Predicting Water Temperature Dynamics of Unmonitored Lakes With Meta-Transfer Learning

Jared D. Willard, Jordan S. Read, Alison P. Appling, Samantha K. Oliver, Xiaowei Jia, Vipin Kumar

Research output: Contribution to journalArticlepeer-review

Abstract

Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer-learning framework that accurately predicts depth-specific temperature in unmonitored lakes (targets) by borrowing models from well-monitored lakes (sources). This method, meta-transfer learning (MTL), builds a meta-learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance. We constructed source models at 145 well-monitored lakes using calibrated process-based (PB) modeling and a recently developed approach called process-guided deep learning (PGDL). We applied MTL to either PB or PGDL source models (PB-MTL or PGDL-MTL, respectively) to predict temperatures in 305 target lakes treated as unmonitored in the Upper Midwestern United States. We show significantly improved performance relative to the uncalibrated PB General Lake Model, where the median root mean squared error (RMSE) for the target lakes is 2.52°C. PB-MTL yielded a median RMSE of 2.43°C; PGDL-MTL yielded 2.16°C; and a PGDL-MTL ensemble of nine sources per target yielded 1.88°C. For sparsely monitored target lakes, PGDL-MTL often outperformed PGDL models trained on the target lakes themselves. Differences in maximum depth between the source and target were consistently the most important predictors. Our approach readily scales to thousands of lakes in the Midwestern United States, demonstrating that MTL with meaningful predictor variables and high-quality source models is a promising approach for many kinds of unmonitored systems and environmental variables.

Original languageEnglish (US)
Article numbere2021WR029579
JournalWater Resources Research
Volume57
Issue number7
DOIs
StatePublished - Jun 29 2021

Bibliographical note

Funding Information:
This work is supported by NSF grant #1934721 under the Harnessing the Data Revolution (HDR) program. The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper. URL: http://www.msi.umn.edu. The authors thank Jennifer Fair and William Farmer for initial review, and Hayley Corson-Dosch for visualization of Figure?2. The authors also thank the Department of the Interior North Central Climate Adaptation Science Center for funding and the USGS Advanced Research Computing, USGS Yeti Supercomputer (https://doi.org/10.5066/F7D798MJ) for infrastructure used for GLM simulations. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Funding Information:
This work is supported by NSF grant #1934721 under the Harnessing the Data Revolution (HDR) program. The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper. URL: http://www.msi.umn.edu . The authors thank Jennifer Fair and William Farmer for initial review, and Hayley Corson‐Dosch for visualization of Figure 2 . The authors also thank the Department of the Interior North Central Climate Adaptation Science Center for funding and the USGS Advanced Research Computing, USGS Yeti Supercomputer ( https://doi.org/10.5066/F7D798MJ ) for infrastructure used for GLM simulations. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Publisher Copyright:
© 2021. The Authors.

Keywords

  • lake temperature
  • machine learning
  • meta learning
  • physics-guided deep learning
  • transfer learning
  • water resources

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