Transferring Hydrologic Data Across Continents – Leveraging Data-Rich Regions to Improve Hydrologic Prediction in Data-Sparse Regions

There is a drastic geographic imbalance in available global streamflow gauge and catchment property data, with additional large variations in data characteristics. As a result, models calibrated in one region cannot normally be migrated to another without significant modifications. Currently in these regions, non-transferable machine learning models are habitually trained over small local data sets. Here we show that transfer learning (TL), in the senses of weight initialization and weight freezing, allows long short-term memory (LSTM) streamflow models that were pretrained over the conterminous United States (CONUS, the source data set) to be transferred to catchments on other continents (the target regions), without the need for extensive catchment attributes available at the target location. We demonstrate this possibility for regions where data are dense (664 basins in Great Britain), moderately dense (49 basins in central Chile), and scarce with only remotely sensed attributes available (5 basins in China). In both China and Chile, the TL models showed significantly elevated performance compared to locally trained models using all basins. The benefits of TL increased with the amount of available data in the source data set, and seemed to be more pronounced with greater physiographic diversity. The benefits from TL were greater than from pretraining LSTM using the outputs from an uncalibrated hydrologic model. These results suggest hydrologic data around the world have commonalities which could be leveraged by deep learning, and synergies can be had with a simple modification of the current workflows, greatly expanding the reach of existing big data. Finally, this work diversified existing global streamflow benchmarks.

An edited version of this paper was published by AGU. Copyright 2021 American Geophysical Union [Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions. Water Resources Research 57, 5 (2021)]

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Work Title Transferring Hydrologic Data Across Continents – Leveraging Data-Rich Regions to Improve Hydrologic Prediction in Data-Sparse Regions
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Open Access
Creators
  1. Kai Ma
  2. Dapeng Feng
  3. Kathryn Lawson
  4. Wen Ping Tsai
  5. Chuan Liang
  6. Xiaorong Huang
  7. Ashutosh Sharma
  8. Chaopeng Shen
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. Water Resources Research
Publication Date March 26, 2021
Publisher Identifier (DOI)
  1. https://doi.org/10.1029/2020WR028600
Deposited October 09, 2024

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Version 1
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  • Created
  • Added Ma_2021__Transfer_data_across_continents_finalmanuscript.pdf
  • Added Creator Kai Ma
  • Added Creator Dapeng Feng
  • Added Creator Kathryn Lawson
  • Added Creator Wen Ping Tsai
  • Added Creator Chuan Liang
  • Added Creator Xiaorong Huang
  • Added Creator Ashutosh Sharma
  • Added Creator Chaopeng Shen
  • Published
  • Updated
  • Updated Publication Date Show Changes
    Publication Date
    • 2021-05-01
    • 2021-03-26