@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}
}
@misc{AyzelVarentsovaErinaetal.2019,
  author    = {Ayzel, Georgy and Varentsova, Natalia and Erina, Oxana and Sokolov, Dmitriy and Kurochkina, Liubov and Moreydo, Vsevolod},
  title     = {OpenForecast},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1338},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-47329},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-473295},
  pages     = {17},
  year      = {2019},
  abstract  = {The development and deployment of new operational runoff forecasting systems are a strong focus of the scientific community due to the crucial importance of reliable and timely runoff predictions for early warnings of floods and flashfloods for local businesses and communities. OpenForecast, the first operational runoff forecasting system in Russia, open for public use, is presented in this study. We developed OpenForecast based only on open-source software and data-GR4J hydrological model, ERA-Interim meteorological reanalysis, and ICON deterministic short-range meteorological forecasts. Daily forecasts were generated for two basins in the European part of Russia. Simulation results showed a limited efficiency in reproducing the spring flood of 2019. Although the simulations managed to capture the timing of flood peaks, they failed in estimating flood volume. However, further implementation of the parsimonious data assimilation technique significantly alleviates simulation errors. The revealed limitations of the proposed operational runoff forecasting system provided a foundation to outline its further development and improvement.},
  language  = {en}
}
@article{AyzelVarentsovaErinaetal.2019,
  author    = {Ayzel, Georgy and Varentsova, Natalia and Erina, Oxana and Sokolov, Dmitriy and Kurochkina, Liubov and Moreydo, Vsevolod},
  title     = {OpenForecast},
  series = {Water : Molecular Diversity Preservation International},
  volume    = {11},
  journal   = {Water : Molecular Diversity Preservation International},
  number    = {8},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2073-4441},
  doi       = {10.3390/w11081546},
  pages     = {17},
  year      = {2019},
  abstract  = {The development and deployment of new operational runoff forecasting systems are a strong focus of the scientific community due to the crucial importance of reliable and timely runoff predictions for early warnings of floods and flashfloods for local businesses and communities. OpenForecast, the first operational runoff forecasting system in Russia, open for public use, is presented in this study. We developed OpenForecast based only on open-source software and data-GR4J hydrological model, ERA-Interim meteorological reanalysis, and ICON deterministic short-range meteorological forecasts. Daily forecasts were generated for two basins in the European part of Russia. Simulation results showed a limited efficiency in reproducing the spring flood of 2019. Although the simulations managed to capture the timing of flood peaks, they failed in estimating flood volume. However, further implementation of the parsimonious data assimilation technique significantly alleviates simulation errors. The revealed limitations of the proposed operational runoff forecasting system provided a foundation to outline its further development and improvement.},
  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}
}