Deciphering Nonlinear Hydrological Process by a Coupled Deep Learning and Physical Based Model in Southern Tibetan Plateau
Abstract Interpretability of deep learning (DL) poses a significant challenge in hydrology modeling, particularly under the complex and frigid conditions of the Tibetan Plateau (TP), which further restricts its application. In this study, we developed a cascade‐style hybrid modeling framework by int...
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| Format: | Article |
| Language: | English |
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Wiley
2025-08-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR038515 |
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| author | Zhanliang Zhu Xiongpeng Tang Jianyun Zhang Yehai Tang Lei Liu Chao Gao Silong Zhang Yanli Liu Junliang Jin Cuishan Liu Bikui Zhao Guoqing Wang |
| author_facet | Zhanliang Zhu Xiongpeng Tang Jianyun Zhang Yehai Tang Lei Liu Chao Gao Silong Zhang Yanli Liu Junliang Jin Cuishan Liu Bikui Zhao Guoqing Wang |
| author_sort | Zhanliang Zhu |
| collection | DOAJ |
| description | Abstract Interpretability of deep learning (DL) poses a significant challenge in hydrology modeling, particularly under the complex and frigid conditions of the Tibetan Plateau (TP), which further restricts its application. In this study, we developed a cascade‐style hybrid modeling framework by integrating the Variable Infiltration Capacity (VIC) model with a two‐dimensional grid long short‐term memory (termed VIC‐LSTM), and incorporated a dual‐layer probe for training and investigating non‐linear hydrological processes. Our objective was to explore the framework's potential for enhancing hydrological simulation accuracy and expanding interpretability. This framework was adopted for the Yarlung Zangbo River Basin (above the Lazi guaged station) in the TP. The results of the VIC‐LSTM demonstrated its effectiveness, achieving a simulated daily streamflow NSE of 0.78 compared to 0.69 for the VIC model during the training period. Moreover, the probe experiments, aided by remote‐sensed images, successfully deciphered the timing signals of snowmelt and glacier melt, with ablation and duration time errors within 2 weeks. The average errors for snowmelt and glacier melt were approximately 7 and 10 days, respectively. The combined spatial and temporal feature quantification indicated that snowmelt and glacier melt contributed 23.7% and 7.7% to the total streamflow, respectively. Additionally, VIC‐LSTM identified that snowmelt signals generally preceded glacier melt signals. These findings underscore the potential of VIC‐LSTM to interpret hydrological processes, increasing confidence in using DL approaches. Therefore, it offers a new perspective for quantifying water resources and understanding hydrological processes in high‐elevation, glacier‐snow coexisting basins. |
| format | Article |
| id | doaj-art-6e4d6dabc26647699a19ec6409d0b0a3 |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-6e4d6dabc26647699a19ec6409d0b0a32025-08-26T12:02:53ZengWileyWater Resources Research0043-13971944-79732025-08-01618n/an/a10.1029/2024WR038515Deciphering Nonlinear Hydrological Process by a Coupled Deep Learning and Physical Based Model in Southern Tibetan PlateauZhanliang Zhu0Xiongpeng Tang1Jianyun Zhang2Yehai Tang3Lei Liu4Chao Gao5Silong Zhang6Yanli Liu7Junliang Jin8Cuishan Liu9Bikui Zhao10Guoqing Wang11Guangdong‐Hong Kong Joint Laboratory for Water Security Beijing Normal University Zhuhai ChinaGuangdong‐Hong Kong Joint Laboratory for Water Security Beijing Normal University Zhuhai ChinaNanjing Hydraulic Research Institute Nanjing ChinaGuangdong‐Hong Kong Joint Laboratory for Water Security Beijing Normal University Zhuhai ChinaGuangdong‐Hong Kong Joint Laboratory for Water Security Beijing Normal University Zhuhai ChinaGuangdong‐Hong Kong Joint Laboratory for Water Security Beijing Normal University Zhuhai ChinaGuangdong‐Hong Kong Joint Laboratory for Water Security Beijing Normal University Zhuhai ChinaNanjing Hydraulic Research Institute Nanjing ChinaNanjing Hydraulic Research Institute Nanjing ChinaNanjing Hydraulic Research Institute Nanjing ChinaGuangdong Research Institute of Water Resources and Hydropower Guangzhou ChinaNanjing Hydraulic Research Institute Nanjing ChinaAbstract Interpretability of deep learning (DL) poses a significant challenge in hydrology modeling, particularly under the complex and frigid conditions of the Tibetan Plateau (TP), which further restricts its application. In this study, we developed a cascade‐style hybrid modeling framework by integrating the Variable Infiltration Capacity (VIC) model with a two‐dimensional grid long short‐term memory (termed VIC‐LSTM), and incorporated a dual‐layer probe for training and investigating non‐linear hydrological processes. Our objective was to explore the framework's potential for enhancing hydrological simulation accuracy and expanding interpretability. This framework was adopted for the Yarlung Zangbo River Basin (above the Lazi guaged station) in the TP. The results of the VIC‐LSTM demonstrated its effectiveness, achieving a simulated daily streamflow NSE of 0.78 compared to 0.69 for the VIC model during the training period. Moreover, the probe experiments, aided by remote‐sensed images, successfully deciphered the timing signals of snowmelt and glacier melt, with ablation and duration time errors within 2 weeks. The average errors for snowmelt and glacier melt were approximately 7 and 10 days, respectively. The combined spatial and temporal feature quantification indicated that snowmelt and glacier melt contributed 23.7% and 7.7% to the total streamflow, respectively. Additionally, VIC‐LSTM identified that snowmelt signals generally preceded glacier melt signals. These findings underscore the potential of VIC‐LSTM to interpret hydrological processes, increasing confidence in using DL approaches. Therefore, it offers a new perspective for quantifying water resources and understanding hydrological processes in high‐elevation, glacier‐snow coexisting basins.https://doi.org/10.1029/2024WR038515deep learninghybrid modelinghydrological processsnowmelt runoffTibetan Plateau |
| spellingShingle | Zhanliang Zhu Xiongpeng Tang Jianyun Zhang Yehai Tang Lei Liu Chao Gao Silong Zhang Yanli Liu Junliang Jin Cuishan Liu Bikui Zhao Guoqing Wang Deciphering Nonlinear Hydrological Process by a Coupled Deep Learning and Physical Based Model in Southern Tibetan Plateau Water Resources Research deep learning hybrid modeling hydrological process snowmelt runoff Tibetan Plateau |
| title | Deciphering Nonlinear Hydrological Process by a Coupled Deep Learning and Physical Based Model in Southern Tibetan Plateau |
| title_full | Deciphering Nonlinear Hydrological Process by a Coupled Deep Learning and Physical Based Model in Southern Tibetan Plateau |
| title_fullStr | Deciphering Nonlinear Hydrological Process by a Coupled Deep Learning and Physical Based Model in Southern Tibetan Plateau |
| title_full_unstemmed | Deciphering Nonlinear Hydrological Process by a Coupled Deep Learning and Physical Based Model in Southern Tibetan Plateau |
| title_short | Deciphering Nonlinear Hydrological Process by a Coupled Deep Learning and Physical Based Model in Southern Tibetan Plateau |
| title_sort | deciphering nonlinear hydrological process by a coupled deep learning and physical based model in southern tibetan plateau |
| topic | deep learning hybrid modeling hydrological process snowmelt runoff Tibetan Plateau |
| url | https://doi.org/10.1029/2024WR038515 |
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