Flood risks: Difference between revisions
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Winsemius, H. C., L. P. H. Van Beek, B. Jongman, P. J. Ward and A. A. Bouwman (2012). A framework for global river flood risk assessments. Hydrology and Earth System Sciences Discussions 9 (8), pp. 9611–9659 (DOI: 10.5194/hessd-9-9611-2012). | Winsemius, H. C., L. P. H. Van Beek, B. Jongman, P. J. Ward and A. A. Bouwman (2012). A framework for global river flood risk assessments. Hydrology and Earth System Sciences Discussions 9 (8), pp. 9611–9659 (DOI: 10.5194/hessd-9-9611-2012). | ||
|InputVar=Land cover; Temperature and precipitation (external); Population per gridcell; GDP | |InputVar=Land cover; Temperature and precipitation (external); Population per gridcell; GDP per gridcell; | ||
|OutputVar=River discharge; Flooded depth; Flooded fraction; Affected GDP; GDP and population risk; | |OutputVar=River discharge; Flooded depth; Flooded fraction; Affected GDP; GDP and population at risk; | ||
|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]]). | |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; and to a variety of large companies for assessing the risks related to regional investments. | 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; and 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) 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 higher resolution is achieved using a specially developed downscaling algorithm and more detailed regional impact models. Impacts 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. | To meet this demand, the effect module ‘GLObal Flood Risks with IMAGE Scenarios’ (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 higher resolution is achieved using a specially developed downscaling algorithm and more detailed regional impact models. Impacts 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. | ||
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Revision as of 12:22, 9 December 2013
| Component is implemented in: |
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| Related IMAGE components |
| Models/Databases |
| Key publications |
| References |
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
<ul><li>Property "HasKeyReference" (as page type) with input value "Ward, P. J., B. Jongman, F. Sperna Weiland, A.A. Bouwman, R. van Beek., M. F. P. Bierkens., W. Ligtvoet and H. C. Winsemius (2013). Assessing flood risk at the global scale: model setup, results, and sensitivity. Environmental Research Letters. 8 044019 (doi:10.1088/1748-9326/8/4/044019).Winsemius, H. C., L. P. H. Van Beek, B. Jongman, P. J. Ward and A. A. Bouwman (2012). A framework for global river flood risk assessments. Hydrology and Earth System Sciences Discussions 9 (8), pp. 9611–9659 (DOI: 10.5194/hessd-9-9611-2012)." contains invalid characters or is incomplete and therefore can cause unexpected results during a query or annotation process.</li> <!--br--><li>Property "HasReference" (as page type) with input value "Barredo, J. I., D. Saurí and M. C. Llasat (2012). Assessing trends in insured losses from floods in Spain 1971–2008. Nat. Hazards Earth Syst. Sci. 12 (5), pp. 1723–1729 (DOI: 10.5194/nhess-12-1723-2012). Bierkens, M. F. P. and L. P. H. Van Beek (2009). Seasonal predictability of European discharge: NAO and hydrological response time. Journal of Hydrometeorology 10 (4), pp. 953–968 (DOI: 10.1175/2009JHM1034.1). Bouwer, L. M., P. Bubeck and J. C. J. H. Aerts (2010). Changes in future flood risk due to climate and development in a Dutch polder area. Global Environmental Change 20 (3), pp. 463–471 (DOI: 10.1016/j.gloenvcha.2010.04.002). Hinkel, J. and R. J. T. Klein (2009). Integrating knowledge to assess coastal vulnerability to sea-level rise: The development of the DIVA tool. Global Environmental Change 19 (3), pp. 384–395 (DOI: 10.1016/j.gloenvcha.2009.03.002). IPCC (2012). Managing the risks of extreme events and aisasters to advance climate change adaptation. A special report of working groups I and II of the Intergovernmental Panel on Climate Change, C.B.Field, V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, . . . P.M. Midgley (eds.), Cambridge University Press, Cambridge, UK / New York, NY, USA. Loveland, T. R., B. C. Reed, J. F. Brown, D. O. Ohlen, Z. Zhu, L. Yang and J. W. Merchant (2000). Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing 21, pp. 1303-1330. UNISDR (2005). Hyogo framework for action 2005 - 2015: Building the resilience of nations and communities to disasters, United Nations, UNISDR, Geneva. UNISDR (2011). Global assessment report on disaster risk reduction, United Nations UNISDR, Geneva, Switzerland. Van Beek, L. P. H., Y. Wada and M. F. P. Bierkens (2011). Global monthly water stress: 1. Water balance and water availability. Water Resources Research 47 (7), pp. W07517 (DOI: 10.1029/2010WR009791). Wada, Y., L. P. H. van Beek, D. Viviroli, H. H. Dürr, R. Weingartner and M. F. P. Bierkens (2011). Global monthly water stress: 2. Water demand and severity of water stress. Water Resources Research 47, pp. W07518 (DOI: 201110.1029/2010WR009792). Winsemius, H. C., L. P. H. Van Beek, B. Jongman, P. J. Ward and A. A. Bouwman (2012). A framework for global river flood risk assessments. Hydrology and Earth System Sciences Discussions 9 (8), pp. 9611–9659 (DOI: 10.5194/hessd-9-9611-2012)." contains invalid characters or is incomplete and therefore can cause unexpected results during a query or annotation process.</li></ul>