IMAGE - User contributions [en]

Statistics of inundation extent - grid

2014-04-02T10:05:42Z

ArnoBouwman:

{{VariableTemplate
|Label=Flooded fraction
|Description=Annual statistics of flooded fraction per grid cell.
|Dimension=time, worldgrid
|VariableType=model (end-indicator)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Expected value of affected GDP - grid

2014-04-02T10:04:30Z

ArnoBouwman:

{{VariableTemplate
|Label=Affected GDP
|Description=Annual expected value of exposed GDP to floods per grid cell or region. GDP is an estimation for the damage caused by floods.
|Dimension=time, region, worldgrid
|Unit=US$
|VariableType=model (end-indicator)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Expected nr of affected people - grid

2014-04-02T10:02:19Z

ArnoBouwman:

{{VariableTemplate
|Label=to floods exposed population..
|Description=Annual expected number of exposed population to floods per grid cell or region.
|Dimension=region, worldgrid
|Unit=people
|VariableType=model (end-indicator)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Statistics on river discharge - grid

2014-04-02T10:01:13Z

ArnoBouwman:

{{VariableTemplate
|Label=River discharge
|Description=Annual statistics of river discharge per grid cell.
|Dimension=time, worldgrid
|Unit=m3
|VariableType=model (end-indicator)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Statistics on inundation depth - grid

2014-04-02T10:00:37Z

ArnoBouwman:

{{VariableTemplate
|Label=Flooded depth
|Description=Annual statistics of water depth in case of floods per grid cell.
|Dimension=time, worldgrid
|Unit=meters
|VariableType=model (from/to model)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Statistics on inundation depth - grid

2014-04-02T10:00:02Z

ArnoBouwman:

{{VariableTemplate
|Label=Flooded depth
|Description=Annual statistics of water depth in case of floods.
|Dimension=time, worldgrid
|Unit=meters
|VariableType=model (from/to model)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Topography, elevation - grid

2014-04-02T09:58:19Z

ArnoBouwman:

{{VariableTemplate
|Label=Topography
|Description=Global high resolution map of topography and elevation obtained from NASA's Shuttle Radar Topography Mission. Digital Elevation Model.
|Dimension=worldgrid
|VariableType=external parameter
|Source2=HydroSHEDS database;
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModelDriver=
|ExternalModel=
}}

Flood statistics - grid

2014-04-02T09:53:58Z

ArnoBouwman:

{{VariableTemplate
|Label=Annual flood statistics.
|Description=Annual statistics of water depth and the flooded fraction per grid cell.
|Dimension=time, worldgrid
|VariableType=external parameter
|BasedOn2=water volumes
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModel=
}}

Coastal storm surges

2014-04-02T09:51:30Z

ArnoBouwman:

{{VariableTemplate
|Label=Coastal storm surges
|Description=Global coastline data set with estimates on storm surge/tide water levels for a large number of coast segments.
|Dimension=other
|Unit=meter
|VariableType=external parameter
|Source2=DIVA model;
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModel=
}}

Coastal storm surges

2014-04-02T09:38:34Z

ArnoBouwman:

{{VariableTemplate
|Label=Coastal storm surges
|Description=Coastline data set with storm surges, storm tides.
|Dimension=other
|Unit=meter
|VariableType=external parameter
|Source2=DIVA model;
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModel=
}}

Coastal storm surges

2014-04-02T09:36:34Z

ArnoBouwman:

{{VariableTemplate
|Label=Coastal storm surges
|Description=Coastline data set with storm surges, storm tides.
|Unit=meter
|VariableType=external parameter
|Source2=DIVA model;
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModel=
}}

Statistics of inundation extent - grid

2014-03-04T10:55:33Z

ArnoBouwman:

{{VariableTemplate
|Label=Flooded fraction
|Description=Annual statistics of flooded fraction. Estimate of flood hazard.
|Dimension=time, worldgrid
|VariableType=model (end-indicator)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Expected value of affected GDP - grid

2014-03-04T10:54:44Z

ArnoBouwman:

{{VariableTemplate
|Label=Affected GDP
|Description=Annual expected value of to floods exposed GDP.
|Dimension=time, worldgrid
|Unit=US$
|VariableType=model (end-indicator)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Topography, elevation - grid

2014-03-04T10:53:57Z

ArnoBouwman:

{{VariableTemplate
|Label=Topography
|Description=Global topography and elevation. Digital Elevation Model.
|Dimension=worldgrid
|VariableType=parameter
|Source2=HydroSHEDS database;
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModelDriver=
|ExternalModel=
}}

Topography - grid

2014-03-04T10:53:57Z

ArnoBouwman:

Daily climate dataset - grid

2014-03-04T10:53:12Z

ArnoBouwman:

{{VariableTemplate
|Label=bias corrected daily climate input.
|Description=Bias corrected daily precipitation, temperature and potential evaporation input.
|Dimension=time, worldgrid
|VariableType=parameter
|Source2=EU-watch database
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModel=
}}

Population - grid

2014-03-04T10:52:07Z

ArnoBouwman:

{{VariableTemplate
|Label=Population per gridcell
|Description=Number of people per country is scaled down to number of people per gridcell.
|Dimension=time, worldgrid
|Unit=people
|VariableType=driver
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=Population projections
|Source=UN; IIASA;
|BasedOn=Own downscaling
|Source3=
|Reference2=
|ExternalModel=
}}

GDP per capita - grid

2014-03-04T10:51:14Z

ArnoBouwman:

{{VariableTemplate
|Label=GDP per capita per gridcell
|Description=Scaled down from GDP per capita per country using urbanization map.
|Dimension=time, worldgrid
|Unit=per gridcell
|VariableType=driver
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=Economic development
|Source=OECD;
|BasedOn=Own downscaling
|Source3=
|Reference2=
|ExternalModel=
}}

EU-watch database

2014-03-04T10:48:57Z

ArnoBouwman:

{{ComputerModelTemplate
|Subject=Climate
|Description=a meteorological forcing dataset (based on ERA-40) for land surface and hydrological models (1901-2001). Five variables are at 6 hourly resolution and five variables are at 3 hourly resolution
|Creator=CEH
|ExternalURL=http://www.eu-watch.org/
|FrameworkRelation=used data source
|Component=
}}

CEH

2014-03-04T10:48:26Z

ArnoBouwman: Created page with "{{ResearchOrganisationTemplate |Description=The Centre for Ecology & Hydrology is the UK's Centre of Excellence for integrated research in terrestrial and freshwater ecosystem..."

{{ResearchOrganisationTemplate
|Description=The Centre for Ecology & Hydrology is the UK's Centre of Excellence for integrated research in terrestrial and freshwater ecosystems and their interaction with the atmosphere.

|URL=http://www.ceh.ac.uk/
}}

EU-watch database

2014-03-04T10:47:42Z

ArnoBouwman: Created page with "{{ComputerModelTemplate |Subject=Climate |Description=a meteorological forcing dataset (based on ERA-40) for land surface and hydrological models (1901-2001). Five variables a..."

{{ComputerModelTemplate
|Subject=Climate
|Description=a meteorological forcing dataset (based on ERA-40) for land surface and hydrological models (1901-2001). Five variables are at 6 hourly resolution and five variables are at 3 hourly resolution
|Creator=CEH;
|ExternalURL=http://www.eu-watch.org/
|FrameworkRelation=used data source
|Component=
}}

USGS

2014-03-04T10:39:38Z

ArnoBouwman: Created page with "{{ResearchOrganisationTemplate |Description=United States Geological Survey The USGS is a science organization that provides impartial information on the health of our ecos..."

{{ResearchOrganisationTemplate
|Description=United States Geological Survey

The USGS is a science organization that provides impartial information on the health of our ecosystems and environment, the natural hazards that threaten us, the natural resources we rely on, the impacts of climate and land-use change, and the core science systems that help us provide timely, relevant, and useable information.

|URL=http://www.usgs.gov/
}}

HydroSHEDS database

2014-03-04T10:37:59Z

ArnoBouwman:

{{ComputerModelTemplate
|Subject=Elevation
|Description=Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales.

HydroSHEDS is a mapping product that provides hydrographic information for regional and global-scale applications in a consistent format. It offers a suite of geo-referenced data sets (vector and raster) at various scales, including river networks, watershed boundaries, drainage directions, and flow accumulations. HydroSHEDS is based on high-resolution elevation data obtained during a Space Shuttle flight for NASA's Shuttle Radar Topography Mission (SRTM).

|Creator=WWF; USGS
|ExternalURL=http://hydrosheds.cr.usgs.gov/index.php
|FrameworkRelation=used data source
|Component=
}}

Topography, elevation - grid

2014-03-03T15:10:23Z

ArnoBouwman:

{{VariableTemplate
|Label=Topography
|Description=Global topography and elevation. Digital elevation model
|VariableType=parameter
|Source2=HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales)
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModelDriver=
|ExternalModel=
}}

Topography - grid

2014-03-03T15:10:23Z

ArnoBouwman:

Daily climate dataset - grid

2014-03-03T15:05:25Z

ArnoBouwman:

{{VariableTemplate
|Label=bias corrected daily climate input.
|Description=bias corrected daily precipitation, temperature and potential evaporation input.
|Dimension=time, worldgrid
|VariableType=parameter
|BasedOn2=EU-watch database
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModel=
}}

Flood statistics - grid

2014-03-03T15:04:33Z

ArnoBouwman:

{{VariableTemplate
|Label=Annual flood statistics.
|Description=Annual statistics of flooded depth and flooded fraction.
|Dimension=time, worldgrid
|VariableType=parameter
|BasedOn2=water volumes
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModel=
}}

Coastal storm surges

2014-03-03T15:03:40Z

ArnoBouwman:

{{VariableTemplate
|Label=Coastal storm surges
|Description=Coastline data set with storm surges.
|Unit=meter
|VariableType=parameter
|Source2=DIVA model;
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModel=
}}

Statistics on river discharge - grid

2014-03-03T14:58:27Z

ArnoBouwman:

{{VariableTemplate
|Label=River discharge
|Description=Annual statistics of river discharge.
|Dimension=time, worldgrid
|Unit=m3
|VariableType=model (end-indicator)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Statistics of inundation extent - grid

2014-03-03T14:57:42Z

ArnoBouwman:

{{VariableTemplate
|Label=Flooded fraction
|Description=Annual statistics of flooded fraction. Estimate of flood hazard
|Dimension=time, worldgrid
|VariableType=model (end-indicator)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Statistics on inundation depth - grid

2014-03-03T14:56:43Z

ArnoBouwman:

{{VariableTemplate
|Label=Flooded depth
|Description=Annual statistics of flooded depth. Estimate of flood hazard.
|Dimension=time, worldgrid
|Unit=meters
|VariableType=model (from/to model)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Expected value of affected GDP - grid

2014-03-03T14:55:53Z

ArnoBouwman:

{{VariableTemplate
|Label=Affected GDP
|Description=Annual expected value of to floods exposed GDP. Flood risk indicator.
|Dimension=time, worldgrid
|Unit=US$
|VariableType=model (end-indicator)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Expected value of affected GDP - grid

2014-03-03T14:55:23Z

ArnoBouwman:

{{VariableTemplate
|Label=Affected GDP
|Description=Annual xxpected value of to floods exposed GDP. Flood risk indicator.
|Dimension=time, worldgrid
|Unit=US$
|VariableType=model (end-indicator)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Expected nr of affected people - grid

2014-03-03T14:54:43Z

ArnoBouwman:

{{VariableTemplate
|Label=to floods exposed population..
|Description=Annual expected number of to floods exposed population.
|Dimension=worldgrid
|Unit=people
|VariableType=model (end-indicator)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Coastal storm surges

2014-03-03T14:52:02Z

ArnoBouwman:

{{VariableTemplate
|Description=Coastline data set with storm surges.
|Unit=meter
|VariableType=parameter
|Source2=DIVA model;
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModel=
}}

Flood statistics - grid

2014-03-03T14:51:30Z

ArnoBouwman:

{{VariableTemplate
|Label=Annual flood statistics.
|Description=Annual statistics of flooded depth and flooded fraction.
|Dimension=worldgrid
|VariableType=parameter
|BasedOn2=water volumes
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModel=
}}

Expected nr of affected people - grid

2014-03-03T14:50:21Z

ArnoBouwman:

{{VariableTemplate
|Description=Annual expected number of to floods exposed people.
|Unit=people
|VariableType=model (end-indicator)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Expected value of affected GDP - grid

2014-03-03T14:49:46Z

ArnoBouwman:

{{VariableTemplate
|Label=Affected GDP
|Description=Annual Expected value of Exposed GDP. Flood risk indicator.
|Dimension=time, worldgrid
|Unit=US$
|VariableType=model (end-indicator)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Daily climate dataset - grid

2014-03-03T14:46:21Z

ArnoBouwman:

{{VariableTemplate
|Label=bias corrected daily climate input
|Description=bias corrected daily precipitation, temperature and potential evaporation input
|Dimension=time, worldgrid
|VariableType=parameter
|BasedOn2=EU-watch database
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModel=
}}

Flood statistics - grid

2014-03-03T14:44:04Z

ArnoBouwman:

{{VariableTemplate
|Description=Annual statistics of flooded depth and flooded fraction.
|Dimension=worldgrid
|VariableType=parameter
|BasedOn2=water volumes
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModel=
}}

Flood statistics - grid

2014-03-03T14:43:30Z

ArnoBouwman:

{{VariableTemplate
|Description=Annual statistics flooded depth and annual statistics flooded fraction
|Dimension=worldgrid
|VariableType=parameter
|BasedOn2=water volumes
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModel=
}}

Coastal storm surges

2014-03-03T14:42:16Z

ArnoBouwman:

{{VariableTemplate
|Description=Coastline data set with storm surges.
|Unit=meter
|VariableType=parameter
|Source2=DIVA model; DINAS
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|ExternalModel=
}}

GLOFRIS model

2014-03-03T14:39:38Z

ArnoBouwman:

{{ComputerModelTemplate
|Subject=Flood risk assessment
|Description=GLOFRIS uses among other inputs results from the core models and calculates the flood risks based on climate change.
|Creator=PBL; Deltares; UU; IVM;
|FrameworkRelation=associated model
|Component=Flood risks;
}}

Expected value of affected GDP - grid

2014-03-03T12:40:26Z

ArnoBouwman:

{{VariableTemplate
|Label=Affected GDP
|Description=Annual Expected value of Exposed GDP. Flood risk indicator
|Dimension=time, worldgrid
|VariableType=model (end-indicator)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Expected nr of affected people - grid

2014-03-03T12:38:26Z

ArnoBouwman:

{{VariableTemplate
|Description=Annual Expected nr of Exposed people
|VariableType=model (end-indicator)
|Source2=
|BasedOn2=
|Reference2=
|DriverGroup=
|Source=
|BasedOn=
|Reference=
|Source3=
|Reference2=
|ExternalModel=
}}

Flood risks

2014-03-03T12:37:22Z

ArnoBouwman: Undo revision 17583 by ArnoBouwman (talk)

{{ComponentTemplate2
|IMAGEComponent=Scenario drivers;land cover and use;Human development
|ExternalModel=PCR-GLOBWB model; DIVA model;
|KeyReference=Ward et al., 2013; Winsemius et al., 2012;
|Reference=UNISDR, 2011;
IPCC, 2012;
Bouwer et al., 2010;
Barredo et al., 2012;
UNISDR, 2005;
Bierkens and Van Beek, 2009;
Hinkel and Klein, 2009;
|InputVar=Land cover, land use - grid; Population - grid; GDP per capita - grid; Land cover, land use - grid;
|OutputVar=Expected nr of affected people - grid; Expected value of affected GDP - grid; Statistics of flooded depth - grid; Statistics of flooded fraction - grid; Statistics of river discharge - grid;
|Parameter=Topography - grid; Coastal storm surges; Flood statistics - grid; Historical climate dataset - 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 km2). 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 km2 resolutions.
|ComponentCode=FR
|AggregatedComponent=Impacts
|FrameworkElementType=impact component
}}

Flood risks

2014-03-03T12:31:16Z

ArnoBouwman:

{{ComponentTemplate2
|IMAGEComponent=Scenario drivers;land cover and use;Human development
|ExternalModel=PCR-GLOBWB model; DIVA model;
|KeyReference=Ward et al., 2013; Winsemius et al., 2012;
|Reference=UNISDR, 2011;
IPCC, 2012;
Bouwer et al., 2010;
Barredo et al., 2012;
UNISDR, 2005;
Bierkens and Van Beek, 2009;
Hinkel and Klein, 2009;
|InputVar=Land cover, land use - grid; Population - grid; GDP per capita - grid; Land cover, land use - grid;
|Parameter=Topography - grid; Coastal storm surges; Flood statistics - grid; Historical climate dataset - 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;
|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 km2). 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 km2 resolutions.
|ComponentCode=FR
|AggregatedComponent=Impacts
|FrameworkElementType=impact component
}}

Flood risks/Policy issues

2013-12-19T08:18:02Z

ArnoBouwman:

{{ComponentPolicyIssueTemplate
|Status=On hold
|Description=The [[GLOFRIS model]] can be used to assess a wide range of scenarios based on information on land cover and climate change. The model, for instance, has been applied to analyse the impact of floods on Bangladesh. This case study was published in Winsemius et al. ([[Winsemius et al., 2012]]) and is summarised below.

The study compares the current situation with the impacts of changes in climate and socioeconomic conditions. The left panel in the maps on the right shows the pattern of a flood occurring once every 30 years according to GLOFRIS, scaled down to 1 x 1 km2 over Bangladesh, under current climate conditions. The right-hand panels in the maps on the right show the resulting expected values of damage under current climate and socioeconomic conditions, based on the two methods mentioned above.
Risk has been computed using these two methods for the reference situation and the two climate data sets. Under the current climate conditions, the projected annual damage in [[HasAcronym::GDP]] is around [[HasAcronym::USD]]2010 740 million, according to the land-use method (asset damage), and USD2010 2183 million using the population method (affected GDP). The graph on the right shows that under the scenarios of hazard and exposure changes, these values increase by a factor of 22 to 30 and 21 to 28, respectively, depending on the climate model used. The effects of simulated change in exposure only (increase by a factor of 11 and 7, respectively) were found to be much higher than those of climate change (increase by a factor of 3 to 4, depending on climate model and impact assessment method used). Calculations show that in the case of Bangladesh, the impacts of population and economic growth are much more important for future risks than the impacts of climate change. It will, therefore, be of interest to consider how the changes in socioeconomic conditions can be combined with a reduction in flood risk.
|Example=To date, the model has not been used extensively to model specific policy interventions. Obviously, measures taken elsewhere in the IMAGE model to prevent climate change would reduce flood risks as well.
}}

Flood risks/Description

2013-12-19T08:13:44Z

ArnoBouwman:

{{ComponentDescriptionTemplate
|Status=On hold
|Reference=Loveland et al., 2000; Van Beek et al., 2011; Wada et al., 2011; Hinkel and Klein, 2009;
|Description===Model core==
The purpose of the [[GLOFRIS model]] ([[Winsemius et al., 2012]]; [[Ward et al., 2013]]) is to estimate the effect of land cover and climate change on flood risks in river catchments and coastal areas on a global level. The global flood risks are expressed in the projected number of people affected, annually, and in GDP value. GLOFRIS uses land-cover input from [[IMAGE land use model|IMAGE]] and climate time series, such as the IPCC GCM projections. These input data drive a global hydrological model ([[PCR-GLOBWB model|PCR-GLOBWB]], the computational core of the module). PCR-GLOBWB calculates where and when flooding events may occur, and calculates the inundation extent and depth needed to estimate flood risks. PCR-GLOBWB has features (daily time steps and proper accounting of the relationship between non-linear soil moisture and run-off) that make this model appropriate for simulating flooding events. The spatial resolution currently used by the model is 0.5 x 0.5 degrees (and 5 x 5 minute resolution, currently under development). The different model steps of the main GLOFRIS module are shown in the model flow diagram of GLOFRIS on the right.

==Land cover==
The land-cover map ‘Global Land Cover Characterization’ ([[HasAcronym::GLCC]]) ([[Loveland et al., 2000]]) is the basis of the parameters of the PCR-GLOBWB hydrological model (model flow diagram of GLOFRIS on the right). These parameters express the hydrological characteristics of different land-cover types. IMAGE and PCR-GLOBWB are linked by lookup tables that translate the IMAGE land-cover classification into that of GLCC.

==Flood hazard==
The hydrological model PCR-GLOBWB ([[Van Beek et al., 2011]]; [[Wada et al., 2011]]) requires data on daily precipitation, potential evaporation and temperature that are consistent with the IMAGE scenario. Daily data are required because that reflects intermonthly and interannual climate variability and its effect on flood risk.
The PCR-GLOBWB model includes a routing component on river flooding that estimates inundation fractions and average inundation depths on a time-step basis. These inundation fractions and depths are used 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 fractions 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.

==Downscaling==
GLOFRIS estimates flood risk on two scales: that of 0.5 x 0.5 degrees for global analyses and that of 1 x 1 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. This means that, for each year of simulation, the most extreme water level and inundated fraction 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 0.5 x 0.5 degree maps of the extent and depth of annual maximum inundation to 1 x 1 km2, using a high-resolution digital elevation model. A scale down is needed, because the spatial variability of both flood hazards and flood exposure may be large and are not well represented on the coarser scales of IMAGE and PCR-GLOBWB. Therefore, a more accurate estimation of flood risk is obtained by converting the results to a higher resolution. This downscaling procedure may also include the risk of coastal flooding (Model flow diagram of the downscaling routine on the right).
For the scaling down in river catchments, annual extreme values of inundation depths and fractions are transformed into bankfull volumes and excess volumes per 0.5 degree cell. The bankfull volume represents the volumetric capacity of a river channel within a particular grid cell and is estimated according to flood volume, associated with a user-defined return period in which flood volumes will not lead to exceeding the bankfull volume (return period threshold in the model flow diagram of the downscaling routine on the right) under current climate and land-cover conditions. The excess bankfull volume for each year is scaled down by estimating a water level from identified river pixels, determined by the user-defined stream threshold (see Model flow diagram of the downscaling routine on the right) that generates a flood volume in the surrounding connected pixels, resulting in the same flood volume, estimated from the 0.5 x 0.5 degree results. The method is therefore mass conservative with respect to the PCR-GLOBWB results on 0.5 degree cells.

==Coastal flood==
Coastal flood hazard maps are established using the [[DIVA model|DIVA database]]. DIVA contains estimates on 1-, 10-, 100- and 1000-year coastal water levels, along a large number of coasts around the world ([[Hinkel and Klein, 2009]]). These coastal flood probabilities are combined with those on river flooding by finding the upstream connected pixels within the high-resolution elevation map that have a lower elevation than the coastal water levels. In this computation it is assumed that, as a wave moves inland, its height will be reduced as the water spreads over the surface, resulting in lower water levels inland than at the coast.

==Flood risk==
After the high-resolution flood hazard maps have been established, the annual extreme values can be combined to form average annual flood hazard maps and flood risk maps. At this scale, also more local detail can be applied about cropland locations, high-resolution maps on population and GDP or other exposure data of interest to the user. The resulting flood hazard maps can be combined with these high-resolution maps on population and GDP and, if possible, more localised damage models.

==More information==
More information about GLOFRIS, its underlying models and methods, as well as the downscaling module can be found in Winsemius et al. and Ward et al. ([[Winsemius et al., 2012]]; [[Ward et al., 2013]]).
}}

Flood risks/Description

2013-12-19T08:11:48Z

ArnoBouwman:

{{ComponentDescriptionTemplate
|Status=On hold
|Reference=Loveland et al., 2000; Van Beek et al., 2011; Wada et al., 2011; Hinkel and Klein, 2009;
|Description===The flood model==
The purpose of the [[GLOFRIS model]] ([[Winsemius et al., 2012]]; [[Ward et al., 2013]]) is to estimate the effect of land cover and climate change on flood risks in river catchments and coastal areas on a global level. The global flood risks are expressed in the projected number of people affected, annually, and in GDP value. GLOFRIS uses land-cover input from [[IMAGE land use model|IMAGE]] and climate time series, such as the IPCC GCM projections. These input data drive a global hydrological model ([[PCR-GLOBWB model|PCR-GLOBWB]], the computational core of the module). PCR-GLOBWB calculates where and when flooding events may occur, and calculates the inundation extent and depth needed to estimate flood risks. PCR-GLOBWB has features (daily time steps and proper accounting of the relationship between non-linear soil moisture and run-off) that make this model appropriate for simulating flooding events. The spatial resolution currently used by the model is 0.5 x 0.5 degrees (and 5 x 5 minute resolution, currently under development). The different model steps of the main GLOFRIS module are shown in the model flow diagram of GLOFRIS on the right.

==Land cover.==
The land-cover map ‘Global Land Cover Characterization’ ([[HasAcronym::GLCC]]) ([[Loveland et al., 2000]]) is the basis of the parameters of the PCR-GLOBWB hydrological model (model flow diagram of GLOFRIS on the right). These parameters express the hydrological characteristics of different land-cover types. IMAGE and PCR-GLOBWB are linked by lookup tables that translate the IMAGE land-cover classification into that of GLCC.

==Flood hazard.==
The hydrological model PCR-GLOBWB ([[Van Beek et al., 2011]]; [[Wada et al., 2011]]) requires data on daily precipitation, potential evaporation and temperature that are consistent with the IMAGE scenario. Daily data are required because that reflects intermonthly and interannual climate variability and its effect on flood risk.
The PCR-GLOBWB model includes a routing component on river flooding that estimates inundation fractions and average inundation depths on a time-step basis. These inundation fractions and depths are used 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 fractions 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.

==Downscaling==
GLOFRIS estimates flood risk on two scales: that of 0.5 x 0.5 degrees for global analyses and that of 1 x 1 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. This means that, for each year of simulation, the most extreme water level and inundated fraction 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 0.5 x 0.5 degree maps of the extent and depth of annual maximum inundation to 1 x 1 km2, using a high-resolution digital elevation model. A scale down is needed, because the spatial variability of both flood hazards and flood exposure may be large and are not well represented on the coarser scales of IMAGE and PCR-GLOBWB. Therefore, a more accurate estimation of flood risk is obtained by converting the results to a higher resolution. This downscaling procedure may also include the risk of coastal flooding (Model flow diagram of the downscaling routine on the right).
For the scaling down in river catchments, annual extreme values of inundation depths and fractions are transformed into bankfull volumes and excess volumes per 0.5 degree cell. The bankfull volume represents the volumetric capacity of a river channel within a particular grid cell and is estimated according to flood volume, associated with a user-defined return period in which flood volumes will not lead to exceeding the bankfull volume (return period threshold in the model flow diagram of the downscaling routine on the right) under current climate and land-cover conditions. The excess bankfull volume for each year is scaled down by estimating a water level from identified river pixels, determined by the user-defined stream threshold (see Model flow diagram of the downscaling routine on the right) that generates a flood volume in the surrounding connected pixels, resulting in the same flood volume, estimated from the 0.5 x 0.5 degree results. The method is therefore mass conservative with respect to the PCR-GLOBWB results on 0.5 degree cells.

==Coastal flood==
Coastal flood hazard maps are established using the [[DIVA model|DIVA database]]. DIVA contains estimates on 1-, 10-, 100- and 1000-year coastal water levels, along a large number of coasts around the world ([[Hinkel and Klein, 2009]]). These coastal flood probabilities are combined with those on river flooding by finding the upstream connected pixels within the high-resolution elevation map that have a lower elevation than the coastal water levels. In this computation it is assumed that, as a wave moves inland, its height will be reduced as the water spreads over the surface, resulting in lower water levels inland than at the coast.

==Flood risk==
After the high-resolution flood hazard maps have been established, the annual extreme values can be combined to form average annual flood hazard maps and flood risk maps. At this scale, also more local detail can be applied about cropland locations, high-resolution maps on population and GDP or other exposure data of interest to the user. The resulting flood hazard maps can be combined with these high-resolution maps on population and GDP and, if possible, more localised damage models.

More information about GLOFRIS, its underlying models and methods, as well as the downscaling module can be found in Winsemius et al. and Ward et al. ([[Winsemius et al., 2012]]; [[Ward et al., 2013]]).
}}