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Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs
This dataset includes model inputs that describe local weather conditions for Sparkling Lake, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited 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 local weather conditions for Sparkling Lake, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited 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",
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{
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"downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.5d98e0dbe4b0c4f70d1186f3.xml"
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|
| identifier | http://datainventory.doi.gov/id/dataset/USGS_5d98e0dbe4b0c4f70d1186f3 |
| keyword |
[
"US",
"USGS:5d98e0dbe4b0c4f70d1186f3",
"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 | -89.7037723045351, 46.002272195262, -89.6957319045477, 46.0152963952417 |
| theme |
[
"Geospatial"
]
|
| title | Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs |
