Advances in Decision Support Systems for Flood Disaster Management: Challenges and Opportunities

Natural variations in the global climate are governed by complex interactions among the atmosphere, oceans, and land cover. Modern climate models suggest that these variations will continue, but with larger magnitudes and greater variability due to human influences. This is expected to increase the risk of flood disaster events. To improve flood risk management, a flood decision support system architecture is proposed that capitalizes on the latest advances in remote sensing, geographic information systems, hydrologic models, numerical weather prediction, information technology, and decision theory. Specifically, the dynamic climate prediction system developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences, is discussed in the context of flood management and planning in the Yangtze River valley, China.

[1]  Robert E. Livezey,et al.  An operational multifield analog/antianalog prediction system for United States seasonal temperatures: 1. System design and winter experiments , 1988 .

[2]  A. T. Anderson,et al.  Flood hazards studies in the Mississippi River basin using remote sensing , 1974 .

[3]  I. Turton,et al.  Web-based public participation geographical information systems: an aid to local environmental decision-making , 2000 .

[4]  Francisco Nunes Correia,et al.  Floodplain Management in Urban Developing Areas. Part II. GIS-Based Flood Analysis and Urban Growth Modelling , 1999 .

[5]  A. K. Sahai,et al.  All India summer monsoon rainfall prediction using an artificial neural network , 2000 .

[6]  E. Kalnay,et al.  Ensemble Forecasting at NCEP and the Breeding Method , 1997 .

[7]  F. Molteni,et al.  The ECMWF Ensemble Prediction System: Methodology and validation , 1996 .

[8]  D. Phillip Guertin,et al.  Information technology in watershed management decision making , 2004 .

[9]  Nicholas Pinter,et al.  One Step Forward, Two Steps Back on U.S. Floodplains , 2005, Science.

[10]  M. Neil Ward,et al.  Prediction of seasonal rainfall in the sahel region using empirical and dynamical methods , 1991 .

[11]  S. Hastenrath.  Recent Advances in Tropical Climate Prediction , 1995 .

[12]  G. Hammer,et al.  Prediction of global rainfall probabilities using phases of the Southern Oscillation Index , 1996, Nature.

[13]  J. Stedinger.  Frequency analysis of extreme events , 1993 .

[14]  A. Barnston.  Linear Statistical Short-Term Climate Predictive Skill in the Northern Hemisphere , 1994 .

[15]  Xixi Lu,et al.  Application of Remote Sensing in Flood Management with Special Reference to Monsoon Asia: A Review , 2004 .

[16]  M. Clark,et al.  Putting water in its place: a perspective on GIS in hydrology and water management , 1998 .

[17]  Michael Mcfaul,et al.  One Step Forward, Two Steps Back , 2000 .

[18]  W. G. Strand,et al.  How Much More Global Warming and Sea Level Rise? , 2005, Science.

[19]  Timothy N. Stockdale.  An overview of techniques for seasonal forecasting , 2000 .

[20]  J. Houghton,et al.  Climate change 2001 : the scientific basis , 2001 .

[21]  Andy Haines,et al.  The human health consequences of flooding in Europe and the implications for public health: a review of the evidence , 2003 .

[22]  John A. Knaff,et al.  An El Niño–Southern Oscillation Climatology and Persistence (CLIPER) Forecasting Scheme , 1997 .

[23]  F. Atger,et al.  The Skill of Ensemble Prediction Systems , 1999 .

[24]  E. Lorenz,et al.  The predictability of a flow which possesses many scales of motion , 1969 .

[25]  Yong Wang,et al.  An efficient method for mapping flood extent in a coastal floodplain using Landsat TM and DEM data , 2002 .

[26]  Andre Zerger,et al.  Beyond Modelling: Linking Models with GIS for Flood Risk Management , 2004 .

[27]  T. Saaty.  Decision making — the Analytic Hierarchy and Network Processes (AHP/ANP) , 2004 .

[28]  Phillip A. Arkin,et al.  The IRI Seasonal Climate Prediction System and the 1997/98 El Niño Event , 1999 .

[29]  Gis Industry,et al.  A Discussion on Basic Problems of 3D GIS , 2001 .

[30]  M. Ward,et al.  Prediction of seasonal rainfall in the north nordeste of Brazil using eigenvectors of sea‐surface temperature , 2007 .

[31]  I. J. Barton,et al.  Monitoring floods with AVHRR , 1989 .

[32]  C. Revenga,et al.  Fragmentation and Flow Regulation of the World's Large River Systems , 2005, Science.

[33]  M. Islam,et al.  Development Priority Map for Flood Countermeasures by Remote Sensing Data with Geographic Information System , 2002 .

[34]  Jim W. Hall,et al.  Integrated flood risk management in England and Wales , 2003 .

[35]  S. Jonkman.  Global Perspectives on Loss of Human Life Caused by Floods , 2005 .

[36]  George Alan Blackburn,et al.  A real-time hydrological model for flood prediction using GIS and the WWW , 2003, Comput. Environ. Urban Syst..

[37]  Anthony Rosati,et al.  The Impact of Ocean Initial Conditions on ENSO Forecasting with a Coupled Model , 1997 .

[38]  T. Wigley.  The Climate Change Commitment , 2005, Science.

[39]  Bin Wang,et al.  A Contrast of the East Asian Summer Monsoon–ENSO Relationship between 1962–77 and 1978–93* , 2002 .

[40]  Oscar K. Huh,et al.  Study of river flood hydrology in Bangladesh with AVHRR data , 1989 .

[41]  F. Tangang,et al.  Forecasting ENSO Events: A Neural Network–Extended EOF Approach. , 1998 .

[42]  D. Holzworth,et al.  The value of skill in seasonal climate forecasting to wheat crop management in a region with high climatic variability , 1996 .

[43]  David S. Richardson,et al.  A probability and decision‐model analysis of PROVOST seasonal multi‐model ensemble integrations , 2000 .

[44]  S. Simonovic,et al.  Computer-based Model for Flood Evacuation Emergency Planning , 2005 .

[45]  D. Mileti.  Disasters by Design: A Reassessment of Natural Hazards in the United States , 1999 .

[46]  L. Smith.  Satellite remote sensing of river inundation area, stage, and discharge: a review , 1997 .

[47]  A. Haines.  Climate change 2001: the scientific basis. Contribution of Working Group 1 to the Third Assessment report of the Intergovernmental Panel on Climate Change [Book review] , 2003 .

[48]  Arun Kumar,et al.  A Multiseason Climate Forecast System at the National Meteorological Center. , 1994 .

[49]  K. Wernstedt,et al.  CLIMATE FORECASTS IN FLOOD PLANNING: PROMISE AND AMBIGUITY 1 , 2002 .

[50]  Tim Palmer,et al.  The prospects for seasonal forecasting—A review paper , 1994 .

[51]  Bin He,et al.  The application of Web GIS in emergency flood control system. , 2004 .

[52]  A. Barnston,et al.  Prediction of ENSO Episodes Using Canonical Correlation Analysis , 1992 .

[53]  S. Clandillon,et al.  The contribution of spaceborne SAR and optical data in monitoring flood events: examples in northern and southern France , 1997 .

[54]  W. Landman,et al.  Retro‐active skill of multi‐tiered forecasts of summer rainfall over southern Africa , 2001 .

[55]  Frank Swiaczny,et al.  Time-integrative geographic information systems , 2001 .

[56]  T. Barnett,et al.  A Two-Tiered Approach to Long-Range Climate Forecasting , 1993, Science.

[57]  Yi-Ming Wei,et al.  The assessment of vulnerability to natural disasters in China by using the DEA method , 2004 .

[58]  G. F. White,et al.  The Challenge of the Mississippi Flood , 1993 .

[59]  Balaji Rajagopalan,et al.  Epochal changes in Indian Monsoon-ENSO precursors , 1999 .

[60]  David Ingle Smith,et al.  Riding the storm: a comparison of uncertainty modelling techniques for storm surge risk management , 2002 .

[61]  Carlo Giupponi,et al.  Towards the development of a decision support system for water resource management , 2005, Environ. Model. Softw..