This matters because return periods underpin design standards, hazard maps, planning rules and investment cases across climate and natural hazard events. When the people making those decisions read “1 in 100” as “once a century”, they systematically underestimate how likely the event is within the timeframe they are actually responsible for. You can test any combination of event and timeframe in our Return period simulator.

Why “1 in 100 year” is easy to misread

A 1 in 100 year event is one with a 1% annual exceedance probability (AEP). In any given year, there is a 1% chance that an event of that size or larger will occur. The probability applies to each year independently, so a flood this year does not make one next year any less likely.

A useful comparison is rolling a 100 sided die once a year. You might roll a 100 twice in a row, or go 300 years without one. Over a very long record, the rolls average out to one in every 100 years, which is where the term “return period” comes from. Over the 10, 30 or 50 years that most decisions cover, that long-run average says little about what will actually happen.

The wording creates two common misreadings. The first is that a 1 in 100 year event is unlikely to occur in our lifetime. The second is that once one has occurred, the next is a century away. Both lead to decisions that carry more risk than the people making them realise.

How likely an event is over the life of a decision

The chance of at least one event over a period of n years follows from the annual probability:

P = 1 − (1 − AEP)ⁿ

For a 1% AEP event over 30 years, P = 1 − 0.99³⁰ = 0.26, or about 26%. The table applies the same calculation to the timeframes that shape common decisions.

The Return period simulator runs this calculation for any event and decision timeframe, and simulates 1,000 possible futures to show how outcomes can vary.

What this changes for decisions

The right timeframe depends on who is deciding and what they are committing to. The same 1% AEP event looks very different through each lens.

• Governance and elected members. Over a three year term, a 1% AEP event has a 3% chance of occurring. That is low enough to feel deferrable, yet the infrastructure and land-use decisions made in that term will carry the risk for decades.

• Long-term and asset management planning. Over a 10 year Long-Term Plan cycle, the chance is close to one in ten. Across a programme of many assets or places, at least one being affected becomes far more likely again.

• Infrastructure design. An asset designed to a 1% AEP standard with a 100 year life has around a 63% chance of meeting its design event. The design standard defines the event the asset must withstand, not an event it is unlikely to see.

• Property and development. Over a 30 year mortgage, a 1% AEP flood has around a one in four chance of occurring. For homeowners, lenders and insurers, that is a material risk, and it explains why property-level hazard information draws so much attention.

In each case, the useful question shifts from “how rare is this event?” to “how likely is it within the period we are responsible for, and what would it mean if it happened?”

Where AEP and ARI part ways

The average recurrence interval (ARI) is the long-run average time between events of a given size. For rare events, ARI and 1 in x AEP are almost identical: a 1 in 100 AEP event has an ARI of 99.5 years. For frequent events, they separate, because more than one event can occur in the same year. A 50% AEP (1 in 2) event has an ARI of about 1.44 years, not two.

This is one reason Australian Rainfall and Runoff moved towards AEP and exceedances per year (EY) as its preferred terms. EY is the average number of times an event is equalled or exceeded each year, and it is most useful for frequent events such as those expected several times a year. The exact relationships are EY = −ln(1 − AEP) and ARI = 1 / EY.

For practitioners, the practical rule is to use AEP when communicating rare events and to check conversions carefully for anything more frequent than about 5% AEP.

Pairing words with numbers

Planning documents often translate probabilities into verbal labels. The Ministry for the Environment’s coastal hazards guidance uses a scale consistent with IPCC guidance, where 1% or less is “exceptionally unlikely”, 10% or less is “very unlikely”, 33% or less is “unlikely” and 33 to 66% is “about as likely as not”.

The timeframe changes the label. A 1% AEP event is exceptionally unlikely in a single year, very unlikely over 10 years, unlikely over 30 years and about as likely as not over 50 years. A label quoted without its timeframe can therefore understate the risk by several categories.

Research by Peters et al. (2025) found that most people prefer to receive numbers when making decisions, and rate information that includes them as clearer and more useful. The most reliable approach is to state the timeframe, the percentage and the verbal label together: “a 26% chance over 30 years, which is unlikely but far from rare”.

When the annual probability itself is changing

Every figure in this article assumes the annual probability stays constant. For many climate and natural hazard events, that assumption is weakening. Sea-level rise, changing rainfall intensity and shifting storm patterns mean an event estimated as 1% AEP from historical records may become more frequent over the life of an asset.

Where that is the case, the chance of experiencing the event over 50 or 100 years will be higher than the table shows. Long-lived decisions therefore need hazard estimates for future conditions, regular review as modelling improves and, for adaptation, triggers that signal when a change of pathway is warranted.

From probability to consequence

Probability tells you how likely an event is within the life of a decision. It does not tell you what the event would mean. Two assets with the same 1% AEP exposure can carry very different risk if one serves a critical network function or isolates a community when it fails. The decision depends on those consequences: which facilities are damaged, who loses access and how long services are disrupted.

Resilience Explorer® connects climate and natural hazard evidence to consequences across assets, infrastructure, services and communities, so planning and investment decisions rest on what an event would mean as well as how likely it is. To see how return periods play out over the timeframes you work with, try the Return period simulator.

Frequently asked questions

What is the chance of a 1 in 100 year flood in 30 years?

About 26%, or roughly one in four.

Is a 1 in 100 year flood the same as a 1% AEP flood?

Yes. Both describe an event with a 1% chance of being equalled or exceeded in any year. AEP is generally preferred because it avoids implying the event happens on a schedule.

Can two 1% AEP events happen in consecutive years?

Yes. Each year carries the same 1% chance, regardless of what happened the year before.

What is the chance of a 1 in 100 year event over 100 years?

About 63%. Over a century, the event is more likely to occur than not.

What is the difference between AEP and ARI?

AEP is the chance of an event in any single year. ARI is the long-run average time between events. They are nearly equivalent for rare events but differ for frequent ones: a 50% AEP event has an ARI of about 1.44 years.

Does climate change affect return periods?

Yes. Where the frequency or intensity of climate and natural hazard events is changing, historical return periods may understate future likelihood, so estimates need regular review.

References

Ball, J. et al. (eds). (2019). Australian Rainfall and Runoff: A guide to flood estimation. Commonwealth of Australia.

Environment Canterbury. (2024). Flood frequency.

Intergovernmental Panel on Climate Change. (2010). Guidance note for lead authors of the IPCC Fifth Assessment Report on consistent treatment of uncertainties.

Ministry for the Environment. (2017). Coastal hazards and climate change guidance for local government, Appendix F: Hazard occurrence.

Peters, E., Blow, A., Chapman, D. A., and Shoots-Reinhard, B. (2025). The power of numbers in natural hazard communication.