Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “grassland”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 397 records · Page 22

Quantitative stable isotope probing (qSIP) and cross-domain networks reveal bacterial-fungal interactions in the hyphosphere

Interactions between fungi and bacteria have the potential to substantially influence soil carbon dynamics in soil, but we have yet to fully identify these interactions and partners in their natural environment. In this study, we stacked two powerful methods, 13 C quantitative stable isotope probing (qSIP) and cross-domain co-occurrence network, to identify interacting fungi and bacteria in a California grassland soil. We used in-field whole plant 13 CO 2 labeling along with sand-filled ingrowth bags (that trap fungi and hyphae-associated bacteria) to amplify the signal of fungal-bacterial interactions, separate from the bulk soil background. We found a total of 54 bacterial ASVs and 9 fungal OTUs that were significantly 13 C-enriched. These were saprotrophic and biotrophic fungi, and motile, sometimes predatory bacteria. Among these, 70% of all 13 C-enriched bacteria identified were motile. Notably, we detected fungal-bacterial network links between a fungal OTU of the genus Alternaria and several bacterial ASVs of the genera Bacteriovorax, Mucilaginibacter, and Flavobacterium, providing empirical evidence of their direct interactions through C exchange. We observed a strong positive co-occurrence pattern between predatory bacteria of the phylum Bdellovibrionota and fungal OTUs, suggesting the transfer of C across the soil food web. To date, our ability to associate microbial co-occurrence network patterns with biological interactions is limited, but the incorporation of qSIP allowed us to more precisely detect interacting partners by narrowing in on the taxa that were actively incorporating plant-fixed, fungal-transported labeled substrates. Together, these approaches can help build a mechanistic understanding of the complex nature of fungal-bacterial interactions in soil.

59 BASIC BIOLOGICAL SCIENCES↗

Data for Robust Paths to Net Greenhouse Gas Mitigation and Negative Emissions via Advanced Biofuels

This zip file contains a UNIX-format DayCent model executable, input files, automation code, and associated directory structure necessary to re-produce the DayCent analysis underlying the manuscript. The main script “autodaycent.py” (written for Python 2.7) opens an interactive command line routine that facilitates: Calibrating the DayCent pine growth model; Initializing DayCent for a set of case studies sites; Executing an ensemble of model runs representing case study site reforestation, grassland restoration, or conversion to switchgrass cultivation; and Results analysis & generation of manuscript Fig. 3. Note that the interactive analysis code requires that all input files to be contained in the directory structure as uploaded, without modification. Executable versions of the DayCent model compatible with other operating systems are available upon request.

Feedstock Production↗

Data for Climate vs Energy Security: Quantifying the Trade-offs of BECCS Deployment and Overcoming Opportunity Costs on Set-Aside Land

Bioenergy with carbon capture and storage (BECCS) sits at the nexus of the climate and energy security. We evaluated trade-offs between scenarios that support climate stabilization (negative emissions and net climate benefit) or energy security (ethanol production). Our spatially explicit model indicates that the foregone climate benefit from abandoned cropland (opportunity cost) increased carbon emissions per unit of energy produced by 14–36%, making geologic carbon capture and storage necessary to achieve negative emissions from any given energy crop. The toll of opportunity costs on the climate benefit of BECCS from set-aside land was offset through the spatial allocation of crops based on their individual biophysical constraints. Dedicated energy crops consistently outperformed mixed grasslands. We estimate that BECCS allocation to land enrolled in the Conservation Reserve Program (CRP) could capture up to 9 Tg C year–1 from the atmosphere, deliver up to 16 Tg CE year–1 in emissions savings, and meet up to 10% of the US energy statutory targets, but contributions varied substantially as the priority shifted from climate stabilization to energy provision. Our results indicate a significant potential to integrate energy security targets into sustainable pathways to climate stabilization but underpin the trade-offs of divergent policy-driven agendas.

Field Data↗

Microbial extracellular enzyme activity with simulated climate change

It is critical to understand the consequences of environmental change for the microbial regulation of carbon and nutrient cycling. Specifically, understanding microbial community traits, such as extracellular enzyme activity, can help inform nutrient cycling models and address knowledge gaps. We analyzed data on extracellular enzyme activities and litter decomposition from an 18-month experiment in which microbial communities were reciprocally transplanted along a climate gradient in Southern California. Communities were from desert, scrubland, grassland, pine–oak, and subalpine ecosystems. We aimed to test how enzyme activities responded to climate change following transplantation and how those responses related to decomposition rates. We hypothesized that microbial communities would specialize on their native climate conditions, resulting in higher enzyme activities when transplanted back into their native climate. We investigated the relationship between extracellular enzyme Vmax values, substrate mass loss, and microbial biomass as well as variation in these variables across the climate gradient. We found little evidence for climate specialization, and there was rarely a reduction in enzyme functioning after microbial communities were transplanted into new climate conditions. Moreover, observed differences in decomposition were not related to changes in extracellular enzyme potential, although there were significant differences in enzyme activities and decomposition rates across sites. These results suggest that direct, physiological impacts of climate are likely to be important for enzyme-mediated decomposition, but climate specialization will not constrain the microbial response to climate change in our system.

54 ENVIRONMENTAL SCIENCES↗

Hybrid predictive modeling approach simulated evapotranspiration and ecosystem respiration data

This dataset includes measured data used for developing hybrid-predictive-modeling (HPM) approach and simulated evapotranspiration and ecosystem respiration data across several Fluxnet sites, SNOTEL sites and East River locations (Chen et al., 2020 in review). Fluxnet sites considered in this study include: CA-OAS, CA-OBS, US-NR1, US-TON, US-VAR, US-SRM, US-WHS, US-WKG. Snotel sites Butte (ER-BT), Porphyry Creek (ER-PK) and Schofield Pass (ER-SP) are included as well 16 east river locations with different vegetation types denoted by evergreen forests (EF), deciduous forests (DF), riparian shrublands (RS) and meadow grassland (MS). This data package includes: 1) A summary spread sheet of the sites and acronyms for data variables. 2) Processed Fluxnet datasets that are obtained from Fluxnet.fluxdata.org with HPM estimated evapotranspiration (ET) and ecosystem respiration (RECO). Trained models and metrics for parameters for the Fluxnet sites were added in an update on November 2, 2020; 3) Processed SNOTEL datasets obtained from www.nrcs.usda.gov/snow/ and HPM estimated ET and RECO; 4) Weather data at east river locations obtained from DAYMET (daymet.ornl.gov) as well as HPM estimated ET and RECO. NDVI datasets are obtained from Landsat 5, 7 and 8 on Google Earth Engine platform. Detailed QA/QC and sampling methods of measured data are available at the original data source.

54 ENVIRONMENTAL SCIENCES↗

Intra-canopy leaf trait variation facilitates high LAI and compensatory growth in a clonal woody encroaching shrub in the tallgrass prairie

This data package contains the data for Tooley et al. (2022) Intra-canopy leaf trait variation facilitates high leaf area index and compensatory growth in a clonal woody encroaching shrub. The purpose of this project was to evaluate the leaf ecophysiological traits of Cornus drummondii C.A. Mey to better understand how C. drummondii maximizes photosynthetic rates across large gradients of light availability in its short, dense canopies. We also evaluate how C. drummondii modifies its allocation of leaf traits in response to grassland disturbance. High LAI canopies and a compensatory growth response to disturbance are two key factors contributing to the success of C. drummondii and other woody species responsible for the encroachment of grasslands.The data package contains ecophysiological measurements, including leaf morphological, physiological, and chemical traits; photosynthetic measurements, leaf carbon and nitrogen stable isotopes, leaf area index, and photosynthetically active radiation. All measurements were taken vertically across canopies of the clonal woody-encroaching shrub Cornus drummondii under browsed, grazed, and control (unbrowsed and ungrazed) conditions.Files:SeasonalMeasurements_Archive.csv - Data for all seasonal measurements used in the study.LeafPhotoSeasonalRaw_Archive.zip - zip file containing raw data from an li-6400 for each sampling period in the study.LAI_Archive.xlsx - Data for LAI Measurements.ACiandLightCurveResults_Archive.xlsx - Contains parameters extracted from A-ci and Light Response curves along with the morphological and chemical traits for each leaf.ACiRaw Archive.xlsx - Raw data from Li-6400 for A-ci response curvesLightCurves_Raw.zip - zip file containing raw data for Li-6400 light response curves. Excel file format: "IslandID"-"Depth"-lc.xlsx (or .csv)DataCollectionMethods.docx - file containing the methods of data collection, processing, and QA/QC for all data files. File also contains a list of excel file headers, abbreviations, and their units.

54 ENVIRONMENTAL SCIENCES↗

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025

This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: v{x}-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_v{x}-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_v{x}-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_v{x}-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_v{x}-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗

Daily evapotranspiration changes during heatwaves at 32 NEON sites, 2019-2021

This dataset provides partitioned evapotranspiration (ET, the combined loss of water from soil and plant surfaces) anomalies during heatwave events—soil evaporation (E) and transpiration (T)—for 268 heatwave events across 32 National Ecological Observatory Network (NEON) flux sites in the contiguous United States from 2019–2021. Using an ensemble of four high-frequency turbulence methods (Flux-variance Similarity, Conditional Eddy Covariance [CEC], CEC with Water-Use Efficiency, and Conditional Eddy Accumulation; see Zahn and Bou-Zeid 2024), half-hourly transpiration-to-evapotranspiration (T/ET) ratios were derived from 20 hertz (Hz, cycles per second) eddy covariance measurements of carbon dioxide (CO₂) and water vapor (H₂O) concentrations. The dataset spans six vegetation types including evergreen and deciduous forests, grasslands, cultivated crops, shrublands, and emergent herbaceous wetlands. Data Package Contents: The dataset includes a single CSV (comma-separated values) file containing daily anomalies (deviations from baseline conditions) for transpiration (Delta_T), evaporation (Delta_E), total evapotranspiration (Delta_ET), and T/ET ratio (Delta_T_ET) during each day of identified heatwave events. The file also includes site codes, dates, heatwave event identifiers, and day-of-heatwave indicators. The CSV file can be opened with spreadsheet software (Microsoft Excel, Google Sheets) or programming environments (Python, R, MATLAB). This resource enables researchers to investigate ecosystem-specific responses to thermal extremes, validate land surface model partitioning of ET fluxes, and examine feedbacks between water cycling and surface energy balance during heatwaves. The dataset is particularly valuable for studies linking vegetation hydraulic strategies to climate resilience, as it captures the divergent responses of shallow-rooted versus deep-rooted ecosystems. Potential applications include improving drought early warning systems, informing irrigation management strategies, and advancing our mechanistic understanding of land-atmosphere interactions under extreme heat conditions.

Day of Heatwave↗

AmeriFlux US-KM4 KBS Marshall Farms Smooth Brome Grass (Ref)

This is the AmeriFlux version of the carbon flux data for the site US-KM4 KBS Marshall Farms Smooth Brome Grass (Ref). Site Description - The site was recruited to the USDA’s Conservation Reserve Program (CRP) grassland in 1987 when it was planted to smooth brome grass. The grass was cut every three years but left in place until 2009; uncut since then.

Robertson, G. Philip↗

AmeriFlux US-KM1 KBS Marshall Farms Corn

This is the AmeriFlux version of the carbon flux data for the site US-KM1 KBS Marshall Farms Corn. Site Description - The site was planted to smooth brome grass under the USDA’s Conservation Reserve Program (CRP) grassland since 1987 before conversion. The grass was cut every three years but left in place. The site was converted to no-till soybean in 2009 and to no-till continuous corn from 2010 onwards.

Robertson, G. Philip↗

AmeriFlux US-KM2 KBS Marshall Farms Prairie

This is the AmeriFlux version of the carbon flux data for the site US-KM2 KBS Marshall Farms Prairie. Site Description - The site was planted to smooth brome grass under the USDA’s Conservation Reserve Program (CRP) grassland since 1987 before conversion. The grass was cut every three years but left in place. The site was converted to no-till soybean in 2009 and to perennial native prairie from 2010 onwards. Starting from 2011, the prairie is harvested every autumn/fall.

Robertson, G. Philip↗

AmeriFlux US-CMW Charleston Mesquite Woodland

This is the AmeriFlux version of the carbon flux data for the site US-CMW Charleston Mesquite Woodland. Site Description - Site is a riparian mesquite woodland located along an old alluvial terrace along the banks of the San Pedro River in southeastern Arizona. Site climate is semiarid with monsoonal (Jul-Sep) rainfall. The mesquite trees access groundwater from an alluvial aquifer recharged elsewhere. Site water balance is not closed and ET greatly exceeds precipitation. The understory grasses and forbs are rainfall dependent. This groundwater-dependent ecosystem is far more ebulliant and productive than the upland vegetation around it (shrublands and grasslands).

Scott, Russell↗

AmeriFlux US-KM3 KBS Marshall Farms Switchgrass

This is the AmeriFlux version of the carbon flux data for the site US-KM3 KBS Marshall Farms Switchgrass. Site Description - The site was planted to smooth brome grass under the USDA’s Conservation Reserve Program (CRP) grassland since 1987 before conversion. The grass was cut every three years but left in place. The site was converted to no-till soybean in 2009 and to perennial switchgrass from 2010 onwards. Starting from 2011, the switchgrass is harvested every autumn/fall.

Robertson, G. Philip↗

AmeriFlux US-LS2 San Pedro River Lewis Springs Savanna

This is the AmeriFlux version of the carbon flux data for the site US-LS2 San Pedro River Lewis Springs Savanna. Site Description - A riparian savanna located on an old alluvial terrace about 0.5 km from the banks of the entrenched San Pedro River in southeastern Arizona USA. Site climate is semiarid with monsoonal (Jul-Sep) rainfall. The trees access groundwater (depth of ~7 m)from an alluvial aquifer recharged elsewhere. Therefore, site water balance is not closed and ET greatly exceeds precipitation. The perennial bunchgrasses use primarily rainfall. This groundwater-using ecosystem is far more ebulliant and productive than the upland vegetation around it (shrublands and grasslands) outside of the San Pedro River corridor.

Scott, Russell↗

AmeriFlux US-SRS Santa Rita Experimental Range Mesquite Savanna

This is the AmeriFlux version of the carbon flux data for the site US-SRS Santa Rita Experimental Range Mesquite Savanna. Site Description - The study site is located in the Santa Rita Experimental Range, approximately 45 km south of Tucson, Arizona, within the Sonoran Desert. The rangeland has undergone a shift from a semiarid grassland to a savanna due to the encroachment of the woody leguminous tree Prosopis velutina Woot., or velvet mesquite. A pair of adjacent watersheds of similar size ( 1.08 ha and 1.10 ha) was established to investigate the effects of mesquite removal on hydrologic processes. In 1974, diesel oil was applied basally to all trees in one watershed, with reapplication as needed and dead tree removal for wood, whereas the other watershed, where the flux tower is located, has continued in the woody plant encroachment process. In 2016, an herbicide process was applied to the area around the EC flux tower.

Vivoni, Enrique R.↗

AmeriFlux US-xAE NEON Klemme Range Research Station (OAES)

This is the AmeriFlux version of the carbon flux data for the site US-xAE NEON Klemme Range Research Station (OAES). Site Description - The 1,560-acre Klemme Range Research Station is a grassland site managed for livestock grazing and pasture, located in the middle of the Rolling Red Plains Resource Area. The Rolling Red Plains extends from south of the Red River to north of the Oklahoma/Kansas border consisting of approximately 9.4 million acres, which occupies a significant portion of Western Oklahoma excluding the Oklahoma Panhandle.

Network), NEON (National Ecological Observatory↗

AmeriFlux US-xSJ NEON San Joaquin Experimental Range (SJER)

This is the AmeriFlux version of the carbon flux data for the site US-xSJ NEON San Joaquin Experimental Range (SJER). Site Description - Located at The San Joaquin Experimental Range, in the western foothills of the Sierra Nevada, this 18.2 kilometer terrestrial field site is a mix of open woodlands, shrubs and grasslands with low density cattle grazing. SJER is an ecosystem research experimental area. It was originally conceived as a cooperative interdisciplinary research center to identify cost-effect methods of commercial livestock production in the annual grass-oak pine woodlands, while maintaining the integrity of the ecosystem. More recently, research objectives have expanded and diversified to include research projects making contributions to our knowledge of the patterns and processes working in this ecosystem.

Network), NEON (National Ecological Observatory↗