U.S. Department of EnergyOffice of Scientific and Technical Information
WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from Machine-Learning-Informed Sites across the Contiguous United States (v6)
Abstract
This dataset supports a broader study examining hyporheic zone respiration rates to improve predictive models at a contiguous United States (CONUS) scale. The CONUS-Scale Model-Sample Study (CM) was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the 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. Sampling began in April 2022 and ended in October 2023. In addition to the widely distributed CONUS sites, a more spatially focused sampling occurred in the Yakima River Basin, WA in summer 2022. Data from this more spatially intensive sampling occurred under the label “Second Spatial Study (SSS)” and were also included in the machine learning models. Other data types collected from SSS that were not part of CM were published in a separate data package (https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566). This data package was originally published in February 2023. It was updated in June 2023 (v2; new and modified files); December 2023 (v3; new and modified files); June 2024 (v4; new and modified files); April 2024 (v5; new andmore »
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- Pacific Northwest National Laboratory
- University of Arizona
- Parallel Works
- Gram Northwest
- Publication Date:
- DOE Contract Number:
- AC02-05CH11231
- Research Org.:
- River Corridor and Watershed Biogeochemistry SFA
- Sponsoring Org.:
- ESS-DIVE; U.S. DOE > Office of Science > Biological and Environmental Research (BER)
- Subject:
- 54 ENVIRONMENTAL SCIENCES; ATP; Adenosine triphosphate; Aerobic respiration rate; Biogeochemistry; Catchment; DO; DOC; Dissolved organic carbon; EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > ECOSYSTEM FUNCTIONS > RESPIRATION RATE; EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SURFACE WATER; EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SURFACE WATER > SURFACE WATER CHEMISTRY; EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > WATER QUALITY/WATER CHEMISTRY > GASES > DISSOLVED OXYGEN; EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > WATER QUALITY/WATER CHEMISTRY > NUTRIENTS > NITROGEN; EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > WATER QUALITY/WATER CHEMISTRY > NUTRIENTS > NITROGEN COMPOUNDS; EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > WATER QUALITY/WATER CHEMISTRY > WATER CHARACTERISTICS > NITROGEN COMPOUNDS; EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > WATER QUALITY/WATER CHEMISTRY > WATER CHARACTERISTICS > WATER ION CONCENTRATIONS; EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > WATER QUALITY/WATER CHEMISTRY > WATER CHARACTERISTICS > WATER TEMPERATURE; ESS-DIVE CSV File Formatting Guidelines Reporting Format; ESS-DIVE File Level Metadata Reporting Format; ESS-DIVE Hydrologic Monitoring Reporting Format; ESS-DIVE Location Metadata Reporting Format; ESS-DIVE Sample ID and Metadata Reporting Format; ESS-DIVE Water-Soil-Sediment Chemistry Reporting Format; FTICR-MS; Freshwater; Gravimetric moisture; Hydraulic exchange; Hydrobiogeochemical function; Hydrology; Hyporheic zone; ICON; Iron (II); ML; Machine learning; Mechanistic model; ModEx; Model-experiment iteration; NPOC; Non-purgeable organic carbon; Organic matter; Percent Saturation; Percent carbon; Percent nitrogen; River; River corridor; Sediment grain size; Sediment surface area; Specific surface area; Stream; Total nitrogen; Watershed; X-ray diffraction; XRD; related identifiers
- OSTI Identifier:
- 1923689
- DOI:
- https://doi.org/10.15485/1923689
Citation Formats
Forbes, Brieanne, Barnes, Morgan, Boehnke, Brandon T., Bowden, Mark E., Chen, Xingyuan, Cornwell, Kali, Crawford, Mekayla, Delgado, Dillman, Fulton, Stephanie G., Garayburu-Caruso, Vanessa A., Gary, Stefan, Goldman, Amy E., Gonzalez, Brianna I., Grieger, Samantha, Hammond, Glenn E., Jiang, Peishi, Kaufman, Matthew H., Laan, Maggi, Li, Bing, Li, Zhi, McKever, Sophia A., Mudunuru, Maruti K., Muller, Katherine A., Myers-Pigg, Allison, Ocejo, Juan Alberto, Otenburg, Opal, Pelly, Aaron, Peta, Kelsey, Powers-McCormack, Beck, Regier, Peter, Renteria, Lupita, Roebuck, Alan, Scheibe, Timothy D., Son, Kyongho, Tfaily, Malak M., Torgeson, Joshua M., Stegen, James C., and WHONDRS Consortium, The. WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from Machine-Learning-Informed Sites across the Contiguous United States (v6). United States: N. p., 2023.
Web. doi:10.15485/1923689.
Forbes, Brieanne, Barnes, Morgan, Boehnke, Brandon T., Bowden, Mark E., Chen, Xingyuan, Cornwell, Kali, Crawford, Mekayla, Delgado, Dillman, Fulton, Stephanie G., Garayburu-Caruso, Vanessa A., Gary, Stefan, Goldman, Amy E., Gonzalez, Brianna I., Grieger, Samantha, Hammond, Glenn E., Jiang, Peishi, Kaufman, Matthew H., Laan, Maggi, Li, Bing, Li, Zhi, McKever, Sophia A., Mudunuru, Maruti K., Muller, Katherine A., Myers-Pigg, Allison, Ocejo, Juan Alberto, Otenburg, Opal, Pelly, Aaron, Peta, Kelsey, Powers-McCormack, Beck, Regier, Peter, Renteria, Lupita, Roebuck, Alan, Scheibe, Timothy D., Son, Kyongho, Tfaily, Malak M., Torgeson, Joshua M., Stegen, James C., & WHONDRS Consortium, The. WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from Machine-Learning-Informed Sites across the Contiguous United States (v6). United States. doi:https://doi.org/10.15485/1923689
Forbes, Brieanne, Barnes, Morgan, Boehnke, Brandon T., Bowden, Mark E., Chen, Xingyuan, Cornwell, Kali, Crawford, Mekayla, Delgado, Dillman, Fulton, Stephanie G., Garayburu-Caruso, Vanessa A., Gary, Stefan, Goldman, Amy E., Gonzalez, Brianna I., Grieger, Samantha, Hammond, Glenn E., Jiang, Peishi, Kaufman, Matthew H., Laan, Maggi, Li, Bing, Li, Zhi, McKever, Sophia A., Mudunuru, Maruti K., Muller, Katherine A., Myers-Pigg, Allison, Ocejo, Juan Alberto, Otenburg, Opal, Pelly, Aaron, Peta, Kelsey, Powers-McCormack, Beck, Regier, Peter, Renteria, Lupita, Roebuck, Alan, Scheibe, Timothy D., Son, Kyongho, Tfaily, Malak M., Torgeson, Joshua M., Stegen, James C., and WHONDRS Consortium, The. 2023.
"WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from Machine-Learning-Informed Sites across the Contiguous United States (v6)". United States. doi:https://doi.org/10.15485/1923689. https://www.osti.gov/servlets/purl/1923689. Pub date:Thu Feb 02 23:00:00 EST 2023
@article{osti_1923689,
title = {WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from Machine-Learning-Informed Sites across the Contiguous United States (v6)},
author = {Forbes, Brieanne and Barnes, Morgan and Boehnke, Brandon T. and Bowden, Mark E. and Chen, Xingyuan and Cornwell, Kali and Crawford, Mekayla and Delgado, Dillman and Fulton, Stephanie G. and Garayburu-Caruso, Vanessa A. and Gary, Stefan and Goldman, Amy E. and Gonzalez, Brianna I. and Grieger, Samantha and Hammond, Glenn E. and Jiang, Peishi and Kaufman, Matthew H. and Laan, Maggi and Li, Bing and Li, Zhi and McKever, Sophia A. and Mudunuru, Maruti K. and Muller, Katherine A. and Myers-Pigg, Allison and Ocejo, Juan Alberto and Otenburg, Opal and Pelly, Aaron and Peta, Kelsey and Powers-McCormack, Beck and Regier, Peter and Renteria, Lupita and Roebuck, Alan and Scheibe, Timothy D. and Son, Kyongho and Tfaily, Malak M. and Torgeson, Joshua M. and Stegen, James C. and WHONDRS Consortium, The},
abstractNote = {This dataset supports a broader study examining hyporheic zone respiration rates to improve predictive models at a contiguous United States (CONUS) scale. The CONUS-Scale Model-Sample Study (CM) was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the 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. Sampling began in April 2022 and ended in October 2023. In addition to the widely distributed CONUS sites, a more spatially focused sampling occurred in the Yakima River Basin, WA in summer 2022. Data from this more spatially intensive sampling occurred under the label “Second Spatial Study (SSS)” and were also included in the machine learning models. Other data types collected from SSS that were not part of CM were published in a separate data package (https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566). This data package was originally published in February 2023. It was updated in June 2023 (v2; new and modified files); December 2023 (v3; new and modified files); June 2024 (v4; new and modified files); April 2024 (v5; new and modified files); and September 2025 (v6; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocols; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) surface water major cations and anions and averages; (4) sediment grain size data; (5) sediment iron (II) data and averages; (6) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment specific surface area; (11) sediment percent carbon and nitrogen; (12) sediment gravimetric moisture and averages; (15) sediment X-ray diffraction (XRD) data; (16) sediment adenosine triphosphate (ATP) and averages; (17) a subfolder with sediment incubation respiration data, scripts, and plots; (18) surface water and sediment FTICR methods; and (19) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS).The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. 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.},
doi = {10.15485/1923689},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Feb 02 23:00:00 EST 2023},
month = {Thu Feb 02 23:00:00 EST 2023}
}
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