Articles | Volume 379
https://doi.org/10.5194/piahs-379-151-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/piahs-379-151-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Coupling physically based and data-driven models for assessing freshwater inflow into the Small Aral Sea
Institute of Earth and Environmental Science, University of Potsdam, 14476 Potsdam, Germany
Institute of Water Problems, Russian Academy of Sciences, 119333 Moscow, Russia
Alexander Izhitskiy
Shirshov Institute of Oceanology, Russian Academy of Science, 117997 Moscow, Russia
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Proc. IAHS, 379, 139–144, https://doi.org/10.5194/piahs-379-139-2018, https://doi.org/10.5194/piahs-379-139-2018, 2018
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Short summary
Presented paper is our first step in developing a geoscientific stack of models for an assessment of the Small Aral Sea basin current hydrological conditions within the interdisciplinary SMASHI project (smashiproject.github.io). Based on coupling state-of-the-art physically-based hydrological and machine learning models we have developed the skillful model for the Syr Darya river runoff prediction. This result is the key to understanding water balance trends in vulnerable Aral Sea region.
Presented paper is our first step in developing a geoscientific stack of models for an...