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Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 5 Model prediction data
Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. state of Minnesota. Uncalibrated models used default configurations (PB0; see Read et al. 2019 for details) of the General Lake Model version 3.1 (Hipsey et al. 2019) and no parameters were further adjusted according to model fit with observations. Process-Guided Deep Learning (PGDL; see Read et al. 2019 and Jia et al. 2019) models were deep learning models pre-trained PB0 outputs and a physical constraint for energy conservation as a loss term. After pre-training, these PGDL models were training on actual temperature observations.
Complete Metadata
| @id | http://datainventory.doi.gov/id/dataset/dfec92b85197b762e3234b014818b13f |
|---|---|
| bureauCode |
[ "010:12" ] |
| identifier | USGS:5e5d0bb9e4b01d50924f2b36 |
| spatial | -96.8588358267624,43.5115807324613,-90.0362369790191,49.3749961973185 |
| theme |
[ "geospatial" ] |
