@misc{AyzelIzhitskiy2018,
  author    = {Ayzel, Georgy and Izhitskiy, Alexander},
  title     = {Coupling physically based and data-driven models for assessing freshwater inflow into the Small Aral Sea},
  series = {Innovative Water Resources Management in a Changing Environment - Understanding and Balancing Interactions between Humankind and Nature},
  volume    = {379},
  journal   = {Innovative Water Resources Management in a Changing Environment - Understanding and Balancing Interactions between Humankind and Nature},
  editor    = {Xu, Z Peng},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {2199-899X},
  doi       = {10.5194/piahs-379-151-2018},
  pages     = {151 -- 158},
  year      = {2018},
  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).},
  language  = {en}
}
@misc{AyzelIzhitskiy2018,
  author    = {Ayzel, Georgy and Izhitskiy, Alexander},
  title     = {Coupling physically based and data-driven models for assessing freshwater inflow into the Small Aral Sea},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {703},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42787},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-427873},
  pages     = {151 -- 158},
  year      = {2018},
  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).},
  language  = {en}
}
@article{Ayzel2018,
  author    = {Ayzel, Georgy V.},
  title     = {Runoff predictions in ungauged arctic basins using conceptual models forced by reanalysis data},
  series = {Water Resources},
  volume    = {45},
  journal   = {Water Resources},
  publisher = {Pleiades Publ.},
  address   = {New York},
  issn      = {0097-8078},
  doi       = {10.1134/S0097807818060180},
  pages     = {S1 -- S7},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}