Integrated Catchment Classification Across China Based on Hydroclimatological and Geomorphological Similarities Using Self-Organizing Maps and Fuzzy C-Means Clustering for Hydrological Modeling

Abstract. Accurately identifying similar catchments is crucial for transferring model parameters and improving hydrological modeling, especially in ungauged regions with varied climates and topographies. This study presents an integrated method for catchment classification by combining Self-Organizing Maps artificial neural network (SOM) and Fuzzy C-Means clustering (FCM), utilizing hydrometeorological and geomorphological data. We evaluated six climate indices and fifteen landscape characteristics for catchments across China, identifying key variables through correlation and principal component analyses. The optimal classification produced six distinct climate regions and 35 catchment types with unique streamflow patterns. Validation using ten catchments confirmed the effectiveness of the SOM-FCM approach. The study underscores the importance of considering both climate and landscape factors for a comprehensive classification of catchments, offering valuable insights for hydrological model predictions in ungauged areas and enhancing our understanding of hydrological processes at various timescales.