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DOE OSTI2018

A Scalable HPC Insider Threat Monitoring System

Cyber Equations, Inc. is reporting on the results of its DOE SBIR Phase I project for the development of a Scalable HPC Insider Threat Monitoring System. This final report to the Department of Energy for Task DE-FOA-0001618 covers the period from 02/21/2017 to 02/20/2018 and summarizes the larger research accomplishments, which can be divided in theoretical and laboratory experiments. One of the many challenges for the U.S. Department of Energy (DOE) is the support of the high-performance computing (HPC) centers while providing quality information services, strategic vision, leadership, technical expertise, and capabilities necessary to enable the DOE to deliver effective, efficient, economical, and secure HPC services. In order to realize a secure DOE laboratory enterprise, DOE facilities operations must counter a wide range of cyber insider threats, from unsophisticated adversaries up to advanced, nation state sponsored actors. The DOE laboratories also presents an enormous insider attack surface of an ever-growing number of computers, networks, and devices all running a myriad of software. In addition, the DOE laboratories are connected to a variety of external organizations which multiplies the possibility of insider threat. During this SBIR project Cyber Equations developed a strategic combination of supervised and unsupervised machine learning techniques to control scale and classify whether entities, such as hosts, actors, or documents, are showing suspicious insider behavior. We conducted experiments using real cyber data to show that changes in observable behavior (e.g., changes in outbound traffic patterns) are indicative of attacks executing (e.g., insiders exfiltrating data, bots calling home, or an attack spreading). The features and methods we use, have been used in the anomaly detection research and development community for a significant amount of time and robust implementations are openly available. K-Means clustering, Decision Trees, and Support Vector Machine based classifiers are readily available through the R language and open source H20 machine learning framework. The Cyber Equations approach is novel in its use of these classifiers combined with Hadoop based parallel processing technology. The five stages of processing used by CyberInsider improve scalability and reduce the high number of false positives normally associated with network anomaly detection. In summary, the Cyber Equations has successfully accomplished all of its Phase I tasks and demonstrated the feasibility of the Insider Threat Monitoring System.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Improving Land-Surface Modeling of Evapotranspiration Processes in Tropical Forests (Final Report)

Land surface models are used to represent terrestrial processes that shape global climate; examples of these processes include evaporation, plant water use, and photosynthesis. While much progress has been made to improve and refine these models, some hydrological processes are not well captured, which hinders our ability to understand land-atmosphere interactions and ultimately to predict impacts of climate change on water resources. The inadequate representation of evapotranspiration may partly explain why global climate models do not match observed precipitation patterns. Multiple factors contribute to this problem. In moist tropical regions, high humidity, leaf wetness, and cloud cover combine to suppress forest water use and possibly reduce forest growth in ways that are poorly understood. Mountainous areas pose additional difficulties, as standard modeling and measurement techniques are not readily applied in rough terrain. In this project, we aimed to improve our understanding and modeling of fluxes of water vapor and carbon dioxide to and from tropical forests. To do this, we collected a suite of data from the mountainous rainforest surrounding the Texas A&M Soltis Center in San Ramon, Costa Rica. From 2014 to 2018, a suite of variables were measured, including: in-canopy profiles of water vapor, carbon dioxide, temperature, light, and leaf wetness; transpiration, evapotranspiration, carbon flux, and soil respiration; and local weather conditions. Leaf-scale measurements were used to find photosynthesis rates and derive their parameters. Using this data set, we were able to better quantify the relationships between assimilation and transpiration and their main drivers in tropical forests. These variables were namely available light, vapor pressure deficit, and leaf wetness, but not soil moisture, which exerted no detectable control. We also showed the vertical variability in canopy microclimates and how neglecting forest layers in models may misrepresent overall fluxes. The data were then used to test and refine evapotranspiration and energy budget formulations in the Community Land Model. Fluxes and energy budget components were compared to simulated values from CLM4.5 and CLM.5. The biggest discrepancies arose from hillslope shading effects, particularly mismatches in afternoon carbon fluxes, with notable differences in transpiration rates and sap flux due to capacitance in large trees. Secondary, but still significant issues included unrealistic leaf wetness and leaf drying rates, as well as air and vegetation temperatures, both of which were likely linked to CLM’s formulations controlling wet leaf and sunlit/shaded leaf fractions. Modeling multiple layers in the canopy via the newly released CLM-ml and making adjustments to existing parameters both show promise in resolving these issues. In total, the project has produced 5 peer-reviewed articles, 24 presentations, 2 theses, 4 public outreach items, and 4 publically available datasets, to date. Additional products are expected pending the completion of an additional thesis. Data has also been shared with several collaborative networks, including PHENOCAM and SAPFLUXNET.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Toward a Predictive Understanding of the Response of Belowground Microbial Carbon Turnover to Climate Change Drivers in a Boreal Peatland

Peatlands are wetlands that act as giant "carbon banks", storing approximately one-third of Earth's soil carbon. Peatland carbon banks cover large areas in northern latitudes including the Arctic and evidence from previous work suggests that northern peatlands store a lot of carbon partly because they are cold and microbes don't work very fast to decompose organic matter in the cold. The concern is that with increasing temperatures, microbes will be become more active and release more carbon from the "bank" as greenhouse gases, carbon dioxide and natural gas (methane). However, there are many more factors that could determine why peatlands store so much carbon such as the fact that they are acidic, low in oxygen, or because they contain a peat moss carpet. All of these factors have been shown to slow down the activity of microbes that release greenhouse gases through the decomposition of organic matter. For example, peat mosses naturally produce organic chemical compounds that are thought to poison microbes, thereby limiting their activity. For this reason, peat moss was used in the past as a food preservative. As the amount of carbon dioxide increases in Earth's atmosphere, it also predicted that plants will become more active and produce more usable organic matter for microbes. Therefore, this project addressed the resilience of the peatland carbon bank to warming and the accumulation carbon dioxide in the atmosphere. The project was conducted at the Marcell Experimental Forest (MEF), northern Minnesota, where the Oak Ridge National Lab (ORNL) has established an experimental site known as Spruce and Peatland Response Under Climatic and Environmental Change (SPRUCE). The SPRUCE site simulates environmental change at the ecosystem scale, allowing us to understand how all organisms, from microbes to trees, will respond to higher temperatures and increased carbon dioxide in the atmosphere. From 2014 to 2017, the response of peatlands to environmental change was evaluated through multiple field campaigns each year along with laboratory research. Environmental changes were initiated in stages; soil heating began in 2014, air heating in 2015, and carbon dioxide enrichment in 2016. It was shown that after 1 year, soil heating alone resulted in an exponential increase in methane release to the atmosphere. However, this response was due solely to surface processes and not degradation of deep peat, which represents much older carbon stores. Studies in the laboratory showed that only the top 20–30 cm of peat from experimental plots had higher methane production rates at elevated temperatures. Further investigations using the latest advanced analytical chemistry and microbiology methods confirmed that greenhouse gas production primarily originated from decomposition of organic matter produced recently by plants and not from old peat. No differences in microbial abundances, dissolved organic matter concentrations or degradative enzyme activities were observed among treatments after 1 year of soil heating alone. The activity and not the abundance of microbial communities responded over this time scale. Subsequent field investigations from 2015 to 2017 supported these initial investigations that changes in chemistry and the release of gases from microbial activity was primarily occurring at the surface of the peatland. Thus, our initial results over the first few years of simulated environmental change indicated that the large store of deep peat carbon will be resistant to anaerobic degradation under future climatic warming. Peatland soils are poor in mineral nutrients, and it was thought previously that the decomposition of plant matter in the soil is controlled by processes leading primarily to methane production (methanogenesis), ultimately producing similar amounts of carbon dioxide and methane during organic matter degradation. However, we observed that much more carbon dioxide is produced than methane from the surface peatland. Using a combination of state of the art molecular techniques, this project showed that novel microbial processes involving the respiration or “breathing” of organic matter itself may be responsible for most of gas release from the peatland as carbon dioxide. This has opened up a whole new area of research in how wetlands respond to environmental change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Using CESM-RESFire to understand climate–fire–ecosystem interactions and the implications for decadal climate variability

Large wildfires exert strong disturbance on regional and global climate systems and ecosystems by perturbing radiative forcing as well as the carbon and water balance between the atmosphere and land surface, while short- and long-term variations in fire weather, terrestrial ecosystems, and human activity modulate fire intensity and reshape fire regimes. The complex climate–fire–ecosystem interactions were not fully integrated in previous climate model studies, and the resulting effects on the projections of future climate change are not well understood. Here we use the fully interactive REgion-Specific ecosystem feedback Fire model (RESFire) that was developed in the Community Earth System Model (CESM) to investigate these interactions and their impacts on climate systems and fire activity. We designed two sets of decadal simulations using CESM-RESFire for present-day (2001–2010) and future (2051–2060) scenarios, respectively, and conducted a series of sensitivity experiments to assess the effects of individual feedback pathways among climate, fire, and ecosystems. Our implementation of RESFire, which includes online land–atmosphere coupling of fire emissions and fire-induced land cover change (LCC), reproduces the observed aerosol optical depth (AOD) from space-based Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products and ground-based AErosol RObotic NETwork (AERONET) data; it agrees well with carbon budget benchmarks from previous studies. We estimate the global averaged net radiative effect of both fire aerosols and fire-induced LCC at –0.59±0.52 W m –2 , which is dominated by fire aerosol–cloud interactions (–0.82±0.19 W m –2 ), in the present-day scenario under climatological conditions of the 2000s. The fire-related net cooling effect increases by ~170 % to –1.60±0.27 W m –2 in the 2050s under the conditions of the Representative Concentration Pathway 4.5 (RCP4.5) scenario. Such considerably enhanced radiative effect is attributed to the largely increased global burned area (+19 %) and fire carbon emissions (+100 %) from the 2000s to the 2050s driven by climate change. The net ecosystem exchange (NEE) of carbon between the land and atmosphere components in the simulations increases by 33 % accordingly, implying that biomass burning is an increasing carbon source at short-term timescales in the future. High-latitude regions with prevalent peatlands would be more vulnerable to increased fire threats due to climate change, and the increase in fire aerosols could counter the projected decrease in anthropogenic aerosols due to air pollution control policies in many regions. We also evaluate two distinct feedback mechanisms that are associated with fire aerosols and fire-induced LCC, respectively. On a global scale, the first mechanism imposes positive feedbacks to fire activity through enhanced droughts with suppressed precipitation by fire aerosol–cloud interactions, while the second one manifests as negative feedbacks due to reduced fuel loads by fire consumption and post-fire tree mortality and recovery processes. These two feedback pathways with opposite effects compete at regional to global scales and increase the complexity of climate–fire–ecosystem interactions and their climatic impacts.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest

In tropical forests, leaf phenology-particularly the pronounced dry-season green-up-strongly regulates biogeochemical cycles of carbon and water fluxes. However, uncertainties remain in the understanding of tropical forest leaf phenology at different spatial scales. Phenocams accurately characterize leaf phenology at the crown and ecosystem scales but are limited to a few sites and time spans of a few years. Time-series satellite observations might fill this gap, but the commonly used satellites (e.g. MODIS, Landsat and Sentinel-2) have resolutions too coarse to characterize single crowns. To resolve this observational challenge, we used the PlanetScope constellation with a 3m resolution and near daily nadir-view coverage. We first developed a rigorous method to cross-calibrate PlanetScope surface reflectance using daily BRDF-adjusted MODIS as the reference. We then used linear spectral unmixing of calibrated PlanetScope to obtain dry-season change in the fractional cover of green vegetation (GV) and non-photosynthetic vegetation (NPV) at the PlanetScope pixel level. We used the Central Amazon Tapajos National Forest k67 site, as all necessary data (from field to phenocam and satellite observations) was available. For this proof of concept, we chose a set of 22 dates of PlanetScope measurements in 2018 and 16 in 2019, all from the six drier months of the year to provide the highest possible cloud-free temporal resolution. Our results show that MODIS-calibrated dry-season PlanetScope data (1) accurately assessed seasonal changes in ecosystem-scale and crown-scale spectral reflectance; (2) detected an increase in ecosystem-scale GV fraction (and a decrease in NPV fraction) from June to November of both years, consistent with local phenocam observations with R 2 around 0.8; and (3) monitored large seasonal trend variability in crown-scale NPV fraction. Finally, our results highlight the potential of integrating multi-scale satellite observations to extend fine-scale leaf phenology monitoring beyond the spatial limits of phenocams.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2011

Tracking Climate Effects on Plant-Pollinator Interaction Phenology with Satellites and Honey Bee Hives

Background/Question/Methods: The complexity of plant-pollinator interactions, the large number of species involved, and the lack of species response functions present challenges to understanding how these critical interactions may be impacted by climate and land cover change on large scales. Given the importance of this interaction for terrestrial ecosystems, it is desirable to develop new approaches. We monitor the daily weight change of honey bee (Apis mellifera) colonies to record the phenology of the Honey Bee Nectar Flow (HBNF) in a volunteer network (honeybeenet.gsfc.nasa.gov). The records document the successful interaction of a generalist pollinator with a variety of plant resources. We extract useful HBNF phenology metrics for three seasons. Sites currently exist in 35 states/provinces in North America, with a concentration in the Mid-Atlantic region. HBNF metrics are compared to standard phenology metrics derived from remotely sensed vegetation indices from NASA's MODIS sensor and published results from NOAA's A VHRR. At any given time the percentage of plants producing nectar is usually a sma11 fraction of the total satellite sensor signal. We are interested in determining how well the 'bulk' satellite vegetation parameters relate to the phenology of the HBNF, and how it varies spatially on landscape to continental scales. Results/Conclusions: We found the median and peak seasonal HBNF dates to be robust, with variation between replicate scale hives of only a few days. We developed quality assessment protocols to identify abnormal colony artifacts. Temporally, the peak and median of the HBNF in the Mid-Atlantic show a significant advance of 0.58 d/y beginning about 1970, very similar to that observed by the A VHRR since 1982 (0.57 d/y). Spatially, the HBNF metrics are highly correlated with elevation and winter minimum temperature distribution, and exhibit significant but regionally coherent inter-annual variation. The relationship between median of the spring HBNF with the "Green-up" metric from the 500 meter MODIS NDVI phenology product, for sites throughout the Eastern US 2000-2009, is well described by a single linear fit (r(exp 2) = 0.72). We conclude.that for the tree-dominated areas of the Eastern US at least the spring HBNF can be tracked very well by MODIS phenology. Analysis of other regions and seasons is presently underway but with more limited data. Spatial patterns in the eastern US and management implications will be presented and discussed.

Esaias, Wayne E.↗

DOE OSTI2018

Central and Eastern North America Ground-Motion Characterization (NGA-East)

This document is the final project report of the Next Generation Attenuation for Central and Eastern North America (CENA) project (NGA-East). The NGA-East objective was to develop a new ground-motion characterization (GMC) model for the CENA region. The GMC model consists of a set of new ground-motion models (GMMs) for median and standard deviation of ground motions and their associated weights to be used with logic-trees in probabilistic seismic hazard analyses (PSHA). NGA-East is a large multidisciplinary project coordinated by the Pacific Earthquake Engineering Research Center (PEER), at the University of California. The project has two components: (1) a set of scientific research tasks, and (2) a model-building component following the framework of the “Seismic Senior Hazard Analysis Committee (SSHAC) Level 3” (Budnitz et al. 1997; NRC 2012). Component (2) is built on the scientific results of component (1) of the NGA-East project. This report documents the tasks under component (2) of the project. Under component (1) of NGA-East, several scientific issues were addressed, including: (a) development of a new database of ground motion data recorded in CENA; (b) development of a regionalized ground-motion map for CENA, (c) definition of the reference site condition; (d) simulations of ground motions based on different methodologies; and (e) development of numerous GMMs for CENA. The scientific tasks of NGA-East were all documented as a series of PEER reports. The scope of component (2) of NGA-East was to develop the complete GMC. This component was designed as a SSHAC Level 3 study with the goal of capturing the ground motions’ center, body, and range of the technically defensible interpretations in light of the available data and models. The SSHAC process involves four key tasks: evaluation, integration, formal review by the Participatory Peer Review Panel (PPRP), and documentation (this report). Key tasks documented in this report include review and evaluation of the empirical groundmotion database, the regionalization of ground motions, and screening sets of candidate GMMs. These are followed by the development of new median and standard deviation GMMs, the development of new analyses tools for quantifying the epistemic uncertainty in ground motions, and the documentation of implementation guidelines of the complete GMC for PSHA computations. Appendices include further documentation of the relevant SSHAC process and additional supporting technical documentation of numerous sensitivity analyses results. The PEER reports documenting component (1) of NGA-East are also considered “attachments” to the current report and are all available online on the PEER website (https://peer.berkeley.edu/). The final NGA-East GMC model includes a set of 17 GMMs defined for 24 ground-motion intensity measures, applicable to CENA in the moment magnitude range of 4.0 to 8.2 and covering distances up to 1500 km. Standard deviation models are also provided for site-specific analysis (single-station standard deviation) and for general PSHA applications (ergodic standard deviation). Adjustment factors are provided for consideration of source-depth effects and hanging-wall effects, as well as for hazard computations at sites in the Gulf Coast region.

58 GEOSCIENCES↗

DOE OSTI2026

Terrestrial laser scanning data (Levels 0 and 1) from Urban Biogeochemistry Pilot Project sites, Knoxville, Tennessee, Jul 2024 - Jul 2025

This data package contains data from terrestrial laser scanning (TLS) at five urban park sites in Knoxville, Tennessee, USA. All parks include open-grown and/or closed-canopy trees and mixed nearby land use. These study sites were established as part of the Urban Biogeochemistry Pilot Project, which has an overall goal of better understanding how hydrobiogeochemical cycling is altered within the human environment. These five sites represent a gradient of urbanization, and were instrumented to understand hydrological and biogeochemical cycling (e.g., soil moisture, soil physical properties and biogeochemistry, tree transpiration, species type). The TLS data archived here were collected to provide detailed, three-dimensional information about forest structure. Specifically, data were collected to allow tree- and stand-level characterization of woody structure and leaf area. TLS scans were placed to capture the area around trees with sap flow sensors, and as much of a 50 m radius area around the meteorological station as possible given site property limits. Derived products will allow upscaling of water content and transpiration data. This data package contains the following data: - High-level files document further details of the campaign and data package: 1_CampaignSummary.csv provides details about the campaign and study site, 2_ScanAreasDetail.csv provides details about each separate scan area (groups of scans post-processed into a single point cloud), 3_TerrestrialLidarSensor.csv provides further technical details about the Riegl VZ-400i TLS sensor, TLS_CSV_dd.csv is a CSV Data Dictionary providing information about the fields in CSV files following the ESS-DIVE CSV File Formatting Guidelines Reporting Format, TLS_flmd.csv is a File Level Metadata file providing information about each file in the data package following the ESS-DIVE File Level Metadata Reporting Format, and README.txt is a text file describing the overall project and file structure. - Level 0 data are the raw data (.PROJ folders) as recorded by the Riegl VZ-400i TLS instrument before scan co-registration and post-processing with the Riegl's proprietary RiSCAN PRO software, which requires a license. - Level 1 data contain post-processed, co-registered data from each scan area. The "PointClouds" folder for each scan area contains a .las file with 1 cm resolution point cloud data exported from RiSCAN PRO. These are the main files likely to be of interest to most users and can be further processed with any software capable of manipulating .las files (e.g. Python, R CloudCompare). The "Project Information" folder contains log files from post-processing in RiSCAN PRO that may be of interest to users who want to see detailed records of post-processing, including all PDF reports generated by RiSCAN PRO. The "ScanPositions" folder contains information about the final position of all TLS scans, after post-processing, in multiple formats. The file ScanPositions_*.csv provides final geo-referenced scan positions, and the file SOP_backup_*.csv can be used in RiSCAN PRO to restore the co-registered scan positions if users wish to re-process raw data (Level 0 .PROJ folders) with RiSCAN PRO software (e.g., subsample to a different resolution, exclude a certain scan position, or apply different filters on reflectance or deviation values) without redoing time-consuming co-registration steps.

54 ENVIRONMENTAL SCIENCES↗