TY - GEN A1 - Ayzel, Georgy A1 - Izhitskiy, Alexander T1 - Climate change impact assessment on freshwater inflow into the Small Aral Sea T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - During the last few decades, the rapid separation of the Small Aral Sea from the isolated basin has changed its hydrological and ecological conditions tremendously. In the present study, we developed and validated the hybrid model for the Syr Darya River basin based on a combination of state-of-the-art hydrological and machine learning models. Climate change impact on freshwater inflow into the Small Aral Sea for the projection period 2007–2099 has been quantified based on the developed hybrid model and bias corrected and downscaled meteorological projections simulated by four General Circulation Models (GCM) for each of three Representative Concentration Pathway scenarios (RCP). The developed hybrid model reliably simulates freshwater inflow for the historical period with a Nash–Sutcliffe efficiency of 0.72 and a Kling–Gupta efficiency of 0.77. Results of the climate change impact assessment showed that the freshwater inflow projections produced by different GCMs are misleading by providing contradictory results for the projection period. However, we identified that the relative runoff changes are expected to be more pronounced in the case of more aggressive RCP scenarios. The simulated projections of freshwater inflow provide a basis for further assessment of climate change impacts on hydrological and ecological conditions of the Small Aral Sea in the 21st Century. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1071 KW - Small Aral Sea KW - hydrology KW - climate change KW - modeling KW - machine learning Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-472794 SN - 1866-8372 IS - 1071 ER - TY - JOUR A1 - Ayzel, Georgy A1 - Izhitskiy, Alexander T1 - Climate Change Impact Assessment on Freshwater Inflow into the Small Aral Sea JF - Water N2 - During the last few decades, the rapid separation of the Small Aral Sea from the isolated basin has changed its hydrological and ecological conditions tremendously. In the present study, we developed and validated the hybrid model for the Syr Darya River basin based on a combination of state-of-the-art hydrological and machine learning models. Climate change impact on freshwater inflow into the Small Aral Sea for the projection period 2007-2099 has been quantified based on the developed hybrid model and bias corrected and downscaled meteorological projections simulated by four General Circulation Models (GCM) for each of three Representative Concentration Pathway scenarios (RCP). The developed hybrid model reliably simulates freshwater inflow for the historical period with a Nash-Sutcliffe efficiency of 0.72 and a Kling-Gupta efficiency of 0.77. Results of the climate change impact assessment showed that the freshwater inflow projections produced by different GCMs are misleading by providing contradictory results for the projection period. However, we identified that the relative runoff changes are expected to be more pronounced in the case of more aggressive RCP scenarios. The simulated projections of freshwater inflow provide a basis for further assessment of climate change impacts on hydrological and ecological conditions of the Small Aral Sea in the 21st Century. KW - Small Aral Sea KW - hydrology KW - climate change KW - modeling KW - machine learning Y1 - 2019 U6 - https://doi.org/10.3390/w11112377 SN - 2073-4441 VL - 11 IS - 11 PB - MDPI CY - Basel ER - TY - GEN A1 - Ayzel, Georgy A1 - Izhitskiy, Alexander ED - Xu, Z Peng T1 - Coupling physically based and data-driven models for assessing freshwater inflow into the Small Aral Sea T2 - Innovative Water Resources Management in a Changing Environment – Understanding and Balancing Interactions between Humankind and Nature N2 - 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). Y1 - 2018 U6 - https://doi.org/10.5194/piahs-379-151-2018 SN - 2199-899X VL - 379 SP - 151 EP - 158 PB - Copernicus CY - Göttingen ER - TY - GEN A1 - Ayzel, Georgy A1 - Izhitskiy, Alexander T1 - Coupling physically based and data-driven models for assessing freshwater inflow into the Small Aral Sea T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - 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). T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 703 KW - climate-change KW - river-basin KW - runoff KW - catchments KW - Asia Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-427873 SN - 1866-8372 IS - 703 SP - 151 EP - 158 ER -