Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values)
Data and Resources
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| Field | Value |
|---|---|
| accessLevel | public |
| bureauCode | {010:00} |
| catalog_@context | https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld |
| catalog_conformsTo | https://project-open-data.cio.gov/v1.1/schema |
| catalog_describedBy | https://project-open-data.cio.gov/v1.1/schema/catalog.json |
| identifier | 3ca59f8b-9584-45da-8cdc-64eafedc9249 |
| metadata_type | geospatial |
| modified | 2020-08-20 |
| old-spatial | {"type": "Polygon", "coordinates": [[[-94.2609062308, 42.5692312673], [-87.9475441739, 42.5692312673], [-87.9475441739, 48.6427837912], [-94.2609062308, 48.6427837912], [-94.2609062308, 42.5692312673]]]} |
| publisher | Climate Adaptation Science Centers |
| resource-type | Dataset |
| source_datajson_identifier | true |
| source_hash | 774d26afc4c21629b462ab51e5858ecce377eb4c |
| source_schema_version | 1.1 |
| spatial | {"type": "Polygon", "coordinates": [[[-94.2609062308, 42.5692312673], [-87.9475441739, 42.5692312673], [-87.9475441739, 48.6427837912], [-94.2609062308, 48.6427837912], [-94.2609062308, 42.5692312673]]]} |
| theme | {geospatial} |
| Groups |
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| Tags |
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| isopen | False |
| license_id | notspecified |
| license_title | License not specified |
| maintainer | U.S. Geological Survey (Point of Contact) |
| maintainer_email | jread@usgs.gov |
| metadata_created | 2025-11-22T07:16:01.505124 |
| metadata_modified | 2025-11-22T07:16:01.505128 |
| notes | This dataset provides model specifications used to estimate water temperature from a process-based model (Hipsey et al. 2019). The format is a single JSON file indexed for each lake based on the "site_id". 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). |
| num_resources | 5 |
| num_tags | 20 |
| title | Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) |