Search NASA⌕ Search

SEARCH · Search NASA

Results for “Colorado River”

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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Adaptive Graph Learning with Transformer for Multi-Reservoir Inflow Prediction

Reservoir inflow prediction is crucial for water resource management, yet existing approaches mainly focus on single-reservoir models that ignore spatial dependencies among interconnected reservoirs. We introduce AdaTrip as an adaptive, time-varying graph learning framework for multi-reservoir inflow forecasting. AdaTrip constructs dynamic graphs where reservoirs are nodes with directed edges reflecting hydrological connections, employing attention mechanisms to automatically identify crucial spatial and temporal dependencies. Evaluation on thirty reservoirs in the Upper Colorado River Basin demonstrates superiority over existing baselines, with improved performance for reservoirs with limited records through parameter sharing. Additionally, AdaTrip provides interpretable attention maps at edge and time-step levels, offering insights into hydrological controls to support operational decision-making. Our code is available at https://github.com/humphreyhuu/AdaTrip.

Hu, Pengfei [ORNL] (ORCID:0009000367130950)↗
DOE OSTI2023

Continental Scale Hydrostratigraphy: Comparing Geologically Informed Data Products to Analytical Solutions

Abstract This study synthesizes two different methods for estimating hydraulic conductivity (K) at large scales. We derive analytical approaches that estimate K and apply them to the contiguous United States. We then compare these analytical approaches to three‐dimensional, national gridded K data products and three transmissivity (T) data products developed from publicly available sources. We evaluate these data products using multiple approaches: comparing their statistics qualitatively and quantitatively and with hydrologic model simulations. Some of these datasets were used as inputs for an integrated hydrologic model of the Upper Colorado River Basin and the comparison of the results with observations was used to further evaluate the K data products. Simulated average daily streamflow was compared to daily flow data from 10 USGS stream gages in the domain, and annually averaged simulated groundwater depths are compared to observations from nearly 2000 monitoring wells. We find streamflow predictions from analytically informed simulations to be similar in relative bias and Spearman's rho to the geologically informed simulations. R ‐squared values for groundwater depth predictions are close between the best performing analytically and geologically informed simulations at 0.68 and 0.70 respectively, with RMSE values under 10 m. We also show that the analytical approach derived by this study produces estimates of K that are similar in spatial distribution, standard deviation, mean value, and modeling performance to geologically‐informed estimates. The results of this work are used to inform a follow‐on study that tests additional data‐driven approaches in multiple basins within the contiguous United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Hydrologic windows into the crystalline basement and their controls on groundwater flow patterns across the Paradox Basin, western USA

Conceptual models of sedimentary basin groundwater flow systems typically assume that the crystalline basement acts as an impermeable boundary and can be neglected. In this study, we use hydrologic models constrained by isotopic and geochemical datasets to argue that the La Sal Mountains, Utah, USA, act as a hydrologic window into the Paradox Basin’s lower aquifer system and underlying crystalline basement. We conducted a sensitivity study in which we varied crystalline basement/laccolith permeability as well as fault zone connectivity along a cross-sectional transect from the La Sal Mountains to Lisbon Valley. When the crystalline basement/laccolith units are set at relatively permeable levels (10–14 m 2 ), simulated tracers that include total dissolved solids, oxygen isotopic composition of pore fluids (δ 18 O), and groundwater residence times are in closest agreement with field measurements. Model results indicate that pore fluids in the basal aquifer system underlying the Paradox Formation confining unit are a mixture of relatively young meteoric fluids and older Paradox Formation brines. The presence of faults did not significantly modify fluid exchange between the upper and lower aquifer systems. This was due, in part, to underpressuring within the Paradox Formation. Our study concludes that the Paradox Basin represents a regional recharge area for the Colorado Plateau, with groundwater discharge occurring along the Colorado River within the Grand Canyon some 375 km away to the southwest. This is only possible with a permeable crystalline basement. Our findings help explain the genesis of Mississippi Valley-type ore deposits of the US Midcontinent, where the presence of a permeable basement may be useful in addressing issues related to solute mass and energy balance.

54 ENVIRONMENTAL SCIENCES↗
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↗
DOE OSTI2020

NEON AOP foliar trait maps, maps of model uncertainty estimates, and conifer map, East River, CO 2018

This data package contains mapped trait estimates and their uncertainties, and conifer map, for the National Ecological Observatory Network's Airborne Observation Platform survey data acquired over the Upper East River, Colorado in 2018. For full details, please see associated reference. in brief, trait models were developed independently for needle and non-needle leaf species using partial least squares regression (PLSR) using ground data from additional datasets: doi:10.15485/1618130, doi:10.15485/1618132, and doi:10.15485/1631278, merged with extracted spectral data from doi:10.15485/1618131. We separated vegetated pixels into needle and non-needle classes in order to generate a classification map based on the spectral differences between these leaf types (conifer.tif). We trained a deep learning model with custom architecture, detailed in Chadwick et al. In Press. The model performed with 0.998 true positive rate and 0.982 true negative rate, with ‘positives’ being non-needle identification. We then utilized PLSR to generate models of foliar traits for each leaf type. So that we could also map uncertainty in these predictions, we generated ten different models for needle and non-needle leaf species using different testing holdout sets of discrete sites. Each of these models was developed with a 100-fold cross validation procedure that utilized a 70% training set and 30% validation set with each fold, and then assessed based on the 10% of testing sites that were not included in that model’s development. The mean predicted value across the 10 models is used for the trait estimate in each pixel across the study area. The models are applied according to the leaf type designation in the conifer.tif map. The errors are the standard deviation across the 10 different models developed, with high error suggesting instability in model prediction and areas where values may not be reliable for ecological inference. These maps are only applied to areas with a NDVI > 0.5 to exclude non-vegetated areas. Shade masks could be applied to these data (doi:10.15485/1618131), but have not been for this data package. These data are also available on Google Earth Engine: https://code.earthengine.google.com/?asset=users/kdc/ER_NEON

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

ParFlow-CLM model simulation files for Maina et al., Journal of Hydrology, 2022

This dataset contains the files to run a ParFlow-CLM integrated hydrologic model simulation for Maina et al., HESS, 2022. It also contains the associated daily pressure output of those simulations. Simulations are for a hillslope-similarity approach based on seasonal groundwater changes in the East River, Colorado. Comparisons are made to different clustering, or functional zonation approaches. We assess the ability of these clustering approaches to identify and categorize hillslopes with similar static characteristics, hydroclimate, land surface processes, and subsurface dynamics in a mountainous watershed. ParFlow binary files (.pfb) are model specific, and can be modified/viewed with conversion to .sa, .silo, or .vtk file formats. ParFlow input scripts are in the .tcl file format. Associated post-processing scripts are included as .f90 files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Geophysical survey associated with NEON AOP survey, East River, CO 2018

The package contains data layers developed and used in Falco et al. 2024: “EcoImaging: Advanced Sensing to Investigate Plant and Abiotic Hierarchical Spatial Patterns in Mountainous Watersheds". The package is part of the DOE Watershed Function Science Focus Area (SFA) project and includes geophysical measurements collected at the East River, Colorado, in conjunction with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey conducted in June 2018. This dataset provide soil geophysical information and were used to investigate soil-plant relationships. The dataset consists of: - NEON_2018_EMI_survey.zip: the electromagnetic induction (EMI) survey as shape-file; - NEON_plot_TDR.csv: plot‑level data from Time‑Domain Reflectometry (TDR) measurements, providing: * volumetric water content (VWC) in percent (%); * soil temperature in degrees Celsius (°C); - file level metadata (flmd.csv) - data dictionary (dd.csv) file 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.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2026

Soil physical and chemical measurements for topsoils collected during NEON campaign in East River, CO (06/14/2018-06/28/2018)

The package is part of the DOE Watershed Function Science Focus Area (SFA) project and includes soil physical and chemical measurements from topsoils collected at the East River, Colorado, in conjunction with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey conducted in June 2018. The soil measurements include soil bulk density, soil volumetric water content, soil microbial biomass C (Carbon), N (Nitrogen) and C:N (C to N ratio), soil DNA yield, soil total extractable organic C, soil total extractable N, soil extractable nitrate, soil extractable ammonium, soil dissolved inorganic N, soil dissolved organic N, soil pH, soil TOC400 (total organic carbon at 400°C), soil ROC (residual oxidizable carbon), soil TIC (total inorganic carbon), soil TOC (total organic carbon), soil TC (total carbon), soil N, soil OM (organic matter) loss on ignition. Additional associated site metadata can be found in the ESS-DIVE package 10.15485/1618130. The dataset includes (1) 2018_NEON_soil_physical_chemical_measurements.csv: soil physical and chemical measurements indexed by soil sample IGSNs; (2) samples.csv: sample metadata file used to register International Generic Sample Numbers (IGSNs); (3) flmd.csv: file level metadata file; and (4) dd.csv: data dictionary file. 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.

2018 NEON and 2025 CHESS (Catchment Hydrology and ↗
DOE OSTI2022

2021 Annual Report: Atmospheric Radiation Measurement

In fiscal year 2021 (FY2021), the COVID-19 pandemic continued to have an impact on activities within the Atmospheric Radiation Measurement (ARM) user facility. Travel was limited, which affected field activities and forced the continuation of virtual meetings. However, the ability to travel expanded significantly through the year, bringing some return to normalcy, and throughout the year, there was a great deal of activity dedicated to advancing the facility. Because of COVID, ARM twice delayed the TRacking Aerosol Convection interactions ExpeRiment (TRACER) in the Houston, Texas, area. Originally planned to launch in the spring, TRACER started October 1, 2021, one month after the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign began near Crested Butte, Colorado. ARM teams worked onsite and remotely to make sure both campaigns could launch on this new schedule that was set early in the year. Data from SAIL and TRACER will be critical to improving earth system models, each contributing to different sets of issues. SAIL measurements will provide insights into how precipitation forms and water travels through the Upper Colorado River Basin. TRACER scientists want to know whether tiny atmospheric particles can influence the severity of thunderstorms. In October 2020, the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition ended after 13 months. MOSAiC data from ARM and other organizations are already helping scientists better understand how ice, atmosphere, and ocean systems are connected in the central Arctic. This report discusses some early investigations from MOSAiC, along with other ARM campaigns and activities generating prolific research. In this report, you will also learn how research activities moved forward during the pandemic at ARM’s fixed-location atmospheric observatories. There were important science applications from measurements across the facility. ARM spent a significant amount of FY2021 looking back—and ahead. In November 2020, ARM had its Triennial Review. This review is held every three years to evaluate ARM’s effectiveness in science, operations, and management. Overall, the reviewers had positive feedback regarding the breadth and impact of science activities using ARM data and the way ARM strives to meet changing user needs. The reviewers also provided some recommendations for strengthening ARM going forward. Incorporating feedback from the review, we finalized an updated Decadal Vision document that will help guide ARM priorities in the coming years. This report describes the four themes driving ARM’s Decadal Vision.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

SAIL Supermicron Bioaerosol (SSB) Field Campaign Report

The Surface Atmosphere Integrated Field Laboratory (SAIL) Supermicron Bioaerosol (SSB) study was designed to complement the larger Atmospheric Radiation Measurement (ARM) SAIL science goals to understand aerosol processes and aerosol-cloud interactions in the Colorado River’s East River Watershed Basin. Supermicron aerosol, particles larger than one micron in diameter, and bioaerosols, primary particles containing biological material, are important for understanding atmospheric processes and sources within high mountainous terrain. For example, they can interact with clouds and the water cycle such as by serving as nuclei for cloud droplets and ice crystals that can eventually result in precipitation events. These particles can also be recycled back to the surface via wet and dry deposition, where they can alter plant and microbial growth and/or be reemitted again, forming a positive feedback loop within the Earth system known as bioprecipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Financial Analysis of the Smallmouth Bass Flows implemented at the Glen Canyon Dam during 2025

The Glen Canyon Dam (GCD) is a Colorado River Storage Project (CRSP) power resource that is a component of the Salt Lake City Area Integrated Projects (SLCA/IP). The 2016 record of decision (ROD) for the GCD long-term experimental and management plan (LTEMP) final Environmental Impact Statement (EIS) specifies criteria for GCD monthly water releases, daily and hourly operating limits, and experimental releases.

Ploussard, Quentin [Argonne National Laboratory (A↗
DOE OSTI2023

Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods

Long short-term memory (LSTM) networks have demonstrated successful applications in accurately and efficiently predicting reservoir releases from hydrometeorological drivers including reservoir storage, inflow, precipitation, and temperature. However, due to its black-box nature and lack of process-based implementation, we are unsure whether LSTM makes good predictions for the right reasons. In this work, we use an explainable machine learning (ML) method, called SHapley Additive exPlanations (SHAP), to evaluate the variable importance and variable-wise temporal importance in the LSTM model prediction. In application to 30 reservoirs over the Upper Colorado River Basin, United States, we show that LSTM can accurately predict the reservoir releases with NSE ≥ 0.69 for all the considered reservoirs despite of their diverse storage sizes, functionality, elevations, etc. Additionally, SHAP indicates that storage and inflow are more influential than precipitation and temperature. Moreover, the storage and inflow show a relatively long-term influence on the release up to 7 days and this influence decreases as the lag time increases for most reservoirs. These findings from SHAP are consistent with our physical understanding. However, in a few reservoirs, SHAP gives some temporal importances that are difficult to interpret from a hydrological point of view, probably because of its ignorance of the variable interactions. SHAP is a useful tool for black-box ML model explanations, but the hydrological processes inferred from its results should be interpreted cautiously. More investigations of SHAP and its applications in hydrological modeling is needed and will be pursued in our future study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

GUC CSU X-Band Precipitation Extracted Radar Columns and In-Situ Sensors (RadCLss)

For the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign, a scanning X-Band dual-polarimetric radar provided by Colorado State University (CSU) was deployed to estimate precipitation within the Upper Colorado River Basin. In order to evaluate precipitation estimates from the CSU X-Band Radar (XPRECIPRADAR), radar columns are extracted above field sites of interest to the SAIL campaign. These radar columns are then matched to in-situ sensors at these locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

GUC CSU X-Band Precipitation Radar Surface QUantitatIve pRecipitation Estimation (SQUIRE)

For the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign, a scanning X-Band dual-polarimetric radar provided by Colorado State University (CSU) was deployed to estimate precipitation within the Upper Colorado River Basin. In order to evaluate precipitation estimates from the CSU X-Band Radar (XPRECIPRADAR), Surface QUantitatIve pRecipitation Estimation (SQUIRE) was created to provide the SAIL community with a gridded dataset of precipitation estimates for comparison with hydrology and atmospheric models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

WRF East River simulation Oct 2021 - Dec 2022

These are the Weather Research and Forecasting (WRF) regional climate model simulations for supporting the analysis of temperature, precipitation, and other hydroclimate variables and evaluating SAIL data. The WRF model has three nested domains centered at the SAIL location (East River, Colorado) for the SAIL period from Oct 01, 2021 to Dec 31, 2022. We used the BSU subgrid-scale physics schemes, CFSR meteorological forcing datasets, and the topographic shading radiation schemes in our WRF simulation. Detailed information on the model configuration can be found at https://doi.org/10.5194/egusphere-2022-437

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Fluorescent Particle Vertical Profiles (Counts and Fractions) at SAIL

This dataset contains vertically resolved fluorescent aerosol number concentrations and fluorescent fractions of total aerosol number concentrations measured with the ARM Tethered Balloon System during the Surface Atmosphere Integrated Field Laboratory campaign near Gothic, Colorado. Measurements were collected during April and June 2023 as part of the ARM–EMSL FICUS project “Characterization of the Aerobiome during the ARM Surface-Atmosphere Integrated Laboratory Campaign,” which was designed to characterize the vertical distribution of bioaerosol-relevant particles in the Colorado River Basin. The dataset includes profiles of fluorescent particle concentrations measured with a Fluorescent Portable Optical Particle Sizer (FPOPS) in the approximately 0.5–3 µm diameter range. For fluorescent fractions of total aerosol number concentrations, we compare FPOPS measurements to POPS on the same TBS flight.

Fluorescent particle concentration↗
NASA NTRS1971

Recognition on space photographs of structural elements of Baja California

Gemini and Apollo photographs provide illustrations of known structural features of the peninsula and some structures not recognized previously. An apparent transform relationship between strike-slip and normal faulting is illustrated by the overlapping vertical photographs of northern Baja California. The active Agua Blanca right-lateral strike-slip fault trends east-southeastward to end at the north end of the Valle San Felipe and Valle Chico. The uplands of the high Sierra San Pedro Martir are a low-relief surface deformed by young faults, monoclines, and warps, which mostly produce west-facing steps and slopes; the topography is basically structural. The Sierra Cucapas of northeasternmost Baja California and the Colorado River delta of northwesternmost Sonora are broken by northwest-trending strike-slip faults. A strike-slip fault is inferred to trend northward obliquely from near Cabo San Lucas to La Paz, thence offshore until it comes ashore again as the Bahia Concepcion strike-slip fault.

Hamilton, W.↗