Item talk:Q336158
From geokb
{
"DOI": {
"doi": "10.5066/p97cghzh",
"prefix": "10.5066",
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"identifiers": [],
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"creators": [
{
"name": "Rahmani, Farshid",
"nameType": "Personal",
"givenName": "Farshid",
"familyName": "Rahmani",
"affiliation": [],
"nameIdentifiers": []
},
{
"name": "Lawson, Kathryn",
"nameType": "Personal",
"givenName": "Kathryn",
"familyName": "Lawson",
"affiliation": [],
"nameIdentifiers": [
{
"schemeUri": "https://orcid.org",
"nameIdentifier": "https://orcid.org/0000-0003-0075-7911",
"nameIdentifierScheme": "ORCID"
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},
{
"name": "Ouyang, Wenyu",
"nameType": "Personal",
"givenName": "Wenyu",
"familyName": "Ouyang",
"affiliation": [],
"nameIdentifiers": []
},
{
"name": "Appling, Alison",
"nameType": "Personal",
"givenName": "Alison",
"familyName": "Appling",
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"nameIdentifiers": [
{
"schemeUri": "https://orcid.org",
"nameIdentifier": "https://orcid.org/0000-0003-3638-8572",
"nameIdentifierScheme": "ORCID"
}
]
},
{
"name": "Oliver, Samantha",
"nameType": "Personal",
"givenName": "Samantha",
"familyName": "Oliver",
"affiliation": [],
"nameIdentifiers": [
{
"schemeUri": "https://orcid.org",
"nameIdentifier": "https://orcid.org/0000-0001-5668-1165",
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},
{
"name": "Shen, Chaopeng",
"nameType": "Personal",
"givenName": "Chaopeng",
"familyName": "Shen",
"affiliation": [],
"nameIdentifiers": [
{
"schemeUri": "https://orcid.org",
"nameIdentifier": "https://orcid.org/0000-0002-0685-1901",
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]
}
],
"titles": [
{
"title": "Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data"
}
],
"publisher": "U.S. Geological Survey",
"container": {},
"publicationYear": 2020,
"subjects": [
{
"subject": "Hydrology, Water Quality, Water Resources"
}
],
"contributors": [],
"dates": [
{
"date": "2020",
"dateType": "Issued"
}
],
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"relatedIdentifier": "10.1088/1748-9326/abd501",
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{
"description": "This data release provides all data and code used in Rahmani et al. (2020) to model stream temperature and assess results. Briefly, we used a subset of the USGS GAGES-II dataset as a test case for temperature prediction using deep learning methods. The associated manuscript explores the value of including stream discharge as a predictor in the temperature models, including the value of predicted discharge from a separate model when no discharge measurements are available. The data are organized into these items: Spatial Information - Locations of the 118 monitoring sites used in this study Observations - Water temperature observations for the 118 sites used in this study Model Inputs - Model inputs, including basin attributes, weather drivers, and discharge Models - Code and configurations for the stream temperature models Model Predictions - Predictions of stream water temperature Model Evaluation - Performance metrics for each stream temperature model This research was funded by the Integrated Water Prediction Program at the US Geological Survey.",
"descriptionType": "Abstract"
}
],
"geoLocations": [],
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