Search NASA⌕ Search

SEARCH · Search NASA

Results for “river network”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Wasatch Environmental Observatory: A mountain to urban research network in the semi‐arid western US

Abstract The 2085 km 2 Jordan River Basin, and its seven sub‐catchments draining the Central Wasatch Range immediately east of Salt Lake City, UT, are home to an array of hydrologic, atmospheric, climatic and chemical research infrastructure that collectively forms the Wasatch Environmental Observatory (WEO). WEO is geographically nested within a wildland to urban land‐use gradient and built upon a strong foundation of over a century of discharge and climate records. A 2200 m gradient in elevation results in variable precipitation, temperature and vegetation patterns. Soil and subsurface structure reflect systematic variation in geology from granitic, intrusive to mixed sedimentary clastic across headwater catchments, all draining to the alluvial or colluvial sediments of the former Lake Bonneville. Winter snowfall and spring snowmelt control annual hydroclimate, rapid population growth dominates geographic change in lower elevations and urban gas and particle emissions contribute to episodes of severe air pollution in this closed‐basin. Long‐term hydroclimate observations across this diverse landscape provide the foundation for an expanding network of infrastructure in both montane and urban landscapes. Current infrastructure supports both basic and applied research in atmospheric chemistry, biogeochemistry, climate, ecology, hydrology, meteorology, resource management and urban redesign that is augmented through strong partnerships with cooperating agencies. These features allow WEO to serve as a unique natural laboratory for addressing research questions facing seasonally snow‐covered, semi‐arid regions in a rapidly changing world and an excellent facility for providing student education and research training.

Follstad Shah, Jennifer J.↗
DOE OSTI2020

Spatiotemporal Variability in Transport and Reactive Processes Across a First- to Fifth-Order Fluvial Network

Fluvial networks integrate and transport constituents from terrestrial and aquatic ecosystems. To date, most research on water quality dynamics has focused on process understanding at individual streams and, as a result, there is a lack of studies analyzing how small-scale, physical and biogeochemical drivers scale across fluvial networks. We performed tracer tests in five stream orders of the Jemez River continuum in New Mexico, USA, to quantify reach-scale hyporheic exchange during two different seasonal periods to address: how do hyporheic zone contributions to riverine processing change with space and time? And does the spatiotemporal variability of hyporheic exchange scale across fluvial networks? Combining conservative (i.e., bromide) and reactive (i.e., resazurin) tracer analyses with solute transport modeling, we found a dominance of reaction-limited transport conditions in space (for all stream orders) and time (two contrasting flow regimes). Moreover, we found a trend toward more reaction-limited conditions with increasing discharge across all stream orders. While our studied fluvial network did not consistently follow the expectations of decreasing hyporheic exchange contributions with increasing stream order, as it has been previously suggested from modeling studies, we found that processing rate coefficients and Damköhler numbers consistently decreased along the continuum with increasing discharge. Our findings suggest that knowledge transferability of hyporheic zone processing along fluvial networks may be possible if the scaling patterns that we found hold true in other catchments. Thus, more research on scaling patterns along fluvial networks is highly needed.

Gootman, Kaylyn S.↗
DOE OSTI2021

Schema Elements for Granta Annual Report

Granta: Materials Intelligence, also known as Granta:MI or Granta, is a commercial database software by Ansys, Inc. that is utilized by the Nuclear Security Enterprise (NSE) to organize and store materials data relevant to the complex. The software was first adopted by individual sites in the early 2000’s, but in FY20, enterprise-wide licensing was purchased for the NSE by the Product Realization Integrated Digital Enterprise (PRIDE) program. Currently, there are 375 floating licenses available for users at all sites. There are also two shared instances of Granta; a classified production instance on the Enterprise Secure Network (ESN) and an unclassified development instance. Additionally, some individual sites such as Los Alamos National Laboratory (LANL), Lawrence Livermore National Laboratory (LLNL), Sandia National Laboratories (SNL), Kansas City National Security Campus (KCNSC), and Savannah River National Laboratory (SRNL) host their own local Granta instances on their own networks that, in general, only their employees can access.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Leaf mass per area and leaf water content measurements from field survey in association with NEON AOP survey, East River, CO 2018

Leaf area, wet, and dry weights for leaf samples from sites where field sampling was conducted during the 2018 National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) imaging spectroscopy and lidar surveys in Gunnison County, Colorado. The sampling sites were located across East River, Washington Gulch, Slate River, and Coal Creek watersheds and contained a mixture of meadow, shrub, and tree sampling sites. This data package contains the leaf area and wet and dry weights for leaves from tree and shrub sampling sites, as well as samples for meadow species present within a given sampling area. Associated data packages contain species composition information at the plot level (DOI: 10.15485/1618130), as well as additional biogeochemical, microbial, and geophysical data as they become available. For full documentation, please see associated reference.This package contains zipped folders of all scans that were used for area calculations for both sampling sites (tree/shrub, site_scans.zip) as well as representative samples from a daily sampling area of meadow species that were found in the 1 m plots (meadow_area_scans.zip). It also contains the macro code that was used in ImageJ for processing of the area calculations. Finally, it includes excel files with the area (cm2), wet weight (g), dry weight (g), leaf mass per area (LMA, g/m2), and leaf water content (%) for each set of scans.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Assessing Sediment Inputs into the Shoshone River in Wyoming to Determine Areas for Protection and Restoration Practices

In 2016, a routine repair operation at the Willwood Dam released tons of built-up sediment into the Shoshone River, polluting the river and killing thousands of fish. This release greatly concerned the communities that rely on the river for farming, recreation, and tourism. In partnership with the Wyoming Department of Environmental Quality (WDEQ), Shoshone River Partners, and the United States Geological Survey (USGS) Wyoming-Montana Water Science Center, this multipart project illustrated the application of high-resolution satellite imagery and hydrological modeling techniques to identify major tributaries contributing to sediment influxes into the Shoshone River between the Buffalo Bill Dam and the Willwood Dam. The team used PlanetScope and Sentinel-2 Multi-Spectral Instrument (MSI) satellite images to assess changes in the surface reflectance of the river in response to precipitation events. To determine these storm events, the team selected dates of interest using Global Precipitation Measurement (GPM) Integrated Multi-Spectral Retrieval (IMERG) and gauge precipitation data from the Global Historical Climatology Network Daily (GHCNd). The continuation of this research further quantified sediment influx in the Shoshone River following snowfall and melt events using the Soil & Water Assessment Tool (SWAT). The precipitation analysis and sediment contribution maps helped the partner organizations prioritize their current decision making and best management practices to specific sites along the river. The results demonstrated the feasibility of using PlanetScope data for categorizing sediment in the Shoshone River.

Caroline Williams↗
DOE OSTI2024

Scenario Planning Management Actions to Restore Cold Water Stream Habitat: Comparing Mechanistic and Statistical Modeling Approaches

ABSTRACT Under the United States Clean Water Act, states are required to periodically assess state waters to determine compliance with water quality criteria (including temperature) and then to develop total maximum daily loads (TMDLs) for impaired waters as necessary to bring them into compliance. We compared the performance of mechanistic stream temperature models (HeatSource, QUAL2K, and QUAL2Kw) applied to the mainstem of three TMDL watersheds (Middle Fork John Day, OR; Wind River, WA; South Fork Nooksack, WA) with that of spatial stream network (SSN) models applied to the full watersheds and used these to evaluate the potential effectiveness of restoration strategies. SSN models performed well with slightly lesser accuracy (RMSE = 0.47–0.87) for mainstem predictions than mechanistic models (RMSE = 0.4) but provided additional benefits to inform management, including information on spatial and temporal heterogeneity of restoration effectiveness throughout the watershed. Of the four scenarios considered (restoration of riparian zones to potential natural vegetation, channel narrowing, increasing flow by restricting irrigation withdrawals, and combined applications), riparian zone restoration was consistently the most effective in reducing temperatures at the outlet, mainstem, and throughout the watersheds. Predicted restoration effectiveness for thermal regimes varied significantly both within and among watersheds. A focus on water quality criteria exceedance only at the watershed outlet or along the mainstem reach can obscure knowledge of restoration potential for fish habitat in tributaries and headwaters, potential for creation of thermal refuge areas along the mainstem critical for maintaining migration corridors, and thermal regime heterogeneity across space and time.

Fuller, M. R.↗
NASA NTRS1986

Rapid evolution of a jet streak circulation in a pre-convective environment

An analysis of the April 10, 1979 Red River Valley severe weather outbreak, using a three-hourly rawinsonde network, indicates that the preconvection environment is influenced by upper-level and lower-level tropospheric jet streaks (ULJs and LLJs) that act to destabilize the atmosphere, and contribute to low-level heat and moisture transports and convergence that act to initiate the storm system. Transformation of an indirect circulation noted within the exit region of the ULJ at 1200 and 1500 GMT is observed within a six-hour period. Dramatic changes are found in the jet streak circulations over a short period of time as the system deviates from that approximated by the geostrophic momentum approximation, and these deviations suggest that adjustments asssociated with ULJs in this case could not be resolved using a simplified two-dimensional approach.

Kocin, P. J.↗
NASA NTRS2006

Flood and Landslide Applications of High Time Resolution Satellite Rain Products

Experimental, potentially real-time systems to detect floods and landslides related to heavy rain events are described. A key basis for these applications is high time resolution satellite rainfall analyses. Rainfall is the primary cause for devastating floods across the world. However, in many countries, satellite-based precipitation estimation may be the best source of rainfall data due to insufficient ground networks and absence of data sharing along many trans-boundary river basins. Remotely sensed precipitation from the NASA's TRMM Multi-satellite Precipitation Analysis (TMPA) operational system (near real-time precipitation at a spatial-temporal resolution of 3 hours and 0.25deg x 0.25deg) is used to monitor extreme precipitation events. Then these data are ingested into a macro-scale hydrological model which is parameterized using spatially distributed elevation, soil and land cover datasets available globally from satellite remote sensing. Preliminary flood results appear reasonable in terms of location and frequency of events, with implementation on a quasi-global basis underway. With the availability of satellite rainfall analyses at fine time resolution, it has also become possible to assess landslide risk on a near-global basis. Early results show that landslide occurrence is closely associated with the spatial patterns and temporal distribution of TRMM rainfall characteristics. Particularly, the number of landslides triggered by rainfall is related to rainfall climatology, antecedent rainfall accumulation, and intensity-duration of rainstorms. For the purpose of prediction, an empirical TMPA-based rainfall intensity-duration threshold is developed and shown to have skill in determining potential areas of landslides. These experimental findings, in combination with landslide surface susceptibility information based on satellite-based land surface information, form a starting point towards a potential operational landslide monitoring/warning system around the globe.

Adler, Robert F.↗
DOE OSTI2024

X-Band Radar and Surface-Based Observations of Cold-Season Precipitation in Western Colorado’s Complex Terrain

Abstract Hydrologic processes associated with intermountain cold-season precipitation in the Upper Colorado River basin have important impacts on avalanche forecasting and water resource management. However, traditional weather radar networks struggle with observations in this complex terrain. Data collected during the Study of Precipitation, the Lower Atmosphere, and the Surface for Hydrometeorology (SPLASH) and its sister campaign, Surface Atmosphere Integrated Field Laboratory (SAIL) in the East River watershed of western Colorado, are used to examine a multistorm period from 23 December 2021 to 1 January 2022 that contributed 35% of the total winter precipitation in this watershed. Dual-polarization X-band radar and disdrometer measurements show ∼30-mm differences in precipitation amount at two sites in proximity over four distinct storm events within the period. Wind patterns, synoptic forcings, microphysical characteristics of precipitation, and surface meteorology are analyzed to explain the observed spatial variability of cold-season precipitation in complex mountainous terrain. Analysis shows that differences over time within this event are mainly accounted for by synoptic forcings, such as frontal passages; differences between sites are accounted for by the impact of variations in local wind patterns on precipitation microphysics. Patterns of surface precipitation intensity are compared and found to be correlated with X-band radar signatures; a relationship between a strong dendritic growth stage and intense low-density surface precipitation is reinforced by this study. This relationship demonstrates the importance of particle growth mechanisms on surface snowfall patterns in high-altitude complex terrain, underscoring the importance of realistic microphysical parameterizations. Significance Statement The amount and density of snowpack from western Colorado winter storms have significant impacts on water resources in the Upper Colorado River basin. Snowpack characteristics are affected by small-scale differences in how snow forms in the atmosphere. These differences are hard to study in the complex terrain of the Rockies, but data from the SPLASH and SAIL field campaigns allows us to investigate how snow crystal formation and mountain-driven wind patterns affect snow near the surface. Our study finds that snow crystal growth varies over small space and time scales and is likely controlled by the terrain beneath a given location and resultant local wind patterns. These results imply that predicting snowpack in the Rockies requires properly representing local wind patterns and crystal growth processes in models.

Heflin, Stella↗
DOE OSTI2021

Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks

Abstract Streamflow forecasting over gauged and ungauged basins play a vital role in water resources planning, especially under the changing climate. Increased availability of large sample hydrology data sets, together with recent advances in deep learning techniques, has presented new opportunities to explore temporal and spatial patterns in hydrological signatures for improving streamflow forecasting. The purpose of this study is to adapt and benchmark several state‐of‐the‐art graph neural network (GNN) architectures, including ChebNet, Graph Convolutional Network (GCN), and GraphWaveNet, for end‐to‐end graph learning. We explicitly represent river basins as nodes in a graph, learn the spatiotemporal nodal dependencies, and then use the learned relations to predict streamflow simultaneously across all nodes in the graph. The efficacy of the developed GNN models is investigated using the Catchment Attributes and MEteorology for Large‐sample Studies (CAMELS) data set under two settings, fixed graph topology (transductive learning), and variable graph topology (inductive learning), with the latter applicable to prediction in ungauged basins (PUB). Results indicate that GNNs are generally robust and computationally efficient, achieving similar or better performance than a baseline model trained using the long short‐term memory (LSTM) network. Further analyses are conducted to interpret the graph learning process at the edge and node levels and to investigate the effect of different model configurations. We conclude that graph learning constitutes a viable machine learning‐based method for aggregating spatiotemporal information from a multitude of sources for streamflow forecasting

Sun, Alexander Y.↗
DOE OSTI2022

Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks

Abstract. Quantifying the spatiotemporal dynamics in subsurface hydrological flows over a long time window usually employs a network of monitoring wells. However, such observations are often spatially sparse with potential temporal gaps due to poor quality or instrument failure. In this study, we explore the ability of recurrent neural networks to fill gaps in a spatially distributed time-series dataset. We use a well network that monitors the dynamic and heterogeneous hydrologic exchanges between the Columbia River and its adjacent groundwater aquifer at the U.S. Department of Energy's Hanford site. This 10-year-long dataset contains hourly temperature, specific conductance, and groundwater table elevation measurements from 42 wells with gaps of various lengths. We employ a long short-term memory (LSTM) model to capture the temporal variations in the observed system behaviors needed for gap filling. The performance of the LSTM-based gap-filling method was evaluated against a traditional autoregressive integrated moving average (ARIMA) method in terms of error statistics and accuracy in capturing the temporal patterns of river corridor wells with various dynamics signatures. Our study demonstrates that the ARIMA models yield better average error statistics, although they tend to have larger errors during time windows with abrupt changes or high-frequency (daily and subdaily) variations. The LSTM-based models excel in capturing both high-frequency and low-frequency (monthly and seasonal) dynamics. However, the inclusion of high-frequency fluctuations may also lead to overly dynamic predictions in time windows that lack such fluctuations. The LSTM can take advantage of the spatial information from neighboring wells to improve the gap-filling accuracy, especially for long gaps in system states that vary at subdaily scales. While LSTM models require substantial training data and have limited extrapolation power beyond the conditions represented in the training data, they afford great flexibility to account for the spatial correlations, temporal correlations, and nonlinearity in data without a priori assumptions. Thus, LSTMs provide effective alternatives to fill in data gaps in spatially distributed time-series observations characterized by multiple dominant frequencies of variability, which are essential for advancing our understanding of dynamic complex systems.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Florida Atlantic Coast Telemetry (FACT) Array: A Working Partnership

The Florida Atlantic Coast Telemetry (FACT) Array is a collaborative partnership of researchers from 24 different organizations using passive acoustic telemetry to document site fidelity, habitat preferences, seasonal migration patterns, and reproductive strategies of valuable sportfish, sharks, and marine turtles. FACT partners have found that by bundling resources, they can leverage a smaller investment to track highly mobile animals beyond a study area typically restrained in scale by funds and manpower. FACT is guided by several simple rules: use of the same type of equipment, locate receivers in areas that are beneficial to all researchers when feasible, maintain strong scientific ethics by recognizing that detection data on any receiver belongs to the tag owner, do not use other members detection data without permission and acknowledge FACT in publications. Partners have access to a network of 480 receivers deployed along a continuum of habitats from freshwater rivers to offshore reefs and covers 1100 km of coastline from the Dry Tortugas, Florida to South Carolina and extends to the Bahamas. Presently, 49 species, (25 covered by Fisheries Management Plans and five covered by the Endangered Species Act) have been tagged with 2736 tags in which 1767 tags are still active.

Scheidt, Douglas↗
DOE OSTI2022

Riparian vegetation shade restoration and loss effects on recent and future stream temperatures

River temperatures are expected to increase this century harming species requiring cold-water habitat unless restoration activities protect or improve habitat availability. Local shading by riparian vegetation can cool water temperatures, but uncertainty exists over the scaling of this local effect to larger spatial extents. Here, we evaluate this issue using a regional spatial stream network temperature model with covariates representing shade effects to predict mean August stream temperatures across 78,195 km of tributaries flowing into the Columbia River in the northwestern United States. We evaluate nine scenarios predicting stream temperatures for three riparian shade conditions (current, restored, and no riparian vegetation) within three different climate periods (2000s, 2040s, and 2080s). Results suggest riparian shade restoration (2000s climate) could decrease mean August stream temperatures by 0.62°C across the study network. Under the same restored shade conditions, temperature predictions for tributaries at their confluence with the Columbia River range from 0.02 to 2.08°C cooler than under current shade conditions. The climate warming effect predicted for the 2040s and 2080s, however, is greater than the cooling effect from restoring riparian shade. Streams less than 10-m bankfull width cooled more frequently with riparian shade restoration. In Oregon, the proportion of fish habitat for salmon and trout rearing and migration that meet temperature numeric water quality criteria could be increased by 20% under restored shade conditions although net habitat declines may still occur in the future. We conclude riparian vegetation restoration could partially mitigate future warming and help maintain cold-water habitats that function as thermal refuges if implemented strategically.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Molecular Engineering of 2D Nanomaterial Field-Effect Transistor Sensors: Fundamentals and Translation across Innovation Spectrum

We report over the last decade, 2D layered nanomaterials have attracted significant attention across the scientific community due to their rich and exotic properties. Various nanoelectronic devices based on these 2D nanomaterials have been explored and demonstrated, including those for environmental applications. Here, the fundamental attributes of 2D layered nanomaterials for field-effect transistor (FET) sensors and tunneling FET (TFET) sensors, which provide versatile detection of water contaminants such as heavy-metal ions, bacteria, nutrients, and organic pollutants, are discussed. The major challenges and opportunities are also outlined for designing and fabricating 2D nanomaterial FET/TFET sensors with superior performance. Translation of these FET/TFET sensors from fundamental research to applied technology is illustrated through a case study on graphene-based real-time FET water sensors. A second case study centers on large-scale sensor networks for water-quality monitoring to enable intelligent drinking water and river-water systems. Overall, 2D nanomaterial FET sensors have significant potential for enabling a human-centered intelligent water system that can likely be applied to other precarious water supplies around the globe.

2D nanomaterials↗
DOE OSTI2024

Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers

Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.

Discrete Wavelet Transform (DWT)↗
DOE OSTI2020

Soil bulk density and texture data collected during field survey associated with NEON AOP survey, East River, CO 2018

The purpose of soil core sampling was to determine soil bulk density, volumetric water content, and soil texture across the sites that were sampled in association with the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) in 2018. Soil cores were collected from surface (2.5-7.5 cm) and subsurface (50-55cm) depths at sampling sites across East River, Washington Gulch, Slate River, and Coal Creek watersheds in Gunnison, Colorado during the summer of 2018. Surface samples were weighed for determination of soil water content prior to dry down for bulk density calculations. For all samples, we measured dry weight and weight of < 2mm fraction and conducted soil textural analysis. We provide metadata for the samples, raw data files, python code that was used to calculate bulk density, water content, and texture fraction densities from primary datasets, and a final csv file with the calculated soil bulk density, volumetric water content, and soil texture data. Additional associated site metadata and vegetation fractional cover information can be found in the associated data release.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Landsat hydrobiological classification for an inland fresh water marsh within Everglades National Park

The considered investigation is concerned with the application of Landsat Multispectral Scanner (MSS) data to the classification of vegetative communities and the establishment of flow vectors for the Shark River Slough in Everglades National Park, Florida. A systematic array of 'ground truth' was established utilizing comprehensive hydrologic field data and conventional high altitude infrared aerial photography. A control network was defined that represented all hydrobiological zones (those wetland vegetative communities that directly influence the rate of overland sheet flow) in the Shark River Slough. These data were then directly applied to the Landsat imagery utilizing an interactive multispectral processor which generated hydrographic maps of the slough and defined the surface radiance characteristics of each hydrobiological system. It was found that the application of Landsat imagery for hydrologic applications in a wetlands area, such as the Shark River Slough in Everglades National Park, is definitely a viable tool for resource management.

Rose, P. W.↗
DOE OSTI2026

Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.

2018 NEON and 2025 CHESS Campaigns↗