GAR 2015 Main Report

Table 3.1 Probabilities for different return periods

Figure 3.3 Sensitivity of results to input data

(Source: UNISDR.)

(Source: CIMNE-INGENIAR, 2014.)

Probability refers to the frequency of occurrence or the return period of losses associated with hazardous events. The concept of return period is often misunderstood. If a loss has a 500-year return period, this does not mean that the loss occurs every 500 years. If the loss occurred today, it does not mean that it will not recur for another 500 years. What it really means is that on average it occurs once every 500 years.

For example, if there were records of losses of different intensities over 1,000 years (Figure 3.2), it can be seen that nine losses exceeded an intensity of 60 over that period. The intervals between these losses fluctuate between 60 and 200 years. However, losses of an intensity of 60 were exceeded every 100 years on average, and that is the return period. Expressed in a different way, the annual probability of a loss exceeding an intensity of 60 is 0.1 per cent.

The PML for different return periods can therefore be expressed as the probability of a given loss amount being exceeded over different periods of time (Table 3.1). Thus, even in the case of a thousand-year return period, there is still a 5 per cent probability of a PML being exceeded over a 50-year time frame. This metric is relevant, for example, to the planners and designers of infrastructure projects, where investments may be made for an expected lifespan of 50 years.

In the development of risk models, many different data sets are used as input components. The level of uncertainty is directly linked to the quality of the input data. On many occasions during model development, expert judgment and proxies are used in the absence of empirical data, and the results are very sensitive to most of these assumptions and variations in input data. As such, all the AAL and PML figures presented in this chapter should be considered indicators of the order of magnitude of the risks, not as exact values.

For example, PML curves developed with the same hazard and exposure models change when different vulnerability functions are used, although the order of magnitude remains the same (Figure 3.3). Better data quality and advances in science and modelling methodologies reduce the level of uncertainty, but it is crucial to interpret the results of any risk assessment against the backdrop of unavoidable uncertainty.

	57 Table 3.1 Probabilities for different return periods Figure 3.3 Sensitivity of results to input data (Source: UNISDR.) (Source: CIMNE-INGENIAR, 2014.) Probability refers to the frequency of occurrence or the return period of losses associated with hazardous events. The concept of return period is often misunderstood. If a loss has a 500-year return period, this does not mean that the loss occurs every 500 years. If the loss occurred today, it does not mean that it will not recur for another 500 years. What it really means is that on average it occurs once every 500 years. For example, if there were records of losses of different intensities over 1,000 years (Figure 3.2), it can be seen that nine losses exceeded an intensity of 60 over that period. The intervals between these losses fluctuate between 60 and 200 years. However, losses of an intensity of 60 were exceeded every 100 years on average, and that is the return period. Expressed in a different way, the annual probability of a loss exceeding an intensity of 60 is 0.1 per cent. The PML for different return periods can therefore be expressed as the probability of a given loss amount being exceeded over different periods of time (Table 3.1). Thus, even in the case of a thousand-year return period, there is still a 5 per cent probability of a PML being exceeded over a 50-year time frame. This metric is relevant, for example, to the planners and designers of infrastructure projects, where investments may be made for an expected lifespan of 50 years. In the development of risk models, many different data sets are used as input components. The level of uncertainty is directly linked to the quality of the input data. On many occasions during model development, expert judgment and proxies are used in the absence of empirical data, and the results are very sensitive to most of these assumptions and variations in input data. As such, all the AAL and PML figures presented in this chapter should be considered indicators of the order of magnitude of the risks, not as exact values. For example, PML curves developed with the same hazard and exposure models change when different vulnerability functions are used, although the order of magnitude remains the same (Figure 3.3). Better data quality and advances in science and modelling methodologies reduce the level of uncertainty, but it is crucial to interpret the results of any risk assessment against the backdrop of unavoidable uncertainty.	Page 1 Page 10 Page 20 Page 30 Page 40 Page 47 Page 48 Page 49 Page 50 Page 51 Page 52 Page 53 Page 54 Page 55 Page 56 Page 57 Page 58 ->Page 59 Page 60 Page 61 Page 62 Page 63 Page 64 Page 65 Page 66 Page 67 Page 68 Page 69 Page 70 Page 71 Page 80 Page 90 Page 100 Page 110 Page 120 Page 130 Page 140 Page 150 Page 160 Page 170 Page 180 Page 190 Page 200 Page 210 Page 220 Page 230 Page 240 Page 250 Page 260 Page 270 Page 280 Page 290 Page 300 Page 310