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At least 1,027 records · Page 57Linked to original sources

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↗

USGS2024

Chlorophyll a in lakes and streams of the United States (2005–2022)

The concentration of chlorophyll a in phytoplankton and periphyton represents the amount of algal biomass. We compiled an 18-year record (2005–2022) of pigment data from water bodies across the United States (US) to support efforts to develop process-based, machine learning, and remote sensing models for prediction of harmful algal blooms (HABs). To our knowledge, this dataset of nearly 84,000 sites and over 1,374,000 pigment measurements is the largest compilation of harmonized discrete, laboratory-extracted chlorophyll data for the US. These data were compiled from the Water Quality Portal (WQP) and previously unpublished U.S. Geological Survey’s National Water Quality Laboratory (NWQL) data. Data were harmonized for reporting units, pigment type, duplicate values, collection depth, site name, negative values, and some extreme values. Across the country, data show great variation by state in sampling frequency, distribution, and methods. Uses for such data include the calibration of models, calibration of field sensors, examination of relationship to nutrients and other drivers, evaluation of temporal trends, and other applications addressing local to national scale concerns.

Nature Scientific Data↗

USGS2019

Measurement of cyanobacteria bloom magnitude using satellite remote sensing

Cyanobacterial harmful algal blooms (cyanoHABs) are a serious environmental, water quality and public health issue worldwide because of their ability to form dense biomass and produce toxins. Models and algorithms have been developed to detect and quantify cyanoHABs biomass using remotely sensed data but not for quantifying bloom magnitude, information that would guide water quality management decisions. We propose a method to quantify seasonal and annual cyanoHAB magnitude in lakes and reservoirs. The magnitude is the spatio-temporal mean of weekly or biweekly maximum cyanobacteria biomass for the season or year. CyanoHAB biomass is quantified using a standard reflectance spectral shape-based algorithm that uses data from Medium Resolution Imaging Spectrometer (MERIS). We demonstrate the method to quantify annual and seasonal cyanoHAB magnitude in Florida and Ohio respectively during 2003-2011 and rank the lakes based on median magnitude over the study period. The new method can be applied to Ocean Land Color Imager (OLCI) on Sentinel-3 data for assessment of cyanoHABs and the change over time, even with issues such as variable data acquisition frequency or sensor calibration uncertainties between satellites. CyanoHAB magnitude can support monitoring and management decision-making for recreational and drinking water sources.

Florida, Ohio↗

USGS2025

2022 McKinney rain-on-wildfire event, dissolved oxygen sags, and a fish kill on the Klamath River, California

The longitudinal propagation of water-quality and ecological impairments in rivers during and after wildfires remain poorly understood. In Northern California, the 2022 McKinney Fire burned 243 km 2 of the Klamath National Forest, with 83% of the burned area classified as moderate to high severity. During the active wildfire, a high-intensity monsoonal rain event triggered sediment-laden flooding and runoff-initiated debris flows, causing extreme water-quality impairments and a 95 km fish kill zone along the main-stem Klamath River. This rain-on-wildfire event produced a flood wave that outpaced a sediment pulse, diminishing the dilution effect of the floodwaters. A network of high-frequency water-quality sensors recorded water-quality impairments that propagated 296 km downstream. Impairments at the nearest monitoring station, situated 71 km downstream from the fire perimeter, included dissolved oxygen sags to zero (anoxia) for 5.25 h, turbidity spikes exceeding 1000 FNU, a doubling of specific conductance from 175 to 415 µS/cm (at 25 °C), and pH anomalies of 0.5 units from 7.8 to 7.3. This novel rain-on-wildfire event triggered the first flush of fire-scar material during an active wildfire, resulting in water-quality impairments unprecedented in the historical monitoring data for the river spanning 2012 to 2022. This study provides new insights into the potential role of rain-on-wildfire events in generating extreme downstream water-quality and ecological impairments in a more fire-prone future.

California, Oregon↗

USGS2024

Concepts and evolution of urban hydrology.

Urbanization and climate change are exacerbating the flood risk and ecosystem degradation in urban catchments, with traditional stormwater management systems often overwhelmed. In this Review, we discuss changes in urban hydrology and approaches to stormwater management. Roughly 90% of rainfall on impervious surfaces and drainage infrastructure becomes run-off, enhancing rainfall export away from cities and leading to local water scarcity and downstream flooding and pollution. Projected increases in urban populations (68% in cities by 2050) and rainfall intensity (~12% in the 10-year and 50-year recurrence interval intensity, under 1.5 °C warming) will exacerbate these issues. Transforming stormwater systems is thus urgently needed, to mitigate flood risk and also to address community desires for environmental protection and enhanced water security. Opportunities include rain gardens and other nature-based stormwater control measures (which restore natural flows and offer other ecosystem services), smart sensor monitoring networks and real-time management (which sustain natural flow regimes, mitigate flood risk and protect ecosystem services) and stormwater harvesting (to avoid local water scarcity). Community acceptance of stormwater harvesting is as high as 96% and stormwater is a substantial resource, with volumes often exceeding demand in some parts of the world. Delivering additional transformations globally requires research into strategies to incentivize engagement and investment, and policies to guide governance of decentralized networks.

Nature Reviews Earth & Environment↗

USGS2023

Supervised discretization for decluttering classification models

Presented here is the first demonstration of supervised discretization to ‘declutter’ multivariate classification data in chemical sensor applications. The performance of multivariate classification models is often limited by the non-informative chemical variance within each target class; decluttering methods seek to reduce within-class variance while retaining between-class variance. Supervised discretization is shown to declutter classes in a manner that is superior to the state-of-the-art External Parameter Orthogonalization (EPO) by constructing a more parsimonious model with fewer parameters to optimize and is, consequently, less susceptible to overfitting and information loss. The comparison of supervised discretization and EPO is performed on three classification applications: X-ray fluorescence spectra of pine ash where the pine was grown in three distinct soil types, laser induced breakdown spectroscopy of colored artisanal glasses, and laser induced breakdown spectroscopy of exotic hardwood species.

Analyst↗

USGS2002

The significance of pockmarks to understanding fluid flow processes and geohazards

Underwater gas and liquid escape from the seafloor has long been treated as a mere curiosity. It was only after the advent of the side-scan sonar and the subsequent discovery of pockmarks that the scale of fluid escape and the moonlike terrain on parts of the ocean floor became generally known. Today, pockmarks ranging in size from the 'unit pockmark' (1-10 m wide, <0.6 m deep) to the normal pockmark (10-700 m wide, up to 45 m deep) are known to occur in most seas, oceans, lakes and in many diverse geological settings. In addition to indicating areas of the seabed that are 'hydraulically active', pockmarks are known to occur on continental slopes with gas hydrates and in association with slides and slumps. However, possibly their potentially greatest significance is as an indicator of deep fluid pressure build-up prior to earthquakes. Whereas only a few locations containing active (bubbling) pockmarks are known, those that become active a few days prior to major earthquakes may be important precursors that have been overlooked. Pockmark fields and individual pockmarks need to be instrumented with temperature and pressure sensors, and monitoring should continue over years. The scale of such research calls for a multinational project in several pockmark fields in various geological settings.

Geofluids↗

USGS2018

New methods for predicting and measuring dispersion in rivers

To develop a better predictive tool for dispersion in rivers over a range of temporal and spatial scales, our group has developed a simple Lagrangian model that is applicable for a wide range of coordinate systems and flow modeling methodologies. The approach allows dispersion computations for a large suite of discretizations, model dimensions (1-, 2-, or 3-dimensional), spatial and temporal discretization, and turbulence closures. As the model is based on a discrete non-interacting particle approach, parallelization is straightforward, such that simulations with large numbers of particles are tractable. Results from the approach are compared to dispersion measurements made with conventional Rhodamine WT dye experiment in which typical at-a-point sensors are employed to determine concentration. The model performs well, but spatial resolution for experiments over large and or complex river flows was inadequate for model testing. To address this issue, we explored the idea of measuring spatial concentrations in river flows using hyperspectral remote sensing. Experiments both for idealized channels and real rivers show that this technique is viable and can provide high levels of spatial detail in concentration measurements with quantitatively accurate concentrations.

Conference Paper↗