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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Predicting Switchgrass Biomass Yields Using a Spectral Vegetation Index Derived from Multispectral Satellite Imagery

Successful scaling of perennial bioenergy crop production requires a landscape design that optimizes the benefits of finite lands for people, communities, and environments. Utilizing marginal areas is the key to sustainable bioenergy crop production (Ssegane et al., 2015, 2016). Marginal areas are often small-sized lands and unevenly distributed across the agricultural landscape (Ssegane et al., 2016); thus, a systematic, semi-automated remote sensing method is needed as an effective means of estimating bioenergy crop yields across landscapes. Argonne National Laboratory (Argonne) is currently developing a tool, Scaling Up Perennial Bioenergy Economics and Ecosystem Services Tool (SUPERBEEST), to identify marginal agricultural lands and quantify environmental and economic effects of perennial bioenergy crop production systems. The tool aims to provide users a path to foster the sustainable and productive integration of bioenergy crops in the Midwestern agricultural landscape. Reliable, cost-effective, and timely estimation of bioenergy crop yields using remote sensing would help calculate and track the success of integrated bioenergy crops in the landscape for those communities. Argonne previously conducted feasibility studies for estimating biomass yields for bioenergy feedstock, corn and perennial grass using spectral vegetation indices (SVIs)1 derived from optical imagery (Hamada et al., 2015, 2021). In both studies, SVIs, more specifically those sensitive to plant chlorophyll or nitrogen contents, showed potential for estimating or predicting biomass yields with a correlation of determination (R2) ranging from 0.54 to 0.96, indicating a value for further investigation as a viable means of quantifying bioenergy feedstock production across large landscapes. Thus, the goal of this study is to evaluate the feasibility of use of SVIs as a means of estimating or predicting switchgrass biomass yields at harvest using publicly available multispectral satellite imagery. The feasibility analysis was performed using four study areas of mature switchgrass located in Virginia. Objectives are to (1) examine the SVIs and establish their relationships with switchgrass biomass yields at harvest, (2) develop a parsimonious image processing model for predicting at-harvest yields by applying the relationships with the most promising spectral index and (3) map switchgrass yields predicted by the image processing model across the study sites. The calibration to field data will rely on switchgrass biomass yields determined by the baling method, representing a potential challenge to the analysis but an important practical aspect for future applications. With this research design, the study aimed to gain insights into enabling remote sensing-based estimation of bioenergy crop yields in a reliable, cost- effective, and timely manner across large, heterogeneous landscapes.

09 BIOMASS FUELS↗
NASA NTRSDate not supplied

Using Earth Observations to Map Bull Kelp in the Puget Sound, Washington, to Support Conservation and Restoration

Bull kelp (Nereocystis luetkeana) is a critical component of nearshore ecosystems in the Puget Sound region of the Salish Sea. The Port of Seattle and Washington State Department of Natural Resources (DNR) have identified possible declines in bull kelp extent and changes in its distribution throughout the Central Puget Sound near Seattle, Washington. Bull kelp losses threaten critical ecological services and marine habitat, as well as important cultural resources. However, these changes are not well tracked or understood due to the expensive and time-intensive nature of traditional kelp canopy monitoring methods. The Port of Seattle and Washington DNR partnered with the NASA DEVELOP team to explore the feasibility of using Earth observations between 2016 and 2021 obtained from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) as a potential tool to monitor and map bull kelp. Our team identified a variety of challenges that need to be addressed before this approach can be utilized as an effective means for identifying or mapping nearshore urban kelp beds. We found that neither Sentinel-2 nor Landsat 8 significantly differentiates between kelp and no-known kelp using the Normalized Difference Vegetation Index (NDVI) or Normalized Difference Red-Edge Blue (NDREB). While the tidal and current filtering methods discussed here may be beneficial for identifying promising single image dates for kelp classification, the filters we used reduced the number of images each year to the point that modeling or mapping yearly kelp extent or creating time series of kelp did not appear to be feasible.

Mike Hitchner​↗
NASA NTRS2022

Puget Sound Water Resources: Using Earth Observations to Map Bull Kelp in the Puget Sound, Washington, to Support Conservation and Restoration

Bull kelp (Nereocystis luetkeana) is a critical component of nearshore ecosystems in the Puget Sound region of the Salish Sea. The Port of Seattle and Washington State Department of Natural Resources (DNR) have identified possible declines in bull kelp extent and changes in its distribution throughout the Central Puget Sound near Seattle, Washington. Bull kelp losses threaten critical ecological services and marine habitat, as well as important cultural resources. However, these changes are not well tracked or understood due to the expensive and time-intensive nature of traditional kelp canopy monitoring methods. The Port of Seattle and Washington DNR partnered with the NASA DEVELOP team to explore the feasibility of using Earth observations between 2016 and 2021 obtained from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) as a potential tool to monitor and map bull kelp. Our team identified a variety of challenges that need to be addressed before this approach can be utilized as an effective means for identifying or mapping nearshore urban kelp beds. We found that neither Sentinel-2 nor Landsat 8 significantly differentiates between kelp and no-known kelp using the Normalized Difference Vegetation Index (NDVI) or Normalized Difference Red-Edge Blue (NDREB). While the tidal and current filtering methods discussed here may be beneficial for identifying promising single image dates for kelp classification, the filters we used reduced the number of images each year to the point that modeling or mapping yearly kelp extent or creating time series of kelp did not appear to be feasible.

Mike Hitchner↗
DOE OSTI2023

Sustainable Supply Chains by Integrating Life Cycle Modeling and Techno-Ecological Synergy with Application to Mitigation of Harmful Algal Blooms

Harmful algal blooms (HABs) are a challenge in water bodies across the planet and are often caused by fertilizer runoff from intensively farmed lands. This chapter describes a framework by which producers of biomass-based products could manage their supply chain to encourage mitigation of HABs and their life cycle environmental impacts, while also considering economic implications. The emphasis is on corn-based products such as ethanol in the region around Lake Erie in Northwest Ohio. It shows how wetland ecosystems could be designed synergistically with farming practices, transportation of the corn, and conversion and use of ethanol. The resulting techno-ecologically synergistic supply chains can be environmentally and economically superior to conventional techno-centric supply chains. The resulting designs can help ethanol manufacturers choose between farms, encourage farmers to adopt wetland ecosystems for intercepting and mitigating nutrient runoff, and determine the location of biorefineries for optimizing environmental and economic objectives.

air quality regulation↗
DOE OSTI2022

Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?

Abstract Aim Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m). Location Eastern USA. Time period 2017–2018. Taxa studied Trees. Methods With a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics. Results Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity. Main conclusions We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Photosynthetic responses to temperature across the tropics: a meta-analytic approach

Background and Aims Tropical forests exchange more carbon dioxide (CO 2 ) with the atmosphere than any other terrestrial biome. Yet, uncertainty in the projected carbon balance over the next century is roughly three times greater for the tropics than other for ecosystems. Our limited knowledge of tropical plant physiological responses, including photosynthetic, to climate change is a substantial source of uncertainty in our ability to forecast the global terrestrial carbon sink. Methods Here, we used a meta-analytic approach, focusing on tropical photosynthetic temperature responses, to address this knowledge gap. Our dataset, gleaned from 18 independent studies, included leaf-level light-saturated photosynthetic (A sat ) temperature responses from 108 woody species, with additional temperature parameters (35 species) and rates (250 species) of both maximum rates of electron transport (J max ) and Rubisco carboxylation (V cmax ). We investigated how these parameters responded to mean annual temperature (MAT), temperature variability, aridity and elevation, as well as also how responses differed among successional strategy, leaf habit and light environment. Key Results Optimum temperatures for A sat (T optA ) and J max (T optJ ) increased with MAT but not for V cmax (T optV ). Although photosynthetic rates were higher for ‘light’ than ‘shaded’ leaves, light conditions did not generate differences in temperature response parameters. T optA did not differ with successional strategy, but early successional species had ~4 °C wider thermal niches than mid/late species. Semi-deciduous species had ~1 °C higher T optA than broadleaf evergreen species. Most global modelling efforts consider all tropical forests as a single ‘broadleaf evergreen’ functional type, but our data show that tropical species with different leaf habits display distinct temperature responses that should be included in modelling efforts. Conclusions This novel research will inform modelling efforts to quantify tropical ecosystem carbon cycling and provide more accurate representations of how these key ecosystems will respond to altered temperature patterns in the face of climate warming.

Carter, Kelsey R. [Oak Ridge National Laboratory (↗
DOE OSTI2025

Integrated Modeling Driven Evaluation of Opportunities for Climate‐Resilient Perennial Biomass Crop Plantings in Flood‐Prone Agricultural Landscapes

Adapting to future climate change in flood-prone landscapes will require climate-resilient agricultural systems. Planting perennial crops, like switchgrass and willow, along river corridors can mitigate future flooding while supporting bioenergy markets. We developed an integrated assessment linking climate, hydrologic, and inundation model results to assess future flood risk to river-adjacent agricultural lands in the Mid-Atlantic Region (MAR) and explore this opportunity. We produced ensemble streamflow projections for every MAR stream using a hydrologic model driven by a suite of downscaled and bias-corrected Coupled Model Intercomparison Project Phase 6 climate projections. We then conducted high-resolution inundation mapping based on projected flood frequencies for baseline and future periods. Results show that in the near-term future, at least two-thirds of the streams will experience 100-year floods more severe than the baseline 200-year floods. Riparian zones are projected to face a median rise of inundation by 9.5%–24.1%. Results show that there is an opportunity to mitigate flooding in over half of MAR's counties with the quantities of switchgrass and willow plantings anticipated for mature bioenergy markets, even under the most extreme (200-year) flood events. Our integrated modeling framework can guide similar regions to evaluate opportunities for flood-resilient agricultural systems under climate change.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2024

Biodiversity loss reduces global terrestrial carbon storage

Natural ecosystems store large amounts of carbon globally, as organisms absorb carbon from the atmosphere to build large, long-lasting, or slow-decaying structures such as tree bark or root systems. An ecosystem’s carbon sequestration potential is tightly linked to its biological diversity. Yet when considering future projections, many carbon sequestration models fail to account for the role biodiversity plays in carbon storage. Here, we assess the consequences of plant biodiversity loss for carbon storage under multiple climate and land-use change scenarios. We link a macroecological model projecting changes in vascular plant richness under different scenarios with empirical data on relationships between biodiversity and biomass. We find that biodiversity declines from climate and land use change could lead to a global loss of between 7.44-103.14 PgC (global sustainability scenario) and 10.87-145.95 PgC (fossil-fueled development scenario). This indicates a self-reinforcing feedback loop, where higher levels of climate change lead to greater biodiversity loss, which in turn leads to greater carbon emissions and ultimately more climate change. Conversely, biodiversity conservation and restoration can help achieve climate change mitigation goals.

Biodiversity↗
DOE OSTI2024

Biomass yield potential, feedstock quality, and nutrient removal of perennial buffer strips under continuous zero fertilizer application

Perennial-based buffer strips have been promoted as having the potential for improving ecosystem services from riparian areas while producing biomass as livestock feed or as a bioenergy feedstock. Both biomass production and nutrient removal of buffer strips are substantially influenced by the vegetation types for the multipurpose perennial buffers. In this 2016–2019 study in western Illinois, two perennial cropping systems, including forage crops composed of cool-season grass mixtures (forage system) and bioenergy crops made up of warm-season grass mixtures (bioenergy system), were used to establish buffer strips for assessing biomass production, feedstock quality, nutrient removal, and buffer longevity. Treatments for this study reflecting agronomic practices included (1) two harvests occurring in summer (at anthesis) and fall (after complete senescence) and (2) one harvest in fall for the forage system (two-cut vs. one-cut forage) and (3) one fall harvest for the bioenergy system (one-cut bioenergy). Successively harvesting without any fertilizer input resulted in a yield decline in forage biomass over 3 years by approximately 30 % (6.3 to 4.4 Mg DM ha –1 (dry matter) with a rate of 1.0 Mg ha –1 yr –1 ) in the two-cut forage and by 35 % (4.9 to 3.2 Mg DM ha –1 with a rate of 0.9 Mg ha –1 yr –1 ) in the one-cut forage systems. The feed quality also decreased over the years by showing declined rates of 12.9 (crucial protein), 0.9 (calcium), 0.7 (copper), and 1.3 g kg –1 DM yr –1 (zinc). Empirical models predicted enteric CH 4 emissions from cattle ranged from 225.7 to 242.6 g per cow per day based on the feed nutritive values. In contrast, bioenergy biomass yield increased by 27 % from 4.9 to 6.7 Mg DM ha –1 with a consistent quality (cellulose of ~ 397.9 g kg –1 ; hemicellulose of ~ 299.4 g kg –1 ), corresponding to the increased total theoretical ethanol yield from 1.8 × 10 3 to 2.4 × 10 3 L ha –1 (~ 33 % increase). Annual nutrient removals of N, P, K, Ca, and Mg were significantly higher in the forage systems (e.g., two-cut: 52.6–106.9 kg N ha –1 ; one-cut: 44.5–84.1 kg N ha –1 ) than those in the bioenergy system (e.g., 25.9–34.4 kg N ha –1 ); however, the removal rate declined rapidly over 3 years (e.g., ~ 49 % reduction) as the annual biomass yield declined in the forage systems. This on-farm field study demonstrated the potential of the perennial crop used as buffer strip options for biomass production and buffer sustainability at the edge of the field.

09 BIOMASS FUELS↗
DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

Lin, Xinming↗
DOE OSTI2025

A global dataset of terrestrial biological nitrogen fixation

Biological nitrogen fixation (BNF) is the main natural source of new nitrogen inputs in terrestrial ecosystems, supporting terrestrial productivity, carbon uptake, and other Earth system processes. We assembled a comprehensive global dataset of field measurements of BNF in all major N-fixing niches across natural terrestrial biomes derived from the analysis of 376 BNF studies. The dataset comprises 32 variables, including site location, biome type, N-fixing niche, sampling year, quantification method, BNF rate (kg N ha −1 y −1 ), the percentage of nitrogen derived from the atmosphere (%N dfa ), N fixer or N-fixing substrate abundance, BNF rate per unit of N fixer abundance, and species identity. Overall, the dataset combines 1,207 BNF rates for trees, shrubs, herbs, soil, leaf litter, woody litter, dead wood, mosses, lichens, and biocrusts, 152 herb %N dfa values, 1,005 measurements of N fixer or N-fixing substrate abundance, and 762 BNF rates per unit of N fixer abundance for a total of 424 species across 66 countries. This dataset facilitates synthesis, meta-analysis, upscaling, and model benchmarking of BNF fluxes at multiple spatial scales.

Reis Ely, Carla R. [Oregon State Univ., Corvallis,↗
DOE OSTI2025

Monthly averages of ED2 model simulations initialized with airborne lidar structure, Jan 1981-Dec 2018, Brazilian Amazon

Deforestation and forest degradation (selective logging, fires, fragmentation) have impacted nearly 40% of the original extent of the Brazilian Amazon, and have markedly impacted forest structure across the region. To date, few studies analysed how shifts in forest structure from degradation influence the forest sensitivity to climate extremes, because of the complex interactions between forest structure and micro-environmental conditions. To address this knowledge gap, we carried out a series of simulations across the Brazilian Amazon using the Ecosystem Demography Model (ED2), using observed forest structure derived from 541 airborne lidar transects (375 ha each) and two scenarios representing forest recovery and expansion of degradation to investigate how shifts in forest structure impact ecosystem function under near-average and extreme climate conditions, as part of the manuscript Longo et al 2025 "Degradation and Deforestation Increase the Sensitivity of the Amazon Forest to Climate Extremes". This dataset provides the output results from the ED2 model simulations for the three simulations at monthly time scales, in NetCDF format. For all simulations, we used bias-corrected hourly reanalyses (WFDE5) for most meteorological drivers, except for precipitation, which was obtained from CHIRPS. The meteorological drivers used in the study span 38 years (Jan 1981–Dec 2018). The output results correspond to the last 38 years of simulation (one full cycle of meteorological drivers), in which ED2 simulations used static stand structure (i.e., the forest structure was held constant). The following files are provided:ED2_emean_Global_R004_BrAmaz_s1c0t0l0f0.nc. This corresponds to the Control simulation. The forest structure was obtained from the airborne lidar.ED2_emean_Global_R005_BrAmaz_s1c0t1l1f0.nc. This corresponds to the Degraded simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that expanded deforestation and selective logging across the Amazon.ED2_emean_Global_R006_BrAmaz_s1c0t1l0f0.nc. This corresponds to the Recovery simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that completely halted deforestation and degradation, allowing degraded forests to recover for 38 years.We also provide file ED2_zones_R004_BrAmaz_s1c0t0l0f0.nc, which classifies each grid cell into zones used in the reference manuscript: 1: Southeast. 2: South. 3: West. 4: Central. 5: Northeast. 6: North. 7: Northwest". Index 0 corresponds to grid cells excluded from sub-region analyses because they were dominated by flooded forests, deforestation, and naturally non-forest vegetation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Models for Facilitating Government-Funded Activities in the Post-ISS LEO Ecosystem

The International Space Station National Lab (ISSNL) was established through Congressional authority in 2005 and has been managed by a nonprofit called the Center for the Advancement of Science in Space (CASIS) since 2011 to support research, development, education, and commercial activities in low-Earth orbit (LEO). NASA leadership is considering next steps for microgravity and orbital activities in LEO in preparation for the retirement of the International Space Station (ISS) and transition to Commercial LEO Destinations (CLDs) in the 2030 timeframe. Specifically, our study team within the NASA Office of Technology, Policy, and Strategy addressed the question: What are potential models for an ISS National Lab facilitating government-funded or subsidized activities on a commercial LEO platform after the transition of the ISS to one or more private platforms? To inform the trade space of potential models, we conducted over 40 discussions with stakeholders internal and external to NASA and reviewed over 35 documents related to LEO legislation, commercialization, models, and activities. Following the extensive literature review and discussions with diverse stakeholders, we defined and evaluated six models, in addition to the current ISSNL model. We assessed each model across three possible future scenarios (dynamic, steady, and limited growth of the commercial LEO market and activities) and across five stakeholder-driven model evaluation criteria (ability to meet NASA’s needs, adaptability, opportunity for collaboration, market sustainability, and equity and accessibility). The six models in the future trade space were: • Government Research Broker: Customizable research efforts on transport vehicles and CLDs • Innovation Campus: Modern terrestrial campus with workforce focus • Anchor Tenant: Long-term, substantial agreement for leasing space on a single CLD • Fee for Service: Free market approach with NASA-sponsored grants and service/data buys • Matchmaker: Neutral third-party connecting users to platforms • Institute Network: Network of separate but related efforts to enable commercial scaling The Government Research Broker model performed best across the scenarios, followed by Innovation Campus, Anchor Tenant, and Fee for Service. While Matchmaker and Institute Network exhibited positive aspects, the two models performed most favorably in future scenarios with well-established communities and markets. The six models presented in this study are representative of the potential trade space and are illustrative examples. NASA leadership can adjust models as desired to align to their priorities more closely by using combinations of the unique model features provided in our analysis. The optimal model is likely a combination of features from multiple models. Building upon this work to best position NASA and the diverse stakeholders of LEO activities for success post-ISS, further work will explore the near-term impact of preparing for the transition of the ISS to CLDs by identifying opportunities for modifying the current ISSNL-CASIS partnership. ACKNOWLEDGEMENTS We thank the many reviewers and discussants, internal and external to NASA, that contributed to the work.

Erica Rodgers↗
DOE OSTI2025

Assessing Simulations of Forest Hurricane Disturbance and Recovery in Puerto Rico by ELM-FATES Using Field Measurements

In the past three decades, Puerto Rico (PR) experienced five hurricanes that met or exceeded category three, and they caused severe forest structural damage and elevated tree mortality. To improve our mechanistic understanding of hurricane impacts on tropical forests and assess hurricane-affected forest dynamics in Earth system models, we use in situ forest measurements at the Bisley Experimental Watersheds in Northeast PR to evaluate the Functionally Assembled Terrestrial Ecosystem Simulator coupled with the Energy Exascale Earth System Model Land Model (ELM-FATES). The observations show that before Hurricane Hugo, 77.3% of the aboveground biomass (AGB) is from the shade-tolerant plant function type (PFT). The Hugo-induced mortality rates are over ~50%, and they induce a ~39% AGB reduction, which recovers to a level like the pre-Hugo condition in 2014, following a second, lower intensity hurricane, Georges. We perform numerical experiments that simulate damage from Hugo and Georges on the forests, including defoliation, sapwood and structural biomass damage, and hurricane-induced mortality. ELM-FATES can reasonably represent coexistence between the two PFTs–light-demanding and shade-tolerant–for both the pre-Hugo and post-Hugo conditions. The model represents a reasonable size distribution of mid-and large-sized trees although it underestimates AGB, likely due to the overestimated nonhurricane mortality. ELM-FATES temporarily stimulated leaf biomass and diameter increment after Georges, an effect that should be tested with observations of future hurricane defoliation events. This research indicates that addressing model-data mismatches in tree mortality and understory dynamics are essential to simulation of more extreme hurricane effects under climate change.

58 GEOSCIENCES↗
DOE OSTI2022

Nature-based solutions in mountain catchments reduce impact of anthropogenic climate change on drought streamflow

Quantifying how well Nature-based Solutions can offset anthropogenic climate change impacts is important for adaptation planning, but has rarely been done. Here we show that a widely-applied Nature-based Solution in South Africa – invasive alien tree clearing – reduces the impact of anthropogenic climate change on drought streamflow. Using a multi-model joint-attribution of climate and landscape-vegetation states during the 2015–2017 Cape Town “Day Zero” drought, we find that anthropogenic climate change reduced streamflow by 12–29% relative to a counterfactual world with anthropogenic emissions removed. This impact on streamflow was larger than corresponding reductions in rainfall (7–15%) and reference evapotranspiration (1.7–2%). Clearing invasive alien trees could have ameliorated streamflow reductions by 3–16% points for moderate invasions levels. Preventing further invasive alien tree spread avoided potential additional reductions of 10–27% points. Total clearing could not have offset the anthropogenic climate change impact completely. Invasive alien tree clearing is an important form of catchment restoration for managing changing hydroclimatic risk, but will need to be combined with other adaptation options as climate change accelerates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Capturing soil moisture and salinity changes in flooded coastal forests using electrical resistivity and induced polarization

Geophysical methods provide high-resolution spatial measurements of physical quantities sensitive to changes in soil moisture and salinity and can be used to monitor soil hydrological responses to flooding. However, extracting quantitative hydrological information from geophysical data remains challenging. In this study, we extended existing petrophysical models to estimate soil moisture and salinity from electrical measurements to address this challenge. We manipulated two hydrologically isolated 2000 m 2 experimental plots by simultaneously inundating them with 265 m 3 of either freshwater or estuarine water. Repeated electrical resistivity and induced polarization measurements were used to image the water and solute infiltration along two transects that are 100 and 42 m in length. Petrophysical models derived from laboratory multi-salinity electrical measurements were used to estimate changes in soil moisture and fluid salinity from field measurements of real and imaginary conductivity during the ecosystem-scale flooding experiment. The real conductivity increased by ∼100% in the freshwater plot and ∼570% in the saltwater plot. The change in imaginary conductivity in the freshwater plot was <1 mS/m, whereas that of the estuarine water plot was ∼5 mS/m. Real conductivity shows dependence on soil moisture content with a coefficient of determination (R 2 ) > 0.7, while the imaginary conductivity shows a dependence on soil salinity with R 2 > 0.6. The results validate the use of electrical resistivity for estimating changes in soil moisture content in response to flooding. Combining electrical resistivity imaging with induced polarization measurements provides the possibility to account for changes in pore fluid conductivity.

Adebayo, Moses B. [Univ. of Toledo, OH (United Sta↗
DOE OSTI2022

A Conterminous USA-Scale Map of Relative Tidal Marsh Elevation

Abstract Tidal wetlands provide myriad ecosystem services across local to global scales. With their uncertain vulnerability or resilience to rising sea levels, there is a need for mapping flooding drivers and vulnerability proxies for these ecosystems at a national scale. However, tidal wetlands in the conterminous USA are diverse with differing elevation gradients, and tidal amplitudes, making broad geographic comparisons difficult. To address this, a national-scale map of relative tidal elevation ( Z * MHW ), a physical metric that normalizes elevation to tidal amplitude at mean high water (MHW), was constructed for the first time at 30 × 30-m resolution spanning the conterminous USA. Contrary to two study hypotheses, watershed-level median Z * MHW and its variability generally increased from north to south as a function of tidal amplitude and relative sea-level rise. These trends were also observed in a reanalysis of ground elevation data from the Pacific Coast by Janousek et al. (Estuaries and Coasts 42 (1): 85–98, 2019). Supporting a third hypothesis, propagated uncertainty in Z * MHW increased from north to south as light detection and ranging (LiDAR) errors had an outsized effect under narrowing tidal amplitudes. The drivers of Z * MHW and its variability are difficult to determine because several potential causal variables are correlated with latitude, but future studies could investigate highest astronomical tide and diurnal high tide inequality as drivers of median Z * MHW and Z * MHW variability, respectively. Watersheds of the Gulf Coast often had propagated Z * MHW uncertainty greater than the tidal amplitude itself emphasizing the diminished practicality of applying Z * MHW as a flooding proxy to microtidal wetlands. Future studies could focus on validating and improving these physical map products and using them for synoptic modeling of tidal wetland carbon dynamics and sea-level rise vulnerability analyses.

Holmquist, James R. (ORCID:0000000325466766)↗
DOE OSTI2026

Heterogeneous Multi-Domain Dataset Synthesis to Facilitate Privacy and Risk Assessments in Smart City IoT

The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly those arising from cross-modal data linkage across heterogeneous sensing platforms. To address these challenges, this paper introduces a comprehensive, statistically grounded framework for generating synthetic, multimodal IoT datasets tailored to Smart City research. The framework produces behaviorally plausible synthetic data suitable for preliminary privacy risk assessment and as a benchmark for future re-identification studies, as well as for evaluating algorithms in mobility modeling, urban informatics, and privacy-enhancing technologies. As part of our approach, we formalize probabilistic methods for synthesizing three heterogeneous and operationally relevant data streams—cellular mobility traces, payment terminal transaction logs, and Smart Retail nutrition records—capturing the behaviors of a large number of synthetically generated urban residents over a 12-week period. The framework integrates spatially explicit merchant selection using K-Dimensional (KD)-tree nearest-neighbor algorithms, temporally correlated anchor-based mobility simulation reflective of daily urban rhythms, and dietary-constraint filtering to preserve ecological validity in consumption patterns. In total, the system generates approximately 116 million mobility pings, 5.4 million transactions, and 1.9 million itemized purchases, yielding a reproducible benchmark for evaluating multimodal analytics, privacy-preserving computation, and secure IoT data-sharing protocols. To show the validity of this dataset, the underlying distributions of these residents were successfully validated against reported distributions in published research. We present preliminary uniqueness and cross-modal linkage indicators; comprehensive re-identification benchmarking against specific attack algorithms is planned as future work. This framework can be easily adapted to various scenarios of interest in Smart Cities and other IoT applications. By aligning methodological rigor with the operational needs of Smart City ecosystems, this work fills critical gaps in synthetic data generation for privacy-sensitive domains, including intelligent transportation systems, urban health informatics, and next-generation digital commerce infrastructures.

IoT↗