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Risk Calculations#

Risk profiles#

The OpenQuake engine can produce risk profiles, i.e. estimates of average losses and maximum probable losses for all countries in the world. Even if you are interested in a single country, you can still use this feature to compute risk profiles for each province in your country.

However, the calculation of the risk profiles is tricky. Starting from version 3.25 the recommended way is to first generate a common stochastic event set and then run all calculations starting from it. In this way events and ruptures are consistent for all countries.

You can compute the common stochastic event set by running an event based calculation without specifying the sites and with the parameter ground_motion_fields set to false. Currently, one must specify a few global site parameters in the precalculation to make the engine checker happy, but they will not be used since the ground motion fields will not be generated in the precalculation. The ground motion fields will be generated on-the-fly in the subsequent individual country calculations, but not stored in the file system.

Here are some tips on how to prepare the required job.ini files. To be concrete, let’s consider the 13 countries of South America. You will have a hazard file to generate the Stochastic Event Set (SES) as follows:

$ cat job_SAM.ini
calculation_mode = event_based
source_model_logic_tree_file = ssmLT.xml
gsim_logic_tree_file = gmmLTrisk.xml
site_model_file =
   Site_model_Argentina.csv
   Site_model_Bolivia.csv
   ...
ground_motion_fields = false
number_of_logic_samples = 2000
ses_per_logic_tree_path = 1
investigation_time = 50
truncation_level = 3
maximum_distance = 300

Notice that the site model files will not be used directly, since there is no calculation of the GMFs involved in this phase. However, the site model files will be imported and then the subsequent risk calculations will be able to associate the exposure sites to the hazard sites and to use the closest site parameters.

The engine will automatically concatenate the site model files for all 13 countries and produce a single site collection. It is FUNDAMENTAL FOR PERFORMANCE to have reasonable site model files, i.e. you should not compute the hazard at the location of every single asset, but rather you should use a variable-size grid fitting the exposure.

The engine provides a command oq prepare_site_model which is meant to generate sensible site model files starting from the country exposures and the global USGS vs30 grid. It works by using a hazard grid so that the number of sites can be reduced to a manageable number. Please refer to the manual in the section about the oq commands to see how to use it, or try oq prepare_site_model --help.

For reference, we were able to compute the hazard for all of South America on a grid of half million sites and 1 million years of effective time in a few hours in a machine with 120 cores, generating half terabyte of GMFs.

Then you will have 13 different risk files with a format like the following:

$ cat job_Argentina.ini
calculation_mode = event_based_risk
exposure_file = Exposure_Argentina.xml
structural_vulnerability_file = vulnerability.xml
...
$ cat job_Bolivia.ini
calculation_mode = event_based_risk
exposure_file = Exposure_Bolivia.xml
structural_vulnerability_file = vulnerability.xml
...

It should be mentioned that you are not forced to use a risk file for each contry. In theory you could run the entire South America in a single calculation, by simply specifying the exposure_file as follows:

exposure_file =
  Exposure_Argentina.xml
  Exposure_Bolivia.xml
  ...

The engine will automatically build a single asset collection for the entire continent of South America. In order to use this approach, you need to collect all the vulnerability functions in a single file and the taxonomy mapping file must cover the entire exposure for all countries. Moreover, the exposure must contain a field specifying the country (in GEM’s exposure models, this is typically encoded in a field called ID_0). Then the aggregation by country can be done with the option

aggregate_by = ID_0

There are however disadvantages of the single file approach:

  1. if only the exposure of a country change, and not the others, you still have to recompute everything

  2. for continental scale calculations it is very likely to run out of memory, so splitting by contry can be the only viable option.

Sometimes, one is interested in finer aggregations, for instance by country and also by occupancy (Residential, Industrial or Commercial); then you have to set

aggregate_by = ID_0, OCCUPANCY
reaggregate_by = ID_0

reaggregate_by is a new feature of engine 3.13 which allows to go from a finer aggregation (i.e. one with more tags, in this example 2) to a coarser aggregation (i.e. one with fewer tags, in this example 1). Actually the command oq reaggregate has been there for more than one year; the new feature is that it is automatically called at the end of a calculation, by spawning a subcalculation to compute the reaggregation. Without reaggregate_by the aggregation by country would be lost, since only the result of the finer aggregation would be stored.

Caveat: GMFs are split-dependent#

You should understand that splitting a calculation by countries is a tricky operation. In general, if you have a set of sites and you split it in disjoint subsets, and then you compute the ground motion fields for each subset, you will get different results than if you do not split.

To be concrete, if you run a calculation for Chile and then one for Argentina, you will get different results than running a single calculation for Chile+Argentina, even if you have precomputed the ruptures for both countries, even if the random seeds are the same and even if there is no spatial correlation. Many users are surprised but this fact, but it is obvious if you know how the GMFs are computed. Suppose you are considering 3 sites in Chile and 2 sites in Argentina, and that the value of the random seed in 123456: if you split, assuming there is a single event, you will produce the following 3+2 normally distributed random numbers:

>>> np.random.default_rng(123456).normal(size=3)  # for Chile
array([ 0.1928212 , -0.06550702,  0.43550665])
>>> np.random.default_rng(123456).normal(size=2)  # for Argentina
array([ 0.1928212 , -0.06550702])

If you do not split, you will generate the following 5 random numbers instead:

>>> np.random.default_rng(123456).normal(size=5)
array([ 0.1928212 , -0.06550702,  0.43550665,  0.88235875,  0.37132785])

They are unavoidably different. You may argue that not splitting is the correct way of proceeding, since the splitting causes some random numbers to be repeated (the numbers 0.1928212 and -0.0655070 in this example) and actually breaks the normal distribution.

In practice, if there is a sufficiently large event-set and if you are interested in statistical quantities, things work out and you should see similar results with and without splitting. But you will never produce identical results. Only the classical calculator does not depend on the splitting of the sites, for event based and scenario calculations there is no way out.

For instructions on generating, reusing, and inspecting an SES datastore, see Stochastic event sets and calculation workflows.

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