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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Investigation of onshore wind farm wake recovery with in situ aircraft measurements during AWAKEN

The share of wind power for electricity supply is increasing worldwide. This highly variable resource requires the improved prediction of power output for network stability. The interaction between wind farm wakes and the atmospheric boundary layer (ABL) introduces uncertainties in power production that warrant detailed investigation. The flow downwind of wind farms is characterized by a reduction in wind speed and an increase in turbulence, which both vary with atmospheric conditions. During the American WAKE experimeNt (AWAKEN), the Technische Universität Braunschweig conducted measurement flights with a research aircraft upwind and downwind of onshore wind farms in the southern Great Plains in Oklahoma in the USA. This study utilizes data from 20 flights conducted at approximately hub height in September 2023 to investigate the wind field variability downwind of the wind farms and vertical profiles to observe atmospheric stratification. The flights were aligned perpendicular to the main wind direction downwind of the King Plains and Armadillo Flats wind farms. Additionally, lidar data from both upwind and downwind ground-based measurement sites and sonic anemometer data were used for comprehensive analysis. Results indicate that under stable ABL conditions, the wake persists at greater downwind distances with a higher velocity deficit in the wake relative to the undisturbed flow compared to unstable stratification. In homogeneous terrain under stable conditions, wake recovery to 95 % occurs between a distance of 4.5 and 9 km downwind of the wind farm. In the semi-complex terrain characterized by shallow hills, slopes, and valleys, the wake exhibits a higher velocity deficit compared to homogeneous terrain, while in some cases the wake was amplified by the terrain resulting in higher velocity deficit 10 km downwind of the wind farm compared to the measurements closer to the wind farm. The turbulent kinetic energy (TKE) and “TKE difference” was found to be a valuable measure in understanding wakes in a semi-complex terrain, showing a clear wake recovery and formation depending on the stratification of the ABL.

17 WIND ENERGY↗
DOE OSTI2024

Haasttse‐baad Tessera Ring Complex: A Valhalla‐Type Impact Structure on Venus?

Abstract Venus preserves ∼1,000 impact craters, yet to date no impact basins larger than 300 km in diameter—common in the oldest terrains on Mercury, Mars and the Moon—are recognized on Venus. The tessera terrain is Venus' oldest recognized terrain. We describe a ∼1,500 km‐diameter concentric ring‐graben complex preserved on Haasttse‐baad Tessera, Venus that we identify as the Haasttse‐baad Tessera Ring Complex (HTRC). Based on geologic relations and numerical modeling, we propose that the HTRC may represent a Valhalla‐type multiring impact basin formed late during the evolution of its host ribbon‐tessera terrain (rtt). Formation of Valhalla‐type impact basins could involve a unique three‐layer target rheology with a thin elastic layer above a low viscosity layer above a deep strong layer. This multi‐layer rheological sandwich is consistent with crustal rheology previously proposed for the formation of Venus' rtt. If the HTRC is a Valhalla‐type impact basin, it would be Venus' oldest, and currently largest, impact structure, providing a rare window into Venus' ancient past and with implications for early crustal processes on Venus.

58 GEOSCIENCES↗
DOE OSTI2025

Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability

Snow plays a critical role in carbon cycling, vegetation dynamics, and permafrost hydrology at high latitudes by influencing surface energy exchange. Predicting snow distribution patterns is essential for understanding the evolution of Arctic ecosystems, yet scaling process-level knowledge to landscape predictions remains challenging. Here, we analyze snow depth (2019 and 2022), terrain elevation, and vegetation height from a watershed on the Seward Peninsula, Alaska, to examine how topography and shrubs shape snow redistribution across spatial scales. We find that snow depth is strongly coupled to terrain at scales below ∼60 m but becomes increasingly decoupled at larger scales. The topographic model of snow depth variation, which transforms terrain data to align with these scale-dependent snow patterns, is well correlated with local snow depth variations (linear fit R 2 > 0.5 for 85% of 100-m patches). A machine learning reconstruction of shrub canopy snow trapping reveals a simple exponential relationship between canopy structure and snow accumulation ( R 2 = 0.59), highlighting the combined influence of topography and vegetation on snow distribution. Together, these empirical relationships capture much of the observed snow variability in the watershed ( R 2 = 0.49, root mean square error (RMSE) = 30 cm), though systematic limitations persist in areas of strong scour and at coarser scales where wind-terrain interactions are more complex. These findings provide a framework for more efficient snow depth prediction and offer insights to improve snow-vegetation feedback representation in Earth System Models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

NGEE Arctic Integrated Modeling (IM3): Improved snow-vegetation interaction

This data product represents the integration of new code capability for arctic tundra snow-vegetation-terrain interactions into the Energy Exascale Earth System Model (E3SM), through the E3SM Land Model (ELM) component. This code integration is the result of collaborative effort between the NGEE Arctic project and the E3SM project. The NGEE Arctic project developed a total of six Integrated Modeling (IM) modules informed by observations and experiments. New ELM capability represented by this data product (IM3) falls into three categories: 1) Downscaling from gridcell to topographic unit level when working through the existing coupler bypass code. 2) Four new parameters (taper, stocking, bendresist, and vegshape) have been added to ELM to allow for flexible definition of snow-vegetation interactions. 3) Vegshape and bendresist parameters are used to calculate the fraction of leaf area and/or stem area buried by snow for a given snow depth. This data record consists of a single document (pdf format) that describes the theoretical basis for the snow-vegetation-terrain interactions added to ELM, and describes the modifications made to the ELM code. The Methods section of this metadata record includes a link to the public E3SM code repository where the exact code modifications as integrated in E3SM can be accessed. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

Thornton, Peter E [ORNL] (ORCID:0000000247595158)↗
DOE OSTI2026

Wind River Experimental Forest Subcanopy Tower Information Sheet

Wind River was one of three sites that collected 3d sonic anemometer data for an ICOS subcanopy observation study. The three sites were defined by the following features and terrain: a deciduous broadleaf forest in flat terrain (Lanžhot, Czech Republic), a coniferous forest in mountainous terrain (Renon, Italy), and a tall conifer forest in mountain-valley terrain (Wind River, USA). The Wind River subcanopy towers were deployed in a high LAI, old-growth evergreen conifer forest and collected approximately 11 months of data. The site is an ecologically rich temperate rainforest in the western Cascade Mountains, and the biological carbon sink and source strength has been measured since 1998 using eddy covariance on the top of a 74 m tall flux tower (currently called the Wind River NEON tower). Additionally, forest inventory records date back to the 1920s. In 2024, four subcanopy towers were installed near the Wind River NEON tower to measure wind flow in the understory canopy layer for better understanding canopy flow coupling and decoupling in the subcanopy and how this affects the interpretation of overstory fluxes. The subcanopy tower installation was done by Lawrence Livermore National Laboratory and Washington State University (WSU) with collaborations from the University of Utah and the National Ecological Observatory Network (NEON).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

AmeriFlux US-SD2 Shatto Ditch Paired Cropland - Site 2 (Corn/Soy; Cover Crops)

This is the AmeriFlux version of the carbon flux data for the site US-SD2 Shatto Ditch Paired Cropland - Site 2 (Corn/Soy; Cover Crops). Site Description - US-SD2, located on gently sloped terrain, is an actively managed farmland operated by working farmers following a conventional no-till corn–soybean rotation in the U.S. Midwest. US-SD2 is one of two paired working farm sites which are relatively close by; both are managed using similar conventional practices with the key difference being that the paired site (US-SD1) incorporates cover crops into its rotation, along with minor differentiating terrain and soil type, where US-SD1 is located on mostly flat terrain with some gentle hills. This paired design enables direct site-to-site comparisons to assess the impacts of cover cropping on carbon, water, and energy fluxes.

Key, Kesondra [Indiana University - Bloomington]↗
DOE OSTI2026

AmeriFlux FLUXNET-1F US-SD2 Shatto Ditch Paired Cropland - Site 2 (Corn/Soy; Cover Crops)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-SD2 Shatto Ditch Paired Cropland - Site 2 (Corn/Soy; Cover Crops). This is the FLUXNET version of the carbon flux data for the site US-SD2 Shatto Ditch Paired Cropland - Site 2 (Corn/Soy; Cover Crops) produced by applying the standard ONEFlux (1F) software. Site Description - US-SD2, located on gently sloped terrain, is an actively managed farmland operated by working farmers following a conventional no-till corn–soybean rotation in the U.S. Midwest. US-SD2 is one of two paired working farm sites which are relatively close by; both are managed using similar conventional practices with the key difference being that the paired site (US-SD1) incorporates cover crops into its rotation, along with minor differentiating terrain and soil type, where US-SD1 is located on mostly flat terrain with some gentle hills. This paired design enables direct site-to-site comparisons to assess the impacts of cover cropping on carbon, water, and energy fluxes.

Key, Kesondra [Indiana University - Bloomington]↗
DOE OSTI2025

Lidar-Based Evaluation of HRRR Performance in California’s Diablo Range

The performance of the NOAA High-Resolution Rapid Refresh (HRRR) model for capturing low-level winds near a wind energy production site during summer 2019 is evaluated. This study catalogs the ability of HRRR to predict boundary layer dynamics relevant to wind energy interests over complex terrain, which has presented challenges for weather and energy forecasting. Performance is evaluated by comparing HRRR output to wind-profiling Doppler lidars at Lawrence Livermore National Laboratory Site 300. HRRR captured the diurnal profile of horizontal winds in the observed 150-m layer, despite strong underpredictions (∼4 m s −1 ) during evening and nighttime hours. These underpredictions may be a result of local speedup flows observed by the lidars, which were unresolved in HRRR due to their small spatial extent. HRRR bias magnitude relative to observations was found to be minimal during days with synoptic-scale troughs and strong 850-hPa geopotential gradients, while bias magnitude was maximal during days with synoptic ridging and weak 850-hPa geopotential gradients. To translate wind speed predictions to energy forecasting, generic turbine models were used to estimate power generation for turbines characteristic of the nearby Altamont Pass Wind Resource Area. Results show that HRRR-based energy estimates predicted daytime power generation adequately relative to lidar-based estimates with an 18-h lead time (bias magnitude < 0.4 MW from 0900 to 1400 LT) but overpredicted power during the rest of the diurnal cycle (bias > 1 MW). These results demonstrate conditions under which HRRR performs well for wind energy applications in complex terrain, while highlighting biases that require further investigation to support usage of a high-resolution model for wind energy forecasts.

Boundary layer↗
DOE OSTI2025

Water Stable Isotopes in Precipitation, Rivers, and Groundwater Across an Elevation Gradient in the Sierra Nevada Mountains (USA) Reflect Source Elevation

Understanding watershed processes is critical to predict the impacts of climate change and forest management on water resources. However, collecting hydrological data in mountainous terrain is challenging. Precipitation, river water, and groundwater H and O stable isotope data can provide insights into processes occurring at the mountain range scale. Water δ 2 H and δ 18 O values in precipitation vary with terrain elevation; thus, the resulting isotopic lapse rates of precipitation, groundwater, and river water have the potential to elucidate watershed processes and source elevations of major rivers. We analysed H and O stable isotope data of precipitation, groundwater, and river water over the course of one Water Year (Oct 2016—Oct 2017) in the Sierra Nevada mountains of California, USA. We calculated elevation-dependent isotopic lapse rates of these waters to estimate the source elevation of major rivers draining the west flank of the Sierra Nevada mountains. We also investigated the Cosumnes River's watershed in more detail to determine how river flow may be more fully partitioned. Here, we found that H and O stable isotopes in precipitation are temporally variable, but isotopic lapse rates are generally consistent with prior studies. However, groundwater samples across an elevation gradient provide a more consistent and accessible isotopic lapse rate to predict river water source elevations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Comparison of Pre‐Construction and Operational Wake Loss Estimates for Land‐Based Wind Plants

The overall bias between pre‐construction energy yield assessment (EYA) estimates of wind plant energy production and the achieved operational production is improving in the wind industry, but uncertainty remains high for individual wind plants. Wake effects within wind plants are one of the largest sources of energy loss considered in the EYA process, and previous work shows wake loss estimates to be a major source of disagreement among wind energy consultants who perform EYAs. To better understand the accuracy of wake loss predictions, we compare overall operational wake loss estimates based on supervisory control and data acquisition data to pre‐construction estimates provided by six wind energy consultants for five land‐based wind plants in North America. By augmenting existing approaches for quantifying operational wake losses, we estimate wake losses during the period of record for which operational data are available as well as the expected long‐term wake losses, based on historical reanalysis weather data, to which the EYA estimates are compared. To account for power variations at different turbine locations caused by terrain‐induced wind resource heterogeneity, we correct the operational wake loss estimates using predicted freestream wind speed variations from the Wind Systems Engineering Reynolds‐averaged Navier–Stokes (RANS) tool. We identify long‐term corrected operational wake losses between 1.9% and 6.4% for the five plants, with a mean loss of 4%. For the project deemed most acceptable for operational wake loss assessment, which is located in the simplest terrain and isolated from neighboring plants, the mean EYA wake loss estimate is within 0.7 percentage points of the operational value of 6.4%. For most of the remaining plants, results suggest that wake losses are generally overpredicted by 2.6–6.3 percentage points. However, operational wake losses may be underestimated for many of these projects because of spatial wind resource variations not captured by the RANS model, external wake effects that are unaccounted for in the estimation process, and wind plant blockage effects. To better understand factors that contribute to the observed wake losses, we investigate operational wake losses as a function of wind direction and wind speed. As expected, wake losses are generally concentrated near wind directions that are aligned with rows of closely spaced turbines and at below‐rated wind speeds; however, for some projects, the energy produced by the wind plant exceeds the estimated potential energy of the plant without wake interactions for certain wind directions and wind speeds, suggesting inaccurate assumptions in the wake loss estimation method for those plants. Lastly, we compare predicted and operational wake losses for individual wind turbines, finding that even when overall wake losses are predicted accurately, large uncertainty exists at the turbine level.

17 WIND ENERGY↗
DOE OSTI2025

A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport

High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume dispersion in complex terrain. However, their high computational cost makes them impractical for applications requiring rapid responses or iterative processes, such as optimization, uncertainty quantification, or inverse modeling. To address this challenge, this work introduces the Dual-Stage Temporal Three-dimensional UNet Super-resolution (DST3D-UNet-SR) model, a highly efficient deep learning model for plume dispersion predictions. DST3D-UNet-SR is composed of two sequential modules: the temporal module (TM), which predicts the transient evolution of a plume in complex terrain from low-resolution temporal data, and the spatial refinement module (SRM), which subsequently enhances the spatial resolution of the TM predictions. We train DST3D-UNet-SR using a comprehensive dataset derived from high-resolution large eddy simulations (LES) of plume transport. We propose the DST3D-UNet-SR model to significantly accelerate LES of three-dimensional (3D) plume dispersion by three orders of magnitude. Additionally, the model demonstrates the ability to dynamically adapt to evolving conditions through the incorporation of new observational data, substantially improving prediction accuracy in high-concentration regions near the source.

3D temporal sequences↗
DOE OSTI2026

A two-stage optical fusion framework for wildfire severity mapping across the conterminous United States

Accurate wildfire severity mapping (WSM) is essential for post-fire recovery planning, erosion risk assessment, ecosystem monitoring, and disaster risk reduction. Although Landsat and Sentinel optical imagery have been widely used for burn severity assessment, the added value of fusing multiple optical sensors has not been sufficiently quantified across diverse fire events, particularly since the launch of Landsat-9. This study evaluates whether multisensor optical fusion improves wildfire severity mapping relative to single-sensor baselines using Sentinel-2, Landsat-8, and Landsat-9 imagery across 40 wildfire events in the conterminous United States. We tested a two-stage fusion framework that combines feature-level fusion with pixel-level dimensionality reduction. First, feature-level fused datasets were created through early fusion by combining standardized post-fire bands from each sensor into a single predictor stack. Both raw reflectance bands and pairwise spectral transforms were retained to capture within- and cross-sensor spectral interactions. Second, Linear Discriminant Analysis was applied to both single-sensor and fused datasets to produce comparable low-dimensional feature spaces. Six machine-learning classifiers were then used to benchmark model performance with repeated spatially buffered train–test splits. Results show that Landsat-9 was the strongest single-sensor baseline. Among the fusion strategies, Sentinel-2 + Landsat-9 produced the most consistent improvement and reduced performance variability. Landscape-condition analysis further showed that this fusion was most beneficial in shrubland-dominated and high-terrain fires, where it achieved the highest overall mean accuracy and the fewest failures. In contrast, its benefits were less reliable in evergreen forests, mixed vegetation, and low- to moderate-elevation terrain. In operational settings, the Sentinel-2 + Landsat-9 configuration offers a practical solution for post-fire recovery planning, erosion-risk assessment, watershed management, and ecological monitoring when field observations are available and timely satellite-based information is needed.

Landsat↗
DOE OSTI2026

Optimizing fluvial flood mitigation strategies: A multi-objective approach for cost-effective and socially-aware infrastructure feasibility analysis

Effective levee planning must balance capital cost, risk reduction, and community priorities. These objectives are rarely optimized together. This study presents a feasibility phase, simulationin-the-loop framework that couples terrain-based flood modeling with a socially aware multiobjective optimizer. Flood risk is measured as Expected Annual Exposed Population (EAEP), obtained by integrating exposure over Annual Exceedance Probability (AEP) nodes, mirroring the Hydrologic Engineering Center's Flood Damage Reduction Analysis (HEC-FDA) expected-annual formulation but with people rather than dollars. Exposure per scenario is computed by overlaying binary inundation masks with a population surface at the tract level. Distributional fairness is encoded through a Group Benefit Share (GBS) constraint that requires high-SVI tracts to receive at least a baseline share of annualized benefits. Capital cost is represented by a height-dependent unit-cost model suitable for screening. This study addresses the two-objective problem, minimize cost and expected annual exposure subject to the GBS constraint, using Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and leveraging Pareto front for feasibility phase decision making. Implemented with terrain-based flood modeling, GeoFlood, for rapid scenario evaluation, the framework is demonstrated in Southeast Texas. The results reveal clear trade-offs among cost, risk, and social benefits and identify non-dominated levee height configurations that satisfy the benefit-share floor. The contributions are a scalable decision support method that operationalizes expected annual population-based risk, embeds enforceable benefit-sharing guarantees, and uses lightweight simulation to explore large design spaces before higher fidelity design stages.

Flood mitigation↗