Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values)
Description
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).
Resources
| Name |
Format |
Description |
Link |
|
0 |
|
https://doi.org/10.5194/gmd-12-473-2019 |
|
0 |
|
http://dx.doi.org/10.1029/2019WR024922 |
|
0 |
|
http://dx.doi.org/10.5066/F7DV1H10 |
|
0 |
CSDGM IMPORT ERROR: No digtinfo/formcont |
http://dx.doi.org/10.5066/P9AQPIVD |
Tags
- usgs-5d8a2257e4b0c4f70d0ae513
- reservoirs
- hybrid-modeling
- modeling
- temperate-lakes
- deep-learning
- united-states
- thermal-profiles
- us
- climate-change
- water
- temperature
- machine-learning