Geologists need help classifying microscope rock images of sigma clasts; a type of mantled porphyroclasts widely used as kinematic indicators in rocks. Knowledge about the shear sense of sigma clast during formation (either CCW or CW shearing) gives insights into rock formation history. This work reports on early investigation of machine learning techniques for automatic detection and classification of sigma clasts and their rotation from photomicrographs. Convolutional Neural Networks (CNNs) are used to extract and leverage defining features of sigma clasts, such as shape, color, texture, and tail direction to improve accuracy. We leverage existing models that are pre-trained on very large collections of images, and use transfer learning techniques to apply them to microstructure images. We used YOLOv3 to identify different sigma clasts in a given image. We also experimented with other large pre-trained models such as ResNet50, VGG19, InceptionV3 with two additional layers trained specifically on our dataset. In order to facilitate exploration of different models with different settings, we are developing a computational experimentation environment to visualize different CNN network layers, classification heatmaps, and comparative metrics. Finally, since models perform better when more data are available, we are developing a web application to collect additional data from geoscientists and incentivize their participation in open science. The website allows researchers to upload images of rock microstructures, showing them the classification of the images based on the best models available, and allows them to correct any errors which can be used to improve the models.

We present several methods for improving plant reconstruction from multiple 3D observations. Producing 3D data useful for plant phenotyping requires proximal sensing (e.g. line scanner, depth camera) at multiple incident angles (φ) and often with multiple passes. These resulting individual point clouds must then be assembled into a single point cloud for analysis. Our interest in improving the registration of individual plants is focused specifically on observations made within field settings which present additional challenges over laboratory 3D scans, where background, overlap and light conditions can be controlled. To develop these methods, we use several season’s worth of data from the University of Arizona’s Field Scanalyzer located in Maricopa, Arizona. Our approach prioritizes: (1) plant completeness, (2) noise reduction, (3) temporal similarity and (4) computational efficiency. The first priority is accomplished simply by prioritizing individual point clouds that contain the majority of the individual plant. 3D field scanning can result in component point clouds that are from near-identical φ and cover the same portions of the individual plant. This results in both additional noise and uncertainties due to small georeferencing errors and plant movement between scans. Thus, we remove the data that is furthest in time with non-unique φ in order to achieve priorities 2 and 3. Our method results in small scene reconstruction which has low memory and computational demands. In order to improve registration further, we investigate iterative closest point (ICP) registration fitting using weights defined by crop height distributions and semantic segmentation point labeling.

Abstract It has been debated globally that the COVID-19 lockdown had significantly diminished the emission levels of anthropogenic greenhouse gases (GHGs). However, different countries possess different footprints of GHGs emission. In regions with inconsistent air quality observation, spaceborne sensors can provide synoptic assessment of air quality with time-based environmental decision making. In this study, we utilised satellite data to quantify the temporal dynamics of carbon monoxide (CO) and nitrogen dioxide (NO2) between the pre-lockdown (January–March 2020), lockdown (April–July 2020) and post-lockdown (August–September 2020) periods in Nigeria. Periodic TROPOspheric Monitoring Instrument (TROPOMI) datasets were acquired from the Google Earth Engine Sentinel-5 Explorer and the Copernicus Open Access Hub. The Population-Weighted Mean Concentration (PWEC) of CO and NO2 was computed using raster-based population data and place-based air quality estimates. The associated economic correlates were computed using data mined from TROPOMI and available health records of Nigeria. Satellite data analysis showed that aggregate CO reduced by 35.1% (25.32⋅105 tons) and 9.06% (6.54⋅105 tons) and NO2 plummeted by 32.81% (22,500 tons) and 11.63% (5,360 tons) during the lockdown and post-lockdown periods across the 36 States of the country. While mobility rate dwindled substantially, mortality rate savings from the exposure to damaging effects of the GHGs were roughly $ 14 million (CO) and $10 million (NO2). The fluxes in CO and NO2 suggest that anthropogenic interference in air quality accounting can aid the understanding of the convoluted human–nature relationships for sustainable environmental management.

Biochar is the carbon rich product of organic waste pyrolysis under oxygen-free conditions. Biochar properties are inherited from the raw materials used and can be enhanced with production methods. The versatility of the raw materials, along with the tweaks in production methods, enable biochars to have a widely varied range of properties, that make it suitable for a multitude of applications. This versatility, in turn, makes characterization, and performance monitoring challenging especially in large scale applications, typical of biochar use. Biochars have unique electrical properties that could be used to fill this gap. The complex electrical properties of biochar remain largely unstudied; this work explores them for different types and concentrations of biochar in porous media. Early measurements on the complex conductivity signatures of biochars, show promising results for both characterization and mass/volume estimation. Biochar surface properties appear have a profound effect on complex conductivity signals, suggesting the potential use of such methods as characterization and monitoring tools, even in large scale operations.

Due to the high spectral resolution goals (R > 2x10^4) for the upcoming Full-sun Ultraviolet Rocket Spectrograph (FURST), instrument calibration will be particularly important. The Solar Physics groups at NASA MSFC and Montana State University (MSU) have been developing the tools necessary to achieve this goal. These include improved tracking of error propagation, in-situ monitoring of the camera gain with a radioactive Fe-55 source, and even better wavelength calibration. This presentation will focus on the latter. We will highlight the development of a calibration method which uses a two-dimensional second-order polynomial to map pixels to wavelength under a simulated noisy diagnostic lamp signal. Additionally, we have introduced a tilted CCD in order to overcome the Nyquist limit. With this as the background, we have been investigating an effect known well among ground-based imaging: geocoronal absorption. We have been looking into how much this effect will be present in the atmosphere at sounding-rocket altitudes (~100-200km). Many studies have found ways to correct for these so-called “Telluric” lines. However, it may be that these lines can in fact be a useful tool to further improve our calibration, rather than simply a nuisance to be corrected for!

Arctic amplification is a near-universal feature of climate change in simulations. However, climate models disagree in its magnitude and in its spatial and seasonal expression. Lower tropospheric stability (LTS = T_{850hpa} - T_{2m}) has been linked to Arctic amplification through its influence on radiative cooling efficiency and vertical propagation of surface fluxes. Using monthly mean output from the Community Earth System Model Large Ensemble (CESM LE) we find that internal variability in CESM LE is insufficient to explain the differences in LTS distributions over the Arctic Ocean found in CMIP3 and CMIP5 multi-model ensembles.To facilitate comparison with prior work we compare the CESM LE output to the ECMWF interim reanalysis (ERA-I) for the period 1979-2005. Over the ocean surfaces north of 60°, LTS exhibits a bimodal distribution. Dividing model and reanalysis output into open water and sea ice domains based on a sea ice concentration (SIC) threshold of 15% confirms LTS bimodality is the result of summing distinct distributions. Over sea ice, median NDJF LTS is 3.6 K in ERA-I and ranges from 5.7 K to 6.9 K in the CESM LE. Interquartile range of NDJF LTS is 4.7 K in ERA-I and varies from 9.6 K and 10.5 K across the ensemble. Spatial and seasonal patterns of LTS are qualitatively similar in the model and reanalysis: over ice LTS is positive through most of the year and slightly negative in the summer, and interannual variability is highest near the ice edge. However, the seasonal cycle of stability is stronger in CESM LE. We find that stability during early spring is consistently higher in CESM LE than in ERA-I. The enhanced variability over the central Arctic in CESM LE appears to be the result of variation in sea ice thickness.

The Solver of Hydrological Unstructured Domain (SHUD) is an integrated multi-process, multi-scale, multi-timestep hydrologic model where the dominant hydrological processes are fully coupled using the semi-discrete Finite Volume Method. The hydrologic processes in land surface, aquifer and river are fully coupled and solved together. The high spatial and temporal resolution in SHUD provides detailed and reliable hydrologic metrics in a watershed. The SHUD System consists of SHUD (the hydrologic model) and SHUDtoolbox (a data processing tool kit). The new SHUD system provides capabilities for public data downloads, pre-processing, hydrologic modeling, automatic calibration, post-processing and spatial visualization, and is fully open-source and ready for hydrological modelers to use. Here we introduce the philosophy of SHUD, from perceptual to computational structures of watershed hydrology, and select two watersheds (one in Africa and the other in California) as examples to demonstrate the workflow and capabilities of SHUD System. In modeling these examples, we exploit national/global public datasets and exemplify the data management, hydrologic analysis, model calibration and visualization capabilities. This modeling system not only supports quick deployment of hydrologic modeling functionality, but also benefits community modeling in other hydrology-related research fields, such as limnology, agriculture, climate change and Coupled-Natural-Human systems.

Migration is a complex and interdisciplinary problem involving multiple factors such as social interactions, resource scarcity, and geographical features. These factors must be incorporated in migration models, but how? We feel that the issue how different factors should be incorporated is not carefully addressed in existing models. Configuring factors in ways that are theoretically unsound can lead to false migration patterns and undermine the usefulness of models; indeed, factor configurations may be more critical than the factors themselves or other inputs. Therefore, we ask: i) How important is factor configuration to output results comparing with other inputs?; ii) How do different factor configurations produce different migration patterns?; and iii) How can multimodality of certain output distributions be controlled in a management perspective? To address the questions, we develop a “toy” migration agent-based model (ABM) and explore three possible configurations between two factors: i) two factors are perfectly substitutable (ADD), ii) both factors are indispensable (AND), and iii) either is enough (OR). ABM results are analyzed by global sensitivity analysis (GSA) and Monte-Carlo Filtering (MCF). The relative importance of factor configurations quantified by GSA emphasizes why we need to consider how the factors are incorporated. Depending on factor configurations, we also observe unimodal or multimodal output distributions. MCF is then applied to the ABM-GSA results to address how policymakers should control certain inputs to sustain systems with desirable outputs. Altogether, we have integrated ABM, GSA, and MCF to disentangle complexity of migration models and better understand underlying mechanisms and patterns of migration.

Over 270 major dams have been constructed in the Southeastern United States (SEUS) during the past century, changing natural flow patterns and affecting stream temperatures. Projected increases in air temperature combined with changes in precipitation may result in water scarcity and affect maximum stream temperatures during the summer for some regions in the SEUS. Currently existing reservoirs mitigate water shortages during drought by releasing more water but reducing residence time, the ratio of reservoir volume to inflow. Regulating stream temperature in the summer can be done by either increasing residence time or releasing more water. In this study, we investigate the extent to which the current reservoir infrastructure can be used to mitigate the impacts of climate change under current reservoir regulations as well as the range of operating rules that could minimize climate change impacts on both streamflow and river temperature. We use the Variable Infiltration Capacity (VIC) hydrological model to simulate runoff, which is then used as input to a large-scale river routing-reservoir model (MOSART-WM) to simulate reservoir operations and produce regulated streamflow. VIC and MOSART-WM outputs are then used as input to a stream temperature model that accounts for thermal stratification in reservoirs (RBM-res). Climate change projections are based on two representative concentration pathways (RCPs) and multiple global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We compare modeled changes with those from a model implementation that does not include any reservoirs and which therefore lacks any flow regulation (VIC->MOSART-RBM) to evaluate the resilience of current reservoir infrastructures. We also evaluate different reservoir operating rules (residence time versus low flow mitigation) to investigate the extent to which the current reservoir system can be used to mitigate the impacts of climate changes on both streamflow and stream temperature.

Low-current tributary-estuaries and embayments along the margin of the Hudson River are uniquely at risk for harmful algal blooms of cyanobacteria (cyanoHABs) due to rising temperatures as a result of climate change. An increased prevalence of cyanoHABs in near-shore, low-current sections of the Hudson River could be extremely harmful to nearby communities, aquatic organisms and wildlife. To address this increased risk, it is imperative to understand the current in-stream and upstream abiotic environmental controls (nutrients, water temperature, etc.) on the current background levels of cyanobacteria within the Hudson River. It is also important to understand how these controls and cyanobacterial populations vary spatially with relation to the higher risk, lower-flow sections along the margins of the Hudson River. Locations of tributary-estuaries of special concern within the Hudson Valley include Esopus Creek in Saugerties, Rondout Creek in Kingston, and Wappingers Creek in Wappingers, NY. Other locations of concern are embayments along the Hudson River such as Long Dock Park in Beacon, Port Ewen in Kingston and Norrie Point in Staatsburg, NY. Given the lower-flow nature of these sites, elevated surface water temperatures are likely a result of settled, striated layers from decreased current. These locations are also susceptible to growth of the invasive species Trapa natans or commonly known as the European water chestnut. High concentrations of nutrients like nitrogen and phosphorous within the water chestnut bloom and the captured sunlight from metabolic processes like photosynthesis can create an ideal microhabitat for harmful algae like cyanobacteria. The background levels of cyanobacteria in outflows of tributaries, and their lower-flow estuary extensions were observed alongside the water quality within the water chestnut blooms of these sites at varying depths. By studying the weekly changes in background abundance of cyanobacteria and their drivers occurring at contrasting locations along the Hudson River, it was found that the strongest controls included turbidity, temperature and levels of phosphorous. In locations of low turbidity and high surface water temperatures, the background levels of cyanobacteria were higher in these lower-flow areas than in areas with increased turbidity. Cyanobacteria was found in greater number within water chestnut blooms than in whole water samples outside the area of the bloom. High surface temperature and riverbed temperature also related to higher levels of cyanobacteria. Given the concluded information, it is apparent that invasive water chestnuts within lower-flow extensions of the Hudson River hold a greater threat than originally understood; creating an ideal habitat for potential cyanoHABs in the wake of climate change.

Anorthosites are attractive paleomagnetic recorders as silicate-hosted magnetite inclusions can be single-domain and be shielded from alteration. However, petrofabrics within anorthosites may result in magnetic remanence anisotropy that is potentially detrimental to recovering paleomagnetic direction and intensity. The Beaver River diabase of the North American Midcontinent Rift contains abundant nearly 100 percent plagioclase anorthosite xenoliths that are hypothesized to have been liberated from the lower crust by the magma enroute to becoming embedded in shallow crustal sills. In this study, we compare the remanent paleomagnetic directions recorded by anorthosite xenoliths to those of the Beaver River diabase host rocks. Given that both lithologies should record the same thermal remanent magnetization, this comparison provides a means to assess the effects of remanence anisotropy on the paleodirection recorded by the anorthosites. Thermal and anhysteretic remanence (TRM and ARM) anisotropy experiments, which are typically used to assess for anisotropy, can be compared to the natural remanence of the diabase and anorthosite in this geologic experiment that was conducted 1.1 billion years ago. Paleodirection data from the interior of the largest (>300 m) anorthosite xenoliths also have the potential to test their hypothetical lower crustal origin. An origin below the Curie depth would result in a full thermal remanence from the time of diabase emplacement, while a shallower origin from above the Curie depth could have resulted in a distinct remanence direction in the xenolith interior that predates the intrusion (with samples from the exterior having acquired a Beaver River diabase coeval thermal remanence in either scenario). Overall, this novel geological association between diabase and anorthosite provides a means to assess the effects of remanence anisotropy providing valuable context for efforts to use anorthosites to understand the ancient geomagnetic field.

Sediment settling velocities are commonly estimated from process-based or parametric data-driven approaches. The process-based approach has theoretical constraints due to the unclear settling physics; the parametric data-driven approach is limited by its mathematical assumptions. To overcome these limitations, this study compiles an aggregated sediment settling experimental database from literature and develops a non-parametric data-driven model to estimate the non-cohesive sediment settling velocity in water. A cross-comparison against five process-based equations and a parametric data-driven equation demonstrates the higher accuracy and better consistency of the new model in estimating sediment settling velocities under various physical regimes. The data-driven model also shows an easy-implemented self-update capability by assimilating theoretical data generated from the process-based equations. The updated model, leveraging experimental and theoretical data of sediment settling process, further improves the accuracy and reduces the uncertainty in estimating sediment settling velocities. This approach illustrates the value of integrating experimental and theoretical knowledge in estimating the complex process in sediment transport, and provides an alternative framework for future sediment transport exploration.

The insular Caribbean is a region influenced by Atlantic Ocean climate variability. Effects of low-frequency atmospheric circulation patterns on the precipitation of the Caribbean have been well documented. However, individual modes of variability are usually only considered in isolation. Here we analyse the combined and individual effects of the North Atlantic Oscillation (NAO) and the Atlantic Meridional Mode (AMM) on insular Caribbean precipitation. This work focuses on the Early Rainfall Season (ERS, April-July), which explains much of the interannual variability in precipitation for this region, from 1960-2016. Correlation analysis compare monthly NAO and AMM indices from the National Oceanic and Atmospheric Administration (NOAA) against monthly Caribbean precipitation from the Climate Research Unit (CRU) year-by-year climate variables by country. Sea surface temperature (SST) and sea level pressure (SLP) composites using NOAA data were also created to analyse regional patterns. Analysis of the results show that the NAO and AMM presented a correlation of opposite signs and affected the Eastern Caribbean (from Dominican Republic to Grenada) during ERS, resulting in precipitation anomalies above/below ± 10%. The combined and individual effects of NAO and AMM indicate that Feb-Mar NAO and AMM are significant correlated to May-Jun Eastern Caribbean precipitation anomalies. More frequent and consistent regional effects on precipitation anomalies, and more regionally spread and persistent SLP and SST were registered when both NAO and AMM occurred together in the previous winter. These results could be helpful in seasonal forecasting, by indicating whether a wetter or drier ERS would be expected based on the previous season NAO and AMM activity.

The Southwest U.S. comprising of the four states-Arizona, New Mexico, Colorado, and Utah-is the hottest and driest region of the United States. Most of the precipitation arrives during the winter season, but the summer precipitation makes a significant contribution to the reliability of water resources and the health of ecology. However, summer precipitation and its extremes, over this region exhibit high degree of spatial and temporal variability. In this study we developed a novel spatial Bayesian hierarchical model to capture the space-time variability of –summer season 3-day maximum precipitation over the southwest U.S. In modeling framework, the data layer the extremes at each station are assumed to be distributed as Generalized Extreme Value (GEV) distribution with non-stationary parameters. In addition, the extremes across space is assumed to be related via a Gaussian Copula. In the process layer, the parameters are modeled as a linear function of large scale climate variables and regional mean precipitation covariates. This is akin to a Generalized Linear Model (GLM). The parameters of the covariates at each station are spatially modeled using spatial Gaussian processes to capture the spatial dependency and enable generating the spatial field of the hydroclimate extremes. The likelihood estimates of the GLM at each station form the initial priors. The posterior distribution of the model parameters and consequently the predictive posterior GEV distribution of the hydroclimate extremes at any arbitrary location, or grid and for any year are obtained. The model is demonstrated by application to extreme summer precipitation at 73 stations from this region. The model validation indicates that return levels and their associated uncertainty have a well-defined spatial structure and furthermore, they capture the historical variability very well. The posterior distribution of the GEV parameters were generated on a 1/8th degree grid, providing maps of various return levels for all the years. Maps of return levels provide information about the spatial and temporal variations of the risk of extreme precipitation in the Southwest U.S. that will be of immense help in management and planning of natural resources and infrastructure.

Conventional earthquake detection methods suffer significant degradations in completeness during high-rate sequences such as aftershock sequences or volcanic swarms. Missed earthquakes during the early periods of aftershock sequences can affect aftershock forecasts and hazard estimates. Missed events during volcanic unrest sequences can impact rate estimations, leading to the sequence being mis-characterized. Much recent work has addressed how matched-filters can be used to overcome some aspects of catalog incompleteness during high-rate sequences, by detecting similar events using cross-correlation. Here we describe the application of open-source (GPL v3.0) software to the near-real-time implementation of matched-filter earthquake detection. Our software (RT-EQcorrscan) is written in Python, and leverages the extensive Obspy package, as well as EQcorrscan and Obsplus to provide matched-filter methods and database handling respectively. RT-EQcorrscan is designed to be modular, so that users can readily utilize only the components they require, or make use of pre-built command-line utilities controlled by simple that can handle thousands of templates over tens of channels of seismic data within the processing capacity (memory and CPU usage) of a standard desktop personal computer. Detections are made within a few seconds of data arriving, with latency due to data delivery and a requirement for full network move-out. At the same time, RT-EQcorrscan has an overarching “Reactor” module to listen to a web-service and respond to new events. If an event occurs that meets user-defined criteria, the Reactor will initiate a near-real-time matched-filter process encompassing the region surrounding the trigger event. Subsequent trigger events in different regions can also be handled with threaded operations. This system is backed-up by a constantly updating template database built on Obsplus, allowing groups of templates to be rapidly deployed. In this presentation we will discuss the key implementation details, as well as showcasing some examples of the system in operation.

Plate tectonics and mantle plumes are two of the most fundamental solid-Earth processes that operated through much of the Earth’s history. For the past 300 million years, mantle plumes are known to derive mostly from two large low shear velocity provinces (LLSVPs) above the core-mantle boundary, referred to as the African and Pacific superplumes. Whether such LLSVPs and their plume products are stationary through Earth history, or dynamically linked to global plates motions and subduction geometry, remains a first-order question for the geoscience community. Here, we demonstrate that transition elements (Ni, Cr and Fe/Mn) in basaltic rocks can be used as a tool to trace plume-related magmatism through Earth history. An analysis of the global petro-chemical database indicates the presence of a direct relationship between the intensity of plume magmatism and the supercontinent cycle. Such results favour the presence of a dynamic supercontinent-superplume coupling over the fixed-LLSVP geodynamic model. In addition, our analysis shows a consistent sudden drop in MgO, Ni and Cr at ~3.2–3.0 billion years ago, possibly indicating an abrupt change in mantle temperature at the start of global plate tectonics.

Planning for hydropower, water resources management, and climate change adaptation requires statistically unbiased hydrologic predictions. However, all hydrologic models contain systematic errors, e.g., incorrect mathematical representations of physical processes and effects of uncertainties in data sources. Statistical post-processing, or bias correction, is often used to reduce the effects of these systematic errors in model outputs. A large number of techniques for performing bias correction has been developed, primarily in the context of correcting statistical properties of independent locations. However, when bias correcting streamflow predictions within the same stream network, this assumption of spatial independence breaks down. Independently bias correcting locations from the headwaters to the mouth of a river system destroys the spatial consistency of the streamflow across a river network. We describe work toward maintaining spatial consistency in streamflow bias correction using a number of locations in the western United States. We simulate the hydrology of the Columbia River in the Pacific Northwestern United States, a river system that spans a number of hydroclimatic and flow regimes that contains a large number of flow gages. We develop a mapping from the modeled output at the gages with flow observations, which we use as the basis for training a machine learning (ML) model to perform the site-specific bias correction. We then apply the ML model to local streamflow contributions for each river segment, including river segments without flow observations. Finally, we combine the local bias corrections across the stream network, to create accumulated bias-corrected streamflow time series that are spatially-consistent across the stream network. We compare our method against several commonly used bias correction techniques to evaluate both model performance and spatial consistency.

Critical freshwater resources lying within mid-latitude mountain glaciers are vulnerable to a rapidly changing climate. The Lehman Rock Glacier is the only extant glacier mapped within the Great Basin National Park in Nevada. As part of an effort to understand this specialized alpine environment, we have been studying this area and conducting observations with annual student research visitations since 2005. Deploying mixed methods including an embedded sensor network, paleoclimate reconstructions, hydrological observations, and unmanned aerial system (UAS) operations, our team has documented diverse evidence of climate change over interannual to millennial scales. Starting in 2015, we conducted annual surveys of the rock glacier to measure topographic changes. Initially, we used balloon-borne photogrammetry, capturing 600+ images over about 0.1 km2 at an altitude of 500m above ground level (AGL). Despite impartial terrain coverage (50-80%) caused by limited control of the balloon rig in the air, digital elevation modeling (DEM) differencing resolved a net volume loss of 5,300m3 between 2015 and 2016. Submission of a Certification Of Approval (COA) granted our team permission to fly a UAV for the first time within the National Park in 2018 and 2019 to map the rock glacier. UAS surveying over successive days in August with > 80% horizontal and vertical overlap helped achieved 100% coverage with 900+ photos. Using previous year’s DEMs, we have optimized autonomous flight planning at 80m AGL and 168m AGL at 8.5mh and 20kmh, respectively. We will present our most recent computations of the glacier changes from years 2018 and 2019 and discuss how UAS instrumentation techniques are helping us observe changing glacier conditions at centimeter-scale resolution, better understand ecosystem relationships, and improve capabilities to model future landscapes all while mitigating mountain safety issues.