Runoff predictions in ungauged arctic basins using conceptual models forced by reanalysis data
- 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.
Author details: | Georgy V. AyzelORCiDGND |
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DOI: | https://doi.org/10.1134/S0097807818060180 |
ISSN: | 0097-8078 |
ISSN: | 1608-344X |
Title of parent work (English): | Water Resources |
Publisher: | Pleiades Publ. |
Place of publishing: | New York |
Publication type: | Article |
Language: | English |
Date of first publication: | 2018/12/10 |
Publication year: | 2018 |
Release date: | 2021/06/25 |
Tag: | Arctic; hydrologic modeling; reanalysis; runoff; ungauged basins |
Volume: | 45 |
Number of pages: | 7 |
First page: | S1 |
Last Page: | S7 |
Funding institution: | Geo.X, the Research Network for Geosciences in Berlin and Potsdam; Russian Science FoundationRussian Science Foundation (RSF) [16-17-10039] |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften |
DDC classification: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
Peer review: | Referiert |