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USGS2026

USGS 2025 critical minerals review

The United States Geological Survey (USGS) provides scientific information for the Department of Interior and the nation, consistent with its original mission expressed in the Organic Act of 1879 (43 U.S.C. 31): “the classification of the public lands and examination of the geological structure, mineral resources, and products within and outside the national domain.” Legislation such as the Energy Act of 2020 and the 2022 Infrastructure Investment and Jobs Act (43 USC 31l) and recent executive actions (Executive Orders 14154 , 14153, 14241 Secretary’s Orders 3417, 3418, 3422, 3436) underscore the importance of mineral resources and focus USGS activities on mapping and assessing mineral resources, with a particular focus on those presently identified as critical, both in ground and above ground in mine wastes. This article reviews selected activities and accomplishments by the USGS Mineral Resources Program related to critical minerals in 2025. Highlights include a new List of Critical Minerals, a first-ever national mine waste inventory, international minerals partnerships, mineral resource assessment advancements, and national data collection activities and outcomes of the Earth Mapping Resources Initiative (Earth MRI). The selected contributions are not comprehensive but are intended to demonstrate USGS leadership in critical mineral mapping and assessment, the importance of domestic and global partnerships, and the breadth of research activities that are responsive to national needs and priorities.

Mining Engineering↗
USGS2026

HyFlood: A surrogate-model-based framework for compound coastal flooding

Compound coastal flooding is a major threat to low-lying coastal regions and is expected to intensify under future climate change projections. However, modeling the joint interaction of waves, storm surge, tides, and rainfall remains computationally demanding, limiting the development of fast and reliable forecast tools. Here we present HyFlood, a hybrid statistical-numerical downscaling framework capable of computing and mapping high-resolution compound flood hazards while substantially reducing the computational cost compared with fully process-based hydrodynamic modeling. HyFlood combines statistical sampling and selection algorithms with a cascade of reduced-complexity surrogate models that emulate nearshore wave transformation, surf-zone hydrodynamics, and coastal, fluvial, and pluvial flooding. The surrogate models employ machine-learning and regression algorithms applied to a low-dimensional representation of the flooding outputs, obtained through statistical dimensionality reduction. The framework is demonstrated in southern O'ahu, Hawai'i, a region exposed to elevated sea levels driven by tides, waves, and storm surge along with frequent precipitation-driven flash flooding. Validation of the surrogates against the physics-based model outputs demonstrates that HyFlood accurately reproduces daily maxima of spatially distributed flooding depths. This hybrid approach offers a scalable and efficient tool to better quantify how changes in flooding drivers translate into hazard and impact assessments, and to support compound-flood risk assessments and climate-change adaptation planning.

Hawaii↗
USGS2026

From landslide susceptibility to risk assessment in the conterminous U.S.

Understanding the spatial distribution of landslide prone-areas and what consequences they may have is important for risk management and land-use planning. In the United States, although landslides occur in every state, a comprehensive landslide risk assessment is still missing. Existing efforts, such as the Federal Emergency Management Agency (FEMA)’s National Risk Index, rely on aggregated products and coarse cartographic units, limiting their geomorphological and practical accuracy. In this study, we present a methodological advance for landslide risk assessment across large areas with incomplete and sparse data. We apply our procedures to the conterminous United States by integrating geomorphologically meaningful partitions and spatial and temporal probability data-driven models. Landslide susceptibility is estimated using a Generalized Additive Mixed Model incorporating a bias capture/correction scheme to account for inventory inaccuracies (reference Area Under the Curve = 0.75). The exceedance probabilities of landslide occurrence are defined for three temporal scenarios (2, 5, and 10 year). Then, we explore the associated potential economic consequences for human settlements and agricultural areas. The findings indicate that the spatial variability of risk is primarily controlled by exposure rather than by susceptibility/hazard alone. The mean risk increases by ∼170% from the 2-year to the 10-year scenario. Beyond its quantitative outcomes, this study offers a blueprint for continental or sub-continental scale landslide risk assessments, demonstrating both the opportunities and current limitations.

Engineering Geology↗
USGS2025

Global patterns of coseismic landslide runout mobility differ from aseismic landslide trends

Coseismic landslides significantly contribute to human and economic losses during and immediately following earthquakes, yet very little data on the runout of such landslides exist. While well-established behavior of aseismic (e.g., hydrologically triggered) landslide runout mobility suggests strong correlation between landslide size and mobility, limited studies of coseismic landslide runout find conflicting mobility trends. We present a global dataset of runout lengths produced from a new automated method for estimating landslide runout, developed and validated using 1726 manually mapped landslides from five unique earthquakes. We then apply the automated runout tool to 23 global earthquake-induced landslide inventories, producing a compiled database of 73,665 measured and estimated runout lengths of coseismic landslides to assess mobility trends. We find a significant divergence between well-established aseismic mobility trends and that of coseismic landslides, with far greater scatter and more complex mobility patterns in earthquake-triggered landslides. As a function of landslide size, we observe global coseismic landslide mobility patterns are bilinear, becoming increasingly less mobile with increasing size above some threshold. This discordance between aseismic and coseismic landslide mobility may be a function of landslide type, kinematics, hydrology, and or setting that systematically differ between triggering mechanisms and should be explored in more depth to develop predictive models of these unique runout patterns. These results suggest hazard and risk models for coseismic landslides may significantly under-predict or over-predict impacts, depending on the size of triggered landslides.

Engineering Geology↗
USGS2025

Irrigated agriculture influences selenium levels in an endangered marsh bird

Selenium bioaccumulation in aquatic food webs poses risks to wildlife, particularly in wetlands receiving irrigation runoff. The Salton Sea, California’s largest lake, is primarily sustained by agricultural drainage. This drainage creates wetland habitat along the lakeshore that many bird species depend on, including the federally endangered Yuma Ridgway’s rail ( Rallus obsoletus yumanensis ). However, these marshes may pose an ecological trap – attracting rails despite high selenium exposure. We captured rails during the 2020–2023 breeding seasons and compared rail selenium levels within three types of marshes (fed with irrigation runoff, Colorado River water, or groundwater). We collected blood, breast feathers, and head feathers of rails in all three water sources for selenium comparisons. We tagged adult rails with GPS transmitters to locate nests and foraging locations where we collected eggshells, unhatched eggs, and prey. We assessed selenium exposure by collecting multiple prey species commonly eaten by rails in all three water sources. Selenium concentrations varied among sampling locations. Selenium concentrations in most sample types were predominately influenced by water source and marsh inflow velocity (sometimes in combination with marsh size). Distance to inflow, however, did not influence selenium concentrations in any sample type. Selenium concentrations were highest in agricultural-fed marshes compared to river-fed and spring-fed marshes. Increased marsh inflow velocities resulted in lower selenium concentrations. Given the risk of an ecological trap, our results suggest that supplementing wetlands with Colorado River water could mitigate selenium bioaccumulation in Yuma Ridgway’s rails.

California↗
USGS2025

Multi-scale predictors of Northern Long-eared Bat (Myotis septentrionalis) occupancy in the United States

Historically, Myotis septentrionalis (Northern Long eared Bat) was among the most common forest-interior species in North America. Largely due to high mortality from white-nose syndrome, this species has experienced severe population declines across its range. To create an updated species distribution map representing summer occupancy probabilities from 2017 to 2022, we integrated stationary acoustic data with live-capture data from the database of the North American Bat Monitoring Program into a multi-scale, multi-method occupancy modeling framework. Our results provide data-driven predictions with quantified uncertainty for summer occupancy probabilities for Northern Long-eared Bats at 2 spatial scales across the range of the species, while also accounting for inherent observation biases (e.g., imperfect detection).

Journal of North American Bat Research↗
USGS2026

Remote compositional analyses of space-weathered lunar maria

Visible-to-shortwave infrared (VSWIR) reflectance spectroscopy has revolutionized our understanding of planetary surface compositions. However, space-weathering processes on airless bodies complicate quantitative compositional analyses. Here, we present a framework to isolate the signatures of space weathering in VSWIR spectra of lunar maria by leveraging radiative transfer modeling under the assumptions that (i) a space-weathered target can be expressed as a mixture of fresh and fully space-weathered components and (ii) remaining signatures can be modeled by including agglutinates as an end-member component. We first validate this approach against laboratory spectra of space-weathered Apollo mare soils of known mineral compositions using a probabilistic Markov Chain Monte Carlo implementation of the Hapke radiative transfer model. Second, we illustrate how this approach can be applied to orbital Moon Mineralogy Mapper data. The proposed space-weathering correction workflow for lunar maria could be expanded to other lunar lithologies and applied to existing and future data sets.

Planetary Science Journal↗
USGS2026

Statewide surficial geologic map of Nebraska underscores Quaternary landscape evolution from the High Plains to the Central Lowland

Surficial geologic mapping in Nebraska has been conducted primarily at the 1:24,000 scale since the mid-1990s, although there have also been limited efforts to map generalized Quaternary and surficial geology within the state and the region. We compiled and evaluated disparate maps (1:24,000–1:1,000,000) and datasets—including geologic, soil and soil parent material, and geomorphic maps as well as LiDAR derivatives—to produce a single 1:500,000 scale surficial geologic map of Nebraska that is the first of its kind. This new map provides a coarse-scale surficial geologic map that will be incorporated into a nationwide U.S. Geological Survey Quaternary geologic map. It also reflects the variation and uniqueness of physical landscapes in the state, where the Great Plains and Central Lowland physiographic provinces meet, further developing a richer interdisciplinary understanding of regional geomorphology in the heart of North America.

Nebraska↗
USGS2026

Synthesizing a twelve-year sediment trap time series of planktic foraminiferal flux in the Gulf of America (Mexico)

Sediment trap time series provide powerful frameworks for testing hypotheses about planktic foraminiferal assemblage composition, seasonality, and geochemical responses to environmental variability, all of which are central to improving paleoceanographic reconstructions. We present high resolution foraminiferal assemblage data from a long-running sediment trap (2008–2020) in the northern Gulf of America (Mexico). This study summarizes the species composition, seasonality, and size distribution of the fifteen most abundant species of planktic foraminifera, which account for 98% of total flux. Foraminiferal flux peaks in winter and reaches a minimum in summer, following the seasonal pattern of primary production in the northern Gulf. Winter assemblages are dominated by non-spinose taxa, whereas spinose taxa prevail during summer. Across nearly all species, average monthly test size covaries with temperature, independent of seasonal flux trends. Notably, Trilobatus sacculifer and Neogloboquadrina dutertrei show a significant decline in relative abundance and flux during 2017–2020, nearly disappearing from the assemblage.

Journal of Foraminiferal Research↗
USGS2026

Spatio-temporal modeling for assessing geoenergy resources: A workflow applied to gas in place variation in coal beds

The ability to estimate spatio-temporal changes in hydrocarbon reservoir properties and energy resources within pore volumes is essential for optimizing production, reservoir management, geologic energy storage, and safety in underground mining operations. In coal seams, predicting remaining methane gas-in-place (GIP) is critical for quantifying producible gas and improving mine safety and productivity through effective ventilation planning. Although such changes are commonly evaluated using physics-based numerical simulation models, these approaches often require extensive data, calibration effort, and time. This study presents a spatio-temporal geostatistical modeling approach that bridges the gap between purely spatial models and full numerical simulations. The method is applied to a case study of coal seam degasification in the Mary Lee coal group, Black Warrior Basin, Alabama, USA, to estimate GIP evolution over time within a selected mining district. The analysis uses published data from prior natural gas production history-matching of degasification using vertical wells. Empirical spatial and temporal statistics were calculated for reservoir pressure and water saturation, and spatio-temporal variogram models were fitted to experimental variograms. These models provided the structural basis for spatio-temporal kriging, integrated with spatial estimates of time-invariant parameters (porosity, density, and thickness) to estimate GIP. This approach enabled estimation of GIP changes over time, including periods without data. Boxplots of GIP estimates indicated systematic depletion and decreasing spatial variability, reflecting the impacts of degasification. Comparison with cumulative gas production from empirical well records showed approximately 85% agreement based on a relative similarity metric. Spatio-temporal GIP estimates were also used to estimate methane emissions to longwall ventilation systems and compared with reported emissions from the U.S. EPA Greenhouse Gas Reporting Program, showing similar distributions (≈80%) given data limitations. Overall, this integrated modeling approach provides time-dependent GIP estimates with broader implications for resource assessment applications.

Alabama↗
USGS2025

Avian navigation: Comparing the olfactory navigational “map” and the infrasound direction-finding hypotheses to aeronautics

Animal navigation has long been a fascinating but bewildering subject. Humans and animals might well share similar navigational strategies because they developed within the same physical environments. A “map-and-compass” model has been proposed to explain the two-step avian navigational process, but the “map” step has remained elusive. Although scalar values from bicoordinate geomagnetic or atmospheric olfactory gradients have been considered foundational to the avian map, neither has proved convincing engendering decades of controversy. The olfactory map, and an alternative infrasound direction-finding (IDF) hypothesis, are discussed in this review. The olfactory map hypothesis currently requires extensive stable gradients of trace-odor ratios, but such gradients are highly unlikely within a turbulent and rapidly mixed lower atmosphere. The IDF hypothesis, on the other hand, postulates a two-step navigational model analogous to the maritime and aeronautical radio direction-finding technique. This review was also written to encourage further investigation, and direct testing, of the acoustic navigational process. The IDF hypothesis, at present, appears the better explanation of observed avian navigational behavior and accuracy within the atmosphere’s physical environment.

Journal of Comparative Physiology A↗
USGS2025

National population exposure and evacuation potential in the United States to earthquake-generated tsunami threats

Previous efforts to characterize tsunami threats to people have focused primarily on individual scenarios in specific areas but have not recognized multiple scenarios across an entire country. This study addresses this gap by quantifying population exposure and evacuation potential in the United States to 102 earthquake-related, tsunami-hazard zones, including 92 local scenarios, 8 distant scenarios, and 2 probabilistic products. Geospatial path-distance modeling quantified evacuation potential and the influence of departure delays. We focused on residents to support other national, multi-hazard risk analyses. Millions of residents are in distant-tsunami zones, and hundreds of thousands of residents are in local-tsunami zones. In 41 scenarios, there is at least one resident that may have insufficient time to evacuate before wave arrival. Tens of thousands of residents may have insufficient time to evacuate from local tsunamis that impact the U.S. Pacific Northwest or Puerto Rican coastlines. The largest improvements in evacuation potential may come from reducing departure delays in some areas but may involve vertical-evacuation structures or changing land use in other areas.

International Journal of Disaster Risk Reduction↗
USGS2024

Self-guided decision support groundwater modelling with Python

The GMDSI tutorial notebooks repository provides learners with a comprehensive set of tutorials for self-guided training on decision-support groundwater modelling using Python-based tools. Although targeted at groundwater modelling, they are based around model-agnostic tools and readily transferable to other environmental modelling workflows. The tutorials are divided into three parts. The first covers fundamental theoretical concepts. These are intended as background reading for reference on an as-needed basis. Tutorials in the second part introduce learners to some of the core concepts parameter estimation in a groundwater modelling context, as well as providing a gentle introduction to the PEST, PEST++ and pyEMU software. Lastly, the third part demonstrates how to implement highly-parameterized applied decision-support modelling workflows. The tutorials aim to provide examples of both “how to use” the software as well as “how to think” about using the software. A key advantage to using notebooks in this context is that the workflows described run the same code as practitioners would run on a large-scale real- world application. Using a small synthetic model facilitates rapid progression through the workflow.

Journal of Open Source Education↗
USGS2026

Groundwater spatial variability within an atoll island: Assessing shallow aquifer heterogeneity with geophysical and physicochemical measurements

This study examines the spatial variability of shallow groundwater on Dhigelaabadhoo Island using electromagnetic induction surveys, groundwater monitoring, and sediment analyses. The research reveals how variations in island morphology—such as differences in elevation, reef flat width, and sediment composition—affect the spatial distribution of groundwater lenses and the overall aquifer dynamics. Saltwater intrusion is especially pronounced in low elevated areas, with narrow reef flat plate and areas where higher hydraulic conductivity—driven by the presence of coarser sediments—is observed, whereas regions characterized by finer sediments, higher elevation, and wider reef flat plates tend to support more symmetric and less saline groundwater lenses. The geophysical investigations reveal that tidal oscillations alter groundwater movement by markedly changing water levels and conductivity, thereby underscoring the critical need to account for temporal dynamics in atoll coastal aquifer systems and the importance of integrating tidal dynamics into the aquifer zone. The findings highlight the significant role of intrinsic morphological and external hydrodynamic factors in shaping groundwater distribution on atoll islands, offering critical insights for sustainable freshwater resource management.

Dhigelaabadhoo Island↗
USGS2025

Divide and conquer: Separating the two probabilities in seismic phase picking

There are two fundamental probabilities in the seismic phase picking process – the probability of the existence of a seismic phase (detection probability) and the probability of correctly identifying the phase arrival time (timing probability). The nearly ubiquitous approach in developing deep learning phase picking models is to use a kernel, such as a truncated Gaussian, to mask the labeled phase arrival time, and train a segmentation model. Once a model is trained, the times of the peaks in the output are taken as phase arrival times (picks) and the height of the peaks are taken as “probability” of the picks. Here, we show that this “probability” represents neither the detection nor the timing probabilty because this approach forces the output to follow the shape of the kernel. We introduce an approach using two models to estimate these two distinct probabilities. We use a binary classifier with a calibrated confidence to address the detection probability and a multi-class classifier to obtain a probability mass function to address the timing probability. This new approach makes the deep learning-based phase picking process more interpretable and gives us options to logically control seismic monitoring workflows.

Geophysical Journal International↗
USGS2025

Simulation of the impacts of projected climate change on groundwater resources in the urban, semiarid Yucaipa Valley watershed, southern California using an integrated hydrologic model

Managing water resources in semiarid watersheds is challenging due to limited supply and uncertain future climate conditions. This paper examines the impact of future climate changes on an urban watershed in southern California using an integrated hydrologic model. GSFLOW modeling software is used to simulate the nonlinear relationships between climate trends and precipitation partitioning into ET, runoff, and subsurface storage. Four global circulation models (GCMs), each with two greenhouse-gas scenarios, RCP45 and RCP85 are used to project future climate conditions. GCMs include the CanESM2, CNRM-CM5, HadGEM2-ES, and MIROC5 models. The model's simulated hydrologic conditions are compared with historical data to assess changes in water budgets and groundwater supply. Results indicate decreased groundwater storage in most scenarios due to increased natural evapotranspiration, vegetation consumptive use, and streamflow out of the watershed. Only scenarios with substantially increased future precipitation show increased groundwater storage. The study also highlights increased future aridity despite the rise in precipitation and large precipitation events forecast by GCMs, which increase the risk of urban floods and decrease stream leakage and water available to vegetation.

California↗
USGS2025

Total uncertainty quantification in inverse solutions with deep learning surrogate models

We propose an approximate Bayesian method for quantifying the total uncertainty in inverse partial differential equation (PDE) solutions obtained with machine learning surrogate models, including operator learning models. The proposed method accounts for uncertainty in the observations, PDE, and surrogate models. First, we use the surrogate model to formulate a minimization problem in the reduced space for the maximum a posteriori (MAP) inverse solution. Then, we randomize the MAP objective function and obtain samples of the posterior distribution by minimizing different realizations of the objective function. We test the proposed framework by comparing it with the iterative ensemble smoother and deep ensembling methods for a nonlinear diffusion equation with an unknown space-dependent diffusion coefficient. Among other applications, this equation describes the flow of groundwater in an unconfined aquifer. Depending on the training dataset and ensemble sizes, the proposed method provides similar or more descriptive posteriors of the parameters and states than the iterative ensemble smoother method. Deep ensembling underestimates uncertainty and provides less-informative posteriors than the other two methods. Our results show that, despite inherent uncertainty, surrogate models can be used for parameter and state estimation as an alternative to the inverse methods relying on (more accurate) numerical PDE solvers.

Journal of Computational Physics↗
USGS2025

Wet meadow regeneration through restoration of biophysical feedbacks

Wet meadows are globally significant ecosystems that provide critical hydrological, ecological, and biogeochemical functions, yet their extent has declined dramatically due to land use changes and hydrologic alteration. These sedge-dominated wetlands exist at the drier end of the wetland gradient, maintained by shallow groundwater and periodic inundation. This paper is a global synthesis of the ecological, geomorphic, and hydrological dynamics of wet meadows, with an emphasis on alluvial systems, to inform effective restoration strategies. We compare wet meadows to other wetlands, classify them into palustrine, lacustrine, and alluvial types, then focus on alluvial wet meadows and discuss how their formation and persistence depend on ground and surface water interactions, sediment deposition and flow obstructions, all mediated by biological processes. In particular, we highlight the role of hydric graminoids in resisting erosion and maintaining soil cohesion, how beaver promote meadow persistence, and the significance of wet meadows as carbon sinks. We also present stratigraphic evidence demonstrating that incision, often triggered by anthropogenic activity or changing climate, is the primary mechanism of alluvial wet meadow degradation, resulting in water table decline and shifts in vegetation composition. Restoration requires reversing these incisional processes through techniques that elevate water tables, disperse flow and retain sediment—methods traditionally associated with either soil conservation or stream restoration. These include nature-based solutions that create obstructions such as beaver dams and their analogues, rock and wood-based obstructions and incision trench or gully filling and grading. Given their multifunctional value—including but not limited to flood attenuation, biodiversity support, and carbon sequestration—wet meadows warrant a focused restoration framework. This review advocates for a valley-floor scale restoration paradigm that integrates hydrological reconnection, sediment retention, and biological reinforcement to ensure long-term resilience of these systems in the face of changing climate and land use pressures.

Frontiers in Environmental Science↗