In 2014, media artist Daniela de Paulis first presented at ASTRON in The Netherlands the possibility of radio-transmission of brain activity as part of her project ‘’COGITO in Space”, for which laboratory-grade EEG recordings are analyzed and converted to sound in real-time, using an open-source interstellar EEG-transmission protocol designed for the project by Guillaume Dumas and Michael Sanders and integrated in the EEGsynth fieldtrip software package. The 25m dish antenna of the Dwingeloo radio telescope in The Netherlands instantly transmits this audio-stream into space while the participant’s brain activity is recorded. The antenna uses amateur radio equipment, with a SingleSideBand (SSB) 120W power transmission with a fixed dish position. By spreading transmissions over the sky chances of possible detection by an alien civilization are limited. One of the challenges of the project was the real-time conversion of 32-channel EEG into a mono 3kHz audio signal for a linear SSB modulated radio transmission, including the 3D electrode positions that would allow the reconstruction of the cortical activity and topography by a hypothetical receiver.

The community of Kotzebue, located on the coast of Kotzebue Sound, which is northeast of the Bering Straits adjacent to the Chukchi Sea, is reliant on the waters around Kotzebue Sound for food and economy. There have been reports of cyanobacterial blooms in these waters around Kotzebue but they have not been systematically studied yet, because the region is sparsely populated with few in-situ observations. Cyanobacteria often form surface blooms in freshwater and coastal ecosystems which can be detected using remote sensing techniques. Cyanobacteria are found to have low nutritional value and many species of cyanobacteria produce cyanotoxins, and thus can be harmful to aquatic life and cause public health hazards. In addition, consumption of decaying cyanobacterial blooms by microbes depletes oxygen level which can lead to hypoxia, adversely impacting the benthic community. As the Arctic is warming twice as fast as the rest of the planet due to climate change, thawing permafrost is releasing nutrients that might be enhancing cyanobacterial blooms in the coastal, marine and lacustrine waters of Alaska. In this study, we used remote sensing to study phytoplankton biomass, turbidity and cyanobacterial blooms between mid-June to end of September each year from 2013 to 2019 when the waters around Kotzebue are ice-free. Using images from Landsat-8 and Sentinel-2, processed using ACOLITE software, we investigated spatial and temporal changes in water quality parameters such as turbidity and chlorophyll concentration between June and September. We used a combination of true-color images and fai (floating algal index) to detect cyanobacterial blooms. There were about two scenes from Sentinel-2 and about one scene from Landsat-8, for a total of about three scenes every week between June and September. Of these, only 49% of the images were cloud-free. Of the cloud-free images, 29% were found to have a cyanobacterial bloom between August and September for an average of two to four scenes every year. Most of the cyanobacterial blooms were detected in Kobuk Lake near Kotzebue, and nearby sites in Hotham Inlet and Selawik Lake. In 2013, 68% of the images were cloudy which was the highest in the observed years and no cyanobacterial blooms were detected.

An upper-crustal intrusive network in the 201.5 Ma, rift-related Central Atlantic Magmatic Province is exposed in the western Newark basin (PA, USA). Alpha-MELTS modeling was used to track magma evolution starting with initial pyroxene crystallization at depth (1000-500 MPa); plagioclase crystallized during ascent in the upper crust. For magma emplaced at 5-6 km depth (170 MPa), six MELTS models were generated to bracket different composition, H2O (1-3 wt.%), and crystallinity (28-49 vol.%). Corresponding magma viscosities evolved from 3 to 1624 Pa-sec (predicted using Giordano et. al 2008; Moitra and Gonnermann 2014). Detailed crystal mush structures in a diabase sill are revealed in a dimension stone quarry. Ubiquitous asymmetric modal layers a few mm thick comprising plag-rich layers (PRL, 75% modal plag) overlying more pyx-rich layers outline the tops of hundreds of dm-m scale flow lobes in the quarry. Tabular plag in PRL show shape-preferred orientations, tiling, and pressure shadows around larger pyx that resemble analog experiments on particle slurries and indicate flow with limited mechanical compaction. During magma emplacement, recursive interactions of propagation, sorting, and crystallization self-organized as flow lobes with plag entrained and aligned along lobe tops. Our calculations show plag separation can reduce bimodal suspension viscosity; a positive feedback likely enhanced by shear thinning and crystal alignments. EDS analyses and X-ray maps show that plag has oscillatory-zoned cores (An82-67) with patchy-zoned mantles (An67) filled in by An66-63. In PRL, plag are cemented together by An62-55; Na-rich rims occur next to qtz-Kspar pockets. By the end of cementation, PRL liquid volume was significantly reduced to 11-18% compared with 28-45% in overall magma based on MELTS models for An62-55 plag. Diabase suspension viscosity increased to >6000 Pa-sec; PRL viscosity cannot be modeled by equations based on random packing. PRL with aligned interlocking crystals were more rigid and less permeable than surrounding diabase. Upward flow of magma after modal layer development was channelized into pipes truncated and deflected by PRL. Thus, lateral flow during emplacement developed sub-vertical heterogeneities that exemplify complex mush rheology over m-scale distances.

We present a new approach for the analysis of high-resolution digital camera photographs taken by photographers who have fortuitously been able to capture rare events such as the glowing sky phenomenon known as STEVE. This method is especially effective with a time lapse series of images of the night sky taken under constant camera settings with a steady pointing. Stars, planets and satellites seen in such images can be used to determine precise and accurate registration of camera pixels to coordinates of angular altitude and azimuth. The location of satellites in the image enables precise and accurate synchronization of the images. We apply these techniques to the series of photographs of STEVE taken on 25 July 2016. We confirm the altitude structure previously found for STEVE. We find it most likely that the green picket fence features often seen during STEVE events are produced by auroral electron precipitation. With the precipitation assumption, we are able to extract novel information about the energy spectrum of the particles responsible for the production of STEVE luminosity in this particular event. Similar analyses of archived digital photographs may constitute a treasure trove of important data for improved understanding of rare and transient events such as STEVE.

The advocacy activities necessary to sustain healthy watersheds and improve impaired ones ultimately rely on the democratic process, and therefore depend on a public that values our coastal resources and understands the role that water quality plays in maintaining that value. We contend that an opportunity exists to improve the temporal and spatial density of monitoring by reducing the cost of collecting measurements, while simultaneously fostering an informed and invested public. We envision a distributed water quality monitoring sensor network, composed of low-cost ($1000-$2000) profiling devices we call TideRiders, built and operated by private citizens and local educational organizations and supported by an institution-hosted centralized data and control portal. The TideRider concept engages the public not just in the collection of data but also in the building, deployment, operation, and recovery of these robot sensors. TideRiders will carry a suite of basic water quality instrumentation (temperature, conductivity, and dissolved oxygen), transmit data and accept commands over the cellular network, and can sample surface and bottom waters by surfacing and submerging on a programmable schedule. Operators will harness tidal currents to move their TideRiders deliberately around an embayment, essentially by surfacing in a favorable tide and anchoring on the bottom in an adverse tide. A network of TideRiders deployed in tidally-dominated estuaries like Buzzards Bay and Narragansett Bay could provide basic water quality data at several-hour intervals for weeks at a time by “virtually mooring” in center-bay locations that are otherwise only accessible by boat and therefore typically sampled less frequently than shore stations. We present preliminary field results from a series of prototypes designed and built by students. The prototype devices utilize a novel low-cost semi-passive shallow-water buoyancy engine and were constructed for less than $1000 in parts.

The frequency, size, and intensity of wildfires in California have increased substantially in recent years, leading to widespread mandatory evacuations affecting millions of residents. However, because evacuation orders are implemented by local agencies, there is limited quantitative evidence on the scope of evacuations statewide. In order to improve the understanding of wildfire evacuations, we assembled information on historical evacuation orders for two distinct wildfire-prone regions --- Fresno and Sonoma county --- in California. This data was used to understand how the frequency and extent of evacuations have changed over time. We then combined this information with census data to characterize which populations have been most affected by evacuation orders. Ultimately, our work aims to quantify this important element of wildfire impacts in key regions around California. Collectively, it provides a starting point for a public database of evacuation orders that could be used by researchers and policymakers to better understand dynamics and improve decision-making around wildfire evacuations.

Since the start of production in 1968 in the Groningen gas field (Netherlands) considerable land subsidence (>30 cm) has occurred above the field. Variability in reservoir compaction has led to earthquakes on reactivated Mesozoic age reservoir faults. Even though the impact of this seismicity (MW ≤3.6) on society has been large, due to substantial structural damage to buildings, surface deformation induced by the co-seismic slip has been too small to detect using geodetic data. It is possible that differential compaction across faults is not only accommodated by seismic slip, but also by aseismic slip (e.g., creep). Aseismically slipping reservoir faults would relax the stresses in the reservoir and, thus, reduce the severity of the seismicity. In this study we explore the potential occurrence of aseismic slip on the reservoir faults. We perform a sensitivity analysis to investigate whether aseismic slip on the different reservoir faults has the capacity to produce detectable surface signals. We use the analytical Okada (1992) model of slip on a discrete dislocation in a uniform elastic half-space to simulate the deformations originating from slip on a wide range of fault geometries, representing the variability in the field. Unsurprisingly, laterally extensive faults with strong compaction contrasts across them (large differential slip magnitudes) produce the largest surface signals. To determine which potentially aseismically slipping faults produce surface signals that could be detectable in persistent scatterer InSAR time series, we analyze the surface patterns for large differential displacements across large length scales, since InSAR observations are most sensitive to spatially extensive patterns with high spatial gradients. We use the results of the sensitivity analysis to guide our search for patterns originating from aseismically slipping reservoir faults in PS-InSAR time series data of the Groningen area. First results show that these specific patterns are rare, indicating that the amount of aseismic slip is limited. For faults lacking surface signals related aseismic slip, the results of sensitivity analysis are used to determine upper limits for the aseismic differential slip magnitudes.

Water management practices in cities around the world are faced with growing social and environmental pressures. Unfortunately, the linear “take-make-waste” approach, previously recognized as the most conclusive practice to address water-related issues, has been found to be unsustainable due to its dependence on the limited availability of energy and resources. It is, therefore, necessary to change the current linear approach dominant in most cities across the world to one that utilizes a high degree of reuse and recycling that is known as “One Water”. The goal of this study is to evaluate a series of expert interviews that were conducted with utilities across the US and Canada to gain insights into implementing One Water principles. Interpreting several interviews is the key step to provide water managers with an understanding of the perspective and required actions towards transitions in urban water management. The results indicated that although several pressures were described in the expert interviews responses, climate change was the most frequently described pressure, followed by water quality impairments and population growth. Moreover, it has been identified that the studied cities have implemented several strategies such as green infrastructure, recycled water, desalination, and stormwater management to achieve this holistic approach. The thematic analysis revealed that all cities demonstrated the importance of cultural change to break down silos and support various technological solutions. Further investigations revealed that cities encounter several barriers that inhibit the One Water transition. One of the most frequently discussed barriers was related to financial challenges in most cities, especially in light of the pandemic when substantial cities lost their revenue. In addition to the financial challenges, lack of regulatory process and framework, institutional barriers for expanding One Water strategies, short-term thinking, lack of collaboration, community resistance to change, lack of public support, and water rights were mentioned by participants as the top barriers.

Plant architecture is an important contributing factor for enhanced yield production and quality. The architecture traits are analyzed for crop health monitoring and genetic manipulation for generating high yielding varieties. Computer vision methods applied on 3D pointcloud allow more accurate extraction of architecture traits but consume more time and memory compared to 2D images. This study aims to design light weight 3D deep network for Cotton plant part segmentation and derive seven architectural traits of mainstem height, mainstem diameter, branch inclination angle, branch diameter, and number of branches, nodes, and cotton bolls. The pointcloud data is collected using FARO LiDAR scanner. The mainstem, branches and cotton bolls are manually annotated using Open3D. The preprocessing steps of denoising, normalization and down sampling are applied. 3D Deep network is designed to sample 1024, 512 and 256 points where neighborhood aggregation is performed at radius levels of 1cm, 5cm, and 30cm respectively. Features for remaining points are interpolated. The features from each radius level are concatenated and passed to multi-layer perceptron for pointwise classification. Results indicate that mean IoU and accuracy of 84% and 94% are achieved respectively. A 6.5 times speedup in inference time and 2.4 times reduction in memory consumption compared to Pointnet++ is gained. After applying postprocessing on part segments, an R square value of more than 0.8 and mean absolute percentage error of less than 11% are achieved on all derived architecture traits. The trait extraction results indicate potential utility of this process in plant physiology and breeding programs.

Plant roots exhibit distinct architectural organization and overall shape. Current concepts to quantify architectural variation assume a homogeneous phenotype for a given genotype. However, this assumption neglects the observable variation in root architecture for two reasons: (i) sampling strategies are designed to capture architectural variation only for the most common phenotype, and (ii) traits are often measured locally within a root system and ignore the architectural organization. Here, we introduce a new concept: the phenotypic spectrum of crop roots to quantify architectural variation as the number of architecture types for one genotype in a specific environment. We use the shape descriptor DS-curve to characterize the whole root system architecture. Using DS curves as a core, we developed a computing pipeline that combines Kmeans++ clustering, outlier filtering and the Fréchet distance as a similarity metric to classify types of root architectures. Subsequently, we applied this pipeline to analyze a field dataset including three common bean (Phaseolus vulgaris) genotypes DOR364 (n=797), L88_57 (n=1772), and SEQ7 (n=768) under non-limiting and water-stressed conditions in 2015 and 2016. We found DOR364 showed five different root architecture types across environments, while L88_57 and SEQ7 showed four. The total variation within classified root architecture types of DOR364, L88_57, and SEQ reduced by 58.59%, 50.19% and 53.01%, compared to the variation of the complete data sets. DOR364 had stable fractions of root architecture types across environments. In contrast, L88_57 and SEQ7 showed more variation in their fractions. There was no significant biomass difference among root architecture types for all studied genotypes within each environment. As such, we hypothesize that the phenotypic spectrum might buffer the impact of environmental stresses as an acclimatization strategy by changing the composition of root architecture types at the population level.

To assist plant scientists, geneticists, and growers to understand crop-environment interactions, plant phenotyping is a powerful tool for improving crop cultivars and developing decision support systems in farm management. Recent trends use LiDAR to capture three-dimensional (3D) information from plants to analyze traits vital to plant growth and development. However, current terrestrial-based 3D analysis methodologies are time and labor intensive and can be a bottleneck when large agricultural fields need to be analyzed. Robotic technologies can be used to accelerate the field-based measurements of relevant plant features and optimize the high-throughput phenotyping process. In this paper, we present a robotic system with a 3D LiDAR and a data processing pipeline for efficient, high-throughput field phenotyping of cotton crops. The robotic system consists of a Husky robotic platform equipped with a FARO Focus 3D laser scanner. The components of the system are integrated under the ROS framework to ensure interoperability and data integrity and availability at any given time. The data processing pipeline involves the data collection, registration, and analysis tasks for measuring crop traits at the plot level—canopy height, volume, and light interception—and estimating yield. This work demonstrates a crop phenotyping platform that leverages two off-the-shelf equipment for the quantitative assessment of cotton plant traits in the field. This methodology can be extended to other agricultural crops contributing to the advancement of plant phenomics.

Ground-based microwave radiometry is a common tool to estimate profiles of the atmosphere. With a high temporal resolution radiometers have became an alternative to atmospheric sounding like radiosondes. However remote sensing radiometry requires the use of inversion algorithms, where methods like linear-, quadratic-regression or Artificial Neural Network are commonly used. The present study implements a Bayesian inversion technique as alternative to the state-of-the-art retrieval algorithms provided by the radiometer’s manufacturer firmware. The Bayesian inversion provides advantages over other established methods, namely: the use of a-priori suited for the specific climatology under observation, the estimation of the most likely profile along with its uncertainty obtained from the posteriori distribution, and the feasibility to add synergistic observations from other instruments to increase retrieval capabilities. To estimate the uncertainties resulting from the Bayesian and firmware retrieval algorithms, synthetic radiometer data have been created by means of radiative transfer simulations using radiosonde profiles as descriptor of atmospheric states. These synthetic data mimics the instrument’s firmware binary files letting the radiometer to perform retrievals as real measurements. By analyzing the differences from retrieval results relative to the known true profile we assess uncertainty metrics to characterize the algorithms. It has been found that Bayesian inversion reproduces more accurately the profile vertical structure as compared to the firmware, specially for humidity profiles. Absolute errors have been strongly reduced mainly at the lower atmosphere. The study concludes that Bayesian inversion for ground-based atmospheric profiling produces results resembling observations by radiosondes when a suitable a-priori distribution is used.

To prevent serious problems occurring in abandoned mines such as ground subsidence, it is commonly carried out to fill cavities with some materials. During or after the cavity-filling process, we need to monitor distribution of filling materials in abandoned mines. Various geophysical methods, such as microgravity, electrical resistivity, ground penetrating radar, and seismic methods, have been used to describe abandoned mines themselves or to monitor distribution of filling materials. Microgravity, electrical resistivity, ground penetrating radar, and microseismic methods can be used to detect cavities, but may have some limitation in monitoring material distributions. In this study, we apply the seismic reflection method to image distribution of filling materials in near-surface abandoned mines. As the imaging methods, we use full waveform inversion and reverse time migration. In addition, we apply seismic interferometry to obtain better results. The full waveform inversion and reverse time migration methods are applied to four models which can mainly appear in abandoned mines. Through numerical examples, we investigate feasibility of seismic reflection method for describing filling material distribution in abandoned mine. Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2017R1A2B4002031) and by the project funded by the Ministry of Oceans and Fisheries, Korea (D11603317H480000112).

Studies on the molecular mechanisms of microbial adaptation in chaotropic and low water activity (aw) environments are poorly understood. Chaotropic environments are characterized as salt rich, MgCl2 and CaCl2, which lowers the availability of water for biological processes. PATRIC, an integrated genomic browsing tool containing vast libraries of sequenced genomes, can help us identify unique genetic markers in chaophilic and xerophilic microbes. Halophilic microbes are characterized as obligate hypersaline with the ability to tolerate exposure to chaotropic agents. Microbes with the greatest tolerance in these extreme environments must have advanced adaptive methods. Halobacterium salinarum and Haloquadratum walsbyi are chaotolerant and well adapted to low water activity. Haloquadratum walsbyi is unique among the halophilics as having the highest tolerance for chaotropes and its square shape. Performing comparative genomics using fully sequenced halophilic archaea such as Halobacterium salinarum NRC-1, a model halophile, and Haloquadratum walsbyi C23, we were able to identify genes that confer adaptation to chaotropic and low aw environments, as well as individual adaptations that may be responsible for the varying levels of tolerance in chaotropic environments . Characterizing genes associated with chaotolerance and low aw adaptations can help elucidate the cellular functions that make these microbes unique. Chaotropic brines may be used as analogs to study the origin of life and the possibility of suitable environments hosting extremophilic microbes on other planets like the Martian brines and the icy moons of Europa; therefore, studying the microbiome of chaotropic environments are essential in the field of astrobiology.

The potential commonality of prebiotic chemical processes on Titan and the primitive Earth makes Titan a prime body of astrobiological interest. Amino acid synthesis can occur if the abundant simple organics on Titan’s surface can mix with liquid water. Because events that melt surface ice, such as impacts, are rare, it is essential to recognize how long the synthesized molecules remain intact on Titan’s surface. The degradation of biomolecules in extraterrestrial environments can be estimated by combining theoretical work about energy deposition on the surface with experimental results from irradiation of organic molecules. We modelled the destruction of amino acids on the surface of Titan, something absent in current literature. We chose Glycine, Alanine, and Phenylalanine as our molecules of interest due to relevant experimental results for their radiation stability at Titan temperatures. Titan’s thick atmosphere prevents solar radiation and energetic particles trapped in Saturn’s magnetosphere from reaching the surface. The dominant source of energetic radiation at the surface of Titan is the diminished flux of Galactic Cosmic Rays (GCR’s) that penetrate the atmosphere. Sittler Jr et al. (Icarus, 2019) modeled surface GCR flux to be ~10^-9 ergs/cm^3/s. Using the GCR flux, in conjunction with the half-life doses at T=100 K from Gerakines et al. (Icarus, 2012), we estimate the half-lives to be 7.69 x 10^12;, 5.07 x 10^12, and 5.82 x 10^12 years for Glycine, Alanine and Phenylalanine, respectively. These extraordinarily long half-lives on Titan’s surface, as compared to similar calculations for amino acids on Mars, Europa, or Pluto, are directly the result of reduced energy deposition due to the atmosphere. We thus conclude that the degradation of these three amino acids by GCR flux is insignificant over geological time, and will not be an essential factor in interpreting the chemistry from Titan’s surface samples from future missions, such as Dragonfly.

Floods are convincingly the most frequent and widespread natural hazard across the world. With an ample amount of literature forecasting increase in its frequency and magnitude further in the future, highly credible and efficient algorithms and tools are crucial for real-time flood monitoring. In this study, a highly efficient tool, Multi-Mission Flood Mapper, has been developed to delineate flood inundation extent without any human intervention from SAR images captured by multiple microwave SAR satellite missions, including ALOS PALSAR CEOS, ALOS 2 CEOS, COSMO-SkyMed, ENVISAT ASAR, ERS 1/2 CEOS, ERS 1/2 SAR(.E1, .E2), ICEYE, JERS CEOS, KOMPSAT-5, PAZ, RADARSAT-1 & -2, RCM, SAOCOM, SeaSat, Sentinel-1, TerraSAR-X, and TanDEM-X. The efficacy of the developed tool is assessed by performing a test on a significant number of flood events in India having diverse flooding patterns and landforms. To manifest the performance of the tool, the step-by-step processing at the backend of the tool is discussed in detail in this study by taking a flood event along the Ganga River in India as a case study. The algorithm of the tool includes various processing steps: pre-processing that incorporate applying orbit file, calibrate to sigma naught, speckle filtering, terrain correction and linear to decibel conversion; thematic analysis that involves multi-segmentation and Otsu’s thresholding techniques; post-processing that consists of the elimination of hill shadows, applying majority filter, and masking out permanent water bodies. Thus derived flood inundation layer is observed to be highly accurate compared to the master image. The total time taken by the tool for processing is about 4 minutes for the given image. The developed tool would be beneficial for rapid flood inundation map generation on a timely basis for flood monitoring and relief management during a disaster. In addition, the flood inundation layers can also be used for calibration/validation of hydrological/hydraulic models, geospatial planning, and generating flood hazard maps. Also, the Multi-Mission Flood Mapper tool is facilitated with a user-friendly Graphical User Interface (GUI), making it look simple and easy to use.

Diverse, complex data are a significant component of Earth Science’s “big data” challenge. Some earth science data, like remote sensing observations, are well understood, are uniformly structured, and have well-developed standards that are adopted broadly within the scientific community. Unfortunately, for other types of Earth Science data, like ecological, geochemical and hydrological observations, few standards exist and their adoption is limited. The synthesis challenge is compounded in interdisciplinary projects in which many disciplines, each with their own cultures, must synthesize data to solve cutting edge research questions. Data synthesis for research analysis is a common, resource intensive bottleneck in data management workflows. We have faced this challenge in several U.S. Department of Energy research projects in which data synthesis is essential to addressing the science. These projects include AmeriFlux, Next Generation Ecosystem Experiment (NGEE) - Tropics, Watershed Function Science Focus Area, Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE), and a DOE Early Career project using data-driven approaches to predict water quality. In these projects, we have taken a range of approaches to support (meta)data synthesis. At one end of the spectrum, data providers apply well-defined standards or reporting formats before sharing their data, and at the other, data users apply standards after data acquisition. As these projects continue to evolve, we have gained insights from these experiences, including advantages and disadvantages, how project history and resources led to choice of approach, and enabled data harmonization. In this talk, we discuss the pros and cons of the various approaches, and also present flexible applications of standards to support diverse needs when dealing with complex data.

Greenhouse gas (GHG) emission metrics, that is, conversion factors to evaluate the emissions of non-CO2 climate forcers on a common scale with CO2, serve crucial functions upon the implementation of the Paris Agreement. While different metrics have been proposed, they have not been investigated under a range of pathways, including those significantly overshooting the temperature targets of the Paris Agreement. Here we show that cost-effective metrics that minimize the overall cost of climate mitigation are time-dependent, primarily determined by the period remaining before the eventual stabilization, and strongly influenced by temperature overshoot. Our study suggests that flexibility should be maintained to adapt the choice of metrics in time as the future unfolds, if cost-effectiveness is a key consideration for global climate policy, instead of hardwiring the 100-year Global Warming Potential (GWP100) as a permanent feature of the Paris Agreement implementation as is currently under negotiation.