Global flood hazard mapping using statistical peak flow estimates

Our aim is to produce a world map of flooded areas for a 100 year return period, using a method based on large rivers peak flow estimates derived from mean monthly discharge time-series. Therefore, the map is supposed to represent flooding that affects large river floodplains, but not events triggered by specific conditions like coastal or flash flooding for instance. We first generate for each basin a set of hydromorphometric, land cover and climatic variables. In case of an available discharge record station at the basin outlet, we base the hundred year peak flow estimate on the corresponding time-series. Peak flow magnitude for basin outlets without gauging stations is estimated by statistical means, performing several regressions on the basin variables. These peak flow estimates enable the computation of corresponding flooded areas using hydrologic GIS processing on digital elevation model.

[1]  Debbie J. Dupuis,et al.  Parameter and quantile estimation for the generalized extreme-value distribution: a second look , 1999 .

[2]  P. Peduzzi,et al.  Assessing global exposure and vulnerability towards natural hazards: the Disaster Risk Index , 2009 .

[3]  Gregory Giuliani,et al.  The PREVIEW Global Risk Data Platform: a geoportal to serve and share global data on risk to natural hazards , 2011 .

[4]  Yuzuru Matsuoka,et al.  Development of highly accurate global polygonal drainage basin data , 2009 .

[5]  Direction régionale de l'environnement,et al.  Les cours d’eau , 2010 .

[6]  Pankaj K. Agarwal,et al.  TerraStream: from elevation data to watershed hierarchies , 2007, GIS.

[7]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[8]  R. Kachroo,et al.  Flood frequency analysis of southern Africa: II. Identification of regional distributions , 2000 .

[9]  J. V. Sutcliffe,et al.  Regional flood frequency analysis in arid and semi-arid areas , 1992 .

[10]  Farrokh Nadim,et al.  The Global Risk Analysis for the 2009 Global Assessment Report on Disaster Risk Reduction , 2009 .

[11]  B. Rudolf,et al.  World Map of the Köppen-Geiger climate classification updated , 2006 .

[12]  S. K. Sando,et al.  Techniques for estimating peak-flow magnitude and frequency relations for South Dakota streams , 1998 .

[13]  James P. Verdin,et al.  A topological system for delineation and codification of the Earth's river basins , 1999 .

[14]  M. Parlange,et al.  Statistics of extremes in hydrology , 2002 .

[15]  J. Bravard,et al.  Les cours d'eau. Dynamique du systeme fluvial , 2000 .

[16]  Roland Schulze,et al.  Flood frequency analysis at ungauged sites in the KwaZulu-Natal Province, South Africa , 2001 .

[17]  J. V. Sutcliffe,et al.  A worldwide comparison of regional flood estimation methods and climate , 1997 .

[18]  Kwabena Asante,et al.  Global Flood Modelling : Statistical Estimation of Peak-Flow Magnitude , 2006 .

[19]  C. R. Liermann.  Ecohydrologic impacts of dams: A global assessment , 2007 .

[20]  P. Döll,et al.  Development and validation of a global database of lakes, reservoirs and wetlands , 2004 .

[21]  Robert S. Chen,et al.  Natural Disaster Hotspots: A Global Risk Analysis , 2005 .

[22]  R. K. Kachroo,et al.  Flood frequency analysis of southern Africa: I. Delineation of homogeneous regions , 2000 .

[23]  T. D. Mitchell,et al.  An improved method of constructing a database of monthly climate observations and associated high‐resolution grids , 2005 .

[24]  B. Rudolf,et al.  A New Monthly Precipitation Climatology for the Global Land Areas for the Period 1951 to 2000 , 2004 .