report on garm

Risk Assessment – Garm (TJ)

FOUNDATIONS FOR RISK ASSESSMENT & MANAGEMENT PROJECT GROUP 4

2015/10/12

ERJAUTZ, JULIAN (19910410T432) GARRELS, LANA (19911209T385) MARTEY, MICHELLE (19891026T349) RÜRUP, ANNA (19910323T305) DO, ANH (19930120P225)

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Executive Summary The city of Garm, due to its geographical location and features, is subjected to a number of different

natural hazards. Three major risks to the people of Garm are identified to be: earthquakes, mudflows and

floods. Focusing on the main objective of analysing possible fatalities, these risk assessment quantifies the

magnitude of the three risks. Using the general framework as outlined in Tehler, 2015, this report

elaborates on the first five steps of risk assessment for the city of Garm: identify value/objective (fatalities),

describe the context/background, identify risk scenarios, analyse consequences and likelihoods and finally,

risk presentation. Details of the methodology, risk scenarios and their consequences and likelihoods,

calculations of societal and individuals risks as well as a sensitivity analysis are presented in the report.

The risk assessment carries out a number of calculations to determine the magnitude of the risks. To assist

understanding of the results, some key figures are included in the report. A map outlining the structural

model of the city is constructed to identify risk areas. Event trees for each of the risks are also presented

to identify risk scenarios and calculate their likelihood. As crucial content for the risk presentation, two

diagrams of Frequency-Number (FN) curves summarizes visually the consequences and probability of

those scenarios. Finally, a tornado diagram in the sensitivity analysis provides information about the

uncertainties in the risk assessment.

In summary, this assessment finds that the expected fatalities per year due to earthquakes is 230, floods

is 7 and mudflows is 7. However, these numbers are not the concluding statement. The uncertainties in

the model, completeness and parameters are very important in the justification of obtaining the expected

fatalities results. It is found that uncertainties in the frequency of occurrence of a small earthquake and

the possibility that an adobe building will collapse in a small earthquakes are two parameters that affect

the result of the assessment the most. The discussion chapter of the report provides insight to how these

numbers can be interpreted to estimate the level of hazards affecting Garm. Bringing together the

assumptions, limitations and uncertainties discussed throughout the report, the conclusion highlights

social and economic aspects that lead to different risk distributions in the city. In addition, based on this

rough risk assessment, suggestions for a more detailed analysis are presented.

With the quantification of 3 natural hazards in Garm, further decisions on risk reducing measure are made

possible – step 6 of the risk assessment framework. This is however not the scope of this report.

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Table of Contents

Executive Summary

1. Introduction ........................................................................................................................................... 1

1.1. Background & Purpose .................................................................................................................. 1

1.2. Content .......................................................................................................................................... 1

2. Methodology ......................................................................................................................................... 2

2.1. Procedure ...................................................................................................................................... 2

2.2. Assumptions .................................................................................................................................. 3

2.3. Limitations ..................................................................................................................................... 4

3. Scenarios and Likelihood ....................................................................................................................... 4

3.1. Structural Model............................................................................................................................ 4

3.2. Event Trees .................................................................................................................................... 5

3.2.1. Event Tree for Earthquakes ................................................................................................... 5

3.2.2. Event Tree for Floods ............................................................................................................ 5

3.2.3. Event tree for mudflows ........................................................................................................ 6

3.3. Table of Risk Scenarios .................................................................................................................. 6

4. Societal Risk ........................................................................................................................................... 7

4.1. Earthquakes ................................................................................................................................... 7

4.2. Floods ............................................................................................................................................ 7

4.3. Mudflows ....................................................................................................................................... 8

4.4. FN-Curves ...................................................................................................................................... 9

5. Individual Risk ...................................................................................................................................... 10

6. Sensitivity Analysis .............................................................................................................................. 11

7. Discussion and Conclusion .................................................................................................................. 14

8. Reference List ...................................................................................................................................... 16

Appendixes

Appendix 1 – Excel Sheet for Data and Calculations ................................................................................. A

Appendix 2 – Information on Construction Types in Garm ....................................................................... B

Appendix 3 – Hazard Frequencies ............................................................................................................. C

Appendix 4 – Large Version Structural Model ........................................................................................... C

Appendix 5 – Table of Scenario Summaries .............................................................................................. D

Appendix 6 – Individual Risks plotted on Map of Garm ............................................................................ E

Appendix 7 – Geographical Area Measurements in Google Earth Screenshots ........................................ F

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Table of Figures Figure 1 Heat map of earthquakes since 1973 near Tajikistan ..................................................................... 1

Figure 2 – General framework for risk assessment ...................................................................................... 2

Figure 3 - Structural model for the risk assessment in form of a map .......................................................... 4

Figure 4 – Event tree earthquakes ................................................................................................................ 5

Figure 5 – Event tree floods .......................................................................................................................... 5

Figure 6 – Event tree mudflows .................................................................................................................... 6

Figure 7 – Frequency-Fatalities-Curve of individual hazards in Garm ........................................................... 9

Figure 8 - Frequency-Fatalities-Curve of all hazards in Garm combined ...................................................... 9

Figure 9 – Individual Risks plotted on map of Garm ................................................................................... 10

Figure 10 – Tornado diagram displaying sensitivity analysis results ........................................................... 13

Appendix figure 1 - The probability that a building collapses, given a certain earthquake risk scenario ..... B

Appendix figure 2 – Population in different construction types ................................................................... B

Appendix figure 3 – Structural Model Map Garm ......................................................................................... C

Appendix figure 4 - IR plotted on Map of Garm ........................................................................................... E

Table of Tables

Table 1 – Assumptions made in the risk assessment process ....................................................................... 3

Table 2 – Example calculations of IR ........................................................................................................... 10

Table 3- Sensitivity Analysis Parameter & Variation ................................................................................... 12

Appendix table 1 – Estimated hazards frequencies ...................................................................................... C

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1. Introduction

1.1. Background & Purpose Tajikistan is a mountainous, landlocked country, with a stagnant economy and is considered one of the

poorest country in Central Asia (United Nations [UN], 2014). This risk assessment concerns the city of Garm

(or Gharm) in the Rhast Valley, next to the river Vakhsh. Although the river provides many economic

benefits, such as hydroelectric power and irrigation, it also poses a number of geological risks to the city.

Unusually high rainfall can cause rising water level in the river leading to floods that affect the population

area close to the river banks. Rain water can also saturate the ground and cause mudflows in areas further

away from the river at a higher elevation. In addition, the city is considerably close to an area of high

frequency of earthquakes as illustrated in the map in figure 1 (United Nations [UN], 2014).

The geological position and

economic situation may create

difficulties for evacuation and

rescue efforts, therefore an in-

depth understanding of the

risks can be crucial to ensuring

the safety of people in this

region.

1.2. Content Fulfilling this purpose, this risk

assessment follows the

general framework as outlined by

Henrik Tehler (2015) which will be

further introduced in chapter two, the methodology. The methodology also contains the motivations for

the assumptions that have been made. The values guiding the analysis are related to the prevention of

fatalities, which is the primary concern. Following the methodology, the context of the City of Garm as

described above is further elaborated on as a model, illustrated by a map showing the different threatened

zones at different geographical location. Based on this, the different risk scenarios are described. A

summary of the main risks that this assessment focuses on include:

Earthquakes: two analysed categories are small earthquakes, which have a magnitude of 6 or less

on the Richter scale and large earthquakes with a magnitude higher than 6 (UPSeis, n.d.). In case

of an earthquake, the whole city is exposed to the same peak acceleration and the type of

construction determines whether a house collapses. Please see appendix 2 for the expert

information provided on these building types. With the current level of rescue capacities, the

likelihood of dying in a collapsed house is 90% (Garm Case, n.d.).

Floods: according to the Garm Case (n.d.), a small flood is when the water level rises 1.5m above

normal water levels, and a large flood is when the water level is 3m above normal. The estimated

mortality rate is 2%.

Figure 1 Heat map of earthquakes since 1973 near Tajikistan with red indicating the most severe areas (maptd, 2011)

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Mudflows: a large mudslide is 250m wide and a small one is 150m wide. Mudflows flowing towards

the city destroy everything in its way. Therefore, the same mortality rate as for earthquake

collapsed buildings is chose, 90%. Further reasoning for this assumption is provided in the

methodology on page 6.

The estimated frequencies for these events are presented in appendix 3 and the values are expert

judgements on the frequency of occurrence of all three analysed hazard types.

Subsequently, the analysis quantifies the individual and societal risk of death of each of the above risks

caused by natural disasters with calculations involving the likelihoods and consequences of different risk

scenarios. The results of these calculations are then presented in a map of individual risks and different

FN-curves. Finally, a sensitivity analysis is conducted to examine the robustness of the analysis and

therefore provide a conclusion of the risk analysis. By quantifying these risks, an understanding of the

magnitude of such disasters for the city of Garm are gained and enable further risk reducing decisions.

2. Methodology

2.1. Procedure This chapter describes the project’s methodological framework. The overall aim of the analysis is to

answer the following three questions associated with risk assessment (Tehler, 2015, p.5):

• What can happen?

• How likely is it?

• What will the consequences be?

In order to successfully answer the

questions, the project is based on

the data provided by the case study

and the “General Framework for

Risk Assessment” by H. Tehler (see

figure 2), which was used to guide

the analysis in a structured manner

by working through the steps from

the value setting to the eventual

risk evaluation. The latter is not a

part of this project, but will be

addressed in the individual

assignments succeeding this

project.

The values of the risk assessments

(step one) were set by case study: the hazards were assessed with a focus on possible fatalities. Thus, the

threat for human life guided the analysis. Moreover, step two, the general context and system description

Figure 2 – General framework for risk assessment (Tehler, 2015)

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were mostly given by the case as well, and a corresponding google earth map was available, which served

as a structural model (chapter 3). Based on this context information, step three was carried out by the

constructions of scenario trees for all identified hazards (chapter 3). The frequencies and probabilities in

the trees (step three and four combined) were calculated from the given data based on the assumptions

presented below. Consequences as in step four and risk presentation in step five have been separated into

two focus areas: the societal risk and the individual risk for the people of Garm. The societal risk is

presented in form of FN-curves and expected consequences, which are according to the value set, the

expected fatalities. The individual risk were plotted on a map of Garm, which served as a structural model

previously. In order to enable the consequence calculations, the geographical sizes of Garm’s population

areas, threatened areas, and exposed areas were necessary, which were obtained by measurements from

Google Earth with the polygon ruler function. Eventually, the robustness of certain parameters were tested

through a sensitivity analysis and the variations were presented in a tornado diagram. Although not being

an explicit step of Tehler’s risk assessment framework, the sensitivity analysis assists the process of

choosing appropriate risk measures, as more knowledge about uncertain aspects is given. All necessary

calculations for the steps elaborated above were made in an excel sheet (see appendix 1). The necessary

assumptions to enable the risk assessment were carefully discussed and decided upon. Additional research

and referencing was conducted with guidance of the American Psychological Association’s referencing

style (American Psychological Association [APA], 2015).

2.2. Assumptions Certain assumptions were necessary for the assessment, as not all data was provided. These assumptions

as well as the factors provided by experts, for example the frequency intervals, were subject to the

sensitivity analysis to gain knowledge about the uncertainty related to these factors. The following

assumptions have been made:

Table 1 – Assumptions made in the risk assessment process

1 The expert-judged data provided in the case is reliable.

2 The average values of the expert data are adequate to obtain results that are as reliable as possible, but a sensitivity analysis is needed.

3 The measurements in google earth with the polygon ruler function contain the necessary level of detail. More detailed measurements would not have a significant impact on the consequences.

4 In order to obtain the exposed mudflow area measurements, simplified area length and width values were used, as displayed in appendix 7 on page vi. This was necessary in order to efficiently obtain the required measurements.

5 The analysed hazards are primary hazards and are not affected by a decrease in the rescue capacities due to being a secondary hazard. For example, the mudflows are assumed not to be a secondary hazard of an earthquake.

6 Therefore, the assumed mortality rate of mudflows is 90%, which is similar to the expert-judged mortality rate for earthquakes. The 10% of the affected population that can be rescued when buildings are destroyed (both assumed for earthquakes and mudflows) due to available resources might be less in case of one hazard being a secondary hazard initiated by the first one, as capacities are already used.

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7 It is also assumed that the data separation in day and night means that 50% of the 24 hour day is night and the other half is day. This is assumed for 365 days of a year, making no difference between weekends, holidays or special occasions. The impact that another day-night ration would have on the valued fatalities, is tested in the sensitivity analysis.

2.3. Limitations

In addition to the fact that all uncertainties and assumptions are naturally limitations of the project

outcome, some other constraints need to be considered. The three analysed hazards are only a choice of

risks for the city of Garm, as accidents, diseases or highly unlikely phenomena such as asteroids can also

be risks. Furthermore, the values could have been broadened by looking at health threats and economic

damage as well, but this would exceed the scope of the project. Moreover, the sensitivity analysis could

have been done with all factors that are possible to change and for all hazard types, but it was decided to

keep the analysis efficient and within the scope of the project. Finally, the assessment team did not have

any background information on the experts and therefore no certainty exists about how the experts

arrived at the provided numbers.

3. Scenarios and Likelihood This chapter focusses on the description of the specific scenarios and the corresponding probabilities of

their occurrence. In order to develop adequate the scenarios, the context and the situation need to be

sufficiently clarified. The context description in the introduction of this report is therefore illustrated by

the following map, which serves as a structural model for the assessment.

3.1. Structural Model The map in Figure 3 displays

the different population and

hazard affected areas of the

city of Garm as well as the

risk frontiers of floods and

mudflows. Derived from this

context, three scenario trees

for each hazard type were

developed. A larger version

of the model and its detailed

description, also showing the

various population areas is

included in appendix 4.

Figure 3 - Structural model for the risk assessment in form of a map

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3.2. Event Trees

As mentioned above, the analysis focusses on earthquakes, floods and mudflows. To appropriately display

the different scenarios and their frequency, the event tree method was chosen, which enables a

representation of the scenarios in a logical order and calculate the frequencies as well as probabilities.

The event tree for earthquakes and floods look similar in structure and contain four scenarios whereas the

mudflow tree includes twelve scenarios due to the different mudflow areas with different probabilities.

An indicating event, which differs from the optimal case S0, was taken and placed as a starting point. The

average frequency of the hazards was based on expert’s opinions, as explained above. Following this, each

event was divided into small and big events with their individual probability, followed by a division in day

and night with a probability of 0.5. The following calculation exemplifies the method used for each branch

of the scenario trees.

As an Example the calculation of Scenario 1: Earthquake F0.0825 * Small P0.909 * Day P 0.5 = S1 0.0375 = P Small earthquake during the day

3.2.1. Event Tree for Earthquakes

Figure 4 – Event tree earthquakes

3.2.2. Event Tree for Floods

Figure 5 – Event tree floods

Earthquake

F=0.0825/year

Earthquake

F=0.0825/year

Small

P=0.909

Small

P=0.909

day

P=0.5

day

P=0.5S1 = 0.0375/year

S2=0.0375/yearnight

P=0.5

night

P=0.5

S3=0.0038/year

S4=0.0038/year

Large

P=0.091

Large

P=0.091

day

P=0.5

day

P=0.5

night

P=0.5

night

P=0.5

Flood

F=0.305/year

Flood

F=0.305/year

Small

P=0.902

Small

P=0.902

day

P=0.5

day

P=0.5S5 = 0.1375/year

S6=0.1375/yearnight

P=0.5

night

P=0.5

S7=0.015/year

S8=0.015/year

Large

P=0.098

Large

P=0.098

day

P=0.5

day

P=0.5

night

P=0.5

night

P=0.5

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3.2.3. Event tree for mudflows

Figure 6 – Event tree mudflows

3.3. Table of Risk Scenarios

The table of Risk Scenarios in appendix 5 summarizes the results of the twenty risk scenarios displayed in

the trees above as well as the consequences, covered by the societal risk section (Chapter 4). The scenarios

are further evaluated in the following sections and the table serves as an overview of all essential factors

needed. The table shows the different scenarios, the areas which are affected by the hazards and the

estimated frequency of occurrence as well as the estimated number of fatalities.

Mudflow

F=0.118/year

Mudflow

F=0.118/year

Area 1

P=0.36

Area 1

P=0.36

small

P=0.71

small

P=0.71

day

P=0.5

day

P=0.5S9 = 0.015/year

night

P=0.5

night

P=0.5S10=0.015/year

large

P=0.29

large

P=0.29

day

P=0.5

day

P=0.5S11=0.00625/year

night

P=0.5

night

P=0.5S12=0.00625/year

Area 2

P=0.38

Area 2

P=0.38

small

P=0.67

small

P=0.67

day

P=0.5

day

P=0.5S13=0.015/year

night

P=0.5

night

P=0.5S14=0.015/year

large

P=0.33

large

P=0.33

day

P=0.5

day

P=0.5S15=0.0075/year

night

P=0.5

night

P=0.5S16=0.0075/year

Area 3

P=0.26

Area 3

P=0.26

small

P=0.82

small

P=0.82

day

P=0.5

day

P=0.5S17=0.0125/year

night

P=0.5

night

P=0.5S18=0.0125/year

large

P=0.18

large

P=0.18

day

P=0.5

day

P=0.5S19=0.00275/year

night

P=0.5

night

P=0.5S20=0.00275/year

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4. Societal Risk Societal risk is a measure of the number of people who might be affected by a hazard/risk (CCPS, 2000). It

can be expressed in terms of the frequency distribution of the hazard (F) and number of fatalities (N),

which can be combined to the form of an FN (frequency-number) curve as well as in terms of expected

consequences. The FN-curve is a plot of the cumulative frequency against consequences (number of

fatalities). Refer for all specific calculations to the excel sheet in appendix 1.

4.1. Earthquakes As explained in the context introduction, expert knowledge of earthquakes in Garm indicates that different

buildings have different probabilities of collapsing in the event of either a small or large earthquake and

90% of the people trapped in the collapsed building will die (see appendix 2 for further information).

This information is used to determine the fatalities from earthquakes in the different buildings, in different

population areas. For example: 50% of the Adobe buildings will collapse if a small earthquake occurs. Once

total number of people who are in Adobe buildings at night is established, the total number of fatalities

can be calculated as follows (refer to appendix 1 and 2 for the data).

Formula: Number of fatalities (N): N = Ae * D * M Ae = building types (all population in the type)

N(fatalities) Adobe building, small earthquake, night = 0.9*(0.5*6010) = 2704.5 People

This is calculated for all buildings in all scenarios (see appendix 1).

The expected consequence E(X), is the total number of expected fatalities of all risk scenarios. It is the sum

of the product of the each scenario’s frequency with the total number of fatalities in the particular

scenario.

Total Fatalities for all buildings in Scenario 2 (Small earthquake, night)= 3438

Frequency (F) - (as in event tree) = 0.0375

This means the expected consequences for all earthquake scenarios are: E(X) Earthquakes= 229.8797.

4.2. Floods The determination of the number of fatalities as a result of floods is slightly different from earthquakes.

Unlike earthquakes, which are experienced in the entire area, floods are restricted to specific “threatened”

areas, such as along rivers or flood plains.

According to the provided map data, the population areas exposed to flood risks are area 1, 2, 6,7,8,9 and

10. Furthermore, the severity of the risk is determined by the size of the flood and the 2% mortality rate.

As explained in the methodology, the area measurement were conducted in google earth and the

measurements can be found in appendix 7. These measurements were then used to determine the

population density of the areas affected by flooding. As an example, see the following calculation of flood

risk for large flood, during the day in population area 2:

Population density= Total population/ threatened area

= 1200/0.92km2= 1304 people/km2

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Number of people in exposed area = 1304* 0.22 (size of area exposed to large flood) = 286.956

N = D * M because Ae = At

Area 2 fatalities (large flood, day) = 0.02* 286.956 = 5.73

This calculation is replicated for all population areas and all flood risk scenarios, eventually computing the

total fatalities.

E(X) = ∑ f(Scenario)* N( total fatalities in each scenario)

E(X) Flood = 6.8579

4.3. Mudflows The process of determining the societal risk that arises from mudflows is the same as that of floods as

highlighted above. However, with the assumption that there are 90% mortality rate in the exposed areas,

which are, contrary to the floods, unequal to the threatened area. Therefore, the whole scenario

consequence calculation is comparable to floods besides that formula for fatalities, which is for the

mudflows:

N = Ae * D * M because Ae ≠ At

Thus, the exposed and threatened areas were measured and calculated (please see appendix 1) and based

on the scenario consequences, the following expected consequences were determined.

The E(X) = ∑ f(Scenario)* N( total fatalities in each scenario)

E(X) Mudflow = 6.833

From the sections above, it can be concluded that the expected consequences for earthquake risks is on

average 230 deaths per year, while floods and mudflows have an expected consequence of 7 deaths

respectively.

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4.4. FN-Curves Based on the findings from the calculations above, the data can be plotted into FN curves as shown below.

See appendix 1 for the individual curves.

Figure 7 – Frequency-Fatalities-Curve of individual hazards in Garm (see appendix 1)

Reflecting upon the FN-curves, higher fatalities arise from earthquakes in Garm compared to floods or

mudflows, which is due to the fact that earthquakes affect a larger geographical area as compared to the

other two hazards. However, the frequency of the earthquakes is considerably much less compared to the

mudflows and floods respectively. Combining all three hazards, the following FN-curve can be developed.

The FN Curves can later be used to determine the acceptability of the risk, such as basing on the ALARP

concept.

Figure 8 - Frequency-Fatalities-Curve of all hazards in Garm combined (see appendix 1)

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

1 10 100 1000 10000

Fre

qu

en

cy

No of Fatalities

FN-Curves Hazards Garm

Earthquakes

Floods

Mudflows

0,001

0,101

0,201

0,301

0,401

0,501

0,601

1 10 100 1000 10000

Fre

qu

en

cy

No of Fatalities

FN-Curve Combined Hazards

10

5. Individual Risk The map shown in figure 9 displays the different location-specific individual risks of dying (IR) that were

identified for the flood and mudflow threatened areas of the city of Garm, when using the average values

for frequencies. Irrespective of the population area, the IR for a small flood is 0.0061 while the IR for a

large flood is 0.0006 (flood IR = yellow). Both are determined by the formula IR = f (scenario) * M, as Ae =

At. Please see appendix 6 for a larger version of the map.

Figure 9 – Individual Risks plotted on map of Garm

However, the mudflow threatened areas (red/purple) vary per population area in the location-specific IR,

which is due to the fact that the possibly exposed areas are not equal to the threatened areas. The

corresponding formula is IR = f (scenario) * M * (Ae/ At), as Ae ≠ At. Please see the excel sheets 1 for

further information on the calculations for IR of floods and mudflows as well as the example calculations

below.

Table 2 – Example calculations of IR

Hazard Formula Calculation

Mudflow large Area 1 (population area 3)

F(large mudflow)*M*(Ae / At) 0.03*0.9*(0.3506/1.08) = 0.0088

Mudflow small Area 1 (population area 3)

F(small mudflow)*M*(Ae / At) 0.013*0.9*(0.0273/0.35) + 0.03*0.9*(0.3506/1.08) = 0.0096

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Overall, it can be concluded that the IR is higher in the areas threatened by both small as well as large

floods/ mudflows as the frequency of small and large hazards are accumulated. Furthermore, it is

identified that the centre of Garm is least dangerous for individuals, the danger of earthquakes being

excluded. The individual risk of earthquakes is not plotted on the map, as the whole city is exposed to an

earthquake in case of its occurrence. Thus, not the area location, but the presence in a specific building

type with a certain probability of collapsing during an earthquake determines the individual earthquake

risk. As no information is given on the specific map location of the building types, the IR of earthquakes is

not plotted on the map.

6. Sensitivity Analysis The quantification of the risk associated with the different hazards present in Garm might lead to a false

impression of certainty about the results. These results rely on assumptions and simplification and cannot

account completely for the complexity of the real world. Since the risk analysis is about future events,

there will always be some degree of uncertainty (Rausand, 2011, p. 497). The question is, if the results are

robust enough to guide decision-making. Therefore, this section of the report is an attempt to document

where in the risk assessment process uncertainty occurs. In order to do this, three categories of

uncertainty are used: model uncertainty, completeness uncertainty and parameter uncertainty, as

presented by Rausand (2011, p. 500ff.) The aim is to help the reader interpret the results and to point out

where additional research might be useful.

First of all, there is some model uncertainty (Rausand, 2011, 500) inherent in the assessment. It was

decided to use separate event trees for the three hazards and treat them as unrelated events, however,

in reality there are linkages between them. Earthquakes often cause mudslides, landslides in turn can block

the river which can lead to floods. Moreover, some factors, for example the occurrence of an event in

summer or winter, could have an impact on the consequences, since, for instance, it seems plausible that

people may be more likely to be outside their houses (and hence not in a collapsed building in case of an

earthquake) during the summer months. Nevertheless, this could not be included in the scenarios due to

a lack of input data and the scope of the project. This could be investigated further, if a new risk assessment

were to be undertaken. On the other hand, it should be kept in mind that if the number of different, more

detailed scenarios is too big, this will not only make the assessment more difficult but also harder to

understand for the decision makers.

Secondly, it is important to remember that there will always be a completeness uncertainty, i.e. it can

never be assured that all possible hazards are included, because it is unknown what is not known. In a

future risk assessment of Garm there could be a brainstorming to identify other possible hazards.

Thirdly, there is uncertainty about the following values for the different parameters used in the

assessment, since all are based on either assumptions or expert estimates.

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Frequency and magnitudes of hazard occurrences

Location of people in Garm

length of day/night

location of people in different building types

distribution of people within the population areas

total number of inhabitants

Mortality

likelihoods of building collapse

mortality rates for different hazard scenarios

In order to find out how much of an impact the uncertainty about these parameters has on the results, a

one way sensitivity analysis was carried out. Due to the fact that earthquakes cause the biggest number

of expected fatalities by a very large margin, it was decided to focus on the expected number of fatalities

due to earthquakes, as variations in these scenarios would have the biggest impact on the total number

of expected fatalities from all hazards. Table 3 displays the parameters tested and the reasoning behind

choosing the specific parameters as well as the variation used.

Table 3- Sensitivity Analysis Parameter & Variation

Parameter Value used

in risk assessment

Data source Variation for

sensitivity analysis

Reasoning

Frequency of small

earthquakes 0.075 year mean value

of the range provided by

expert

0.05; 0.1 By providing the frequency as an

interval the expert already expressed her uncertainty about the data. Thus, the minimum and maximum values of the provided interval are used to test

the effect of this uncertainty.

Frequency of large

earthquakes 0.0075 year 0.005; 0.01

Length of a day 12 hours

assumption by

assessment team

10 hours; 14 hours

This parameter measures when people are in different building types and

population areas, however, besides this varying naturally, the experts who provided the data did not give any

indication on the length of a day. For this reason the ratio of day and night

was slightly varied.

Number of people in adobe

buildings

1850 day 6010 night

provided by expert

+500 people in adobe -500 people in

reinforced concrete; -500

people in adobe +500 people in

concrete buildings;

People’s behaviour is hard to model and anticipate and the number of people in the building is subject to variability for example due to a celebration, season, etc. It was attempted to show this by moving a number of people between

adobe buildings and reinforced concrete buildings, however, of course one can conceive of endless combinations of

such movements.

13

Mortality rate in collapsed

building

90% 85%; 95% Rescue capacity due to roads blocked by a landslide or a simultaneously occurring

flood.

Probability of adobe buildings collapsing in a

small earthquake

50% 30%; 70%

Not every building even in the same category will have the same likelihood of collapse. Adobe buildings were chosen, because these are one of the most basic

building types, often build by the inhabitants themselves and not

regulated by building codes, thus variability might be quite big depending

on skills of the builder, age, design of the structure etc.

The tornado diagram in figure 10 illustrates the results of the sensitivity analysis. As can be seen in the

diagram, most of the parameters tested only change the expected number of fatalities by around +/- 15

fatalities or 6%. The uncertainty about the frequency of small earthquakes and the likelihood of building

collapse seem to have the biggest impact on the overall result, changing by around 25%. Hence, in a future

assessment uncertainty could be reduced by doing further research on these parameters. This could be

done for example by consulting multiple experts, looking at historical data or asking local stakeholders.

However, as the uncertainty for both these parameters is essentially aleatory, i.e. caused by natural

variation (Rausand, 2011, p. 499), the uncertainty cannot be completely reduced.

Figure 10 – Tornado diagram displaying sensitivity analysis results

Keeping these uncertainties in mind, the results of this risk assessment may serve as a good basis for

making more informed decisions about mitigation. The results may vary slightly, but the varying levels of

threat stemming from the different hazards in terms of expected numbers of fatalities is still clear.

243

244

245

245

283

291

217

215

215

215

177

169

150 170 190 210 230 250 270 290

Mortality rate

Percentage of day time

Number of people in building types

Frequency for large earthquake

P of adobe building collapsing in small earthquake

Frequency for small earthquake

Sensitivity Analysis Results

14

7. Discussion and Conclusion The results of this risk assessment show that the inhabitants of Garm are exposed to rather big risks from

natural hazards, the potentially most catastrophic but least frequent one being earthquakes. It is

noteworthy that these risks are not distributed equally within the city, both in terms of geographic

location, but also in terms of the building type people are staying in. Mudflows and floods only threaten

parts of the city and while earthquakes will hit the entire city, there is also a geographic component to

this. Adobe buildings are most vulnerable to earthquakes and this building type is not distributed equally

within the city. Assuming that the location of people at night indicates where their homes are, it can be

deduced that the vast majority of these adobe structures seem to be located in population areas 3 to 6

and population area 10, where between 70% and 80% of the area population is in adobe buildings at night.

Looking at the population data, these areas also seem to be the city’s main residential areas, as 7250 of

Garm’s 9500 inhabitants spend the night in one of these areas. These parts of the city are also part of the

areas threatened by mudflows (areas 3-5) and floods (areas 6 and 10). During the day, many people are

concentrated in the city centre (area 1 and 2), where mostly reinforced masonry and steel buildings are

found, which are much safer during earthquakes. In addition, these areas are also safe from mudflows and

albeit there is a flood risk in area 1, this does not cause many fatalities. For this reason, the overall risk of

fatalities due to the natural hazards in Garm is higher at night than during the day. For more information

on the population in the different areas, please see the large version of the structural model in appendix

4.

It might be worth mentioning, that there is also probably a socioeconomic component to the level of risk

an individual in Garm is exposed to, since adobe buildings are cheaper to construct, and thus, often

inhabited by the poorer parts of the population. The individual risk map underlines this consideration, as

unpopular areas due to higher IR are clearly recognizable. However, with Tajikistan being the poorest

country in Central Asia, this probably concerns a large part of Garm’s population (in fact 6010 of its 9500

inhabitants spend the night in adobe buildings). Further research to confirm these speculations could help

shape future policy making.

Furthermore, it should be underlined that this risk assessment, only considers fatalities resulting directly

from these three natural hazards. However, any of the three hazards could have further consequences

threatening human lives and health. For example, even if people are not inside their house when it

collapses or gets flooded, they will still be left homeless which could be catastrophic in itself, especially in

the winter. Moreover, food insecurity is a major threat in Tajikistan affecting almost one third of the

population (World Food Programme, 2015). Natural disasters may exacerbate this problem, as access to

food could be hindered, for example by blocked roads or livestock as well as harvest could be destroyed.

In addition to this, there might be other things that human beings value at risk from natural disasters in

Garm. For instance, while a flood will probably not cause many fatalities, it can potentially be very

destructive to the city’s infrastructure and cause high economic losses. This applies to the two other

hazards.

Moreover, the country underwent a civil war from 1992 to 1997, during which the Rasht Valley was the

centre of opposition (Markowitz, 2011, p. 2013). Even after the war, this area continued to be the location

15

of violent clashes between local leaders and government forces, a hazard in itself that was outside the

scope of this assessment (CIA, 2015). It is conceivable that natural disasters may lead to further political

tensions and civil unrest (for more on this refer to Ahlerup 2009).

These additional consequences could be subject of a more thorough risk assessment that looks at a

broader range of values. Ideally, this would include local stakeholders to find out which values are most

important to them and therefore looked at more closely.

The overall conclusion can be drawn that Garm is exposed to various risk. The most severe hazard in terms

of high fatalities are earthquakes, which is also contributing significantly to the overall risk of Garm, as

depicted in the combined FN-curve. Moreover, the construction types of houses and the hazard frequency

are highly influential on the risk assessment, which requires further consideration of these factors.

Although the three natural hazards are possible to be analysed, the additional factors that might change

the outcome of a mitigation process need to be carefully considered.

16

8. Reference List

Ahlerup, P. (2009). Earthquakes and Civil War. Working paper. Department of Economics. University of

Gothenburg. Retrieved October 8, 2015, from website:

https://gupea.ub.gu.se/bitstream/2077/21202/1/gupea_2077_21202_1.pdf

APA. (2015). Learning APA Style. Retrieved October 8, 2015, from website:

http://www.apastyle.org/learn/index.aspx

CCPS. (2000). Guidelines for Chemical Process Quantitative Risk Analysis. New York: Center for Chemical Process Safety, American Institute of Chemical Engineers (Chapter 4-4.3: Risk Measures p.399).

CIA. (2015). The World Factbook. Retrieved October 8, 2015, from website: https://www.cia.gov/library/publications/the-world-factbook/geos/ti.html

Maptd. (2011). Nuclear Power Plant Locations and Global Seismic Activity. Retrieved September 23,

2015, from http://maptd.com/map/earthquake_activity_vs_nuclear_power_plants/

Markowitz, L. P. (2013). State Erosion: Unbootable Resources and Unruly Elites in Central Asia. Cornell

University Press.

Rausand, M. (2011). Uncertainty and Sensitivity Analysis. In M. Rausand (Ed. 1), Risk Assessment Theory, Methods, and Applications (pp. 497- 513). Published by John Wiley & Sons, Inc., Hoboken, New

Jersey.

Tehler, H. (2015, August). A general framework for risk assessment V 1.4. Division of risk management and societal safety - Lund University

United Nations. (2014). Tajikistan: Building a Democracy. [Video file] Retrieved September 23, 2015,

from http://www.youtube.com/watch?v=C1ey-4PO7fE

UPSeis. (n.d.). Earthquakes Magnitude Scale and Classes. Retrieved September 23, 2015, from website:

http://www.geo.mtu.edu/UPSeis/magnitude.html.

World Food Programme. (2015). Tajikistan Overview. Retrieved October 8, 2015, from website:

http://vam.wfp.org/CountryPage_overview.aspx?iso3=TJK

A

Appendixes

Appendix 1 – Excel Sheet for Data and Calculations

Due to technical constraints, the excel sheet is added as a pdf on the following pages

without corresponding page numbers.

Caption:

Day Night Total

Population area 1 1400 100 1500

Population area 2 1200 700 1900 Day 0.5

Population area 3 500 2500 3000 Night 0.5








Outside population area 3000 0 3000

Earthquake scenario Small Large

Small 0.05 0.1 50% 100%

Large 0.01 0.01 30% 100% → 90%

10% 50%

5% 25%

Reinforced concrete

Steel

Probability building collapses

If a building collapses, 90% of the people in the building

will die

warning text

Population area 10

Population area 9

Population area 8

Population area 7Population area 1

Population area 2

Population area 3-5

Population area 6

Number of people

Earthquakes

Frequency

headline total result

Adobe

Unreinforced masonry

Building type Population, day Population, night

Adobe

Reinforced concrete

Steel

60%

10%

Unreinforced masonry 20% 50%

Reinforced concrete 60% 20%

50%

0%


Adobe 10% 30%


Adobe

Building Type

Population area

All Data presented in Garm Case

explanatory text input data linked cell


0%

30%

0%

50%

check cell

10% 10%

Unreinforced masonry 30%

60%

0%

30%

Reinforced concrete 60%

Steel 0%

Steel 0% 0%


80%



Steel 0% 0%

Building type Population, day


Adobe 70% 80%

20% 20%


0%

Building type Population, dayPopulation, night

Steel 10% 0%

Adobe 70%

Population areas

People in types of buildings in percentage

50% 100%

Steel 50% 0%

30%


0%

Steel 40%

Probability of it being day/night

Assumption


Adobe 0% 0%


Population, night

Adobe 0%


Steel 0% 0%



Adobe 70% 70%

Reinforced concrete

0%


Flood scenario Min Max → 2%

Small 0.05 0.5

Large 0.01 0.05

Mudflow scenario

Small Max Min Max Min

Large 0.02 0.005 0.05 0.01 → 90%

0.025 0.005 0.05 0.01

0.01 0.001 0.04 0.01

km² km²

km² km²width km (simplified)

Population area 3 - Mudflow area 1 length km (simplified)

km² km² [mudflow width * length (length Ae = length At)]width km (simplified)

length km (simplified) Population area 8

km²

Population area 4 - Mudflow area 2

km² km²width km (simplified)

length km (simplified)

km²


km²

km² mudflow width * length (length Ae = length At)

km² mudflow width * length (length Ae = length At)

0.0273 0.3506

0.15850.025

1.44 1.23

0.25 0.6341

1.37 0.36 0.78

Total area size

0.1383

0.92

0.25

0.0138 0.0625

0.13 0.23

Population area 6

Total area sizeSize of small flood affected

area

Size of large flood affected

area

0.8 0.02 0.04

Size of small mudflow

threatened area

Size of large mudflow

threatened area

0.68 0.04 0.1



exposed area


exposed area

0.94

0.78 0.0026 0.04

0.95 0.04 0.14

Total area size Size of small mudflow Size of large mudflow

Population area 10

0.34 0.1 0.18


exposed area


exposed area

Size of small flood affected

area


area

0.5 0.41 0.49

Population area 9


area


area


threatened area


threatened areaTotal area size

Size of small flood affected

area


area


area


area

0.2734 1.4026

0.771.28

Small mudflow (per

year)

Mudflow area 3

Mudflow area 2

Mudflow area 1

0.22

3 meter above normal


exposed area


exposed area

0.47 0.33 0.45


area


area

Total area sizeSize of small mudflow

threatened area


threatened area

Population area 2

Total area size

Estimated flood frequencies per year

Risk scenario

Small flood

Large flood

2.21 0.35 1.08

Large mudflow (per

year)

Population area 1


area


area

0.92 0.0016

Estimated mudflows frequencies per yearMudflows

Width (kilometer)

0.1

0.25

Find markings on electronic map

Sizes of population areas & affected/ threatened areas & exposed areas (Google earth measurements)

All measurements made in Google earth are displayed in the

appendixes of the report

Floods

Water level rise 2% of people within affected area will drown -->

calculate population density1.5 meter above normal

Case text "… destroying everything in its way." -->

assumption: 100% housing collapse & 90% of people in

collapsed houses die

width km (simplified)length km (simplified)

Persons / km²

Day Night

Population area 1 2979 213










Total population density of total areas

population density = persons per square

kilometer (see calculation in case p.5)

km² mudflow width * length (length Ae = length At) 0.0098 0.0660

0.41 0.53

0.0976 0.2642


exposed area


exposed area

Caption:

Min Max Average 0.0375

Small 0.05 0.1 0.0750 0.0375

Large 0.005 0.01 0.0075 0.0038

0.0038

0.0825

Probability

Small 0.9091

Large 0.0909

Day 0.5

Night 0.5

Number of people in types of buildings

Population area 1

Steel 140 0

Population area 7

Reinforced concrete 840 50


Unreinforced masonry 420

Earthquake data & calculations

explanatory text input data linked cell headline warning text check cell total result


Earthquake scenario

Total frequency of earthquakes per year

Earthquake scenarioProbabilities are calculated from frequencies by dividing the frequency

scenario small (or large or others) by the total frequency of the event

happening. E.g. P small earthquake = F small / F total --> 0,075

/0,0825

S3: Large, day

S4: Large, night

Frequencies F(scenario)Frequency

S1: Small, day

S2: Small, night

Estimated earthquake frequencies per year

Assumption: total F is

based on average

50

Adobe 0 0

Population area 2

90

Steel 120 0

Unreinforced masonry 150 270


Adobe 120 210 Adobe 50



Population area 8



540

Population area 3

Steel 0 0

Reinforced concrete 300

Adobe 350 2000 Adobe 0


0

280Reinforced concrete 50 0

0

Steel

Adobe 0

0 0

Population area 9

Steel 0 0

490 800

% of people in building types given in sheet 'All data'

multiplied by total area population e.g.

D33 = PRODUKT('All data'!B10;'All data'!D26:E26)

Population area 4


Adobe 210 1280



Steel 200 0



Population area 6




Adobe

Earthquake

F=0.0825/year

Small

P=0.909

day

P=0.5S1 = 0.0375/year

S2=0.0375/yearnight

P=0.5

S3=0.0038/year

S4=0.0038/year

Large

P=0.091

day

P=0.5

night

P=0.5

Day Night

1850 6010

1380 2330

2560 1160

710 0

6500 9500

N fatalities

832.50

372.60

230.40

31.95

Total 1467.45

N fatalities

2704.50

629.10

104.40

0.00

Total 3438.00

N fatalities

1665.00

1242.00

1152.00

159.75

Total 4218.75

N fatalities

5409.00

2097.00

522.00

0.00

100% 1850

Unreinforced masonry 2330

2330

Reinforced concrete 1160 50%

N fatalities = population

in collapsed buildings (D * P) *90% 90% of people

in collapsed building die --> see sheet 'All data' O56

580

116

Steel 0 5% 0

Popu. in buildings P (building collapse)

710

16316500

Steel 365%

Earthquake scenarios consequences - N fatalities

6500 4688

Scenario 4: Large earthquake, night

Building type Popu. in buildings P (building collapse) Popu. in collapsed buildings

9500 3820

Scenario 3: Large earthquake, day


Adobe 1850

30% 699

Reinforced concrete 1160 10%

Steel 0 25% 0

1380

Reinforced concrete 2560 50% 1280

Steel 710 25% 178

Adobe 6010 100% 6010

Unreinforced masonry 1380 100%

Unreinforced masonry 2330 100%

Scenario 2: Small earthquake, night


Adobe 6010 50% 3005

250 0Steel 0 0

Reinforced concrete 250Reinforced concrete 30 0

Unreinforced masonry 0 0Unreinforced masonry 60 320

0

Steel 0 0


630


Adobe 280

Population area 10


150

Steel



Population area 5


Adobe 350 1000


Reinforced concrete

Steel

Total

Total no° of people in building types

Steel 0 0

Adobe

∑ of calculations above

Scenario 1: Small earthquake, day

Building type

Adobe

Popu. in collapsed buildings

925

414

256

50%

30%

10%

1850

1380

2560


Reinforced concrete

Total 8028.00

S1

S2

S3

S4

Total E(N) 229.8797

E(N)=∑ f scenarios*N scenarios

e.g. PRODUKT(L86;J10)

9500 8920

55.0294

128.925

15.8203

30.1050

Expected Number of fatalities E(N)

Caption:

Min Max Average (Ø) Frequencies f(scenario)

Small 0.05 0.5 0.275 0.1375

Large 0.01 0.05 0.03 0.1375

0.305 0.015

0.015

Probability

Small 0.9016

Large 0.0984

day 0.5

night 0.5

N

N

N

N

N

N

Ø total F/year (E13) * P small or large

(C18,C19) * P day or night (B23,B24)Ø f small or large / Ø F

total per year

Floods


based on average

Population area 6

Population area 8

Large flood, day Large flood, night Small flood, day


0.7000 1.0000 0.3500

S5: Small, day

S8: Large, night

Small flood, day

5.2941 1.5882 2.9412

Population area 7

5.7391 3.3478 0.0407

Frequency of occurrence

26.8085 1.9149 19.6596

Population area 2




Total frequency of floods per year

Flood scenario

Population area 1

Population affected by flood scenarios (consequences - N fatalities)

S6: Small, night

S7: Large, day

total result

Flood data & calculations

explanatory text input data linked cell headline warning text check cell

Estimated flood frequencies per year


1.4706 2.6471 0.5882

9.8000 7.8400 8.2000

Small flood, night

1.4043

Small flood, night

0.0237

Small flood, night

0.5000

Small flood, night

1.0588

Small flood, night

6.5600

Small flood, night

0.8824

values of D and Ae (Ae = At) presented in sheet 'All data'

e.g. B32= PRODUKT('All data'!B122;'All data'!F87:G87)*0.02

N fatalities = population

density (D) * exposed area (Ae) *2% (2% of people in

Ae die --> see sheet 'All data' O64)

Small flood, night

Population area 10


Population area 9

Large flood, day Large flood, night

Flood

F=0.305/year

Small

P=0.902

day

P=0.5S5 = 0.1375/year

S6=0.1375/yearnight

P=0.5

S7=0.015/year

S8=0.015/year

Large

P=0.098

day

P=0.5

night

P=0.5

N

31.8063

10.4892

50.2226

19.2611

∑ of calculations above

Total fatalities scenarios N(scenario)

0.0600

S6 Small flood, night


0.7533

E(N)=∑ f(scenario)*N(scenario)

(f= J11-J14; N= C60-63)

0.2889

4.3734

M= N/ (D*Ae) = 0,02 [provided in case]

0.4103 0.9231 0.0267

S7 Large flood, day

S8 Large flood, night

S5 Small flood, day

Individual risk of death IR

0.0006

IR=∑f(scenario)*M --> because Ae = At

0.0061

1.4423

6.8579

frequency large flood (J13+J14) * 0.02

S7: Large, day

S8: Large, night

S5: Small, day

S6: Small, night

Total E(N)

IR small flood

IR large flood

frequency small flood (J11+J12)* 0.02 + frequency large

flood (J13+J14) * 0.02

Caption:

Max Min Average Max Min Average0.020 0.005 0.013 0.050 0.010 0.030 0.043 (E9 + H9)

0.025 0.005 0.015 0.050 0.010 0.030 0.045 (E10 + H10)

0.010 0.001 0.006 0.040 0.010 0.025 0.031 (E11 + H11)

0.118

Area 1 0.360 0.015

Area 2 0.381 0.015

Area 3 0.258 0.006

Total 1 0.006

0.015

0.015

large small 0.008

Area 1 0.294 0.706 0.008

Area 2 0.333 0.667

Area 3 0.180 0.820 0.013

0.013

0.003

0.003

Day 0.5

Night 0.5

N

N

N

0.0835 Ae/ At M f(scenario) IR

0.4176 0.3246 0.03 0.0088

0.4462 0.0780 0.013 0.0096

2.2312

0.1432 0.2032 0.0300 0.00549

0.5675 0.0694 0.015 0.0064

0.4689

1.8363 0.4714 0.0300 0.0127

0.1144 0.2450 0.015 0.0160

0.2059

0.1137 0.2717 0.025 0.0061

0.2047 0.1062 0.006 0.0066

6.8333

S20: Large mudflow, night

5.5677 27.8387 71.3992 356.9959

Total E(N)


S9: Small mudflow, day

S10: Small mudflow, night

S11: Large mudflow, day





O 17-30 --> sum of day and night Individual risk of death IR


based on average

Problem with cell connection, so not

directly connected to 'All data'

IR large mudflow

IR small mudflow

Mudflow area 2 - Population area 5


IR areas = ∑f(scenario) * M * (Ae / At)

90%


IR large mudflow

IR small mudflow


IR large mudflow

IR small mudflow

IR large mudflow

IR small mudflow

IR small mudflows = accumulated large & small

mudflow e.g.R57

= PRODUKT(O57;P56;Q57)+R56

M = 90% --> see explanation 'All data'!Q74

E(N)=∑ f(scenario)*N(scenario)

(f= O17-30; N= BEHK 42, 46, 50)

37.8306 62.5255 244.8396

Mudflow area 3 - Population area 7S17: Small mudflow, day S18: Small mudflow, night S19: Large mudflow, day S20: Large mudflow, night

9.1521 16.4737 41.3603 74.4485





N fatalities = Ae * D * M

e.g. S1 = PRODUKT('All data'!D99:E99;'All

data'!B124;'All data'!Q74)

Mudflow area 2 - Population area 4 & 5S13 : Small mudflow, day S14: Small mudflow, night S15: Large mudflow, day S16: Large mudflow, night

9.5483

Mudflow area 1 - Population area 3S10: Small mudflow, nightS9: Small mudflow, day S11: Large mudflow, day S12: Large mudflow, night

F(scenario) =

total F mudflows * P mudflow area * P mudflow size * P time

e.g. F13*B17*C25*B31

Estimated mudflows frequencies per year

Population affected by mudflow scenarios (consequences - N fatalities)

Average frequency small or large / total

frequency areas

(E9-11; H9-11 / J9-11)

Frequency of occurrence Total frequency of mudflows per area

(J9 / F13)

(J10 / F13)

(J11 / F13)

Probability of mudflow being big or small


Frequencies f(scenario)

S9: Area 1, small, day

S10: Area 1, small, night

S11: Area 1, large, day

S12: Area 1, large, night



Large mudflow (per Small mudflow







Mudflow data & calculations

explanatory text input data linked cell headline warning text check cell

Mudflow area 1

Mudflow area 2

Mudflow area 3

total result

Probability of mudflow in an area given a mudflow occurred

Total frequency of mudflows in all areas per year Ø F =

Mudflow

F=0.118/year

Area 1

P=0.36

small

P=0.71

day

P=0.5S9 = 0.015/year

night

P=0.5S10=0.015/year

large

P=0.29

day

P=0.5S11=0.00625/year

night

P=0.5S12=0.00625/year

Area 2

P=0.38

small

P=0.67

day

P=0.5S13=0.015/year

night

P=0.5S14=0.015/year

large

P=0.33

day

P=0.5S15=0.0075/year

night

P=0.5S16=0.0075/year

Area 3

P=0.26

small

P=0.82

day

P=0.5S17=0.0125/year

night

P=0.5S18=0.0125/year

large

P=0.18

day

P=0.5S19=0.00275/year

night

P=0.5S20=0.00275/year

Caption:

Risk

ScenarioDescription Frequency

Cumulative

FrequencyNo. fatalities Fatalities

S4Earthquake

(large, night) 0.00375 0.00375 8028.00 8028

S3Earthquake

(large, day) 0.00375 0.0075 4218.75 8028

S2Earthquake

(small, night) 0.0375 0.045 3438.00 4218.75

S1Earthquake

(small, day) 0.0375 0.0825 1467.45 4218.75

3438

3438

1467.45

1467.45

100

Frequencies and fatalities

connected to sheet

'Earthquakes'

J10-J13 & L86-L110

Cumulative frequencies

examples: D10 = D9 + C10 and

D11 = D10 + C11

0.0076

0.045

0.045

0.0825

0.0826

Cumulative

Frequency

0

0.00375

0.0038

0.0075

Earthquake scenarios - cumulative frequencies For FN-curve

FN-Curve Earthquakes


0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

100 1000 10000

Fre

qu

en

cy

No of Fatalities

FN-Curve Earthquakes

Caption:

Risk

ScenarioDescription Frequency

Cumulative

FrequencyNo. fatalities Fatalities

S7Flood

(large, day) 0.015 0.015 50.2226 50.2226

S5Flood

(small, day) 0.1375 0.1525 31.8063 50

S8Flood

(large, night) 0.015 0.1675 19.2611 32

S6Flood

(small, night) 0.1375 0.305 10.4892 31.80635

19.26109

19

10.48917

10

1

Frequencies and fatalities

connected to sheet

'Floods'

J11-J14 & L86-L110


examples: D10 = D9 + C10

and D11 = D10 + C11

0.1525

0.1675

0.1675

0.305

0.305

Cumulative

Frequency

0

0.015

0.015

0.1525

Flood scenarios - cumulative frequencies For FN-curve

FN-Curve Floods


0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1 10 100

Fre

qu

en

cy

No of Fatalities

FN Curve Floods

Caption:

Fatalities

Risk

Scenario

Description Frequency Cumulative

Frequency

No.

fatalities 396.6622

S12 Mudflow area 1

(large, night) 0.006 0.006 396.6622 396.6622

S16 Mudflow area 2

(large, night) 0.008 0.014 272.044 272.044

S20 Mudflow area 3

(large, night) 0.003 0.017 82.7206 272.044

S11 Mudflow area 1

(large, day) 0.006 0.023 79.3324 82.7206

S15 Mudflow area 2

(large, day) 0.008 0.031 69.4727 82.7206

S19 Mudflow area 3

(large, day) 0.003 0.034 45.9559 79.3324

S14 Mudflow area 2

(small, night) 0.015 0.049 42.0341 79.3324

S10 Mudflow area 1

(small, night) 0.015 0.064 30.9318 69.4727

S18 Mudflow area 3

(small, night) 0.013 0.077 18.3041 69.4727

S13 Mudlflow area 2

(small, day) 0.015 0.092 10.6092 45.9559

S17 Mudlflow area 3

(small, day) 0.013 0.105 10.169 45.9559

S9 Mudlflow area 1

(small, day) 0.015 0.12 6.1864 42.0341

42.0341

30.9318

30.9318

18.3041

18.3041

10.6092

10.6092

10.169

10.169

6.1864

6.1864

1

Mudflow scenarios - cumulative frequencies

FN-Curve Mudflows


For FN-curve

0.031

Cumulative

Frequency

0

0.006

0.006

0.014

0.014

0.017

0.017

0.023

0.023

0.031

0.034

0.034

0.049

0.049

0.064

Problem with cell connection,

so not directly connected to

'All data'

0.12

0.12


examples: D10 = D9 + C10

and D11 = D10 + C11

0.077

0.077

0.092

0.092

0.105

0.105

0.064

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

1 10 100 1000

Fre

qu

en

cy

No of Fatalities

FN Curve Mudflows

Caption:

Risk

ScenarioFrequency No. fatalities Description

Cumulative

FrequencyNo° Frequency

S4 0.0038 8028 Earthquake (large, night) 0.0038 8028 0.0001

S3 0.0038 4218.75 Earthquake (large, day) 0.0076 8028 0.0038

S2 0.0375 3438 Earthquake (small, night) 0.0451 4218.75 0.0038

S1 0.0375 1467.45 Earthquake (small, day) 0.0826 4218.75 0.0076

S12 0.006 396.6622 Mudflow area 1 (large, night) 0.0886 3438 0.0076

S16 0.008 272.044 Mudflow area 2 (large, night) 0.0966 3438 0.0451

S20 0.003 82.7206 Mudflow area 3 (large, night) 0.0996 1467.45 0.0451

S11 0.006 79.3324 Mudflow area 1 (large, day) 0.1056 1467.45 0.0826


S7 0.015 50 Flood (large, day) 0.1286 396.6622 0.0886


S14 0.015 42.0341 Mudflow area 2 (small, night) 0.1466 272.044 0.0966

S5 0.1375 32 Flood (small, day) 0.2841 82.7206 0.0966


S8 0.015 19 Flood (large, night) 0.3141 79.3324 0.0996


S13 0.015 10.6092 Mudlflow area 2 (small, day) 0.3421 69.4727 0.1056

S17 0.013 10.169 Mudlflow area 3 (small, day) 0.3551 69.4727 0.1136

S6 0.1375 10 Flood (small, night) 0.4926 50 0.1136

S9 0.015 6.1864 Mudlflow area 1 (small, day) 0.5076 50 0.1286

45.9559 0.1286

45.9559 0.1316

42.0341 0.1316

42.0341 0.1466

32 0.1466

32 0.2841

30.9318 0.2841

30.9318 0.2991

19 0.2991

19 0.3141

18.3041 0.3141

18.3041 0.3271

10.6092 0.3271

10.6092 0.3421

10.169 0.3421

10.169 0.3551

10 0.3551

10 0.4926

6.1864 0.4926

6.1864 0.5076

1 0.5076

Problem with cell

connection, so not directly

connected to 'All data'

All scenarios - cumulative frequencies For FN-curve

FN-Curve Mudflows

input data linked cell headline warning text check cell total result

0.001

0.101

0.201

0.301

0.401

0.501

0.601

1 10 100 1000 10000

Fre

qu

en

cy

No of Fatalities

FN curve Combined Hazards

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1 10 100 1000 10000Fr

eq

ue

ncy

No of Fatalities

FN Curves Hazards Garm

Earthquakes

Floods

Mudflows

Caption:

Min Avrg Max

Frequency for small earthquake 0.05 0.075 0.1

E(N) 169 230 291

Frequency for large earthquake 0.005 0.0075 0.01

E(N) 215 230 245

Percentage of day time 0.58 0.5 0.42

E(N) Earthquakes 215 230 244

Number of people in building types500 people

less in adobe

buildings

1850/6010

500 people

more in

adobe

buildings

215 230 245

Mortality rate 0.85 0.9 0.95

217 230 243

P of adobe building collapsing in

small earthquake 0.3 0.5 0.7

E(N) Earthquakes 177 230 283

low base high

Mortality rate 217 230 243

Percentage of day time 215 230 244

Number of people in building types 215 230 245

Frequency for large earthquake 215 230 245

P of adobe building collapsing in

small earthquake 177 230 283Frequency for small earthquake 169 230 291

Problem with cell connection, so not

directly connected to 'All data'

Numbers derived from

redoing our calculations

with different values for

the parameters, varying

them one at a time

Sensitivity Analysis


Earthquake frequency interval

243

244

245

245

283

291

217

215

215

215

177

169

150 170 190 210 230 250 270 290

Mortality rate

Percentage of day time

Number of people in building types

Frequency for large earthquake

P of adobe building collapsing in small earthquake

Frequency for small earthquake

Sensitivity Analysis Results

B

Appendix 2 – Information on Construction Types in Garm

The following data related to the different construction types and the population in the buildings is

provided by the Garm case and based on expert judgements (Garm Case, n.d.).

Appendix figure 1 - The probability that a building collapses, given a certain earthquake risk scenario

Appendix figure 2 – Population in different construction types

C

Appendix 3 – Hazard Frequencies The following estimated frequencies are the expert judgements on the frequency of occurrence of all

three analysed hazard types. The data is retrieved from the Garm Case (n.d.).

Appendix 4 – Large Version Structural Model The structural model in form of a simplified map of Garm is supposed to simplify the risk assessment.

Population areas as well as hazard affect zones are displayed.

Appendix table 1 – Estimated hazards frequencies

Min Max

Small earthquake 0.05 0.1 Large earthquake 0.005 0.01 Small flood 0.05 0.5 Large flood 0.01 0.05 Mudflow area 1 small flood 0.01 0.05 Mudflow area 1 large flood 0.005 0.02 Mudflow area 2 small flood 0.01 0.05 Mudflow area 2 large flood 0.005 0.025 Mudflow area 3 small flood 0.01 0.04 Mudflow area 3 large flood 0.001 0.01

Appendix figure 3 – Structural Model Map Garm

D

Appendix 5 – Table of Scenario Summaries As explained in the main body, the table in appendix table 2 summarizes the results of the twenty risk

scenarios displayed in the trees above as well as the consequences. All displayed values were calculated

in the scenario tree section and the societal risk chapter. The table shows the different scenarios, the areas

which are affected by it and its estimated frequency of occurrence as well as the estimated number of

fatalities in the case that a scenario occurs.

Appendix table 2 - Risk scenarios with frequencies and consequences

Risk Scenario

Effect zone Frequency No. fatalities Description

S1 Entire city of Garm 0.0375 1467.45 Earthquake (small, day)

S2 0.0375 3438.00 Earthquake (small, night)

S3 0.0038 4218.75 Earthquake (large, day)

S4 0.0038 8028.00 Earthquake (large, night)

S5 Population areas 1,2,6,7,8,9,10

0.1375 32 Flood (small, day)

S6 0.1375 10 Flood (small, night)

S7 0.015 50 Flood (large, day)

S8 0.015 19 Flood (large, night)

S9 Population area 3 0.015 6.1864 Mudflow area 1 (small, day)

S10 0.015 30.9318 Mudflow area 1 (small, night)

S11 0.006 79.3324 Mudflow area 1 (large, day)

S12 0.006 396.6622 Mudflow area 1 (large, night)

S13 Population areas 4 and 5

0.015 10.6092 Mudflow area 2 (small, day)




S17 Population area 7 0.013 10.1690 Mudflow area 3 (small, day)




E

Appendix 6 – Individual Risks plotted on Map of Garm

Appendix figure 4 - IR plotted on Map of Garm

F

Appendix 7 – Geographical Area Measurements in Google Earth Screenshots

Total area measurements (given in properties) Example:

Other areas:

Population area 2 3.87 km 0.92 km²









G

Affected areas population area 1 (ruler measurements)

Affected areas population area 2

H


I

1) Width At

2) Lenght At =

Length Ae

calculated:

At total/ At width

3) Mudflow (Ae) width: 250 or 100m

Ae =

Ae width * Ae

length

J


K


L


M


N


O


P


report on garm

Engineering