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505 records · Page 22Linked to original sources
USGS2024

A new water temperature modeling approach to predict thermal habitat suitability for nonnative cichlids in Florida rivers

As global temperatures increase, the spatiotemporal arrangement of thermal habitats in Florida rivers may shift, creating the potential for greater dispersal and establishment of nonnative tropical freshwater fishes. To understand how water temperature changes may affect the spatial distribution of these nonnative species, more effective water temperature prediction models are necessary. Currently, most models employ either a generalized air–water temperature relationship or require expensive and complicated tools to measure hydrometeorological factors (e.g. groundwater input). Thus, we developed a novel modeling approach that is accurate, accessible, and cost-effective in allowing fisheries managers to project water temperatures in rivers across Central and North Florida. To characterize the potential for nonnative fishes to spread northward, we evaluated two hardy and abundant species currently found primarily in South Florida: Mayan Cichlid ( Mayaheros urophthalmus ) and Oscar ( Astronotus ocellatus ). Our results show an increase in thermally suitable winter days for both species in 10 of 11 rivers studied, consistent with predicted water temperature warming under 16 climate-change scenarios spanning different levels of air temperature warming (+1 °C, +2 °C, +3 °C, +4 °C) and precipitation/groundwater thermal sensitivity (0, 0.33, 0.66, 1). Considering resource limitations, fisheries managers can use our water temperature modeling approach to predict effects of climate change on Mayan Cichlid and Oscar survival, growth, and dispersal and take actions to manage potential northward movement of these species.

Florida↗
USGS2025

Gaps in water quality modeling of hydrologic systems

This review assesses gaps in water quality modeling, emphasizing opportunities to improve next-generation models that are essential for managing water quality and are integral to meeting goals of scientific and management agencies. In particular, this paper identifies gaps in water quality modeling capabilities that, if addressed, could support assessments, projections, and evaluations of management alternatives to support ecosystem health and human beneficial use of water resources. It covers surface water and groundwater quality modeling, dealing with a broad suite of physical, biogeochemical, and anthropogenic drivers. Modeling capabilities for six constituents (or constituent categories) are explored: water temperature, salinity, nutrients, sediment, geogenic constituents, and contaminants of emerging concern. Each constituent was followed through the coupled atmospheric-hydrologic-human system, with prominent modeling gaps described for a diverse array of relevant inputs, processes, and human activities. Commonly identified modeling gaps primarily fall under three types: (1) model gaps, (2) data gaps, and (3) process understanding gaps. In addition to potential solutions for addressing specific individual modeling limitations, some broad approaches (e.g., enhanced data collection and compilation, machine learning, reduced-complexity modeling) are discussed as ways forward for tackling multiple gaps. This gap analysis establishes a framework of diverse approaches that may support improved process representation, scale, and accuracy of models for a wide range of water quality issues.

Water↗
USGS2024

Estimating domestic self-supplied water use in Rhode Island, 2014–21

Water withdrawal from private groundwater wells is often unaccounted for in water planning studies, and water from private wells can be a source of exposure to environmental contaminants. The sizes of populations that depend on private wells for domestic water use and the amounts of water that are withdrawn from these wells are generally poorly represented in data collection efforts because of the challenges of locating, metering, or gathering withdrawal information from individual property owners. To address this problem, the U.S. Geological Survey, in cooperation with the Rhode Island Water Resources Board, estimated the volume of water withdrawn from domestic self-supply wells and the populations who use them for the State of Rhode Island at a 30-meter pixel spatial resolution and one-month temporal resolution between July 2014 and June 2021. The number of people reliant on domestic self-supply wells has increased in Rhode Island over the study period; however, the statewide estimate of total water withdrawal has not statistically increased. Withdrawals from private wells are largest in the inland areas of the western part of the State, and the towns of Scituate and Charlestown have the highest estimated withdrawals. Statewide monthly withdrawals ranged from 3.987 million gallons per day in March 2018 to 7.767 million gallons per day in September 2016. The median per capita domestic water use rate was 46.0 gallons per capita per day.

Rhode Island↗
USGS2025

Review and synthesis of the applications of machine learning to coalbed methane recovery

Over the last 30 years, a substantial literature has evolved on the use of machine learning (ML) to assess, predict, and improve the efficiency of coalbed methane (CBM) recovery. In the United States, the production of CBM declined as shale gas production matured, but CBM continues to be an important energy resource in other parts of the world. ML applications that have the potential to improve CBM reservoir management and production forecasts, and to increase exploration and operational efficiency, are still of significant interest. The integration of geostatistical techniques into the CBM ML applications has been largely absent but represents an opportunity for improvement. The literature demonstrates the widespread interest in, and applicability of, ML algorithms applied to CBM problems, and that they continue to result in improvements in predictive performance. However, (1) much of the research is more academic than operational, (2) many results are based on simulations, or small or proprietary datasets, (3) ML performance information can be inconsistent and sometimes entirely omitted, (4) most methodologies are unique to the specific CBM situation and likely not generalizable, (5) no standard data repositories are available to directly compare the performance of competing algorithms, and (6) the spatial component is often omitted. Finally, relatively new ML protocols involving causality analysis and reinforced learning, as well as hybrid workflows combining both supervised and unsupervised learning, are anticipated to dominate the future investigations. Integration of geostatistical and geospatial analysis with ML should enhance performance.

Book chapter↗
USGS2024

Seismic tomography 2023

Seismic tomography is the most abundant source of information about the internal structure of the Earth at scales ranging from a few meters to thousands of kilometers. It constrains the properties of active volcanoes, earthquake fault zones, deep reservoirs and storage sites, glaciers and ice sheets, or the entire globe. It contributes to outstanding societal problems related to natural hazards, resource exploration, underground storage, and many more. The recent advances in seismic tomography are being translated to nondestructive testing, medical ultrasound, and helioseismology. Nearly 50 yr after its first successful applications, this article offers a snapshot of modern seismic tomography. Focused on major challenges and particularly promising research directions, it is intended to guide both Earth science professionals and early‐career scientists. The individual contributions by the coauthors provide diverse perspectives on topics that may at first seem disconnected but are closely tied together by a few coherent threads: multiparameter inversion for properties related to dynamic processes, data quality, and geographic coverage, uncertainty quantification that is useful for geologic interpretation, new formulations of tomographic inverse problems that address concrete geologic questions more directly, and the presentation and quantitative comparison of tomographic models. It remains to be seen which of these problems will be considered solved, solved to some extent, or practically unsolvable over the next decade.

Bulletin of the Seismological Society of America↗
USGS2026

The United States Magnetotelluric Array and the National Impedance Map

The United States Magnetotelluric Array (USMTArray) data set, collected in the years 2006–2024, consists of more than 1,700 long-period magnetotelluric stations covering the entirety of the contiguous United States on a quasi-regular 70 km grid. Funding across multiple federal agencies was critical to sustaining this effort to its completion. Important components of the project included active guidance and participation from the MT community, the open and timely availability of all data, and the application of consistent instrumentation and robust data processing. Together with parallel advancement in the development of publicly available three-dimensional (3D) inversion codes, the USMTArray has revitalized the US magnetotelluric community and increased the visibility of magnetotellurics within the Earth-science community. Taken as a whole, these data are visualized as the National Impedance Map, which, together with a 3D synthesis conductivity model of the nation, reveals the electrical architecture of the contiguous US. USMTArray data are used by researchers worldwide for fundamental and applied studies, including investigations of continental architecture and evolution, estimation of hazards to critical infrastructure due to geomagnetic storms, and assessment of the nation's undiscovered geothermal and mineral resources. We here review the history and development of the project, discuss the challenges and successes in its execution, present the National Impedance Map and synthesis conductivity model, and highlight the breadth of research stemming from this rich data set.

conterminous United States↗
USGS2025

Geologic map of the Greater Antilles and Virgin Islands

Introduction The geologic map of the Greater Antilles and Virgin Islands is a compilation of information from the literature, integrated to provide a seamless geologic map of the region. This map was prepared to serve as a base map for a mineral resource assessment of the region. Several small-scale regional geologic maps of the region have been prepared in the past. This report supersedes an earlier version of the geologic map of the Greater Antilles and the Virgin Islands that was released as U.S. Geological Survey (USGS) Open-File Report 2019–1036. For this report, the regional geologic overview shown on sheet 1 covers Cuba; the island of Hispaniola, which includes Haiti and Dominican Republic; Jamaica; the Cayman Islands; Puerto Rico; and the U.S. and British Virgin Islands. Sheet 2 shows the geology of Cuba and Cayman Islands, sheet 3 the geology of Jamaica, sheet 4 the geology of Hispaniola, and sheet 5 the geology of Puerto Rico and Virgin Islands. Accompanying the maps are three tables: table 1 lists the map units (in alphabetical order by map-unit label), their ages, and the countries or territories in which they can be found. These are listed in alphabetical order for ease in locating unit names from the symbols on the maps. Table 2 is a list of sources for the geologic map compilation of Puerto Rico. Table 3 shows formations in the Devil’s Race Course Group of Jamaica.

Greater Antilles, Virgin Islands↗
USGS2024

Groundwater-Surface water interactions research: Past trends and future directions

Interactions between groundwater and surface water sustain groundwater-dependent ecosystems and regulate river temperature and biogeochemical cycles, amongst many other processes. These interactions occur in freshwater environments including rivers, springs, lakes, and wetlands, and in coastal environments via tidal pumping, submarine groundwater discharge, and seawater intrusion. Here, we explore groundwater-surface water interactions research using bibliometric analyses of titles, abstracts, and keywords from 20,275 journal papers published between 1970 and 2023 extracted from Scopus. Analyses show that research into groundwater-surface water interactions is highly multi-disciplinary, with growing contributions from the social and biological sciences. The number of groundwater-surface water interactions papers is rapidly increasing with over 1200 papers published per year since 2020. Drawing on our data-driven approach and expert knowledge, we synthesise current research trends and identify critical future research directions. Despite the thousands of papers on groundwater-surface water interactions, important processes are still difficult to quantify or predict at meaningful spatial scales to inform water-resources management. We see benefits in future groundwater-surface water interactions research focusing on: (1) using new technologies including internet-of-things-based sensors, uncrewed vehicles, and remote-sensing approaches for data collection to inform groundwater-surface water interactions at large scales, (2) seeking approaches to upscale site-specific findings to better inform management, and (3) continuing the movement towards multi-disciplinary investigations to better inform the understanding of groundwater-surface water interactions and processes that will enable better management outcomes.

Journal of Hydrology↗
USGS2025

Assessing earthquake risks to lifeline infrastructure systems in the United States

The security and economic stability of the United States rely heavily on robust lifeline infrastructure systems and yet the risks to such systems are seldom quantified at the national scale. For example, while earthquake risks to buildings in the United States have been investigated at the national scale regularly, such risks to gas pipelines have rarely been investigated nationally. In this paper, we use examples from two critical infrastructure sectors to demonstrate (1) the nature of earthquake risks to lifeline infrastructure systems, (2) complexities involved in regional seismic risk assessments, and (3) how such risks change with time. We found that bridge risks can be underestimated by at least 64 % when viewed from repair costs instead of traffic demands and that regional risks can be underestimated by 19 % when spatial correlations of ground motion are ignored. Further, exceedance of traffic demand can be 50 times more likely to occur when viewed at the regional scale than when viewed at an individual bridge. Similarly, exceedance of repairs can be 180 times more likely to occur when viewed at the pipeline network level than at a segment-specific level. Finally, sensitivity analyses with the 2018 and 2023 USGS National Seismic Hazard Models indicate an increase in bridge risk of at least 24 % and an increase in exposed gas pipeline mileage of 43 %. The evolution of risks, complexities involved in assessments, and limited resources jointly underscore the need for more routine updates to nationwide seismic risk assessments of lifeline systems in the United States.

International Journal of Critical Infrastructure P↗
USGS2024

On connecting hydro-social parameters to vegetation greenness differences in an evolving groundwater-dependent ecosystem

Understanding groundwater-dependent ecosystems (i.e., areas with a relatively shallow water table that plays a major role in supporting vegetation health) is key to sustaining water resources in the western United States. Groundwater-dependent ecosystems (GDEs) in Colorado have non-pristine temporal and spatial patterns, compared to agro-ecosystems, which make it difficult to quantify how these ecosystems are impacted by changes in water availability. The goal of this study is to examine how key hydrosocial parameters perturb GDE water use in time and in space. The temporal approach tests for the additive impacts of precipitation, surface water discharge, surface water mass balance as a surrogate for surface–groundwater exchange, and groundwater depth on the monthly Landsat normalized difference vegetation index (NDVI). The spatial approach tests for the additive impacts of river confluences, canal augmentation, development, perennial tributary confluences, and farmland modification on temporally integrated NDVI. Model results show a temporal trend (monthly, 1984–2019) is identifiable along segments of the Arkansas River at resolutions finer than 10 km. The temporal impacts of river discharge correlate with riparian water use sooner in time compared to precipitation, but this result is spatially variable and dependent on the covariates tested. Spatially, areal segments of the Arkansas River that have confluences with perennial streams have increased cumulative vegetation density. Quantifying temporal and spatial dependencies between the sources and effects of GDEs could aid in preventing the loss of a vulnerable ecosystem to increased water demand, changing climate, and evolving irrigation methodologies.

Colorado↗
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

Hydrogeology of unconsolidated and bedrock aquifers along the Salmon River, including Malone, Franklin County, New York

The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, investigated the hydrogeology of the unconsolidated and bedrock aquifers along the Salmon River corridor in northern Franklin County, New York. The study area covers roughly 147 square miles and includes the village of Malone and parts of the Towns of Malone, Bellmont, Burke, Constable, Westville, Bangor, Duane, and Franklin, New York. Groundwater is the primary source for water supply within the study area. Eighty-three percent of all residents use public water supplied from two production wells that draw water from a thick, highly productive sand-and-gravel aquifer likely receiving induced infiltration from the Salmon River. Twenty-four percent of the 187 verified domestic wells in the study area outside of the production well service area boundary are screened in typically discontinuous deposits of stratified sand and sand and gravel. Seventy-six percent of the wells are completed in bedrock aquifers including the Potsdam Sandstone, metamorphic rocks, Theresa Formation, and unknown bedrock. Characterizing and understanding potential groundwater resources is critical for protecting the quality of the groundwater. The information in this report may be used to guide delineation of groundwater contributing areas, assess potential threats to aquifers from both point and nonpoint sources, respond to contamination from spills or leaks from underground storage facilities or other sources, and to support assessments for future development of municipal water supplies.

New York↗
USGS2025

A new groundwater energy transport model for the MODFLOW hydrologic simulator

Heat transport in the subsurface is an important aspect of research related to the effects of a warming climate on ecological services (i.e., cold-water refugia); the development of geothermal resources for energy banking schemes (i.e., aquifer thermal energy storage [ATES]); and the effects of temperature on other aspects of groundwater quality, such as nutrient cycling. Historically, simulation of heat transport using the MODFLOW groundwater simulator and related codes was performed by scaling the input parameters of a solute-transport model to emulate heat transport. However, that approach required additional pre- and post-processing of input and output and could not account for the variation in effective thermal storage and transport properties during transient, unsaturated flow, for example. True heat-transport capabilities in the context of MODFLOW were first introduced in a variant called USG-Transport. More recently, a new groundwater energy-transport (GWE) model type has been added to MODFLOW 6, the core version of the MODFLOW hydrologic simulator. GWE supports the simulation of heat transport on structured or unstructured grids as well as within and between features of advanced packages that represent streams, lakes, multi-aquifer wells, and the unsaturated zone. GWE is integrated within MODFLOW 6 and is accessible through the FloPy Python package and the MODFLOW 6 application programming interface (API). An example simulation demonstrates conduction between grid cells through both the water and the solid aquifer material, including thermal bleeding from saturated overburden cells into a groundwater flow field.

Groundwater↗
USGS2024

U.S.-Mexico Borderland & vegetation community map

People on both sides of the United States-Mexico border need a high-resolution, binational vegetation community map that spans the entire United States-Mexico borderlands. Traditionally, mapping efforts in this region were impeded by complex logistics related to the international border, differing national needs and plans, and resource allocations and priorities. To address this need, scientists from the U.S. Geological Survey (USGS) Southwest Biological Science Center partnered with the Sonoran Joint Venture, the U.S. Fish and Wildlife Service (FWS) Migratory Bird Program, data engineers from the Department of Biosystems Engineering at the University of Arizona, and collaborators from the Wildlands Network, the Borderlands Program to produce the first prototype land cover map within the overlapping Mojave Desert, Sonoran Desert, and the North American Bird Conservation Initiative’s Bird Conservation Region 33 (BCR33) using Landsat satellite data . BCR33 is an area of high biodiversity, providing habitat for bird species of concern and other wildlife. The land cover map supports FWS recovery plan efforts related to conservation planning activities for many species, including Yellow-billed Cuckoo ( Coccyzus americanus ), Cactus Ferruginous Pygmy-Owl ( Glaucidium brasilianum cactorum ), Southwestern Willow Flycatcher ( Empidonax traillii extimus ), Yuma Ridgway’s Rail ( Rallus obsoletus yumanensis ), Bendire’s thrasher ( Toxostoma bendirei ), LeConte’s thrasher ( Toxostoma lecontei ), Masked Bobwhite ( Colinus virginianus ridgwayi ), jaguar ( Panthera onca ), and endangered plants such as Bartram’s stonecrop ( Graptopetalum bartramii ) and the Pima pineapple cactus ( Coryphantha robustispina ssp. robustispina ). In 2024, a Phase-II map for the full BCR33 region was completed, increasing the understanding of the binational nature of natural communities. The published map and associated paper can be found here .

Borderland↗
USGS2026

Where will the cat cross the road? Comparing camera and GPS-based models for identifying wildlife corridors

Designing effective wildlife corridors is a critical conservation challenge in fragmented landscapes. GPS-based step selection functions strongly predict dispersal corridors and connectivity, but GPS collaring can be expensive and invasive. Camera-based occupancy models are widely used for connectivity analyses but may involve trade-offs in data resolution. Despite widespread use of both approaches, few studies have directly compared them using concurrent datasets. We developed a stacked single-species, single-season occupancy model and a Circuitscape connectivity surface for mountain lions (Puma concolor) on Washington’s Olympic Peninsula, USA, and compared them with a connectivity surface from an existing integrated step selection function. Both models predicted mountain lion GPS locations well, with binned Spearman rank correlations of 1 for Circuitscape and 0.96 for the step selection function, though step selection better identified habitat use by dispersers. Connectivity predictions were moderately correlated across the landscape ( r = 0.26), but agreement was strongest in human-dominated areas most critical for corridor planning. We conclude that GPS-based approaches are advantageous when data collection is feasible and the focus is on dispersal or fine-scale movement. However, camera-based approaches may be preferable for multi-species monitoring, large spatial and temporal scales, noninvasive sampling, when resources are limited, or when fine-scale or dispersal-specific inference is not required.

Washington↗
USGS2024

Parsimonious high-resolution landslide susceptibility modeling at continental scales

Landslide susceptibility maps are fundamental tools for risk reduction, but the coarse resolution of current continental-scale models is insufficient for local application. Complex relations between topographic and environmental attributes characterizing landslide susceptibility at local scales are not transferrable across areas without landslide data. Existing maps with multiple susceptibility classifications under-represent landslide potential in moderate and gently sloping terrain. We leverage an extensive landslide database ( N = 613,724), a high-resolution digital elevation model (10-m), and high-performance computing resources, to develop a new nationwide susceptibility map for the contiguous United States, Hawaii, Alaska, and Puerto Rico. We calculate four alternative linear and nonlinear thresholds of topographic slope and relief using an objective split-sample calibration. We down-sample our results to a 90-m grid to account for uncertainty in the digital elevation model and landslide position, and evaluate these thresholds' ability to differentiate areas of greater susceptibility. The less conservative nonlinear model optimally balances our priorities of capturing observed landslides (99%) while minimizing area covered by susceptible terrain (43%). Independent evaluation with four statewide landslide inventories ( N = 172,367) reinforces our model selection but highlights spatially variable performance. Therefore, we propose a novel approach to susceptibility classification using the concentration of landslide-prone terrain within each down-sampled grid. While landslides are possible within any cells containing susceptible terrain, those with the highest concentration capture the majority of observed landslides. Our new map characterizes landside susceptibility more consistently than prior models; our transparent classification approach also provides flexibility for accommodating different tolerances in risk reduction measures.

AGU Advances↗
USGS2026

Software to support remote sensing of river discharge based on critical flow theory

Water resource management requires accurate observations of streamflow but standard field methods for measuring river discharge ( Q ) are costly and can be hazardous for equipment and personnel. Remote sensing has become a viable alternative, but many image-based techniques require field data for calibration and depth and velocity can seldom be mapped with a single sensor. A new approach based on critical flow theory, in contrast, allows both of these attributes to be inferred from readily available image data. This technique only pertains to sites with standing waves, called undular hydraulic jumps (UHJs), but a recent investigation demonstrated its potential to provide accurate discharge estimates. This paper introduces software designed to facilitate Inferring Q from UHJs Identified in River Images (InQUIRI). The package includes modules for retrieving data from image servers, making the measurements of wavelength and width required to calculate discharge, inferring a representative wavelength from a profile digitized along a wave train, combining multiple estimates to obtain an ensemble median discharge, and assessing accuracy via comparison to gage records from the U.S. Geological Survey. By making these steps easier to implement, InQUIRI enables users to apply the workflow to a variety of UHJ-containing images. Accumulating more case studies, some successful and others less so, would help constrain the range of applicability of the critical flow approach and foster development of refined guidelines for selecting and measuring waves. The software described herein could play an important role in promoting informed use of this new technique for non-contact streamflow measurement.

Arizona, Colorado, New Mexico, Utah↗
USGS2026

Hydrothermal manganese-oxide mineralization of a carbonate ooze, Samoan hotspot region, South Pacific Ocean

Low-temperature hydrothermal manganese oxides occur throughout the global oceans. However, the hydrothermal replacement of a carbonate ooze by manganese oxides is described here for the first time. The 24 samples dredged from three locations around the Territory of American Samoa in the South Pacific Ocean include six samples with remnant carbonate and volcaniclastic sediments, which we refer to as “low Mn,” and 18 samples in which the mineralization is pervasive and has replaced most original sediment, termed “high Mn.” The 18 high-Mn samples exhibit a mean Mn content of 51 wt.%. Higher Li and Mn contents and lower Fe contents in the high-Mn samples indicate a hydrothermal origin and distinguish these samples from hydrogenetic ferromanganese crusts. Mn-oxide layers are up to 90 mm thick, some with columns to 44 mm long and 10 mm wide, the magnitude of which has not been described previously. Samples are composed of birnessite and 10 Å phyllomanganate minerals. Textures of the thickest Mn layers indicate mineralization below the seabed from ascending fluids during multiple hydrothermal pulses. Mineralization took place by complete to partial replacement and cementation of foraminiferal sediments intermixed with volcaniclastic sediments in varying amounts. Our results highlight the production of carbonate sediment-hosted hydrothermal Mn oxides from multiple hydrothermal sources within the Samoan volcanic chain. The potential extensive distribution on the regional scale of this newly described mineralization process and unique element enrichments raise questions about its broader distribution globally and potential importance to hydrothermal processes, element mass balance, and seabed mineral resources.

Geochemistry, Geophysics, Geosystems↗