It has become apparent in recent years that scientists need to find new ways to communicate and connect with the public to increase science literacy and trust of scientific results. To address these issues, the Time Scavengers website (timescavengers.blog) was created. This website is maintained and continuously added to by a team of collaborators including graduate students, post docs, museum staff, professors, avocational scientists, educators, and an editor. The website also includes static pages on the scientific method, geology, and climate science methods, as well as a number of resources for educators and others interested in science. The collaborators contribute regular blog posts on a variety of topics related to being a scientist, including the work we do in the field, learning new methods, and various aspects of our academic and career paths. One of our more popular blogs is called ‘Meet the Scientist’, which showcases diverse scientists in many different fields, from graduate students to experienced professional scientists, both U.S.-based and international. The website has reached almost 63,000 unique visitors in the two years since it was created, reaching folks speaking 155 languages in 196 countries. Using data from Google Analytics and social media accounts, including Facebook, Twitter, and Instagram, we examined some of the trends related to our broad international reach, to determine if any specific posts or types of posts attracted more international or non-English speaking visitors. Besides examining the general geographic reach over time, a few more specific comparisons were conducted. We examined whether or not Meet the Scientist posts featuring international scientists attracted more international visitors than those featuring U.S.-based scientists. We also analyzed data forField Excursions posts that described places people could visit to see if they attracted site visitors from those areas described in the post or had a broader national and international reach. Preliminary data indicate that posts about international scientists reach more countries, on average, than those featuring U.S. scientists, and geographic-specific posts reach a broad national and international audience.

Formation of surficial sulfate– and halide–bearing salts by syn–eruptive ash–gas interactions is known to occur during volcanic eruptions. For reactions between aluminosilicates and the gas SO2, at high temperature regimes (T≥ 600 °C), the controlling mechanism is the outward chemical diffusion of alkalis and alkaline earth metals, predominantly Ca2+, that result in sulfate salt formation, mostly CaSO4, on glass surfaces. However, most of the experimental research has been conducted for SO2–reactions with pure crystal–free, aluminosilicate glass, to simplify the complexities of crystal–bearing systems. Here, we tested high temperature SO2–reactions using particles of a rhyolitic, crystal–bearing dome material from a 2013 eruption of Santiaguito volcano (Guatemala), by exposing 2 g of particles to 25 sccm of SO2; at 600–800 °C, for 5–60 min each time. We then compare our results with those of previous studies using pure glass particles, aiming to determine the influence of crystal fraction and type on the occurrence and efficiency of gas–ash reactions. We conducted chemical and microscopic analysis of pre– and post–treated samples and observed that diffusion of Ca2+ is reduced in crystal–bearing samples relative to crystal–free samples at the same conditions. The rate of slow–down of the diffusion process appears to be dependent on the crystal volume fraction, providing a mechanism to account for this effect a priori. SEM images also showed that surface componentry strongly affects presence of CaSO4, as salts appear to be absent on specific surface spots corresponding to crystal phases. Our results illustrate the need for ash-gas reaction studies to further consider both the effect of bulk– and surface–componentry, in order to more accurately assess syn-eruptive gas uptake by ash.

Coastal flooding associated with hurricanes and other major storm events along the U.S. Coast results from complex interactions between freshwater flows, tides, storm surge, and wave effects. We have developed a two-way coupled model consisting of the National Water Model (NWM), the Advanced Circulation Ocean Model (ADCIRC), and WAVEWATCH III (WWIII) to quantify these interactions and compute total water levels in the coastal zone after significant riverine and coastal flooding events. This coupled continental coastal model covers the US Gulf and Atlantic Coasts, extending from the US-Canada border to the US-Mexico border. The Delft3D FM, D-Flow Flexible Mesh (D-Flow FM) model simulates coastal flooding on a 2D unstructured mesh within the National Water Model (NWM)/ADCIRC/WWIII coupled system. We developed a high quality 2D unstructured mesh using a sizing function that assigns element sizes based on proximities of coastal features at given spatial locations. Data sources used to identify relevant coastal features included NWM streamlines, the National Hydrography Dataset (NHD), and United States Army Corps of Engineers (USACE) data, allowing integration of D-Flow FM with the NWM and optimization of the number of computational points. The system obtains freshwater inflow boundary conditions to D-Flow FM from the NWM channel network. Offshore water levels boundary conditions for D-Flow FM come from the coupled ADCIRC-WWIII model. Domain sub-setting keeps runtimes within reasonable limits, as it does execution of the detailed hydrodynamic model within a user-defined area enclosing the storm landfall site. The advantage of this approach comes from the fact that the same coupled model setup allows simulation of coastal flooding for different storm events; only the sub-setting enclosure and the atmospheric forcing require updating from case to case. Model validation, consisting of water level comparisons against observations from simulations using the coupled system for historical storm events. The model simulations satisfactorily reproduced observed spatial and temporal variations of total water levels. In conclusion, this study presents performance of the sub-setting approach in reducing runtime considerably without compromising the accuracy of the coupled modeling system solution.

Generation of 2D meshes with reduced number of elements while yielding accurate results is a major challenge in coastal numerical models. High-quality 2D unstructured meshes were generated using sizing functions, which were computed from Euclidean distances to coastal features at given spatial locations and assigned element sizes based on calculated distances. The coastal features consist of National Water Model (NWM) streamlines, National Hydrography Dataset (NHD), NOAA Medium Resolution Shoreline and bathymetric features from the United States Army Corps of Engineers (USACE). This approach allows improved integration of the hydrodynamic D-Flow Flexible Mesh (D-Flow FM) model into the hydrological NWM and results in an optimum number of computational points. The method grants the user flexibility to control element sizes and avoids manual iterative procedures by determining an optimal element-sizing function that defines small element scales in regions where geometrical and physical characteristics exist, with larger scales elsewhere. Newly created continental-scale meshes on the Atlantic Ocean, Gulf of Mexico and Pacific Ocean coastlines demonstrate the application of the proposed method for automatic generation of unstructured, high-quality 2D meshes.

The influence of shield wires on Geomagnetically Induced Currents (GICs) in power systems is considered. For the most simple power network, with one single transmission line and one shield wire connecting two substations, we derive the expressions for the voltage source and the resistance of the Thévenin equivalent circuits that, in parallel with the substations grounding resistances, produce the same effects on GICs as the full circuit. Our model extends results from previous studies that considered the effect of shield wires resistances by also including the induced geoelectric field.

We seek to model the coupled evolution of a planet and a civilization through the era when energy harvesting by the civilization drives the planet into new and adverse climate states. In this way we ask if “anthropocenes” of the kind humanity is experiencing might be a generic feature of planet-civilization evolution. In this study we focus on the effects of energy harvesting via combustion and vary the planet’s initial chemistry and orbital radius.

The subdued topography of the Turkana Depression separates the elevated Ethiopian and Kenyan Plateaus in East Africa. Mechanisms to explain its topography are debated because constraints on upper mantle structure and dynamics are lacking. Attempts to understand the role of the mantle below Turkana in the evolution of rifting between the Main Ethiopian and Southern East African rifts and the onset of Ethiopian Flood Basalt volcanism are also hindered by limited data availability. Here, recently deployed seismic networks in Turkana and neighboring Uganda enable us to develop a new absolute P-wavespeed tomographic model (AFRP21) to image mantle structure below the Turkana depression. Additionally, we use P-to-s receiver functions to map the mantle transition zone (MTZ) discontinuity structure. In the shallow mantle, broadly distributed slow wavespeeds reside below the Main Ethiopian rift. To the south, slow wavespeeds occur in a focused zone below the East African rift, but beneath the northern Turkana depression these are cross-cut by a narrow E-W band of fast wavespeeds. At upper MTZ depths slow wavespeeds are broadly continuous below the East African rift but begin to separate into two distinct anomalies at the base of the MTZ. While receiver functions reveal a broadly thinned MTZ below Cenozoic rift-related magmatism in East Africa, the thinnest transition zone exists below the Turkana Depression. Slow wavespeeds and a thinned MTZ below the Turkana Depression indicate hot upwelling material, thus its low-lying nature is not due to the lack of underlying dynamic support. Instead, the depressed topography may be better explained by Mesozoic-Cenozoic E-W rifting associated with the imaged shallow fast wavespeed band. Furthermore, the main eruptive phase of Ethiopian Flood basalt volcanism may be associated with the African plate’s position over the anomalously thinned MTZ in Turkana at ~30Ma.

The quantification and monitoring of photosynthesis are essential to understand the global carbon cycle and vegetation’s responses to climate. Among the different remotely-sensed photosynthesis-related variables, Sun-Induced chlorophyll a Fluorescence (SIF) is especially promising since it results directly from photochemical energy conversion but uncertainties still complicate its interpretation. Recent studies have pointed to the influences of vegetation biochemistry and structure on radiative transfer as the main confounding factors for the use of SIF as a photosynthesis proxy. Leaf-level fluorescence research has shown that such influences may be removed by adjusting the raw fluorescence signal to the emitting leaf’s spectra and we suggest that this can be upscaled to the landscape level. In this study we present and test new Spectrally-Adjusted SIF formulations (SASIFs), along with previously proposed SIF modifications and other acknowledged photosynthesis productivity proxies, against carbon-flux data from vegetation of diverse structure. Accordingly, we used Gross Primary Productivity (GPP) data spanning periods from two to seven years, from 27 FLUXNET sites classified into different Land Cover Classes (LCCs) as defined by the International Geosphere-Biosphere Programme (IGBP). The data tested against GPP was calculated with GOME-2 SIF data, MODIS reflectance and spectral vegetation indices, and it included: NIRV, SIF from the red and the far-red frequency peaks, SIF normalized by the cosine of the Sun’s zenith angle, SIF-yield, new SASIFs and FLUXCOM GPP. The relationships between all variables and FLUXNET GPP were tested using time-series decomposition, site- and LCC-specific Kendall’s rank correlation tests and linear mixed model analysis. Results show that one of our new SASIFs has the best overall correlation to FLUXNET GPP among all tested data. Our LCC-specific analysis demonstrates the influences of biochemistry, phenology, temporal resolution and vegetation structure on the relationships between the tested variables. Results support the idea that chlorophyll fluorescence can be complemented with reflectance data improving our ability to monitor vegetation productivity and predict climate-driven changes to standing biomass in spite of their particular limitations.

Bangladesh, a small and over populated country in Southeast Asia occupies most of the Bengal Basin that results from sediments derived from the collision of India with Asia. The basin is filled with a 19 km thick sequence of Cenozoic sediments deposited by the mighty rivers Ganges and Brahmaputra. Unconsolidated Holocene sediments susceptible to seismic amplification characterize the upper part of the Cenozoic sequence. Bangladesh sits a top on three tectonic plates; India, Tibet and Burma. The India plate is colliding with the Tibet subplate to the north, which gives rise to great Himalayas, while to the east it is subducting beneath Burma and Sunda slivers, which gave rise to Indo-Burma arc. The Surma basin of NE Bangladesh is being underthrust under the Shillong massif producing the 2-km high plateau. The Indo-Burma fold and thrust belt results from the oblique subduction of the thick sediments of the Bengal Basin on the India plate that has deformed into a series of north-south trending en-echelon folds and thrust faults. The faults rooting these folds and the underlying megathrust are capable of generating devastating earthquakes in and around Bangladesh. Past earthquakes have brought changes to the landscape, avulsion of rivers Brahmaputra and Meghna, migration of human settlements, and widespread sand liquefactions and sand and/or mud eruptions. Our GPS study demonstrated that the landward extension of Andaman-Sumatra subduction zone into Indo-Burma subduction in deltaic Bangladesh is active. The present day India-Burma oblique convergence rate is 17 mm/y and that the décollement beneath the fold-thrust belt is locked (Steckler et. al., 2016). The western part of the subduction zone over a shallow décollement shows little seismicity whereas the eastern part shows moderate seismicity of magnitude 4 to 6. Based on the GPS velocity across the fold belt and seismicity the Indo-Burma subduction zone can be potentially be divided into locked western segment and slipping eastern segment, analogous to Cascadia subduction zone. Fold belt parallel shortening across Dauki Fault in Shillong is 7 mm/yr, which is another potential source of a large earthquake. The huge population might be severely ravaged by devastating earthquakes from both these sources.

High-resolution mapping and monitoring of global inland surface water bodies are critical to address challenges in sustainable water management practices. Planet currently operates the largest constellation of Earth Observation satellites and collects images at very high spatial (0.5 m - 5 m) and temporal (near-daily) resolutions. Here, we use PlanetScope data (resampled to 3 m) to develop a holistic and fully automated pipeline running on the Google Cloud Platform for monitoring global inland surface water. We incorporate the openly-available Global Reservoir and Dam (GRanD) data set into a three-stage supervised learning approach which initiates with an unsupervised label-generation step consisting of k-means clustering and NIR-based thresholding. We then rank the labels generated from these steps and the water labels extracted from the latest ESRI 10 m land cover data based on image contours. The best (noisy) labels having the least number of contours from this unsupervised learning stage are bootstrapped to train a deep-learning based semantic segmentation model (U-Net) on a KubeFlow pipeline. We subsequently create a new refined dataset by using these model predictions as labels which are passed to a Stochastic Gradient Descent (SGD)-based multi-temporal supervised label refinement stage (SGD classifier running on the same label for multiple input scenes). Finally, we iterate over the SGD based-supervised and U-Net-based label refinement steps to successively denoise the bootstrapped data until we obtain an acceptable test accuracy (F1 score > 0.9). Visual inspection of the results obtained over different climatic regions, terrains, and seasons across the globe shows that our approach works quite well. We also aggregate these predictions to detect temporal changes in surface water area. However, the model predictions exhibit high uncertainty in agricultural areas and complex terrains characterized by hill shadows and clouds. This issue could potentially be mitigated using hard-negative mining. Nevertheless, with the nearly-daily imaging capability of Planet, the high-fidelity surface water maps developed using this proposed supervised learning approach could be beneficial to the global water community for dealing with water security issues as part of the UN sustainable development goals.

Compound extremes exhibit greater adverse impacts than their univariate counterparts. Studies have reported changes in frequency and the spatial extent of extremes in India; however, investigation of compound extremes is in the infancy state. This study investigates the historical variation of compound dry and hot extremes (CDHE) and compound wet and cold extremes (CWCE) during the Indian summer monsoon period from 1951-2019 using monthly data. Results are analyzed for 10 identified homogeneous regions for India. Our results unraveled that CDHE (CWCE) frequency has increased (decreased) by 1-3 events per decade for the recent period (1977-2019) relative to the base period (1951-1976). Overall, there is increasing (decreasing) pattern of CDHE (CWCE) is high across North-central India, Western India, North-eastern India and South-eastern coastlines. Our findings help in identification of the parts of the country were affected by frequent and widespread CDHE during the recent period, which is alarming. More detailed assessments are required to disentangle the complex physical process of compound extremes to improve risk management options.

Compound dry and hot extremes (CDHE) during the Indian summer monsoon significantly affect agriculture. Due to climate change, the frequency, spatial extent and severity of CDHE have changed over several parts of the world. Understanding the variability of CDHE is critical for designing adaptation strategies to reduce the adverse impacts on agricultural systems. In particular, traditional assessments have focused on the variability of frequency and spatial extent using the quantile-based approach. However, counting the number of events excess over the threshold helps to understand the variability in frequency and spatial extent but fails to detect the changes in the severity. Further, limited studies have investigated the changes in CDHE severity over India. Hence, in the present study, the variability of CDHE severity is assessed during the summer monsoon from 1951 to 2020 over homogenous regions of India using a copula-based Standardized Compound Event Indicator. A significant increase in the severity of CDHE during the summer season was found in eight homogenous regions out of ten. The most vulnerable regions are northeast India and peninsular India, and interestingly, a significant decrease in the severity is observed for the north rain-belt Western Himalayan region. In addition, a significant increase in the spatial extent of the CDHE severe category is also found in all the homogenous regions over the past three decades. This study highlights that severe CDHE is associated with a high risk of severe agricultural drought for a large part of the country. Uni-variate assessments based on precipitation or temperature can underestimate the risks associated with CDHE if there is a strong dependence among the drivers.

Recent Innovations in Precision Agriculture (PA) are driven by Computer Vision and Data Processing systems to quantify plant parameters. Quantitative analysis of Plant Phenotyping in PA and monitoring morphological traits is a protracting process, precluding the objective and phenotyping pipeline. Greenhouses growing Genetically Modified (GM) crops need to be maintained at constant environmental and simulated conditions. Multiple parameters have to be controlled and regulated inside a greenhouse for effective growth of crops and yield maximisation. Not at all times are these factors derived and so, yield maximisation in greenhouse is an experimental approach to new varieties. For deduced environmental parameters and conditions for certain crops, few other biotic and abiotic factors can hinder or affect growth in certain ways that are not always factored in during calculating parameters conducive for plant growth. Such factors may not always be affecting parametral calculations, but transpose visual cues on plant growth environment such as spectral change in soil values, or minute changes like leaf reflectance or visible changes in plant stimuli to biotic factors. Plant growth is inclusive of multiple environmental variables, and yield maximisation approaches are experimental to finding the optimum derived value for these variables. Computer Vision provides a catalytic approach to predicting optimum parameters for yield maximization in phenomics. Computer Vision and Generative Adversarial Networks (GAN)’s offer a catalytic approach to the time-consuming process, providing a solution to the phenotyping bottleneck. This research proposes a concept of curating data of plant growth over time to predict conditional growth and responsive stimuli of the plant under different situations and how this can affect crop yield. The method proposed here is a non-invasive approach to the existing destructive biomass estimation methods and Frameworks. This methodology of the research focuses on utilizing image parameters modelled using a time series Progressively Growing Generative Adversarial Networks PGGAN to map plant growth patterns and progressive variance in biomass of plant in the Spatio-Temporal Domain. These Generative networks evaluate and predict based on merely raw pixel input excluding dependence on further constraints, feature vectors or parameters influencing data.

Supercritical CO2 (ScCO2) invades oilwell cement under geological CO2 sequestration conditions. With the penetration of ScCO2, cement structure prone to damage when the coupled effects of capillarity and carbonation were found. Microstructural evolution of oilwell cement samples was investigated by the CT scanning and the quantitative image-based analysis and show that ScCO2 with the high humid condition would penetrate much deeper than the dry ScCO2 because of the capillarity effects. Due to the deep saline condition in the sequestration formation, the penetration of ScCO2 was retarded by the salt deposition, comparing with the ultrapure water (UP water) conditions. For further assessment of this coupled mechanism, the permeability property and contact angle changes were proposed to analyse the interface region between ScCO2, saline/UP water and oilwell cement.

Roots are the interface between the plant and the soil and play a central role in multiple ecosystem processes. With intensification of agricultural practices, rhizosphere processes are being disrupted and are causing degradation of the physical, chemical, and biotic properties of soil. Improvement of ecosystem service performance is rarely considered as a breeding trait due to the complexities and challenges of belowground evaluation. Advancements in root phenotyping and genetic tools are critical in accelerating ecosystem service improvement in cover crops. Here I will present root phenotyping approaches for assessing ecosystem service in a prospective cash cover crop; pennycress (Thlaspi arvense L.). In development is a large format mesocosm system that will allow 3D root system architecture analysis of multiple plants. Using this system, we will be assessing how variation in pennycress root system architecture can affect ecosystem service and abiotic stress tolerance with the plant to scale from single plant to canopy level traits.

Data publication with DOI assignment has become common practice. The Citation Service for Coupled Model Intercomparison Project Phase 6 (CMIP6) was requested by the scientific panel WGCM, which is part of the World Climate Research Programme (WCRP) to enable references of the CMIP6 data in the upcoming Sixth IPCC Assessment Report (AR6). Expectations for data citations include aspects of data usage metrics to receive credit and reproducibility of published research findings. These two are difficult to combine, because a meaningful data usage metrics requires information on large data collections such as experiment data while reproducibility relies on individual datasets. In addition, data usage metrics rely on data users citing the data in the reference list of a scholarly publication and the publisher to provide data references in the articles’ metadata. Neither is a given. Organizations like the Coalition for Publishing Data in the Earth and Space Sciences (COPDESS) play an important role for the required ongoing community change. The Reliquaries approach together with PID Graph implementations being developed in the Data Citation Community of Practice subgroup shows promise for combining reproducability and credit expectations. The contribution will present survey results on CMIP6 participants’ expectations, discuss gaps in the current citation system and investigate, how new ideas can help to close these gaps. Reference: Stockhause, M. and Lautenschlager, M., 2017. CMIP6 Data Citation of Evolving Data. Data Science Journal, 16, p.30. DOI: http://doi.org/10.5334/dsj-2017-030

In the Arctic, the spatial distribution of boreal forest cover and soil profile transition characterizing the taiga-tundra ecological transition zone (TTE) is experiencing an alarming transformation. The SIBBORK-TTE model provides a unique opportunity to predict the spatiotemporal distribution patterns of vegetation heterogeneity, forest structure change, arctic-boreal forest interactions, and ecosystem transitions with high resolution scaling across broad domains. Within the TTE, evolving climatological and biogeochemical dynamics facilitate moisture signaling and nutrient cycle disruption, i.e. permafrost thaw and nutrient decomposition, thereby catalyzing land cover change and ecosystem instability. To demonstrate these trends, in situ ground measurements for active layer depth were collected to cross-validate below-ground-enhanced modeled simulations from 1996-2017. Shifting trends in permafrost variability (i.e. active layer depth) and seasonality were derived from model results and compared statistically to the in situ data. The SIBBORK-TTE model was then run to project future below-ground conditions utilizing CMIP6 scenarios. Upon visualization and curve-integrated analysis of the simulated freeze-thaw dynamics, the calculated performance metric associated with annual maximum active layer depth rate of change yielded 76.19%. Future climatic conditions indicate an increase in active layer depth and shifting seasonality across the TTE. With this novel approach, spatiotemporal variation of active layer depth provides an opportunity for identifying climate and topographic drivers and forecasting permafrost variability and earth system feedback mechanisms.

We examine long-term energetic heavy ion measurements including three planets’ magnetospheres, focusing on Fe ions (specifically, but not exclusively, Fe+) in and near Earth’s magnetosphere. We compare Fe data to that of other energetic ion species with masses greater than C (carbon) and consider the relationship(s) of energetic Fe ion measurements at the three planets to internal (ionospheres, exospheres, moons, rings, and trapped radiation) and external (solar wind and interplanetary dust) source candidates. Fe+ has been observed at Earth and Saturn, but not yet at Jupiter, as our observations there were brief. The measurements are from two functionally identical charge-energy-mass ion spectrometers: one on Geotail (~87-212 keV/e), orbiting Earth at ~9-30 Re; and the other on Cassini (~83-167 keV/e), in interplanetary space, during Jupiter flyby, and at ~4-20 Rs on its constantly varying orbits around Saturn. These ion spectrometers efficiently separate energetic light and heavy ions by mass, as well as lower charge state ions from higher charge state ions by mass-per-charge. Energetic low charge state ions often derive from magnetospheric sources, while energetic high charge state ions most often derive from the solar wind. We also enlist heavy ion measurements closer to the Earth from AMPTE/CCE which are used for C and Fe radiation-belt- modeling content, consideration, and estimation.