Displays

Due to climate change and rapid urbanization, urban flooding has become one of the major natural hazards threatening the safety of people and their properties and affecting the overall sustainability of cities across the globe, especially developing countries such as China. Flood modelling has now provided an indispensable tool to support urban flood risk assessment and management, and inform the planning of cities that are more resilient to flooding.

Hydraulic structures, e.g. regulation gates and pumping stations, play an important role in urban flood risk management. However, direct simulation of these hydraulic structures is not a current practice in 2D urban flood modelling. This work presents and applies a robust numerical approach to directly simulate the effects of hydraulic structures in a 2D high-resolution urban flood model. An additional computational module is developed and fully coupled to a GPU-accelerated finite volume shock-capturing urban flood model to directly simulate the highly transient flood waves through hydraulic structures. The improved flood model is applied to  reproduce a flood event induced by Typhoon “Lekima” in 2019 in Yuhuan, Zhejiang Province, China. At 3m resolution, the model is able to simulate the complete process of the flood event in nearly 3.5 times faster than real time, demonstrating the efficiency and robustness of the new fully coupled model for high-resolution food modelling in cities. Further simulations are performed to systemically investigate the effect of hydraulic structures and different operational regulations on flood dynamics and associated risks, demonstrating the importance of directly considering hydraulic structures and their operations in 2D high-resolution urban flood modelling.

How to cite: Cui, Y., Liang, Q., Wang, G., Zeng, J., and Hu, J.: High-Resolution Simulation of Hydraulic Structures in a Typhoon Induced Urban Flood Event, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3232, https://doi.org/10.5194/egusphere-egu2020-3232, 2020.

Typhoon often brings heavy rainfall, floods and storm surges to estuaries and may cause devastating disaster loss, especially in the downstream coastal urban areas, so a timely modeling of disaster loss is of great importance to emergency management. However, the complexity of interaction between river flood and storm surge imposes great challenges to the simulation of coastal flood in urban cities. At the same time, the local characteristics of building contents such as their types, values and vulnerabilities in different cities may also vary greatly. Haikou city, located in Hainan Island of China, was flooded due to the cascading effects of the upstream flood from Nandu River basin and the strong storm surge caused by strong winds of typhoon Rammasun during July 18 to 20, 2014.

In this study, firstly, the water from Nandu River was simulated with hydrological model and one-dimensional hydraulic model, and the coastal storm surge was modeled with a numerical surge model. The outputs of these models were used as the boundary conditions of two-dimensional hydraulic model, coupled with SWMM to reflect urban surface flow. Based on the above models, the maximum flood depth in Haikou city were derived. The inundation depth of Nandu River Estuary and riverside area is about 4 meters, while it of urban areas is relatively shallow. Secondly, the boundary of all the buildings in Haikou city and their geographic distribution were collected, and the values of contents were estimated building by building based on questionnaire survey data. Finally, based on the vulnerability curves developed in the past study, the direct economic loss of residential building contents were estimated. The results can provide a firm basis for the prediction of future loss before TC landing.

How to cite: Zhang, H., Yu, L., and Fang, W.: Modeling Direct Economic Loss of Residential Building Contents Induced by River Flood and Storm Surge: A Case Study on Typhoon Rammasun in Haikou City of China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4331, https://doi.org/10.5194/egusphere-egu2020-4331, 2020.

Tropical cyclones (TCs) often bring multiple hazards to offshore and onshore areas, including wind, rainfall, riverine flood, wave and storm surge. These hazards usually interact with each other and cause greater amplified hazard intensity. In the coastal areas, wave may damage coastal defense system like sea walls and dykes, and overtopping storm surge could hence become severe flooding due to the breach of the dykes. The probability distributions of wave and surge, as univariate respectively, have been studies and used in the design in various research. However, far less investigations on their joint probability distribution have been carried out in the past.

In this study, the dataset of hourly surge height, and significant wave height of 89 TC events impacting along Hainan Island during 1949~2013 was obtained, which are simulated numerically with ADCIRC and SWAN respectively. Following that, 4 types of probability distributions for univariate were used to fit the marginal distribution of storm surge and wave. Secondly, Frank, Clayton and Gumbel Copula were tried to construct the joint probability distribution of wave and surge, and the optimal Copula was determined by K-S test and AIC, BIC criteria. Based on the optimal Copula selected for each area of interest, the joint return period of wave and surge was estimated.

The results show that, 1) the annual maximum value of the storm surge height and significant wave height of Hainan Island has a relatively obvious geographical distribution regularity. 2) GEV and Gumbel are the most optimal distribution for storm surge height and significant wave height respectively. 3) Clayton Copula is the best model for fitting joint probability of storm surge and wave. The estimated joining probability distribution can help the determination of design standard, and typical TC disaster scenario development.

How to cite: Cheng, M. and Fang, W.: Joint probability of storm surge and wave along Hainan Island based on bi-variate copulas analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4361, https://doi.org/10.5194/egusphere-egu2020-4361, 2020.

Landslide dams occur when the debris from a landslide blocks, fully or partially, a river channel or floodplain. The landslide event often occurs during periods of heavy, intense rainfall, for example during hurricanes and tropical storms. This means that the blocked river is usually at high flow when the dam occurs, resulting in large volumes of water building up behind the dam. Due to the unconsolidated nature of the material blocking the river and large volumes of water behind it, it often does not take long for the dam to fail, releasing an enormous pulse of flood water down the river system. This flood pulse can cause enormous damage and modelling estimates show it can result in a flood peak from a catchment in the order of 3 to 4 times the flood peak that might be expected from the catchment under none-landside conditions. The island of Dominica in the Caribbean has suffered recently from two major catastrophic events, 2015 Tropical Storm Erica and 2017 Hurricane Maria. During these events there were many such landslide dam burst that brought significant damage to infrastructure such as bridges, housing as well as loss of lives.

We report on current research into understanding landslide dam risk on the island, funded by the World Bank as part of efforts to increase resilience of the islands infrastructure to hurricane induced hazards. The island has over one hundred main river systems, all of which are relatively steep due to the volcanic nature of the island and have therefore have significant landslide risk. We are aiming to answer the following questions with our research: (i) Which river catchments are most at risk from these dam-burst events and why; (ii) What evidence is available for landslides that blocked rivers during the last two major events; (iii) What are the scale of these events. We are carrying out geospatial analysis using a combination of landslide susceptibility mapping, river proximity analysis and LiDAR data recently collected for the island as well as landslide inventories for validation.

We will be using the understanding gained from this research to identify catchments most at risk, what infrastructure is exposed to this risk, what mitigation might be effective in reducing the risk, and finally what design changes to the infrastructure could be made to make it more resilient to these hazards.

How to cite: Trigg, M., Carr, A., and Trigg, S.: Identifying factors leading to hurricane induced landslide dam flood risk in Dominica, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11281, https://doi.org/10.5194/egusphere-egu2020-11281, 2020.

The methodology for Probabilistic Safety Assessment (PSA) of Nuclear Power Plants (NPPs) has been used for decades by practitioners to better understand the most probable initiators of nuclear accidents by identifying potential accident scenarios, their consequences, and their probabilities. However, despite the remarkable reliability of the methodology, the Fukushima Dai-ichi nuclear accident in Japan, which occurred in March 2011, highlighted a number of challenging issues (e.g. cascading event - cliff edge - scenarios) with respect to the application of PSA questioning the relevance of PSA practice, for such low-probability but high-consequences external events. Following the Fukushima Dai-ichi accident, several initiatives at the international level, have been launched in order to review current practices and identify shortcomings in scientific and technical approaches for the characterization of external natural extreme events and the evaluation of their consequences on the safety of nuclear facilities.

The H2020 project “New Approach to Reactor Safety ImprovementS” (NARSIS, 2017-2021) aims at proposing some improvements to be integrated in existing PSA procedures for NPPs, considering single, cascade and combined external natural hazards (earthquakes, flooding, extreme weather, tsunamis). It coordinates the research efforts of eighteen partners encompassing leading universities, research institutes, technical support organizations (TSO), nuclear power producers and suppliers, reactor designers and operators from ten countries.

The project will lead to the release of various tools together with recommendations and guidelines for use in nuclear safety assessment, including a Bayesian-based multi-risk framework able to account for causes and consequences of technical, social/organizational and human aspects and as well as a supporting Severe Accident Management decision-making tool for demonstration purposes.

The NARSIS project has now been running for two years and a half, and the first set of deliverables and tools have been produced as part of the effort of the consortium. Datasets have been collected, methodologies tested, states of the art have been produced, and various criteria and plans developed. First results have started to emerge and will be presented here.

How to cite: Foerster, E., Bazargan-Sabet, B., Daniell, J., Gehl, P., Vardon, P. J., Duvvuru Mohan, V. K., Rastiello, G., Štrubelj, L., and Ragon, F.: New trends in Multihazards Probabilistic Safety Assessment for nuclear installations: the H2020-NARSIS Project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5860, https://doi.org/10.5194/egusphere-egu2020-5860, 2020.

Power plants are essential for modern life and blackouts are a frequent observation during natural disasters. Thus, assessing the specific hazards for power plant facilities is a crucial component of community risk management. However, multi-hazard assessments are rare and risk studies only rely on independent perils.

For the European power plant sites, a multi-hazard assessment has been taken out considering earthquakes, flood, tornados and lightning. Each peril is considered independently. For each power plant location, return period curves for the relevant impact metrics like ground motion or wind speed have been compiled based on a variety of hazard model inputs. Those curves have been combined to provide threshold exceedance curves for any multi-hazard combination.

The results have been facilitated within a software developed for the H2020 EURATOM NARSIS project (New Approach to Reactor Safety ImprovementS) to allow users to define relevant thresholds for different power plant system components like offsite power generators or the connecting road network. In addition, it allows to explore the site-specific hazard combinations. The tool is based on the Electron development framework.

This study provides a general overview on the multi-hazard situation of Europe’s primary power producers and highlights sites where multi-hazard combinations may lead to infrastructure disruption. The results come with an easy-to-use tool to quickly assess the relevant metrics. It is hoped that these findings can help to increase the overall resilience of the European power network.

How to cite: Schaefer, A., Daniell, J., and Wenzel, F.: A multi-hazard assessment of Europe’s power plants, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13108, https://doi.org/10.5194/egusphere-egu2020-13108, 2020.

How to cite: Pittore, M., Gómez Zapata, J. C., Brinckmann, N., Weatherill, G., Babeyko, A., Harig, S., Mahdavi, A., Proß, B., Rosero Velasquez, H. F., Straub, D., Krautblatter, M., Frimberger, T., Langbein, M., Geiß, C., and Schoepfer, E.: Towards an integrated framework for distributed, modular multi-risk scenario assessment , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19097, https://doi.org/10.5194/egusphere-egu2020-19097, 2020.

In scenario-based and probabilistic single-hazard risk and loss estimation over urban building portfolios, it is customary to use specific exposure/vulnerability schemas that entail a set of mutually exclusive, collectively exhaustive (MECE) building classes, each associated with a fragility/vulnerability model focusing on the specific reference hazard., In a multi-risk application, where the same built structure can be subjected to the action of different natural hazards, possibly in close succession, a number of different schemas should be then jointly applied. Another option would be using a single set of building classes with as many fragility / vulnerability models as the considered natural hazards, as in the case for instance of the HAZUS multi-hazard framework. Unfortunately the latter approach requires a multi-hazard calibration that is rarely attainable with consistent results, while the former approach is complicated by the need for harmonizing different types of building classes. Furthermore, although fragility surfaces for independent hazards have been recently reported, they do not consider the nonlinear contribution of the different failure mechanisms (e.g. earthquake and tsunami) to the overall damage of a single asset. A timely update of the exposure model accounting for the progressive damage accumulation, thus describing a dynamic vulnerability framework, is then required.

We propose an alternative, innovative approach based on three main components: 1) a comprehensive multi-hazard building taxonomy able to address most of the building attributes driving the vulnerability with respect to different hazards, 2) a generalized description of the damage state of a building based on a set of low-level observable damage types and 3) a methodology to implement probabilistic mapping across different hazard-dependent building schemas and damage states.

A matrix describing the degree of compatibility between building types from two different schemas is estimated, partially making use of the fuzzy scores methodology suggested by Pittore et al., 2018. Since two building schemas may have different number of damage states (e.g. four in seismic, and six in tsunami), and are associated to different physical damage descriptions, the probability of the damage states conversion between the different schemas is also required.

This transparent and flexible formulation allows the implementation of multi-risk scenario assessment exploiting single-risk fragility/vulnerability models available in literature for a wide range of natural hazards. A preliminary state-dependency of these fragility models is based on expert knowledge. This work has been carried out within the scope of the RIESGOS project and exemplified in a study area in South America and further highlights the importance of defining accurate exposure models and compatible damage states descriptions in a multi- hazard-risk context. 

How to cite: Gomez- Zapata, J. C., Pittore, M., Brinckmann, N., and Shinde, S.: Dynamic physical vulnerability: a Multi-risk Scenario approach from building- single- hazard fragility- models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18379, https://doi.org/10.5194/egusphere-egu2020-18379, 2020.

Low probability events occurring in sequence, within a limited operational time (damage and recovery window between events), are a key consideration in multi-hazard safety assessments of nuclear power plants (NPPs). Cascading effects from hazards and associated event sequences could potentially have a significant impact on risk estimates. The Bayesian network can act as a framework to consider aforementioned statistical dependencies between various hazards in multi-risk analyses of nuclear power plants.

Within the EU project NARSIS (New Approach to Reactor Safety Improvements), a Bayesian network-based risk assessment framework was developed to perform multi-hazard risk assessment of NPPs.

The Bayesian network method was applied for an external-event related station blackout (SBO) scenario at a NPP. Earthquake, flooding, and tornado were among the hazards considered at a decommissioned NPP site location in Europe. Both hazard dependency in time as well as a cascading scenario was also considered. The hazards, their interactions and the fragilities of selected systems, structures and components within the nuclear power plant were represented in the network and their probability distributions were obtained based on the multi-hazard and fragility approaches adopted within the NARSIS project.

Sensitivity analyses in the network were used to identify key hazards and interactions. Most influential hazard combinations and ranges of intensity measures were identified through diagnostic inference in the network. Discretisation of continuous variables (hazard curves in this case) is a key aspect of performing inference in Bayesian networks. The effect of various levels of discretisation of hazard probability distributions was assessed, to identify suitable discretisations of hazard data.

This application demonstrates the use and advantages of the Bayesian network methodology, developed in the NARSIS project, for probabilistic safety assessments of NPPs.

How to cite: D. Mohan, V. K., Vardon, P., Daniell, J., Gehl, P., Schafer, A., van Gelder, P., Natarajan, V., Molenaar, C., Foerster, E., and Ragon, F.: Application of Bayesian Networks in Multi-Hazard Safety Assessment of Nuclear Power Plants, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21036, https://doi.org/10.5194/egusphere-egu2020-21036, 2020.

There are many ways to quantify initiating event probability and most of them are described in document “Defining initiating events for purposes of probabilistic safety assessment”, developed by the International Atomic Energy Agency . This guide describes seven methods: engineering evaluation or technical study of plant; reference to previous probabilistic safety assessment; EPRI list of initiating events; logical classification; plant energy balance fault tree; analysis of operation experience for actual plant; failure mode and effect analysis. In practice, currently many of PSA specialists use EPRI list of IEs, which has been originally prepared for single hazard and application to multiple hazards is not straightforward. Therefore other approaches are considered. In the paper combined method based on fragility functions and Bayesian network is proposed in order to elaborate for easier and more accurate approximation of the probability of initiating events caused by multiple hazards. In this respect, first of all, events fragility functions for hazards considered or multi-hazard fragility function are needed, which can have various form like , for example, parameterized fragility functions or logit fragility function. The next step is to develop a model the Bayesian network with the implementation of derived fragility functions. This can be performed using widely available computing programs for interactive building Bayesian network models .Depending on the hazards considered, the Bayesian network should be then developed accordingly. Example of such Bayesian network will be given.

Finally after calculating the probability of initiating events using this combined method, the results can be used in Event Trees and Fault Trees already developed for considered nuclear installation, in order to update the estimations of probabilities. Such an approach has also practical meaning as it will reduce man-month costs in comparison with the approach based on building full PSA models in Bayesian network.

How to cite: Kaszko, A., Kowal, K., and Potempski, S.: Quantification of initiating events probability based on fragility functions and Bayesian network applied for multi-hazard, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21900, https://doi.org/10.5194/egusphere-egu2020-21900, 2020.

Within the course of the EU project NARSIS (New Approach to Reactor Safety ImprovementS), sites of decommissioned nuclear power plants (NPPs) were investigated for external hazards using a multi-hazard approach.

The starting point was a review of existing multi-hazard frameworks, as well as their application to real world locations. From this knowledge, after significant screening, external hazards were analysed at different site locations in Europe using stochastic event sets for earthquake, flood, lightning, tornadoes, tsunami, hail and other perils in order to identify key scenarios along the hazard curves. These were built from existing national and supranational stochastic event sets.

The joint probability at each site of certain threshold events occurring was calculated, and relevant risk scenarios were chosen based on these hazard thresholds. Most importantly, the concept of joint operational time windows was investigated. Because the overall hazard for events is generally low, the chance of two low probability events is often screened out. However, during the damage and recovery window of these events (the operational time), the joint probabilities are much higher, thus affecting the infrastructure. Including the cascading effects, aftershocks, secondary effects and associated event sequences, provides a new insight into the probabilities of multi-hazard events and the implications for multi-risk.

Historical events from the loss database CATDAT and other records are chosen where joint operational time windows have occurred to show empirical examples of joint occurrences and cascades in the past for European and international examples.

Joint probabilities for significant events at decommissioned NPPs are presented within the NARSIS project and the application to multi-risk within Probabilistic Safety Assessments (PSA), however it is the application to other industrial types and infrastructure which shows the need for integration of multi-hazard (coinciding or cascading) events into operational management plans as well as important thought processes for building standards and use.

How to cite: Daniell, J., Schaefer, A., Winter, H., Gehl, P., Vardon, P., Mohan, V., Molenaar, C., Natarajan, V., Foerster, E., and Ragon, F.: A European multi-hazard assessment for Nuclear Power Plants with applications to other infrastructure types with operational time windows, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8829, https://doi.org/10.5194/egusphere-egu2020-8829, 2020.

Many countries face high risk to multiple natural hazards - such as earthquakes, floods, storms or extreme heat - which jeopardize human security around the globe. At the same time, a significant part of the world’s population is still living in poverty, often facing an increased vulnerability to hazards. On top it is often the most poor and vulnerable living in high-risk places. Hence, they are often disproportionally affected by disasters, facing significant development set-backs after disastrous events.

In response, many risk management and poverty reduction strategies have been applied in accordance with global agendas - such as the Sendai Framework for Disaster Risk Reduction – to strengthen disaster resilience. These range from early warning systems to the deployment financial mechanisms, and several specific disaster risk management (DRM), climate change adaptation (CCA), as well as livelihood support and other social protection (SP) programmes. Thus, many different streams have evolved, which, however, work towards similar goals and objectives. Those efforts are therefore often scattered and lacking cooperation and integration, hence preventing or hampering effective, well-coordinated risk management.

To overcome this deficit, Adaptive Social Protection has recently emerged as a promising approach to integrate SP, CCA and DRM with the goal to effectively build resilience to climate-related and other natural hazards. Best practices from several countries underline that this integrative approach provides a meaningful risk management option for the most vulnerable. However, ASP is still not well researched and lacks consideration on international level and in most countries’ approaches to build resilience.

This presentation will outline results from an extensive systematic literature review and highlight recent ASP developments and best practices from around the globe, as well as accenting protection gaps and entry points for ASP implementation. Drawing on this review and an ongoing study on the country of Indonesia, steps towards an effective ASP approach in will be discussed that will help building resilience in a multi-hazard environment.

How to cite: Sett, D. and Sandholz, S.: Adaptive Social Protection - an innovative approach to build resilience in a multi-hazard environment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19463, https://doi.org/10.5194/egusphere-egu2020-19463, 2020.

In order to support a more integrated approach in the early phases of Risk Management Process, a multi-hazard index was defined to assess the spatial and temporal interaction between natural hazards affecting the same area in a specific timeframe. The Coimbra municipality (western-central Portugal) was used as case study. This territory is an example of the persistent occurrence of potentially dangerous natural events: e.g., floods, landslides and forest fires. Using weighting methodologies, numerical values were assigned to each hazard-related factor (weights) and their categories (ratings). To minimize subjectivity/bias in weighting and rating-assignment processes, several quantitative methods were applied, including probabilistic frequency distribution, multi-criteria analysis and artificial neural networks. Monothematic hazard index quantification and subsequently multi-hazard assessment were implemented in a geographic information system. In few hotspots, the relationship between the observed multi-hazard manifestations and the predicted multi-hazard occurrences was recognized. A framework containing the main hazardous processes and most of the complex relationships/interconnections between them was established. The different degree of multi-hazard zones was mapped. This map can be used to support the implementation of actions to mitigate exposure and vulnerability to these hazards, as well as to promote the territorial management and the development of a resilient municipal system.

How to cite: Dimuccio, L., Cunha, L., and Figueiredo, R.: Defining a multi-hazard index in territorial planning framework: application on the Coimbra municipality (western-central Portugal), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9598, https://doi.org/10.5194/egusphere-egu2020-9598, 2020.

This paper focuses on the assessment of the multi-hazard natural disaster susceptibility and disaster risk in the Belt and Road (B&R) region. It is expected to provide a reference for cooperation in disaster risk reduction among B&R countries. Based on historical disaster data from 1980 to 2018, the disaster susceptibility of the B&R countries to multi-hazard has been analyzed using random forest model. The multi-hazard risk was further assessed based on the disaster susceptibility and Monte-Carlo method. Results show that regions with high susceptibility to meteorological hazards are mostly distributed in central Africa and the coastal areas of all continents. While Himalayan-Mediterranean seismic zone is susceptible to geological hazards. Due to the different distribution of regional exposures, the risks of economic loss and the risk of population casualties also appear differently. For economic loss risk, in grid scale very high and high level take 21% area. Europe, southeast China coast, and the Indian peninsula present higher economic loss risks. In population casualties risk, very high and high level take 15% area and in national scale the central and southern parts of Eurasia show higher population casualties risk. The results provide a comprehensive analysis of the spatial and temporal distribution, sensitivity, and disaster risk of natural disasters in B&R region, and provides a reference for regional disaster prevention and reduction cooperation.

How to cite: Chai, D., Wang, M., and Liu, K.: Risk analysis of multi-hazard in the Belt and Road region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13697, https://doi.org/10.5194/egusphere-egu2020-13697, 2020.

Volcanic eruption sequences are often very long in length, and can cause significant downtimes and damage to infrastructure. Over the course of the H2020 EURATOM NARSIS project (New Approach to Reactor Safety ImprovementS), a review of volcanic sources for Europe was undertaken including historical impacts, source parameters and potential events to impact nuclear facilities either directly or operationally.

The development of a volcano database of Eruption Source Parameters in order to estimate the tephra dispersal and its risk for the surrounding population and infrastructure is an important component for volcanic risk modelling.

Each eruption is unique with different Eruption Source Parameters (ESPs), depending on the volcano and its surrounding area. The aim of the project is to create a global volcano database including the ESPs. With the help of this database it is possible to generate a risk map of tephra dispersal for future volcanic eruptions. This map can be used to estimate the potential risk of tephra fall for the surrounding population and infrastructure (development of isopahcs).

The ESPs includes the plume height, the duration of the eruption, the volume, the mass and the grain-size distribution of the erupted material. Using local wind data it is possible to model an eruption and its range with open-source software packages, like Fall3D. A review of such open-source software packages has been undertaken. To get the ESPs of a volcano, which represent its typical eruptions, the eruption history of the volcano needs to be known. This is done using databases like the Smithsonian GVP, VOGRIPA and LaMEVE, as well as other reports and scientific articles.

The intended result is a volcano database containing 1547 volcanoes. The ESPs for each volcano will be given in ranges to be able to determine the minimal and maximal effects of an eruption. However, data of previous eruptions will not in all cases be able to be found for every volcano. For these volcanoes, assumptions based on the behaviour of similar volcanoes need to be made. This will then be combined with the socioeconomic impacts of historic eruptions as have been collected as part of the CATDAT Damaging Volcanic Eruptions Database, as well as a reanalysis of historical eruptions for ashfall.

How to cite: Neuweiler, A., Daniell, J., Schaefer, A., and Wenzel, F.: A global volcanic eruption source parameter database with application to determination of ashfall risk to infrastructure, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13896, https://doi.org/10.5194/egusphere-egu2020-13896, 2020.