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    • Process-guided deep learning water temperature predictions: 5 Model 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.

    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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    Dates

    Metadata Created Date May 31, 2023
    Metadata Updated Date June 15, 2024

    Metadata Source

    Harvested from DOI EDI

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date May 31, 2023
    Metadata Updated Date June 15, 2024
    Publisher Climate Adaptation Science Centers
    Maintainer
    @Id http://datainventory.doi.gov/id/dataset/0d8896d9c5855b8dc857553ad90aefff
    Identifier cec2780a-4455-408e-9bb5-b25a7154c23f
    Data Last Modified 2020-08-20
    Category geospatial
    Public Access Level public
    Bureau Code 010:00
    Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
    Metadata Catalog ID https://datainventory.doi.gov/data.json
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Harvest Object Id 117cb45a-39be-4c68-8ae2-cba8d9f4c156
    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 34564971863f51d1177cb550d984f3ee29ed558a6cc916d9ba121c424a641d89
    Source Schema Version 1.1
    Spatial {"type": "Polygon", "coordinates": -94.2609062308, 42.5692312673, -94.2609062308, 48.6427837912, -87.9475441739, 48.6427837912, -87.9475441739, 42.5692312673, -94.2609062308, 42.5692312673}

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