Geology Reports⌕ Search

SEARCH · Geology Reports

Results for “Environmental Science and Technology - Water”

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

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

1,666 records · Page 13Linked to original sources
USGS2024

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

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

California↗
USGS2024

Classification of lakebed geologic substrate in autonomously collected benthic imagery using machine learning

Mapping benthic habitats with bathymetric, acoustic, and spectral data requires georeferenced ground-truth information about habitat types and characteristics. New technologies like autonomous underwater vehicles (AUVs) collect tens of thousands of images per mission making image-based ground truthing particularly attractive. Two types of machine learning (ML) models, random forest (RF) and deep neural network (DNN), were tested to determine whether ML models could serve as an accurate substitute for manual classification of AUV images for substrate type interpretation. RF models were trained to predict substrate class as a function of texture, edge, and intensity metrics (i.e., features) calculated for each image. Models were tested using a manually classified image dataset with 9-, 6-, and 2-class schemes based on the Coastal and Marine Ecological Classification Standard (CMECS). Results suggest that both RF and DNN models achieve comparable accuracies, with the 9-class models being least accurate (~73–78%) and the 2-class models being the most accurate (~95–96%). However, the DNN models were more efficient to train and apply because they did not require feature estimation before training or classification. Integrating ML models into benthic habitat mapping process can improve our ability to efficiently and accurately ground-truth large areas of benthic habitat using AUV or similar images.

Michigan, Wisconsin↗
USGS2025

Integrating marine historical ecology into management of Alaska’s Pacific cod fishery for climate readiness

The Pacific cod ( Gadus macrocephalus ) fishery was closed in 2020 after a rapid decline in biomass caused by the marine heat waves of 2014–2019. Pacific cod are exceptionally thermally sensitive and management of this fishery is now challenged by increasingly unpredictable climate conditions. Fisheries monitoring is critical for climate readiness, but short-term monitoring data may be inadequate for recognizing and anticipating change under rapid climate changes. We propose an interdisciplinary, marine historical ecology framework that looks to long-term records (local and traditional knowledge, history, archaeology, and paleoclimatology) to capture a long range of ecological variability and provide historical context for management. In order to connect to contemporary fisheries management, this framework must be built on a common vocabulary and an understanding of the key metrics used in fisheries stock assessments. Here, we propose metrics derived from Pacific cod stock assessment and synthesize information relevant to understanding the effects of past warming periods on cod populations across the Gulf of Alaska and Bering Sea. This case study provides a framework for thinking about how to use these historical records in the context of fisheries management under rapidly changing climate conditions.

Alaska↗
USGS2025

Cave records reveal recent origin of North America’s deepest canyon

We explore how and when Hells Canyon, North America’s deepest river gorge (~2,400 m deep), formed, addressing these fundamental questions first posed by W. Lindgren [ The Gold Belt of the Blue Mountains of Oregon (1901)]. Existing hypotheses about the canyon’s formation and timing of incision remain speculative due to a lack of direct constraints and geomorphic analysis in the canyon. Herein, we combine cosmogenic nuclide dating of cave-bound river deposits, river profile analysis, and numerical modeling to provide the first direct age constraints and systematic analysis of incision processes at work in Hells Canyon. Our study reveals a significant drainage capture triggered rapid incision at ~2.1 ± 1.0 Ma, establishing the Snake River’s modern route into the Columbia River system. The increased drainage area and subsequent increase in stream power resulted in the rapid incision of Hells Canyon and the formation of tributary knickpoints (KPs) that decrease in elevation away from the capture location. Cosmogenic dating of cave deposits indicates incision rates increased from ~0.01 to ~0.16 mm y −1 . Numerical modeling of the stream capture supports these observations, demonstrating how abrupt drainage area increase drives rapid river incision. Our findings from Hells Canyon provide a well-constrained example of how drainage capture can dramatically shape the evolution of a major river gorge.

Idaho, Oregon, Washington↗
USGS2026

Magnetic storms and geoelectric hazards

Magnetic storms induce geoelectric fields at Earth's surface that can interfere with grounded long-line systems. The September 1859 storm disrupted global telegraph operations, the March 1989 storm caused a blackout in Canada and interfered with electric-power-transmission systems in the United States, and other storms have had related impacts. The geographic and temporal dependence of geoelectric fields are functions of both geomagnetic variation and local surface impedance, which differ considerably across different geological regions. These dependencies can be mapped across the contiguous United States by combining magnetotelluric impedance tensors with ground magnetometer time series. This review illustrates such mapping for the 1989 storm and shows that power-system interference was experienced where surface impedance is high, and when and where geoelectric fields were intense. Statistical analyses indicate that storms comparable to that of March 1989 occur roughly once every four solar cycles. Ongoing developments in numerical modeling and real-time monitoring are anticipated to enable prediction of geoelectric hazards. ▪ Magnetic storms can induced electric fields in the solid Earth that interfere with electric-power-transmission systems. ▪ Geoelectric hazards depend on the storm-time geomagnetic disturbance and the electrical conductivity structure of Earth. ▪ Historically, impacts on telecommunication and power-transmission systems in the United States have been concentrated in the East and Midwest. ▪ The future occurrence of a magnetic superstorm could cause widespread disruption of electric-power-transmission systems.

Annual Review of Earth and Planetary Sciences↗
USGS2026

Multiple-well monitoring site adjacent to the Midway- Sunset and Buena Vista Oil Fields, Kern County, California

Groundwater quality in and around oil fields in the Southern San Joaquin Valley is of interest to many California residents that rely heavily on groundwater for domestic, commercial, and agricultural use. To help assess the effects of historical oil-field activities and natural geologic sources on groundwater near the southwest margins of the Kern County Groundwater Subbasin, a multiple-well monitoring site was installed near the administrative boundary between the Midway-Sunset and Buena Vista Oil Fields in Kern County, California. The installation of the Midway-Sunset Buena Vista multiple-well monitoring site (MSBV) supports regional analysis of the relations of oil and gas sources to groundwater quality by providing information about the geology, hydrology, geophysical properties, and water quality of the alluvial and upper Tulare aquifers in areas where groundwater data were limited. Data collected from the site included drill cuttings, whole core samples, sidewall core samples, mud-gas analysis, borehole geophysical logs, depth to water measurements, and water quality samples. Whole cores were scanned using dual energy computed tomography. Subsamples of selected cores were analyzed for density, porosity, specific retention, and bulk minerology. Thin sections of the subsamples were prepared, photographed, and examined. Two samples were analyzed using scanning electron microscope technology to examine the microporosity of diatomite laden sediment. Instrumentation installed in the wells collect hourly depth to water measurements. Analysis of the data show there is 355 feet of alluvium overlying the Tulare Formation at the well site. The contact between the two formations is an aquitard resulting in a perched aquifer in the alluvium and unconfined aquifer in the Tulare Formation. The alluvium is more heterogenous and finer grained than the Tulare Formation resulting in markedly higher porosity in the alluvium compared to the Tulare Formation. Higher specific retention observed in the alluvium is attributed to the finer grained sediment and greater abundance of reworked diatomite (as represented by opal-CT [cristobalite-tridymite]) compared to the Tulare Formation. Total dissolved solids (TDS) approached or exceeded 10,000 milligrams per liter (mg/L) in the alluvium from approximately 176 to 242 feet below land surface and at the top of the Amnicola clay at approximately 670 feet below land surface within the Tulare Formation. Elevated TDS, chloride, and boron concentrations in the alluvium and on top of the Amnicola clay likely reflect groundwater that is mixed with oil-field water. Water chemistry and modern-aged groundwater in the alluvial monitoring well (MSBV #3) are consistent with the oil-field water in the alluvium being derived from documented historical surface disposal of oil-field water upslope (northwest) of the site. Water chemistry and pre-modern groundwater age in the deeper Tulare monitoring well (MSBV #1) on top of the Amnicola clay are consistent with oil-field fluids derived from upslope natural geologic sources or old oil wells that leak in the subsurface. Shallow groundwater in the Tulare (MSBV #2) is not affected by mixing with oil-field sources.

EarthArXiv↗
USGS2026

Evaluation of best management practices at an edge-of-field site in the Eagle Creek watershed, Ohio, 2012–20

In 2010, the U.S. Geological Survey worked in partnership with the Great Lakes Restoration Initiative and the Natural Resources Conservation Service to identify farm fields in priority watersheds—watersheds critically important to Great Lake health. Intensive best management practices were implemented in five States to test the efficacy of best management practices, also referred to as agricultural conservation practices, to reduce sediment and nutrient runoff on agricultural fields. These fields were chosen as representatives of the priority watersheds because their farming practices and geographical conditions were common among farms in those watersheds. A farm site located near Findlay, Ohio, in the Eagle Creek watershed used two conservation practices, cover crop (Natural Resources Conservation Service practice standard 340) and variable rate technology (VRT; Natural Resources Conservation Service practice standard 590), which represent common practices in the Maumee priority watershed. This study monitored surface runoff and subsurface tile runoff at the Eagle Creek watershed site. The effects of cover crop and VRT nutrient application on sediment and nutrient runoff were assessed from October 2012 to September 2020. Cover crops were applied in fall of 2016, 2017, and 2018, coinciding with VRT applications. The following parameters were analyzed as part of the study: runoff discharge; peak discharge; and concentrations and loads of total phosphorus, particulate phosphorus, dissolved reactive phosphorus, and suspended sediment. High peak runoff events disproportionally affected nutrient concentrations and loads in runoff. A threshold of the 85th percentile of the peak discharge was chosen to assess the effect of cover crop and VRT nutrient application on nutrient mitigation at each gage. Runoff events below the 85th percentile were considered small, and runoff events over the 85th percentile were considered large. Concentrations from collected water-quality samples were used for the analysis; estimated concentrations were excluded from the statistical evaluation. All surface runoff parameters during small runoff events, except surface runoff volume, were significantly lower in the period with cover crops and VRT implementation. Contrarily, subsurface runoff parameters during small events were not statistically different between the periods with and without cover crops and VRT implementation. Surface and subsurface parameters during large events were also not statistically different between the periods with and without cover crops and VRT implementation. These results indicate cover crops and VRT may improve water quality during small runoff events, but additional best management practices that mitigate large runoff events may lead to greater water-quality improvement given the contribution of large events to overall losses.

Ohio↗
USGS2024

Iodine-129 in the eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho, 2021–22

Between the 1950s and 1980s, wastewater generated at the Idaho National Laboratory contained Iodine-129 ( 129 I); this wastewater was discharged directly into the eastern Snake River Plain (ESRP) aquifer through a deep disposal well, unlined infiltration ponds, or leaked from distribution systems below industrial facilities. During 2021–22, the U.S. Geological Survey, in cooperation with the U.S. Department of Energy and the Idaho Department of Environmental Quality Idaho National Laboratory Oversight Program, collected groundwater samples from 64 monitoring wells in the ESRP aquifer, 6 of which are part of a multilevel monitoring system, to determine the concentration of 129 I in the groundwater. These samples were analyzed by accelerator mass spectrometry as part of a long-term ongoing study to track trends and occurrences of this carcinogenic, long-lived radionuclide in the environment. Concentrations ranged from slightly above the locally determined background concentration of 5.4×10 −6 picocuries per liter, to just below the U.S. Environmental Protection Agency’s maximum contaminant level of 1 picocurie per liter. Discharge of wastewater containing 129 I has been discontinued to the aquifer, and long-term trends from a subset (n=15) of sampled wells show decreasing 129 I concentrations over the last three decades. Concentrations of 129 I in groundwater from monitoring wells near facilities at the Idaho National Laboratory are affected by episodic recharge from an ephemeral surface-water source and by the fracture-flow dominated hydrologic regime in the ESRP aquifer. The spatially focused sampling effort has also identified a low-level 129 I plume that affects long-term water quality near and downgradient from the Advanced Test Reactor Complex in the southwestern part of the facility that had not been clearly defined in previous sampling efforts, although the definition of the plume is somewhat limited by available data.

Idaho↗
USGS2026

Uranium—Deposits, production and resources, market dynamics, and supply chain risks

Introduction Interest in nuclear power for the generation of electricity has risen with the increase in the need for more diverse baseload power, enhanced energy security, and the development of new technologies, such as small modular reactors (SMRs), which could provide power for remote areas, industrial applications, and artificial intelligence (AI) data centers. In 2024, the U.S. Department of Energy received $2.7 billion in congressional funding to bolster the domestic uranium production and nuclear fuel supply chain and address reliance on imports from foreign suppliers. In 2025, the U.S. Government issued several Executive and Secretary’s orders aimed at revitalizing the U.S. nuclear sector. If SMRs are to be as widely deployed in the United States and worldwide as envisioned, demand for uranium (nuclear reactor fuel) will likely increase. After the Fukushima nuclear accident in 2011, the market spot price of uranium began a decline, followed by a decrease in U.S. and global uranium exploration and mine development expenditures that led to a uranium supply deficit until 2020, when prices started to recover, prompting a resurgence in uranium exploration and development. In January of 2024, the uranium spot price rose to a 17-year high $106 (U.S. dollars) per pound of U3O8 (triuranium oxide, commonly known as “yellowcake”), which is expected to increase uranium exploration, mine development, and uranium production domestically and worldwide.

Fact Sheet↗
USGS2025

Avak Creek oil occurrence, North Slope, Alaska: Newly discovered oil seep on Native lands, near village of Utqiagvik

An unknown occurrence of oil was detected near Avak Creek on Native lands on the North Slope of Alaska. Determining the source of oil was imperative for allowing stakeholders (Federal, State, and local government agencies and the landowner, an Alaska Native corporation) to make timely and informed decisions and mount a mitigation response, if required. The regional and local geological framework of the Avak Creek site was constructed using seismic surveys, well data, and basin modeling results, to identify local petroleum systems, map structural geometry and faults, define source rock thermal maturity distributions, and infer likely oil-migration pathways. Molecular hydrocarbon fingerprints (biomarkers, diamondoids, compound-specific isotopes) of the oil were compared to those of local and regional oil seeps, exploration well tests, and produced oils. Biomarker acid distributions characterized the history and extent of petroleum biodegradation. Integrating subsurface and geochemical parameters, the oil is interpreted to be a natural seep generated locally, predominantly from the Brookian Lower Cretaceous Hue Shale/gamma-ray zone, rather than an anthropogenic source of pollution. Results highlight sophisticated analytical technologies used to characterize complex, compositionally altered hydrocarbons. Results also advance our understanding of Brookian source rock distribution, subsurface petroleum migration pathways, and Arctic Alaska petroleum systems.

Alaska↗
USGS2025

Lithium from magma to mine in an early Yellowstone hotspot caldera

Renewable energy technologies rely on the extraction of metals not historically in high demand, such as lithium (Li), for which ore deposit models are incompletely understood. One of the world’s largest Li deposits is hosted in lake sediments of the 16.4 Ma McDermitt caldera, which formed during the early stages of Yellowstone hotspot volcanism in the western United States. Eruptive and posteruptive mobility of Li are major challenges in elucidating deposit formation. Melt inclusions preserved in quartz crystals provide a means to assess pre-eruptive magmatic Li contents. Concentrations of Li determined by ion microprobe for melt inclusions in a McDermitt rhyolite lava are 400−1350 ppm, compared to 20−70 ppm Li in matrix rhyolite glasses. Synthesis with melt inclusion data for eight additional calderas demonstrates a recurrence of Li-rich rhyolitic magmas (200−2000 ppm Li) in the western part of the Yellowstone hotspot track. However, unlike the multicyclic caldera complexes with overlapping fault networks that may have compromised Li retention, the McDermitt caldera remained a closed hydrologic system throughout its evolution. Modeling indicates 100 km 3 of resurgent magma could yield 25−150 Mt Li in a magmatic fluid and supports accumulation of Li-rich magmatic fluid in a closed intracaldera lake, followed by evaporative concentration and sequestration of Li within clay minerals to generate the McDermitt deposit.

Idaho, Nevada, Oregon, Wyoming↗
USGS2024

Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico

Hurricane Maria induced about 70 000 landslides throughout Puerto Rico, USA, including thousands each in three municipalities situated in Puerto Rico's rugged Cordillera Central range. By combining a nonlinear soil-depth model, presumed wettest-case pore pressures, and quasi-three-dimensional (3D) slope-stability analysis, we developed a landslide susceptibility map that has very good performance and continuous susceptibility zones having smooth, buffered boundaries. Our landslide susceptibility map enables assessment of potential ground-failure locations and their use as landslide sources in a companion assessment of inundation and debris-flow runout. The quasi-3D factor of safety, F 3 , showed strong inverse correlation to landslide density (high density at low F 3 ). Area under the curve (AUC) of true positive rate (TPR) versus false positive rate (FPR) indicated success of F 3 in identifying head-scarp points (AUC = 0.84) and source-area polygons (0.85 ≤ AUC ≤ 0.88). The susceptibility zones enclose specific percentages of observed landslides. Thus, zone boundaries use successive F 3 levels for increasing TPR of landslide head-scarp points, with zones bounded by F 3 at TPR = 0.75, very high; F 3 at TPR = 0.90, high; and the remainder moderate to low. The very high susceptibility zone, with 118 landslides km −2 , covered 23 % of the three municipalities. The high zone (51 landslides km −2 ) covered another 10 %.

Puerto Rice↗
USGS2026

An improved empirical model for predicting postfire debris-flow volume in the western United States

Reliable estimates of debris-flow volume can be used to help predict the magnitude of debris-flow hazards following wildfire in the western United States. In this study, we compiled and used a database of 227 postfire debris-flow volumes that were collected across the western United States to develop a multiple linear regression model for predicting postfire debris-flow volume. We explored 36 predictor variables related to rainfall, terrain, and fire characteristics, and selected the model with the combination of variables that yielded the most accurate predictions of debris-flow volume. We evaluated model performance against the entire volume database, as well as against four subsets of volume data from southern California, the Intermountain West, the Southwest, and regions with limited volume data, such as northern California and Washington. We also compared model performance against 3 existing postfire debris-flow volume models that were developed for use in southern California, the Intermountain West, and the Southwest. We demonstrate that the new volume model performs as well as the regional models in the regions for which they were developed and outperforms existing models when applied to volumes from data-limited regions in the western United States. These results indicate that the debris-flow volume model introduced in this study can be used to improve postfire hazard assessments across the western United States, especially outside of southern California.

Arizona, California, Colorado, New Mexico, Utah, W↗
USGS2026

High-resolution landscape-scale maps of nutrients, phytoplankton, and related water-quality constituents in Suisun Bay and the Sacramento–San Joaquin Delta, California, during 2018—Results from high-resolution underway surveys

We examined the abundance and distribution of nutrients and phytoplankton in the tidal aquatic environments of the Sacramento–San Joaquin Delta (Delta) and Suisun, Grizzly, and Honker Bays in California by completing three spatial surveys that used continuous underway high frequency (1 hertz) measurements and sampling onboard a high-speed boat. Surveys were conducted in May, July, and October 2018. Water was sampled continuously during surveys to simultaneously collect information about the concentration and spatial distribution of all major nutrient forms together with information about the major classes of phytoplankton and associated standard field measurements of water quality, such as dissolved oxygen, dissolved organic matter, pH, salinity, specific conductance, turbidity, and water temperature. The results show a greater than 50-fold variation in nutrient and phytoplankton concentrations across space and time, providing evidence of the dynamic environmental processes that shape the ways nutrients interact with and affect Delta aquatic habitats.

Data Report↗
USGS2025

An exploration of the relative influence of physical models for Omori’s law

Omori’s law states that the rate of aftershocks decays as a function of inverse time. There are multiple physical explanations that we reduce into a nonlinear mixed effects relation of three terms: (1) a Rate/State expression that can account for static/dynamic and viscoelastic triggering caused directly by the mainshock, (2) a fluid diffusion triggering term, and (3) a randomized secondary triggering (cascade) term. We fit free physical-model parameters to an observed aftershock sequence through two nonlinear regression methods to find the relative contributions of physics-based models in an observed aftershock sequence. Results from both methods show that Rate/State models overpredict aftershock rates by ∼0–30%. Secondary aftershocks cause a net negative contribution (seismicity rate reduction that corrects overprediction by other terms) ranging between ∼0 and 30%. All regression solutions yield negative secondary triggering contributions without being guided to do so. A physical explanation for this is that aftershock occurrence relieves stress from the crust, ultimately causing the sequence to extinguish itself. Fluid diffusion triggering contributions range from ∼0 to 20%. Diffusion processes are observed to be shorter in time than the full duration of an aftershock sequence and they are also spatially limited, diminishing their influence. Our results apply to an aftershock decay curve from the 2016 Central Apennines earthquake sequence, meaning that our specific results may not be general. Our primary conclusion is that any one physical model cannot alone fit the observed sequence as well as the combination of three we investigated.

Frontiers in Earth Science↗
USGS2026

Dynamic drainage reorganization in Eastern Tibet: Insights from the Yangtze River first bend

The modern drainage network of eastern Tibet is widely believed to have developed through a series of river capture and flow reversal events; however, the timing and mechanisms driving this reorganization remain contentious. Among these events, the river capture that formed the First Bend of the Yangtze River (YFB) stands out as both iconic and particularly debated. Here we present sedimentary provenance data from the Late Miocene–Quaternary Dali Basin, located south of the YFB, which indicate that a southward-flowing Jinsha River (i.e., the present-day upper Yangtze River) sourced sediment to the Dali basin at ∼7.4–6.4 Ma in a drainage configuration different from that of today. Because this interval postdates the initial establishment of a near-modern Jinsha River system prior to the Miocene, our results imply at least two discrete fluvial reorganizations occurred at the YFB—one preceding ∼7.4 Ma and another following ∼6.4 Ma. By integrating these findings with landscape evolution modeling, we infer that the initiation of rapid uplift of the Yulong-Haba Mountains and the Diancang Shan may have been responsible for these drainage reorganizations. These results underscore that Cenozoic drainage systems on the eastern Tibetan Plateau have evolved dynamically on a short timescale of ∼10 5 –10 6 -year, rather than remaining in a long-term stationary configuration on ∼10 7 -year timescales.

eastern Tibetan Plateau, first bend of the Yangtze↗
USGS2025

Machine learning provides reconnaissance-type estimates of carbon dioxide storage resources in oil and gas reservoirs

Oil and gas reservoirs represent suitable containers to sequester carbon dioxide (CO 2 ) in a supercritical state because they are accessible, reservoir properties are known, and they previously contained stored buoyant fluids. However, planners must quantify the relative magnitude of the CO 2 storage resource in these reservoirs to formulate a comprehensive strategy for CO 2 mitigation. Even reconnaissance-type estimates of CO 2 storage resources of known oil and gas reservoirs may require complicated calculations involving 1) estimates of recoverable oil and gas, 2) reservoir properties (depth, temperature, pressure, etc.), and 3) the physical qualities of the retained fluids. We demonstrate the application of machine learning (ML) algorithms to bypass these computations to yield more rapid estimates of CO 2 storage resources in reservoirs capable of hosting CO 2 in a supercritical state. ML algorithms are computationally efficient because they do not impose the strong assumptions on the data-generating process that standard statistical or engineering procedures require. Further, ML algorithms can capture highly complex, particularly nonlinear, relationships among predictor variables. We demonstrate the application of four different ML algorithms using data from onshore and offshore oil and gas reservoirs in Europe, and show they perform well when predictions are compared to engineering estimates. The proposed methods and models provide an effective and novel way to more rapidly and directly determine the subsurface CO 2 storage capacity of oil and gas reservoirs around the world, information that operators, researchers, and policymakers alike require to meet energy transition and decarbonization goals.

Frontiers in Enviornmental Science↗
USGS2026

Hazard potential of compound flooding from rainfall, storm surge, and groundwater in coastal New York and Connecticut

Compound flood events, the co-occurrence of multiple flood drivers, can result in flood hazard potential exceeding that of any single driver alone. To evaluate compound flooding in a semi-urbanized coastal area, historical records dating back to 1970 are used to study the co-occurrences of high precipitation, storm surge, and shallow groundwater conditions along the coastlines of New York and Connecticut. Joint return periods for coincident precipitation-surge events were computed using statistical dependence models and compared to the assumption of independence as a ratio, referred to here as a return period adjustment. Results indicate distinct seasonality where compound events in the area disproportionately occur in the cold season between October and April. Return period adjustments range from a factor of 1 to almost 9, demonstrating the range in precipitation-storm surge dependence across the study area. Across all 24 station triad locations, groundwater levels were elevated during times of precipitation- surge co-occurrence, reflecting the tendency for coastal storms and shallow groundwater conditions to co-occur seasonally. The result is a pseudo-trivariate compound flood hazard score and corresponding hazard map that integrates dependence between daily precipitation-surge events and overall monthly groundwater levels (as a precondition) into a relative compound hazard score. The location with the highest compound flood hazard score is on the south shore of Long Island, as well as locations across coastal Connecticut where groundwater levels compound the co-occurrence of heavy precipitation and storm surge.

Connecticut, New York↗