Operational issues and appendixes

In this section a few operational or more practical issues, like tables, “how-to” hints or frequently asked questions, software tricks or demos are explained in a more detailed way.

Hazard and Risk mapping

Directly associated to flood simulations, or frequently the promoter, is the calculation of the Flood Risk, nowadays strongly linked to Climate-Change scenarios.

The Risk is a function of Hazard, Exposure, and Vulnerability, being:

  1. Hazard, with two components:

    • Intensity: direct result of simulations that output rasters with Water Depth, Velocities, and Extents.

    • Probability of hazard: linked to the Return Period of the forcing boundary: a river discharge for fluvial floods, a rainfall for pluvial floods, or the sea level for coastal floods.

  2. Exposure: related to accounting Land Uses, type of buildings and infrastructures, all analysed within a GIS Framework. Nowadays a correct and affordable pixel resolution for Risk calculations is as fine as 1-2 meters.

  3. Vulnerability: through the damage curve that relates usually Water Depth versus Damage having particular dependencies on the physical structures or terrain contents, if the loss is direct or indirect, tangible (economic loss) or intangible (population, injuries or loss of life).

Tipically a Total-Damage is calculated as a sum over Polygons, considering an averaged Water-Depth(WD) over each surface Polygon (S):

\[TD=\sum_{Polygs} S_{Polyg} V_{Max-Dmg} \Theta_{Polyg}(\overline{WD})\]

A more complex analysis can be held by using the product of Depth times Velocity, specially when it is directly applied to population or cars, using the so called vulnerability curves.

Annual Expected Damage:

The expected annual damage (EAD), also known as averaged annual damage (AAD), although the former is more used to predict, is the average of flood damages calculated over a number of events, where the total damage for each event is weighted by its probability in a year, that weight can be:

\[W_i=1.-exp \left(\frac{-1} {T_i}\right) \; or \; W_i=\frac{1} {T_i}-\frac{1} {T_{i+1}}\]

Tabulating validation data sources

From Bates, 2022, we can extract a table with the most common sources and ranges for model validation.

ValSRC

Urban scenarios: street meshing

While working with building blocks across urban scenarios, the most accurate and flexible approach for meshing is the Delaunay tessellation, in this case with GMSH mesh generator. The steps will be described during a live course.

StreetMsh