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535 records · Page 6Linked to original sources
USGS2025

Land cover change within wetland complexes at Dixie Meadows, Churchill County, Nevada: 2015 – 2023

Dixie Meadows, Nevada, is a system of geothermal springs and seeps that feed a complex of marshes and wetland meadows that are located within lands managed by the Bureau of Land Management (BLM) and the Department of Defense (DOD). A previous U.S. Geological Survey report documented variability in satellite imagery-based land cover classifications for seven wetland complexes at near monthly time intervals between October 2015 and January 2022. This report presents additional data, extending analysis to November 2023. Land cover classifications between October 2015 and November 2023 demonstrated an association between vegetation cover characteristics and surface moisture, with Class 1 having dry, bare soil or sparse upland vegetation, Class 2 having moist, bare soil or sparse to small vegetation, Class 3 having dense green vegetation with potentially saturated soil conditions, Class 4 having a mix of shallow surface water, saturated soil, and dense green vegetation, and Class 5 having open surface water. Most of the wetland complexes occur close to spring outflows primarily within land managed by the DOD, though portions are also within BLM lands. The intervening and surrounding landscape outside of the wetland complexes assessed in this study are managed by the BLM. As a result, Class 1 land covers had the largest areal coverage for BLM managed lands. Classes 2 and 3 land covers were primarily mapped inside the wetland complexes and thus had the largest area coverage within DOD managed lands. Class 4 was almost exclusively mapped within the wetland complexes and thus was largely contained within DOD managed lands. Class 5 (open water) was exclusively mapped in and adjacent to a single wetland complex with catchment ponds on land managed by the BLM. The distribution of these land cover classes over the study period was seasonally and annually variable. Land cover areas of Classes 1 and 2 were larger during the spring months. Conversely, land cover areas of Classes 3 and 4 tended to be greatest during the summer or fall. These patterns might be influenced by differences in seasonal water sources and phenology.

Nevada↗
USGS2026

Scenarios to assess the future water availability in the Mississippi River Valley Alluvial Aquifer for the Cache River and Grand Prairie Regions of Arkansas

The U.S. Geological Survey, as part of the Arkansas Groundwater Initiative, developed forecast scenarios using previously calibrated MODFLOW 6 groundwater models that focused on the Cache and Grand Prairie Critical Groundwater Areas to assess the impact of future climate and water management strategies on the Mississippi River Valley alluvial aquifer. A Soil Water Balance model was used to forecast recharge and irrigation water use. The forecast scenario period was from January 1, 2019, through December 31, 2055, with monthly stress periods. Twenty scenarios were simulated and included seven alternate climate forecasts, five 13 general groundwater pumping reduction scenarios (round 1), and groundwater pumping reduction scenarios by crop type and for the Bayou Meto Water Management Project and Grand Prairie Area Demonstration Project (round 2). Declines in saturated thickness within the Cache Critical Groundwater Area were larger for 18 of the 20 scenarios as compared to outside of the Critical Groundwater Area. The largest average increase in saturated thickness inside the Critical Groundwater Area was 6.4 m which occurred for the round 1, 50 percent reduction scenario. Automatic reductions in groundwater pumping by MODFLOW 6 in the Cache simulation ranged from 0.02 to 13.1 percent of total groundwater pumping. For the Grand Prairie model domain, the average change in saturated thickness of the Mississippi River Valley alluvial aquifer inside the Critical Groundwater Area for the forecast period ranged between -6.6 to 1.7 m. The average saturated thickness of the Mississippi River Valley alluvial aquifer inside the Grand Prairie Critical Groundwater Area declined for 16 of the 20 scenarios. The average reduction in requested groundwater pumping for all scenarios inside the Grand Prairie Critical Groundwater Area was 25.1 percent, and the largest reduction was 46.5 percent.

ESS Open Archive↗
USGS2025

Cancer risk and estimated lithium exposure in drinking groundwater in the US

Importance Lithium is a naturally occurring element in drinking water and is commonly used as a mood-stabilizing medication. Although clinical studies have reported associations between receiving lithium treatment and reduced cancer risk among patients with bipolar disorder, to our knowledge, the association between environmental lithium exposure and cancer risk has never been studied in the general population. Objectives To evaluate the association between exposure to lithium in drinking groundwater and cancer risk in the general population. Design, Setting, and Participants This cohort study included participants with electronic health record and residential address information but without cancer history at baseline from the All of Us Research Program between May 31, 2017, and June 30, 2022. Participants were followed up until February 15, 2023. Statistical analysis was performed from September 2023 through October 2024. Exposure Lithium concentration in groundwater, based on kriging interpolation of publicly available US Geological Survey data on lithium concentration for 4700 wells across the contiguous US between May 12, 1999, and November 6, 2018. Main Outcome and Measures The main outcome was cancer diagnosis or condition, obtained from electronic health records. Stratified Cox proportional hazards regression models were used to estimate the hazard ratios (HRs) and 95% CIs for risk of cancer overall and individual cancer types for increasing quintiles of the estimated lithium exposure in drinking groundwater, adjusting for socioeconomic, behavioral, and neighborhood-level variables. The analysis was further conducted in the western and eastern halves of the US and restricted to long-term residents living at their current address for at least 3 years. Results A total of 252 178 participants were included (median age, 52 years [IQR, 36-64 years]; 60.1% female). The median follow-up time was 3.6 years (IQR, 3.0-4.3 years), and 7573 incident cancer cases were identified. Higher estimated lithium exposure was consistently associated with reduced cancer risk. Compared with the first (lowest) quintile of lithium exposure, the HR for all cancers was 0.49 (95% CI, 0.31-0.78) for the fourth quintile and 0.29 (95% CI, 0.15-0.55) for the fifth quintile. These associations were found for all cancer types investigated in both females and males, among long-term residents, and in both western and eastern states. For example, for the fifth vs first quintile of lithium exposure for all cancers, the HR was 0.17 (95% CI, 0.07-0.42) in females and 0.13 (95% CI, 0.04-0.38) in males; for long-term residents, the HR was 0.32 (95% CI, 0.15-0.66) in females and 0.24 (95% CI, 0.11-0.52) in males; and the HR was 0.01 (95% CI, 0.00-0.09) in western states and 0.34 (95% CI, 0.21-0.57) in eastern states. Conclusions and Relevance In this cohort study of 252 178 participants, estimated lithium exposure in drinking groundwater was associated with reduced cancer risk. Given the sparse evidence and unknown mechanisms of this association, follow-up investigation is warranted.

contiguous United States↗
USGS2024

Automated, near real-time ground-motion processing at the U.S. Geological Survey

We describe automated ground‐motion processing software named gmprocess that has been developed at the U.S. Geological Survey (USGS) in support of near‐real‐time earthquake hazard products. Because of the open‐source development process, this software has benefitted from the involvement and contributions of a broad community and has been used for a wider range of applications than was initially envisioned. Here, we give an overview and introduction to the software, including how it has leveraged other open‐source libraries. We highlight some key features that gmprocess provides, compare response spectra calculated with the automated processing approach of gmprocess to the response spectra provided by the Next Generation Attenuation projects, and summarize projects that have utilized gmprocess. These use‐cases demonstrate that this software development effort has been successfully leveraged in earthquake research activities both within and outside the USGS.

Seismological Research Letters↗
USGS2025

Estimating the probability of export restrictions to inform mineral criticality

To assess risks associated with advanced technologies’ supply chain disruptions, governmental agencies and others have developed mineral “criticality” assessments, with criticality described using the economic impact and probability of supply chain disruptions. Previous work developed subjective supply risk indicators to approximate this probability, typically combining several factors such as supply diversity and trading partners’ political stability, where indicator weightings can substantially impact results. This work explicitly quantifies export barrier probability using an ensemble of machine learning classifiers, with probability estimates informed by exogenous variables, including prior barrier implementation and global export dominance. Major differences in high-probability countries and commodities are observed across models, but the ensemble method highlights Indonesia, China, Tanzania, and the United States as particularly high risk. The Supplementary Data File provides export barrier probability estimates for each analyzed country-commodity pair, enabling a direct, quantitative, objective contribution to assessing mineral criticality, enhancing risk identification and prioritization for policymakers.

Resources, Conservation, and Recycling↗
USGS2026

Increasing artificial light at night enhances salmonid predator foraging efficiency in an urbanized lake

Artificial light at night (ALAN) poses a threat to ecosystems globally. It includes both direct and indirect light, or skyglow, which occurs when ALAN scatters in the atmosphere, extending beyond its original source. We analyzed ALAN trends in Lake Washington, WA, from 2014 to 2023 using Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light measurements, evaluated the relationship between in situ and satellite measurements, and modeled predator search volumes for a juvenile salmon predator, cutthroat trout ( Oncorhynchus clarki ), as a function of ambient light and turbidity conditions. Open water regions experienced significant increases in ALAN, while nearshore areas primarily showed no or negative trends, revealing the role of skyglow in shaping open water light environments. Using a visual foraging model for cutthroat trout, we found that juvenile salmon at the shallow southern pelagic site experienced light 28 times brighter, resulting in a 168% greater predation vulnerability than those at the northern site. In the Ship Canal, a narrow corridor for outmigrating salmon, predator search volumes were 249% higher than at the southern site. These contrasts in predation vulnerability demonstrate how local conditions influence predator–prey dynamics and provide critical insight for targeting mitigation of both nearshore and distant light sources.

Washington↗
USGS2026

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

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

Journal of Hydrology↗
USGS2025

Evolution of permeability and strength recovery of shear fracture under hydrothermal conditions

Geothermal energy is a clean and renewable resource that depends on the ability to move water through hot rock. In many locations, the ability to move water through rock requires the presence of extensive natural or human-made systems of fractures. However, these fracture systems are influenced by a variety of complex processes that occur at the temperature and pressure conditions found in geothermal reservoirs. To make geothermal systems more efficient, it is important to understand how these fractures evolve over time in response to these processes. This study explored how fractures in rock change under high-temperature and pressure conditions, like those found in geothermal reservoirs, through laboratory experiments and numerical simulations. In general fractures, tend to close due to pressure from the surrounding rock preventing the flow of water. One set of laboratory experiments revealed that fractures in granitic rock can weaken over time at elevated temperatures. These weak fractures can then slip slowly potentially keeping the fracture open for a longer period. This study also showed that at the highest temperature examined (250 °C) large differences between the chemical composition of the rock and the fluid moving through it could improve water flow temporarily likely due to the water dissolving parts of the rock. Flow rate predicted by models shows overall good agreement with the flow rate measured in our experiments but with some long time-scale variations that may be due to mechanical wear of the fracture surface. A second set of experiments found that, over the temperature range examined, the closure of fractures was caused by brittle failure driven by high stresses at points where fracture surfaces were in contact. This result suggests that fracture closure in geothermal reservoirs could be counteracted by maintaining high pore pressures. Additionally, we also tested a new method using electrical resistance to measure fracture closure in controlled systems with high precision.

Final Technical Report↗
USGS2026

High-resolution transboundary vegetation community maps of the Sonoran and Mojave Desert ecoregion to support critical landscape conservation planning and habitat management needs

We produced a 30-m resolution binational land cover map of Bird Conservation Region 33 (BCR 33) for the U.S. North American Bird Conservation Initiative. The region covers large portions of the Sonoran and Mojave Deserts. The map can support the U.S. Fish and Wildlife Service (FWS) Migratory Bird Program’s recovery planning efforts and constitutes the first known binational land cover dataset spanning sections of the United States–Mexico border and using a consistent classification system for both countries. The mapped region includes 152 distinct land cover classes, covering a total area of 38,421,453 ha (148,345 mi 2 ), of which 13,148,345 ha (52,706 mi 2 ) are located in Mexico and 24,770,640 ha (95,639 mi 2 ) in the United States. We primarily used Landsat 8 (OLI) imagery, supplemented by limited ground surveys from two field campaigns, drone-based aerial data, and existing vegetation classification frameworks from both countries. The classification applied a data-fusion approach integrating 30-m Landsat 8 imagery, decadal phenology metrics from vegetation indices, and a random forest model trained mainly with datasets from a comprehensive national mapping project from the U.S. Geological Survey (USGS) GAP Analysis Project (GAP) and federal wildland fire agencies’ Landscape Fire and Resource Management Planning Tools (LANDFIRE) (GAP/LANDFIRE) [United States side] and the National Institute of Statistics and Geography (INEGI) [Mexico side] as well as land cover maps and opportunistic open-access and field observations. Mapping of the full BCR 33 region was carried out in two phases: 1) Phase I, the prototype map, covered a smaller portion of the transboundary area and identified 31 land cover classes, and 2) Phase II, the full BCR 33 map (refer to Figure 1), which resulted in 152 land cover classes. Using a Random Forest classifier, we achieved an overall prediction accuracy of 92% for the Phase I map and 87% for the Phase II full region map. This slight decrease can be attributed to working on a larger, more complex area with a greater number of land cover classes. No formal validation was conducted, aside from using a subset of the collected field observations and training data to assess model performance during and after training. The training sites were further verified using Google Earth (Google, 2026) imagery. Two undergraduate students who worked for over a year visually inspected imagery and open access public images to confirm each training site during model training using in-house developed, online, visual tools. A portion of this field training data was reserved for model validation, and the corresponding results are to be presented in later sections. The project developed an end-to-end, medium- and fine-resolution remote sensing–based data fusion mapping approach. This effort produced a map (Nagler et al., 2025) and the online tools to support a dynamic, live, online map for visualizing the transboundary vegetation communities in BCR 33. The toolset is currently hosted by the University of Arizona (UofA) Vegetation Index and Phenology (VIP) Lab to support FWS partners (https://vip.arizona.edu/viplab_data_explorer?LCM_BCR33). The online map is designed to allow rapid updates using new training, validation, or correction data, making it dynamic and maintainable. The approach we took established a framework for rapid updating and correction of land cover maps, as the model can be quickly retrained with new field observations, updated training data, or other sources. This enables dynamic mapping and change detection of the region’s vegetation. This framework is an advance in data fusion and crowdsourced mapping of complex, vulnerable regions, providing support to regional stakeholders and the wider user community. This transboundary map can inform the protection, conservation, and restoration of vegetation, habitat, and ecosystems, particularly for threatened and endangered species across the two nations using consistent and harmonized binational mapping systems. Beyond supporting land management decisions and stakeholders in the transboundary desert ecoregions, this BCR 33 mapping effort establishes a foundation for future rapid, low-cost, cross-border land cover mapping that can benefit and advance ecosystem management.

Arizona, Baja California, California, Nevada, Sina↗
USGS2000

Suspended-sediment characteristics in the Housatonic River Basin, western Massachusetts and parts of eastern New York and northwestern Connecticut, 1994-96

Suspended-sediment concentrations, discharges, loads, and yields were determined for eight subbasins in the Housatonic River Basin in western Massachusetts, eastern New York, and northwestern Connecticut from April 1994 through March 1996. Suspended-sediment samples were collected at three continuous-record sediment stations and at four partial-record sediment stations. Suspended-sediment concentrations in samples collected during the period of study ranged from less than 0.5 to 3,400 milligrams per liter, and concurrent streamflows ranged from 0.03 to 126 cubic feet per second per square mile at the seven stations. Median suspended-sediment concentrations in samples collected at each station ranged from 7 to 61 milligrams per liter. Median streamflows during suspended-sediment sampling ranged from 1.86 to 5.88 cubic feet per second per square mile. Instantaneous suspended-sediment yields ranged from less than 0.005 to 185 tons per day per square mile, and medians ranged from 0.03 to 1.12 tons per day per square mile at the seven stations. Total suspended-sediment loads (mass) from April 1994 through March 1996 at the continuous-record sediment stations were 11,603 tons at Housatonic River near Great Barrington, 7,929 tons at Green River, and 54,347 tons at Housatonic River near Ashley Falls. Suspended-sediment load during January 1996 at the Green River station accounted for about 54 percent of the total suspended-sediment load for the Green River during the 2-year study. Suspended-sediment load on January 19 and 20, 1996, at the Green River station accounted for about 50 percent of the January 1996 suspended-sediment load, or about 27 percent of the total suspended-sediment load during the 2-year study. This large suspended-sediment transport was the result of rainfall and snowmelt on January 19 and 20, 1996--the equivalent of a 5- to 6-inch rain storm in the Green River subbasin. Total suspended-sediment loads during the 2-year study at the partial-record sediment stations were 3,052 tons at Williams River, 1,758 tons at Ironworks Brook, and 17,927 tons at Konkapot River. Suspended-sediment yields from April 1994 through March 1996 at the continuous-record sediment stations were 21 (tons/yr)/mi 2 at Housatonic River near Great Barrington, 78 (tons/yr)/mi 2 at Green River, and 58 (tons/yr)/mi 2 at Housatonic River near Ashley Falls. Suspended-sediment yields during the 2- year study at the partial-record sediment stations were 35 (tons/yr)/mi 2 at Williams River, 78 (tons/yr)/mi 2 at Ironworks Brook, and 147 (tons/yr)/mi 2 at Konkapot River. Suspended-sediment yields were estimated for two subbasins in the Housatonic River Basin--Schenob Brook at Sheffield, a partial-record sediment station, and the area adjacent to the Housatonic River between Great Barrington and Ashley Falls. The estimate of suspended-sediment yield at Schenob Brook at Sheffield of 82 (tons/yr)/mi 2 is comparable to the yield determined for the Green River and Ironworks Brook. The estimate of suspended-sediment yield for the area adjacent to the Housatonic River between Great Barrington and Ashley Falls was 395 (tons/yr)/mi 2 . This estimated suspended-sediment yield was 2.7 to 18.8 times greater than that estimated for any of the other subbasins. Several basin and land-use characteristics thought to affect suspended-sediment transport in the subbasins were compared to the suspended-sediment yields. The characteristics that seemed to affect suspended-sediment discharge were dams, which contributes to decreased yields; Hadley, Limerick, Linlithgo, Saco, and Winooski silt loam soils (high erodibility soils), which contributes to increased yields; stratified-drift deposits, which contributes to increased yields; and agricultural and open land, which contributes to increased yields. The effect of stratified-drift deposits on suspended-sediment discharge is thought to be greater when those deposits are of glaciolacustrine (generally clay, silt, and fine sand), rather than glaciofluvial (clay, silt, sand, gravel, and cobbles) origin. The silt loam soils, glaciolacustrine deposits, and agricultural and open land are interrelated, inasmuch as the silt loam soils generally are associated with glaciolacustrine deposits and agricultural activities.

Connecticut, Massachusetts, New York↗
USGS2026

Shallow differentiation of primitive arc magmas at the Jurassic Emigrant Gap mafic complex, Sierra Nevada, California

The Emigrant Gap composite pluton exposes ultramafic to silicic intrusive rocks that preserve the chemical evolution of primitive mafic arc magmas and their open-system interactions in the upper crust during mid-Jurassic growth of the Sierra Nevada batholith (California). We present field and petrographic observations and mineral and whole-rock chemistry of the ~35-km 2 ultramafic to dioritic Emigrant Gap mafic complex and an adjacent penecontemporaneous ~90-km 2 granodiorite that together make up the composite pluton. In the Emigrant Gap mafic complex, four roughly central masses of dunite, wehrlite, and olivine clinopyroxenite are surrounded by weakly layered gabbronorite and non-layered diorite. The ultramafic rocks are cumulates formed from near-liquidus minerals of primitive arc magmas that accumulated in steep feeder zones with substantial modification by melt–mush reaction as primitive liquids repeatedly transited the mush-filled conduits. The dominant gabbronoritic rocks are the variably accumulative products of more advanced crystallization–differentiation of arc tholeiitic basalts and basaltic andesites. The adjacent granodiorite intrusion originated separately and preserves field and geochemical evidence for assimilation of metasedimentary rocks. Open-system hybridization between the gabbronoritic mushes and the granodioritic magma produced an intervening body of two-pyroxene diorite. We infer that the ultramafic rocks and gabbronorite of the Emigrant Gap mafic complex crystallized from near-primitive arc basaltic to basaltic andesitic magmas at ~0.15–0.3 GPa, with estimated f O 2 of ≥FMQ +1 and dissolved H 2 O concentrations of only ~0.5–2 wt %. Notably, the Emigrant Gap composite pluton is distinct from other Mesozoic plutons in the Sierra Nevada batholith because of (1) its abundance of mafic and ultramafic rocks that crystallized from relatively primitive mafic melts and (2) the low inferred H 2 O concentrations of its parental magmas, indicated by a near absence of igneous amphibole and by the intermediate rather than calcic compositions of plagioclase. A Jurassic regional extension event probably accounts for the formation of relatively dry primitive arc magmas, as well as for their ascending to the upper crust.

California↗
USGS2026

Ungulate migrations of the Western United States, volume 6

This report, volume 6 in the “Ungulate Migrations of the Western United States” report series, showcases the migrations of 23 ungulate herds in the Western United States. The report series is produced by the Corridor Mapping Team (CMT). Led by the U.S. Geological Survey, the CMT is a collaboration among 11 State agencies, as well as regional and Federal partners, and an expanding number of Tribal wildlife agencies. The CMT was initiated in response to the U.S. Department of the Interior Secretarial Order 3362, which was signed in 2018 and provided Federal support to expand existing research efforts to study ungulate populations and conserve their migrations throughout the Western United States. Including this volume, the report series has detailed the migrations of 237 unique ungulate herds throughout the Western United States and continues to serve as a valuable resource to guide local and regional management, policy, and on-the-ground work necessary to maintain intact and functional ungulate migrations. This report highlights several guiding principles of the CMT that facilitate collaboration among the diverse set of partners and contribute to the program’s continued successes. Notably, raw global positioning system data are not shared among participating agencies and the U.S. Geological Survey, delineating migration corridors and seasonal ranges relies on empirical data, the CMT provides flexible approaches to participating State and Tribal partners, and regular CMT meetings create a framework for open communication among agency partners that supports transboundary mapping of migrations. The 237 ungulate migrations that have been included in the report series are an expanding inventory, which can help maintain ungulate migrations in perpetuity.

Arizona, California, Colorado, Idaho, Montana, Nev↗
USGS2024

Geomorphic map of the Umatilla River corridor, Oregon

This map portrays the distribution of landforms along the Umatilla River in northeastern Oregon and covers a corridor 127 kilometers long from the confluence of the Umatilla River with the Columbia River upstream to Meacham Creek. The map encompasses the valley bottom and extends about 1 kilometer up the adjoining hillslopes. Map data are intended to support water quality and fisheries enhancement efforts pursuant to the First Foods, a resource-management approach that focuses on traditionally gathered foods including water, fish, big game, roots, and berries and calls attention to the reciprocity between people and the foods upon which humans depend. The Umatilla River drains about 6,300 square kilometers on the northwest slope of the Blue Mountains in northeast Oregon. Most of the drainage basin is underlain by Miocene basalt flows of the Columbia River Basalt Group. Younger, weakly lithified, late Miocene and early Pliocene gravel deposits of local origin (for example, McKay Formation) are mapped in a few places. Upland surfaces are mantled with windborne silt (loess) correlative with deposits elsewhere known as the Palouse Formation. Surfaces below an elevation of about 340 meters were inundated repeatedly by large Pleistocene glacial outburst floods, most emanating from glacial Lake Missoula in western Montana. In backflooded areas such as the lower Umatilla River valley, Missoula floods deposited extensive slack-water silt. Areas mapped as open water, active channel and tie channel, flood basin, valley bottom, and modified land constitute the geomorphic floodplain: the area subject to occasional inundation by the Umatilla River. Deposits and landforms within the floodplain are inset into Missoula flood deposits and hence postdate the 20–15-kilo-annum Missoula floods. Some floodplain deposits are no more than a few centuries old, as indicated by substantial erosion and deposition during the Umatilla River flood of February 2020, the largest since systematic measurements began in October 1903. Deposits and landforms of the floodplain are transient features within the longer-term incision of the Umatilla River into mid-Miocene flood basalts and younger gravel of the McKay Formation.

Oregon↗
USGS2025

An inset groundwater-flow model to evaluate the effects of layering configuration on model calibration and assess managed aquifer recharge near Shellmound, Mississippi

The U.S. Geological Survey has developed a high-resolution inset groundwater-flow model in the Mississippi Delta as part of an interdisciplinary collaboration coordinated by the Mississippi Alluvial Plain project to provide a tool that stakeholders can use to support water-resource management decisions. Groundwater withdrawals from the Mississippi River Valley alluvial (MRVA) aquifer have been vital to support agricultural production in the region, but substantial groundwater-level declines near Shellmound, Mississippi, have caused concerns for long-term sustainability of the aquifer. To better understand the subsurface and try to mitigate the long-term groundwater-level declines, stakeholders have undertaken actions including a Groundwater Transfer and Injection Pilot (GTIP) project using a riverbank filtration-based managed aquifer recharge approach. The pilot project consisted of extracting groundwater near the Tallahatchie River and reinjecting it into the aquifer 3 kilometers west where water levels have substantially declined. A high-resolution airborne electromagnetic (AEM) survey was also completed to collect electrical resistivity data to support the GTIP project and the development of the groundwater model. The inset groundwater-flow model was developed to (1) integrate the AEM data into the optimal layering configuration of the MRVA aquifer that the available observation data can support through calibration, and (2) assess the potential effect of the GTIP project on the groundwater levels. The AEM data were processed into three different layering configurations leading to the development of model A (18 layers), model B (16 layers), and model C (8 layers), all at a 100- x 100-meter cell spatial resolution using the U.S. Geological Survey modular finite-difference flow model 6 code with Newton-Raphson formulation. The model development process integrated recent advances in modeling, such as the incorporation of AEM data, the use of outputs from the soil-water-balance (SWB) model, and the Aquaculture and Irrigation Water-Use Model, and was facilitated by robust automation using the open-source python packages Modflow-setup and SFRmaker. Using Parameter Estimation ++ Iterative Ensemble Smoother, the three numerical groundwater-flow models (models A, B, and C) were calibrated against a set of observations, which included aquifer groundwater levels, streamflows, stream stage, and aquifer transmissivity. Results indicate that the detailed representation of MRVA aquifer layers in model A produced the best calibrated model by history matching, and the integration of data representing surficial connectivity played a key role in improving groundwater recharge and enhancing the ability of the model to match groundwater levels in the cone of depression. A forecast model simulated the managed aquifer recharge approach, and the results indicated that, given average irrigation and recharge conditions (2010–15), the GTIP project has the potential to induce groundwater-level increases of as much as 3 meters around the injection site, but a sustained increase would require repetition in subsequent years of water transfer at 2022 rates or above.

Mississippi↗
USGS2026

Multiple machine-learning estimation of groundwater levels and trends for the regional Mississippi River Valley alluvial aquifer

The Mississippi River Valley alluvial aquifer provides irrigation, public, and domestic water supplies across the south-central United States. Declining groundwater levels require improved characterization of changing conditions. Traditional potentiometric-surface mapping does not use all available water-level data or quantify uncertainty. To address these limitations, we developed a data-driven multiple machine-learning (MML) framework delivered through two open-source R packages. The covMRVAgen1 software assembles covariates to 155,960 monthly groundwater levels from 57,695 wells; the mmlMRVAgen1 software trains Cubist and Random Forest models, blends them, and makes 1-kilometer gridded predictions of monthly potentiometric surfaces for the period January 1980–December 2022. The MML approach provides a methodological foundation for region-scale spatiotemporal groundwater prediction and uncertainty quantification, generating 90-percent prediction limits with appropriate empirical coverage. Model performance is acceptable, with a root-mean-square error of about 4.2 feet, standard deviation of 24.82 feet, and a normalized Nash–Sutcliffe efficiency of 0.973.

Arkansas, Illinois, Louisiana, Mississippi, Missou↗
USGS2025

REDPy: A Python tool for automated repeating earthquake detection and visualization

Detecting and cataloging seismic events are among the most fundamental tasks in seismology. Many standardized tools for these tasks exist, including the open‐source package repeating earthquake detector in Python (REDPy). REDPy generates an organized catalog of seismic events from continuous waveform data, in which events are automatically separated into groups (“families”) by their waveform similarity through cross‐correlation. REDPy also automatically generates various outputs that allow a user to visualize important trends in the catalog, which may be used in real time or in retrospective analyses to allow rapid identification of interesting features. The code was designed for near‐real‐time volcano monitoring but is applicable across a broad range of use cases in seismology and seismoacoustics. In this article, the utility and performance of REDPy are demonstrated on two highly seismogenic volcanic eruption sequences: the onset of the dome‐building eruption of Mount St. Helens, Washington, from 2004 to 2005, and the entirety of the summit caldera collapse sequence of Kīlauea, Hawai‘i, in 2018. This article is meant to be a companion to the documentation of the code; in addition to detailing the basic required inputs, script functionality, and resulting outputs, the reasonings behind several important design decisions are also discussed.

Seismological Research Letters↗
USGS2026

Teach me how to pycap: A high-capacity well decision support tool using analytical solutions in Python

Regulatory agencies in humid temperate environments rely on timely evaluations of streamflow depletion and drawdown to protect aquatic ecosystems and existing water users. Numerical models offer detailed insights, but their complexity and time demands often preclude their practical use in rapid decision-making. We present pycap-dss, an open-source Python package that implements a suite of analytical solutions for estimating streamflow depletion and drawdown. The tool supports superposition of multiple wells and time-varying pumping, enabling cumulative impact assessments in situations with multiple wells and streams. The software is modular and extensible, allowing users to interchange solutions or add new analytical methods. A YAML-based configuration supports batch processing of multiple wells, and an optional AnalysisProject class facilitates integration with regulatory workflows. Rigorous unit and regression testing ensures computational reliability, and continuous integration supports ongoing development. We demonstrate deterministic examples of drawdown where multiple solutions are readily compared and streamflow depletion with multiple wells in the Central Sands region of Wisconsin. We also show the value of Monte Carlo analyses of streamflow depletion in the same Central Sands example, leveraging computational efficiency to evaluate the uncertainty of individual and cumulative streamflow depletion calculations from over 200 high-capacity wells.

Wisconsin↗
USGS2025

A method to obtain remotely sensed grain size distributions from nonplanar granular deposits

Constraining the grain size distribution of granular deposits with complex surfaces is difficult with existing approaches. Field and laboratory techniques are time consuming and limited by the maximum grain size that laboratories can accommodate. In this study, we present a new method to identify the coarse fraction of the grain size distribution at a debris-flow fan deposit surveyed with terrestrial laser scanning (TLS) in Glenwood Canyon, Colorado, USA. This method is a novel grain segmentation algorithm developed for application to point cloud data of deposits with complex surfaces and angular grains ranging in size from centimeters to a meter. This approach combines an existing random forest machine learning method with a novel iterative clustering algorithm. We compared the grain size distribution from our algorithm with a Wolman pebble count conducted in the field, and found a root mean squared error of less than 2 cm from the 5th to 95th percentile of the grain size distribution of grains ranging from cobble to boulder sized (6.3–78 cm in our application). Finally, we compared our new algorithm with an existing open-source grain segregation algorithm, and our method outperformed the selected alternative when applied to the debris-flow deposit point cloud.

Colorado↗