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1,662 records · Page 88Linked to original sources
USGS2026

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

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

Alaska↗
USGS2026

pySATSI: A Python package for computing focal mechanism stress inversions

We introduce pySATSI, a Python package for computing earthquake focal mechanism stress inversions. This algorithm can handle a wide variety of types of stress inversion problems with a single script and can duplicate many capabilities of preceding methodologies. We also add new capabilities that include spatiotemporally variable inversion grids, damped stress estimates for clusters with few or no focal mechanisms, and variable fault‐plane ambiguities that the user can assign to individual events. In addition, we added the ability to use damped stress inversions with fault‐plane ambiguity probabilities that are weighted by fault instabilities. Our algorithm is computationally efficient with faster runtimes than previous algorithms, scales well for large datasets, and can be easily parallelized.

Seismological Research Letters↗
USGS2025

Ambient field seismology in critical zone hydrological sciences

Passive ambient noise monitoring is an emerging tool in environmental seismology, leveraging the ambient seismic field to assess temporal variations in shallow subsurface properties. This review focuses on the potential and challenges of using scattered coda waves from noise correlation functions to monitor critical zone dynamics. The sensitivity of seismic velocities to various environmental factors, including precipitation, snowmelt, atmospheric pressure, and groundwater fluctuations, underscores the method’s versatility. While coda waves excel in detecting subtle changes due to their scattered nature, ballistic waves provide higher spatial resolution, albeit with challenges in source stability. Advances in seismic sensing, including distributed acoustic sensing and low-cost geophone networks, have enabled high-resolution monitoring of hydrological processes, subsurface deformation, and seismic hazards. Integrating seismic data with hydrological models provides insights into water storage, pore pressure changes, and soil moisture dynamics. However, limitations in spatial resolution, calibration with ground truth data, and coupled effects between environmental factors remain key challenges. This review emphasizes the importance of interdisciplinary approaches in refining methodologies, enhancing sensor deployments, and addressing data gaps. Passive seismic monitoring offers opportunities to understand critical zone processes and their broader impacts on seismic hazards and environmental sustainability.

Comptes Rendus. Géoscience↗
USGS2026

Ductile and brittle Rio Grande Rift deformation in Oligocene granite records a two-stage rift history in southern Colorado

The timing and nature of early deformation in the Rio Grande Rift remains poorly constrained. We present evidence for the earliest structural signature of rift extension in the Sangre de Cristo Range, southern Colorado, based on new geologic mapping, structural analysis, rock magnetic data, and thermochronology. These analyses focus on the ~30.0 Ma granite of Chokecherry Canyon, which hosts discrete low-angle mylonitic shear zones and a distributed, gently SW-dipping protomylonitic fabric. Incremental stretching axes, stretching lineations, and Kmax magnetic lineations plunge gently WSW. Quartz microstructures and crystallographic orientations indicate dominantly coaxial strain in the protomylonite and general shear in the discrete shear zones. Quartz c-axis opening-angle thermometry suggests deformation at ~420–540°C. Thermal modeling of ⁴⁰Ar/³⁹Ar K-feldspar data indicates rapid post magmatic cooling below the brittle–plastic transition, supporting shear-zone formation immediately after emplacement. Slow cooling from ~20–13 Ma was followed by renewed rapid cooling at ~13 Ma, interpreted as the onset of extensional exhumation along the Sangre de Cristo Fault System. These results show that extension in the northern Rio Grande Rift was active by ~30 Ma, earlier than previously recognized. We propose a two-stage model for northern Rio Grande Rift evolution: Stage I (30–23 Ma) records ENE–WSW extension localized in low-angle mylonitic shear zones associated with mid-crustal intrusions; Stage II (≤18 Ma) reflects brittle high-angle normal faulting, focused exhumation, and rift narrowing. Stage I magmatism and deformation along the western range front likely established crustal weaknesses that guided later fault development.

Colorado↗
USGS2026

Extreme precipitation variability and soil texture controls on water-table response

Extreme precipitation events (EPEs), a key class of hydrometeorological extremes, are intensifying globally under climate change; however, their effects on water-table dynamics across varying soil textures remain poorly understood. To better understand the impacts of EPEs, we conducted one-dimensional modeling to evaluate water-table response time, displacement, recession time, and total recharge under EPEs of 0.20 m, 0.40 m, and 0.60 m amounts, applied over 1-, 7-, and 20-day durations across twelve soil textures. The results show that coarse soils (i.e., sand) respond within days, while fine soils (i.e., clay) may take over 200 days. Water-table displacement ranged from 0.30 to 1.64 m and increased with EPE magnitude. The time it took for water tables to recede ranged from 1.2 to 3.0 years. A first-order estimate of total possible recharge, calculated from porosity and displacement, ranged from 17% (clay) to 97% (sand), averaging ~63% across soil textures. These findings highlight that recharge is primarily governed by EPE magnitude and soil properties, not event duration. This modeling effort provides new insight into how soil texture modulates groundwater response to extreme precipitation, informing future water budget and resilience assessments.

Water↗
USGS1998

Methods and guidelines for effective model calibration; with application to UCODE, a computer code for universal inverse modeling, and MODFLOWP, a computer code for inverse modeling with MODFLOW

This report documents methods and guidelines for model calibration using inverse modeling. The inverse modeling and statistical methods discussed are broadly applicable, but are presented as implemented in the computer programs UCODE, a universal inverse code that can be used with any application model, and MODFLOWP, an inverse code limited to one application model. UCODE and MODFLOWP perform inverse modeling, posed as a parameter-estimation problem, by calculating parameter values that minimize a weighted least-squares objective function using nonlinear regression. Minimization is accomplished using a modified Gauss-Newton method, and prior, or direct, information on estimated parameters can be included in the regression. Inverse modeling in many fields is plagued by problems of instability and nonuniqueness, and obtaining useful results depends on (1) defining a tractable inverse problem using simplifications appropriate to the system under investigation and (2) wise use of statistics generated using calculated sensitivities and the match between observed and simulated values, and associated graphical analyses. Fourteen guidelines presented in this work suggest ways of constructing and calibrating models of complex systems such that the resulting model is as accurate and useful as possible.

Water-Resources Investigations Report↗
USGS2024

Telecommunications fiber for sensing earthquake aftershocks: Progress and hurdles

Aftershocks offer valuable clues to earthquake behavior. The challenge: quickly deploying sensors to capture the early details of earthquake ruptures within the zone of aftershocks. Telecommunication fibers might be an answer, providing denser networks in otherwise difficult areas, potentially faster than traditional methods.

Conference Paper↗
USGS2025

Preventing overfitting when using tree-based methods for mapping hydrothermal favorability

Ensemble tree-based algorithms are robust tools for estimating sparsely distributed resources with non-linear dependencies (e.g., hydrothermal systems). These algorithms naturally accommodate the threshold conditions necessary to enable and support hydrothermal systems (e.g., having sufficient heat and permeability) and are simpler than many other non-linear machine learning strategies (e.g., artificial neural networks), which is an advantage when working with few labeled examples from which to learn. In previous work, we used eXtreme Gradient Boosting (XGBoost) to produce regional prediction and uncertainty maps of hydrothermal favorability; however, recent studies suggest that, even when properly applied, XGBoost has some risk of overfitting when there are few labeled examples from which to learn. To evaluate overfitting when constructing hydrothermal favorability maps with tree-based methods, we compare XGBoost with Extremely Randomized Trees (ExtraTrees), another ensemble tree-based algorithm that has the potential to underfit when using few labeled examples. We hold all other modeling parameters constant, resulting in two contrasting favorability maps of conventional geothermal resources for the Great Basin. Our results indicate that ExtraTrees demonstrably reduces overfitting compared with XGBoost. After considering overall performance, we conclude that ExtraTrees provides a more suitable modeling approach than XGBoost for the purposes of conventional hydrothermal resource assessments.

Conference Paper↗
USGS2024

Reduced injection rates and shallower depths mitigated induced seismicity in Oklahoma

The proximity of wastewater disposal to the Precambrian basement is a critical factor influencing induced earthquake rates in the Central United States, but the impact of reducing injection depths has not been widely demonstrated. Beginning in 2015, state regulatory efforts in Oklahoma and Kansas mandated that wells injecting into the lower Arbuckle Group, a basal sedimentary unit, be backfilled with cement (i.e. “plugged back”) so that they inject into shallower formations. This plug back activity gives us a unique opportunity to investigate the relationship between injection depth and induced seismicity rate. To evaluate the impact that decreased injection rates and plug backs had on the seismicity rates, we create a suite of rate-state earthquake models. Observed seismicity rates are best fit when only lower Arbuckle volumes are considered, suggesting the lower Arbuckle injectors were primarily responsible for the seismicity and that plug backs were effective at isolating the injected volumes to shallower formations. Our models demonstrate that if these wells had not been plugged back, seismicity rates would be multiple times larger than they are today. We find that the combination of well plug backs and injection volume decreases can be effective strategies for reducing induced seismicity rates.

Oklahoma↗
USGS2024

Correction to A regime shift in sediment export from a coastal watershed during a record wet winter, California: Implications for landscape response to hydroclimatic extremes

In the referenced article, the authors would like to correct text in the first paragraph on page 2571, Figure 9 and its caption. The changes reflect an error made in the processing of the rainfall intensity-duration data used to compare storms to published debris flow triggering thresholds. The correctly processed data does not change the interpretations made in the paper but does correctly indicate that the investigated storms did not exceed the rainfall intensity – duration threshold of Cannon (1988) but did significantly exceed the debris flow triggering threshold of Wieczorek (1987).

Earth Surface Processes and Landforms↗
USGS2026

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

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

Remote Sensing Applications: Society and Environme↗
USGS2025

USGS addresses needs for lithium calibration and quality control materials for pLIBS analysis

Lithium (Li) is a globally important commodity used for energy storage, national defense, human health, and advanced technologies. Lithium resource development requires identifying deposits with elevated concentrations and optimal mineralogy, typically associated with select clays and pegmatites. Lithium is a light, highly reactive alkali metal with low atomic mass that is difficult to detect and quantify using conventional portable geochemical techniques such as X-ray fluorescence (XRF). However, portable laser-induced breakdown spectroscopy (pLIBS) is a powerful analytical technique for lithium exploration due to its ability to analyze solids quickly with minimal preparation. The expanded utility of pLIBS is hampered by the lack of matrix-matched calibration and quality control (QC) materials. The United States Geological Survey (USGS) has developed in-house lithium calibration and QC materials for lithium in clay and pegmatite matrices to address this limitation. We present the workflow and implementation of a custom-built matrix specific calibration on a SciAps Z-300 pLIBS, using proprietary Profile Builder software. The implementation of the custom calibration and quality control standards enables us to collect semiquantitative results directly from the pLIBS while in the field. Ultimately, this calibration has improved confidence in sample selection and collection in the field, providing more efficient site characterization.

Conference Paper↗
USGS2026

A diatom-based quantitative sea-ice proxy for the Bering and Chukchi seas

Sea ice affects Earth's climate system on both regional and global scales. Its incorporation into climate can be used to achieve more accurate predictions of future climate. However, instrumental records of sea-ice concentration do not extend earlier than 1978. In an effort to extend this record, we constructed a proxy using the generalized additive model based on relative abundances of five easy-to-identify diatom species found in sediment samples across the Bering and Chukchi seas. Here we present the first quantitative diatom-based sea-ice proxy developed for Beringia. The developed proxy has been applied to two sediment cores in the Bering Sea ranging from 0 to 25.7 ka (HLY0204 51JPC) and 369 to 430 ka (IODP Exp 323 Site U1345) and one in the Chukchi Sea ranging from 2.7 to 10 ka (HLY0204 24JPC). The obtained reconstructions of sea-ice concentrations are similar, but not identical to previously published qualitative and nearby records based on other proxies. Because our results are quantitative, they can be incorporated into regional climate models. The proxy is publicly available as an R Shiny application (app) and can be applied to any diatom count from marine sediments in the region.

Bering Sea, Chukchi Sea↗
USGS2024

New developments at the Center for Engineering Strong-Motion Data (CESMD)

The Center for Engineering Strong-Motion Data (CESMD), an internationally utilized joint center of the U.S. Geological Survey (USGS) and the California Geological Survey (CGS), provides a single access point for earthquake strong-motion records and station metadata from the CGS California Strong-Motion Instrumentation Program (CSMIP), the USGS National Strong-Motion Project (NSMP), the USGS Advanced National Seismic System, and other affiliates. The CESMD has been continuously improving its webtools to facilitate the access of strong-motion data and metadata for use in post-earthquake response and for scientific and engineering research applications. The Center provides raw and processed strong-motion data via the Engineering Data Center (EDC) and the Virtual Data Center (VDC) web portals. This paper focuses on the strong-motion products provided by the EDC where more than 48,000 records with peak ground accelerations greater than 0.1% g from over 2400 earthquakes are currently hosted. and on the ongoing efforts to develop data access tools and applications. The new developments and ongoing efforts in the EDC include: 1) enhancements to the CESMD webservices to facilitate access to station metadata, earthquake information, and strong motion records 2) new features to the interactive map interface, improving the visualization and access to earthquake, station, and record information, 3) efforts to develop a new web application tool for data format conversion from a number of data formats, 4) efforts to unify varying waveform data formats into a consistent format, 5) ongoing efforts to compile seismic station site geology, measured or inferred Vs30 values, shear-wave profiles, NEHRP site class, and available structural instrument deployment schematics, and 6) a special studies pages for research topic-specific ground motion datasets that offer uniform processing of records from a variety of sources.

Conference Paper↗
USGS2024

Wildfire, extreme precipitation and debris flows, oh my! Channel response to compounding disturbances in a mountain stream in the Upper Colorado Basin, USA

Compounding changes in climate and human activities stand to increase sediment input to rivers in many landscapes, including via discrete perturbations such as post-fire debris flows. Because sediment supply is a dominant control on river morphology, understanding mountain river responses to sediment regime perturbations is critical to predicting and addressing downstream effects to infrastructure, water security and aquatic habitat. A growing body of literature explores the causes, likelihood, size and composition of post-fire debris flows, but the channel response to these disturbances remains poorly studied. This study used repeat field surveys, time-lapse photographs and pre- and post-disturbance remote sensing datasets to document and analyse space- and time-varying channel response to post-fire debris flows along a steep mountain stream in the Upper Colorado River Basin, USA. Specifically, we evaluated channel morphology and bed composition changes, correlations between channel changes and valley and channel attributes, and the relative importance of spring snowmelt versus summer monsoon events. Several cross-sectional channel change types were observed from lidar a month after post-fire debris-flow events, including channelized and braided incision into deposits, incision into the pre-fire channel bed, bank erosion and no change. Channel changes were most correlated with pre-fire channel width, valley width and unit stream power, and these relationships could be tested in other burned locations to evaluate their transferability. Repeat channel surveys before and after snowmelt indicate rapid recovery and channel narrowing following major sediment disturbances, although sediment deposits remained in the channel margins. Together, these results highlight the importance of field and remote sensing-based channel surveys to improve understanding of, and potential to predict, mountain channel response to compounding climate disturbances.

Earth Surface Processes and Landforms↗
USGS2025

Combining scanning electron microscopy, X-ray diffraction, and X-ray fluorescence to characterize shear zones at the Pogo gold deposit, Alaska

This study employs a multi-method analytical approach to characterize the mineralogical, geochemical, and textural properties of fault rocks from the Pogo gold mine in the Yukon-Tanana Upland, central Alaska. Specifically, we examine cataclasites, to document the structural and geochemical evolution of shear zones and their associations with gold mineralization. To investigate the shear zone, we integrate portable X-ray fluorescence (pXRF), scanning electron microscopy-based automated mineralogy (SEM-AM), X-ray diffraction (XRD), and high-resolution micro-X-ray fluorescence (micro-XRF) mapping. These methods collectively provide insights into bulk and trace element chemistry, mineralogical composition, and deformation-related textures across multiple scales. Handheld pXRF enables rapid geochemical screening, guiding SEM-AM and XRD analyses to ensure consistent mineralogical interpretation. X-ray diffraction identifies and quantifies crystalline phases, while SEM-AM produces high-resolution mineral maps, revealing mineral abundances, grain-scale textures, and gold associations. Micro-XRF mapping further refines our understanding by showing visual trace element distributions at sub-millimetre resolution. By integrating these techniques, we improve our understanding of the nature and geochemistry of Pogo shear zones, their role in gold mineralization, and support metallurgical processing strategies. This approach enhances exploration models and resource characterization for structurally complex gold deposits.

Alaska↗
USGS2024

Preparing for today's and tomorrow's water-resources challenges in eastern Long Island, New York

Freshwater is a vital natural resource. Although New York is a water-rich State, the wise and economical use of water resources is needed to ensure that there is enough water of adequate quality for both human and ecological needs—both for today and for tomorrow. Nowhere in New York is this more evident than in Nassau and Suffolk Counties on Long Island, where the public water supply is obtained from the sole-source aquifers located directly beneath the nearly 3 million people who live there. In 2023, in eastern Long Island’s Suffolk County, groundwater was pumped from these aquifers by more than 1,100 public water-supply wells to meet the needs of about 1.5 million people.

New York↗
USGS2024

GRAPES: Earthquake early warning by passing seismic vectors through the grapevine

Estimating an earthquake's magnitude and location may not be necessary to predict shaking in real time; instead, wavefield-based approaches predict shaking with few assumptions about the seismic source. Here, we introduce GRAph Prediction of Earthquake Shaking (GRAPES), a deep learning model trained to characterize and propagate earthquake shaking across a seismic network. We show that GRAPES’ internal activations, which we call “seismic vectors”, correspond to the arrival of distinct seismic phases. GRAPES builds upon recent deep learning models applied to earthquake early warning by allowing for continuous ground motion prediction with seismic networks of all sizes. While trained on earthquakes recorded in Japan, we show that GRAPES, without modification, outperforms the ShakeAlert earthquake early warning system on the 2019 M7.1 Ridgecrest, CA earthquake.

Shimane/HiroshimaPrefectures↗