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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Aboveground Rather Than Belowground Productivity Drives Variability in Miscanthus × giganteus Net Primary Productivity

Quantifying the carbon (C) uptake of Miscanthus × giganteus ( M × g ) in both aboveground and belowground structures (e.g., net primary productivity (NPP)) and differences among methodological approaches is crucial. Our objectives were to directly measure Mxg NPP and evaluate the effects of nitrogen application, location, and belowground biomass sampling methods. We hypothesize that increased nitrogen application increases the overall NPP of M × g and that quantifying rhizome biomass using excavations will produce the lowest variability between replicates. We collected biomass from mature M × g stands from three locations in Iowa with three nitrogen application rates and one site in Illinois. We destructively sampled at two time points, when rhizome mass is anticipated to be at a minimum (initial) and anticipated to be at its maximum (peak). Biomass was collected from 1 × 1 m quadrats in which one in-clump and one beside-clump cores were collected and then excavated to 30 cm depth to extract all rhizomes. We found that aboveground M × g NPP ranged from 15.4 Mg DM ha –1 year –1 to 36.4 Mg DM ha –1 year–1 and belowground M × g NPP ranged from 4.4 Mg DM ha –1 year –1 to 19.6 Mg DM ha –1 year –1 . M × g NPP varied across sites, fertilization, and calculation assumptions. Aboveground NPP (yield) was on average 68.7% of the total NPP. Root-to-shoot ratios at peak biomass decreased with nitrogen application rate, from an average of 1.9 for 0 N plots to 0.89 for 224 N fertilized plots. There was more variation in core data than from excavations; however, when in-clump and beside-clump cores were averaged together, core and excavation averages were not different. Overall, these results show that the range of mature M × g NPP is driven by aboveground productivity, influenced by nitrogen application and site. Our results provide useful data to constrain agro-ecosystem models and provide crucial insights for future perennial belowground sampling.

Hartman, Theodore [Univ. of Illinois at Urbana-Cha↗
DOE OSTI2023

FUN-BioCROP model with litter decomposition parameters derived from the LIDET dataset

This repository contains the code and data necessary to run the FUN-BioCROP (Fixation and Uptake of Nitrogen-Bioenergy Carbon, Rhizosphere, Organisms, and Protection) model with litter decomposition parameters derived from a modified Monte Carlo simulation that used the Long-term Intersite Decomposition Experiment Team dataset. Related publication:Juice, S.M., Ridgeway, J.R., Hartman, M.D., Parton, W.J., Berardi, D.M., Sulman, B.N., Allen, K.E., & Brzostek, E.R. Reparameterizing litter decomposition using a simplified Monte Carlo method improves litter decay simulated by a microbial model and alters bioenergy soil carbon estimates. Description of Files: FUNBioCROP_LIDET Study.Rmd R code with FUN-BioCROP model that can be run with 10 different sets of parameters for litter decomposition (Baseline, LIDET, or eight other best parameter sets identified in the modified Monte Carlo simulation. CORPSE Functions_Bioenergy_V2.R Code with CORPSE model functions, called by FUNBioCROP_LIDET Study.Rmd Model Input Data: bulk.csv, bulk_till.csv, rhizo.csv, rhizo_till.csv, litter.csv Initial C and N (kg C or N/m2) pool values for each soil compartment, final values from spin up. All five files have the same columns: (Column - Description - Units) uFastC - Unprotected fast decomposing carbon - kg carbon/m2 uSlowC - Unprotected slow decomposing carbon - kg carbon/m2 uNecroC - Unprotected necromass carbon - kg carbon/m2 pFastC - Protected fast decomposing carbon - kg carbon/m2 pSlowC - Protected slow decomposing carbon - kg carbon/m2 pNecroC - Protected necromass carbon - kg carbon/m2 livingMicrobeC - Carbon in living microbial biomass - kg carbon/m2 uFastN - Unprotected fast decomposing nitrogen - kg nitrogen/m2 uSlowN - Unprotected slow decomposing nitrogen - kg nitrogen/m2 uNecroN - Unprotected necromass nitrogen - kg nitrogen/m2 pFastN - Protected fast decomposing nitrogen - kg nitrogen/m2 pSlowN - Protected slow decomposing nitrogen - kg nitrogen/m2 pNecroN - Protected necromass nitrogen - kg nitrogen/m2 inorganicN - Inorganic nitrogen - kg nitrogen/m2 CO2 - Carbon in carbon dioxide - kg carbon/m2 livingMicrobeN - Nitrogen in living microbial biomass - kg nitrogen/m2 Model Input Data: FluxTower_AvgSoilT.csv: Average daily soil temperature (oC) at 10 cm depth at University of Illinois Urbana-Champaign (UIUC) Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: FluxTower_AvgSoilVWC.csv: Average daily soil volumetric water content (VWC) at 10 cm depth at UIUC Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: input_CCS_LIDET Study.csv: This file has daily data to run FUN-BioCROP (Column - Description - Units): yr - calendar year - year doy - day of year (1 to 365) (no leap year) - day anpp - aboveground NPP (DayCent) - kg C/m2/day bnpp - belowground NPP (DayCent) - kg C/m2/day aglivc - live aboveground biomass carbon (DayCent) - kg C/m2 bglivcj - live juvenile fine root biomass carbon (DayCent) - kg C/m2 bglivcm - live mature fine root biomass carbon (DayCent) - kg C/m2 aglivn - live aboveground biomass nitrogen (DayCent) - kg N/m2 bglivnj - live juvenile fine root biomass nitrogen (DayCent) - kg N/m2 bglivnm - live mature fine root biomass nitrogen (DayCent) - kg N/m2 nyr - simulation year - year cult - indicates a cultivation event (0 or 1) crop - indicates a new crop (0 or 1) fert - indicates a fertilizer event (0 or 1) frst - indicates the first day of the growing season (0 or 1) harv - indicates a harvest event (0 or 1) last - indicates the end of the growing season (0 or 1) croptype - crop type (0=none; 1=alfalfa; 2=corn; 3=grass clover pasture; 4=soybean; 5=wheat) cropsrl - crop specific root length - mm/g root cultrhizmix - fraction of rhizosphere mixed with bulk soil during cultivation (0.0-1.0) - fraction cultlitmix - fraction of litter mixed with bulk soil during cultivation (0.0-1.0) - fraction harvremov - fraction of above ground biomass removed during harvest (0.0-1.0) - fraction fertamt - fertilization amount - g N/m2 lifehist - plant life history (0 = annual, 1 = perennial) froot_turnover_c - amount of C in fine root turnover - kg C/m2 froot_turnover_n - amount of N in fine root turnover - kg N/m2 agrd_turnover_c - amount of C in aboveground biomass turnover - kg C/m2 agrd_turnover_n - amount of N in aboveground biomass turnover - kg N/m2 leaf_litter_fastfrac - Fast decomposing fraction of leaf litter (0.0-1.0) - fraction root_litter_fastfrac - Fast decomposing fraction of root litter (0.0-1.0) - fraction root_diameter - root diameter - mm root_length - root length - mm root/m2 rhizo_frac - fraction of total soil volume that is rhizosphere (0.0 - 1.0) - fraction date - date in format YYYY-MM-DD Instructions: Save the model code ("FUN-BioCROP_LIDET Study.Rmd") and accompanything files (data streams and CORPSE function code) in the same folder. In model code "Chunk 3: Load CORPSE Data Streams" set the working directory (setwd) to the folder with the files saved in step #1. In "Chunk 5: Define LIDET parameter sets" select the litter decomposition parameter set to be used in the run, and comment out all other sets. If changing any parameter values, edit them in "Chunk 6: Load parameters." Run all chunks up to and including "Chunk 10: Prepare Data for Export." In "Chunk 11: Export Output Data" edit data frames for export and filenames, as necessary. "Chunk 12: Graph Total Soil C" makes a figure of C remaining over the model run period. Description of each model chunk (in file FUN-BioCROP_LIDET Study.Rmd): Chunk 1: Remove all functions, clear memory. Removes all functions from R environment, clears the memory. Chunk 2: Load Packages. Loads packages necessary to run the code. Chunk 3: Load CORPSE Data Streams. Sets the working directory and loads the data files necessary to run CORPSE. Chunk 4: Load CORPSE Functions. Loads the R script with CORPSE functions from the working directory, "CORPSE Functions_Bioenergy_V2.R". Chunk 5: Define LIDET parameter sets. Has ten different parameter sets for litter decomposition tested in this study: Baseline parameters, LIDET parameters, and the other 8 best performing parameter sets identified in the modified Monte Carlo. To run the model, all but one parameter set must be commented out. Chunk 6: Load Parameters. Loads all fixed parameters to run the model. Data frame with definitions of parameters is in the CORPSE function script "CORPSE Functions_Bioenergy_V2.R" Chunk 7: Prepare Data Streams. Takes data streams loaded in Chunk 3 and puts them in the format necessary to run the model. The model is coded to run at least two sites at a time, so if only one site is being run it must be run in duplicate. Individual data tables of daily values are created in this chunk from the input data file. Chunk 8: Set Initial Conditions. Creates data tables of soil C and N pools for each soil compartment (rhizo_till, rhizo, bulk_till, bulk, litter) and loads initial values into the data tables. Creates lists for each soil compartment to hold model output. Chunk 9: Load FUN Data and Set Up Matrices. Uses DayCent data to calculate FUN input data: root and leaf N demand, total N demand, plant CN, leaf N available for retranslocation, and litter production. Creates matrices for FUN model outputs. Chunk 10: Run Model. Runs the model. Chunk 11: Prepare Data for Export. Combines data from each day saved as lists into data frames for each soil compartment. Adds values from all soil compartments together to calculate total soil values, creates separate data frames for each soil C and N pool (e.g., protected slow C) for the total soil value. Adds different C and N pools together to calculate total soil C and N for all layers. Creates data frame of ratio of protected to unprotected SOC. Organizes FUN data for export. Chunk 12: Export Results. Exports CSV files of model results to the working directory. Chunk 13: Graph Total Soil C. Makes figure of C remaining over time. Related Links: Original FUN-BioCROP model: https://github.com/BrzostekEcologyLab/FUN-BioCROP LIDET dataset: https://andlter.forestry.oregonstate.edu/data/abstract.aspx?dbcode=TD023

Juice, Stephanie↗
DOE OSTI2020

Degradation of REBCO coated conductors due to a combination of epoxy impregnation, thermal cycles, and quench: Characteristics and a method of alleviation

REBa2Cu3Ox (REBCO) coated conductors are an enabling technology for >1 GHz nuclear magnetic resonance spectrometers that need >23.5 T solenoids, compact high-field fusion reactors beyond the International Thermonuclear Experimental Reactor, and 20 T class accelerator magnets. An in-depth understanding of stress and strain management is critically needed for these applications. Here, we report experimental data and an analysis of damages of REBCO-coated conductors in several scenarios relevant for their applications in high-field magnets and unique to their multi-layer thin film structure. The examined scenarios include failure induced by quench, failure induced by epoxy impregnation, and failure induced by a combination of epoxy impregnation and quench. The root cause is film blistering and peeling induced by thermally interfacial stresses characteristic of multilayered heteroepitaxial thin film structures on thick substrates utilized. The quantitative stress analysis is introduced using simple and easy to use formulas and clearly indicates how materials and structural characteristics affect the magnitude and the distribution of stresses and deflections. Finally, a simple method is introduced for removing degradation of REBCO coated conductors due to epoxy impregnation. Our data and analysis help our understanding of the practical limit of REBCO-coated conductors and can be utilized as a guidance for the physical design of REBCO coated conductors and high-current cables assembled from them and for minimizing damages.

Yin, Shijian (ORCID:0000000224312274)↗
DOE OSTI2022

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network

Abstract Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein–protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision–recall curve of 0.63 and a Cohen’s Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network’s information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.

Wang, Xiaowen↗
DOE OSTI2021

Assimilation of a Coordinated Fleet of Uncrewed Aircraft System Observations in Complex Terrain: EnKF System Design and Preliminary Assessment

Abstract Uncrewed aircraft system (UAS) observations collected during the 2018 Lower Atmospheric Process Studies at Elevation—a Remotely Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting Model using an ensemble Kalman filter. The benefit of UAS observations was assessed for a terrain-driven (drainage and upvalley) flow event that occurred within Colorado’s San Luis Valley (SLV) using independent observations. The analysis and prediction of the strength, depth, and horizontal extent of drainage flow from the Saguache Canyon and the subsequent transition to upvalley and up-canyon flow were improved relative to that obtained both without data assimilation (benchmark) and when only surface observations were assimilated. Assimilation of UAS observations greatly improved the analyses of vertical variations in temperature, relative humidity, and winds at multiple locations in the northern portion of the SLV, with reductions in both bias and the root-mean-square error of roughly 40% for each variable relative to the benchmark run. Despite these noted improvements, some biases remain that were tied to measurement error and/or the impact of the boundary layer parameterization on vertically spreading the observations, both of which require further exploration. The results presented here highlight how observations obtained with a fleet of profiling UAS improve limited-area, high-resolution analyses and short-term forecasts in complex terrain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

On the oceanic origin for the enhanced seasonal cycle of SST in the midlatitudes under global warming

Climate models project an enhancement in SST seasonal cycle over the midlatitude oceans under global warming. However, the maintaining mechanism for this enhancement is still under debate. This study investigates the SST seasonal cycle enhancement under quadrupling CO2 using a set of partially coupled experiments, in which the contribution from direct CO2 forcing (without the effect of wind changes) and wind effects can be isolated. Results indicate that both the direct CO2 and total wind effects contribute to the enhancement in the SST seasonal cycle, with the former (latter) effect being more important in the Northern Hemisphere (Southern Hemisphere). Further decomposition of the wind effect into the wind stress feedback and wind-evaporation-SST (WES) feedback through the change of wind speed reveals the importance of the wind stress-driven ocean response in the change of SST seasonal cycle, a result in contrast to a previous study that ascribed the midlatitude SST seasonal cycle change to the thermodynamic WES feedback. The direct CO2 effect regulates SST seasonal cycle mainly through the mediating effects of the ocean mixed layer depth (MLD): i) the CO2 induced warming leads to MLD shoaling, thus reducing its effective heat capacity; ii) the climatological MLD is greater in winter than summer, given the same amount of energy flux from the surface, SST warming in winter should be smaller. Notably, the surface wind seasonal cycle change due solely to the direct CO2 effect is found to bear a great resemblance to the full wind response, implicating that the direct CO2 effect is the root source for the enhancement of the midlatitude SST seasonal cycle. This is further supported by an ocean-alone experiment that replicates the SST seasonal cycle enhancement under a spatially and temporally homogeneous surface thermal forcing into the ocean.

Liu, Fukai↗
DOE OSTI2025

Mapping the gas density with the kinematic Sunyaev-Zel’dovich and patchy screening effects: A self-consistent comparison

The secondary anisotropies of the cosmic microwave background (CMB) provide a wealth of astrophysical and cosmological information. Pairing measurements of the CMB temperature map obtained by DR5 of the Atacama Cosmology Telescope (ACT) with the imaging survey conducted by the Dark Energy Spectroscopic Instrument for the purposes of target selection, DECaLS DR9, we investigate two effects that are sensitive to the gas density 𝜏: kinematic Sunyaev-Zel’dovich (kSZ) and patchy screening or anisotropic screening (resulting from the Thomson scattering of CMB photons away from the line-of-sight by free electrons). In particular, we measure the stacked profiles of the gas density around luminous red galaxies (LRGs) at a mean redshift of 𝑧 ≈ 0.7. We detect the kSZ signal at 7.2⁢𝜎, and we find a signal at ∼ 4.1⁢𝜎 for the patchy screening estimator, which is in excess relative to the kSZ signal. We attribute this excess to contamination from CMB lensing. Here, we demonstrate the effect of lensing using 𝑁-body simulations, and we show that the screening signal is dominated by it. Accounting for lensing, our measurement places a 95% upper bound on the optical depth of the Extended DESI LRG sample of 𝜏 < 2.5 10 −4 for a mean value of the sample of 𝜏 ≈ 1.6 10 −4 . Furthermore, via hydro simulations, we show that the underlying optical depth signal measured by both effects (after removing the CMB lensing contribution) is in perfect agreement when adopting either a compensated aperture photometry (CAP) filter or a high-pass filter. Consistent with previous measurements, we see evidence for excess baryonic feedback around DESI LRGs in the patchy screening measurement. In the future, when both effects can be measured with high signal-to-noise, one can measure the amplitude ratio between them, which is proportional to the root-mean-square velocity of the host halo sample, and even place constraints on velocity-sensitive models such as modified gravity and phantom dark energy.

Astrophysical & cosmological simulations↗
DOE OSTI2020

Using respiration quotients to track changing sources of soil respiration seasonally and with experimental warming

Developing a more mechanistic understanding of soil respiration is hampered by the difficulty in determining the contribution of different organic substrates to respiration and in disentangling autotrophic-versus-heterotrophic and aerobic-versus-anaerobic processes. Here, we use a relatively novel tool for better understanding soil respiration: the apparent respiration quotient (ARQ). The ARQ is the amount of CO 2 produced in the soil divided by the amount of O 2 consumed, and it changes according to which organic substrates are being consumed and whether oxygen is being used as an electron acceptor. We investigated how the ARQ of soil gas varied seasonally, by soil depth, and by in situ experimental warming ( +4 ° C) in a coniferous-forest whole-soil-profile warming experiment over 2 years. We then compared the patterns in ARQ to those of soil δ 13 CO 2 . Our measurements showed strong seasonal variations in ARQ, from ≈0.9 during the late spring and summer to ≈0.7 during the winter. This pattern likely reflected a shift from respiration being fueled by oxidized substrates like sugars and organic acids derived from root and root respiration during the growing season to more reduced substrates such as lipids and proteins derived from microbial necromass during the winter. This interpretation was supported by δ 13 CO 2 values, which were lower, like lipids, in the winter and higher, like sugars, in the summer. Furthermore, experimental warming significantly changed how both ARQ and δ 13 CO 2 responded to soil temperature. Wintertime ARQ and δ 13 CO 2 values were higher in heated than in control plots, probably due to the warming-driven increase in microbial activity that may have utilized oxidized carbon substrates, while growing-season values were lower in heated plots. Experimental warming and phenology change the sources of soil respiration throughout the soil profile. The sensitivity of ARQ to these changes demonstrates its potential as a tool for disentangling the biological sources contributing to soil respiration.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Quarterly Soil Core and Root Analyses from the Missouri Ozarks AmeriFlux (MOFLUX) Site, Ashland, Missouri, 2017-2023

This dataset contains quarterly soil core measurements from the Missouri Ozarks AmeriFlux (MOFLUX) site located at the University of Missouri’s Thomas H. Baskett Wildlife Research and Education Area near Ashland, Missouri. These data will be used to parameterize an ensemble of MOFLUX-optimized soil carbon-nitrogen models, used to simulate carbon (C) and nitrogen (N) cycling responses to future hydroclimatic scenarios and the trajectory of soil C stocks with concomitant forest decline. Beginning in 2017, eight soil cores were collected approximately quarterly near plot 1 of the southeast transect, near the automated soil respiration flux chambers, from 0–15 cm depth. Data are currently available through 2023 (2017-06-14 to 2023-11-13); additional observations will be appended to this dataset as they become available. Cores were analyzed for gravimetric moisture content, pH, total carbon and nitrogen, texture, microbial biomass carbon and nitrogen, and extractable dissolved organic carbon and nitrogen. This dataset contains one data file in comma separate (*.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (*.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Development of observation-based global multilayer soil moisture products for 1970 to 2016

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model – ESM – simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multilayer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5∘ resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1; Wang and Mao, 2021) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations (mean bias from −0.044 to 0.033 m3 m−3, root mean square errors from 0.076 to 0.104 m3 m−3, Pearson correlations from 0.35 to 0.67) and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Three of the new SM products, which were produced by applying any of the three merging methods to the source datasets excluding the ESMs, had lower bias and root mean square errors and higher correlations than the ESM-dependent merged products. The ESM-independent products also showed a better ability to capture historical large-scale drought events than the ESM-dependent products. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors, except that the ESM-dependent products underestimated the low-frequency temporal variability in SM and overestimated the high-frequency variability for the 50–100 cm depth. Based on these evaluation results, the three ESM-independent products were finally recommended for future applications because of their better performances than the ESM-dependent ones. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Pore-resolved investigation of turbulent open channel flow over a randomly packed permeable sediment bed

Pore-resolved direct numerical simulations are performed to investigate the interactions between streamflow turbulence and groundwater flow through a randomly packed porous sediment bed for three permeability Reynolds numbers, Re K = 2.56 , 5.17 and 8.94, representative of natural stream or river systems. Time–space averaging is used to quantify the Reynolds stress, form-induced stress, mean flow and shear penetration depths, and mixing length at the sediment–water interface (SWI). Here, the mean flow and shear penetration depths increase with Re K and are found to be nonlinear functions of non-dimensional permeability. The peaks and significant values of the Reynolds stresses, form-induced stresses, and pressure variations are shown to occur in the top layer of the bed, which is also confirmed by conducting simulations of just the top layer as roughness elements over an impermeable wall. The probability distribution functions (p.d.f.s) of normalized local bed stress are found to collapse for all Reynolds numbers, and their root-mean-square fluctuations are assumed to follow logarithmic correlations. The fluctuations in local bed stress and resultant drag and lift forces on sediment grains are mainly a result of the top layer; their p.d.f.s are symmetric with heavy tails, and can be well represented by a non-Gaussian model fit. The bed stress statistics and the pressure data at the SWI potentially can be used in providing better boundary conditions in modelling of incipient motion and reach-scale transport in the hyporheic zone.

turbulence simulation↗
DOE OSTI2023

On Magnitudes and Inferred Yields of the 39 Underground Nuclear Test Explosions at the Novaya Zemlya Test Site

ABSTRACT We review the previously compiled seismic magnitudes for underground nuclear explosions at the Union of the Soviet Socialist Republics Novaya Zemlya Test Site, and analyze these data in the context of published details about individual tests and official Russian reports of annual cumulative explosive energy release at the test site. We derive relationships between teleseismic body wave magnitude mb and explosive yield Y by predicting the yield of each test using mb and minimizing the difference between predicted and reported annual cumulative yield. A linear relationship between mb and logY with a slope of 0.82 provides a good fit to the annual total yield, with a root mean square (rms) misfit of 0.095 logY units. A spectral magnitude relationship based on the explosion source model of Mueller and Murphy (1971) with a single-free-parameter provides a slightly better fit. For the 10 tests for which individual yields can be directly deduced from the official reports, the rms misfit of reported yields is less than 0.1 logY units or about 25%. More complex relationships that differentiate between the northern and southern test areas, or take account of variations in the scaled depth of burial, do not provide substantially better fits to the reported yields.

Geochemistry & Geophysics↗
DOE OSTI2024

Using phenology to unravel differential soil water use and productivity in a semiarid savanna

Savannas are water-limited ecosystems characterized by two dominant plant types: trees and an understory primarily made up grass. Different phenology and root structures of these plant types complicate how savanna primary productivity responds to changes in water availability. We tested the hypothesis that productivity in savannas is controlled by the temporal and vertical distribution of soil water content (SWC) and differences in growing season length of understory and tree plant functional types. To quantify the relationship between tree, understory, and savanna-wide phenology and productivity, we used PhenoCam and satellite observations surrounding an eddy covariance tower at a semiarid savanna site in Arizona, USA. We distinguished between SWC across two different depth intervals (shallow, <0–30 cm and deep, >30–100 cm). We found that tree greenness increased with SWC at both depths, while understory greenness was only sensitive to the shallower SWC measurements. Onset of ecosystem dormancy, estimated from satellite observations close to the eddy covariance tower, explained more variability in annual gross primary productivity (GPP) than in other phenometrics. Higher SWC led to an extended growing season, caused by delayed dormancy in trees, but the understory showed no evidence of delayed dormancy in wetter periods. We infer that the timing of ecosystem scale dormancy, driven by trees, is important in understanding changes in a savanna's GPP. These findings highlight the important effects of rainfall during the winter. These findings suggest that savanna GPP is conditional on different responses to moisture availability in each of the dominant vegetation components.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Soil resistance and recovery during neotropical forest succession

The recovery of soil conditions is crucial for successful ecosystem restoration and, hence, for achieving the goals of the UN Decade on Ecosystem Restoration. Here, we assess how soils resist forest conversion and agricultural land use, and how soils recover during subsequent tropical forest succession on abandoned agricultural fields. Our overarching question is how soil resistance and recovery depend on local conditions such as climate, soil type and land-use history. For 300 plots in 21 sites across the Neotropics, we used a chronosequence approach in which we sampled soils from two depths in old-growth forests, agricultural fields (i.e. crop fields and pastures), and secondary forests that differ in age (1–95 years) since abandonment. We measured six soil properties using a standardized sampling design and laboratory analyses. Soil resistance strongly depended on local conditions. Croplands and sites on high-activity clay (i.e. high fertility) show strong increases in bulk density and decreases in pH, carbon (C) and nitrogen (N) during deforestation and subsequent agricultural use. Resistance is lower in such sites probably because of a sharp decline in fine root biomass in croplands in the upper soil layers, and a decline in litter input from formerly productive old-growth forest (on high-activity clays). Soil recovery also strongly depended on local conditions. During forest succession, high-activity clays and croplands decreased most strongly in bulk density and increased in C and N, possibly because of strongly compacted soils with low C and N after cropland abandonment, and because of rapid vegetation recovery in high-activity clays leading to greater fine root growth and litter input. Furthermore, sites at low precipitation decreased in pH, whereas sites at high precipitation increased in N and decreased in C : N ratio. Extractable phosphorus (P) did not recover during succession, suggesting increased P limitation as forests age. These results indicate that no single solution exists for effective soil restoration and that local site conditions should determine the restoration strategies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Irradiation-assisted cracking of SA508-304L weldments with 308L groove filler and 309L butter in hot water immersion constant rate extension tests

Low alloy SA508 bainitic steel is often welded to 300 series austenitic stainless steel in nuclear power plants. These dissimilar metal weldments use 309L butter applied to the low alloy steel and 308L groove filler material to join the 309L to austenite. High alloy steels (such as the 308L and 309L material used here) have been identified as having greater susceptibility to irradiation-assisted stress corrosion cracking (IASCC). Evidence is presented here of cracking under tensile loads in 309L weldment butter in LWR hydrothermal conditions that correlates irradiation induced changes at δ ferrite–γ austenite phase boundaries and γ–γ grain boundaries; specifically intergranular Cr 23 C 6 precipitates. Cracking is not observed in irradiated 308L groove filler under the same conditions. Both the 309L butter and 308L filler material have a duplex or mixed δ–γ phase microstructure due to solidification during the welding procedure. We have performed constant extension rate autoclave immersion tests in BWR normal water chemistry hot water (288 °C, 10 MPa, 2000 wt. ppb dissolved oxygen, <100 nS/cm conductivity, neutral pH) of proton irradiated (2 MeV, approximately 3 displacements per atom at a depth of 10 μm) tensile specimens. Proton irradiation to the dpa levels used here induced Cr 23 C 6 carbide precipitation at γ–γ grain boundaries and δ–γ phase boundaries in the 309L butter but not the 308L filler. Cracking was observed in the proton-irradiated volume of 309L at these interfaces in the vicinity of the Cr 23 C 6 precipitates. We hypothesis the SA508 is the source of carbon in the 309L butter. Cr depletion at γ–γ grain boundaries was also observed and may also be a root cause of cracking. Furthermore, the 309L butter is under significant tensile residual stress in the weldment that adds to applied tensile loads and this too may be a contributing factor.

309L weld butter↗
DOE OSTI2024

Evaluating the effects of heatwave events on hydrological processes in the contiguous United States (2003–2022)

Extreme heat and drought conditions are affecting water availability in many regions worldwide, leading to negative impacts on human societies, agriculture, and ecosystems. However, current research lacks comprehensive spatiotemporal analysis examining the interplay between multiple hydrological factors and heatwave events, especially in the context of climate change. This research broadly pertains to understanding the dynamics of hydrological factors and their potential responses to heatwave during warm seasons across the contiguous United States for the period from 2003 to 2022. Utilizing data from the Global Land Data Assimilation System (GLDAS), we analyzed surface runoff, evapotranspiration (ET), precipitation, Groundwater Storage (GWS), Root Zone Soil Moisture (RZSM), and Total Water Storage (TWS) to discern annual patterns and the impacts of heatwave. Further, the spatial patterns of heatwave highlighted a higher occurrence in the western, central, and northeastern U.S., with longer average durations in the western and south-central regions. These events are predominantly dry, characterized by low Relative Humidity (RH), except in the southeastern U.S., where heatwave coincide with high RH levels. Post-heatwave analysis indicated a reduction in GWS, TWS, RZSM, and ET, alongside an increase in surface runoff, RH, and precipitation. An in-depth examination of rainfall and temperature dynamics during heatwave revealed weak correlations between rainfall and temperature, as well as between rainfall and heatwave duration, highlighting the complex nature of these interactions. The study also found an enhanced probability of rainfall following heatwave, particularly in the eastern regions, drawing attention to the potential for increased flood risks post-heatwave. Our findings contribute to the growing body of knowledge on the impacts of heatwave on hydrological factors, providing valuable insights for climate change adaptation and water resource management strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Estimating Soil Thermal Inertia Profiles From the Passive Equilibration of a Temperature Probe

Knowledge of the distribution of soil thermal properties is important for understanding subsurface hydrological and biogeochemical processes. This study describes and evaluates quick thermal profiling (QTP), a new measurement technique aimed at providing rapid, depth-resolved measurements of soil thermal inertia at numerous locations across the landscape. A cylindrical probe with temperature sensors at multiple depths is quickly inserted into the ground, and soil thermal inertia is estimated from how quickly the probe temperature equilibrates with the soil. To this end, a finite volume heat transfer model is used to generate temperature equilibration time series across combinations of controlling factors, and a gridded search inversion approach is applied to infer soil thermal inertia. Field tests in the Arctic indicate that QTP measurements have a minimum uncertainty of 0.14 J m −2 K −1 s −1/2 and covary with dual-probe heat pulse thermal analyzer measurements (concordance correlation coefficient = 0.56) with a root-mean-square error of 0.40 J m −2 K −1 s −1/2 . Besides demonstrating the value of QTP for estimating thermal inertia, this study identifies various sources of measurement uncertainty, particularly probe-soil contact resistance and frictional heating. Further, analysis of soil samples indicates that thermal inertia can be used to estimate thermal conductivity and dry bulk density in the studied area, although such inferences are highly site-specific. Overall, the QTP method holds promise to generate thermal inertia data products and to complement other characterization approaches for advancing understanding of soil properties across far more locations than is currently possible.

Lamb, J. R. [Lawrence Berkeley National Laboratory↗
DOE OSTI2022

Spontaneous Exciton Collapse in a Strongly Flattened Ellipsoidal InSb Quantum Dot

Abstract Electronic and excitonic states in an InSb strongly flattened ellipsoidal quantum dot (QD) with complicated dispersion law are theoretically investigated within the framework of the geometric adiabatic approximation in the strong, intermediate, and weak quantum confinement regimes. For the lower levels of the spectrum, the square root dependence of energy on QD sizes is revealed in the case of Kane’s dispersion law. The obtained results are compared to the case of a parabolic (standard) dispersion law of charge carriers. The possibility of the accidental exciton instability is revealed for the intermediate quantum confinement regime. For the weak quantum confinement regime, the motion of the exciton's center-of-gravity is quantized, which leads to the appearance of additional Coulomb-like sub-levels. It is revealed that in the case of the Kane dispersion law, the Coulomb levels shift into the depth of the forbidden band gap, moving away from the quantum confined level, whereas in the case of the parabolic dispersion law, the opposite picture is observed. The corresponding selection rules of quantum transitions for the interband absorption of light are obtained. New selection rules of quantum transitions between levels conditioned by 2D exciton center of mass vertical motion quantization in a QD are revealed. The absorption threshold behavior characteristics depending on the QDs geometrical sizes are also revealed.

97 MATHEMATICS AND COMPUTING↗