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DOE OSTI2024

Building Stock Models for Embodied Carbon Emissions—A Review of a Nascent Field

Building stock modeling emerges as a critical tool in the strategic reduction of embodied carbon emissions, which is pivotal in reshaping the evolving construction sector. This review provides an overall view of modern methodologies in building stock modeling, homing in on the nuances of embodied carbon analysis in construction. Examining 23 seminal papers, our study delineates two primary modeling paradigms—top-down and bottom-up—each further compartmentalized into five innovative methods. This study points out the challenges of data scarcity and computational demands, advocating for methodological advancements that promise to refine the precision of building stock models. A groundbreaking trend in recent research is the incorporation of machine learning algorithms, which have demonstrated remarkable capacity, improving stock classification accuracy by 25% and urban material quantification by 40%. Furthermore, the application of remote sensing has revolutionized data acquisition, enhancing data richness by a factor of five. This review offers a critical examination of current practices and charts a course toward an environmentally prudent future. It underscores the transformative impact of building stock modeling in driving ecological stewardship in the construction industry, positioning it as a cornerstone in the quest for sustainability and its significant contribution toward the grand vision of an eco-efficient built environment.

Hu, Ming (ORCID:0000000325831161)↗

DOE OSTI2024

An Evaluation of Sustainable Power System Resilience in the Face of Severe Weather Conditions and Climate Changes: A Comprehensive Review of Current Advances

Natural disasters pose significant threats to power distribution systems, intensified by the increasing impacts of climate changes. Resilience-enhancement strategies are crucial in mitigating the resulting social and economic damages. Hence, this review paper presents a comprehensive exploration of weather management strategies, augmented by recent advancements in machine learning algorithms, to show a sustainable resilience assessment. By addressing the unique challenges posed by diverse weather conditions, we propose flexible and intelligent solutions to navigate disaster complications effectively. This proposition emphasizes sustainable practices that not only address immediate disaster complications, but also prioritize long-term resilience and adaptability. Furthermore, the focus extends to mitigation strategies and microgrid technologies adapted to distribution systems. Through statistical analysis and mathematical formulations, we highlight the critical role of these advancements in mitigating severe weather conditions and ensuring the system reliability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Reclassification of ASFV into 7 Biotypes Using Unsupervised Machine Learning

In 2007, an outbreak of African swine fever (ASF), a deadly disease of domestic swine and wild boar caused by the African swine fever virus (ASFV), occurred in Georgia and has since spread globally. Historically, ASFV was classified into 25 different genotypes. However, a newly proposed system recategorized all ASFV isolates into 6 genotypes exclusively using the predicted protein sequences of p72. However, ASFV has a large genome that encodes between 150–200 genes, and classifications using a single gene are insufficient and misleading, as strains encoding an identical p72 often have significant mutations in other areas of the genome. We present here a new classification of ASFV based on comparisons performed considering the entire encoded proteome. A curated database consisting of the protein sequences predicted to be encoded by 220 reannotated ASFV genomes was analyzed for similarity between homologous protein sequences. Weights were applied to the protein identity matrices and averaged to generate a genome-genome identity matrix that was then analyzed by an unsupervised machine learning algorithm, DBSCAN, to separate the genomes into distinct clusters. We conclude that all available ASFV genomes can be classified into 7 distinct biotypes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

PREDICTING CORONAL MASS EJECTIONS USING MACHINE LEARNING METHODS

Of all the activity observed on the Sun, two of the most energetic events are flares and coronal mass ejections (CMEs). Usually, solar active regions that produce large flares will also produce a CME, but this is not always true. Despite advances in numerical modeling, it is still unclear which circumstances will produce a CME. Therefore, it is worthwhile to empirically determine which features distinguish flares associated with CMEs from flares that are not. At this time, no extensive study has used physically meaningful features of active regions to distinguish between these two populations. As such, we attempt to do so by using features derived from (1) photospheric vector magnetic field data taken by the Solar Dynamics Observatory ’s Helioseismic and Magnetic Imager instrument and (2) X-ray flux data from the Geostationary Operational Environmental Satellite’s X-ray Flux instrument. We build a catalog of active regions that either produced both a flare and a CME (the positive class) or simply a flare (the negative class). We then use machine-learning algorithms to (1) determine which features distinguish these two populations, and (2) forecast whether an active region that produces an M- or X-class flare will also produce a CME. We compute the True Skill Statistic, a forecast verification metric, and find that it is a relatively high value of ∼0.8 ± 0.2. We conclude that a combination of six parameters, which are all intensive in nature, will capture most of the relevant information contained in the photospheric magnetic field.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Characterization of Near-Earth Asteroids Using KMTNET-SAAO

We present here VRI spectrophotometry of 39 near-Earth asteroids (NEAs) observed with the Sutherland, South Africa, node of the Korea Microlensing Telescope Network (KMTNet). Of the 39 NEAs, 19 were targeted, but because of KMTNet’s large 2° × 2° field of view, 20 serendipitous NEAs were also captured in the observing fields. Targeted observations were performed within 44 days (median: 16 days, min: 4 days) of each NEA’s discovery date. Our broadband spectrophotometry is reliable enough to distinguish among four asteroid taxonomies and we were able to confidently categorize 31 of the 39 observed targets as either an S-, C-, X-, or D-type asteroid by means of a Machine Learning algorithm approach. Our data suggest that the ratio between “stony” S-type NEAs and “not-stony” (C+X+D)-type NEAs, with H magnitudes between 15 and 25, is roughly 1:1. Additionally, we report ∼1 hr light curve data for each NEA, and of the 39 targets, we were able to resolve the complete rotation period and amplitude for six targets and report lower limits for the remaining targets.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

The Clustering of High-redshift (2.9 ≤ $z$ ≤ 5.1) Quasars in SDSS Stripe 82

We present a measurement of the two-point autocorrelation function of photometrically selected high-$z$ quasars over ~100 deg 2 on the Sloan Digital Sky Survey Stripe 82 field. Selection is performed using three machine-learning algorithms in a six-dimensional optical/mid-infrared color space. Optical data from the Sloan Digital Sky Survey are combined with overlapping deep mid-infrared data from the $Spitzer$ IRAC Equatorial Survey and the $Spitzer$-HETDEX Exploratory Large-Area survey. Our selection algorithms are trained on the colors of known high-$z$ quasars. The selected quasar sample consists of 1378 objects and contains both spectroscopically confirmed quasars and photometrically selected quasar candidates. These objects span a redshift range of 2.9 ≤ $z$ ≤ 5.1 and are generally fainter than $i$ = 20.2, a regime that has lacked sufficient number density to perform autocorrelation function measurements of photometrically classified quasars. We compute the angular correlation function of these data, marginally detecting quasar clustering. We fit a single power law with an index of $δ$ = 1.39 ± 0.618 and amplitude of $θ_0$ = 0$^{\prime}_{.}$71 ± 0$^{\prime}_{.}$546 . A dark matter model is fit to the angular correlation function to estimate the linear bias. At the average redshift of our survey ($\langle z\rangle =3.38$), the bias is $b$ = 6.78 ± 1.79. Using this bias, we calculate a characteristic dark matter halo mass of 1.70–9.83$\times {10}^{12}{h}^{-1}\,{M}_{\odot }$. Our bias estimate suggests that quasar feedback intermittently shuts down the accretion of gas onto the central supermassive black hole at early times. If confirmed, these results hint at a level of luminosity dependence in the clustering of quasars at high-$z$.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Identifying the 3FHL Catalog. I. Archival Swift Observations and Source Classification

We present the results of an identification campaign of unassociated sources from the Fermi Large Area Telescope 3FHL catalog. Out of 200 unidentified sources, we selected 110 sources for which archival Swift-XRT observations were available, 52 of which were found to have exactly one X-ray counterpart within the 3FHL 95% positional uncertainty. In this work, we report the X-ray, optical, IR, and radio properties of these 52 sources using positional associations with objects in various catalogs. The Wide-field Infrared Survey Explorer color–color plot for sources suggests that most of these belong to the blazar class family. The redshift measurements for these objects range from z = 0.277 to z = 2.1. Additionally, under the assumption that the majority of these sources are blazars, three machine-learning algorithms are employed to classify the sample into flat spectrum radio quasars or BL Lacertae objects. Finally, these suggest that the majority of the previously unassociated sources are BL Lac objects, in agreement with the fact the BL Lac objects represent by far the most numerous population detected above 10 GeV in 3FHL.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

The Circular Velocity Curve of the Milky Way from 5–25 kpc Using Luminous Red Giant Branch Stars

We present a sample of 254,882 luminous red giant branch (LRGB) stars selected from the APOGEE and LAMOST surveys. By combining photometric and astrometric information from the Two Micron All Sky Survey and Gaia survey, the precise distances of the sample stars are determined by a supervised machine-learning algorithm: the gradient-boosted decision trees. To test the accuracy of the derived distances, member stars of globular clusters (GCs) and open clusters are used. The tests by cluster member stars show a precision of about 10% with negligible zero-point offsets, for the derived distances of our sample stars. The final sample covers a large volume of the Galactic disk(s) and halo of 0 < R < 30 kpc and |Z| ≤ 15 kpc. The rotation curve (RC) of the Milky Way across the radius of 5 ≲ R ≲ 25 kpc has been accurately measured with ~54,000 stars of the thin disk population selected from the LRGB sample. The derived RC shows a weak decline along R with a gradient of -1.83 ± 0.02 (stat.) ± 0.07 (sys.) km s -1 kpc -1 , in excellent agreement with the results measured by previous studies. The circular velocity at the solar position, yielded by our RC is 234.04 ± 0.08 (stat.) ± 1.36 (sys.) km s -1 , again in great consistency with other independent determinations. From the newly constructed RC, as well as constraints from other data, we have constructed a mass model for our Galaxy, yielding a mass of the dark matter halo of M 200 = (8.05 ± 1.15) × 10 11 M ⊙ with a corresponding radius of R 200 = 192.37 ± 9.24 kpc and a local dark matter density of 0.39 ± 0.03 GeV cm -3 .

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

QUOTAS: A New Research Platform for the Data-driven Discovery of Black Holes

We present QUOTAS, a novel research platform for the data-driven investigation of supermassive black hole (SMBH) populations. While SMBH data—observations and simulations—have grown in complexity and abundance, our computational environments and tools have not matured commensurately to exhaust opportunities for discovery. To explore the BH, host galaxy, and parent dark matter halo connection—in this pilot version—we assemble and colocate the high-redshift, z > 3 quasar population alongside simulated data at the same cosmic epochs. As a first demonstration of the utility of QUOTAS, we investigate correlations between observed Sloan Digital Sky Survey (SDSS) quasars and their hosts with those derived from simulations. Leveraging machine-learning algorithms (ML), to expand simulation volumes, we show that halo properties extracted from smaller dark-matter-only simulation boxes successfully replicate halo populations in larger boxes. Next, using the Illustris-TNG300 simulation that includes baryonic physics as the training set, we populate the larger LEGACY Expanse dark-matter-only box with quasars, and show that observed SDSS quasar occupation statistics are accurately replicated. First science results from QUOTAS comparing colocated observational and ML-trained simulated data at z3 are presented. QUOTAS demonstrates the power of ML, in analyzing and exploring large data sets, while also offering a unique opportunity to interrogate theoretical assumptions that underpin accretion and feedback models. QUOTAS and all related materials are publicly available at the Google Kaggle platform. (The full data set—observational data and simulation data—are available at: https://www.kaggle.com/ and the codes are available at:https://www.kaggle.com/datasets/quotasplatform/quotas)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

On the Physical Nature of Lyα Transmission Spikes in High-redshift Quasar Spectra

We investigate Lyman-alpha (Lyα) transmission spikes at 5.2 < z < 6.8 using synthetic quasar spectra from the “Cosmic Reionization on Computers” simulations. We focus on understanding the relationship between these spikes and the properties of the intergalactic medium (IGM). Disentangling the complex interplay between IGM physics and the influence of galaxies on the generation of these spikes presents a significant challenge. To address this, we employ Explainable Boosting machines, an interpretable machine learning algorithm, to quantify the relative impact of various IGM properties on the Lyα flux. Our findings reveal that gas density is the primary factor influencing absorption strength, followed by the intensity of background radiation and the temperature of the IGM. Ionizing radiation from local sources (i.e., galaxies) appears to have a minimal effect on Lyα flux. The simulations show that transmission spikes predominantly occur in regions of low gas density. Our results challenge recent observational studies suggesting the origin of these spikes in regions with enhanced radiation. We demonstrate that Lyα transmission spikes are largely a product of the large-scale structure, of which galaxies are biased tracers.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the Extended Baryon Oscillation Spectroscopic Survey and from the Second Phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its rst two years of operation (July 2014{2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the rst phase began operations in 2000. New in DR14 is the rst public release of data from the extended Baryon Oscillation Sky Survey (eBOSS); the rst data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as The Cannon; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scienti c use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network

Solar energetic particles (SEPs) are an essential source of space radiation, and are hazardous for humans in space, spacecraft, and technology in general. In this paper, we propose a deep-learning method, specifically a bidirectional long short-term memory (biLSTM) network, to predict if an active region (AR) would produce an SEP event given that (i) the AR will produce an M- or X-class flare and a coronal mass ejection (CME) associated with the flare, or (ii) the AR will produce an M- or X-class flare regardless of whether or not the flare is associated with a CME. The data samples used in this study are collected from the Geostationary Operational Environmental Satellite's X-ray flare catalogs provided by the National Centers for Environmental Information. We select M- and X-class flares with identified ARs in the catalogs for the period between 2010 and 2021, and find the associations of flares, CMEs, and SEPs in the Space Weather Database of Notifications, Knowledge, Information during the same period. Each data sample contains physical parameters collected from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Experimental results based on different performance metrics demonstrate that the proposed biLSTM network is better than related machine-learning algorithms for the two SEP prediction tasks studied here. We also discuss extensions of our approach for probabilistic forecasting and calibration with empirical evaluation

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

The Dark Energy Survey: Cosmology Results with ∼1500 New High-redshift Type Ia Supernovae Using the Full 5 yr Data Set

Abstract We present cosmological constraints from the sample of Type Ia supernovae (SNe Ia) discovered and measured during the full 5 yr of the Dark Energy Survey (DES) SN program. In contrast to most previous cosmological samples, in which SNe are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being an SN Ia, we find 1635 DES SNe in the redshift range 0.10 < z < 1.13 that pass quality selection criteria sufficient to constrain cosmological parameters. This quintuples the number of high-quality z > 0.5 SNe compared to the previous leading compilation of Pantheon+ and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints, we combine the DES SN data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning 0.025 < z < 0.10. Using SN data alone and including systematic uncertainties, we find Ω M = 0.352 ± 0.017 in flat ΛCDM. SN data alone now require acceleration ( q 0 < 0 in ΛCDM) with over 5 σ confidence. We find ( Ω M , w ) = ( 0.264 − 0.096 + 0.074 , − 0.80 − 0.16 + 0.14 ) in flat w CDM. For flat w 0 w a CDM, we find ( Ω M , w 0 , w a ) = ( 0.495 − 0.043 + 0.033 , − 0.36 − 0.30 + 0.36 , − 8.8 − 4.5 + 3.7 ) , consistent with a constant equation of state to within ∼2 σ . Including Planck cosmic microwave background, Sloan Digital Sky Survey baryon acoustic oscillation, and DES 3 × 2pt data gives (Ω M , w ) = (0.321 ± 0.007, −0.941 ± 0.026). In all cases, dark energy is consistent with a cosmological constant to within ∼2 σ . Systematic errors on cosmological parameters are subdominant compared to statistical errors; these results thus pave the way for future photometrically classified SN analyses.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

RODEM Jet Datasets

We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in particle physics. These datasets encompass a diverse range of jet sources, including quark/gluon jets, jets from the decay of W bosons, top quarks, and heavy new-physics particles. The datasets provide detailed substructure information, including jet kinematics, constituent kinematics, and track displacement details, enabling a wide range of applications in jet tagging, anomaly detection, and generative modelling.

FOS: Physical sciences↗

DOE OSTI2018

Impacts of air pollutants from fire and non-fire emissions on the regional air quality in Southeast Asia

Abstract. Severe haze events in Southeast Asia caused by particulate pollution have become more intense and frequent in recent years. Widespread biomass burning occurrences and particulate pollutants from human activities other than biomass burning play important roles in degrading air quality in Southeast Asia. In this study, numerical simulations have been conducted using the Weather Research and Forecasting (WRF) model coupled with a chemistry component (WRF-Chem) to quantitatively examine the contributions of aerosols emitted from fire (i.e., biomass burning) versus non-fire (including fossil fuel combustion, and road dust, etc.) sources to the degradation of air quality and visibility over Southeast Asia. These simulations cover a time period from 2002 to 2008 and are driven by emissions from (a) fossil fuel burning only, (b) biomass burning only, and (c) both fossil fuel and biomass burning. The model results reveal that 39 % of observed low-visibility days (LVDs) can be explained by either fossil fuel burning or biomass burning emissions alone, a further 20 % by fossil fuel burning alone, a further 8 % by biomass burning alone, and a further 5 % by a combination of fossil fuel burning and biomass burning. Analysis of an 24 h PM 2.5 air quality index (AQI) indicates that the case with coexisting fire and non-fire PM 2.5 can substantially increase the chance of AQI being in the moderate or unhealthy pollution level from 23 to 34 %. The premature mortality in major Southeast Asian cities due to degradation of air quality by particulate pollutants is estimated to increase from ∼ 4110 per year in 2002 to ∼ 6540 per year in 2008. In addition, we demonstrate the importance of certain missing non-fire anthropogenic aerosol sources including anthropogenic fugitive and industrial dusts in causing urban air quality degradation. An experiment of using machine learning algorithms to forecast the occurrence of haze events in Singapore is also explored in this study. All of these results suggest that besides minimizing biomass burning activities, an effective air pollution mitigation policy for Southeast Asia needs to consider controlling emissions from non-fire anthropogenic sources.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Impacts of air pollutants from fire and non-fire emissions on the regional air quality in Southeast Asia

Severe haze events in Southeast Asia caused by particulate pollution have become more intense and frequent in recent years, degrading air quality, threatening human health, and interrupting economic and societal activities. Widespread biomass burning activities are a major source of severe haze events in Southeast Asia. On the other hand, particulate pollutants from human activities other than biomass burning also play an important role in degrading air quality in Southeast Asia. In this study, numerical simulations have been conducted using the Weather Research and Forecasting (WRF) model coupled with a chemistry component (WRF-Chem) to quantitatively examine the contributions of aerosols emitted from fire (i.e., biomass burning) versus non-fire (including fossil fuel combustion, road and industrial dust, land use, and land change, etc.) sources to the degradation of air quality and visibility over Southeast Asia. These simulations cover a time period from 2002 to 2008 and were respectively driven by emissions from: (a) fossil fuel burning only, (b) biomass burning only, and (c) both fossil fuel and biomass burning. Across ASEAN 50 cities, these model results reveal that 39 % of observed low visibility days can be explained by either fossil fuel burning or biomass burning emissions alone, a further 20 % by fossil fuel burning alone, a further 8 % by biomass burning alone, and a further 5 % by a combination of fossil fuel burning and biomass burning. The remaining 28 % of observed low visibility days remain unexplained, likely due to emissions sources that have not been accounted for. Further analysis of 24-hr PM 2.5 Air Quality Index (AQI) indicates that comparing to the simulated result of the case with stand-alone non-fire emissions, the case with coexisting fire and non-fire PM 2.5 can substantially increase the chance of AQI being in the moderate or unhealthy pollution level from 23 % to 34 %. The premature mortality among major Southeast Asian cities due to degradation of air quality by particulate pollutants is estimated to increase from ~ 4110 per year in 2002 to ~ 6540 per year in 2008. In addition, we demonstrate the importance of certain missing non-fire anthropogenic aerosol sources including anthropogenic fugitive and industrial dusts in causing urban air quality degradation. An exploratory experiment of using machine learning algorithms to forecasting the occurrence of haze events in Singapore is also demonstrated in this study. All these results suggest that besides minimizing biomass burning activities, an effective air pollution mitigation policy for Southeast Asia needs to consider controlling emissions from non-fire anthropogenic sources.

Lee, Hsiang-He (ORCID:0000000251407324)↗

DOE OSTI2024

A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps

Effective monitoring of global water resources is increasingly critical due to climate change and population growth. Advancements in remote sensing technology, specifically in spatial, spectral, and temporal resolutions, are revolutionizing water resource monitoring, leading to more frequent and high-quality surface water extent maps using various techniques such as traditional image processing and machine learning algorithms. However, satellite imagery datasets contain trade-offs that result in inconsistencies in performance, such as disparities in measurement principles between optical (e.g., Sentinel-2) and radar (e.g., Sentinel-1) sensors and differences in spatial and spectral resolutions among optical sensors. Therefore, developing accurate and robust surface water mapping solutions requires independent validations from multiple datasets to identify potential biases within the imagery and algorithms. However, high-quality validation datasets are expensive to build, and few contain information on water resources. For this purpose, we introduce a globally sampled, high-spatial-resolution dataset labeled using 3 m PlanetScope imagery. Our surface water extent dataset comprises 100 images, each with a size of 1024×1024 pixels, which were sampled using a stratified random sampling strategy covering all 14 biomes. We highlighted urban and rural regions, lakes, and rivers, including braided rivers and coastal regions. We evaluated two surface water extent mapping methods using our dataset – Dynamic World, based on Sentinel-2, and the NASA IMPACT model, based on Sentinel-1. Dynamic World achieved a mean intersection over union (IoU) of 72.16 % and F1 score of 79.70 %, while the NASA IMPACT model had a mean IoU of 57.61 % and F1 score of 65.79 %. Performance varied substantially across biomes, highlighting the importance of evaluating models on diverse landscapes to assess their generalizability and robustness. Our dataset can be used to analyze satellite products and methods, providing insights into their advantages and drawbacks. Our dataset offers a unique tool for analyzing satellite products, aiding the development of more accurate and robust surface water monitoring solutions. The dataset can be accessed via https://doi.org/10.25739/03nt-4f29.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: An artificial neural network approach

Upscaling instantaneous evapotranspiration retrieved at any specific time-of-day (ET i ) to daily evapotranspiration (ET d ) is a key challenge in mapping regional ET using polar orbiting sensors. Various studies have unanimously cited the shortwave incoming radiation ( R S ) to be the most robust reference variable explaining the ratio between ET d and ET i . This study aims to contribute in ET i upscaling for global studies using the ratio between daily and instantaneous incoming shortwave radiation ( R Sd / R Si ) as a factor for converting ET i to ET d . This paper proposes an artificial neural network (ANN) machine-learning algorithm first to predict R Sd from R Si followed by using the R Sd / R Si ratio to convert ET i to ET d across different terrestrial ecosystems. Using R Si and R Sd observations from multiple sub-networks of the FLUXNET database spread across different climates and biomes (to represent inputs that would typically be obtainable from remote sensors during the overpass time) in conjunction with some astronomical variables (e.g. solar zenith angle, day length, exoatmospheric shortwave radiation), we developed the ANN model for reproducing R Sd and further used it to upscale ET i to ET d . The efficiency of the ANN is evaluated for different morning and afternoon times of day, under varying sky conditions, and also at different geographic locations. R S -based upscaled ET d produced a significant linear relation ( R 2 = 0.65 to 0.69), low bias (-0.31 to -0.56 MJ m -2 d -1 ; approx. 4 %), and good agreement (RMSE 1.55 to 1.86 MJ m -2 d -1 ; approx. 10 %) with the observed ET d , although a systematic overestimation of ET d was also noted under persistent cloudy sky conditions. Inclusion of soil moisture and rainfall information in ANN training reduced the systematic overestimation tendency in predominantly overcast days. An intercomparison with existing upscaling method at daily, 8-day, monthly, and yearly temporal resolution revealed a robust performance of the ANN-driven R S -based ET i upscaling method and was found to produce lowest RMSE under cloudy conditions. Sensitivity analysis revealed variable sensitivity of the method to biome selection and high ET d prediction errors in forest ecosystems are primarily associated with greater rainfall and cloudiness. As a result, the overall methodology appears to be promising and has substantial potential for upscaling ET i to ET d for field and regional-scale evapotranspiration mapping studies using polar orbiting satellites.

54 ENVIRONMENTAL SCIENCES↗