Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values)
Dates
Publication Date
2019-11-13
Start Date
1980-04-01
End Date
2018-12-31
Citation
Read, J.S., Jia, X., Willard, J., Appling, A.P., Zwart, J.A., Oliver, S.K., Karpatne, A., Hansen, G.J.A., Hanson, P.C., Watkins, W., Steinbach, M., and Kumar, V., 2019, Data release: Process-guided deep learning predictions of lake water temperature: U.S. Geological Survey data release, https://doi.org/10.5066/P9AQPIVD.
Summary
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).
Summary
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).
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02_config.xml Original FGDC Metadata
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16.24 KB
application/fgdc+xml
glm_config.json
262.14 KB
application/json
Related External Resources
Type: Related Primary Publication
Read, J. S., Jia, X., Willard, J., Appling, A. P., Zwart, J. A., Oliver, S. K., et al ( 2019). Process‐guided deep learning predictions of lake water temperature. Water Resources Research, 55. https://doi.org/10.1029/2019WR024922
