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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Predicting river turbidity in Pine Island Bayou using machine learning techniques coupled with variational mode decomposition

Elevated turbidity levels pose significant public health risks by facilitating the transport of harmful pollutants, including metals, organic compounds, and pathogenic microorganisms into the surface water. These conditions create serious challenges for public recreational water use and drinking water treatment, leading to economic losses and health risks. This study utilizes water monitoring data in Pine Island Bayou, Texas, and develops a Sequence-to-Sequence (S2S) model to predict turbidity using Attention-based Gated Recurrent Units with Encoder-Decoder (AT-GRU-ED) and Long Short-Term Memory (LSTM), coupled with Variational Mode Decomposition (VMD). Compared to the model without VMD, the model demonstrates satisfactory 72-hour turbidity prediction performance, achieving MAEs of 2.60 and 3.29 NTU (reductions of 53% and 58%), RMSEs of 21.08 and 31.49 NTU (reductions of 82% and 80%), and R² values of 0.96 and 0.84 on the validation and test sets, respectively. Feature importance analysis reveals that water temperature is the dominant factor influencing seasonal turbidity patterns, while real-time hourly rainfall significantly contributes to short-term variability. Turbidity typically peaks within 48 hours after rainfall events due to lagged effects from surface runoff and upstream flow. Findings suggest suspending recreational water use and water supply pumping for three days after heavy rainfall can benefit public health and improve water treatment processes. Discharges above 100 m3/s are found to accelerate sediment dilution and transport, reducing turbidity levels more quickly after the peak. In conclusion, the proposed model demonstrates reliable 72-hour turbidity prediction, supporting decision-making for water treatment plant operations and providing early warning for public recreational water use.

Deep learning↗
DOE OSTI2019

Arctic River Delta Morphologic Variability and Implications for Riverine Fluxes to the Coast

Arctic riverine fluxes are anticipated to increase as the Arctic warms and have a large impact on the Arctic ocean. Deltas modify the spatial and temporal distributions of riverine fluxes, but no thorough studies have been conducted to analyze Arctic delta morphologies to determine their influence on land–ocean fluxes. We performed an analysis of six high–latitude deltas (Colville, Kolyma, Lena, Mackenzie, Yenisei, and Yukon) to characterize delta morphologies and determine the influence of morphology on the distribution of fluxes to the coast. All six deltas deliver material to the coast at discrete locations across small areas despite differences in delta shoreline length. Large Arctic deltas exhibit large variability in channel width, which we hypothesize is due to a feedback with ice cover and retreat that favors the growth of large channels over geologic timescales. Spatial variability in island sizes suggests variability in channel activity, island nourishment, and susceptibility to drowning by sea level rise. Potential lake storage is highest on the Mackenzie delta, thus providing a means for reducing nutrient and sediment loading of the coastal ocean. Here, the connected lakes are also prevalent on the Colville and Yukon deltas, suggesting that these deltas can filter riverine fluxes even when the deltas are not flooded. Differences in Arctic delta morphologies can be explained by varying levels of riverine and marine influence, antecedent topography, and local channel dynamics. Ice cover also plays a large role in controlling Arctic delta morphologies and dynamics that has not been previously represented in interpretations of existing delta metrics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Self‐Potential Tomography Preconditioned by Particle Swarm Optimization—Application to Monitoring Hyporheic Exchange in a Bedrock River

Abstract A self‐potential (SP) data‐inversion algorithm was developed and tested on an analytical model of electrical‐potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP‐monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock‐lined riverbed. The algorithm combined stochastic source‐localization by particle‐swarm‐optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least‐squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical‐model data to evaluate the algorithm performance. Results indicated that true parameters of single‐source models were inverted and approximated with small residual error, whereas inversion of analytical‐model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non‐uniqueness of the inverse‐model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point‐scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP‐monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Assessing the Role of Hydrodynamics in Enhancing Height-Above-the-Nearest-Drainage Derived Synthetic Rating Curves: A Comparative Study in the Wu River Basin, Taiwan

The conventional approach to generating synthetic rating curves (SRC) using the Height-Above-the-Nearest-Drainage (HAND) method typically relies on the assumption of uniform flow, such as Manning's equation, to establish stage-discharge ratings. The zero-physics application of the uniform flow equation is insufficient for capturing detailed hydraulic features (e.g., backwater effect) and neglects the hydraulic effects from adjacent channels. This lack of hydrodynamic computation can impact the accuracy and effectiveness of riverine flood risk estimation and management. To reduce this foreseeable error, we introduce the HAND-hd workflow, which integrates sophisticated hydrodynamic computations in the production of HAND-based SRC with hydrodynamic features (SRC hd ). The results indicate that SRC hd demonstrates consistent agreement with both gauge observations and benchmark solutions. Additionally, the comparative analysis suggests that SRC hd provides notable improvements in stage-discharge ratings over conventional HAND-based SRCs, particularly in channels with mild bed gradients, where it reduces water stage prediction errors and percent biases. In steeper channel segments, SRC hd maintains comparable accuracy to conventional methods. The comprehensive evaluation in this study emphasizes the potential discrepancies and inaccuracies associated with the adoption of the uniform flow assumption in the conventional HAND-SRCs and addresses the necessity of including hydrodynamic physics in the application of HAND-based SRC (e.g., inundation map) in channels with mild gradients.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Time Series Surface Temperature of Variably Inundated Sediment across 30 North American Rivers

This dataset supports a broader study examining drivers of organic matter chemistry in variably inundated hyporheic zone sediments and further linking that chemistry to biogeochemical rates. The dataset provides surficial temperature time series that can be used to infer the dynamics of inundation prior to the collection of sediments. Those inferred inundation histories can then be used to help interpret variation in the organic matter chemistry. There are related data that will be published, such as FTICR-MS data on organic matter chemistry and sediment moisture. A data package with those data is forthcoming.This dataset is comprised of two folders: (1) ECA1_iButtonData and (2) ECA1_SitePhotos. The ECA1_iButtonData folder contains: (1) file-level metadata, (2) data dictionary, (3) field metadata, (4) installation methods, (5) iButton deployment protocol, (6) readme, and (7) folder of individual time series temperature csv files for each iButton sensor deployed. The ECA1_SitePhotos folder contains site photographs taken in the field. All files are .csv, .txt, .pdf, or .jpg.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

STATISTICAL ANALYSIS OF IN-SERVICE ULTRASONIC INSPECTION DATA OF WASTE TANKS AT THE SAVANNAH RIVER Site-25021

CONCLUSIONS AND RECOMMENDATIONS •“There is no evidence of pit depth growth over the last 15-years demonstrating the effectiveness of the corrosion control program. •“Incipient pits” are not growing and are likely pre-service pits. • No need to continue measuring these tiny pits. However, due to the random nature of pit initiation and the potentially accelerated growth of a pit, periodic monitoring for pit growth is prudent. 1. No reportable pits were identified. All identified pits are far less than the reportable depth of 25% of the nominal wall thickness. 2. No cracking was identified. 3. There is no evidence that active, systemic corrosion has occurred in Tank 29 during its 38-year service history as of 2009.

Harris, Stephen P. [Savannah River National Labora↗
DOE OSTI2025

Probabilistic Hazard Assessment for Tornadoes, Straight-Line Wind, and Extreme Precipitation at the Savannah River Site

Recent data sets for three meteorological phenomena with the potential to inflict damage on SRS facilities – tornadoes, straight-line winds, and heavy precipitation – are analyzed using appropriate statistical techniques to estimate the occurrence probabilities for these events in the future. Summaries of the results for DOE-mandated return periods and comparisons to similar calculations performed in 2013 by Werth et al. (W2013) are given. Using tornado statistics for i) the combined states of Georgia and South Carolina, and ii) a 2⁰ square area surrounding SRS, we calculated the probability per year of any location at SRS being struck by a tornado (the ‘strike’ probability) and the probability that any point will experience winds above set thresholds. The strike probability was calculated to be 7.04E-4 (1 chance in 1420) per year and tornadic wind speeds for DOE mandated return periods of 50,000 years (corresponding to wind design category 3 (WDC-3), and 125,000 years (meeting WDC-4) (USDOE, 2016) were estimated to be 132 mph and 147 mph, respectively. By contrast, default tornado wind speeds taken from ANSI/ANS-2.3-2011 are somewhat higher: 161 mph for return periods of 50,000 years and 173 mph every 125,000 years (ANS, 2011). Although the ANS and the SRS evaluation used the same basic model (Ramsdell and Rishel, 2007), the region defined in ANS 2.3 that encompasses the SRS also includes areas of the Great Plains and lower Midwest, regions with much higher occurrence frequencies of strong tornadoes. The SRS straight-line wind values associated with various return periods were calculated by fitting existing wind data to a GEV1 distribution and extrapolating the values for any return period from the tail of that function. For the DOE mandated return periods, we expect straight-line winds of 117 mph every 2500 years (the required WDC-3 standard) and 125 mph every 6250 years (WDC-4) at any point within the SRS. These values are similar to those from the ANS-2.3-2011 report, which has wind speeds of 125mph and 133 mph for return periods of 2500 years and 6250 years, respectively. For extreme precipitation, we compared the fits of two different theoretical extreme-value distributions and applied the one that fit the data best for each of several accumulation periods. The DOE mandated 6-hr accumulated rainfall for return periods of 10,000 years (corresponding to precipitation design category 3 (PDC-3) and 25,000 years (PDC-4) were estimated as 9.1 inches and 10.1 inches, respectively. For the 24-hr rainfall return periods of 10,000 years and 25,000 years, total rainfall estimates were 12.02 inches and 13.17 inches, respectively, higher than comparable values provided in the W2013 report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Unlocking the Tight Oil Reservoirs of the Powder River Basin, Wyoming

The project focused on detailed geologic characterization, geomechanical studies, well completion optimization, stimulation monitoring, and field development strategies. A key aspect involved partnerships with industry and academic collaborators such as Occidental Petroleum, Southern Illinois University, Britt Rock Mechanics and Piri Technologies. Data acquisition included drilling, logging, coring, deployment of fiber optics, and microseismic monitoring. The project emphasized feedback loops for continuous model updating and integration of economic evaluations to guide development strategies.

02 PETROLEUM↗
NASA NTRS2004

Cooperative satellite-based flood detection, mapping, and river monitoring in near real time

The North Atlantic Oscillation (NAO), the Pacific-North American (PNA) teleconnection pattern, and the El Nino-Southern Oscillation (ENSO) combine to influence the planetary wave structure over the northern hemisphere. Floods and droughts are associated around the world with ENSO through such teleconnections, and improved flood prediction relies on understanding them better. The scientific study of floods, and consistent measurements thereof, are needed in order to allow 'Greenhouse warming' predictions about flooding to be tested, and the hydrologic effects of other phenomena such as ENSO to be evaluated. The needed tasks are: 1) detection/warning of flooding, 2) flood magnitude assessment, 3) flood inundation mapping, and 4) preservation of the record of flooding. Accomplishing these same tasks provides direct local societal benefits as well: they can save lives and reduce economic loss. We emphasize that the basic science observations need not be divorced from the immediate practical applications: both can occur together, and just as is the case for meteorological remote sensing.

Shuttle Radar Topography Mission (SRTM)↗