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.

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.

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.

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.

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 M_W). 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.

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.

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.

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.

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.

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.

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.

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.