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

    Metadata Updated: October 29, 2023

    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 June 1, 2023
    Metadata Updated Date October 29, 2023

    Metadata Source

    Harvested from DOI EDI

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date June 1, 2023
    Metadata Updated Date October 29, 2023
    Publisher Climate Adaptation Science Centers
    Maintainer
    @Id http://datainventory.doi.gov/id/dataset/6f6ee602ec42aefeaa4333430d75fdc1
    Identifier f963b3c6-758b-410f-9de7-fa4f974d57e1
    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 b05543b3-ce67-4e74-a88e-9577e1973714
    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 984a50710c7fa7416e6c75b684172b45b953eb3309cb434a8e8d0373b8fb4767
    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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