Articles | Volume 30, issue 7
https://doi.org/10.5194/hess-30-2013-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-2013-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Integrated catchment classification across China based on hydroclimatological and geomorphological similarities using self-organizing map and fuzzy c-means clustering for hydrological modeling
Jiefan Niu
State Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210098, China
Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
State Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210098, China
Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
China Meteorological Administration Hydro-Meteorology Key Laboratory, Hohai University, Nanjing, Jiangsu, 210024, China
Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
Xi Li
State Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210098, China
Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu, 210024, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210024, China
Hongjun Bao
CORRESPONDING AUTHOR
China Meteorological Administration Hydro-Meteorology Key Laboratory, Hohai University, Nanjing, Jiangsu, 210024, China
National Meteorological Center, China Meteorological Administration, Beijing, 100081, China
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Short summary
This study developed a new method for classifying catchments, combining machine learning techniques with climate and landscape data. By analyzing catchments across China, we identified six climate regions and 35 unique catchment types, each with distinct streamflow patterns. This classification method improves hydrological predictions, especially in areas lacking direct data.
This study developed a new method for classifying catchments, combining machine learning...