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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Role of Bedrock Circulation Depth and Porosity in Mountain Streamflow Response to Prolonged Drought

Quantitative understanding is lacking on how the depth of active groundwater circulation in bedrock affects mountain streamflow response to a multi-year drought. We use an integrated hydrological model to explore the sensitivity of a variety of streamflow metrics to bedrock circulation depth and porosity under a plausible extreme drought scenario lasting up to 5 years. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. With drought, a deeper circulation system with higher drainable porosity more effectively buffers minimum flow and significantly limits perennial stream loss in comparison to a shallow circulation system. Streamflow buffering is accomplished through extensive groundwater storage loss. However, deeper circulation systems experience prolonged recovery from drought in comparison to storage-limited shallow systems. Research highlights the importance of characterizing the deeper bedrock hydrogeology in mountainous watersheds to better understand and predict drought impacts on stream ecosystem health and water resource sustainability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Impact of El Niño‐Southern Oscillation and Madden‐Julian Oscillation on the US Puget Sound Regional Hydroclimate

El Niño-Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO) are two major modes of climate variability with global hydroclimate impacts. However, their impacts often depend on the local climate and geography, resulting in large regional differences. In this study, we examined the connection of ENSO and MJO to the hydroclimate conditions and extremes in the Puget Sound (PS) basin located in the US Pacific Northwest coast. The results indicate that ENSO significantly modulates the cold season temperature and temperature-mediated hydrologic processes. El Niño cold seasons feature less snow accumulation and intensified surface runoff, even if the precipitation amount is similar to La Niña cold seasons. Therefore, El Niño causes more snow drought (in the form of compound dry and warm snow drought) and shifts the surface runoff seasonality by reducing runoff in the subsequent warm season. MJO phases 6–7 trigger more extreme precipitation, temperature, snowmelt, and runoff in the PS region at 0–9–day lags, and such connections are robust regardless of how the ENSO signals are removed. Meanwhile, MJO modulates large-scale extreme weather systems (e.g., atmospheric rivers) with significant enhancement during phases 6–7. ENSO impacts have intensified in the 2001–2020 period, whereas MJO impacts showed some phase shift in this period. This study reveals ENSO and MJO phases 6–7 as useful predictors of the PS hydroclimate anomalies/extremes at seasonal and daily scales, respectively. Utilizing these findings holds the potential to improve regional water resources prediction and management.

ENSO↗
DOE OSTI2024

Anthropogenic aerosols mask increases in US rainfall by greenhouse gases

A comprehensive understanding of human-induced changes to rainfall is essential for water resource management and infrastructure design. However, at regional scales, existing detection and attribution studies are rarely able to conclusively identify human influence on precipitation. Here we show that anthropogenic aerosol and greenhouse gas (GHG) emissions are the primary drivers of precipitation change over the United States. GHG emissions increase mean and extreme precipitation from rain gauge measurements across all seasons, while the decadal-scale effect of global aerosol emissions decreases precipitation. Local aerosol emissions further offset GHG increases in the winter and spring but enhance rainfall during the summer and fall. Our results show that the conflicting literature on historical precipitation trends can be explained by offsetting aerosol and greenhouse gas signals. At the scale of the United States, individual climate models reproduce observed changes but cannot confidently determine whether a given anthropogenic agent has increased or decreased rainfall.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Precipitation regime changes in High Mountain Asia driven by cleaner air

High Mountain Asia (HMA) has experienced a spatial imbalance in water resources in recent decades, partly due to a dipolar pattern of precipitation changes known as South Drying-North Wetting. These changes can be influenced by both human activities and internal climate variability. While climate projections indicate a future widespread wetting trend over HMA, the timing and mechanism of the transition from a dipolar to a monopolar pattern remain unknown. Here, we demonstrate that the observed dipolar precipitation change in HMA during summer is primarily driven by westerly- and monsoon-associated precipitation patterns. The weakening of the Asian westerly jet, caused by uneven emission of anthropogenic aerosols, favors the dipolar precipitation trend during 1951-2020. Additionally, the phase transition of the interdecadal Pacific oscillation induces an out-of-phase precipitation change between the core region of the South Asian monsoon and southeastern HMA. Under medium or high emission scenarios, corresponding to a global warming of 0.6-1.1 °C compared to the present, the dipolar pattern is projected to shift into a monopolar wetting trend in the 2040s. This shift of precipitation patterns is mainly attributed to the intensified jet stream resulting from reduced emissions of anthropogenic aerosols. In conclusion, these findings underscore the importance of considering the impact of aerosol emission reduction in future social planning by policymakers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evidence of human influence on Northern Hemisphere snow loss

Documenting the rate, magnitude and causes of snow loss is essential to benchmark the pace of climate change and to manage the differential water security risks of snowpack declines. So far, however, observational uncertainties in snow mass have made the detection and attribution of human-forced snow losses elusive, undermining societal preparedness. Here we show that human-caused warming has caused declines in Northern Hemisphere-scale March snowpack over the 1981–2020 period. Using an ensemble of snowpack reconstructions, we identify robust snow trends in 82 out of 169 major Northern Hemisphere river basins, 31 of which we can confidently attribute to human influence. Most crucially, we show a generalizable and highly nonlinear temperature sensitivity of snowpack, in which snow becomes marginally more sensitive to one degree Celsius of warming as climatological winter temperatures exceed minus eight degrees Celsius. Such nonlinearity explains the lack of widespread snow loss so far and augurs much sharper declines and water security risks in the most populous basins. Together, our results emphasize that human-forced snow losses and their water consequences are attributable—even absent their clear detection in individual snow products—and will accelerate and homogenize with near-term warming, posing risks to water resources in the absence of substantial climate mitigation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Stress-tolerant non-conventional microbes enable next-generation chemical biosynthesis

Microbial chemical production is a rapidly growing industry, with much of the growth fueled by advances in synthetic biology. New approaches have enabled rapid strain engineering for the production of various compounds; however, translation to industry is often problematic because native phenotypes of model hosts prevent the design of new low-cost bioprocesses. Here, in this work, we argue for a new approach that leverages the native stress-tolerant phenotypes of non-conventional microbes that directly address design challenges from the outset. Growth at high temperature, high salt and solvent concentrations, and low pH can enable cost savings by reducing the energy required for product separation, bioreactor cooling, and maintaining sterile conditions. These phenotypes have the added benefit of allowing for the use of low-cost sugar and water resources. Non-conventional hosts are needed because these phenotypes are polygenic and thus far have proven difficult to recapitulate in the common hosts Escherichia coli and Saccharomyces cerevisiae.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A Dataset of 3D Structural and Simulated Transport Properties of Complex Porous Media

Physical processes that occur within porous materials have wide-ranging applications including - but not limited to - carbon sequestration, battery technology, membranes, oil and gas, geothermal energy, nuclear waste disposal, water resource management. The equations that describe these physical processes have been studied extensively; however, approximating them numerically requires immense computational resources due to the complex behavior that arises from the geometrically-intricate solid boundary conditions in porous materials. Here, we introduce a new dataset of unprecedented scale and breadth, DRP-372: a catalog of 3D geometries, simulation results, and structural properties of samples hosted on the Digital Rocks Portal. The dataset includes 1736 flow and electrical simulation results on 217 samples, which required more than 500 core years of computation. This data can be used for many purposes, such as constructing empirical models, validating new simulation codes, and developing machine learning algorithms that closely match the extensive purely-physical simulation. This article offers a detailed description of the contents of the dataset including the data collection, simulation schemes, and data validation.

3D images↗
DOE OSTI2023

Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States

Abstract Land surface models such as the Community Land Model Version 5 (CLM5) are essential tools for simulating the behavior of the terrestrial system. Despite the extensive application of CLM5, limited attention has been paid to the underlying uncertainties associated with its hydrological parameters and how these uncertainties affect water resource applications. To address this long-standing issue, we use five meteorological datasets to conduct a comprehensive hydrological parameter uncertainty characterization of CLM5 over the hydroclimatic gradients of the conterminous United States. Key datasets produced from the uncertainty characterization experiment include: a benchmark dataset of CLM5 default hydrological performance, parameter sensitivities for 28 hydrological metrics, and large-ensemble outputs for CLM5 hydrological predictions. The presented datasets will assist CLM5 calibration and support broad applications, such as evaluating drought and flood vulnerabilities. The datasets can be used to identify the hydroclimatological conditions under which parametric uncertainties demonstrate substantial effects on hydrological predictions and clarify where further investigations are needed to understand how hydrological prediction uncertainties interact with other Earth system processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Large Ensemble Global Dataset for Climate Impact Assessments

We present a self-consistent, large ensemble, high-resolution global dataset of long-term future climate, which accounts for the uncertainty in climate system response to anthropogenic emissions of greenhouse gases and in geographical patterns of climate change. The dataset is developed by applying an integrated spatial disaggregation (SD) - bias-correction (BC) method to climate projections from the MIT Integrated Global System Model (IGSM). Four emission scenarios are considered that represent energy and environmental policies and commitments of potential future pathways, namely, Reference, Paris Forever, Paris 2 °C and Paris 1.5 °C. The dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity. We demonstrate the dataset’s ability to represent climate-change responses across various regions of the globe. This dataset can be used to support regional-scale climate-related impact assessments of risk across different applications that include hydropower, water resources, ecosystem, agriculture, and sustainable development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Unified 0.25-degree gridded infrastructure-critical extreme weather for the United States from 1979 to 2100

Extreme weather events can severely disrupt critical infrastructure, triggering cascading effects on power, transportation, and essential services. However, standard weather and climate datasets often lack specialized variables necessary for hazard assessments. We present a unified dataset of infrastructure-critical weather and climate variables across the United States at 0.25° resolution, covering daily or sub-daily intervals from 1979 to 2100. The dataset includes temperature, dew point, wind gusts, precipitation partitioned by rain, snow, and freezing rain or ice pellets, lightning, and wildfire metrics. Historical conditions (1979-2023) are synthesized from observations and reanalysis products, while future projections are derived from 14 CMIP6 global climate models (historical, SSP245, and SSP585 experiments). Physically based and data-driven methods are used to estimate variables not directly provided by existing models. By integrating these variables into a single unified dataset, we enable consistent, high-resolution assessments of weather-related infrastructure risks across past and future periods, supporting wide-ranging applications in energy, transportation, water resources, emergency management, and beyond.

Climate and Earth system modelling↗
DOE OSTI2024

Floods of Egypt’s Nile in the 21st century

Extreme precipitation and flooding events are rising globally, necessitating a thorough understanding and sustainable management of water resources. One such setting is the Nile River’s source areas, where high precipitation has led to the filling of Lake Nasser (LN) twice (1998–2003; 2019–2022) in the last two decades and the diversion of overflow to depressions west of the Nile, where it is lost mainly to evaporation. Using temporal satellite-based data, climate models, and continuous rainfall-runoff models, we identified the primary contributor to increased runoff that reached LN in the past two decades and assessed the impact of climate change on the LN’s runoff throughout the twenty-first century. Findings include: (1) the Blue Nile subbasin (BNS) is the primary contributor to increased downstream runoff, (2) the BNS runoff was simulated in the twenty-first century using a calibrated (1965–1992) rainfall-runoff model with global circulation models (GCMs), CCSM4, HadGEM3, and GFDL-CM4.0, projections as model inputs, (3) the extreme value analysis for projected runoff driven by GCMs’ output indicates extreme floods are more severe in the twenty-first century, (4) one adaptation for the projected twenty-first century increase in precipitation (25–39%) and flood (2%-20%) extremes is to recharge Egypt’s fossil aquifers during high flood years.

Climate change↗
DOE OSTI2023

Sulfate reduction accelerates groundwater arsenic contamination even in aquifers with abundant iron oxides

Groundwater contamination by geogenic arsenic is a global problem affecting nearly 200 million people. In South and Southeast Asia, a cost-effective mitigation strategy is to use oxidized low-arsenic aquifers rather than reduced high-arsenic aquifers. Aquifers with abundant oxidized iron minerals are presumably safeguarded against immediate arsenic contamination, due to strong sorption of arsenic onto iron minerals. However, preferential pumping of low-arsenic aquifers can destabilize the boundaries between these aquifers, pulling high-arsenic water into low-arsenic aquifers. We investigate this scenario in a hybrid field-column experiment in Bangladesh where naturally high-arsenic groundwater is pumped through sediment cores from a low-arsenic aquifer, and detailed aqueous and solid-phase measurements are used to constrain reactive transport modelling. Here we show that elevated groundwater arsenic concentrations are induced by sulfate reduction and the predicted formation of highly mobile, poorly sorbing thioarsenic species. Furthermore, this process suggests that contamination of currently pristine aquifers with arsenic can occur up to over 1.5 times faster than previously thought, leading to a deterioration of urgently needed water resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Projected runoff declines from plant physiological effects on precipitation

Abstract The impact of plants on runoff under high atmospheric CO 2 is a major uncertainty for future water resources. Theory and Earth system models (ESMs) suggest that stricter plant stomatal regulation under high CO 2 will reduce transpiration, potentially boosting runoff. Yet, across a 12-member ensemble of idealized ESM simulations that isolate plant responses to CO 2 , we show that lower transpiration robustly enhances runoff over only 5% of modelled global land area. Precipitation changes are five times more important than transpiration changes in driving runoff responses and are a significant signal of CO 2 physiological forcing over 31–57% of land areas across models. Crucially, ESMs largely disagree on where physiologically forced precipitation changes occur but agree that plant responses in most locations are as likely to reduce runoff as increase it. These results imply that large model uncertainties in precipitation responses, rather than transpiration responses, explain why ESMs disagree on plant physiologically driven runoff changes.

Lesk, Corey S. (ORCID:0000000308684040)↗
DOE OSTI2026

Material efficiency technologies in the food and beverage industry

The U.S. food and beverage (F&B) sector is a major contributor to manufacturing gross domestic product and supports substantial employment and economic activity, while exerting significant pressures on land and water resources. At the same time, the industry faces growing expectations to balance its resource-intensive operations without compromising cost competitiveness. Material inefficiencies across the F&B value chain, particularly in raw material use and product loss/waste, lead to substantial financial losses and resource depletion. Thus, the F&B sector requires adoption of solutions and measures to avoid food wastage, reduce raw material consumption and valorize waste to high-value added products. This work presents a comprehensive understanding of the various technology solutions available for the F&B sector. The following two research questions are addressed: “What are the mid-to-high Technology Readiness Level technologies or measures to reduce material use and enable waste valorization in the F&B sector? What are the barriers to their commercial deployment? Additionally, what targeted research and development efforts are needed to overcome these barriers and accelerate their scale-up?” The findings are intended to support evidence-based decision-making, guide strategic investment, and help stakeholders strengthen resilience and competitiveness across the F&B sector.

Nain, Preeti [ORNL] (ORCID:0000000258358959)↗
DOE OSTI2025

River Dissolved Oxygen Prediction Using Machine Learning Models and Wireless Sensor Measurements

Simultaneous flooding&heat and droughts&heat events can potentially destabilize hydro-meteorological conditions to deteriorate the water quality of Neches River. Machine learning (ML) models utilizing wireless sensor measurements have been applied to predict water quality and optimize various water management strategies. This study aims to develop ML models to predict dissolved oxygen (DO) prediction under various hydro-meteorological conditions and enhance water management decision-making. Wireless sensor measurements of DO, water temperature, sample depth, conductivity, turbidity, and pH, along with discharge from the United States Geological Survey stations, are collected for model inputs at the Pine Island Bayou C749 station (PIB-C749) and Neches River Saltwater Barrier (SWB). Multilayer perceptron neural networks, recurrent neural networks, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) with and without attention mechanism (AT) are tested to determine the best model, which is applied the rolling forecast method to predict 14-day DO. Traditional and recurrent transfer learning (TL and RTL) methods are adopted to overcome insufficient data at the SWB. The input feature importance analysis using the integrated gradients (IG) algorithm is applied to determine dominant inputs. The results show LSTM-based models are capable handling long sequential data. AT-BiLSTM and RTL-LSTM demonstrate the best performance at the PIB-C749 (RMSE=0.054) and the SWB (RMSE=0.028), respectively. TL and RTL methods significantly improve model performance at the SWB. DO, temperature, and pH show higher importance, consistent with hydrodynamics and water chemistry. Both best models are applied to predict 14-day DO and demonstrate reasonable performance for decision-making. Hydro-meteorological conditions of 2017 flood and 2012 drought events are simulated and reveal that possible hypoxia occurs after flooding due to increasing temperature and turbidity, and DO concentration decreases significantly under heat and drought conditions. In conclusion, LSTM-based models utilizing wireless sensor data can be a timely and effective approach to make appropriate decisions on water resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Equity and modeling in sustainability science: Examples and opportunities throughout the process

Equity is core to sustainability, but current interventions to enhance sustainability often fall short in adequately addressing this linkage. Models are important tools for informing action, and their development and use present opportunities to center equity in process and outcomes. This Perspective highlights progress in integrating equity into systems modeling in sustainability science, as well as key challenges, tensions, and future directions. We present a conceptual framework for equity in systems modeling, focused on its distributional, procedural, and recognitional dimensions. We discuss examples of how modelers engage with these different dimensions throughout the modeling process and from across a range of modeling approaches and topics, including water resources, energy systems, air quality, and conservation. Synthesizing across these examples, we identify significant advances in enhancing procedural and recognitional equity by reframing models as tools to explore pluralism in worldviews and knowledge systems; enabling models to better represent distributional inequity through new computational techniques and data sources; investigating the dynamics that can drive inequities by linking different modeling approaches; and developing more nuanced metrics for assessing equity outcomes. We also identify important future directions, such as an increased focus on using models to identify pathways to transform underlying conditions that lead to inequities and move toward desired futures. By looking at examples across the diverse fields within sustainability science, we argue that there are valuable opportunities for mutual learning on how to use models more effectively as tools to support sustainable and equitable futures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A multifunctional copper single-atom electrocatalyst aerogel for smart sensing and producing ammonia from nitrate

Despite modern chemistry’s success in providing affordable fertilizers for feeding the population and supporting the ammonia industry, ineffective nitrogen management has led to pollution of water resources and air, contributing to climate change. Here, we report a multifunctional copper single-atom electrocatalyst-based aerogel (Cu SAA) that integrates the multiscale structure of coordinated single-atomic sites and 3D channel frameworks. The Cu SAA demonstrates an impressive faradaic efficiency of 87% for NH 3 synthesis, as well as remarkable sensing performance with detection limits of 0.15 ppm for NO 3 − and 1.19 ppm for NH 4 + . These multifunctional features enable precise control and conversion of nitrate to ammonia in the catalytic process, facilitating accurate regulation of the ammonium and nitrate ratios in fertilizers. We thus designed the Cu SAA into a smart and sustainable fertilizing system (SSFS), a prototype device for on-site automatic recycling of nutrients with precisely controlled nitrate/ammonium concentrations. The SSFS represents a forward step toward sustainable nutrient/waste recycling, thus permitting efficient nitrogen utilization of crops and mitigating pollutant emissions. This contribution exemplifies how electrocatalysis and nanotechnology can be potentially leveraged to enable sustainable agriculture.

Science & Technology - Other Topics↗
DOE OSTI2024

Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene

Climate change and human activities alter the global freshwater cycle, causing nonstationary processes as its distribution shifting over time, yet a comprehensive understanding of these changes remains elusive. Here, we develop a remote sensing–informed terrestrial reanalysis and assess the nonstationarity of and interconnections among global water cycle components from 2003 to 2020. We highlight 20 hotspot regions where terrestrial water storage exhibits strong nonstationarity, impacting 35% of the global population and 45% of the area covered by irrigated agriculture. Emerging long-term trends dominate the most often (48.2%), followed by seasonal shifts (32.8%) and changes in extremes (19%). Notably, in mid-latitudes, this encompasses 34% of Asia and 27% of North America. The patterns of nonstationarity and their dominant types differ across other water cycle components, including precipitation, evapotranspiration, runoff, and gross primary production. These differences also manifest uniquely across hotspot regions, illustrating the intricate ways in which each component responds to climate change and human water management. Our findings emphasize the importance of considering nonstationarity when assessing water cycle information toward the development of strategies for sustainable water resource usage, enhancing resilience to extreme events, and effectively addressing other challenges associated with climate change.

Science & Technology - Other Topics↗