Lumped hydrological models is an Occam' razor for runoff modeling in large Russian Arctic basins

This study is aimed to investigate the possibility of three lumped hydrological models to predict daily runoff of large-scale Arctic basins for the modern period (1979-2014) in the case of substantial data scarcity. All models were driven only by meteorological forcing reanalysis dataset without any additional information about landscape, soil or vegetation cover properties of studied basins. We found limitations of model parameters calibration in ungauged basins using global optimization algorithm and confirmed the hypothesis about its equifinality without robustness violation. Model parameters regionalization based on the whole parameters set transfer across studied watersheds was performed and showed good efficiency for prediction in ungauged basins. We run a blind test of the proposed methodology for ensemble runoff predictions on five subbasins which had only periodical monthly observations, and it showed promising results for current freshwater resources assessment for a broad domain of the Russian Arctic.