Shifted dominant flood drivers of an alpine glacierized catchment in the Tianshan region revealed through interpretable deep learning
Abstract The Kumalak River, a typical alpine glacierized catchment in the Tianshan region, experiences complex flooding driven by glacier meltwater, snowmelt, and rainfall. However, the mechanisms driving these processes under climate change remain unclear. To address this, a SWAT-Glacier hydrologic...
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Nature Portfolio
2025-01-01
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| Series: | npj Climate and Atmospheric Science |
| Online Access: | https://doi.org/10.1038/s41612-025-00918-z |
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| author | Wenting Liang Weili Duan Yaning Chen Gonghuan Fang Shan Zou Zhi Li Zewei Qiu Haodong Lyu |
| author_facet | Wenting Liang Weili Duan Yaning Chen Gonghuan Fang Shan Zou Zhi Li Zewei Qiu Haodong Lyu |
| author_sort | Wenting Liang |
| collection | DOAJ |
| description | Abstract The Kumalak River, a typical alpine glacierized catchment in the Tianshan region, experiences complex flooding driven by glacier meltwater, snowmelt, and rainfall. However, the mechanisms driving these processes under climate change remain unclear. To address this, a SWAT-Glacier hydrological model and a degree–day factor model were used for snowmelt, glacier meltwater, and rainfall calculations. Two Long Short-Term Memory (LSTM) models (LSTM-SG and LSTM-DDF) were developed using these inputs, and additive decomposition and integrated gradient methods were applied to interpret flood mechanisms. Glacier meltwater was found to dominate annual maximum flood (AMF) events, while snowmelt drove annual spring maximum flood (AMFSp) events. For AMF events (1960–2018), contributions were 10.01–12.21% from snowmelt, 60.49–60.92% from glacier meltwater, and 26.86–29.50% from rainfall. For AMFSp events (1961–2018), contributions were 48.49–56.08% from snowmelt, 16.12–22.08% from glacier meltwater, and 27.79–29.42% from rainfall. These findings provide critical insights for enhancing flood prediction and optimizing water resource management. |
| format | Article |
| id | doaj-art-6c5d17b7abef46f487b197c170573bd7 |
| institution | Kabale University |
| issn | 2397-3722 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Climate and Atmospheric Science |
| spelling | doaj-art-6c5d17b7abef46f487b197c170573bd72025-01-26T12:22:33ZengNature Portfolionpj Climate and Atmospheric Science2397-37222025-01-018111210.1038/s41612-025-00918-zShifted dominant flood drivers of an alpine glacierized catchment in the Tianshan region revealed through interpretable deep learningWenting Liang0Weili Duan1Yaning Chen2Gonghuan Fang3Shan Zou4Zhi Li5Zewei Qiu6Haodong Lyu7State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesState Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesAbstract The Kumalak River, a typical alpine glacierized catchment in the Tianshan region, experiences complex flooding driven by glacier meltwater, snowmelt, and rainfall. However, the mechanisms driving these processes under climate change remain unclear. To address this, a SWAT-Glacier hydrological model and a degree–day factor model were used for snowmelt, glacier meltwater, and rainfall calculations. Two Long Short-Term Memory (LSTM) models (LSTM-SG and LSTM-DDF) were developed using these inputs, and additive decomposition and integrated gradient methods were applied to interpret flood mechanisms. Glacier meltwater was found to dominate annual maximum flood (AMF) events, while snowmelt drove annual spring maximum flood (AMFSp) events. For AMF events (1960–2018), contributions were 10.01–12.21% from snowmelt, 60.49–60.92% from glacier meltwater, and 26.86–29.50% from rainfall. For AMFSp events (1961–2018), contributions were 48.49–56.08% from snowmelt, 16.12–22.08% from glacier meltwater, and 27.79–29.42% from rainfall. These findings provide critical insights for enhancing flood prediction and optimizing water resource management.https://doi.org/10.1038/s41612-025-00918-z |
| spellingShingle | Wenting Liang Weili Duan Yaning Chen Gonghuan Fang Shan Zou Zhi Li Zewei Qiu Haodong Lyu Shifted dominant flood drivers of an alpine glacierized catchment in the Tianshan region revealed through interpretable deep learning npj Climate and Atmospheric Science |
| title | Shifted dominant flood drivers of an alpine glacierized catchment in the Tianshan region revealed through interpretable deep learning |
| title_full | Shifted dominant flood drivers of an alpine glacierized catchment in the Tianshan region revealed through interpretable deep learning |
| title_fullStr | Shifted dominant flood drivers of an alpine glacierized catchment in the Tianshan region revealed through interpretable deep learning |
| title_full_unstemmed | Shifted dominant flood drivers of an alpine glacierized catchment in the Tianshan region revealed through interpretable deep learning |
| title_short | Shifted dominant flood drivers of an alpine glacierized catchment in the Tianshan region revealed through interpretable deep learning |
| title_sort | shifted dominant flood drivers of an alpine glacierized catchment in the tianshan region revealed through interpretable deep learning |
| url | https://doi.org/10.1038/s41612-025-00918-z |
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