Articles | Volume 30, issue 7
https://doi.org/10.5194/hess-30-2013-2026
https://doi.org/10.5194/hess-30-2013-2026
Research article
 | 
14 Apr 2026
Research article |  | 14 Apr 2026

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, Ke Zhang, Xi Li, and Hongjun Bao

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Cited articles

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Addor, N., Nearing, G., Prieto, C., Newman, A., Le Vine, N., and Clark, M. P.: A ranking of hydrological signatures based on their predictability in space, Water Resour. Res., 54, 8792–8812, https://doi.org/10.1029/2018WR022606, 2018. 
Berghuijs, W. R., Sivapalan, M., Woods, R. A., and Savenije, H. H. G.: Patterns of similarity of seasonal water balances: A window into streamflow variability over a range of time scales, Water Resour. Res., 50, 5638–5661, https://doi.org/10.1002/2014WR015692, 2014. 
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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.
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