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At least 91 records · Page 5Linked to original sources
USGS2020

Lessons from a post-eruption landscape

From March to May 1980, magma rose high into Mount St. Helens (MSH), swelling and—as it turned out—destabilizing its north flank. Scientists knew the volcano had been highly active at times over the past 40,000 years, but the mountain, located amid the Cascade Range in southwestern Washington, had been mostly quiet since the mid-19th century. The collapse of the north flank on 18 May shattered that quiet, triggering a cascade of events that left resounding impressions not only on those who witnessed and studied them but also on the surrounding landscape [Lipman and Mullineaux, 1981; Waitt, 2015]. The eruption of MSH also provided unparalleled opportunities for advancing several disciplines [e.g., Shore et al., 1986; Newhall, 2000; Franklin and MacMahon, 2000]. Although not as conspicuously as in volcanology, the eruption and its aftermath led to an intensification of research investigating biophysical impacts of eruptions and subsequent responses [e.g., Dale et al., 2005; Pierson and Major, 2014; Crisafulli and Dale, 2018]. Long-term research on the biophysical responses at MSH has provided important new insights, challenged long-standing ideas, and provided many societal benefits. The fortieth anniversary of the eruption this year offers a timely opportunity to reflect on these insights and influences. This long-term vantage is important because sustained, place-based studies following landscape disturbances are rare; because the MSH eruption spurred the greatest depth and breadth of multidisciplinary studies of biophysical responses to landscape disturbance; and because these responses created some of the most significant societal challenges to emerge after the eruption. We summarize key biophysical disturbances and responses, highlight salient insights, and suggest actions that can extend the usefulness of these insights to volcanically vulnerable communities worldwide.

Washington↗
USGS2021

Down to Earth with nuclear electromagnetic pulse: Realistic surface impedance affects mapping of the E3 geoelectric hazard

An analysis is made of Earth-surface geoelectric fields and voltages on electricity transmission power-grids induced by a late-phase E3 nuclear electromagnetic pulse (EMP). A hypothetical scenario is considered of an explosion of several hundred kilotons set several hundred kilometers above the eastern-midcontinental United States. Ground-level E3 geoelectric fields are estimated by convolving a standard parameterization of E3 geomagnetic field variation with magnetotelluric Earth-surface impedance tensors derived from wideband measurements acquired across the study region during a recent survey. These impedance tensors are a function of subsurface three-dimensional electrical conductivity structure. Results, presented as a movie-map, demonstrate that localized differences in surface impedance strongly distort the amplitude, polarization, and variational phase of induced E3 geoelectric fields. Locations with a high degree of E3 geoelectric polarization tend to have high geoelectric amplitude. Uniform half-space models and one-dimensional, depth-dependent models of Earth-surface impedance, such as those widely used in government and industry reports informing power-grid vulnerability assessment projects, do not provide accurate estimates of the E3 geoelectric hazard in complex geological settings. In particular, for the Eastern-Midcontinent, half-space models can lead to (order-one) overestimates/underestimates of EMP-induced geovoltages on parts of the power grid by as much as 1,000 volts (a range of 2,000 volts)—comparable to the amplitudes of the geovoltages themselves.

Earth and Space Sciences↗
USGS2021

Watershed sediment yield following the 2018 Carr Fire, Whiskeytown National Recreation Area, northern California

Wildfire risk has increased in recent decades over many regions, due to warming climate and other factors. Increased sediment export from recently burned landscapes can jeopardize downstream infrastructure and water resources, but physical landscape response to fire has not been quantified for some at-risk areas, including much of northern California, USA. We measured sediment yield from three watersheds (13–29 km 2 ) that drain to Whiskeytown Lake, California, within the area burned by the 2018 Carr Fire. Structure-from-Motion photogrammetry on aerial images combined with sonar bathymetric mapping of submerged areas indicated first-year post-fire sediment yields of 4,080 ± 598 t/km 2 (Brandy Creek), 2,700 ± 527 t/km 2 (Boulder Creek), and 305 ± 58.0 t/km 2 (Whiskey Creek)—some of the first post-fire yields measured in northern California and 64, 42, and 4.8 times greater than pre-fire yields, respectively. These were measured during a wet year and resulted largely from rilling erosion and fluvial sediment transport, without post-fire debris flows. Rilling preferentially developed in contact with dirt roads, aided by thin soils and exposed bedrock, and on slopes vegetated by chaparral pre-fire. The second post-fire year (a dry year) was characterized by fluvial reworking and delta progradation of the first-year deposits and relatively little new sediment export. First-year sedimentation of 111,000 m 3 represented minor loss of storage capacity in Whiskeytown Lake but would be detrimental to smaller reservoirs; in general, increased sediment yields from western US watersheds as fire and extreme rainfall increase will likely pose risks to water quality and storage.

California↗
USGS2021

Fitting jet noise similarity spectra to volcano infrasound data

Infrasound (low-frequency acoustic waves) has proven useful to detect and characterize subaerial volcanic activity, but understanding the infrasonic source during sustained eruptions is still an area of active research. Preliminary comparison between acoustic eruption spectra and the jet noise similarity spectra suggests that volcanoes can produce an infrasonic form of jet noise from turbulence. The jet noise similarity spectra, empirically derived from audible laboratory jets, consist of two noise sources: large-scale turbulence (LST) and fine-scale turbulence (FST). We fit the similarity spectra quantitatively to eruptions of Mount St. Helens in 2005, Tungurahua in 2006, and Kīlauea in 2018 using nonlinear least squares fitting. By fitting over a wide infrasonic frequency band (0.05–10 Hz) and restricting the peak frequency above 0.15 Hz, we observe a better fit during times of eruption versus non-eruptive background noise. Fitting smaller overlapping frequency bands highlights changes in the fit of LST and FST spectra, which aligns with observed changes in eruption dynamics. Our results indicate that future quantitative spectral fitting of eruption data will help identify changes in eruption source parameters such as velocity, jet diameter, and ash content which are critical for effective hazard monitoring and response.

Earth and Space Science↗
USGS2021

Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement

Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.

Earth and Space Science↗
USGS2021

Clustering supported classification of ChemCam data from Gale crater, Mars

The Chemistry and Camera (ChemCam) instrument on board the MSL rover Curiosity has collected a very large and unique dataset of in-situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot LIBS (laser-induced breakdown spectroscopy) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a dataset is ideally suited for the application of statistical methods for the recognition of patterns that are difficult to observe by humans. In this work, we develop an approach relying on the feature extraction method non-negative matrix factorization (NMF) and the repetition of k-means clustering to classify ChemCam spectra. A strong consistency of the clustering results among the repetitions were found, which allowed us to identify six clusters representing the dominant compositions measured by ChemCam in Gale crater so far. By tracking clusters across the rover traverse from landing to sol 2756, we are able to provide a chemostratigraphic overview of Gale crater from the ChemCam perspective. Transitions between major geologic groups (such as the Bradbury and the Mt. Sharp groups) are identifiable demonstrating that they are compositionally distinct, consistent with previous work. Compositional differences between their members also appear in the results. Furthermore, a first approach in which a random forest classifier was trained and validated with the obtained cluster assignments, reveals promising results for predicting cluster memberships of new ChemCam LIBS data acquired after sol 2756.

Earth and Space Science↗
USGS2021

Improving the usability of Galileo and Voyager images of Jupiter’s moon, Europa

NASA's Voyager 1, Voyager 2, and Galileo spacecraft acquired hundreds of images of Jupiter's moon Europa. These images provide the only moderate- to high-resolution views of the moon's surface and are therefore a critical resource for scientific analysis and future mission planning. Unfortunately, uncertain knowledge of the spacecraft's position and pointing during image acquisition resulted in significant errors in the location of the images on the surface. The result is that adjacent images are poorly aligned, with some images displaced by more than 100 km from their correct location. These errors severely degrade the usability of the Voyager and Galileo imaging data sets. To improve the usability of these data sets, we used the U.S. Geological Survey Integrated Software for Imagers and Spectrometers to build a nearly global image tie-point network with more than 50,000 tie points and 135,000 image measurements on 481 Galileo and 221 Voyager images. A global least-squares bundle adjustment of our final Europa tie-point network calculated latitude, longitude, and radius values for each point by minimizing residuals globally, and resulted in root mean square (RMS) uncertainties of 246.6 m, 307.0 m, and 70.5 m in latitude, longitude, and radius, respectively. The total RMS uncertainty was 0.32 pixels. This work enables direct use of nearly the entire Galileo and Voyager image data sets for Europa. We are providing the community with updated NASA Navigation and Ancillary Information Facility Spacecraft, Planet, Instrument, C-matrix (pointing), and Events kernels, mosaics of Galileo images acquired during each observation sequence, and individual processed and projected level 2 images.

Earth and Space Science↗
USGS2022

Temperatures of the Lacus Mortis region of the Moon

Over 11 years of data acquired by the Diviner Lunar Radiometer Experiment instrument aboard Lunar Reconnaissance Orbiter have been compiled into a comprehensive data set of surface temperatures in the Lacus Mortis region which includes the landing ellipse of the Astrobotic Mission One lander mission. These data provide diurnal brightness temperatures at 128 pixels per degree (ppd) spatial resolution and 0.1 hr of local time resolution. From this data set, we highlight several features that display variations in radiative and thermophysical properties in the Lacus Mortis region and characterize the temperatures of the Astrobotic Mission One landing ellipse. We identify distinctly contrasting properties of materials in the walls of Bürg crater, hummocks of materials on the southeast margin of the mare basalts, and materials exposed or excavated by impacts. Additionally, we describe an exceptionally rocky fault scarp that predates the formation of Bürg crater suggesting the observed boulders are replenished on a timescale <1 Ga. Within the Astrobotic landing ellipse, temperatures are observed to range from ∼88 to ∼359 K with sunrise and sunset local times constrained to 5.8–6.3 hr and 17.8 and 18.1 hr respectively.

Earth and Space Science↗
USGS2022

Integrating Bayesian networks to forecast sea-level rise impacts on barrier island characteristics and habitat availability

Evaluation of sea-level rise (SLR) impacts on coastal landforms and habitats is a persistent need for informing coastal planning and management, including policy decisions, particularly those that balance human interests and habitat protection throughout the coastal zone. Bayesian networks (BNs) are used to model barrier island change under different SLR scenarios that are relevant to management and policy decisions. BNs utilized here include a shoreline change model and two models of barrier island biogeomorphological evolution at different scales (50 and 5 m). These BNs were then linked to another BN to predict habitat availability for piping plovers ( Charadrius melodus ), a threatened shorebird reliant on beach habitats. We evaluated the performance of the two linked geomorphology BNs and further examined error rates by generating hindcasts of barrier island geomorphology and habitat availability for 2014 conditions. Geomorphology hindcasts revealed that model error declined with a greater number of known inputs, with error rates reaching 55% when multiple outputs were hindcast simultaneously. We also found that, although error in predictions of piping plover nest presence/absence increased when outputs from the geomorphology BNs were used as inputs in the piping plover habitat BN, the maximum error rate for piping plover habitat suitability in the fully-linked BNs was only 30%. Our findings suggest this approach may be useful for guiding scenario-based evaluations where known inputs can be used to constrain variables that produce higher uncertainty for morphological predictions. Overall, the approach demonstrates a way to assimilate data and model structures with uncertainty to produce forecasts to inform coastal planning and management.

New York↗
USGS2022

Regional-scale mapping of landscape response to extreme precipitation using repeat lidar and object-based image analysis

Extreme precipitation events may cause flooding, slope failure, erosion, deposition, and damage to infrastructure over a regional scale, but the impacts of these events are often difficult to fully characterize. Regional-scale landscape change occurred during an extreme rain event in June 2012 in northeastern Minnesota. Landscape change was documented by 8,000 km 2 of airborne lidar data collected before and after the event. Following improved alignment of the lidar point data and reducing error using insight from analysis of extensive stable areas, elevation differences were classified into map objects representing geomorphic change in relation to process and landscape position using object-based image analysis. This remote mapping compares favorably to field and imagery-based mapping and provides the basis for volumetric sediment budgeting. Elevation differences in these objects indicate that 4.5 × 10 6 ± 1.0 × 10 6 m 3 of sediment was eroded in the study area. Of this, 2.5 × 10 6 ± 3.3 × 10 5 m 3 was deposited in deposits on hillslopes and valley floors, and 2.0 × 10 6 ± 4.6 × 10 5 m 3 were removed from watersheds and exported to the Saint Louis River Estuary and Lake Superior. Multivariate logistic regression analysis emphasized that topographic slope and presence of glaciolacustrine clay lithology are the primary control on landslide occurrence, and landslides occur most frequently on slopes within tens of meters of stream channels. These results provide the basis to anticipate the impacts of similar future storm events. Because precipitation events are forecast to continue to increase in frequency and intensity owing to climate change, characterizing and anticipating their effects may support hazard planning.

Minnesota↗
USGS2023

Simulating debris flow and levee formation in the 2D shallow flow model D-Claw: Channelized and unconfined flow

Debris flow runout poses a hazard to life and infrastructure. The expansion of human population into mountainous areas and onto alluvial fans increases the need to predict and mitigate debris flow runout hazards. Debris flows on unconfined alluvial fans can exhibit spontaneous self-channelization through levee formation that reduces lateral spreading and extends runout distances compared to unchannelized flows. Here we modify the D-Claw shallow flow model in two ways that are hypothesized to generate levees. We evaluate these modifications with observations from a large-scale flume experiment. We investigate model performance when including the effect of two different friction sub-models, as well as the inclusion of segregation effects on granular permeability. Results show that, for a wide range of plausible model input parameters, simulations including the effects of segregation promoted modeled levee formation, whereas simulations without the effects of segregation did not create levees. Further, using a forward predictive framework, simulations with the effects of segregation were more likely to better model the magnitude of debris flow depth and runout distance, whereas simulation timing of the debris flow was affected by the choice of friction sub-model. Our results indicate that including the effects of segregation on granular permeability can improve the likelihood of better predictions of debris flow depth and runout prior to an event occurring.

Earth and Space Science↗
USGS2023

Exploring the influence of input feature space on CNN-based geomorphic feature extraction from digital terrain data

Many studies of Earth surface processes and landscape evolution rely on having accurate and extensive data sets of surficial geologic units and landforms. Automated extraction of geomorphic features using deep learning provides an objective way to consistently map landforms over large spatial extents. However, there is no consensus on the optimal input feature space for such analyses. We explore the impact of input feature space for extracting geomorphic features from land surface parameters (LSPs) derived from digital terrain models (DTMs) using convolutional neural network (CNN)-based semantic segmentation deep learning. We compare four input feature space configurations: (a) a three-layer composite consisting of a topographic position index (TPI) calculated using a 50 m radius circular window, square root of topographic slope, and TPI calculated using an annulus with a 2 m inner radius and 10 m outer radius, (b) a single illuminating position hillshade, (c) a multidirectional hillshade, and (d) a slopeshade. We test each feature space input using three deep learning algorithms and four use cases: two with natural features and two with anthropogenic features. The three-layer composite generally provided lower overall losses for the training samples, a higher F1-score for the withheld validation data, and better performance for generalizing to withheld testing data from a new geographic extent. Results suggest that CNN-based deep learning for mapping geomorphic features or landforms from LSPs is sensitive to input feature space. Given the large number of LSPs that can be derived from DTM data and the variety of geomorphic mapping tasks that can be undertaken using CNN-based methods, we argue that additional research focused on feature space considerations is needed and suggest future research directions. We also suggest that the three-layer composite implemented here can offer better performance in comparison to using hillshades or other common terrain visualization surfaces and is, thus, worth considering for different mapping and feature extraction tasks.

Earth and Space Science↗
USGS2024

A novel surface energy balance method for thermal inertia studies of terrestrial analogs

Surface thermal inertia derived from satellite imagery offers a valuable tool for remotely mapping the physical structure and water content of planetary regolith. Efforts to quantify thermal inertia using surface temperatures on Earth, however, have consistently yielded large uncertainties and suffered from a lack of reproducibility. Unlike dry or airless bodies, Earth's abundant water and dense atmosphere lead to dynamic thermophysical conditions that are a greater challenge to model than on a world like Mars. In this work, an approach was developed using field experiments to inform and fine-tune a thermophysical model of terrestrial sediment and calculate an inherent thermal inertia value with higher precision and less initial knowledge of the sediment than has previously been achieved remotely on Earth. A thermal inertia derived for a basaltic tephra site in Northern Arizona was replicated within 1% between different field seasons, demonstrating reproducibility. Model-derived values were validated in situ by two different thermophysical field probes to within 8% of the measured mean values. Analog studies such as this hold the promise of improved interpretations of surface materials on Mars, and an accurate thermal model for Earth is the key step to enabling translation between the two worlds.

Earth and Space Science↗
USGS2024

Remote sensing large-wood storage downstream of reservoirs during and after dam removal: Elwha River, Washington, USA

Large wood is an integral part of many rivers, often defining river-corridor morphology and habitat, but its occurrence, magnitude, and evolution in a river system are much less well understood than the sedimentary and hydraulic components, and due to methodological limitations, have seldom previously been mapped in substantial detail. We present a new method for this, representing a substantial advance in automated deep-learning-based image segmentation. From these maps, we measured large wood and sediment deposits from high-resolution orthoimages to explore the dynamics of large wood in two reaches of the Elwha River, Washington, USA, between 2012 and 2017 as it adjusted to upstream dam removals. The data set consists of a time series of orthoimages (12.5-cm resolution) constructed using Structure-from-Motion photogrammetry on imagery from 14 aerial surveys. Model training was optimized to yield maximum accuracy for estimated wood areas, compared to manually digitized wood, therefore model development and intended application were coupled. These fully reproducible methods and model resulted in a maximum of 15% error between observed and estimated total wood areas and wood deposit size-distributions over the full spatio-temporal extent of the data. Areal extent of wood in the channel margin approximately doubled in the years following dam removal, with greatest increases in large wood in wider, lower-gradient sections. Large-wood deposition increased between the start of dam removal (2011) and winter 2013, then plateaued. Sediment bars continued to grow up until 2016/17, assisted by a partially static wood framework deposited predominantly during the period up to winter 2013.

Washington↗
USGS2024

Post-fire sediment yield from a central California watershed: Field measurements and validation of the WEPP model

In a warming climate, an intensifying fire regime and higher likelihood of extreme rain are expected to increase watershed sediment yield in many regions. Understanding regional variability in landscape response to fire and post-fire rainfall is essential for managing water resources and infrastructure. We measured sediment yield resulting from sequential wildfire and extreme rain and flooding in the upper Carmel River watershed (116 km 2 ), on the central California coast, USA, using changes in sediment volume mapped in a reservoir. We determined that the sediment yield after fire and post-fire flooding was 854–1,100 t/km 2 /yr, a factor of 3.5–4.6 greater than the long-term yield from this watershed and more than an order of magnitude greater than during severe drought conditions. In this first large-scale field validation test of the WEPPcloud/ wepppy framework for the Water Erosion Prediction Project (WEPP) model on a burned landscape, WEPP predicted 81%–106% of the measured sediment yield. These findings will facilitate assessing and predicting future fire effects in steep watersheds with a Mediterranean climate and indicate that the increasingly widespread use of WEPP is appropriate for evaluating post-fire hillslope erosion even across 100-km 2 scales under conditions without debris flows.

California↗
USGS2025

Post-wildfire sediment fluxes and turbidity plumes in a coastal-draining watershed

Coastal watersheds impacted by wildfires experience higher erosion resulting in increased sediment delivery to the ocean that alters limiting factors (i.e., light) for marine organisms. With increasing wildfire magnitude and severity, it is critical to explore changes in riverine discharges to the ocean to assess cascading hazards associated with wildfires. In situ data, remotely sensed turbidity data, and hydrological model (Soil and Water Assessment Tool “SWAT”) simulations have been adapted to capture and investigate fire-related land use change impacts on Malibu Creek, California, USA. Modifying SWAT land cover inputs using burn severity data had minimal impact on simulations, requiring additional parameterization for acceptable model performance. Remotely sensed turbidity, in situ discharge, rating curve sediment loads, and SWAT simulated discharge and sediment loads increased following the Woolsey Fire. When compared to in situ and rating curve data in similar non-fire water years, the 2019 Woolsey Fire water year in situ discharge was 1.8 times higher, SWAT simulated discharges were 1.4–1.7 times higher, and rating curve sediment load was 1.3 times higher. However, the SWAT simulated sediment loads were slightly lower (0.8–0.9 times) than rating curve sediment loads in similar non-fire water years. Mean coastal turbidity increased to 18.2 Formazin Nephelometric Unit (FNU) during the first storm post-fire (mean background value of 4.3 FNU). Synergies between methods demonstrated rapid coastal sediment exports (remote sensing) and ongoing erosion post-fire (SWAT). These data are essential to understanding fire-related marine ecological changes and implementing effective management and conservation initiatives.

California↗
USGS2025

Post-fire sediment yield from a western Sierra Nevada watershed burned by the 2021 Caldor Fire

Watershed sediment yield commonly increases after wildfire, often causing negative impacts to downstream infrastructure and water resources. Post-fire erosion is important to understand and quantify because it is increasingly placing water supplies, habitat, communities, and infrastructure at risk as fire regimes intensify in a warming climate. However, measurements of post-fire sediment mobilization are lacking from many regions. We measured sediment yield from a forested, heavily managed 25.4-km 2 watershed in the western Sierra Nevada, California, over 2 years following the 2021 Caldor Fire, by repeat mapping of a reservoir where sediment accumulated from terrain with moderate to high soil burn severity. Sediment yield was less than the geochronology-derived long-term average in the first year post-fire (conservatively estimated at 21.8–28.0 t/km 2 ), low enough to be difficult to measure with uncrewed airborne system (UAS) and bathymetric sonar survey methods that are most effective at detecting larger sedimentary signals. In the second year post-fire the sediment delivery was 1,560–2,010 t/km 2 , an order of magnitude above long-term values, attributable to greater precipitation and intensive salvage logging. Hillslope erosion simulated by the Water Erosion Prediction Project (WEPP) model overestimated the measured amount by a factor of 90 in the first year and in the second year by a factor (1.9) that aligned with previously determined model performance in northern California. We encourage additional field studies, and validation of erosion models where feasible, to further expand the range of conditions informing post-fire hazard assessments and management decisions.

California↗