Method & Results

Motivation

Probabilistic Forecasting

Loss Estimation Probabilistic Loss Assessment

Literature

Communicating time-varying seismic riskduring an earthquake sequence

Discussion & Conclusion

Marcus Herrmann1, J. Douglas Zechar1, Stefan Wiemer1

1 Swiss Seismological Service, ETH Zürich, Switzerland;  marcus.herrmann@sed.ethz.ch

SEISMICRISK

Long-term

e.g., building code,excercises

Socio-EconomicIMPACT

loss + damage(human, structural, financial)

earth structureearthquakessoil

creates

SEISMICHAZARD

EXPOSURE

VULNERABILITYbuildings population

MITIGATIONShort-term

e.g., evacuation,communication

MITIGATION

acts on

results in

²

²

²

expresses

may

modify

may

modify

may

redistribute

may

redistribute

estimates of whichmay drive

estimates of whichmay drive

estimates of whichmay drive

estimates of whichmay drive

Schematic overview of the components of risk and where mitiga-tion actions may inter-vene. The red part is subject in the present work. Importantly, the figure illustrates that hazard and risk are not interchangeable.

Basel

Switzerland

Germany

France

0 10 20 km

0 1 2 3 4 5 6 7 8 9 101234567

Time after mainshock [days]

Mag

nitu

de

MainshockAftershocks

Foreshocks

7 8 9 10 110

1

2

3

4

5

Fata

lities

(m

ean)

[% o

f pop

ulat

ion]

EMS Intensity7 8 9 10 110

5000

10000

15000

Sum

med

fata

lities

EMS Intensity

City of Baselouter zone

most probable valueprobable boundpossible bound g VI uncertainties

1.61.5

5250

1031910

Cost–Benefit Analysis

Probabilistic Loss Curve (PLC)

1 min after mainshock:

When people and their environment are not properly prepared, earthquakes pose a serious threat. Unfortunately, earthquakes cannot be predicted. The scientific and engineer-ing community therefore attempt ways to reduce adverse effects of future disasters by mitigating the potential socio-economic impact and improving the resilience of the community. Recently, an earthquake scenario exercise (SeiSmo-12) simulated a repeat of the 1356 Basel earthquake. We use the simulated earthquake catalog to explore how we might provide decision support and information throughout such a crisis and ongoing seismic sequence. Specifically, we describe a method that extends recent devel-opments in short-term seismicity forecasting: by including seismic risk* assessment, we can make predictive statements about earthquake consequences on short-term. To support decision-makers and inform the public, we employ cost-benefit analysis and consider a short-term mitigation action: evacuating people in vulnerable buildings. A proposed warning approach may eventually facilitate communication.*Seismic risk delivers a more direct expression of the socio-economic impact than seismic hazard, but one must char-acterize vulnerability and exposure to estimate risk; risk assessment brings together a variety of data, models, and assumptions; but it also introduces further sources of uncertainty.

Based on information of the continuous seismic activ-ity (potential clustering), the Short-Term Earthquake Probability (STEP) model [2] produces time-varying rate forecasts of seismicity (≠ “prediction”). STEP, an aftershock forecasting framework, fundamentally relies on two empirical relations: Gutenberg–Rich-ter and Omori–Utsu. Probabilistic forecasts show in-creasing benefit for short-term defense against earth-quakes [3]. STEP subsequently converts forecast rates to short-term seismic hazard—expressing the earth-quake forecast in terms of ground motion. This re-quires PSHA (probabilistic seismic hazard assessment), using a suitable attenuation relation (Cauzzi et al. 2014, next-gen Swiss ShakeMap) and site amplifica-tion factors of ground shaking. To generate complete maps, we calculate background rates and background hazard beforehand.

Seismicity

With the loss estimation (left box ←) and the short-term hazard (upper box ↑) issued at the same EMS intensities [5] (shaking levels), we can quantify the short-term seismic risk—varying in time and for each district. We do that by constructing so-called probabilistic loss curves (PLC) for each settlement which allow us different expressions of the risk: for example

(1) the probability of an individual dying Pindiv —as shown varying in time for three regions in the Figure on the right;

(2) fixing the probability of exceedance to, e.g., 10 % and visualize the corresponding loss esti-mate in a map to get a spatial impression of the risk.

We use the loss estimation routine of Qlarm [4] for estimating the time-invariant loss for a range of EMS [5] intensity levels. This uncovers the sensitivity of buildings and their inhabitants to ground shaking independent of any seismic event—a vulnerability analysis in the proper sense.

for each settlement

But what does the quantified risk mean for deci-sion-makers or the public? They get challenged by the low-probabilities and the complexity of involved processes. Solving the decision problem requires an agreement on the acceptable risk Paccept—a condition that can be assumed as “safe.” To find this level, we employ cost–benefit analysis (CBA) [1] and compare the socio-economic costs of an evacuation with its benefits (i.e. rescuing lifes).To facilitate the commmunication of results in a crisis for support, we proposed an alarm lev-el scale that correllates with the "cost-effective-ness" (Pindiv/Paccept).Note: Such an economic perspective might be adequate for decision-makers, but not for an individual.

Settlement stock(representative)

To estimate the potential loss, we first need information about the elements at risk: settlement data for each district and municipality provides population numbers and a building in-ventory according to a known building typology [5].

Distribution of building classesZoning of regional settlements

Vulnerability analysis using Qlarm

Vulnerability of buildings; showing the relative amount of buildings that will at least experience “extensive damage” [5]—assuming that a fixed intensity of EMS IX is experienced in each settlement.

Vulnerability of people within buildings; showing the relative amount of inhabi-tants that will remain uninjured in case of experienced EMS IX in each settlement.

Fatality curves; uncertainties originate from linguistic (qualitative) definitions in EMS-98 (e.g., “many”, “most”, “few”). Left: Summed over zone-related settlements in the Basel zone and outer zone. Right: Normalized by the settlement’s population and averaged for each of the two zones.

van Stiphout, T., S. Wiemer, and W. Marzocchi (2010). “Are short-term evacuations warranted? Case of the 2009 L’Aquila earthquake”. Geophysical Research Letters, 37(6), 1–5. doi:10.1029/2009GL042352

Gerstenberger, M. C., S. Wiemer, L. M. Jones, and P. A. Reasenberg (2005). “Real-time forecasts of tomor-row’s earthquakes in California”. Nature, 435(7040), 328–331. doi:10.1038/nature03622

Jordan, T. H., Y.-T. Chen, P. Gasparini, R. Madariaga, I. Main et al. (2011). “Operational Earthquake Fore-casting: State of Knowledge and Guidelines for Implementation”. Annals of Geophysics. doi:10.4401/ag-5350

Trendafiloski, G., & M. Wyss (2009). “Loss estimation module in the second generation software Qlarm”. Sec-ond International Workshop on Disaster Casualties 15-16 June 2009 (pp. 1–10).

Grünthal, G. (1998). “European Macroseismic Scale 1998”. Luxembourg: European Seismological Commission

ABCDEF

EMS-98Vulnerability

Classes

uncoversettlements where

evacuations are cost-effective

Short-term loss forecast

(for the next 24 hours, issued 3 days after the M6.6)

PSHA(probabilistic seismic hazard assessment)

* every forecast refers to the next 24 hoursThe time designation in the upper left corner of each forecast is relative to the mainshock’s occurrence.

–1 min

+1 min

+24 hours

+7 days +7days

+24 hours

+1min

− 1min using an intensity predic-tion equation (IPE) and distant-dependent sigma; site amplification factors are considered

EMS 9

Amou

nt o

f bui

ldin

gs e

xcee

ding

dam

age

grad

e 3

[%]

20

25

30

35

40

45

50max

min

5244

45

4730

3638

35

3741

33

37

3940

343140

2333

27

31

3124

28

24

33

34

26

3025

28

26

3731

27 33

26

32

24

25

25

25

27

24

27

25

26

21

25

27

26

23

25

30

26

27

31

29

28

28

33

29

32

29

24

29

27

29

31

31

30

31

26

19

27

32

29

27

31

Unin

jure

d pe

ople

[% o

f set

tlem

ent p

opul

atio

n]

91

92

93

94

95

96

97

98max

min

9192

92

9296

9594

96

9594

95

95

9394

969693

9795

98

96

9798

97

98

96

96

98

9798

97

97

9597

98 96

98

96

98

98

98

97

97

97

97

97

97

98

97

97

97

97

97

97

98

98

97

97

97

97

96

97

97

97

98

97

98

98

97

97

97

97

98

98

97

96

97

97

97

EMS9

Hazard forecastRate forecast

[1]

[2]

[3]

[4]

[5]

•District-wise and even building-wise mitigation actions improve the cost-effectiveness considerably, but may be hardly practicable up to now

→ More effort on e.g., emergency system, building information and monitoring•Whether an evacuation can be justified before a possibly impending main-

shock depends largely on the size of the foreshocks•Main limitation: current short-term forecasting models—the basis of our risk as-

sessment—are not particularly skillful at forecasting large events•Evacuations are recommended everywhere after the M6.6

•At later times, alarm levels vary among the region, making our approach partic-ularly relevant for rescue and recovery efforts

•The earthquake scenario only approximates the historic 1356 event; nobody knows about the details of the next earthquake in the region

•But our spatial risk estimates can give a conditional indication of the relative seismic risk among the localities

•We consider location-dependent and time-varying alarm levels to add val-ue to personal decision-making and raising public risk awareness

•Our method can be applied in any region, provided sufficient data (inventory, etc.)

red color indi-cates a justified (cost-effective)

mitigation action(evacuation)

10 -3 10 -2 10 -1 100

Probability of exceeding EMS VI in 24h10 -6 10 -5 10 -4 10 -3 10 -2 10 -1 100

Seismicity rate for M ̧ 3.0 in 24h

download this posteras PDF

Germany

7.4 7.5 7.6 7.7

47.5

47.6

47.7

City of Baselouter zone

Switzerland

France Germany

0 10 20 30 40 50 60 70 80 90 100 110 12010−5

10−4

10−3

10−2

10−1

100

Prob

abili

ty o

f exc

eeda

nce

in 2

4h

Estimated fatalities

+1 minafter M6.6

EMS 11

EMS 10

EMS 9EMS 9.5

EMS 8.5

EMS 10.5

extract for each district

Pindiv = ∫ PLCNpeople

~ AlarmPindiv

Paccept

Paccept = CostLoss

expectedfatalities

settlementpopulation

... of evacuatinga person

value of ahuman life

individual riskof dying

acceptablerisk

Fatalities0 100 200 300 400 500 600 700 800 900 1000Pr

obab

ility

of e

xcee

danc

e in

the

nex

t 24

h

10 -5

10-4

10-3

10-2

10-1

100

Whole regionCity of BaselSurrounding region

M6.6

M6.5

M6.0

M5.5

M5.0

M6.6M6.6

PLCs express the probability of exceeding certain losses. The five PLCs (black) display the daily risk for the whole region immediately after the corresponding magnitude. Regional PLCs (orange and blue) are shown for the time 1 minute after the mainshock

Fata

lity

num

ber t

o be

exc

eede

dfo

r a p

roba

bilit

y of

10

% in

24h

0.1

1

10

60

108

2054

173085

33

5930

4352

121412949

315

20

70

263

2

9

69

17

18

1710

7

5

5544

31 25

32

28

4

17

4

2

3

2

1

21

4

25

1

1

1

18

1

1

2

4

0

3

12

0

8

1

4

6

1

1

1

1

2

11

1

11

1

0

1

2

1

396431736

City of Basel:Surrounding area:

All:

+1 minafter M6.6

+3 daysafter M6.6

0

1

2

3

4

Alar

m le

vel

“cos

t-effe

ctive

to e

vacu

ate”

threshold

The curvy look is because we (1) issued loss estimation and hazard forecast at distinct intensities; and (2) incorporat-ed uncertainty information from the loss estimation (→ smoothing)

Time after mainshock [hours]-4 0 6 12 18 24

Indi

vidu

al p

roba

bilit

y of

dyi

ng

10-8

10-7

10-6

10-5

10-4

10-3

10-2

10-1

Time after mainshock [days]100 101 102 103 104 105 106

Tota

l for

ecas

t ra

te (

M>

3.0)

10-4

10-3

10-2

10-1

100

101

102

103

1234567

234567

MainshockAftershocks

Foreshocks

Mag

nitu

de

1 m

onth

1 ye

ar

10 y

ears

100

year

s

1000

yea

rs

City of Basel

Risk forecast

Outer zone

Cost–bene�t threshold

Total rate M > 3.0Seismicity forecast

Wholeregion}

(individual probability of dying)

1

Time-varying risk probabilities (colored curves) and seismicity rates (gray line, right axis) using in-formation available at that time (retrospective). The risk curves indicate the probability that any inhabitant will die in the next 24 hours.