Item talk:Q327696

{

 "DOI": {
   "doi": "10.5066/f7fq9vtd",
   "prefix": "10.5066",
   "suffix": "f7fq9vtd",
   "identifiers": [],
   "alternateIdentifiers": [],
   "creators": [
     {
       "name": "Start, J.Jeffrey",
       "nameType": "Personal",
       "givenName": "J.Jeffrey",
       "familyName": "Start",
       "affiliation": [],
       "nameIdentifiers": [
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           "nameIdentifier": "https://orcid.org/0000-0001-5909-0010",
           "nameIdentifierScheme": "ORCID"
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     },
     {
       "name": "Carlson, Carl S.",
       "nameType": "Personal",
       "givenName": "Carl S.",
       "familyName": "Carlson",
       "affiliation": [],
       "nameIdentifiers": []
     }
   ],
   "titles": [
     {
       "title": "Supporting Datasets Used in the General Groundwater-Model Construction System Version 0.1"
     }
   ],
   "publisher": "U.S. Geological Survey",
   "container": {},
   "publicationYear": 2018,
   "subjects": [
     {
       "subject": "Hydrogeology,Water Resources"
     }
   ],
   "contributors": [],
   "dates": [
     {
       "date": "2018",
       "dateType": "Issued"
     }
   ],
   "language": null,
   "types": {
     "ris": "DATA",
     "bibtex": "misc",
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       "relatedIdentifier": "10.1029/2017wr021531",
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   "descriptions": [
     {
       "description": "General Groundwater-Model Construction System Version 0.1 (Genmod0.1) Groundwater residence-time distributions (GRTD) are critical for assessing lag times between activities at the land surface and the emergence of related solutes in the baseflow of streams. However, GRTD can not be measured directly, they must be inferred from an analysis of data using models. Glacial aquifers present challenges to modeling approaches because they are spatially discontinuous and have highly variable properties. An innovative approach developed by the U.S. Geological Survey (USGS) uses machine learning techniques in conjunction with numerical models that results in a rapid and robust way of generating GRTD. The main idea is to simulate groundwater flow in subregional type locales (for example at the HUC8 scale using the General Groundwater-Model Construction System Version 0.1 [Genmod0.1]) and then apply machine learning to extract variables to predict GRTD across a region. The technique has been applied to glacial aquifers in the U.S. and compared to tracer data in wells in the work of Starn and Belitz (2018) which is currently in review. The Python (https://www.python.org/) Jupyter Notebooks (Perez and Granger, 2007) of Genmod (gw-general-models) and GRTD (gw-res-time) are located in respective repositories on the official USGS organization on Github (see section \"Related External Resources\" below). This data release serves to provide links to those repositories and to provide supporting datasets used by Genmod and GRTD as part of the supplemental material used to generate 30 general models from which water-particle residence time distributions were determined in the work of Starn and Belitz (2018). Descriptions of files found in the \"Attached Files\" section below (files with the \".7z\" extension are 7-Zip files, http://www.7-zip.org/): \"General_Groundwater-Model_Construction_System_Version_0point1_Metadata.xml\" structure where the components of the General Groundwater-Model Construction System Version 0.1 (Genmod) are organized on a users' computer system. \"shapefiles_30_model_domains.7z\": A zip file containing 30 individual shapefiles, one for each model domain. \"WSW_points.7z\": One shapefile of points with water-elevation data for groundwater and surface-water sites from the USGS National Water Information System (NWIS). \"Watersheds.7z\": One shapefile that includes all 30 model domain areas. \"Wells.7z\": A shapefile of wells sampled as part of the USGS National Water Quality Assessment Program (NAWQA). \"Tracer input.7z\": A spreadsheet file with tracer data. To use this spreadsheet, the program TracerLPM available from the USGS software site at https://www.usgs.gov/software/tracerlpm must be installed on the users' system. Installation may require administrator rights. \"factor_added_geology.7z\": One shapefile of the Quaternary Atlas where geologic units were mapped to either coarse or fine sediments (report describing this result is in review). Place this file in the 'input_data/Geology' folder. Links to the USGS GitHub repositories for the python jupyter notebooks for creating a general model using Genmod (gw-general-models) and determining groundwater residence time distributions from that model using GRTD (gw-res-time) are provided below in the section \"Related External Resources\". References: Perez, Fernando, and Granger, B.E., 2007, IPython--A system for interactive scientific computing: Computing in Science and Engineering, v. 9, no. 3, p. 21-29. [Also available at https://doi.org/10.1109/MCSE.2007.53.] Starn, J. J. and Belitz, K. (2018), Regionalization of groundwater residence time using metamodeling. Water Resour. Res.. Accepted Author Manuscript. . doi:10.1029/2017WR021531 [Also available at: https://doi.org/10.1029/2017WR021531]",
       "descriptionType": "Abstract"
     }
   ],
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   "fundingReferences": [],
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