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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The Role of Retardation, Attachment and Detachment Processes during Microbial Coal-Bed Methane Production after Organic Amendment

Microbially enhanced coal-bed methane could allow for a more sustainable method of harvesting methane from un-mineable coaldbeds. The model presented here is based on a previously validated batch model; however, this model system is based on upflow reactor columns compared to previous experiments and now includes flow, transport and reactions of amendment as well as intermediate products. The model implements filtration and retardation effects, biofilm decay, and attachment and detachment processes of microbial cells due to shear stress. The model provides additional insights into processes that cannot be easily observed in experiments. This study improves the understanding of complex and strongly interacting processes involved in microbially enhanced coal-bed methane production and provides a powerful tool able to model the entire process of enhancing methane production and transport during microbial stimulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Mapping of a Novel Zero-Liquid Discharge Desalination System Based on Humidification–Dehumidification onto the Field of Existing Desalination Technologies

It is well-established that increasing demands for fresh water are paving the way for desalination technologies. However, this correlates with an increase in brine production whose treatment and disposal can be complicated and expensive. This paper presents a thermodynamic model to bound the operation and development of a novel Humidification–Dehumidification-based system featuring Zero-Liquid Discharge and off-grid capabilities. The model employs conservation laws to find feasible state points to meet a baseline operation of 10 kg/h of product water separated from a hypersaline feed stream with 100 g/kg salt concentration. The system incurs in a 1039 kWh/m3 energy intensity that can be supplied completely by an electric source or in combination with heating steam. Follow-up sensitivity analysis highlights the robustness of the system in handling variations of 25% in product flowrate and 75% in feed salinity, practically without incurring any additional energy demands. The proposed system operating costs between 72 USD/m3 and 96 USD/m3 are comparable to those of existing brine disposal techniques. Furthermore, an operational map of existing desalination technologies suggests a niche characterized by high recovery rates and high feed salinities that are generally unfulfilled by conventional desalination methods. Overall, the proposed system shows potential for off-grid hypersaline brine treatment. This study sets the stage for future development of physics-based and data-driven predictive models as the proposed system iterates into a pilot plant deployment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A ‘Nuclear Bomb’ or Just ‘a Joke’? Groundwater Models May Help Communicate Nuanced Risks to the Great Salt Lake

The Great Salt Lake entered the zeitgeist of environmental concern in 2022 when a coalition of scientists and activists warned in a highly publicized report that the lake might be just five years away from complete desiccation, a possibility one state official warned was tantamount to an “environmental nuclear bomb”. Shortly thereafter, an unpredicted and unprecedented pluvial winter resulted in an increase in inflow, temporarily halting the lake’s decline and prompting Utah’s governor to mock the dire prediction as “a joke”, an outcome that speaks to the tension between agenda-setting and trust-building that researchers face when sharing worst-case warnings, particularly those based on short-term variability. Here, we describe a robust relationship between the lake and groundwater in the surrounding region and demonstrate how coupled models can thus be used to improve lake elevation predictions, suggesting that while the situation may not be as dire as some have warned, the lake remains at long-term risk as a result of climate warming. We further suggest that efforts to communicate the risk of future desiccation should be informed by stochastic variability and guided by long-term fluctuations in the total water storage of the endorheic lake’s watershed.

Environmental Sciences & Ecology↗
DOE OSTI2020

Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling

Abstract. Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles. However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed the basic theory and state-of-the-art approaches for estimating global terrestrial ET, including remote-sensing-based physical models, machine-learning algorithms and land surface models (LSMs). We then utilized 4 remote-sensing-based physical models, 2 machine-learning algorithms and 14 LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the ensemble means of annual global terrestrial ET estimated by these three categories of approaches agreed well, with values ranging from 589.6 mm yr-1 (6.56×104 km3 yr-1) to 617.1 mm yr-1 (6.87×10 4 km3 yr -1 ). For the period from 1982 to 2011, both the ensembles of remote-sensing-based physical models and machine-learning algorithms suggested increasing trends in global terrestrial ET (0.62 mm yr -2 with a significance level of p<0.05 and 0.38 mm yr -2 with a significance level of p<0.05, respectively). In contrast, the ensemble mean of the LSMs showed no statistically significant change (0.23 mm yr -2 , p>0.05), although many of the individual LSMs reproduced an increasing trend. Nevertheless, all 20 models used in this study showed that anthropogenic Earth greening had a positive role in increasing terrestrial ET. The concurrent small interannual variability, i.e., relative stability, found in all estimates of global terrestrial ET, suggests that a potential planetary boundary exists in regulating global terrestrial ET, with the value of this boundary being around 600 mm yr -1 . Uncertainties among approaches were identified in specific regions, particularly in the Amazon Basin and arid/semiarid regions. Improvements in parameterizing water stress and canopy dynamics, the utilization of new available satellite retrievals and deep-learning methods, and model–data fusion will advance our predictive understanding of global terrestrial ET.

58 GEOSCIENCES↗
DOE OSTI2023

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations

Abstract. Deep learning (DL) and machine learning (ML) are widely used in hydrological modelling, which plays a critical role in improving the accuracy of hydrological predictions. However, the trade-off between model performance and computational cost has always been a challenge for hydrologists when selecting a suitable model, particularly for probabilistic post-processing with large ensemble members. This study aims to systematically compare the quantile regression forest (QRF) model and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) model as hydrological probabilistic post-processors. Specifically, we evaluate their ability in dealing with biased streamflow simulations driven by three satellite precipitation products across 522 nested sub-basins of the Yalong River basin in China. Model performance is comprehensively assessed using a series of scoring metrics from both probabilistic and deterministic perspectives. Our results show that the QRF model and the CMAL-LSTM model are comparable in terms of probabilistic prediction, and their performances are closely related to the flow accumulation area (FAA) of the sub-basin. The QRF model outperforms the CMAL-LSTM model in most sub-basins with smaller FAA, while the CMAL-LSTM model has an undebatable advantage in sub-basins with FAA larger than 60 000 km2 in the Yalong River basin. In terms of deterministic predictions, the CMAL-LSTM model is preferred, especially when the raw streamflow is poorly simulated and used as input. However, setting aside the differences in model performance, the QRF model with 100-member quantiles demonstrates a noteworthy advantage by exhibiting a 50 % reduction in computation time compared to the CMAL-LSTM model with the same ensemble members in all experiments. As a result, this study provides insights into model selection in hydrological post-processing and the trade-offs between model performance and computational efficiency. The findings highlight the importance of considering the specific application scenario, such as the catchment size and the required accuracy level, when selecting a suitable model for hydrological post-processing.

Geology↗
DOE OSTI2024

Synthesis of historical reservoir operations from 1980 to 2020 for the evaluation of reservoir representation in large-scale hydrologic models

Abstract. All the major river systems in the contiguous United States (CONUS) (and many in the world) are impacted by dams, yet reservoir operations remain difficult to quantify and model due to a lack of data. Reservoir operation data are often inaccessible or distributed across many local operating agencies, making the acquisition and processing of data records quite time-consuming. As a result, large-scale models often rely on simple parameterizations for assumed reservoir operations and have a very limited ability to evaluate how well these approaches match actual historical operations. Here, we use the first national dataset of historical reservoir operations in the CONUS domain, ResOpsUS, to analyze reservoir storage trends and operations in more than 600 major reservoirs across the US. Our results show clear regional differences in reservoir operations. In the eastern US, which is dominated by flood control storage, we see storage peaks in the winter months with sharper decreases in the operational range (i.e., the difference between monthly maximum and minimum storage) in the summer. In the more arid western US where storage is predominantly for irrigation, we find that storage peaks during the spring and summer with increases in the operational range during the summer months. The Lower Colorado region is an outlier because its seasonal storage dynamics more closely mirrored those of flood control basins, yet the region is classified as arid, and most reservoirs have irrigation uses. Consistent with previous studies, we show that average annual reservoir storage has decreased over the past 40 years, although our analyses show a much smaller decrease than previous work. The reservoir operation characterizations presented here can be used directly for development or evaluation of reservoir operations and their derived parameters in large-scale models. We also evaluate how well historical operations match common assumptions that are often applied in large-scale reservoir parameterizations. For example, we find that 100 dams have maximum storage values greater than the reported reservoir capacity from the Global Reservoirs and Dams database (GRanD). Finally, we show that operational ranges have been increasing over time in more arid regions and decreasing in more humid regions, pointing to the need for operating policies which are not solely based on static values.

Geology↗
DOE OSTI2021

Attribution of the role of climate change in the forest fires in Sweden 2018

In this study, we analyse the role of climate change in the forestfires that raged through large parts of Sweden in the summer of 2018 from ameteorological perspective. This is done by studying the Canadian FireWeather Index (FWI) based on sub-daily data, both in reanalysis data sets(ERA-Interim, ERA5, the Japanese 55 year Reanalysis, JRA-55, and Modern-Era Retrospective analysis for Research and Applications version 2, MERRA-2) and three large-ensemble climatemodels (EC-Earth, weather@home, W@H, and Community Earth System Model, CESM) simulations. The FWI, based on reanalysis, correlates well with the observed burnt area in summer (r=0.6 to 0.8). We find that the maximum FWI in July 2018 had return times of ~24 years (90 % CI, confidence interval, > 10 years) for southern and northern Sweden. Furthermore, we find a negative trend of the FWI for southern Sweden over the 1979 to 2017 time period in the reanalyses, yielding a non-significant reduced probability of such an event. However, the short observational record, large uncertainty between the reanalysis products and large natural variability of the FWI give a large confidence interval around this number that easily includes no change, so we cannot draw robust conclusions from reanalysis data. The three large-ensembles with climate models point to a roughly 1.1 (0.9 to 1.4) times increased probability (non-significant) for such events in the current climate relative to preindustrial climate. For a future climate (2 °C warming), we find a roughly 2 (1.5 to 3) times increased probability for such events relative to the preindustrial climate. The increased fire weather risk is mainly attributed to the increase intemperature. The other main factor, i.e. precipitation during summer months, isprojected to increase for northern Sweden and decrease for southern Sweden. We, however, do not find a clear change in prolonged dry periods in summer months that could explain the increased fire weather risk in the climate models. In summary, we find a (non-significant) reduced probability of such eventsbased on reanalyses, a small (non-significant) increased probability due to global warming up to now and a more robust (significant) increase in the risk for such events in the future based on the climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Key Accomplishments & Results: Sustainable Wastewater Infrastructure of the Future

This fact sheet summarizes the key accomplishments and results from DOE's Better Buildings Sustainable Wastewater Infrastructure of the Future (SWIFt) Accelerator. The Accelerator was a 3-year effort between DOE and state and local jurisdictions to promote energy data management practices in wastewater operations in an effort to increase energy efficiency in wastewater facilities around the country. Accelerator participants shared implementation plans, and DOE combined the results into a final toolkit.

wastewater, water resource recovery facilities, en↗
DOE OSTI2025

Digital Twin Applications in the Water Sector: A Review

As cities develop and resource demands rise, the water sector faces crucial challenges to deliver reliable, sustainable, and efficient services. Digital Twins (DTs), virtual replicas of physical systems, offer a promising tool to transform how we manage water infrastructure. Originally developed in the aerospace industry, DTs are now gaining traction in the water sector, enabling real-time monitoring, simulation, and predictive control of water and wastewater treatment, collection and distribution networks, and water reclamation and reuse systems. While still emerging in the water sector, DTs have shown potential to enhance operational efficiency, reduce environmental impacts, and support smarter, more resilient water management. This review study provides a comprehensive overview of current DT applications in the water sector, highlighting successful case studies, technical challenges, and knowledge gaps. It also explores how DTs can help bridge the water–energy nexus by optimizing resources utilized across interconnected systems. By synthesizing recent advances and identifying future research directions, this paper illustrates how DTs can play a central role in building sustainable, adaptive, and digitally-enabled water infrastructure.

digital twin↗
DOE OSTI2022

Drivers of Future Physical Water Scarcity and Its Economic Impacts in Latin America and the Caribbean

Future water scarcity is a global concern with significant impacts on the energy, water, and land (EWL) sectors. Countries in Latin America and the Caribbean (LAC) are significant producers of agricultural goods consumed around the world, so disruptions resulting from land and water scarcity in LAC are a source of global supply chain risk. Understanding where water scarcity in LAC could occur and what could drive that scarcity to emerge is critical for strategic resource management and planning, both regionally and globally. Assessing future water scarcity impacts in LAC is challenging given the multisector dynamic interactions among the EWL sectors and the multiple uncertainties acting across different spatial scales. We use scenario discovery to explore the drivers of future water scarcity, considering assumptions related to climate, socioeconomics, and the EWL sectors. Understanding what factors drive uncertainty across outcomes can help stakeholders in targeting long-term solutions and future data collection efforts. To illuminate these dynamics, we use a database consisting of a large ensemble of scenarios representing diverse worlds simulated using the Global Change Analysis Model. We quantify future water scarcity and its economic impacts across the scenarios using three metrics: (1) physical water scarcity, (2) water price, and (3) crop profit. We find that physical water scarcity and water price are driven primarily by reservoir storage capacity assumptions, highlighting the importance of strategic water infrastructure development in maintaining future water availability and accessibility. Crop profit is driven by a combination of water supply and demand assumptions, which emphasizes the complex nature of EWL multisector dynamics. While most of LAC is poised to have abundant land and water resources available for development, water basins in Mexico and along the Pacific coast of South America experience high levels of severity and uncertainty across the future scenarios for at least one of the metrics. We find that the drivers of extreme scarcity vary spatially and across the metrics, further highlighting the heterogeneity of the region and the importance of considering multiple metrics to assess water scarcity vulnerability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Water Security: Trends, Capabilities, and Research Directions to Secure Water Infrastructure

Water and wastewater sector is target rich and resource poor ~153k water utilities, serve 80% of US population ~16k publicly-owned wastewater systems in the US serve 75% of the population Need scalable solutions to fit small and medium to large systems Federal attention to critical infrastructure continues to grow – particularly in the water sector Increase in water sector incidents and threats for large scale disruption – particularly by nation-state actors and their proxies EPA is the SRMA DHS CISA focuses on critical infrastructure protection across sectors They must work together to secure WWW systems Research capabilities to enable secure water systems Current and future threats Resilience – natural disasters, accidents, cyber-physical attacks

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

The uncertainties resulting from the escalating penetration of renewable energy resources pose severe challenges to the efficient operation of modern power systems. Hydroelectricity is characterized by its flexibility, controllability, and reliability, and thus becomes one of the most ideal energy resources to hedge against such uncertainties. This paper studies the mid-term integrated generation and maintenance scheduling of a cascaded hydroelectric system (CHS) consisting of multiple cascaded reservoirs and hydroelectric units. To precisely describe the mid-term water regulation policies, the hydraulic coupling relationship and water-energy nexus of CHS are incorporated into the proposed optimization model. The uncertainties of natural water inflow and the power outputs of wind/solar energy generation are taken into consideration and captured via a stochastic process modeled by a scenario tree. A multistage stochastic optimization (MSO) approach is developed to coordinate the complementary operations of multiple energy resources, by optimizing the mid-term water resource management, generation scheduling, and maintenance scheduling of CHS. The proposed MSO model is formulated as a large-scale mixed-integer linear program that presents significant computational intractability. To address this issue, a tailored Benders decomposition algorithm is developed. Two real-world case studies are conducted to demonstrate the capability and characteristics of the proposed model and algorithm. The computational results show that the proposed MSO model can exploit the flexibility of hydroelectricity to efficiently respond to variable wind and solar power, and reserve water resources for the generation in peak months to reduce the consumption of fossil fuel. Furthermore, the proposed solution approach also exhibits promising computational efficiency when handling large-scale models.

13 HYDRO ENERGY↗
DOE OSTI2023

Moisture Absorbing and Water Self‐Releasing from Hybrid Hydrogel Desiccants

Abstract Atmospheric moisture is a valuable resource for fresh water and potentially sustainable energy. However, direct harvesting water from moisture (the vapor form) remains the most challenging. Hybrid desiccants made from hydrogels embedded with salt offer promise in absorbing moisture. However, the desorption process often requires additional energy to heat the samples. Here, poly(acrylic acid) (PAA) hydrogels embedded with lithium chloride are prepared, demonstratng simultaneous moisture absorption and self‐release of liquid water at room temperature with 50–90% relative humidity. The water self‐releasing process can be separated into two distinct stages: 1) surface release, where water droplets grow on the hydrogel surface due to differences in nucleation and diffusion rates, and 2) bulk release, triggered by the collapse of polymer chains, subsequently releasing water from the hydrogel network. Factors such as salt concentration, hydrogel crosslinking density, and film thickness are investigated to better understand the moisture absorption and water‐releasing processes. Moreover, hydrophobic domains are introduced onto the salt‐embedded PAA films, creating an edge effect that enhances the droplet growth rates. When the hydrophobic domains are patterned, the movement of released water can be guided, resulting in a threefold increase in water removal rate attributed to gravitational force.

Chemistry↗
DOE OSTI2021

Simulated Adhesive Response to Aging (CRADA 453 Final Report)

The goal of this project was to develop a suite of molecular models to investigate adhesive performance as a result of the presence of different levels of moisture. These different levels of moisture are accepted in the adhesives community to represent different states of aging. First, our team modeled the adhesive’s crosslinked structure. Next, we modeled adding water to the crosslinked structure and used high performance computing resources to simulate water diffusion through the bulk adhesive and possible sites for chain scission. Finally, we modeled the response of the simulated adhesive to tensile loads at various levels of moisture content. These simulations were validated by experimental characterization by the industry partner. We found that the Young’s modulus of the adhesive decreased by about 30% when the water content increased to 3 wt.%. Our simulations also identified the likely site where the polymer chain could break. Our integration of molecular level simulations and mechanical property predictions using high performance computing with experimental studies of a hydrated epoxy system has yielded fundamental insights that will help industry advance lightweight joining technologies.

36 MATERIALS SCIENCE↗