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The 50 state update to the 2023 United States National Seismic Hazard Model (NSHM) is the latest in a sequence published by the U. S. Geological Survey (USGS). The 2023 NSHM is intended for use in building codes and similar applications at return periods of 475 years (corresponding to exceedance probabilities of 10% in 50 years) or longer. In reviewing the model, the NSHM Program Steering Committee, consisting of the authors of this paper, considered the characteristics of “best available science” that are applicable to the NSHM. Best available science must perform better than the previous NSHM, and there should be no available alternatives that could improve the models. The following are suggested characteristics of “best available science”:A) Clear objectivesB) Rigorous conceptual modelC) Timely, relevant and inclusiveD) Verified and reproducibleE) Validated intermediate and final modelsF) Replicable within uncertaintiesG) Peer reviewedH) Permanent documentationThis article focuses on the justification for, and intent of, the above criteria for best available science.

The impact of liquefaction on a regional scale is not well understood or modeled with traditional approaches. This paper presents a method to quantitatively assess liquefaction hazard and risk on a regional scale, accounting for uncertainties in soil properties, groundwater conditions, ground-shaking parameters, and empirical liquefaction potential index equations. The regional analysis is applied to a case study to calculate regional occurrence rates for the extent and severity of liquefaction and to quantify losses resulting from ground shaking and liquefaction damage to residential buildings. We present a regional-scale metric to quantify the extent and severity of liquefaction. A sensitivity analysis on epistemic uncertainty indicates that the two most important factors on output liquefaction maps are the empirical liquefaction equation, emphasizing the necessity of incorporating multiple equations in future regional studies, and the ground motion model, highlighting the same necessity for the peak ground acceleration input. Furthermore, the disaggregation of seismic sources reveals that triggering earthquakes for various extents of liquefaction originate from multiple sources, though primarily nearby faults and large magnitude ruptures. This finding indicates the value of adopting regional probabilistic analysis in future studies to capture the diverse sources and spatial distribution of liquefaction. [ABSTRACT FROM AUTHOR]

Seismic fragility models provide a probabilistic relation between ground-motion intensity and damage, making them a crucial component of many regional risk assessments. Estimating such models from damage data gathered after past earthquakes is challenging because of uncertainty in the ground-motion intensity the structures were subjected to. Here, we develop a Bayesian estimation procedure that performs joint inference over ground-motion intensity and fragility model parameters. When applied to simulated damage data, the proposed method can recover the data-generating fragility functions, while the traditionally used method, employing fixed, best-estimate, intensity values, fails to do so. Analyses using synthetic data with known properties show that the traditional method results in flatter fragility functions that overestimate damage probabilities for low-intensity values and underestimate probabilities for large values. Similar trends are observed when comparing both methods on real damage data. The results suggest that neglecting ground-motion uncertainty manifests in apparent dispersion in the estimated fragility functions. This undesirable feature can be mitigated through the proposed Bayesian procedure. [ABSTRACT FROM AUTHOR]

The intensity of an atmospheric river (AR) is only one of the factors influencing the damage it will cause. We use random forest models fit to hazard, exposure, and vulnerability data at different spatial and temporal scales in California to predict the probability that a given AR event will cause flood damage, as measured by National Flood Insurance Program (NFIP) claims. We first demonstrate the usefulness of data-driven models and interpretable machine learning to identify and describe drivers of AR flood damage. Hazard features, particularly measures of AR intensity such as total precipitation, increase the probability of damage with increasing values up to a threshold point, after which the probability of damage saturates. Although hazard is generally the most important risk dimension across all models, exposure and vulnerability contribute up to a third of the explanatory power. Exposure and variability features generally increase the probability of damage with increasing values, apart from a few instances which can be explained by physical intuition, but tend to affect the probability of damage less for the largest AR events. Comparisons between random forest models at different spatial and temporal scales showed general agreement. We then examine limitations inherent in publicly available exposure, vulnerability, and loss data, focusing on the difference in temporal resolution between variables from different risk dimensions and discrepancies between NFIP claims and total flood losses, and describe how those limitations may affect the model results. Overall, the application of interpretable machine learning to understand the contributions of exposure and vulnerability to AR-driven flood risk has identified potential community risk drivers and strategies for resilience, but the results must be considered in the context of the data that produced them. [ABSTRACT FROM AUTHOR]

Modern research often involves the collection or analysis of data and the use of specialized computer algorithms. Traditional text articles thus provide only partial documentation of a research study. Readers have limited ability to reproduce or utilize work if the source data are not available or if it relies on an algorithm that is described, but code is not provided. Fortunately, a wide variety of tools are now available to support the publication of research data and code. The effort required to publish data is now relatively small, and the benefits can be immense. This opinion article discusses trends toward increased sharing in academic publishing. It describes opportunities and resources to support data and code sharing and describes the benefits for both authors and readers. Finally, it discusses how Earthquake Spectra is providing resources and enhancing its policies to establish the sharing of data as the default procedure when publishing in the journal, and encourage the sharing of code and other resources. [ABSTRACT FROM AUTHOR]

In the United States, assistance from the Department of Housing and Urban Development (HUD) plays an essential role in supporting the postdisaster recovery of states with unmet housing needs. HUD requires data on unmet needs to appropriate recovery funds. Ground truth data are not available for months after a disaster, however, so HUD uses a simplified approach to estimate unmet housing needs. State authorities argue that HUD's simplified approach underestimates the state's needs. This article presents a methodology to estimate postdisaster unmet housing needs that is accurate and relies only on data obtained shortly after a disaster. Data on the number of damaged buildings are combined with models for expected repair costs. Statistical models for aid distributed by the Federal Emergency Management Agency (FEMA) and the Small Business Administration (SBA) are then developed and used to forecast funding provided by those agencies. With these forecasts, the unmet need to be funded by HUD is estimated. The approach can be used for multiple states and hazard types. As validation, the proposed methodology is used to estimate the unmet housing needs following disasters that struck California in 2017. California authorities suggest that HUD's methodology underestimated the state's needs by a factor of 20. Conversely, the proposed methodology can replicate the estimates by the state authorities and provide accounts of losses, the amount of funding from FEMA and SBA, and the total unmet housing needs without requiring data unavailable shortly after a disaster. Thus, the proposed methodology can help improve HUD's funding appropriation without delays. [ABSTRACT FROM AUTHOR]

This article proposes a model to quantify dependencies in component damage in the Federal Emergency Management Agency (FEMA) P-58 seismic performance assessment methodology and to simulate damage realizations with the desired dependencies. The model is compatible with the prior FEMA P-58 procedure and can quantify more realistic dependencies in component damage with only minor changes to the calculation algorithm and model parameters. This article introduces the proposed model and compares it with the prior procedure. Example calculations are then used to illustrate the quantitative impacts of component damage dependencies on building-level performance metrics. The model is relatively simple to conceptualize and parameterize, so that the degree of dependency can be easily estimated and documented. Given the improved conceptual framing of the problem, and the significant changes it sometimes produces in building-level performance predictions, this model represents an improvement to the general FEMA P-58 seismic performance assessment methodology. [ABSTRACT FROM AUTHOR]

This article investigates the correlation coefficients of ground-motion intensity measures for ground motions containing near-fault directivity velocity pulses. These correlation coefficients are necessary to quantify conditional multivariate intensity measure distributions and generate realizations from them for ground-motion selection. The empirical correlations between intensity measures representing ground-motion amplitude, frequency content, duration, and cumulative effects are calculated using the RotD50 definition and compared with published models. The impact of intensity measure definition as in RotD50, RotD100, and the geometric mean is also scrutinized. The sensitivity of the results to the considered ground motion set and the reference ground motion model are addressed in the computations. The results are compared with those from only non-directivity and mixed data sets based on the NGA-West2 database. The results indicate that the adopted data set has the largest influence on the variability of the empirically computed correlation coefficients. Given the multiple sources that contribute to uncertainty in these calculations, the authors conclude that existing models for predicting median correlation coefficients based on mixed data sets are sufficient for use with directivity, non-directivity, and mixed ground motions. [ABSTRACT FROM AUTHOR]

Ground-motion correlation models play a crucial role in regional seismic risk modeling of spatially distributed built infrastructure. Such models predict the correlation between ground-motion amplitudes at pairs of sites, typically as a function of their spatial proximity. Data from physics-based simulators and event-to-event variability in empirically derived model parameters suggest that spatial correlation is additionally affected by path and site effects. Yet, identifying these effects has been difficult due to scarce data and a lack of modeling and assessment approaches to consider more complex correlation predictions. To address this gap, we propose a novel correlation model that accounts for path and site effects via a modified functional form. To quantify the estimation uncertainty, we perform Bayesian inference for model parameter estimation. The derived model outperforms traditional isotropic models in terms of the predictive accuracy for training and testing data sets. We show that the previously found event-to-event variability in model parameters may be explained by the lack of accounting for path and site effects. Finally, we examine implications of the newly proposed model for regional seismic risk simulations. [ABSTRACT FROM AUTHOR]