Probabilistic Hazard Maps Research Articles

Quantifying uncertainties in tropical cyclone wind hazard assessment due to synthetic track stochastic variability for Southeast Asia

Extreme tropical cyclones (TCs) present serious threat to Southeast Asian communities, causing widespread loss of lives and property damages. This study investigates the uncertainties in probabilistic TC wind hazard assessment due to stochastic variability in the underlying synthetic TC track ensembles. We focus on landfall TCs in the Philippines and Vietnam using multiple realisations from a single synthetic TC generation model. It is found that the maximum 3-s gusts in the Philippines are 233.3 km/h and 301.6 km/h under 100-year and 500-year return periods (RP). The coefficient of variance (CV) for the ensemble means falls within 0.05–0.20 over the main landmass. In Vietnam, the maximum gusts under 100-year and 500-year RPs are 207.6 km/h and 286.1 km/h, respectively, with a CV range of 0.01–0.13. Large uncertainties mainly concentrate in high TC activity zones where the peak gusts from individual synthetic event set can be up to 20% higher than the ensemble outputs. Low TC activity zones are also prone to high ensemble spread in model outputs under short RPs, although these uncertainties tend to decrease as RP increases. The probabilistic hazard model is shown to be most sensitive to TC landfall frequency and maximum intensity. Influence from other track parameters, such as TC size and forward speed, can become important for long exposure of TC trajectories. Such insights enable better understanding on the key drivers of sensitivities in the probabilistic hazard maps. Our study complements the conventional hazard assessment using synthetic TC tracks by explicitly assessing the uncertainties from the underlying stochastic event catalogue. The calculated CVs arising from synthetic track stochastic variability can be compared and/or combined with uncertainty measures on the underlying TC hazard model. This ensures proper quantification and communication of full model uncertainties for more rigorous TC risk assessment and in subsequent risk mitigation.

New Methodology for Unbiased Ground-Motion Intensity Conversion Equations

ABSTRACT Ground-motion intensity conversion equations (GMICEs) allow for conversions between ground-motion amplitude and shaking intensity. The current methods used to develop GMICEs model the dependence of the intensity on the peak ground-motion (PGM) parameter. For some models, there is a second step that models the magnitude and distance dependence of the residuals from the initial regression. We show that this approach for developing GMICEs works well for estimating the intensity from median ground motions, but for ground-motion values away from the medians, the intensities estimated by the GMICE can have large bias, with overprediction for positive PGM residuals and underprediction for negative PGM residuals. The bias is due to an implicit assumption in the current approach that there is a direct causal relation between intensity and the ground-motion parameter and that the residuals of the intensity and ground-motion parameter are fully correlated. We present two alternative methodologies for developing GMICEs that do not suffer from this bias. The first method includes the magnitude and distance scaling of the GMICE in the initial regression for the scaling with the PGM. The second method excludes the magnitude and distance terms but includes the number of standard deviations of the PGM (ϵ) as an additional parameter in the GMICE. Using a synthetic data set of intensity and peak ground acceleration values, we show that the GMICE developed using the proposed method leads to more accurate estimates of the intensity than current methods. We also discuss implications of using GMICEs based on the proposed methods for the evaluation of probabilistic hazard maps and as input to ShakeAlert estimates.

Real-time probabilistic assessment of volcanic hazard for tephra dispersal and fallout at Mt. Etna: the 2021 lava fountain episodes

Starting from February 2021, Mt. Etna (Italy) experienced a period of intense explosive activity with 17 lava fountain episodes between 16 February and 1 April 2021. During the eruptive cycle, the Istituto Nazionale di Geofisica e Vulcanologia-Osservatorio Etneo (INGV-OE) issued 62 alert notifications known as VONAs (Volcano Observatory Notice for Aviation) to inform the aeronautical authorities about the volcanic activity. We present an automated VONA-based workflow aimed at real-time assessment of the volcanic hazard due to tephra fallout at Mt. Etna. When a VONA reporting tephra emission is issued by INGV-OE, numerical simulations accounting for atmospheric and eruptive uncertainties are automatically initialized to produce probabilistic hazard maps of tephra fallout and atmospheric dispersal. We applied the workflow to three lava fountains that occurred during the 2021 eruptive cycle. To test the modelling results, we compared the simulated ground load with field data, and the extent and position of the simulated volcanic cloud with the observed or estimated volcanic cloud from the Toulouse Volcanic Ash Advisory Center. Overall, we found a good match between simulated and observed quantities (tephra loads and volcanic cloud position), especially when accurate information on eruptive conditions (column height and duration) are supplied by the VONAs. Finally, through a statistical analysis, we found that column height and wind field are fundamental in determining tephra ground accumulation. For this reason, these parameters should be constrained by observational data as accurately as possible when performing numerical simulations, especially in the line of developing operational workflows.

Seismic Risk Assessment of Nagpur City using the Geographic Information System

Earthquakes are the major threat to mankind accounting for almost half of the fatalities brought about by all of the natural hazards and desert their traces in societies for years. It is well known that seismic risk cannot be eliminated but minimized. Risk studies lay down the roadmap to prepare and minimize the aftermath of any disastrous event. Geographic Information System (GIS) has been proved as an effectual tool for seismic risk assessment study. This study presents the seismic risk assessment of Nagpur city using the GIS framework. Albeit per Indian Standard (IS) 1893-2016 [1] seismic zonation of Nagpur city comes under zone II (relatively low seismic hazard zone), the seismic risk being a combination of hazard, elements at risk and, vulnerability cannot be denied. Similar to other Indian cities the city of Nagpur had a heterogeneous development pattern. Even though Nagpur city is municipalized in well distributed wards, many intra wards distributions show housing clusters of different socioeconomic status. Therefore, using the satellite imagery different socioeconomic housing clusters were identified and mapped in the ArcGIS platform. Six socioeconomic clusters were identified using satellite imagery for Nagpur. From the field survey of randomly selected typical clusters, 14 prevailing Model Building Types (MBTs) were identified. Census data were used to find the population density and its distribution throughout the city. Seismic hazard has been estimated from IS 1893-2016 [1] as well as using the probabilistic hazard map of NDMA [2]. Selected MBTs were assessed for their seismic vulnerability, seismic risk in terms of casualties and damage is calculated. Finally, thematic maps showing damages arrived from the various approach are shown and discussed.

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.

Probabilistic Hazard Maps of Volcanic Ash Dispersion and Fallout from Lascar Volcano: Implications for Argentine Territory and Surroundings Areas

Probabilistic Hazard Maps of Volcanic Ash Dispersion and Fallout from Lascar Volcano: Implications for Argentine Territory and Surroundings Areas

Probabilistic Machine Learning with Low-Cost Sensor Networks for Occupational Exposure Assessment and Industrial Hygiene Decision Making.

Occupational exposure assessments are dominated by small sample sizes and low spatial and temporal resolution with a focus on conducting Occupational Safety and Health Administration regulatory compliance sampling. However, this style of exposure assessment is likely to underestimate true exposures and their variability in sampled areas, and entirely fail to characterize exposures in unsampled areas. The American Industrial Hygiene Association (AIHA) has developed a more realistic system of exposure ratings based on estimating the 95th percentiles of the exposures that can be used to better represent exposure uncertainty and exposure variability for decision-making; however, the ratings can still fail to capture realistic exposure with small sample sizes. Therefore, low-cost sensor networks consisting of numerous lower-quality sensors have been used to measure occupational exposures at a high spatiotemporal scale. However, the sensors must be calibrated in the laboratory or field to a reference standard. Using data from carbon monoxide (CO) sensors deployed in a heavy equipment manufacturing facility for eight months from August 2017 to March 2018, we demonstrate that machine learning with probabilistic gradient boosted decision trees (GBDT) can model raw sensor readings to reference data highly accurately, entirely removing the need for laboratory calibration. Further, we indicate how the machine learning models can produce probabilistic hazard maps of the manufacturing floor, creating a visual tool for assessing facility-wide exposures. Additionally, the ability to have a fully modeled prediction distribution for each measurement enables the use of the AIHA exposure ratings, which provide an enhanced industrial decision-making framework as opposed to simply determining if a small number of measurements were above or below a pertinent occupational exposure limit. Lastly, we show how a probabilistic modeling exposure assessment with high spatiotemporal resolution data can prevent exposure misclassifications associated with traditional models that rely exclusively on mean or point predictions.

THE USE OF GEOGRAPHIC INFORMATION SYSTEM FOR FLOOD PREDICTIONS

The article examines the issue of using geographic information systems (GIS) and WEBGIS technologies to predict areas likely to be flooded. Probabilistic flood hazard maps (0.5%, 0.1% and 1% flood risks) were used, obtained from a model installation in an area subject to flood risk, in this case, the municipality of Ungheni in the Republic Moldova, which has been flooded in the past. The application of GIS technologies is necessary to prevent floods affecting households, infrastructure and to minimize its effects. For this purpose, were analyzed the previous floods that occurred in the region. Also were applied and overlaid thematic digital maps such as Land Use, river network, DTM, Delineation Methodology of water bodies which represents the transposition of the EU Water Framework Directive 2000/60/EC and at the end were uploaded on an ESRI Web GIS platform http://www.dbga.md/siga.html. The Coordinate system used here was WGS-84.

Probabilistic hazard maps for operational use: the case of SO2 air pollution during the Holuhraun eruption (Bárðarbunga, Iceland) in 2014–2015

The Holuhraun fissure eruption (Iceland) in 2014–2015, which originated from the Barðarbunga volcanic system, was exceptional in several respects. It lasted 6 months and, throughout its duration, it released up to 9.6 Mt of SO2 in the atmosphere. The main recorded hazard affecting the entire country over the 6 months was the constant presence of a low-level gas cloud that led to recurrent air pollution episodes. The Icelandic Meteorological Office responded to this human health hazard by (1) setting up a forecasting system to anticipate the distribution of SO2 over Iceland and (2) preparing probabilistic hazard maps to support the decisions taken by the Icelandic Civil Protection in demarcating the accessible area around the eruption site. This paper introduces some technical aspects of the application of the CALPUFF numerical model to this eruption like the SO2 dispersal forecasting setup, the volcanic source numerical description, and the Monte Carlo procedure adopted for the creation of the probabilistic hazard maps. CALPUFF-based maps were created in January 2015, when the eruption was still ongoing, with the assumption that the eruption would be continuing with the same intensity. Maps for the entire country and for a smaller domain were produced, the latter showing the likelihood to exceeding an hourly concentration of 2600 μg/m3 (1 ppm) of SO2 for the spring season, a level chosen by the Icelandic Civil Protection for the delineation of the area of restricted access around the eruption site. As during the eruption there was no time for a rigorous evaluation of the model accuracy, we then undertook a retrospective analysis of CALPUFF model performance comparing the forecasted hourly SO2 concentration with real-time measurements at key-sites. The model did reproduce the hourly observations (the maximum within a 24-running window) with a level of agreement of 50.6% in Mývatn (85 km from the eruption site) and 50.4% in Reykjavik (258 km), when instances of null pairs have been removed. In Mývatn, the model overestimated the concentration more than 22% of the time. In Hofn (104 km), the model accuracy is 81.7% and occurrences of underestimation are higher than 11% of the time. In Reyðarfjorður (at 124 km), the model accuracy is assessed to be 82.7% and the model overestimates occurring 15.1% of the time. Possible explanations for the observed mismatch between model results and measurements include the spatial resolution of meteorological data field, the capability into reproducing chemical reactions of SO2 in atmosphere, and the reduced extension of the numerical domain. In addition, the model performance is strongly dependent on the source descriptors (e.g., strength of the SO2 flux and injection height) that, in this contribution, have been kept constant over long periods—neglecting, in this way, the natural variability of a dynamic emission of SO2. This consideration points toward the need of frequent and high-quality observation data for the initialization of dispersal numerical model. In light of this retrospective analysis, the probabilistic hazard maps possibly over-estimated the area exposed to high level of SO2 concentration. All the same, this paper reports on how quantitative probabilistic hazard mapping can be used for mitigating the health risk of volcanic SO2 emissions during a volcanic crisis to the benefit of operational hazard monitoring in support an effective crisis response.

A probabilistic seismic hazard map for the metropolitan France

This paper presents the development of a probabilistic seismic hazard analysis (PSHA) model to compute seismic hazard maps for the French territory taking into account 15 years of research and development in the area. Since 2002, when the first probabilistic hazard map was computed for France, many new data became available leading to new studies and experience gained. This 2017 PSHA version for France incorporates significant improvements over previous version. In particular, the recent SIGMA project 2010–2016 produced a number of outputs which are used in the present analysis: a homogenized earthquake catalogue in moment magnitude (Mw), a Bayesian methodology to compute distributions of maximum magnitudes, ground motion prediction equations specifically developed for the French territory, new seismotectonic analysis conducted based on geological, structural, geophysical, neotectonic and seismological data. Preliminary comparison of median PGA values at 475 years return period with results obtained independently for neighboring countries (Germany, Switzerland, and Italy) reveals a fair agreement. Comparison with the 2013 European Seismic Hazard Model (ESHM13, SHARE project) and the model for France developed in 2002 indicates that 2017 PSHA version leads to lower hazard.

Dynamic Probabilistic Hazard Mapping in the Long Valley Volcanic Region CA: Integrating Vent Opening Maps and Statistical Surrogates of Physical Models of Pyroclastic Density Currents

AbstractIdeally, probabilistic hazard assessments combine available knowledge about physical mechanisms of the hazard, data on past hazards, and any precursor information. Systematically assessing the probability of rare, yet catastrophic hazards adds a layer of difficulty due to limited observation data. Via computer models, one can exercise potentially dangerous scenarios that may not have happened in the past but are probabilistically consistent with the aleatoric nature of previous volcanic behavior in the record. Traditional Monte Carlo‐based methods to calculate such hazard probabilities suffer from two issues: they are computationally expensive, and they are static. In light of new information, newly available data, signs of unrest, and new probabilistic analysis describing uncertainty about scenarios the Monte Carlo calculation would need to be redone under the same computational constraints. Here we present an alternative approach utilizing statistical emulators that provide an efficient way to overcome the computational bottleneck of typical Monte Carlo approaches. Moreover, this approach is independent of an aleatoric scenario model and yet can be applied rapidly to any scenario model making it dynamic. We present and apply this emulator‐based approach to create multiple probabilistic hazard maps for inundation of pyroclastic density currents in the Long Valley Volcanic Region. Further, we illustrate how this approach enables an exploration of the impact of epistemic uncertainties on these probabilistic hazard forecasts. Particularly, we focus on the uncertainty of vent opening models and how that uncertainty both aleatoric and epistemic impacts the resulting probabilistic hazard maps of pyroclastic density current inundation.

Statistical theory of probabilistic hazard maps: a probability distribution for the hazard boundary location

Abstract. The study of volcanic flow hazards in a probabilistic framework centers around systematic experimental numerical modeling of the hazardous phenomenon and the subsequent generation and interpretation of a probabilistic hazard map (PHM). For a given volcanic flow (e.g., lava flow, lahar, pyroclastic flow, ash cloud), the PHM is typically interpreted as the point-wise probability of inundation by flow material. In the current work, we present new methods for calculating spatial representations of the mean, standard deviation, median, and modal locations of the hazard's boundary as ensembles of many deterministic runs of a physical model. By formalizing its generation and properties, we show that a PHM may be used to construct these statistical measures of the hazard boundary which have been unrecognized in previous probabilistic hazard analyses. Our formalism shows that a typical PHM for a volcanic flow not only gives the point-wise inundation probability, but also represents a set of cumulative distribution functions for the location of the inundation boundary with a corresponding set of probability density functions. These distributions run over curves of steepest probability gradient ascent on the PHM. Consequently, 2-D space curves can be constructed on the map which represents the mean, median, and modal locations of the likely inundation boundary. These curves give well-defined answers to the question of the likely boundary location of the area impacted by the hazard. Additionally, methods of calculation for higher moments including the standard deviation are presented, which take the form of map regions surrounding the mean boundary location. These measures of central tendency and variance add significant value to spatial probabilistic hazard analyses, giving a new statistical description of the probability distributions underlying PHMs. The theory presented here may be used to aid construction of improved hazard maps, which could prove useful for planning and emergency management purposes. This formalism also allows for application to simplified processes describable by analytic solutions. In that context, the connection between the PHM, its moments, and the underlying parameter variation is explicit, allowing for better source parameter estimation from natural data, yielding insights about natural controls on those parameters.

Tephra fallout hazard assessment at Tacaná volcano (Mexico)

Tacaná volcano is one of the dormant volcanoes in Mexico. Its activity is demonstrated by recent phreatic explosions occurred in 1949 and 1986. At least four Plinian to sub Plinian eruptions have been recorded in the eruptive history of this volcano, which produced pumice and ash deposits that blanketed a vast area straddling the Mexican and Guatemalan territory. Currently, there is no quantitative tephra hazard study of the volcano fallout deposits nor of the dispersion of ash in the atmosphere.In this work, we present the first probabilistic tephra hazard assessment for the Tacaná volcano. Probabilistic hazard maps were computed both, for the tephra deposit, and for critical thresholds of airborne ash concentration at different flight levels. The FALL3D numerical model was used to perform hazard maps for a Plinian scenario defined on the basis of the Sibinal Pumice eruption occurred ∼23.5 kyrs ago, which is the most studied deposit of this type in the volcano's surroundings, even when just few of their most distal outcrops are preserved. Results of this work show that at ground level, the disturbance for this type of hazard would affect mainly Guatemalan territory and the state of Chiapas in Mexico. It could cause serious repercussions on the economy of the region, which is characterized mainly by cocoa, coffee and banana plantations. The ash dispersion into the atmosphere could disturb the airspace and major airports in Mexican territory and neighboring countries. In fact, probability ash dispersal maps show that the level of affectation to the air traffic would involve the possible closure of at least 23 airports in Mexico, Guatemala, Belize, Honduras, El Salvador and Nicaragua. Another collateral damage would be the remobilization of ash by lahars considering the near 4000 mm of rain in average poured yearly in the Soconusco region that would increase the sediment load of the Coatán and Suchiate rivers, and then could produce a severe damage to the lowlands, which in the case of the Mexican territory, is the most populated one.

Towards Quantitative Volcanic Risk of Pyroclastic Density Currents: Probabilistic Hazard Curves and Maps Around Somma‐Vesuvius (Italy)

AbstractPyroclastic density currents (PDCs) are hot flowing mixtures of gas and pyroclasts that can cause widespread loss of life and structural damage around the erupting volcano. Hazard assessments that include quantification of aleatory and epistemic uncertainty are a necessary step toward calculating volcanic risk of PDCs in an accurate and complete manner. We develop a three‐stage procedure to quantify such uncertainties for dense PDCs. First, the TITAN2D model is parameterized to simulate the PDC phenomenology at the target volcano. Second, TITAN2D is coupled with Polynomial Chaos Quadrature to propagate aleatory uncertainty from model parameters to hazard intensity measures (flow depth and speed). Third, the TITAN2D‐PCQ analysis is merged with the Bayesian Event Tree for Volcanic Hazard to include other volcano‐specific aleatory uncertainty and estimates of epistemic uncertainty. A comprehensive collection of probabilistic hazard curves and maps for flow depth and speed around the volcano is obtained through this methodology and its application is illustrated at Somma‐Vesuvius (Italy). Our results indicate that, given an eruption from the current central crater, exceedance probabilities are around 30% (aleatory uncertainty only) and between 10% and 60% (aleatory and epistemic uncertainty), for flow depth = 1 m and flow speed = 2 m/s, over the first 2–3 km around the vent. Dense PDCs faster than 30 m/s may cover areas about 50 km2 around the vent, on average, 1 every 10 eruptions. This type of probabilistic hazard assessment represents a crucial step toward quantitative volcanic risk of dense PDCs at Somma‐Vesuvius and worldwide.

Flood mapping under uncertainty: a case study in the Canadian prairies

In Canada, approaches for hazard mapping involve using a hydraulic model to generate a flood extent map that distinguishes between inundated and non-inundated areas in a deterministic way. The authors adapt a probabilistic approach to obtain flood hazard maps by accounting for uncertainty in boundary conditions and calibration parameters of a hydrodynamic model, while considering extent, probability, and depth of inundation as important flood indicators. The Qu’Appelle River in the Canadian prairies is considered as a case study to demonstrate the benefits of a probabilistic approach to obtain flooding indicators. The probability and depth of inundation are obtained for all locations in the floodplain, and their uncertainties are evaluated. A sensitivity analysis to determine factors affecting the extent of flooding at multiple locations along the river is also carried out. Further, the criterion to distinguish between flooded and non-flooded areas is modified and its subsequent effect on the flooding indicators is also evaluated. Results show low variability in depth and probability of inundation at most locations along the floodplain. It is also observed that the influence of the roughness parameters on the flooding extents is higher in the steeper stretches of the river, compared to the flatter stretches. Another component of the presented work focusses on evaluating an existing topography-based hazard map for Canada that classifies the country into different levels based on severity of flooding, by comparing it with the probabilistic hazard map obtained in this study. The comparison indicates a good level of agreement between both maps and highlights the reliability of using the topography-based hazard map where hydraulic modeling is infeasible.

Probabilistic hazard modelling of rain-triggered lahars

Probabilistic quantification of lahar hazard is an important component of lahar risk assessment and mitigation. Here we propose a new approach to probabilistic lahar hazard assessment through coupling a lahar susceptibility model with a shallow-layer lahar flow model. Initial lahar volumes and their probabilities are quantified using the lahar susceptibility model which establishes a relationship between the volume of mobilised sediment and exceedance probabilities from rainfall intensity-frequency-duration curves. Rainfall-triggered lahar hazard zones can then be delineated probabilistically by using the mobilised volumes as an input into lahar flow models. While the applicability of this model is limited to rain-triggered lahars, this approach is able to reduce the reliance on historic and empirical estimates of lahar hazard and creates an opportunity for the generation of purely quantitative probabilistic lahar hazard maps. The new approach is demonstrated through the generation of probabilistic hazard maps for lahars originating from the Mangatoetoenui Glacier, Ruapehu volcano, New Zealand.

3‐D ballistic transport of ellipsoidal volcanic projectiles considering horizontal wind field and variable shape‐dependent drag coefficients

AbstractAn innovative 3‐D numerical model for the dynamics of volcanic ballistic projectiles is presented here. The model focuses on ellipsoidal particles and improves previous approaches by considering horizontal wind field, virtual mass forces, and drag forces subjected to variable shape‐dependent drag coefficients. Modeling suggests that the projectile's launch velocity and ejection angle are first‐order parameters influencing ballistic trajectories. The projectile's density and minor radius are second‐order factors, whereas both intermediate and major radii of the projectile are of third order. Comparing output parameters, assuming different input data, highlights the importance of considering a horizontal wind field and variable shape‐dependent drag coefficients in ballistic modeling, which suggests that they should be included in every ballistic model. On the other hand, virtual mass forces should be discarded since they almost do not contribute to ballistic trajectories. Simulation results were used to constrain some crucial input parameters (launch velocity, ejection angle, wind speed, and wind azimuth) of the block that formed the biggest and most distal ballistic impact crater during the 1984–1993 eruptive cycle of Lascar volcano, Northern Chile. Subsequently, up to 106 simulations were performed, whereas nine ejection parameters were defined by a Latin‐hypercube sampling approach. Simulation results were summarized as a quantitative probabilistic hazard map for ballistic projectiles. Transects were also done in order to depict aerial hazard zones based on the same probabilistic procedure. Both maps combined can be used as a hazard prevention tool for ground and aerial transits nearby unresting volcanoes.

Investigating the effects of smoothing on the performance of earthquake hazard maps

We explore whether less detailed probabilistic hazard maps might perform better by assessing how smoothing Japan's national earthquake hazard maps affects their fit to a 510-year record of shaking. As measured by the fractional exceedance metric implicit in such probabilistic hazard maps, simple smoothing over progressively larger areas improves the maps' performance such that in the limit a uniform map performs best. However, using the squared misfit between maximum observed shaking and that predicted as a metric, map performance improves up to a ~75-150 km smoothing window, and then decreases with further smoothing. This result suggests that the probabilistic hazard models and the resulting maps may be over-parameterized, in that including too high a level of detail to describe past and future earthquakes may lower the maps' ability to predict future shaking.

A framework to quantify uncertainty in simulations of oil transport in the ocean

AbstractAn uncertainty quantification framework is developed for the DeepC Oil Model based on a nonintrusive polynomial chaos method. This allows the model's output to be presented in a probabilistic framework so that the model's predictions reflect the uncertainty in the model's input data. The new capability is illustrated by simulating the far‐field dispersal of oil in a Deepwater Horizon blowout scenario. The uncertain input consisted of ocean current and oil droplet size data and the main model output analyzed is the ensuing oil concentration in the Gulf of Mexico. A 1331 member ensemble was used to construct a surrogate for the model which was then mined for statistical information. The mean and standard deviations in the oil concentration were calculated for up to 30 days, and the total contribution of each input parameter to the model's uncertainty was quantified at different depths. Also, probability density functions of oil concentration were constructed by sampling the surrogate and used to elaborate probabilistic hazard maps of oil impact. The performance of the surrogate was constantly monitored in order to demarcate the space‐time zones where its estimates are reliable.

Probabilistic seismic hazard assessment in SE-Spain based on macroseismic site histories

In many countries such as Spain earthquake databases still mainly comprise macroseismic data from felt effects. The full exploit of this information is of basic importance for seismic risk assessment and emergency planning, given the strict link between macroseismic intensity and damage. A probabilistic procedure specifically developed to handle macroseismic data, mostly relying on site information and seismogenic-source free, has been applied to evaluate seismic hazard in SE-Spain (Alicante-Murcia region). Present seismicity is moderate-low with largest magnitudes slightly over Mw5.0. The historical record includes very destructive earthquakes, maximum EMS98 intensities reaching IX–X and X in the nineteenth century (e.g., Torrevieja 1829 earthquake). Very recently, two events in the area on 11 May 2011 (Mw4.5, Mw5.2) killed nine people, injured 300, and produced important damage in the city of Lorca. Regional hazard maps for the area together with specific hazard curves at selected localities are obtained. Results are compared with the maximum observed intensities in the period 1300–2012, and with the values in the seismic hazard map from the Spanish Building Code in force. In general, the maximum felt intensity values are closer to the hazard values calculated for 2 % probability of exceedance in 50 years, using felt and expected intensity. The intensity-based probabilistic hazard maps obtained through the applied approach reduce the inherent smoothing of those based on standard probabilistic seismic hazard assessment approaches for the region, allowing identifying possible over- or sub-estimates of site hazard values, providing very valuable information for risk reduction strategies or for future updates of the building code hazard maps.