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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Time series of surface velocity and stage have been collected simultaneously. Surface velocity was measured using an array of newly developed continuous-wave microwave sensors. Stage was obtained from the standard U.S. Geological Survey (USGS) measurements. The depth of the river was measured several times during our experiments using sounding weights. The data clearly showed that the point of zero flow was not the bottom at the measurement site, indicating that a downstream control exists. Fathometer measurements confirmed this finding. A model of the surface velocity expected at a site having a downstream control was developed. The model showed that the standard form for the friction velocity does not apply to sites where a downstream control exists. This model fit our measured surface velocity versus stage plots very well with reasonable values of the parameters. Discharges computed using the surface velocities and measured depths matched the USGS rating curve for the site. Values of depth-weighted mean velocities derived from our data did not agree with those expected from Manning's equation due to the downstream control. These results suggest that if real-time surface velocities were available at a gauging station, unstable stream beds could be monitored. Journal of Hydraulic Engineering ?? ASCE.
Acceleration response records obtained during the October 1, 1987 Whittier‐Narrows earthquake ( M s = 5.6 ) "> ( M s = 5.6 ) from the 32‐story, vertically irregular 1100 Wilshire Finance Building, of which the cross section changes from a nominally rectangular shape to a triangular one, are analyzed. The building was instrumented with 21 channels of synchronized sensors, consisting of 12 uniaxial accelerometers deployed throughout the superstructure and connected to a central recording system and three triaxial strong‐motion accelerographs in the basement. At each of the four levels of the superstructure, three uniaxial accelerometers were deployed, two parallel and at a distance apart, and the third one orthogonal to the parallel pair. The records permit the identification of the predominantly coupled torsional‐translational response of the building and the associated frequency at approximately 0.7–0.8 Hz from the difference between accelerations recorded by the parallel pairs of accelerometers at different levels. This mode corresponds to the second torsional mode. A translational mode at 0.3 Hz is also identified from the records. The center of rigidity of any instrumented level is identified by minimizing the coherence between the relative accelerations and the translational acceleration. Employing system identification techniques provides the modal damping percentage for the second torsional mode as 6–7%.
The objective of this paper is to present preliminary analyses of a set of acceleration response records obtained during the October 17, 1989 Loma Prieta earthquake (Ms = 7.1) from the 60-story vertically tapered, pyramid-shaped Trans-america Building-a landmark of San Francisco. The building was instrumented in 1985 with 22 channels of synchronized sensors consisting of 13 uniaxial accelerometers deployed throughout the structure and connected to a central recording system and three triaxial strong-motion accelerographs at three different levels of the structure. No free-field accelerographs are at the site. The acceleration records permit the study of the behavior of this unique structure. The predominant translational response of the building and the associated frequency at approximately 0.28 Hz are identified from the records and their Fourier amplitude spectra. The records do not indicate any significant torsional motion. However, there is rocking type soil-structure interaction, and an associated frequency of approximately 2.0 Hz is identified from the Fourier amplitude spectra of the differential motions between the ground level and that at the basement. In addition, the response spectra for the basement motions indicate significant resonance in both directions at a period of approximately 0.5 seconds.
Seismic behavior and performance of the new Self- Anchored Suspension (SAS) Bridge of the San Francisco Bay Bridge System is studied using response data recorded during the October 14, 2019, 𝑀𝑤4.6 Pleasant Hill earthquake. The new bridge went into service within the last decade as a replacement for the older truss bridge that spanned between Yerba Buena Island and East Bay. During the October 19, 1989, M6.9 Loma Prieta earthquake, which occurred ∼100 km away from the Bay Bridge, a section of the upper deck of the old truss bridge fell onto the lower deck—thus closing this important lifeline between San Francisco and East Bay. The new SAS Bridge (as well as the rest of the Bay Bridge) is instrumented by the California Strong Motion Instrumentation Program (CSMIP). The unique SAS Bridge is suspended by a single tower that is pivotal in trafficking the cable and hanger system to support the eastbound (E) and westbound (W) decks. At both the west and east ends of the SAS, there is a hinge system that connects the W and E decks to the skyways leading to highways. For the west side, the SAS is led to a tunnel at Yerba Buena Island. The response data analyses highlight the complex and yet identifiable coupled response of the deck, tower, and cable system. Using system identification methods including spectral analyses of both acceleration and displacement time history data, the fundamental frequencies (periods) and critical damping percentages are extracted for the main components (tower, deck, and cables) of the bridge where the sensors are deployed. Frequencies (periods) are then compared with the values computed during the design and analysis process of the bridge. The analyses in this paper showed that there is strong evidence of a beating effect attributed to low critical damping percentages and coupled modes. A possible correlation of fundamental periods of such suspension bridges with their span lengths is discussed. The beating effect and period versus span length can be significant topics for further research.
Remote sensing of the earth promises to become an operational tool of the engineer and the scientist within the next few years. There are no real technical limits on the uses of data from space sensors. However, it is not practical to fly special missions, particularly in space, for each specific use. General-purpose, earth-sensing missions aimed at meeting a wide variety of requirements must be defined. Engineers must help define earth-sensing missions, based on the concepts of repetitive coverage, multispectral response, and spatial correlation.
The KidSAT/EarthKAM program is a NASA educational initiative that allows students to develop and operate a remote sensing space program. The first phase of the program consisted of a student developed camera that is flown on Space Shuttle missions in the nadir window. A network of students develop a series of image acquisition requests, based on approved science proposals, that are combined into a set of instrument commands that are uplinked to the space shuttle. Acquired images are downlinked and archived for student access in completing their research. The International Space Station provides a unique platform for the next generation of KidSAT/EarthKAM instruments, which will allow more students to participate in the program and enable development of more sophisticated instruments. Follow-on instruments consist of Earth-looking multispectral digital sensors mounted in the optical window and on the express pallet, and radar instruments located on the express pallet. Use of the International Space Station will allow routine data acquisition and enable the instrument to be part of regular science curricula unconstrained by the Space Shuttle launch schedule.
Earlier papers have described how observed data from classical accelerometers deployed in structures or from differential GPS with high sampling ratios deployed at roofs of tall buildings can be configured to establish seismic health monitoring of structures. In these configurations, drift ratios 1 are the main parametric indicator of damage condition of a structure or component of a structure. Real‐time measurement of displacements are acquired either by double integration of accelerometer time‐series data, or by directly using GPS. Recorded sensor data is then related to the performance level of a building. Performance‐based design method stipulates that for a building the amplitude of relative displacement of the roof of a building (with respect to its base) indicates its performance. Usually, drift ratio is computed using relative displacement between two consecutive floors. When accelerometers are used, a specific software is used to compute displacements and drift ratios in realtime by double integration of accelerometer data from several floors. However, GPS‐measured relative displacements are limited to being acquired only at the roof with respect to its reference base. Thus, computed drift ratio is the average drift ratio for the whole building. Until recently, the validity of measurements using GPS was limited to long‐period structures ( T >1  s ) "> ( T >1 s ) because GPS systems readily available were limited to 10–20 samples per seconds (sps) capability. However, presently, up to 50 sps differential GPS systems are available on the market and have been successfully used to monitor drift ratios [1,2]—thus enabling future usefulness of GPS to all types of structures. Several levels of threshold drift ratios can be postulated in order to make decisions for inspections and/or occupancy. Experience with data acquired from both accelerometers and GPS deployments indicates that they are reliable and provide pragmatic alternatives to alert the owners and other authorized parties to make informed decisions and select choices for pre‐defined actions following significant events.
While recording fish habitat use by electronic sensors, biologgers can also be viewed as autonomous environmental monitoring systems with the organism as a vehicle. This dual perspective has provided novel results from marine ecosystems, but has not been applied to freshwater ecosystems. To understand limitations in fresh water, we evaluated miniature depth and temperature recorders as aquatic monitoring systems in a Laurentian Great Lake: Erie. As part of an acoustic telemetry study, biologgers were opportunistically implanted in a subsample of walleye Sander vitreus . Biologgers recorded temperature and depth at half-hour intervals for up to 1 year. Recaptures provided six biologgers for analysis of seasonal temperature patterns and lake stratification, key variables for understanding dimictic lakes. Depth-resolved temperature patterns showed close correspondence with independent weather buoy measurements. Because the buoy was deployed late in the season, biologger data provided improved estimates of the start of stratification, which had important implications for understanding development of hypoxia in the hypolimnion. Drawbacks to biologger data included imprecise knowledge of fish location and reliance on tag recoveries from the fishery. Optimistically, our results show how biologgers could be part of a monitoring approach that integrates limnological surveys with fisheries science.