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Proc. IAHS, 379, 151-158, 2018
https://doi.org/10.5194/piahs-379-151-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Pre-conference publication
05 Jun 2018
Coupling physically based and data-driven models for assessing freshwater inflow into the Small Aral Sea
Georgy Ayzel1,2 and Alexander Izhitskiy3 1Institute of Earth and Environmental Science, University of Potsdam, 14476 Potsdam, Germany
2Institute of Water Problems, Russian Academy of Sciences, 119333 Moscow, Russia
3Shirshov Institute of Oceanology, Russian Academy of Science, 117997 Moscow, Russia
Abstract. The Aral Sea desiccation and related changes in hydroclimatic conditions on a regional level is a hot topic for past decades. The key problem of scientific research projects devoted to an investigation of modern Aral Sea basin hydrological regime is its discontinuous nature – the only limited amount of papers takes into account the complex runoff formation system entirely. Addressing this challenge we have developed a continuous prediction system for assessing freshwater inflow into the Small Aral Sea based on coupling stack of hydrological and data-driven models. Results show a good prediction skill and approve the possibility to develop a valuable water assessment tool which utilizes the power of classical physically based and modern machine learning models both for territories with complex water management system and strong water-related data scarcity. The source code and data of the proposed system is available on a Github page (https://github.com/SMASHIproject/IWRM2018).
Citation: Ayzel, G. and Izhitskiy, A.: Coupling physically based and data-driven models for assessing freshwater inflow into the Small Aral Sea, Proc. IAHS, 379, 151-158, https://doi.org/10.5194/piahs-379-151-2018, 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...
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