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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Global drivers of local water stresses and global responses to local water policies in the United States

Abstract The rapid depletion of US groundwater resources and rising number of dying wells in the Western US brings attention to the significance of groundwater governance and sustainability restrictions. However, such restrictions on groundwater withdrawals are likely to generate spillover effects causing further environmental stresses in other locations and adding to the complexity of sustainability challenges. The goal of this paper is to improve our understanding of the implications of growing global food demand for local sustainability stresses and the implications of local sustainability policies for local, regional, and global food production, land use, and prices. We employ SIMPLE-G-US (Simplified International Model of agricultural Prices, Land use, and the Environment—Gridded version for the United States) to distangle the significance or remote changes in population and income for irrigation and water resources in the US. Then we examine the local-to-global impacts of potential US groundwater sustainability policies. We find that developments in international markets are significant, as more than half of US sustainability stresses by 2050 are caused by increased commodity demand from abroad. Furthermore, a US sustainable groundwater policy can cause overseas spillovers of US production, thereby potentially contributing to environmental stresses elsewhere, even as groundwater stress in the US is alleviated. These unintended consequences could include deforestation due to cropland expansion, as well as degradation in water quality due to intensification of production in non-targeted areas.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Assessing the methane mitigation potential of innovative management in US rice production

Abstract Rice is an important global crop while also contributing significant anthropogenic methane (CH 4 ) emissions. To support the future of rice production, more information is needed on the impacts of sustainability-driven management used to grow rice with lower associated methane emissions. Recent support for the impacts of different growing practices in the US has prompted the application of a regional methodology (Tier 2) to estimate methane emissions in different rice growing regions. The methodology estimates rice methane emissions from the US Mid-South (MdS) and California (Cal) using region-specific scaling factors applied to a region-specific baseline flux. In our study, we leverage land cover data and soil clay content to estimate methane emissions using this approach, while also examining how changes in common production practices can affect overall emissions in the US. Our results indicated US rice cultivation produced between 0.32 and 0.45 Tg CH 4 annually, which were approximately 7% and 42% lower on average compared to Food and Agriculture Organization of the UN (FAO) and US Environmental Protection Agency (EPA) inventories, respectively. Our estimates were 63% greater on average compared to similar methods that lack regional context. Introducing aeration events into irrigation resulted in the greatest methane reductions across both regions. When accounting for differences between baseline and reduction scenarios, the US MdS typically had higher mitigation potential compared to Cal. The differences in cumulative mitigation potential across the 2008–2020 period were likely driven by lower production area clay content for the US MdS compared to Cal. The added spatial representation in the Tier 2 approach is useful in surveying how impactful methane-reducing practices might be within and across regions.

Environmental Sciences & Ecology↗
DOE OSTI2024

Agrivoltaic system design tools for managing trade-offs between energy production, crop productivity and water consumption

Abstract Agrivoltaic systems that locate crop production and photovoltaic energy generation on the same land have the potential to aid the transition to renewable energy by reducing the competition between food, habitat, and energy needs for land while reducing irrigation requirements. Experimental efforts to date have not adequately developed an understanding of the interaction among local climate, array design and crop selection sufficient to manage trade-offs in system design. This study simulates the energy production, crop productivity and water consumption impacts of agrivoltaic array design choices in arid and semi-arid environments in the Southwestern region of the United States. Using the Penman–Monteith evapotranspiration model, we predict agrivoltaics can reduce crop water consumption by 30%–40% of the array coverage level, depending on local climate. A crop model simulating productivity based on both light level and temperature identifies afternoon shading provided by agrivoltaic arrays as potentially beneficial for shade tolerant plants in hot, dry settings. At the locations considered, several designs and crop combinations exceed land equivalence ratio values of 2, indicating a doubling of the output per acre for the land resource. These results highlight key design axes for agrivoltaic systems and point to a decision support tool for their development.

Environmental Sciences & Ecology↗
DOE OSTI2024

Urban morphology and urban water demand: a case study in the land constrained Los Angeles region using urban growth modeling

The interactions between population growth, urban morphology, and water demand have important implications for water resources and supply in urban regions. Water use for irrigation comprises a significant fraction of urban water demand, and is potentially influenced by long-term changes in urban morphology. To investigate this, we used spatially explicit projections of urban land development intensity (fraction impervious area) generated from a 30 m resolution urban growth model for the Los Angeles (LA) region. Recent historical data on water use and high resolution landcover were used to establish relationships between green area, urban development intensity, and outdoor water demand. These relationships were then used to project outdoor and total water demand in 2100 using the urban growth model outputs. We considered two different population scenarios informed by the shared socioeconomic pathway (SSP) projections for the region (SSP3 and SSP5), and three scenarios of urban development intensification. Our analysis is resolved for over 80 water providers in the region, from the urban core to suburban fringe, and highlights diverse demand responses influenced by initial urban form and water demand attributes. Assumptions about outdoor water use factors based on recent water supply data were found to be nearly as influential on future outdoor demand as the urban growth scenario settings. Compared to previous studies, our work is unique in coherently linking high resolution SSP population scenarios, urban land cover evolution, and urban water demand projections, demonstrating the approach for the LA region—the largest population center in the western United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Field-scale mapping of California crop water productivity to inform water management in critically overdrafted groundwater basins

Crop water productivity (CWP) metrics can reveal how the dynamics of crop production and water use change across space and time. We use field-scale satellite inputs from 2016–2021 to estimate potential water savings for four crops (almonds, grapes, walnuts, and citrus—which collectively account for approximately one-third of California’s cropland area), within critically overdrafted groundwater sub-basins of California’s San Joaquin Valley. These annual estimates of field-level water savings potential are based on locally achievable CWP values for each crop type. Our findings show considerable spatial variation in CWP and potential water savings within each sub-basin. We find that increasing CWP to peak efficiency (defined as improving fields to the 95th percentile of observed CWP) for four crops could meet up to 36% of the estimated annual overdraft in San Joaquin Valley. For comparison, fallowing 5% of the four crop type fields in the same study area could potentially reduce annual overdraft by 11%. By delivering results at the field scale, our work can inform targeted interventions by irrigation district managers and more efficient allocation of state incentives for improved water management. For example, we estimate that state grant funding for water efficiency upgrades could have amplified potential water savings threefold by targeting investments to the least efficient fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Analyzing Land Characteristics Associated with Historical Large-Scale Solar Photovoltaic Deployment in the United States

Large-scale solar (LSS) deployment has accelerated rapidly across the United States, yet comprehensive understanding of its siting patterns remains limited. Here, we present a national-scale geospatial assessment of land characteristics converted for LSS development and apply statistical analyses to evaluate whether installations are disproportionately concentrated within specific land types. We find that siting patterns are strongly correlated with high solar irradiance, underscoring the role of resource quality in shaping deployment decisions. LSS projects are disproportionately concentrated on agricultural lands and shrubland. Within agricultural areas, installations predominantly occur on croplands and are strongly associated with irrigated systems. Notably, more than 90% of projects are deployed on agricultural land with Productive, Versatile, and Resilient (PVR) index values of land quality exceeding the national median. These findings highlight the need for strategic siting frameworks that reconcile solar development with the conservation of agriculturally valuable land.

14 SOLAR ENERGY↗
DOE OSTI2025

Community composition and abundance of wild bees at row crop-grassland interfaces in west central Nebraska

Abstract Perennial mixed forb and grassland habitats are crucial to conservation of pollinators and connectivity of habitats in intensely farmed landscapes. This study aims to understand the effects of land use on the pollinator community by describing bee abundance, species richness and community composition in perennial conservation grasslands and adjacent annual row crops located in west central Nebraska. In 2022 and 2023, we collected and identified bees via sticky traps at 4 locations (center and edge of adjacent grasslands and crop fields) at 6 replicated sites. We collected 1,768 specimens from sticky traps, resulting in 70 species within 28 genera. Halictidae accounted for 84% of the specimens collected. Bee abundance was influenced by the simple effects of land use (grassland vs. crops), edge adjacency, and the month and year of collection. Differences in bee abundance within a collection date were found mostly in early 2022 (May and June) and late 2023 (September), when the crop center location was generally the lowest, with some evidence for spillover of bees from the grassland into the crop edge during the early summer months. Bee species richness was affected only by month and was not significantly different by land use and edge adjacency. Bee community composition overlapped across the 4 locations, although there were significant dissimilarities between crop fields and grasslands. Surveys of the plant community revealed very low abundance of blooming stems and plant taxonomic richness at crop locations for all sampling periods, while grassland locations were comparatively high and varied over time. Plant communities showed no overlap between crop field and grassland locations. Overall, we found that conservation grasslands, while not seeded specifically with pollinator-attractive forbs, provide diverse resources to support wild bee communities in west central Nebraska; crop edges may also provide non-plant resources such as nesting sites and irrigation water. Going forward, better understanding pollinator species composition and resource utilization relative to land use characteristics and drought conditions will allow for better tailoring of conservation efforts and management strategies in Nebraska and across the larger region.

Entomology↗
DOE OSTI2025

Developmentally-specific physiological and metabolic responses support drought resilience in switchgrass and constrains biofuel yield

Switchgrass (Panicum virgatum) is a promising bioenergy crop due in part to its resilience to drought stress. However, the significance of drought timing remains poorly understood, both from a plant biology perspective and its impact on downstream biofuel production. This study determines the developmental stage-specific physiological and metabolic responses of switchgrass to drought stress and its implications for biofuel production using a custom-built programmable irrigation system. Vegetative, flowering, and senescence-stage drought significantly reduced carbon dioxide assimilation, and stomatal conductance without affecting biomass yield. Metabolic profiling revealed significant accumulation of glucose, fructose, quinic acid, shikimate and GABA during vegetative-stage drought, while flowering and senescence stages exhibited limited metabolic changes. Similarly, specialized metabolites also displayed distinct developmental patterns, with vegetative-stage drought driving the most pronounced metabolic alterations. Thermochemically-treated and hydrolyzed switchgrass biomass from vegetative-stage drought showed elevated lignocellulose-derived compounds and saponins with the latter most positively correlating with fermentation lag times. Conversely, senescence-stage drought enhanced ethanol yields while lowering saponin levels in the hydrolysates. While vegetative-stage drought enhanced physiological resilience, it compromises downstream biofuel production by introducing fermentation inhibitors, particularly saponins.

biofuel↗
DOE OSTI2021

Capturing in-field root system dynamics with RootTracker

Abstract Optimizing root system architecture offers a promising approach to developing stress tolerant cultivars in the face of climate change, as root systems are critical for water and nutrient uptake as well as mechanical stability. However, breeding for optimal root system architecture has been hindered by the difficulty in measuring root growth in the field. Here, we describe the RootTracker, a technology that employs impedance touch sensors to monitor in-field root growth over time. Configured in a cylindrical, window shutter-like fashion around a planted seed, 264 electrodes are individually charged multiple times over the course of an experiment. Signature changes in the measured capacitance and resistance readings indicate when a root has touched or grown close to an electrode. Using the RootTracker, we have measured root system dynamics of commercial maize (Zea mays) hybrids growing in both typical Midwest field conditions and under different irrigation regimes. We observed rapid responses of root growth to water deficits and found evidence for a “priming response” in which an early water deficit causes more and deeper roots to grow at later time periods. Genotypic variation among hybrid maize lines in their root growth in response to drought indicated a potential to breed for root systems adapted for different environments. Thus, the RootTracker is able to capture changes in root growth over time in response to environmental perturbations.

Aguilar, Jeffrey J.↗
DOE OSTI2019

Climatic and physiological regulation of the bimodal xylem formation pattern in Pinus pinaster saplings

Abstract Seasonality in tree cambial activity and xylem formation encompass large variation in environmental conditions. Abiotic stressors such as warming or drought also modulate plant behavior at species and individual level. Despite xylem formation susceptibility to carbon (C) and water availability, it is still unknown which are the key physiological variables that regulate xylogenesis, and to what extent plant performance contributes to further explain the number of cells in the different phases of xylem development. Xylogenesis and physiological behavior was monitored in saplings of Pinus pinaster Aiton, a bimodal growth pattern species, distributed in different irrigation regimes. Xylogenesis and plant physiological behavior were compared between treatments and the relationship between climate, physiology and the number of cells in the cambium, enlargement and cell-wall thickening phases was evaluated. Xylogenesis regulation shifted from physiological to climatic control as cell differentiation advanced to mature tracheids. The number of cells in the cambium increased with assimilation rates and decreased with the water potential gradient through the plant. Enlargement was the most susceptible phase to plant relative water content, whereas no physiological variable contributed to explain the number of cells in the wall thickening phase, which declined as temperatures increased. All treatments showed a bimodal growth pattern with a second growth period starting when primary growth was completed and after plants had experienced the highest summer hydraulic losses. Our study demonstrates the importance of including physiological responses and not only climate to fully understand xylogenesis, with special attention to the enlargement phase. This is critical when studying species with a bimodal growth pattern because the second growth peak responds to internal shifts of C allocation and may strongly depend on plant hydraulic responses and not on a fine tuning of cambial activity with soil water availability.

Garcia-Forner, Núria↗
DOE OSTI2023

Measurement and applications: Exploring the challenges and opportunities of hierarchical federated learning in sensor applications

Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays [2], [3].

Po-Leen Ooi, Melanie↗
DOE OSTI2024

How do drought and heat affect the response of soybean seed yield to elevated O 3 ? An analysis of 15 seasons of free–air O 3 concentration enrichment studies

The coincidence of rising ozone concentrations ([O 3 ]), increasing global temperatures, and drought episodes is expected to become more intense and frequent in the future. A better understanding of the responses of crop yield to elevated [O 3 ] under different levels of drought and high temperature stress is, therefore, critical for projecting future food production potential. Using a 15-year open-air field experiment in central Illinois, we assessed the impacts of elevated [O 3 ] coupled with variation in growing season temperature and water availability on soybean seed yield. Thirteen soybean cultivars were exposed to a wide range of season-long elevated [O 3 ] in the field using free-air O 3 concentration enrichment. Elevated [O3] treatments reduced soybean seed yield from as little as 5.3% in 2005 to 35.2% in 2010. Although cultivars differed in yield response to elevated [O 3 ] (R), ranging from 17.5% to –76.4%, there was a significant negative correlation between R and O 3 dosage. Soybean cultivars showed greater seed yield losses to elevated [O 3 ] when grown at drier or hotter conditions compared to wetter or cooler years, because the hotter and drier conditions were associated with greater O 3 treatment. However, year-to-year variation in weather conditions did not influence the sensitivity of soybean seed yield to a given increase in [O 3 ]. Collectively, this study quantitatively demonstrates that, although drought conditions or warmer temperatures led to greater O 3 treatment concentrations and O 3 -induced seed yield reduction, drought and temperature stress did not alter soybean's sensitivity to O 3 . Our results have important implications for modeling the effects of rising O 3 pollution on crops and suggest that altering irrigation practices to mitigate O 3 stress may not be effective in reducing crop sensitivity to O 3 .

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Comparisons of biomass, water use efficiency and water use strategies across five genomic groups of Populus and its hybrids

Abstract Genetic improvement and hybridization in the Populus genus have led to the development of genotypes exhibiting fast growth, high rooting ability and disease resistance. However, while large biomass production is important for bioenergy crops, efficient use of resources including water is also important in sites lacking irrigation and for maintaining ecosystem water availability. In addition, comparison of water use strategies across a range of growth rates and genetic variability can elucidate whether certain strategies are shared among the fastest growing and/or most water use efficient genotypes. We estimated tree water use throughout the second growing season via sapflow sensors of 48 genotypes from five Populus taxa; P. deltoides W. Bartram ex Marshall × P. deltoides (D × D), P. deltoides × P. maximowiczii A. Henry (D × M), P. deltoides × P. nigra L. (D × N), P. deltoides × P. trichocarpa Torr. & Gray (D × T) and P. trichocarpa × P. deltoides (T × D) and calculated average canopy stomatal conductance ( G S ). We regressed G S and atmospheric vapor pressure deficit (VPD) wherein the slope of the relationship represents stomatal sensitivity to VPD. At the end of the second growing season, trees were harvested, and their dry woody biomass was used to calculate whole tree water use efficiency (WUE T ). We found that D × D and D × M genotypes exhibited differing water use strategies with D × D genotypes exhibiting high stomatal sensitivity while retaining leaves while D × M genotypes lost leaf area throughout the growing season but exhibited low stomatal sensitivity. Across measured taxa, biomass growth was positively correlated with WUE T , and genotypes representing each measured taxa except D × N and T × D had high 2‐year dry biomass of above 6 kg/tree. Overall, these data can be used to select Populus genotypes that combine high biomass growth with stomatal sensitivity and WUE T to limit the negative impacts of bioenergy plantations on ecosystem water resources.

09 BIOMASS FUELS↗
DOE OSTI2020

Plant Hosts Modify Belowground Microbial Community Response to Extreme Drought

Drought stress negatively impacts microbial activity, yet the magnitude of stress responses is likely dependent on a diversity of belowground interactions. Populus trichocarpa individuals and no-plant bulk soils were exposed to extended drought (~0.03% gravimetric water content [GWC] after 12 days), rewet, and a 12-day “recovery” period to determine the effects of plant presence in mediating soil microbiome stability to water stress. Plant metabolomic analyses indicated that drought exposure increased host investment in C and N metabolic pathways (amino acids, fatty acids, phenolic glycosides) regardless of recovery. Several metabolites positively correlated with root-associated microbial alpha-diversity, but not those of soil communities. Soil bacterial community composition shifted with P. trichocarpa presence and with drought relative to irrigated controls, whereas soil fungal composition shifted only with plant presence. However, root fungal communities strongly shifted with drought, whereas root bacterial communities changed to a lesser degree. The proportion of bacterial water-stress opportunistic operational taxonomic units (OTUs) (enriched counts in drought) was high (~11%) at the end of drying phases and maintained after rewet and recovery phases in bulk soils, but it declined over time in soils with plants present. For root fungi, opportunistic OTUs were high at the end of recovery in drought treatments (~17% abundance), although relatively not responsive in soils, particularly planted soils (<0.5% abundance for sensitive or opportunistic). These data indicate that plants modulate soil and root-associated microbial drought responses via tight plant-microbe linkages during extreme drought scenarios, but trajectories after extreme drought vary with plant habitat and microbial functional groups.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

pnnl/femp-water-balance

Analysis and reporting for water and irrigation usage PNNL developed the Water Balance Tool, which provides a method to determine potable water consumption, by end-use, of a campus. A water balance compares the total water supply to the volume of water consumed by water-using equipment and applications. The output of the tool provides the estimated potable water consumption by major end-use and compares the sum of the end-uses to the total potable water supplied to the campus.

Kobold, Mark A.↗
DOE OSTI2020

Near-Real-Time Forecast of Satellite-Based Soil Moisture Using Long Short-Term Memory with an Adaptive Data Integration Kernel

Nowcasts, or near-real-time (NRT) forecasts, of soil moisture based on the Soil Moisture Active and Passive (SMAP) mission could provide substantial value for a range of applications including hazards monitoring and agricultural planning. To provide such a NRT forecast with high fidelity, we enhanced a time series deep learning architecture, long short-term memory (LSTM), with a novel data integration (DI) kernel to assimilate the most recent SMAP observations as soon as they become available. The kernel is adaptive in that it can accommodate irregular observational schedules. Testing over the CONUS, this NRT forecast product showcases predictions with unprecedented accuracy when evaluated against subsequent SMAP retrievals. It showed smaller error than NRT forecasts reported in the literature, especially at longer forecast latency. The comparative advantage was due to LSTM’s structural improvements, as well as its ability to utilize more input variables and more training data. The DI-LSTM was compared to the original LSTM model that runs without data integration, referred to as the projection model here. We found that the DI procedure removed the autocorrelated effects of forcing errors and errors due to processes not represented in the inputs, for example, irrigation and floodplain/lake inundation, as well as mismatches due to unseen forcing conditions. The effects of this purely data-driven DI kernel are discussed for the first time in the geosciences. Furthermore, this work presents an upper-bound estimate for the random component of the SMAP retrieval error.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Two (or more) for one: Identifying classes of household energy- and water-saving measures to understand the potential for positive spillover

A key component of behavior-based energy conservation programs is the identification of target behaviors. A common approach is to target behaviors with the greatest energy-saving potential. The concept of behavioral spillover introduces further considerations, namely that adoption of one energy-saving behavior may increase (or decrease) the likelihood of other energy-saving behaviors. This research aimed to identify and describe household energy- and water-saving measure classes within which positive spillover is likely to occur (e.g., adoption of energy-efficient appliances may correlate with adoption of water-efficient appliances), and explore demographic and psychographic predictors of each. Nearly 1,000 households in a California city were surveyed and asked to report whether they had adopted 75 different energy- and/or water-saving measures. Principal Component Analysis and Network Analysis based on correlations between adoption of these diverse measures revealed and characterized eight water-energy-saving measure classes: Water Conservation, Energy Conservation, Maintenance and Management, Efficient Appliance, Advanced Efficiency, Efficient Irrigation, Green Gardening, and Green Landscaping. Understanding these measure classes can help guide behavior-based energy program developers in selecting target behaviors and designing interventions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗