Opportunities, challenges and pitfalls in characterizing plant water-use strategies
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Environmental Sciences & Ecology↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Biomass Production with Conservation Practices for Two Iowa Watersheds
Hydrologic modeling was used to estimate potential changes in nutrients, suspended sediment, and streamflow in various biomass production scenarios with conservation practices under different landscape designs. Two major corn and soybean croplands were selected for study: the South Fork of the Iowa River watershed and the headwater of the Raccoon River watershed. A physically based model, the Soil and Water Assessment Tool, was used to simulate hydrology and water quality under different scenarios with conservation practices and biomass production. Scenarios are based on conservation practices and biomass production; riparian buffer (RB), saturated buffer, and grassed waterways; various stover harvest rates of 30%, 45%, and 70% with and without winter cover crops; and conversion of marginal land to switchgrass. Conservation practices and landscape design with different biomass feedstocks were shown to significantly improve water quality while supporting sustainable biomass production. Model results for nitrogen, phosphorus, and suspended sediments were analyzed temporally at spatial scales that varied from hydrologic response units to the entire watershed. With conservation practices, water quality could potentially improve by reducing nitrogen loads by up to 20%–30% (stover harvest with cover crop), phosphorus loads by 20%–40% (RB), and sediment loads by 30%–70% (stover harvest with cover crop and RB).
54 ENVIRONMENTAL SCIENCES↗
Wildfire smoke impacts lake ecosystems
Abstract Wildfire activity is increasing globally. The resulting smoke plumes can travel hundreds to thousands of kilometers, reflecting or scattering sunlight and depositing particles within ecosystems. Several key physical, chemical, and biological processes in lakes are controlled by factors affected by smoke. The spatial and temporal scales of lake exposure to smoke are extensive and under‐recognized. We introduce the concept of the lake smoke‐day, or the number of days any given lake is exposed to smoke in any given fire season, and quantify the total lake smoke‐day exposure in North America from 2019 to 2021. Because smoke can be transported at continental to intercontinental scales, even regions that may not typically experience direct burning of landscapes by wildfire are at risk of smoke exposure. We found that 99.3% of North America was covered by smoke, affecting a total of 1,333,687 lakes ≥10 ha. An incredible 98.9% of lakes experienced at least 10 smoke‐days a year, with 89.6% of lakes receiving over 30 lake smoke‐days, and lakes in some regions experiencing up to 4 months of cumulative smoke‐days. Herein we review the mechanisms through which smoke and ash can affect lakes by altering the amount and spectral composition of incoming solar radiation and depositing carbon, nutrients, or toxic compounds that could alter chemical conditions and impact biota. We develop a conceptual framework that synthesizes known and theoretical impacts of smoke on lakes to guide future research. Finally, we identify emerging research priorities that can help us better understand how lakes will be affected by smoke as wildfire activity increases due to climate change and other anthropogenic activities.
54 ENVIRONMENTAL SCIENCES↗
Protein Dynamics by Two-Dimensional Infrared Spectroscopy
Proteins function as ensembles of interconverting structures. The motions span from picosecond bond rotations to millisecond and longer subunit displacements. Characterization of functional dynamics on all spatial and temporal scales remains challenging experimentally. Two-dimensional infrared spectroscopy (2D IR) is maturing as a powerful approach for investigating proteins and their dynamics. We outline the advantages of IR spectroscopy, describe 2D IR and the information it provides, and introduce vibrational groups for protein analysis. We highlight example studies that illustrate the power and versatility of 2D IR for characterizing protein dynamics and conclude with a brief discussion of the outlook for biomolecular 2D IR.
Chemistry↗
A Primer on Phased Array Radar Technology for the Atmospheric Sciences
The scientific community has expressed interest in the potential of phased array radars (PARs) to observe the atmosphere with finer spatial and temporal scales. Although convergence has occurred between the meteorological and engineering communities, the need exists to increase access of PAR to meteorologists. Here, we facilitate these interdisciplinary efforts in the field of ground-based PARs for atmospheric studies. We cover high-level technical concepts and terminology for PARs as applied to studies of the atmosphere. A historical perspective is provided as context along with an overview of PAR system architectures, technical challenges, and opportunities. Envisioned scan strategies are summarized because they are distinct from traditional mechanically scanned radars and are the most advantageous for high-resolution studies of the atmosphere. Further, open access to PAR data is emphasized as a mechanism to educate the future generation of atmospheric scientists. Finally, a vision for the future of operational networks, research facilities, and expansion into complementary radar wavelengths is provided.
47 OTHER INSTRUMENTATION↗
The Surface Atmosphere Integrated Field Laboratory (SAIL) Campaign
The science of mountainous hydrology spans the atmosphere through the bedrock and inherently crosses physical and disciplinary boundaries: land-atmosphere interactions in complex terrain enhance clouds and precipitation, while watersheds retain and release water over a large range of spatial and temporal scales. Limited observations in complex terrain challenge efforts to improve predictive models of the hydrology in the face of rapid changes. The Upper Colorado River exemplifies these challenges, especially with ongoing mismatches between precipitation, snowpack, and discharge. Consequently, the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility has deployed an observatory to the East River Watershed near Crested Butte, Colorado between September 2021 and June 2023 to measure the main atmospheric drivers of water resources, including precipitation, clouds, winds, aerosols, radiation, temperature and humidity. This effort, called the Surface Atmosphere Integrated Field Laboratory (SAIL), is also working in tandem with DOE-sponsored surface and subsurface hydrologists and other federal, state, and local partners. SAIL data can be benchmarks for model development by producing a wide range of observational information on precipitation and its associated processes, including those processes that impact snowpack sublimation and redistribution, aerosol direct radiative effects in the atmosphere and in the snowpack, aerosol impacts on clouds and precipitation, and processes controlling surface fluxes of energy and mass. Preliminary data from SAIL’s first year showcase the rich information content in SAIL’s many data-streams and support testing hypotheses that will ultimately improve scientific understanding and predictability of Upper Colorado River hydrology in 2023 and beyond.
54 ENVIRONMENTAL SCIENCES↗
A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability
Stochastic reduced models are an important tool in climate systems whose many spatial and temporal scales cannot be fully discretized or underlying physics may not be fully accounted for. One form of reduced model, the linear inverse model (LIM), has been widely used for regional climate predictability studies—typically focusing more on tropical or midlatitude studies. However, most LIM fitting techniques rely on point estimation techniques deriving from fluctuation–dissipation theory. In this methodological study we explore the use of Bayesian inference techniques for LIM parameter estimation of sea surface temperature (SST), to quantify the skillful decadal predictability of Bayesian LIM models at high latitudes. We show that Bayesian methods, when compared to traditional point estimation methods for LIM-type models, provide better calibrated probabilistic skill, while simultaneously providing better point estimates due to the regularization effect of the prior distribution in high-dimensional problems. We compare the effect of several priors, as well as maximum likelihood estimates, on 1) estimating parameter values on a perfect model experiment and 2) producing calibrated 1-yr SST anomaly forecast distributions using a preindustrial control run of the Community Earth System Model (CESM). Finally, we employ a host of probabilistic skill metrics to determine the extent to which an LIM can forecast SST anomalies at high latitudes. We find that the choice of prior distribution has an appreciable impact on estimation outcomes, and priors that emphasize physically relevant properties enhance the model’s ability to capture variability of SST anomalies.
54 ENVIRONMENTAL SCIENCES↗
Hard x-ray – optical four-wave mixing using a split-and-delay line
New, hard x-ray free electron lasers (FEL) produce intense femtosecond-to-attosecond pulses at angstrom wavelengths, giving access to the fundamental spatial and temporal scales of matter. These revolutionary light sources open the door to applying the suite of nonlinear, optical spectroscopy methods at hard x-ray photon energies. Nonlinear spectroscopy with hard x-rays can allow for measuring the coherence properties of short wavelength excitations with atomic specificity and for understanding how high energy excitations couple to other degrees of freedom in atomic, molecular or condensed-phase systems. As a step in this direction, here we present hard x-ray, optical four-wave mixing (4WM) measurements done at 9.8 keV at the split-and-delay line at the x-ray correlation spectroscopy (XCS) hutch of the Linac Coherent Light Source (LCLS). In this work, we create an x-ray transient grating (TG) from a pair of crossing x-ray beams and diffract optical laser pulses at 400 nm from the TG. The key technical advance here is being able to independently vary the delays of the x-ray pulses. Measurements were made in 3 different solid samples: bismuth germinate (BGO), zinc oxide (ZnO) and yttrium aluminum garnet (YAG). The resulting phase-matched, 4WM signal is measured in two different ways: by varying the x-ray, x-ray pulse delay which can reveal both material and light source coherence properties and also by varying the optical laser delay with respect to the x-ray TG to study how the x-ray excitation couples to the optical properties. Although no coherent 4WM signal was seen in these measurements, the absence of this signal gives important information on experimental requirements for detecting this in future work. Also, our laser-delay scans, although not a new measurement, were applied to different materials than in past work and reveal new examples x-ray induced lattice dynamics in solids. This work represents a key step towards extending nonlinear optics and time-resolved spectroscopy into the hard x-ray regime.
47 OTHER INSTRUMENTATION↗
Spatiotemporal evolution of melt ponds on Arctic sea ice
Melt ponds on sea ice play an important role in the Arctic climate system. Their presence alters the partitioning of solar radiation: decreasing reflection, increasing absorption and transmission to the ice and ocean, and enhancing melt. The spatiotemporal properties of melt ponds thus modify ice albedo feedbacks and the mass balance of Arctic sea ice. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition presented a valuable opportunity to investigate the seasonal evolution of melt ponds through a rich array of atmosphere-ice-ocean measurements across spatial and temporal scales. In this study, we characterize the seasonal behavior and variability in the snow, surface scattering layer, and melt ponds from spring melt to autumn freeze-up using in situ surveys and auxiliary observations. We compare the results to satellite retrievals and output from two models: the Community Earth System Model (CESM2) and the Marginal Ice Zone Modeling and Assimilation System (MIZMAS). During the melt season, the maximum pond coverage and depth were 21% and 22 ± 13 cm, respectively, with distribution and depth corresponding to surface roughness and ice thickness. Compared to observations, both models overestimate melt pond coverage in summer, with maximum values of approximately 41% (MIZMAS) and 51% (CESM2). This overestimation has important implications for accurately simulating albedo feedbacks. During the observed freeze-up, weather events, including rain on snow, caused high-frequency variability in snow depth, while pond coverage and depth remained relatively constant until continuous freezing ensued. Both models accurately simulate the abrupt cessation of melt ponds during freeze-up, but the dates of freeze-up differ. MIZMAS accurately simulates the observed date of freeze-up, while CESM2 simulates freeze-up one-to-two weeks earlier. This work demonstrates areas that warrant future observation-model synthesis for improving the representation of sea-ice processes and properties, which can aid accurate simulations of albedo feedbacks in a warming climate.
54 ENVIRONMENTAL SCIENCES↗
Data-model files associated with the manuscript "The Effects of Spatial and Temporal Resolution of Gridded Meteorological Forcing on Watershed Hydrological Responses" (Shuai et al., 2022 HESS)
This data package contains the model inputs and outputs used in "The Effects of Spatial and Temporal Resolution of Gridded Meteorological Forcing on Watershed Hydrological Responses" (Shuai et al., 2022 HESS). The data.zip file contains the data used to drive the model simulations. The model.zip file contains the XML input file for ATS. The notebook.zip file contains the Jupyter notebooks for pre- and post- processing model results. The figures.zip file contains the raw figures associated with the manuscript.Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at high temporal (hourly to daily) and spatial resolution (10s of meters) has become efficient and computationally affordable. These hyperresolution watershed models require high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow, and distributed variables such as evapotranspiration, snow water equivalent (SWE), and groundwater table driven by three publicly available, gridded meteorological forcings (GMFs) -- Daily Surface Weather and Climatological Summaries (Daymet), Parameter-elevation Regressions on Independent Slopes Model (PRISM), and North American Land Data Assimilation System (NLDAS). By comparing various spatial resolutions (ranging from 400 m to 4 km) of PRISM, the simulated streamflow only becomes marginally worse when spatial resolution of meteorological forcing is coarsened to 4 km (or 30% of the watershed area). However, the 4 km resolution has much worse performance than finer resolution in spatially distributed variables such as SWE. Using temporally disaggregated PRISM, we compared models forced by different temporal resolutions (hourly to daily), sub-daily resolution preserves the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Conversely, the simulated streamflow shows better performance using daily resolution compared to that using sub-daily resolution. Our findings suggest that the choice of GMF and its spatiotemporal resolution depends on the quantity of interest and its spatial and temporal scale, which may have important implications on model calibration and watershed management decisions.
54 ENVIRONMENTAL SCIENCES↗
Sap flow data from San Lorenzo, Panama (PA-SLZ), 2019
This data package contains sap flow data from the NGEE Tropics site in San Lorenzo, Panama (PA-SLZ) from 22 January 2019 to 31 December 2019. Sap flow measurements quantify plant-level transpiration, which varies across spatial and temporal scales. It is a key trait in understanding plant hydraulic functioning. Here sap flow was measured using ‘Granier-type’ thermal dissipation probes. Raw data downloaded from each data logger are in the 'Raw data' folder as CSV files, along with the Campbell data logger programs which can be read when opened with a text editor. The data was processed using the AquaFlux package in R. R code for preparing the data for use in AquaFlux is in the 'R code for pre-processing data folder'; it is saved as a text file that can be copy and pasted into R. The processed data for each data logger is in the 'Processed data' folder as CSV files. Documentation for the Granier sensors and AquaFlux package is in the ‘Manuals’ folder as PDF files.
54 ENVIRONMENTAL SCIENCES↗
Tethys: A Spatiotemporal Downscaling Model for Global Water Demand
Humans use water for many important tasks, such as drinking, growing food, and cooling power plants. Since future water demands depend on complex global interactions between economic sectors (e.g., demand for wheat in one country causing demand for water to grow that wheat in another country), it is often modeled at coarse spatial and temporal scales as part of models that account for complex, multi-sector system dynamics. However, models that project future water availability typically simulate physical processes at much finer scales. Tethys enables integration between these kinds of models by downscaling region-scale water demand projections using sector-specific proxies and formulas.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
The Birth of Minerals: From Single Step to Multiple Step Mechanisms
Mineral formation from ions in aqueous solutions begins with complex initial stages, where amorphous and liquid-like precursors play pivotal roles before crystalline growth occurs. Both classical and non-classical nucleation and growth theories, introduced in previous chapters, offer explanations, each with their own strengths and limitations, for the complex intermediate phases observed in experimental research. Analytical techniques play a critical role in detecting and characterizing precursor phases, offering valuable insights into nucleation and growth mechanisms across various temporal and spatial scales. In conclusion, molecular dynamics and modelling provide in-depth perspectives on these phases, allowing for a closer examination of their nucleation and growth mechanisms at the molecular level, and revealing the intricate processes that govern their behaviour.
amorphous↗
Kinetic effects of self-organization in low-temperature magnetized plasmas (Final Report for DE-SC0019045: 9/1/2018-8/31/2019)
The research goal of this Early Career Research Program project is to advance the science of low temperature magnetized plasmas using first-principles kinetic, e.g., solving the Boltzmann equation, models. A better understanding of the plasma transport properties, such as electrical resistivity and thermal conductivity must be obtained to better control and design magnetized plasma sources. A key physical phenomenon in low-temperature magnetized plasmas is the self-organization of plasma flows, e.g., coherent plasma structures and transition between different discharge modes. Understanding the physics of coherent plasma structures has been identified as one of the four important frontier topics in a recent workshop by the Office of Fusion Energy Science. These self-emerging plasma patterns results from the nonlinear coupling between plasma constituents (ions, electrons, and neutral atoms), electromagnetic fields, and materials. The physics of low-temperature magnetized plasmas is particularly complex because (i) the collisionless instabilities affect collisional phenomena (ionization, transport, etc.) and vice versa and (ii) the presence of materials (sheath, electron injection, etc.) influences the bulk plasma properties in low-temperature plasma (LTP) devices. Computational models of these plasma flows remain challenging because the plasma density can vary several orders of magnitudes, a wide range of temporal and spatial scales must be resolved, the phenomena are inherently three-dimensional, and various physical processes, such as wall interaction, instabilities, inelastic collisions, etc., must be simultaneously and self-consistently taken into account. Understanding the fundamental transport mechanisms in cross-field configurations enables additional controllability of plasma properties and electron energy distribution functions (EEDFs).
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Functional, Hierarchical Colloidal Liquid Crystal Gels and Liquid Crystal Elastomers Nanocomposites. Final Report
Biological systems rely on a hierarchy of dynamic, complex and reconfigurable compartments to carry out functions essential for life. This hierarchical organization serves as an amplifier that allows highly localized, molecular events to propagate into the mesoscale, resulting in dynamic functional properties of biological systems that have not yet been fully realized in synthetic material designs. We pursued a program of research in which we recreated such principles using synthetic liquid crystals (LC) as a versatile platform. We elucidated new hierarchical design strategies that heavily leverage surface anchoring, coexisting phases and interfacial tension gradient to realize equilibrium and non-equilibrium, dynamic mesoscale phenomena in the context of micrometer-sized LC droplets containing multiple compartments. Specifically, we unmasked how equilibrium and dynamical phenomena emerged from hierarchical organizations in reconfigurable, thermotropic liquid crystalline assemblies. The long-term impact of the project is to advance new hierarchical designs of complex soft matter systems in which biomimetic principles, including the propagation of events or information over multiple temporal and spatial scales, are enacted in a facile manner.
36 MATERIALS SCIENCE↗
Laser-Produced Coherent X-ray Sources. Final report
This is the final report for the DOE-AMO DE-FG02-05ER15663 grant. Using a high-peak-power laser system at the Extreme Light Laboratory of the University of Nebraska-Lincoln, we develop novel femtosecond radiation sources and use them to investigate ultrafast photo-induced processes. Pulses of high energy electrons and x-rays are generated when an optical pulse is focused to ultra-high intensity. The radiation is used to probe the evolution of matter under highly non-equilibrium conditions with atomic-scale temporal and spatial resolution. Moreover, the facility is small enough to fit in a university laboratory and is operated by graduate students and postdocs.
74 ATOMIC AND MOLECULAR PHYSICS↗
Learning from learning machines: improving the predictive power of energy-water-land nexus models with insights from complex measured and simulated data
Focal Area(s): Insights gleaned from complex data (observed and simulated) using AI; Predictive modeling through the use of AI techniques and AI-derived model components, including physics- and knowledge-informed models; Energy-water-land nexus and integrated energy systems – models of MultiSector dynamics. Science Challenge: Scientific communities are in need of tools for the computational integration of physics-based models, experimental data, and empirical/observational studies across a broad range of temporal and spatial scales to explore and meet EESSD grand challenges. We require AI algorithms for the discovery of process-drivers in the Earth-energy-human system and to link them with complex measured and simulated data. We aim to understand the energy-water-land nexus, particularly under extreme forcing scenarios and rare events, leveraging scale-aware AI process models, including probabilistic uncertainties, and benefiting from the emerging 5G-enabled landscape-scale sensing and edge computing capabilities. These models are needed to identify instabilities and tipping points that manifest extreme system behaviors with consequences for integrated energy systems and the environment. This work requires fundamental advances in uncertainty quantification, in particular, to identify and model unlikely but catastrophic outliers. Specifically, we need models that are interpretable to domain scientists and, essentially, explainable to public and private stake holders.
54 ENVIRONMENTAL SCIENCES↗
Predictive Understanding of Compound and Cascading Extremes and Their Impacts
We seek to answer the following questions: What interactions across spatial and temporal scales drive multivariate compound and cascading water cycle extremes, and how can we leverage understanding of such interactions to improve predictive understanding of these events and quantification of their impacts?
54 ENVIRONMENTAL SCIENCES↗