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Comparisons of shoreline positions from satellite-derived and traditional field- and remote-sensing techniques

Satellite-derived shorelines (SDS) have the potential to help researchers answer critical coastal science questions and support work to predict coastal change by filling in the spatial and temporal gaps present in current field-based and remote-sensing data collection methods. The U.S. Geological Survey conducted comparison analyses of traditionally sourced shorelines and SDS in diverse coastal landscapes to determine how SDS could be used in ongoing and future work across varied coastal environments and provided some initial findings that could be used for implementation. Using CoastSeg, a browser-based program for SDS detection and mapping, SDS for the period 1984–2023 for multiple locations across the United States were compared to shoreline positions from traditionally sourced shoreline data. In this report, the authors present these comparisons alongside lessons learned and challenges encountered when building SDS workflows in different coastal locations. Results show that individual SDS have larger uncertainty and yet produced similar linear trends to sparser, traditionally sourced shoreline data; because SDS methods provide orders of magnitude more data than traditional shoreline-detection methods, they can be used to evaluate shoreline behaviors. Refining average scalar slopes used in tidal corrections did not result in substantial decreases in uncertainty. Using lessons from this work to outline needs for regional implementation, initial setup time would be considerable, being on the order of weeks. However, once complete, shoreline detections and analyses are fast (on the order of minutes to hours) and achievable using a desktop computer.

Alaska, Florida, Massachusetts, Washington

Design and function of the Autonomous Benthic Imaging and Surveying System (ABISS) for remote sensing of lake and seabed environments

Lake and seabed environments are home to fisheries and other biota that are important to ecosystems and economies, yet these environments and the species that use them are difficult to accurately assess and monitor. Traditional benthic survey techniques, like bottom trawling used by the U.S. Geological Survey, are limited by substrate constraints, poor spatial resolution and precision, and operational depth limits, hindering accurate assessment of benthic species and habitats. In response to these limitations, the U.S. Geological Survey developed the Autonomous Benthic Imaging and Surveying System, a camera system integrated into underwater vehicles, to capture high-resolution images of the lakebed. The system uses color and stereo cameras to collect imagery, which can be analyzed using computational methods to detect organisms and (or) characterize habitat features, such as geologic substrate types. The system has been integrated into autonomous underwater vehicles and into an underwater housing used by self-contained underwater breathing apparatus (SCUBA) divers. Although the engineering of the system was motivated by the need for data collection in the Great Lakes, it has potential to collect high quality data in any aqueous setting with sufficient water clarity and safe operating conditions. The Autonomous Benthic Imaging and Surveying System can operate across diverse depths and light conditions to map and quantify ecological patterns that were difficult or impossible to assess using traditional methods. The Autonomous Benthic Imaging and Surveying System offers the potential for accurate and precise monitoring and assessment of native benthic biota, invasive species, and habitat, potentially providing natural resource managers with improved information to support decision making about benthic resource management.

Great Lakes

Identification of representative earthquakes for probabilistic tsunami hazard analysis (PTHA) using earthquake rupture forecasts and machine learning

As probabilistic tsunami hazard analysis (PTHA) focuses more on assessments for localized, populous regions, techniques are needed to identify a subsample of representative earthquake ruptures to make the computational requirements for producing high-resolution hazard maps tractable. Moreover, the greatest epistemic uncertainty in seismic PTHA is related to source characterization, which is often poorly defined and subjective. We address these two salient issues by applying streamlined earthquake rupture forecasts (ERFs), based on combinatorial optimization methods, to an unsupervised machine learning workflow for identifying representative ruptures. ERFs determine the optimal distribution of a millennia-scale sample of earthquakes by inverting the observed slip rate on major faults. We use two previously developed combinatorial optimization ERFs, integer programming and greedy sequential, to produce the optimal location of ruptures with seismic moments sampled from a regional Gutenberg–Richter magnitude–frequency distribution. These ruptures in turn are used to calculate peak nearshore tsunami amplitude, using computationally efficient tsunami Green's functions. An unsupervised machine learning workflow is then used to identify a small subsample of the earthquakes input to ERFs for onshore PTHA analysis. We eliminate epistemic uncertainty related to source distribution under traditional PTHA analysis; in its place, a quantifiable, less subjective and generally smaller uncertainty related to the input to ERFs is included. The Nankai subduction zone is used as a test case, where previous ERFs have been conducted. Results indicate that the locations of representative earthquakes are sensitive to choice of magnitude–area relation and to whether a minimum cumulative stress objective is imposed on the fault. In general, incorporating ERFs into PTHA provide a physically self-consistent method to incorporate fault slip information in determining representative earthquakes for onshore PTHA, eliminating a major source of epistemic uncertainty.

Nankai subduction zone

Development of a hydrogeologic visualization model for western Sarpy County, Nebraska

Population in western Sarpy County, Nebraska, has steadily increased over the last several decades and has led to increased groundwater use for domestic purposes. To meet the increase in demand, the Papio-Missouri River Natural Resources District is seeking to use all available sources of groundwater in western Sarpy County. Additionally, elevated groundwater nitrate plus nitrite as nitrogen concentrations were detected, indicating the need to better understand the groundwater quality of the area. Although the general geology of the area is understood, the area does not have detailed information on the extent of the various aquifers, particularly the Dakota aquifer. To characterize these aquifers, the Papio-Missouri River Natural Resources District invested in airborne electromagnetic surveys of the area to better understand the subsurface geology. Although these surveys improved understanding of the groundwater systems in the area, the Papio-Missouri River Natural Resources District wanted to integrate the subsurface information with available water-quality and groundwater-level data. In response, the U.S. Geological Survey, in cooperation with the Papio-Missouri River Natural Resources District, the Nebraska Natural Resources Commission, and the Nebraska Department of Natural Resources, assembled geologic, hydrogeologic and nitrate plus nitrite as nitrogen information for the selected area into a three-dimensional visualization computer software package called GeoScene3D. The completed GeoScene3D project was assembled to provide a visualization of the groundwater systems and associated water-quality results in Sarpy County and to provide the Papio-Missouri River Natural Resources District managers with information that can be used to make more informed groundwater resource-planning decisions in the future. This report details the development of a three-dimensional model created within GeoScene3D to visualize the subsurface, particularly the Dakota Sandstone in western Sarpy County.

Nebraska

Representation of surface-water flows using Gradient-Related Discharge in an Everglades Network

The Everglades Depth Estimation Network interpolates water-level gage data to produce daily water-level elevations for the Everglades in south Florida. These elevations were used to estimate flow vectors (gradients and directions) and volumetric flow rates using the Gradient-Related Discharge in an Everglades Network (GARDEN) application developed by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers. Flow rates in both the east-west and north-south directions were computed on a 400-meter square grid using modified parameters in the Manning’s equation. The frictional resistance parameter in the Manning’s equation was calibrated to measured flow rates at coastal creeks fed by Everglades Depth Estimation Network boundary flows. Levees and other features that act as barriers to flow were defined as “no-flow” grid cells where vectors were set to zero. The flow volume magnitudes were calibrated with 2020 daily values of coastal river flows, and verification was performed using 2021 data. Within a given day, the measured coastal river flows fluctuate more than the GARDEN boundary flows because of tidal and wind forcings. Because the GARDEN boundary flows were the upstream water source for the coastal rivers, calibration focused on matching average daily flow volumes rather than daily fluctuations. The Pearson’s correlation coefficient is 0.766 for the 2020 calibration period and 0.566 for the 2021 verification period. Applying GARDEN to periods with hydraulic-control-structure releases allows the propagation of structure flows to be seen in the daily flow-vector maps along with the multiday response of flows farther downgradient. Flow vectors may be overestimated near control structures because of difficulties in resolving the water gradient downstream from the structure. Flow vectors farther from the structure are more accurate than those near the structure.

Florida

Peak-discharge frequency and potential extreme peak discharge for natural streams in the Brazos River basin, Texas

The 2-, 5-, 10-, 25-, 50-, and 100-year peak discharges were estimated for 186 streamflow-gaging stations with at least 8 years of data for natural streams in and near the Brazos River Basin, Texas. Multiple regression equations were developed to estimate peak-discharge frequency for the 2-, 5-, 10-, 25-, 50-, and 100-year recurrence intervals for each of three hydrologic regions that compose the Brazos River Basin. The equations for each region are a function of significant basin characteristics (explanatory variables). The significant explanatory variables among six that were tested are the contributing drainage area and stream slope for regions 1 and 2 and the contributing drainage area for region 3. For the three sets of equations, the coefficient of determination ranges from 0.59 to 0.93, and the standard error ranges from 0.184 to 0.391 log units. A larger coefficient of determination and a lower standard error generally are associated with the equations for hydrologic regions 2 and 3. Statistics from the regression analysis allow computation of the prediction interval associated with a given significance level for a peak-discharge frequency estimate. The regression equations can be used to estimate peak discharges for sites at, near, or away from sites with streamflow-gaging stations. The potential extreme peak-discharge curves as related to contributing drainage area were estimated for each of the three hydrologic regions from measured extreme peaks of record at 186 sites with streamflow-gaging stations and from measured extreme peaks at 37 sites without streamflow-gaging stations in and near the Brazos River Basin. The potential extreme peak-discharge curves generally are similar for hydrologic regions 1 and 2, and the curve for region 3 consistently is below the curves for regions 1 and 2, which indicates smaller peak discharges.

Texas

Extending the Boore and Abrahamson (2023) modified square-root-impedance method for the development of site amplifications consistent with the full-resonance approach to a range of VS30 values

The square-root-impedance (SRI) method is commonly used to approximate the seismic site amplifications computed using the full-resonance (FR) method for gradient shear-wave velocity ( V S ) profiles that are smoothly varying with depth. The SRI site amplifications have been observed to systematically underpredict the FR site amplifications by a ratio of FR/SRI amplifications around 1.05 to 1.3 across a wide frequency range (Boore, 2013). Recently, Boore and Abrahamson (2023; hereafter, BA23) related this difference in the SRI and FR methods to differences in the exponent η of the ratio of seismic impedances between the two methods. They proposed the implementation of a modified frequency-dependent η in the SRI method to improve its match to the FR site amplifications. This modified η was derived using only five V S profiles. We investigate the performance of the BA23 η for a wide range of realistic gradient V S profiles with V S30 ranging from 180 to 1500 m/s. These gradient V S profiles are constructed using two power-law functions of depth and are constrained by the assigned VS30 value, the depth and velocity of the half-space, and depths to shear-wave velocity horizons of 1.0 and 2.5 km/s ( Z 1.0 and Z 2.5 ) based on western United States sites. Despite observing a V S30 dependence of η, we find that the BA23 η generally works reasonably well for the range of V S profiles analyzed. Using the VS30 -dependent η derived in this study results in improvements in matching the FR site amplification compared to using the BA23 η. These improvements are more pronounced for the soft-site conditions and become modest to negligible for the stiff site conditions

Bulletin of the Seismological Society of America

Site response models based on geometric parameters for southern California sedimentary basins

Site response in sedimentary basins is influenced by complex three-dimensional (3D) features, including trapping of seismic waves, focusing of seismic energy and basin resonance. Current ground motion models (GMMs) incorporate basin effects using one-dimensional parameters like V S30 and shear wave velocity isosurface depths, which are limited in capturing lateral and 3D effects. To address these limitations, we develop seismic site response models based on novel parameters that represent multi-dimensional properties of the Los Angeles Basin (LAB) geometry and shear wave velocity. We define a basin shape for the LAB using depth to subsurface geologic interfaces associated with the oldest sedimentary deposits (depth to a particular shear wave velocity horizon, i.e., 1.5 km/s - z 1.5 ) and the depth to the crystalline basement ( z cb ) which are determined using geologic cross sections and community seismic velocity model profiles. We explore a suite of geometric descriptors computed for the LAB and southern California, from which three parameters with the greatest predictive potential are selected and evaluated using empirical ground motion residual analyses in combination with the Boore et al. GMM. The results demonstrate that the zonal heterogeneity index ( ), standard deviation of the absolute difference between z 1.5 and z cb ( ) and standard deviation of z cb ( ) each provide a reduction in site-to-site variability ( ϕ S2S ) of empirical GMMs. The reduction in ϕ S2S is period-dependent, with average decreases of 3%, 26% and 6% for , , and , respectively. Although these reductions are modest from an engineering application perspective, they are statistically significant, underscoring the inherent difficulty in fully characterising complex basin effects. Collectively, these findings indicate that the inclusion of basin-specific geometric parameters yields measurable, albeit incremental, improvements in site response prediction and establishes a framework for the progressive refinement of seismic hazard characterisation within sedimentary basins.

California

A partially nonergodic ground-motion model for Fourier amplitude spectra for the San Francisco Bay area, California, USA

We develop a partially nonergodic ground-motion model (GMM) for Fourier amplitude spectra for the San Francisco Bay Area, California, USA, using the Bayless and Abrahamson (2019) GMM as a reference ergodic GMM and developing location-dependent adjustments to the predicted median and variance. We compile regional ground-motion data from moment magnitude (𝑀 w ) >3 earthquakes occurring during 2000–2022 for which magnitude information is available in the U.S. Geological Survey Comprehensive Catalog (Guy et al., 2015). The data set predominantly consists of records from 𝑀 w 3.5–4.5 earthquakes but includes three well-recorded 𝑀 w > 5 events. Ground-motion residuals are evaluated using the time-averaged shear-wave velocity in the top 30 m (𝑉 S30 ) from the California-specific map of Thompson et al. (2018) and basin-depth site parameters from the seismic velocity model of Aagaard and Hirakawa (2021). The 𝑉 S30 dependence and basin-depth scaling of the reference ergodic GMM of Bayless and Abrahamson (2019) are evaluated and modified with the updated data set. We compute maps of site adjustments using a varying-coefficient model that considers the spatial correlation structure and uncertainties at each observation location. The spatial covariance model is developed using ground-motion residuals that are standardized by the uncertainty model, which allows for consideration of the aleatory variability in developing the site adjustments. The covariance model is fit considering the means and standard deviations of the site terms at all locations. The use of partially nonergodic median adjustments results in modified variance components of the within-event variability. Due to the low number of large-magnitude earthquakes that control seismic hazard in the data set, we do not modify between-event variance; however, we present adjustments to site-to-site variability for use in partially nonergodic hazard assessments.

California

The 2023 Alaska National Seismic Hazard Model

US Geological Survey (USGS) National Seismic Hazard Models (NSHMs) are used extensively for seismic design regulations in the United States and earthquake scenario development, as well as risk assessment and mitigation for both buildings and infrastructure. This 2023 update of the long-term, time-independent Alaska NSHM includes substantial changes to both the earthquake rupture forecast (ERF) and ground motion models (GMMs). The ERF includes numerous additions to the finite-fault model, considers two deformation models, and introduces updated declustering and smoothing algorithms in the gridded background seismicity model. For the Alaska–Aleutian subduction zone, megathrust earthquakes occur on an updated structural and segmentation model, and the moment magnitude (M) 8+ rupture and rate model include a logic tree branch that considers slip rates derived from geodetic models of interface coupling. The megathrust model considers multiple models of down-dip width, and magnitudes are computed using newly developed scaling relations. For subduction intraslab events and subduction interface events with M < 7, the 2023 update uses a smoothed seismicity model with rupture depths derived from Slab2. The 2023 model updates GMMs in all tectonic settings using the recently published Next Generation Attenuation Subduction (NGA-Sub) GMMs for subduction interface and intraslab events, and the NGA-West2 GMMs for active crustal settings. Collectively, additions and updates to the Alaska NSHM result in hazard increases across most of south-central Alaska relative to the previous model, published in 2007. These changes are primarily due to the adoption of updated rate models for the large-magnitude interface events and the NGA-Sub GMMs that have much higher aleatory variability (sigma), consistent with global observations, and that include models of epistemic uncertainty.

Alaska

SURF: An automated method for building nonplanar 3D fault models from earthquake hypocenters

Accurately characterizing 3D fault geometry is vital for improving our understanding of earthquake behavior and informing the development of seismic hazard models. Despite their importance, subsurface fault structures tend to be poorly constrained because of limitations in observational data. Improvements to the seismic networks and earthquake detection algorithms have increased the precision and volume of earthquake catalogs, which help illuminate detailed subsurface fault structure and provide the most direct information available about fault geometries at depth. We present a Python package to automate generating 3D fault geometries directly from hypocentral seismicity patterns. This method begins with clustering events based on their spatial density, identifying coherent patterns. Nearby clusters are then merged based on the similarity of their orientations. We fit nonplanar surfaces using support vector regression to balance surface accuracy with minimal deviations from planarity. The fault models are output as quadrilateral meshes at user‐defined resolution. In the process of generating the 3D fault surfaces, we compute the spatial density of seismicity around the surface and the planarity as quantitative metrics of the model outputs. As a proof of concept, we apply this approach to the San Andreas–Calaveras fault junction region and the 2019 Ridgecrest earthquake sequence, both in California, which contain complex subparallel faults well defined at the Earth’s surface and abundant microseismicity. These case studies demonstrate the method’s ability to model complex fault structures, including long continuous fault surfaces, crossing faults, variably dipping segments, and subparallel faults. We test the method on both standard network catalogs and double‐difference relocated catalogs. We find that our seismicity‐based fault model results align with published 3D models that incorporate additional constraints and interpretations ( Plesch et al. , 2020 ; Aagaard and Hirakawa, 2021 ). This workflow provides a low‐user‐input solution for estimating fault geometries at depth from earthquake catalogs.

California

Estimation of reservoir storage capacity and geomorphic change detection analysis from a multibeam bathymetric survey of Randy Poynter Lake, Rockdale County, Georgia

Rockdale County Department of Water Resources has a directive to update estimates of the reservoir storage capacity of Randy Poynter Lake, located in northern Georgia, and to assess recent sedimentation and associated storage capacity loss. In 2022, the U.S. Geological Survey completed a multibeam bathymetric survey of Randy Poynter Lake to update storage capacity estimates and to quantify storage capacity change since the first multibeam bathymetric survey in 2012 in consideration of estimated errors inherent to bathymetric surveys. Data from the 2022 survey were used to generate contours of the reservoir as well as compute storage capacity at regular increments of water-surface elevation. Storage capacity comparisons between 2012 and 2022 at Randy Poynter Lake show minimal changes that are within the estimated uncertainties, with consistent or slightly increased storage capacities observed at most water-surface elevations and reductions observed at the remaining few elevations. Comparison of the multibeam bathymetric data collected in 2012 with data collected in 2022 further allowed for a formal geomorphic change detection analysis to map, quantify, and infer causation of morphological change over time with respect to a level of detectable change. The volume change in Randy Poynter Lake for the decade between 2012 and 2022 was slightly net-depositional and within the estimated uncertainty. The spatial distribution of sediment deposition was primarily concentrated in the northern portion of the lake, where the principal tributary flows into Randy Poynter Lake. The results of the geomorphic change analysis were used to further understand the future implications to storage capacity change. Despite the challenges of confirming systematic biases because of uncertainties exceeding the observed changes, insights from the study help predict long-term reservoir sediment accumulation, indicating a reservoir half-life extending about 650 years from 2022 on the basis of the current sediment yield estimates.

Georgia

Framework for mapping liquefaction hazard–Targeted design ground motions

Liquefaction-induced ground failure poses substantial challenges to geotechnical earthquake engineering design. Current approaches for designing against liquefaction hazards, as specified in most seismic provisions, focus on estimating a liquefaction factor of safety ( 𝐹⁢𝑆𝐿 ) and typically characterize earthquake loading using design parameters based on probabilistic or deterministic ground motion levels. Because 𝐹⁢𝑆𝐿 is estimated deterministically, this basis of design neglects considerable uncertainties for estimating liquefaction triggering and its consequences and results in a lack of liquefaction-specific design criteria, particularly as structural design has advanced toward risk-targeted performance objectives. This study presents a framework for developing liquefaction-targeted design criteria based on a minimum acceptable return period of liquefaction, informed by probabilistic liquefaction hazard analysis (PLHA). PLHA quantifies annualized rates of liquefaction by considering contributions from (1) the full ground-motion probability space, and (2) uncertainties in liquefaction triggering using probabilistic models. PLHA is used in this study to characterize the current, effective return periods of 𝐹⁢𝑆𝐿 ( 𝑇𝑅,𝐹⁢𝑆 ) obtained from conventional liquefaction hazard analysis (CLHA) using uniform-hazard ground motions. 𝑇𝑅,𝐹⁢𝑆 is evaluated in a parametric study of nearly 100 sites throughout the conterminous United States. The results indicate large geographic variations in acceptable liquefaction hazard levels, with implied 𝑇𝑅,𝐹⁢𝑆 ranging between approximately 1,000 to 3,000 years. To address these inconsistencies without the computational demands of full PLHA, a framework is proposed for developing a liquefaction-targeted design peak ground acceleration, 𝑃⁢𝐺⁢𝐴𝐿 , for use in liquefaction models that result in consistent liquefaction design levels across all geographic locations. The mapped 𝑃⁢𝐺⁢𝐴𝐿 is shown to be somewhat sensitive to site-specific properties, and adjustment factors are developed and presented. The proposed 𝑃⁢𝐺⁢𝐴𝐿 mapping procedure produces 𝐹⁢𝑆𝐿 estimates that are consistent with those obtained from full PLHA at a target 𝑇𝑅,𝐹⁢𝑆 , providing a promising roadmap to incorporating PLHA concepts into current liquefaction design methods.

Journal of Geotechnical and Geoenvironmental Engin

The GorDAS Distributed Acoustic Sensing experiment above the Cascadia locked zone and subducted Gorda Slab

The southernmost portion of the Cascadia Subduction zone in Northern California produces high rates of moderate and large earthquakes owing to subduction of the Gorda slab and deformation associated with the Mendocino Triple Junction. Distributed Acoustic Sensing (DAS) is rapidly advancing as a method for detecting earthquakes and imaging crustal structure. We have begun a long-term DAS monitoring experiment on buried telecom fiber in Arcata, California, with the goal of increasing the available recordings of moderate to large earthquakes as well as imaging seismogenic structures. We have recorded over a year's worth of data, including most aftershocks of the 2022 M w 6.4 Ferndale earthquake, though not the mainshock itself. The dataset includes numerous magnitude 3.5 and larger earthquakes including the 2023/01/01 M w 5.4 Rio Dell earthquake. Here we present initial results comparing an earthquake detection algorithm, run in real-time on the processing unit of the interrogator system, with both the ShakeAlert earthquake early warning system as well as a post-processed earthquake catalog developed with deep-learning phase-picker algorithms. The rapid onboard processing of the detector demonstrates the potential utility of DAS-based edge computing for earthquake early warning. We also verify the quality of the strain waveforms both in terms of peak amplitudes and waveform similarity using about five months of nodal seismometer data. These instruments were deployed roughly every 300 m along the ~15km long cable and validate large variations in peak strain over short distances that are seen in the DAS data. All data from time windows surrounding both the local and teleseismic earthquakes are publicly available, which will improve our understanding of both the performance of DAS systems in moderate earthquakes and earthquake hazards associated with the Gorda subduction zone.

California

Suitability of ground-motion models for seismic hazard assessment in Puerto Rico and the U.S. Virgin Islands

We perform linear mixed-effects analyses with a ground-motion dataset to evaluate how well ground-motion models (GMMs) fit active crustal, subduction interface, and subduction intraslab earthquakes in Puerto Rico and the US Virgin Islands. Most of the GMMs reproduce the spatial variation in peak ground motions with earthquake magnitude and rupture distance but predict ground motions 0.3–1.0 natural log units (35%–270%) greater than observations. Two GMMs developed for Puerto Rico that are based on ground-motion records from mostly magnitude 4–5 earthquakes do not perform as well. We attribute the period-dependent overprediction in observed ground motions to differences between the observed site response and the linear site response in the GMMs. Consequently, we developed region-specific GMMs by adjusting the period-dependent linear site response coefficients and period-dependent constant coefficients to remove most of the bias between predicted ground motions and observations. For the analysis, we compile ground-motion records and process waveforms to build a dataset with 10,127 records at 72 stations from 849 magnitude 4.0–6.4 earthquakes between 1 January 2006 and 31 March 2024. The earthquakes include active crustal, subduction interface, and subduction intraslab events. We evaluate the GMMs using the time-averaged shear wave speed in the top 30 m ( ), which we compile from site surveys and proxy values computed from horizontal to vertical spectral ratios. Site terms exhibit strong consistency across GMMs and crustal and subduction earthquakes, indicating that the linear mixed-effects analysis successfully isolates the effects of local site response. The event terms show little spatial correlation and more substantial variability than in other regions, which we attribute to uncertainties in the earthquake magnitudes. This analysis guides the selection of the GMMs for the 2025 update of the National Seismic Hazard Model for Puerto Rico and the US Virgin Islands.

Puerto Rico, U.S. Virgin Islands

Global variability of the composition and temperature at the 410-km discontinuity from receiver function analysis of dense arrays

Seismic boundaries caused by phase transitions between olivine polymorphs in Earth's mantle provide thermal and compositional markers that inform mantle dynamics. Seismic studies of the mantle transition zone often use either global averaging with sparse arrays or regional sampling from a single dense array. The intermediate approach of this study utilizes many densely spaced seismic arrays distributed around the globe. We systematically compute teleseismic P-to-S receiver functions for each seismic array and invert for the 1-D seismic velocity structure of the mantle transition zone beneath each array to facilitate a comparison between densely sampled regions. We stack 3,600 receiver functions on average at 67 arrays in total. The stack is used in a probabilistic inversion to estimate the mantle transition zone interface depths and velocities beneath each array. We focus on the 410-km discontinuity (410) because it is a prominent seismic interface that is clearly linked to a single mineral phase transition between olivine and wadsleyite. The depths and velocity contrasts of the 410 are mapped to temperatures and compositions using mineral physics constraints. The depth of the 410 ranges from ∼405–440 km, which is consistent with a ∼360 K temperature range in a dry mantle and a ∼260 K temperature range in a wet mantle (2 wt. % water). The Vs contrast across the 410 ranges from ∼2.5–8 %, which is consistent with ∼20–70 vol. % olivine composition in a dry mantle and ∼25–80 vol. % in a wet mantle. The bulk composition of the upper mantle near the 410-km discontinuity is typically considered to be well-mixed because there is no thermodynamic impediment to convection at the olivine to wadsleyite phase transition. However, the wide range of inferred olivine content from our study suggests that there are large lateral variations in the bulk composition of the upper mantle near the 410-km discontinuity.

Earth and Planetary Science Letters

Groundwater drought in the United States: Spatial and temporal variability

Many communities and ecosystems in the United States that are dependent on groundwater are potentially adversely affected by groundwater drought. We computed yearly groundwater-drought metrics and mean groundwater levels at well locations across the conterminous United States (CONUS), using data from wells and remotely sensed and modeled Gravity Recovery and Climate Experiment Drought Monitor Data Assimilation (GRACE-DADM). We also modeled the probability of low or high human impact at each well location. The spatial distribution of groundwater-drought duration and severity from 2001 to 2020 for 1,510 wells shows longer maximum duration and higher maximum severity events in drier regions like the Southwest than in wetter regions like the Northeast. Based on 613 wells in CONUS from 1981 to 2020, there are many significant decreases in drought duration and severity in the Northeast and many significant increases in annual-mean groundwater levels. In contrast, there are many significant increases in drought metrics and decreases in mean water levels in parts of the Southeast. There are major differences in trends from 2001 to 2020 between well-based and GRACE-DADM-based groundwater metrics in some CONUS regions and a very low correlation between trends at individual locations across CONUS. A potential reason for this disparity is the low GRACE-DADM resolution (∼12 km) and the potential for a large amount of groundwater variation at the local scale. Also, GRACE-DADM represents shallow, unconfined aquifers which may not match the screened interval of the monitoring wells we evaluated. Large spatial gaps in long-term, high frequency, and quality-assured groundwater-well monitoring data present a challenge for understanding groundwater-drought variability across CONUS. Remote sensing tools such as GRACE can help but cannot fully replace well monitoring, as highlighted by our study results. Substantially more long-term monitoring wells would more accurately represent groundwater-drought trends and spatial variability across CONUS, particularly in western regions.

conterminous United States

As above, so below? A framework for integrating long-term water quantity trends reveals divergent patterns in groundwater and low streamflow across the United States

Climate, land-use, and disturbance drive long-term global trends in groundwater levels and streamflow. At large scales, these trends are typically considered separately, despite the well-established concept that groundwater and surface water comprise a single resource. Joint trend assessment at national scales is challenging because it requires pairing and aggregating data from spatially disparate streamflow and groundwater monitoring sites for which no established framework exists. Here, we evaluate alternative approaches for integrating groundwater and streamflow data to enable joint trend analysis—a critical step toward understanding how water-budget components respond concurrently and interactively to environmental drivers. Mann–Kendall trends were computed for individual groundwater (annual mean depth) and streamflow (annual low of 7 d averages) sites across the U.S over 21- (2000–2020), 31- (1990–2020), and 41-year (1980–2020) periods. Regional Kendall trends were calculated using five spatially contiguous and noncontiguous regional classifications for aggregation based on subsurface (e.g. aquifer, geology) and surface (e.g. watershed, landscape) characteristics. Site-level results revealed contrasting trends, with tendencies toward increasing low flows (wetting) and increasing groundwater depths (drying). Agreement between streamflow and groundwater trends increased with regional aggregation and longer timeframes, though persistent skew toward streamflow wetting and groundwater drying remained. Results varied by region and trend period, with notable consistencies: unified drying in the West/Southwest and wetting in the Upper Midwest. Directional mismatches in long-term trends were prominent in the High Plains and Mississippi Alluvial Plain, whereas near-term mismatches were most evident in the Northwest. Aggregation by hydrologic landscape regions (HLR) yielded the greatest agreement between groundwater and streamflow trends. These findings indicate that coupled responses may represent combined influences of climate, relief, and geology, as captured by HLR, more strongly than geography or geology alone. Integrated water availability assessments may benefit from a multi-characteristic classification framework to treat groundwater and surface water as a unified resource.

Environmental Research: Water