Impact of catchment classification on streamflow regionalization in ungauged catchments

Hydrological parameter transfer between dissimilar catchments is bound to be associated with errors. As a result of that, hydrological prediction at ungauged sites gets affected. If the catchments are similar to each other in some way, then it may reduce the error in prediction. The logic mentioned above is applied in the current study, where three regionalization approaches, i.e., Inverse Distance Weighted, Kriging and global mean based on spatial proximity between gauged and ungauged catchments, were applied on 32 catchments in India. Before application of regionalization techniques, 32 catchments were categorized into four homogeneous groups using the self-organizing map method. The improvement in streamflow regionalization was checked for both calibration (1995–2006) and validation (2007–2011) while moving from unclassified to classified catchments. Results suggest that prior classification of catchments into homogeneous groups helps in improving the regionalization output. Fifty per cent of the total catchments displayed 10% improvement, while 30% of the catchments showed more than 10% improvement. The results imply that an appropriate combination of a hydrological model (Soil and Water Assessment Tool), regionalization technique and classification method will yield better results for ungauged catchments in streamflow prediction.