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Why do Insurers Prefer longer Return Period Design?

In the context of natural catastrophes (NatCats), the return period refers to the average time interval or frequency at which a specific event, such as a flood, hurricane, or earthquake, is expected to occur or be surpassed in a given location. It is a statistical concept used to assess the likelihood and recurrence of extreme events.

How does Return Period Work?

The return period is often expressed in years and is derived from historical data, mathematical models, and statistical analysis of past occurrences. For example, if a flood has a return period of 50 years in a certain area, it means that, on average, a flood of similar magnitude is expected to occur once every 50 years in that location.

It is important to note that the return period does not guarantee that an event will occur exactly at the specified interval. It is a statistical estimation that provides a measure of the event’s frequency and helps in assessing risk, designing infrastructure, and determining insurance premiums. Higher return periods indicate less frequent but more severe events, while lower return periods suggest more frequent but less severe events.

Insurers’ View

The return period is a key parameter used in risk assessment, emergency planning, and decision-making processes related to infrastructure development, land-use planning, and insurance coverage in areas vulnerable to natural hazards. By understanding the return period, stakeholders can better prepare for and manage the potential impacts of NatCat events.

How to determine the Return Period?

Determining the return period of a specific event typically involves analyzing historical data, conducting statistical analysis, and applying mathematical models. Here’s a general overview of the process:

  1. Data Collection: Gather relevant historical data related to the specific event of interest. This may include records of past occurrences, such as floods, hurricanes, or earthquakes, in the region of concern. The data should ideally cover a sufficiently long period to capture a representative sample of events.
  2. Data Analysis: Analyze the collected data using statistical techniques to assess the frequency and magnitude of the events. Common methods include plotting the data on a frequency-magnitude graph, fitting probability distributions, and calculating return periods based on the observed data.
  3. Probability Estimation: Apply statistical models, such as the Generalized Extreme Value (GEV) distribution or the Gumbel distribution, to estimate the probability of an event of a particular magnitude occurring in a given year. These models help extrapolate the data beyond the observed range and estimate the likelihood of extreme events.
  4. Return Period Calculation: Once the probability is estimated, the return period can be calculated by taking the reciprocal of the probability. For example, if a flood event has a 1% chance of occurring in a given year, its return period would be 1 divided by 0.01, resulting in a 100-year return period.

It is important to note that the accuracy of return period calculations depends on several factors:

  1. Data Quality: The accuracy of the return period estimation heavily relies on the quality, reliability, and representativeness of the historical data used. Longer and more comprehensive data sets tend to yield more accurate results.
  2. Statistical Assumptions: The accuracy of the return period calculation is influenced by the assumptions made in the statistical models used. The choice of distribution and the assumptions about stationarity and independence can impact the accuracy of the estimated return period.
  3. Climate Change Considerations: Return period calculations are based on historical data, which may not fully capture the potential impact of climate change. As climate patterns shift, the accuracy of return period estimates may be affected, and additional considerations may be required to account for future changes.
  4. Uncertainty: There is inherent uncertainty in return period calculations due to the limited sample size, assumptions made, and potential variability in extreme events. Return periods should be interpreted as estimates rather than precise predictions.

To improve the accuracy and reliability of return period estimates, it is common practice to combine historical data analysis with other approaches, such as climate modeling, expert judgment, and risk assessment techniques that consider future scenarios and uncertainties associated with climate change.

Case Study: Limitations of accurately estimating return periods

Hurricane Harvey brought record-breaking rainfall and caused widespread flooding in the Houston metropolitan area. The event was unprecedented in terms of its rainfall intensity and the resulting flooding. Many areas experienced flooding that exceeded the estimates based on historical data and return period calculations.

The estimated return period for extreme rainfall events in the Houston region was based on historical data and statistical analysis. However, Hurricane Harvey’s rainfall far exceeded the estimated return period, catching many residents, emergency responders, and even experts by surprise.

Several factors contributed to the inaccuracy of the estimated return period:

  1. Climate Change: The estimates were based on historical data, which did not fully account for the potential impact of climate change on extreme weather events. Climate change can intensify rainfall patterns and increase the likelihood of extreme events, leading to underestimation of return periods.
  2. Limited Data: The estimation relied on historical records of rainfall events, but the available data may not have captured rare or extreme events that occurred before the recorded period. This limited data set can result in underestimating the true return period.
  3. Infrastructure and Land Use Changes: The estimates did not fully consider changes in infrastructure, urban development, and land use patterns that can affect rainfall runoff and flood risk. These changes can alter the hydrological characteristics of an area and impact the frequency and magnitude of flooding events.

The case of Hurricane Harvey demonstrates the challenges of accurately estimating return periods, particularly in the face of changing climate patterns and evolving environmental conditions. It underscores the need for ongoing data collection, improved modeling techniques, and consideration of climate change factors to enhance the accuracy of return period estimations and effectively manage the risks associated with natural catastrophes.

Few More Examples in Asia

  1. Uttarakhand Floods, India (2013): In June 2013, the state of Uttarakhand in northern India experienced devastating floods and landslides triggered by heavy rainfall. The event resulted in significant loss of life and infrastructure damage. The estimated return period for such extreme rainfall events in the region was based on historical data, but the severity and magnitude of the 2013 floods exceeded the estimated return period. Climate change, inadequate infrastructure, and land use changes were some of the factors that contributed to the inaccuracy of the estimation.
  2. Typhoon Haiyan, Philippines (2013): Typhoon Haiyan, one of the most powerful typhoons on record, struck the Philippines in November 2013. It caused widespread destruction, particularly in the central part of the country. The intensity and impact of Typhoon Haiyan exceeded the estimated return period for typhoons in the region. The estimation was based on historical data, but the storm’s unprecedented strength and the vulnerability of the affected areas led to significant underestimation of the event’s severity.

These examples highlight the challenges of accurately estimating return periods for natural events, even in highly vulnerable regions.

Factors such as climate change, inadequate historical data, and evolving environmental conditions can lead to inaccuracies in estimations. It emphasizes the importance of continuously updating and improving risk assessment methods, considering climate change impacts, and implementing adaptive measures to manage the risks associated with natural catastrophes.