Can transfer learning improve hydrological predictions in the alpine regions?

The Tibetan Plateau (TP) is an important Asian water tower for livelihood, irrigation, hydropower, and downstream ecosystems. Hydrological predictions in the TP have long been limited by sparse and discontinuous observations. Transfer learning (TL) technology may improve hydrological predictions by pre-training deep learning (DL) models on data-rich areas and then applying them to data-limited areas. However, the extent to which the DL and TL models work in alpine regions and the physical knowledge they provide remain unclear. Models were pretrained using data from 671 catchments across the U.S. and fine-tuned using data from four basins around the TP. Our results show that streamflow data with different temporal resolutions (monthly or daily) have little effect on discharge predictions when using DL. The number of discharge observations needed to enable acceptable performance of the DL models (Nash-Sutcliffe Efficiency coefficient greater than 0.6) depends on the hydrological characteristics of the catchments, in particular, how closely they conform to a general rainfall-runoff system. Climate forcing data determine the performance of streamflow prediction, whereas other attributes (i.e., soil and geology) have less significant impacts on prediction. The effectiveness of the DL and TL models is limited because groundwater contributes significantly to river discharge. This study provides an updated understanding of the application of DL and TL to quantify hydrological changes in the global cryosphere environment.

© This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

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Work Title Can transfer learning improve hydrological predictions in the alpine regions?
Access
Open Access
Creators
  1. Yingying Yao
  2. Yufeng Zhao
  3. Xin Li
  4. Dapeng Feng
  5. Chaopeng Shen
  6. Chuankun Liu
  7. Xingxing Kuang
  8. Chunmiao Zheng
Keyword
  1. Tibetan Plateau
  2. deep learning
  3. transfer learning
  4. streamflow prediction
License CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
Work Type Article
Publisher
  1. Journal of Hydrology
Publication Date October 2023
Publisher Identifier (DOI)
  1. https://doi.org/10.1016/j.jhydrol.2023.130038
Deposited October 14, 2024

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Version 1
published

  • Created
  • Added Yao_2023__Transfer_Learning_Alpine_finalmanuscript.pdf
  • Added Creator Yingying Yao
  • Added Creator Yufeng Zhao
  • Added Creator Xin Li
  • Added Creator Dapeng Feng
  • Added Creator Chaopeng Shen
  • Added Creator Chuankun Liu
  • Added Creator Xingxing Kuang
  • Added Creator Chunmiao Zheng
  • Published
  • Updated
  • Updated Keyword, Publication Date Show Changes
    Keyword
    • Tibetan Plateau, deep learning, transfer learning, streamflow prediction
    Publication Date
    • 2023-10-01
    • 2023-10