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1,658 records · Page 7Linked to original sources
USGS2024

Factors contributing to pesticide contamination in riverine systems: The role of wastewater and landscape sources

Wastewater treatment plant (WWTP) discharges can be a source of organic contaminants, including pesticides, to rivers. An integrated model was developed for the Potomac River watershed (PRW) to determine the amount of accumulated wastewater percentage of streamflow (ACCWW) and calculate predicted environmental concentrations (PECs) for 14 pesticides in non-tidal National Hydrography Dataset Plus Version 2.1 stream segments. Predicted environmental concentrations were compared to measured environmental concentrations (MECs) from 32 stream sites that represented a range of ACCWW and land use to evaluate model performance and to assess possible non-WWTP loading sources. Statistical agreement between PECs and MECs was strongest for insecticides, followed by fungicides and herbicides. Principal component analysis utilizing optical fluorescence and ancillary water quality data identified wastewater and urban runoff sources. Pesticides that indicated relatively larger sources from WWTPs included dinotefuran, fipronil, carbendazim, thiabendazole, and prometon whereas imidacloprid, azoxystrobin, propiconazole, tebuconazole, and diuron were more related to urban runoff. In addition, PECs generally comprised a low proportion of MECs, which indicates possible dominant loading sources beyond WWTP discharges. Cumulative potential toxicity was higher for sites with greater ACCWW and/or located in developed areas. Imidacloprid, fipronil, and carbendazim accounted for the largest portion of predicted potential toxicity across sites. The chronic aquatic life toxicity benchmarks for freshwater invertebrates were exceeded for 82 % of the imidacloprid detections ( n = 28) and 47 % of the fipronil detections ( n = 19). These results highlight the ecological implications of pesticide contamination from WWTP discharges and also the potential legacy effects from accumulated soil and groundwater sources. Pesticide management strategies that mitigate both current and historical impacts may improve the health of aquatic ecosystems.

Potomac River watershed↗
USGS2026

Birds, breakpoints, and baselines: How citizen science data can reveal ecological boundaries in Kenya’s Upper Tana watershed

Tropical watersheds are increasingly threatened by climate change, land-use conversion, and resource extraction, yet conventional biodiversity monitoring in these systems is often spatially and temporally limited. Citizen science offers a complementary approach, enabling biodiversity data collection over large areas that can supplement professional scientific surveys. We analyzed 10 years (2012–2022) of Upper Tana Watershed bird data from the Kenya Bird Map project, covering 114 pentads (9 × 9 km) within a 17,000 km 2 watershed to assess patterns of bird community composition and distribution across this watershed and to also evaluate the effects of environmental variables, seasonality, and sampling effort to help inform improvements in future citizen science projects. Citizen (or community) scientists recorded 575 species (>50% of Kenya’s total avifauna) in 74 families. Asymptotic species accumulation indicates that most probable species present in the watershed were detected. Threshold indicator taxa analysis revealed distinct ecological boundaries along elevation (∼1,500 m), precipitation (∼1,100 mm), and mean temperature (∼19°C) gradients, corresponding to a turnover from xeric savanna to mesic montane forest assemblages. Notably, bird communities showed little seasonal differentiation between wet and dry periods, consistent with dominance by resident year-round species. Data limitations including uneven survey distribution and frequency, absence of abundance metrics, and coarse representation of local environmental conditions that likely reduced our ability to detect fine-scale species–habitat relationships. Addressing these gaps through spatially balanced sampling at greater resolution, greater survey frequency in underrepresented areas, and improved capture of habitat metrics could strengthen the use of citizen-science bird data for watershed bioassessment. Our findings demonstrate that structured citizen-science initiatives can identify ecological boundaries and inform adaptive management of tropical socio-ecological systems under rapid environmental change.

Upper Tana watershed↗
USGS2026

Multi-objective optimization of a hydro-economic model in an over-allocated agricultural basin

Groundwater depletion for agricultural irrigation poses significant environmental and economic challenges. This study introduces a proof-of-concept that combines hydro-economic modeling, scenario-based modeling, and multi-objective optimization to manage pumping curtailment in an over-allocated basin in the western United States. Three optimization scenarios were evaluated, each offering different degrees of management flexibility. Results reveal that scenarios with finer spatial resolution achieved greater environmental benefits per unit profit loss. Additionally, strategies allowing fractional reductions in curtailed wells–rather than complete shutdowns based on water rights seniority–substantially improved efficiency, highlighting the value of increased decision-making flexibility. Although scenario testing can aid stakeholder engagement and strategy exploration, multi-objective optimization provides a systematic framework to quantify tradeoffs between competing objectives. This combined approach demonstrates promise for building consensus and supporting the design of sustainable water management strategies that balance agricultural livelihoods with ecosystem preservation.

Oregon↗
USGS2026

A diatom-based quantitative sea-ice proxy for the Bering and Chukchi seas

Sea ice affects Earth's climate system on both regional and global scales. Its incorporation into climate can be used to achieve more accurate predictions of future climate. However, instrumental records of sea-ice concentration do not extend earlier than 1978. In an effort to extend this record, we constructed a proxy using the generalized additive model based on relative abundances of five easy-to-identify diatom species found in sediment samples across the Bering and Chukchi seas. Here we present the first quantitative diatom-based sea-ice proxy developed for Beringia. The developed proxy has been applied to two sediment cores in the Bering Sea ranging from 0 to 25.7 ka (HLY0204 51JPC) and 369 to 430 ka (IODP Exp 323 Site U1345) and one in the Chukchi Sea ranging from 2.7 to 10 ka (HLY0204 24JPC). The obtained reconstructions of sea-ice concentrations are similar, but not identical to previously published qualitative and nearby records based on other proxies. Because our results are quantitative, they can be incorporated into regional climate models. The proxy is publicly available as an R Shiny application (app) and can be applied to any diatom count from marine sediments in the region.

Bering Sea, Chukchi Sea↗
USGS2024

Framework for implementing damping scaling factors in U.S. Geological Survey National Seismic Hazard Models

Traditionally, probabilistic seismic hazard analysis (PSHA) has focused on calculating ground motion hazard curves for elastic, 5%-damped pseudo spectral accelerations, Sa(T,5%), which are used as the basis for engineering design parameters and targets for ground motion selection and modification. However, structures and geotechnical systems can exhibit a wide range of damping ratios both above and below the 5% level, depending on the construction material, structural system, nonstructural elements, or subsurface soil properties. When spectral parameters at such damping levels are required for certain applications, 5%-damped accelerations have traditionally been extracted from PSHA-based hazard curves and adjusted outside of the hazard integral using damping scaling factors (DSF) such as those from Newmark & Hall (1982). Recent advances in the development of more rigorous and comprehensive damping scaling models (e.g., Rezaeian et al., 2014; Rezaeian et al., 2021) have allowed for the modeling of means and standard deviations of DSFs as functions of earthquake source and path properties for crustal, intraslab, and subduction interface tectonic environments. These DSF models can be applied to ground motion model (GMM) estimates of Sa(T,5%) for a given earthquake rupture scenario to produce a corresponding mean and standard deviation Sa at a specified damping ratio β, Sa(T,β). In this study, the DSF models of Rezaeian et al. (2014) and Rezaeian et al. (2021) are implemented within the U.S. Geological Survey National Seismic Hazard Model (NSHM) PSHA framework to calculate probabilistic hazard curves for spectral accelerations at damping ratios from 0.5% to 30%. The DSF models are applied directly to the mean and standard deviation of Sa(T,5%) predictions from each GMM in the NSHM logic tree. Resulting hazard curves and uniform hazard and risk spectra for Sa(T,β) are presented for several geographic locations and compared with corresponding spectra estimated using current design practices by applying the same DSFs outside of the PSHA calculation. Key differences between the two methods for estimating Sa(T,β) are discussed, and potential strategies are presented for the implementation and usage of the hazard-consistent Sa(T,β) in building codes. Comparing the results to those from DSFs used in current design practices that are mainly based on Newmark & Hall (1982) is not explored in this study.

Conference Paper↗
USGS2025

Subduction zone earthquake catalog separation tool: Implementation in the USGS 2025 Puerto Rico and U.S. Virgin Islands National Seismic Hazard Model

The U.S. Geological Survey (USGS) periodically releases updates to National Seismic Hazard Model (NSHM) for the United States and its territories leveraging current scientific knowledge and methodologies to guide public policy, building codes, and risk assessments regarding potential ground shaking due to earthquakes that may result in infrastructure damage. In subduction zones, there is a need to separate the earthquake catalog into tectonic regimes to create specific seismicity models for which the most appropriate ground‐motion models are then applied. Here, we describe newly developed methods and software, called CatSep, that classifies subduction zone events into three primary tectonic regimes: crustal, interface, and intraslab. This method incorporates information about the location of the earthquake relative to the subducting slab, the depth of the Mohorovičić discontinuity, and the earthquake’s moment tensor. Applying this method is a first step in the NSHM workflow for regions covering U.S. subduction zones. Results using this subduction zone earthquake catalog separation tool for the 2025 Puerto Rico and U.S. Virgin Islands NSHM earthquake catalog are presented and analyzed.

Puerto Rico, U.S. Virgin Islands↗
USGS2026

Computational electromagnetic geophysics for groundwater system studies: A review on established practices and recent advances

Identifying effective solutions for locating groundwater resources and ensuring the quality of drinking water is increasingly urgent, given the challenges posed by climate change and population growth. This review investigates electromagnetic geophysical imaging techniques, in both time- and frequency-domain, that can provide valuable insights for groundwater assessment. We explore computational electromagnetic methods used to evaluate electromagnetic data and several recent hydrogeophysical case studies. As open-source frameworks for modeling electromagnetic geophysical problems become available, a broader range of researchers can interpret their data with computationally advanced software. We provide an overview of documented open-source codes for evaluating electromagnetic data and analyze various hydrological targets in relation to their electromagnetic surveying technique and the computational method applied. Furthermore, we evaluate the potential of advanced computational techniques, including three-dimensional modeling, non-deterministic inversion and machine learning, to couple geophysical with numerical groundwater modeling and apply it in groundwater system studies. Despite obstacles such as complexity and resource demands, our findings indicate that the quantification and integration of predictive uncertainties from both electromagnetic and hydrological data and simulations would significantly improve the reliability of hydrogeophysical models. This can lead to a deeper understanding of groundwater systems and improved management practices.

Journal of Hydrology↗
USGS2025

ARCHI: A new R package for automated imputation of regionally correlated hydrologic records

Missing data in hydrological records can limit resource assessment, process understanding, and predictive modeling. Here, we present ARCHI (Automated Regional Correlation Analysis for Hydrologic Record Imputation), a new, open-source software package in R designed to aggregate, impute, cluster, and visualize regionally correlated hydrologic records. ARCHI imputes missing data in “target” records by linear regression using more complete “reference” records as predictors. Automated imputation is implemented using a novel, iterative algorithm that allows each site to be considered a target or reference for regression, growing the pool of complete references with each imputed record until viable gap-filling ceases. Users can limit artifacts from spurious correlations by specifying model-acceptance criteria and applying geospatial, correlation, and group-based filters to control reference selection. ARCHI provides additional functions for visualizing results, clustering records with similar correlation structures, evaluating holdout data, and interactive parameterization with an accessible and intuitive graphical user interface (GUI). This methods brief provides an overview of the ARCHI package, modeling guidelines, and benchmarking on two regional groundwater-level datasets from the Central Valley, CA and Long Island, NY. We evaluate ARCHI alongside widely used multivariate imputation software to highlight and contextualize its computational efficiency, imputation accuracy, and model transparency when applied to large, groundwater-level datasets.

California, New York↗
USGS2026

Cultural benefits knowledge and social learning in Elwha River dam removal and ecosystem decision-making

In a retrospective case study of the seminal Elwha River dam removal in Washington State, USA, we explore pathways through which ecosystem services (ES) knowledge can inform environmental decision-making. We specifically investigate how knowledge of cultural benefits linked to the Elwha River ecosystem was made available. Through interviews and historical document review we identify diverse forms of cultural benefits knowledge (i.e., how individuals or groups know the contributions of an ecosystem to their identities, experiences, and capabilities), including both knowledge in the form of practices – embodied or enacted forms of knowledge, and knowledge in the form of products – quantitative or written documentation. Recognizing and learning from the diverse forms in which benefits knowledges are conveyed can support more accurate and equitable consideration of the diverse values, well-beings, and human-nature relationships linked to ecosystems. With this case study, we illuminate pathways for meaningful consideration of multiple knowledge forms, and the plural ecosystem values they convey, with relevance for environmental decision processes in the Puget Sound and beyond.

Washington↗
USGS2025

Evaluating mass flow meter measurements from chambers for greenhouse gas emissions from orphan wells and other point sources

This study evaluates the performance of a rigid gas flux chamber equipped with a mass flow meter (MFM) for measuring gas emissions from leaking orphan wells and similar pressure-driven gas point sources. We conducted a series of laboratory and field experiments to evaluate the sensitivity, stability, and dynamic range of an MFM chamber system and found an optimal method for sealing the chamber to the ground to isolate the emission source. From these results, we estimate the effects of different soil gas permeabilities on measurements and identify the uncertainty of environmental processes that can impact measurements. Simulations of an MFM chamber are compared to those of a dynamic flux chamber to contrast the data derived with both methodologies and illustrate the potential for measuring high variability leaks with the MFM chamber. Using a low flow resistance MFM and a chamber well-sealed to the ground, it is possible to measure leaks down to 1.08 x 10 -3 cubic meters per hour (m 3 h −1 ) (refenced to 25°/1 atm), corresponding to 0.77 grams per hour (g h −1 ) methane or 2.11 g h −1 carbon dioxide, with a mean uncertainty of 0.89 % relative standard deviation. Environmental processes such as heated gas inside the chamber from solar gain, wind blowing across the chamber vent, and changing humidity in the chamber, can cause variation in MFM measurements. Over 11 d of continuous monitoring under varying weather conditions, the standard deviation of the environmentally sourced signals was found to be 7.40 x 10 -3 m 3 h −1 (equivalent to or 5.27 g h −1 methane or 14.45 g h −1 carbon dioxide). Strategies to obtain the highest quality data from MFM chambers include burying the edges of the chamber below the surface sufficiently deep to seal the chamber edges against gas flow and soaking the dirt with water to lower the chances of escaping gases, while monitoring the gas flow and adjusting the chamber seal to achieve a maximum flow rate.

Atmospheric Measurement Techniques↗
USGS2025

Disturbance is the primary determinant of food chain length when the top predator is constant

Food chain length (FCL) is a primary determinant of food web structure and is hypothesized to be influenced by habitat size, productivity, and disturbance. Understanding the environmental characteristics that determine food chain length can assist in understanding how food webs may be impacted due to changes in habitats and environmental characteristics. This study examines the impact of hydrologic disturbance on stream food webs when the top predator is constant. We analyzed FCL in less disturbed groundwater flashy streams and more disturbed runoff flashy streams using stable isotopes. Despite no difference in species richness or fish density, food chains in more disturbed streams had a lower FCL compared to food chains in more stable streams. Assemblage analysis showed that flow regime and drainage area significantly impacted individual species abundances. The more disturbed runoff flashy streams had higher proportions of primary consumer fish, such as the algivorous Campostoma sp. (Stonerollers), which likely drives the reduced FCL. Drainage area and land cover had non-significant relationships with FCL. Shifting community structure due to hydrologic variability likely leads to differences in diet of Micropterus dolomieu (Smallmouth Bass), and thus a difference in FCL.

Arkansas, Missouri, Oklahoma↗
USGS2026

Transit time modeling framework for predicting freshwater salinization in urban catchments

The salinity of inland freshwaters is rising globally, particularly in urban watersheds where winter road deicers are widely applied. Attributing stream salinity dynamics to specific sources and transport pathways remains challenging due to episodic salt inputs, engineered drainage, and strong coupling between hydrology and subsurface storage. We present a modeling framework that couples climate-driven deicer build-up and wash-off with transient transit time distribution theory to simulate salt transport through drainage, interflow, and groundwater pathways. Applied to an urban watershed in Northern Virginia (USA), the model reproduces ten years of high-frequency stream salinity measurements across daily-to-decadal timescales. The calibrated model implies an average deicer application of 206 tonnes Cl −1 yr −1 , or roughly one 20 kg bag of rock salt person −1 yr −1 when normalized by the 20,000 people living in the watershed. In winter months, higher infiltration routes a large fraction of snowmelt and deicers into shallow subsurface pathways, enhancing vadose-zone and interflow contributions to stream salinity. Limited subsurface storage capacity and seasonal hydrologic turnover flush excess chloride from the vadose zone and groundwater during subsequent summer storms. By linking climate-driven deicer inputs, hydrologic connectivity, and stream water age, the framework provides a transferable basis for diagnosing and managing freshwater salinization in urban watersheds.

Virginia↗
USGS2024

Deep-ocean macrofaunal assemblages on ferromanganese and phosphorite-rich substrates in the Southern California Borderland

Mineral-rich hardgrounds, such as ferromanganese (FeMn) crusts and phosphorites, occur on seamounts and continental margins, gaining attention for their resource potential due to their enrichment in valuable metals in some regions. This study focuses on the Southern California Borderland (SCB), an area characterized by uneven and heterogeneous topography featuring FeMn crusts, phosphorites, basalt, and sedimentary rocks that occur at varying depths and are exposed to a range of oxygen concentrations. Due to its heterogeneity, this region serves as an optimal setting for investigating the relationship between mineral-rich hardgrounds and benthic fauna. This study characterizes the density, diversity, and community composition of macrofauna (>300 μm) on hardgrounds as a function of substrate type and environment (depth and oxygen ranges). Rocks and their macrofauna were sampled quantitatively using remotely operated vehicles (ROVs) during expeditions in 2020 and 2021 at depths above, within, and below the oxygen minimum zone (OMZ). A total of 3,555 macrofauna individuals were counted and 416 different morphospecies (excluding encrusting bryozoans and hydrozoans) were identified from 82 rocks at depths between 231 and 2,688 m. Average density for SCB macrofauna was 11.08 ± 0.87 ind. 200 cm −2 and mean Shannon-Wiener diversity per rock (H′ [loge] ) was 2.22 ± 0.07. A relationship was found between substrate type and macrofaunal communities. Phosphorite rocks had the highest H′ of the four substrates compared on a per-rock basis. However, when samples were pooled by substrate, FeMn crusts had the highest H′ and rarefaction diversity. Of all the environmental variables examined, water depth explained the largest variance in macrofaunal community composition. Macrofaunal density and diversity values were similar at sites within and outside the OMZ. This study is the first to analyze the macrofaunal communities of mineral-rich hardgrounds in the SCB, which support deep-ocean biodiversity by acting as specialized substrates for macrofaunal communities. Understanding the intricate relationships between macrofaunal assemblages and mineral-rich substrates may inform effects from environmental disruptions associated with deep-seabed mining or climate change. The findings contribute baseline information useful for effective conservation and management of the SCB and will support scientists in monitoring changes in these communities due to environmental disturbance or human impact in the future.

California↗
USGS2026

The digital archivist: Automating legacy macroseismic data processing using large language models

Macroseismic data are a key resource to investigate shaking and damage from preinstrumental and early instrumental eras. However, data are often stored as inconsistently formatted reports describing observed shaking and damage, making manually parsing and interpreting accounts labor‐intensive. We introduce a novel workflow using Google’s Gemini 2.5 Pro large language model (LLM) to automate the extraction and structuring of macroseismic observations from summary reports. We apply this workflow to the 22 March 1957 M 5.3 Daly City, California, earthquake as a case study. We used Gemini to extract addresses, originally assigned modified Mercalli intensity values, and descriptions from each report. To address coordinate precision limits, addresses were geocoded via Google’s Geocoding application programming interface. This workflow yielded over 2300 geocoded intensity reports for the Daly City earthquake. We use the geocoded accounts, with the original report intensity assignments, to develop a shaking intensity map that in some respects rivals modern Did You Feel It? Maps. We also extract and present data for the 9 February 1971 M L 6.7 Sylmar, California, earthquake. Our results demonstrate the potential of LLMs for reliably extracting and analyzing large, unstructured macroseismic datasets. LLMs offer a scalable solution for rapidly digitizing macroseismic archives, enabling their broader use to constrain ground‐motion models in modern seismic hazard analysis and to improve our understanding of site effects in urban areas. The concepts explored here may also be applied to the handling of other legacy seismological and earth science data.

Seismological Research Letters↗
USGS2025

A review of post-wildfire adaptations of surface-water-quality models: Synthesis, gaps, and opportunities

As wildfires increasingly affect water-supply watersheds, the demand for models to predict water-quality responses is increasing. This work reviews and synthesizes existing post-wildfire applications of water-quality models in the context of geographic and ecohydrological distribution, hydrologic and water-quality response process representation, model parameterization, model and input data scales, model calibration data availability, as well as calibration and performance evaluation approaches. Emphasis is placed on models that simulate water-quality output, rather than sediment and erosional response as the primary focus. Here, identified gaps and opportunities to advance the post-wildfire application of water-quality models include: 1. applying models in under-represented geographic and ecohydrologic regions, 2. simulating multiple streamflow generation mechanisms, including groundwater, with an emphasis on shifting dominant flow pathways as the landscape recovers following wildfire, 3. adding studies that include the simulation of metals, 4. incorporating more biogeochemical and in-stream processes to model applications, 5. applying finer spatial and temporal resolution of precipitation data input as well as finer spatial resolution hydrologic response units, 6. implementing fully distributed grid or element models or finer resolution response units to capture burn severity heterogeneity, 7. collecting enhanced water-quality data for model calibration and validation, 8. conducting model-intercomparison studies, and 9. developing model parameter value guidance in post-wildfire applications. These identified gaps and opportunities may assist users in deciding on key processes and approaches to consider in modeling post-wildfire water-quality conditions.

Science of the Total Environment↗
USGS2025

Case study of deep learning image segmentation for the purposes of rapid 2D petrographic analysis in volcanic rocks

Automation using deep learning methods is a useful alternative to manual methods of petrographic segmentation, but often requires user familiarity with coding and/or algorithms. We examine the Dragonfly TM program's deep learning tools for application by users with a variety of skill levels as a method for petrographic image segmentation. An image processing methodology, bimodal image stacking, was created for low-input-data, high-efficacy training of models which can then be applied to varied samples. Using backscatter electron images we show that the resulting model segmentations agree with manual segmentation total and modal crystallinity values within 5%, and calculated plagioclase crystal size distribution (CSD) values within 2σ, despite limitations in discriminating mafic phases. Model creation and training takes <24 hours, 1–3 hours of which are supervised, and the resultant model can then be applied to new uncharacterized samples in <15 minutes per image. This allows for non-experts to create and utilize deep learning models to segment images of variable brightness and texture, at low user-time cost and resulting in size and shape data which are within uncertainty of manual segmentation. While some limitations are noted (for example, sieve-textured phases may need manual correction, and different minerals with similar BSE intensity may not be resolved as separate phases), this methodology can be utilized for general application of models to wide ranges of volcanic crystalline and bubble textures, and to create a library of models for rapid petrological analysis during volcanic eruptions.

Alaska↗
USGS2025

False positives in the identification of dynamic earthquake triggering

Dynamic earthquake triggering is commonly identified through the temporal correlation between increased seismicity rates and global earthquakes that are possible triggering events. However, correlation does not imply causation. False positives may occur when unrelated seismicity rate changes coincidently occur at around the time of candidate triggers. We investigate the expected false positive rate in Southern California with global M ≥ 6 earthquakes as candidate triggers. We compute the false positive rate by applying the statistical tests used by DeSalvio and Fan (2023), https://doi.org/10.1029/2023jb026487 to synthetic earthquake catalogs with no real dynamic triggering. We find a false positive rate of ∼3.5%–8.5% when realistic earthquake clustering is present, consistent with the 95% confidence typically used in seismology. However, when this false positive rate is applied to the tens of thousands of spatial-temporal windows in Southern California tested in DeSalvio and Fan (2023), https://doi.org/10.1029/2023jb026487 , thousands of false positives are expected. The expected false positive occurrence is large enough to explain the observed apparent triggering following 70% of large global earthquakes (DeSalvio & Fan, 2023, https://doi.org/10.1029/2023jb026487 ), without requiring any true dynamic triggering. Aside from the known triggering from the nearby El Mayor-Cucapah, Mexico, earthquake, the spatial and temporal characteristics of the reported triggering are indistinguishable from random false positives. This implies that best practice for dynamic triggering studies that depend on temporal correlation is to estimate the false positive rate and investigate whether the observed apparent triggering is distinguishable from the correlations that may occur by chance.

JGR Solid Earth↗
USGS2024

The 2023 U.S. 50-state National Seismic Hazard Model: Changes in 2023 compared to 2018 ground motions

We present the 2023 U.S. National Seismic Hazard Model (NSHM) for all 50 states that applies new smoothed seismicity, fault rupture, and ground motion models. New data and methods are introduced in the 2023 earthquake rupture forecast that include: new earthquake catalogs - excluding induced earthquakes, alternative declustering methods, spatially smoothed seismicity distributions, full-catalog scaled rates to account for aftershocks, updated CEUS-WUS attenuation boundary, new magnitude-scaling equations, new geodetic and geologic deformation models, and alternative fault system solutions accounting for a more complete representation of epistemic uncertainty potential for earthquake generation in Alaska, Hawaii, and the conterminous U.S. Improved ground motion models consider new Next Generation Attenuation NGASubduction, modified NGA-East, and adjustments to account for regional biases in ground shaking observations. Semi-empirical and 3D simulations of ground motion are applied to account for shaking at 21 oscillator periods, 2 peak motions, and 8 site conditions. Site effects models are constructed for western U.S. basins (Seattle, Portland/Tualatin, San Francisco, Central Valley of California, Los Angeles, and Salt Lake City) and for sites with deep sedimentary wedges found across the central and eastern U.S. Gulf Coast and Atlantic coastal plain regions. These models result in substantial changes compared to the older NSHMs and are differentiated for the earthquake rupture forecast and ground motion model changes to display sensitivities and impacts.

Conference Paper↗