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Process-guided deep learning water temperature predictions: 5a Lake Mendota detailed prediction data

Dates

Publication Date
Start Date
1980-04-01
End Date
2018-12-31

Citation

Read, J.S., Jia, X., Willard, J., Appling, A.P., Zwart, J.A., Oliver, S.K., Karpatne, A., Hansen, G.J.A., Hanson, P.C., Watkins, W., Steinbach, M., and Kumar, V., 2019, Data release: Process-guided deep learning predictions of lake water temperature: U.S. Geological Survey data release, https://doi.org/10.5066/P9AQPIVD.

Summary

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 [...]

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05a_prediction_me.xml
Original FGDC Metadata

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39.38 KB application/fgdc+xml
me_similar_predict_pb.csv 98.32 MB text/csv
me_similar_predict_dl.csv 72.69 MB text/csv
me_similar_predict_pgdl.csv 86.72 MB text/csv
me_season_predict_pb.csv 16.39 MB text/csv
me_year_predict_pb.csv 16.35 MB text/csv
me_season_predict_dl.csv 14.39 MB text/csv
me_year_predict_dl.csv 14.52 MB text/csv
me_season_predict_pgdl.csv 14.39 MB text/csv
me_year_predict_pgdl.csv 14.38 MB text/csv
me_predict_pb0.csv 12.64 MB text/csv

Purpose

Fisheries biology, limnological research, and climate science.

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  • National and Regional Climate Adaptation Science Centers
  • Northeast CASC

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