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Results for “New Zealand Journal of Geology and Geophysics”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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1,689 records · Page 79Linked to original sources
USGS2024

Chloride concentrations in groundwater from the western part of the Southern Hills regional aquifer system, Louisiana, 2021–22

Groundwater is heavily used for public supply and industrial uses in the Baton Rouge, Louisiana, area. Lowered water levels resulting from groundwater withdrawals have induced the movement of saltwater towards wells in East Baton Rouge and West Baton Rouge Parishes. Saltwater intrusion has the potential to affect water supply infrastructure, reduce water availability for some uses, and increase treatment costs. To document current conditions, samples were collected from 161 wells screened in 10 aquifers of the Southern Hills regional aquifer system during November 2021 through February 2022. The results were compared with historical data to identify where chloride concentrations are increasing, which could indicate that saltwater intrusion is occurring. Saltwater intrusion, to varying degrees and areal extents, was observed in most of the 10 aquifers. The limited availability of monitoring wells near or within some of the known saltwater plume areas restricts tracking of the movement or delineation of the plumes’ current extents.

Louisiana↗
USGS2026

From model to action: Identifying the gaps in coastal marsh models for decision making

Coastal salt marsh modeling projects are designed to answer questions about where salt marshes may migrate, how they can migrate, and how they will be impacted by changing water levels. While this information is vital for climate-resilient marsh conservation, translating these findings into actionable results remains a challenge. The authors examined the actionability of marsh modeling efforts in the United States by analyzing data collected from semi-structured interviews of marsh model users ( n = 24) across two ongoing projects along the U.S. East Coast and the Gulf of America (Gulf of Mexico). By qualitatively analyzing the interview data, the authors found that the tasks of practitioners who use coastal marsh model outputs fall into three major themes: (1) marsh restoration, (2) planning with uncertainty, and (3) conserving habitat for marsh-reliant species. For each of these themes, the authors identify unmet needs, including high spatial resolution information for local planning, accessible descriptions of uncertainty to increase user confidence, and the incorporation of human dimensions data (e.g., human alterations to the landscape such as impoundments and culverts) for a comprehensive understanding of the coastal salt marsh. Marsh modeling projects should strategize how to fulfil these unmet user needs so that marsh modeling outputs can better support decisions related to marsh conservation and restoration.

Gulf of America, United States East Coast↗
USGS2025

Groundwater budget for the surficial aquifer surrounding Lake Nokomis, Minneapolis, Minnesota

During prolonged periods of above-average precipitation, rising groundwater levels have the potential to cause damage to and interfere with underground infrastructure and building foundations. To understand the relations between precipitation and groundwater in the vicinity of Lake Nokomis, the U.S. Geological Survey, in collaboration with the University of Minnesota, quantified five components of the groundwater budget: groundwater recharge, change in surficial aquifer storage, surficial aquifer groundwater discharge to Lake Nokomis, groundwater evapotranspiration, and groundwater discharge to underlying bedrock aquifers. Field data, geologic records, and empirical calculation methods were used to quantify groundwater budget components for April 2023 through April 2024. Lake water budget data indicate that Lake Nokomis is a flowthrough system during periods with no outflow through the weir, with groundwater inputs equal to outputs. Roughly 40 percent of precipitation that fell in the study area was added to the surficial aquifer as recharge. Uncertainty in the vertical hydraulic conductivity resulted in wide-ranging estimates (spanning three orders of magnitude) of water discharging from the surficial aquifer to the underlying bedrock aquifer. Drought conditions persisted for the duration of this study and were not representative of the conditions that motivated this study. This study is a start towards understanding relations between precipitation, Lake Nokomis levels, and groundwater levels that could affect local underground infrastructure.

Minnesota↗
USGS2024

Predicted exposure of communities in southeastern United States to climate-related coastal hazards

A rigorous analysis of 21st Century multi-hazard exposure for U.S. Southeast Atlantic coastal communities indicates that up to 70% of residents will be exposed daily to shallow and emerging groundwater by ~2100, 15 times higher than from surficial flooding alone. This threat further exacerbates other coastal stressors, such as flooding, subsidence, and beach erosion, that impact these vulnerable coastal communities and ecosystems under expected future climate change scenarios.

Florida, Georgia, North Carolina, South Carolina, ↗
USGS2025

International data gaps at the Center for Engineering Strong Motion Data

The Center for Engineering Strong Motion Data (CESMD) is utilized by seismologists, engineers, and disaster management professionals in the US and has historically achieved and distributed waveforms from across the globe for significant earthquakes. The increased access to the waveforms via Web API (Application Programming Interface) offers a unique opportunity to provide the community complete datasets, sampling a variety of tectonic environments and geologic conditions, increasing the number of available ground motion records for use in ground motion models (GMMs) and improving the accuracy of earthquake engineering evaluations. The objective of this study is to programmatically identify gaps in global event data from the past decade and backfill missing data gaps at CESMD. We first compare the CESMD catalog with the Advanced National Seismic System (ANSS) Comprehensive Earthquake Catalog identifying regions and time periods where strong-motion data is limited or inadequate. To backfill datasets at CESMD for significant events, we pinpoint regions and time intervals that lack information, creating a list of events for which we’d like to obtain data. An important facet of this work is identifying the source of data and metadata across earthquake repositories around the world and integrating these data repositories into our current strong-motion data processing workflow. In parallel with these newly processed datasets, we are developing a script to produce data origination citations to include provenance and attribution information to associate with respective datasets at CESMD. We showcase our methodology for identifying and filling data gaps at CESMD using three case studies (the 2018 Anchorage Alaska earthquake sequence, seismicity associated with the 2018 Hawaiian Kilauea volcano eruption, and several earthquakes in Turkey) and then outline our strategy to apply our data gap backfilling methods on an international scale.

Conference Paper↗
USGS2025

Extremophile hotspots linked to containerized industrial waste dumping in a deep-sea basin

Decaying barrels on the seafloor linked to DDT contamination have raised concerns about the public health implications of decades old industrial waste dumped off the coast of Los Angeles. To explore their contents, we collected sediment cores perpendicular to five deep-sea barrels. The concentration of DDT and its breakdown products were highly elevated relative to control sites yet did not vary with distance from the barrels, suggesting that they were not associated with the contamination. Sediment cores collected through white halos surrounding three barrels were enriched in calcite and had elevated pH. The associated microbial communities were low diversity and dominated by alkalophilic bacteria with metagenome-assembled genomes adapted to high pH. A solid concretion sampled between a white halo and barrel was composed of brucite, a magnesium hydroxide mineral that forms at high pH. Based on these findings, we postulate that leakage of containerized alkaline waste triggered the formation of mineral concretions that are slowly dissolving and raising the pH of the surrounding sediment pore water. This selects for taxa adapted to extreme alkalinity and drives the precipitation of “anthropogenic” carbonates forming white halos, which serve as a visual identifier of barrels that contained alkaline waste. Remarkably, containerized alkaline waste discarded >50 years ago represents a persistent pollutant creating localized mineral formations and microbial communities that resemble those observed at some hydrothermal systems. These formations were observed at one-third of the visually identified barrels in the San Pedro Basin and have unforeseen, long-term consequences for benthic communities in the region.

California↗
USGS2024

Parameter ESTimation with the Gauss–Levenberg–Marquardt algorithm: An intuitive guide

In this paper, we review the derivation of the Gauss–Levenberg–Marquardt (GLM) algorithm and its extension to ensemble parameter estimation. We explore the use of graphical methods to provide insights into how the algorithm works in practice and discuss the implications of both algorithm tuning parameters and objective function construction in performance. Some insights include understanding the control of both parameter trajectory and step size for GLM as a function of tuning parameters. Furthermore, for the iterative Ensemble Smoother (iES), we discuss the importance of noise on observations and show how iES can cope with non-unique outcomes based on objective function construction. These insights are valuable for modelers using PEST, PEST++, or similar parameter estimation tools.

Groundwater↗
USGS2025

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↗
USGS2024

Earthquake-triggered ground-failure inventory associated with the M7.1 2018 Southcentral Alaska earthquake

The 30 November 2018, magnitude (Mw) 7.1 earthquake in Southcentral Alaska triggered substantial landslides, liquefaction, and ground cracking throughout the region, resulting in widespread geotechnical damage to buildings and infrastructure. Despite a challenging reconnaissance and remote-sensing environment, we constructed a detailed digital inventory of ground failure associated with the event from several sources. Sources included information derived from remotely sensed data, and data compiled from literature, social media postings, and earthquake damage information compiled by local, state, and federal agencies. Each instance of ground failure within the inventory contains information on the location and type of observed ground failure, and the methods and data used to document the occurrence. Where high-quality data, such as LIDAR or satellite imagery, were available and showed the ground-failure instance clearly, the extent is mapped as a polygon or polyline. All other locations are mapped as points. There are a total of 886 ground-failure instances documented within the inventory (400 landslides, 286 liquefaction features, and 200 features unattributed to specific processes). A semi-quantitative confidence scheme is used to describe mapping certainty associated with each ground-failure feature. This inventory represents a relatively moderate ground-failure-triggering event that occurred in a subarctic environment. This data paper describes the content within the inventory, the inventory data collection procedures, and limitations of the data. Events of this type are not often documented in detail; thus, adding the inventory data to the US Geological Survey Open Repository of Earthquake-Triggered Ground-Failure Inventories further diversifies the datasets available to the scientific community to be used to better understand and model earthquake-triggered ground failure.

Alaska↗
USGS2026

Season and antecedent conditions impact concentration-discharge relationships for dissolved organic carbon and alkalinity in southeast Alaskan watershed

Fluvial export of dissolved carbon plays an important role in watershed-scale biogeochemistry. Predicted changes in climate are expected to impact watershed hydrologic regimes, and in turn, the sources and export of dissolved carbon from watersheds. Here, we utilize high resolution measurements of discharge and dissolved carbon concentration to examine how concentration-discharge (CQ) relationships vary seasonally and during high flow events over the main runoff season (May–October) in a temperate forested watershed in Southeast Alaska. Concentration-discharge relationships for dissolved organic carbon (DOC) and alkalinity demonstrated strong seasonal patterns, with more linear relationships in May and June versus other months. Changing power law model slopes ( b values; the exponent in a power law regression between runoff and carbon yields) indicated potentially shifting watershed sources (biogenic vs. geologic) and contrasting dominant flowpaths (shallow vs. deeper groundwater) for DOC and alkalinity over the sampling period. During the largest storm event of the study, DOC and alkalinity b values shifted from an overall pattern of transport (mean b = 1.58 values >1.0 indicate transport limitation) and source limitation (mean b = 0.48, values <1.0 indicate source limitation) to chemostatic (DOC, b = 0.99; alkalinity, b = 1.019). In June through August, patterns in hysteresis index suggest that CQ relationships were altered when storms followed in close succession to each other. Together, these findings indicate that seasonal and antecedent flow conditions play a role in dissolved carbon export from forested watersheds. Understanding these dynamics, particularly during winter months, will become increasingly important as changes to hydroclimate impact riverine carbon export.

Alaska↗
USGS2025

Long‐period ground motions from dynamic rupture simulations of large earthquakes on the creeping Hayward–Calaveras–Rodgers Creek fault system

he Hayward, Calaveras, and Rodgers Creek faults in the San Francisco Bay region of California have a high probability of producing a large earthquake in the next decades. Although these faults creep, the creep is insufficient to keep up with their relatively rapid slip rates on their deepest sections, so they have been storing tectonic strain since their last large earthquakes, with the Hayward’s and Rodgers Creek’s more than 150 yr ago. We do not know what the next large Hayward–Calaveras–Rodgers Creek earthquakes will look like or how strongly they will shake the San Francisco Bay region. Harris et al. (2021) used the 3D dynamic (spontaneous) rupture method to simulate large earthquakes on these creeping faults. In this article, we examine the resulting simulated long‐period ( T > 1 s) ground shaking from 0 to 50 km distance, for earthquakes nucleating on the Hayward fault and earthquakes nucleating on the Rodgers Creek fault. We compare these simulated long‐period ground motions with the Boore et al. (2014) well‐established empirically based ground‐motion model suitable for the slowest material velocity in our 3D velocity structure. We find that the simulated long‐period ground motions from the creeping‐fault earthquake scenarios produce a reasonable agreement with the empirical expectations if frictional cohesion is included only where it is appropriate.

California↗
USGS2026

Assessing natural recharge in Indian Wells Valley, California: A Basin Characterization Model case study

The communities in Indian Wells Valley (IWV), in the northern Mojave Desert in California, rely on groundwater for domestic and agricultural use. Mountain front recharge from the surrounding Sierra Nevada is the main source of natural recharge to the valley. Increased urbanization, agricultural development, and groundwater pumping during recent decades put IWV in a state of critical overdraft. The U.S. Geological Survey Basin Characterization Model, version 8 (BCMv8) was used to evaluate historical and future climate and hydrologic conditions in IWV. The BCMv8 estimated natural recharge in IWV at 10.7 million cubic meters (Mm 3 ) per year for the period from 1981 to 2010. Future patterns of water balance variables using three future climate scenarios, hot-wet, hot-dry, and warm-moderately wet, were calculated for mid-century (2040–69) and end-of-century (2070–99) periods. Results for both wet models projected an increase in recharge in both periods, whereas the hot-dry model projected a decrease in recharge in both periods. All models reported a large increase in seasonal variability in recharge, indicating more future availability and frequent occurrences of drought years. All climate scenarios projected an increase in climatic water deficit in both periods. These increases in irrigation demand and variability of water supply highlight the importance of strategic management planning for the sustainability of water resources in IWV.

California↗
USGS2024

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↗
USGS2025

The National Map Corps—Federal Emergency Management Agency and Oak Ridge National Laboratory pilot project report

This report provides an overview of the U.S. Geological Survey National Map Corps —Federal Emergency Management Agency and Oak Ridge National Laboratory pilot project in St. James Parish, Louisiana, that began in February 2024 and ended at the end of March 2024. The project used the power of The National Map Corps’ volunteer community to improve building classifications in the original Federal Emergency Management Agency’s U.S.A. Structures dataset. The report highlights the project’s completion and details the work and results achieved through a collaborative effort to enhance geospatial data quality and utility.

Open-File Report↗
USGS2026

A practical framework for identifying genetic subpopulations and ESUs: Insights for IUCN assessments and broader management

Species conservation assessments evaluate extinction risk, and recovery potential, advancing species persistence through guiding resource prioritization and planning. Assessment frameworks, including the International Union for Conservation of Nature Red List and Green Status of Species, typically focus on species as a whole. Importantly, they do not routinely account for genetically distinct units or do not have standardized methods of unit delineation. This limits the representation of genetically distinct components, including adaptive genetic diversity that underpins long-term resilience and recovery. Incorporating standardized within-species units like subpopulations and Evolutionarily Significant Units (ESUs) into species assessments could help address this oversight. However, identifying and delineating such units remain challenging, particularly when molecular data are limited. Here, we propose a flexible framework that integrates molecular and non-molecular evidence to identify both subpopulations and ESUs across taxa, providing a practical tool to incorporate within-species diversity into conservation assessments.

BioScience↗
USGS2025

Overview of The SCEC/USGS Community Stress Drop Validation Study using the 2019 Ridgecrest earthquake sequence

We present initial findings from the ongoing Community Stress Drop Validation Study to compare spectral stress‐drop estimates for earthquakes in the 2019 Ridgecrest, California, sequence. This study uses a unified dataset to independently estimate earthquake source parameters through various methods. Stress drop, which denotes the change in average shear stress along a fault during earthquake rupture, is a critical parameter in earthquake science, impacting ground motion, rupture simulation, and source physics. Spectral stress drop is commonly derived by fitting the amplitude‐spectrum shape, but estimates can vary substantially across studies for individual earthquakes. Sponsored jointly by the U.S. Geological Survey and the Statewide (previously, Southern) California Earthquake Center our community study aims to elucidate sources of variability and uncertainty in earthquake spectral stress‐drop estimates through quantitative comparison of submitted results from independent analyses. The dataset includes nearly 13,000 earthquakes ranging from M 1 to 7 during a two‐week period of the 2019 Ridgecrest sequence, recorded within a 1° radius. In this article, we report on 56 unique submissions received from 20 different groups, detailing spectral corner frequencies (or source durations), moment magnitudes, and estimated spectral stress drops. Methods employed encompass spectral ratio analysis, spectral decomposition and inversion, finite‐fault modeling, ground‐motion‐based approaches, and combined methods. Initial analysis reveals significant scatter across submitted spectral stress drops spanning over six orders of magnitude. However, we can identify between‐method trends and offsets within the data to mitigate this variability. Averaging submissions for a prioritized subset of 56 events shows reduced variability of spectral stress drop, indicating overall consistency in recovered spectral stress‐drop values.

California↗
USGS2025

Structural controls on splay fault rupture dynamics during Cascadia megathrust earthquakes

Great subduction earthquakes ( M w ≥ 8.0) can generate devastating tsunamis by rapidly displacing the seafloor and overlying water column. These potentially tsunamigenic seafloor offsets result from coseismic fault slip and deformation beneath or within the accretionary wedge. The mechanics of these shallow rupture phenomena and their dependence on subduction zone properties remain unresolved, partly due to the sparsity of offshore observations of shallow megathrust earthquake deformation. Here, we analyze how offshore structure influences shallow rupture mechanics and slip partitioning using 3D dynamic earthquake simulations of the Cascadia subduction zone (CSZ) megathrust with and without variably dipping seaward- or landward-vergent splay faults in the wedge that sole into the megathrust. Resulting tradeoffs between splay and megathrust slip reveal structural controls on rupture partitioning, with greater splay slip leading to less shallow megathrust slip updip. Gently dipping and seaward-vergent splays host more slip than those with steeper, landward-vergent splays. To isolate the underlying mechanisms, we compare models with Andersonian and plunging principal stresses. Results suggest distinct static and dynamic processes control the dip- and vergence-dependence of splay rupture: static (mis)alignment relative to far-field tectonic loading favors slip on more optimally oriented, shallowly dipping splay faults. In contrast, dynamic stress interactions of an updip-propagating megathrust rupture front with the free surface and potential branch faults favor forward branching onto seaward-vergent splays and inhibit backward branching onto landward-vergent splays. Resulting seafloor displacements suggest splay fault structure may influence coseismic tsunami source processes, highlighting the importance of dynamically viable rupture scenarios in subduction hazard assessments.

Cascadia subduction zone↗
USGS2026

Comparative crop yield forecasting using satellite-derived biophysical and agro-climatic predictors in Sub-Saharan Africa

Timely and accurate crop yield forecasting is central to food security early warning systems, particularly in climate-vulnerable regions. While operational forecasting frameworks commonly rely on precipitation and vegetation indices such as NDVI, their ability to provide actionable lead time remains limited. Here, we evaluate the added value of satellite-derived biophysical Essential Climate Variables (ECVs): Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FAPAR), for forecasting millet yield in Burkina Faso (BF) and maize yield in South Africa (ZA) and Malawi (MW). Using Random Forest models, we quantify forecast skill across the growing season at both national and sub-national scales. Results show that LAI and FAPAR provide effective forecast lead times of approximately 4 months in BF, 2 months in ZA, and up to 6 months in MW relative to harvest. At peak performance, Mean Absolute Percentage Error (MAPE) reaches 19.8% (LAI) and 23.8% (FAPAR) in BF, 12.0% and 9.8% in ZA, and 21.8% and 20.8% in MW, respectively. Across countries, biophysical parameters often outperform NDVI and precipitation, particularly in arid and semi-arid regions. At the sub-national level, LAI and FAPAR enable classification of administrative units into high and moderate-skill forecast units, revealing strong spatial heterogeneity linked to crop dominance. However, forecast skill declines where the target crop is not the dominant type, highlighting an important limitation for operational deployment. Overall, the findings suggest that satellite-derived biophysical parameters can provide earlier and more spatially resolved yield signals than commonly used predictors, with potential to improve the timeliness and effectiveness of food security early warning systems.

Remote Sensing Applications: Society and Environme↗