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Use of MLCM3 Software for Flash Flood Modeling and Forecasting

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Accurate and timely flash floods forecasting, especially, in ungauged and poorly gauged basins, is one of the most important and challenging problems to be solved by the international hydrological community. In changing climate and variable anthropogenic impact on river basins, as well as due to low density of surface hydrometeorological network, flash flood forecasting based on “traditional” physically based, or conceptual, or statistical hydrological models often becomes inefficient. Unfortunately, most of river basins in Russia are poorly gauged or ungauged; besides, lack of hydrogeological data is quite typical. However, the developing economy and population safety necessitate issuing warnings based on reliable forecasts. For this purpose, a new hydrological model, MLCM3 (Multi-Layer Conceptual Model, 3rd generation) has been developed in the Russian State Hydrometeorological University. The model showed good results in more than 50 tested basins.
Rocznik
Strony
177--185
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
autor
  • Russian State Hydrometeorological University, Malookhtinsky 98, Saint Petersburg 195196, Russia
autor
  • Russian State Geological Prospecting University, Miklouho-Maklay’s St. 23, Moscow 17997, Russia
autor
  • Russian State Hydrometeorological University, Malookhtinsky 98, Saint Petersburg 195196, Russia
  • Saint-Petersburg Mining University, 2, 21 Line, Vasilievsky Island, Saint Petersburg, 199026, Russia
autor
  • VNU University of Science, Vietnam National University (VNU), 334 Nguyen Trai, Thanh Xuan, Hanoi, Vietnam
autor
  • VNU University of Science, Vietnam National University (VNU), 334 Nguyen Trai, Thanh Xuan, Hanoi, Vietnam
  • Russian State Hydrometeorological University, Malookhtinsky 98, Saint Petersburg 195196, Russia
Bibliografia
  • 1. Alfonso L., Jonoski A, Solomatin D. 2010. Multiobjective optimisation of operational responses for contaminant flushing in water distribution networks, Journal of Water Resources Planning and Management, 136(1), 48-58.
  • 2. Barrett, D. 2008. Improved stream flow forecasting by coupling satellite observations, in situ data and catchment models using data assimilation methods, eWaterCRC Technical Report.
  • 3. Burnash, R.J.C., R.L. Ferral, and R.A. McGuire, 1973. A generalized streamflow simulation system – Conceptual modeling for digital computers. Technical Report, Joint Federal and State River Forecast Center, U.S. National Weather Service and California Department of Water Resources, Sacramento.
  • 4. Johansson B. 1997, Development and test of the distributed HBV-96 hydrological model; Journal of Hydrology, 201 (1-4), 272-288
  • 5. Kayastha N., Ye J., Solomatine D.P., Fenicia F., Kuzmin V. 2013. Fuzzy committees of specialized rainfall-runoff models: further enhancements and tests; Hydrology and Earth System Sciences, 11, 4441-4451.
  • 6. Kuzmin V.A. 2001. Selection and parametrization of prediction models of river flow; Russian Meteorology and Hydrology, 3, 64-68.
  • 7. Kuzmin V.A. 2001. Short-term forecasting of disastrous high waters and floods; Russian Meteorology and Hydrology, 6, 67-71.
  • 8. Kuzmin V., Seo D.-J., Koren V. 2008. Fast and efficient optimization of hydrologic model parameters using a priori estimates and stepwise line search. Journal of Hydrology, 353(1-2), 109-128.
  • 9. Kuzmin V.A. 2009. Basic principles of automatic calibration of multi-parameter models used in operational systems of flash flood forecasting. Russian Meteorology and Hydrology, 34 (6), 384-391.
  • 10. Pivovarova I.I. 2016. Optimization methods for hydroecological monitoring systems. Journal of Ecological Engineering, 17(4), 30-34.
  • 11. Phong V.V. Le, Kumar P, Dang H.V., Valocchi A.J. 2015. GPU-based high-performance computing for integrated surface-subsurface flow modeling. Environmental Modeling & Software, 73, 1-13.
  • 12. Reed S., Koren V., Smith M., Zhang Z.. Moreda F. 2004. Overall distributed model intercomparison project results, J. Hydrol., 298(1-4), 27-60.
  • 13. Refsgaard, J.C. 1997. Parameterization, calibration, and validation of distributed hydrological models. Journal of Hydrology, 198, 69-97.
  • 14. Sokolova D.V., Kuzmin V.A. 2017. Use of «MLCM3» software for flash flood forecasting.,Conference paper: European Geosciences Union General Assembly 2017, Vienna, Austria http://meetingorganizer.copernicus.org/EGU2017/EGU2017–9498.pdf Accessed: 28 April, 2017.
  • 15. World Meteorological Organization. Guide to Hydrological Practices. 1994. WMO-No. 168. Fifth edition. 1994. Geneva, Switzerland.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-82959abc-fae4-4aa7-9c18-344e4c5f86c0
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