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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Modeling sediment diagenesis processes on riverbed to better quantify aquatic carbon fluxes and stocks in a small watershed of the Mid-Atlantic region

Background: Despite the widely recognized importance of aquatic processes for bridging gaps in the global carbon cycle, there is still a lack of understanding of the role of riverbed processes for carbon flows and stocks in aquatic environments. Here, we added a sediment diagenesis and sediment carbon (C) resuspension module into the SWAT-C model and tested it for simulating both particulate organic C (POC) and dissolved organic C (DOC) fluxes using 4 years of monthly observations (2014–2017) in the Tuckahoe watershed (TW) in the U.S. Mid-Atlantic region. Results: Sensitivity analyses show that parameters that regulate POC deposition in river networks are more sensitive than those that determine C resuspension from sediments. Further analyses indicate that allochthonous contributions to POC and DOC are about 36.6 and 46 kgC ha –1 year –1 , respectively, while autochthonous contributions are less than 0.72 kgC ha –1 year –1 for both POC and DOC (less than 2% of allochthonous sources). The net deposition of POC on the riverbed (i.e., 11.4 kgC ha –1 year –1 ) retained ca. 31% of terrestrial inputs of POC. In addition, average annual buried C was 0.34 kgC ha –1 year –1 , accounting for only 1% of terrestrial POC inputs or 3% of net POC deposition. The results indicate that about 79% of deposited organic C was converted to inorganic C (CH 4 and CO 2 ) in the sediment and eventually released into the overlying water column. Conclusion: This study serves as an exploratory study on estimation of C fluxes from terrestrial to aquatic environments at the watershed scale. We demonstrated capabilities of the SWAT-C model to simulate C cycling from uplands to riverine ecosystems and estimated C sinks and sources in aquatic environments. Overall, the results highlight the importance of including carbon cycle dynamics within the riverbed in order to accurately estimate aquatic carbon fluxes and stocks. The new capabilities of SWAT-C are expected to serve as a useful tool to account for those processes in watershed C balance assessment.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2008

Absorption and Attenuation Coefficients Using the WET Labs ac-s in the Mid-Atlantic Bight: Field Measurements and Data Analysis

Ocean color algorithms are based on the parameterization of apparent optical properties as a function of inherent optical properties. WET Labs underwater absorption and attenuation meters (ac-9 and ac-s) measure both the spectral beam attenuation [c (lambda)] and absorption coefficient [a (lambda)]. The ac-s reports in a continuous range of 390-750 nm with a band pass of 4 nm, totaling approximately 83 distinct wavelengths, while the ac-9 reports at 9 wavelengths. We performed the ac-s field measurements at nine stations in the Mid-Atlantic Bight from water calibrations to data analysis. Onboard the ship, the ac-s was calibrated daily using Milli Q-water. Corrections for the in situ temperature and salinity effects on optical properties of water were applied. Corrections for incomplete recovery of the scattered light in the ac-s absorption tube were performed. The fine scale of spectral and vertical distributions of c (lambda) and a (lambda) were described from the ac-s. The significant relationships between a (674) and that of spectrophotometric analysis and chlorophyll a concentration of discrete water samples were observed.

Ohi, Nobuaki↗
DOE OSTI2022

Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning

Stream temperature (Ts) is an important water quality parameter that affects ecosystem health and human water use for beneficial purposes. Accurate Ts predictions at different spatial and temporal scales can inform water management decisions that account for the effects of changing climate and extreme events. In particular, widespread predictions of Ts in unmonitored stream reaches can enable decision makers to be responsive to changes caused by unforeseen disturbances. In this study, we demonstrate the use of classical machine learning (ML) models, support vector regression and gradient boosted trees (XGBoost), for monthly Ts predictions in 78 pristine and human-impacted catchments of the Mid-Atlantic and Pacific Northwest hydrologic regions spanning different geologies, climate, and land use. The ML models were trained using long-term monitoring data from 1980–2020 for three scenarios: (1) temporal predictions at a single site, (2) temporal predictions for multiple sites within a region, and (3) spatiotemporal predictions in unmonitored basins (PUB). In the first two scenarios, the ML models predicted Ts with median root mean squared errors (RMSE) of 0.69–0.84 °C and 0.92–1.02 °C across different model types for the temporal predictions at single and multiple sites respectively. For the PUB scenario, we used a bootstrap aggregation approach using models trained with different subsets of data, for which an ensemble XGBoost implementation outperformed all other modeling configurations (median RMSE 0.62 °C).The ML models improved median monthly Ts estimates compared to baseline statistical multi-linear regression models by 15–48% depending on the site and scenario. Air temperature was found to be the primary driver of monthly Ts for all sites, with secondary influence of month of the year (seasonality) and solar radiation, while discharge was a significant predictor at only 10 sites. The predictive performance of the ML models was robust to configuration changes in model setup and inputs, but was influenced by the distance to the nearest dam with RMSE <1 °C at sites situated greater than 16 and 44 km from a dam for the temporal single site and regional scenarios, and over 1.4 km from a dam for the PUB scenario. Our results show that classical ML models with solely meteorological inputs can be used for spatial and temporal predictions of monthly Ts in pristine and managed basins with reasonable (<1 °C) accuracy for most locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Mid-Atlantic US observations of radiocarbon in CO 2 : fossil and biogenic source partitioning and model evaluation

Accurately quantifying regional anthropogenic CO 2 fluxes is fundamental to improving our understanding of the carbon cycle and for creating effective carbon mitigation policies, and the radiocarbon to total carbon ratio in atmospheric CO 2 (Δ 14 CO 2 ) is a robust tracer of fossil fuel CO 2 that can discriminate between biogenic and fossil fuel CO 2 sources. NASA's Atmospheric Carbon and Transport-America (ACT-America) airborne mission between 2016 and 2019 aimed to improve the accuracy of regional greenhouse gas flux estimates, through refining our understanding and characterization of fluxes and flux uncertainties in models. Δ 14 CO 2 observations from 26 flights are presented for examining seasonal CO 2 source partitioning in the Mid-Atlantic USA. Observed variability in boundary layer CO 2 at timescales ranging from intra-day to seasonal was largely driven by biogenic CO 2 (CO 2bio ) variability that ranged from −19.7 ppm in summer to 16.2 ppm in fall, while fossil fuel CO 2 (CO 2ff ) variability remained at 3.3±2.0 ppm. Carbonyl sulfide uptake was well-correlated with CO 2bio uptake, and examining this relationship, as well as that between CO 2 and CO 2bio variability reinforces the seasonal extent of gross primary productivity response throughout ACT-America. We use airborne Δ 14 CO 2 flask sampling alongside in situ carbon monoxide measurements to calculate high-frequency CO 2ff and evaluate the magnitude and diurnal variability of modeled CO 2ff , deducing likely transport errors in an example flight. Although ACT-America CO 2ff signals were attenuated due to the broad source regions sampled, results illustrate the value of Δ 14 CO 2 sampling and observation-based methodologies for regional CO 2 flux attribution, evaluation and improvement of modeled CO 2 .

Baier, Bianca C. [National Oceanic and Atmospheric↗
DOE OSTI2023

Biogeochemistry of upland to wetland soils, sediments, and surface waters across Mid-Atlantic and Great Lakes coastal interfaces

Transferable and mechanistic understanding of cross-scale interactions is necessary to predict how coastal systems respond to global change. Cohesive datasets across geographically distributed sites can be used to examine how transferable a mechanistic understanding of coastal ecosystem control points is. To address the above research objectives, data were collected by the EXploration of Coastal Hydrobiogeochemistry Across a Network of Gradients and Experiments (EXCHANGE) Consortium – a regionally distributed network of researchers that collaborated on experimental design, methodology, collection, analysis, and publication. The EXCHANGE Consortium collected samples from 52 coastal terrestrial-aquatic interfaces (TAIs) during Fall of 2021. At each TAI, samples collected include soils from across a transverse elevation gradient (i.e., coastal upland forest, transitional forest, and wetland soils), surface waters, and nearshore sediments across research sites in the Great Lakes and Mid-Atlantic regions (Chesapeake and Delaware Bays) of the continental USA. The first campaign measures surface water quality parameters, bulk geochemical parameters on water, soil, and sediment samples, and physicochemical parameters of sediment and soil.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic

Abstract. As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer timescales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the sensitivity of model output from the Weather Research and Forecasting model (WRF) 4.2.1 to different SST products. We first evaluate three different data sets: the Multiscale Ultrahigh Resolution (MUR25) SST analysis, a daily, 0.25∘ × 0.25∘ resolution product; the Operational Sea Surface Temperature and Ice Analysis (OSTIA), a daily, 0.054∘ × 0.054∘ resolution product; and SSTs from the Geostationary Operational Environmental Satellite 16 (GOES-16), an hourly, 0.02∘ × 0.02∘ resolution product. GOES-16 is not processed at the same level as OSTIA and MUR25; therefore, the product requires gap-filling using an interpolation method to create a complete map with no missing data points. OSTIA and GOES-16 SSTs validate markedly better against buoy observations than MUR25, so these two products are selected for use with model simulations, while MUR25 is at this point removed from consideration. We run the model for June and July of 2020 and find that for this time period, in the Mid-Atlantic, although OSTIA SSTs overall validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height (140 m) wind characterization performance on monthly timescales. Additionally, during hours-long flagged events (< 30 h each) that show statistically significant wind speed deviations between the two simulations, both simulations once again demonstrate similar validation performance (differences in bias, earth mover's distance, correlation, and root mean square error on the order of 10−1 or less), with GOES-16 winds validating nominally better than OSTIA winds. With a more refined GOES-16 product, which has been not only gap-filled but also assimilated with in situ SST measurements in the region, it is likely that hub-height winds characterized by GOES-16-informed simulations would definitively validate better than those informed by OSTIA SSTs.

17 WIND ENERGY↗
DOE OSTI2025

The Remarkable 2024 North Atlantic Mid‐Season Hurricane Lull

Abstract The 2024 North Atlantic (hereafter Atlantic) hurricane season started quickly, with the earliest Category 5 on record (Beryl) and three hurricanes forming through 14 August. Following Ernesto's dissipation on 20 August, the Atlantic hurricane season became extremely quiet during the climatological peak of hurricane season, with only one Category 2 hurricane (Francine) and one tropical storm through 23 September. Several environmental factors likely contributed to this unexpected, prolonged lull. During mid‐to‐late August, subseasonal conditions were broadly favorable for Atlantic hurricanes, but a northward shift in African easterly wave emergence latitude yielded fewer tropical cyclone seed disturbances that also traversed unfavorably cool ocean water. During early‐to‐mid September, subseasonal variability driven by the Madden‐Julian oscillation was less conducive to hurricane activity, with several bouts of increased vertical wind shear across the central Atlantic. Throughout most of the lull, the tropical Atlantic was anomalously dry and subsident, suppressing hurricane formation chances. Plain Language Summary The 2024 North Atlantic (hereafter Atlantic) hurricane season was anticipated to be extremely active, with seasonal forecasts calling for a potentially historic season. Following a busy start including the earliest Category 5 Atlantic hurricane on record (Beryl), the season became very quiet between 20 August–23 September, with only one Category 2 hurricane (Francine) and one tropical storm forming. Following the lull, the Atlantic became extremely busy again, with seven hurricanes forming from 25 September through the end of the season–the most on record for this period. This paper focuses on the surprising lull and attributes it to several different factors, including a late‐August northward shift in African easterly wave tracks (organized thunderstorm complexes spanning thousands of kilometers that often serve as Atlantic hurricane seeds). This northward shift brought these systems out over colder water and into a drier airmass, suppressing their hurricane formation chances. During the first 3 weeks of September, a large‐scale tropical phenomenon known as the Madden‐Julian oscillation, resulted in increased sinking motion, drier mid‐levels and increased vertical wind shear–all of which typically reduce Atlantic hurricane activity. Overall, the lull demonstrates how seed disturbances and other environmental factors can strongly modulate aggregate seasonal tropical cyclone activity. Key Points Following an active start, the 2024 Atlantic hurricane was quiet during the climatological peak with only 1 tropical storm and 1 hurricane The lull was not anticipated by seasonal forecasting agencies, which called for an extremely active 2024 Atlantic hurricane season Factors reducing storm count included a northward shift in African easterly waves, broad‐scale subsidence, and reduced mid‐level moisture

Klotzbach, P J↗
NASA NTRS2020

An Examination of the Impact of Grid Spacing on WRF Simulations of Wintertime Precipitation in the Mid-Atlantic United States

A large set of deterministic and ensemble forecasts was produced to identify the optimal spacing for forecasting U.S. East Coast snowstorms. WRF forecasts were produced on cloud-allowing (~1-km grid spacing) and convection-allowing (3–4 km) grids, and compared against forecasts with parameterized convection (>~10 km). Performance diagrams were used to evaluate 19 deterministic forecasts from the winter of 2013–14. Ensemble forecasts of five disruptive snowstorms spanning the years 2015–18 were evaluated using various methods to evaluate probabilistic forecasts. While deterministic forecasts using cloud-allowing grids were not better than convection-allowing forecasts, both had lower bias and higher success ratios than forecasts with parameterized convection. All forecasts were underdispersive. Nevertheless, forecasts on the higher-resolution grids were more reliable than those with parameterized convection. Forecasts on the cloud-allowing grid were best able to discriminate areas that received heavy snow and those that did not, while the forecasts with parameterized convection were least able to do so. It is recommended to use convection-resolving and (if computationally possible) to use cloud-allowing forecast grids when predicting East Coast winter storms.

Snowstorms↗
DOE OSTI2024

Multimodal, microspectroscopic speciation of legacy phosphorus in two US mid-Atlantic agricultural soils

To understand phosphorus (P) mobility in agricultural soils and its potential environmental risk, it is essential to directly measure solid phase P speciation. Often, bulk P K-edge X-ray absorption near edge structure (XANES) spectroscopy followed by linear combination fitting (LCF) is utilized to determine the solid P phases in soil. However, this method may limit results to only a few major phases. Additionally, XANES spectra for different P species may have very similar features, leading to an over- or underestimate of their contribution to LCF. Here, an improved P speciation by pairing multimodal microbeam-X-ray fluorescence (µ-XRF) mapping coupled with µ-XANES (microbeam-X-ray absorption near edge structure) analysis to directly speciate major and minor P phases on the micron scale is provided. We combined maps of both tender (P, sulfur, aluminum, and silicon) and hard energy (calcium, iron [Fe], and manganese) elements to evaluate the elemental co-locations with P. To better account for uncertainty assigning XANES peaks to individual compounds, a more quantitative fingerprinting by “spectral feature analysis” was completed. With this analysis, an R-factor is reported for the fit. These results were compared to traditional LCF. Pre-edge fitting results revealed the presence of a two-component pre-edge feature for phosphate adsorbed to ferrihydrite. Additionally, phytate co-precipitated with ferrihydrite (Phytate-Fe-Cop) had a pre-edge feature, indicating direct association with Fe. Lastly, a unique P species associated with manganese oxide was identified in the soil via multimodal mapping and µ-XANES. These results allow for better prediction of P dissolution and mobility.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Extreme flood return levels in a U.S. mid-Atlantic estuary using 40-year fluvial-coastal model simulations

Using an integrated watershed-coastal modeling framework, we conducted long-term historical simulations (1980-2019) of fluvial and coastal flooding in the Delaware Bay and River, a vulnerable estuarine system in the U.S., at high spatial resolutions. By applying process-based models, we generated physically consistent and spatially detailed estimates of estuarine, riverine, and surge-driven extreme water level compared to previous studies that used field data only. We then evaluated changes in the magnitude of flood events using the 40-year simulations and detrended Floodwater Depth values with stationary extreme value analysis. Our detailed assessment of spatial-varying extreme values revealed how different flood-generation mechanisms can dominate various zones in the estuary. The datasets produced through this work will be valuable for long-term flood hazard mitigation planning in coastal communities in the Delaware Bay and River region. Additionally, this work will serve as a benchmark for other coastal flood hazard modeling communities worldwide, aiding them in systematically modeling long-term and continuous extreme flood events.

Coastal and Riverine Flooding↗
DOE OSTI2022

Evaluation of coupled wind-wave model simulations of offshore winds in the Mid-Atlantic Bight using lidar-equipped buoys.

From 2014 to 2017, two Department of Energy buoys equipped with Doppler lidar were deployed off the U.S. East Coast to provide long term measurements of hub-height wind speed in the marine environment. In this study, we performed simulations of selected cases from the deployment using a 5-km configuration of the Weather Research and Forecasting (WRF) model, to see if simulated hub height speeds could produce closer agreement with the observations than existing reanalysis products. For each case we performed two additional simulations: one in which marine surface roughness height was one-way coupled to forecast wave parameters from a standalone WaveWatch III (WW3) simulation, and another in which WRF and WW3 were two-way coupled using the Coupled-Ocean-Atmosphere-Wave-Sediment-Transport (COAWST) framework. It was found that all the 5-km WRF simulations improved 90-m wind speed statistics for the tropical cyclone case of 08 May 2015 and the cold frontal case of 25 Mar 2016, but not the nor-easter of 18 Jan 2016. The impact of wave coupling on buoy-level (4 m) wind speed was modest and case dependent, but when present, the impact was typically seen at 90 m as well, being as large as 10% in stable conditions. One-way wave coupling consistently reduced wind speeds, improving biases for 25 Mar 2016 but worsening them for 08 May 2015. Two-way wave coupling mitigated these negative biases, improved wave field representation and statistics, and mostly improved 4-m wind field correlation coefficients, at least at the VA buoy, largely due to greater self-consistency between wind and wave fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

EXCHANGE Campaign 2 - Microbial Biomass Collapse (EC2-MBC): Carbon Availability Under Prolonged Flooding in Mid-Atlantic and Great Lakes Coastal Ecosystems

Our collective research focus is on investigating the interaction between carbon availability and microbial demands during prolonged flooding. Through laboratory experiments utilizing freshly collected soil samples from diverse transition zone environments, we aim to simulate flooding and understand the mechanisms governing carbon cycling under anoxic conditions. Our primary objective is to elucidate how carbon becomes available to microbes and how they utilize it, discerning between maintenance and investment processes. By examining soils from regions with differing native redox conditions, our aim is to gain comprehensive insights into carbon dynamics across various soil types. Future data types will be added to the ESS-DIVE data package as they are completed and will be version-controlled in the Change History section of EC2_MBC_README.pdf. This data package includes: EC2_MBC_README.pdf flmd.csv dd.csv EC2_SampleIdentificationKey.csv EC2_SoilMoistureKey.csv Processed_GHGFlux_Entisols.csv Processed_GHGFlux_Inceptisols.csv Processed_GHGFlux_Ultisols.csv Processed_Porewater_Iron_II.csv Processed_Porewater_Nutrients.csv Processed_Porewater_Sulfate.csv Processed_Probe_Measurements.csv Processed_Soil_HydrolyticEnzymes.csv Processed_Soil_MicrobialCN.csv Processed_Soil_NPOC_TN_SaltExtracted.csv Processed_Soil_NPOC_TN_WaterExtracted.csv Processed_Soil_Nutrients_OlsenPExtracted.csv Processed_Soil_Nutrients_SaltExtracted.csv Processed_Soil_Nutrients_WaterExtracted.csv Processed_DO_Probe.zip Processed_Temperature_Probe.zip

Carbon Cycling↗
DOE OSTI2026

EXCHANGE Campaign Degradation (ECD): Understanding Decomposition Dynamics Across Mid-Atlantic and Great Lakes Coastal Ecosystems

The EXploration of Coastal Hydrobiogeochemistry Across a Network of Gradients and Experiments (EXCHANGE) Degradation Experiment (EXCHANGE-D) is an in situ experiment designed to assess organic matter decomposition rates across coastal terrestrial-aquatic interfaces (TAIs), from coastal uplands through transition zones to wetlands. Through a network of partner scientists and coastal sites, we are testing how environmental gradients shape decomposition and carbon dynamics across terrestrial-aquatic interfaces. Using standardized tea bag substrates deployed across a network of diverse coastal sites, we compare decomposition rates at different fresh- and salt-water TAIs to develop transferable knowledge that improves the representation of organic matter degradation in coastal ecosystem models. For more information, please see https://compass.pnnl.gov/FME/EXCHANGE. This is Version 1 of the data package, which includes: ecd_README.pdf flmd.csv dd.csv ecd_soil_weom_L2.csv ecd_soil_ph_conductivity_L2.csv ecd_soil_gwc_L2.csv ecd_soil_teabag_degradation_L2.csv ecd_readme.pdf

coastal soils↗