Probabilistic Risk Assessment Methodology Research Articles

A new methodology for probabilistic flood displacement risk assessment: the case of Fiji and Vanuatu

This paper presents an enhanced probabilistic flood displacement risk assessment methodology. Several techniques have been proposed to estimate the number of people at risk of being displaced triggered due to climatic extremes. Among these methods, the probabilistic approach is promising for its quantitative nature and versatility at different scales. However, it has so far been limited to assessing loss of housing as the sole cause of displacement. The proposed methodology addresses this limitation by considering two additional elements beyond the traditional evaluation of housing loss: the likelihood of losing means of livelihood, directly included in the computation, and the likelihood of losing access to essential services, such as schools and health centers, provided as a factor to increase the propensity to displace. This new methodology is applied to assess flood disaster displacement risk in Fiji and Vanuatu, where climate change, coupled with the vulnerability of exposed assets, poses an existential threat to these Pacific islands, potentially leading to internal and cross-border population movements. Different climate scenarios were considered: current climate conditions (1979–2016 period), medium-term projected climate conditions (2016–2060), and long-term projected climate conditions (2061–2100). The average annual displacement increases in Fiji and Vanuatu by a factor of 3 and 4, respectively, in the projected long-term pessimistic climate scenario compared to current conditions. Depending on the country and climate change scenario, 20 to 40% of these displacements stem from loss of livelihoods as a dominant factor, highlighting the importance of considering this aspect in the vulnerability approach. The outcomes of these scenarios serve as the foundation for implementing displacement risk adaptation and management measures. This novel quantitative methodology holds significant potential for applications in larger domains and even globally.

Proposal of uncertainty analysis methodology for L1PRA using Markov state-transition model

ABSTRACT Following the severe accident at the Fukushima Daiichi Nuclear Power Plant in 2011, the revised nuclear safety regulation in Japan requires continuous safety improvement and states that PRA methods that reflect the latest knowledge should be used in activities related to continuous safety improvement. In this context, the construction of PRA models for the digital RPS (DRPS) has been addressed as an important issue within the Working Group on Risk Assessment (WGRISK) of the OECD/NEA, and several studies have been conducted. And there are challenges in aligning them with the conventional probabilistic risk assessment methodology. In a previous study, the authors developed a simultaneous differential equation describing the relationship between state transitions and state probabilities based on Markov state transition diagrams to calculate them numerically. However, the analytical method for uncertainty analysis commonly used in conventional PRA evaluations is not explicitly presented. The purpose of this study is to provide a methodology for more accurate evaluation of core damage frequency in nuclear power plants equipped with digital RPS, taking into account the uncertainties, and to contribute to the continuous improvement of safety.

Performance based probabilistic seismic risk assessment for urban heritage. An example in Pla del Remei Area (Valencia)

The assessment of the seismic behaviour of historic residential buildings and the estimation of their possible losses in the event of an earthquake, is a must for defining strategic mitigation plans to prevent irreplaceable heritage losses. In this study an integrated performance based probabilistic risk assessment methodology is developed. An archival study and a field survey allow to identify architectural and construction characteristics of heritage residential buildings in urban areas and determine realistic structural models. These are analysed by using a limit state approach, coded in the FaMIVE method, considering different construction hypotheses, to produce capacity curves which support the identification of a discrete number of typologies representative of the entire building stock in the area. Their fragility functions are then derived using the modified N2 method. Because of the difficulty in quantifying the expected probable losses in purely economic terms, given the heritage value of these assets, losses are computed in terms of damaged floor surface area and mean damage ratio. These have been obtained through the earthquake loss estimation platform SELENA, considering different possible seismic scenarios. The procedure is applied to masonry residential buildings in Pla del Remei area of Valencia, Spain, built between the end of the 19th Century and the end of the Spanish War (1939). This neighbourhood embodies the cultural values, construction techniques and historic legacy of a new and brief era of modernity, inspired by the new urban theories and architectural styles of Eclecticism and Modernism. Despite Valencia being located in an area of low to moderate seismicity, the results show that the maximum percentage of built damaged area ranges from 5.8 to 11.6% for 475 years return period, increasing to 33.59–51.59% for 975 years return period. The high level of resolution of the study allows mapping and identifying the structures at higher risk and is therefore a valuable tool to support sensitive and targeted retrofitting policies.

A Limited-Scope Probabilistic Risk Assessment Study to Risk-Inform the Design of a Fuel Storage System for Spent Pebble-Filled Dry Casks

This limited-scope study demonstrates the application of probabilistic risk assessment (PRA) methodologies to a spent fuel storage system for spent pebble-filled dry cask with a focus only on the necessary PRA technical elements sufficient to risk-inform the spent fuel storage system design. A dropping canister scenario in a silo of the spent fuel storage system is analyzed through an initiating event (IE) identification from the Master Logic Diagram (MLD); event sequence analysis (ES) by establishing the event tree; data analysis (DA) for event sequence quantification (ESQ) with uncertainty quantification; mechanistic source term (MST) analysis by using ORIGEN; radiological consequence analysis (RC) by deploying MicroShield, and risk integration (RI) by showing the Frequency-Consequence (F-C) target curve in the emergency area boundary (EAB). Additionally, a sensitivity study is conducted using the ordinary least square (OLS) regression method to assess the impact of variables such as failed pebble numbers, their location in the canister, and building wall thickness. Furthermore, the release categories grouped from the end states in the event tree are verified as safety cases through the F-C curve. This study highlights the implementation of PRA elements in a logical and structured manner, using appropriate methodologies and computational tools, thereby showing how to risk-inform the design of a dry cask system for storing spent pebble-filled fuel.

Integrated Safety and Security Analysis of Nuclear Power Plants Using Dynamic Event Trees

Nuclear security relies on the method of vital area identification (VAI) to determine which locations within the nuclear power plant (NPP) need to be protected from radiological sabotage. The VAI methodology uses fault trees (FTs) and event trees (ETs) to identify locations in the NPP that contain vital equipment: structures and components that may result in reactor significant core damage if direct or indirect sabotage occurred. However, the traditional FT/ET process cannot fully capture the dynamics of NPP systems and mitigating measures at play. Existing safety systems or possible operator procedures may be able to avert or mitigate core damage despite the loss of one or more vital areas. Dynamic probabilistic risk assessment (DPRA) methodologies are those that, unlike traditional probabilistic risk assessment, explicitly consider time effects when modeling a system. One common DPRA methodology is that of the use of dynamic event trees (DETs) that drive computer models of a system with user-specified branching conditions to account for uncertainties in a scenario. The DPRA process allows analysts to explore the uncertainties and state space of a scenario in a systematic fashion. A scenario was developed that uses the novel leading simulator/trailing simulator methodology to perform a DET analysis of a combined nuclear safety and nuclear security analysis. The scenario under consideration models the successful sabotage of a vital area by adversaries and determines the effects of timing and the extent of sabotage, as well as possible recovery actions, on the state of the plant. The results of this integrated analysis include the timing and extent of core damage as well as the extent of any radiological release that may occur as a result of sabotage.

Probabilistic urban cascading multi-hazard risk assessment methodology for ground shaking and post-earthquake fires

A probabilistic methodology is presented for assessing cascading multi-hazard risk for ground shaking and post-earthquake fires at a regional scale. The proposed methodology focuses on direct economic losses to buildings caused by the combined effect of ground shaking and post-earthquake fires and evaluates the exceedance probability of the regional shaking–fire losses in a predefined future time period by comprehensively considering the effects of various uncertain factors on the losses via Monte Carlo simulations. Probabilistic seismic risk assessments are extended by integrating post-earthquake fire models with seismic activity models, ground motion prediction equations, and seismic fragility functions. The fire models include post-earthquake ignition models, a weather model, a physics-based urban fire spread model, and a fire brigade response model. This integrated modeling enables the incorporation of the following uncertain factors with causal relationships into the assessments: earthquake occurrence, ground motion intensity distribution, damage to buildings resulting from ground shaking, post-earthquake ignition occurrence and occupant firefighting, weather condition, fire brigade response time including time to detection, and damage to buildings resulting from post-earthquake urban fire spread. To demonstrate the methodology, a realistic case study is conducted for a historical urban area with closely spaced wooden buildings in Kyoto, Japan, focusing on possible large earthquakes along major active faults. Contrary to conventional single-hazard approaches, the results highlight the impact of multi-hazard consideration on risk assessments. This indicates that the methodology can be a useful tool for more appropriately understanding earthquake risk and promoting risk-informed decision-making in urban communities for risk reduction.

Development of probabilistic risk assessment methodology against strong wind for sodium-cooled fast reactors

Development of probabilistic risk assessment methodology against strong wind for sodium-cooled fast reactors

Probabilistic Wildfire risk assessment methodology and evaluation of a supply chain network

Increasing wildfire risks have significantly disrupted supply chain systems in the United States, which accounts for a large portion of wildfire-induced economic losses and affects the regional and national economies. While it is important to understand wildfire effects on supply chains in meeting customer needs and achieving regional economic stability, such effects have not yet been extensively studied. This paper proposes a probabilistic framework for quantitatively assessing wildfire risk to a supply chain network. It provides rigorous probabilistic descriptions of wildfire ignition likelihood and growth, the interaction between supply chain components and wildfire, consequent component damage, and network-level performance reduction. A hypothetical forest-residuals-to-sustainable-aviation-fuel supply chain network is utilized as an illustrative example to demonstrate the capability and applicability of the proposed framework. The simulation results indicate that wildfire-induced damages to feedstock nodes are insignificant (i.e., 0.1% reduction in total feedstock availability per year), whereas total supply chain cost still increases considerably due to high unmet demand penalty and detours. Moreover, the findings further demonstrate the weakness of network configuration under wildfire risk: a failure of the processing facility located in the Inland Northwest of the United States results in significant increases in total transportation costs and time, which suggests that the construction of additional processors in this region could reduce wildfire risk significantly. Thus, the proposed framework can be used as a planning tool to evaluate network performance subject to a set of what-if scenarios and assess the effect of pre- and post-wildfire risk mitigation measures.

A Risk Approach to the Management for the Pre-defueled Phase in the Decommissioning Transition Stage

We discuss the specific risk significance in the extended pre-defueled (PD) phase of the decommissioning process, particularly if spent fuels are still in the core due to the low-power and shutdown refueling plant operating state (POS). The issue of full-core discharge capability after permanent shutdown during the PD phase motivated this study on the evolution of system risks using a reference plant design of the two-unit/BWR-4/Mark-I. The effects of the reactor core and the spent fuel pool (SFP) on the incorporative risks are explored. The probabilistic risk assessment methodology, including the technical elements, is systematically developed by defining two primary configurations from the internal event analysis under the models 30, 60, 180, 365, and 942 days after permanent shutdown, respectively. The movable refueling gate between the reactor core and the SFP, as well as the residual heat removal (RHR) system, have been subjected to two sensitivity studies on system configurations in order to examine the induced impacts by the refueling gate and cooling systems. MELCOR, a realistic thermal-hydraulic code, is utilized to determine the decay heat levels and the success criteria after shutdown. The two operator tasks are assumed to be independent in the situation of decreasing decay heat after shutdown and a long time available for human actions. In addition, the WinNUPRA software package is used for the fuel uncovery sequence quantification. Plant-centered loss-of-offsite power (LOOP), flow diversion loss-of-coolant accidents (LOCAs) to the suppression pool via the RHR system, switchyard-centered LOOPs, and LOCAs in the connected systems via the RHR, have proven to be the most significant initiating events for the configurations. When compared to the low-power and shutdown refueling POS, the realistic quantification results in terms of fuel uncovery frequencies and the evolution of the risk profile for the basic and sensitivity configurations meet the expectations under the PD-phase condition of low-decay heat levels.

Dynamic probabilistic risk assessment of seismic-induced flooding in pressurized water reactor by seismic, flooding, and thermal-hydraulics simulations

ABSTRACT Probabilistic risk assessment (PRA) is an essential approach to improving the safety of nuclear power plants. However, this method includes certain difficulties, such as modeling of combinations of multiple hazards. Seismic-induced flooding scenario includes several core damage sequences, i.e. core damage caused by earthquake, flooding, and combination of earthquake and flooding. The flooding fragility is time-dependent as the flooding water propagates from the water source such as a tank to compartments. Therefore, dynamic PRA provides realistic results and insights related to time-dependent accident sequences. This study analyzed the risk of seismic-induced flooding events by coupling seismic, flooding, and thermal-hydraulics simulations, considering the dependency between multiple hazards explicitly. For requirements of safety improvement, especially in light of the Fukushima Daiichi Nuclear Power Plant accident, sensitivity analysis was performed on the seismic fragilities, and the effectiveness of alternative steam generator injection by a portable pump was estimated. We demonstrate the use of this simulation-based dynamic PRA methodology to evaluate the risk induced by a combination of hazards.

An integrated probabilistic risk assessment methodology for maritime transportation of spent nuclear fuel based on event tree and hydrodynamic model

Spent nuclear fuel maritime transportation (SNFMT) accident may cause radiation hazards to personnel, vessels, and the ocean environment. Current risk assessment methods of SNFMT lack full consideration and quantification of the risk indicators. In this work, an integrated probabilistic risk assessment (IPRA) methodology incorporating multiple risk factors-based accident probability model and public dose-based radiological consequence model quantitatively is proposed for SNFMT. First, from the sociotechnical-environmental risk perspective, the SMCETC (Ship, Management, Crew, Environment, Tank, Channel) comprehensive risk indicators are identified for ET-FT modeling. Second, considering the effects of continuous emissions, water depth, tidal cycle, and radioactive decay, a shallow water equations-based hydrodynamic model is established to simulate the radionuclide concentration in coastal water. Third, the ET-FT model-based accident frequency and the radionuclide concentration-based population radiation consequence are integrated, and subsequently the public radioactive risks are obtained. Finally, a case study is presented to demonstrate the feasibility and value of the proposed method. The time-related public radioactive risks were quantified and 28 highly safety importance risk factors were found and ranked. The proposed IPRA methodology integrates deterministic and probabilistic modeling perspectives, and provides a comprehensive risk assessment tool for SNFMT.

A machine learning methodology for probabilistic risk assessment of process operations: A case of subsea gas pipeline leak accidents

Subsea gas pipeline leak may cause the catastrophic consequences, e.g., offshore fire and explosion, and the overturning of floating offshore structures. Efficient risk assessment is critical to prevent the unexpected offshore accident due to subsea gas pipeline leak. The modern technology advancement urges the need for developing new risk assessment approach in the case that a large volume of data becomes available. A new methodology comprising of BRANN implemented with BN is proposed for dynamic risk assessment of process operations, which can capture the nonlinear and the non-sequential features of accident escalation. BN is used to model the accident scenario from causations to consequences considering the conditional dependencies among accident contributory factors. Subsequently, BN model as an informative base is mapped into BRANN model. The data-driven nature makes it better to capture the uncertainty among the cause-consequence relationships. The practicability of the methodology is illustrated by a case study of subsea gas pipeline leak accident. It is observed that BRANN model is superior in the network performance and prediction accuracy. This methodology can help to perform more effectively real-time risk assessment of process operations.

Optimal Bayesian MCMC based fire brigade non-suppression probability model considering uncertainty of parameters

The fire brigade non-suppression probability model is a major factor that should be considered in evaluating fire-induced risk through fire probabilistic risk assessment (PRA), and also uncertainty is a critical consideration in support of risk-informed performance-based (RIPB) fire protection decision-making. This study developed an optimal integrated probabilistic fire brigade non-suppression model considering uncertainty of parameters based on the Bayesian Markov Chain Monte Carlo (MCMC) approach on electrical fire which is one of the most risk significant contributors. The result shows that the log-normal probability model with a location parameter (μ) of 2.063 and a scale parameter (σ) of 1.879 is best fitting to the actual fire experience data. It gives optimal model adequacy performance with Bayesian information criterion (BIC) of −1601.766, residual sum of squares (RSS) of 2.51E-04, and mean squared error (MSE) of 2.08E-06. This optimal log-normal model shows the better performance of the model adequacy than the exponential probability model suggested in the current fire PRA methodology, with a decrease of 17.3% in BIC, 85.3% in RSS, and 85.3% in MSE. The outcomes of this study are expected to contribute to the improvement and securement of fire PRA realism in the support of decision-making for RIPB fire protection programs.

Safety and Risk of Operating Facilities: Rapid Assessment Methodology

Two methods for methodology uses “risk indices” (dam condition index I), as well as “fuzzy logic methods” to combine the original quantitative and qualitative (expert) information on the operated dam condition. The approach applied is in accordance with the recommendations of IEC 31010: 2019. Risk Management — Risk Assessment Techniques. Deterministic assessments of the condition in the form of “risk indices” were also used as input data in assessing the probability of the failure and in developing a probabilistic risk assessment methodology. The original database, as well as the damage assessment scale, modified in the course of the research, summarize the experience of surveys and examination of safety declarations of more than 180 hydraulic works in Russia. A description is given of the methods of initial assessment and digitization (quantification) of the condition index I, as well as combining the initial quantitative and qualitative (expert) information about various damages. The practical feasibility and the possibility of categorizing (with fuzzy boundaries) conditions and levels of damage to hydraulic structures are shown. With regard to various conditions and levels of damage, proposals are made for practical actions to ensure the safety of dams in the process of monitoring, inspection, development of a reconstruction project and its expertise. As a result of the research, the dependence of the probability of the failure pfailure on the average value of the Iср index has been established and the graph “pfailure – Iср” is presented, which is well described by the exponential and is convenient for practical application. The value of I ср is determined according to the data of visual and instrumental control of the dam condition of the as well as according to expert estimates. The methodology for creation the indicated graph is presented. The creation of this graph became possible on the basis of: statistical processing, proof of the “normality” of the distribution of the I indices and the estimation (according to the distribution functions constructed for each level of damage) the probability of the failure pfailure, as well as during the survey and examination of the dam project. Proposals are formulated for the practical application of the proposed methods for assessing the risk of operating dams, and the near-term prospect of research in the field of risk assessment and ensuring dam safety is formulated.

Probabilistic Wildfire Risk Assessment Methodology and Evaluation of a Supply Chain Network

Probabilistic Wildfire Risk Assessment Methodology and Evaluation of a Supply Chain Network

Perfluorooctanoic Acid (PFOA) and Perfluorooctanesulfonic Acid (PFOS) in Surface Water of China: National Exposure Distributions and Probabilistic Risk Assessment.

This study presents a comprehensive application of the probabilistic risk assessment methodology for perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), which are two types of perfluoroalkyl acids frequently studied in recent years. The exposure characteristics of PFOA and PFOS in Chinese surface water on a nationwide scale were summarized. Individual predicted no-effect concentration (PNEC) and the sensitivities for taxonomic groups of primary producers, invertebrates, and vertebrates were derived by the species sensitivity distributions method. Both hazard quotients (HQs) and joint probability curves were calculated to assess the risks to aquatic organisms. Among seven Chinese river basins, the mean concentrations of PFOA and PFOS in the Yangtze River Basin were the highest (58ng/L and 22ng/L, respectively), while the lowest concentrations (< 1ng/L) were in the Songhua River Basin. The acute PNEC value was 2.43mg/L for PFOA and 0.96mg/L for PFOS, and the chronic PNEC value was 0.0067mg/L for PFOA and 0.0012mg/L for PFOS, respectively. The sensitivities of different taxonomic groups revealed higher sensitivity of primary producers for PFOA and higher sensitivity of invertebrates for PFOS. The acute HQs of PFOA and PFOS were less than 1. The probabilities of exposure concentrations exceeding 5th percentile toxicity value of the chronic data for all aquatic organisms were 1.65% for PFOA and 1.23% for PFOS, respectively, suggesting a low probability of effects to aquatic organisms. Compared with the risk scenarios worldwide, the ecological risks for chronic effects decreased in the order of PFOS (worldwide) > PFOA (China) > PFOS (China) > PFOA (worldwide).

Guided simulation for dynamic probabilistic risk assessment of complex systems: Concept, method, and application

Probabilistic risk assessment (PRA) is a systematic process of examining how engineered systems work to ensure safety. With the growth of the size of dynamic systems and the complexity of the interactions between hardware, software, and humans, it is extremely difficult to enumerate risky scenarios by the traditional PRA methods. In this study, a new dynamic probabilistic risk assessment methodology is proposed that employs a new exploration strategy to generate risky scenarios. The proposed methodology consists of three main modules, including simulation, planner, and scheduler. In this methodology, the engineering knowledge of the system is explicitly used to guide the simulation module to achieve higher efficiency and accuracy. The engineering knowledge is reflected in the planner module which is responsible for generating plans as a high-level map to guide the simulation. The scheduler module is responsible for guiding the simulation by controlling the timing and occurrence of the random events. In this paper, modules of the proposed methodology, and their interactions are explained in detail. The developed methodology is used to perform risk assessment of a Space Shuttle ascent phase, and results show the effectiveness of the proposed platform.

Dynamic probabilistic risk assessment of decision-making in emergencies for complex systems, case study: Dynamic positioning drilling unit

Decision-making in emergency situations is a risky and uncertain process due to the limited information and lack of time. Some key problem parameters, such as the time required to complete important response tasks, must be estimated and are therefore prone to errors. Other parameters, such as the probability of occurrence of a consequential event, will typically change as the response operation progresses. As a result, there should be a dynamic probabilistic risk assessment framework to assess the risk level of decision scenarios and facilitate the decision-making process.In this paper, a methodology for dynamic probabilistic risk assessment of decision making in emergencies for complex marine systems is proposed. In this method, a dynamic event sequence diagram is introduced that helps to quantify events probabilities as a function of time, as well as environmental and operational variables, considering events interdependencies and uncertainties. In addition, the effects of time required11Time required refers to the time needed to perform each level of decision-making process (detection, diagnosis, decision-making, execution). and time available22Time available is the time remaining before an accident happens in a system. It is derived from the dynamic simulator of the system. The dynamic simulator calculates the time remaining before a collision based on the system components status, operation and environmental conditions at the time of incident. for performing a decision in emergency are considered in the risk model. In this methodology, probabilistic models including Bayesian network and Monte Carlo simulation are utilized to quantify the uncertain behavior of the decision-making process in complex marine systems.A computational study is also conducted to evaluate the methodology performance, in terms of effectiveness and efficiency. Computational results show that the proposed approach can obtain optimal solutions for large and practical problem sizes.

A paradigm shift in Natech risk management: Development of a rating system framework for evaluating the performance of industry

An increasing trend in the number of reported natural hazard-triggered technological accidents (Natechs) worldwide has led to the development of contributions in Natech risk assessment and management, risk reduction and emergency response strategies, risk communication, risk perception, and risk governance. Yet, although some studies have underscored the need for resilient industries, only recently researchers have called for an area-wide perspective, where a paradigm shift in Natech risk management fosters a holistic systems approach. That approach should contemplate the facilities and their surrounding environment, and encourage multi-stakeholder interaction to advocate for more resilient societies. However, there are currently no methodologies for evaluating, rating, and certifying industrial facilities' level of preparedness to Natech risk; nor are there any tools available to inform the governments and the public, on industry's Natech risk management practices. In response to this need, we propose Natech RateME, a comprehensive, area-wide risk management, and rating system framework. The proposed framework is premised on a probabilistic risk assessment methodology, that can support industrial facilities' management of onsite and offsite risk from complex events and that rates their performance in terms of minimum life losses. By evaluating their safety performance, their engagement to develop cooperation mechanisms with local authorities and neighboring communities, and their commitment to disclose risk information, among other aspects, the framework, and the rating system, seek to contribute to overall territorial resilience. To do so, Natech RateME considers the interaction between infrastructure systems present at industrial parks, the technical and organizational systems, governance, risk communication, and community participation, constituting the first approach to support the strengthening of prevention and mitigation measures by enhancing facilities' and governments' abilities to manage Natech risk comprehensively.

Probabilistic cascading multi-hazard risk assessment methodology using graph theory, a New Zealand trial

Probabilistic multihazard risk assessment from natural hazard is still a challenge today. Current limitations are the number of different hazards that can be included in the assessment, the capacity to output detailed spatial results and access to data for inverse fitting models. A novel quantitative multi-hazard framework is proposed which permit to model the interactions and triggering of perils of different types. Franz Josef on the West Coast of South Island, New Zealand, is used as a case study where the interactions and impacts of earthquake, rainfall, landslide, landslide dam and flooding hazards on the road network, stopbanks and housing is stochastically quantified. The results show that losses are earthquake-dominated, while rarer events show an influence of landsliding on the losses. Due to the complex interaction between hazards allowed by the modelling framework, while initial perils reach a “loss ceiling” with reducing probabilities, it also supercharges secondary perils that can cause greater and greater losses. Combining a network framework with iterative simulations is shown to provide inherent advantages by its discrete nature. In-depth exploration of the data outputs grants insight into the interconnected disaster system on a granular level. The risk assessment herein pointed out the seriousness of the multihazard threat to the Franz Josef township.