Hazard Local Context Data
The negative impacts of climate change and extreme weather are manifest through ‘hazards’, also referred to as perils. The Climate Risk Engines can analyse a range of hazards (Figure 6). Figure 6: Hazards analysed by the Climate Risk Engines are constantly expanding. Hazard data can be obtained directly or constructed through context data. For example, Fluvial (riverine) and Pluvial (surface water) flood data is purchased from specialist flood modelling companies.
Context Data
Context data is used to construct a hazard or to provide the preconditions for damage and disruption. Contextual information includes vegetation, soils and climatic zones. For sea level rise in particular, historical data is gathered for tidal extremes, deep water wave size and periodicity around the coast, as well as tectonic movement trends. The context data used in the Climate Risk Engines includes data, models and maps that cover: • Digital Elevation Model • Astronomic tides • Recorded tide gauge • Tectonic land movement • Storm surge and wave set-up • Global Severe Convection Environment Occurrence • Forest Cover and Ground Fuel Mapping • Ground Fuel Attenuation Mapping • Landslip susceptibility • Fluvial flood • Pluvial flood • Soil composition maps • Bathymetry
Digital Elevation Model – DEM
Resolution: 5-30m | Availability: Global A Digital Elevation Model (DEM) is 3D representation of the elevation of bare ground (bare Earth), the topographic surface of the Earth excluding trees, buildings, and any other surface objects. DEMs are created from a variety of sources such as Light Detection And Ranging (LiDAR), and are typically represented as a raster, a grid of squares, also known as a heightmap when representing elevation. Sources of Elevation data include: • Shuttle Radar Topography Missions (STRMs) provide the underlying data to some of the best global models. These datasets are referenced to the EGM96, the geoid used by the international GPS system. Some countries have reprocessed this data for their own purposes (e.g. Australia), and some have refined this data for the world e.g. Japan, EU DEM (Copernicus). • LiDAR data, is now seen as the gold standard (though still with problems) due to its much greater horizontal resolution, and higher accuracy (vertically and horizontally). These datasets are often referenced to the country’s own datums. • Actual surveyed elevations for some point on the property.
Tectonic Land Movement
Resolution: 111km | Availability: Global Tectonic land movement or land which is rising or falling needs to be included in the calculation of coastal inundation impacts – as it will exacerbate or temper sea level rise impacts. Glacial Isostatic Adjustments (GIA) is the continual vertical movement of land due to changes in mass due to the melting of ice-age glaciers. For a given location the Climate Risk Engines access local vertical land movement gauges and global gridded data.
Tide Data
Resolution: NA | Availability: Global Tides are caused by the gravitational pull of the Sun and the Moon (and other celestial bodies, to a much smaller extent). The constantly changing direction of gravity from these bodies produce sets of harmonic waves in the Earth's oceans. The result of this force is to increase and decrease sea levels in a semi-regular way. These forces are typically called astronomical tides, and they are highly predictable. The Climate Risk Engines have access to over 1,580 tide gauges around the world, and this is augmented by synthetic tide gauges to establish higher effective resolution in some countries.
Global Severe Convection Environment Occurrence Map
Resolution: 111km | Availability: Global The Climate Risk Engines use an in-house model to predict the future probability of small convective storms around the world. The Global Severe Convection Environment Occurrence Map is a high resolution gridded map of relating severe storm environments to regular thunderstorms occurrence.
Forest Cover and Ground Fuel Mapping
Resolution: 30m | Availability: Global Global tree cover data are per pixel estimates peak of growing season tree canopy cover based on satellite data.
Ground Fuel Attenuation Mapping
Resolution: ~4.5km | Availability: Global Forest fires are mainly maintained and distributed at ground level rather than through the canopy, therefore canopy cover alone is an inadequate guide to wildfire risk. The Climate Risk Engines also use various data sources to estimate the reduction in the ground level fuel load.
Raster Depth Grid – Fluvial and Pluvial Flood Maps
Resolution: 5-30m | Availability: Global Flood depth rasters for multiple event return frequencies are used in the Climate Risk Engines to calculate the probability of flood damage. Multiple layers per location are used from multiple commercial and government sources. There are separate layers for Fluvial (Riverine) and Pluvial (Surface Water) flooding. For each hazard, these come as GeoTIFF files that contain the flood depths for a given return period. Typically, there are 6-7 of these flood layers, at return periods such as 20, 50, 100, 200, 500, 1000, 1500, or 10,000/PMF (probable maximum flood).
Landslip Susceptibility Map
Resolution: 1km | Availability: Global Landslip requires steep slopes, but are made more susceptible by nearby roads or rail, deforestation, a major tectonic fault nearby and weak bedrock. The Climate Risk Engines use integrated landslip susceptibility mapping as one of the inputs for computing landslip risk.
Soil composition maps
Resolution: 250m | Availability: Global The Climate Risk Engines use national soil composition maps to determine soil type and its likely susceptibility to hazard conditions.
Bathymetry Data
Resolution: 450m | Availability: Global Bathymetry data is important when computing storm surge and wave impacts at the near shore. The Climate Risk Engines can use a variety of models. The gridded bathymetric data used is a global terrain model for both the ocean and the land, providing elevation data on a 15 arc-second grid, where height is represented by positive values and depth is represented by negative values of elevation.
