Comparison of deep learning models and a typical process-based model in glacio-hydrology simulation

Glacier hydrology has profound implications for socio-economic development and nature conservation in arid Central Asia. Process-based hydrological models, which are the traditional tools used to simulate glacier melting, have made considerable contributions to advance our understanding of glacio-hydrology. Simultaneously, deep learning (DL) models have achieved excellent performance in many complex tasks and provide high accuracy. However, it is uncertain whether glacio-hydrological studies can benefit from the application of DL models. In this study, to help us assess water resource change for glacier-influenced regions, we used DL models to simulate glacio-hydrological processes in the Urumqi Glacier No. 1 in northwest China. First, we proposed a newly DL model called Exogenous Regularization Network (ERNet), which focuses on the relationship between exogenous (temperature and precipitation) and endogenous (runoff) variables, balancing the roles of different variables in the simulation process. Second, we compared ERNet with a stacked long short-term memory (LSTM) model and a process-based glacio-hydrology model, FLEXG. Experiments showed that compared with the other two models, ERNet not only performed well in runoff and peak flow simulations but also displayed superior transferability. Third, given that the DL model is data-driven, we experimentally compared the importance of air temperature and precipitation to glacial runoff processes. The results show that air temperature plays a dominant role in glacier runoff generation. We believe that the proposed model provides a useful predictive tool and that the results shed light on the future implication in cold region hydrology.

© 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 Comparison of deep learning models and a typical process-based model in glacio-hydrology simulation
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Open Access
Creators
  1. Xi Chen
  2. Sheng Wang
  3. Hongkai Gao
  4. Jiaxu Huang
  5. Chaopeng Shen
  6. Qingli Li
  7. Honggang Qi
  8. Laiwen Zheng
  9. Min Liu
Keyword
  1. FLEXG model
  2. Glacio-hydrology
  3. Long short-term memory
  4. Runoff simulation
  5. Urumqi Glacier No. 1
License CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
Work Type Article
Publisher
  1. Journal of Hydrology
Publication Date November 16, 2022
Publisher Identifier (DOI)
  1. https://doi.org/10.1016/j.jhydrol.2022.128562
Deposited December 06, 2023

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Version 1
published

  • Created
  • Added Chen_2022_Glacio-hydrology_finalmanuscript-1.pdf
  • Added Creator Xi Chen
  • Added Creator Sheng Wang
  • Added Creator Hongkai Gao
  • Added Creator Jiaxu Huang
  • Added Creator Chaopeng Shen
  • Added Creator Qingli Li
  • Added Creator Honggang Qi
  • Added Creator Laiwen Zheng
  • Added Creator Min Liu
  • Published
  • Updated Keyword, Publication Date Show Changes
    Keyword
    • FLEXG model, Glacio-hydrology, Long short-term memory, Runoff simulation, Urumqi Glacier No. 1
    Publication Date
    • 2022-12-01
    • 2022-11-16
  • Updated