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The high computational requirements of physically based fully distributed hydrological models (PDHM) constrain the use of all available general circulation models (GCMs) to assess climate change impacts. Here, an approach of ensembling GCMs using clustering based on future climatological variables was compared with model democracy while using a PDHM, SHETRAN, forced with six GCMs. The methodology is applied to hydrological projections in Netravathi River basin from the present to the near (2021–2050) and far (2071–2100) future. The results demonstrate that some GCMs project increase (50%, 30%) while others show decrease (10%, 11%) in the far future relative to the historical period (1980–2005) for streamflow and sediment load, respectively. The spread in the projection of climatological and hydrological variables from ensembled GCMs was retained as in model democracy whereas actual evapotranspiration showed overestimation relative to individual GCMs in the far future due to limitations of the clustering approach. Hence, we suggest employing individual GCMs for hydrological impact studies.

Comparison of river basin-scale hydrologic projections from a clustering based ensemble and model democracy approach using SHETRAN