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

ABSTRACT

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.

KEYWORDS:

• Physically based distributed model
• SHETRAN
• climate change impacts
• GCM ensemble
• k-means clustering

Editor A. Fiori Associate Editor M. Sadegh

Editor A. Fiori Associate Editor M. Sadegh

Acknowledgements

We thank the Directorate of Economics and Statistics, Karnataka, and the India Meteorological Department for sharing rainfall and meteorological data for this study; the Central Water Commission for providing hydrological data; the modelling groups for making their data available through NEX-GDDP; and Dr Birkinshaw, School of Civil Engineering and Geosciences, Newcastle University, UK, for great assistance in clearing all the SHETRAN-related queries. The first author acknowledges Dr Madhusoodhanan and Dr Jayasankar for helpful assistance in climate model data analysis. The authors thank the Ministry of Human Resource Development (MHRD) of the Government of India for funding the research fellowship of the first author. The authors acknowledge the editorial board and anonymous reviewers for their critical comments, which improved the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data and scripts that support the findings of this study are available from the corresponding author upon request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/02626667.2022.2092404