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DOE OSTI2023

Detecting Permafrost Active Layer Thickness Change From Nonlinear Baseflow Recession

We report permafrost underlies about one fifth of the global land area and affects ground stability, freshwater runoff, soil chemistry, and surface-atmosphere gas exchange. The depth of thawed ground overlying permafrost (active layer thickness) has broadly increased across the Arctic in recent decades, coincident with a period of increased streamflow, especially the lowest flows (baseflow). Mechanistic links between active layer thickness and baseflow have recently been explored using linear reservoir theory, but most watersheds behave as nonlinear reservoirs. We derive theoretical nonlinear relationships between long-term average saturated soil thickness $\bar{η}$(proxy for active layer thickness) and long-term average baseflow. When applied to 38 years of daily streamflow data for the Kuparuk River basin on the North Slope of Alaska, the theory predicts $\bar{η}$ increased 0.17 ± 0.22[2σ] cm a -1 between 1983 and 2020 (6.4 ± cm total). The rate of increase nearly doubled to 0.29 ± 0.31 cm a -1 between 1990 and 2020, during which time local field measurements from Circumpolar Active Layer Monitoring sites indicate the active layer increased 0.31 ± 0.22 cm a -1 . The predicted rate of increase more than doubled again between 2002 and 2020, outpacing a near doubling of observed active layer thickening, consistent with trends in terrestrial water storage inferred from Gravity Recovery and Climate Experiment satellite gravimetry and Modern-Era Retrospective Analysis for Research and Applications climate reanalysis. Overall, hydrologic change is accelerating in the Kuparuk River basin, and we provide a theoretical framework for estimating basin-scale changes in active layer water storage from streamflow measurements.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2020

230Th Normalization: New Insights on an Essential Tool for Quantifying Sedimentary Fluxes in the Modern and Quaternary Ocean

230Th normalization is a valuable paleoceanographic tool for reconstructing high‐resolution sediment fluxes during the late Pleistocene (last ~500,000 years). As its application has expanded to ever more diverse marine environments, the nuances of 230Th systematics, with regard to particle type, particle size, lateral advective/diffusive redistribution, and other processes, have emerged. We synthesized over 1000 sedimentary records of 230Th from across the global ocean at two time slices, the late Holocene (0–5,000 years ago, or 0–5 ka) and the Last Glacial Maximum (18.5–23.5 ka), and investigated the spatial structure of 230Th‐normalized mass fluxes. On a global scale, sedimentary mass fluxes were significantly higher during the Last Glacial Maximum (1.79–2.17 g/sq.cmkyr, 95% confidence) relative to the Holocene (1.48–1.68 g/sq.cmkyr, 95% confidence). We then examined the potential confounding influences of boundary scavenging, nepheloid layers, hydrothermal scavenging, size‐dependent sediment fractionation, and carbonate dissolution on the efficacy of 230Th as a constant flux proxy. Anomalous 230Th behavior is sometimes observed proximal to hydrothermal ridges and in continental margins where high particle fluxes and steep continental slopes can lead to the combined effects of boundary scavenging and nepheloid interference. Notwithstanding these limitations, we found that 230Th normalization is a robust tool for determining sediment mass accumulation rates in the majority of pelagic marine settings (>1,000 m water depth).

Costa, Kassandra M.↗

NASA NTRS2007

A First Approach to Global Runoff Simulation using Satellite Rainfall Estimation

Many hydrological models have been introduced in the hydrological literature to predict runoff but few of these have become common planning or decision-making tools, either because the data requirements are substantial or because the modeling processes are too complicated for operational application. On the other hand, progress in regional or global rainfall-runoff simulation has been constrained by the difficulty of measuring spatiotemporal variability of the primary causative factor, i.e. rainfall fluxes, continuously over space and time. Building on progress in remote sensing technology, researchers have improved the accuracy, coverage, and resolution of rainfall estimates by combining imagery from infrared, passive microwave, and space-borne radar sensors. Motivated by the recent increasing availability of global remote sensing data for estimating precipitation and describing land surface characteristics, this note reports a ballpark assessment of quasi-global runoff computed by incorporating satellite rainfall data and other remote sensing products in a relatively simple rainfall-runoff simulation approach: the Natural Resources Conservation Service (NRCS) runoff Curve Number (CN) method. Using an Antecedent Precipitation Index (API) as a proxy of antecedent moisture conditions, this note estimates time-varying NRCS-CN values determined by the 5-day normalized API. Driven by multi-year (1998-2006) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis, quasi-global runoff was retrospectively simulated with the NRCS-CN method and compared to Global Runoff Data Centre data at global and catchment scales. Results demonstrated the potential for using this simple method when diagnosing runoff values from satellite rainfall for the globe and for medium to large river basins. This work was done with the simple NRCS-CN method as a first-cut approach to understanding the challenges that lie ahead in advancing the satellite-based inference of global runoff. We expect that the successes and limitations revealed in this study will lay the basis for applying more advanced methods to capture the dynamic variability of the global hydrologic process for global runoff monlto~ngin real time. The essential ingredient in this work is the use of global satellite-based rainfall estimation.

Hong, Yang↗

NASA NTRS2017

Online Multi-Modal Learning and Adaptive Information Trajectory Planning for Autonomous Exploration

In robotic information gathering missions, scientists are typically interested in understanding variables which require proxy measurements from specialized sensor suites to estimate. However, energy and time constraints limit how often these sensors can be used in a mission. Robots are also equipped with cheaper to use navigation sensors such as cameras. In this paper, we explore a challenging planning problem in which a robot is required to learn about a scientific variable of interest in an initially unknown environment by planning informative paths and deciding when and where to use its sensors. To tackle this we present two innovations: a Bayesian generative model framework to automatically learn correlations between expensive science sensors and cheaper to use navigation sensors online, and a sampling based approach to plan for multiple sensors while handling long horizons and budget constraints. Our approach does not grow in complexity with data and is anytime making it highly applicable to field robotics. We tested our approach extensively in simulation and validated it with real data collected during the 2014 Mojave Volatiles Prospector Mission. Our planning algorithm performs statistically significantly better than myopic approaches and at least as well as a coverage-based algorithm in an initially unknown environment while having added advantages of being able to exploit prior knowledge and handle other intricacies of the real world without further algorithmic modifications.

learning↗

NASA NTRS1994

EUVAC: A solar EUV flux model for aeronomic calculations

This paper presents a new solar Extreme Ultraviolet (EUV) flux model for aeronomic calulations (EUVAC), which is based on the measured F74113 solar EUV reference spectrum. The model provides fluxes in the 37 wavelength bins that are in widespread use. This paper also presents cross sections to be used with the EUVAC flux model to calculate photoionization rates. The flux scaling for solar activity is accomplished using a proxy-based on the F10.7 index and its 81-day average together with the measured solar flux variation from the EUVS instrument on the Atmosphere Explorer E satellite. This new model produces 50-575 A integrated EUV fluxes in good agreement with rocket observations. The solar cycle variation of the chromospheric fluxes agrees well with the measured variation of the Lyman alpha flux between 1982 and 1988. In addition, the theoretical photoelectron fluxes, calculated using the new EUV flux model, are in good agreement with the solar minimum photoelectron fluxes from the Atmosphere Explorer E satellite and also with the solar maximum photoelectron fluxes from the Dynamics Explorer satellite. Its relative simplicity coupled with its ability to reproduce the 50-575 A solar EUV flux as well as the measured photoelectron spectrum makes the model well suited for aeronomic applications. However, EUVAC is not designed to accurately predict the solar flux variability for numerous individual lines.

Richards, P. G.↗

DOE OSTI2017

Pececillo

Pececillo is a proxy-app for the open source Truchas metal processing code (LA-CC-15-097). It implements many of the physics models used in Truchas: free-surface, incompressible Navier-Stokes fluid dynamics (e.g., water waves); heat transport, material phase change, view factor thermal radiation; species advection-diffusion; quasi-static, elastic/plastic solid mechanics with contact; electomagnetics (Maxwell's equations). The models are simplified versions that retain the fundamental computational complexity of the Truchas models while omitting many non-essential features and modeling capabilities. The purpose is to expose Truchas algorithms in a greatly simplified context where computer science problems related to parallel performance on advanced architectures can be more easily investigated. While Pececillo is capable of performing simulations representative of typical Truchas metal casting, welding, and additive manufacturing simulations, it lacks many of the modeling capabilites needed for real applications.

Jibben, Zechariah↗

DOE OSTI2016

Redshift Measurement and Spectral Classification for eBoss Galaxies with the Redmonster Software

“Cosmological redshift surveys” are experiments conducted with astronomical telescopes, imagers, and spectrographs, which map the three-dimensional structure of the universe on the largest scales. These maps are delineated by the positions of galaxies, quasars, and intergalactic hydrogen clouds. When interpreted in the context of Einstein’s theory of gravity, these maps can be used to infer the nature of the contents of the universe, including the mysterious “dark energy” that is driving the expansion of the universe to accelerate. While the directional positions of galaxies and other objects can be measured directly in images of the sky, the third dimension of their position (i.e., their distance from the Earth and the Milky Way Galaxy) must be measured by spectrographs that distribute their light as a function of frequency, enabling a measurement of their cosmological Doppler shift (or “redshift”), which serves as an observable proxy for distance. The largest cosmological redshift surveys, such as the “eBOSS” experiment of the fourth Sloan Digital Sky Survey, collect spectroscopic data for hundreds of thousands to millions of galaxies. Future experiments such as the Dark Energy Spectroscopic Instrument will in turn collect tens of millions of spectra. To be feasible, redshift measurement methods in datasets of this scale must be made with automated software. This paper describes the algorithms, astrophysical templates, and implementation of a new redshift measurement software package that is optimized to run on large numbers of spectra with relatively low signal-to-noise ratio, typical of the most ambitious current and future cosmological redshift surveys. The software is demonstrated on spectroscopic data from the eBOSS survey, with performance that meets the scientific requirements of that experiment. The software is implemented in a general framework that will allow application to spectra from the DESI project in the future.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

Pececillo

Carlson, Neil↗

DOE OSTI2018

Small-scale characterization of vine plant root water uptake via 3-D electrical resistivity tomography and mise-à-la-masse method

The investigation of plant roots is inherently difficult and often neglected.Being out of sight, roots are often out of mind. Nevertheless, roots play a key rolein the exchange of mass and energy between soil and the atmosphere, in addition tothe many practical applications in agriculture. In this paper, we propose amethod for roots imaging based on the joint use of two electricalnoninvasive methods: electrical resistivity tomography (ERT) andmise-à-la-masse (MALM). The approach is based on the key assumption that theplant root system acts as an electrically conductive body, so that injectingelectrical current into the plant stem will ultimately result in the injectionof current into the subsoil through the root system, and particularly throughthe root terminations via hair roots. Evidence from field data, showing thatvoltage distribution is very different whether current is injected into thetree stem or in the ground, strongly supports this hypothesis. The proposedprocedure involves a stepwise inversion of both ERT and MALM data thatultimately leads to the identification of electrical resistivity (ER)distribution and of the current injection root distribution in thethree-dimensional soil space. This, in turn, is a proxy to the active (hair)root density in the ground. We tested the proposed procedure on syntheticdata and, more importantly, on field data collected in a vineyard, where theestimated depth of the root zone proved to be in agreement with literature onsimilar crops. The proposed noninvasive approach is a step forward towards abetter quantification of root structure and functioning.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2021

Highland Lakes Water Resources: Using NASA Earth Observations to Improve Detection Systems for Harmful Algal Events in the Highland Lakes in Central Texas

Beginning in 2019, harmful algal events in Austin, Texas, caused canine deaths in the Lady Bird Lake and Lake Travis reservoirs. These reservoirs are part of the larger Highland Lakes chain, managed by the Lower Colorado River Authority (LCRA) and the City of Austin Department of Watershed Protection (CoA DWP), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable algal event conditions in central Texas, the LCRA and CoA DWP partnered with NASA DEVELOP to improve algal event early-warning systems through the application of remote sensing and machine learning. An Earth observation-based algal monitoring system will assist the responsible agencies in predicting algal conditions and communicating hazards to the public. The NASA DEVELOP team utilized Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data to produce products including chlorophyll-a concentrations, cyanobacteria detections, turbidity, and water surface temperature. Chlorophyll-a concentrations were retrieved with a pre-trained machine learning model (mixture density network) and spectral indices, while the other products were derived from spectral indices. In situ field data were used to validate and quantify uncertainties for each product. The validations show strong correlations for chlorophyll-a and water surface temperature. Time series analyses of chlorophyll-a concentrations show peaks in the severe drought years (2015 and 2016). This project's resulting products enable monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain and will ultimately support water management, decision making, and risk communication.

Kaitlynn Hietpas↗

DOE OSTI2020

Harvesting the low-hanging fruit of high energy savings -- Virtual Occupancy using Wi-Fi Data

Approximately 20% of primary energy consumed in the U.S. is attributed to HVAC use. Ideally, HVAC operation would be driven by actual building occupancy, but lack of reliable occupancy information often results in the use of conservative static schedules. This disparity is even more pronounced in a college campus, where the function of each space differs by building (classrooms, offices, libraries) and the class schedules change frequently -- every semester, day of week, and hour. While several research papers propose the use of counts of the Wi-Fi connections (e.g., phones, computers) as a proxy for occupancy, few real-world implementations exist. This paper describes the development and deployment of an open-source Wi-Fi-to-Occupancy software library in 65 buildings of a college campus, and the planned integration with the building energy management and control system at the building scale. Over a year of Wi-Fi data was gathered into distinct academic periods, including fall and spring semester, academic breaks, and summer sessions. Patterns such as students moving between classrooms, closing laptops before exams, etc., can be visualized from the data. Approximating occupancy from Wi-Fi data presents challenges which we address in this project -- for example, identifying static devices, or estimating the ratio of devices per person. Utilizing real-time occupancy data to inform optimal HVAC schedules and ventilation rates creates the potential to identify and reduce energy waste. Other potential applications include forecasting occupancy, and using Wi-Fi data to predict peak demands. Finally, the paper discusses how to easily scale these tools to other buildings.

Pritoni, Marco↗

DOE OSTI2017

High-Dimensional Intrinsic Interpolation Using Gaussian Process Regression and Diffusion Maps

This article considers the challenging task of estimating geologic properties of interest using a suite of proxy measurements. The current work recast this task as a manifold learning problem. In this process, this article introduces a novel regression procedure for intrinsic variables constrained onto a manifold embedded in an ambient space. The procedure is meant to sharpen high-dimensional interpolation by inferring non-linear correlations from the data being interpolated. The proposed approach augments manifold learning procedures with a Gaussian process regression. It first identifies, using diffusion maps, a low-dimensional manifold embedded in an ambient high-dimensional space associated with the data. It relies on the diffusion distance associated with this construction to define a distance function with which the data model is equipped. This distance metric function is then used to compute the correlation structure of a Gaussian process that describes the statistical dependence of quantities of interest in the high-dimensional ambient space. The proposed method is applicable to arbitrarily high-dimensional data sets. Here, it is applied to subsurface characterization using a suite of well log measurements. The predictions obtained in original, principal component, and diffusion space are compared using both qualitative and quantitative metrics. Considerable improvement in the prediction of the geological structural properties is observed with the proposed method.

58 GEOSCIENCES↗

DOE OSTI2023

Examining Infrasound Propagation at High Spatial Resolution Using a Nodal Seismic Array

Infrasound—acoustic waves in the atmosphere below 20 Hz—is a useful monitoring tool. Topography and atmospheric structure strongly control infrasound propagation, and at common source–receiver distances neither of these effects can be ignored when quantitative source constraints are sought. Detailed spatial measurements of the infrasound wavefield would inform propagation models and improve source estimates. However, the “large-N” deployment strategy now well-known in seismology has not yet been realized for infrasound studies. Here, we use the 900-node seismic array from the 2014 Imaging Magma Under St. Helens (iMUSH) experiment as a proxy for a large-N infrasound network, by leveraging acoustic–seismic coupled arrivals. The active-source component of iMUSH consisted of 23 shallowly buried explosions around Mount Saint Helens volcano; these explosions produced epicentral infrasound recorded on the nodes. We find that the bulk presence of ground-coupled infrasound on the nodes is controlled by wind noise and source–receiver distance, with observed arrivals for eight explosions. Explosions with the most extensive coupling produce complex spatial waveform patterns across the array. These patterns are related to both topographic and atmospheric propagation effects, as well as spatially variable site (coupling) effects. We compare our observations to simple topographic diffraction and high-resolution wind advection models, and full-wave numerical simulations. We find strong spatial correlations between (a) coupled arrival strength and modeled topographic obstruction and (b) coupled arrival time and along-path winds. Our seismoacoustic analyses and results are applicable to other existing and future nodal seismic data sets and can expand the utility of such deployments.

58 GEOSCIENCES↗

DOE OSTI2020

Diagnosing the sensitivity of grounding-line flux to changes in sub-ice-shelf melting

Abstract. Using a numerical ice flow model, we study changes in ice shelf buttressing and grounding-line flux due to localized ice thickness perturbations, a proxy for localized changes in sub-ice-shelf melting. From our experiments, applied to idealized (MISMIP+) and realistic (Larsen C) ice shelf domains, we identify a correlation between a locally derived buttressing number on the ice shelf, based on the first principal stress, and changes in the integrated grounding-line flux. The origin of this correlation, however, remains elusive from the perspective of a theoretical or physically based understanding. This and the fact that the correlation is generally much poorer when applied to realistic ice shelf domains motivate us to seek an alternative approach for predicting changes in grounding-line flux. We therefore propose an adjoint-based method for calculating the sensitivity of the integrated grounding-line flux to local changes in ice shelf geometry. We show that the adjoint-based sensitivity is identical to that deduced from pointwise, diagnostic model perturbation experiments. Based on its much wider applicability and the significant computational savings, we propose that the adjoint-based method is ideally suited for assessing grounding-line flux sensitivity to changes in sub-ice-shelf melting.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2018

NASA's GMAO Atmospheric Motion Vectors Simulator: Description and Application to the MISTiC Winds Concept

An atmospheric wind vectors (AMVs) simulator was developed by NASA's GMAO to simulate observations from future satellite constellation concepts. The synthetic AMVs can then be used in OSSEs to estimate and quantify the potential added value of new observations to the present Earth observing system and, ultimately, the expected impact on the current weather forecasting skill. The GMAO AMV simulator is a tunable and flexible computer code that is able to simulate AMVs expected to be derived from different instruments and satellite orbit configurations. As a case study and example of the usefulness of this tool, the GMAO AMV simulator was used to simulate AMVs envisioned to be provided by the MISTiC Winds, a NASA mission concept consisting of a constellation of satellites equipped with infrared spectral midwave spectrometers, expected to provide high spatial and temporal resolution temperature and humidity soundings of the troposphere that can be used to derive AMVs from the tracking of clouds and water vapor features. The GMAO AMV simulator identifies trackable clouds and water vapor features in the G5NR and employs a probabilistic function to draw a subset of the identified trackable features. Before the simulator is applied to the MISTiC Winds concept, the simulator was calibrated to yield realistic observations counts and spatial distributions and validated considering as a proxy instrument to the MISTiC Winds the Himawari-8 Advanced Imager (AHI). The simulated AHI AMVs showed a close match with the real AHI AMVs in terms of observation counts and spatial distributions, showing that the GMAO AMVs simulator synthesizes AMVs observations with enough quality and realism to produce a response from the DAS equivalent to the one produced with real observations. When applied to the MISTiC Winds scanning points, it can be expected that the MISTiC Winds will be able to collect approximately 60,000 wind observations every 6 hours, if considering a constellation composed of 12 satellites (4 orbital planes). In addition, one of the main expected impacts of the MISTiC Winds concept is the ability to derive water vapor feature tracking AMVs below 500-400 hPa, an unique feature among the water vapor AMVs derived from the current Earth observing system.

Carvalho, David↗

NASA NTRS2018

NASA's GMAO Atmospheric Motion Vectors Simulator: Description and Application to the MISTiC Winds Concept

Carvalho, David↗

NASA NTRSDate not supplied

Illustrating the Spatiotemporal Complexity of No2 Columns Using A Multi-Perspective Observing System: Moving Toward Geostationary Product Validation and Applications

As a precursor to secondary pollutants like ozone and PM2.5, nitrogen dioxide (NO2) is crucial to understand when addressing air quality issues. However, due to NO2’s short lifetime during the daytime and complexity of emission sources in urbanized regions, interpreting datasets from ground or satellite perspectives alone are challenged by variance in spatial and temporal resolutions. High resolution airborne mapping (< 1 km) of NO2 column densities across morning, midday, and afternoon add a unique perspective toward interpreting satellite data with respect to ground-measurements. This presentation focuses on the interpretation of spatiotemporal complexity of NO2 columns from the Synergistic TEMPO Air Quality Science Study (STAQS). The mission’s goal is to integrate geostationary observations from Tropospheric Emissions: Monitoring of Pollution (TEMPO) with traditional and enhanced air quality monitoring to improve the understanding of air quality science for increased societal benefit. We will demonstrate the interweaved perspective of NO2 columns from ground-based Pandora spectrometers and satellite-based observations (e.g., TROPOMI) as compared to high spatial resolution airborne observations from the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS). This includes the evaluation of each dataset through comparison to each other to identify potential biases in data products and the impact of heterogeneity on these comparisons. Airborne data will also be used as a proxy for geostationary observations with morning, midday, and afternoon raster maps collected over four cities (Los Angeles, Chicago, Toronto, and New York City). Finally, recent research outcomes will be presented to demonstrate how airborne and geostationary observations can be used to evaluate emission inventories and air quality models.

Laura Judd↗

DOE OSTI2016

A butterfly diagram and carrington maps for century-long Ca II K spectroheliograms from the Kodaikanal observatory

The century-long (1907–2007) Ca ii K spectroheliograms from the Kodaikanal Solar Observatory (KSO) are calibrated, processed, and analyzed to follow the evolution of the bright on-disc structures called plages, possible representatives of magnetic activity on the Sun. This is the longest data set studied in Ca ii K to date, covering about 9.5 cycles of 11 yr periods. Plages are segmented with area ⩾1 arcmin{sup 2} using global thresholds for individual full disc images and subsequent application of a morphological closing operation. The plage index is calculated and is seen to have a close positive correlation with the fractional disc area covered by plages. The newly generated plage area cycle (from KSO) was compared with the same from the Mount Wilson Observatory (correlation 95.6%) for the overlapping years, i.e., 1915–2000. This study illustrates the time–latitude distribution of plage centroids by rendering a butterfly diagram (as observed for sunspots). The 3D visualization of the diagram shows one-to-one mapping between plage location, time, and area. This work further delineates the positional correlation between magnetic patches and plage regions through the comparison of synoptic maps derived from both KSO Ca ii K images and space-based full disc line-of-sight magnetograms. Regular synoptic magnetograms from ground-based observatories are available only after 1970s. Thus the long term Ca ii K data from KSO can be used as a proxy for estimating magnetic activity locations and their strengths at earlier times.

79 ASTRONOMY AND ASTROPHYSICS↗