Runoff Predictions in Ungauged Arctic Basins Using Conceptual Models Forced by Reanalysis Data
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Due to global warming, the problem of assessing water resources and their vulnerability to climate drivers in the Arctic region has become a focus in the recent years. This study is aimed at investigating three lumped hydrological models to predict daily runoff of large-scale Arctic basins 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 the studied basins. Model parameter regionalization based on transferring the whole parameter set showed good efficiency for predictions in ungauged basins. We run a blind test of the proposed methodology for ensemble runoff predictions on five sub-basins, for which only monthly observations were available, and obtained promising results for current water resources assessment for a broad domain of ungauged basins in the Russian Arctic.
Keywords:hydrologic modeling runoff ungauged basins reanalysis Arctic
This publication was supported by Geo.X, the Research Network for Geosciences in Berlin and Potsdam. The model development and evaluation part (Section 4.1–4.2) was supported by the Russian Science Foundation, project no. 16-17-10039. River runoff data were kindly provided by the Global Runoff Data Centre (GRDC), D-56068 Koblenz, Germany. Georgy Ayzel thanks James Bennett and Guillaume Thirel for their contribution to the publication’s idea distillation, useful recommendations, and positive criticism.
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