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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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Environmental contaminants in aquatic ecosystems threaten both human and ecosystem health. Western Montana’s rivers possess great economic and ecological value, yet the status of contaminants in these systems can be unclear. Contaminants such as brominated flame-retardants, heavy metals, and pharmaceuticals negatively affect ecosystem health by traveling through the food chain and bioaccumulating in apex predators. Exposure to these contaminants can result in neurological, endocrine, and reproductive impairments in wildlife and humans. American mink (Neovison vison) and North American river otter (Lontra canadensis) have a predominantly aquatic diet and serve as reliable indicator species of environmental health. Working Dogs for Conservation (WD4C) uses detection dogs to locate scat samples of these species which are assessed for contaminants by the Virginia Institute of Marine Science (VIMS). With Software for Assisted Habitat Modeling (SAHM), the team generated current (2013-2020) and projected (2021-2040) habitat suitability models for mink and otter utilizing NASA Earth observations from Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Global Precipitation Measurement Integrated Multi-Satellite Retrievals for GPM (GPM IMERG), Shuttle Radar Topography Mission (SRTM), and Soil Moisture Active Passive (SMAP). The habitat suitability maps’ study region encompassed Flathead National Forest and Blackfeet Indian Reservation. Additionally, the team created site accessibility and precipitation anomaly maps that display the viability of survey locations. The current habitat suitability models performed well (AUC-PR=0.88). The most important predictor variables for suitable habitat were distance to rivers, elevation, and land cover. These end products will further inform WD4C survey site selection and contaminant monitoring.
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The Greenland Ice Sheet discharges ice to the ocean through hundreds of outlet glaciers. Recent acceleration of Greenland outlet glaciers has been linked to both oceanic and atmospheric drivers. Here, we leverage temporally dense observations, regional climate model output, and newly developed time series analysis tools to assess the most important forcings causing ice flow variability at one of the largest Greenland outlet glaciers, Helheim Glacier, from 2009 to 2017. We find that ice speed correlates most strongly with catchment-integrated runoff at seasonal to interannual scales, while multi-annual flow variability correlates most strongly with multi-annual terminus variability. The disparate time scales and the influence of subglacial topography on Helheim Glacier’s dynamics highlight different regimes that can inform modeling and forecasting of its future. Notably, our results suggest that the recent terminus history observed at Helheim is a response to, rather than the cause of, upstream changes.
Global avian population decline since the 1970s is largely attributable to habitat loss and degradation from anthropogenic disturbances. NASA DEVELOP’s Rhode Island Ecological Conservation team partnered with the Audubon Society of Rhode Island to compute land use land cover (LULC) maps of Rhode Island to aid in the conservation of the state’s 140 bird species. This project aimed to support the partner’s land acquisition strategies with updated and specific LULC classifications showing potential bird-habitat locations across the state. We incorporated remotely sensed data from Landsat 8 and 9 Operational Land Imager (OLI) into LULC maps using unsupervised classification techniques in ArcGIS Pro and supervised classification in Google Earth Engine. We generated six land classifications for 2023, which showed land cover dominated by upland habitats (forests, scrub/shrub, and grasslands), followed by development. We used TerrSet’s Land Change Modeler to forecast LULC change through 2043, using 2011 and 2021 National Land Cover Database (NLCD) land cover maps derived from Landsat 8 and 9 imagery. Project results suggest that non-urban upland and wetland habitats will decrease over time, while development will continue to encroach on non-urban avian habitats. Our maps and associated data will allow for more efficient land acquisition and management efforts to support avian habitat conservation across Rhode Island. Our study shows that data acquisition and processing from open data sources is feasible and further analysis can be done through GIS classification tools. More analysis is needed beyond this study to obtain more detailed land cover maps, though Audubon can aid its targeted conservation efforts with our current, historic, and forecasted LULC maps.
"Extreme weather events, such as hurricanes, tornadoes, floods, droughts, heatwaves, and blizzards, can cause widespread damage, disrupting ecosystems, agricultural production, and economies. The frequency and intensity of these events have been increasing, likely due to climate change, raising concerns and the need for more accurate analysis and predictions. NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) has migrated its long-term historical datasets, including precipitation data from MERRA-2 reanalysis, GLDAS land data assimilation, and IMERG satellite observations, to the cloud. This cloud-stored data enables scientists and researchers to utilize cloud computing for advanced modeling and forecasting of extreme weather events, eliminating the need to download large datasets. In this presentation, we will provide an overview of the cloud-based data and services managed by GES DISC; demonstrate methods for accessing and analyzing time series data stored in the cloud; and compare results across various datasets to address critical questions related to extreme precipitation. We will present use cases including: 1. Determining the average total precipitation in California during January and February from 2000 to 2024, and identifying anomalous precipitation in 2021. 2. Calculating the 10, 20, 50, and 100-year return periods for maximum daily rainfall based on 25 years of historical precipitation data (2000-2024) for Maryland.
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NREL staff used the dSolar (distributed solar) model to forecast the adoption of distributed, behind-the-meter PV through the year 2050 for 9 different scenarios. The scenarios varied in their assumptions about a carbon tax, the cost of PV systems in the future, and what credit would be given for excess generation once current net metering policies expire.
Language in social media is extremely dynamic: new words emerge, trend and disappear, while the meaning of existing words can fluctuate over time. This work addresses several important tasks of visualizing and predicting short term text representation shift, i.e. the change in a word’s contextual semantics. We study the relationship between short-term concept drift and representation shift on a large social media corpus – VKontakte collected during the Russia-Ukraine crisis in 2014 – 2015. We visualize short-term representation shift for example keywords and build predictive models to forecast short-term shifts in meaning from previous meaning as well as from concept drift. We show that short-term representation shift can be accurately predicted up to several weeks in advance and that visualization provides insight into meaning change. Our approach can be used to explore and characterize specific aspects of the streaming corpus during crisis events and potentially improve other downstream classification tasks including real-time event forecasting in social media.
Developers have identified many non-technical barriers to geothermal power development, including access to land. Activities required for accessing land, such as environmental review and private and public leasing can take a considerable amount of time and can delay or prevent project development. This paper discusses the impacts to available geothermal resources and deployment caused by land access challenges, including tribal and cultural resources, environmentally sensitive areas, biological resources, land ownership, federal and state lease queues, and proximity to military installations. In this analysis, we identified challenges that have the potential to prevent development of identified and undiscovered hydrothermal geothermal resources. We found that an estimated 400 MW of identified geothermal resource potential and 4,000 MW of undiscovered geothermal resource potential were either unallowed for development or contained one or more significant barriers that could prevent development at the site. Potential improvement scenarios that could be employed to overcome these barriers include (1) providing continuous funding to the U.S. Forest Service (USFS) for processing geothermal leases and permit applications and (2) the creation of advanced environmental mitigation measures. The model results forecast that continuous funding to the USFS could result in deployment of an additional 80 MW of geothermal capacity by 2030 and 124 MW of geothermal capacity by 2050 when compared to the business-as-usual scenario. The creation of advanced environmental mitigation measures coupled with continuous funding to the USFS could result in deployment of an additional 97 MW of geothermal capacity by 2030 and 152 MW of geothermal capacity by 2050 when compared to the business-as-usual scenario. The small impact on potential deployment in these improvement scenarios suggests that these 4,400 MW have other barriers to development in addition to land access. In other words, simply making more resources available for development does not increase deployment; however, impacts to deployment could increase when coupled with other improvements (e.g., permitting, market and/or technology improvements).
Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement is critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting is gaining pace in the recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregated in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance of freight systems. Addressing this research need, this paper presents the 'Freight Mobility Energy Productivity' metric to quantify freight performance of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, which shows intuitive results in quantifying freight performance. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight efficiency as a result of vehicle powertrain technology improvement. Results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the efficiency of existing as well as future freight trends and technologies.
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Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement are critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting is gaining pace in the recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregate in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance or productivity of freight systems. Addressing the research need, this paper presents the “Freight Mobility Energy Productivity” metric to quantify freight productivity of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, which shows intuitive results in quantifying freight productivity. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight productivity as a result of vehicle electrification. The relative differences in Freight Mobility Energy Productivity scores help identify which zones benefit from the vehicle powertrain technology improvement. The results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the productivity of existing as well as future freight trends and technologies.
Pyrofusion sources are compact, low power systems which are capable of producing short intense pulses of neutrons from D-D fusion reactions. A model structure has been derived from the underlying physics principles and it is shown that the dominant non-linear dynamics pose significant challenges to the system control. This paper presents the methods under development to construct, estimate and analyse nonlinear grey-box models of Pyrofusion systems from measured data. The methods have been trialled on data sets obtained from the publications of previous Pyrofusion research groups. The results indicate the promising potential application of these methods to the system identification of profusion device characteristics to support model verification, pulse characterisation and prediction of operational capabilities. The identified models are shown to exhibit satisfactory analogous cause-and-effect behaviour. (authors)
This study assesses the predictive skill of eight North American Multimodel Ensemble (NMME) models in predicting the Indian Ocean dipole (IOD). We find that the forecasted ensemble-mean IOD–El Niño–Southern Oscillation (ENSO) relationship deteriorates away from the observed relationship with increasing lead time, which might be one reason that limits the IOD predictive skill in coupled models. We are able to improve the IOD predictive skill using a recently developed stochastic dynamical model (SDM) forced by forecasted ENSO conditions. The results are consistent with the previous result that operational IOD predictability beyond persistence at lead times beyond one season is mostly controlled by ENSO predictability and the signal-to-noise ratio of the Indo-Pacific climate system. The multimodel ensemble (MME) investigated here is found to be of superior skill compared to each individual model at most lead times. Importantly, the skill of the SDM IOD predictions forced with forecasted ENSO conditions were either similar or better than those of the MME IOD forecasts. Moreover, the SDM forced with observed ENSO conditions exhibits significantly higher IOD prediction skill than the MME at longer lead times, suggesting the large potential skill increase that could be achieved by improving operational ENSO forecasts. We find that both cold and warm biases of the predicted Niño-3.4 index may cause false alarms of negative and positive IOD events, respectively, in NMME models. Many false alarms for IOD forecasts at lead times longer than one season in the original forecasts disappear or are significantly reduced in the SDM forced by forecasted ENSO conditions.