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This is the AmeriFlux version of the carbon flux data for the site US-CS4 Central Sands Irrigated Agricultural Field. Site Description - Heartland Farms Center-Pivot Irrigated Potato Field
This is the AmeriFlux version of the carbon flux data for the site US-CS5 Central Sands Irrigated Agricultural Field. Site Description - Heartland Farms Center-Pivot Irrigated Potato Field
This is the AmeriFlux version of the carbon flux data for the site US-CS6 Central Sands Irrigated Agricultural Field. Site Description - Worzella Farms Center-Pivot Irrigated Potato Field
This is the AmeriFlux version of the carbon flux data for the site US-CS8 Central Sands Irrigated Agricultural Field. Site Description - Worzella Farms Center-Pivot Irrigated Potato Field
Machine learning (ML) catalogs contain many more earthquakes than routine catalogs, but their performance in phase picking and earthquake detection has not been fully evaluated. We develop station‐level detection probabilities using logistic regression and combine them across a seismic network to compute spatial magnitude‐of‐completeness fields. We apply this approach to two catalogs from the 2016–2017 Central Italy sequence that were constructed from the same seismic network, one routine and one ML‐based. At the station level, the ML picker increases detection sensitivity by identifying smaller magnitude events and detecting earthquakes at greater distances. Spatially, the magnitude of completeness decreases substantially, with median values shifting from 1.6 to 0.5 for P waves and from 1.7 to 0.5 for S waves. However, the ML catalog also shows greater variability in station‐level performance than the routine catalog. These results demonstrate that ML‐based improvements in detectability are widespread but spatially nonuniform, highlighting their benefits, their limitations, and the potential for further improvements.
The 2016–2017 central Italy seismic sequence occurred on an 80 km long normal-fault system. The sequence initiated with the Mw 6.0 Amatrice event on 24 August 2016, followed by the Mw 5.9 Visso event on 26 October and the Mw 6.5 Norcia event on 30 October. We analyze continuous data from a dense network of 139 seismic stations to build a high-precision catalog of ~900,000 earthquakes spanning a 1 yr period, based on arrival times derived using a deep-neural-network-based picker. Our catalog contains an order of magnitude more events than the catalog routinely produced by the local earthquake monitoring agency. Aftershock activity reveals the geometry of complex fault structures activated during the earthquake sequence and provides additional insights into the potential factors controlling the development of the largest events. Activated fault structures in the northern and southern regions appear complementary to faults activated during the 1997 Colfiorito and 2009 L’Aquila sequences, suggesting that earthquake triggering primarily occurs on critically stressed faults. Delineated major fault zones are relatively thick compared to estimated earthquake location uncertainties, and a large number of kilometer-long faults and diffuse seismicity were activated during the sequence. These properties might be related to fault age, roughness, and the complexity of inherited structures. The rich details resolvable in this catalog will facilitate continued investigation of this energetic and well-recorded earthquake sequence.
Conference paper presented at 17th International Conference on Greenhouse Gas Control Technologies (GHGT-17), Calgary, Alberta, Canada, October 20–24, 2024. This study estimated the accuracy of the predicted 5-year carbon dioxide (CO 2 ) and pressure plumes for a hypothetical geological storage project injecting CO 2 into the Broom Creek Formation near central North Dakota.
A 10-year study (2004-2013) was conducted to determine mosquito distribution and the prevalence of West Nile Virus (WNV) on the Nevada National Security Site (NNSS) in south central Nevada. Mosquito traps baited with dry ice were set overnight at sites where standing water provided potential breeding sites for mosquitoes. Captured mosquitoes were identified to species and tested for WNV. A total of 556 mosquitoes representing 6 species were captured and identified at 20 sites around the NNSS during 124 surveys. A vast majority (89%) of mosquitoes were Culex tarsalis. None of the mosquitoes tested were positive for WNV although 2 samples, both Culiseta inornata, were suspect for WNV in 2005 and 2006, both from Well 3 Pond. Additionally, 24 birds representing 5 species were opportunistically collected and tested for WNV with no positive results. Results from this study reveal patterns of mosquito distribution across the NNSS and suggest that NNSS workers have a very low risk of being exposed to WNV.
The compendium of Hanford site sediment mineralogy data provided in this report was initiated in 2012 to support flux mitigation technologies and real-time monitoring that could limit contaminant fluxes to the groundwater in the Central Plateau. Recent efforts in advancing spectral induced polarization as a method for monitoring contaminant transformations for active biogeochemical remedies have underscored the need for a published compendium of sediment characterization data. Although the data are primarily focused on the mineralogical composition of Hanford sediments, other soil measurements such as particle size distribution such as particle size distribution and carbon content are provided. The data have been reproduced from the original reports and are provided here for information only (FIO).
This report discusses the results of baseline geochemical data from a carbon dioxide (CO 2 ) enhanced oil recovery (EOR) field in the Permian Basin’s Central Basin Platform. This report focuses on understanding the variability in geochemistry during normal oil field practices, including the transition from water flooding to a water-alternating-gas (WAG) technique. The primary objectives of this study were to focus on 1) determining the best general geochemical parameters to identify produced water intrusion into overlying groundwaters, 2) observing if there was any intrusion during the sampling period, and 3) identifying changes in produced water following CO 2 injection.
The purpose of this groundwater sampling plan (referred to herein as “the Plan”) is to describe the approach for collecting and analyzing groundwater samples to meet the objectives of the U.S. Department of Energy (DOE), Environmental Management (EM) Nevada Program’s Underground Test Area (UGTA) Activity. The Plan is focused on the Central and Western Pahute Mesa corrective action units (CAUs 101 and 102). The remaining UGTA CAUs are in the final stage of the UGTA strategy (Closure Report [CR] stage), and monitoring requirements for these CAUs are described in their respective CRs. The Plan supersedes the Nevada National Security Site Integrated Groundwater Sampling Plan and its ensuing Record of Technical Change (ROTC) (DOE/EMNV, 2019b). The Plan is designed to ensure compliance with the UGTA Quality Assurance Plan (QAP) and its ensuing ROTC (DOE/EMNV, 2018), and the Federal Facility Agreement and Consent Order (FFACO) (1996, as amended). The Plan is implemented by the Environmental Program Services (EPS) contractor. Implementation of the Plan provides data to complete corrective actions prescribed by the FFACO to protect the public from groundwater contaminated by historical underground nuclear testing. The Plan’s scope comprises sample collection and analysis requirements for assessing the extent of groundwater contamination from underground nuclear testing. Sampling wells located in the communities surrounding the Nevada National Security Site (NNSS) is performed by other organizations (e.g., management and operating [M&O] contractor) and is therefore not included within the Plan. The results of the community sampling are presented in the annual NNSS environmental reports.
This report is an update to the Phase II Corrective Action Investigation Plan for Corrective Action Units 101 and 102: Central and Western Pahute Mesa, Nevada Test Site, Nye County, Nevada (NNSA/NSO, 2009) activities resulting from data collection and analysis conducted between 2009 and 2019. This report contains two sections: (1) a summary of how the new data have informed the updated approach to complete the characterization activities under the corrective action investigation (CAI), and (2) a table documenting the specific activities identified in the CAIP with a status and expected outcome of each activity (see Table A-1 in Appendix A). As stated in Sections 1.5 and 1.5.1 of the CAIP, the CAIP was conceptualized to be iterative, with continuous review and assessment of the data, data analysis, and modeling to provide a guide for decisions regarding additional work. This report provides the revised plan, based on an assessment of the data, to complete the characterization activities identified in the CAIP.
EPOC uses the Deep Dive process to discuss and analyze current and planned science use cases and anticipated data output of a particular use case, site, or project to help inform the strategic planning of a campus or regional networking environment. This includes understanding future needs related to network operations, network capacity upgrades, and other technological service investments. A Deep Dive comprehensively surveys major research stakeholders’ plans and processes in order to investigate data management requirements over the next 5–10 years. Deep Dives help ensure that key stakeholders have a common understanding of the issues and the actions that a campus or regional network may need to undertake to offer solutions. The EPOC team leads the effort and relies on collaboration with the hosting site or network, and other affiliated entities that participate in the process. EPOC organizes, convenes, executes, and shares the outcomes of the review with all stakeholders. Between December 2020 and August 2021, staff members from the Engagement and Performance Operations Center (EPOC) met with researchers and staff at University of Central Florida (UCF) for the purpose of a Campus-Wide Deep Dive into research drivers. The goal of this meeting was to help characterize the requirements for five campus research use cases and to enable cyberinfrastructure support staff to better understand the needs of the researchers they support.
This summarizes technical highlights from the DOE Early Career Project entitled “Understanding the Roles of Cloud Microphysics and Land Surface Coupling Feedbacks in Multi-Scale Predictions of Central US Summer Hydroclimate” led by PI Mike Pritchard at the University of California. The project’s original aims were to investigate trade-offs of cloud superparameterization such as the effects of microphysical assumptions on mesoscale convective systems (Section I) and the representation of land-atmosphere coupling under explicit convection (Section II). Under the same broader theme of water cycle dynamics, superparameterization, and land interaction, the project evolved to further investigate the climate dynamics of irrigation, vegetation and streamflow (Section III), the dynamics of the Madden- Julian Oscillation (Section IV), as well as the use of modern machine learning to replace traditional sub grid approaches for climate simulation (Section V). Twenty peer-reviewed journal publications were produced as a result of this project (Section VI).
The Gluonic Excitations (GlueX) experiment is designed to search for exotic hybrid mesons using photoproduction, and to study the hybrid meson spectrum predicted from Lattice Quantum Chromodynamics. For the first time, the GlueX Central Drift Chamber was controlled autonomously using machine learning (ML) to calibrate in real time while recording cosmic ray tracks. We demonstrate the ability of a Gaussian Process to predict the gain correction calibration factor used to determine a high voltage setting that will stabilize the CDC gain in response to changing environmental conditions; this is in contrast to the traditional, computationally expensive method of calibrating raw data after data collection is complete.
This document is the primary output of the Proton Intensity Upgrade Central Design Group (PIU CDG) which was convened in the summer of 2022. The Group was asked to study options and provide recommendations for upgrading the Fermilab Accelerator Complex including increased reliability and capability and a focus on increasing the proton intensity to the LBNF beamline. Several approaches and combinations of approaches were examined for capability, cost, schedule, risks, and necessary R&D
The Arctic is warming faster than any other region on Earth, causing glaciers to melt, frozen ground to thaw, and the Arctic Ocean's ice to shrink. These consequential changes induce feedback loops that exacerbate warming and affect weather and climate worldwide. Microscopic airborne particles called "aerosols" and clouds in the sky are crucial for regulating heat and light reaching the Arctic surface. However, the magnitude of their effects is not adequately quantified, especially in the central Arctic, where they impact temperatures directly over the sea ice. Unique aerosols called "ice nucleating particles" (INPs), which play a significant role in cloud ice production, remain understudied. Understanding how ice forms in clouds is critical in the Arctic, as it affects cloud lifespan, interactions with heat and light, and precipitation. In this project, we conducted the first-ever observations of INPs in the middle of the Arctic over a whole year, covering the entire period when sea ice grows and melts. Furthermore, these are the first observations of INPs in different size ranges throughout the year, anywhere in the world. We use DNA sequencing to evaluate the presence of various types of microorganisms, while INP measurements on seawater, sea ice, snow, and meltwater samples help assess potential local Arctic sources of INPs. The results from this work are currently being used to improve the accuracy of Arctic cloud formation in various models.
We have developed and implemented a machine learning based system to calibrate and control the GlueX Central Drift Chamber at Jefferson Lab, VA, in near real-time. The system monitors environmental and experimental conditions during data taking and uses those as inputs to a Gaussian process (GP) with learned prior. The GP predicts calibration constants in order to recommend a high voltage (HV) setting for the detector that maintains consistent detector performance (gain and resolution) throughout data taking. This approach is in stark contrast to traditional detector operations in which the detector operates at fixed HV and its calibration parameters vary quite considerably with time. Additionally, the ML based system utilizes uncertainty quantification to correct the recommended control parameters when appropriate. We will present results from the ML system autonomously during the Charged Pion Polarizability (CPP) experiment conducted in Hall D at Jefferson Lab.