Articles | Volume 379
Proc. IAHS, 379, 151–158, 2018
Proc. IAHS, 379, 151–158, 2018
Pre-conference publication
05 Jun 2018
Pre-conference publication | 05 Jun 2018

Coupling physically based and data-driven models for assessing freshwater inflow into the Small Aral Sea

Georgy Ayzel and Alexander Izhitskiy

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Cited articles

Apel, H., Abdykerimova, Z., Agalhanova, M., Baimaganbetov, A., Gavrilenko, N., Gerlitz, L., Kalashnikova, O., Unger-Shayesteh, K., Vorogushyn, S., and Gafurov, A.: Statistical forecast of seasonal discharge in Central Asia for water resources management: development of a generic linear modelling tool for operational use, Hydrol. Earth Syst. Sci. Discuss.,, in review, 2017. a
Ayzel, G.: LHMP: First major release,, 2016. a
Ayzel, G.: Use of machine learning techniques for modeling of snow depth, Ice and Snow, 34–44,, 2017. a
Ayzel, G. and Izhitskiy, A.: Data, code, and results for the paper “Coupling physically based and data-driven models for assessing freshwater inflow into the Small Aral Sea”,, 2017. a
Ayzel, G. V., Gusev, E. M., and Nasonova, O. N.: River runoff evaluation for ungauged watersheds by SWAP model. 2. Application of methods of physiographic similarity and spatial geostatistics, Water Resour., 44, 547–558,, 2017. a
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 ( 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.