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Process-guided deep learning water temperature predictions: 3c All lakes historical inputs
This dataset includes model inputs that describe weather conditions for the 68 lakes included in this study. Weather data comes from gridded estimates (Mitchell et al. 2004). There are two comma-separated files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[
"010:12"
]
|
| contactPoint |
{
"fn": "Jordan S. Read",
"@type": "vcard:Contact",
"hasEmail": "mailto:jread@usgs.gov"
}
|
| description | This dataset includes model inputs that describe weather conditions for the 68 lakes included in this study. Weather data comes from gridded estimates (Mitchell et al. 2004). There are two comma-separated files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD). |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "Digital Data",
"format": "XML",
"accessURL": "http://dx.doi.org/10.5066/P9AQPIVD",
"mediaType": "application/http",
"description": "Landing page for access to the data"
},
{
"@type": "dcat:Distribution",
"title": "Original Metadata",
"format": "XML",
"mediaType": "text/xml",
"description": "The metadata original format",
"downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5d98e0a3e4b0c4f70d1186ee.xml"
}
]
|
| identifier | http://datainventory.doi.gov/id/dataset/USGS_5d98e0a3e4b0c4f70d1186ee |
| keyword |
[
"MN",
"Minnesota",
"US",
"USGS:5d98e0a3e4b0c4f70d1186ee",
"United States",
"WI",
"Wisconsin",
"biota",
"climate change",
"deep learning",
"environment",
"hybrid modeling",
"inlandWaters",
"machine learning",
"modeling",
"reservoirs",
"temperate lakes",
"temperature",
"thermal profiles",
"water"
]
|
| modified | 2020-08-20T00:00:00Z |
| publisher |
{
"name": "U.S. Geological Survey",
"@type": "org:Organization"
}
|
| spatial | -94.2609062307949, 42.5692312672573, -87.9475441739278, 48.6427837911633 |
| theme |
[
"Geospatial"
]
|
| title | Process-guided deep learning water temperature predictions: 3c All lakes historical inputs |
