PROBABILISTIC-BASED HURRICANE RISK ASSESSMENT AND ...

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due to climate change. Similar research is needed to investigate the potential effects ofclimate change on hurricane risk assessments in the U.S.1.1.2.1 Monte Carlo Simulation (MCS)A Monte Carlo Simulation (MCS) can be used to estimate the damage risk to incorporatethe epistemic uncertainty in change in hurricane wind speed, i.e. the wind increase is avariable. How climate change will affect regional climates and pertinent variables(epistemic uncertainty) is not well known, limiting the ability to predict consequentialeffects.Figure 1.1 shows the results of the MCS for the annual damage in Miami-Dade County,Florida, assuming a mean increase in wind speed of 5% with COV of 0.50 in year 50.This figure is for illustration purposes only, as significant refinement is required toestimate probability distributions for future mean temperatures or other climatic variables(e.g. the COV for changes in wind speed) when new information is available (IPCC2007). A presentation like this figure can also be modified to represent the cumulativedamage of a region to allow making statements with certain confidence level, such as “Iam 95% confident that the wind damage for this region will be under $1.0 billion for anincrease in wind speed of 5% over 50 years”. Such statement can be very useful in riskcommunication for decision making.7
Page 6 and 7: 4.3 Design of Power Distribution Po
Page 8 and 9: 6.6 Conclusions and Future Work ...
Page 10 and 11: List of FiguresFigure 1.1:Simulatio
Page 12 and 13: Figure 4.10: Effects of Degradation
Page 14 and 15: List of TablesTable 2.1: Percentage
Page 16 and 17: PrefaceThe research presented in th
Page 18 and 19: AcknowledgementsI would like to sta
Page 20 and 21: AbstractStudies are suggesting that
Page 22 and 23: Chapter 1__________________________
Page 24 and 25: 1.1 Literature Review and Critical
Page 26 and 27: assumed, between now and 2080, it c
Page 32 and 33: to be uniform and the use of a coas
Page 34 and 35: vulnerability function as a percent
Page 36 and 37: ased, accounting for the resistance
Page 38 and 39: size, forward speed, forecast track
Page 40 and 41: estimates direct damages, i.e. repa
Page 42 and 43: Figure 1.2: Generalized Schematic o
Page 44 and 45: Eq. (1.5). By means of the required
Page 46 and 47: amp = 11− ξ(DL)Pcr(1.9)P cr = P
Page 48 and 49: poles in the U.S., as no such data
Page 51 and 52: distribution) to effectively repres
Page 53 and 54: of various adaptation/mitigation st
Page 55 and 56: Chapter 6 presents the published jo
Page 57 and 58: Bender, M. A., Knuston, T.R., Tuley
Page 59 and 60: FEMA (2003) RSDE field workbook: pr
Page 61 and 62: Jain, V.K., and Davidson, R. (2007)
Page 63 and 64: Leicester, R.H., Bubb, C.T.J., Dorm
Page 65 and 66: Pielke, R.A.J., and Pielke, R.A.S.
Page 67 and 68: Stewart, M. G. and Wang, X. (2011b)
Page 69 and 70: Wang, C.-h., Leicester, R.H., and N
Page 71 and 72: 2.1 AbstractThis paper presents a p
Page 73 and 74: the other hand, if Hurricane Ike ha
Page 75 and 76: speed if there is no climate change
Page 77 and 78: foreshore locations, when there is
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The cumulative regional loss estima
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Figure 2.4 shows the expected damag
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approximately $1.49 billion, with a
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scenarios. The reduced cumulative d
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2.9 Cost Effectiveness of Adaptatio
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housee in Miami-Dade County, then t
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3.503.00Net Benefit, E b ($ billion
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5.004.00Net Benefit, E b ($ billion
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2.00Net Benefit, E b ($ billion )1.
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Cost of Retrofit (C st )35%30%25%20
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nt100(A2.1b)The LCC for adaptation
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[11] Landsea CW. Counting Atlantic
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[32] Moreno D, Murray N, Lerma V, G
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Chapter 3__________________________
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ecent years (Goldenberg et al. 2001
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Larsen et al. (2007) conducted a st
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straightforward regressive format.
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F D (v, t) = 100 v ≥ 41.1 m/s (3.
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where j represents the exposure sit
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Figure 3.1: Map of United States wi
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3. 25% decrease in frequency, 10% i
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Table 3.5: Annual Damage Risk at ye
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9, the cumulative damage costs incr
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cumulative damage cost for Climate
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identified. The wind speed is assum
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identifying a typical residential b
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from the figures it is apparent tha
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Figure 3.7: Damage Cost per 100,000
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hurricanes over 50 years, were cons
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Elsner, J.B., Kossin, J.P., and Jag
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Leicester, R.H., Bubb, C.T.J., Dorm
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Pratson, L.F., and Haxby, W.F. (199
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Zigomanis, A. (2007) Land Prices St
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4.1 AbstractStorm related power out
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intensity/frequency may change as a
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uniform) reliabilities for distribu
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account for the P-Δ effect in util
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4.4.3 Degradation of timber polesTh
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Then the time in which decay reache
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2 2 2COV d = COV wood + COV climate
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Figure 4.5: Flowchart of the Develo
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4.6.1 Design of Distribution PolesT
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Table 4.3: Required Circumference a
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Table 4.5: Statistics for Wind Load
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Cases 1 and 4, which resulted in la
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Table 4.7: Mean and COV of Resistan
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10.9Probability of Failure0.80.70.6
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This analysis assesses the impacts
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0.400.350.30P f0.250.200.150.100.05
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4.6.6 Effects of Degradation and Cl
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degradation is not considered, the
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other climate change scenarios are
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Ellingwood, B. R., and Tekie, P.B.
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Peel, M. C., Finlayson, B. L., and
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Wolfe, R. and Moody, R. (1997) Stan
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5.1 AbstractThis paper presents a r
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are more exposed to hurricane winds
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the load being designed for (i.e. w
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structural capacity of a distributi
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distribution poles of the required
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categories, the replacement cost fo
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ecommends that the discount rate fo
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COV of 20% for the resistance. The
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for details on how the equations ou
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The updated annual p f is determine
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Table 5.4: Cost of the “Do Nothin
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eplacing poles with stronger ones a
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Figure 5.6: 10th, 50th, and 90 thMi
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Four mitigation measures were explo
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Columbia Broadcasting System (CBS)
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Reed, D.A., Powell, M.D., and Weste
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Chapter 6__________________________
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(W&PE 2003). Social vulnerability i
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variation in surge height under var
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6.3.1 Hurricane Wind Models and Hur
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model that estimates surge height b
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model will be used to determine the
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Next, the weights for the dimension
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where ζ 1 refers to the proportion
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to be 2.2 m, 2.5 m, 2.6 m, and 2.7
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and older) will increase significan
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and maximum values are determined u
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Table 6.5: CCSVI(50) for the weight
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estimated to be 7.60; therefore, th
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The CCSVI can be established for va
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6.6 Conclusions and Future WorkIt i
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CCSP (2008) Impacts of Climate Chan
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Li, Y., and Ellingwood, B.R. (2006)
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Vickery, P.J., Wadhera, D., Twisdal
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parameters should be assessed throu
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systems. Because of infrastructure
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per hour are combined to estimate t
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Community Social Vulnerability Inde
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Willis, J.A.B., Lee, B.E., and Wyat
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not possible at this time; however,
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ReferencesAdachi, T. and Ellingwood
Page 271 and 272:
Bjarnadottir, S., Li, Y. and Stewar
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Das, P.K., Sinha, M.C., and Balasub
Page 275 and 276:
Georgiou, P.N., Davenport, A.G., an
Page 277 and 278:
Jagger, T., and J. Elsner, (2006) C
Page 279 and 280:
Larsen, T., Porter, K., Zadeh, M.,
Page 281 and 282:
Mitsuta, Y., Fujii, T., and Nagashi
Page 283 and 284:
Office of Best Practice Regulation
Page 285 and 286:
Reed, D.A. (2008) Electric utility
Page 287 and 288:
http://www.csiro.au/resources/~/med
Page 289 and 290:
Van der Sande, C.J., de Jong, S.M.,
Page 291 and 292:
Wolfe, R., Bodig, J., and Lebow, P.
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km-% of houses located further than
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Hanover County, and Galveston Count
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From Table AI.3, it can be seen tha
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strengthening stays the same. From
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number of houses needed to be retro
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the number of distribution poles th
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enefits for the 56 m/s which is ass
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een identified, a multivariate regr
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Table AIII.1 shows the results of t
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This is documentation for Chapter 2
Page 313 and 314:
This is documentation for Chapter 4
Page 315 and 316:
6. the right to post your revised t
Page 317 and 318:
Psychology Press authors reporting
Page 319:
This is documentation for Figure 3.
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