Flood risks/Description: Difference between revisions

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PCR-GLOBWB includes a routing component on river flooding that estimates inundation proportions and average inundation depths on a time-step basis to estimate flood risk. GLOFRIS scenarios typically cover a 30-year or longer climatological model run. From this time series, annual extreme values of the inundated proportions and water depths are derived and summarised in an extreme value probability distribution. This probability distribution is subsequently used for annual projections on the damage of flood risk.  
PCR-GLOBWB includes a routing component on river flooding that estimates inundation proportions and average inundation depths on a time-step basis to estimate flood risk. GLOFRIS scenarios typically cover a 30-year or longer climatological model run. From this time series, annual extreme values of the inundated proportions and water depths are derived and summarised in an extreme value probability distribution. This probability distribution is subsequently used for annual projections on the damage of flood risk.  


GLOFRIS estimates flood risk on two scales 0.5x0.5 degrees for global analyses, and 1x1 km2 for specific case studies. On a global scale, the extreme value probability distribution is directly combined with data on population and GDP, using a linear flood level–damage relationship. Thus for each year of simulation, the most extreme water level and inundated proportion from PCR-GLOBWB is used to calculate the maximum damage (in GDP or population) per grid cell.  
GLOFRIS estimates flood risk on two scales 0.5x0.5 degrees for global analyses, and 1x1 km<sup>2</sup> for specific case studies. On a global scale, the extreme value probability distribution is directly combined with data on population and GDP, using a linear flood level–damage relationship. Thus for each year of simulation, the most extreme water level and inundated proportion from PCR-GLOBWB is used to calculate the maximum damage (in GDP or population) per grid cell.  


An algorithm is implemented to scale down the 0.5x0.5 degrees maps of the extent and depth of annual maximum inundation to 1x1 km2, using a high-resolution digital elevation model. A scale down is needed because the spatial variability of flood hazards and flood exposure may be large and not well represented on the coarser scales in IMAGE and PCR-GLOBWB. A more accurate estimation of flood risk is obtained by converting the results to a higher resolution. The downscaling procedure may also include the risk of coastal flooding (see the flowchart, bottom).
An algorithm is implemented to scale down the 0.5x0.5 degrees maps of the extent and depth of annual maximum inundation to 1x1 km<sup>2</sup>, using a high-resolution digital elevation model. A scale down is needed because the spatial variability of flood hazards and flood exposure may be large and not well represented on the coarser scales in IMAGE and PCR-GLOBWB. A more accurate estimation of flood risk is obtained by converting the results to a higher resolution. The downscaling procedure may also include the risk of coastal flooding (see the flowchart, bottom).


===Downscaling===
===Downscaling===

Revision as of 10:40, 1 July 2014

link to framework components overview

Parts of Flood risks/Description

  1. Introduction page
  2. Model description
  3. Policy issues
  4. Data, uncertainty and limitations
  5. Overview of references
Component is implemented in:
Components:
Related IMAGE components
Models/Databases
Key publications
References
GLOFRIS, the flood risk model in IMAGE 3.0
Flowchart Flood risks. See also the Input/Output Table on the introduction page.

Model description of Flood risks

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