Flood risks: Difference between revisions

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{{ComponentTemplate2
{{ComponentTemplate2
|IMAGEComponent=Scenario drivers;land cover and use;Human development
|IMAGEComponent=Drivers; Human development; Land cover and land use;
|ExternalModel=PCR-GLOBWB model; DIVA model;
|Model-Database=PCR-GLOBWB model; DIVA model;
|KeyReference=Ward et al., 2013; Winsemius et al., 2012;
|KeyReference=Ward et al., 2013; Winsemius et al., 2012;
|Reference=UNISDR, 2011;
|Reference=UNISDR, 2011;
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Bierkens and Van Beek, 2009;
Bierkens and Van Beek, 2009;
Hinkel and Klein, 2009;
Hinkel and Klein, 2009;
|InputVar=Land cover, land use - grid; Population - grid; GDP per capita - grid; Land cover, land use - grid;
|InputVar=Land cover, land use - grid; Population - grid; GDP per capita - grid; Land cover, land use - grid; Temperature - grid; Precipitation - grid;
|Parameter=Topography - grid; Coastal storm surges; Flood statistics - grid; Historical climate dataset - grid;
|Parameter=Coastal storm surges; Flood statistics - grid; Daily climate dataset - grid; Topography, elevation - grid
|OutputVar=Expected nr annual exposed people - grid; Expected value of annual exposed GDP - grid; Statistics of flooded depth - grid; Statistics of flooded fraction - grid; Statistics of river discharge - grid;
|OutputVar=Expected nr of affected people - grid; Expected value of affected GDP - grid; Statistics on inundation depth - grid; Statistics of inundation extent - grid; Statistics on river discharge - grid;
|Description=Flooding is the most frequent and costly natural hazard, affecting a majority of countries on a regular basis ([[UNISDR, 2011]]) ([[IPCC, 2012]]). Over the past few decades, the economic damage as a result of flooding has increased in most regions, primarily due to a growth in population and wealth in flood-prone areas ([[Bouwer et al., 2010]]; [[UNISDR, 2011]]; [[Barredo et al., 2012]]).
The largest changes in economic loss and mortality from flooding are observed in developing countries, although data scarcities are hampering flood risk assessments. In order to evaluate current flood risk as well as possible changes under global change scenarios, there is a demand for rapid cost-effective assessments based on available global data sets. Such assessments are of interest to various users, for example:
* to international financing institutes for assessing which investments in natural disaster risk reduction would be the most promising;
* to national institutes for monitoring progress in risk reduction activities, such as those related to the implementation of the Hyogo Framework for Action ([[UNISDR, 2005]]);
* to insurance and reinsurance companies that need to justify their insurance coverage;
* to a variety of large companies for assessing the risks related to regional investments.
 
To meet this demand, the effect module ‘GLObal Flood Risks with IMAGE Scenarios’ ([[GLOFRIS model|GLOFRIS]]) has been developed for IMAGE 3.0, in a joint effort, by [[Deltares]], [[PBL]] Netherlands Environmental Assessment Agency, Utrecht University ([[UU]]) and the Institute for Environmental Studies of VU University Amsterdam ([[IVM]]). The GLOFRIS model estimates the combination of river and coastal flood risks by integrating the global hydrological model [[PCR-GLOBWB model|PCR-GLOBWB]] ([[Bierkens and Van Beek, 2009]]) and the global sea-level rise impacts model  [[DIVA model|DIVA]] ([[Hinkel and Klein, 2009]]), using climate scenario data from complex climate models and downscaled socioeconomic scenarios from [[IMAGE land use model|IMAGE]] . GLOFRIS may be used for assessing current and future flood risks related to climate, changing land-cover patterns and changing socioeconomic conditions for any world region. This can be done, globally, at a resolution of 0.5 x 0.5 degrees and, regionally, at a higher resolution (1 x 1 km<sup>2</sup>). The lower resolution results in annual statistics on flooded depth and the fraction of the cell that is flooded. These variables in combination with the population and GDP are used to calculate the impact on 0.5 x 0.5 degrees. The higher resolution is achieved using a specially developed downscaling algorithm and more detailed regional impact models. Impacts such as exposed population and exposed GDP for several safety levels can be analysed. Possible applications include the preparation of [[IPCC]] scenarios for flood risk changes at 0.5 degree and 1 km<sup>2</sup> resolutions.
|ComponentCode=FR
|ComponentCode=FR
|AggregatedComponent=Impacts
|AggregatedComponent=Impacts
|FrameworkElementType=impact component
|FrameworkElementType=impact component
}}
}}
<div class="page_standard">
Flooding is the most frequent and costly natural hazard that regularly affects many countries ([[UNISDR, 2011]]; [[IPCC, 2012]]). In the last few decades, economic damage as a result of flooding has increased in most regions, primarily due to growth in population and wealth in flood-prone areas ([[Bouwer et al., 2010]]; [[UNISDR, 2011]]; [[Barredo et al., 2012]]). In relative terms, economic loss and mortality from flooding are highest in developing countries, but lack of reliable and complete data remains an important issue for damage estimates.
To evaluate current flood risk and how the risks may change under future global change scenarios, rapid cost-effective assessments based on available global data are required. Such assessments are required, for instance, by international financing institutes to assess investment in risk reduction of natural disasters and by national institutes to monitor progress in risk reduction, such as under the Hyogo Framework for Action ([[UNISDR, 2005]]), by companies to justify insurance coverage and to assess risks to regional investments.
GLObal Flood Risks with IMAGE Scenarios ([[GLOFRIS model|GLOFRIS]]) was developed for IMAGE 3.0 jointly by Deltares; PBL Netherlands Environmental Assessment Agency; Utrecht University; and the Institute for Environmental Studies, VU University Amsterdam. GLOFRIS estimates river and coastal flood risks by integrating the global hydrological model [[PCR-GLOBWB model|PCR-GLOBWB]] ([[Bierkens and Van Beek, 2009]]) and the global sea-level rise impacts model [[DIVA model|DIVA]] ([[Hinkel and Klein, 2009]]), using climate scenario data from complex climate models and downscaled socio-economic scenarios from IMAGE.
GLOFRIS is used to assess current and future flood risks related to climate, changing land-cover patterns and changing socio-economic conditions in all world regions. This can be done globally at a resolution of 0.5x0.5 degrees and regionally at a higher resolution (1x1 km<sup>2</sup>). The higher resolution is achieved using a specially developed downscaling algorithm and more detailed regional impact models. Impacts for various safety levels can be analysed. Possible applications include the preparation of IPCC scenarios for flood risk changes at 0.5 degree and 1 km<sup>2</sup> resolutions.
{{InputOutputParameterTemplate}}
</div>

Latest revision as of 14:27, 1 April 2020

link to framework components overview

Parts of Flood risks

  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.

Key policy issues

  • How will future flood risk change as a result of socio-economic changes and climate change?
  • What would be the impact of floods, in terms of damage and victims, and where are the hot spots?
  • What would be suitable adaptation strategies and investment options related to flood risk?

Introduction

Flooding is the most frequent and costly natural hazard that regularly affects many countries (UNISDR, 2011; IPCC, 2012). In the last few decades, economic damage as a result of flooding has increased in most regions, primarily due to growth in population and wealth in flood-prone areas (Bouwer et al., 2010; UNISDR, 2011; Barredo et al., 2012). In relative terms, economic loss and mortality from flooding are highest in developing countries, but lack of reliable and complete data remains an important issue for damage estimates.

To evaluate current flood risk and how the risks may change under future global change scenarios, rapid cost-effective assessments based on available global data are required. Such assessments are required, for instance, by international financing institutes to assess investment in risk reduction of natural disasters and by national institutes to monitor progress in risk reduction, such as under the Hyogo Framework for Action (UNISDR, 2005), by companies to justify insurance coverage and to assess risks to regional investments. GLObal Flood Risks with IMAGE Scenarios (GLOFRIS) was developed for IMAGE 3.0 jointly by Deltares; PBL Netherlands Environmental Assessment Agency; Utrecht University; and the Institute for Environmental Studies, VU University Amsterdam. GLOFRIS estimates river and coastal flood risks by integrating the global hydrological model PCR-GLOBWB (Bierkens and Van Beek, 2009) and the global sea-level rise impacts model DIVA (Hinkel and Klein, 2009), using climate scenario data from complex climate models and downscaled socio-economic scenarios from IMAGE.

GLOFRIS is used to assess current and future flood risks related to climate, changing land-cover patterns and changing socio-economic conditions in all world regions. This can be done globally at a resolution of 0.5x0.5 degrees and regionally at a higher resolution (1x1 km2). The higher resolution is achieved using a specially developed downscaling algorithm and more detailed regional impact models. Impacts for various safety levels can be analysed. Possible applications include the preparation of IPCC scenarios for flood risk changes at 0.5 degree and 1 km2 resolutions.

Input/Output Table

Input Flood risks component

IMAGE model drivers and variablesDescriptionSource
Population - grid Number of people per gridcell (using downscaling). Drivers
GDP per capita - grid Scaled down GDP per capita from country to grid level, based on population density. Drivers
Land cover, land use - grid Multi-dimensional map describing all aspects of land cover and land use per grid cell, such as type of natural vegetation, crop and grass fraction, crop management, fertiliser and manure input, livestock density. Land cover and land use
Temperature - grid Monthly average temperature. Atmospheric composition and climate
Precipitation - grid Monthly total precipitation. Atmospheric composition and climate
External datasetsDescriptionSource
Coastal storm surges Estimates on storm surge/tide water levels for a large number of coast segments. DIVA model
Daily climate dataset - grid Bias corrected daily precipitation, temperature and potential evaporation input. This data set is according to the monthly Precipitation and Temperature. EU-watch database
Flood statistics - grid Annual statistics of water depth and the flooded fraction per grid cell. water volumes
Topography, elevation - grid Global high resolution map of topography and elevation from NASA Shuttle Radar Topography Mission. Digital Elevation Model. HydroSHEDS database

Output Flood risks component

IMAGE model variablesDescriptionUse
Statistics on inundation depth - grid Annual statistics of water depth in flooded areas of a grid cell.
Statistics on river discharge - grid Annual statistics on river discharge. Final output
Expected nr of affected people - grid Population expected to be exposed to floods per year. Final output
Expected value of affected GDP - grid GDP expected to be exposed to floods per year. Final output
Statistics of inundation extent - grid Annual statistics of flooded fraction per grid cell. Final output
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