Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs
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).
Resources
| Name |
Format |
Description |
Link |
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0 |
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http://dx.doi.org/10.1029/2019WR024922 |
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0 |
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http://dx.doi.org/10.5066/P9AQPIVD |
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0 |
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https://doi.org/10.5194/gmd-12-473-2019 |
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0 |
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http://dx.doi.org/10.1029/2003JD003823 |
Tags
- reservoirs
- hybrid-modeling
- modeling
- temperate-lakes
- deep-learning
- usgs-5d98e0dbe4b0c4f70d1186f3
- united-states
- thermal-profiles
- us
- climate-change
- water
- temperature
- machine-learning