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DOE OSTI2025

Digital Twin Applications in the Water Sector: A Review

As cities develop and resource demands rise, the water sector faces crucial challenges to deliver reliable, sustainable, and efficient services. Digital Twins (DTs), virtual replicas of physical systems, offer a promising tool to transform how we manage water infrastructure. Originally developed in the aerospace industry, DTs are now gaining traction in the water sector, enabling real-time monitoring, simulation, and predictive control of water and wastewater treatment, collection and distribution networks, and water reclamation and reuse systems. While still emerging in the water sector, DTs have shown potential to enhance operational efficiency, reduce environmental impacts, and support smarter, more resilient water management. This review study provides a comprehensive overview of current DT applications in the water sector, highlighting successful case studies, technical challenges, and knowledge gaps. It also explores how DTs can help bridge the water–energy nexus by optimizing resources utilized across interconnected systems. By synthesizing recent advances and identifying future research directions, this paper illustrates how DTs can play a central role in building sustainable, adaptive, and digitally-enabled water infrastructure.

digital twin↗

DOE OSTI2024

Providing Experimental Infrastructure for Accelerating Advanced Reactor Demonstrations through the National Reactor Innovation Center

A suite of experimental infrastructure projects has been developed by the National Reactor Innovation Center to accelerate advanced reactor demonstrations and facilitate their development, addressing crucial gaps in data, materials characterization, and modeling. First, the Molten Salt Thermophysical Examination Capability (MSTEC) provides a specialized platform for post-irradiation characterization of molten salt reactor fuel, coolant salts, and structural materials, essential for supporting the design and operation of advanced reactors and future commercial molten salt reactor development and licensing. The Virtual Test Bed (VTB) complements these efforts by leveraging advanced modeling and simulation tools to evaluate reactor performance and safety. Serving as a library of reference models, the VTB offers a database of multiphysics reactor models, facilitating rapid safety evaluations and includes continuous software quality assurance, crucial for accelerating deployment while maintaining reliability. Additionally, the Helium Component Test Facility (HeCTF) addresses the need for high-temperature helium-cooled reactor component testing. As the first-of-its-kind facility in the United States, HeCTF emulates high-temperature gas reactor conditions, reducing time and cost associated with component validation, thereby accelerating reactor development. Finally, In-cell Thermal Creep Frames provide a unique solution for obtaining thermal creep data from irradiated materials, critical for materials qualification and licensing. Developed by the National Reactor Innovation Center, these compact frames enable the examination of previously irradiated materials, overcoming traditional limitations and enhancing the understanding of mechanical properties crucial for reactor development. Collectively, these experimental infrastructure projects form a comprehensive framework aimed at expediting advanced reactor demonstrations, fostering innovation, and ensuring the viability of next-generation nuclear energy solutions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Towards time-resolved MicroED grid preparation using mix-and-inject gas dynamic virtual nozzles

Recent progress in gas dynamic virtual nozzle (GDVN) technologies in combination with high-brilliance synchrotron and X-ray free-electron lasers (XFELs) has allowed the visualization of protein dynamics in crystallo by mixing macromolecular protein crystals with a substrate using tunable mixing times on the order of milliseconds to seconds prior to serial X-ray diffraction data collection. This has become the method of choice for high-resolution structure determination of intermediate states. However, such experiments require large counts of crystals of proper sizes for high-resolution data collection, and premium beam times for screening efforts. Cryogenic microcrystal electron diffraction (MicroED) represents a complementary technique that may be a more accessible avenue for time-resolved nanocrystallography compared with serial X-ray diffraction experiments. MicroED can produce full diffraction datasets from just a few submicrometre-thick crystals, and the approach is more readily accessible, requiring standard cryogenic transmission electron microscopy (TEM) equipment available at many universities and institutes. Cryogenic MicroED, like other forms of cryo-EM, begins with rapidly freezing biological material on electron microscopy grids. In the case of MicroED, micro- to nano-crystals (<500 nm thick) are deposited onto electron microscopy grids and plunge-frozen for subsequent electron diffraction data collection. Here, we have incorporated GDVN technology developed originally for XFEL experiments into the freezing process as a first step towards time-resolved studies. We describe the limited deposition efficiency of the model MicroED protein proteinase K on TEM grids using GDVNs, preceding sample vitrification and successful MicroED data collection. We discuss both the initial results from such experiments and the methodological challenges in developing this approach into a reliable workflow for millisecond-to-second time-resolved structural studies of macromolecules. Our results promise a strategy to deposit crystals on grids using GDVNs and determine high-resolution structures by MicroED, constituting a first step towards development of time-resolved MicroED experiments.

MicroED↗

DOE OSTI2023

Modular HF Isolated MV String Inverters Enable a New Paradigm for Large PV Farms

The “Modular HF Isolated MV String Inverters Enable a New Paradigm for Large PV Farms” project focuses on exploring alternative power converter and system-level plant configurations to achieve the lowest cost and highest energy output for a given solar plus storage (e.g., PV + battery) plant, including the use of medium voltage (MV) collection while taking into account detailed models of all elements. To realize this objective, four approaches were utilized (i) employ a novel Medium Voltage String Inverter (MVSI) topology (soft switching solid state transformer – S4T) to convert 1000 Vdc to 4.16 kVac; (ii) plant collection using standard, low-cost overhead MV distribution network; (iii) enable energy storage integration without additional converter cost to achieve dispatchability of the PV resource; and (iv) provide advanced functionality (autonomous operation, track ISO signals for dynamic balancing and ancillary services, and PV farm operation as a virtual grid resource). Subsequently and in alignment with the previously mentioned approaches, the project was structured in five efforts (i) S4T MVSI simulation and design; (ii) system analysis and storage optimization; (iii) financial analysis; (iv) power converter prototype build and test; and (v) regulatory and commercial impact study. The outcomes provided by each effort can be summarized as follows (i) Project explored the use of MV AC distribution architecture for hybrid PV+storage utility-scale PV farms; (ii) Detailed loss and LCOE analysis for AC and DC side BESS architecture, including multiple converter topologies, as well as for proposed MVSI/MDCT systems; (iii) MVSI was built and holds promise but needs lower-cost high-voltage Si-C devices, which does not seem possible in the near term; (iv) MDCT provides a simpler modular building block – validated through HIL and farm level modeling, simulation and experimental validation; (v) 300 kVA MDCT prototype built and tested, technology is being commercialized; and (vi) Regulatory model of utility building PV plants, where PV panels are treated as DC generation (IPP), seems viable and can allow improved grid integration.

14 SOLAR ENERGY↗

DOE OSTI2024

Neutron and photon in-vivo materials characterization at the evolving plasma-material interface in plasma-burning fusion environments

This report includes the first set of MOSS data collected in the context of fusion-relevant plasma-material interactions. The data collected show that the surface curvature and stress irreversibly change during low-energy deuterium ion irradiation. This holds for both tungsten thin films, bulk tungsten, and single-crystal silicon. Meanwhile, argon irradiation did not produce this same effect. The surface morphology of the exposed samples was virtually identical regardless of gas species. There were, however, some surface chemistry differences between argon-exposed and deuterium-exposed samples, mainly concerning the oxide states of the W4f XPS signal. The changes in surface chemistry could be due to hydrogen dynamics on the tungsten surfaces, though further investigation will be required with in-situ irradiation and analysis. The changes in surface stress could also not be explained by temperature alone, as the temperature reached during the ion irradiations was too low to induce non-elastic strain. With this in mind, deuterium retention in the materials could change the surface curvature and stress. To confirm this, further work will be needed to quantify the change in curvature and relate it to ion flux, fluence, and deuterium retention. Unfortunately, when this work was conducted, the experimental setup for thermal desorption spectroscopy was not operating correctly, and that work could not be included in this dissertation. Despite experimental difficulties, this work shows the first results of a promising technique to study deuterium dynamics in tungsten-based reconstituted thin films.

36 MATERIALS SCIENCE↗

DOE OSTI2023

The 100-month Swift catalogue of supergiant fast X–ray transients

Supergiant fast X-ray transients (SFXTs) are high mass X-ray binaries (HMXBs) displaying X-ray outbursts that can reach peak luminosities up to 10 38 erg s -1 and spend most of their lives in more quiescent states with luminosities as low as 10 32 -10 33 erg s -1 . During the quiescent states, less luminous flares are also frequently observed with luminosities of 10 34 -10 35 erg s -1 . The main goal of the comprehensive and uniform analysis of the SFXT Swift triggers presented in this paper is to provide tools to predict whether a transient that has no known X-ray counterpart may be an SFXT candidate. These tools can be exploited for the development of future missions exploring the variable X-ray sky through large field-of-view instruments. We examined all available data on outbursts of SFXTs that triggered the Swift/Burst Alert Telescope (BAT) collected between 2005 August 30 and 2014 December 31, in particular those for which broad-band data, including the Swift/X-ray Telescope (XRT) data, are also available. This work complements and extends our previous catalogue of SFXT flares detected by BAT from 2005 February 12 to 2013 May 31, since we now include the additional BAT triggers recorded until the end of 2014 (i.e. beyond the formal first 100 months of the Swift mission). Due to a change in the mission’s observational strategy, virtually no SFXT triggers obtained a broad-band response after 2014. We processed all BAT and XRT data uniformly by using the Swift Burst Analyser to produce spectral evolution dependent flux light curves for each outburst in the sample. The BAT data allowed us to infer useful diagnostics to set SFXT triggers apart from the general γ-ray burst population, showing that SFXTs uniquely give rise to image triggers and are simultaneously very long, faint, and ‘soft’ hard-X-ray transients. We find that the BAT data alone can discriminate very well the SFXTs from other classes of fast transients, such as anomalous X-ray pulsars and soft gamma repeaters. On the contrary, the XRT data collected around the time of the BAT triggers are shown to be decisive for distinguishing SFXTs from, for instance, accreting millisecond X-ray pulsars and jetted tidal disruption events. The XRT observations of 35 (out of 52 in total) SFXT BAT triggers show that in the soft X-ray energy band, SFXTs display a decay in flux from the peak of the outburst of at least three orders of magnitude within a day and rarely undergo large re-brightening episodes, favouring in most cases a rapid decay down to the quiescent level within three to five days (at most).

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models

Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the above-mentioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

CarbonSAFE North Dakota – Early Successes

Conference presentation for American Institute of Chemical Engineers (AIChE) Annual Meeting, Boston, MA and virtual, November 5–11 and 15–19, 2021. As part of the U.S. Department of Energy’s (DOE’s) CarbonSAFE program, the Energy & Environmental Research Center (EERC) is conducting commercial-scale site characterization and permitting of up to two geologic horizons for the geologic storage of anthropogenic CO2 emissions. These storage horizons would collectively and permanently store nearly 4 million metric tons (MMt) of CO2 per year captured as part of Minnkota Power Cooperative’s (Minnkota’s) Project Tundra. Minnkota’s ambitious initiative to build the world’s largest postcombustion carbon capture and storage facility in central North Dakota demonstrates the existence of a business case for carbon capture, and storage (CCS).

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2025

Why is height-dependent mixing observed in stratocumulus clouds?

Recent aircraft measurements in stratocumulus clouds suggest that entrainment mixing is inhomogeneous (IM) near cloud top and homogeneous (HM) within the cloud. However, this proposed height-dependence of mixing transition is uncertain because of artifacts involved in the aircraft measurements. In this study, we use the Explicit Mixing Parcel Model to simulate mixing scenarios in stratocumulus clouds and reconstruct the virtual aircraft measurements to investigate the mixing signature. Results show that, from the aircraft-measurement perspective, the mixing signature always exhibits IM characteristic near cloud top and HM characteristic within cloud, independent of the types of the local entrainment-mixing process. The appearance of the vertical IM-to-HM transition is essentially a collective behavior of multiple parcels sampled at the same height, experiencing distinct entrainment-mixing-evaporation histories. This bulk view of mixing process, which is widely used for aircraft measurements, could lead to misinterpretations of the true mixing mechanism occurring in clouds. Our result underscores the limitations of using aircraft measurements to identify the entrainment-mixing mechanism at the process level.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

ESS-DIVE Reporting Format for Dataset Package Metadata

ESS-DIVE’s (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) dataset metadata reporting format is intended to compile information about a dataset (e.g., title, description, funding sources) that can enable reuse of data submitted to the ESS-DIVE data repository. The files contained in this dataset include instructions (dataset_metadata_guide.md and README.md) that can be used to understand the types of metadata ESS-DIVE collects. The data dictionary (dd.csv) follows ESS-DIVE’s file-level metadata reporting format and includes brief descriptions about each element of the dataset metadata reporting format. This dataset also includes a terminology crosswalk (dataset_metadata_crosswalk.csv) that shows how ESS-DIVE’s metadata reporting format maps onto other existing metadata standards and reporting formats.Data contributors to ESS-DIVE can provide this metadata by manual entry using a web form or programmatically via ESS-DIVE’s API (Application Programming Interface). A metadata template (dataset_metadata_template.docx or dataset_metadata_template.pdf) can be used to collaboratively compile metadata before providing it to ESS-DIVE.Since being incorporated into ESS-DIVE’s data submission user interface, ESS-DIVE’s dataset metadata reporting format, has enabled features like automated metadata quality checks, and dissemination of ESS-DIVE datasets onto other data platforms including Google Dataset Search and DataCite.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

2021 GeoAI Workshop Report: The Trillion Pixel Challenge

The convergence of geospatial big data with advancements from artificial intelligence, cloud infrastructure, and high-performance computing continues to revolutionize mapping and analysis of Earth's surface in unprecedented detail. Rapid innovations in sensing technologies will soon collect geospatial data in even higher resolution and throughput. These developments offer the potential for breakthroughs in science, policy, and national security via end-to-end GeoAI systems that can provide fresh insights into how humans occupy and alter their environment over time. At the 2021 GeoAI Trillion Pixel workshop, international subject matter experts from government, academia, industry, and nonprofit organizations gathered virtually to discuss the Trillion Pixel GeoAI Challenge. The event focused on six major themes currently influencing scientific innovation and breakthroughs. Particular focus was paid to societal impacts. As an additional takeaway message, the gathering identified remaining application gaps and challenges that are in need of stronger community partnerships and collaborations.

58 GEOSCIENCES↗

DOE OSTI2025

VISIONARY: Virtual Intelligence System for Optimizing Novel Analytical Research Yields

VISIONARY is an AI system that accelerates energy materials discovery by automatically generating hypotheses about structure-property relationships. It analyzes patterns in materials data, identifies promising correlations, and proposes testable scientific hypotheses without human intervention. By streamlining this reasoning process, VISIONARY helps researchers efficiently identify candidate materials with desired properties, significantly speeding up the materials development pipeline for energy applications. During the project, we developed a standalone application. The application uses a combination of papers provided by the user and data collected from FutureHouse’s dataset to build an understanding of the background that the user wants to explore for the hypothesis.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Modeling deformation and failure in AlSi-polyester abradable sealcoating material using microstructure-based finite element simulation

A plasma-sprayed aluminum-silicon (AlSi)/polyester coating is applied in modern gas turbine engines as an abradable sealcoating to maintain tight clearances between the rotating blades and the static casing. While running the engine, the rotating blades “rub” with the abradable coating, which results in extreme strain rate (up to 10 6 ) dynamics and a high-temperature environment. Due to the difficulty of collecting direct measurements, predictive computational models are important for analyzing the deformation and failure of the abradable material, to help meet the design target of avoiding damage to the blade tip and maintaining high fuel efficiency. In this research, a microstructure-based finite element (FE) computational model was developed to capture the complex mechanical behavior of the AlSi/polyester microstructure. The model is based on a virtual representative-volume-element (RVE) of a metal-polymer microstructure, reconstructed from x-ray computed tomography. It models the plastic deformation of, and damage to, each AlSi and polyester constituents, as well as the failure at their interface. The model was calibrated and validated with uniaxial tension and compression experiments, conducted at two temperatures (298 K and 533 K) at an applied strain rate of 10 3 - 10 4 s -1 . The material exhibited strongly asymmetric tension-compression behavior and a sensitivity to temperature, which was well captured by the model. The model was further applied to investigate changes in mechanical behavior due to variations in constituents’ volume fractions, which provides guidance to the microstructural design of AlSi/polyester abradable materials. The model is expected to facilitate the development of improved abradable materials by bypassing the conventional trial-and-error approach and extensive testing requirements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

GRUMDN: A Multi-Task Model for Predicting Human Patterns-of-Life from Stay Transition Data

Understanding human patterns-of-life (PoL) is essential towards ensuring safe and secure indoor facility environment as well as outdoor urban environment. Prediction of human movement in between places of interest is vital in understanding human PoL. Movement between spaces maybe represented and detected in one of the two forms: 1) trajectories: locations measured at regular time intervals by mobile sensors, bluetooth or GPS sensors; or 2) stay transitions: semantic PoI (points of interest) and stay duration data measurable by eventbased sensors that collect data when a check-in or check-out event is detected. Stay transition data provides a more compressed data format compared to trajectories data, especially in situations with longer stay durations, while preserving the information necessary for PoL analysis. Now as introduced briefly in the paper, our deployed end application (Digital Twin of a facility with non-player characters, besides the interactive user in virtual reality) needed a well-performing and validated AI/ML model for simulating high quality stay transitions behavior. In this study we thus primarily present our findings with developing and validating that model, which is a multi-task neural network for stay transition prediction. The neural network consists of two heads, for corresponding two tasks of stay category prediction and stay duration prediction. We evaluated gated recurrent units and multi-layer perceptrons of varying network sizes for stay category prediction; while mixture density networks, noisy generator-only networks, and generative adversarial networks of varying network sizes for stay duration prediction. We have then evaluated four multi-task models, constructed by combining these specialized models, on their ability to predict stay transition data. We tested our models on datasets from two different cases: 1) a simulation-generated dataset of indoor movement within the HFIR (high flux isotope reactor) nuclear reactor facility at Oak Ridge National Laboratory (ORNL); and 2) the GeoLife human mobility dataset of outdoor urban movement available in literature. Our results indicate that GRUMDN, which combines gated recurrent units (GRU) for stay category prediction task, and mixture density networks (MDN) for stay duration prediction task, did overall outperform other multitask models and the current state-of-the-art.

Gunaratne, Chathika [ORNL] (ORCID:0000000225088745↗

DOE OSTI2023

Wood Heater Design Challenge Workshop Report

To support the United States Department of Energy (DOE) Bioenergy Technologies Office’s (BETO) goal of advancing residential wood heating technologies, Brookhaven National Laboratory (BNL), Lawrence Berkeley National Laboratory (LBNL), and the Alliance for Green Heat (AGH) hosted the fifth Wood Heater Design Challenge (WHDC). The Challenge consists of three virtual workshops and a Technology Slam aimed at inspiring and advancing the next generation of residential wood heaters. With an estimated 12.5 million homes in the United States using wood or pellets for space heating, this challenge stands to create a significant impact on the environmental footprint of domestic heating. This report provides a summary of the activities and outcomes from the three virtual workshops held in January, March, and April of 2022. The goal of the workshops was to bring together stakeholders from across the world to discuss research and development (R&D) challenges and opportunities for advancing residential wood heaters in the United States. The workshops focused on three topics: 1. Advances in wood heater design and technology; 2. Advances in instrumentation used for wood heater testing and field data collection; and 3. Adoption of new wood heater technology and integration with other renewables. Each workshop had more than 100 participants each day which included wood heater manufacturers, researchers, academics, government agencies, and policy makers. Overall, a clear message was presented that collaborative input from experts at air quality agencies, industry, academic institutions, and non-profits is necessary to ensure that wood heating technology continues as a mainstream renewable energy option in our changing energy landscape and to facilitate innovative technology solutions. Participants also provided insights and recommendations that may help guide future R&D investments.

09 BIOMASS FUELS↗

DOE OSTI2024

Conditional guided generative diffusion for particle accelerator beam diagnostics

Abstract Advanced accelerator-based light sources such as free electron lasers (FEL) accelerate highly relativistic electron beams to generate incredibly short (10s of femtoseconds) coherent flashes of light for dynamic imaging, whose brightness exceeds that of traditional synchrotron-based light sources by orders of magnitude. FEL operation requires precise control of the shape and energy of the extremely short electron bunches whose characteristics directly translate into the properties of the produced light. Control of short intense beams is difficult due to beam characteristics drifting with time and complex collective effects such as space charge and coherent synchrotron radiation. Detailed diagnostics of beam properties are therefore essential for precise beam control. Such measurements typically rely on a destructive approach based on a combination of a transverse deflecting resonant cavity followed by a dipole magnet in order to measure a beam’s 2D time vs energy longitudinal phase-space distribution. In this paper, we develop a non-invasive virtual diagnostic of an electron beam’s longitudinal phase space at megapixel resolution (1024 × 1024) based on a generative conditional diffusion model. We demonstrate the model’s generative ability on experimental data from the European X-ray FEL.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Dataset: "Widespread Drought-driven Declines in Streamflows and Water quality in the Upper Colorado River Basin (1998-2022)"

This data package contains the associated data and scripts for Nagamoto, E., Ombadi, M., Ciulla, F. et al. Widespread drought-driven declines in streamflows and water quality in the Upper Colorado River Basin during 1998-2022. Commun Earth Environ 7, 734 (2026). https://doi.org/10.1038/s43247-026-03890-5. This purpose of this study was to investigate the impact of the 21st century drought on water quantity and quality at catchments throughout the Upper Colorado River Basin (UCRB). We used stream flow, water temperature, specific conductance, air temperature, precipitation, and catchment attribute data for over 200 sites in the UCRB, collected from the National Water Information System using Basin3D (Varadharajan, 2023), GAGESII (Falcone, 2010), and the Google Earth Engine. We identified years of severe drought between 1998 and 2022 using the Standardized Precipitation Evaporation Index (SPEI), then calculated the relative change percentage of the stream flow, water temperature, and specific conductance from drought versus non-drought years. We used the attribute information from GAGESII to investigate what physical traits of catchments are associated streamflow vulnerability (greater relative change) or resilience to drought. We used land cover data from the National Land Cover Database (USGS, 2024) to assess any changes to physical attributes that may not be represented in the static attributes information in GAGESII. To increase data availability, we modeled stream temperature using methods from Willard, 2023. While the study period is water years 1998 to 2022, the raw water quantity and quality data extends to 1950 and the meteorological data extends to 1980. The data and code can be downloaded via the UCRB_drought.zip. Within the zip, the files are organized as follows: - INPUTS: Contains all input data used in UCRB_Drought_Workflow.ipynb - OUTPUTS: Contains all intermediate data created from UCRB_Drought_Workflow.ipynb as well as final products including the calculated Standardized Evapotranspiration Index (SPEI) - climatic_variables: The code used to collect meteorologic data from Google Earth Engine - feature_importance: The code used for the catchment attributes analysis - preprocessing: Code used in UCRB_Drought_Workflow_Preprocessing.ipynb - pyeto: Code used in UCRB_Drought_Workflow_Preprocessing.ipynb - calculations: Code used in UCRB_Drought_Workflow_Impacts.ipynb - plotting: Code used in UCRB_Drought_Workflow_Impacts.ipynb - README.md - UCRB_Drought_Workflow_Preprocessing.ipynb: The code used to prep raw data for the analysis - UCRB_Drought_Workflow_Impact.ipynb: The code which uses the prepped raw data for analysis, and plots all figures - requirements_ucrb-drought_v2.yml: The requirements file to create a virtual environment and Jupyter Lab kernel to run the code The INPUTS folder is organized into the following major directories and sub-directories. The "RDC_WT_SC_RAW" folder contains raw data for streamflow, water temperature, and specific conductance in a ".h5" file. The "NLCD_RAW" folder contains ".csv" files with annual land cover percentages for counties within the UCRB. The "MET_RAW" folder contains a ".csv" file with monthly meteorological data (air temperature and precipitation) for the sites in the UCRB which was obtained from code in the climatic_variables folder. The "GAGESII" folder contains ".csv" files with physical catchment attribute variables for catchments across the country. The "WT_LSTM_data" folder contains ".csv" files with calculated WT (Willard, 2023) and the associated RMSEs. The "Upper_Colorado_River_Basin_Boundary" folder contains geographic data including a shapefile for plotting in the UCRB_Drought_Workflow.ipynb. The "RESERVOIRS_RAW" folder contains ".csv" files for each reservoir in the UCRB with daily reservoir storage. There are also two files in the INPUTS folder that have combined reservoir storage data and reservoir metadata. The OUTPUTS folder is organized into the following major directories and sub-directories. The "RDC_WT_SC_data" folder contains a folder "Water_year" with the associated cleaned data, metadata, and data availability information in ".csv" files, a folder "Median_Relchange" with the relative change comparing drought to non-drought years in ".csv" files, and a folder "Peak95_Min5_Relchange" that has ".csv" files for the relative change in peak (95th %) and minimum (5th %) variables. The "NLCD_data" folder contains the difference in land cover from the beginning to end of the study period and the percentage of the county that is within UCRB bounds can be found in Nagamoto et al (2025)). The "MET_data" folder contains separated monthly air temperature and precipitation data and the calculated PET in ".csv" files. The "SPEI_data" folder contains ".csv" files with calculated SPEI values (one restricted to the study period and the other with information from the entire MET data period). The "Paper_Tables" folder contains two ".csv" files containing site information and data availability and information about the GAGESII trait aggregated categories. The base directory includes the file “flmd.csv” for a list and description of all files and the file “dd.csv” for data dictionaries. Scripts for preprocessing, analysis, and figure generation are located in the associated GitHub repository found at [https://github.com/iNAIADS/drought-impacts/tree/develop/UCRB-drought]. UPDATE 1: Title and code file updated to match submitted manuscript 10-15-2025. UPDATE 2: Code and data files updated to match revised manuscript 3-4-2026. UPDATE 3: Code and data files updated to match revised manuscript 6-7-2026. ** NOTE: DD and FLMD have not been updated yet. UPDATE 4: Added associated Manuscript information and DD and FLMD have been updated. To cite this code, please use the following BibTeX: @misc{nagamoto2025drought, author = {Emily Nagamoto and Fabio Ciulla and Mohammad Ombadi and Jared Willard and Rosemary Carroll and Charuleka Varadharajan}, title = {Dataset: "Widespread Drought-driven Declines in Streamflows and Water quality in the Upper Colorado River Basin (1998-2022)"}, year = {2025}, doi = {10.15485/2551894}, publisher = {ESS-DIVE Repository}, url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2551894} }

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

On the approximations underpinning fast nuclear cloud models

Fast nuclear cloud models have been used for many decades to predict cloud rise and growth following a nuclear detonation for both emergency response and debris collections mission planning. We present derivations and discussions of the approximations made by such fast nuclear cloud models in common use. In particular, the models we discuss all fundamentally rely on an empirical entrainment parameter to inexpensively model the cloud rise and growth of a buoyant bubble due to entrainment of an ambient air mass on its surface. We show the particular circumstances under which these models are equivalent as well as apply a virtual mass correction throughout them. Aside from some minor differences in representation and the use of the Boussinesq approximation, these models are all very similar and necessitate the specification of an entrainment parameter to accurately determine the cloud rise.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗