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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Competing Use Analysis for Offshore Renewables and Subsea Cables

Demand for abundant and diverse resources in the oceans is growing, necessitating marine spatial planning. To inform development of Marine Hydrokinetic (MHK) and Offshore Wind (OSW) resources, the Department of Energy (DOE) has asked NREL to identify the competing uses areas between promising MHK/OSW sites and submarine power and telecommunications cables. The first step in this work is to identify and quantify the overlap between the MHK/OSW resource availability and existing cable routes. The analysis is done in terms of resource area because the task of quantifying actual impacts on available resource is a non-trivial undertaking that involves subjective decisions of identifying resource opportunities. Quantifying overlap in-terms of resource area?on the other hand?is significantly more straight forward, and useful to marine spatial planners. This dataset of polygons describes recommended setback areas from submarine cables for marine renewable energy Development. Two archive files are provided: 1. NREL Cables Setback-Polygons.zip contains the following GeoJSON files: 1.a. cables_100ft.geojson: The Code of Federal Regulations (CFR 585.301) specifies that the legal right of way for submarine cables is 100 ft (~30 meters) to either side of the cable (i.e., 200 ft wide). 1.b. cables_2z.geojson: The International Cable Protection Committee (ICPC) of the North American Submarine Cable Association (NASCA) recommends setback distances for new facilities as the maximum of 500 m or twice the bottom depth (2z), per ICPC Recommendation 13 No. 2 (Communications Security, Reliability and Interoperability Council IV 2014). For depths 250 m, 2 * depth is to be used. 1.c. cables_3z.geojson: The ICPC of NASCA recommends setback distances for new cables as the maximum of 500 m or three times the bottom depth (3z), per ICPC Recommendation 2 No. 10 (Communications Security, Reliability and Interoperability Council IV 2014). So for depths 167 m, 3 * depth is to be used. 2. NREL Submarine Cable Analysis Code Data Presentations.zip contains all files (code, data, README, etc) from release 0.1 of this Github repository for possibly regenerating the product as submarine cable configurations change: https://github.com/ecoquants/nrel-cables. For more details, please see the link to the full report for which this data product is a supplemental output: Submarine Cable Analysis for U.S. Marine Renewable Energy Development by Ben Best and Levi Kilcher National Renewable Energy Laboratory

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Geospatial Information, Metadata, and Maps for Global River Corridor Science Focus Area Sites (v5)

This dataset provides geospatial information, metadata, and maps for the Pacific Northwest National Laboratory (PNNL) River Corridor Science Focus Area (RC-SFA; https://www.pnnl.gov/projects/river-corridor) sites. The RC-SFA works to transform understanding of spatial and temporal dynamics in river corridor hydrobiogeochemical functions from molecular reaction to watershed and basin scales. The knowledge we gain is used to formulate and test hypotheses and to improve mechanistic representation of river corridor processes and their response to disturbances in multiscale models of integrated hydrobiogeochemical function. The data provided includes Site ID, latitude, longitude, stream name, and common ID (COMID) for sites used across the RC-SFA. The COMID can be used to find and download data from NHDPlus (https://www.epa.gov/waterdata/nhdplus-national-hydrography-dataset-plus) and other platforms. The sites included are non-exhaustive. Sites (including past sites) will be added to this data package in the future. Data generated from the RC SFA can be accessed at https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA. This data package was originally published in April 2023. It was updated in June 2023 (v2; modified files), December 2023 (v3; modified files), January 2025 (v4; modified files), and December 2025 (v5; modified files). See the change history section in the readme for more details. This dataset is comprised of one main data folder. The data folder consists of (1) file-level metadata; (2) data dictionary; (3) readme; (4) methods codes; (5) geospatial information for all RC SFA sites including International Generic Sample Number (IGSN); (6) maps of all sites and sites in Washington State, USA; and (7) a subfolder with the shapefile of all sites. All files are .csv, .pdf, .shp, .cpg, .dbf, .prj, .qmd, or .shx. We thank the Confederated Tribes and Bands of the Yakama Nation for access to field locations where some data were collected in Washington state. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Effects of the U.S. inflation reduction act on SMR economics

The U.S. Inflation Reduction Act (IRA) of 2022 provides a wide array of tax credits and other incentives for low-carbon energy. The technology-neutral clean generation production tax credit (PTC) (Section 45Y of the U.S. Internal Revenue Code) and the technology-neutral investment tax credit (ITC) (Section 48E) lower the net cost of new electricity generation projects with zero or negative greenhouse gas emission rates. We evaluate the impact of the IRA legislation—specifically the PTC and ITC—on the cost-competitiveness of small modular reactors (SMRs). We use the Argonne Low-carbon Energy Analysis Framework (A-LEAF) model to calculate the capacity factor of an SMR with a range of hypothetical variable operating and maintenance (O&M) costs in the Electric Reliability Council of Texas (ERCOT) electricity market. We selected ERCOT for market modeling because of its competitive structure, available data, and extensive use in prior literature. We use a discounted cash flow model to calculate the SMR’s net present value based on the market prices and capacity factors from A-LEAF, hypothetical ranges of capital and variable O&M costs, and other input parameters, with or without the IRA tax credits. We determine the SMR owner’s optimal choice of PTC or ITC for the hypothetical ranges of capital and variable O&M costs. We also evaluate potential shifts in the SMR owner’s optimal choice of PTC or ITC based on historical patterns of nuclear capital cost overruns in the United States. We also assess the sensitivity of our results to longer PTC period and electricity prices from the New England market, which tend to be higher than electricity prices in ERCOT. We find that even with the IRA tax credits, only SMRs with low capital and variable O&M costs would be economically feasible in the low-price ERCOT market scenario modeled. A longer PTC period and higher-price market such as New England, however, would significantly expand the economic feasibility of SMRs in the United States.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Temporal Study 2021-2022: Sample-Based Surface Water Chemistry and Organic Matter Characterization across Watersheds in the Yakima River Basin, Washington, USA (v3)

This dataset supports a broader study examining the drivers of temporal variability in sediment respiration rates in the Yakima River Basin. The dataset provides geochemistry and organic matter characterization data generated from samples collected at weekly or bi-weekly intervals at six sites across the Yakima River Basin in Washington, USA. Related sensor data will be published separately and can be used to link sediment respiration rates to biogeochemical processing rates. The data package was originally published in November 2022. It was updated in November 2023 (v2; modified files) and April 2025 (new and modified files). See the change history section in readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) dissolved inorganic carbon (DIC), dissolved organic carbon (DOC; reported as non-purgeable organic carbon; NPOC), total nitrogen (TN), total suspended solids (TSS), and ions; (5) averaged values from water chemistry data; (6) surface water sampling protocol; (7) sensor protocol; (8) readme; (9) methods codes; (10) international generic sample number (IGSN) mapping file; and (11) folder of high resolution characterization of organic via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). This folder contains two subfolders, one containing the .xml data files and the other containing instructions for using Formularity (https://omics.pnl.gov/software/formularity) and an R script to process the data based on the user's specific needs. All files are .csv, .pdf, .R, .ref, or .xml. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected our data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Spatial Study 2022: Surface Water Samples, Cotton Strip Degradation, and Hydrologic Sensor Data across the Yakima River Basin, Washington, USA (v3)

This dataset supports a broader study examining the drivers of spatial variability in sediment respiration rates in the Yakima River Basin. The dataset provides data and photos generated from sample collection during the same one-week period at 48 sites within multiple rivers throughout the Yakima River Basin in Washington, USA. The contents include surface water geochemistry data; river substrate grain size photos; stream depth data; manual chamber open channel respiration data; and field metadata (including qualitative information on instream and river corridor characteristics). Grain size photos can be used to improve estimates of channel substrate D50 data. The dataset also includes tensile strength and photos from cotton strip field degradation experiments; five-week sensor time series temperature, dissolved oxygen, pressure, pH, specific conductance, chlorophyll A, and turbidity data; plots of the sensor data; and R scripts used to generate the plots. Samples collected during this study were labeled as “Second Spatial Study” or “SSS.” A subset of data from the SSS samples were published in the contiguous United States (CONUS)-Scale Model-Sample (CM) study data package available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1923689 that presents data from across the CONUS. SSS data published in the CM data package were not included in this data package. They include dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC), total nitrogen (TN), grain size, aerobic sediment respiration, dissolved oxygen (DO), and temperature. Parent IDs and Site IDs are consistent between the SSS and CM data packages, and they can be mapped directly so data across packages can be used together. Additionally, sensor data from a similar 2021 spatial study can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1892052 and 2021 sample data can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1898914. The 2021 spatial study had some sites in common with this 2022 spatial study. This dataset is comprised of three photo folders and one main data folder with six subfolders. The photo folders contain photographs and videos of cotton strip retrieval and sediment quadrats. The main data folder consists of (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) total suspended solids (TSS) data and cotton strip tensile strength data and averages; (5) field protocol; (6) readme; (7) methods codes; (8) international generic sample number (IGSN) mapping file; (9) sensor installation methods summary; (10) stream depth and averages; and (11) Ultrameter data and averages. The Sonar subfolder consists of Sonar time-series depth data and a processing script. The BarotrollAtm, DepthHOBO, MantaRiver, miniDOT, and miniDOTManualChamber subfolders contain time-series data, plots, and summary files. All files are .csv, .pdf, .txt, .R, .Rmd, .jpg, .jpeg, .AVI, .mp4, or .mov. The data package was originally published in April 2023. It was updated in August 2023 (v2; modified files) and September 2024 (v3; modified files). See the change history section in the readme for details. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Spatial Study 2021: Sample-Based Surface Water Chemistry and Organic Matter Characterization across Watersheds in the Yakima River Basin, Washington, USA (v3)

This dataset supports a broader study examining the drivers of spatial variability in sediment respiration rates in the Yakima River Basin. The dataset provides geochemistry and organic matter characterization data generated from samples collected during the same two-week period at 47 sites within multiple rivers throughout the Yakima River Basin in Washington, USA. Related sensor data are published at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1892054. This data package was originally published September 2022. It was updated May 2023 (modified files) and November 2024 (new and modified files). See the change history section in the readme for more details. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) dissolved inorganic carbon (DIC), dissolved organic carbon (DOC; reported as non-purgeable organic carbon; NPOC), total nitrogen (TN), total suspended solids (TSS), ions, and benzene polycarboxylic acid (BPCA) concentration and stable isotope data; (5) averaged values from water chemistry data; (6) surface water sampling protocol; (7) sensor protocol (8) readme; (9) methods codes; (10) international generic-sample number (IGSN) mapping file; and (11) folder of high resolution characterization of organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). This folder contains two subfolders, one containing the .xml data files and the other containing instructions for using Formultitude (https://github.com/PNNL-Comp-Mass-Spec/Formultitude) and an R script to process the data based on the user's specific needs. All files are .csv, .pdf, .R, .ref, or .xml. We thank the United States Forest Service, Washington Department of Natural Resources, Washington Department of Fish and Wildlife, Washington State Parks, Confederated Tribes and Bands of the Yakama Nation, and Cowiche Canyon Conservancy for access to field locations where these samples were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

The Building ENergy Demand (BEND) Model: A Comprehensive Composite Building Energy Modeling Framework

The Building ENergy Demand (BEND) model was developed at Pacific Northwest National Laboratory (PNNL) to simulate climate-dependent hourly building energy demand with the ability to disaggregate down to or aggregate up to any geographic area of interest, including counties, states, electric utility service territories, and census regions. The BEND modeling framework is designed to study the effects of climate change, economics, technology advancement, and various mitigation policies on aggregate building energy demand. To make the process computationally feasible, BEND reduces granularity in building characteristics and weather forcing while maintaining enough detail to minimize systematic differences (biases) between BEND results and data from the Energy Information Administration (EIA). Based on the Commercial Building Energy Consumption Survey (CBECS) and Residential Energy Consumption Survey (RECS) databases, BEND establishes a set of representative buildings with predefined sets of building types, sizes, and vintages. These representative buildings are defined for each of the four U.S. census regions and the five climate zones used by the EIA for building surveys (hereafter “EIA climate zones”). BEND’s representative buildings are based on a statistical analysis of the CBECS and RECS databases that infers detailed parameters such as construction type, thermal properties, internal loads, equipment and mechanical systems, and operation schedules from the high-level descriptors in those databases (building type, vintage, size, and location). The representative buildings are simplified and consolidated into a set of prototype buildings for the purpose of simulating building energy demand. Combining the prototype buildings with weather files from selected locations and years, BEND creates a set of simulation-ready building models to be run using the EnergyPlus simulation engine (EnergyPlus). The base spatial unit of each BEND simulation is a subregion that is the intersection of an International Energy Conservation Code climate zone, a census region, and an EIA climate zone; analysis for each spatial unit is influenced by the weather file BEND selects to represent the climate-similar area. The major simulation outputs of interest to BEND are hourly energy consumption values for each simulated year, possibly for multiple fuel types. Based on building-stock weights derived from the CBECS/RECS data, the hourly output streams are aggregated to a single, 8760-hour load profile for each of the major building categories—residential and commercial—with the possibility of segregating by fuel types. To correct for biases inherent in the use of a limited suite of representative buildings, BEND is calibrated against observational targets of total annual building energy consumption and hourly load profiles (including peak demand) obtained for the decision-relevant geographic scales of interest (e.g., total electric load data for individual balancing authorities available from EIA). Based on available data such as population distribution, BEND is able to aggregate both up and down scales, allowing the study of building energy demand over a range of spatial scales (e.g., states or counties within the Western Electricity Coordinating Council, Eastern Interconnection, or the entire U.S.) and/or subsets of building types (e.g., office buildings with particular size ranges).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

SU-A-BRA-01: Introduction

Vic Montemayor - No one has been more passionate about improving the quality and effectiveness of the teaching of Medical Physics than Bill Hendee. It was in August of 2008 that the first AAPM Workshop on Becoming a Better Teacher of Medical Physics was held, organized and run by Bill Hendee. This was followed up in July of 2010 with a summer school on the same topic, again organized by Bill. There has been continued interest in alternate approaches to teaching medical physics since those initial gatherings. The momentum established by these workshops is made clear each year in the annual Innovation in Medical Physics Education session, which highlights work being done in all forms of medical physics education, from one-on-one residencies or classroom presentations to large-scale program revisions and on-line resources for international audiences. This symposium, presented on behalf of the Education Council, highlights the work of three finalists from past Innovation in Education sessions. Each will be presenting their approaches to and innovations in teaching medical physics. It is hoped that audience members interested in trying something new in their teaching of medical physics will find some of these ideas and approaches readily applicable to their own classrooms. Rebecca Howell - The presentation will discuss ways to maximize classroom learning, i.e., increasing the amount of material covered while also enhancing students’ understanding of the broader implications of the course topics. Specifically, the presentation will focus on two teaching methodologies, project based learning and flip learning. These teaching methods will be illustrated using an example of graduate medical physics course where both are used in conjunction with traditional lectures. Additionally, the presentation will focus on our experience implementing these methods including challenges that were overcome. Jay Burmeister - My presentation will discuss the incorporation of active learning techniques into a traditional medical physics classroom course. I will describe these techniques and how they were implemented as well as student performance before and after implementation. Student feedback indicated that these course changes improved their ability to actively assimilate the course content, thus improving their understanding of the material. Shahid Naqvi - My talk will focus on ways to help students visualize crucial concepts that lie at the core of radiation physics. Although particle tracks generated by Monte Carlo simulations have served as an indispensable visualization tool, students often struggle to resolve the underlying physics from a simultaneous jumble of tracks. We can clarify the physics by “coding” the tracks, e.g., by coloring the tracks according to their “starting” or “crossing” regions. The regionally-coded tracks when overlaid with dose distributions help the students see the elusive connection between dose, kerma and electronic disequilibrium. Tracks coded according to local energy or energy-loss rate can illustrate the need for stopping power corrections in electron beams and explain the Bragg peak in a proton beam. Coding tracks according to parent interaction type and order can clarify the often misunderstood distinction between primary and scatter dose. The students can thus see the “whole” simultaneously with the “sum of the parts,” which enhances their physical insight and creates a sustainable foundation for further learning. After the presentations the speakers and moderator will be open to questions and discussion with the audience members. Learning Objectives: Be able to explain Project-Based Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain Flipped Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain active-learning strategies for the teaching of Medical Physics. Be able to explain how Monte Carlo simulations can be used to deepen a student’s understanding of radiation physics and dosimetry.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

SU-A-BRA-02: Making the Most of a One Hour Lecture with Alternative Teaching Methodologies: Implementing Project-Based and Flipped Learning

Vic Montemayor - No one has been more passionate about improving the quality and effectiveness of the teaching of Medical Physics than Bill Hendee. It was in August of 2008 that the first AAPM Workshop on Becoming a Better Teacher of Medical Physics was held, organized and run by Bill Hendee. This was followed up in July of 2010 with a summer school on the same topic, again organized by Bill. There has been continued interest in alternate approaches to teaching medical physics since those initial gatherings. The momentum established by these workshops is made clear each year in the annual Innovation in Medical Physics Education session, which highlights work being done in all forms of medical physics education, from one-on-one residencies or classroom presentations to large-scale program revisions and on-line resources for international audiences. This symposium, presented on behalf of the Education Council, highlights the work of three finalists from past Innovation in Education sessions. Each will be presenting their approaches to and innovations in teaching medical physics. It is hoped that audience members interested in trying something new in their teaching of medical physics will find some of these ideas and approaches readily applicable to their own classrooms. Rebecca Howell - The presentation will discuss ways to maximize classroom learning, i.e., increasing the amount of material covered while also enhancing students’ understanding of the broader implications of the course topics. Specifically, the presentation will focus on two teaching methodologies, project based learning and flip learning. These teaching methods will be illustrated using an example of graduate medical physics course where both are used in conjunction with traditional lectures. Additionally, the presentation will focus on our experience implementing these methods including challenges that were overcome. Jay Burmeister - My presentation will discuss the incorporation of active learning techniques into a traditional medical physics classroom course. I will describe these techniques and how they were implemented as well as student performance before and after implementation. Student feedback indicated that these course changes improved their ability to actively assimilate the course content, thus improving their understanding of the material. Shahid Naqvi - My talk will focus on ways to help students visualize crucial concepts that lie at the core of radiation physics. Although particle tracks generated by Monte Carlo simulations have served as an indispensable visualization tool, students often struggle to resolve the underlying physics from a simultaneous jumble of tracks. We can clarify the physics by “coding” the tracks, e.g., by coloring the tracks according to their “starting” or “crossing” regions. The regionally-coded tracks when overlaid with dose distributions help the students see the elusive connection between dose, kerma and electronic disequilibrium. Tracks coded according to local energy or energy-loss rate can illustrate the need for stopping power corrections in electron beams and explain the Bragg peak in a proton beam. Coding tracks according to parent interaction type and order can clarify the often misunderstood distinction between primary and scatter dose. The students can thus see the “whole” simultaneously with the “sum of the parts,” which enhances their physical insight and creates a sustainable foundation for further learning. After the presentations the speakers and moderator will be open to questions and discussion with the audience members. Learning Objectives: Be able to explain Project-Based Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain Flipped Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain active-learning strategies for the teaching of Medical Physics. Be able to explain how Monte Carlo simulations can be used to deepen a student’s understanding of radiation physics and dosimetry.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

SU-A-BRA-03: Creative Stimulation: A Flexible Hands-On Approach to Building a Deeper Understanding of Critical Concepts in Radiation Physics

Vic Montemayor - No one has been more passionate about improving the quality and effectiveness of the teaching of Medical Physics than Bill Hendee. It was in August of 2008 that the first AAPM Workshop on Becoming a Better Teacher of Medical Physics was held, organized and run by Bill Hendee. This was followed up in July of 2010 with a summer school on the same topic, again organized by Bill. There has been continued interest in alternate approaches to teaching medical physics since those initial gatherings. The momentum established by these workshops is made clear each year in the annual Innovation in Medical Physics Education session, which highlights work being done in all forms of medical physics education, from one-on-one residencies or classroom presentations to large-scale program revisions and on-line resources for international audiences. This symposium, presented on behalf of the Education Council, highlights the work of three finalists from past Innovation in Education sessions. Each will be presenting their approaches to and innovations in teaching medical physics. It is hoped that audience members interested in trying something new in their teaching of medical physics will find some of these ideas and approaches readily applicable to their own classrooms. Rebecca Howell - The presentation will discuss ways to maximize classroom learning, i.e., increasing the amount of material covered while also enhancing students’ understanding of the broader implications of the course topics. Specifically, the presentation will focus on two teaching methodologies, project based learning and flip learning. These teaching methods will be illustrated using an example of graduate medical physics course where both are used in conjunction with traditional lectures. Additionally, the presentation will focus on our experience implementing these methods including challenges that were overcome. Jay Burmeister - My presentation will discuss the incorporation of active learning techniques into a traditional medical physics classroom course. I will describe these techniques and how they were implemented as well as student performance before and after implementation. Student feedback indicated that these course changes improved their ability to actively assimilate the course content, thus improving their understanding of the material. Shahid Naqvi - My talk will focus on ways to help students visualize crucial concepts that lie at the core of radiation physics. Although particle tracks generated by Monte Carlo simulations have served as an indispensable visualization tool, students often struggle to resolve the underlying physics from a simultaneous jumble of tracks. We can clarify the physics by “coding” the tracks, e.g., by coloring the tracks according to their “starting” or “crossing” regions. The regionally-coded tracks when overlaid with dose distributions help the students see the elusive connection between dose, kerma and electronic disequilibrium. Tracks coded according to local energy or energy-loss rate can illustrate the need for stopping power corrections in electron beams and explain the Bragg peak in a proton beam. Coding tracks according to parent interaction type and order can clarify the often misunderstood distinction between primary and scatter dose. The students can thus see the “whole” simultaneously with the “sum of the parts,” which enhances their physical insight and creates a sustainable foundation for further learning. After the presentations the speakers and moderator will be open to questions and discussion with the audience members. Learning Objectives: Be able to explain Project-Based Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain Flipped Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain active-learning strategies for the teaching of Medical Physics. Be able to explain how Monte Carlo simulations can be used to deepen a student’s understanding of radiation physics and dosimetry.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

SU-A-BRA-04: Incorporating Active Learning Into Medical Physics Education

Vic Montemayor - No one has been more passionate about improving the quality and effectiveness of the teaching of Medical Physics than Bill Hendee. It was in August of 2008 that the first AAPM Workshop on Becoming a Better Teacher of Medical Physics was held, organized and run by Bill Hendee. This was followed up in July of 2010 with a summer school on the same topic, again organized by Bill. There has been continued interest in alternate approaches to teaching medical physics since those initial gatherings. The momentum established by these workshops is made clear each year in the annual Innovation in Medical Physics Education session, which highlights work being done in all forms of medical physics education, from one-on-one residencies or classroom presentations to large-scale program revisions and on-line resources for international audiences. This symposium, presented on behalf of the Education Council, highlights the work of three finalists from past Innovation in Education sessions. Each will be presenting their approaches to and innovations in teaching medical physics. It is hoped that audience members interested in trying something new in their teaching of medical physics will find some of these ideas and approaches readily applicable to their own classrooms. Rebecca Howell - The presentation will discuss ways to maximize classroom learning, i.e., increasing the amount of material covered while also enhancing students’ understanding of the broader implications of the course topics. Specifically, the presentation will focus on two teaching methodologies, project based learning and flip learning. These teaching methods will be illustrated using an example of graduate medical physics course where both are used in conjunction with traditional lectures. Additionally, the presentation will focus on our experience implementing these methods including challenges that were overcome. Jay Burmeister - My presentation will discuss the incorporation of active learning techniques into a traditional medical physics classroom course. I will describe these techniques and how they were implemented as well as student performance before and after implementation. Student feedback indicated that these course changes improved their ability to actively assimilate the course content, thus improving their understanding of the material. Shahid Naqvi - My talk will focus on ways to help students visualize crucial concepts that lie at the core of radiation physics. Although particle tracks generated by Monte Carlo simulations have served as an indispensable visualization tool, students often struggle to resolve the underlying physics from a simultaneous jumble of tracks. We can clarify the physics by “coding” the tracks, e.g., by coloring the tracks according to their “starting” or “crossing” regions. The regionally-coded tracks when overlaid with dose distributions help the students see the elusive connection between dose, kerma and electronic disequilibrium. Tracks coded according to local energy or energy-loss rate can illustrate the need for stopping power corrections in electron beams and explain the Bragg peak in a proton beam. Coding tracks according to parent interaction type and order can clarify the often misunderstood distinction between primary and scatter dose. The students can thus see the “whole” simultaneously with the “sum of the parts,” which enhances their physical insight and creates a sustainable foundation for further learning. After the presentations the speakers and moderator will be open to questions and discussion with the audience members. Learning Objectives: Be able to explain Project-Based Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain Flipped Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain active-learning strategies for the teaching of Medical Physics. Be able to explain how Monte Carlo simulations can be used to deepen a student’s understanding of radiation physics and dosimetry.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

SU-A-BRA-05: Panel Discussion

Vic Montemayor - No one has been more passionate about improving the quality and effectiveness of the teaching of Medical Physics than Bill Hendee. It was in August of 2008 that the first AAPM Workshop on Becoming a Better Teacher of Medical Physics was held, organized and run by Bill Hendee. This was followed up in July of 2010 with a summer school on the same topic, again organized by Bill. There has been continued interest in alternate approaches to teaching medical physics since those initial gatherings. The momentum established by these workshops is made clear each year in the annual Innovation in Medical Physics Education session, which highlights work being done in all forms of medical physics education, from one-on-one residencies or classroom presentations to large-scale program revisions and on-line resources for international audiences. This symposium, presented on behalf of the Education Council, highlights the work of three finalists from past Innovation in Education sessions. Each will be presenting their approaches to and innovations in teaching medical physics. It is hoped that audience members interested in trying something new in their teaching of medical physics will find some of these ideas and approaches readily applicable to their own classrooms. Rebecca Howell - The presentation will discuss ways to maximize classroom learning, i.e., increasing the amount of material covered while also enhancing students’ understanding of the broader implications of the course topics. Specifically, the presentation will focus on two teaching methodologies, project based learning and flip learning. These teaching methods will be illustrated using an example of graduate medical physics course where both are used in conjunction with traditional lectures. Additionally, the presentation will focus on our experience implementing these methods including challenges that were overcome. Jay Burmeister - My presentation will discuss the incorporation of active learning techniques into a traditional medical physics classroom course. I will describe these techniques and how they were implemented as well as student performance before and after implementation. Student feedback indicated that these course changes improved their ability to actively assimilate the course content, thus improving their understanding of the material. Shahid Naqvi - My talk will focus on ways to help students visualize crucial concepts that lie at the core of radiation physics. Although particle tracks generated by Monte Carlo simulations have served as an indispensable visualization tool, students often struggle to resolve the underlying physics from a simultaneous jumble of tracks. We can clarify the physics by “coding” the tracks, e.g., by coloring the tracks according to their “starting” or “crossing” regions. The regionally-coded tracks when overlaid with dose distributions help the students see the elusive connection between dose, kerma and electronic disequilibrium. Tracks coded according to local energy or energy-loss rate can illustrate the need for stopping power corrections in electron beams and explain the Bragg peak in a proton beam. Coding tracks according to parent interaction type and order can clarify the often misunderstood distinction between primary and scatter dose. The students can thus see the “whole” simultaneously with the “sum of the parts,” which enhances their physical insight and creates a sustainable foundation for further learning. After the presentations the speakers and moderator will be open to questions and discussion with the audience members. Learning Objectives: Be able to explain Project-Based Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain Flipped Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain active-learning strategies for the teaching of Medical Physics. Be able to explain how Monte Carlo simulations can be used to deepen a student’s understanding of radiation physics and dosimetry.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

SU-A-BRA-00: Education Council Symposium: Revitalizing Your Medical Physics Classroom: Some Examples and Thoughts from the Trenches

Vic Montemayor - No one has been more passionate about improving the quality and effectiveness of the teaching of Medical Physics than Bill Hendee. It was in August of 2008 that the first AAPM Workshop on Becoming a Better Teacher of Medical Physics was held, organized and run by Bill Hendee. This was followed up in July of 2010 with a summer school on the same topic, again organized by Bill. There has been continued interest in alternate approaches to teaching medical physics since those initial gatherings. The momentum established by these workshops is made clear each year in the annual Innovation in Medical Physics Education session, which highlights work being done in all forms of medical physics education, from one-on-one residencies or classroom presentations to large-scale program revisions and on-line resources for international audiences. This symposium, presented on behalf of the Education Council, highlights the work of three finalists from past Innovation in Education sessions. Each will be presenting their approaches to and innovations in teaching medical physics. It is hoped that audience members interested in trying something new in their teaching of medical physics will find some of these ideas and approaches readily applicable to their own classrooms. Rebecca Howell - The presentation will discuss ways to maximize classroom learning, i.e., increasing the amount of material covered while also enhancing students’ understanding of the broader implications of the course topics. Specifically, the presentation will focus on two teaching methodologies, project based learning and flip learning. These teaching methods will be illustrated using an example of graduate medical physics course where both are used in conjunction with traditional lectures. Additionally, the presentation will focus on our experience implementing these methods including challenges that were overcome. Jay Burmeister - My presentation will discuss the incorporation of active learning techniques into a traditional medical physics classroom course. I will describe these techniques and how they were implemented as well as student performance before and after implementation. Student feedback indicated that these course changes improved their ability to actively assimilate the course content, thus improving their understanding of the material. Shahid Naqvi - My talk will focus on ways to help students visualize crucial concepts that lie at the core of radiation physics. Although particle tracks generated by Monte Carlo simulations have served as an indispensable visualization tool, students often struggle to resolve the underlying physics from a simultaneous jumble of tracks. We can clarify the physics by “coding” the tracks, e.g., by coloring the tracks according to their “starting” or “crossing” regions. The regionally-coded tracks when overlaid with dose distributions help the students see the elusive connection between dose, kerma and electronic disequilibrium. Tracks coded according to local energy or energy-loss rate can illustrate the need for stopping power corrections in electron beams and explain the Bragg peak in a proton beam. Coding tracks according to parent interaction type and order can clarify the often misunderstood distinction between primary and scatter dose. The students can thus see the “whole” simultaneously with the “sum of the parts,” which enhances their physical insight and creates a sustainable foundation for further learning. After the presentations the speakers and moderator will be open to questions and discussion with the audience members. Learning Objectives: Be able to explain Project-Based Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain Flipped Learning and how can it be incorporated into a Medical Physics classroom. Be able to explain active-learning strategies for the teaching of Medical Physics. Be able to explain how Monte Carlo simulations can be used to deepen a student’s understanding of radiation physics and dosimetry.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

HarDWR - Harmonized Water Rights Records

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on data sourced from WestDAAT - Changed using a Site ID column to identify unique records to using aa combination of Site ID and Allocation ID - Removed the Water Management Area (WMA) column from the harmonized records. The replacement is a separate file which stores the relationship between allocations and WMAs. This allows for allocations to contribute to water right amounts to multiple WMAs during the subsequent cumulative process. - Added a column describing a water rights legal status - Added "Unspecified" was a water source category - Added an acre-foot (AF) column - Added a column for the classification of the right's owner v1.02 - Added a .RData file to the dataset as a convenience for anyone exploring our code. This is an internal file, and the one referenced in analysis scripts as the data objects are already in R data objects. v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description Here we present an updated database of Western U.S. water right records. This database provides consistent unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of seven broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of inter-sectoral water allocation changes though water markets (Grogan et al., *in review*). Specifically, the data were formatted for use as input to a process-based hydrologic model, Water Balance Model (WBM), with a water rights module (Grogan et al., *in review*). While this specific study motivated the development of the database presented here, water management in the U.S. West is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al., 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. West. We produced the water rights database presented here in four main steps: (1) data collection, (2) data quality control, (3) data harmonization, and (4) generation of cumulative water rights curves. Each of steps (1)-(3) had to be completed in order to produce (4), the final product that was used in the modeling exercise in Grogan et al. (*in review*). All data in each step is associated with a spatial unit called a Water Management Area (WMA), which is the unit of water right administration utilized by the state in which the right came from. Steps (2) and (3) required use to make assumptions and interpretation, and to remove records from the raw data collection. We describe each of these assumptions and interpretations below so that other researchers can choose to implement alternative assumptions an interpretation as fits their research aims. Motivation for Changing Data Sources The most significant change has been a switch from collecting the raw water rights directly from each state to using the water rights records presented in WestDAAT, a product of the Water Data Exchange (WaDE) Program under the Western States Water Council (WSWC). One of the main reasons for this is that each state of interest is a member of the WSWC, meaning that WaDE is partially funded by these states, as well as many universities. As WestDAAT is also a database with consistent categorization, it has allowed us to spend less time on data collection and quality control and more time on answering research questions. This has included records from water right sources we had previously not known about when creating v1.0 of this database. The only major downside to utilizing the WestDAAT records as our raw data is that further updates are tied to when WestDAAT is updated, as some states update their public water right records daily. However, as our focus is on cumulative water amounts at the regional scale, it is unlikely most records updates would have a significant effect on our results. The structure of WestDAAT led to several important changes to how HarWR is formatted. The most significant change is that WaDE has calculated a field known as `SiteUUID`, which is a unique identifier for the Point of Diversion (POD), or where the water is drawn from. This separate from `AllocationNativeID`, which is the identifier for the allocation of water, or the amount of water associated with the water right. It should be noted that it is possible for a single site to have multiple allocations associated with it and for an allocation to be able to be extracted from multiple sites. The site-allocation structure has allowed us to adapt a more consistent, and hopefully more realistic, approach in organizing the water right records than we had with HarDWR v1.0. This was incredibly helpful as the raw data from many states had multiple water uses within a single field within a single row of their raw data, and it was not always clear if the first water use was the most important, or simply first alphabetically. WestDAAT has already addressed this data quality issue. Furthermore, with v1.0, when there were multiple records with the same water right ID, we selected the largest volume or flow amount and disregarded the rest. As WestDAAT was already a common structure for disparate data formats, we were better able to identify sites with multiple allocations and, perhaps more importantly, allocations with multiple sites. This is particularly helpful when an allocation has sites which cross WMA boundaries, instead of just assigning the full water amount to a single WMA we are now able to divide the amount of water between the number of relevant WMAs. As it is now possible to identify allocations with water used in multiple WMAs, it is no longer practical to store this information within a single column. Instead the stAllocationToWMATab.csv file was created, which is an allocation by WMA matrix containing the percent Place of Use area overlap with each WMA. We then use this percentage to divide the allocation's flow amount between the given WMAs during the cumulation process to hopefully provide more realistic totals of water use in each area. However, not every state provides areas of water use, so like HarDWR v1.0, a hierarchical decision tree was used to assign each allocation to a WMA. First, if a WMA could be identified based on the allocation ID, then that WMA was used; typically, when available, this applied to the entire state and no further steps were needed. Second was the spatial analysis of Place of Use to WMAs. Third was a spatial analysis of the POD locations to WMAs, with the assumption that allocation's POD is within the WMA it should belong to; if an allocation still had multiple WMAs based on its POD locations, then the allocation's flow amount would be divided equally between all WMAs. The fourth, and final, process was to include water allocations which spatially fell outside of the state WMA boundaries. This could be due to several reasons, such as coordinate errors / imprecision in the POD location, imprecision in the WMA boundaries, or rights attached with features, such as a reservoir, which crosses state boundaries. To include these records, we decided for any POD which was within one kilometer of the state's edge would be assigned to the nearest WMA. Other Changes WestDAAT has Allowed In addition to a more nuanced and consistent method of assigning water right's data to WMAs, there are other benefits gained from using the WestDAAT dataset. Among those is a consistent categorization of a water right's legal status. In HarDWR v1.0, legal status was effectively ignored, which led to many valid concerns about the quality of the database related to the amounts of water the rights allowed to be claimed. The main issue was that rights with legal status' such as "application withdrawn", "non-active", or "cancelled" were included within HarDWR v1.0. These, and other water rights status' which were deemed to not be in use have been removed from this version of the database. Another major change has been the addition of the "unspecified water source category. This is water that can come from either surface water or groundwater, or the source of which is unknown. The addition of this source category brings the total number of categories to three. Due to reviewer feedback, we decided to add the acre-foot (AF) column so that the data may be more applicable to a wider audience. We added the ownerClassification column so that the data may be more applicable to a wider audience. File Descriptions The dataset is a series of various files organized by state sub-directories. In addition, each file begins with the state's name, in case the file is separate from its sub-directory for some reason. After the state name is the text which describes the contents of the file. Here is each file described in detail. Note that st is a placeholder for the state's name. stFullRecords_HarmonizedRights.csv: A file of the complete water records for each state. The column headers for each of this type of file are: state - The name of the state to which the allocations belong to. FIPS - The two digit numeric state ID code. siteID - The site location ID for POD locations. A site may have multiple allocations, which are the actual amount of water which can be drawn. In a simplified hypothetical, a farm stead may have an allocation for "irrigation" and an allocation for "domestic" water use, but the water is drawn from the same pumping equipment. It should be noted that many of the site ID appear to have been added by WaDE, and therefore may not be recognized by a given state's water rights database. allocationID - The allocation ID for the water right. For most states this is the water right ID, and what is recommended to use should a right be looked up on a given state's water rights database. The water amounts associated with these IDs tend to be finer scaled than those associated with siteID. It should be noted that some allocations may be extracted from multiple sites, particularly for larger Places of Use. ownerClassification - A classification of the types of owners for water rights. The most common is `Private` which incorporates a wide range of entities. Several classifications would be grouped into a government category, most of which are for the U.S. Federal Government. These allocations could be listed as "Federal", "United States of America", or as the names of any number of federal agencies. The last major grouping of entities is for "Native American"s. priorityDate - The date we use as the water right priority date for our modeling analysis. This is the legal priority date when it is available. However, for some rights, specifically from California and New Mexico, we used a pseudo priority date (e.g. well completion date or start of well drilling date) when a legal priority date was not available. The most questionable dates come from New Mexico, where the only date associated with certain water right records was the date the allocation was recorded in the database. As the allocation record creation tended to be within a few months of the filing of the application of the water right, from manually double checking the water rights, and our analysis focuses on aggregating water rights on the timescale of years, we determined it was acceptable to use such dates to include as many records as possible. primaryBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories WestDAAT. This column is the original WaDE category for the primary water use at the PoD site. allocationBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories for WestDAAT. This column is the original WaDE category

Economics↗
DOE OSTI2022

NRAP-Open-IAM: NRAP Open Source Integrated Assessment Model

Note: This is the last version (a2.6.1) of NRAP-Open-IAM released during NRAP Phase II in 2022. The latest version of NRAP-Open-IAM is available here: https://edx.netl.doe.gov/dataset/phase-iii-nrap-open-iam NRAP-Open-IAM is an open-source software product that enables quantification of containment effectiveness and leakage risk at storage sites in the context of system uncertainties and variability. NRAP-Open-IAM represents the next-generation in a line of systems-based computational models developed for quantitative geological carbon storage (GCS) risk assessment. The model comprises a set of reduced-order and analytical models of various components of the GCS system, potential leakage pathways, receptors of concern including impact to groundwater resources and the atmosphere, a framework to support stochastic simulation, time stepping, uncertainty quantification, other analytical functionality for scenario and risk-performance evaluation, and a basic graphical user interface to support scenario development, data input simulation definition, and basic post-processing and results display. As the NRAP Open-IAM functionality continues to evolve, we continue to add to its capability to develop quantitative, probabilistic, and time-dependent profiles of the evolution of risk at a GCS site and evaluate the influence of uncertain parameters on uncertainty in predicted risk. It can be used to quantify the dynamics of reservoir saturation plume and pressure-affected area, for evaluation of the area of potential groundwater impact (i.e., Area of Review) and monitoring requirements to support cost and regulatory analysis, and for consideration of different post-injection site care and closure scenarios. This submission contains the current version of NRAP-Open-IAM available for evaluation and testing. To use the NRAP-Open-IAM, download the source code (https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/4c24a3da-3b40-4ffe-9892-c807ae9f8760) then open the NRAP-Open-IAM user's guide (https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/8b27335a-343c-4836-b8a3-3ad0bdc9e669) to read more about the tool. Installation instructions for Windows, Mac, and Linux can be found in the "installers" folder of the extracted NRAP-Open-IAM folder and describe setup of environment (e.g., Python libraries) needed for proper work of the tool. Test of installation can be done by running "python openiam_setup_tests.py" in the "setup" folder. The installation test also runs a test suite to see if the NRAP-Open-IAM has been installed correctly. To run the test suite separately, run "python iam_test.py" in the "test" folder. User's guide: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/8b27335a-343c-4836-b8a3-3ad0bdc9e669 Developer's guide: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/3bc6ee7d-609d-4eb6-80ba-fa6130ee0313 Reservoir simulation data used in some examples distributed with NRAP-Open-IAM: - Kimberlina: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/eb62cece-61b2-4037-9b6d-32407dde2ab8 - Kimberlina (compartmentalized): https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/366f9530-3b32-4b84-affe-ab2df1d9a8b5 - FutureGen 2.0: https://edx.netl.doe.gov/dataset/futuregen-2-0-1008-simulation-reservoir-lookup-table NRAP-Open-IAM GitLab repository: https://gitlab.com/NRAP/OpenIAM Related publications: - Bacon, D., Yonkofski, C., Brown, C., Demirkanli, D. and Whiting, J., 2019. Risk-based post injection site care and monitoring for commercial-scale carbon storage: Reevaluation of the FutureGen 2.0 site using NRAP-Open-IAM and DREAM. International Journal of Greenhouse Gas Control 90: 102784. - Bacon, D. Demirkanli, D., and White, S., 2020. Probabilistic risk-based Area of Review (AoR) determination for a deep-saline carbon storage site. International Journal of Greenhouse Gas Control 102: 103153. - Harp, D., Oldenburg, C., and Pawar, R., 2019. A metric for evaluating conformance robustness during geologic CO2 sequestration operations. International Journal of Greenhouse Gas Control 85: 100-108. - Lackey, G., Vasylkivska, V., Huerta, N., King, S., and Dilmore, R., 2019. Managing well leakage risks at a geologic carbon storage site with many wells, International Journal of Greenhouse Gas Control, 88 :182-194. - Vasylkivska, V., Dilmore, R., Lackey, G., Zhang, Y., King, S., Bacon, D., Chen, B., Mansoor, K., and Harp, D., 2021. NRAP-Open-IAM: A flexible open-source integrated assessment model for geologic carbon storage risk assessment and management, Environmental Modelling & Software, 143: 105114. Presentations: - Chen, B., Harp, D., and Pawar, R., A data assimilation approach (ES-MDA) coupling with NRAP-Open-IAM for quantifying uncertainty reduction in geological CO2 sequestration. AGUFM 2019: T44A-02. - Chen, B., and Harp, D., Improving risk analysis precision for geologic CO2 sequestration by quantifying the uncertainty reduction before and after acquiring monitoring data. 14th Greenhouse Gas Control Technologies Conference, Melbourne, Australia, 2018, pp. 21-26. - Harp, D., National Risk Assessment Partnership Task 2: Containment Assurance. No. LA-UR-19-28654, Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2019. - Vasylkivska, V., King, S., Bacon, D., Harp, D., Chen, B., Mansoor, K., Onishi, T., Yang, Y., Zhang, Y., and Keating, E., NRAP-Open-IAM: An open-source integrated assessment model, poster, Mastering the Subsurface Through Technology Innovation, Partnerships and Collaboration: Carbon Storage and Oil and Natural Gas Technologies Review Meeting, Pittsburgh, PA, August 13-16, 2018. - Vasylkivska, V., Lackey, G., King, S., Wentworth, A., Huerta, N., Creason, C., DiGiulio, J., Yang, Y., and Dilmore, R., Long-term risk analysis of a geologic CO2 storage project during the post-injection period, SIAM Conference on Computational Science and Engineering, Spokane, WA, February 25-March 1, 2019. - Vasylkivska, V., Overview of the NRAP-Open-IAM tool for carbon storage (beta release), 2019 Annual NRAP Tool Users Meeting, Pittsburgh, PA, August 27, 2019. - Vasylkivska, V., Bacon, D., Chen, B., Dilmore, R., Harp, D., King, S., Lackey, G., Lindner, E., Liu, G., Mansoor, K. and Zhang, Y., NRAP-Open-IAM: A new, open-source code for integrated assessment of geologic carbon storage containment effectiveness and leakage risk, poster, American Geophysical Union Fall Meeting 2020 (virtual meeting), December 2020. - Vasylkivska, V., NRAP open-source integrated assessment model and relevant application, oral presentation, NRAP workshop "NRAP Tools for Geologic Carbon Storage Risk-Based Decision Making" held in conjunction with Groundwater Protection Council (GWPC) 2021 Annual Forum (virtual meeting), Salt Lake City, UT, September 2021. - Vasylkivska, V., NRAP-Open-IAM: open-source integrated assessment model, digital poster/demonstration, software demonstration session, 2022 Carbon Management Project Review Meeting, August 16, 2022

AoR↗
DOE OSTI2023

WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from Machine-Learning-Informed Sites across the Contiguous United States (v6)

This dataset supports a broader study examining hyporheic zone respiration rates to improve predictive models at a contiguous United States (CONUS) scale. The CONUS-Scale Model-Sample Study (CM) was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Sampling began in April 2022 and ended in October 2023. In addition to the widely distributed CONUS sites, a more spatially focused sampling occurred in the Yakima River Basin, WA in summer 2022. Data from this more spatially intensive sampling occurred under the label “Second Spatial Study (SSS)” and were also included in the machine learning models. Other data types collected from SSS that were not part of CM were published in a separate data package (https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566). This data package was originally published in February 2023. It was updated in June 2023 (v2; new and modified files); December 2023 (v3; new and modified files); June 2024 (v4; new and modified files); April 2024 (v5; new and modified files); and September 2025 (v6; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocols; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) surface water major cations and anions and averages; (4) sediment grain size data; (5) sediment iron (II) data and averages; (6) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment specific surface area; (11) sediment percent carbon and nitrogen; (12) sediment gravimetric moisture and averages; (15) sediment X-ray diffraction (XRD) data; (16) sediment adenosine triphosphate (ATP) and averages; (17) a subfolder with sediment incubation respiration data, scripts, and plots; (18) surface water and sediment FTICR methods; and (19) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS).The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. CORRECTION: Carbon and nitrogen content are reported as percentages. The current column headers "01395_C_percent_per_mg" and "01397_N_percent_per_mg" are incorrect. These should read "01395_C_percent" and "01397_N_percent" and will be corrected in the next version of this data package. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

TropiRoot 1.0: Database of tropical root characteristics across environments

Tropical ecosystems contain the world's largest biodiversity of vascular plants. Yet, our understanding of tropical functional diversity and its contribution to global diversity patterns is constrained by data availability. This discrepancy underscores an urgent need to bridge data gaps by incorporating comprehensive tropical root data into global datasets. Here, we provide a database of tropical root characteristics. This new database, TropiRoot 1.0, will be instrumental in evaluating an array of hypotheses pertaining to root functional ecology and plant biogeography, both within the tropics and relative to other global biomes. The data compilation was conducted by the TropiRoot Initiative, in partnership with the Fine-Root Ecology Database (FRED) and the Global Root Trait (GRooT) database, Colorado State University (CSU) and the Smithsonian Tropical Research Institute (STRI). Literature search and data extraction were conducted between 2020 and 2024. Literature was identified using Web of Science, Scopus, and complemented using the expert knowledge of members of TropiRoot. To provide broad environmental and geographical distributions, literature searches included root characteristics (traits) across global change drivers, natural gradients, and from different continents. We adopted FRED standardized data columns and streamlined the format to enhance accessibility for data extraction across various user groups. This optimized framework resulted in a smaller, yet comprehensive datasheet. To make the database compatible with other global root trait initiatives, column identification was standardized following the codes provided by FRED. These efforts culminated in data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 include root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology, and root chemistry. This initiative represents a 30% increase in the currently available data for tropical roots in FRED. TropiRoot 1.0 contains root characteristics from 25 different countries, where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data were available, including soil data, these data were either extracted and included in the database or its availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match those reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models. The data are freely available and should be cited when used.

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