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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Microbiome differences in sugarcane and metabolically engineered oilcane accessions and their implications for bioenergy production

Abstract Oilcane is a metabolically engineered sugarcane ( Saccharum spp. hybrid) that hyper-accumulates lipids in its vegetable biomass to provide an advanced feedstock for biodiesel production. The potential impact of hyper-accumulation of lipids in vegetable biomass on microbiomes and the consequences of altered microbiomes on plant growth and lipid accumulation have not been explored so far. Here, we explore differences in the microbiome structure of different oilcane accessions and non-modified sugarcane. 16S SSU rRNA and ITS rRNA amplicon sequencing were performed to compare the characteristics of the microbiome structure from different plant compartments (leaf, stem, root, rhizosphere, and bulk soil) of four greenhouse-grown oilcane accessions and non-modified sugarcane. Significant differences were only observed in the bacterial microbiomes. In leaf and stem microbiomes, more than 90% of the entire microbiome of non-modified sugarcane and oilcane was dominated by similar core taxa. Taxa associated with Proteobacteria led to differences in the non-modified sugarcane and oilcane microbiome structure. While differences were observed between multiple accessions, accession 1566 was notable in that it was consistently observed to differ in its microbial membership than other accessions and had the lowest abundance of taxa associated with plant-growth-promoting bacteria. Accession 1566 is also unique among oilcane accessions in that it has the highest constitutive expression of the WRI 1 transgene. The WRI 1 transcription factor is known to contribute to significant changes in the global gene expression profile, impacting plant fatty acid biosynthesis and photomorphogenesis. This study reveals for the first time that genetically modified oilcanes associate with distinct microbiomes. Our findings suggest potential relationships between core taxa, biomass yield, and TAG in oilcane accessions and support further research on the relationship between plant genotypes and their microbiomes.

09 BIOMASS FUELS↗
DOE OSTI2024

Coupled Aero-Hydro-Mechanical Hybrid Simulation Testing of Offshore Wind Turbines Subjected to Operational and Extreme Loading Conditions

Understanding the response of the Offshore Wind Turbine (OWT) subjected to realistic applied loads requires modeling the whole structure including its soil-foundation system. This requires unique and innovative testing facilities. OWT systems experience cyclic and dynamic loading due to wind, wave, current, rotor vibrations (i.e., 1P load) and vibrations caused by the blade shadowing effects (2P/3P loads). These loads are complicated in nature and have varying amplitudes, frequencies, and directions. Investigating the response of the entire OWT system including the soil-foundation system under these complex loading conditions, requires: (1) full understanding of the loading characteristics including: the power take-off mechanical load (1P and 3P), and areo- and hydrodynamic loads that the OWT system is subjected to; (2) testing facility with unique multidirectional loading capabilities that allows for simultaneous application of realistic wind, wave and machine loads, axial gravity loads, and induced overturning moments; and (3) unique and cost-effective testing techniques that allow for accurate analysis of the overall response of the OWT system under realistic conditions such as: Real-Time Hybrid Simulation (RTHS).

17 WIND ENERGY↗
DOE OSTI2024

Microbial Ecology and Site Characteristics Underlie Differences in Salinity-Methane Relationships in Coastal Wetlands

Methane (CH 4 ) is a potent greenhouse gas emitted by archaea in anaerobic environments such as wetland soils. Tidal freshwater wetlands are predicted to become increasingly saline as sea levels rise due to climate change. Previous work has shown that increases in salinity generally decrease CH 4 emissions, but with considerable variation, including instances where salinization increased CH 4 flux. We measured microbial community composition, biogeochemistry, and CH 4 flux from field samples and lab experiments from four different sites across a wide geographic range. We sought to assess how site differences and microbial ecology affect how CH 4 emissions are influenced by salinization. CH 4 flux was generally, but not always, positively correlated with CO 2 flux, soil carbon, ammonium, phosphate, and pH. Methanogen guilds were positively correlated with CH 4 flux across all sites, while methanotroph guilds were both positively and negatively correlated with CH 4 depending on site. There was mixed support for negative relationships between CH 4 fluxes and concentrations of alternative electron acceptors and abundances of taxa that reduce them. CH 4 /salinity relationships ranged from negative, to neutral, to positive and appeared to be influenced by site characteristics such as pH and plant composition, which also likely contributed to site differences in microbial communities. The activity of site-specific microbes that may respond differently to low-level salinity increases is likely an important driver of CH 4 /salinity relationships. Our results suggest several factors that make it difficult to generalize CH 4 /salinity relationships and highlight the need for paired microbial and flux measurements across a broader range of sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Impact of Vegetation Assimilation on Flash Drought Characteristics across the Continental United States

Predicting and managing the impacts of flash droughts is difficult owing to their rapid onset and intensification. Flash drought monitoring often relies on assessing changes in root-zone soil moisture. However, the lack of widespread soil moisture measurements means that flash drought assessments often use process-based model data like that from the North American Land Data Assimilation System (NLDAS). Such reliance opens flash drought assessment to model biases, particularly from vegetation processes. Here, we examine the influence of vegetation on NLDAS-simulated flash drought characteristics by comparing two experiments covering 1981–2017: open loop (OL), which uses NLDAS surface meteorological forcing to drive a land surface model using prognostic vegetation, and data assimilation (DA), which instead assimilates near-real-time satellite-derived leaf area index (LAI) into the land surface model. The OL simulation consistently underestimates LAI across the United States, causing relatively high soil moisture values. Both experiments produce similar geographic patterns of flash droughts, but OL produces shorter duration events and regional trends in flash drought occurrence that are sometimes opposite to those in DA. Across the Midwest and Southern United States, flash droughts are 4 weeks (about 70%) longer on average in DA than OL. Moreover, across much of the Great Plains, flash drought occurrence has trended upward according to the DA experiment, opposite to the trend in OL. This sensitivity of flash drought to the representation of vegetation suggests that representing plants with greater fidelity could aid in monitoring flash droughts and improve the prediction of flash drought transitions to more persistent and damaging long-term droughts.

Drought↗
DOE OSTI2022

Disentangling error structures of precipitation datasets using decision trees

Characterizing error structures in precipitation products not only facilitates their proper applications for scientific and practical purposes but also helps improve their retrieval algorithms and processing methods. Despite the fact that multiple precipitation products have been assessed in the literature, factors that affect their error structures remain inadequately addressed. By interpreting 60 binary decision trees, this study disentangles the error characteristics of precipitation products in terms of their spatiotemporal patterns and geographical factors. Three independent precipitation products - two satellite-based and one reanalysis datasets: the Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) late run (IMERG-L), Soil Moisture to Rain-Advanced SCATterometer (SM2RAIN-ASCAT), and the Modern-Era Retrospective analysis for Research and Applications, Version 2 uncorrected precipitation output (MERRA2-UC), are evaluated across the contiguous United States from 2010 to 2019. Here, the ground-based Stage IV precipitation dataset is used as the ground truth. Results indicate that the MERRA2-UC outperforms the IMERG-L and SM2RAIN-ASCAT with higher accuracy and more stable interannual patterns for the analysis period. Decision trees cross-assess three spatiotemporal factors and find that the underestimation of MERRA2-UC occurs in the east of the Rocky Mountains, and SM2RAIN-ASCAT underestimates precipitation over high latitudes, especially in winter. Additionally, the decision tree method ascribes system errors to nine different geographical characteristics, of which the distance to the coast, soil type, and DEM are the three dominant features. On the other hand, the land cover type, topography position index, and aspect are three relatively weak factors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Removable, Sequestration Coatings for Mitigating Hazardous Contaminants Related to Deactivation and Decommissioning Activities

This project is a collaborative effort between InnoSense LLC (ISL) and Oak Ridge National Laboratory (ORNL) aimed at mitigating the mercury (Hg) contamination problem at the Y-12 National Security Complex (NSC). The report summarizes the research and technology development work of the Phase IIB Project, led by Dr. Uma Sampathkumaran at ISL for the period of July 31, 2017–October 31, 2021. The Y-12 site cleanup includes technologies for safe and cost-effective facility demolition, soil remediation, and on-site treatment and disposal of mercury bearing wastes. One of the technology demonstration goals was a reactive strippable coating for targeted pre-demolition and de-contamination to limit Hg mobilization and the volume of debris requiring treatment for disposal. Toward this, ISL has developed a water-based reactive strippable coating (Trap & See-Hg™) to sequester elemental Hg(0) and mercuric Hg(II) species in the coating. The coating provides multiple benefits including (1) Hg vapor suppression, (2) sealing in the hazardous contaminants, (3) visually locating the contaminant through color changes, and (4) remediating the contaminated surfaces upon coating removal. Through collaboration between ORNL and ISL in Phase II work, the team successfully demonstrated the Trap & See-Hg technology to (1) visually locate the presence of Hg on concrete, soil and drywall surfaces, (2) effectively sequester all mercury species (elemental, soluble and insoluble Hg) from lab-generated and ex-situ soil and brick/rubble samples collected from the Y 12 site with 80 to 95% efficiency to capture/reduce Hg vapor concentration, (3) be applied by manual and robotic spray applicators and cure at room ambient, (4) remain a cohesive and peelable coating upon drying, and (5) sequester Hg0 from walls in the alpha-2 building basement at Y-12 during a small scale in-situ field trial. In Phase IIB, ISL’s effort was focused on refining the coating formulations with additives containing passive and reactive sorbents, colorimetric indicator, and thixotropic agents to achieve the desired performance. The paints were then evaluated at ORNL for applicability to metal and concrete surfaces of different sizes and shapes, and soil rubble, Hg-sorption capacity and achieving sag-free coating on surfaces when applied by a manual spray applicator. Ambient cure of the coating resulted in a dry thickness of 16–20 mil. The coatings were removed from smooth or rough surfaces with low to moderate force. Both lab-generated Hg-contaminated samples and soil and brick/rubble samples containing a range of Hg concentrations (collected from the Y-12 site by UCOR) were used in this project. Optimized formulations were applied to these samples and evaluated for ability to suppress Hg vapors, sequester Hg 0 and Hg(II) species and color mapping of mercury species on the test surface. The ORNL team initially identified three to four potential sites at Y-12 and ORNL for potential demonstration. However, the pandemic closed any possible avenues for on-site demonstration of the robotic spray applicator. The metal and concrete samples with low to high Hg content provided by UCOR from an ongoing demolition were subsequently used for ex-situ sample characterization and analysis at ORNL over a period of 25 days. The coatings suppressed Hg vapors with an efficiency of 95–99% from heavily contaminated metal and 60% from concrete substrate. Analysis of total Hg in the peeled paints indicates sorption capacities ranging from 12 µg Hg/g of paint from concrete to 1.2 mg Hg/g of paint from the metal substrates while peeled paints from soil samples could capture a total Hg from 2.3 mg Hg/g to 66 mg/g paint. Toxicity characteristic leaching procedure (TCLP) tests recovered total mercury leached at 0.15±0.0008 µg/L for concrete to 126.81±0.67 µg/L and 377.25±5.67 µg/L for the two metal substrates, to allow for informed decisions on downstream disposal considerations. Together, this Phase IIB demonstrated the potential value of the Trap & See-Hg for Hg uptake, vapor suppression, and color mapping, and may thus be used to enhance worker safety during site cleanup, facility demolition, and downstream waste disposal at Y-12 NSC.

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

Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

Arsenic, a potent carcinogen and neurotoxin, affects over 200 million people globally. Current detection methods are laborious, expensive, and unscalable, being difficult to implement in developing regions and during crises such as COVID-19. This study attempts to determine if a relationship exists between soil’s hyperspectral data and arsenic concentration using NASA’s Hyperion satellite. It is the first arsenic study to use satellite-based hyperspectral data and apply a classification approach. Four regression machine learning models are tested to determine this correlation in soil with bare land cover. Raw data are converted to reflectance, problematic atmospheric influences are removed, characteristic wavelengths are selected, and four noise reduction algorithms are tested. The combination of data augmentation, Genetic Algorithm, Second Derivative Transformation, and Random Forest regression (R 2 =0.840 and normalized root mean squared error (re-scaled to [0,1]) = 0.122) shows strong correlation, performing better than past models despite using noisier satellite data (versus lab-processed samples). Three binary classification machine learning models are then applied to identify high-risk shrub-covered regions in ten U.S. states, achieving strong accuracy (=0.693) and F1-score (=0.728). Overall, these results suggest that such a methodology is practical and can provide a sustainable alternative to arsenic contamination detection.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

Interacting Effects of Watershed and Coastal Processes on the Evolution of Compound Flooding During Hurricane Irene

Abstract In low‐lying estuarine regions, compound flooding (CF) is caused by the co‐occurrence of extreme precipitation, river flooding and storm surge. In recent decades, there has been a rise in the frequency and intensity of pluvial‐coastal CF events in different parts of the U.S. due to the increased frequency of intense precipitation and storm surge events. However, in estuarine and deltaic regions, the CF characteristics depend mainly on the storm tide and river flow interaction. Understanding how the fluvial‐coastal CF may respond to changes to watershed and estuarine characteristics is essential for future CF hazard prediction. This study examined two critical processes: (a) the interplay between antecedent soil moisture conditions and peak river flow, and (b) how the impact of sea level rise (SLR) on storm surge and river flood distribution alters the CF in complex estuaries. As the study area, we selected the Delaware Bay and River, a shallow and convergent estuary in the US Mid‐Atlantic region—where flood hazards during a CF can become more significant than the surge and river flood processes occurring in isolation. For the focal event for the study, we selected Hurricane Irene (2011) because it reportedly produced the most extreme CF over the past two decades in the same region. Ultimately, our results illustrated that the potential changes to the catchment and bay characteristics from the global temperature increase and SLR could significantly modulate the fluvial‐coastal CF variability. The potential increase in global temperature and rainfall intensity might not always exacerbate the CF.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Measuring and modeling soil moisture and runoff at solar farms using a disconnected impervious surface approach

Abstract Ground‐mounted photovoltaic sites are often treated as impervious surfaces in stormwater permits. This ignores the pervious soils beneath and between solar arrays and leads to an overestimation of runoff. Our objective was to improve solar farm stormwater hydrology models by explicitly considering the disconnected impervious nature of solar design and site characteristics. Experimental sites established on utility scale solar farms in Colorado, Georgia, Minnesota, New York, and Oregon had perennial vegetative plantings with mean precipitation ranging from 40.6 to 124.5 cm, and soil texture ranging from loamy sand to clay. Soil moisture measurements were collected beneath arrays, under drip edges, and in the vegetated area between arrays at each site. Hydrus‐3D models for soil moisture and stormwater hydrology were developed that accounted for precipitation falling on solar panels, drip edge redistribution of rainfall, infiltration, and runoff in the pervious areas between solar arrays and beneath panels. Drip edge runoff averaged 3‐ to 10‐times incident precipitation at the New York and Minnesota sites, respectively. Root mean square error values between measured sub‐hourly soil moisture and predicted moisture for large measured single storm events averaged 0.029 across all five sites. Predicted runoff depths were strongly affected by precipitation depth, soil texture, soil profile depth, and soil bulk density. Runoff depths across the five experimental sites averaged 13%, 25%, and 45% of the 2‐, 10‐, and 100‐year design storm depths, clearly showing that these solar farms do not behave like impervious surfaces, but rather as disconnected impervious surfaces with substantial infiltration of runoff in the vegetated areas between and beneath solar arrays.

Agriculture↗
DOE OSTI2025

TARP Identifying Critical Thresholds for Acute Response of Plants and Ecosystems to Water Stress at Walker Branch Watershed, 2002-2005

This dataset contains data from a manipulative field study aimed at identifying critical thresholds for acute response of plants and ecosystems to water stress (TARP) that took place at Walker Branch Watershed in Oak Ridge, Tennessee from 2002-2005 (2002-06-20 to 2005-12-16). The study used understory tents for the removal of 100% of the growing-season throughfall and stem flow, to provide data on the impact of acute drought on mechanisms responsible for growth and mortality of deciduous forest canopy trees representative of common plant functional types (Liriodendron and Quercus). Through three years of manipulation (2003, 2004 and 2005; pretreatment measurements in 2002) various measures of tree response to surface moisture deficits were recorded including root and leaf traits, plant nonstructural carbohydrates status, hourly sapflow, basal area, and periodic observations of foliar photosynthesis and conductance. Additionally, environmental data such as air and soil temperature, soil water content, and soil matric potential were recorded. This dataset contains data 16 files in comma-separate (*.csv) format. Additional metadata are provided: 16 data dictionaries and a file-level metadata file in comma separate (*.csv) format and a user guide in PDF (*.pdf) format.

basal area↗
DOE OSTI2024

Modeling exports of dissolved organic carbon from landscapes: a review of challenges and opportunities

Inland waters receive large quantities of dissolved organic carbon (DOC) from soils and act as conduits for the lateral transport of this terrestrially derived carbon, ultimately storing, mineralizing, or delivering it to oceans. The lateral DOC flux plays a crucial role in the global carbon cycle, and numerous models have been developed to estimate the DOC export from different landscapes. We reviewed 34 published models and compared their characteristics to identify challenges in model applications and opportunities for future model development. We classified these models into three types: indicator-driven, hydrology-forced, and process-based DOC export simulation models. They differ mainly in their environmental inputs, simulation approaches for soil DOC production, leaching from soils to inland waters, and transit through inland waters. It is essential to consider landscape characteristics, climate conditions, available data, and research questions when selecting the most appropriate model. Given the substantial assumptions associated with these models, sufficient measurements are required to benchmark estimates. Accurate accounting of terrestrially derived DOC export to oceans requires incorporating the DOC produced in aquatic ecosystems and deposited with rainwater; otherwise, global export estimates may be overestimated by 40.7%. Additionally, improving the representation of mineralization and burial processes in inland waters allows for more accurate accounting of carbon sequestration through land ecosystems. When all the inland water processes are ignored or assuming DOC leaching is equivalent to DOC export, the loss of soil carbon through this lateral flux could be underestimated by 43.9%.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Dynamics of N 2 O in vicinity of plant residues: a microsensor approach

Plant residues decomposing within the soil matrix are known to serve as hotspots of N 2 O production. However, the lack of technical tools for microscale in-situ N 2 O measurements limits understanding of hotspot functioning. Our aim was to assess performance of microsensor technology for evaluating the temporal patterns of N 2 O production in immediate vicinity to decomposing plant residues. We incorporated intact switchgrass leaves and roots into soil matrix and monitored O 2 depletion and N 2 O production using electrochemical microsensors along with N 2 O emission from the soil. We also measured residue’s water absorption and b-glucosidase activity on the surface of the residue - the characteristics related to microenvironmental conditions and biological activity near the residue. N 2 O production in the vicinity of switchgrass residues began within 0–12 h after the wetting, reached peak at ~0.6 day and decreased by day 2. N 2 O was higher near leaf than near root residues due to greater leaf N contents and water absorption by the leaves. However, N 2 O production near the roots started sooner than near the leaves, in part due to high initial enzyme levels on root surfaces. Electrochemical microsensor is a useful tool for in-situ micro-scale N 2 O monitoring in immediate vicinity of soil incorporated plant residues. Monitoring provided valuable information on N 2 O production near leaves and roots, its temporal dynamic, and the factors affecting it. The N 2 O production from residues measured by microsensors was consistent with the N 2 O emission from the whole soil, demonstrating the validity of the microsensors for N 2 O hotspot studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Fine Ash-Bearing Particles as a Major Aerosol Component in Biomass Burning Smoke

Biomass burning (BB) events are occurring globally with increasing frequency, and their emissions are having more impacts on human health and climate. Large ash particles are recognized as a BB product with major influences on soil and water environments. However, fine-ash particles, which have diameters smaller than several microns and characteristic morphologies and compositions (mainly Ca and Mg carbonates), have not yet been explicitly considered as a major BB aerosol component either in field observations or climate models. This study measured BB aerosol samples using transmission electron microscopy (TEM) and ion chromatography during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign. We show that significant amounts of fine ash-bearing particles are transported >100 km from their fire sources. Our environmental chamber experiments suggest that they can act as cloud condensation and ice nuclei. We also found considerable amounts of fine ash-bearing particles in the TEM samples collected during previous campaigns (Biomass Burning Observation Project and Megacity Initiative: Local and Global Research Observations). These ash particles are commonly mixed with organic matter and make up ~8% and 5% of BB smoke by number and mass, respectively, in samples collected during the FIREX-AQ campaign. The measured ash-mass concentrations are approximately five times and six times greater than those of BB black carbon and potassium, respectively, scaling to an estimated global emission of 11.6 Tg yr -1 with a range of 8.8–16.3 Tg yr -1 . Better characterization and constraints on these fine ash-bearing particles will improve BB aerosol measurements and strengthen assessments of BB impacts on human health and climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine Learning of Key Variables Impacting Extreme Precipitation in Various Regions of the Contiguous United States

Abstract Amplification in extreme precipitation intensity and frequency can cause severe flooding and impose significant social and economic consequences. Variations in extreme precipitation intensity, frequencies, and return periods can be attributed to many physical variables across spatial and temporal scales. Here we employ ensemble machine learning (ML) methods, namely random forest (RF), eXtreme Gradient Boosting (XGB), and artificial neural networks (ANN), to explore key contributing variables to monthly extreme precipitation intensity and frequency in six regions over the United States. We further establish emulators for return periods. Results show that the ML models for intensity perform better in regions with obvious seasonality (i.e., Northern Great Plains, Southern Great Plains, and West Coast) than the other three regions (Northeast, Southwest, and Rocky Mountains), while for frequency the models perform well for most regions. The Shapley additive explanation is used to help explain the relationships between extreme precipitation characteristics and identify top variables for RF and XGB. We find that latent heat flux, relative humidity, soil moisture, and large‐scale subsidence are key common variables across the regions for both monthly intensity and frequency, and their compound effects are non‐negligible. The developed ML models capture the probability and return period of extreme precipitation well for all regions and may be used for decision making (e.g., infrastructure planning and design).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Shrub Expansion Can Counteract Carbon Losses From Warming Tundra

Arctic warming is causing substantial compositional, structural, and functional changes in tundra vegetation including shrub and tree-line expansion and densification. However, predicting the carbon trajectories of the changing Arctic is challenging due to interacting feedbacks between vegetation composition and structure, and surface characteristics. We conduct a sensitivity analysis of the current-date to 2100 projected surface energy fluxes, soil carbon pools, and CO 2 fluxes to different shrub expansion rates under future emission scenarios (intermediate—RCP4.5, and high—RCP8.5) using the Arctic-focused configuration of E3SM Land Model (ELM). We focus on Trail Valley Creek (TVC), an upland tundra site in the western Canadian Arctic, which is experiencing shrub densification and expansion. We find that shrub expansion did not significantly alter the modeled surface energy and water budgets. However, the carbon balance was sensitive to shrub expansion, which drove higher rates of carbon sequestration as a consequence of higher shrubification rates. Thus, at low shrub expansion rates, the site would become a carbon source, especially under RCP8.5, due to higher temperatures, which deepen the active layer and enhance soil respiration. At higher shrub expansion rates, TVC would become a net CO 2 sink under both Representative Concentration Pathway scenarios due to higher shrub productivity outweighing temperature-driven respiration increase. Our simulations highlight the effect of shrub expansion on Arctic ecosystem carbon fluxes and stocks. We predict that at TVC, shrubification rate would interact with climate change intensity to determine whether the site would become a carbon sink or source under projected future climate.

Yazbeck, Theresia [The Ohio State Univ., Columbus,↗
DOE OSTI2021

Impoverishing Roots Will Improve Wheat Yield and Profitability Through Increased Water and Nitrogen Use Efficiencies

We report that more than a 60% increase in crop production is required by the 2050's to feed a growing world population. Understanding how plant functional traits and field management affect crop yields has the potential to improve agricultural productivity, minimize economic and environmental losses, and maximize food security. We explored the influence of winter wheat root characteristics and management on winter wheat growth, yield, and profit using a mechanistic and well-tested ecosystem and crop model, ecosys. We applied and further tested ecosys at an agricultural farm growing winter wheat in Ardmore, Oklahoma, United States. The model accurately predicted observed shoot carbon (R 2 = 0.95), soil moisture (R 2 = 0.67), soil temperature (R 2 = 0.91), and yield (percent error = 17%). Numerical optimization experiments were conducted to explore potential improvements of winter wheat yield and profit by modifying root characteristics, including root radius and root:shoot carbon transfer conductance, and fertilizer inputs. Our results show the potential for simultaneously improving winter wheat yields and profits. The optimum conditions were found to be in the range of root radius between 0.1 and 0.3 mm, carbon transfer conductance between 0.004 and 0.01 h -1 , and the currently applied fertilizer rate of 112 kg ha -1 . Under these conditions, improvements in yields and profits of up to approximately 25% and 110%, respectively, were modeled compared to those under baseline root traits. These improvements were achieved by impoverishing root structures, thereby increasing nutrient allocation to grains. Our results also demonstrate and motivate model structures that integrate the complex network of plant physiology, soil nutrient biogeochemistry, hydrology, and management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Electrical Conductivity of Clayey Rocks and Soils: A Non-Linear Model

It is well-accepted that Archie's law is only applicable to “clean” rocks and soils but fails in “dirty” ones where clay minerals possess an additional component of surface conductivity. Although several models, for example, Waxman-Smits model, were presented to account for this phenomenon, surface conductivity is always inappropriately treated as constant, which actually only holds at high salinities. The essential non-linear characteristic differing over fluid salinities has not been physically or mathematically explained well in those models. Here, we scrutinize the conduction mechanism of clayey rocks and soils and ascribe this non-linear feature to (a) variation of the electrical double layer and (b) the intrinsic clay-and-water conduction pattern. With effective medium theory, we develop an easy-to-use non-linear model that both reflects electrochemical theories and explains the measurement data well. Our model can be used to produce more accurate results for laboratory- and field-scale petrophysical parameter evaluations than the previous models.

58 GEOSCIENCES↗
DOE OSTI2020

System-level performance and degradation of 21 GWDC of utility-scale PV plants in the United States

We assess the performance of a fleet of 411 utility-scale (i.e., >5 MWAC and ground-mounted) photovoltaic (PV) projects totaling 21.1 GWDC (16.3 GWAC) of capacity, which achieved commercial operations in the United States from 2007 to 2016. This fleet of projects contributed more than 50% of all solar electricity generated in the United States in 2017. Using detailed information on individual project characteristics, in conjunction with modeled irradiance data, we assess the extent to which actual first-year performance has lived up to both modeled and stated expectations. We then analyze system-level performance degradation in subsequent years by employing a “fixed effects” regression model to statistically isolate the impact of age on system performance. We find that this fleet of utility-scale PV projects has generally lived up to ex ante expectations for first-year performance but that subsequent system-level degradation—found to be −1.3%/year (±0.2%)—has, on average, been worse than both ex ante expectations (commonly −0.5%/year) and results from past studies (ranging from −0.8%/year to −1.0%/year). We emphasize that −1.3%/year is a system-level estimate that captures more than just module degradation (e.g., including soiling, balance of plant degradation, and downtime for maintenance and/or other events). A side analysis of a variety of project characteristics suggests that system-level degradation rates tend to be of lower magnitude among newer projects and larger projects and at sites with lower long-term average temperatures.

14 SOLAR ENERGY↗