Preprints
https://doi.org/10.5194/hess-2024-54
https://doi.org/10.5194/hess-2024-54
12 Apr 2024
 | 12 Apr 2024
Status: this preprint is currently under review for the journal HESS.

Hybrid Hydrological Modeling for Large Alpine Basins: A Distributed Approach

Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni

Abstract. Large alpine basins provide abundant water resources crucial for hydropower generation, irrigation, and daily life. It is thus crucial to develop high-performance hydrological models for water resources management in large alpine basins. Recently, hybrid hydrological models have come to the forefront, synergizing the exceptional learning capacity of deep learning with the interpretability and physical consistency of process-based models. These models exhibit considerable promise in achieving precision in hydrological simulations. However, a notable limitation of existing hybrid models lies in their failure to incorporate spatial information within the basin and describe alpine hydrological processes, which restricts their applicability in hydrological modeling in large alpine basins. To address this issue, we develop a set of hybrid distributed hydrological models by employing a distributed process-based model as the backbone, and utilizing embedded neural networks (ENNs) to parameterize and replace different internal modules. The proposed models are tested on three large alpine basins on the Tibetan Plateau. Results are compared to those obtained from hybrid lumped models, state-of-the-art distributed hydrological model, and purely deep learning models. A climate perturbation method is further used to test the applicability of the hybrid models to analyze the hydrological sensitivities to climate change in large alpine basins. Results indicate that proposed hybrid hydrological models can perform well in predicting runoff processes and simulating runoff component contributions in large alpine basins. The optimal hybrid model with Nash-Sutcliffe efficiency coefficients (NSEs) higher than 0.87 shows comparable performance to state-of-the-art DL models. The hybrid distributed model also exhibits remarkable capability in simulating hydrological processes at ungauged sites within the basin, markedly surpassing traditional distributed models. Besides, the results also show reasonable patterns in the analysis of the hydrological sensitivities to climate change. Runoff exhibits an amplification effect in response to precipitation changes, with a 10 % precipitation change resulting in a 15–20 % runoff change in large alpine basins. An increase in temperature enhances evaporation capacity and changes the redistribution of rainfall and snowfall and the timing of snowmelt. It further leads to a decrease in the total runoff, the contributions of snowmelt runoff, and the intra-annual variability of runoff. Overall, this study provides a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and improves our understanding about the hydrological sensitivities to climate change in large alpine basins.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni

Status: open (until 07 Jun 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on hess-2024-54', Kunlong He, 16 Apr 2024 reply
    • AC1: 'Reply on CC1', Bu Li, 23 May 2024 reply
      • CC3: 'Reply on AC1', Kunlong He, 23 May 2024 reply
  • RC1: 'Comment on hess-2024-54', Anonymous Referee #1, 23 Apr 2024 reply
  • RC2: 'Comment on hess-2024-54', Anonymous Referee #2, 12 May 2024 reply
  • CC2: 'Comment on hess-2024-54', John Ding, 13 May 2024 reply
    • AC2: 'Reply on CC2', Bu Li, 24 May 2024 reply
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni

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Short summary
This paper developed hybrid distributed hydrological models by employing a distributed model as the backbone, and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and improves understanding about the hydrological sensitivities to climate change in large alpine basins.