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At least 1,747 records · Page 97Linked to original sources
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↗
USGS2025

An integrated sensor network and data driven approach to satellite remote sensing of dissolved organic matter

Traditional remote sensing retrieval models for water quality have historically relied on limited, localized data sets due to the prohibitive costs of extensive field campaigns and logistical challenges of collecting match-up data with satellite overpasses. As a result, these models often lack generalizability across seasons, tides, and sites. Furthermore, small field data sets limit the utility of modern machine learning techniques to advance remote sensing retrieval models. In situ optical sensors deployed in a sensor network to continuously monitor larger water bodies can drastically increase the number of measurements, providing the opportunity to develop new approaches for building robust remote sensing retrieval models by leveraging both remote sensing data and in situ networks as an integrated monitoring system. This study leverages a large “ground-to-space” sensor network that combines an in situ optical sensor network with satellite-based remote sensing to overcome these limitations. Utilizing a large-scale data set from the U.S. Geological Survey's Sacramento—San Joaquin River Delta monitoring network, of dissolved organic matter fluorescence measurements, and remote sensing data from the European Space Agency's Sentinel-2A and -2B satellites, this study implemented a data driven approach for dissolved organic matter models. The data set, consisting of 982 samples collected between 2018 and 2021 was used to train and validate a random forest model ( R 2 = 0.76, RMSE = 6.1 Quinine Sulfate Equivalents), with demonstrated applicability across diverse site conditions, tidal stages, and seasons. This work provides a scalable solution to address critical challenges in water quality monitoring and offers a replicable framework for global water quality management.

Calfornia↗
USGS2025

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

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

Colorado↗
USGS2025

Did they feel it? Legacy maroseismic data illuminates an engimatic 20th century earthquake

The challenges and the importance of preserving legacy instrumental records of earthquakes are now well-recognized (e.g., Richards & Hellweg, 2020, https://doi.org/10.1785/0220200053 ). Seismologists may not be aware of parallel challenges and opportunities with legacy macroseismic data for earthquakes in the United States. For much of the 20th century, macroseismic data were collected by a series of U.S. government agencies using a standard questionnaire distributed on postcards. Published summaries of postcards provide macroseismic data akin to modern Did You Feel It? questionnaire responses. In this paper we focus on the M 6.5 Fickle Hill, California earthquake, on 21 December 1954 (Hellweg et al., 2025) as a proof-of-concept, illustrating the potential of what we dub Did They Feel It? (DTFI) data to improve our understanding of significant 20th century U.S. earthquakes for which instrumental data are sparse. Legacy macroseismic data interpreted following modern conventions can potentially constrain traditional ShakeMaps at a level of detail and accuracy that in some respects rival maps for modern earthquakes. The updated ShakeMap for the 1954 Fickle Hill earthquake, also drawing from recently published media and first-person accounts, supports the location, depth, and stress drop value estimated from available instrumental data (Hellweg et al., 2025).

California↗
USGS2025

Effect of land cover type on 3D deformation recovery from synthetically deformed high-resolution satellite optical imagery

The limits of detection for earthquake surface deformation in the spatial domain have improved with advances in remote sensing imagery data availability, resolution, and analysis. Sub-pixel correlation and digital elevation model (DEM) differencing from sub-meter, earthquake-spanning satellite optical imagery has enhanced surface rupture mapping and deformation measurements. However, knowledge of measurement accuracy and uncertainty is limited. To address this, we construct orthophotos and digital elevation models (DEMs) from repeat high resolution (∼0.5 m) satellite optical imagery along two sections of the Garlock fault, California with clear fault geomorphology and differing land cover. We deform later sets of DEMs and images with synthetic earthquakes containing both diffuse and discrete horizontal and vertical displacements. Sub-pixel image correlation and DEM differencing demonstrate how vegetation degrades recovered displacement accuracy. In barren land cover, horizontal displacements are detectable to an expected ∼1/10th-pixel size. With shrubs, trees, and grass, detectable displacements increase to >1/2-pixel size, and filtering results by correlation score and using elevation values as input rather than image values improves accuracy. Vertical displacement detection thresholds remain lower in vegetation, at >1-pixel size. Higher slope angles degrade displacement recovery, worsened by vegetation. Diminishing seasonal separation improves accuracy over vegetated regions, though not to the level achieved in barren environments. These results will inform research and operational efforts on the utility of high resolution satellite optical imagery for detecting deformation in varied land cover. Furthermore, they reveal where alternative measurements, such as from LiDAR or radar interferometry, are required to mitigate the effects of vegetation and capture fine-scale crustal deformation.

California↗
USGS2026

Patterns and drivers of cliff erosion in Big Sur, California, USA using repeat photogrammetry, 2017–2023

Seacliff erosion in steep terrain poses major risks to transportation and critical infrastructure. In Big Sur, California, USA, seacliff erosion threatens the sustainability of the central coast stretch of California State Route 1, a transportation corridor that is critical to the region's economy. Published cliff retreat rates for the region range from 1 to 40 cm yr −1 , highlighting that high-resolution, process-based studies could enhance understanding of the causes of spatial and temporal variability. We quantified cliff erosion and investigated its drivers along ∼13 km of the Big Sur coastline at week–month timescales during the late fall to early spring wet seasons between January 2017 and June 2023 by analyzing 3D point clouds developed from aerial imagery using four-dimensional structure-from-motion (4D SfM) photogrammetry techniques. We calculated cliff face retreat rates of 2.23 ± 3.06 cm yr −1 (mean ±1 σ ), an order of magnitude lower than long-term estimated rates for the region (which included large deep-seated landslides), but in line with short-term rates reported across California. Change detection imagery comparison, cliff profiles through time, and statistical analysis reveal a cyclical cliff evolution process in which erosion by wave action at the cliff base destabilizes the cliff and primes it for subsequent failure during precipitation events. Although more erosion by volume could be attributed to precipitation-induced increases in soil moisture (784 m 3 km −1 yr −1 ) compared with erosion attributed to wave power (282 m 3 km −1 yr −1 ), our observations underscore the coupled nature of these processes in driving cliff evolution, consistent with established theory and observations.

California↗
USGS2026

Widespread landslide activity in an extreme wet season and implications for regional sediment management, eastern San Francisco Bay area, California

Watershed sediment production is expected to increase in a warmer future with more extreme rain, with cascading effects throughout drainage and sediment-transport networks. This study investigated landscape-scale sediment movement in the eastern San Francisco Bay area, California, USA, during the extreme 2016–2017 wet season that brought major rainfall, landslides, and flooding. Mapping 8,928 landslides across a 1,050-km 2 study area revealed new sediment yield of 510–956 t/km 2 , equivalent to denudation of 193–361 mm/ky. These results correspond closely to long-term denudation rates in the northern and central California Coast Ranges, indicating that mass wasting in very wet years dominates long-term sediment mobilization. However, due to long residence times in drainage networks, the 2017 landslides contributed at most ∼1%–2% of the estimated locally derived fluvial sediment transport to San Francisco Bay. Although the amount of sediment mobilized did not threaten municipal water supplies, small rangeland impoundments in this mixed-use landscape lost storage capacity to new sedimentation. Considering regional sediment supply and demand, even the exceptionally large sediment delivery in an extreme wet year cannot meet the need for sediment to accrete tidal wetlands in the bay. To keep pace with rising sea levels, this abnormally high terrestrial sediment input would need to occur in 50 of the next 75 years, an unlikely occurrence due to the prevalence of recent drought years. Shoreline protection and restoration in the bay would need additional sources of sediment, such as through management of dredged sediment through beneficial-reuse programs.

California↗
USGS2006

Continental crustal evolution observations

How has the continental crust evolved? What are the primary processes responsible for its composition, structure, and mode of deformation? What role do fluids play in deep crustal processes? In the last dozen years, geophysicists have obtained images of the deep continental crust that can be used to examine these questions and refine geologic models of crustal evolution. In this report we summarize recent progress in geophysical studies of the deep continental crust and highlight some of the more important implications of deep crustal processes.

Eos, Earth and Space Science News↗
USGS2006

Speculations on continental crustal evolution

The evolution of the continental crust is a topic that has challenged Earth scientists since the earliest hypotheses of crustal evolution were put forth by such luminaries as Hutton, the 18th century Scottish scientist, and later by Stille (Germany), Argand (France), and Dana (United States). Recent geophysical observations provide important constraints on hypotheses of crustal evolution, and the most important of these observations are reviewed in a companion paper [ Mooney and Meissner , 1991], henceforth referred to as Paper 1. In this article we briefly speculate on crustal evolution using both geological and geophysical data as guidelines. For the past 25 years, the basic framework for models of the evolution of the Earth's crust and lithosphere has been plate tectonics. This framework has been particularly successful in explaining the processes that form and modify the oceanic crust but has had somewhat more limited success in its application to the continental crust. Some of the basic tenets of plate tectonics, as applied to the continental crust, are listed below. Continental masses coalesce and disperse as ocean basins open and close in the Wilson cycle.

Eos, Earth and Space Science News↗
USGS2006

Deep seismic sounding in northern Eurasia

For nearly 40 years, the former Soviet Union has carried out an extensive program of seismic studies of the Earth's crust and upper mantle, known as “Deep Seismic Sounding” or DSS [ Piwinskii , 1979; Zverev and Kosminskaya , 1980; Egorkin and Pavlenkova , 1981; Egorkin and Chernyshov , 1983; Scheimer and Borg , 1985]. Beginning in 1939–1940 with a series of small-scale seismic experiments near Moscow, DSS profiling has broadened into a national multiinstitutional exploration effort that has completed almost 150,000 km of profiles covering all major geological provinces of northern Eurasia [Ryaboy, 1989].

Eos, Earth and Space Science News↗
USGS2006

Kenya Rift International Seismic Project, 1989–1990 experiment

Ever since Gregory's work at the turn of the century, the extensional structures extending through central Kenya have been recognized as the classic example of a continental rift zone. Because of the scale and variety of rift features present, this locality has fascinated geologists and geophysicists since its discovery.

Eos, Earth and Space Science News↗