Process-guided deep learning water temperature predictions: 4c All lakes historical training data
Description
Observed water temperatures from 1980-2018 were compiled for 68 lakes in Minnesota and Wisconsin (USA). These data were used as training data for process-guided deep learning models and deep learning models, and calibration data for process-based models. The data are formatted as a single csv (comma separated values) file with attributes corresponding to the unique combination of lake identifier, time, and depth. Data came from a variety of sources, including the Water Quality Portal, the North Temperate Lakes Long-Term Ecological Research Project, and digitized temperature records from the MN Department of Natural Resources.
Resources
| Name |
Format |
Description |
Link |
|
0 |
sdata.2017.53 |
http://dx.doi.org/10.1038/sdata.2017.53 |
|
0 |
|
http://dx.doi.org/10.1029/2019WR024922 |
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0 |
|
http://dx.doi.org/10.5066/P9AQPIVD |
Tags
- reservoirs
- hybrid-modeling
- usgs-5d8a47bce4b0c4f70d0ae61f
- modeling
- temperate-lakes
- deep-learning
- united-states
- thermal-profiles
- us
- climate-change
- water
- temperature
- machine-learning