It is applicable, but the shapes of the probability distributions used for approximation are quite different. They could include gamma, Pearson type 3, or lognormal but I believe the most common is the Gumbel type 2 (maxima) extreme distribution. Also you need to have a good understanding of what a mean return period is: the mean recurrence interval between events in for a very large number of realisations. In other words an event with 100 years return period will lean to about 1000 realisations in 100 000 years (or 10 000 in 1000 000 years if no change in nature occurs) but for shorter time frames it maybe seem stochastic.

Hi Ijaz Ahmad,

Yes, Average Recurrence Interval (ARI), more commonly known as Return Period, is a completely general technique which can be used to model any type of natural phenomenon. The aim of the technique is to calculate the maximum level of the phenomenon which can be exceeded in any single year. Thus when we say that the annual probability of occurrence of some flood in a given region is 0.02 we are saying that the ARI of this event is 1 in 50 years or that the phenomenon is expected to be exceeded on average once every 50 years. The word 'average' must be stressed here, it does not mean that the phenomenon is exceeded exactly every 50 years.

If you have observations from recording stations the usual way to calculate ARI

is by fitting an Extreme Value Distribution to your observations.

A good reference to this topic is: Coles, S., 2001. An Introduction to Statistical

Modeling of Extreme Values. Springer, London.

Regards,

Augusto

It is an interesting question, and I am not an expert on earthquake risk.  Earthquakes tend to be dependent on the local geology, stratigraphy, plate tectonics.  The location of storm severity is less dependent on surface or subsurface features, more dependent on jet stream and air circulation patterns interacting with as they move over surface features.  Flood recurrence intervals are based primarily on storm data collected or historic records that can be retrieved, and to some degree, this may be the same as for earthquakes.  Since I am not an earthquake expert, I would hesitate to say that they are directly comparable, even though with enough data, the tools to plot flood and earthquake frequencies and severities for a location may be the same or similar.  The time series for floods seems like it is apt to be more frequent and widespread within a climactic zone, while earthquakes frequency and severity tend to be more localized within defined zones, less frequent for most areas, but also with connected aftershocks for a period of time that are related to the initial severe earthquake event.  As long as you understand the differences, the basic methods to assess risk exist, but it should be noted about the average comment made is often applied to data, so 50% of data could be above, 50% below the trend lines.  Even though the lines may appear impressive on plots, the confidence limits help to assess the uncertainty, and should be greatly respected if severity is related to life and property damage.

I don't think it would be applicable because one of the main characteristics of earthquakes is that they cluster in time. In other words when you have a big one it creates aftershocks with a decaying rate in time. This is called the Omori law. So if you want to calculate an average rate (i.e recurrence interval) you have to first account for this clustering effect. This is called "declustering" and the current state of the art is to model the earthquake sequence with a point-process called ETAS (Epidemic Type Aftershock Sequence).

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