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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Environmental Performance Report 2020 (Annual Site Environmental Report per the U.S. Department of Energy Order 231.1B Chg 1)

The National Renewable Energy Laboratory's (NREL's) Environmental Performance Report provides a description of the laboratory's environmental management activities in 2020, including information on environmental and sustainability performance, environmental compliance activities and status, and environmental protection programs, highlights, and successes. The purpose of this report is to ensure that the U.S. Department of Energy (DOE) and the public receive timely, accurate information about events that have positively affected or could adversely affect the health and safety of the public or workers, the environment, or the operations of DOE facilities. This report meets the DOE requirements of the Annual Site Environmental Report (ASER) and has been prepared in accordance with DOE Order 231.1B Chg 1, Environment, Safety and Health Reporting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Improving Discovery, Sharing, and Use of Water Data: Initial Findings and Suggested Future Work

Collaborative management of water resources requires a broad suite of “water data” that extends beyond basic information about water quantity and quality to other related topics such as water infrastructure, aquatic ecosystem health, socioeconomic factors, and power generation. Water data are disparate in nature because they are collected and provided by many entities, and in some cases, remain challenging to access and use. The U.S. Department of Energy’s Water Power Technologies Office initiated a project to characterize relevant categories of water data; describe the current state of accessing, using, and visualizing water data; and outline investigatory pathways for future efforts aimed at improving the discovery, sharing, and use of water data. Input on these topics was solicited from a small but diverse cross section of members of the water resources community. Fourteen broad categories of water data were identified: dams; ecology; flood control; hydroclimatology; hydrography; hydrology; hydropower; management landscape; migratory barriers; recreation and aesthetic importance; socioeconomic; water quality; water availability and use; and weather. Stakeholder perspectives on the accessibility and usability of water data indicate these aspects are affected by a complex set of technical and social factors. However, stakeholders generally agreed that better access to water data can provide a range of benefits to water management, and they stressed the need to generate broad support from water data users and producers. Two investigatory pathways were outlined that, taken together, provide a logical progression toward the goals of the project. The first pathway emphasizes further investigation to better define target audiences and data needs, identify opportunities for collaboration between related efforts, and conduct value demonstration activities to generate further support for improving discovery and access of water data. The second pathway focuses on creating a comprehensive vision for potential solutions that improve the discovery of water data. Several activities that align with the first pathway are suggested for the next phase of the project.

13 HYDRO ENERGY↗
DOE OSTI2021

Psychosocial drivers of land management behaviour: How threats, norms, and context influence deforestation intentions

Understanding how private landholders make deforestation decisions is of paramount importance for conservation. Behavioural frameworks from the social sciences have a lot to offer researchers and practitioners, yet these insights remain underutilised in describing what drives landholders' deforestation intentions under important political, social, and management contexts. Using survey data of private landholders in Queensland, Australia, we compare the ability of two popular behavioural models to predict future deforestation intentions, and propose a more integrated behavioural model of deforestation intentions. We found that the integrated model outperformed other models, revealing the importance of threat perceptions, attitudes, and social norms for predicting landholders' deforestation intentions. Social capital, policy uncertainty, and years of experience are important contextual moderators of these psychological factors. Here, we conclude with recommendations for promoting behaviour change in this deforestation hotspot and highlight how others can adopt similar approaches to illuminate more proximate drivers of environmental behaviours in other contexts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Attention-based convolutional capsules for evapotranspiration estimation at scale

Evapotranspiration (ET) measures the amount of water lost from the Earth's surface to the atmosphere and is an integral metric for both agricultural and environmental sciences. Understanding and quantifying ET is critical for achieving effective management of freshwater and irrigation systems. However, current ET estimation models suffer from a trade-off between accuracy and spatial coverage. In this study, we introduce our model Quench, a neural network architecture that achieves highly-accurate ET estimates over large continuous spatial extents. Quench uses our novel Attention-Based Convolutional Capsule for its neural network layers to identify areas of focus and efficiently extract ET information from satellite imagery. Benchmarks that profile our model's performance show substantive improvements in accuracy, with up to 128% increase in accuracy compared to traditional convolutional-based and process-based models. Finally, Quench also demonstrates consistent model performance over high geospatial variability and a diverse array of regions, seasons, climates, and vegetations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Building Science Advisor: A Web-Based Tool to Assess the Durability of Building Envelope Components

The Department of Energy (DOE) and Oak Ridge National Laboratory (ORNL) have developed an innovative new design tool that will put building science expert advice in the hands of every building designer who cares to manage moisture risk in high-R envelope components. As modern buildings become increasingly more airtight, constructed with modern labor-saving materials, and equipped with air-conditioning, they have become less forgiving to moisture intrusion. The Building Science Advisor (BSA) provides building science knowledge and advice based on expert experience, field measurements, laboratory tests, and computer simulations.BSA users are prompted to enter relevant information about the building location, design, and material selection options like cladding, structural system, and insulation. Influential factors such as climate, building air tightness, material properties, and internal moisture loads are also considered to estimate and compare the moisture durability performance of several design options. If the BSA deems the assembly’s performance unsatisfactory, it will provide the reasons why, and suggest necessary changes to wall design to ensure more robust performance.Using the BSA tool enables building designers to confidently select assembly design characteristics that achieve their design goals with the least moisture durability risk. Links to design-specific guidance are also be provided to help users manage any remaining risk. This tool will further enable DOE’s Building Technologies Office (BTO) to meet its long-term energy goal of a 50% reduction in building energy consumption by reducing builder concern about using highly energy efficient wall systems.

Desjarlais, Andre Omer↗
DOE OSTI2024

Tree drought physiology: critical research questions and strategies for mitigating climate change effects on forests

Droughts of increasing severity and frequency are a primary cause of forest mortality associated with climate change. Yet, fundamental knowledge gaps regarding the complex physiology of trees limit the development of more effective management strategies to mitigate drought effects on forests. Here, in this work, we highlight some of the basic research needed to better understand tree drought physiology and how new technologies and interdisciplinary approaches can be used to address them. Our discussion focuses on how trees change wood development to mitigate water stress, hormonal responses to drought, genetic variation underlying adaptive drought phenotypes, how trees ‘remember’ prior stress exposure, and how symbiotic soil microbes affect drought response. Next, we identify opportunities for using research findings to enhance or develop new strategies for managing drought effects on forests, ranging from matching genotypes to environments, to enhancing seedling resilience through nursery treatments, to landscape-scale monitoring and predictions. We conclude with a discussion of the need for co-producing research with land managers and extending research to forests in critical ecological regions beyond the temperate zone.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The future evolution of energy-water-agriculture interconnectivity across the US

Abstract Energy, water, and agricultural resources across the globe are highly interconnected. This interconnectivity poses science challenges, such as understanding and modeling interconnections, as well as practical challenges, such as efficiently managing interdependent resource systems. Using the US as an example, this study seeks to define and explore how interconnectivity evolves over space and time under a range of influences. Concepts from graph theory and input–output analysis are used to visualize and quantify key intersectoral linkages using two new indices: the ‘Interconnectivity Magnitude Index’ and the ‘Interconnectivity Spread Index’. Using the Global Change Analysis Model (GCAM-USA), we explore the future evolution of these indices under four scenarios that explore a range of forces, including socioeconomic and technological change. Analysis is conducted at both national and state level spatial scales from 2015 to 2100. Results from a Reference scenario show that resource interconnectivity in the US is primarily driven by water use amongst different sectors, while changes in interconnectivity are driven by a decoupling of the water and electricity systems, as power plants become more water-efficient over time. High population and GDP growth results in relatively more decoupling of sectors, as a larger share of water and energy is used outside of interconnected sector feedback loops. Lower socioeconomic growth results in the opposite trend. Transitioning to a low-carbon economy increases interconnectivity because of the expansion of purpose-grown biomass, which strengthens the connections between water and energy. The results highlight that while some regions may experience similar sectoral stress projections, the composition of the intersectoral connectivity leading to that sectoral stress may call for distinctly different multi-sector co-management strategies. The methodology we introduce here can be applied in diverse geographical and sectoral contexts to enable better understanding of where, when, and how coupling or decoupling between sectors could evolve and be better managed.

Khan, Zarrar (ORCID:0000000281478553)↗
DOE OSTI2022

Climate-aware decision-making: lessons for electric grid infrastructure planning and operations

Abstract Climate change poses significant risks to large-scale infrastructure systems and brings considerable uncertainties that challenge historical planning approaches. Here we focus on how climate awareness might be better incorporated into planning and decision-making in the electric power sector. To do so, we consider lessons from decision science literature where researchers have specifically focused on how to make better decisions under uncertainty. We perform a three-part review: of decision science literature on best practices for decision-making under uncertainty, of industry practice documents to understand how new uncertainties may affect the types of decisions electric utilities are making today, and of literature on electricity sector planning under climate change to assess how lessons from decision science might fit into sector-specific analyses. We discuss how characterizations of uncertainty from the decision science literature might guide approaches in the electricity sector to appropriately capture climate-related uncertainties. We also distill three key ideas from the decision science literature that can be incorporated into electricity sector planning to manage these new uncertainties: robustness, adaptive planning, and multi-stakeholder engagement. We offer example recommendations for how these key ideas might be incorporated into one essential type of planning activity, capacity expansion.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Machine Learning Assisted Reservoir Operation Model for Long–Term Water Management Simulation

This study explores strategies for long-term reservoir simulations by combining generic rule-based reservoir management model (RMM) and machine learning (ML) models for two major multipurpose reservoirs — Allatoona Lake and Lake Sidney Lanier in the southeastern United States. First, a standalone RMM is developed to simulate daily release and storage during Water Year 1981–2015. Next, using Long-Short Term Memory (LSTM) as the ML technique, a standalone LSTM model is trained based on reservoir inflow and meteorological observations to simulate reservoir release and estimate reservoir storage through water balance calculation. Three hybrid modeling strategies are developed, one using RMM output as an additional LSTM input (H1), another using LSTM as the initial release estimate in RMM (H2), and the third combining the first two strategies (H3). The Nash–Sutcliffe efficiency (NSE) for release (NSE-r), storage (NSE-s), and their mean (NSE-avg) are used for model evaluation. Overall, H1 improves NSE-r to 0.65 and 0.54 for Allatoona and Lanier, respectively, compared to standalone RMM (0.44 and 0.21); however, its storage trajectory did not produce a physically feasible solution, similar to LSTM. H2 and especially H3 show that they can retain the best features from RMM and LSTM, with H3 NSE-avg being 0.695 and 0.55 for Allatoona and Lanier outperforming RMM (0.615 and 0.29). In conclusion, the findings suggest a robust simulation capacity for large-scale water management in future studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Mixed Waste Landfill Annual Long-Term Monitoring & Maintenance Report (Apr 2015-Mar 2016)

Sandia National Laboratories (SNL) is a multi-purpose engineering and science laboratory owned by the U.S. Department of Energy (DOE)/National Nuclear Security Administration. SNL is managed and operated by Sandia Corporation (Sandia), a wholly-owned subsidiary of Lockheed Martin Corporation. Sandia National Laboratories, New Mexico (SNL/NM) is located within the boundaries of Kirtland Air Force Base (KAFB), southeast of the City of Albuquerque in Bernalillo County, New Mexico. The Mixed Waste Landfill (MWL) is located 4 miles south of SNL/NM central facilities and 5 miles southeast of Albuquerque International Sunport, in the north-central portion of Technical Area (TA)-III. The MWL disposal area comprises 2.6 acres. During operations, the MWL accepted containerized and other low-level radioactive waste and minor amounts of mixed waste from SNL/NM research facilities and off-site DOE and U.S. Department of Defense generators from March 1959 to December 1988. More specific information regarding the MWL inventory and past disposal practices is presented in the MWL Phase 2 RCRA Facility Investigation Report (Peace et al. September 2002) and the extensive MWL Administrative Record.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Incorporating the Role(s) of Human Actors in Complex System Design for Safety and Security

Traditional systems engineering demonstrates the importance of customer needs in scoping and defining design requirements; yet, in practice, other human stakeholders are often absent from early lifecycle phases. Human factors are often omitted in practice when evaluating and down-selecting design options due to constraints such as time, money, access to user populations, or difficulty in proving system robustness through the inclusion of human behaviors. Advances in systems engineering increasingly include non-technical influences into the design, deployment, operations, and maintenance of interacting components to achieve common performance objectives. Furthermore, such advances highlight the need to better account for the various roles of human actors to achieve desired performance outcomes in complex systems. Many of these efforts seek to infuse lessons and concepts from human factors (enhanced decision-making through Crew Resource Management), systems safety (Rasmussen's “drift toward danger”) and organization science (Giddens' recurrent human acts leading to emergent behaviors) into systems engineering to better understand how socio-technical interactions impact emergent system performance. Safety and security are examples of complex system performance outcomes that are directly impacted by varying roles of human actors. Using security performance of high consequence facilities as a representative use case, this article will outline the System Context Lenses to understand how to include various roles of human actors into systems engineering design. Several exemplar applications of this organizing lenses will be summarized and used to highlight more generalized insights for the broader systems engineering community.

42 ENGINEERING↗
DOE OSTI2022

Litter removal impacts on soil biodiversity and eucalypt plantation development in the seasonal tropics

The little layer of tree plantations provides primary nutrients for uptake, buffers changes in soil moisture, and provides habitat and substrate to soil epigeic fauna. However, this layer in eucalypt plantations is often removed to reduce fuel load during the fire season in the Brazilian savanna (Cerrado). Therefore, it is necessary to quantify the effects of changes in litter dynamics on the function of these plantations, on key nutrient cycling processes and on epigeic fauna diversity and abundance. In two adjacent stands (one juvenile and one mature), the consequences of two years of litter removal were quantified as monthly litterfall, leaf and fine wood litter decomposition, epigeic fauna abundance and diversity, soil biogeochemical variables, and tree diameter and basal area increments. Monthly litterfall rates in juvenile and mature stands did not change with litter removal over the study period. Annual litterfall ranged from 4.1 to 4.9 Mg ha -1 a -1 in litter removal plots and from 3.9 to 4.8 Mg ha -1 a -1 in control plots. Fine wood litter decomposition was slower in litter removal plots compared to controls, while leaf decomposition rates were similar in both. Two years of litter removal in the juvenile stand did not affect topsoil biogeochemical parameters but decreased available phosphorus at 20–40 cm depth relative to controls. In the mature stand, total cation exchange capacity (0–20 cm) was higher in controls (6.4 cmol c dm -3 ) relative to litter removal plots (6.3 cmol c dm -3 ), while soil moisture (0–40 cm depth) was lower in litter removal (25.45 m 3 m -3 ) compared to control plots (26 m 3 m -3 ) in the dry season. A non-metric multidimensional scaling ordination revealed an increased homogeneity in epigeic fauna where litter was removed. Litterfall, decomposition, diameter increment, four soil physical parameters and fourteen chemical parameters at 0–20 and 20–40 cm depth explained the differences in soil epigeic fauna composition between litter removal and control plots. Diameter increment decreased with litter removal only in the juvenile stand, which had reached its growth peak. The results indicate that removing excess litter to decrease fuel volume can alter soil biodiversity and edaphic conditions that negatively affect nutrient cycling and tree growth.

54 ENVIRONMENTAL SCIENCES↗