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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 OSTI2020

Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refining system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Search for R-parity-violating supersymmetry in a final state containing leptons and many jets with the ATLAS experiment using $\sqrt{s}=13\hbox{TeV}$ proton–proton collision data

A search for R-parity-violating supersymmetry in final states characterized by high jet multiplicity, at least one isolated light lepton and either zero or at least three b-tagged jets is presented. The search uses 139fb –1 of $\sqrt{s}=13\hbox{TeV}$ proton–proton collision data collected by the ATLAS experiment during Run 2 of the Large Hadron Collider. The results are interpreted in the context of R-parity-violating supersymmetry models that feature gluino production, top-squark production, or electroweakino production. The dominant sources of background are estimated using a data-driven model, based on observables at medium jet multiplicity, to predict the b-tagged jet multiplicity distribution at the higher jet multiplicities used in the search. Machine-learning techniques are used to reach sensitivity to electroweakino production, extending the data-driven background estimation to the shape of the machine-learning discriminant. No significant excess over the Standard Model expectation is observed and exclusion limits at the 95% confidence level are extracted, reaching as high as 2.4 TeV in gluino mass, 1.35 TeV in top-squark mass, and 320 (365) GeV in higgsino (wino) mass.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Seasonal changes in GPP/SIF ratios and their climatic determinants across the Northern Hemisphere

Satellite-derived sun-induced chlorophyll fluorescence (SIF) has been increasingly used for estimating gross primary production (GPP). However, the relationship between SIF and GPP has not been well defined, impeding the translation of satellite observed SIF to GPP. Previous studies have generally assumed a linear relationship between SIF and GPP at daily and longer time scales, but support for this assumption is lacking. Here, we used the GPP/SIF ratio to investigate seasonal variations in the relationship between SIF and GPP over the Northern Hemisphere (NH). Based on multiple SIF products and MODIS and FLUXCOM GPP data, we found strong seasonal hump-shaped patterns for the GPP/SIF ratio over northern latitudes, with higher values in the summer than in the spring or autumn. This hump-shaped GPP/SIF seasonal variation was confirmed by examining different SIF products and was evident for most vegetation types except evergreen broadleaf forests. The seasonal amplitude of the GPP/SIF ratio decreased from the boreal/arctic region to drylands and the tropics. For most of the NH, the lowest GPP/SIF values occurred in October or September, while the maximum GPP/SIF values were evident in June and July. The most pronounced seasonal amplitude of GPP/SIF occurred in intermediate temperature and precipitation ranges. GPP/SIF was positively related to temperature in the early and late parts of the growing season, but not during the peak growing months. These shifting relationships between temperature and GPP/SIF across different months appeared to play a key role in the seasonal dynamics of GPP/SIF. Several mechanisms may explain the patterns we observed, and future research encompassing a broad range of climate and vegetation settings is needed to improve our understanding of the spatial and temporal relationships between SIF and GPP. Nonetheless, the strong seasonal variation in GPP/SIF we identified highlights the importance of incorporating this behavior into SIF-based GPP estimations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Macquarie Island Cloud and Radiation Experiment (MICRE) Field Campaign Report

The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility, the Australian Antarctic Division (AAD), and the Australian Bureau of Meteorology (BOM) collaborated in deploying a variety of ground instrumentation to Macquarie Island between March 2016 and March 2018. Macquarie Island is located at 54.5° S, 158.9° E and is well situated for studying clouds over the Southern Ocean. The primary objective of the March 2016 to March 2018 deployment, hereafter the Macquarie Island Cloud and Radiation Experiment (MICRE), was to collect observations of surface radiation, cloud, precipitation, and aerosol properties. This report describes the instruments deployed and data sets collected, along with a summary of key data quality issues and information on some value-added products that are being developed to assist scientists in using the data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties

This project developed machine learning (ML) methods, lab data sets, and field data to advance geothermal exploration and geothermal energy production. The work had three focus areas. One involved the development of ML methods to use microearthquakes (MEQs) for imaging geothermal reservoir properties and improving subsurface characterization – most importantly the evolution of permeability within the evolving reservoir. This part of the work included development of ML approaches for automated MEQ location, focal mechanism determination and identification of earthquake precursors. The second area focused on using MEQ signals generated by geothermal exploration and production to predict the relationship between fluid injection and seismicity. Here, we extended to reservoir scale our success in using ML to predict laboratory earthquakes and fault zone stress state. The third focus area was on lab experiments. Here, we developed new ML models for lab earthquake prediction and identification of precursors to failure to improve earthquake forecasting and early warning in geothermal settings. Major outcomes of our work include ML models that learn from MEQ signals during geothermal exploration and production to predict induced seismicity. MEQs occur naturally in connection with drilling and energy production. We developed ML methods to use the seismic waves from these events to characterize the elastic, hydraulic and poromechanical properties of reservoirs. Our work illuminated fracture geometry and the evolution of fracture permeability by incorporating seismic coda wave analysis and ML methods to relate fluid injection and seismicity. We significantly expanded laboratory earthquake prediction to include methods that use both passive measurements of microearthquakes within the lab fault zones and also active source acoustic measurements of fault zone elastic properties. These methods can now predict fault zone stress state, time to failure and the magnitude of lab earthquakes. Our work showed that repetitive stick- slip failure events during frictional sliding (the lab equivalent of earthquakes) are preceded by a cascade of micro-failure events that radiate energy in a manner that foretells unstable failure – manifest as laboratory MEQs. We documented a mapping between fracture properties and statistical attributes of elastic radiation. We extended existing works to geothermal reservoir scale and developed ML methods to determine reservoir permeability, fracture properties, and their evolution during geothermal energy production. An attractive feature of ML algorithms is their ability to handle big datasets and reveal patterns and correlations that may remain invisible to conventional analyses. Our work connected data from field, laboratory and intermediate scales to study permeability, stress, strength, fracture stiffness and geometry. At the field scale we used data from the Newberry Volcano field site, UtahFORGE, EGS Collab, and also the Bedretto underground research lab in Switzerland. These data sets are bridging the gap between the lab scale, theory, and reservoir scale. Our work produced plain language summaries to improve public understanding of DOE research. We also developed openly distributed ML and seismicity datasets for use by all researchers and we published connections between induced seismicity in geothermal areas and reservoir properties including permeability, fracture properties, and stress state. Our models are designed for the large data sets of induced seismicity typically associated with geothermal sites. We produced labeled event catalogs and used them on geothermal data to assess how ML can facilitate geothermal production and exploration. All datasets are available on the GDR Productivity: The project produced 32 publications in peer reviewed journals (two are in review). It supported the work of 6 PhD students, 40 conference presentations, 6 keynote talks at national meetings, and mentoring and professional development for 4 postdoctoral fellows.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

ADVERT: An Asynchronous Runtime for Fine-Grained Network Systems

The Data Vortex Network, a novel fine-grained high-radix interconnect, provides the performance and injection rates required by the fine-grained, unpredictable communication patterns of irregular applications. This paper presents ADVERT, an asynchronous runtime system that provides performance and productivity for the fine-grained Data Vortex Network. ADVERT integrates a lightweight memory manager (DVMem) for the user accessible SRAM integrated in the network interface, and a communication library (DVComm) that implements active messaging primitives (remote execution, and, based on this, get and put routines). ADVERT hides the complexity of controlling all the network hardware features through the low-level Data Vortex programming interface, while providing comparable performance. We discuss ADVERT's design and present an analysis of three exemplar irregular kernels (Triangle Count, Page Rank, and GUPS). ADVERT can provide the communication substrate required to build emerging higher level asynchronous many tasking runtimes and partitioned global address space libraries on top of the Data Vortex Network.

Friese, Ryan D.↗
DOE OSTI2024

Radiometric Testing of Germicidal UV Products, Round 2: Upper-Room Luminaires (CALiPER Report)

This report analyzes the independently tested performance of eight germicidal ultraviolet (GUV) upper-room luminaires marketed for use in occupied spaces and purchased between March and June 2023. Three of the luminaires used UV-emitting LEDs, and the remaining five luminaires used low-pressure mercury (LPM) lamps. Product testing covered radiometric and electrical performance for each luminaire. Initial performance was measured for all eight products, and four were additionally measured after 100 h and 500 h of operation. Measured performance was compared against manufacturer or vendor claims. Specifiers and buyers of GUV products need accurate performance claims and data to deploy GUV technology safely and effectively. As with the CALiPER GUV Round 1 report, this CALiPER GUV Round 2 report demonstrates that manufacturers and vendors need significant education and training to accurately test and report the performance of their GUV products. Further development of industry consensus standards and guidelines may address testing limitations and improve test methods, product performance, and the accuracy of performance claims.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Source Term Analysis of Xenon (STAX): An effort focused on differentiating man-made isotope production from nuclear explosions via stack monitoring

An overview of the hardware and software developed for the Source Term Analysis of Xenon (STAX) project is presented which includes the data collection from two stack monitoring systems installed at medical isotope production facilities, infrastructure to transfer data to a central repository, and methods for sharing data from the repository with users. STAX is an experiment to collect radioxenon emission data from industrial nuclear facilities with the goal of developing a better understanding of the global radioxenon background and the effect industrial radioxenon releases have on nuclear explosion monitoring. The final goal of this work is to utilize collected data along with atmospheric transport modeling to calculate the contribution of a peak or set of peaks detected by the International Monitoring System (IMS) to provide desired discriminating information to the International Data Centre (IDC) and National Data Centers (NDCs). Types of data received from the STAX equipment are shown and collected data was used for a case study to predict radioxenon concentrations at two IMS stations closest to the Institute for RadioElements (IRE) in Belgium. The initial evaluation of results indicate that the data is very valuable to the nuclear explosion monitoring community.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling

Satellite precipitation estimates (SPEs) are promising alternatives to gauge observations for hydrological applications (e.g., streamflow simulation), especially in remote areas with sparse observation networks. However, the existing SPEs products are still biased due to imperfections in retrieval algorithms, data sources and post-processing, which makes the effective use of SPEs a challenge, especially at different spatial and temporal scales. In this study, we used a distributed hydrological model to evaluate the simulated discharge from eight quasi-global SPEs at different spatial scales and explored their potential scale effects of SPEs on a cascade of basins ranging from approximately 100 to 130,000 km 2 . The results indicate that, regardless of the difference in the accuracy of various SPEs, there is indeed a scale effect in their application in discharge simulation. Specifically, when the catchment area is larger than 20,000 km2, the overall performance of discharge simulation emerges an ascending trend with the increase of catchment area due to the river routing and spatial averaging. Whereas below 20,000 km 2 , the discharge simulation capability of the SPEs is more randomized and relies heavily on local precipitation accuracy. Our study also highlights the need to evaluate SPEs or other precipitation products (e.g., merge product or reanalysis data) not only at the limited station scale, but also at a finer scale depending on the practical application requirements. Here we have verified that the existing SPEs are scale-dependent in hydrological simulation, and they are not enough to be directly used in very fine scale distributed hydrological simulations (e.g., flash flood). More advanced retrieval algorithms, data sources and bias correction methods are needed to further improve the overall quality of SPEs.

DTVGM↗
DOE OSTI2021

Search for phenomena beyond the Standard Model in events with large $b$-jet multiplicity using the ATLAS detector at the LHC

A search is presented for new phenomena in events characterised by high jet multiplicity, no leptons (electrons or muons), and four or more jets originating from the fragmentation of b -quarks ( b -jets). The search uses 139 fb -1 of $\sqrt{s}$ = 13 TeV proton–proton collision data collected by the ATLAS experiment at the Large Hadron Collider during Run 2. The dominant Standard Model background originates from multijet production and is estimated using a data-driven technique based on an extrapolation from events with low b -jet multiplicity to the high b -jet multiplicities used in the search. No significant excess over the Standard Model expectation is observed and 95% confidence-level limits that constrain simplified models of R-parity-violating supersymmetry are determined. The exclusion limits reach 950 GeV in top-squark mass in the models considered.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

The study of C-13(a, n)O-16 reactions in the Majorana Demonstrator calibration data

Neutron captures and delayed decays of reaction products are common sources of backgrounds in ultra-rare event searches. In this work, we studied 13C(a, n)1640 O reactions induced by a-particles emitted within the calibration sources of the Majorana Demonstrator. These sources are thorium-based calibration standards enclosed in carbon-rich materials. The reaction rate was estimated by using the 6129 keV ?-rays emitted from the excited 1643 O states that are populated when the incoming a-particles have enough energy. Thanks to the excellent energy performance of the Demonstrator’s germanium detectors, these characteristic photons can be clearly observed in the calibration data. Facilitated by Geant4 simulations, a comparison between the observed 6129 keV photon rates and predictions by a TALYS-based software was performed. The measurements and predictions were found to be consistent with each other, albeit with large statistical uncertainties. This agreement provides supports for further background predictions for (a, n)-reactions in future double-beta decay search efforts.

6 ≤ A ≤ 19↗
DOE OSTI2021

A data-driven global soil heterotrophic respiration dataset and the drivers of its inter-annual variability

Soil heterotrophic respiration (SHR), one of the primary carbon fluxes from terrestrial ecosystems to the atmosphere, is important for carbon-climate feedbacks because of its sensitivity to available litter and soil carbon, climatic conditions, and nutrient availability. However, until recently limited SHR data were available, and most published global SHR estimates have either a short time span, coarse spatial resolution, or reply on overly-simple model formulations. To better understand and quantify the global distribution of SHR and its sensitivity to climate variability, we produced a new global SHR dataset using Random Forest algorithms, up-scaling 455 point data from the Global Soil Respiration Database (SRDB 4.0) with gridded fields of climatic, edaphic and productivity as explanatory variables. We estimated a global total SHR of 46.8 Pg C yr-1 over 1985-2013 (95% confidence interval: 38.6-56.3 Pg C yr-1), with a significant increasing trend of 0.03 Pg C yr-2 during this period. We found that the choice of soil moisture datasets contributes more to the difference among these data-driven SHR members rather than that of productivity, temperature and precipitation data sources. We also analyzed the influence of climatic variables on the inter-annual variability (IAV) of our SHR product. Water availability was the dominant driver of IAV at global scales, although the inferred sensitivity depends on the choice of the soil moisture gridded dataset. At the ecosystem scale, temperature strongly controls the IAV of SHR in tropical forests, while water availability dominates in extra-tropical forest and semi-arid regions. Our machine-learning gridded SHR dataset and outputs from process-based land surface models (TRENDYv6) show agreement for a strong association between water variability and SHR IAV at the global scale, but the two approaches lead to different temporal trend globally and different controlling variables for IAV at the ecosystem scale. Our study provides evidence for the pervasive and important role of water availability in driving SHR, indicating both a direct effect limiting decomposition rates and an indirect effect through the amount of fresh organic matter made available to SHR from productivity. In consideration of potential limitations and uncertainties remaining in our data-driven SHR datasets, we call for a more scientifically designed observation network for SHR, more observation data compilation, and increased use of deep learning methods making maximum use of observation data in hand. This will benefit process-based models, and improve our understanding of SHR response to future anomalous environmental conditions.

Yao, Yitong↗
DOE OSTI2025

Data from: 'Abiotic influences on continuous conifer forest structure across a subalpine watershed'

This package archives the core data used for analysis and inference in 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (Worsham et al., 2025). All data were collected in the East River, Washington Gulch, Slate River, and Coal Creek watersheds of Colorado. In the paper, we quantified the relative influence of climate, topographic, edaphic, and geologic factors on conifer stand structure and composition, and their functional relationships, at the watershed scale. We used waveform LiDAR data to derive spatially continuous stand structure metrics. We fused these with a species-level classification map to estimate tree species abundance. We applied generalized additive and generalized boosted models to evaluate the covariability of structural and compositional metrics with abiotic variables. The package contains the essential products required for reproducing our analysis and the tables and figures reported in the publication. The products comprise four classes: (1) geospatial data, (2) tabular data used for inferential analysis, (3) tabular data describing analytical results and performance statistics, and (4) a data user guide. (1) includes discretized waveform LiDAR data, locations and attributes of individual tree crowns, sampling locations and domain boundaries, a canopy height model, and raster files of estimated forest structural and compositional metrics at 100 m grid scale. (2) includes all response and explanatory variable values applied in inferential models. Response variables include conifer forest stand density, basal area, 95th percentile height, quadratic mean diameter, and others. Explanatory variables include climatic water deficit, actual evapotranspiration, elevation, heat load, soil available water content, and others. (3) includes results of training and testing several individual tree detection (ITD) algorithms, as well as inferential modeling results. (4) is a PDF user guide for this data package, including detailed descriptions and data dictionaries for all files. The data package root contains 17 assets: 8 compressed tape archive (.tar.gz) files, 5 comma-separated values (.csv) files, 3 Geographic Tagged Image File Format (GeoTIFF) (.tif) files, and 1 Portable Document Format (.pdf) file. The compressed .tar.gz archives contain ESRI shapefiles (.shp) .tif, compressed LASer (.laz), and .csv files. The archives must first be decompressed using the widely distributed command-line software utility TAR. All other files, including constituent files within the .tar.gz archives, can be opened in the open-source R statistical computing environment. Alternatively, .csv files may also be read in any simple text editor software or Microsoft Excel. Geospatial files including .shp and .tif files can also be opened in GIS software, such as QGIS (open-source) or ESRI ArcGIS (proprietary). The .pdf Data User Guide can be read with Adobe Acrobat Reader or other compatible readers.

2018 NEON and 2025 CHESS Campaigns↗
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 OSTI2022

Hanford Reach Fall Chinook Salmon Productivity Analysis, Brood Years 1975–2016

Updating the Hanford Reach fall Chinook salmon productivity analysis to include seven additional years of data (BY 2010–2016), including three years of record escapements (>150,000 adults), reaffirmed the highly productive nature of this population and the positive effect of flow fluctuation constraints as implemented at Priest Rapids Dam by the VBSA and HRFCPP. The addition of several years of high escapements also revealed that competition among juveniles (and not spawning habitat limitations) is likely the mechanism driving density dependence in the Hanford Reach. The addition of the high escapement brood years also provided evidence for the high recruitment capacity of the population. As such, the WDFW may consider increasing the maximum escapement goal from 42,000 adults to 49,000 adults, which would be more in-line with SMSY as estimated from this update. Given the in-river protections that are in place as part of the HRFCPP, ocean conditions appear to be the primary factor controlling adult production. Therefore, the success of any additional management actions taken in freshwater are likely to be dependent on survival in the ocean. As such, the most effective management actions may be those that limit juvenile competition and density dependent growth effects in the Hanford Reach. Limiting competition would be expected to increase growth, resulting in larger smolts that would be less likely to suffer size-dependent mortality in the marine environment, even during years of poor ocean conditions. Increasing terminal harvest rates when high escapements are anticipated (from downstream dam counts) is one management option that could help to curtail juvenile competition.

Harnish, Ryan A.↗
DOE OSTI2022

Cosmogenic nuclide and solute flux data from central Cuban rivers emphasize the importance of both physical and chemical mass loss from tropical landscapes

We use 25 new measurements of in situ produced cosmogenic 26 Al and 10 Be in river sand, paired with estimates of dissolved load flux in river water, to characterize the processes and pace of landscape change in central Cuba. Long-term erosion rates inferred from 10 Be concentrations in quartz extracted from central Cuban river sand range from 3.4–189 Mg km –2 yr –1 (mean 59, median 45). Dissolved loads (10–176 Mg km –2 yr –1 ; mean 92, median 97), calculated from stream solute concentrations and modeled runoff, exceed measured cosmogenic- 10 Be-derived erosion rates in 18 of 23 basins. This disparity mandates that in this environment landscape-scale mass loss is not fully represented by the cosmogenic nuclide measurements. The 26 Al / 10 Be ratios are lower than expected for steadystate exposure or erosion in 16 of 24 samples. Depressed 26 Al/ 10 Be ratios occur in many of the basins that have the greatest disparity between dissolved loads (high) and erosion rates inferred from cosmogenic nuclide concentrations (low). Depressed 26 Al/ 10 Be ratios are consistent with the presence of a deep, mixed, regolith layer providing extended storage times on slopes and/or burial and extended storage during fluvial transport. River water chemical analyses indicate that many basins with lower 26 Al/ 10 Be ratios and high 10 Be concentrations are underlain at least in part by evaporitic rocks that rapidly dissolve. Our data show that when assessing mass loss in humid tropical landscapes, accounting for the contribution of rock dissolution at depth is particularly important. In such warm, wet climates, mineral dissolution can occur many meters below the surface, beyond the penetration depth of most cosmic rays and thus the production of most cosmogenic nuclides. Our data suggest the importance of estimating solute fluxes and measuring paired cosmogenic nuclides to better understand the processes and rates of mass transfer at a basin scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

AmeriFlux FLUXNET-1F US-WT1 Kress, TX no-till cotton and grain sorghum production

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-WT1 Kress, TX no-till cotton and grain sorghum production. This is the FLUXNET version of the carbon flux data for the site US-WT1 Kress, TX no-till cotton and grain sorghum production produced by applying the standard ONEFlux (1F) software. Site Description - Non irrigated cotton and gain sorghum production

Bednarz, Craig [West Texas A&M University]↗
DOE OSTI2025

Small radius inclusive jet production at the LHC through NNLO+NNLL

The study of hadronic jets and their substructure at hadronic colliders is crucial for improving our understanding of QCD, and searching for new physics. As such, there has been a significant effort to improve their theoretical description. In the small radius limit, inclusive jet production exhibits a universal factorization, enabling the resummation of logarithms which greatly stabilizes theoretical predictions. In this paper, we show how to combine a recently introduced framework for small-R resummation with the Stripper subtraction formalism for fragmentation, enabling next-to-next-to-leading order calculations of small-R inclusive jet production for a wide variety of processes at the LHC. We extract the two-loop constants for the jet functions, enabling for the first time next-to-next-to-leading logarithmic resummation matched to next-to-next-to-leading order perturbative calculation. We compare with CMS data for small-R jet production, and find that our results greatly improve the accuracy of the predictions at small-R, and stabilize the perturbative convergence and error estimates at larger R. Our approach is applicable to a wide class of jet substructure observables exhibiting similar factorization theorems, opening the door to an NNLO jet substructure program at the LHC.

Effective Field Theories↗