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As electric vehicles (EV) become more common, Tribes can guide how charging infrastructure is planned, approved, and built on Tribal lands. Tribes can prepare for future infrastructure projects by updating codes, zoning, parking rules, and permitting process, as well as improving electrical infrastructure and broadband access.
The 2013 New Mexico Mid-Region Travel Survey assessed travel behavior patterns to update a travel demand model for the Albuquerque Metropolitan Planning Area, which consists of Bernalillo County, Valencia County, and southern Sandoval County. It includes the cities of Albuquerque, Rio Rancho, Los Lunas, and Belen as well as some tribal lands. The Mid-Region Council of Governments contracted with Westat to conduct the survey, which included the collection of socio-demographic data and a one-day (24-hour) period of household travel behavior collected during weekdays (Monday through Friday). The survey also included a random selection of a 20% subsample of households (1,023 participants) to take part in a wearable global positioning system, technology-based component of the study, which was used to assess the level of trip under-reporting from the self-reported component of the survey.
NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization” under review. This data package includes five data types: 1) raw photos and videos from drone survey and walking smartphone surveys; 2) images derived from raw videos; 3) manual labeling of reference scales; 4) metadata for all images and photo resolution derived from artificial intelligence (AI) models or manual labels, 5) grain size data obtained from AI models for all photos, 6) metadata and grain size data after quality control, 7) summaries of sample efficiency for all data, and 8) computational fluid dynamics (CFD) data used to support hydro-biogeochemical (HBGC) parameter estimation. Such data is used to 1) demonstrate significant improvements in accuracy, efficiency, and quality control for grain size data collection with the help of AI models, 2) study the spatial heterogeneity of grain size and observation reproducibility based on tens of thousands of data points generated by the AI models, and 3) evaluate the impacts of grain size heterogeneity on key HBGC parameters across sediment-to-reach and hourly-to-yearly scales. In particular, the data package contains 116 folders and 179696 files. The files include 41 videos in .mov format, 64047 photos in .jpg format, 13541 video-derived photos in .png format, 12747 segmentation mask data in .tif format, 12747 segmentation data in .json format, 24771 .csv files that with metadata and grain size for each individual photo as well as water depth and velocity data from CFD and observation, 51791 .txt files of raw AI predicted labels, and 11 flight record data in .srt format. The summary for all metadata and grain size statistics information is included in “Scales_V3_NG.csv” and “Statistics_V3_NG.csv”. The summary for data that pass data quality control (QC) level 0-2 is included in “QCStatistics_V3_NG.csv”. The QC level 0 represents photos whose photo resolution is positive, excluding photos that miss reference scale. The QC level 1 means reference scale circularity uncertainty is less than 5% for smartphone images while representing photo resolution is larger than 0.44 mm/pixel for drone images. The QC level 2 means excluding photos whose grain number is less than 100, a minimum number of grains recommended by classic literature. The summary for each video’s name, length, frame rates, survey area, grain number, survey efficiency, etc. can be found in “QCSummary_V3_NG.csv”. The summary for site name, GPS coordinates, and number of images at each site can be found in “SitesSummary_V3_*.csv” files. Overall computational efficiency summary is reported in Table 4 of accompanying manuscript. Additionally, the nitrate concentration data used in this work was downloaded from an existing dataset published on ESS-DIVE (Boat-Dragged Sensor Hanford Reach.csv; Conner A. et al., 2020). We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Port of Benton, and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the data were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate data collection and optimization of data usage according to their values and worldview.
NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.
This data package is associated with “Point-scale organic-matter decomposition in streambeds is weakly associated with reach-scale respiration” published in EGU Biogeosciences (Stegen et al., 2026; https://doi.org/10.5194/bg-23-3981-2026). It contains cotton strip decomposition rates (Kcd and Kdd) collected across the Yakima River Basin (YRB), Washington, USA. These data were collected to support a broader study examining the drivers of spatial variability in sediment respiration rates in the Yakima River Basin. Associated data used in analysis, metadata, and field protocols can be accessed at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1969566, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1987520. This data package is associated with the repository found at https://github.com/river-corridors-sfa/rcsfa-ST-2B-SSS-cotton-strip. A preliminary version of this data package was published in December 2025 at the time of manuscript submission. It was updated in June 2026, at the time of manuscript acceptance, to include additional metadata (this readme, data dictionary, and file level metadata). The data did not change. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This data package consists of (1) readme; (2) data dictionary (dd); (3) file level metadata (flmd); and (4) four folders: (1) R-scripts; (2) figures; (3) outputs from the scripts; and (4) published data. The published data folder contains a readme directing the user to download data in order to run the R-scripts. All files are .csv, .pdf, .R, .Rmd, and .txt. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.
NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the publication “Non-random processes impacting organic matter chemistry are maximized in mid-order streams” submitted to Limnology and Oceanography (L&O) by Danczak et al. (in review). This package contains data and scripts used to investigate dissolved organic matter (DOM) molecular chemistry and diversification processes across 47 surface-water sampling sites in the Yakima River Basin, Washington, USA, during an August 2021 sampling campaign. The package contains analyses of ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), geochemical measurements, geospatial attributes, molecular diversity, and meta-metabolome ecological null models needed to reproduce the main manuscript results. The underlying field data were pulled from exising data packages at https://doi.org/10.15485/1892052 (Fulton et al., 2022) and https://doi.org/10.15485/1898914 (Grieger et al., 2022). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. We thank the following organizations for providing access to field locations for sample collection: the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, the Confederated Tribes and Bands of the Yakama Nation, and the Cowiche Canyon Conservatory. Research was conducted under Washington State Parks and Recreation Commission Scientific Research Permit #210901. We are grateful to the Yakama Nation Tribal Council and Yakama Nation Fisheries for their collaboration in facilitating sample collection and ensuring data usage aligns with their values and worldview. This data package contains an R-Markdown file for analyses and five folders: (1) Data, (2) Geospatial Data, (3) Supplemental_Files, (5) Figures_pdf, (4) and src. The Data folder contains tabular inputs and derived files used in the manuscript analysis. The Geospatial Data folder contains climate and water-balance, hydrologic, land-cover, population/regional water-use, stream, topographic, and stream-order attribute CSV files. The src folder contains scripts used to process data, run analyses, and generate figures. The Figures_pdf folder contains manuscript figure outputs. The Supplemental_Files folder contains supplemental analysis products. All files are .csv, .pdf, .html, .png, .R, .Rmd, .svg, or .tre. This data package is associated with the rcfsa-RC2-SPS_Null_Modeling repository found at https://github.com/river-corridors-sfa/rcfsa-RC2-SPS_Null_Modeling.
The Wellbore Exploration and Location Logistic System (WELLS) Interactive Application is an interactive tool to enable easy exploration and visualization of the living national wellbore database (WELLS Database (https://edx.netl.doe.gov/dataset/wells_database)). The tool and underlying database were created and are maintained by the National Energy Technology Laboratory (NETL), providing visualization of the more than six million public wellbore records from more than 65 authoritative state, federal, and tribal resources. The WELLS Interactive Application serves up wellbore data from oil, gas, underground injection, research, geothermal, geotechnical, groundwater, and other types of wells in a single, standardized, unified system. In addition to the surface location of these wells, the underlying database combines select key attributes for features such as well age, depth, and operating status. The system also provides users with references back to the original sources used in this unified platform. The underlying data can be accessed through the WELLS Database: https://edx.netl.doe.gov/dataset/wells_database Additional Information: The WELLS Interactive Application (formerly titled CO2-Locate) enables visualization and access to the public wellbore records through an intuitive web-based mapping tool. The WELLS Interactive Application was designed to help users visualize, query, analyze, and download wellbore records. Public wellbore points are included as a layer in the Map page, called Public Wells. Additionally, a multivariate hexagon grid summarizing well density from proprietary well data, called Well Density, is included to identify data gaps between the public and proprietary well data. Filtering functionalities in the tool allow these two layers to be spatially filtered by state, county, or basin as well as by status, type, true vertical depth, and spud year. The WELLS Interactive Application also contains a Near Me tool can be used to search and explore wellbore data within a user-defined distance of a specified location on the map, which can also be downloaded. The Query tool allows users to query the selected or filtered wells in the Public Wells layer and export the data. For additional information on these tool functionalities, see the help documentation on the About page of the tool. Notes for Consideration: The Well Density layer provided in this application is derived from proprietary wellbore data, the records of which do not always contain values for key features (status, type, true vertical depth, or spud year). Therefore, data might not be available when layers are queried for all filter combinations. Additionally, visualizing layers and applying filters may take additional time to load (i.e., draw on the map) due to the large size of the data.
The WINDExchange Offshore Wind Energy Guide is a foundational resource that introduces the major concepts and topics within offshore wind energy: project anatomy and component characteristics; the project development process; siting of turbines and other infrastructure; state and local involvement in offshore wind energy development; community impacts; public engagement in decision making and planning; Tribal considerations; economic impacts; supply chain, ports, and vessels, and workforce. This guide is intended to provide information and resources that can build a foundation of knowledge and understanding about offshore wind energy and create a launching point for readers to seek out additional information about the topics and issues of interest to them.
The goal of this report is to help license applicants, resource agencies, and other members of the hydropower community involved in closed-loop pumped storage hydropower permitting and licensing process, focus the scope of environmental reviews, and more quickly identify impacts with project nexus and potential mitigation measures for these impacts. Pumped storage hydropower (PSH) is an energy storage technology that uses energy to pump water up from a lower reservoir to an upper reservoir where water is stored until electricity is needed and the water is released to a lower reservoir passing through turbines. Closed-loop PSH—PSH that is not continuously connected to a naturally flowing water feature—is one of the lowest greenhouse gas emitting energy storage technologies and is therefore a critical part of the transition to renewable energy (Simon et al. 2023). Proposals for closed-loop PSH facilities in the United States currently account for more than 40% of original licenses and 99% of potential generation capacity in the Federal Energy Regulatory Commission (FERC) hydropower licensing pipeline (Johnson et al. 2023). While closed-loop PSH facilities can have lower environmental impacts than open-loop PSH facilities, no closed-loop facilities have been constructed in the United States to enable direct accounting of project impacts and efficacy of mitigations. Many proposals for closed-loop PSH submitted to FERC are abandoned early in the permitting and licensing process prior to license applications and environmental assessments, so there is little documentation describing potential project impacts and proposed mitigations. The newness of closed-loop PSH proposals in the United States may mean that tribal, federal, and state agencies with authorities for cultural and natural resources protection and management involved in the FERC licensing process may not have experience with closed-loop PSH regulation. Moreover, many proposed closed-loop PSH facilities are in areas that do not have high concentrations of conventional hydropower, so these agencies may also be unfamiliar with the FERC hydropower licensing process. The goal of this report is to help license applicants, resource agencies, and other members of the hydropower community focus the scope of environmental review for the closed-loop PSH development, licensing, and federal authorization process enabling quicker identification of potential impacts, mitigations, and situations where mitigation may not be possible. We found that environmental impacts of closed-loop PSH are highly site-specific, and generalizations about the types of environmental impacts across closed-loop PSH projects are difficult to make. Environmental impacts of closed-loop PSH are like those for open-loop PSH with a few exceptions including water sourcing, which can lead to delays and contention due to potential complexities with water rights, impacts to aquatic resources, and greenhouse gas emission potential. Cultural resource impacts were commonly reported in National Environmental Policy Act (NEPA) documents reviewed and discussed in interviews, but in many cases such impacts cannot be mitigated.
Systems of fixed-position radiation sensors can provide information that assists emergency responders following nuclear and radiological incidents. State, local, tribal, and territorial (SLTT) government agencies that implement systems of fixed-position sensors are faced with numerous decisions regarding sensor selection, quantity, and placement. To develop guidance on implementation of radiation detection systems, we simulated the release of radioactive material in an urban environment using a combination of three models: the Weather Research Forecasting (WRF), Quick Urban and Industrial Complex (QUIC), and Monte Carlo N-Particle (MCNP) models. We then evaluated the performance of several hypothetical sensor systems. The small number of simulations we conducted are not sufficient to generate definitive design guidance for radiation sensor systems, but we did identify trends that would be of interest to emergency planners. For a scenario that releases 1000 curies of Cs-137, radiation detectors were needed at 500-meter intervals to have a high likelihood of event detection and to estimate source location and plume detection. We also noted that optimal detector altitude varied with distance to the source. We recommend additional research in this area be conducted to support developing sensor placement guidelines that expand on a range of locations, isotopes, activity levels, and different weather conditions. Original simulation strategies included a range of environments, additional radioisotopes (Am241 and AmBe), and a larger selection of sensor types. These types of expansions would support SLTT guidance on sensor system recommendations.
This report summarizes results of CMCSCFCB—Critical Minerals in Coaly Strata of the Cherokee-Forest City Basin, a multiyear and multidisciplinary study funded through DE-FOA-0002364 as part of the Department of Energy Office of Fossil Energy and Carbon Management (FECM) Carbon Ore, Rare Earth and Critical Minerals (CORE-CM) Initiative for U.S. Basins. Our working group of state and tribal agencies collected data and made interpretations included herein. It also surveyed the regional potential for critical mineral/material production.
This report provides the final status of the “Enhanced Outcrop Methane Capture (EOMC) Project” which was focused on drilling two new methane capture wells along the Fruitland Formation Outcrop for capture of methane and carbon dioxide which are naturally seeping from the Outcrop. The EOMC Project was focused on evaluating whether novel horizontal drilling technology will result in improved capture of methane and carbon dioxide which are naturally seeping from the Fruitland Formation Outcrop on Southern Ute tribal trust lands within the Southern Ute Indian Reservation (the “Reservation”).
The U.S. Department of Energy, Hanford Field Office (HFO) conducts ecological monitoring at the Hanford Site to collect and maintain data to ensure compliance with an array of environmental laws, regulations, and policies governing HFO activities. Ecological monitoring data provides baseline information about the plants, animals, and habitats under HFO stewardship at the Hanford Site that is required for decision making under the National Environmental Policy Act of 1969 and the Comprehensive Environmental Response, Compensation, and Liability Act of 1980. In addition, ecological monitoring helps ensure that HFO, its contractors, and other entities conducting activities at the Hanford Site are in compliance with DOE/EIS-0222-F, Final Hanford Comprehensive Land-Use Plan Environmental Impact Statement. HFO places priority on monitoring those plant and animal species or habitats with specific regulatory protections or requirements that are rare and/or declining (i.e., federal or state listed endangered, threatened, or sensitive species) or are of significant interest to federal, state, or tribal governments or the public.
Biologists first documented elk on the Hanford Site in 1972; since then, the elk herd known as the Rattlesnake Hills Elk Herd (RHEH) has grown substantially (PNNL-13331, Population Characteristics and Seasonal Movement Patterns of the Rattlesnake Hills Elk Herd: Status Report 2000). Through the 1990s, the core range of the RHEH was focused on the portion of the Hanford Reach National Monument known as the Fitzner-Eberhardt Arid Lands Ecology (ALE) Reserve. More recently, larger numbers of elk have been occupying the U.S. Department of Energy (DOE), Hanford Field Office (HFO), formerly the DOE, Richland Operations Office managed portion- of the Hanford Site, known as central Hanford (Figure 1-1). This began with bachelor groups of bulls occupying the site intermittently, and the herd has grown to resident herds, including bulls, cows, and calves. The Washington Department of Fish and Wildlife (WDFW) established a target herd size for the RHEH of less than 350 animals to minimize damages on adjacent private agricultural lands (Washington State Elk Herd Plan–Yakima Elk Herd [WDFW 2002]). Attempts to control the RHEH population through hunting on private lands and a relocation effort during 2000 have failed to limit growth of the population toward the WDFW target, and the herd exceeds 1,600 according to recent counts (PNNL-13331; DOE/RL- 2023-20, Hanford Annual Site Environmental Report for Calendar Year 2022). Although elk are present on central Hanford year-round, they continue to move between areas offsite, the ALE Reserve, and central Hanford throughout the year, and it is these movements that result in many of the elk-vehicle collisions (EVC) that occur along Hanford Site roads and on the Washington State highways bordering the site. In addition, changes in management strategies on the ALE Reserve, including recent and potential future tribal elk hunts, may alter herd behavior and result in additional animals transiting onto and off central Hanford.
The U.S. Department of Energy, Richland Operations Office (RL) conducts ecological monitoring on the Hanford Site to collect and track data needed to ensure compliance with an array of environmental laws, regulations, and policies governing RL activities. Ecological monitoring data provide baseline information about the plants, animals, and habitat under RL stewardship at the Hanford Site, which is required for accurate ecological impact assessment decision making under the National Environmental Policy Act and the Comprehensive Environmental Response, Compensation, and Liability Act. In addition, ecological monitoring helps ensure that RL, its contractors, and other entities conducting activities on the Hanford Site are in compliance with DOE/EIS-0222-F, Final Hanford Comprehensive Land Use Plan Environmental Impact Statement. RL places priority on monitoring those plant and animal species or habitats with specific regulatory protections or requirements; or that are rare and/or declining (federal or state listed endangered, threatened, or sensitive species) or of significant interest to federal, state, or tribal governments or the public.
The U.S. Department of Energy (DOE), Hanford Field Office (HFO) conducts ecological monitoring on the DOE-managed portion of Hanford Site (hereby referred to as Central Hanford) to collect and track data needed to ensure compliance with an array of environmental laws, regulations, and policies governing HFO activities. Ecological monitoring data provides baseline information about the plants, animals, and habitat under HFO stewardship on the Hanford Site, which is required for accurate ecological impact assessment decision making under the National Environmental Policy Act and the Comprehensive Environmental Response, Compensation, and Liability Act. In addition, ecological monitoring helps ensure that HFO, its contractors, and other entities conducting activities on the Hanford Site are in compliance with DOE/EIS-0222-F, Final Hanford Comprehensive Land Use Plan Environmental Impact Statement. HFO places priority on monitoring plant and animal species or habitats that have specific regulatory protections or requirements; that are rare and/or declining (federal or state listed endangered, threatened, or sensitive species); or are of significant interest to federal, state, or tribal governments or to the public.
In 2024, Idaho National Laboratory (INL) and Pacific Northwest National Laboratory (PNNL) initiated a technical-assistance project to support Cat Creek Energy, LLC, (CCE) in evaluating site suitability for the proposed Corral Summit Pumped Storage Hydropower (PSH) project in south-central Idaho near Mackay Reservoir. The Corral Summit facility incorporates battery storage and photovoltaic (PV) solar arrays in a Trybrid configuration to deliver large-volume long-duration (LVLD) storage solutions for rural electric cooperatives in eastern Idaho. The evaluation process focused on determining the most-suitable location for the upper reservoir of the PSH system, guided by a comprehensive assessment framework spanning multiple categories, including physical characteristics, environmental constraints, building infrastructure, regulatory constraints, cultural resources and sensitivity, social factors, and power market and grid integration. Each site was analyzed based on a ranking scale (0–1), which scores ranging from “severely disfavored” to “highly favored,” allowing detailed comparisons of site-specific conditions. Categories such as hydraulic head, utilities corridor, land ownership, and transmission-grid limitations emerged as key contributors to the overall assessment. Site 2 (Idaho Trust) demonstrated a slight advantage over Site 1 (Bureau of Land Management) primarily due to favorable outcomes in regulatory constraints, building infrastructure, and power-market integration. However, Site 1 outperformed Site 2 in factors related to physical characteristics and social factors. The report emphasizes the need for further evaluation of both sites before clear determination of which site is preferred, due to the limited information available on either site at the time of this report. Key areas of evaluation to clearly define the preferred site are ecological impacts, cultural-resource surveys, and economic-feasibility assessments. For successful project execution, recommended follow-up actions include seismic and geotechnical surveys, groundwater and habitat monitoring, regulatory reviews of water rights and right-of-way agreements, cultural engagement with local tribal governments, and enhanced stakeholder strategies. These efforts will ensure the Corral Summit Trybrid facility meets local energy needs while balancing environmental, social, and regulatory responsibilities.
Through funding from the Department of Energy’s Office of Indian Energy, the Northwest Arctic Borough (NAB) and the Native Village of Noatak (NNV) formed a Tribal Energy Development Organization (TEDO) to implement the Noatak Solar and Battery Project to reduce reliance on costly imported diesel, stabilize energy costs, and strengthen local energy sovereignty.