency, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9686, https://doi.org/10.5194/egusphere-egu24-9686, 2024.

11:40–11:50
|
EGU24-15048
|
NH10.6
|
On-site presentation
Iuliana Armas, Andra-Cosmina Albulescu, and Daniela Dobre

Vulnerability is the most important predictive variable in the risk equation, but it isn't easy to evaluate the best objective approach to quantify it. Another hot topic of debate among scientists is whether vulnerability analysis describes only patterns or can also produce a quantitative value. The need to streamline and provide comparable and easy-to-use results has led to developing vulnerability indicators. Generally, these provide some form of aggregation of underlying factors, often including hazard exposure. Factor selection varies from deductive approaches, based on theoretical understanding, to inductive ones, based on statistical relationships.

For the past thirty years, there have been significant efforts to measure vulnerability, but up to now, the field of vulnerability assessments has been dominated by hierarchical versus inductive approaches.

The hierarchical analysis is a transparent approach, more accessible to stakeholders due to its logical structure and statistical support, and capable of functioning with more available datasets for assessing vulnerabilities in the studied areas. These are the most eloquent reasons for preferring the hierarchical approach in stakeholder territorial management and mitigation policies.

The inductive, statistical approach developed by Cutter (based on the hazards-of-place model, Cutter, 1996) uses the principal component analysis (PCA) to establish vulnerability factors over time and eliminates the biases from aggregated decisions.

Against this background, our study proposes a new model for quantifying vulnerability using an Impact Chain-based approach, taking as an initial case study the powerful flood events and the COVID-19 pandemic that affected Romania in 2020-2021. The hazards, impacts, vulnerability, exposed elements, and adaptation options pertaining to the case study are integrated into a comprehensive Impact Chain that is used as the foundation for the model.

The proposed model relies on factorial techniques and ANOVA, with a focus on identifying statistically significant multiple regressions. It also integrates an optimization procedure that enables either a maximum value response or a minimum accepted value.

This new framework allows for identifying vulnerability's influencing role in unfolding a multi-hazard and pinpointing the potential ways in which vulnerability can be affected by this unfolding. Thus, the model looks at vulnerability with a double lens, assessing its power to induce change by conditioning impacts and adaptation options and its propensity to change by certain impacts and adaptation options working in asynergy. Only by thoroughly analyzing both of these facets and understanding their implications can we produce bias-(more) free vulnerability assessments, particularly in multi-hazard contexts.

 

Keywords: vulnerability, vulnerability approaches, hierarchical approach, inductive approach, Impact Chain

How to cite: Armas, I., Albulescu, A.-C., and Dobre, D.: Impact Chain-based model to assess multi-hazard systemic vulnerability. Case study: Flood and the COVID-19 pandemic in Romania, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15048, https://doi.org/10.5194/egusphere-egu24-15048, 2024.

11:50–12:00
|
EGU24-18352
|
NH10.6
|
On-site presentation
Massimiliano Pittore, Cristine Griffo, Alessandro Mosca, Davide Ferrario, and Piero Campalani

Many areas of the world are prone to several natural hazards, with their occurrences possibly compounded, cascaded, or otherwise connected, either causally or across space and time, and effective risk reduction is only possible if all ensuing relevant threats are considered and analyzed. The examination of multiple hazards for the assessment of risk poses a range of additional challenges partly due to the differing characteristics of underlying processes, partly due to the broader range of consequences and the related risk-driving factors (e.g, exposure and vulnerability).

Considering the increasing availability of data about some components of risk, in the past decade several frameworks and approaches including artifical intelligence and in particular machine learning algorithms have been proposed to support multi-hazard/multi-risk assessment studies.

However, several challenges can be acknowledged that hinder the application of machine learning: the complex interplay among the risk components in multi-hazard contexts, for instance, along with the paucity of available quantitative information on impact (i.e., damage, loss) might impair the development of training datasets of adequate size and quality. Furthermore, information on multi-hazard risk is relying on heteogeneous data, often qualitative. Lastly, but not least, additional uncertainty is associated to the complexity and current lack of consensus in the conceptual definition of high-impact multi-hazard events by the different involved scientific as well as praxis-oriented communities.

In this context, the use of ontologies and semantic data representations may prove useful to tackle the above-mentioned challenges. An ontology is a structured representation of shared knowledge about a specific domain, encoded in the form of axioms, natural language labels, synonyms, definitions and other types of annotation properties. Risk-oriented ontologies can be used for instance to provide a common operational basis to the basic underlying conceptual definition, to be agreed upon and shared across communities with different scientific background. Furthermore, ontologies can be used to access and exploit background knowledge in order to build better predictive models, expand or enrich feature engineering in machine learning or to constrain the search for a solution to an optimization problem (e.g., setting hard constraints based on logical inferences). Formal ontological representations can also provide a consistent support to the development of so-called explainable models, therefore controlling the unnecessary spread of "black box" models in sensitive operational environment such as, e.g., impact forecasting and early warning.

How to cite: Pittore, M., Griffo, C., Mosca, A., Ferrario, D., and Campalani, P.: On the integration of ontological models of risk in artificial intelligence and machine learning applications to advance multi-hazard risk assessment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18352, https://doi.org/10.5194/egusphere-egu24-18352, 2024.

12:00–12:10
|
EGU24-16585
|
NH10.6
|
ECS
|
On-site presentation
Davide Mauro Ferrario, Marcello Sano, Timothy Tiggeloven, Judith Claasen, Elena Petrovska, Margherita Maraschini, Marleen de Ruiter, Silvia Torresan, and Andrea Critto

The escalating frequency and intensity of extreme climate events underscore the need for robust multi-risk assessment methodologies. Conventional approaches often struggle unravelling the intricate interplays among diverse hazards and their impacts on vulnerability and exposure factors. Understanding the complex impact chains and the consequences of extreme climate events on socio-economic and natural systems is crucial for formulating effective risk reduction and preparedness strategies. Artificial Intelligence (AI) has emerged as a powerful tool for analysing intricate environmental data, fusing information from different heterogeneous sources, and modeling non-linear relationships.

A stepwise AI-based framework has been developed to assess the risk induced by extreme climate events—specifically, heatwaves, droughts, storm surges, extreme precipitation, and extreme wind events—in the Veneto Region (North-East Italy). The first step consists in the identification of single hazard spatial and temporal footprints from climate data, using statistical methods (quantiles and percentiles) for identifying anomalies and extreme events and unsupervised machine learning (DBSCAN) for clustering. The second step aims at building multi-hazard event sets, by combining the dynamic single hazard clusters extracted in the first step with static footprints of other hazards, such as wildfires and landslides. In particular, different time lags and spatial overlaps are applied to identify compound or consecutive events. Finally, the third step employs supervised ML algorithms, such as Random Forest, Support Vector Machine (SVM), and Convolutional Neural Networks (CNN), to model multi-hazard susceptibility over different multi-hazard combinations. Footprints of past single and multi-hazard events are used as assessment endpoints to train the ML model and identify the most important vulnerability and exposure factors and multi-risk hotspots within the Veneto region.

This comprehensive approach integrates advanced data driven and AI techniques to enhance the understanding of the complex dynamics associated with multi-risk events. This framework has been applied and tested within the Myriad-EU project, in the Veneto Region case study, demonstrating its efficacy in assessing and predicting the impacts of multi-risk events under different climate change scenarios.

How to cite: Ferrario, D. M., Sano, M., Tiggeloven, T., Claasen, J., Petrovska, E., Maraschini, M., de Ruiter, M., Torresan, S., and Critto, A.: Artificial Intelligence for climate change multi-risk assessment: a Myriad-EU case study in the Veneto Region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16585, https://doi.org/10.5194/egusphere-egu24-16585, 2024.

12:10–12:20
|
EGU24-19323
|
NH10.6
|
ECS
|
Highlight
|
On-site presentation
Duygu Kalkanlı, Seda Kundak, Funda Atun, and Cees J. van Westen

Analyzing multi-hazards requires a comprehensive approach, involving complexities in studying multiple hazards and challenges in visualizing numerous risks due to the abundance of information (Kappes, et.al.2012). Risk perception research, on the other hand, has emerged to aid decision-makers in understanding how people characterize and evaluate different hazards, anticipating behavioral responses, and guiding risk communication. Although the risk perception concept has been integrated into various behavioral theories applied to examine preparedness for numerous hazard types, there remains a gap in understanding which theories are suitable for examining multiple hazard types simultaneously (Gill & Malamud, 2017). Therefore, anthropogenic factors indirectly influencing multi-hazard risk assessment need addressing. Studying human behavior in multi-hazard scenarios presents inherent challenges, primarily due to the retrospective nature of analyses conducted after the event. The lack of direct observation during occurrences hampers the formulation of questions and modeling beforehand, limiting the ability to address perception and recall biases in real time. Despite these challenges, a thorough examination of catastrophes necessitates understanding not only how people behave but also delving into the underlying reasons for their behavior, a longstanding challenge in economics and social sciences (Wilson, 2017).

Virtual Reality (VR) environments emerge as valuable tools for overcoming these challenges. VR facilitates a more natural interaction among participants, providing an ideal setting to explore complex behavioral dynamics in disaster scenarios, previously nearly impossible in controlled settings. Combining the internal validity of laboratory experiments with the external validity of field or natural experiments (Fiore et al., 2009), VR enables repeated experiments with large subject pools, a challenge in real disaster situations. This allows researchers to achieve realistic yet replicable results that traditional methods struggle to attain. In contrast to real-world disasters, VR experiments avoid participant attrition, a common issue in natural research studies introducing biases. Conducting numerous identical experiments with a significant number of participants allows researchers to subtly manipulate factors and interactions, exploring specific questions comprehensively. Participants in VR experiments can engage in multiple scenarios, facilitating the exploration of learning behavior beyond one-time event analyses. Fiore et al. (2009) emphasize that VR participants can experience long-term scenarios in a short time, generating multiple counterfactual scenarios.

While traditional laboratories played a central role in advancing behavioral economics, VR is poised to be vital for inclusive multidisciplinary behavioral research in more realistic environments. It not only addresses methodological challenges associated with disaster research but also opens avenues for nuanced exploration of the reasons behind human behavior in disasters. This study examines the use of VR as an innovative tool for new risk assessment in complex contexts, considering behavioral differences and mobility preferences of participants with and without familiarity with the spatial environment.

How to cite: Kalkanlı, D., Kundak, S., Atun, F., and van Westen, C. J.: Investigating Spatial-Behavioral Patterns in Hazards: A Virtual Reality Study as A Data Gathering Method, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19323, https://doi.org/10.5194/egusphere-egu24-19323, 2024.

12:20–12:30
|
EGU24-567
|
NH10.6
|
ECS
|
On-site presentation
Flavio Alexander Asurza Véliz, Marcel Hürlimann, and Vicente Medina

Flash floods, fluvial floods and shallow landslides triggered by intense rainfall present substantial threats to both human lives and infrastructure. Furthermore, floods and landslides often manifest in a cascading sequence, where an initially lower-consequence event like heavy rainfall can lead to more severe floods and/or landslides, intensifying the impact to affected communities. Losses resulting from these combined hazards may be significantly greater than the sum of losses from individual hazards. Therefore, there is a crucial need to integrate hydrological and geotechnical modelling into an integrated flood–landslide cascading preparedness and hazard management. This research introduces a coupled flood and landslide initiation modelling system, integrating a temperature index-based snowmelt model (SNOW-17), the Coupled Routing and Excess STorage (CREST) model, and the Fast-Shallow Landslide Assessment Model (FSLAM). The proposed approach is evaluated in the Val d’Aran region that experienced multiple landslides and important flooding due to a combination of heavy rainfall and snowmelt in June 2013. The coupled-model involves three main steps: i) The SNOW-17 model is applied to quantify the snow melting process which is further included in ii) the hydrological model CREST in order to estimate soil water content conditions, discharge and flood extent. Later, iii) the FSLAM model generates landslide susceptibility maps based on the hydrological model outputs, and finally iv) a random walk runout model will determine the landslides trajectories and the amount of sediment that may reach the river network. Preliminary results, related to snow, hydrological and landslide model calibration, have shown good statistical performance when comparing modelled daily soil water equivalent and daily hydrographs with observations from 2012-2020. Landslide predictions also showed a good accuracy (72%). Further steps will try to include the cascading effect of sediments being delivered to drainage network during landslides episodes. This study highlights the importance of the physical connection among snow melting, hydrological processes and slope stability, and aims to provide a prototype model system for operational forecasting of floods and landslides.

How to cite: Asurza Véliz, F. A., Hürlimann, M., and Medina, V.: Coupling hydrological and geotechnical models for enhanced flood–landslide cascading disaster modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-567, https://doi.org/10.5194/egusphere-egu24-567, 2024.

Posters on site: Fri, 19 Apr, 16:15–18:00 | Hall X4

The posters scheduled for on-site presentation are only visible in the poster hall in Vienna. If authors uploaded their presentation files, these files are linked from the abstracts below, but only on the day of the poster session.
Display time: Fri, 19 Apr 14:00–Fri, 19 Apr 18:00
Chairpersons: Saman Ghaffarian, Michele Calvello, Marleen de Ruiter
X4.73
|
EGU24-4202
|
NH10.6
|
Umut Lagap and Saman Ghaffarian

Digital Twins (DT) are dynamic digital representations of physical entities ranging from individual systems to entire cities. They leverage real-time data to create accurate models and simulations, offering significant potential for post-disaster risk management (PRM) applications. However, the integration of DT into PRM is still in its infancy, with its full capabilities yet to be realized.

This study introduces the Digital Post-Disaster Risk Management Twinning (DPRMT) paradigm, which aims to harness AI and ML within DT frameworks to reinforce the resilience of urban areas and communities in the face of disasters. A critical review of 335 research papers on DPRMT from reputable databases indicates that existing literature often fails to fully appreciate the dynamic and interconnected nature of disasters, typically relying on static historical data and neglecting important financial, social, and demographic factors in affected communities.

We propose a tansformative DPRMT framework that encompasses six interconnected components. “Entities at Risk” identifies a variety of elements vulnerable during disasters, including human lives, buildings, critical infrastrucres, and social networks. “Data collection and preparation” employ various methods such as remote sensing, crowdsourcing, and social sensing to gather and prepare dynamic data for analysis. Data Processing leverages artificial intelligence and machine learning to validate, fuse, and analyze collected data, enhancing its accuracy and reliability. Digital Modeling encompasses diverse techniques like AI-based modeling, socio-economic modeling, and physical modeling to create computer-based representations of entities at risk, enabling in-depth analysis and prediction. Information Decoding involves comprehensive data and model analysis, integration, and visualization, delivering timely and actionable information to enhance decision-making and transparency. User Interaction and Application ensure effective communication between digital twin models and end-users through various technologies, facilitating real-time information delivery and stakeholder engagement in disaster response and recovery. This framework is designed to fill current gaps in traditional disaster recovery methods by integrating real-time, detailed, and data-driven modeling solutions, fostering improved decision-making in areas such as policy development, resilience assessment, casualty and hazard prediction, resource distribution, evacuation planning, scenario testing, and community involvement.  

Despite the promise of ML in improving DT capabilities for PRM such as data validation, information extraction, predictive maintenance and anomaly detection, the results show that challenges remain, including the need for high-quality and diverse data, privacy concerns, and cost-effectiveness, particularly in less developed countries. The use of remote sensing technologies, such as satellites and drones, is presented as a viable solution to overcome these challenges. These technologies supply high-quality, detailed data on buildings, infrastructure, land cover changes, and post-disaster scenarios while addressing privacy and security concerns. Nonetheless, issues with model generalization persist, necessitating training on varied disaster contexts, managing large datasets, capturing the dynamic nature of disasters, and maintaining transparency in decision-making for practical real-time application. The limitations of current ML methods, especially their time-consuming nature and the need for frequent re-training in evolving disaster scenarios, may impede their seamless integration with DT frameworks. This highlights the need to develop more efficient and rapid ML and Deep Learning models specifically designed for the unique requirements of post-disaster recovery management.

How to cite: Lagap, U. and Ghaffarian, S.: Transforming Post-Disaster Risk Management: A Comprehensive Framework for Digital Twinning with AI and Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4202, https://doi.org/10.5194/egusphere-egu24-4202, 2024.

X4.74
|
EGU24-6680
|
NH10.6
Timothy Tiggeloven, Davide Ferrario, Wiebke Jäger, Judith Claassen, Yuliya Shapovalova, Maki Koyama, Marleen de Ruiter, James Daniell, Silvia Torresan, and Philip Ward

A crucial component of disaster preparedness is the development of a multi-hazard susceptibility map, which plays a vital role in comprehensive risk assessment, resource allocation, land use planning, emergency management, community preparedness, and decision-making. Recently deep learning methods have been showing potential to map susceptibility at a finer resolution. While prior research has predominantly focused on advanced single-hazard or simplified multi-hazard susceptibility mapping, an approach to explore multi-hazard susceptibility mapping using deep learning methods and explainable AI’s remains lacking to date. Addressing this gap, our research employs an ensemble Convolutional Neural Networks, to develop a multi-hazard susceptibility map. Leveraging diverse datasets and the MYRIAD-HESA framework, our analysis considers a range of hazards and their interactions, offering a more integrated view of the complex risk landscape faced by communities. Using Japan as a case study, the resulting susceptibility map serves as a valuable tool for informing land use and urban planning, resilient infrastructure development, and identification of suitable locations for critical facilities. Furthermore, it supports emergency management by facilitating resource prioritization, coordination, evacuation planning, and community awareness. This research contributes to evidence-based decision-making, policy development, and global disaster preparedness efforts.

How to cite: Tiggeloven, T., Ferrario, D., Jäger, W., Claassen, J., Shapovalova, Y., Koyama, M., de Ruiter, M., Daniell, J., Torresan, S., and Ward, P.: Exploring the World of Multi-Hazard Susceptibility Mapping With Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6680, https://doi.org/10.5194/egusphere-egu24-6680, 2024.

X4.75
|
EGU24-9223
|
NH10.6
Philip Ward, Nicole van Maanen, and Marleen de Ruiter and the EO4MULTIHAZARDS team

Natural hazard impacts are becoming increasingly complex, as demonstrated by real world examples of multi-hazards events. This requires major improvements of our current multi-hazard scientific modelling capabilities. High-quality earth observation (EO) data have the potential to contribute to improving our understanding of multi-hazard events and multi-risk impacts. However, to date there have been limited attempts to include EO data into the workflow of multi-hazard analysis, modelling, forecasting and added-value generation. In this contribution, we review recent developments in using EO data in multi-hazard and multi-risk assessment. We examine how EO data can support our practical understanding of multi-(hazard-)risk, and how this can be made accessible, useful and practical. We provide recommendations for improving EO information (tools, methodologies, accessibility, etc.) and an outlook on the potential evolution of using EO in disaster risk management.

How to cite: Ward, P., van Maanen, N., and de Ruiter, M. and the EO4MULTIHAZARDS team: Role of Earth Observation in multi-(hazard-)risk assessment and management , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9223, https://doi.org/10.5194/egusphere-egu24-9223, 2024.

X4.76
|
EGU24-9940
|
NH10.6
|
ECS
|
Highlight
|
Salsabila Prasetya, Irene Manzella, and Cees van Westen

Small Island-Developing States (SDIS) are susceptible to a broad range of risks coupled with a constrained capacity to manage them effectively. The Caribbean is one of three geographical regions in which SDIS are located, with a high vulnerability to multi-hazard events, such as tropical storms and volcanic eruptions. According to the European Commission, the Caribbean is the second most disaster-prone region in the world with extreme climatic events. Based on the EM-DAT database, tropical storms are the most frequent disastrous event in the Caribbean. A tropical storm triggers a combination of coupled hazardous phenomena such as strong winds and heavy rainfall which often leads to floods and landslides. The Caribbean also lies on several active tectonic plates which makes it home to several active volcanoes. There are 21 volcanoes across 11 volcanically active islands.

A low-probability high-impact combination of compounding storm and volcanic event happened in 2021 in Saint Vincent where an eruption of the La Soufriere volcano was followed by a storm which triggered several lahars and other cascading effects. Based on historical event records, volcanic eruptions occur on average every 77 to 94 years in Saint Vincent alternating between effusive and explosive eruptions. Meanwhile, tropical depressions affect the island on average once every 3 years for direct hit or brush and 18 years for major hurricane hit. This study will assess the impact of compounding storms and volcanic events in the Caribbean with a case study from Saint Vincent.  

A comprehensive multi-hazard risk assessment which considers multiple spatial and temporal scales plays a role in disaster risk reduction and response planning. The present work uses a multi-hazard and multi-phase modular framework based on literature review of historical events in Saint Vincent. Several scenarios are developed that show a variety of hazard types and intensities as well as the impacts. Impact chain models are used to present these scenarios. Impact chains are conceptual models based on cause-effect chains that include all major factors and processes leading to specific risks in a specific context. Compounding scenarios developed resulted in impacts much more severe as compared to the individual events. This study highlights the importance of studying compounding risks and the effectiveness of impact chains assessment for better disaster risk reduction planning and mitigation.

 

Keywords: multi-hazard, impact assessment, impact chain, volcanic eruption, storm, hurricane, Caribbean. 

How to cite: Prasetya, S., Manzella, I., and van Westen, C.: Multi-hazard Impact Assessment for Volcanic and Storm Hazards: the Saint Vincent Case Study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9940, https://doi.org/10.5194/egusphere-egu24-9940, 2024.

X4.77
|
EGU24-13832
|
NH10.6
Marcello Sano, Davide Ferrario, Margherita Maraschini, Silvia Torresan, and Andrea Critto

As climate change accelerates and environmental uncertainties mount, traditional models fall short in effectively handling the complexity and fluidity of multi-hazard risk and corresponding resilience measures. Notably, the vast amount of data being collected and the rapid advancements in artificial intelligence offer extraordinary potential. These advancements can equip us to tackle complex climate risks and develop innovative services that empower both government and communities to adapt and thrive.

This review aims to bolster research on the transformative potential of Artificial Intelligence (AI), propelled by Machine Learning (ML) and Big Data (BD), to address the escalating challenges posed by climate change across several key areas. On the one hand, it examines AI's capability to process and integrate diverse data sources, such as satellite imagery, monitoring stations, climate models, social data, with varying spatial and temporal resolutions, including the potential of AI tools in identifying and quantifying cascading and interconnected hazard events and potential resilience measures. On the other hand, the review delves into the development of AI-powered climate services designed to manage climate risk and enhance resilience across various sectors. It evaluates the integration of AI techniques in climate services for dynamic, user-centric platforms that offer actionable insights and decision support. The current and future data constraints and emerging opportunities in implementing these services are explored, alongside strategies to overcome these challenges. Additionally, the review considers the scalability and adoption of AI-powered climate services in the future, highlighting the role of AI in revolutionizing the landscape of climate risk assessment and resilience planning.

In summary, this comprehensive literature review synthesizes insights from multi-hazard risk assessment and resilience building. It aims to bridge the gap between static risk models and the dynamic reality of climate threats, paving the way for a comprehensive AI-driven framework helping building climate resilience.

This research is funded under the European projects MYRIAD-EU (Horizon 2020) and EXPEDITE (Horizon 2021 MSCA).

How to cite: Sano, M., Ferrario, D., Maraschini, M., Torresan, S., and Critto, A.: A review of opportunities and challenges for AI driven multi-hazard risk assessment and resilience enhancement in climate services, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13832, https://doi.org/10.5194/egusphere-egu24-13832, 2024.

X4.78
|
EGU24-12033
|
NH10.6
|
ECS
Cristina Savu, Andra-Cosmina Albulescu, Iuliana Armaș, and Dragos Toma-Dănăilă

In the last years, the impacts of natural hazards have been coupled with and sometimes overshadowed by those of the COVID-19 pandemic. Such co-occurrences added extra layers to risk management and highlighted the need for updated multi-hazard risk models and management plans. While developing the tools, models, and strategies to battle the challenges of the post-pandemic world, an unsettling question lingers: What if the most impactful and feared hazard in a specific area were to occur during a pandemic wave?

This study aims to 1) take an in-depth look at the impact of the COVID-19 pandemic on the hospital system in Bucharest, Romania, and 2) identify the compounded impacts of a powerful earthquake that would potentially affect the city during a pandemic wave, under an Impact Chain-based approach. To this end, two Impact Chains are analysed side by side: one of them presents the actual impacts of the pandemic documented for 2020-2022, and the other focuses on the potential impacts of a powerful earthquake, similar to the one that affected Romania in March 1977 (7.4 MW). The co-occurrence of such powerful hazardous events poses a worst-case scenario for Bucharest, which stands out as the European capital with the highest seismic risk and one of the urban centres severely affected by the COVID-19 pandemic.

 

The Impact Chain centred on the COVID-19 pandemic dwells on a wide range of sources: scientific literature, data collected from hospital administration (e.g., reports on available medical resources, including human resources), official reports from international health care organisms, legislative documents that regulate COVID-19 prevention protocols, official press releases, and grey literature in the form of news reports. The earthquake-based Impact Chain represents a simplified, expert knowledge-based version of a larger chain developed within the Paratus Project as part of the analysis of present and future outcomes of a major earthquake in Bucharest.

 

By juxtaposing the two Impact Chains, this study addresses the research gap concerning the compounded impacts of earthquakes and the COVID-19 pandemic, an area currently open for further investigation. This analysis offers an initial answer to the worrisome question posed earlier, aiding in the preparation for “the worst” in Bucharest.

Keywords: COVID-19 pandemic, earthquake, impact chain, hospital system, Romania

How to cite: Savu, C., Albulescu, A.-C., Armaș, I., and Toma-Dănăilă, D.: Learning from the past to prepare for the worst. Impact Chain on the multi-hazard of COVID-19 pandemic and a powerful earthquake in Bucharest, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12033, https://doi.org/10.5194/egusphere-egu24-12033, 2024.

X4.79
|
EGU24-15139
|
NH10.6
|
Highlight
Cees Van Westen, Bastian van den Bout, Rabina Twayana, Massimiliano Pittore, Ashok Dahal, Manzul Hazarika, and Yu Han

There is a need for the development of databases for representing the complex hazard interactions and cascading impacts of multi-hazard extreme events, such as sequences of earthquake or storm-related events. The concept of impact chains has proven to be a useful concept for conceptually representing the risk related to such complex events, but applications have been mostly used for visualization purposes only.  In the context of the EU PARATUS project, a web-based simulation service is being developed for first and second responders and other stakeholders to evaluate the impact and risk related to multi-hazard events building upon a representation of scenario risk through impact chains. The simulation service includes a series of tools to gather, integrate, and develop new hazard and risk information. The central tool is the impact chain builder, where users can develop their own impact chain of past events, or future disaster events, and is used as a basis for quantifying direct damage and prioritizing secondary losses in different sectors. Several tools for hazard assessment will provide fast estimations of multiple hazards and can be linked to the impact chains. One of these is the FastFlood tool which allows to generate flood extent and depth maps for any area, within seconds, based on global datasets, or more detailed user-supplied data. The tool can also be used to evaluate the effect of risk reduction measures. Also, hazard tools for other processes are developed such as for mass movements, with initiation and runout components and linked to flood events. The hazard data is combined with elements-at-risk data, for exposure analysis in the RiskChanges tool. This tool allows to quantify losses, using a database of vulnerability functions. Multi-hazard losses are calculated using specific combination rules for different hazard interactions. The tool can also be used for evaluating optimal risk reduction alternatives, where the risk components are re-analyzed and the risk reduction is compared with investment in a cost-benefit analysis. Changes in risk for future scenarios, related to climate change, land use change, and population change, for certain future years, can also be analyzed using the tool. Other tools are still under development, such as a tool for collaborative planning. The exact number of components and the final structure of the platform will be determined iteratively through a series of stakeholder consultations, following a user-centered design. The platform is designed flexibly to be able to support stakeholders that work in different sectors, geographic settings, and interacting hazards, and at the same time to address (a number of) their needs for analyzing the impact of compounding multi-hazard events with cascading impacts.  

How to cite: Van Westen, C., van den Bout, B., Twayana, R., Pittore, M., Dahal, A., Hazarika, M., and Han, Y.: A web-based multi-hazard risk simulation service based on impact chains, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15139, https://doi.org/10.5194/egusphere-egu24-15139, 2024.

X4.80
|
EGU24-15767
|
NH10.6
|
ECS
|
|
Amal Sarfraz, Charles Rougé, Lyudmila Mihaylova, Jonathan Lamontagne, Abigail Birnbaum, and Flannery Dolan

In climate risk modelling, the growing trend of simulating large ensembles is driven by the need to understand a wide range of possible future scenarios. This approach generates vast datasets, which presents a challenge: identifying the most critical scenarios that could have significant impacts. While mainstream data patterns offer general insights, outliers provide unique perspectives, specifying areas for further investigation. However, focusing on single outliers is not optimal. Instead, analysing groups of outliers enables a more comprehensive exploration for the identification of patterns in multiple plausible future outcomes.

In this context, we introduce the term ensemble of outliers to describe groups of data points deviating significantly from the mean of the dataset. An ensemble of outliers can help uncover underlying patterns and highlight areas for deeper exploration. These ensembles of outliers, once identified can possess distinct properties and indicate phenomena that are not represented in the rest of the dataset.

Our research proposes a new method to address the challenge of identifying these ensembles of outliers within large datasets. Our methodology, Mahalanobis distance-based Ensemble of Outlier Detection (MEOD) includes Gaussian Mixture Models for probabilistic clustering coupled with Enhanced Mahalanobis distance-based statistical analysis to identify an ensemble of outliers in complex large datasets. MEOD's efficiency is validated through extensive testing on thousands of synthetic datasets, encompassing diverse configurations of both the dataset and an ensemble of outlier characteristics. The results indicate a high degree of accuracy for MEOD, with an average purity of 99.65% and an average F1 score of 0.92.

To demonstrate the utility of MEOD to climate risk assessment, we implement our method on a large dataset of future agricultural production scenarios for the Indus River Basin (IRB). This large dataset was generated using an Integrated Assessment Model, Global Change Analysis Model and encompasses 3,000 scenarios outlining potential socioeconomic, water supply-demand, and land use changes up to the century's end. Our goal is to use MEOD to identify and analyse a critical ensemble of outliers that significantly drives water scarcity in IRB's agricultural sector. We successfully identified 150 scenarios as an ensemble of outliers, characterised by their unique socioeconomic attributes and agricultural practices.

These scenarios predominantly fall into two categories: 1) those involving increased competition for resources due to regional disparities and 2) those incorporating a mix of sustainable and conventional agricultural practices. This dichotomy highlights both overuse and intensive water resource utilisation scenarios, signalling significant agricultural withdrawals and high scarcity risks.

Our findings demonstrate the MEOD's efficiency as a robust, versatile tool for analysing complex, large-scale datasets, providing nuanced insights into intricate data patterns.

How to cite: Sarfraz, A., Rougé, C., Mihaylova, L., Lamontagne, J., Birnbaum, A., and Dolan, F.: Automatic identification of ensembles of critical futures in large datasets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15767, https://doi.org/10.5194/egusphere-egu24-15767, 2024.

X4.81
|
EGU24-16910
|
NH10.6
Michaela Bachmann, Reinhard Mechler, Oscar Higuera Roa, Anna Pirani, Jeremy Pal, Lena Reimann, Maurizio Mazzoleni, Ted Buskop, and Jaroslav Mysiak

With climate change increasingly affecting people, assets and the environment, Climate Risk Assessments (CRA) are seeing strong attention for understanding the scope and scale of climate risks in order to plan and implement adaptation and climate risk management responses.

In the context of the EU Horizon 2020 project CLIMAAX we developed an inclusive and harmonized CRA framework adapted for NUTS-1 and NUTS-2 level. This framework aligns with state-of-the-art methodologies and is further complemented by a user-friendly toolbox tailored for risk quantification across European regions. Our approach integrates insights from UCPM documents, European National Adaptation Plans and Strategies, peer-reviewed literature as well as existing CRA frameworks and international standards to respond to needs, recent advancements and best practices in the CRA field. The framework was collaboratively developed with five European pilot regions to ensure feasibility and applicability while pursuing adaptive flexibility.

The practical need of the CRA framework led to a five-step assessment cycle (with special emphasis on key risk assessment as a novelty), underpinned by a conceptual context addressing principles, technical choices (e.g. future scenarios) and participatory processes. The framework allows for toolbox extension (the risk analysis) as well as indicates entry points for Climate Risk Management and Adaptation options thereby creating a feedback loop within the CRA cycle. To address compound and multi-hazards aspects of risk, the framework is designed to tackle complexity by referring to a variety of options such as workflows for climate risk quantification or qualitative options together with participatory processes and stakeholder inclusion.

The developed CRA framework brings together practical needs and scientific, standardized knowledge. However, further insights are needed to efficiently connect climate risk estimations with climate risk management and adaptation strategies to support communities and regions in their efforts towards building climate resilience.

How to cite: Bachmann, M., Mechler, R., Higuera Roa, O., Pirani, A., Pal, J., Reimann, L., Mazzoleni, M., Buskop, T., and Mysiak, J.: An adaptive and flexible Climate Risk Assessment Framework for regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16910, https://doi.org/10.5194/egusphere-egu24-16910, 2024.

X4.82
|
EGU24-556
|
NH10.6
|
ECS
|
Highlight
|
Nuria Pantaleoni Reluy, Marcel Hürlimann, and Nieves Lantada Zarzosa

Conducting a forensic analysis of catastrophic multi-hazard episodes is a challenging yet essential undertaking to enhance our understanding and preparedness for future events. In this study, the multiple direct damage costs for repairing and replacing the effects of the Gloria storm, which struck Catalonia from January 20 to 23, 2020, are thoroughly examined. The storm, characterized by persistent and intense rainfall coupled with strong winds, resulted in a significant sea-level rise heightened by large waves, numerous slope failures and widespread pluvial and fluvial floods, leading to substantial direct economic losses. While databases of damage and losses provide valuable insights into documenting disaster effects, we propose an integrative approach that combines post-event data compilation with forensic analysis to understand the hazard conditions. The resulting database in our study includes parameters such as geographical location, triggering hazard, exposed element at risk, and cost, providing a comprehensive understanding of the Gloria storm's impact. By interpreting the collected data, we derive with key insights of the economic impacts and severity of the hazards caused by the storm in Catalonia. The compilation of data from 14 different sources revealed extensive repair and replacement costs of approximately 390 million Euros for the damages caused by the Gloria storm. Fluvial and coastal processes were the primary contributors to direct economic losses in Catalonia, with fluvial hazards accounting for 44% and coastal processes for 41%. Slope failures and meteorological hazards accounted for 9% and 5%, respectively, in the overall damages. By complementing this with forensic analysis, the integrated approach allows us to discern how and why these events occurred, whether they were amplified or diminished by management strategies, and what strategies could be applied. Additionally, the study incorporates the development of an impact chain, illustrating potential sequences of events and relationships based on the Gloria Storm case. This analytical diagram serves to better comprehend the interrelationships and cascading effects of different hazards, as well as the environmental and socio-economic factors contributing to the damages. The integrated approach contributes to more effective risk management strategies and enhances the broader field of disaster analysis.

How to cite: Pantaleoni Reluy, N., Hürlimann, M., and Lantada Zarzosa, N.: Forensic insights into Gloria's storm multi-hazard damages in Catalonia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-556, https://doi.org/10.5194/egusphere-egu24-556, 2024.

X4.83
|
EGU24-16163
|
NH10.6
|
ECS
|
Highlight
|
Maria Katherina Dal Barco, Sebastiano Vascon, Silvia Torresan, and Andrea Critto

Over the past three decades, the global climate has experienced a significant and unprecedented increase of temperature, leading to the occurrence of many extreme events worldwide. Coastal areas are particularly vulnerable to the impacts of climate change, due to the high population density, interconnected economic activities and the presence of fragile habitats and ecosystems. The interactions between multiple hazards, acting at different temporal and spatial scales, can amplify the effects on dynamic vulnerability and exposure patterns.

In order to address these complex challenges, an integrated approach becomes crucial, considering the relationships among all risk factors (hazard, exposure, and vulnerability) at the land-sea interface.

Agent-based model (ABM) approaches are able to simulate the interactions between different individuals, households or communities, playing a vital role in the analysis of their responses to environmental hazards (e.g., sea-level rise, heavy precipitation, extreme wind) and adaptation strategies (e.g., beach nourishment, nature-based solutions).

Here we present the development of a local-scale ABM to assess coastal risks caused by climate change on various sectors, such as local communities, tourism, and ecosystems. In particular, the model aims at exploring the interactions among atmospheric and marine hazards (e.g., sea-level rise, extreme precipitation, and wind), exposure and vulnerability factors (e.g., land-use, population) to simulate coastal risks for the municipality of Jesolo (Italy). The ABM will be trained with local-scale records over the 2009-2020 baseline timeframe, and then used to project future climate risk until 2100, under the climate change scenarios (e.g., RCP2.6, 4.5, and 8.5), as well as the potential effect induced by different coastal protection measures and nature-based adaptation strategies (e.g., beach nourishment, groins).

The resulting outcomes could represent a valuable tool to inform stakeholders and decision-makers on climate change adaptation, in line with EU, national and local adaptation strategies. Furthermore, they can be used to improve disaster risk preparedness as well as raise awareness in local communities.

How to cite: Dal Barco, M. K., Vascon, S., Torresan, S., and Critto, A.: Building an agent-based model to assess multi-risk caused by climate change in coastal areas: the case study of the Jesolo municipality (Italy), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16163, https://doi.org/10.5194/egusphere-egu24-16163, 2024.

X4.84
|
EGU24-19199
|
NH10.6
Funda Atun, Federica Romagnoli, Silvia Cocuccioni, Liz Jessica Olaya Calderon, Iuliana Armas, Ruxandra Mocanu, Caglar Goksu, Seda Kundak, Massimiliano Pittore, and Richard Sliuzas

Understanding complex interactions between hazardous events and dynamic risk conditions in today’s geographies requires carefully analyzing the historical data. Learning from the past will contribute to developing models and multi-hazard risk scenarios. Current disaster databases often concentrate on individual hazards and their direct consequences, lacking the ability to attribute impacts resulting from hazard interactions or adequately depict risk pathways from root causes to ensuing losses. Although the Post Disaster Needs Assessment (PDNA) approach is widely used to assess physical damages, economic losses, and recovery costs following major disasters, it proves less straightforward in estimating impacts and losses for future events.

In forensic analysis, when examining post-event conditions, the investigator formulates hypotheses regarding the pre-event conditions and gathers relevant evidence and facts. Forensic investigations of disasters, i.e. FORIN, highlight the necessity to characterize systemic, structural root causes and risk drivers at global, national, and local levels. While historical disaster data is indispensable, acknowledging the dynamic nature of economic, social, and environmental conditions, at the same time it challenges the prevailing notion that "the past is the key to the future."

Within the realm of disaster risk literature, several forensic analysis approaches are present. In the context of the PARATUS project's development of a forensic approach, three specific methodologies are incorporated: Investigation of Disasters (FORIN), Post Event Review Capability (PERC), and Detecting Disaster Root Causes (DKKV). PARATUS approach applies a combination of these three forensic approaches to a set of learning case studies drawn from selected past disaster events to analyze and navigate the complexity of disaster impacts across diverse contexts.

In PARATUS, we employ forensic analysis alongside historical datasets and earth observation across 18 learning case studies. Three primary criteria guide the selection of these case studies: 1) featuring hazard interactions representative of the European context; 2) having an impact on diverse sectors; and 3) global scenarios that could potentially occur in Europe.

How to cite: Atun, F., Romagnoli, F., Cocuccioni, S., Olaya Calderon, L. J., Armas, I., Mocanu, R., Goksu, C., Kundak, S., Pittore, M., and Sliuzas, R.: PARATUS Forensic Analysis Approach of Past Disasters to Develop Quantifiable Multi-Hazard Impact Scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19199, https://doi.org/10.5194/egusphere-egu24-19199, 2024.

X4.85
|
EGU24-19673
|
NH10.6
|
Maria Polese and Gabriella Tocchi

Given the complexity of the urban environment and the intricate social fabric within cities the multi-risk assessment in urban settlements is a particularly challenging task. The nature of urban risk is inherently multi-dimensional, encompassing physical, social, economic, institutional, and environmental factors. Each element of the systems constituting the urban settlement is characterized by different exposure and vulnerability to natural hazards. Moreover, the key features of the exposed elements can vary spatially and temporally, leading to an even more complex estimation of potential across an urban area. Additionally, the interrelated nature of various hazards adds another dimension of complexity to traditional risk frameworks.

This study presents a framework for integrating multiple dimensions in risk analysis. A straightforward risk index that combines multiple hazards and physical, social, and environmental exposure and vulnerability information is proposed. The index is obtained by combining single indicators representative of the aforementioned dimensions, resulting in a more holistic representation of risk. Moreover, selected indicators are combined, defining suitable weights that may reflect stakeholders’ priorities in policymaking. Recognizing the extreme complexity of urban systems and the difficulties in capturing different exposure/vulnerability conditions with a single index, a viable approach is to define a priori the multi-hazard scenario and risk metric of interest and select only the most representative exposure/vulnerability indicators to build the composite risk index. To this end, risk storylines and related impact-chains can be used as a practice-oriented support to guide the selection of the basic elements contributing to the relevant impact scenario and to account for unexpected cascading effects activating different types of vulnerabilities and eventually amplifying the final impact.

This approach allows for ranking regions exposed to multiple hazards and identifying urban critical contexts, i.e., urban areas where potential risk generated by different sources is higher and that are more in need of application of disaster risk reduction strategies. The prioritization of urban areas exposed to natural hazard risks provides several advantages for effective risk management and mitigation strategies. Concentrating efforts on high-risk areas is often more cost-effective, as it minimizes the need for widespread interventions and allows for the efficient allocation of limited resources. Furthermore, by applying a variation of single indicators composing the index, the proposed approach enables accounting for the effect of mitigating actions in risk analysis. the Thus, this tool also represents a helpful mean to evaluate the effectiveness of risk reduction policies.

This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005

How to cite: Polese, M. and Tocchi, G.: Identification of urban critical context using multi-risk composite-index, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19673, https://doi.org/10.5194/egusphere-egu24-19673, 2024.

X4.86
|
EGU24-20072
|
NH10.6
Francesca Renzi, Stefano Bagli, and Paolo mazzoli

The intensifying urban flood risks due to climate change necessitate innovative decision-support systems (DSS) for risk assessment and emergency management. The SaferPlaces cloud web platform emerges as a transformative DSS, utilizing AI-based algorithms and cloud computing power to enhance flood hazard preparedness in urban areas. By enabling users to conduct large-scale, high-speed simulations economically, SaferPlaces stands at the vanguard of urban resilience against flooding.

At its core, SaferPlaces harnesses Digital Twin technology to create a virtual replica of urban environments, allowing for comprehensive pluvial, fluvial, and coastal flood risk assessment. This digital replication assists in formulating risk mitigation strategies, addressing challenges posed by present and future climate conditions. As an ex-ante tool, SaferPlaces integrates climate forecasts into urban planning, fostering scientifically grounded disaster risk reduction and detailed risk profiling.

SaferPlaces distinguishes itself by amalgamating high spatial resolution topographic data, climate information from Copernicus CDS, hydrological data, and open-data platform resources. These inputs feed into advanced models and algorithms, producing high-fidelity hazard and damage estimates. Utilizing Amazon AWS Cloud's computing prowess, the platform generates real-time, cost-effective flood maps for historical and projected climate scenarios.

The platform's user-friendly GUI empowers users to swiftly execute multiple flood hazard and damage scenarios, rendering high-resolution maps within minutes. These maps are readily downloadable, supporting an array of potential stakeholders in their operational tasks. Moreover, SaferPlaces offers specialized tools within its GUI to assist users in identifying, designing, and evaluating the effectiveness of various climate mitigation interventions.

SaferPlaces' cross-sectoral appeal extends to municipal planners, disaster risk reduction professionals, public authorities, environmental and civil protection agencies, emergency organizations, water management entities, insurance sectors, and real estate industries.

Real case application will be discussed in the study

How to cite: Renzi, F., Bagli, S., and mazzoli, P.: AI-based Digital Twin Platform for Flood Risk Intelligence in cities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20072, https://doi.org/10.5194/egusphere-egu24-20072, 2024.

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

The posters scheduled for virtual presentation are visible in Gather.Town. Attendees are asked to meet the authors during the scheduled attendance time for live video chats. If authors uploaded their presentation files, these files are also linked from the abstracts below, but only on the day of the poster session. The button to access Gather.Town appears just before the time block starts. Onsite attendees can also visit the virtual poster sessions at the PICO spot in the corresponding on-site poster hall (e.g. virtual posters of vHall X4 are visible at PICO spot 4).
Display time: Fri, 19 Apr 08:30–Fri, 19 Apr 18:00
Chairperson: Ivan Van Bever
vX4.32
|
EGU24-21937
|
NH10.6
Wonhyun Lee, Alexander Y. Sun, and Bridget R. Scanlon

Coastal areas, facing escalating hazards intensified by climate change, are particularly vulnerable to wind-driven storm surge, waves, and flooding. The unprecedented events of Hurricane Harvey in 2017 highlighted the urgent need to better predict and understand storm-induced impacts in complex coastal environments. This study integrates two numerical modeling frameworks, namely the Delft3D Flexible Mesh (DFM) and Super-Fast INundation of CoastS (SFINCS), to provide a comprehensive approach addressing the challenges of coastal hazards and compound flooding in the Texas Gulf Coast region. This integrated DFM approach incorporates features like surface wave, hydrological, and hydraulic model-coupling, alongside grid nesting procedures to capture the wave and flow dynamics. The variable grid configuration optimally represents bathymetry while improving 
simulation time and accuracy. Model validation against measurements ensures a high level of accuracy, with a focus on estimating spatiotemporal variability in storm-induced surge and flooding. Addressing challenges faced by Texas Gulf Coast communities, a probabilistic surge 
and flood-inundation modeling system employing SFINCS is proposed. This system offers probabilities for different water depth thresholds, supporting surge and flood risk assessments, resilient infrastructure design, and coastal planning decisions. SFINCS, with reduced computational demand, uses essential physics for efficiency and accuracy, overcoming limitations of High-Performance Computing (HPC) systems. The study's outcome includes probabilistic predictions of compound flooding events in the Texas Gulf Coast region, presented through a probabilistic map and data. Stakeholders and end-users will benefit from this information for short and long-term planning and management, contributing to the resilience of coastal communities facing the complex challenges of climate-induced hazards. This integrated approach advances scientific understanding, supports decision-making, and promotes mutual benefit for researchers, policymakers, and coastal communities 
alike

How to cite: Lee, W., Sun, A. Y., and Scanlon, B. R.: Advancing Coastal Resilience through Integrated Modeling of Compound Flooding Events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21937, https://doi.org/10.5194/egusphere-egu24-21937, 2024.