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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

spammR: an R package designed for analysis and integration of spatial multi-omic measurements

Spatial omics is a young and evolving field and as such shows rapid development of novel technologies and analysis methods to measure transcripts, proteins, metabolites, and post-translational modifications at high spatial resolution. These advances in technology have enabled the simultaneous generation of abundance profiles for multiple different omics types and associated microscopy imaging data, as well as their analysis in a spatial context. However, most analytical tools are designed for spatial transcriptomics platforms and are challenging to use in other contexts such as mass spectrometry-based measurements or metagenomics. To this end we present spammR (spatial analysis of multi-omics measurements in R), an R package that enables end-to-end analysis with a specific focus on mass-spectrometry derived spatial omics datasets with (1) smaller sample sizes and spatial sparsity of samples, (2) considerable missingness, and (3) no a-priori knowledge about proteins or genes of interest, relying on a fully data-driven approach.

spammR↗
DOE OSTI2013

High spatial resolution brain functional MRI using submillimeter balanced steady-state free precession acquisition

Purpose: One of the technical advantages of functional magnetic resonance imaging (fMRI) is its precise localization of changes from neuronal activities. While current practice of fMRI acquisition at voxel size around 3 × 3 × 3 mm{sup 3} achieves satisfactory results in studies of basic brain functions, higher spatial resolution is required in order to resolve finer cortical structures. This study investigated spatial resolution effects on brain fMRI experiments using balanced steady-state free precession (bSSFP) imaging with 0.37 mm{sup 3} voxel volume at 3.0 T. Methods: In fMRI experiments, full and unilateral visual field 5 Hz flashing checkerboard stimulations were given to healthy subjects. The bSSFP imaging experiments were performed at three different frequency offsets to widen the coverage, with functional activations in the primary visual cortex analyzed using the general linear model. Variations of the spatial resolution were achieved by removing outerk-space data components. Results: Results show that a reduction in voxel volume from 3.44 × 3.44 × 2 mm{sup 3} to 0.43 × 0.43 × 2 mm{sup 3} has resulted in an increase of the functional activation signals from (7.7 ± 1.7)% to (20.9 ± 2.0)% at 3.0 T, despite of the threefold SNR decreases in the original images, leading to nearly invariant functional contrast-to-noise ratios (fCNR) even at high spatial resolution. Activation signals aligning nicely with gray matter sulci at high spatial resolution would, on the other hand, have possibly been mistaken as noise at low spatial resolution. Conclusions: It is concluded that the bSSFP sequence is a plausible technique for fMRI investigations at submillimeter voxel widths without compromising fCNR. The reduction of partial volume averaging with nonactivated brain tissues to retain fCNR is uniquely suitable for high spatial resolution applications such as the resolving of columnar organization in the brain.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Technical Note: Measuring contrast- and noise-dependent spatial resolution of an iterative reconstruction method in CT using ensemble averaging

Purpose: The spatial resolution of iterative reconstruction (IR) in computed tomography (CT) is contrast- and noise-dependent because of the nonlinear regularization. Due to the severe noise contamination, it is challenging to perform precise spatial-resolution measurements at very low-contrast levels. The purpose of this study was to measure the spatial resolution of a commercially available IR method using ensemble-averaged images acquired from repeated scans. Methods: A low-contrast phantom containing three rods (7, 14, and 21 HU below background) was scanned on a 128-slice CT scanner at three dose levels (CTDI{sub vol} = 16, 8, and 4 mGy). Images were reconstructed using two filtered-backprojection (FBP) kernels (B40 and B20) and a commercial IR method (sinogram affirmed iterative reconstruction, SAFIRE, Siemens Healthcare) with two strength settings (I40-3 and I40-5). The same scan was repeated 100 times at each dose level. The modulation transfer function (MTF) was calculated based on the edge profile measured on the ensemble-averaged images. Results: The spatial resolution of the two FBP kernels, B40 and B20, remained relatively constant across contrast and dose levels. However, the spatial resolution of the two IR kernels degraded relative to FBP as contrast or dose level decreased. For a given dose level at 16 mGy, the MTF{sub 50%} value normalized to the B40 kernel decreased from 98.4% at 21 HU to 88.5% at 7 HU for I40-3 and from 97.6% to 82.1% for I40-5. At 21 HU, the relative MTF{sub 50%} value decreased from 98.4% at 16 mGy to 90.7% at 4 mGy for I40-3 and from 97.6% to 85.6% for I40-5. Conclusions: A simple technique using ensemble averaging from repeated CT scans can be used to measure the spatial resolution of IR techniques in CT at very low contrast levels. The evaluated IR method degraded the spatial resolution at low contrast and high noise levels.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A Review of Bayesian Networks for Spatial Data

We report Bayesian networks are a popular class of multivariate probabilistic models as they allow for the translation of prior beliefs about conditional dependencies between variables to be easily encoded into their model structure. Due to their widespread usage, they are often applied to spatial data for inferring properties of the systems under study and also generating predictions for how these systems may behave in the future. We review published research on methodologies for representing spatial data with Bayesian networks and also summarize the application areas for which Bayesian networks are employed in the modeling of spatial data. We find that a wide variety of perspectives are taken, including a GIS-centric focus on efficiently generating geospatial predictions, a statistical focus on rigorously constructing graphical models controlling for spatial correlation, as well as a range of problem-specific heuristics for mitigating the effects of spatial correlation and dependency arising in spatial data analysis. Special attention is also paid to potential future directions for integration of Bayesian networks with spatial processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

spopt: a python package for solving spatial optimization problems in PySAL

Spatial optimization is a major spatial analytical tool in management and planning, the significance of which cannot be overstated. Spatial optimization models play an important role in designing and managing effective and efficient service systems such as transportation, education, public health, environmental protection, and commercial investment among others. To this end, spopt (spatial optimization) is under active development for the inclusion of newly proposed models and methods for regionalization, facility location, and transportation-oriented solutions (Feng et al., 2021). Spopt is a submodule in the open-source spatial analysis library PySAL (Python Spatial Analysis Library) founded by Dr. Sergio J. Rey and Dr. Luc Anselin in 2005 (Rey et al., 2015, 2021; Rey & Anselin, 2007). The goal of developing spopt is to provide management and decision-making support to all relevant practitioners and to further promote the appropriate and meaningful application of spatial optimization models in practice.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2006

The Impact of the AIRS Spatial Response on Channel-to-Channel and Multi-Instrument Data Analyses

The Atmospheric Infrared Sounder (AIRS) measures the infrared spectrum in 2378 channels between 3.7 and 15.4 microns with a very high spectral resolution of approximately 1200. AIRS footprints are approximately 1.1 by 0.6 degrees. Because AIRS is a grating spectrometer, each channel has a unique spatial response. Image rotation due to the scan mirror causes these spatial responses to rotate. In effect, each channel has 90 spatial responses, one for each scene footprint in the scan line. Although the spatial response for most channels is symmetric and nearly uniform, some channels have significantly asymmetric response. This paper reviews and describes the prelaunch measurements that characterized the spatial response functions. Next, it describes the conversion of the ground-based results into footprint-specific response functions valid in flight. Then we describe the postlaunch validation of the measurements, including centroid location, axes orientations, and a check on the full two-dimensional response functions. This latter check involves comparison of AIRS data with that of the Moderate Resolution Imaging Spectrometer (MODIS), which flies on the same platform as AIRS. An important result is that AIRS/MODIS brightness temperature comparisons are significantly improved (scatter reduced) when the AIRS spatial response is explicitly taken into account. Finally, a status report is given on attempts to fully verify the prelaunch measurements by deriving the AIRS spatial response from flight data alone.

preflight calibaration↗
NASA NTRS2016

Uncertainties in Coastal Ocean Color Products: Impacts of Spatial Sampling

With increasing demands for ocean color (OC) products with improved accuracy and well characterized, per-retrieval uncertainty budgets, it is vital to decompose overall estimated errors into their primary components. Amongst various contributing elements (e.g., instrument calibration, atmospheric correction, inversion algorithms) in the uncertainty of an OC observation, less attention has been paid to uncertainties associated with spatial sampling. In this paper, we simulate MODIS (aboard both Aqua and Terra) and VIIRS OC products using 30 m resolution OC products derived from the Operational Land Imager (OLI) aboard Landsat-8, to examine impacts of spatial sampling on both cross-sensor product intercomparisons and in-situ validations of R(sub rs) products in coastal waters. Various OLI OC products representing different productivity levels and in-water spatial features were scanned for one full orbital-repeat cycle of each ocean color satellite. While some view-angle dependent differences in simulated Aqua-MODIS and VIIRS were observed, the average uncertainties (absolute) in product intercomparisons (due to differences in spatial sampling) at regional scales are found to be 1.8%, 1.9%, 2.4%, 4.3%, 2.7%, 1.8%, and 4% for the R(sub rs)(443), R(sub rs)(482), R(sub rs)(561), R(sub rs)(655), Chla, K(sub d)(482), and b(sub bp)(655) products, respectively. It is also found that, depending on in-water spatial variability and the sensor's footprint size, the errors for an in-situ validation station in coastal areas can reach as high as +/- 18%. We conclude that a) expected biases induced by the spatial sampling in product intercomparisons are mitigated when products are averaged over at least 7 km × 7 km areas, b) VIIRS observations, with improved consistency in cross-track spatial sampling, yield more precise calibration/validation statistics than that of MODIS, and c) use of a single pixel centered on in-situ coastal stations provides an optimal sampling size for validation efforts. These findings will have implications for enhancing our understanding of uncertainties in ocean color retrievals and for planning of future ocean color missions and the associated calibration/validation exercises.

Coastal ocean color↗
NASA NTRSDate not supplied

Twenty Years of Terra MODIS Spatial Performance Using the Spectro-Radiometric Calibration Assembly

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board the NASA’s Earth Observing System Terra satellite has continued successful Earth-sensing operations for over 20 years. To aid in its mission in providing calibrated science data to the worldwide user community, the MODIS instrument is equipped with several on-board calibrators designed to measure changes in the instrument response over time. One such calibrator is the Spectro-Radiometric Calibration Assembly (SRCA), which can provide a source signal for radiometric, spectral, or spatial characterization. When commanded into its spatial calibration mode, the SRCA is able to produce light across the MODIS band spectral range (0.412μm to 14.2μm) at a variety of signal levels thanks to several internal halogen lamps, an IR glow bar, and a neutral density filter. This signal, used in combination with commanded sub-sample measurements of the MODIS detectors, provides a basis for determining changes in the spatial performance of the MODIS spectral bands. This work summarizes the spatial calibration process using the SRCA and presents 20 years of Terra MODIS spatial performance characterized through co-registration between MODIS bands, detectors, and focal plane assemblies. Results from pre-launch testing using the Integration and Alignment Collimator and the SRCA are incorporated in the history of the Terra MODIS mission-long spatial performance. We also note modifications to the spatial characterization methodology brought on by changes to the SRCA’s operational configuration and changes to the MODIS spectral band performance, particularly after the recovery from the safe-mode event in February 2016. Results are compared against the MODIS design specifications.

MODIS↗
NASA NTRS2022

Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City

The surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions is needed. However, the ability to improve understanding of the surface energy balance is limited by the heterogeneity of surfaces in urban areas. This study aims to enhance the understanding of the urban surface energy budget through an innovation in the use of land surface temperature (LST) observations from remote sensing satellites. A LST database with 5–min temporal and 30–m spatial resolution is developed by spatial downscaling of the Geostationary Operational Environmental Satellites—R (GOES–R) series LST product over New York City (NYC). The new downscaling method, known as the Spatial Downscaling Method (SDM), benefits from the fine spatial resolution of Landsat–8 and high temporal resolution of GOES–R, and considers the temporal variation in LST for each land cover type separately. Preliminary results show that the SDM can reproduce the temporal and spatial variability of LST over NYC reasonably well and the downscaled LST has a spatial root mean square error (RMSE) of the order of 2 K as compared to the independent Landsat–8 observations. The SDM shows smaller RMSE of 1.93 K over the tree canopy land cover, whereas RMSE is 2.19 K for built–up areas. The overall results indicate that the SDM has potential to estimate LST at finer spatial and temporal scales over urban regions.

land surface temperature↗
NASA NTRS2024

On-Orbit Spatial Performance Characterization for Thermal Infrared Imagers of Landsat 7, 8, and 9, ECOSTRESS and CTI

In this analysis of the spatial resolving power of thermal imagery products we focus on four satellite instruments that are used in research and applications, for example to monitor land surface temperature and derive evapotranspiration. These are thermal imagers on Landsat 7, Landsat 8, and Landsat 9, as well as the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). We compiled sets of close-in-time, day-time images of bridges surrounded by open water bodies, captured by each of the satellite imagers during cloud-free moments. Where possible, we also included some images captured by the Compact Thermal Imager (CTI), a technology demonstrator that was co-located with ECOSTRESS on the International Space Station in 2019. Bridges were found to provide a sufficient thermal contrast with the water surface to quantify the line-spread function of satellite-based thermal products. The full-width-at-half-max of a gaussian beam model fitted to this transect quantifies the on-orbit spatial resolution of different imagers. The results show some loss of spatial resolving power in the final product delivered to end-users as compared to the at-sensor characterization of spatial resolution. For Landsat 7, 8 and 9, the spatial resolution of the thermal bands is 1.5 times the ground sampling distance of 60 m and 100 m respectively. For the ECOSTRESS the difference is up to twice the sampling distance of 78 by 69 m2. Since spatial resolution is a main driver for instrument design it is important to understand and communicate this discrepancy between pre-flight design parameters and the characteristics of the surface imagery delivered to the user community. The goal of this research is to facilitate an improved fusion of current and future satellite observations into harmonized products with superior temporal and spatial characteristics.

thermal infrared↗
DOE OSTI2024

Stock-specific spatial overlap among seabird predators and Columbia River juvenile Chinook Salmon suggests a mechanism for predation during early marine residence

Abstract Objective Because predation is thought to be the primary source of natural mortality for juvenile salmon first entering the ocean, we sought to identify regions where, on average, stock-specific spatial overlap between the distribution of threatened and endangered juvenile Chinook Salmon Oncorhynchus tshawytscha and abundant fish-eating seabirds (common murres Uria aalge and sooty shearwaters Ardenna grisea) suggests the greatest potential for ocean predation risk to juvenile Chinook Salmon. Methods The relative abundance and spatial distribution of seabird predators and juvenile Chinook Salmon were quantified as part of long-term ecosystem surveys during May 2003–2012 and June 2003–2022. Genetic stock identification methods were used to assign individual fish to their respective stock groups. Stock-specific species distribution models then generated maps and indices of average annual spatial overlap between predators and prey within the survey area. Result There is unequivocal evidence for spatial overlap between common murres, sooty shearwaters, and five genetic groups of interior and lower Columbia River juvenile Chinook Salmon. We found strongly positive (≥0.70) spatial correlations between predator and prey densities in both May and June, although spatial overlap was, in general, greater during May. The region of highest spatial overlap occurred on the inner continental shelf between the Columbia River mouth (46.2°N) and Grays Harbor (47.0°N), a region at the beginning of the juvenile salmon migratory pathway that is strongly affected by freshwater outflow from the river. Conclusion Our findings support the idea that ocean avian predation during early marine residence has the potential to affect marine survival of juvenile Chinook Salmon and should be further investigated to better inform and implement ecological models and possible recovery actions for Chinook Salmon populations of the Columbia River basin.

Zamon, Jeannette E.↗
DOE OSTI2017

On the nonlinearity of spatial scales in extreme weather attribution statements

In the context of continuing climate change, extreme weather events are drawing increasing attention from the public and news media. A question often asked is how the likelihood of extremes might have changed by anthropogenic greenhouse-gas emissions. Answers to the question are strongly influenced by the model used, duration, spatial extent, and geographic location of the event—some of these factors often overlooked. Using output from four global climate models, we provide attribution statements characterised by a change in probability of occurrence due to anthropogenic greenhouse-gas emissions, for rainfall and temperature extremes occurring at seven discretised spatial scales and three temporal scales. An understanding of the sensitivity of attribution statements to a range of spatial and temporal scales of extremes allows for the scaling of attribution statements, rendering them relevant to other extremes having similar but non-identical characteristics. This is a procedure simple enough to approximate timely estimates of the anthropogenic contribution to the event probability. Furthermore, since real extremes do not have well-defined physical borders, scaling can help quantify uncertainty around attribution results due to uncertainty around the event definition. Results suggest that the sensitivity of attribution statements to spatial scale is similar across models and that the sensitivity of attribution statements to the model used is often greater than the sensitivity to a doubling or halving of the spatial scale of the event. The use of a range of spatial scales allows us to identify a nonlinear relationship between the spatial scale of the event studied and the attribution statement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Microscale spatial distribution and soil organic matter persistence in top and subsoil

The spatial distribution of organic substrates and microscale soil heterogeneity significantly influence organic matter (OM) persistence as constraints on OM accessibility to microorganisms. However, it is unclear how changes in OM spatial heterogeneity driven by factors such as soil depth affect the relative importance of substrate spatial distribution on OM persistence. This work evaluated the decomposition and persistence of 13 C and 15 N labeled water-extractable OM inputs over 50 days as either hotspot (i.e., pelleted in 1–2 mm-size pieces) or distributed (i.e., added as OM < 0.07 μm suspended in water) forms in topsoil (0–0.2 m) and subsoil (0.8–0.9 m) samples of an Andisol. We observed greater persistence of added C in the subsoil with distributed OM inputs relative to hotspot OM, indicated by a 17% reduction in cumulative mineralization of the added C and a 10% higher conversion to mineral-associated OM. A lower substrate availability potentially reduced mineralization due to OM dispersion throughout the soil. NanoSIMS (nanoscale secondary ion mass spectrometry) analysis identified organo-mineral associations on cross-sectioned aggregate interiors in the subsoil. On the other hand, in the topsoil, we did not observe significant differences in the persistence of OM, suggesting that the large amounts of particulate OM already present in the soil outweighed the influence of added OM spatial distribution. Here, we demonstrated under laboratory conditions that the spatial distribution of fresh OM input alone significantly affected the decomposition and persistence of OM inputs in the subsoil. On the other hand, spatial distribution seems to play a lower role in topsoils rich in particulate OM. The divergence in the influence of OM spatial distribution between the top and subsoil is likely driven by differences in soil mineralogy and OM composition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Climatic Controls of the Spatial Patterns of Vegetation Phenology in Midlatitude Grasslands of the Northern Hemisphere

Abstract Revealing grassland growing season spatial patterns and their climatic controls is crucial for estimating the spatial heterogeneity of grassland productivity and carbon sequestration. In this study, we first used satellite‐derived normalized difference vegetation index data and a double logistic function to extract the start (SOS), end (EOS), and length of the growing season in midlatitude grasslands of the Northern Hemisphere during 1981–2014. Then, we verified the accuracy of satellite‐derived SOS and EOS using ground‐observed phenological records and gross primary production data at some locations. Moreover, we analyzed the spatial patterns of growing season indicators and their climatic controls. Results show that both SOS and EOS appear first in cool semidesert grasslands (CG), then in temperate grasslands (TG) and alpine grasslands (AG), and finally in warm semidesert grasslands (WG). A delaying tendency of SOS and EOS from north to south was identified in TG of North America. In contrast, an advancing tendency of SOS and EOS from north to south was detected in CG of Central and Western Asia. Further analysis indicates that a spatial opposite effect of spring temperature and precipitation triggers SOS in TG, whereas a spatial synergy effect of spring temperature and precipitation triggers SOS in CG of Asia, WG, and AG. Meanwhile, a spatial synergy effect of autumn temperature and precipitation triggers EOS for TG of North America and AG, whereas a spatial opposite effect of autumn temperature and precipitation determines EOS for CG.

Ren, Shilong↗
DOE OSTI2023

Uncertain Spatial Pattern of Future Land Use and Land Cover Change and Its Impacts on Terrestrial Carbon Cycle Over the Arctic–Boreal Region of North America

Land use and land cover change (LULCC) represents a key process of human-Earth system interaction and has profound impacts on terrestrial ecosystem carbon cycling. As a key input for ecosystem models, future gridded LULCC data is typically spatially downscaled from regional LULCC projections by integrated assessment models, such as the Global Change Analysis Model (GCAM). The uncertainty associated with the different spatial downscaling methods and its impacts on the subsequent model projections have been historically ignored and rarely examined. This study investigated this problem using two representative spatial downscaling methods and focused on their impacts on the carbon cycle over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain, where extensive LULCC is expected. Specifically, we used the Future Land Use Simulation model (FLUS) and the Demeter model to generate 0.25° gridded LULCC data (i.e., LULCC FLUS and LULCC Demeter , respectively) with the same input of regional LULCC projections from GCAM, under both the low (i.e., SSP126) and high (i.e., SSP585) greenhouse gas emission scenarios. The two sets of downscaled LULCC were used to drive the Community Land Model version 5 and prognostically simulate the terrestrial carbon cycle dynamics over the 21st century. The results suggest large spatial-temporal differences between LULCC FLUS and LULCC Demeter , and the spatial distributions of the needleleaf evergreen boreal tree, broadleaf deciduous boreal tree, broadleaf deciduous boreal shrub, and C3 arctic grass are particularly different under both SSP126 and SSP585. Additionally, the spatiotemporal differences are larger under SSP126 than SSP585, due to more intensive LULCC under SSP126 than SSP585 from GCAM projection. The differences in LULCC further lead to large discrepancies in the spatial patterns of projected gross primary productivity, ecosystem respiration, and net ecosystem exchange, which represent more than 79% of the contributions of future LULCC in 2100. Additionally, the difference in carbon flux under SSP126 is generally larger than those under SSP585. This study highlights the importance of considering the uncertainties induced by the spatial downscaling process in future LULCC projections and carbon cycle simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Spatially-resolved fluorescence-detected two-dimensional electronic spectroscopy probes varying excitonic structure in photosynthetic bacteria

Abstract Conventional implementations of two-dimensional electronic spectroscopy typically spatially average over ~10 10 chromophores spread over ~10 4 micron square area, limiting their ability to characterize spatially heterogeneous samples. Here we present a variation of two-dimensional electronic spectroscopy that is capable of mapping spatially varying differences in excitonic structure, with sensitivity orders of magnitude better than conventional spatially-averaged electronic spectroscopies. The approach performs fluorescence-detection-based fully collinear two-dimensional electronic spectroscopy in a microscope, combining femtosecond time-resolution, sub-micron spatial resolution, and the sensitivity of fluorescence detection. We demonstrate the approach on a mixture of photosynthetic bacteria that are known to exhibit variations in electronic structure with growth conditions. Spatial variations in the constitution of mixed bacterial colonies manifests as spatially varying peak intensities in the measured two-dimensional contour maps, which exhibit distinct diagonal and cross-peaks that reflect differences in the excitonic structure of the bacterial proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Nitrogen Fertilization Restructured Spatial Patterns of Soil Organic Carbon and Total Nitrogen in Switchgrass and Gamagrass Croplands in Tennessee USA

Nitrogen (N) fertilizers can potentially alter spatial distribution of soil organic carbon (SOC) and total nitrogen (TN) concentrations in croplands such as switchgrass (SG: Panicum virgatum L.) and gamagrass (GG: Tripsacum dactyloides L.), but it remains unclear whether these effects are the same between crops and under different rates of fertilization. 13 C and 15 N are two important proxy measures of soil biogeochemistry, but they were rarely examined as to their spatial distributions in soil. Based on a three-year long fertilization experiment in Middle Tennessee, USA, the top mineral horizon soils (0–15 cm) were collected using a spatially explicit design within two 15-m 2 plots under three fertilization treatments in SG and GG croplands. A total of 288 samples were collected based on 12 plots and 24 samples in each plot. The fertilization treatments were no N input (NN), low N input (LN: 84 kg N ha -1 in urea) and high N input (HN: 168 kg N ha -1 in urea). The SOC, TN, SOC/TN (C: N), δ 13 C and δ 15 N were quantified and their within-plot variations and spatial distributions were achieved via descriptive and geostatistical methods. Results showed that SG generally displayed 10~120% higher plot-level variations in all variables than GG, and the plot-level variations were 20~77% higher in NN plots than LN and HN plots in SG but they were comparable in unfertilized and fertilized plots in GG. Relative to NN, LN and HN showed more significant surface trends and spatial structures in SOC and TN in both croplands, and the fertilization effect appeared more pronounced in SG. Spatial patterns in C: N, δ 13 C and δ 15 N were comparable among different fertilization treatments in both croplands. The descending within-plot variations were also identified among variables (SOC > TN > δ 15 N > C: N > δ 13 C). This study demonstrated that N fertilizations generally reduced the plot-level variance and simultaneously re-established spatial structures of SOC and TN in bioenergy croplands, which little varied with fertilization rate but was more responsive in switchgrass cropland.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Cosmological backreaction within the Szekeres model and emergence of spatial curvature

This paper discusses the phenomenon of backreaction within the Szekeres model. Cosmological backreaction describes how the mean global evolution of the Universe deviates from the Friedmannian evolution. The analysis is based on models of a single cosmological environment and the global ensemble of the Szekeres models (of the Swiss-Cheese-type and Styrofoam-type). The obtained results show that non-linear growth of cosmic structures is associated with the growth of the spatial curvature Ω{sub R} (in the FLRW limit Ω{sub R} → Ω {sub k} ). If averaged over global scales the result depends on the assumed global model of the Universe. Within the Swiss-Cheese model, which does have a fixed background, the volume average follows the evolution of the background, and the global spatial curvature averages out to zero (the background model is the ΛCDM model, which is spatially flat). In the Styrofoam-type model, which does not have a fixed background, the mean evolution deviates from the spatially flat ΛCDM model, and the mean spatial curvature evolves from Ω{sub R} =0 at the CMB to Ω{sub R} ∼ 0.1 at 0 z =. If the Styrofoam-type model correctly captures evolutionary features of the real Universe then one should expect that in our Universe, the spatial curvature should build up (local growth of cosmic structures) and its mean global average should deviate from zero (backreaction). As a result, this paper predicts that the low-redshift Universe should not be spatially flat (i.e. Ω {sub k} ≠ 0, even if in the early Universe Ω {sub k} = 0) and therefore when analysing low- z cosmological data one should keep Ω {sub k} as a free parameter and independent from the CMB constraints.

79 ASTRONOMY AND ASTROPHYSICS↗