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Lessons from Mexico’s earthquake early warning system
The devastating 2017 Puebla quake provides an opportunity to assess how citizens perceive and use the Mexico City earthquake early warning system.
Data sharing in magnetotellurics
Here, we introduce the first openly available comprehensive database of magnetotelluric (MT) and related electromagnetic data that we developed and matured over the past decade, explain how to access the data, and describe the challenges that had to be overcome to make MT data sharing possible. The database is a helpful tool for MT scientists, and is widely used by the international scientific community in teaching, planning future surveys, and then to leverage the value of new data at the inversion and interpretation stage. It has also turned into a primary resource for researchers in space weather, and for power grid industry. MT impedance, a transfer function that relates geoelectric fields to geomagnetic fields at the Earth’s surface, provides a critical missing component for estimation of geomagnetically induced currents: quasi-direct currents caused by geomagnetic storms that can destroy valuable equipment and cause extended blackouts. We envision that this database will also be useful for researchers in other areas of geophysics, such as seismology, who would like to use real MT data for hands-on learning and joint inversion analysis.
Planetary sensor models interoperability using the community sensor model specification
This paper presents the photogrammetric foundations upon which the Community Sensor Model specification depends, describes common coordinate system and reference frame transformations that support conversion between image sensor (charge‐coupled device) coordinates to some arbitrary body coordinate, and describes the U.S. Geological Survey Astrogeology Community Sensor Model implementation ( https://github.com/USGS-Astrogeology/usgscsm ). We present a new image support data specification that provides the position, pointing, timing, and metadata information necessary to properly locate a pixel or observations location on a body and describe a system architecture designed to explicitly identify the responsibilities of software components within a larger pipeline or analytical environment. This paper concludes with a set of experiments that illustrate positional and pointing error in the sensor location and the impact on the computed surface location.
Aligning climate models with stakeholder needs: Advances in communicating future rainfall uncertainties for south Florida decision makers
Changes in future precipitation are of great importance to climate data users in South Florida. A recent U.S. Geological Survey workshop, “Increasing Confidence in Precipitation Projections for Everglades Restoration,” highlighted a gap between standard climate model outputs and the climate information needs of some key Florida natural resource managers. These natural resource managers (hereafter broadly defined as “climate data users”) need more tailored output than is commonly provided by the climate modeling community. This study responds to these user needs by outlining and testing an adaptable methodology to select output from ensemble climate‐model simulations based on user‐defined precipitation drivers, using statistical methods common across scientific disciplines. This methodology is developed to provide a “decision matrix” that guides climate data users to specify the subset of models most important to their work based on each user's season (winter, summer, and annual) and the condition (dry, wet, neutral, and no threshold events) of interest. The decision matrix is intended to better communicate the subset of models best representing precipitation drivers. This information could increase users' confidence in climate models as a resource for natural resource planning and can be used to direct future dynamical downscaling efforts. This methodology is based in dynamical processes controlling precipitation via remote and local teleconnections. We also suggest that future climate studies in South Florida include high‐resolution climate model runs (i.e., ocean eddy resolving) in conjunction with dynamical downscaling to adequately capture precipitation variability.
Rating fire danger from the ground up
Soil moisture information could improve assessments of wildfire probabilities and fuel conditions, resulting in better fire danger ratings.
Can modeling the geologic record contribute to constraining the tectonic source of the AD 1755 Great Lisbon earthquake?
The precise location of the seismic source of 1755 CE Great Lisbon earthquake is still uncertain. The aim of this work is to use an onland sedimentary record in southern Portugal to test and validate seismic sources for the earthquake. To achieve this, tsunami deposit thicknesses from over 150 cores collected at Salgados in southern Portugal were compared to the results of a tsunami sediment transport model (Delft3D-FLOW) that simulates tsunami propagation, inundation, erosion, and deposition. Five different hypothetical seismic sources were modeled with varying bed roughness coefficients to assess how well they reproduced observed patterns of tsunami deposit thicknesses and dune. Modeled and observed historical tsunami arrival times were also used to test different earthquake sources. Based on these comparisons, three modeled earthquake sources were able to reproduce the observed data, suggesting they should be regarded as somewhat more likely sources for the 1755 earthquake in contrast to four other modeled sources. The fault closest to shore (Marquês de Pombal) yielded the best correlations between model and observations.
Observations of an extreme atmospheric river storm with a diverse sensor network
Observational networks enhance real‐time situational awareness for emergency and water resource management during extreme weather events. We present examples of how a diverse, multitiered observational network in California provided insights into hydrometeorological processes and impacts during a 3‐day atmospheric river storm centered on 14 February 2019. This network, which has been developed over the past two decades, aims to improve understanding and mitigation of effects from extreme storms influencing water resources and natural hazards. We combine atmospheric reanalysis output and additional observations to show how the network allows: (1) the validation of record cool season precipitable water observations over southern California; (2) the identification of phenomena that produce natural hazards and present difficulties for short‐term weather forecast models, such as extreme precipitation amounts and snow level variability; (3) the use of soil moisture data to improve hydrologic model forecast skill in northern California's Russian River basin; and (4) the combination of meteorological data with seismic observations to identify when a large avalanche occurred on Mount Shasta. This case study highlights the value of investments in diverse observational assets and the importance of continued support and synthesis of these networks to characterize climatological context and advance understanding of processes modulating extreme weather.
Lessons from a post-eruption landscape
From March to May 1980, magma rose high into Mount St. Helens (MSH), swelling and—as it turned out—destabilizing its north flank. Scientists knew the volcano had been highly active at times over the past 40,000 years, but the mountain, located amid the Cascade Range in southwestern Washington, had been mostly quiet since the mid-19th century. The collapse of the north flank on 18 May shattered that quiet, triggering a cascade of events that left resounding impressions not only on those who witnessed and studied them but also on the surrounding landscape [Lipman and Mullineaux, 1981; Waitt, 2015]. The eruption of MSH also provided unparalleled opportunities for advancing several disciplines [e.g., Shore et al., 1986; Newhall, 2000; Franklin and MacMahon, 2000]. Although not as conspicuously as in volcanology, the eruption and its aftermath led to an intensification of research investigating biophysical impacts of eruptions and subsequent responses [e.g., Dale et al., 2005; Pierson and Major, 2014; Crisafulli and Dale, 2018]. Long-term research on the biophysical responses at MSH has provided important new insights, challenged long-standing ideas, and provided many societal benefits. The fortieth anniversary of the eruption this year offers a timely opportunity to reflect on these insights and influences. This long-term vantage is important because sustained, place-based studies following landscape disturbances are rare; because the MSH eruption spurred the greatest depth and breadth of multidisciplinary studies of biophysical responses to landscape disturbance; and because these responses created some of the most significant societal challenges to emerge after the eruption. We summarize key biophysical disturbances and responses, highlight salient insights, and suggest actions that can extend the usefulness of these insights to volcanically vulnerable communities worldwide.
Down to Earth with nuclear electromagnetic pulse: Realistic surface impedance affects mapping of the E3 geoelectric hazard
An analysis is made of Earth-surface geoelectric fields and voltages on electricity transmission power-grids induced by a late-phase E3 nuclear electromagnetic pulse (EMP). A hypothetical scenario is considered of an explosion of several hundred kilotons set several hundred kilometers above the eastern-midcontinental United States. Ground-level E3 geoelectric fields are estimated by convolving a standard parameterization of E3 geomagnetic field variation with magnetotelluric Earth-surface impedance tensors derived from wideband measurements acquired across the study region during a recent survey. These impedance tensors are a function of subsurface three-dimensional electrical conductivity structure. Results, presented as a movie-map, demonstrate that localized differences in surface impedance strongly distort the amplitude, polarization, and variational phase of induced E3 geoelectric fields. Locations with a high degree of E3 geoelectric polarization tend to have high geoelectric amplitude. Uniform half-space models and one-dimensional, depth-dependent models of Earth-surface impedance, such as those widely used in government and industry reports informing power-grid vulnerability assessment projects, do not provide accurate estimates of the E3 geoelectric hazard in complex geological settings. In particular, for the Eastern-Midcontinent, half-space models can lead to (order-one) overestimates/underestimates of EMP-induced geovoltages on parts of the power grid by as much as 1,000 volts (a range of 2,000 volts)—comparable to the amplitudes of the geovoltages themselves.
Watershed sediment yield following the 2018 Carr Fire, Whiskeytown National Recreation Area, northern California
Wildfire risk has increased in recent decades over many regions, due to warming climate and other factors. Increased sediment export from recently burned landscapes can jeopardize downstream infrastructure and water resources, but physical landscape response to fire has not been quantified for some at-risk areas, including much of northern California, USA. We measured sediment yield from three watersheds (13–29 km 2 ) that drain to Whiskeytown Lake, California, within the area burned by the 2018 Carr Fire. Structure-from-Motion photogrammetry on aerial images combined with sonar bathymetric mapping of submerged areas indicated first-year post-fire sediment yields of 4,080 ± 598 t/km 2 (Brandy Creek), 2,700 ± 527 t/km 2 (Boulder Creek), and 305 ± 58.0 t/km 2 (Whiskey Creek)—some of the first post-fire yields measured in northern California and 64, 42, and 4.8 times greater than pre-fire yields, respectively. These were measured during a wet year and resulted largely from rilling erosion and fluvial sediment transport, without post-fire debris flows. Rilling preferentially developed in contact with dirt roads, aided by thin soils and exposed bedrock, and on slopes vegetated by chaparral pre-fire. The second post-fire year (a dry year) was characterized by fluvial reworking and delta progradation of the first-year deposits and relatively little new sediment export. First-year sedimentation of 111,000 m 3 represented minor loss of storage capacity in Whiskeytown Lake but would be detrimental to smaller reservoirs; in general, increased sediment yields from western US watersheds as fire and extreme rainfall increase will likely pose risks to water quality and storage.
Fitting jet noise similarity spectra to volcano infrasound data
Infrasound (low-frequency acoustic waves) has proven useful to detect and characterize subaerial volcanic activity, but understanding the infrasonic source during sustained eruptions is still an area of active research. Preliminary comparison between acoustic eruption spectra and the jet noise similarity spectra suggests that volcanoes can produce an infrasonic form of jet noise from turbulence. The jet noise similarity spectra, empirically derived from audible laboratory jets, consist of two noise sources: large-scale turbulence (LST) and fine-scale turbulence (FST). We fit the similarity spectra quantitatively to eruptions of Mount St. Helens in 2005, Tungurahua in 2006, and Kīlauea in 2018 using nonlinear least squares fitting. By fitting over a wide infrasonic frequency band (0.05–10 Hz) and restricting the peak frequency above 0.15 Hz, we observe a better fit during times of eruption versus non-eruptive background noise. Fitting smaller overlapping frequency bands highlights changes in the fit of LST and FST spectra, which aligns with observed changes in eruption dynamics. Our results indicate that future quantitative spectral fitting of eruption data will help identify changes in eruption source parameters such as velocity, jet diameter, and ash content which are critical for effective hazard monitoring and response.
Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement
Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.
Clustering supported classification of ChemCam data from Gale crater, Mars
The Chemistry and Camera (ChemCam) instrument on board the MSL rover Curiosity has collected a very large and unique dataset of in-situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot LIBS (laser-induced breakdown spectroscopy) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a dataset is ideally suited for the application of statistical methods for the recognition of patterns that are difficult to observe by humans. In this work, we develop an approach relying on the feature extraction method non-negative matrix factorization (NMF) and the repetition of k-means clustering to classify ChemCam spectra. A strong consistency of the clustering results among the repetitions were found, which allowed us to identify six clusters representing the dominant compositions measured by ChemCam in Gale crater so far. By tracking clusters across the rover traverse from landing to sol 2756, we are able to provide a chemostratigraphic overview of Gale crater from the ChemCam perspective. Transitions between major geologic groups (such as the Bradbury and the Mt. Sharp groups) are identifiable demonstrating that they are compositionally distinct, consistent with previous work. Compositional differences between their members also appear in the results. Furthermore, a first approach in which a random forest classifier was trained and validated with the obtained cluster assignments, reveals promising results for predicting cluster memberships of new ChemCam LIBS data acquired after sol 2756.
Improving the usability of Galileo and Voyager images of Jupiter’s moon, Europa
NASA's Voyager 1, Voyager 2, and Galileo spacecraft acquired hundreds of images of Jupiter's moon Europa. These images provide the only moderate- to high-resolution views of the moon's surface and are therefore a critical resource for scientific analysis and future mission planning. Unfortunately, uncertain knowledge of the spacecraft's position and pointing during image acquisition resulted in significant errors in the location of the images on the surface. The result is that adjacent images are poorly aligned, with some images displaced by more than 100 km from their correct location. These errors severely degrade the usability of the Voyager and Galileo imaging data sets. To improve the usability of these data sets, we used the U.S. Geological Survey Integrated Software for Imagers and Spectrometers to build a nearly global image tie-point network with more than 50,000 tie points and 135,000 image measurements on 481 Galileo and 221 Voyager images. A global least-squares bundle adjustment of our final Europa tie-point network calculated latitude, longitude, and radius values for each point by minimizing residuals globally, and resulted in root mean square (RMS) uncertainties of 246.6 m, 307.0 m, and 70.5 m in latitude, longitude, and radius, respectively. The total RMS uncertainty was 0.32 pixels. This work enables direct use of nearly the entire Galileo and Voyager image data sets for Europa. We are providing the community with updated NASA Navigation and Ancillary Information Facility Spacecraft, Planet, Instrument, C-matrix (pointing), and Events kernels, mosaics of Galileo images acquired during each observation sequence, and individual processed and projected level 2 images.
Temperatures of the Lacus Mortis region of the Moon
Over 11 years of data acquired by the Diviner Lunar Radiometer Experiment instrument aboard Lunar Reconnaissance Orbiter have been compiled into a comprehensive data set of surface temperatures in the Lacus Mortis region which includes the landing ellipse of the Astrobotic Mission One lander mission. These data provide diurnal brightness temperatures at 128 pixels per degree (ppd) spatial resolution and 0.1 hr of local time resolution. From this data set, we highlight several features that display variations in radiative and thermophysical properties in the Lacus Mortis region and characterize the temperatures of the Astrobotic Mission One landing ellipse. We identify distinctly contrasting properties of materials in the walls of Bürg crater, hummocks of materials on the southeast margin of the mare basalts, and materials exposed or excavated by impacts. Additionally, we describe an exceptionally rocky fault scarp that predates the formation of Bürg crater suggesting the observed boulders are replenished on a timescale <1 Ga. Within the Astrobotic landing ellipse, temperatures are observed to range from ∼88 to ∼359 K with sunrise and sunset local times constrained to 5.8–6.3 hr and 17.8 and 18.1 hr respectively.
Integrating Bayesian networks to forecast sea-level rise impacts on barrier island characteristics and habitat availability
Evaluation of sea-level rise (SLR) impacts on coastal landforms and habitats is a persistent need for informing coastal planning and management, including policy decisions, particularly those that balance human interests and habitat protection throughout the coastal zone. Bayesian networks (BNs) are used to model barrier island change under different SLR scenarios that are relevant to management and policy decisions. BNs utilized here include a shoreline change model and two models of barrier island biogeomorphological evolution at different scales (50 and 5 m). These BNs were then linked to another BN to predict habitat availability for piping plovers ( Charadrius melodus ), a threatened shorebird reliant on beach habitats. We evaluated the performance of the two linked geomorphology BNs and further examined error rates by generating hindcasts of barrier island geomorphology and habitat availability for 2014 conditions. Geomorphology hindcasts revealed that model error declined with a greater number of known inputs, with error rates reaching 55% when multiple outputs were hindcast simultaneously. We also found that, although error in predictions of piping plover nest presence/absence increased when outputs from the geomorphology BNs were used as inputs in the piping plover habitat BN, the maximum error rate for piping plover habitat suitability in the fully-linked BNs was only 30%. Our findings suggest this approach may be useful for guiding scenario-based evaluations where known inputs can be used to constrain variables that produce higher uncertainty for morphological predictions. Overall, the approach demonstrates a way to assimilate data and model structures with uncertainty to produce forecasts to inform coastal planning and management.