Search NASA⌕ Search

SEARCH · Search NASA

Results for “data lake”

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 55 records · Page 3

AmeriFlux CA-KLP Kinoje Lake Peatland

This is the AmeriFlux version of the carbon flux data for the site CA-KLP Kinoje Lake Peatland. Site Description - Ontario Ministry of Environment and Climate Change's Environmental Monitoring and Reporting Branch has established five carbon flux monitoring towers in Ontario’s Far North as part of its Climate Change Modelling and Monitoring program. These long term monitoring stations measure carbon exchange and a suite of soil and meteorological parameters over peatland ecosystems to better understand carbon cycling in Ontario’s Far North. Information produced by these monitoring stations will assist the province in land use planning and the development of climate change adaptation strategies.

Humphreys, Elyn [Carleton University]↗

AmeriFlux US-UTL UFLUX Pelican Lake

This is the AmeriFlux version of the carbon flux data for the site US-UTL UFLUX Pelican Lake. Site Description - This station is on a drip irrigated alflafa field

Menuz, Diane [Utah Geological Survey]↗

Freight Electrification, Moving Goods While Managing Grid Impacts

Analyzing freight data from the Salt Lake City inland port demonstrates significant potential forintegrating battery-electric freight vehicles (BEVs) into key transportation corridors. Our partnership with freight operators like RSD and MWCS, and other companies moving freight around the port, provided valuable fleet telemetry and operationalinsights, highlighting critical corridors on and around I-15 and I-80.

Source record↗

AmeriFlux FLUXNET-1F US-UTL UFLUX Pelican Lake

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-UTL UFLUX Pelican Lake. This is the FLUXNET version of the carbon flux data for the site US-UTL UFLUX Pelican Lake produced by applying the standard ONEFlux (1F) software. Site Description - This station is on a drip irrigated alflafa field

Menuz, Diane [Utah Geological Survey]↗

AmeriFlux FLUXNET-1F US-PFe NW4 Lake-1 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFe NW4 Lake-1 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFe NW4 Lake-1 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the northwestern quadrant of the 10 x 10km study domain. It is located in a red pine and aspen forest (20.1 m height) on the bank of a lake (500m across).

Desai, Ankur [University of Wisconsin-Madison]↗

AmeriFlux FLUXNET-1F US-RC5 Moses Lake on-farm site

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-RC5 Moses Lake on-farm site. This is the FLUXNET version of the carbon flux data for the site US-RC5 Moses Lake on-farm site produced by applying the standard ONEFlux (1F) software. Site Description - The Moses Lake On-farm site operated 2013-2015 as part of a cluster of 5 towers (RC1 to RC5) operated for the Regional Approaches to Climate Change (REACCH) USDA-supported research project. The field was a half-circle irrigated plot with the irrigation pivot located on the edge of the field and the flux tower located next to the center of the pivot. The field was in wheat from tower establishment in June 2013 to harvest in August 2013. A cover crop of arugula and mustard was grown from August to October 2013. Potatoes were grown April to August 2014 and wheat (a spring cultivar planted in fall) was grown October 2014 to June 2015. Soils are coarse sandy loam mollisols in the Timmerman series. The site topography is flat.

Chi, Jinshu [The Hong Kong University of Science a↗

AmeriFlux FLUXNET-1F US-MBP Marcell Bog Lake Peatland

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-MBP Marcell Bog Lake Peatland. This is the FLUXNET version of the carbon flux data for the site US-MBP Marcell Bog Lake Peatland produced by applying the standard ONEFlux (1F) software. Site Description - The study site is a fen within the Marcell Experimental Forest, which has been monitored for fluxes and environmental variables at various points in time. In 2007 an EC tower was established to monitor CO2/H2O fluxes within the peatland. Methane observations were added in 2009 and have been ongoing since. This site has also been referred to as Bog Lake Fen in the past.

Roman, Tyler [USDA Forest Service- International P↗

AmeriFlux US-UTP UFLUX Great Salt Lake Phragmites

This is the AmeriFlux version of the carbon flux data for the site US-UTP UFLUX Great Salt Lake Phragmites. Site Description - The station is in the middle of a stand of invasive Phragmites australis, located on the margin of the Great Salt Lake in Farmington Bay.

Inkenbrandt, Paul [Utah Geological Survey]↗

AmeriFlux FLUXNET-1F US-UTP UFLUX Great Salt Lake Phragmites

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-UTP UFLUX Great Salt Lake Phragmites. This is the FLUXNET version of the carbon flux data for the site US-UTP UFLUX Great Salt Lake Phragmites produced by applying the standard ONEFlux (1F) software. Site Description - The station is in the middle of a stand of invasive Phragmites australis, located on the margin of the Great Salt Lake in Farmington Bay.

Inkenbrandt, Paul [Utah Geological Survey]↗

Estimates of Lake Nitrogen, Phosphorus, and Chlorophyll‐ a Concentrations to Characterize Harmful Algal Bloom Risk Across the United States

Abstract Excess nutrient pollution contributes to the formation of harmful algal blooms (HABs) that compromise fisheries and recreation and that can directly endanger human and animal health via cyanotoxins. Efforts to quantify the occurrence, drivers, and severity of HABs across large areas is difficult due to the resource intensive nature of field monitoring of lake nutrient and chlorophyll‐aconcentrations. To better characterize how nutrients interact with other environmental factors to produce algal blooms in freshwater systems, we used spatially explicit and temporally matched climate, landscape, in‐lake characteristic, and nutrient inventory data sets to predict nutrients and chlorophyll‐aacross the conterminous US (CONUS). Using a nested modeling approach, three random forest (RF) models were trained to explain the spatiotemporal variation in total nitrogen (TN), total phosphorus (TP), and chlorophyll‐aconcentrations across US EPA's National Lakes Assessment (n = 2,062). Concentrations of TN and TP were the most important predictors and, with other variables, the RF model accounted for 68% of variation in chlorophyll‐a. We then used these RF models to extrapolate lake TN and TP predictions to lakes without nutrient observations and predict chlorophyll‐afor ∼112,000 lakes across the CONUS. Risk for high chlorophyll‐aconcentrations is highest in the agriculturally dominated Midwest, but other areas of risk emerge in nutrient pollution hot spots across the country. These catchment and lake‐specific results can help managers identify potential nutrient pollution and chlorophyll‐ahot spots that may fuel blooms, prioritize at‐risk lakes for additional monitoring, and optimize management to protect human health and other environmental end goals.

Environmental Sciences & Ecology↗

Xanthos-Lake Model Source Code

This repository contains the source code for Xanthos-Lake, a lake-modeling extension of the Xanthos framework that introduces a coupled lake component comprising the Xanthos-Lake Snow and Ice Model (xLSIM) and the Xanthos-Lake Water Balance Model (xLWBM). xLSIM is a basin-aware machine-learning model for lake snow, ice, and thermal conditions. It predicts monthly lake ice thickness, snow depth, snow-cover fraction, mixing-layer temperature, and lake ice fraction from meteorological forcing and lake surface-area information. It uses sequence-based deep-learning architectures, including Transformer and hybrid Long Short-Term Memory–Transformer (LSTM–Transformer) models, together with seasonal encoding, multi-lake learning, physical masking, and basin-level cryospheric and non-cryospheric classification. The training workflow uses Ray for scalable execution and includes optional Ray Tune hyperparameter optimization. Model predictions, observations, diagnostics, and feature-importance outputs are written in NetCDF. xLWBM is the water-balance component of the new lake framework. It simulates monthly lake storage, surface area, evaporation, inflow, outflow, and lake–groundwater exchange. It combines physical water-balance equations with calibrated bathymetric relationships, weir-based outlet flow, modified Penman open-water evaporation, groundwater head relaxation, Penman–Monteith snow and ice sublimation, and snow, ice, and thermal conditions supplied by xLSIM. The model calibrates lake parameters against satellite-derived surface-area data, using evaporation-based calibration where surface-area data are unavailable, and supports small, medium, and large lake classes. For large lakes, xLWBM is integrated with the managed-routing workflow so that lake storage and outflow interact directly with downstream river routing and reservoir operations. Together, xLSIM and xLWBM provide Xanthos with a coupled lake-modeling capability. xLSIM supplies the snow, ice, and thermal conditions that affect lake evaporation and snow- and ice-related water exchanges, while xLWBM translates those conditions into dynamic lake storage, surface area, evaporation, and discharge. In return, xLWBM supplies evolving lake surface area to xLSIM. This coupling enables Xanthos to represent lakes as active hydrologic components within basin-scale water-availability and routing simulations.

Machine Learning↗

Lake-Effect Snowstorm Events and Associated Snowfall Totals Integrated from NOAA Storm Reports, ERA5, and HRRR for the Laurentian Great Lakes (1997–2024)

Lake-effect snowstorms are localized, impactful winter weather phenomena that can generate substantial snowfall totals and pose significant challenges for forecasting, transportation, and regional infrastructure. To support the analysis and modeling of these events, this dataset compiles observational reports of lake-effect snowstorms alongside corresponding snowfall estimates derived from gridded atmospheric datasets. The observational component of the data originates from the National Weather Service (NWS) winter storm report, subset to lake-effect snow event type, covering 1997–2024. For each lake-effect snow event, this data provides the impacted county, event start and end datetimes at an hourly resolution, as well as relevant storm narratives. The complementary reanalysis-derived data is sourced from European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and High-Resolution Rapid Refresh (HRRR) gridded data. For both gridded datasets, the maximum total snowfall (in units mm) was extracted, constrained by the county and datetimes specified by the observational report. ERA5 data covers the entire observational period (1997–2024), whereas HRRR data is only available from November 2016 – December 2024. Three CSV files are provided here: (1) the observational lake-effect snow event report, (2) ERA5 maximum snowfall detections for each event, and (3) HRRR maximum snowfall detections for each event. Relevant data from the observational files, such as impacted state and county, event datetimes, and event IDs, were included for convenience. Users can inspect and visualize the data using tools such as Microsoft Excel and Python pandas/matplotlib packages. This dataset may support a variety of applications, including climatological analyses of lake-effect snowfall, evaluation of snowfall representation in atmospheric datasets and numerical weather prediction models, and the development of machine learning approaches for detecting or predicting lake-effect snowfall events.

EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > SOLID↗

Data for "Implications of Zoning Ordinances for Rural Utility-Scale Solar Deployment and Power System Decarbonization in the Great Lakes Region".

This dataset includes the Energy Zoning Database and geographic shapefiles used to identify suitable rural areas for utility-scale solar development. Additionally, it contains the input data required for the capacity expansion model, including information on existing electric generators and their associated costs, solar resource potential, and transmission infrastructure data. These datasets collectively support the analysis presented in the paper, ensuring a comprehensive assessment of zoning regulations, land suitability, and the economic and technical feasibility of solar deployment in rural areas.

Owusu-Obeng, Papa Yaw (ORCID:0000000334385183)↗

AmeriFlux FLUXNET-1F CA-Na1 New Brunswick - 1967 Balsam Fir - Nashwaak Lake Site 01 (Mature balsam fir forest)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-Na1 New Brunswick - 1967 Balsam Fir - Nashwaak Lake Site 01 (Mature balsam fir forest). This is the FLUXNET version of the carbon flux data for the site CA-Na1 New Brunswick - 1967 Balsam Fir - Nashwaak Lake Site 01 (Mature balsam fir forest) produced by applying the standard ONEFlux (1F) software. Site Description - immature balsam fir forest

Bourque, Charles P.-A.↗

Trends and meteorological drivers of extreme daily reservoir evaporation events in the western United States

Extreme daily evaporation from reservoir surfaces can lead to significant short-term water losses, affecting water quality, water supply, and reservoir operation strategies. Historical trends in daily reservoir evaporation events have eluded the scientific and operational communities, largely due to a lack of long-term, consistent data record. This study quantifies trends in extreme daily reservoir evaporation events at 165 major reservoirs located in the western U.S. Here, we use the place-based energy balance and aerodynamic Daily Lake Evaporation Model (DLEM) driven by multiple meteorological data products (RTMA, gridMET, Daymet) to estimate daily evaporation rates at these reservoirs from 1981 to 2022. The results—while are based on different meteorological forcing datasets—consistently indicate that the California, Lower Colorado, and Rio Grande hydrologic regions are more prone to higher daily evaporation extremes. Compared to the rest of western U.S, these three regions also experience a more pronounced increasing trend in the annual maximum daily evaporation rate, at about 0.3 mm day -1 decade -1 during 1981-2022. The results show that heat and dryness are the main drivers to the increasing trend of extreme evaporation, while extreme wind speed is the primary contributor to exceptionally high daily evaporation events across all regions. This phenomenon is particularly prominent in the arid Lower Colorado region, but less significant in the cold and humid Pacific Northwest region. We also find that the correlation between extreme wind speed and extreme evaporation degrades as the time scale increases from daily, to monthly and seasonal. Our findings have strong implications for the pattern and distribution of extreme evaporation events at the western U.S. reservoirs, and illustrate how various drivers influence extreme evaporation across different time scales.

13 HYDRO ENERGY↗

Triple oxygen and hydrogen stable isotope composition of hydroxyl water in Orgueil (CI-type) and Tagish Lake (C2-type) carbonaceous chondrites

The primitive carbonaceous chondrites are of interest to cosmochemical science because they contain relatively large amounts of ‘water’ (H 2 O and/or OH – ) within phyllosilicate minerals. This water is evidence for the accretion of ices by their parent planetesimals, and thus represents an archive of the isotopic compositions of H 2 O in the protoplanetary environments. Here, in this study, we used thermogravimetry-enabled laser spectroscopy (TGA-IRIS) analyses of the Orgueil and Tagish Lake meteorites to make δ 2 H, δ 18 O, and Δ′ 17 O measurements of the H 2 O and OH – contained in the different hydrous minerals that comprise each meteorite. In Orgueil, we measured mass-weighted averages of OH – in the saponite and serpentine phyllosilicate matrix to be δ 2 H = 192 ‰, δ 18 O = 1.5 ‰, which are unquestionably of extraterrestrial origin with Δ′ 17 O = 1.0 ‰. For Tagish Lake, analogous values of OH– in the saponite and serpentine phyllosilicate matrix are δ 2 H = 704 ‰, δ 18 O = 11.3 ‰, and are similarly unambiguously extraterrestrial with Δ′ 17 O = 0.82 ‰. We estimate that the parent H 2 O involved in aqueous alteration of Orgueil had δ 18 O value ≥ +23 ‰. In Orgueil, we interpret the phyllosilicate petrographic relationships, and the δ 18 O values of OH – in saponite and serpentine to indicate that saponite formed first, at a lower temperature by 35 to 53 °C than serpentine. This suggests that the Orgueil parent body experienced increasing temperature during the phase of active aqueous alteration (prograde) which set the δ 18 O OH values of the serpentine and saponite. In the case of Tagish Lake, serpentine formed at a lower temperature by 32 to 60 °C than saponite, for the simplest case with constant δ 18 O H2O values. If serpentine formed first, followed by saponite formation at 32 to 60 °C °C higher temperature, this suggests that Tagish Lake sample TL1 underwent prograde aqueous alteration as the parent body heated up. We find evidence that the parent H 2 O for Orgueil, Tagish Lake sample TL1, and Murchison (based on data from a previous study) may have had Δ′ 17 O values of >0.64 ‰.

Aqueous alteration↗

A low-latitude, high-elevation 10Be production-rate calibration from the Rwenzori Mountains, Uganda

Cosmogenic nuclide surface-exposure dating has become a key tool in geomorphology. Robust application of the technique relies on determination of local nuclide production rates using independently dated calibration sites. However, there are few existing calibration sites in certain regions, particularly in low-latitude, high-elevation regions where cosmogenic nuclide production is theoretically more sensitive to changes in Earth's magnetic field and atmospheric conditions. We present a new cosmogenic beryllium-10 (10Be) production-rate calibration from the equatorial Rwenzori Mountains in Uganda. The calibration is based on radiocarbon dating of basal lake sediments (∼21.3 kyr BP) from Lake Mahoma (2900 m asl) and measured 10Be concentrations from boulders on the crest of a moraine which dams the lake. Our results indicate a time-integrated 10Be production rate of 15.83 ± 0.81 at/g/yr at Lake Mahoma. We then use these calibration data and two public online calculators to calculate ages for moraines in a separate Rwenzori valley for which there exists a minimum-limiting radiocarbon age constraint (∼12 kyr BP) on surficial glacial sediments. This enables us to assess the performance of different parameters used to scale production rates spatially and temporally (e.g., scaling frameworks, geomagnetic field reconstructions, atmospheric models) for surface-exposure age calculation in the region. Our results highlight the sensitivity of low-latitude cosmogenic nuclide surface-exposure ages to discrete calculation parameters, such as scaling framework or reconstructions of Earth's magnetic field, and the utility of determining production rates at sites where limiting age data exists for other nearby landforms.

Anderson, N [Dartmouth College]↗

Expanding Access to Science Participation: A FAIR Framework for Petascale Data Visualization and Analytics

The massive data generated by scientists daily serve as both a major catalyst for new discoveries and innovations, as well as a significant roadblock that restricts access to the data. Here, our paper introduces a new approach to removing Big Data barriers and democratizing access to petascale data for the broader scientific community. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We enable FAIR (Findable, Accessible, Interoperable, and Reusable) access to datasets such as NASA’s petascale climate datasets. Our paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction utilizes state-of-the art progressive compression algorithms and machine-learning insights to power scalable visualization dashboards for petascale data. The result provides users with the ability to identify extreme events or trends dynamically, expanding access to scientific data and further enabling discoveries. We validate our approach by improving the ability of climate scientists to visually explore their data via three fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution. These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.

Computer science↗