Probabilistic Hazard Assessment Research Articles

Probabilistic Hazard Assessment of Bridges under Multihazard Actions during the Life-Cycle Period

Probabilistic Hazard Assessment of Bridges under Multihazard Actions during the Life-Cycle Period

Copula-Based Probabilistic Hazard Assessment Model for Debris Flow Considering the Uncertainties of Multiple Influencing Factors

This paper proposes a probabilistic hazard assessment model for debris flows considering the uncertainties of multiple influencing factors based on copula approaches. Fifty-nine rainfall-induced debris flows occurred between 2001 and 2009 in Taiwan are taken as an illustrative example to validate the proposed approaches. A copula-based probabilistic model is developed to model the joint probability distribution of debris-flow volume V and its influencing factors (e.g., rainfall intensity, RI and landslide area, AL). The developed model is then used to make probabilistic prediction of debris-flow volume for a specific hazard level, and compared with the empirical approaches. The proposed probabilistic model is also used to develop the exceedance probability charts of quantities for a specific debris-flow basin. Results show that the developed V–RI–AL probabilistic model can provide reasonable estimates of debris-flow volume in Taiwan for a specific probability level of 0.94, and show better predictive performance than the empirical relationships by using an independent debris-flow dataset in Taiwan for validation. The developed multivariate joint probabilistic model can also provide the exceedance probability of debris flows through considering the uncertainties of debris flow and its influencing factors, providing a preliminary reference for hazard assessment of the debris flows.

The probable future of toxicology - probabilistic risk assessment.

Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.

SPATIAL ANALYSIS OF VOLCANIC ASH DISTRIBUTION DUE TO VOLCANIC ERUPTION IN JAVA ISLAND

Abstract. Indonesia is located on the Ring of Fire with the most geologically active than any other countries, which makes it vulnerable due to the massive earthquakes and volcanic eruptions. Java Island has the most active volcano with high risks such as human risk and infrastructure from volcanic ash because of volcanic eruptions. The availability of the map of potential volcanic hazards is important to help mitigate the risk caused by volcanic eruptions. However, to the best of the author's knowledge, the distribution of volcanic ash has never been assessed in detail in the disaster-prone hazard map published by the Centre for Volcanology and Geological Hazard Mitigation (CVGHM), Indonesia. This research reported the potential distribution of volcanic ash due to volcanic eruptions in the future in Java island. Following the principles of Probabilistic Hazard Assessment and TephraProb software, the modeling of volcanic ash potential was performed using various parameters such as historical data, eruption source parameter, total grain-size distribution, tephra2 parameter, and the wind speed around the volcanoes as an input. The map shows the distribution of volcanic ash based on the volcanic ash accumulation (kg/m2) and the volcanic ash hazard map is classified into three classes. There are 19 models of volcanic ash distribution with various probabilities of exceedance based on 19 A-type volcanoes on Java Island. This volcano's distribution of volcanic ash tends to the southwest as the wind speed and direction.

Probabilistic volcanic mass flow hazard assessment using statistical surrogates of deterministic simulations

Probabilistic volcanic hazard assessments require (1) an identification of the hazardous volcanic source; (2) estimation of the magnitude-frequency relationship for the volcanic process; (3) quantification of the dependence of hazard on magnitude and external conditions; and (4) estimation of hazard exceedance from the magnitude-frequency and hazard intensity relationship. For volcanic mass flows, quantification of the hazard is typically undertaken through the use of computationally expensive mass flow simulators. However, this computational expense restricts the number of samples that can be used to produce a probabilistic assessment and limits the ability to rapidly update hazard assessments in response to changing source probabilities. We develop an alternate approach to defining hazard intensity through a surrogate model that provides a continuous estimate of simulation outputs at negligible computational expense, demonstrated through a probabilistic hazard assessment of dome collapse (block-and-ash) flows at Taranaki volcano, New Zealand. A Gaussian Process emulator trained on a database of simulations is used as the surrogate model of hazard intensity across the input space of possible dome collapse volumes and configurations, which is then sampled using a volume-frequency relationship of dome collapse flows. The demonstrated technique is a tractable solution to the problem of probabilistic volcanic hazard assessment, with the surrogates providing a good approximation of the simulator, and is generally applicable to volcanic hazard and geo-hazard assessments that are limited by the demands of numerical simulations and changing source probabilities.

Assessing long-term tephra fallout hazard in southern Italy from Neapolitan volcanoes

Abstract. Nowadays, modeling of tephra fallout hazard is coupled with probabilistic analysis that takes into account the natural variability of the volcanic phenomena in terms of eruption probability, eruption sizes, vent position, and meteorological conditions. In this framework, we present a prototypal methodology to carry out the long-term tephra fallout hazard assessment in southern Italy from the active Neapolitan volcanoes: Somma–Vesuvius, Campi Flegrei, and Ischia. The FALL3D model (v.8.0) has been used to run thousands of numerical simulations (1500 per eruption size class), considering the ECMWF ERA5 meteorological dataset over the last 30 years. The output in terms of tephra ground load has been processed within a new workflow for large-scale, high-resolution volcanic hazard assessment, relying on a Bayesian procedure, in order to provide the mean annual frequency with which the tephra load at the ground exceeds given critical thresholds at a target site within a 50-year exposure time. Our results are expressed in terms of absolute mean hazard maps considering different levels of aggregation, from the impact of each volcanic source and eruption size class to the quantification of the total hazard. This work provides, for the first time, a multi-volcano probabilistic hazard assessment posed by tephra fallout, comparable with those used for seismic phenomena and other natural disasters. This methodology can be applied to any other volcanic areas or over different exposure times, allowing researchers to account for the eruptive history of the target volcanoes that, when available, could include the occurrence of less frequent large eruptions, representing critical elements for risk evaluations.

Scenario-based probabilistic hazard assessment for explosive events at the San Salvador volcanic complex, El Salvador

We present a scenario-based, probabilistic hazard assessment for the San Salvador volcanic complex (SSVC), a volcanic field located in the vicinity of San Salvador that includes the El Boquerón stratovolcano and 25 monogenetic vents. We define a set of likely eruption scenarios for tephra fallout and pyroclastic density currents (PDCs). The eruption scenarios range from violent Strombolian eruptions with a significant uncertainty in source position to sub-Plinian and Plinian activity fed from the central cone. The adopted methodology is mainly based on numerical modeling using Tephra2 (adopting the software TephraProb) to study tephra fallout and the branching box model and the branching energy cone model (adopting the programs BoxMapProb 2.0 and ECMapProb 2.0) to describe inertial and frictional PDCs, respectively. Despite the dominant W-WSW-trending winds, numerical results show that Plinian eruptions at El Boquerón volcano are able to deposit thick tephra layers in the metropolitan area of San Salvador city, likely reaching mass loads of the order of 100 kg/m2 (conditional probability of 50%). The simulated sub-Plinian events highlight the seasonal influence of wind patterns. In fact, the conditional probability of significant tephra sedimentation in San Salvador city is strongly reduced when eruptions occur during the rainy season. Numerical modeling of violent Strombolian eruptions is performed considering uncertainty in vent position. Results show that the conditional probability of depositing tephra mass loads higher than 10 kg/m2 at a given point reaches a maximum value of ∼7% on the NW flank of the volcano, at about 8 km from the central crater. On the other hand, very low conditional probabilities (<1%) are obtained for San Salvador city for any relevant threshold (10 kg/m2 or more) of tephra mass load during violent Strombolian events. Regarding PDCs, results show that those produced during large-scale Plinian eruptions are able to invade significant areas of the volcano surroundings, including San Salvador city. PDCs generated from the partial collapse of a sub-Plinian eruption column exhibit maximum inundation probabilities on the N, W and S flanks of the volcano. Cerro El Picacho exerts a significant shield effect on the propagation of these PDCs, with low inundation probabilities for San Salvador city (<3%). Finally, coupling published vent opening probability maps and numerical modeling of small-scale PDCs yields maximum inundation probabilities on the NW flank of the volcano, reaching maximum conditional probabilities of the order of ∼10% and values of about 5% near the village of Nuevo Sitio del Niño.

The EU Center of Excellence for Exascale in Solid Earth (ChEESE): Implementation, results, and roadmap for the second phase

The EU Center of Excellence for Exascale in Solid Earth (ChEESE) develops exascale transition capabilities in the domain of Solid Earth, an area of geophysics rich in computational challenges embracing different approaches to exascale (capability, capacity, and urgent computing). The first implementation phase of the project (ChEESE-1P; 2018–2022) addressed scientific and technical computational challenges in seismology, tsunami science, volcanology, and magnetohydrodynamics, in order to understand the phenomena, anticipate the impact of natural disasters, and contribute to risk management. The project initiated the optimisation of 10 community flagship codes for the upcoming exascale systems and implemented 12 Pilot Demonstrators that combine the flagship codes with dedicated workflows in order to address the underlying capability and capacity computational challenges. Pilot Demonstrators reaching more mature Technology Readiness Levels (TRLs) were further enabled in operational service environments on critical aspects of geohazards such as long-term and short-term probabilistic hazard assessment, urgent computing, and early warning and probabilistic forecasting. Partnership and service co-design with members of the project Industry and User Board (IUB) leveraged the uptake of results across multiple research institutions, academia, industry, and public governance bodies (e.g. civil protection agencies). This article summarises the implementation strategy and the results from ChEESE-1P, outlining also the underpinning concepts and the roadmap for the on-going second project implementation phase (ChEESE-2P; 2023–2026).

Influence of local site effects on seismic risk maps and ranking of Italian municipalities

The latest studies concerning seismic risk assessment in Italy were developed without considering the site geo-lithological effects, thus limiting the assessment to rock soil type. In this study, for the evaluation of site amplification effects, we used the results of recent works based on the Italian seismic microzonation data. As this first study is performed for the entire Italian territory at the municipality scale, the site amplification factors (AFs) have been assigned to the chief town of each of the 7715 Italian municipalities, assuming that the building stock is concentrated in that small area. The AFs have been compared with those foreseen by the Italian building code (NTC2018). The PGA amplification with the new AFs ranges from 1 to 2.2 bringing to an average increase of 75% in the hazard, compared to 27% if using the NTC2018 amplification. The seismic risk has been evaluated using the probabilistic hazard assessment adopted in the NTC2018 and recent vulnerability/exposure models developed for Italy. The residential building stock was subdivided into 5 vulnerability classes (3 for masonry and 2 for concrete) derived from the national census data and further refined using the construction age and building height. The results show that, for a rock site condition, the loss estimates are comparable with those of previous works in terms of casualties, homeless, and economic damage. The introduction of the site effects brings to a significant increase in the expected losses resulting higher than the historical data retrieved from a careful analysis of the major Italian earthquakes. This suggests the importance of developing new fragility curves based on updated AFs. To support the implementation of risk reduction programs, a seismic risk index is introduced, allowing a municipality ranking by risk, a cost estimation of the seismic retrofitting in the highest risk municipalities, and an assessment of the consequent risk reduction.

Practical workflow for assessment of seismic hazard in low enthalpy geothermal systems

It is of vital importance to be able to determine the seismic hazard in advance of any geothermal operation in the subsurface, especially in a densely populated area such as The Netherlands. The author aims to arrive at a practical assessment of the seismic hazard in low-enthalpy geothermal doublet systems specifically designed for heat exchange in porous and permeable aquifers operated on a volume balance, at a depth range of 1800 to 3300 m having temperatures in the range of 60 °C to 100 °C. The article presents a practical workflow aiming to determine the probability distribution for mechanical re-activation along pre-existing weak faults. After presenting the tectonic structural setting the criticality criterion based on shear mobilisation is introduced. Existing stress models are reviewed and a practical manner to estimate and limit all geomechanical input parameters is presented, including fault mechanical properties. The workflow is demonstrated both for early period operation times and at final thermal breakthrough. The uncertainty is addressed through probabilistic logic tree analysis quantifying the variation of the four most uncertain input parameters: fault cohesion and friction coefficient, the thermal stress parameter and the initial minimum Earth stress. The probabilistic hazard assessment is characterised by four output parameters: the expected value, the probability that unity is exceeded and two more probabilities. In case unity is exceeded the range of fault dips prone to mechanical re-activation is shown. Exceedance of this first necessary condition requires the assessment of the other two necessary conditions: seismogeneity and moment magnitude.Article highlightsThe article presents a practical workflow to assess the seismic hazard associated with geothermal operations.To be able to perform the necessary uncertainty analysis, four main input parameters are treated probabilistic.The first necessary condition for seismicity to occur is characterised by four probabilities: the expected probability, the probability that seismicity can occur and two higher probabilities.

Categorizing and Harmonizing Natural, Technological, and Socio-Economic Perils Following the Catastrophe Modeling Paradigm.

The literature on probabilistic hazard and risk assessment shows a rich and wide variety of modeling strategies tailored to specific perils. On one hand, catastrophe (CAT) modeling, a recent professional and scientific discipline, provides a general structure for the quantification of natural (e.g., geological, hydrological, meteorological) and man-made (e.g., terrorist, cyber) catastrophes. On the other hand, peril characteristics and related processes have yet to be categorized and harmonized to enable adequate comparison, limit silo effects, and simplify the implementation of emerging risks. We reviewed the literature for more than 20 perils from the natural, technological, and socio-economic systems to categorize them by following the CAT modeling hazard pipeline: (1) event source → (2) size distribution → (3) intensity footprint. We defined the following categorizations, which are applicable to any type of peril, specifically: (1) point/line/area/track/diffuse source, (2) discrete event/continuous flow, and (3) spatial diffusion (static)/threshold (passive)/sustained propagation (dynamic). We then harmonized the various hazard processes using energy as the common metric, noting that the hazard pipeline’s underlying physical process consists of some energy being transferred from an energy stock (the source), via an event, to the environment (the footprint).

A probabilistic hazard and risk assessment of exposure to metals and organohalogens associated with a traditional diet in the Indigenous communities of Eeyou Istchee (northern Quebec, Canada)

Hunting, trapping, and fishing are part of an Indigenous lifestyle in subarctic Canada. However, this lifestyle may be a route of exposure to contaminants and may pose a risk for the people who rely on a subsistence diet. Monte Carlo simulations for the chemical concentration of eight game species and one fish species were carried out by randomly sampling 10,000 samples from the contaminant measures for each species. We then calculated a probabilistic non-carcinogenic hazard quotient or carcinogenic risk values to estimate the human health risk of exposure to contaminants. Of the species examined, ducks were of concern for potential carcinogenic risk. There was a modeled probabilistic 95th-percentile risk associated with the consumption of ducks and polybrominated biphenyl (PBB) congener 153 in boys, women, and men (1.09 × 10−6, 1.57 × 10−6, and 2.17 × 10−6, respectively) and, to a lesser extent, with geese and polychlorinated biphenyl (PCB) congener 153 in men (1.19 × 10−6). Contaminant concentration in the intake rate (food consumption) for PBB congener 153 exposure risk was more important than that for PCB congener 153, where intake rate had greater relevance. The consumption of waterfowl may increase the exposure to organohalogens; however, there are health and wellness benefits associated with the harvesting and consumption of subsistence foods that must also be considered. We recommend follow-up species-specific studies focused on ducks to clarify and elucidate the results in the present study.

Probabilistic hydro-geomorphological hazard assessment based on UAV-derived high-resolution topographic data: The case of Basento river (Southern Italy)

The combined use of Unmanned Aerial Vehicles (UAVs) and Structure from Motion (SfM) photogrammetry allows the collection and processing of high-resolution data on demand, which is key for the constant and detailed monitoring of the fluvial environment. In addition, through the ever-increasing development of new techniques of mapping and data processing (i.e., UAVs swarm, BVLOS flight missions, high-performance photogrammetry workflow), it is now possible to detect large areas at high-resolution, providing support for spatiotemporal insights into hydro-geomorphological processes and hazard assessment, in order to ensure an effective management and to prevent catastrophic phenomena (i.e., floods).The main goal of this paper is to use UAV-based high-resolution topographic data to constrain the probabilistic hazard assessment of extended reaches of Basento river (Basilicata, Italy). To demonstrate the influence of geomorphology and riverbed sediment on hazard assessment, a sensitivity analysis was carried out on the resolution of the UAV-derived DEMs; on the riverbed roughness coefficient resulting from photo-sieving analysis and on the morphological change detection over short-time ranges (2019–2021). We found that lower resolution DEMs lead to an increase of flooding probability (in several river cross-sections an increase even higher than 99 % resulted), and that a greater roughness coefficient involves an increase in the probability of flooding (with a maximum increase of about 9 %). Moreover, the multitemporal high-resolution outputs resulting from SfM allowed the identification of morphological changes, in short-times, caused by an anthropic modification of the river bank, which significantly affected the flooding hazard.

VIGIL: A Python tool for automatized probabilistic VolcanIc Gas dIspersion modeLling

Probabilistic volcanic hazard assessment is a standard methodology based on running a deterministic hazard quantification tool multiple times to explore the full range of uncertainty in the input parameters and boundary conditions, in order to probabilistically quantify the variability of outputs accounting for such uncertainties. Nowadays, different volcanic hazards are quantified by means of this approach. Among these, volcanic gas emission is particularly relevant given the threat posed to human health if concentrations and exposure times exceed certain thresholds. There are different types of gas emissions but two main scenarios can be recognized: hot buoyant gas emissions from fumaroles and the ground and dense gas emissions feeding density currents that can occur, e.g., in limnic eruptions. Simulation tools are available to model the evolution of critical gas concentrations over an area of interest. Moreover, in order to perform probabilistic hazard assessments of volcanic gases, simulations should account for the natural variability associated to aspects such as seasonal and daily wind conditions, localized or diffuse source locations, and gas fluxes. Here we present VIGIL (automatized probabilistic VolcanIc Gas dIspersion modeLling), a new Python tool designed for managing the entire simulation workflow involved in single and probabilistic applications of gas dispersion modelling. VIGIL is able to manage the whole process from meteorological data processing, needed to run gas dispersion in both the dilute and dense gas flow scenarios, to the post processing of models’ outputs. Two application examples are presented to show some of the modelling capabilities offered by VIGIL.

Validation of the Probabilistic Seismic Hazard Assessment by the Taiwan Earthquake Model through Comparison with Strong Ground Motion Observations

Abstract To validate the probabilistic hazard assessment proposed by the Taiwan Earthquake Model (TEM), we compared it with the strong ground motion observations. We accessed the Taiwan Strong Motion Instrumentation Program (TSMIP) database and reported the maximum ground shaking of each strong-motion station. Comparing the TSMIP observations and the TEM hazard model reveals similar spatial patterns. However, some records indicate significantly higher shaking levels than the model does due to the occurrence of some large events, for example, the 1999 Mw 7.6 Chi-Chi earthquake. Such discrepancies cannot be explained by model parameter uncertainties but by unexpected events in the given short observation period. We have confirmed that, although each seismogenic structure in Taiwan is unlikely to rupture within a short period, the summarized earthquake potentials from all of the structures are significant. In addition, we discuss the impacts of some model parameters, including epistemic uncertainties of source parameters, truncation of standard deviation for ground-motion prediction equations, the Gutenberg–Richter law for area source, and the time-dependent seismicity rate model. The outcomes of this study provide not only crucial information for urban planning on a city scale and building code legislation on a national scale but also suggestions for the next generation of probabilistic seismic hazard assessment for Taiwan as well as other regions.

Probabilistic hazard assessment of landslide-induced river damming

Landslide-induced river damming poses a considerable threat to the safety of humans and infrastructure. Prediction of landslide-induced river damming is of great significance for quantitative risk assessment and emergency response planning. However, owing to the large uncertainties embedded in the input parameters and dynamic numerical models, a reliable physically based prediction of landslide dam formation is still challenging. In this study, we proposed a probabilistic framework to predict rockslide-induced river damming and its associated barrier lake. Both parameter and model uncertainties were considered to reproduce the run-out process and the deposition behavior of the Baige landslide based on dynamic numerical simulation, while calibrating the input parameters through a sequential Bayesian back analysis. The first slide of the Baige landslide was considered to calibrate the input parameters with observations at several deposition depths. The proposed method was validated by predicting the second slide-induced river damming using the calibrated results from the first slide. Furthermore, the second slide with deposition depth observations was employed to update the input parameters again. Through the sequential Bayesian back analysis, the probability of the river damming induced by the potentially unstable rock masses was predicted, which yielded a hazard zonation map of the barrier lake exceeding various water levels. This hazard zonation map may be employed to guide quantitative risk assessment and corresponding emergency response plans for future landslide-induced upstream backwater-inundation.

A proxy-based model for estimating V30 in the Iberian Peninsula

The time-averaged shear-wave velocity in the upper 30 m of the ground, V S 30 , is a key soil descriptor for estimating site response despite its recognized limitations. It is employed in both, site-specific probabilistic hazard assessments (PSHAs) and regional seismic codes. This work presents a model for estimating V S 30 in the Iberian Peninsula as a function of three proxies: topographic slope, geological age and lithology at each site. Tasks accomplished include: 1) gathering existing V s profiles and calculating their V S 30 ; 2) defining an adequate set of representative age and lithological groups; 3) classifying the available V S profiles according to these groups; and 4) carrying out a regression analysis between V S 30 , slopes, age and lithological groups. Based on the regression analysis and the dependency on the slope, some of the initially proposed groups were amalgamated, before proposing the final model. This model considers topographic slope values extracted from a digital elevation model (DEM) with 200 m horizontal resolution, plus six geological age groups and four lithological groups. It provides an estimate of the mean and standard deviation of log V S 30 (and hence V S 30 ), which can be used for sites without direct estimates of velocity profiles (and V S 30 ) in the Iberian Peninsula. •Gathering existing V s profiles and calculating their V S 30 •Defining an adequate set of representative age and lithological groups •Classifying the available V S profiles according to these groups •Carrying out a regression analysis between V S 30 , slopes, age and lithological groups.

Multilayer modelling of waves generated by explosive subaqueous volcanism

Abstract. Theoretical source models of underwater explosions are often applied in studying tsunami hazards associated with subaqueous volcanism; however, their use in numerical codes based on the shallow water equations can neglect the significant dispersion of the generated wavefield. A non-hydrostatic multilayer method is validated against a laboratory-scale experiment of wave generation from instantaneous disturbances and at field-scale subaqueous explosions at Mono Lake, California, utilising the relevant theoretical models. The numerical method accurately reproduces the range of observed wave characteristics for positive disturbances and suggests a relationship of extended initial troughs for negative disturbances at low-dispersivity and high-non-linearity parameters. Satisfactory amplitudes and phase velocities within the initial wave group are found using underwater explosion models at Mono Lake. The scheme is then applied to modelling tsunamis generated by volcanic explosions at Lake Taupō, New Zealand, for a magnitude representing an ejecta volume of 0.1 km3. Waves reach all shores within 15 min with maximum incident crest amplitudes around 0.2 m at shores near the source. This work shows that the multilayer scheme used is computationally efficient and able to capture a wide range of wave characteristics, including dispersive effects, which is necessary when investigating subaqueous explosions. This research therefore provides the foundation for future studies involving a rigorous probabilistic hazard assessment to quantify the risks and relative significance of this tsunami source mechanism.

Considering Engineering Geology Input for Probabilistic Flood Hazard Assessments

ABSTRACT Probabilistic risk assessments were developed in the 1970s as consistent approaches to assessing public health protection by nuclear-facility safety measures. Risk-informed initiatives resulted in the characterization of processes that produce extreme events (hazards) independently from the detrimental effects of such events on people or environment from facility damage (risks), as well as quantifying uncertainties. For example, large dams are designed to perform without uncontrolled reservoir release under seismic motion with 1/10,000 annual frequency. Geologic inputs for seismic hazards include ground motion sources and site response. Probabilistic flood hazards analyses are emerging in response to uncertainty about the effects of climate change, aging flood control structures, and acceptance of probabilistic seismic hazard analyses. Geologic inputs for flood hazard have focused on paleoflood hydrology from slackwater deposits and boulder bars. Procedures are available for calculating probable maximum floods, produced from the most severe combination of meteorological and hydrologic conditions, but not for assessing annual frequencies of such events. Flood routing, the domain of hydrologists and hydraulic engineers, typically stipulates channel stability. What if channels erode during extreme floods, watershed slopes are susceptible to landslides, or landslides reduce channel cross sections? Hydrologists and hydraulic engineers evaluate flood flow and water elevation effects at facilities, whereas engineering geologists need to assess slope response and mobilization of debris under extreme precipitation. Keeping slope assessments consistent with probabilistic approaches is challenging. A real location provides a hypothetical example to illustrate selected aspects of the geological approach and to utilize the results of some available tools.

Workflows for Construction of Spatio-Temporal Probabilistic Maps for Volcanic Hazard Assessment

Probabilistic hazard assessments for studying overland pyroclastic flows or atmospheric ash clouds under short timelines of an evolving crisis, require using the best science available unhampered by complicated and slow manual workflows. Although deterministic mathematical models are available, in most cases, parameters and initial conditions for the equations are usually only known within a prescribed range of uncertainty. For the construction of probabilistic hazard assessments, accurate outputs and propagation of the inherent input uncertainty to quantities of interest are needed to estimate necessary probabilities based on numerous runs of the underlying deterministic model. Characterizing the uncertainty in system states due to parametric and input uncertainty, simultaneously, requires using ensemble based methods to explore the full parameter and input spaces. Complex tasks, such as running thousands of instances of a deterministic model with parameter and input uncertainty require a High Performance Computing infrastructure and skilled personnel that may not be readily available to the policy makers responsible for making informed risk mitigation decisions. For efficiency, programming tasks required for executing ensemble simulations need to run in parallel, leading to twin computational challenges of managing large amounts of data and performing CPU intensive processing. The resulting flow of work requires complex sequences of tasks, interactions, and exchanges of data, hence the automatic management of these workflows are essential. Here we discuss a computer infrastructure, methodology and tools which enable scientists and other members of the volcanology research community to develop workflows for construction of probabilistic hazard maps using remotely accessed computing through a web portal.