On January 6, 2019, OSIRIS-REx first observed particles ejecting from the surface of near-Earth asteroid (101955) Bennu. This ejection event was unexpected and was only captured by chance in a pair of optical navigation images taken by the OSIRIS-REx NavCam 1 imager. With this limited dataset of only two observations per ejected particle, traditional orbit determination to reconstruct the particles’ trajectories was not possible. Therefore, a new technique was developed for reconstruction of the ejection event based on some simplifying assumptions that the particles all ejected from the same location at the same time and that their velocities remained constant after ejection (a reasonable approximation for fast-moving particles given Bennu’s weak gravity). This technique was then applied to reconstruct those particle events observed by the OSIRIS-REx spacecraft at Bennu from January 2019 through June 2019 by Pelgrift et al. (2020). We present a follow-on to that work that applies the same technique to reconstruct the particle ejection events observed in the latter half of OSIRIS-REx proximity operations at Bennu, covering the time span from July 2019 through July 2020. We reconstructed 8 additional events, bringing the total number of Bennu particle ejection events reconstructed using this technique to 19. The new dataset includes the largest event observed to-date, with over 350 individual tracked particles. The new events have particle ejection velocities similar to the previous events, ranging from 5 cm/s to 1.8 m/s. In the full dataset of 19 events, we observed the same trend noted in the original work where the majority of events were estimated to have occurred at mid-latitudes and afternoon local solar times (LST). Reference: Pelgrift, J. Y., Lessac-Chenen, E. J., Adam, C. D., Leonard, J. M., Nelson, D. S., McCarthy, L., et al. (2020). Reconstruction of Bennu particle events from sparse data. Earth and Space Science. 7, e2019EA000938. https://doi.org/10.1029/2019EA000938

This paper represents the first national-level (United States) estimate of the economic impacts of vibriosis cases as exacerbated by climate change. Vibriosis is an illness contracted through foodborne and waterborne exposures to various Vibrio species (e.g., non-V. cholerae O1 and O139 serotypes) found in estuarine and marine environments, including within aquatic life such as shellfish and finfish. Objectives The objective of this study was to project climate-induced changes in vibriosis related to sea surface temperatures (SSTs) and associated economic impacts in the U.S. Methods Our analysis constructed three logistic regression models by Vibrio species, using vibriosis data sourced from the Cholera and Other Vibrio Illness Surveillance (COVIS) system and using historical SSTs. We relied on previous estimates of the cost-per-case of vibriosis to estimate future total annual medical costs, lost income from productivity loss, and mortality-related indirect costs throughout the U.S. We separately report results for V. parahaemolyticus, V. vulnificus, V. alginolyticus, and “V. spp” given the different associated health burden of each. Results By 2090, increases in SST are estimated to result in a 51 percent increase in cases annually relative to the baseline era (1995) under Representative Concentration Pathway (RCP) 4.5 and a 108 percent increase under RCP8.5. The cost of these illnesses is projected to reach over $5.2 billion annually under RCP4.5 and $7.3 billion annually under RCP8.5, relative to $2.2 billion in the baseline (2018 dollars), equivalent to 140 percent and 234 percent increases respectively. Discussion Vibriosis incidence is likely to increase in the U.S. under moderate and unmitigated climate change scenarios through increases in SST, resulting in a substantial burden of morbidity and mortality, and costing millions of dollars. These costs are mostly attributable to deaths, primarily from exposure to V. vulnificus. Evidence suggests that other factors, including sea surface salinity, may contribute to further increases in vibriosis cases in some regions of the U.S. and should be investigated.

The work of Monin and Obukhov has enabled a description of turbulent processes in the Atmospheric Boundary Layer using flux-variance similarity functions. These functions, also called Integral Turbulence Characteristics (ITC), are used to characterize the state of turbulence at all frequencies. However, due to the non-universality of ITC models, more investigations are necessary, especially in tropical regions where low wind conditions frequently occur. This study aims at investigating whether these normalized standard deviations obey the Monin-Obukhov Similarity Theory (MOST) above a forest site in the Sudanian climate, and at identifying the appropriate ITC models for this ecosystem. Data were collected from a 18m tower equipped with an Eddy Covariance system, above the clear forest at Bellefoungou’s village, Northwest of Benin, West Africa. The turbulence intensity parameters calculated for five years and half, were analyzed according to wind speed, stability conditions and seasons. From their relationships with the stability parameter, data driven models were then obtained by the nonlinear least squares. The results showed that, all similarity functions follow MOST with a 1/3 power law whatever the stratification of the atmosphere during all the seasons excepted the temperature which had a parabolic shape in near neutral condition (-0.05 < ζ < 0.1). A seasonal dependence of all ITCs was evidenced under stable conditions. Indeed, roughness length and strong winds which dominating especially in the dry season favored more efficient turbulent exchange at the site. We also showed that the heat transfer is relatively more efficient than H2O transfer under both stability conditions. The established temperature and CO2 similarity models are found to be closer, and for some given stratification conditions, to those already existing in literature. But a noteworthy finding is that the models often used to assign a quality criterion to turbulent fluxes showed an overestimation relatively to those established ‘locally’ for u and w through all atmospheric stratification.

This contribution highlights part of training events designed to collect, analyze, and manage water and water-related data (e.g., climate, weather, land, soils) and information products for the purposes of reducing water-related risks, and improving regional responses to environmental emergencies in the Mekong region. In this work, we discuss multiple tools and applications developed by National Aeronautics and Space Administration (NASA) scientists to lower technical barriers through current web technologies and leveraging data sharing capabilities among existing programs and institutions within different parts of the Mekong region. The aim of this training contribution is to leverage a well-established suite of tools that include but are not limited to remote sensing precipitation data adjustment techniques, i.e., the SWATOnline visualization and modeling system, and the NASAaccess data toolkit. The collaborative training events, which this contribution is part of, are administered by the United States Department of State (DOS) and the Ministry of Foreign Affairs - Republic of Korea under the Mekong-US Partnership and its Mekong Water Data Initiative facilitated by Sustainable Infrastructure Partnership (SIP) program. The Mekong Water Data is a DOS Initiative consists of multiple efforts and programs with an overarching objective of building the capacity of Mekong riparian countries and the Mekong River Commission Secretariat (MRCS), National Mekong Committees and line agencies in the Lower Mekong countries to improve the management of the Mekong River.

Thunderstorms exhibiting anomalous charge structures (ACSs, i.e., anomalous storms) are comprised by more riming ice carrying net positive versus negative charge, thought to result from increased mixed-phase cloud liquid water content (LWC). Anomalous storms are rarely observed in the United States (US) outside of the Great Plains (GP) region, where environmental conditions that suppress warm precipitation efficiency and support robust updrafts are thought to favor increased mixed-phase LWC. Two rare anomalous supercells in the Southeastern (SE) US exhibited similar charge structure characteristics as observed in GP anomalous storms, including a deep positive (negative) charge layer associated with riming (non-riming) ice in the lower (upper) mixed-phase region. However, most characteristics associated with SE anomalous environments were not consistent with those in the GP. A more rigorous evaluation of hypotheses concerning ACS development and their observation in the SE compared electrical, kinematic, microphysical, and environmental properties between the two anomalous and two SE normal supercells (i.e., exhibiting normal charge structures). Similar metrics of warm precipitation efficiency were observed in each. However, lower relative humidity in the charging region of the SE anomalous storms uniquely matched environmental characteristics in GP anomalous storms and differentiated SE anomalous from normal environments, suggesting the relative importance of saturation ratio alongside LWC in positive charging of riming ice. Differences were also observed in charge region characteristics and flash locations between a SE anomalous and normal storm. As riming ice increased in the negative charge region of the ACS, flash initiation locations were increasingly observed in stronger updrafts and updraft gradients compared with the normal storm. The evolution of microphysical characteristics of the negative charge region in the anomalous storm suggested an increase in normal alongside anomalous charging, indicating that variability in charging polarity may impact spatial flash relationships with the updraft. Further work is needed to diagnose whether particle-scale charging variability influences flash rate relationships with convective parameters such as updraft or graupel volume.

Solar energy, if carefully planned, can contribute to the attainment of global climate mitigation goals by reducing reliance on fossil fuel energy. It has been suggested that large-scale photovoltaic solar farms envisioned over the Sahara desert would reduce surface albedo, leading to increased rainfall and vegetation cover that would benefit the regional environment while meeting the world’s energy demand. However, adverse remote effects resulting from atmospheric teleconnections could offset such regional benefits. We use state-of-the-art Earth-system model simulations to evaluate the global impacts of Sahara solar farms. Our results indicate a redistribution of precipitation causing Amazon droughts and forest degradation, and global surface temperature rise and sea-ice loss, particularly over the Arctic due to increased polarward heat transport, and northward expansion of deciduous forests in the Northern Hemisphere. We also identify reduced El Niño-Southern Oscillation and Atlantic Nino variability and enhanced tropical cyclone activity. All these remote effects are in line with the global impacts of the Sahara land-cover transition ~6,000 years ago when Sahara desert was wetter and greener. The improved understanding of the forcing mechanisms of massive Sahara solar farms can be helpful for the future site selection of large-scale desert solar energy facilities.

Soil moisture is a critical component in many meteorological, hydrological, and agricultural applications, and understanding its spatial and temporal dynamics is vital for the understanding of these processes. Satellite-based remote sensing offers the ability to synoptically capture this spatiotemporal information over large areas, compared to more site-based in-situ field measurements. In this study, we use Sentinel-1 SAR imagery of the River Thames catchment, United Kingdom, over the period 2015 - 2020. A backscatter normalisation process is applied to account for the use of multiple satellite viewing geometries. A change-detection algorithm utilising backscatter power is then applied to the timeseries, to estimate relative surface soil moisture (rSSM) across the study area. To determine information across the large river watershed, smaller sub-catchments, and intra-field scales, the rSSM time series is replicated at multiple spatial scales (1 km, 500m, 250m, and 100m). Although positive biases are present during the growing season of arable farmland, comparison with rainfall data and in-situ soil moisture probes shows there is good agreement with the temporal cycle of soil moisture. These data are being used to evaluate natural flood management by land use and management across a wide area to better understand relationships between surface wetness and water storage in relation to land cover and underlying geology for the Landwise project (Landwise-NFM.org).

Climate change and deforestation influence the rainfall patterns in the tropics, thereby increasing the risk of drought-induced forest-to-savanna transitions. Forest ecosystems respond to these changing environmental conditions by adapting various drought coping strategies driven by different magnitudes of water-stress (i.e., defined here as a deficit in soil water availability inhibiting plant growth due to change in rainfall patterns). A better understanding of forest dynamics in response to the water-stress conditions is, therefore, crucial to determine the rainforest’s present ecohydrological conditions, as well as project a possible rainforest-savanna transition scenario. However, our present understanding of such transitions is entirely based on rainfall, which does not consider the adaptability of vegetation to droughts by utilizing subsoil moisture in a quantifiable metric. Using remote-sensing derived root zone storage capacity (Sr) and tree cover, we analyze the water-stress and drought coping strategies of the rainforest-savanna ecosystems in South America and Africa. The results from our empirical and statistical analysis allows us to classify the ecosystem’s adaptability to droughts into four key classes of drought coping strategies: lowly water-stressed forest (shallow roots, high tree cover), moderately water-stressed forest (investing in Sr, high tree cover), highly water-stressed forest (trade-off between investments in Sr and tree cover) and savanna-grassland regime (competitive rooting strategy, low tree cover). This study concludes that the ecosystems’ responses are primarily focused on allocating carbon in the most efficient way possible to maximize their hydrological benefits. The insights from this study suggest remote sensing-based Sr as an important indicator revealing important subsoil forest dynamics and opens new paths for understanding the ecohydrological state, resilience, and adaptation dynamics of the tropical ecosystems under a rapidly changing climate.

Context: Late on 2013 August 19, a coronal mass ejection (CME) erupted from an active region located near the far-side central meridian from Earth’s perspective. The event and its accompanying shock was remotely observed by the STEREO-A, STEREO-B and SOHO spacecraft. The interplanetary (IP) counterpart (ICME) intercepted MESSENGER near 0.3 au, and both STEREO-A and STEREO-B, near 1 au, which were separated by 78 degrees in longitude. Aims: The main objective of this study is to follow the radial and longitudinal evolution of the ICME throughout the heliosphere, and to examine possible scenarios for the different magnetic flux- rope (MFR) signatures observed on the solar disk, and measured at the locations of MESSENGER and STEREO-A, separated by 15 degrees in heliolongitude, and at STEREO-B, which detected the ICME flank. Methods: Solar disk observations are used to estimate the ‘MFR type’ and the graduated cylindrical shell model is used to reconstruct the CME in the corona. The analysis of in-situ data, namely, plasma and magnetic field, is used to estimate the global IP shock geometry and to derive the MFR type at different in-situ locations. The elliptical cylindrical analytical model is used for the in-situ MFR reconstruction. Results: The MFR structure detected at STEREO-B belongs to the same magnetic structure detected at MESSENGER and STEREO-A. The different helicity deduced at STEREO-B, might be due to the spacecraft intercepting one of the legs of the MFR, while STEREO-A and MESSENGER are crossing through the core of the structure. The opposite polarity measured at MESSENGER and STEREO-A arises because the two spacecraft measure a curved, highly distorted and rather complex MFR topology. The ICME may have suffered additional distortion in its evolution in the heliosphere, resulting in different expansion and arrival time of the IP shock flanks at 1 au.

The Maranhão reservoir (~180 hm3) is mainly used for energy production and irrigation of 15360 ha in the Sorraia Valley, in southern Portugal. The necessity to save water in the Autumn/Winter rainfall season to be used for irrigation of Spring/Summer crops creates a sensible management situation regarding the control of floods and their impact on downstream areas in years with extreme precipitation events. Streamflow forecast is then essential to improve the reservoir’s management regarding water storage. This study addresses the estimation of the daily streamflow in the watershed draining to the Maranhão reservoir (2311 km3) following two different approaches. Firstly, the physically based hydrological model, MOHID-Land, was calibrated/validated for estimating daily streamflow in the study area following physically processes and using a finite volume approach, which require considerable amount of input data. Secondly, a data-driven model composed of an artificial neural network (ANN) was used with the same purpose. This ANN model was selected from a previous work where different models (multi-layer perceptron, convolutional neural network, and long short-term memory model) were tested with different scenarios for the combination of input variables. Both models were optimized considering the observed streamflow in the Ponte Vila Formosa station, which drains 30% of the Maranhão watershed, being after applied to the entire domain. The results for the Maranhão watershed were then validated considering a mass balance considering the reservoir’s outflow, level, and water consumed values. The ANN model had a better performance predicting streamflow than the physically based model, and with less calibration effort. However, the physically based model can give much more information about the entire system. Lastly, it is expected that a physically based model can correctly estimate the streamflow for extreme events not considered in the calibration and validation datasets, but for the ANN models this question should be carefully addressed since data-driven models are event-based.

Computational software for planetary science and astrobiology is vital for understanding how complex systems interact, and for the analysis of sparse spacecraft data. PlanetProfile is an open source framework for modeling the interior of planetary bodies, especially ocean worlds, and is available in Matlab. We have rebuilt PlanetProfile in Python to improve its accessibility, as Python is free and widely used in scientific computing. PlanetProfile features such as the calculation of induced magnetic fields and seismic properties permit wider availability of these important tools for research purposes. An upcoming release of PlanetProfile contains the converted and streamlined software. New features such as a graphical user interface are in development that will support new users in taking advantage of these valuable tools.

Most remote sensing-based surface energy balance (SEB) models are limited by data availability and physical constraints to fully capture the non-linear and temporally varying nature of atmospheric, biophysical, and environmental controls on evapotranspiration (ET). As such, currently, no single SEB model is considered to work best under all conditions particularly in irrigated croplands where surface moisture conditions could change dramatically in a short amount of time. Hence, irrigation water management based on a single remotely sensed ET model is often required to cope with model limitations and data latency issues, which could lead to unsustainable and unreliable accounting of water use over time. The recent inception of ensemble-based ET modeling takes the advantage of the strengths of the several SEB models under different conditions and is found to perform better as compared to an Individual model. Yet, challenges remain in how high-temporal ET outputs from different models are accurately assembled in a way that yields the most reliable estimates of ET across any environmental and surface conditions. Specifically, existing simple or Bayesian average and machine learning-based ensemble approaches have not been able to optimally utilize the comprehensive suite of existing SEB models and the availability of multiple remotely sensed datasets. Here, we discuss the utility of convolutional neural networks (CNNs) to assemble the outputs from a host of SEB models that can robustly capture the non-linear dynamics of ET under all conditions. We will also discuss the advantage and potential limitations of using the CNN-based ensemble ET modeling framework with respect to the individual, simple or Bayesian average, and other machine learning approaches and their implications for use in allocating water use across critically dry regions. Several ensemble models will be trained using eddy covariance flux data globally and will be evaluated based on their ability to estimate ET from MODIS and Landsat sensors with both individual and fused products and minimal weather inputs. The results can provide useful insights into how multiple datasets and SEB models could be optimally utilized to accurately monitor crop water status and support sustainable water resource management in drylands.

This study investigates how course design for a first year introductory class on climate change impacts student understanding of this global issue. The course was developed to situate the scientific knowledge of climate change alongside the political, economic, and social dimensions of this human-caused crisis. This intentional course development was to give students a more holistic understanding of the causes, impacts, and solutions. The curriculum is widely interdisciplinary with scientific concepts, like the functioning of the climate system and tools of climate system research, taught alongside politics, economics, media analysis, and social justice. A teaching as research project was conducted to assess how adding the sociopolitical context impacted student's ability to situate their knowledge of the scientific basis of climate change using systems thinking. As climate change, and scientific research, operate within the social and political landscape students became more informed citizens through learning these connections. Student participants in the study have a range of backgrounds and were all majors within the college of natural sciences during their first semester of college, though not all majoring in earth sciences

After 35 years of fairly steady lava effusion from the Pu’u ‘O’o vent along Kilauea’s mid portion of the East Rift Zone, a drastic change in the volcano’s behavior was initiated in May 2018. A series of new fissures opened between May 3rd and May 9th along the eastern end of the subaerial ERZ ( >40 km from the summit caldera), feeding a few short lived, relatively small volume flows. The products from the early phase of the eruption were dominated by plagioclase and clinopyroxene phenocrysts, gabbroic glomerocrysts and other crystal clots likely picked up prior to eruption. The initial activity transitioned between May 12th and around May 28th to steadier fountaining, higher effusion rate activity, leading to the formation of larger volume lava flows and the ensuing destruction of hundreds of houses and structures within the Puna area. This period marked the arrival of olivine-bearing Pu’u ‘O’o-like magma to the eruption site. Since the end of May, activity has focused on a unique vent (Fissure 8). This contribution examines the crystal cargo from the different magmas involved in the initial four weeks of the eruption, and their history through chemical zoning patterns of crystals. We discuss the magmatic origin of mineral assemblages from the evolved basalt end-member, and present initial results on timescales of magma interactions prior to their arrival at the surface from diffusion modeling in olivine.

The EarthCube Data Discovery Studio (DDStudio) integrates several technical components into an end-to-end data discovery and exploration system. Beyond supporting dataset search across multiple data sources, it lets geoscientists explore the data using Jupyter notebooks; organize the discovered datasets into thematic collections which can be shared with other users; edit metadata records and contribute metadata describing additional datasets; and examine provenance and validate automated metadata enhancements. DDStudio provides access to 1.67 million metadata records from 40+ geoscience repositories, which are automatically enhanced and exposed via standard interfaces in both ISO-19115 and in schema.org markup; the latter can be used by commercial search engines (Google, Bing) to index DDStudio content. For geoscience end users, DDStudio provides a custom Geoportal-based user interface which enables spatio-temporal, faceted, and full-text search, and provides access to additional functions listed above. Key project accomplishments over the last year include: - User interface improvements, based on design advice from a Science Gateways Community Institute (SGCI) usability team, who conducted user interviews, performed usability testing, and analyzed a dozen of other search portals to identify the most useful features. This work resulted in a streamlined user interface, particularly in presentation of search results and in management of thematic collections. - The earlier effort to publish DDStudio content using schema.org markup resulted in significant usage increase. With over 900K records indexed by Google, nearly half of the roughly 1000 unique users per month are now accessing DDStudio via referrals from Google. - The added ability to harvest and process JSON-LD metadata makes it possible to integrate EarthCube GeoCodes content into DDStudio, and work with this content using DDStudio’s user interface. - New application domains include joint work with the library community, and interoperation with DataMed, a similar system that indexes 2.3 million biomedical datasets.

Knowing how many plants have germinated in a farmer’s or researcher’s field is central to crop management and research. Plants are commonly counted by walking the field and counting the plants in each row. This technique is labor-intensive, slow, and error-prone. Automating stand counts using RGB imagery from Unmanned Aerial Vehicles (UAV) is an obvious solution. We propose DeepMaizeCounter, a robust computational system that provides accurate stand counts for research and production fields from imagery captured by freely flown, inexpensive drones and processed with an inexpensive computer. DeepMaizeCounter exploits mosaics computed from RGB videos, using a YOLOv4 model that is trained to recognize seedling maize plants in the V2–V10 growth stages (approximately 10–40cm in height), singly or in groups of two and three plants, and determines its accuracy on these classes of seedlings. We evaluated DeepMaizeCounter against in-field and on-frame manual stand counts for a number of different maize lines in both nursery and production fields. DeepMaizeCounter can reliably distinguish corn from weeds and other grasses, counting only the maize. The network is light and able to run 175 test frames in 6 seconds, or 29 frames per second. This opens the prospect that DeepMaizeCounter can eventually be deployed on cheap platforms for real-time counting.

Plant growth and development is impacted by the ability to capture resources including sunlight, determined in part by the arrangement of plant parts throughout the canopy. This is a very complex trait to describe, but has a major impact on downstream traits such as biomass or grain yield per acre. Though some is known about genetic factors contributing to leaf angle, maturity, and leaf size and number, these discrete traits do not encompass the structural complexity of the canopy. In addition, modeling and prediction for plant developmental traits using genomics or phenomics are usually conducted separately. We have developed proof-of-concept models that incorporate spatio-temporal factors from drone-acquired LiDAR features in a maize diversity panel to predict plant growth and development over time to improve our understanding of the biology of canopy formation and development. Briefly, voxel models for probability of beam penetration into the foliage were generated from 3D LiDAR scans collected at seven dates throughout crop canopy development. From the same plots, key architectural features of the maize canopy were measured by hand: stand count; plant, tassel, and flag leaf height; anthesis and silking dates; ear leaf, total leaf, and largest leaf number; and largest leaf length and width. We develop a self-supervised autoencoding neural network architecture that separately encodes plant temporal growth patterns for individual genotypes and plant spatial distributions for each plot. Then, leveraging the resulting latent space encoding of the LiDAR scans, we train and demonstrate accurate prediction of hand-measured crop traits.

Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of entire, full sized root systems of crop plants, mesocosm growth containers were developed with an internal volume of 45 ft3 (1.27 m3). Mesocosms allow for unconstrained root growth, excavation and preservation of 3-dimensional RSA, and modularity that facilitates the use of a variety of sensors. Sensors arrays monitoring matric potential, temperature and CO2 levels are buried in a grid formation at depths of 1.25, 2.75, & 4.25 ft to assess environmental fluxes at regular intervals. Additionally, 3-dimensional water availability can be measured using ERT inside of root mesocosms. Methods of 3D data visualization of fluxes were developed to allow for comparison with root architectural traits. Following harvest, the recovered root system can be digitally reconstructed through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. Initial metrics inferred from the 3D models include root system biomass (occupied voxels), volume, flatness, convex hull volume and solidity with depth. Root systems are finally dissected and biomass measurements are made in a 3-dimensional matrix of the growth zone, while the crown is saved for X-ray CT analysis.