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Modelling Flood Risk
Committed To Securing Livelihoods
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Modelling Risk of High-Resolution Inundation
Flood damage accounts for an increasing amount of catastrophe losses as a result of our changing climate. As more and more of the global population and wealth are accumulated in regions that will have some measure of probability of inundation occurrence of a 1-in-100-year event, a serious task arises for (re)insurers who face growing concentrations and severity of risk. Furthermore, flood hazard maps will have to be updated more frequently as they will quickly become outdated and more sophisticated methods will be necessary to assess, quantify and manage the risk.
By considering the range complex numerical or other measurable factors that are associated with damaging floods, governments, institutions,
agencies, etc. will be able to make better informed decisions, develop policy, guidelines and effective risk management strategies. (Re-)insurers and other industries will be able to recognise more profitable business opportunities.
Infinite Observations ZineQx Flood Models capture all aspects of flood risk, including fluvial (riverine) and pluvial (surface water) flooding, at very high spatial resolutions. By combining our Hur Cat Models with our Flood Models we obtain comprehensive view of tropical cyclone-induced flooding, which is in addition to other sources of flooding, an important flood risk and are able to offer our expertise in regions with coastal and inland flood risk.


Global Flood Catastrophe Models
Modelled Regions :
Click a region to see countries or islands where courage is available.
North America
Europe
Middle East
Central America & Caribbean
Asia & Pacific
Latin America
Africa
Australia
North America
Canada
United States
Mexico
Central America
Belize
Costa Rica
El Salvador
Honduras
Nicaragua
Panama
Caribbean
The Greater Antilles
Cuba
Puerto Rico
Jamaica
Cayman Islands
Hispaniola:
Haiti
Dominican Republic
Lucayan Archipelago
Bahamas
Turks and Caicos Islands
Lesser Antilles (Leeward Islands)
Anguilla (UK)
Saint Martin (Fr.)
Sint Maarten (Neth.)
Saint-Barthélemy (Fr.)
Saba (Neth.)
Sint Eustatius (Neth.)
Saint Kitts
Nevis
Antigua
Barbuda
Redonda
Bermuda
Montserrat (UK)
Guadeloupe (Fr.)
La Désirade (Fr.)
Marie-Galante (Fr.)
Les Saintes archipelago (Fr.)
St. Thomas (US VI)
St. John (US VI)
St. Croix (US VI)
Water Island (US VI)
Tortola (UK VI)
Virgin Gorda (UK VI)
Anegada (UK VI)
Jost Van Dyke (UK VI)
Aruba (Neth.)
Curaçao (Neth.)
Bonaire (Neth.)
La Orchila
La Tortuga
La Blanquilla
Margarita Island
Coche
Cubagua
Los Roques Archipelago
Lesser Antilles (Windward Islands)
Dominica
Martinique (Fr.)
Saint Lucia
Barbados
Saint Vincent
Grenadines
Carriacou
Petite Martinique
Grenada
Trinidad
Tobago
South America
Argentina
Bolivia
Chile
Cayman Islands
Colombia
Ecuador
Guyana
Guiana (Fr.)
Paraguay
Peru
Suriname
Uruguay
Venezuela
Europe
Andorra
Austria
Belgium
Bulgaria
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Iceland
Ireland
Italy
Latvia
Liechtenstein
Lithuania
Luxembourg
Malta
Monaco
Netherlands
Norway
Poland
Russia
Spain
Sweden
Switzerland
Turkey
United Kingdom
Vatican City
Asia & Pacific
East Asia
China
Hong Kong (China)
Macau (China)
Japan
North Korea
South Korea
Taiwan
South Asia
Bangladesh
India
Maldives
Sri Lanka
South Asia
Brunei
Cambodia
Indonesia
Laos
Malaysia
Myanmar
Philippines
Singapore
Thailand
Timor-Leste
Vietnam
Australasia
Australia
New Zealand
Melanesia
Fiji
New Caledonia (Fr)
Papua New Guinea
Solomon Islands
Vanuatu
Micronesia
Guam (US)
Kiribati
Marshall Islands
Nauru
Northern Mariana Islands (US)
Wake Island (US)
Polynesia
American Samoa (US)
Cook Islands (NZ)
Easter Island (Ch)
French Polynesia (Fr)
Niue (NZ)
Norfolk Island (A)
Tonga
Tuvalu
Samoa
Wallis and Futuna (Fr)
Understanding Flood Risk Exposure Models



Data Driven Flood Modelling
Infinite Observations estimates that by 2040, over a third of the current agricultural area will be subject to increase inundation, thus having negative implications for human health and economies worldwide. Over half of the population in the most exposed small island developing nations are exposed to some form of flooding exacerbated by sea level rise. In large countries trillions of worth in GDP is exposed to hurricanes. Increasingly severe climate conditions impose growing economic pressure, which impacts sovereign credit risk. Infinite Observations uses global granular data along with detailed analytics for sovereign climate risk data that focuses on key risk drivers such as the amount of agricultural area, population, and GDP (PPP) are/will be exposed to climate hazard of flood risk. This cutting-edge methodology helps to expand the understanding of climate risk that sovereign countries are facing and aid decision and policymakers, investors and credit institutions.
Quantifying Flood Hazard Losses
To capture flood risk Infinite Observations takes into account: (1) Potential flooding that affects several river basins are caused by large-scale frontal systems of storms and small-scale torrential downpours that frequently cause localized inundations, such as convective summer storms. To be able to capture both (large- and small scale) types of storms a Global Circulation Model (GCM) is coupled with a high-resolution Numerical Weather Prediction (NWP) model. (2) High-resolution precipitation data, snow build-up and snowmelt is considered to be able to take local soil-water balance into account. (3) A flood routing scheme is employed where surface run-off is routed downstream along the river network. (4) The inundation depth is determined using a physically based hydraulic model that converts river flow into water level, or elevation. (5) Average Annual Losses estimates (AAL) are derived for each flood event based on an exposure database that contains flood defence systems, population, building data that are grouped into different structural classes and structural characteristics.




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