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At least 1,009 records · Page 56Linked to original sources
USGS2021

Robust earthquake early warning at a fraction of the cost: ASTUTI Costa Rica

We show that a fixed smartphone network can provide robust Earthquake Early Warning for at least two orders of magnitude less cost than scientific-grade networks. Our software and cloud-based data architecture that we have constructed for the Alerta Sismica Temprana Utilizando Teléfonos Inteligentes (ASTUTI; Earthquake Early Warning Utilizing Smartphones) network in Costa Rica is easily scaled and exported. Implementation comprises provisioning and installing modern smartphones in judicious locations. Stand-up time for regionally operational networks can be on the order of days. We evaluated a non-parametric ground-motion detection and alerting strategy that would alert the entire Costa Rican population of any event with a ground motion detection threshold of 0.55–0.65 %g at four neighboring stations. During a 6-month evaluation period ASTUTI detected and alerted on five of 13 earthquakes with M w 4.8–5.3 that caused felt Modified Mercalli Intensity shaking levels of 4.3–6. The system did not produce any false alerts and the undetected events did not produce wide-spread or significant felt shaking. System latencies were less than or similar to scientific-grade latencies. Alerts for all five detected events would have reached the capital city, San Jose, before strong S -wave shaking. This would have afforded time for Drop Cover Hold On actions by most residents. Two of the five alerts were triggered by P -waves suggesting that smartphone-based networks could approach the fastest theoretical EEW performance, especially with future expected improvements in smartphone sensors and processing algorithms.

AGU Advances↗
USGS2021

Projecting climate dependent coastal flood risk with a hybrid statistical dynamical model

Numerical models for tides, storm surge, and wave runup have demonstrated ability to accurately define spatially varying flood surfaces. However these models are typically too computationally expensive to dynamically simulate the full parameter space of future oceanographic, atmospheric, and hydrologic conditions that will constructively compound in the nearshore to cause both extreme event and nuisance flooding during the 21st century. A surrogate modeling framework of waves, winds, and tides is developed in this study to efficiently predict spatially varying nearshore and estuarine water levels contingent on any combination of offshore forcing conditions. The surrogate models are coupled with a time-dependent stochastic climate emulator that provides efficient downscaling for hypothetical iterations of offshore conditions. Together, the hybrid statistical-dynamical framework can assess present day and future coastal flood risk, including the chronological characteristics of individual flood and wave-induced dune overtopping events and their changes into the future. The framework is demonstrated at Naval Base Coronado in San Diego, CA, utilizing the regional Coastal Storm Modeling System (CoSMoS; composed of Delft3D and XBeach) as the dynamic simulator and Gaussian process regression as the surrogate modeling tool. Validation of the framework uses both in-situ tide gauge observations within San Diego Bay, and a nearshore cross-shore array deployment of pressure sensors in the open beach surf zone. The framework reveals the relative influence of large-scale climate variability on future coastal flood resilience metrics relevant to the management of an open coast artificial berm, as well as the stochastic nature of future total water levels.

California↗
USGS2022

Synthetic evaluation of infrasonic multipole waveform inversion

Acoustic source inversions estimate the mass flow rate of volcanic explosions or yield of chemical explosions and provide insight into potential source directionality. However, the limitations of applying these methods to complex sources and their ability to resolve a stable solution have not been investigated in detail. We perform synthetic infrasound waveform inversions that use 3-D Green’s functions for a variety of idealized and realistic deployment scenarios using both a flat plane and Yasur volcano, Vanuatu as examples. We investigate the ability of various scenarios to retrieve the input source functions and relative amplitudes for monopole and multipole (monopole and dipole) inversions. Infrasound waveform inversions appear to be a robust method to quantify mass flow rates from simple sources (monopole) using deployments of infrasound sensors placed around a source, but care should be taken when analyzing and interpreting results from more complex acoustic sources (multipole) that have significant directional components. In the examples we consider the solution is stable for monopole inversions with a signal-to-noise ratio greater than five and the dipole component is small. For most scenarios investigated, the vertical dipole component of the multipole explosion source is poorly constrained and can impact the ability to recover the other source term components. Because multipole inversions are ill-posed for many deployments, a low residual does not necessarily mean the proper source vector has been recovered. Synthetic studies can help investigate the limitations and place bounds on information that may be missing using monopole and multipole inversions for potentially directional sources.

Journal of Geophysical Research-Solid Earth↗
USGS2021

Dissolved Fe supply to the central Gulf of Alaska is inferred to be derived from Alaskan glacial dust that is not resolved by dust transport models

Re-examination of previously published dissolved iron time-series data from Ocean Station Papa in the central Gulf of Alaska (GoA) reveals 33-70% increases in the dissolved iron inventories occurring between September and February of successive years, implying a source of Fe to this region during autumn or early winter. Because I can virtually rule out many possible iron sources at this time of year, I suggest Alaskan glacial dust is the likely iron source. Large plumes of such dust are known to be generated regularly in the autumn by anomalous offshore winds and channelled through mountain gaps, simultaneously from several locations spanning ∼1000 km of the northern Gulf of Alaska coastline. Large dust flux events occur when below-freezing, low-humidity air temperatures persist for many days during the autumn. I suggest that existing state-of-the-art global dust models fail to reproduce this Alaskan dust flux because the model spatial resolution is too coarse to resolve the high winds through the narrow mountain gaps that generate the dust. Future work that could help to confirm this Fe source to the central GoA includes time-series profiles of iron concentrations, and ancillary information from sensor-equipped profiling floats. If this mechanism of Fe supply to the central GoA were confirmed, it would imply this Alaskan dust is transported ≥ 1100 km from the coast, more than twice as far as has been visually documented from satellite observations.

Alaska↗
USGS2022

Atmospheric river storm flooding influences tidal marsh elevation building processes

Disturbances are a key component of ecological processes in coastal ecosystems. Investigating factors that affect tidal marsh accretion and elevation change is important, largely due to accelerating sea-level rise and the ecological and economic value of wetlands. Sediment accumulation rates, elevation change, and flooding were examined at five marshes along a riverine-tidal gradient in the northern San Francisco Bay-Delta, California, USA during an Atmospheric River storm event in 2017 using Surface Elevation Tables (SETs), feldspar marker horizons (MH), and continuous water-level sensors. Our results showed that localized marsh flooding increased during the storm event, but not evenly across sites. Marsh surface elevation increased the most at the tidal freshwater marsh site in response to the storms, with an average surface elevation gain of 45.6 ± 13.1 mm, and the least at a tidal saline marsh with an average surface elevation gain of 4.0 ± 1.2 mm. A marsh located on the large embayment did not exhibit an immediate response to the storm but had a surface elevation gain of 21.5 ± 13.7 mm 6 months after the storm. During the storm period, marsh distance to the bay was the strongest predictor of elevation change, followed by SET-MH elevations. Conversely, during non-storm periods, SET-MH elevation was a relatively strong predictor of elevation change. Atmospheric Rivers appear to be a major factor affecting short-term spatial and temporal variability in flooding and sedimentation rates in tidal marsh systems. Incorporating information about storms into monitoring could increase our understanding of how episodic storms can impact marshes.

Journal of Geophysical Research: Biogeosciences↗
USGS2023

Lightning rings and gravity waves: Insights into the giant eruption plumefrom Tonga’s Hunga Volcano on 15 January 2022

On 15 January 2022, Hunga Volcano in Tonga produced the most violent eruption in the modern satellite era, sending a water-rich plume at least 58 km high. Using a combination of satellite- and ground-based sensors, we investigate the astonishing rate of volcanic lightning (>2,600 flashes min −1 ) and what it reveals about the dynamics of the submarine eruption. In map view, lightning locations form radially expanding rings. We show that the initial lightning ring is co-located with an internal gravity wave traveling >80 m s −1 in the stratospheric umbrella cloud. Buoyant oscillations of the plume's overshooting top generated the gravity waves, which enhanced turbulent particle interactions and triggered high-current electrical discharges at unusually high altitudes. Our analysis attributes the intense lightning activity to an exceptional mass eruption rate (>5 × 10 9 kg s −1 ), rapidly expanding umbrella cloud, and entrainment of abundant seawater vaporized from magma-water interaction at the submarine vent.

Hunga Volcano↗
USGS2023

River geomorphology affects biogeochemical responses to hydrologic events in a large river ecosystem

Shifts in the frequency and intensity of high discharge events due to climate change may have important consequences for the hydrology and biogeochemistry of rivers. However, our understanding of event-scale biogeochemical dynamics in large rivers lags that of small streams. To fill this gap, we used high-frequency sensor data collected during four consecutive summers from a main channel and backwater site of the Upper Mississippi River. We identified high discharge events and calculated event concentration-discharge responses for both physical-chemical (nitrate, turbidity, and fluorescent dissolved organic matter) and biological (chlorophyll-a and cyanobacteria) constituents using metrics of hysteresis and slope. We found a range of responses across events, particularly for nitrate. Although fluorescent dissolved organic matter (FDOM) and turbidity exhibited more consistent responses across events, contrasting hysteresis metrics indicated that FDOM was flushed to the river from more distant sources than turbidity. Biological responses (chlorophyll a and cyanobacteria) differed more between sites than physical and chemical constituents. Lastly, we found that the event characteristics best explaining concentration responses differed between sites, with event magnitude more frequently related to responses in the main channel, and antecedent wetness conditions associated with response variation in the backwater. Our results indicate that event responses in large rivers are distinct across the diverse habitats and biogeochemical components of a large floodplain river, which has implications for local and downstream ecosystems as the climate shifts.

Minnesota, Wisconsin↗
USGS2023

Aquatic carbon export and dynamics in mountain headwater streams of the western U.S.

Mountain headwater streams actively cycle carbon, receiving it from terrestrial landscapes and exporting it through downstream transport and gas exchange with the atmosphere. Although their importance is now widely recognized, aquatic carbon fluxes in headwater streams remain poorly characterized. In this study, aquatic carbon fluxes were measured in 15 mountain headwater streams and were used in a geostatistical analysis to determine how landscape characteristics influence aquatic carbon fluxes. In-stream sensors were used to measure fluorescent dissolved organic matter (fDOM) (a surrogate for dissolved organic carbon (DOC)) at a subset of sites to characterize dynamic responses to hydroclimatic events. Wetlands have a positive influence on aquatic carbon fluxes, whereas perennial snow/ice has the opposite effect, reflecting differences in soil organic matter content in these landscapes. Mean annual temperature (MAT) has a complex influence on DOC, with peak DOC exports in basins with MAT of 0–2°C. Precipitation has a strong positive influence on aquatic carbon fluxes, and declining snowpacks in the western United States may reduce future aquatic carbon exports. fDOM (and by implication DOC) and

Journal of Geophysical Research: Biogeosciences.↗
USGS2024

Dynamics, monitoring and forecasting of tephra in the atmosphere

Explosive volcanic eruptions inject hot mixtures of solid particles (tephra) and gasses into the atmosphere. Entraining ambient air, these mixtures can form plumes rising tens of kilometers until they spread laterally, forming umbrella clouds. While the largest clasts tend to settle in proximity to the volcano, the smallest fragments, commonly referred to as ash (≤2 mm in diameter), can be transported over long distances, forming volcanic clouds. Tephra plumes and clouds pose significant hazards to human society, affecting infrastructure, and human health through deposition on the ground or airborne suspension at low altitudes. Additionally, volcanic clouds are a threat to aviation, during both high-risk actions such as take-off and landing and at standard cruising altitudes. The ability to monitor and forecast tephra plumes and clouds is fundamental to mitigate the hazard associated with explosive eruptions. To that end, various monitoring techniques, ranging from ground-based instruments to sensors on-board satellites, and forecasting strategies, based on running numerical models to track the position of volcanic clouds, are efficiently employed. However, some limitations still exist, mainly due to the high unpredictability and variability of explosive eruptions, as well as the multiphase and complex nature of volcanic plumes. In the next decades, advances in monitoring and computational capabilities are expected to address these limitations and significantly improve the mitigation of the risk associated with tephra plumes and clouds.

Reviews of Geophysics↗
USGS2024

Controls on lake pelagic primary productivity: Formalizing the nutrient-color paradigm

Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. Lake primary production is strongly controlled by inputs of nutrients and colored dissolved organic matter. While past studies have developed mathematical models of this nutrient-color paradigm, broad empirical tests of these models are scarce. We used data from 58 diverse and globally distributed temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. These lakes varied widely in size (0.02-2300 km2), pelagic gross primary production (20-8000 mg C m-2 d-1), and other characteristics. Across these diverse systems, and given relatively limited inputs, model predictions of primary production were highly correlated with observed values derived from high-frequency sensor data. Our analysis provides a model structure, including calibrated parameter estimates, that may be broadly useful for understanding current and future patterns in lake primary production.

JGR Biogeosciences↗
USGS2025

Over, under, and through: Hydrologic connectivity and the future of coastal landscape salinization

Seawater intrusion (SWI) affects coastal landscapes worldwide. Here we describe the hydrologic pathways through which SWI occurs - over land via storm surge or tidal flooding, under land via groundwater transport, and through watersheds via natural and artificial surface water channels—and how human modifications to those pathways alter patterns of SWI. We present an approach to advance understanding of spatiotemporal patterns of salinization that integrates these hydrologic pathways, their interactions, and how humans modify them. We use examples across the East Coast of the United States that exemplify mechanisms of salinization that have been reported around the planet to illustrate how hydrologic connectivity and human modifications alter patterns of SWI. Finally, we suggest a path for advancing SWI science that includes (a) deploying standardized and well-distributed sensor networks at local to global scales that intentionally track SWI fronts, (b) employing remote sensing and geospatial imaging techniques targeted at integrating above and belowground patterns of SWI, and (c) continuing to develop data analysis and model-data fusion techniques to measure the extent, understand the effects, and predict the future of coastal salinization.

Water Resources Research↗
USGS2025

The first instrumentally detected hydrothermal explosion in Yellowstone National Park

Hydrothermal explosions are one of the geological hazards most likely to impact people in Yellowstone National Park, but their frequency is poorly known. Infrasound and seismic sensors identified an explosion in Norris Geyser Basin on 15 April 2024, at 14:56 MDT (20:56 UTC)—the first instrumentally detected hydrothermal explosion in the Yellowstone region. The event affected an area tens of meters across, resulting in fractured ground, a shallow explosion crater, and a field of ejecta. There were no immediate geophysical precursors, but in the preceding years elevated discharge of thermal water altered the color, temperature, and level of a nearby small lake. Expanded seismo-acoustic monitoring in Yellowstone National Park could be useful for detecting small hydrothermal explosions and constraining their frequency, magnitude, energy release, and locations—information that could be used to better assess and mitigate hazards for the millions of people that visit the park each year.

Wyoming↗
USGS2025

Ubiquity and causes of soil water preferential flow across 17 ecoregions

Preferential flow (PF) in soil causes the rapid transport of water, nutrients, and contaminants into the subsurface, influencing groundwater recharge and streamflow. Data scarcity has hindered the quantification of PF occurrence and the identification of its drivers across diverse ecoregions. We address this gap by analyzing high-frequency, multi-depth soil moisture data across 17 ecoregions in the USA, using ∼1,500 sensors at 40 sites. We discovered that PF is widespread, with sites experiencing PF in up to 60% of rainfall events ≥2 mm. Multiple approaches consistently show that PF is more likely to occur with increased peak rainfall intensity, finer textured material, low soil moisture variability, humid climate, and higher net primary productivity. This suggests that PF patterns could shift with projected climate changes, increasing uncertainty in predictions of groundwater recharge, water quality, and streamflow generation.

conterminous United States and Puerto Rico↗
USGS2026

Towards affordable wetland evapotranspiration monitoring using the Variance-Bowen Ratio method: Insights from three contrasting wetlands

Accurate measurement of evapotranspiration (ET) is essential for sustainable water management. Standard methods such as eddy covariance (EC) are costly, while alternatives such as surface renewal are cheaper but require calibration and complex data processing. This study evaluates the utility of the Variance-Bowen Ratio (VBR) method for estimating ET across three California’s wetlands. Using data from 2023, half-hourly latent heat flux (λE) and daily/monthly ET from VBR were compared with EC at one non-tidal (site A) and two tidal (sites B and C) wetlands. λE VBR consistently underestimated λE EC , with root mean squared errors (RMSE) of 61.2 W m -2 at sites A, 106.1 W m -2 at site B, and 137.2 W m -2 at site C, largely due to storage fluxes across sites. Temporal integration improved VBR’s performance at tidal sites, where compensating water heat storage errors yielded low daily and monthly biases (site B: RMSE = 0.78 mm/d and 12 mm/month; r = 0.93; site C: RMSE = 0.90 mm/d and 13 mm/month; r = 0.93), with reduced major axis (RMA) regression slopes of 0.98 and ~0.91. In contrast, biomass heat storage at site A caused persistent biases (RMSEs = 0.97 mm/d and 23 mm/month; daily and monthly RMA slopes ~0.75; r = 0.85). These results highlight VBR’s limitations in environments with substantial storage fluxes. Despite this, VBR is cost-effective for estimating daily and monthly ET, with sensor costs at least tenfold lower than EC and simpler setup, making it suitable for ET monitoring in resource-limited and hard-to-access regions.

California↗
USGS2006

Seismic noise on Rarotonga: Surface versus downhole

Seismic noise data are presented from the new Global Seismographic Network station, RAR, on the Island of Rarotonga in the South Pacific. Data from the first new borehole site in the GSN are compared with a surface vault installation. Initial indications from the data show that borehole siting on a small island significantly reduces long-period (>20 s) horizontal seismic noise levels during the daytime, but little or no improvement is evident at periods shorter than 20 s or on the vertical component. The goal of the Incorporated Research Institutions for Seismology (IRIS) GSN program is broad, uniform coverage of the Earth with a 128-station network. To achieve this goal and provide coverage in oceanic areas, many stations will be sited on islands. A major siting consideration for these new stations is whether to build a surface vault or drill a borehole. Neither option is inexpensive. The costs for drilling a cased hole and a borehole sensor are large, but the benefit of a borehole site is that seismic noise is reduced during certain periods when a surface installation may be subject to wind, weather, and thermal effects. This benefit translates into recording greater numbers of smaller earthquakes and higher signal-to-noise ratio.

Eos, Earth and Space Science News↗
USGS2018

To catch a quake

A revolution in seismic detection technology is underway, capturing unprecedented observations of earthquakes and their impacts. These sensor innovations provide real-time ground shaking observations that could improve emergency response following damaging earthquakes and may advance our understanding of the physics of earthquake ruptures.

Nature Communications↗
USGS2023

Increasing hypoxia on global coral reefs under ocean warming

Ocean deoxygenation is predicted to threaten marine ecosystems globally. However, current and future oxygen concentrations and the occurrence of hypoxic events on coral reefs remain underexplored. Here, using autonomous sensor data to explore oxygen variability and hypoxia exposure at 32 representative reef sites, we reveal that hypoxia is already pervasive on many reefs. Eighty-four percent of reefs experienced weak to moderate (≤153 µmol O 2 kg −1 to ≤92 µmol O 2 kg −1 ) hypoxia and 13% experienced severe (≤61 µmol O 2 kg −1 ) hypoxia. Under different climate change scenarios based on four Shared Socioeconomic Pathways (SSPs), we show that projected ocean warming and deoxygenation will increase the duration, intensity and severity of hypoxia, with more than 94% and 31% of reefs experiencing weak to moderate and severe hypoxia, respectively, by 2100 under SSP5-8.5. This projected oxygen loss could have negative consequences for coral reef taxa due to the key role of oxygen in organism functioning and fitness.

Nature Climate Change↗
USGS2023

A 1.2 billion pixel human-labeled dataset for data-driven classification of coastal environments

The world’s coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over a range of space and time scales. Understanding and predicting coastline dynamics necessitates frequent observation from imaging sensors on remote sensing platforms. Machine Learning models that carry out supervised (i.e., human-guided) pixel-based classification, or image segmentation, have transformative applications in spatio-temporal mapping of dynamic environments, including transient coastal landforms, sediments, habitats, waterbodies, and water flows. However, these models require large and well-documented training and testing datasets consisting of labeled imagery. We describe “Coast Train,” a multi-labeler dataset of orthomosaic and satellite images of coastal environments and corresponding labels. These data include imagery that are diverse in space and time, and contain 1.2 billion labeled pixels, representing over 3.6 million hectares. We use a human-in-the-loop tool especially designed for rapid and reproducible Earth surface image segmentation. Our approach permits image labeling by multiple labelers, in turn enabling quantification of pixel-level agreement over individual and collections of images.

Scientific Data↗