| 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}
|
| Gruppi |
- AmeriGEOSS
- National Provider
- North America
|
| Tag |
- amerigeo
- amerigeoss
- ckan
- climate-change
- deep-learning
- geo
- geoss
- hybrid-modeling
- machine-learning
- modeling
- national
- north-america
- reservoirs
- temperate-lakes
- temperature
- thermal-profiles
- united-states
- us
- usgs-5d8a2257e4b0c4f70d0ae513
- water
|
| 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 |
2025-11-22T18:03:18.192680
|
| metadata_modified |
2025-11-22T18:03:18.192684
|
| 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 |
4
|
| num_tags |
20
|
| title |
Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values)
|