Flood inference simulation using surrogate modelling for the Yellow River multiple reservoir system

The Yellow River, in China, is one of the largest hydro systems in the world. Flooding is a major problem for the river, and therefore over the last 50 years a large number of interventions have been made in its reaches and tributaries, in order to control the flooding events in the lowland area, downstream of the Huayuankou hydrological station. The development of new technologies and approaches to decision support has raised possibilities for creating new ways of managing the river and reducing loss of life, in the case of flooding, for the people living within the embankment area of the river. Given the importance of the river for the development of economic activity in China, it is essential to increase the understanding of the general flooding processes triggered by several reservoir operation scenarios, and then, after applying them to a flooding model of a specific area, to test the findings. The main goal of the research presented here is to investigate and develop the statistical inference between the operation of reservoirs on the Yellow River and a set of variables related to the downstream flooding, such as the total flooding volume and the peak discharge. The research shows that it is possible to use such inference models as decision support tools, by reducing the number of explanatory variables to be included in the simulations carried out to determine the appropriate reservoir operation.

[1]  Deepti Rani,et al.  Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation , 2010 .

[2]  Christoph C. Raible,et al.  Climate change: Water cycle shifts gear , 2005, Nature.

[3]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[4]  D. Alsdorf,et al.  Water slope and discharge in the Amazon River estimated using the shuttle radar topography mission digital elevation model , 2005 .

[5]  GuoWei Liu.  On the geo-basis of river regulation in the lower reaches of the Yellow River , 2011, Science China Earth Sciences.

[6]  Andrew Fredrick Gilmore,et al.  A Study of Optimization of Reservoir Operations of the Colorado River , 1999 .

[7]  Dimitri Solomatine,et al.  A study of the climate change impacts on fluvial flood propagation in the Vietnamese Mekong Delta , 2012 .

[8]  John Turner,et al.  State of the Antarctic and Southern Ocean climate system , 2009 .

[9]  Johannes Cullmann,et al.  Flood routing modelling with Artificial Neural Networks , 2006 .

[10]  Andreja Jonoski,et al.  Climate change impact on flood hazard, vulnerability and risk of the Long Xuyen Quadrangle in the Mekong Delta , 2012 .

[11]  E. Onyari,et al.  Integrated modelling for flood risk mitigation in Romania: case study of the Timis–Bega river basin , 2010 .

[12]  D. Moorhead,et al.  Increasing risk of great floods in a changing climate , 2002, Nature.

[13]  Ioana Popescu,et al.  Model-Based Optimization of Downstream Impact during Filling of a New Reservoir: Case Study of Mandaya/Roseires Reservoirs on the Blue Nile River , 2011, Water Resources Management.

[14]  Bryan A. Tolson,et al.  Review of surrogate modeling in water resources , 2012 .

[15]  William W.-G. Yeh,et al.  Reservoir Management and Operations Models: A State‐of‐the‐Art Review , 1985 .

[16]  P. Bates,et al.  Utility of different data types for calibrating flood inundation models within a GLUE framework , 2005 .

[17]  Johannes Cullmann,et al.  PAI-OFF: A new proposal for online flood forecasting in flash flood prone catchments , 2008 .

[18]  G. Gary Wang,et al.  Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions , 2010 .

[19]  P. Bates,et al.  Progress in integration of remote sensing–derived flood extent and stage data and hydraulic models , 2009 .

[20]  Arthur van Dam,et al.  Efficient scheme for the shallow water equations on unstructured grids with application to the Continental Shelf , 2011 .

[21]  S. Li.  Adaptive Multi-reservoir-based Flood Control and Management for the Yellow River: Towards a Next Generation Software System , 2013 .

[22]  Kerry R. Pearn,et al.  The impact of future socio-economic and climate changes on agricultural land use and the wider environment in East Anglia and North West England using a metamodel system , 2008 .

[23]  Sanjay B. Joshi,et al.  Metamodeling: Radial basis functions, versus polynomials , 2002, Eur. J. Oper. Res..

[24]  Robert F. Chen,et al.  Modelling complex flood flow evolution in the middle Yellow River basin, China , 2008 .

[25]  Guangqian Wang,et al.  Sedimentation problems and management strategies of Sanmenxia Reservoir, Yellow River, China , 2005 .

[26]  Ashish Sharma,et al.  Selection of a kernel bandwidth for measuring dependence in hydrologic time series using the mutual information criterion , 2001 .

[27]  Manfred W. Ostrowski,et al.  Optimum multi-objective reservoir operation with emphasis on flood control and ecology , 2009 .

[28]  V. Merwade,et al.  Uncertainty Quantification in Flood Inundation Mapping Using Generalized Likelihood Uncertainty Estimate and Sensitivity Analysis , 2012 .

[29]  Andrea Castelletti,et al.  A general framework for Dynamic Emulation Modelling in environmental problems , 2012, Environ. Model. Softw..

[30]  M. Ratto,et al.  Using recursive algorithms for the efficient identification of smoothing spline ANOVA models , 2010 .

[31]  Stephan C. Kramer,et al.  A conservative unstructured scheme for rapidly varied flows , 2008 .

[32]  Ximing Cai,et al.  Input variable selection for water resources systems using a modified minimum redundancy maximum relevance (mMRMR) algorithm , 2009 .

[33]  Asaad Y. Shamseldin,et al.  Application of surrogate artificial intelligent models for real‐time flood routing , 2013 .

[34]  Ezio Todini,et al.  A mass conservative and water storage consistent variable parameter Muskingum-Cunge approach , 2007 .

[35]  Jery R. Stedinger,et al.  Water Resources Systems Planning And Management , 2006 .

[36]  Toshio Koike,et al.  Decision support for dam release during floods using a distributed biosphere hydrological model driven by quantitative precipitation forecasts , 2010 .

[37]  Andreja Jonoski,et al.  DECISION SUPPORT SYSTEMS FOR FLOOD MANAGEMENT IN THE TIMIS BEGA CATCHMENT , 2012 .

[38]  John W. Labadie,et al.  Optimal Operation of Multireservoir Systems: State-of-the-Art Review , 2004 .

[39]  M. Vanclooster,et al.  Metamodelling: Theory, concepts and application to nitrate leaching modelling , 2006 .

[40]  Venkatesh Merwade,et al.  Uncertainty in Flood Inundation Mapping: Current Issues and Future Directions , 2008 .

[41]  Dimitri Solomatine,et al.  Cloud and cluster computing in uncertainty analysis of integrated flood models , 2013 .

[42]  Thomas E. Croley,et al.  Application of a distributed large basin runoff model in the Great Lakes basin , 2007 .

[43]  Daniele de Rigo,et al.  Neuro-dynamic programming for designing water reservoir network management policies , 2007 .

[44]  Ioana Popescu,et al.  River cross-section extraction from the ASTER global DEM for flood modeling , 2012, Environ. Model. Softw..

[45]  Ioana Popescu,et al.  Modeling the Inference between Upstream Inflow Hydrographs and Downstream Flooded Areas in a Reservoir Driven System , 2014 .

[46]  Ioana Popescu,et al.  Parametric and physically based modelling techniques for flood risk and vulnerability assessment: A comparison , 2013, Environ. Model. Softw..

[47]  Joseph H. A. Guillaume,et al.  Characterising performance of environmental models , 2013, Environ. Model. Softw..

[48]  Shawn E. Gano,et al.  Comparison of Three Surrogate Modeling Techniques: Datascape r , Kriging, and Second Order Regression , 2006 .

[49]  J. Lund,et al.  Derived Operating Rules for Reservoirs in Series or in Parallel , 1999 .

[50]  H. Ross,et al.  Participatory development of a Bayesian network model for catchment‐based water resource management , 2010 .

[51]  Germán Poveda,et al.  Feedbacks between Hydrological Processes in Tropical South America and Large-Scale Ocean–Atmospheric Phenomena , 1997 .

[52]  G. G. Wang,et al.  Metamodeling for High Dimensional Simulation-Based Design Problems , 2010 .

[53]  Peter C. Young,et al.  A unified approach to environmental systems modeling , 2009 .

[54]  J. A. Cunge,et al.  On The Subject Of A Flood Propagation Computation Method (Musklngum Method) , 1969 .

[55]  Peter C. Young,et al.  State Dependent Parameter metamodelling and sensitivity analysis , 2007, Comput. Phys. Commun..

[56]  Eric F. Wood,et al.  Effects of soil moisture aggregation on surface evaporative fluxes , 1997 .

[57]  Pan Liu,et al.  Dynamic control of flood limited water level for reservoir operation by considering inflow uncertainty , 2010 .

[58]  Andreja Jonoski,et al.  Distance Learning in Support of Water Resources Management: An Online Course on Decision Support Systems in River Basin Management , 2012, Water Resources Management.

[59]  Holger R. Maier,et al.  Water Distribution System Optimization Using Metamodels , 2005 .

[60]  Holger R. Maier,et al.  Input determination for neural network models in water resources applications. Part 1—background and methodology , 2005 .

[61]  Caspar J. M. Hewett,et al.  Initiation of the Upper Mississippi River Basin Observatory , 2010 .

[62]  Alcigeimes B. Celeste,et al.  Evaluation of stochastic reservoir operation optimization models , 2009 .

[63]  Ralph A. Wurbs.  Reservoir‐System Simulation and Optimization Models , 1993 .

[64]  Ioana Popescu,et al.  Application of a coastal modelling code in fluvial environments , 2011, Environ. Model. Softw..