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Juvenile Salmonid Emigration Monitoring in the Lower American River, California.

Overview Operation of rotary screw traps on the Lower American River is part of a collaborative effort by the U.S. Fish and Wildlife Service, Pacific States Marine Fisheries Commission, and the California Department of Fish and Wildlife. The primary objectives of the study are to collect data that can be used to estimate the passage of juvenile fall-run Chinook Salmon Oncorhynchus tshawytscha and to quantify the raw catch of steelhead Oncorhynchus mykiss as well as winter, spring, and late fall runs of Chinook Salmon. Secondary objectives of the trapping operations focus on collecting biological data on juvenile salmonids and gathering environmental data that will be used to develop models that correlate environmental parameters with salmonid size, temporal presence, abundance, and production. The data package contains seven datasets including: raw catch, trap operation, environmental, and trap efficiency data. Raw Catch – Chinook Dataset This dataset covers ALL Chinook Salmon captured by the rotary screw traps. This spreadsheet includes biological data on: 1) unmarked fall-, spring-, and winter-run Chinook Salmon 2) marked (adipose clipped OR "fin clip") hatchery origin Chinook Salmon 3) recaptured marked fall-run (BBY OR "Pigment / Dye", Photonic Dye, and VIE OR "Elastomer") Chinook Salmon utilized in trap efficiency trials. Raw Catch – Steelhead Dataset This dataset covers ALL steelhead captured by the rotary screw traps. This spreadsheet includes biological data on: 1) unmarked (natural origin) steelhead 2) marked (adipose clipped OR "fin clip") hatchery origin steelhead 3) recaptured marked steelhead utilized in trap efficiency trials. Raw Catch – ByCatch Dataset This dataset provides biological data on ALL catch (EXCLUDING Chinook Salmon or steelhead) captured by the rotary screw traps. All catch in this table is of natural origin. Trap Operations Dataset This dataset provides trap operation data for each trap visit. Specifically, it includes data on the visit

openCC (other)Aug 2025View details →

O. mykiss passages at the Stanislaus River weir, 2005-2025

The Central Valley Project Improvement Act (CVPIA) provides funds to aid the San Joaquin Basin Steelhead Collaborative and initiate a Steelhead Life-Cycle Monitoring Program. This dataset includes observations of O. mykiss in the Stanislaus River during the steelhead spawning migration period, September through May. Fish are observed at an Alaskan-style weir outfitted with a VAKI Riverwatcher fish counting device. The counting device provides visual records of upstream passing individuals that are later reviewed by an experienced biologist. During times when the counting device is offline, a continuous video feed is reviewed to provide counts and identifications of passing fish. The length of passing fish is estimated using a known body depth to length ratio derived from individuals trapped and measured at the weir. The weir also has a trap box that, when closed, allows investigators to physically capture fish for measurements, biological sample collection, and injection of a PIT tag. There is also a PIT tag antenna affixed to the weir allowing detection of PIT tagged fish. There are three datasets currently associated with this project, 1) passage data for all O. mykiss from 2005 through 2025, 2) O. mykiss that were captured and processed from the trap, and 3) PIT tag detections from the PIT antenna.

openCC0Aug 2024View details →

Lower American River restoration snorkel surveys at the project and control reaches

Sacramento Water Forum will implement spawning and rearing habitat enhancement projects on the Lower American River at the Lower Sailor Bar, Nimbus Basin, and Upper River Bend reaches. Enhancements include installation of gravel to restore over 18 acres of spawning habitat and in-channel/floodplain grading to create over 14 acres of rearing habitat. Construction of Lower Sailor Bar and Nimbus Basin were completed in summer 2022 and Upper River Bend was completed in summer 2023.The goal of the projects is to increase existing spawning and rearing habitat for salmonids under typical flows. This work supports effectiveness monitoring for this project, including spawning and rearing (snorkel) surveys before and after restoration. This work also informs performance metrics and adaptive management strategies.

openCC (other)Mar 2025View details →

Monitoring juvenile Chinook salmon outmigration using rotary screw traps on the Sacramento River near Tisdale Weir

The Tisdale RST sampling site is operated by the California Department of Fish and Wildlife (CDFW) to obtain information on the temporal distribution, relative abundance, and composition of race and species of juvenile Chinook salmon (Oncorhynchus tshawytscha) and steelhead trout (O. mykiss) emigrating from the upper Sacramento River and tributaries to the Sacramento-San Joaquin Delta (Delta). The project collects data near the Tisdale Weir on the Sacramento River, using two paired rotary screw traps (RSTs) outfitted with two 8-ft diameter cones. The RST monitoring site at Tisdale Weir was established meet a requirement of the 2011 amendment to the reasonable and prudent alternative (RPA) of the 2009 biological and conference opinion (BO) on the long-term operations of the Central Valley Project (CVP) and State Water Project (SWP). The amendment required the Bureau of Reclamation (USBR) and the California Department of Water Resources (DWR) to fund a new juvenile salmonid monitoring site on the Sacramento River between Red Bluff Diversion Dam (RBDD) and Knights Landing. The purpose of the new site was to provide early warning of fish movement and determine survival of listed fish species leaving spawning habitat in the upper Sacramento River. CDFW issued ITP 2081-2019-066-00 to DWR on March 31, 2020, for the long-term operation of the SWP in the Delta. Condition 7.5.2 of the ITP requires the development and establishment of a spring-run Chinook salmon juvenile production estimate (JPE) to increase understanding regarding the impacts water operations have on the spring-run Chinook salmon population in the Sacramento River watershed and inform the development of minimization measures to reduce take of spring-run Chinook salmon at Delta fish salvage facilities. Data from the Tisdale RST will be used along with other datasets from juvenile salmonid monitoring programs in the Sacramento River Watershed to inform the development of JPE modeling approaches. Salmonid data c

openCC (other)Dec 2025View details →

Monitoring juvenile Chinook salmon outmigration using rotary screw traps on the Lower Feather River

CDFW issued Incidental Take Permit No. 2081-2019-006-00 (ITP) to the California Department of Water Resources (DWR) on March 31, 2020, for the long-term operation of the State Water Project (SWP) in the Sacramento San Joaquin Delta (Delta). Condition 7.5.2 of the ITP requires the development and establishment of a spring-run Chinook salmon (Oncorhynchus tshawytscha) juvenile production estimate (JPE) to increase understanding of the impacts that water operations have on the spring-run Chinook salmon population in the Sacramento River watershed and to inform the development of minimization measures to reduce take of spring-run Chinook salmon at Delta fish salvage facilities. As a part of the JPE effort, CDFW began operating a new rotary screw trap (RST) monitoring station on the lower Feather River near River Mile 17, approximately 1 mile downstream of Star Bend Park and Boat Ramp near Olivehurst, in January 2022. This RST location represents the lowest point in the Feather River Watershed where juvenile salmon are sampled with an RST prior to entering the Sacramento River and includes salmon emigrating from the Yuba River. The expanded juvenile monitoring effort will help resource agencies and water managers identify numbers of salmon emigrating from the Feather River Watershed and contributing to the spring-run Chinook salmon population entering the Delta. Monitoring is conducted annually from October through June utilizing a pair of eight-foot rotary screw traps (RST). Data collected at the Lower Feather River RST site provides information on the temporal distribution, relative abundance, and race composition of juvenile Chinook salmon; and temporal distribution and relative abundance of steelhead trout (O. mykiss) emigrating from the Feather River and tributaries, including the Yuba River, to the Delta. Salmonid data collected from the Lower Feather River RST, among other datasets, is also used by the Salmon Monitoring Team (SaMT) to understand the movement of ju

openCC0Aug 2025View details →

Lower American River steelhead and Chinook stranding surveys, California, 2016 - 2025

Steelhead stranding surveys have been conducted on the Lower American River (LAR) for the years 2016-2025. Reclamation’s mission is to manage, develop and protect water and related resources in an environmentally and economically sound manner in the interest of the American people. In the National Marine Fisheries Service (NMFS) most recent biological opinion (2009), the presence of dams was identified as the most influential stressor to steelhead on the American River because it blocks passage to historic spawning and rearing habitat. Thus, Reclamation is required to monitor the effects of flow regulation by dams on the steelhead life stages present in the river system. Congruently, Reclamation has committed to significant restoration actions including salmonid spawning and rearing habitat rehabilitation on the Lower American River, which also require accurate and robust monitoring. These surveys support the mission and monitoring requirements of Reclamation by collecting the stranding data required to effectively conduct analyses of the effects of regulating Folsom and Nimbus dams on this federally listed species’ critical life stage development and support operational decision making.

openCC (other)Dec 2024View details →

Distribution and habitat use of juvenile steelhead and other fishes of the lower Feather River

Understanding how fish presence is related to habitat features is useful in restoration planning and monitoring as better information about how fish use habitat may lead to more impactful restoration projects. The California Department of Water Resources (DWR), conducted a two-year study of microhabitat and mesohabitat in Feather River. The goal of this study was to identify relationships between habitat conditions (depth, substrate, velocity, and cover) and where juvenile Chinook salmon and steelhead occur. Snorkel surveys were conducted monthly March through August in 2001 and 2002 across 29 different sites, which were selected at random (13 in Low Flow Channel, and 16 in High Flow Channel). Each sampling section covered an area 25 meters long by 4 meters wide, running parallel to riverbank. These data were published to support the Healthy Rivers and Landscapes Science Program.

openCC (other)Mar 2025View details →

Redd survey data of Chinook salmon in the Feather River

Since 2014, the California Department of Water Resources (DWR) has conducted annual Chinook salmon redd surveys. The objective of this data collection effort is to quantify and understand potential shits in redd distribution and potential habitat differences between historic and restored sites. The redd surveys are conducted in the uppermost 16 miles of the lower Feather River though have been concentrated in the uppermost two miles of the lower Feather River in, and adjacent to the Gravel Supplementation Areas (GSAs). Redd surveys are conducted less frequently in the 14 miles downstream of the GSAs. Surveys typically begin in mid-September at the onset of spawning and generally conclude at the end of November. This dataset represents an extensive time series that could be used to identify habitat conditions where Chinook salmon spawn, how these conditions have changed over time, especially in areas where restoration has occurred. These data were published to support the Healthy Rivers and Landscapes Program.

GRIME AI Water Segmentation Model for the USGS Platte River near Grand Island, NE, 2023-2024

Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the Platte River, near Grand Island, NE, 2023-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/NE_Platte_River_near_Grand_Island for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated during this process. This project w

GRIME AI Water Segmentation Model for the USGS Monitoring Site Chippewa River at Grand Ave at Eau Claire, WI, 2023-2024

Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS Monitoring Site Chippewa River at Grand Ave at Eau Claire, WI, 2023-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/WI_Chippewa_River_at_Grand_Ave_at_Eau_Claire for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically gen

GRIME AI Water Segmentation Model for the USGS Monitoring Site at East River at County Trunk HWY ZZ near Greenleaf, WI, 2023-2024

Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for USGS Monitoring Site at East River at County Trunk HWY ZZ near Greenleaf, WI. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/WI_East_River_at_HWY_ZZ_near_Greenleaf for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated duri

GRIME AI Water Segmentation Model for the USGS Monitoring Site at Missouri River at Hermann, MO, 2022-2024

Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS Monitoring Site at Missouri River at Hermann, MO, 2022-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/MO_Missouri_River_at_Hermann for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown".Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated during this process. Th

GRIME AI Water Segmentation Model for the USGS Monitoring Site at Pecos River near Acme, NM, 2022-2024

Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) USGS Monitoring Site at Pecos River near Acme, NM, 2022-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/NM_Pecos_River_near_Acme for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated during this process. This project was

Juvenile Salmonid Emigration Monitoring in the Stanislaus River at Caswell Memorial State Park, California, 2017-2025

Overview Operation of rotary screw traps on the lower Stanislaus River at Caswell Memorial State Park is part of the U.S Fish and Wildlife Service’s Anadromous Fish Restoration Program and Comprehensive Assessment and Monitoring Program under the National Marine Fisheries Service Reasonable and Prudent Alternatives actions and Central Valley Project Improvement Act. The primary objectives of the study are to collect data that can be used to estimate the passage of juvenile fall-run Chinook Salmon Oncorhynchus tshawytscha and to quantify the raw catch of steelhead Oncorhynchus mykiss. Secondary objectives of the trapping operations focus on collecting biological data on juvenile salmonids and gathering environmental data that will be used to develop models that correlate environmental parameters with salmonid size, temporal presence, abundance, and production. The data package contains seven datasets including: raw catch, trap operation, environmental, and trap efficiency data. Raw Catch – Chinook Dataset This dataset covers ALL Chinook Salmon captured by the rotary screw traps. This spreadsheet includes biological data on: 1) unmarked fall- and spring-run Chinook Salmon 2) recaptured marked fall-run (BBY OR "Pigment / Dye", Photonic Dye, Fin Clip, and VIE OR "Elastomer") Chinook Salmon utilized in trap efficiency trials. Raw Catch – Steelhead Dataset This dataset covers ALL steelhead captured by the rotary screw traps. All steelhead captured are unmarked and presumed to be natural origin steelhead. Raw Catch – ByCatch Dataset This dataset provides biological data on ALL catch (EXCLUDING Chinook Salmon or steelhead) captured by the rotary screw traps. All catch in this table is of natural origin. Trap Operations Dataset This dataset provides trap operation data for each trap visit. Specifically, it includes data on the visit type, trap functioning status, start and end sampling dates and times, total revolutions and instantaneous revolution speeds, livewell intake st

openCC (other)Dec 2022View details →

Multi-locus DNA metabarcoding of western spotted skunk diet in the McKenzie River Ranger District of the Willamette National Forest from 2017-2019

There are increasing concerns about the declining population trends of small mammalian carnivores around the world. Their conservation and management is often challenging due to limited knowledge about their ecology and natural history. To address one of these deficiencies for western spotted skunks (Spilogale gracilis), we investigated their diet in the Oregon Cascades of the Pacific Northwest during 2017 –2019. We collected 130 spotted skunk scats opportunistically and with detection dog teams and identified prey items using DNA metabarcoding and mechanical sorting. Western spotted skunk diet consisted of invertebrates such as wasps, millipedes, and gastropods, vertebrates such as small mammals, amphibians, and birds, and plants such as Gaultheria, Rubus, and Vaccinium. Diet also consisted of items such as black-tailed deer that were likely scavenged. Comparison in diet by season revealed that spotted skunks consumed more insects during the dry season (June –August), particularly wasps (75% of scats in the dry season), and marginally more mammals during the wet season(September –May). We observed similar diet in areas with no record of human disturbance and areas with a history of logging at most spatial scales, but scats collected in areas with older forest within a skunk’s home range (1 km buffer) were more likely to contain insects. Western spotted skunks provide food web linkages between aquatic, terrestrial, and arboreal systems and serve functional roles of seed dispersal and scavenging. Due to their diverse diet and prey-switching, western spotted skunks may dampen the effects of irruptions of prey, such as wasps during dry springs and summers. By studying the natural history of western spotted skunks in the Pacific Northwest forests while they are still abundant, we provide key information necessary to achieve the conservation goal of keeping this common species common.

Carbon and nitrogen content and stable isotope compositions from particulate organic matter samples from lagoon, river, and open ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing

Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods, and analyzed for particulate organic carbon (POC) and particulate organic nitrogen (PON) content and stable isotopic composition.

openCC0Sep 2025View details →

Water column and sediment porewater nutrient concentrations from lagoon, river, and ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing

Several water types (lagoon, river, ocean) and surface sediment porewater samples from the coastal Beaufort Sea system were sampled seasonally to investigate temporal and spatial shifts in nutrient dynamics. Surface and bottom water samples were collected in April, June, July, and August and analyzed for ammonium, nitrate + nitrite, orthophosphate, and silica.

Dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) from river, lagoon, and open ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing

Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods and analyzed for dissolved organic carbon and total dissolved nitrogen content.

openCC0Dec 2025View details →

Total dissolved nitrogen (TDN), dissolved organic carbon (DOC), radiocarbon (14C-DOC), and stable carbon (13C-DOC) of surface waters from the Canning River watershed, 2019 and 2021

Sites along the Canning River mainstem and contributing streams near the Kavik River Camp, Alaska, were visited to track changes in stream and river total dissolved nitrogen (TDN) concentration, dissolved organic carbon (DOC) concentration, and the stable carbon (13C) and radiocarbon (14C) isotopic composition of DOC across transitions between the Brooks Range, Brooks foothills, and Arctic Coastal Plain. The dataset also includes water samples collected from lakes, springs, groundwater, and streams and rivers outside the Canning River watershed. Water samples were collected in late April and early August 2019 and in late July and early August 2021. Data include measurements of individual samples for TDN (milligrams nitrogen per liter), DOC (milligrams carbon per liter), carbon-13 of DOC (reported as delta-13C, per mil), carbon-14 of DOC (reported as fraction modern), and analytical error in the fraction modern values. Additional water chemistry data for these samples can be found in Koch et al. (2024). References: Koch, J. C., Connolly, C. T., Repasch, M., Best, H. R., Couvillion, C. S., Hunt, A. (2024). [Dataset] Hydrochemistry and age date tracers from springs, streams, and rivers in the Arctic National Wildlife Refuge, 2019-2022, U.S. Geological Survey data release, https://doi.org/10.5066/P95CXJIT.