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Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data

Metadata Updated: June 15, 2024

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. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added physical constraint for energy conservation as a loss term. These models were pre-trained with uncalibrated Process-Based model outputs (PB0) before training on actual temperature observations. Zip files for each lake contain four files, one for each of PB, PB0, DL, and PGDL.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date June 1, 2023
Metadata Updated Date June 15, 2024

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date June 1, 2023
Metadata Updated Date June 15, 2024
Publisher Climate Adaptation Science Centers
Identifier 2dc76bea-2874-4392-bbd6-f2e422e7b681
Data Last Modified 2020-08-20
Category geospatial
Public Access Level public
Bureau Code 010:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id 2df8f130-7b0b-476d-b0cc-530e218dd9f2
Harvest Source Id 52bfcc16-6e15-478f-809a-b1bc76f1aeda
Harvest Source Title DOI EDI
Metadata Type geospatial
Old Spatial -94.2609062308,42.5692312673,-87.9475441739,48.6427837912
Source Datajson Identifier True
Source Hash c5767a3ddaed8463b1d5efa776ed299d51cadc73684b1e367ae1e94ede6e0a5e
Source Schema Version 1.1
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