Session 6. Flood risk management September 29, 2016 Room 424 6.1 Theories, methods and technologies of hydrological forecasts 14.00–14.201. The new paradigm in hydrological forecasting (ensemble predictions and their improving based on assimilation of observation data) Lev Kuchment, Victor Demidov (RAS Institute of Water Problem, Russia) 14.20–14.402. The hydrological forecast models of the Siberian rivers water regime Dmitry Burakov (Krasnoyarsk State Agrarian University, Krasnoyarsk Center for Hydrometeorology and Monitoring of the Environment, Russia), Evgeniya Karepova (Institute of Computational Modeling, Siberian Branch of RAS, Russia) 14.40–15.003. Short-term forecasts method of water inflow into Bureyskaya reservoir Yury Motovilov (RAS Institute of Water Problems, Russia), Victor Balyberdin (SKM Market Predictor, Russia), Boris Gartsman, Alexander Gelfan (RAS Institute of Water Problems, Russia), Timur Khaziakhmetov (RusHydro Group, Russia), Vsevolod Moreydo (RAS Institute of Water Problems, Russia), Oleg Sokolov (Far Eastern Regional Hydrometeorological Research Institute, Russia) 15.00–15.204. Forecast of spring floods on the upper Ob river Alexander Zinoviev, Vladimir Galаkhov, Konstantin Koshelev (Institute of Water and Environmental Problems, Siberian Branch of RAS, Russia) 15.20–15.405. Regional hydrological model: the infrastructure and framework for hydrological prediction and forecasting Andrei Bugaets (RAS Institute of Water Problems, Far Eastern Regional Research Hydrometeorological Institute, Russia), Boris Gartsman (RAS Institute of Water Problems, Russia), Leonid Gonchukov (RAS Institute of Water Problems, Far Eastern Regional Research Hydrometeorological Institute, Russia), Oleg Sokolov (Far Eastern Regional Research Hydrometeorological Institute, Russia), Kwan Tun Lee (National Taiwan Ocean University, Taiwan), Yury Motovilov, Vitaly Belikov, Vsevolod Moreydo, Andrey Kalugin, Andrey Aleksyuk, Inna Krylenko, Alexey Rumyantsev (RAS Institute of Water Problems, Russia) 6.2 Methods of risk assessment and management of hazardous hydrological phenomena 16.30–16.506. The development of methods of preventing water’s adverse impact based on the analysis of data on extreme precipitation Dmitry Klimenko (Perm State National Research University, Russia) 16.50–17.107. Towards a comprehensive approach to evaluation of loss and damage from floods: identifying missing components Ekaterina Makarova (National Research University Higher School of Economics, Russia) 17.10–17.308. Spatial variability of surface soil moisture at the field scale under the framework of geostatistics and satellite remotely sensed imagery Otto Correa Rotunno Filho, Kary de Paiva, Afonso Augusto Magalhaes de Araujo, Vitor Paiva Alcoforado Rebello (Laboratory for Water Resources and Environment, Civil Engineering Programme, Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering, Federal University of Rio de Janeiro, Brazil) 17.30–17.509. Hydrological balance in the large Russian river basins from GRACE satellites Leonid Zotov (National Research University Higher School of Economics, Russia), Natalia Frolova (M.V. Lomonosov Moscow State University, Russia), Elena Kyzyngasheva September 30, 2016 Room 424 6.2 Methods of risk assessment and management of hazardous hydrological phenomena (continued) 10.00–10.2010. Urbanization and its impact on watersheds flood responses Yangbo Chen (Sun Yat-Sen University, China) 10.20–10.4011. Assessment of hazard of inundations at regional and local levels Natalia Frolova, Svetlana Agafonova, Maria Kireeva, Inna Krylenko, Dmitry Magritsky, Alexey Sazonov (M. V. Lomonosov Moscow State University, Russia) 10.40–11.0012. Hydrological extreme projections: modeling and uncertainty issues Alexander Gelfan, Yury Motovilov, Inna Krylenko, Andrey Kalugin, Alexander Lavrenov (RAS Institute of Water Problems, Russia) 11.00–11.2013. Adaptation to floods in the Amur river basin: considering the environment Oxana Nikitina (World Wildlife Fund (WWF-Russia), Eugene Simonov (Rivers without Boundaries International Coalition, Russia), Peter Osipov (WWF-Russia), Evgeny Egidarev (WWF-Russia), Pacific Institute of Geography, Far East Branch of RAS, Russia), Andrey Shalikovsky (Eastern Branch of Russian Research Institute for Integrated Water Management and Protection, Russia) 11.30–12.00 Coffee break 12.00–12.2014. Hydrodynamic modeling as a tool for flood hazard assessment in river mouths: case study of the White Sea Andrei Alabyan, Serafima Lebedeva, Evgenia Panchenko (M.V. Lomonosov Moscow State University, Russia) 12.20–12.4015. Risk optimization of the NPP flooding Vitaly Belikov, Natalia Borisova, Alexey Rumyantsev (RAS Institute of Water Problems, Russia) 12.40–13.0016. Necessity of considering outburst floods originating in the catchment area for large dams design Alexander Strom (Geodynamics Research Center – branch of JSC “Hydroproject Institute”, Russia), Anatoly Zhirkevich, Ekaterina Shilina (JSC “Hydroproject Institute”, Russia) Kuchment L.S. and Demidov V. N. The new paradigm in hydrological forecasting (ensemble predictions and their improving based on assimilation of observation data) In recent decades, a significant advance in the development of methods of hydrological forecasts and their practical application has been observed. Implementation into operative practice of runoff generation models and improving hydrometeorological information has provided an opportunity to create the hydrological forecasting systems which can produce continuous predictions for different lead times. The accuracy and reliability of hydrological forecasting has been significantly increased. However, for decreasing the risk in water resources control the more urgent becomes the problem of how to ensure more comprehensive and at the same time more cautious use of forecasting information taking into account of possible errors and uncertainties in the available forecasts. In this regard, in the last 5-7 years intensified investigations have carried out for qualitative change in hydrological forecasting paradigm: deterministic (single) forecasts are replaced by probabilistic forecasts based on so-called ensemble prediction and data assimilation techniques .This transfer became possible as a result of recent achievements in computational power and physically based modeling. The probabilistic forecasts are more valuable than deterministic forecasts because they can be used not only to predict the most likely event but also to assess the probability of occurrence of extreme and rare events. The ensemble prediction where forecasts are obtained on the basis of the ensembles of input data and different scenarios of the multiple uncertainties (model errors, errors in initial conditions and inputs, etc) is one of the most promising techniques of accounting for uncertainties and obtaining of probabilistic forecasts. An important advantage of ensemble prediction is the ability to more accurately forecast if new information about the measurements of the characteristics of the state of the hydrological system or the predicted values entries. Many flood forecasting centers around the world use now the ensemble prediction based on meteorological forecasting (Cloke and Pappenberger,2009). Data assimilation techniques give an opportunity to improve probabilistic forecasts using the merging, in an optimal way, the information present in imperfect models and inaccurate observation data. It is clear that ensemble forecasts, however, cannot account for all of uncertainties and it is necessary to carry out researches of possibilities of taking into account of most important uncertainties for various runoff generation processes and hydrological models as well as for different forecast horizons and available hydrometeorological observations. In the Water Problems Institute of RAS, the investigations of different techniques of ensemble predicting have been carried out for long-term and short-term forecasting of the spring runoff of several Russian river basins. The technique of long-term (with the lead time of 2-3 months) ensemble forecasting of the spring runoff volumes and the peak discharges was based on the use of the distributed physically based model of runoff generation and ensemble simulation of input data. The model includes description of snow accumulation and melt, soil freezing and redistribution of soil moisture during autumn and winter period and processes of runoff generation after the beginning of spring snowmelt period. Ensembles of input data consist of historical daily temperature, precipitation and air humidity observations for previous years or the Monte Carlo simulations of these time series. The physically based model is applied to calculate, using meteorological data for the autumn- winter period, initial river basin conditions before forecasting (commonly, the soil moisture and depth of frozen soil; sometimes, the snow water equivalent) and to estimate the runoff hydrographs during the lead-time period. Continuous simulation of runoff generation processes for each snowmelt flood began from 1 May of the previous year. Using ensembles of input data, we have an opportunity to calculate ensembles of possible hydrographs, determine probability distribution of the forecasted flood volumes and peak discharges, and estimate the forecast uncertainty caused by different ways of assigning meteorological data during the lead time period. The case studies have been carried out for the Sosna River basin (catchment area is 16400