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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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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Evaluation of the Event Driven Phenology Model Coupled with the VegET Evapotranspiration Model Through Comparisons with Reference Datasets in a Spatially Explicit Manner

A new model coupling scheme with remote sensing data assimilation was developed for estimation of daily actual evapotranspiration (ET). The scheme represents a mix of the VegET, a physically based model to estimate ET from a water balance, and an event driven phenology model (EDPM), where the EDPM is an empirically derived crop specific model capable of producing seasonal trajectories of canopy attributes. In this experiment, the scheme was deployed in a spatially explicit manner within the croplands of the Northern Great Plains. The evaluation was carried out using 2007-2009 land surface forcing data from the North American Land Data Assimilation System (NLDAS) and crop maps derived from remotely sensed data of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). We compared the canopy parameters produced by the phenology model with normalized difference vegetation index (NDVI) data derived from the MODIS nadir bi-directional reflectance distribution function (BRDF) adjusted reflectance (NBAR) product. The expectations of the EDPM performance in prognostic mode were met, producing determination coefficient (r2) of 0.8 +/-.0.15. Model estimates of NDVI yielded root mean square error (RMSE) of 0.1 +/-.0.035 for the entire study area. Retrospective correction of canopy dynamics with MODIS NDVI brought the errors down to just below 10% of observed data range. The ET estimates produced by the coupled scheme were compared with ones from the MODIS land product suite. The expected r2=0.7 +/-.15 and RMSE = 11.2 +/-.4 mm per 8 days were met and even exceeded by the coupling scheme0 functioning in both prognostic and retrospective modes. Minor setbacks of the EDPM and VegET performance (r2 about 0.5 and additional 30 % of RMSR) were found on the peripheries of the study area and attributed to the insufficient EDPM training and to spatially varying accuracy of crop maps. Overall the experiment provided sufficient evidence of soundness and robustness of the EDPM and VegET coupling scheme, assuring its potential for spatially explicit applications.

Kovalskyy, V.↗
NASA NTRS1997

Physiological Response of Plants Grown on Porous Ceramic Tubes

This research involves the manipulation of the root-zone water potential for the purposes of discriminating the rate limiting step in the inorganic nutrient uptake mechanism utilized by higher plants. This reaction sequence includes the pathways controlled by the root-zone conditions such as water tension and gradient concentrations. Furthermore, plant based control mechanisms dictated by various protein productions are differentiated as well. For the nutrients limited by the environmental availability, the kinetics were modeled using convection and diffusion equations. Alternatively, for the nutrients dependent upon enzyme manipulations, the uptakes are modeled using Michaelis-Menten kinetics. In order to differentiate between these various mechanistic steps, an experimental apparatus known as the Porous Ceramic Tube - Nutrient Delivery System (PCT-NDS) was used. Manipulation of the applied suction pressure circulating a nutrient solution through this system imposes a change in the matric component of the water potential. This compensates for the different osmotic components of water potential dictated by nutrient concentration. By maintaining this control over the root-zone conditions, the rate limiting steps in the uptake of the essential nutrients into tomato plants (Lycopersicon esculentum cv. Cherry Elite) were differentiated. Results showed that the uptake of some nutrients were mass transfer limited while others were limited by the enzyme kinetics. Each of these were adequately modeled with calculations and discussions of the parameter estimations provided.

Tsao, David↗
NASA NTRS2011

The Effect of Plant Cultivar, Growth Media, Harvest Method and Post Harvest Treatment on the Microbiology of Edible Crops

Systems for the growth of crops in closed environments are being developed and tested for potential use in space applications to provide a source of fresh food. Plant growth conditions, growth media composition and harvest methods can have an effect on the microbial population of the plant, and therefore should be considered along with the optimization of plant growth and harvest yields to ensure a safe and palatable food crop. This work examines the effect of plant cultivar, growth media, and harvest method on plant microbial populations. Twelve varieties of leafy greens and herbs were grown on a mixture of Fafard #2 and Arcillite in the pillow root containment system currently being considered for the VEGGIE plant growth unit developed by Orbitec. In addition, ,Sierra and Outredgeous lettuce varieties were grown in three different mixtures (Fafard #2, Ardllite, and Perlite/Vermiculite). The plants were analyzed for microbial density. Two harvest methods, "cut and come again" (CACA) and terminal harvest were also compared. In one set ofexpe'riments red leaf lettuce and mizuna were grown in pots in a Biomass Production System for education. Plants were harvested every two weeks by either method. Another set of experiments was performed using the rooting pillows to grow 5 varieties of leafy greens and cut harvesting at different intervals. Radishes were harvested and replanted at two-week intervals. Results indicate up to a 3 IOglO difference in microbial counts between some varieties of plants. Rooting medium resulted in an approximately 2 IOglO lower count in the lettuce grown in arscillite then those grown in the other mixtures. Harvest method and frequency had less impact on microbial counts only showing a significant increase in one variety of plant. Post harvest methods to decrease the bacterial counts on edible crops were investigated in these and other experiments. The effectiveness of PRO-SAN and UV-C radiation is compared.

Hummerick, Mary P.↗
DOE OSTI2020

The small secreted effector protein MiSSP7.6 of Laccaria bicolor is required for the establishment of ectomycorrhizal symbiosis

Summary To establish and maintain a symbiotic relationship, the ectomycorrhizal fungus Laccaria bicolor releases mycorrhiza‐induced small secreted proteins (MiSSPs) into host roots. Here, we have functionally characterized the M YCORRHIZA‐ i NDUCED S MALL S ECRETED P ROTEIN OF 7 . 6 kDa ( MiSSP7 . 6 ) from L . bicolor by assessing its induced expression in ectomycorrhizae, silencing its expression by RNAi, and tracking in planta subcellular localization of its protein product. We also carried out yeast two‐hybrid assays and bimolecular fluorescence complementation analysis to identify possible protein targets of the MiSSP7.6 effector in Populus roots. We showed that MiSSP7 . 6 expression is upregulated in ectomycorrhizal rootlets and associated extramatrical mycelium during the late stage of symbiosis development. RNAi mutants with a decreased MiSSP7 . 6 expression have a lower mycorrhization rate , suggesting a key role in the establishment of the symbiosis with plants. MiSSP7.6 is secreted, and it localizes both to the nuclei and cytoplasm in plant cells. MiSSP7.6 protein was shown to interact with two Populus Trihelix transcription factors. Furthermore, when coexpressed with one of the Trihelix transcription factors, MiSSP7.6 is localized to plant nuclei only. Our data suggest that MiSSP7.6 is a novel secreted symbiotic effector and is a potential determinant for ectomycorrhiza formation.

Kang, Heng↗
NASA NTRS2020

The NASA MODIS-VIIRS Continuity Cloud Optical Properties Products

The NASA Aqua MODIS and Suomi National Polar-Orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) climate data record continuity cloud properties products (CLDPROP) were publicly released in April 2019 with an update later that year (Version 1.1). These cloud products, having heritage with the NASA Moderate-resolution Imaging Spectroradiometer (MODIS) MOD06 cloud optical properties product and the NOAA GOES-R Algorithm Working Group (AWG) Cloud Height Algorithm (ACHA), represent an effort to bridge the multispectral imager records of NASA’s Earth Observing System (EOS) and NOAA’s current generation of operational weather satellites to achieve a continuous, multi-decadal climate data record for clouds that can extend well into the 2030s. CLDPROP offers a “continuity of approach,” applying common algorithms and ancillary datasets to both MODIS and VIIRS, including utilizing only a subset of spectral channels available on both sensors to help mitigate instrument differences. The initial release of the CLDPROP_MODIS and CLDPROP_VIIRS data records spans the SNPP observational record (2012-present). Here, we present an overview of the algorithms and an evaluation of the intersensor continuity of the core CLDPROP_MODIS and CLDPROP_VIIRS cloud optical property datasets, i.e., cloud thermodynamic phase, optical thickness, effective particle size, and derived water path. The evaluation includes analyses of pixel-level MODIS/VIIRS co-locations as well as spatial and temporal aggregated statistics, with a focus on identifying and understanding the root causes of individual dataset discontinuities. The results of this evaluation will inform future updates to the CLDPROP products and help scientific users determine the appropriate use of the product datasets for their specific needs.

satellite remote sensing↗
NASA NTRS1992

Frozen orbits in the J2 + J3 problem

An analytical derivation of frozen orbit eccentricities and their location over the range of possible orbital inclinations in the J2 + J3 problem is presented. A gravitational field with only J2 and J3 terms is considered, because the equation defining frozen orbits in this field is an algebraic equation of the third order and an analytical formula for roots of this equation exists. An equation for the frozen orbit eccentricity is derived in a convenient form using only two independent parameters: the inclination and a parameter which is the product of the ratio of the radius of the central body to the orbital semimajor axis and the ratio of the J2 and J3 coefficients. The equation is solved, and, on the basis of its roots, frozen orbits in the J2 + J3 problem are classified.

Kiedron, Krystyna↗
DOE OSTI2024

Severe and mild drought cause distinct phylogenetically linked shifts in the blue grama (Bouteloua gracilis) rhizobiome

Plants rely on a diverse rhizobiome to regulate nutrient acquisition and plant health. With increasing severity and frequency of droughts worldwide due to climate change, untangling the relationships between plants and their rhizobiomes is vital to maintaining agricultural productivity and protecting ecosystem diversity. While some plant physiological responses to drought are generally conserved, patterns of root exudation (release of small metabolites shown to influence microbes) and the consequential effects on the plant rhizobiome can differ widely across plant species under drought. To address this knowledge gap, we conducted a greenhouse study using blue grama ( Bouteloua gracilis ), a drought-tolerant C4 grass native to shortgrass prairie across North American plains, as a model organism to study the effect of increasing drought severity (ambient, mild drought, severe drought) on root exudation and the rhizobiome. Our previous results demonstrated physiological effects of increasing drought severity including an increase in belowground carbon allocation through root exudation and shifts in root exudate composition concurrent with the gradient of drought severity. This work is focused on the rhizobiome community structure using targeted sequencing and found that mild and severe drought resulted in unique shifts in the bacterial + archaeal and fungal communities relative to ambient, non-droughted controls. Specifically, using the change in relative abundance between ambient and drought conditions for each ZOTU as a surrogate for population-scale drought tolerance (e.g., as a response trait), we found that rhizobiome response to drought was non-randomly distributed across the phylogenies of both communities, suggesting that Planctomycetota , Thermoproteota (formerly Thaumarchaeota ), and the Glomeromycota were the primary clades driving these changes. Correlation analyses indicated weak correlations between droughted community composition and a select few root exudate compounds previously implicated in plant drought responses including pyruvic acid, D-glucose, and myoinositol. This study demonstrates the variable impacts of drought severity on the composition of the blue grama rhizobiome and provides a platform for hypothesis generation for targeted functional studies of specific taxa involved in plant-microbe drought responses.

Goemann, Hannah M.↗
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↗
NASA NTRS1989

Control of water and nutrients using a porous tube - A method for growing plants in space

A plant nutrient delivery system that uses a microporous, hydrophilic tube was developed with potential application for crop production in the microgravity of space. The tube contains a nutrient solution and delivers it to the roots. Pumps attached to the tubing create a very small suction that holds the solution within the tube. This system was used to grow wheat for 107 d in a controlled environment at suctions of 0.40, 1.48, or 2.58 kPa. The water absorbed through the pores of the tube by baby diaper sections decreased as suction increased. Correspondingly, final plant biomass, seed number, and spikelet number also tended to decrease as suction increased. The reduced yield at higher suction suggests that the plants experienced water stress, although all suctions were below those typical of soils at field capacity.

Dreschel, Thomas W.↗
NASA NTRS2006

Quality Interaction Between Mission Assurance and Project Team Members

Mission Assurance independent assessments started during the development cycle and continued through post launch operations. In operations, Health and Safety of the Observatory is of utmost importance. Therefore, Mission Assurance must ensure requirements compliance and focus on process improvements required across the operational systems including new/modified products, tools, and procedures. The deployment of the interactive model involves three objectives: Team member Interaction, Good Root Cause Analysis Practices, and Risk Assessment to avoid reoccurrences. In applying this model, we use a metric based measurement process and was found to have the most significant effect, which points to the importance of focuses on a combination of root cause analysis and risk approaches allowing the engineers the ability to prioritize and quantify their corrective actions based on a well-defined set of root cause definitions (i.e. closure criteria for problem reports), success criteria and risk rating definitions.

quality↗
NASA NTRS2022

Thermal Hydraulic Disaggregation of SMAP Soil Moisture Over the Continental United States

Thermal Hydraulic disaggregation of Soil Moisture (THySM) algorithm was implemented to downscale NASA’s Soil Moisture Active Passive (SMAP) Enhanced soil moisture (SM) product to 1 km over the continental United States (CONUS). This algorithm was developed by combining thermal inertia theory with a soil hydraulic-based approach that considers fine-scale SM spatial distribution driven by both heat fluxes and hydraulic conductivity in soils. Relative soil wetness values were estimated using land surface temperature and normalized difference vegetation index for the thermal inertia model and using soil properties for the hydraulic model. The relative soil wetness values at 1 km from both models were then combined by using weighting functions whereby the spatial distribution of SM was governed more by thermal fluxes during times of strong heat transport and infiltration during moisture abundant soil conditions. THySM values were evaluated using in situ SM measurements from SMAP Core Validation Sites (CVS), the USDA Soil Climate Analysis Network, and the NOAA Climate Reference Network over CONUS. THySM shows higher accuracy than the SMAP / Sentinel-1 (SPL2SMAP_S) 1 km SM product when compared to in situ measurements. The accuracy of THySM is 0.048 m3/m3 based on unbiased root mean square error (ubRMSE), outperforming SPL2SMAP_S by 0.01-0.02 m3/m3. The ubRMSE of THySM 1km SM over the SMAP grassland/rangeland-dominated CVS sites is better than 0.04 m3/m3, which meets the SMAP mission SM accuracy requirement applied at 9 and 36 km.

SMAP↗
NASA NTRS2022

A Discussion of the Failure of a Quad Diode Module and Efforts to Assure the Flight Spares

In 2019 the International Space Station (ISS) experienced an on on-orbit failure that affected 1 of 28 Battery Charge Discharge Units (BCDUs). Telemetry pointed to a short circuit failure of a Power Rectifier Quad Diode Module. Astronauts removed the failed unit from service which was then returned to Earth for failure analysis. The failure analysis confirmed that the quad diode module had failed short circuit. The investigation identified silver dendrites had grown on the insulated, sloped edge of the mesa semiconductor die of the failed diode and also a 2nd diode in the same quad diode module. Voids between the diode’s protective encapsulating ring and the die provided space within which dendrites were able to form and cause catastrophic failure. The NASA Engineering & Safety Center (NESC) convened a EEE Parts Sub-Team to investigate root cause and to assist with risk assessment for all of the flight diode modules (4 distinct production lots) and the flight spares (from a 5th lot). Analysis of the original diode manufacturer’s read and record screening test data identified some lots which contained diodes with parametric instabilities that could signal conditions suited for silver dendrite formation. Computed Tomography (CT) X-ray screening inspection was performed on flight spare modules to identify diodes that do not exhibit voids between the edge of die and encapsulating ring as a mitigation against dendrite formation.

Jay Brusse↗
DOE OSTI2021

Importance of suberin biopolymer in plant function, contributions to soil organic carbon and in the production of bio-derived energy and materials

Abstract Suberin is a hydrophobic biopolymer of significance in the production of biomass-derived materials and in biogeochemical cycling in terrestrial ecosystems. Here, we describe suberin structure and biosynthesis, and its importance in biological (i.e., plant bark and roots), ecological (soil organic carbon) and economic (biomass conversion to bioproducts) contexts. Furthermore, we highlight the genomics and analytical approaches currently available and explore opportunities for future technologies to study suberin in quantitative and/or high-throughput platforms in bioenergy crops. A greater understanding of suberin structure and production in lignocellulosic biomass can be leveraged to improve representation in life cycle analysis and techno-economic analysis models and enable performance improvements in plant biosystems as well as informed crop system management to achieve economic and environmental co-benefits.

09 BIOMASS FUELS↗
DOE OSTI2021

Data from: "Warming of alpine tundra enhances belowground production and shifts community towards resource acquisition traits"

This archive contains data used to draw conclusions in “Warming of alpine tundra enhances belowground production and shifts community towards resource acquisition traits”, by Yang et al. 2020. Data were collected on Niwot Ridge, in an alpine meadow within the Alpine Treeline Warming Experiment (ATWE) field sites in Colorado, USA. Samples were also processed in the U.S. Geological Survey Forest and Rangeland Ecosystem Science Center, in Boise, Idaho. File formats in this archive include comma-separated values (.csv), portable document format (.pdf), Microsoft Excel (.xlsx), and two types of geospatial files: keyhole markup language (.kml), and ESRI shapefiles (.shp). Leaf scans are .jpg images, and root scans are .tiff/.tif images.The .csv files can be opened using R, Microsoft Excel, or any simple text-editing software such as TextEdit and Notepad. Microsoft Excel files can be opened using Microsoft Excel, and .pdf files can be opened with Adobe Acrobat Reader, Preview, or other compatible programs. Scanned images can be opened using any photo and/or picture viewing software.The .kml file can be opened using Google Earth and Google Maps, and the shapefiles can be opened by any programs compatible with shapefiles, such as the ArcGIS Desktop suite, and QGIS.------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Measures of belowground net primary productivity (BNPP) are required to understand whether aboveground net primary production (ANPP) changes reflect changes in allocation or are indicative of a whole plant NPP response. Plant functional traits provide a key way to scale from the individual plant to the community level, and provide insight into drivers of NPP responses to environmental change. We used infrared heaters to warm an alpine plant community at Niwot Ridge, Colorado, and applied supplemental water to compensate for soil water loss induced by warming. We measured ANPP, BNPP, and leaf and root functional traits across treatments after 5 years of continuous warming. Community-level ANPP and total NPP (ANPP + BNPP) did not respond to heating or watering, but BNPP increased in response to heating. Heating decreased community-level leaf dry matter content and increased total root length, indicating a shift in strategy from resource conservation to acquisition in response to warming.

13C/12C isotope ratio↗
DOE OSTI2021

A daily, 250 m and real-time gross primary productivity product (2000–present) covering the contiguous United States

Abstract. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time and observation-based GPP products. To address this critical gap, here we leverage a state-of-the-art vegetation index, near-infrared reflectance of vegetation (NIRV), along with accurate photosynthetically active radiation (PAR), to produce a SatelLite Only Photosynthesis Estimation (SLOPE) GPP product for the contiguous United States (CONUS). Compared to existing GPP products, the proposed SLOPE product is advanced in its spatial resolution (250 m versus >500 m), temporal resolution (daily versus 8 d), instantaneity (latency of 1 d versus >2 weeks) and quantitative uncertainty (on a per-pixel and daily basis versus no uncertainty information available). These characteristics are achieved because of several technical innovations employed in this study: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRV (SANIRV) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Cropland Data Layer (CDL) product to predict dynamic C4 crop fraction. Through developing a parsimonious model with only two slope parameters, the proposed SLOPE product explains 85 % of the spatial and temporal variations in GPP acquired from 49 AmeriFlux eddy-covariance sites (324 site years), with a root-mean-square error (RMSE) of 1.63 gC m−2 d−1. The median R2 over C3 and C4 crop sites reaches 0.87 and 0.94, respectively, indicating great potentials for monitoring crops, in particular bioenergy crops, at the field level. With such a satisfactory performance and its distinct characteristics in spatiotemporal resolution and instantaneity, the proposed SLOPE GPP product is promising for biological and environmental research, carbon cycle research, and a broad range of real-time applications at the regional scale. The archived dataset is available at https://doi.org/10.3334/ORNLDAAC/1786 (download page: https://daac.ornl.gov/daacdata/cms/SLOPE_GPP_CONUS/data/, last access: 20 January 2021) (Jiang and Guan, 2020), and the real-time dataset is available upon request.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Modeling the mechanisms of coastal vegetation dynamics and ecosystem responses to changing water levels

Coastal forests are increasingly experiencing mortality due to inundation by fresh- and seawater, leading to their replacement by marshes. These shifts alter vegetation composition, biogeochemical cycling, carbon storage, and hydrology. Using a hydraulically enabled ecosystem demography model (FATES-Hydro), we conducted numerical experiments to investigate the mechanisms behind inundation-driven forest loss and the ecosystem-scale consequences of forest-to-marsh transitions. We compared mortality processes and their effects across broadleaf and conifer trees at two coastal sites – Lake Erie (freshwater) and Chesapeake Bay (saline). Our simulations show that hydraulic failure, driven by root loss under prolonged flooding, is the primary mortality mechanism across both tree types and sites. Forest replacement by marsh reduced ecosystem-scale leaf area index (LAI), gross primary production (GPP), transpiration, and deep soil water uptake in conifer forests, while broadleaf forests experienced smaller changes due to lower initial LAI and greater marsh compensation. Marsh invasion occurred following canopy thinning driven by tree mortality. These findings suggest that, under similar root loss, hydraulic failure dominates coastal tree mortality regardless of species or water type, with denser forests experiencing stronger ecosystem impacts. Our study identifies key mortality mechanisms and offers testable hypotheses for future empirical studies on coastal vegetation change.

Ding, Junyan [Occidental College, Los Angeles, CA ↗
NASA NTRS2004

Lunar Simulants: JSC-1 is Gone; The Need for New Standardized Root Simulants

A workshop was held in 1991 to evaluate the status of simulated lunar regolith material and to make recommendations on future requirements and production of such material. As an outgrowth of that workshop, a group centered at Johnson Space Center (JSC) teamed with James Carter of the University of Texas at Dallas and Walter Boles of Texas A&M University to produce and distribute a new standardized lunar regolith simulant termed JSC-1. Carter supervised the field collection, shipping, processing, and initial packaging and transportation of JSC-1. Boles stored and distributed JSC-1. About 25 tons were created and distributed to the lunar science and engineer community; none is left for distribution. JSC-1 served an important role in concepts and designs for lunar base and lunar materials processing. Its chemical and physical properties were described by McKay et al., with its geotechnical properties described by Klosky et al.. While other lunar regolith simulants were produced before JSC-1, they were not standardized, and results from tests performed on them were not necessarily equivalent to test results performed on JSC-1. JSC-1 was designed to be chemically, mineralogically, and texturally similar to a mature lunar mare regolith (low titanium). The glass-rich character of JSC-1 (approx. 50%) produced quite different properties compared to other simulants that were made entirely of comminuted crystalline rock, but properties similar to lunar mare near surface regolith.

Carter, James L.↗
NASA NTRS2021

NASA’s MODIS/VIIRS Global Water Reservoir Product Suite from Moderate Resolution Remote Sensing Data

Global reservoir information can not only benefit local water management but can also improve our understanding of the hydrological cycle. This information includes water area, elevation, and storage; evaporation rate and volume values; and other characteristics. However, operational wall-to-wall reservoir storage and evaporation monitoring information is lacking on a global scale. Here we introduce NASA’s new MODIS/VIIRS Global Water Reservoir product suite based on moderate resolution remote sensing data—the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). This product consists of 8-day (MxD28C2 and VNP28C2) and monthly (MxD28C3 and VNP28C3) measurements for 164 large reservoirs (MxD stands for the product from both Terra (MOD) or Aqua (MYD) satellites). The 8-day product provides area, elevation, and storage values, which were generated by first extracting water areas from surface reflectance data and then applying the area estimations to the pre-established Area–Elevation (A–E) relationships. These values were then further aggregated to monthly, with the evaporation rate and volume information added. The evaporation rate and volume values were calculated after the Lake Temperature and Evaporation Model (LTEM) using MODIS/VIIRS land surface temperature product and meteorological data from the Global Land Data Assimilation System (GLDAS). Validation results show that the 250 m area classifications from MODIS agree well with the high-resolution classifications from Landsat (R^(2) = 0.99). Validation of elevation and storage products for twelve Indian reservoirs show good agreement in terms of R^(2) values (0.71–0.96 for elevation, and 0.79–0.96 for storage) and normalized root-mean-square error (NRMSE) values (5.08–19.34% for elevation, and 6.39–18.77% for storage). The evaporation rate results for two reservoirs (Lake Nasser and Lake Mead) agree well with in situ measurements (R^(2) values of 0.61 and 0.66, and NRMSE values of 16.25% and 21.76%). Furthermore, preliminary results from the VIIRS reservoir product have shown good consistency with the MODIS based product, confirming the continuity of this 20-year product suite. This new global water reservoir product suite can provide valuable information with regard to water-sources-related studies, applications, management, and hydrological modeling and change analysis such as drought monitoring.

Yao Li↗