Loss

Schema attributes

The loss schema enables to store information about hazard impact over exposure as a function of vulnerability. Loss datasets are directly linked to the hazard, exposure, and vulnerability datasets which were used to model losses. When no vulnerability model is applied, the potential loss is estimated as the sum of all exposed value. Losses can be expressed in form of map or in form of a curve, both sharing the same attributes and metrics.

classDiagram Model -- Map Model -- Curve Model: Hazard type Model: Exposure category Model: Calculation method Model: Link data class Map{ Occurrence frequency Time reference Impact type Loss type Loss metric Loss unit } class Curve{ Occurrence frequency Time reference Impact type Loss type Loss metric Loss unit }

The main attributes of the loss model describe the hazard and process for which the loss are calculated, the method of calculation (to discern empirical events from simulated scenarios) and the category of asset on which losses insist. The schema includes the direct links to the original dataset of hazard, exposure, and vulnerability that were used to calculate the loss.

Required Attribute Description Type
* Hazard type Main hazard type from list of options
  • Coastal Flood
  • Convective Storm
  • Drought
  • Earthquake
  • Extreme Temperature
  • Flood
  • Landslide
  • Tsunami
  • Volcanic
  • Wildfire
  • Strong Wind
  • Multi-Hazard
Hazard process Specific hazard process Options list
Calculation method How the scenario was calculated
  • Inferred
  • Simulated
  • Observed
* Exposure occupancy Destination of use of the asset
  • Residential
  • Commercial
  • Industrial
  • Infrastructure
  • Healthcare
  • Educational
  • Government
  • Crop
  • Livestock
  • Forestry
  • Mixed
* Exposure category Category of asset suffering the losses
  • Buildings
  • Indicators
  • Infrastructures
  • Crops, livestock and forestry
* Value type Element on which loss insist
  • Structure
  • Content
  • Product
  • Other
Hazard link Hazard dataset that was used to calculate loss URL
Exposure link Exposure dataset that was used to calculate loss URL
Vulnerability link Vulnerability dataset that was used to calculate loss URL


When the scenario modelled refers to a specific period of time, this can be specified in terms of dates, period span and reference year. For example, an observed flood event that occurred from 1.10.2009 (time start) to 3.10.2009 (time end), spanning over 3 days (time span). When precise time collocation is unknow or inapplicabile, a general reference date such as "2009" is used to identify events (time year). This is also useful to specify future scenario, e.g. time year: 2050.

Required Attribute Description Type
Time start The time at which the modelled scenario starts Date
Time end The time at which the modelled scenario ends Date
Time span The duration of the modelled period Number
Time year One reference year to univocally identify the scenario Date (year)


When instead the hazard scenario is represented in probabilistic terms, the occurrence probability (frequency distribution) of hazard can be expressed in different ways. The most common way to communicate this is the "return period", expressed as the number of years after which a given hazard intensity could occurr again: RP 100 indicates that that event has a probability of once in 100 years. This attirbute can indicate individual layer frequency (RP100) or a range of frequencies for a collection of layers (RP10-100).

Required Attribute Description Type
Frequency type The frequency of occurrence of the present event
  • Rate of Exceedence
  • Probability of Exceedence
  • Return Period
Occurrence probability For probabilistic scenario, the occurrence probability is expressed according to frequency type Text


Additional attributes are specific to loss, describing the type of impact, the type of loss, the loss metric and the unit used to measure it.

Required Attribute Description Type
* Impact The type of impact
  • Direct
  • Indirect
  • Total
* Loss type The type of loss
  • Ground up
  • Insured
* Metric Type of loss metric
  • Average Annual Losses
  • Annual Average Loss Ratio
  • Probable Maximal Loss
* Unit Cost unit of measure Unit code


Examples

Losses can be rapresented in many different way: regular raster grids, points, or polygons. Often, the loss data consist of measures aggregated at the administrative unit level.

Flood loss scenarios for Afghanistan, 2050

Schema attributes for loss map related to future river flood hazard scenarios (2050) over all types of exposure occupancies for Afghanistan.

Flood losses in Afghanistan

The losses are higher in the most densely built-up area of Kabul.

Flood losses in Kabul

Required Attribute Example
* Hazard type Flood
Hazard process River flood
* Exposure occupancy Mixed
* Exposure category Buildings
* Value type Structure
Hazard link Dataset
Exposure link
Vulnerability link
Time year 2050
Frequency type Return Period
Occurrence probability RP 5-1000 years
* Impact Direct
* Loss type Ground up
* Metric Average Annual Losses
* Unit USD


Losses can be investigated as total or for individual exposed asset and infrastructure elements.

Road losses


Observed losses

Insert example of recorded empirical losses.

Screenshot

Required Attribute Example
* Hazard type Earthquake
* Analysis type Probabilistic
* Calculation method Simulated
Frequency type Return Period
Occurrence probability 1000 years
Occurence time (start) 800
Occurence time (end) 2001
Occurence time (span) 1200 years
* Hazard process Ground motion
* Unit of measure PGA (g)