Hazard Assessment Model Research Articles

Research status of earthquake forecasting in hydraulic-fracturing induced earthquakes

In the new types of industrial activities including unconventional energy extraction associated with shale gas and hot dry rock, gas reservoir operations, CO2 geological storage, undergoing research on induced earthquake forecasting has become one of the forward positions of current seismology. As for the intense actual demand, the immature research on induced earthquake forecasting has already been applied in pre-assessment of site safety and seismic hazard and risk management. This work will review systematically recent advances in earthquake forecasting induced by hydraulic fracturing during industrial production from four aspects: earthquake occurrence probability, maximum expected magnitude forecasting, seismic risk analysis for engineering and social applications and key scientific problems. In terms of earthquake occurrence probability, we introduce statistical forecasting models such as an improved ETAS and non-stationary ETAS and physical forecasting models such as Seismogenic Index (SI) and hydro-mechanism nucleation. Research on maximum expected magnitude forecasting has experienced four stages of linear relationship with net injection volume of fluid, power exponential relationship and physical forecasting regarding fault parameters. For seismic risk analysis, we focus on probabilistic seismic hazard assessment and quantitative geological susceptibility model. Furthermore, this review is extended to key scientific problems that contain obtaining accurate fault scale and environmental stress state of reservoir, critical physical process of runaway rupture, complex mechanism of fault activation as well as physical mechanism and modeling of trailing effect. This work in understanding induced earthquake forecasting may contribute to unconventional energy development and production, seismic hazard mitigation, emergency management and scientific research as a reference.

Hazard assessment of extreme heat during summer maize growing season in Haihe Plain, China

AbstractThe characteristics of extreme heat events (EHE) are changing under global warming. A comprehensive assessment of extreme heat hazards is important for a better understanding of extreme heat distribution and agricultural mitigation planning. In the present study, an extreme heat hazard assessment model is defined by its probability and severity. Continuous days and accumulative temperatures are used to describe the severity of EHE. Daily maximum temperature data from 26 stations in the Haihe Plain during 1960–2019 are used to identify EHE during the summer maize growing seasons. This region is the main summer maize planting area and suffers from numerous EHE. Copula functions are used to estimate the joint probability distributions of continuous days and the accumulative temperature of EHE. The fitted probabilities are then used in the hazard assessment model. The results reveal that the most severe extreme heat hazard areas are found in the western Haihe Plain, the frequency, average continuous days and accumulative temperature of EHE are the largest in this area. The extreme heat hazards are decreased to the northeastern coast area. Annual variations in extreme heat hazard show two change points near 1968 and 1996 that divide the research period into three main stages: higher, lower, and higher extreme heat hazard stage. These findings provide a comprehensive view of extreme heat over the Haihe Plain, which can be used for better extreme heat management and agriculture planning.

Dynamic hazard assessment of group-occurring debris flows based on a coupled model

A scientific hazard zoning of debris flows can ensure that human activities are not performed therein, thereby reducing economic loss as much as possible. In this study, by considering the formation background conditions of mountain hazards and dynamic rainfall conditions, the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model (TRIGRS) and Flow path assessment of gravitational hazards at a Regional scale (Flow-R) are organically combined to establish a new coupled model for debris flow dynamic hazard assessment from the viewpoint of landslides providing the material source of debris flows. The coupled model was employed in Qingping township, Sichuan province of China, which was severely damaged by the “5·12” Wenchuan earthquake in 2008. Evaluation results show that the accuracy of the coupled model is 87.98%, which is significantly higher than that of the static Flow-R model (75.51%). This study not only considers the effect of the formation condition of mountain hazards and rainfall-induced shallow landslides on the source of debris flows, but also realises real-time hazard assessment and prediction of group-occurring debris flows. The coupled model used in this study provides a new perspective and is paramount for improving the accuracy and reliability of debris flow hazard assessment.

Back calculation and hazard prediction of a debris flow in Wenchuan meizoseismal area, China

On August 20, 2019, many catastrophic debris flows broke out in meizoseismal area of Wenchuan M s 8.0 earthquake in China under the influence of continuous heavy rainfall. This paper takes the Chutou gully debris flow event occurred on August 20, 2019 as an example. The 3-D numerical simulation software, RAMMS, is used to back calculate the event and predict the future hazard. Coulomb and viscous turbulent friction, μ and ξ are calibrated in RAMMS. Numerical simulation reveals the movement process of Chutou gully from the aspects of flow depth, velocity, discharge, and run-out solid materials volume. The simulation results show that more than two-thirds of the solid materials are still deposited in the main channel, which could provide material basis for the re-occurrence of high hazard debris flow. In addition, based on the intensity and probability of debris flow, the hazard of debris flow is divided into high, middle, and low degrees. According to the simulation results and hazard assessment model, the hazard map of Chutou gully debris flow in various rainstorm return periods (20, 50, 100, and 200 years) is established, which can provide guidance for the future land-use planning and debris flow prevention works.

Railway Alignment Optimization in Mountainous Regions Considering Spatial Geological Hazards: A Sustainable Safety Perspective

Sustainable railway construction and operation are threatened by densely occurring geological hazards in complex mountainous regions. Thus, during the alignment optimization process, it is vital to reduce the harmful impacts of geological hazards to a railway. However, current alignment-related studies solely consider such threats in existing geological hazard regions and, outside these regions, slight attention has been devoted to the assessment of potential hazardous impacts along the alignment. To this end, this paper proposes a novel railway alignment optimization model considering both existing and potential geological hazards based on quantitative geological hazard evaluation criteria from a sustainable safety perspective. More specifically, a geohazard zone classification method, within which an energy–slope model is integrated, is first developed. Three geohazard regions, namely the geohazard outbreak region, buffer region and fuzzy region, can then be obtained. Afterward, a spatial geological hazard assessment model is constructed considering the geological danger of three kinds of geohazards (debris flows, landslides and rockfalls) and railway construction vulnerability. This model is incorporated into a previous cost–hazard bi-objective alignment optimization model. Finally, the effectiveness of the proposed model is verified by applying it to a real-life case of the Sichuan–Tibet railway. The results show that this method can effectively optimize mountain railway alignments by concurrently reducing geological hazards and costs, which is beneficial to railway safety and sustainable construction and operation.

A health hazard assessment model of fine particles generated from coal-fired boilers and its case analysis

A novel health hazard assessment model of fine particles generated from five groups of coal-fired boilers is built based on AHP. In our assessment model, some important factors are taken into account, including the number from uncontrolled emission, and mass from uncontrolled emission, the particle size distribution of Rosin-Rammler function of fine particles, and the total content of toxic chemical compositions (including Cr, V, Mn, Ni, Pb, Cd, As, Se, Hg, OC and EC). By calculation, PM2.5 from the industrial grate-fired boilers are much more harmful to human health than those from the other types of coal-fired boilers, which is not recommended to choose grate-fired burners as a burner of a new boiler or a modification boiler. PM2.5 from industrial fluidized-bed boilers are more harmful to human health than that of utility CFB boilers; PM2.5 from the large-capacity utility pulverized coal boilers (⩾450MW) are more harmful to human health than those from the small-capacity utility pulverized coal boilers (<450MW). It is necessary to install a high performance dust removal device.

Computer vision technologies for safety science and management in construction: A critical review and future research directions

Recent years have seen growing interests in developing and applying computer vision technologies to solve safety problems in the construction industry. Despite the technological advancements, there is no research that exams the theoretical links between computer vision technology and safety science and management. Thus, the objectives of this paper are to: (1) investigate the current status of applying computer vision technology to construction safety, (2) examine the links between computer vision applications and key research themes of construction safety, (3) discuss the theoretical challenges of applying computer vision to construction safety, and (4) recommend future research directions. A five-step review approach was adopted to search and analyze peer-reviewed academic journal articles. A three-level computer vision development framework was proposed to categorized computer vision applications in the construction industry. The links between computer vision and three main safety research traditions: safety management system, behavior-based safety program, and safety culture, were discussed. The results suggest that the majority of past efforts were focused on object recognition, object tracking, and action recognition, with limited research focused on recognizing unsafe behavior. There are even fewer studies aimed at developing vision-based safety assessment and prediction systems. Based on the review findings, four future research directions are suggested: (1) develop and test a behavioral-cues-based safety climate measure, (2) develop safety behavior datasets, (3) develop a formal hazard identification and assessment model, and (4) develop criteria to evaluate the real impacts of vision-based technologies on safety performance.

Verification of seismic risk models using observed damages from past earthquake events

The accuracy of a seismic risk model to predict future losses is a significant challenge as it depends on many parameters, each with important uncertainties, that are present in all components of the model. The aim of this study is to present a process that can be followed to validate the efficiency of a predictive risk model for seismic risk assessment at urban or regional scale, through comparison of estimated physical damages with those recorded at past seismic events, discussing at the same time the uncertainties involved in the different steps of this process. The proposed process is applied for the verification of the open-access uniform European Seismic Risk Model (ESRM20), considering two major past seismic events in Greece, i.e. the Athens 1999 Mw 5.9 and the Thessaloniki 1978 Mw 6.5 earthquakes, for which we possess relatively good damage reports for a representative portion of the two metropolitan agglomerations. Despite the numerous uncertainties and the unavoidable simplifications, a good comparison is found between the estimated and observed damage, especially for the city of Thessaloniki. Regarding the distribution of damage, it is anticipated that the most heavily damaged typologies for both case studies are found to be the low-to-medium-rise, non-ductile masonry buildings. The analyses also revealed the importance of the credibility of the adopted exposure model and of the adequate selection of ground motion models for seismic hazard assessment. Τhis challenging work and the positive results acquired so far aim to contribute to the efforts towards increasing the resilience of cities.

A decade of seismicity in metropolitan France (2010–2019): the CEA/LDG methodologies and observations

We summarize ten years of the French seismicity recorded by the Geophysical and Detection Laboratory (LDG) of the French Alternative Energies and Atomic Energy Commission (CEA) network from 2010 to 2019. During this period, 25 265 natural earthquakes were detected by the LDG and located within metropolitan France and its immediate vicinity. This seismicity contributes to more than 47% of the natural earthquakes instrumentally recorded since 1962 (mainly due to the improvement of network capacity), and includes about 28% of the most significant earthquakes with a magnitude ML ≥ 4.0. Recent seismic events therefore significantly expand the available national catalogues. The spatial distribution of 2010–2019 earthquakes is broadly similar to the previously recorded instrumental pattern of seismicity, with most of the seismic activity concentrated in the French Alps, the Pyrenees, Brittany, the upper Rhine Graben and the Central Massif. A large part of the seismic activity is related to individual events. The largest earthquakes of the last ten years include the November 11, 2019 Le Teil earthquake with ML 5.4 and epicentral intensity VII–VIII, which occurred in the Rhone valley; the April 28, 2016 La Rochelle earthquake with ML 5.1 and epicentral intensity V, which occurred at the southernmost extremity of the Armorican Massif in the vicinity of the Oléron island; and the April 7, 2014 Barcelonnette earthquake with ML 5.1 and epicentral intensity V–VI, which occurred in the Ubaye valley in the Alps. In 2019, two other moderate earthquakes of ML 5.1 and ML 4.9 stroke the western part of France, in Charente-Maritime and Maine-et-Loire departments, respectively. The recent moderate earthquake occurrences and the large number of small earthquakes recorded give both the potential to revise some regional historical events and to determine more robust frequency-magnitude distributions, which are critical for seismic hazard assessment but complex due to low seismicity rates in France. The LDG seismic network installed since the early 1960s also allows a better characterization of the temporal structure of seismicity, partly diffused and in the form of mainshock-aftershocks sequences or transient swarms. These aspects are important in order to lower the uncertainties associated to seismogenic sources and improve the models in seismic hazard assessment for metropolitan France.

A PCA spatial pattern based artificial neural network downscaling model for urban flood hazard assessment

We present two statistical models for downscaling flood hazard indicators derived from upscaled shallow water simulations. These downscaling models are based on the decomposition of hazard indicators into linear combinations of spatial patterns obtained from a Principal Component Analysis (PCA). Artificial Neural Networks (ANNs) are used to model the relationship between low resolution (LR) and high resolution (HR) information drawn from hazard indicators. In both statistical models, the PCA features, i.e. the linear weights of the spatial patterns, of the LR hazard indicator are taken as inputs to the ANNs. In the first model, there is one ANN per HR cell where the hazard indicator is to be estimated and the output of the ANN is the hazard indicator value at that cell. In the second model, there is a single ANN for the whole HR mesh whose outputs are the PCA features of the HR hazard indicator. An estimate of the hazard indicator is obtained by combining the ANN’s outputs with the HR spatial patterns. The two statistical downscaling models are evaluated and compared at estimating the water depth and the norm of the unit discharge, two common hazard indicators, on simulations from five synthetic urban configurations and one field-test case. Analyses are carried out in terms of relative absolute errors of the statistical downscaling model with respect to the LR hazard indicator. They show that (i) both statistical downscaling models provide estimates that are more accurate than the LR hazard indicator in most cases and (ii) the second downscaling model yields consistently lower errors for both hazard indicators for all flow scenarios on all configurations considered. The statistical models are three orders of magnitude faster than HR flow simulations. Used in conjunction with upscaled flood models such as porosity models, they appear as a promising operational alternative to direct flood hazard assessment from HR flow simulations.

Western Mexico seismic source model for the seismic hazard assessment of the Jalisco-Colima-Michoacán region

The Mexican subduction zone, the Gulf of California spreading center, as well as the triple junction point around the Jalisco and the Michoacan Blocks, represents the most active seismogenic belts inducing seismic hazard in the Jalisco-Colima-Michoacan region. Herein, considering such seismotectonic setting, we develop a new seismic source model for the surrounding of Jalisco-Colima-Michoacan to be used as an input in the assessment of the seismic hazard of the region. This new model is based on revised Poissonian earthquake (1787–2018) and focal mechanism (1963–2015) catalogs, as well as crustal thickness data and all information about the geometry of the subducting slabs. The proposed model consists of a total of 37 area sources, comprising all the three different possible categories of seismicity: shallow crustal, interface subduction, and inslab earthquakes. A special care was taken during the delimitation of the boundaries for each area source to ensure that they represent a relatively homogeneous seismotectonic region and to include a relatively large number of earthquakes that enable us to compute as reliable as possible seismicity parameters. Although the sources were delimited following the standard criteria of assessing the probabilistic seismic hazard, they are also characterized in terms of their seismicity parameters (annual rate of earthquakes above Mw 4.0, b-value, and maximum expected magnitude), mean seismogenic depth, as well as the predominant stress regime. The proposed model defines and characterizes regionalized potential seismic sources that can contribute to the seismic hazard at the Jalisco-Colima-Michoacan region, providing the necessary information for seismic hazard estimates.

Earthquake ground motion modeling of induced seismicity in the Groningen gas field

AbstractA physics‐based numerical approach is used to characterize earthquake ground motion due to induced seismicity in the Groningen gas field and to improve empirical ground motion models for seismic hazard and risk assessment. To this end, a large‐scale (20 km × 20 km) heterogeneous 3D seismic wave propagation model for the Groningen area is constructed, based on the significant bulk of available geological, geophysical, geotechnical, and seismological data. Results of physics‐based numerical simulations are validated against the ground motion recordings of the January 8, 2018, ML 3.4 Zeerijp earthquake. Taking advantage of suitable models of slip time functions at the seismic source and of the detailed geophysical model, the numerical simulations are found to reproduce accurately the observed features of ground motions at epicentral distances less than 10 km, in a broad frequency range, up to about 8 Hz. A sensitivity analysis is also addressed to discuss the impact of 3D underground geological features, the stochastic variability of seismic velocities and the frequency dependence of the quality factor. Amongst others, results point out some key features related to 3D seismic wave propagation, such as the magnitude and distance dependence of site amplification functions, that may be relevant to the improvement of the empirical models for earthquake ground motion prediction.

Flood Hazard Assessment in Data-Scarce Watersheds Using Model Coupling, Event Sampling, and Survey Data

The application of hydrologic and hydrodynamic models in flash flood hazard assessment is mainly limited by the availability of robust monitoring systems and long-term hydro-meteorological observations. Nevertheless, several studies have demonstrated that coupled modeling approaches based on event sampling (short-term observations) may cope with the lack of observed input data. This study evaluated the use of storm events and flood-survey reports to develop and validate a modeling framework for flash flood hazard assessment in data-scarce watersheds. Specifically, we coupled the hydrologic modeling system (HEC-HMS) and the Nays2Dflood hydrodynamic solver to simulate the system response to several storm events including one, equivalent in magnitude to a 500-year event, that flooded the City of Tena (Ecuador) on 2 September, 2017. Results from the coupled approach showed satisfactory model performance in simulating streamflow and water depths (0.40 ≤ Nash-Sutcliffe coefficient ≤ 0.95; −3.67% ≤ Percent Bias ≤ 23.4%) in six of the eight evaluated events, and a good agreement between simulated and surveyed flooded areas (Fit Index = 0.8) after the 500-year storm. The proposed methodology can be used by modelers and decision-makers for flood impact assessment in data-scarce watersheds and as a starting point for the establishment of flood forecasting systems to lessen the impacts of flood events at the local scale.

Evaluating the performance of random forest for large-scale flood discharge simulation

The machine learning algorithm ‘random forest’ has been applied in many areas of water resources research including discharge simulation. Due to low setup and operation cost, random forest could represent an alternative approach to physical and conceptual hydrological models for large-scale hazard assessment in multiple catchments. Yet, the applicability of random forest to flood discharge simulation requires further exploration, especially with respect to heterogeneous catchments and daily temporal resolution. In this study, we simulate flood event and peak discharge on a daily time scale for 95 study basins in Canada and the USA. We comparatively evaluate the predictive performance of random forest against the conceptual hydrological modeling package ‘hydromad’ and assess the influence of catchment characteristics on model performance. Our analysis showed that random forest is competitive to hydromad in the simulation of low and medium flood magnitudes. However, both models exhibit inaccuracies for higher flood events. Relating catchment characteristics to model skill, we found that primarily climatic conditions and elevation affect the flood simulation capability. We conclude that random forest provides a low-cost and, yet, competitive alternative to conventional rainfall-runoff models in large-scale flood discharge simulation. Nevertheless, without further model advancements, the presented models only provide robust discharge predictions for small and medium magnitude floods in low altitude catchments with warm temperate climate.

Anatomy of a crustal-scale accretionary complex: Insights from deep seismic sounding of the onshore western Makran subduction zone, Iran

Abstract The Makran subduction zone has produced M 8+ earthquakes and subsequent tsunamis in historic times, hence indicating high risk for the coastal regions of southern Iran, Pakistan, and neighboring countries. Besides this, the Makran subduction zone is an end-member subduction zone featuring extreme properties, with one of the largest sediment inputs and the widest accretionary wedge on Earth. While surface geology and shallow structure of the offshore wedge have been relatively well studied, primary information on the deeper structure of the onshore part is largely absent. We present three crustal-scale, trench-perpendicular, deep seismic sounding profiles crossing the subaerial part of the accretionary wedge of the western Makran subduction zone in Iran. P-wave travel-time tomography based on a Monte Carlo Markov chain algorithm as well as the migration of automatic line drawings of wide-angle reflections reveal the crustal structure of the wedge and geometry of the subducting oceanic plate at high resolution. The images shed light on the accretionary processes, in particular the generation of continental crust by basal accretion, and provide vital basic information for hazard assessment and tsunami modeling.

Susceptibility Mapping of Soil Water Erosion Using Machine Learning Models

Soil erosion is a serious threat to sustainable agriculture, food production, and environmental security. The advancement of accurate models for soil erosion susceptibility and hazard assessment is of utmost importance for enhancing mitigation policies and laws. This paper proposes novel machine learning (ML) models for the susceptibility mapping of the water erosion of soil. The weighted subspace random forest (WSRF), Gaussian process with a radial basis function kernel (Gaussprradial), and naive Bayes (NB) ML methods were used in the prediction of the soil erosion susceptibility. Data included 227 samples of erosion and non-erosion locations through field surveys to advance models of the spatial distribution using predictive factors. In this study, 19 effective factors of soil erosion were considered. The critical factors were selected using simulated annealing feature selection (SAFS). The critical factors included aspect, curvature, slope length, flow accumulation, rainfall erosivity factor, distance from the stream, drainage density, fault density, normalized difference vegetation index (NDVI), hydrologic soil group, soil texture, and lithology. The dataset cells of samples (70% for training and 30% for testing) were randomly prepared to assess the robustness of the different models. The functional relevance between soil erosion and effective factors was computed using the ML models. The ML models were evaluated using different metrics, including accuracy, the kappa coefficient, and the probability of detection (POD). The accuracies of the WSRF, Gaussprradial, and NB methods were 0.91, 0.88, and 0.85, respectively, for the testing data; 0.82, 0.76, and 0.71, respectively, for the kappa coefficient; and 0.94, 0.94, and 0.94, respectively, for POD. However, the ML models, especially the WSRF, had an acceptable performance regarding producing soil erosion susceptibility maps. Maps produced with the most robust models can be a useful tool for sustainable management, watershed conservation, and the reduction of soil and water loss.

California Historical Intensity Mapping Project (CHIMP): A Consistently Reinterpreted Dataset of Seismic Intensities for the Past 162 Yr and Implications for Seismic Hazard Maps

AbstractHistorical seismic intensity data are useful for myriad reasons, including assessment of the performance of probabilistic seismic hazard assessment (PSHA) models and corresponding hazard maps by comparing their predictions to a dataset of historically observed intensities in the region. To assess PSHA models for California, a long and consistently interpreted intensity record is needed. For this purpose, the California Historical Intensity Mapping Project (CHIMP) has compiled a dataset that combines and reinterprets intensity information that has been stored in disparate and sometimes hard-to-access locations. The CHIMP dataset also includes new observations of intensity from archival research and oral history collection. Version 1 of the dataset includes 46,502 intensity observations for 62 earthquakes with estimated magnitudes ranging from 4.7 to 7.9. The 162 yr of shaking data show observed shaking lower than expected from seismic hazard models. This discrepancy is reduced, but persists, if historical intensity data for the largest earthquakes are smoothed to reduce the effects of spatial undersampling. Possible reasons for this discrepancy include other limitations of the CHIMP dataset, the hazard models, and the possibility that California seismicity throughout the historical period has been lower than the long-term average. Some of these issues may also explain similar discrepancies observed for Italy and Japan.

Current Approaches and Techniques in Physiologically Based Pharmacokinetic (PBPK) Modelling of Nanomaterials.

There have been efforts to develop physiologically based pharmacokinetic (PBPK) models for nanomaterials (NMs). Since NMs have quite different kinetic behaviors, the applicability of the approaches and techniques that are utilized in current PBPK models for NMs is warranted. Most PBPK models simulate a size-independent endocytosis from tissues or blood. In the lungs, dosimetry and the air-liquid interface (ALI) models have sometimes been used to estimate NM deposition and translocation into the circulatory system. In the gastrointestinal (GI) tract, kinetics data are needed for mechanistic understanding of NM behavior as well as their absorption through GI mucus and their subsequent hepatobiliary excretion into feces. Following absorption, permeability (Pt) and partition coefficients (PCs) are needed to simulate partitioning from the circulatory system into various organs. Furthermore, mechanistic modelling of organ- and species-specific NM corona formation is in its infancy. More recently, some PBPK models have included the mononuclear phagocyte system (MPS). Most notably, dissolution, a key elimination process for NMs, is only empirically added in some PBPK models. Nevertheless, despite the many challenges still present, there have been great advances in the development and application of PBPK models for hazard assessment and risk assessment of NMs.

The influence of source- and ground-motion model choices on probabilistic seismic hazard levels at 6 sites in France

In this study focused on France, we explore the uncertainties related to choices made while building a source model for hazard assessment and we quantify the impact on probabilistic hazard estimates. Earthquake recurrence models are initially built from the French Seismic CATalog (FCAT, Manchuel et al. in Bull Earthq Eng, 2018. https://doi.org/10.1007/s10518-017-0236-1). We set up a logic tree that includes two alternative seismogenic source models (ESHM13 and Baize et al. in Bull Soc Géol Fr 184(3):225–259, 2013), two versions of FCAT catalog, two alternative declustering algorithms, and three alternative minimum magnitudes for earthquake recurrence modeling. We calculate the hazard for six cities (i.e. Nantes, Lourdes, Clermont-Ferrand, Briançon, Nice and Strasbourg) that are located in source zones with a minimum amount of data to work with. Results are displayed for the PGA and spectral period 0.2 s, at return periods 475 and 5000 years. Exploration of the logic tree shows that the parameters with the most impact on hazard results are the minimum magnitude used in the recurrence modeling (up to 31%) and the selection of the seismogenic source model (up to 30%). We also use the SHARE European Earthquake Catalog (SHEEC, Woessner et al. in Bull Earthquake Eng, 2015. https://doi.org/10.1007/s10518-015-9795-1) to build earthquake recurrence models and compare hazard values obtained with the FCAT logic tree. Comparisons are limited because of the low number of events available in some sources in SHEEC; however, results show that, depending on the site considered, the earthquake catalog selection can also strongly impact the hazard estimates (up to 50%). The FCAT logic tree is combined with four ground-motion models (Bindi et al. in Bull Earthq Eng 12(1):391–430, 2014; Boore et al. in Earthq Spectra 30(3):1057–1085, 2014; Cauzzi et al. in Bull Earthq Eng 13(6):1587–1612, 2015. https://doi.org/10.1007/s10518-014-9685-y; Drouet and Cotton in Bull Seismol Soc Am 105(4):1883–1902, 2015) to account for the epistemic uncertainty on the prediction of ground-motion. Exploration of the logic tree shows that the contribution of ground-motion model uncertainties can be larger than, equivalent to, or lower than the contribution of the source-model uncertainties to the overall hazard variability. Which component controls overall uncertainty depends on the site, spectral period and return period. Finally, exploring the logic tree provides a distribution for the ratios between hazard levels at 5000 and 475 years return periods, revealing that the ratios only slightly depend on source-model uncertainties, vary strongly from site to site, and can take values between 3 and 5, which is significantly higher than what is commonly assumed in the engineering community.

Assessing coastal hazard from extreme storms with a phase resolving wave model: Case study of Narragansett, RI, USA

Coastal hazard at the residential scale, resulting from the combined effects of storm surge and waves including dynamic set-up and wave runup, is assessed for the local 100-year Synthetic Design Storm (SDS) in Narragansett, RI, USA, based on high resolution (2 m) simulations with the nonlinear phase-resolving Boussinesq wave model FUNWAVE. The accuracy of wave-flow-structure interaction modeling is improved using a built-up DEM, defined by adding coastal structures to the bare-earth DEM. In addition, bathymetry and topography are modified to reflect post-storm conditions based on simulating the same storm with the morphodynamic model XBeach. Comparisons of FUNWAVE results with those of the phase-averaged model STWAVE, for the same storm conditions, show that including individual wave effects increases the inundated area by 13% and maximum water levels (TWL) across the shoreline by 11%, in average; however, in high runup areas (such as along exposed structures), the predicted maximum water levels can increase by up to 100%. FUNWAVE predicts maximum runups on the order of ∼1.5Hs (with Hs the offshore significant wave height), which is consistent with empirical formulae for steep seawalls (Van der Meer, 2002), and these occur at the most exposed structures, which historical records show have been periodically damaged by large storms. The maximum momentum forces predicted by FUNWAVE are much larger than those derived from STWAVE simulations (based on linear wave theory and assuming Rayleigh distributed waves), and also occur on the most exposed structures. The study provides a quantitative assessment of the local coastal hazard associated with the selected SDS at the scale of individual structures, and stresses the importance of accurately modeling individual wave effects, runups and associated forces in coastal hazard assessment modeling studies.