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Results for “International Journal of Critical Infrastructure Protection”

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1,661 records · Page 89Linked to original sources
USGS2025

Permafrost–wildfire interactions: active layer thickness estimates for paired burned and unburned sites in northern high latitudes

As the northern high-latitude permafrost zone experiences accelerated warming, permafrost has become vulnerable to widespread thaw. Simultaneously, wildfire activity across northern boreal forest and Arctic/subarctic tundra regions impacts permafrost stability through the combustion of insulating organic matter, vegetation, and post-fire changes in albedo. Efforts to synthesis the impacts of wildfire on permafrost are limited and are typically reliant on antecedent pre-fire conditions. To address this, we created the FireALT dataset by soliciting data contributions that included thaw depth measurements, site conditions, and fire event details with paired measurements at environmentally comparable burned and unburned sites. The solicitation resulted in 52 466 thaw depth measurements from 18 contributors across North America and Russia. Because thaw depths were taken at various times throughout the thawing season, we also estimated end-of-season active layer thickness (ALT) for each measurement using a modified version of the Stefan equation. Here, we describe our methods for collecting and quality-checking the data, estimating ALT, the data structure, strengths and limitations, and future research opportunities. The final dataset includes 48 669 ALT estimates with 32 attributes across 9446 plots and 157 burned–unburned pairs spanning Canada, Russia, and the United States. The data span fire events from 1900 to 2022 with measurements collected from 2001 to 2023. The time since fire ranges from 0 to 114 years. The FireALT dataset addresses a key challenge: the ability to assess impacts of wildfire on ALT when measurements are taken at various times throughout the thaw season depending on the time of field campaigns (typically June through August) by estimating ALT at the end-of-season maximum. This dataset can be used to address understudied research areas, particularly algorithm development, calibration, and validation for evolving process-based models as well as extrapolating across space and time, which could elucidate permafrost–wildfire interactions under accelerated warming across the high-northern-latitude permafrost zone. The FireALT dataset is available through the Arctic Data Center ( https://doi.org/10.18739/A2RN3092P , Talucci et al., 2024).

Arctic↗
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

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↗
USGS2025

Tracing mercury from land to river: Global sources, retention, and implications for sustainability

Mercury (Hg) pollution in river systems is a global sustainability challenge. Yet the transport, transformation, and retention of Hg within global rivers remain poorly quantified, particularly in regions with sparse observations such as Southeast Asia and Africa, hindering effective pollution mitigation and reinforcing geographic inequities in scientific knowledge and environmental governance. Here, we present the first global, high-resolution simulation of riverine Hg dynamics using a process-based model that traces Hg from land-based sources through river networks to the ocean. Under a realistic scenario, we estimate that ~1,900 megagrams per year (Mg/yr) of Hg enters global rivers, including 1,500 Mg/yr from human-induced sources and 400 Mg/yr from soil erosion. Nearly half of this flux (~1,000 Mg/yr) is retained in reservoirs and dams, which act as major sinks. While such retention limits downstream delivery to the oceans, it also heightens in-reservoir Hg methylation risks. By bridging the gap between Hg releases and observed riverine exports, our framework offers a scalable tool for data-limited regions, promotes data access, and supports global freshwater and pollution-management strategies.

EarthArXiv↗
USGS2026

Tracing mercury from land to river: Global sources, retention, and implications for sustainability

Mercury (Hg) pollution in river systems is a global sustainability challenge. Yet the transport, transformation, and retention of Hg within global rivers remain poorly quantified, particularly in regions with sparse observations such as Southeast Asia and Africa, hindering effective pollution mitigation and reinforcing geographic inequities in scientific knowledge and environmental governance. Here, we present the first global, high-resolution simulation of riverine Hg dynamics using a process-based model that traces Hg from land-based sources through river networks to the ocean. Under a realistic scenario, we estimate that ∼1900 megagrams per year (Mg/yr) of Hg enters global rivers, including 1500 Mg/yr from human-induced sources and 400 Mg/yr from soil erosion. Nearly half of this flux (∼1000 Mg/yr) is retained in reservoirs and dams, which act as major sinks. While such retention limits downstream delivery to the oceans, it also heightens in-reservoir Hg methylation risks. By bridging the gap between Hg releases and observed riverine exports, our framework offers a scalable tool for data-limited regions, promotes data access, and supports global freshwater and pollution-management strategies.

Environmental Science & Technology↗
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↗
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↗
USGS2026

Incorporating location uncertainty improves inference with stop-level North American Breeding Bird Survey data

Ecological models should account for uncertainty to be most effective and useful. Yet, uncertainty from model covariates—unlike that from other sources, such as sampling error or process variability—is seldom explicitly incorporated. This can cause underestimates of uncertainty to cascade through model parameter estimates, predictions, and downstream uses. Burner et al. proposed a method for quantifying uncertainty in covariates and incorporating it into models using informative Bayesian priors. This method was applied to stop-level Breeding Bird Survey (BBS) analyses, where land cover uncertainty at each stop arises from substantial stop location uncertainty. A limited validation of model-estimated land cover, using stops with known locations, indicated the method’s potential effectiveness, but it was not rigorously evaluated. We conduct a robust simulation-based test, generating stop locations, extracting land cover, and simulating bird communities across 210 BBS routes in the upper Midwest. We compare 3 models: a “known” model with true land cover, a “naive” model assuming consistent 800-m stop spacing, and a “full” model using informative priors to estimate land cover. Species parameter estimates and predicted prevalence patterns across gradients in land cover from the full model approached those of the known model and were substantially closer to the true values used in simulations relative to those from the naive model. Naive model parameters were more biased relative to the other models, and credible intervals of predicted species prevalence rarely included the true simulated values. The full model also produced land cover covariate estimates closer to true simulation values relative to the mean informative priors. Our results show that, for the BBS, informative priors enable more accurate stop-level analyses despite location uncertainty. In contrast, naive models that ignore this uncertainty yield poor inferences. More broadly, we demonstrate empirically the utility of informative priors to account for covariate uncertainty in ecological models.

Michigan, Minnesota, Wisconson↗
USGS2024

Groundwater flow model for the Des Moines River alluvial aquifer near Des Moines, Iowa

Des Moines Water Works (DMWW) is a regional municipal water utility that provides residential and commercial water resources to about 600,000 customers in Des Moines, Iowa, and surrounding municipalities in central Iowa. DMWW has identified a need for increased water supply and is exploring the potential for expanding groundwater production capabilities in the Des Moines River alluvial aquifer, where it operates two radial collector wells (RCWs). The U.S. Geological Survey, in cooperation with DMWW, completed a study of the Des Moines River alluvial aquifer and interactions of the RCWs with the aquifer; no previously published model has included the existing well locations, which is the focus of this model. A conceptual and numerical groundwater flow model have been developed to characterize the Des Moines River alluvial aquifer under existing conditions, to simulate water levels observed in the RCWs, and to provide publicly accessible hydrologic data and research that advance understanding of the regional hydrologic system and can potentially be used in the future to evaluate groundwater production scenarios. Model performance was assessed by comparing observed and simulated groundwater levels that included water level elevations, water level changes, water level inequality observations, surface water streamflow, and change in surface water volume from upstream to downstream. Water table elevation in the aquifer layers is on average slightly overestimated with average absolute value error less than 1.5 meters at both RCWs and less than 2.5 meters for all observation wells in the alluvial aquifer layers. The model also accurately simulated water tables greater than the RCW design minimum (a water level threshold at which RCW pumping is reduced) in all timesteps for which water level observation data existed. Water table elevation error was higher in other model layers that were not the focus of the study, and the model did not accurately match streamflow targets.

Iowa↗
USGS2024

Simulating present and future groundwater/surface-water interactions and stream temperatures in Beaver Creek, Kenai Peninsula, Alaska

In many places, coldwater ecosystems are facing increasing pressure from anthropogenic warming. This study examined stream temperatures and the water balance in the Beaver Creek watershed on the Kenai Peninsula in south-central Alaska—an area that is experiencing rapid warming. Low-gradient streams near the Kenai coast provide important spawning and rearing habitat for salmon but may be especially vulnerable to rising temperatures, because of long residence times, inflows from abundant riparian wetlands, and reliance on groundwater discharge that may also warm, or decrease in volume with rising evapotranspiration. In recent decades, observed maximum 7-day temperatures have consistently exceeded statistical (regression-based) projections. Here we simulate total streamflows and temperatures with a physics-based model that links the Soil Water Balance, MODFLOW 6 and SNTEMP simulation codes on a 7-day timestep. The model is based on existing data and groundwater levels, instream flows, and stream temperatures collected during 2019–23. Future climate scenarios were developed for 2023–50 from downscaled climate projections. Results indicate that groundwater discharge is about 64 percent of the total streamflow during the months of May through September. Total streamflow and groundwater discharge are expected to remain similar to current conditions through 2050. Stream temperatures are expected to rise; by midcentury, near the Beaver Creek mouth the model predicts 34 to 63 additional days per year with average weekly temperatures above 13 degrees Celsius, 14 to 81 additional days with average weekly temperatures above 15 degrees Celsius, and routine exceedances of 20 degrees Celsius during the warmest periods. Projected stream temperatures vary spatially. Areas of high groundwater inflows in the lower main stem and some tributaries may be most resilient to warming air temperatures during dry conditions. During storm events, groundwater-dominated tributaries may have the coolest stream temperatures.

Alaska↗
USGS2024

Field observations and logs from the Rose Hip trench exposure across a north-facing scarp within the Seattle Fault Zone, southern Bainbridge Island, Washington

The Seattle Fault Zone is an approximately 70-km-long, east-west-trending zone of south-dipping blind reverse faults within the Puget lowland region in Washington. Because of the proximity, the Seattle Fault Zone poses a significant earthquake hazard to the Puget sound and Seattle metropolitan regions. We present preliminary mapping and trench-site information from a paleoseismic investigation across a newly identified active fault scarp located within the hanging wall of the Seattle Fault Zone on southern Bainbridge Island, Washington. The trench exposed monoclinally folded Miocene bedrock, fractured and faulted glacial-related deposits, and laminated lacustrine deposits capped by slope-derived colluvium. The observations from this investigation record late Pleistocene to Holocene north-vergent folding and faulting along this new fault scarp.

Washington↗