Elective Exercises

All the exercises in this section can be chosen and done in any order. You need to fully answer at least one of them.

Exercise 1 : Manipulate the impact matrix

Save your script in a jupyter notebook impmat_YOUR_NAME.ipynb.

  • Extract the impact matrix and the frequency array from the Impact object used in the mandatory exercise 1.

  • Compute the average annual aggregated impact using this data.

  • Compute also the maximum, average and standard deviation of the impact over all exposures points and all events.

Tip: the impact matrix is obtained as impact.imp_mat and the frequency as impact.frequency

Tip: the impact matrix is stored as a sparse matrix as implemented in the scipy module. The scipy.sparse.csr_matrix object can be manipulated much like a normal matrix and can be converted to a numpy 2-d array if needed. Sparse matrices are used to reduce the amount of require memory when a lot of entries are identical (for example 0).

Exercise 2 : Use future scenario

Save your script in a jupyter notebook future_YOUR_NAME.ipynb.

Based on the hazard, exposures and impact functions from Exercise 1 we shall “simulate” a future scenario.

Simulate climate change.

In principle, this requires the a combination of complex physical models (for instance Global Circulation Models) to generate future climate scenario (time series of physical variables such as temperature, precipitation, wind speed) and hazard models (for instance hydrological flood models to translate precipitation information into flood event). This would go way beyond this course.

A simpler approach is to consider scenarios which might be only loosely based on physical modelling, or make use of previous scientific studies that evaluated change in intensity and frequency of hazard with the previous approach.

This allows for ‘what-if’ types of questions:

  • Multiply the storm intensity with 10%. Multiply the storm frequency with 5%.

  • How does the impact change? Is this a good way to model climate change?

  • Find values in the literature that give estimates for the change in intensity AND frequency of European winter storms? Can you implement these changes?

Simulate future exposure

Just like for future climate, evaluating how exposure will change in the future requires a complex projection process based on economic and demographic models.

Estimating the change in money-value for the exposures can be proxied by the change in macro-economical indicators such as the GDP.

  • Look for long-time GDP change data and estimate the growth until 2050.

  • Multiply the exposures asset value by this value. How does the impact change?

  • Is GDP a good measure of asset-value growth?

  • How would you model future exposure?

Simulate vulnerability change.

This represents how the response to the hazard changes. It can include adaptation measure (e.g. early warnings systems that allow people to prepare and better protect themselves), or increase in vulnerability (a country might be subject to an economic crisis which leads to a decrease in financial means to cope with natural hazards which increase the vulnerability).

It can also represent behaviour change or difference: Someone that has never seen a tsunami will not recognize it and not move away from the ocean when the water retreats. While a village that has experienced a tsunami will have a memory, will recognize the early signs and move to safety.

  • Modify the impact function to a high-vulnerability curve and a low-vulnerability curve

  • How does the impact change?

Tip : You can change the impact (or vulnerability) functions by directly assigning/changing the paa or mdd attributes of an ImpactFunc object. For instance, if you want to decrease the percentage of affected assets (PAA) of the impact function of Exercise 1 by 10%, you could use this code

impf_reduced_paa = ImpfStormEurope.from_welker()
impf_reduced_paa.paa *= 0.9

Exercise 3 : Use another hazard

Save your script in a jupyter notebook other_hazard_YOUR_NAME.ipynb.

Can you, using all the resources available in the CLIMADA documentation, do a climate risk study as showed above for a different country (for example Mozambique, Vietnam, Bolivia, Japan, …) and a different hazard (for example river flood, tropical cyclones, wildfires, …) than presented here?

Tip : Many hazards and exposures exist precomputed on the CLIMADA API.