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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Denudation, solute export, landscape evolution modeling, and geographic information system data for the East River watershed, Colorado, USA (2020-2024)

This data package contains geographic information system (GIS) layers and tabular datasets associated with the study of lithologic controls on denudation, solute export, carbon-scaling relationships, and transient landscape evolution in the East River watershed near Crested Butte, Colorado, USA. The package includes GIS layers used to produce the Figure 2 map, including drainage, hillshade, lithology, sample locations, and basin polygons, together with comma-separated value (CSV) tables and matching CSV data dictionaries. One group of tables reports sample-level and catchment-level information for river-sediment samples analyzed for in situ-produced cosmogenic beryllium-10 (10Be), including sample names, outlet elevations, geographic coordinates, upstream drainage area, rock-type classes, production-rate scaling scheme, analyzed nuclide, catchment-averaged denudation rates, and associated lower and upper analytical uncertainties. Sample and catchment attributes provide the basis for comparing denudation rates across intrusive, shale, sedimentary, and mixed-lithology settings. A second group of tables reports supporting information for landscape-evolution modeling and the mapped geologic framework of the study area. Included files list parameter values and definitions for the two-phase landscape-evolution simulations, summarize full-domain model erosion fluxes and topographic metrics for different simulation configurations, provide a fixed-area carbon-model scaling table, and summarize mapped geologic units within the East River study domain, including geologic code, formation name, lithologic description, mapped area, and lithologic class grouping. Model outputs and geologic summaries support interpretation of transient landscape behavior and its relation to the mapped distribution of shale, intrusive, sedimentary, and surficial units. A third group of tables reports hydrologic and hydrochemical information used to quantify dissolved export from the watershed. Included files provide site-level values for drainage area, mean annual solute export, standard error of annual export, area-normalized solute yield, and equivalent weathering rate for five East River monitoring sites, along with metadata describing the number, sampling cadence, and date range of discharge records and partial and full total dissolved solids observations used in the solute-yield analyses. The package also contains a supplementary daily ion-load time series with daily mean discharge, discharge observation counts, dissolved concentrations, and daily loads for calcium, magnesium, sodium, potassium, chloride, sulfate, nitrate, fluoride, dissolved silica, charge-balance bicarbonate, and total dissolved solids. The package contains GIS files, comma-separated value files (.csv), CSV data dictionaries, a file-level metadata table, a package-tree text file, and a readme text file.

10Be↗
DOE OSTI2024

Stable Isotope and Geochemical Evidence for Hydrological Isolation in an Arctic Coastal Plain Landscape, Barrow, Alaska, 2013

Data include results from water chemistry and water isotope analyses for samples collected in Barrow, Alaska during July and September 2013. Samples were from surface and soil pore waters from 15 locations: 3 locations from interlake polygonal terrain, 6 locations associated with interlake drainages, and 6 locations within or at the outlets of different aged drained thaw lake basins (DTLBs). Samples were taken in different drainage flow types at three different depths at each location in and around the Barrow Environmental Observatory. This dataset includes one .csv data file and one .pdf user guide.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Prospects of the ICESat-2 Laser Altimetry Mission for Savanna Ecosystem Structural Studies Based on Airborne Simulation Data

The next planned spaceborne lidar mission is the Ice, Cloud and land Elevation Satellite 2 (ICESat-2), which will use the Advanced Topographic Laser Altimeter System (ATLAS) sensor, a photon counting technique. To pre-validate the capability of this mission for studying three dimensional vegetation structure in savannas, we assessed the potential of the measurement approach to estimate canopy height in an oak savanna landscape. We used data from the Multiple Altimeter Beam Experimental Lidar (MABEL), an airborne photon counting lidar sensor developed by NASA's Goddard Space Flight Center. ATLAS-like data was generated using the MATLAS simulator, which adjusts MABEL data's detected number of signal and noise photons to that expected from the ATLAS instrument. Transects flown over the Tejon ranch conservancy in Kern County, California, USA were used for this work. For each transect we chose to use data from the near infrared channel that had the highest number of photons. We segmented each transect into 50 m, 25 m and 14 m long blocks and aggregated the photons in each block into a histogram based on their elevation values. We then used an automated algorithm to identify cut off points where the cumulative density of photons from the highest elevation indicates the presence of the canopy top and likewise where such cumulative density from the lowest elevation indicates the mean terrain elevation. MABEL derived height metrics were moderately correlated to discrete return lidar (DRL) derived height metrics r(sub 2) and RMSE values ranging from 0.60 to 0.73 and 2.9 m to 4.4 m respectively) but MATLAS simulation resulted in more modest correlations with DRL indices r(sub 2) ranging from 0.5 to 0.64 and RMSE from 3.6 m to 4.6 m). Simulations also indicated that the expected number of signal photons from ATLAS will be substantially lower, a situation that reduces canopy height estimation precision especially in areas of low density vegetation cover. On the basis of the simulated data, there is reason to believe that the ability of ICESat-2 to estimate height in savannas will be comparable to the original ICESat mission although the respective sensors have different measurement principles.

Gwenzi, David↗
NASA NTRS1985

Agricultural applications for thermal infrared multispectral scanner data

The use of the Thermal Infrared Multispectral Scanner (TIMS) data in agricultural landscapes is discussed. The TIMS allows for narrow-band analysis in the 8.2-11.6 micron range at spatial resolutions down to 5 meters in cell size. A coastal plain region in SE Alabama was studied using the TIMS. The crop/plant vigor, canopy density, and thermal response changes for soils obtained from thermal imagery are examined. The application of TIMS data to hydrologic and topographic issues, inventory and conservation monitoring, and the enhancement and extraction of cartographic features is described.

Pelletier, R. E.↗
NASA NTRS2015

Continental Spatio-Temporal Data Analysis with Linear Spectral Mixture Model Using FOSS

This work demonstrates the development and implementation of a Fully Constrained Least Squares (FCLS) unmixing model developed in C++ programming language with OpenCV package and boost C++ libraries in the NASA Earth Exchange (NEX). Visualization of the results is supported by GRASS GIS and statistical analysis is carried in R in a Linux system environment. FCLS was first tested on computer simulated data with Gaussian noise of various signal-to-noise ratio, and Landsat data of an agricultural scenario and an urban environment using a set of global end members of substrate (soils, sediments, rocks, and non-photosynthetic vegetation), vegetation that includes green photosynthetic plants and dark objects which encompasses absorptive substrate materials, clear water, deep shadows, etc. For the agricultural scenario, a spectrally diverse collection of 11 scenes of Level 1 terrain corrected, cloud free Landsat-5 TM data of Fresno, California, USA were unmixed and the results were validated with the corresponding ground data. To study an urbanized landscape, a clear sky Landsat-5 TM data were unmixed and validated with coincident World View-2 abundance maps (of 2 m spatial resolution) for an area of San Francisco, California, USA. The results were evaluated using descriptive statistics, correlation coefficient, RMSE, probability of success, boxplot and bivariate distribution function. Finally, FCLS was used for sub-pixel land cover analysis of the monthly WELD (Wen-enabled Landsat data) repository from 2008 to 2011 of North America. The abundance maps in conjunction with DMSP-OLS nighttime lights data were used to extract the urban land cover features and analyze their spatial-temporal growth.

Landsat Satellites↗
DOE OSTI2023

Temporal and spatial resolution of distal protein motions that activate hydrogen tunneling in soybean lipoxygenase

The enzyme soybean lipoxygenase (SLO) provides a prototype for deep tunneling mechanisms in hydrogen transfer catalysis. This work combines room temperature X-ray studies with extended hydrogen–deuterium exchange experiments to define a catalytically-linked, radiating cone of aliphatic side chains that connects an active site iron center of SLO to the protein–solvent interface. Employing eight variants of SLO that have been appended with a fluorescent probe at the identified surface loop, nanosecond fluorescence Stokes shifts have been measured. We report a remarkable identity of the energies of activation ( E a ) for the Stokes shifts decay rates and the millisecond C–H bond cleavage step that is restricted to side chain mutants within an identified thermal network. These findings implicate a direct coupling of distal protein motions surrounding the exposed fluorescent probe to active site motions controlling catalysis. While the role of dynamics in enzyme function has been predominantly attributed to a distributed protein conformational landscape, the presented data implicate a thermally initiated, cooperative protein reorganization that occurs on a timescale faster than nanosecond and represents the enthalpic barrier to the reaction of SLO.

Science & Technology - Other Topics↗
DOE OSTI2024

Understanding spatial and temporal drivers of variation in tree hydraulic processes and their consequences for climate feedbacks (Final Technical Report)

This is the final technical report from the first phase of a project that changed institutions. The grant was titled “Understanding spatial and temporal drivers of variation in tree hydraulic processes and their consequences for climate feedbacks.” The overall objectives of this project were to (1) provide model‐compatible datasets of key plant hydraulic traits and status for model evaluation, parameterization and validation and (2) use these data to pinpoint ecosystem responses to a changing hydroclimate by addressing both long‐term climatic drying and episodic extreme droughts. We planned to address the objectives with three research activities to quantify plant responses to chronic water stress and episodic drought: (1) generate high frequency observations of soil and plant hydraulic data across different landscape positions at multiple sites, (2) quantify plant hydraulic trait plasticity in response to experimental soil moisture reduction in situ in two central hardwood forests, and (3) simulate the carbon consequences of incorporating plant hydrodynamics and plant acclimation to water stress in the DOE‐sponsored plant hydrodynamics model FATES‐HYDRO. As of the transfer of this project to another institution, we had made substantial progress on activities 1 and 2, and started activity 3.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

Analysis of terrestrial conditions and dynamics

Land spectral reflectance properties for selected locations, including the Goddard Space Flight Center, the Wallops Flight Facility, a MLA test site in Cambridge, Maryland, and an acid test site in Burlington, Vermont, were measured. Methods to simulate the bidirectional reflectance properties of vegetated landscapes and a data base for spatial resolution were developed. North American vegetation patterns observed with the Advanced Very High Resolution Radiometer were assessed. Data and methods needed to model large-scale vegetation activity with remotely sensed observations and climate data were compiled.

Goward, S. N.↗
NASA NTRS2018

Modeling and Observations of Outlet Canyons from Lake Overflow Floods on Early Mars

Numerous observations from both orbital remote sensing [1-3] and Mars Curiosity [4] suggest that lakes were once part of the martian landscape. From orbital data, one of the key lines of evidence for past paleolakes is the existence of several hundred valley network-fed basins – usually craters – that have outlet valleys that remain perched above their floors. The existence of outlets requires that water ponded to the point that it overflowed confining topography. Beyond recognizing these landforms, there has been only limited work reconstructing the morphometry, formative hydrology, and incision history for these outlets. Here, we describe our recently published observations of outlets and ongoing numerical modeling looking at these factors.

Fassett, Caleb I.↗
NASA NTRS1984

Spatial variation analyses of Thematic Mapper data for the identification of linear features in agricultural landscapes

A need exists for digitized information pertaining to linear features such as roads, streams, water bodies and agricultural field boundaries as component parts of a data base. For many areas where this data may not yet exist or is in need of updating, these features may be extracted from remotely sensed digital data. This paper examines two approaches for identifying linear features, one utilizing raw data and the other classified data. Each approach uses a series of data enhancement procedures including derivation of standard deviation values, principal component analysis and filtering procedures using a high-pass window matrix. Just as certain bands better classify different land covers, so too do these bands exhibit high spectral contrast by which boundaries between land covers can be delineated. A few applications for this kind of data are briefly discussed, including its potential in a Universal Soil Loss Equation Model.

Pelletier, R. E.↗
NASA NTRS1993

Landscape and geomorphic survey of Zhamanshin area, Northern Kazakhstan: Preliminary report on 1992 field trip data

The June 1992 geographical expedition to Zhamanshin crater area made a landscape and geomorphic survey of this impact structure. Observational points in the area of 300 sq km give new data on the geographical processes in the impact crater. The data obtained could be applied to search for unknown impact structures anywhere through the traces of such features in landscapes.

Burba, G. G., Jr.↗
NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 1: Development of models predicting surface shelter temperatures

Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%-95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. In addition, root-mean-square errors (rmse's) were over 3 C for GHCN models and over 2 C for COOP models for winter months, and near 2 C for GHCN models and near 1.5 C for COOP models for summer months.

Epperson, David L.↗
NASA NTRS1998

Thermal Infrared Remote Sensing for Analysis of Landscape Ecological Processes: Methods and Applications

Thermal Infrared (TIR) remote sensing data can provide important measurements of surface energy fluxes and temperatures, which are integral to understanding landscape processes and responses. One example of this is the successful application of TIR remote sensing data to estimate evapotranspiration and soil moisture, where results from a number of studies suggest that satellite-based measurements from TIR remote sensing data can lead to more accurate regional-scale estimates of daily evapotranspiration. With further refinement in analytical techniques and models, the use of TIR data from airborne and satellite sensors could be very useful for parameterizing surface moisture conditions and developing better simulations of landscape energy exchange over a variety of conditions and space and time scales. Thus, TIR remote sensing data can significantly contribute to the observation, measurement, and analysis of energy balance characteristics (i.e., the fluxes and redistribution of thermal energy within and across the land surface) as an implicit and important aspect of landscape dynamics and landscape functioning. The application of TIR remote sensing data in landscape ecological studies has been limited, however, for several fundamental reasons that relate primarily to the perceived difficulty in use and availability of these data by the landscape ecology community, and from the fragmentation of references on TIR remote sensing throughout the scientific literature. It is our purpose here to provide evidence from work that has employed TIR remote sensing for analysis of landscape characteristics to illustrate how these data can provide important data for the improved measurement of landscape energy response and energy flux relationships. We examine the direct or indirect use of TIR remote sensing data to analyze landscape biophysical characteristics, thereby offering some insight on how these data can be used more robustly to further the understanding and modeling of landscape ecological processes.

Quattrochi, Dale A.↗
DOE OSTI2022

Developing an Interactive Landscape for Mobility Resources: Preprint

As the world continues to be increasingly driven by data, the ways researchers and professionals sort and collect this data is critical. In the world of mobility data, new levels of data from public transportation systems, location services, and other means are being lost due to how little organization exists. Much of the data is proprietary, and there are few if any de jure or even de facto standards connecting data. There is also little knowledge about the gaps that exist in the data. In this project, we created an interactive landscape where mobility resources are categorized and organized in an easy to use, living document. We made this landscape with open-source code from the CNCF Cloud Native Landscape and repurposed it to the mobility data's needs. Additionally, unlike previous sources that organize mobility data, this document can be updated through GitHub by those in the field to keep its sources relevant. Following the creation of a beta version of the landscape, we conducted several interviews with industry researchers and professionals to ensure the landscape would be useful. The result is an online hub where mobility researchers and resource creators can easily access research and collaborate.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Assessing biogeographic survey gaps in bacterial diversity knowledge: A global synthesis of freshwaters

Freshwaters account for 0.8% of Earth's surface area, yet support >10% of known plant and animal species making them disproportionately biodiverse. Modern molecular techniques have begun to reveal microbial diversity, but application of these approaches to address global microbial biogeography is relatively unknown in freshwaters. Our aim was to identify gaps in microbial data coverage along climatic and landscape disturbance gradients and among terrestrial biomes and hydrographic regions for all freshwater ecosystems and three freshwater habitat types: lakes and reservoirs (lentic); streams and rivers (lotic); and wetlands. We reviewed literature on microbial diversity in freshwaters surveyed using 16S ribosomal RNA sequencing which identify microbial taxa. We georeferenced survey locations and used a geographic information system to identify and map gaps in survey coverage using open-source data for climate, landscape disturbance, terrestrial biomes, and freshwater ecoregions. In our study, we compiled 3,425 georeferenced survey locations reported from 963 studies. Streams were surveyed most frequently (60.8% of survey locations), followed by lakes (33.5%) and wetlands (5.6%). Surveys were concentrated in North America, central and western Europe, and Southeast Asia; 35% of freshwater ecoregions were surveyed at least once across freshwater habitat types, whereas 23%, 23%, and 12% were surveyed at least once for lentic, lotic, and wetland habitat types, respectively. The climatic gap analysis indicated coverage is high for temperate regions but lacking in the tropics and Arctic, particularly for wetland ecosystems. Our assessment revealed high climatic coverage of freshwater microbial diversity knowledge, but expansive ecoregional gaps attributable to biased sampling near research institutions in North America, western Europe, and China. Future surveys should target ecoregions in Africa, South America, Central Asia, Australia, and Antarctica. An essential next step will be to curate and disseminate sequencing efforts to facilitate the study of processes driving global diversity patterns.

16S rRNA↗
DOE OSTI2021

The Role of Mobility Data Hubs in an Integrated Decarbonized Transportation Future

The landscape for connected mobility ecosystems is evolving rapidly as information and communication technologies lower the cost and complexity of connecting people to places, integrating transportation modes and collecting data regarding such movements. These developments have been key to unlocking new business opportunities, particularly through mobility services. While the mechanisms for data collection, processing, and transfer have made significant advances in the past decade, the broader landscape of mobility data architectures and data users remains largely unresolved. It is unclear as to whether the result will converge towards a framework that resembles a coherent quilt or a disjointed patchwork of competing visions. Initial approaches to mobility data collection and provisioning have been largely siloed - by mode or software - or held for exclusive use, however several key players are quickly realizing the need and opportunities enabled through integrated mobility data eco-systems, or mobility data hubs as referred to in this paper. As the business case for hosting mobility data hubs evolves, there is great uncertainty regarding their impact to either advance or exacerbate sustainable mobility (e.g., seamless connectivity across modes, decreased energy consumption and greenhouse gas emissions, etc.). Groups such as the United Nations and World Bank have identified data platforms as a key enabler of realizing environmental and social benefits. If designed with decarbonization in mind, we hypothesize that enhanced observability provided by these ever-expanding mobility data hubs can facilitate energy and emissions reductions that are otherwise limited by transactional barriers and knowledge asymmetry that is inherent to a more siloed approach. In this sense, integration of mobility data can help to create a competitive playing field where value is not determined by exclusivity of data, but rather the quality and uniqueness of a given service. The goals of this paper are to 1) identify key players and data architectures that are emerging in a service-based mobility market, 2) explore several use cases where mobility data hubs have enabled greater sustainability outcomes, and 3) discuss key issues that will need to be resolved to fully leverage emerging mobility data hubs towards a sustainable transportation future.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2024

Snow Distribution Patterns Revisited: A Physics-Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub-Arctic

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data and scripts associated with “Allometric scaling of hyporheic respiration across basins in the Pacific Northwest USA"

This data package is associated with the publication “Allometric scaling of hyporheic respiration across basins in the Pacific Northwest USA” submitted to JGR-Biogeosciences (Regier et al. 2025).This study used reach-scale modeled estimates of hyporheic aerobic respiration made by the River Corridor Model (Fang et al. 2020) and watershed characteristics across the Willamette and Yakima River basins to explore potential allometric scaling (i.e., power-law relationships between size and function) of cumulative hyporheic respiration across catchment-to-basin scales. Scaling was explored quantitatively via the R2, slope, and y-intercept of relationships between cumulative hyporheic respiration and watershed area, divided into hyporheic exchange flux (HEF) quantiles. We also explored relationships between allometric scaling and other watershed characteristics through linear regression, spatial patterns, and mutual information analyses. Our results also suggest variability of hyporheic respiration allometry for middle exchange flux quantiles, and in relation to land-cover. Our findings provide initial evidence that allometric scaling may be useful for predicting hyporheic biogeochemical dynamics across watersheds from reach to basin scales. This data package is associated with the GitHub repository found at https://github.com/peterregier/rc_wrb_yrb_scaling. The data package is organized into several key directories. The “data” folder contains multiple CSV files, including landscape heterogeneity, scaling analysis, and watershed boundary data. The “figures” folder has all figure files in both PDF and PNG formats. Core analysis scripts and figure generation scripts are in the “scripts” directory, systematically numbered for sequential execution. The root directory includes essential project files; please see the file ending in “flmd.csv” for a list and description of all files contained in this data package and the file ending in “dd.csv” for data dictionaries used to describe tabular column headers.

54 ENVIRONMENTAL SCIENCES↗