Process-based water temperature predictions in the Midwest US: 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. states of Minnesota and Wisconsin. General Lake Model verion 2 process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error for 449 lakes (PBALL). Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations for 7,150 lakes.
Complete Metadata
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|
| description | Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. General Lake Model verion 2 process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error for 449 lakes (PBALL). Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations for 7,150 lakes. |
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| identifier | http://datainventory.doi.gov/id/dataset/USGS_5db819a8e4b0b0c58b5a4c45 |
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"IA",
"IL",
"IN",
"Illinois",
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|
| modified | 2021-07-27T00:00:00Z |
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{
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| spatial | -104.016631682319, 37.1076610090681, -83.0716153148296, 49.3749961973185 |
| theme |
[
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|
| title | Process-based water temperature predictions in the Midwest US: 5 Model prediction data |
