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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

An integrative framework reveals widespread gene flow during the early radiation of oaks and relatives in Quercoideae (Fagaceae)

ABSTRACT Although the frequency of ancient hybridization across the Tree of Life is greater than previously thought, little work has been devoted to uncovering the extent, timeline, and geographic and ecological context of ancient hybridization. Using an expansive new dataset of nuclear and chloroplast DNA sequences, we conducted a multifaceted phylogenomic investigation to identify ancient reticulation in the early evolution of oaks (Quercus). We document extensive nuclear gene tree and cytonuclear discordance among major lineages ofQuercusand relatives in Quercoideae. Our analyses recovered clear signatures of gene flow against a backdrop of rampant incomplete lineage sorting, with gene flow most prevalent among major lineages ofQuercusand relatives in Quercoideae during their initial radiation, dated to the Early‐Middle Eocene. Ancestral reconstructions including fossils suggest ancestors ofCastanea + Castanopsis,Lithocarpus, and the Old World oak clade probably co‐occurred in North America and Eurasia, while the ancestors ofChrysolepis, Notholithocarpus, and the New World oak clade co‐occurred in North America, offering ample opportunity for hybridization in each region. Our study shows that hybridization—perhaps in the form of ancient syngameons like those seen today—has been a common and important process throughout the evolutionary history of oaks and their relatives. Concomitantly, this study provides a methodological framework for detecting ancient hybridization in other groups.

Biochemistry & Molecular Biology↗
DOE OSTI2023

Multi-Unit Dwelling Plug-in EV Charging Innovation Pilots (Final Report)

Nearly one-third of residences in the U.S. are multi-unit dwellings (MUDs), e.g., apartments and condominiums, and MUDs with five or more units account for approximately 45% of rental households. While 80% of EV charging takes place at home, less than 5% of home charging takes place at MUDs. With public electric vehicle (EV) charging still underdeveloped, lack of access to reliable home charging is a major barrier to EV adoption for MUD residents. Challenges to siting electric vehicle supply equipment (EVSE) at MUDs include the high upfront cost of EVSE installation, physical and/or electrical infrastructure constraints, a lack of clear incentives for property managers to invest in installing EV charging for tenants, and a limited number of EV charging service providers that offer solutions adapted to the unique needs of MUDs. Through award DE-EE0008473 from the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE), Center for Sustainable Energy (CSE), Energetics, and Forth, along with a diverse team of partners, led a three-year project to address barriers to EV charging at MUDs by developing an online toolkit geared toward residents, homeowner associations (HOAs), and property managers. The project, referred to as Vehicle Charging Innovations – Multi-Unit Dwellings (VCI-MUD project), engaged stakeholders across the country to identify real and perceived barriers to EV charging at MUDs and explored innovative technologies that attempt to alleviate the identified barriers. Over the course of three and a half years, the project conducted 60 interviews with industry stakeholders, compiled findings in an easy-to-use toolkit, and disseminated the toolkit across national, regional, state, and local channels. Key findings and outcomes of the VCI-MUD project include: Identifying six primary barriers to the installation of EV charging at MUDs; Developing five fact sheets and eight case studies highlighting innovative charging solutions to address barriers, including real-world operational and financial data; Developing a user-friendly, online empowerment toolkit with five important points for “making your pitch” to support EV charging to assist residents, property managers, building managers, and HOAs explore options for MUD charging. The VCI-MUD online toolkit was designed with replicability in mind. It includes general tools and guides to evaluate EV charging demand, gauge readiness for EV charging installation, and develop actionable plans. The fact sheets and case studies highlight the diversity of emerging MUD EV charging solutions, featuring different geographic and structural installation scenarios and providing interested parties with a menu of options, rather than prescribing a one-size-fits-all solution. The following resources are included in the toolkit to provide MUD stakeholders with all the information needed to navigate EV charging installation at their MUD location: Empowerment Toolkit – Easy-to-read FAQ overview, stakeholder roles and responsibilities, and additional resources for MUD EV charging installations; Charging Basics – Glossary of terms and descriptions of charging features, installation and operating expenses; EV Charging Survey Templates – Resident-to-Resident, Property Manager-to-Resident and HOA-to-Resident template letters with pre-populated template questions; Technology Selection Tool – Charging barrier fact sheets and case study examples; Installation Checklist – Submittal document requirements for EVSE installations; MUD Building Self-Evaluation Survey – Self-guided evaluation of potential barriers to EV charging installation at MUD locations; Curbside Resources – Case studies and fact sheets for curbside charging options; Find a Certified Electrician – Approved list of certified EV charging installers; “Right-to-Charge” State Legal References – Legal requirements for charging equipment at MUD locations. The online toolkit was promoted extensively in the final six months of the project and will continue to be disseminated by the Clean Cities Coalitions and other partners after the end of the VCI-MUD project to encourage, support, and demonstrate viable solutions for vehicle charging infrastructure in MUDs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Spatially calibrating polycyclic aromatic hydrocarbons (PAHs) as proxies of area burned by vegetation fires: Insights from comparisons of historical data and sedimentary PAH fluxes

Many regions worldwide have experienced increasing wildfire activity in recent years and climate changes are predicted to result in more frequent and severe fires. Reconstruction of past fire activity offers paleoenvironmental context for modern and future burning. Pyrogenic polycyclic aromatic hydrocarbons (PAHs) have been increasingly used as a molecular biomarker for fire occurrence in the paleorecord and offer opportunity for nuanced reconstructions of fire characteristics. A suite of PAHs are produced during combustion, and the emission amount and assemblage is influenced by many variables including fuel type, fire temperature, and oxygen availability. Despite recent advances in understanding the controls and taphonomy of these biomass burning markers, the spatial scale of this proxy is unknown. In this paper, measurements of PAH fluxes preserved in a lake sediment archive from the Sierra Nevada, California were compared with a historical geographic information system dataset of area burned up to 150 km distance from the lake to determine the spatial scales for which these biomarkers are reliable proxies of burning. Comparisons of PAH fluxes with charcoal accumulation rates in the same sediments suggest that pyrogenic particulate transport modulates low to mid-molecular weight PAHs via adsorption. Overall, the results indicate that PAH records integrate a combination of spatial signals of area burned and measurement of individual PAHs may enable cross-scale paleofire reconstructions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Are coupled renewable-battery power plants more valuable than independently sited installations?

Coupled renewable-battery powerplants differ from the traditional concept of independent siting of electricity resources within transmission networks. Prior research on the value proposition and cost savings from coupling did not consider the geographic constraint of co-location. This paper fills the gap by assessing how pricing volatility differences between nodes within electricity markets impact the system value of coupled renewable-battery projects as compared to independent VRE and battery installations. We use wholesale power market prices from 2012–2019 across the seven main U.S. independent system operators (ISOs) with a linear optimization program to compare the electricity market value of coupled projects to the value of the same underlying sub-components, deployed separately. We find that additional value from adding a 4-hour battery sized to 50% of renewable-plant nameplate capacity is $\$$10/MWh across ISOs on average. The highest boost occurs in California ($\$$15/MWh), where the value of adding storage to solar rises over time in tandem with increased solar penetration in the region. If renewables and batteries are deployed independently, we estimate that $\$$12.5/MWh of additional value could be achieved because of more flexibility on battery siting and operation. The $\$$12.5/MWh coupling penalty is reduced to $\$$1.6/MWh when considering alternative approaches to integrating battery storage. This result implies that renewable-battery power plants will play an increasing role in electricity systems if they can be built for $\$$2–$\$$13/MWh less than independent projects of comparable size. However, the wide regional variation in coupling penalties, along with the importance of conditions captured in our sensitivity cases, suggests the tradeoff between coupling penalties and savings will vary by situation. Therefore, roles exist for independent and coupled projects from a system optimization perspective.

14 SOLAR ENERGY↗
DOE OSTI2026

1981 Bay Area Metropolitan Transportation Commission Household Survey

The San Francisco Bay Area's Metropolitan Transportation Commission (MTC) conducted the study in 1981, collecting household and travel information by telephone from 6,209 households in the MTC's nine-county jurisdiction. The sample gave good geographic coverage and provided a good representation of the population groups in the Bay Area. The survey was primarily intended to measure weekday travel; however, the size of the weekend travel sample was sufficient to enable MTC to make inferences about weekend trip generation. Travel days in the sample were spread out evenly over the three-month interviewing period to eliminate seasonal bias.

1Hz data↗
DOE OSTI2024

Fractal Nanostructured Solar Selective Surfaces for Next Gen Concentrating Solar Power (Final Report)

This project reports a novel coating with enhanced solar absorptance and reduced thermal emittance with high efficiency at elevated temperature for next-generation concentrated solar power (CSP) plants, with targeted operating temperatures around 750°C. Highly textured single and multimetallic oxide coatings were electrodeposited onto Inconel substrate by systematically varying the composition and process parameters. The optimized coating exhibited micro-to-nano structures designed to match the wavelengths in the visible region of the solar spectrum. These structures facilitate resonant absorption of solar radiation, significantly boosting solar absorption and accommodating thermal stress during high temperature exposure. A high solar absorptance exceeding 0.985 and a low thermal emittance below 0.5, yielding a thermal efficiency near 95%, was achieved for the optimized coatings without any anti-reflective overcoat, that remained robust after 750 h of isothermal exposure to 750°C in air. The coatings are also robust to severe mechanical and environmental stressors. The innovative approach presented in this study demonstrates the potential for tailoring air-stable solar absorber coatings to achieve high absorption, low emittance, and excellent high-temperature endurance, meeting the rigorous demands of next-generation CSP systems. A technoeconomic analysis reveals the economic advantage of the coatings for Gen3 CSP installations in different geographical zones globally.

14 SOLAR ENERGY↗
DOE OSTI2020

Emulating climate extreme indices

We use simple pattern scaling and time-shift to emulate changes in a set of climate extreme indices under future scenarios, and evaluate the emulators' accuracy. We propose a metric for the error in emulation in the context of initial condition ensembles, to specifically characterize the role of internal variability in the emulation performance. Our metric separates systematic emulation errors from unavoidable discrepancies between emulated and target values due to internal variability. We compute the metricis at grid-point scale, and we show geographically resolved results, or aggregate them at global scale. We demonstrate the use of our error metric in the emulation of a suite of temperature and precipitation extreme indices. We test and compare simple pattern scaling and time-shift using a range of trajectories spanning targets inspired by the Paris agreement -- warming to 1.5C and 2.0C from the pre-industrial baseline -- and two of the longer-established trajectories, RCP4.5 and RCP8.5. With this suite of scenarios we can test the effects on the performance of the size of the temperature gap between emulation origin and target. We find that for most indices emulation the dominant source of discrepancy is internal variability. For at least one index, however, counting exceedances of a high temperature threshold, significant portions of the globally aggregated discrepancy and its regional pattern originate from the systematic emulation error. This error exceeds internal variability of both the target and the emulated quantities in large coherent regions at low latitudes, and the explanation can be found in the differential behavior of temperature distributions across latitudes. The metric also highlights a fundamental difference in the two methods related to the simulation of internal variability, which is dampened significantly by simple pattern scaling. This aspect is of consequence when using these methods for specific applications, where preserving variability for uncertainty quantification is deemed important. With this study we offer our metric as a diagnostic tool, facilitating the formulation of scientific hypotheses on the reasons for the error. In the meantime, we show that for many impact relevant indices by now traditional emulation techniques can be accurate within the variations unavoidably introduced by internal variability, establishing the fundamental condition for using their emulation in impact modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Keep it short: Exploring the impacts of configuration choices on the recent economics of solar-plus-battery and wind-plus-battery hybrid energy plants

Coupled renewable-battery powerplants differ from the traditional concept of independent siting of electricity resources within transmission networks. Prior research on the value proposition and cost savings from coupling did not consider the geographic constraint of co-location. This paper fills the gap by assessing how pricing volatility differences between nodes within electricity markets impact the system value of coupled renewable-battery projects as compared to independent VRE and battery installations. We use wholesale power market prices from 2012–2019 across the seven main U.S. independent system operators (ISOs) with a linear optimization program to compare the electricity market value of coupled projects to the value of the same underlying sub-components, deployed separately. We find that additional value from adding a 4-hour battery sized to 50% of renewable-plant nameplate capacity is $\$10$/MWh across ISOs on average. The highest boost occurs in California ($\$15$/MWh), where the value of adding storage to solar rises over time in tandem with increased solar penetration in the region. If renewables and batteries are deployed independently, we estimate that $\$12.5$/MWh of additional value could be achieved because of more flexibility on battery siting and operation. The $\$12.5$/MWh coupling penalty is reduced to $\$1.6$/MWh when considering alternative approaches to integrating battery storage. This result implies that renewable-battery power plants will play an increasing role in electricity systems if they can be built for $\$2$–$\$13$/MWh less than independent projects of comparable size. However, the wide regional variation in coupling penalties, along with the importance of conditions captured in our sensitivity cases, suggests the tradeoff between coupling penalties and savings will vary by situation. Therefore, roles exist for independent and coupled projects from a system optimization perspective.

14 SOLAR ENERGY↗
DOE OSTI2023

Insights from application of a hierarchical spatio-temporal model to an intensive urban black carbon monitoring dataset

Existing regulatory pollutant monitoring networks rely on a small number of centrally located measurement sites that are purposefully sited away from major emission sources. While informative of general air quality trends regionally, these networks often do not fully capture the local variability of air pollution exposure within a community. Recent technological advancements have reduced the cost of sensors, allowing air quality monitoring campaigns with high spatial resolution. The 100×100 black carbon (BC) monitoring network deployed 100 low-cost BC sensors across the 15 km 2 West Oakland, CA community for 100 days in the summer of 2017, producing a nearly continuous site-specific time series of BC concentrations which we aggregated to one-hour averages. Leveraging this dataset, we employed a hierarchical spatio-temporal model to accurately predict local spatio-temporal concentration patterns throughout West Oakland, at locations without monitors (average cross-validated hourly temporal R 2 =0.60). Using our model, we identified spatially varying temporal pollution patterns associated with small-scale geographic features and proximity to local sources. In a sub-sampling analysis, here we demonstrated that fine scale predictions of nearly comparable accuracy can be obtained with our modeling approach by using ~30% of the 100×100 BC network supplemented by a shorter-term high-density campaign.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A geospatial environmental and techno-economic framework for sustainable phosphorus management at livestock facilities

Nutrient pollution of waterbodies is a major worldwide water quality problem. Excessive use and discharge of nutrients can lead to eutrophication and algal blooms in fresh and marine waters, resulting in environmental problems associated with hypoxia, public health issues related to the release of toxins and freshwater scarcity. A promising option to address this problem is the recovery of nutrient releases prior to being discharged into the environment. Driven by the sustainable materials management concept, the COW2NUTRIENT (Cattle Organic Waste to NUTRIent and ENergy Technologies) framework is developed for the techno-economic evaluation and selection of nutrient recovery systems at livestock facilities. Furthermore, environmental vulnerability to nutrient pollution determined through a geographic information system (GIS)-based model and techno-economic information of different state-of-the-art nutrient management technologies are combined in a multi-criteria decision analysis (MCDA) model, resulting in the selection and economic analysis of the most suitable process for each studied livestock facility. This framework has been employed for studying the implementation of sustainable phosphorus management systems at 2,217 livestock facilities in the Great Lakes area, resulting in capital expenses of 2.5 billion USD if only phosphorus recovery technologies are installed, and up to 5.2 billion USD if nutrient management is combined with biogas and power production. However, considering potential economic incentives for the recovery of phosphorus, net revenues up to 230 million USD per year can be achieved. Therefore, the framework presented reveals the potential of implementing nutrient management systems at regional scale for the abatement of phosphorus releases from livestock facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Dose Assessment Model for Radioactive Waste Exposed by Gully Erosion at West Valley - 20513

The Western New York Nuclear Service Center (WNYNSC), located approximately 48 km south of Buffalo, New York, is the site of a former nuclear fuel reprocessing and radioactive waste disposal facility. Spent nuclear fuel was processed there from 1966 to 1972, leaving behind radioactive and chemical wastes in two disposal areas and a waste tank farm. Site operations also resulted in releases of radioactivity to site soils, groundwater, and to surface waters draining the site. The New York State Energy Research and Development Authority (NYSERDA) and the U.S. Department of Energy (DOE) are collaborating in a process of decision making for decommissioning those facilities remaining at the WNYNSC following the completion of Phase 1 decommissioning. Neptune and Company, Inc. (Neptune) was contracted to develop a probabilistic performance assessment (PPA) computer model to assist the agencies in this process. The PPA Model includes a contaminant transport component focusing on the movement of contaminants within and among environmental media including groundwater and surface water transport, contaminant translocation by plants and animals, diffusion, and erosion. The model also includes evaluation of potential exposure and health effects for a Resident Farmer exposure scenario, where the Resident Farmer represents a critical group, described as that group of individuals reasonably expected to receive the greatest exposure to residual radioactivity for any applicable set of circumstances. The West Valley Site is located in the glaciated Allegheny Plateau region of western New York State. The waste reprocessing and disposal areas were constructed on a relatively fat area of plateau dissected by drainages of Buttermilk Creek, including Erdman Brook, Franks Creek, and Quarry Creek. An important aspect of the contaminant transport component of the PPA model is consideration of erosive processes such as slumping of the stream slopes and the advance of gullies from these streams. These erosion processes remove material from the plateau, growing the size of the creek valleys and making them wider and deeper. Of particular interest for the impact of erosion is radioactive waste contained in the Nuclear Regulatory Commission (NRC)-Licensed Disposal Area (NDA), the New York State-Licensed Disposal Area (SDA), and residual radiological inventory in the underground storage tanks at the Waste Tank Farm (WTF). The PPA Model is organized around geographically-defined facilities which were constructed upon the plateau, including the NDA, SDA, and WTF. Ongoing stream erosion processes will potentially transfer radioactive waste and residual inventory from these facilities to the ground surface on adjacent hillslope areas where erosion has breached the facility. Hence, it is important to evaluate the consequences of potential exposures to a Resident Farmer on the hillslopes below a breached facility. Two interrelated aspects of the dose assessment model related to hillslope exposure are discussed: 1) representation of the physical processes related to transport of radionuclides from facilities onto the hillslopes, and from the hillslopes into adjoining creeks where contaminated material migrates downstream with surface water and sediment, and; 2) adaptation of the activities associated with the Resident Farmer scenario to assess potential exposures to contamination in the hillslope areas. This discussion will cover the conceptual basis of the hillslope exposure and transport models, and also implementation in the PPA computer model. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

A Near-Real-Time Model for Predicting Electricity Disruptions in Texas During Winter Storms

There has been an increase in extreme weather events, posing a threat to power grid systems, potentially influenced by factors such as population growth, changes in ecosystems, land cover, and land use in the service area, as well as the growth of certain vegetation types. This research seeks to develop a predictive model to mitigate potential damages caused by future winter storms. This research utilizes the Light Gradient Boosting Machine (LightGBM), incorporating the number of power outages experienced at the county level, geographic details, weather information, and lagged outage and lagged weather data. The developed models were broadly divided into two groups, with six models in each group - one group without optimization and another with optimization, totaling 12 trained models. For model optimization, Bayesian optimization was employed using Root Mean Squared Error (RMSE) as the objective function. In results, when comparing Group 2 (the optimized group) with Group 1 (the non-optimized group), it was found that optimization did not always lead to a reduction in RMSE and Mean Absolute Error (MAE). However, in terms of Mean Directional Accuracy (MDA), while all results in Group 1 were below the baseline accuracy of 0.33, all results in Group 2 exceeded 0.33, with some cases showing an increase of more than three times the baseline. The results indicated that, in the optimized model group, Population and Pressure were the most influential factors when using current weather data and geographical information. When using lagged data, lagged recorded outages and lagged Pressure emerged as the most significant factors. Among the 12 developed models, the L-1-2-O model showed the lowest RMSE and MAE, as well as the highest accuracy, with values of 390.62 households and 168.13 households, respectively. To normalize the RMSE and MAE values, each metric was divided by the average number of households among the counties in Texas. For the L-1-2-O model, the scaled RMSE was 0.88% and the scaled MAE was 0.38%. In terms of MDA, which indicates the accuracy of the prediction direction, the L-1-O model achieved the highest score of 0.41. Although this study focused on Texas, which suffered the greatest impact from the winter storms in 2021, with additional validation, the methodology used in this research could be applied to other regions.

Lee, Jangjae [Texas A & M Univ., College Station, ↗
DOE OSTI2025

Diurnal urban rainfall anomalies across different landscapes

Urbanization influences regional climates and extreme weather. While previous research has documented urban-induced precipitation for specific cities or storm cases, diurnal urban precipitation anomalies across different landscapes remain poorly understood. Analyzing diurnal and seasonal variations of urban precipitation anomalies across 175 US cities, we found strong urban effects on precipitation especially during summer afternoons. Large, inland cities can receive 10% more summer afternoon rainfall, and the pattern varies with urban heat islands and wind conditions. Coastal cities, under the influence of sea-land breeze, experience three times amplified daily urban precipitation enhancements than inland cities. Urban impacts are also noted for mountain cities, affecting their topography-dominated precipitation patterns. With increasing thresholds of extreme precipitation, most US cities experience more intense rainfall compared to their rural backgrounds. This study enhances our understanding of hitherto poorly documented diurnal aspects of urban rainfall through geographically distributed, landscape-specific rainfall anomalies.

Science & Technology - Other Topics↗
DOE OSTI2025

MAIA: A new detector concept for a 10 TeV muon collider

Muon colliders offer a compelling opportunity to explore the TeV scale and conduct precision tests of the Standard Model, all within a relatively compact geographical footprint. This paper introduces a new detector concept, MAIA (Muon Accelerator Instrumented Apparatus), optimized for $\sqrt{s}=10$ TeV $\mu\mu$ collisions. The detector features an all-silicon tracker immersed in a 5T solenoid field. High-granularity silicon-tungsten and iron-scintillator calorimeters surrounding the solenoid capture high-energy electronic and hadronic showers, respectively, and support particle-flow reconstruction. The outermost subsystem comprises an air-gap muon spectrometer, which enables standalone track reconstruction for high-momentum muons. The performance of the MAIA detector is evaluated in terms of differential particle reconstruction efficiencies and resolutions. Beam-induced background (BIB) simulations generated in FLUKA are overlaid with single particle gun samples to assess detector reconstruction capabilities under realistic experimental conditions. Even with BIB, reconstruction efficiencies exceed 95% for energetic tracks, photons, and neutrons in the central region of the detector. This paper outlines promising avenues of future work, including forward region optimization and opportunities for enhanced flavor/boosted object tagging, and addresses the technological assumptions needed to achieve the desired detector performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Reduced Erosion Augments Soil Carbon Storage Under Cover Crops

ABSTRACT Cover crops, a promising strategy to increase soil organic carbon (SOC) storage in croplands and mitigate climate change, have typically been shown to benefit soil carbon (C) storage from increased plant C inputs. However, input‐driven C benefits may be augmented by the reduction of C outputs induced by cover crops, a process that has been tested by individual studies but has not yet been synthesized. Here we quantified the impact of cover crops on organic C loss via soil erosion (SOC erosion) and revealed the geographical variability at the global scale. We analyzed the field data from 152 paired control and cover crop treatments from 57 published studies worldwide using meta‐analysis and machine learning. The meta‐analysis results showed that cover crops widely reduced SOC erosion by an average of 68% on an annual basis, while they increased SOC stock by 14% (0–15 cm). The absolute SOC erosion reduction ranged from 0 to 18.0 Mg C −1 ha −1 year −1 and showed no correlation with the SOC stock change that varied from −8.07 to 22.6 Mg C −1 ha −1 year −1 at 0–15 cm depth, indicating the latter more likely related to plant C inputs. The magnitude of SOC erosion reduction was dominantly determined by topographic slope. The global map generated by machine learning showed the relative effectiveness of SOC erosion reduction mainly occurred in temperate regions, including central Europe, central‐east China, and Southern South America. Our results highlight that cover crop‐induced erosion reduction can augment SOC stock to provide additive C benefits, especially in sloping and temperate croplands, for mitigating climate change.

Huang, Wenjuan [Department of Ecology, Evolution, ↗
DOE OSTI2020

Temporal Analysis and Scene Change Detection in Multispectral Overhead Imagery

Scene change detection can be a tedious and time consuming process especially when concerning large geographical areas, and the process can be even more cumbersome when analyzing changes in an area over large spans of time. Developing a useful way to help analysts recognize at what points in time significant changes to a scene have occurred can allow them to better focus their efforts in characterizing events. Applications include: Facility monitoring, Construction chronology, Monitoring of vehicle/aircraft activity, Characterization of larger sequences of events. In large areas exceeding hundreds to thousands of square kilometers in size, it can be difficult localizing when scene changes have occurred. Analysts can spend hours going through imagery to try to identify new construction, monitor facility activities, monitor vehicle movement, etc. where the object of interest may only be a few square meters. Our goal is to help cut down this time by giving analysts change maps with hot spots of change, allowing them to focus on regions that have experienced actual change in time frames they're interested in. Additionally, by combining these change maps into layers within a data cube, analysts can examine the change maps from a temporal perspective, allowing events to be characterized over spans of time. By opening the data cube in an imaging software capable of separating the layers, we can analyze the change maps sequentially, allowing us to examine scene changes occurring over time. As an example, we examined overhead imagery from Planet Labs of what appears to be a parking lot on Fort Irwin over the course of a year using ENVI, a geospatial satellite imagery analysis software. Using ENVI, we generate a graph of changes over time, and notice a particular segment near the end of our analysis window where no changes are detected. Examination of the actual satellite imagery reveals that during this time span, the parking lot was empty. This could be due to facility shutdown for maintenance or upgrades, or possibly even total workforce/vehicle fleet movement. Information like this could help analysts better characterize events, as well as to help create clearer timelines in larger sequences of events. Workflow steps: - Collect multiple maps of the same AOI (Area of Interest) during a time span of interest; - Generate change maps from AOI maps; - Generate data cube from change maps. An analyst can use the data cube to help inspect an AOI for activities within a time span of interest. If an event of interest is discovered, the analyst can then refer to the maps corresponding to the appropriate dates and times in the data cube to see precisely what is transpiring. The biggest objective being worked on is improving the change detection methodology employed. We currently use PCA-EM (Principal Component Analysis with Expectation Maximization), but we are currently focusing on implementing IR-MAD (Iteratively Reweighted Multivariate Alteration Detection) to be used in conjunction with PCA-EM in an effort to decrease false positivity and noise in the change maps we generate.

42 ENGINEERING↗
DOE OSTI2021

Role of underlying surface, rainstorm and antecedent wetness condition on flood responses in small and medium sized watersheds in the Yangtze River Delta region, China

Floods are becoming more frequent and are hard to control due to the shortage of water conservancy projects in small and medium sized basins, especially in developing regions. Understanding the hydrologic responses and estimated flood characteristics to storm events can help to predict flood disasters and form better effective mitigation and adaptation strategies. Here, using observations of several representative watersheds and Artificial Neural Networks (ANN) and Principal Component Regression (PCR) models, we conducted data-driven analyses to examine the flood responding characteristics. We quantified the relative contributions of the influencing factors to the variation of each flood characteristic. Statistical analysis of the observations shows that the drainage area plays a key role in determining the distribution of lag time and peak discharge. Rainstorm variability has direct influence on floods, and typhoon-induced rainstorms with high total rainfall and rainfall intensity generate higher lag time, flood peak, unit discharge and runoff depth, but lower runoff coefficient. The ANN and PCR models accurately predicted the variations of flood features using the driving factors including physical geographical characteristics, rainstorm features, and antecedent condition. Physical geographic characteristics are key influential factors of lag time, flood peak and runoff coefficient, while the rainstorm features control the magnitude of unit discharge and runoff depth. These results indicate that floods are mainly affected by rainstorm features and physical geographic characteristics in the Yangtze River delta, and it might become more damaging with the increasing rainfall extremes and sprawling impervious surfaces in a changing environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Willow Biomass Crops Are a Carbon Negative or Low-Carbon Feedstock Depending on Prior Land Use and Transportation Distances to End Users

Few life cycle assessments (LCAs) on willow biomass production have investigated the effects of key geographically specific parameters. This study uses a spatial LCA model for willow biomass production to determine spatially explicit greenhouse gas (GHG) emissions and energy return on investment (EROI), including land use conversion from pasture and cropland or grassland. There were negative GHG emissions on 92% of the land identified as suitable for willow biomass production, indicating this system’s potential for climate change mitigation. For willow planted on cropland or pasture, life cycle GHG emissions ranged from −53.2 to −176.9 kg CO2eq Mg-1. When willow was grown on grassland the projected decrease in soil organic carbon resulted in a slightly positive GHG balance. Changes in soil organic carbon (SOC) associated with land use change, transportation distance, and willow yield had the greatest impacts on GHG emissions. Results from the uncertainty analysis exhibited large variations in GHG emissions between counties arising from differences in these parameters. The average EROI across the entire region was 19.2. Willow biomass can be a carbon negative or low-carbon energy source with a high EROI in regions with similar infrastructure, transportation distances, and growing conditions such as soil characteristics, land cover types, and climate.

09 BIOMASS FUELS↗