FROM RISK MANAGEMENT TO QUANTITATIVE DISASTER RESILIENCE A PARADIGM SHIFT

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1 S. P. Smonovc, Int. J. of Safety and Securty Eng., Vol. 6, No. 2 (2016) FROM RISK MANAGEMENT TO QUANTITATIVE DISASTER RESILIENCE A PARADIGM SHIFT S. P. SIMONOVIC Department of Cvl and Envronmental Engneerng, The Unversty of Western Ontaro, London, Ontaro, Canada ABSTRACT There are practcal lnks between dsaster rsk management and sustanable development leadng to the reducton of dsaster rsk and re-enforcng reslence as a new development paradgm. There has been a notceable change n dsaster management approaches, movng from dsaster vulnerablty to dsaster reslence; the latter vewed as a more proactve and postve approach. As hazard s ncreasng, at the same tme, t erodes reslence. In the past, standard dsaster management consdered arrangements for preventon, mtgaton, preparedness and recovery, as well as response. However, over the last 10 years substantal progress has been made n establshng the role of reslence n sustanable development. Multple case studes around the world reveal lnks between attrbutes of reslence and the capacty of complex systems to absorb dsturbance whle stll beng able to mantan a certan level of functonng. There s a need to focus more on acton-based reslence plannng. Dsasters do not mpact everyone n the same way. It s clear that the problems assocated wth sustanable human wellbeng call for a paradgm shft. Use of reslence as an approprate matrx for nvestgaton arses from the ntegral consderaton of overlap between: (a) physcal envronment (bult and natural); (b) socal dynamcs; (c) metabolc flows; and (d) governance networks. Ths paper provdes an orgnal systems framework for quantfcaton of reslence. The framework s based on the defnton of reslence as the ablty of systems to absorb dsturbance whle stll beng able to contnue functonng. The dsturbance depends on spatal and temporal perspectves and drect nteracton between mpacts of dsturbance and system adaptve capacty to absorb dsturbance. Keywords: adaptve capacty, natural dsasters, reslence, system performance. 1 INTRODUCTION From the 1980s to the last decade, the annual economc losses caused by natural dsasters have ncreased from $50 bllon to $180 bllon and of these losses, 75% are lnked to extreme weather events. The trend suggests that losses wll contnue to ncrease n future years due to economc development, populaton growth, rapd urbanzaton and clmate change. In order to mtgate the sgnfcant damages assocated wth natural dsasters and extreme hydrometeorologcal events n partcular, t s recommended to ntegrate dsaster rsk management schemes nto varous plannng, desgn and operatonal polces [1, 2]. The terms hazard, vulnerablty, dsaster and rsk cover a very broad range of phenomena and are nterpreted and understood by dfferent people n dfferent ways [3]. Many defntons of dsasters are lmted by notons of mpact and damage. The term such as dsaster rsk and dsaster losses are essentally our nterpretatons of the negatve economc and socal consequences of natural events. Human judgment s subject to value systems that dfferent groups of people may have and therefore these terms may be subject to dfferent defntons. The dsaster rsk, at varous locatons, may ncrease by human actvty lke Ths paper s part of the Proceedngs of the 10th Internatonal Conference on Rsk Analyss (RISK 2016) WIT Press, ISSN: (paper format), ISSN: X (onlne), DOI: /SAFE-V6-N

2 86 S. P. Smonovc, Int. J. of Safety and Securty Eng., Vol. 6, No. 2 (2016) napproprate land use practces. Also, the dsaster rsk may be reduced by protecton structures and/or effectve emergency plannng. The real dsaster rsk therefore, stems from the lkelhood that a major hazardous event wll occur unexpectedly, and t wll mpact negatvely on people and ther welfare. Many hazard mpacts result from a combnaton of physcal exposure and human vulnerablty to hazard. Physcal exposure reflects the type of hazardous event that can occur, and ts statstcal pattern, at a partcular locaton. The human vulnerablty reflects key soco-economc factors such as the number of people at rsk, the extent of defence works and the ablty of the populaton to antcpate and cope wth dsaster. Tradtonal dsaster rsk management s defned as the combnaton of three elements: () the hazard that s n the context of ths work the probablty of occurrence of a hazardous event; () exposure that s the locaton of people, property, nfrastructure and ndustry relatve to the hazard; and () vulnerablty that s the susceptblty of people, property, nfrastructure and ndustry to damage caused by the hazard [1]. In order to manage dsaster rsk, measures are taken to reduce the vulnerablty of the system components exposed to the hazards. More recently, however, there has been a shft from the tradtonal, vulnerablty-drven approach to dsaster reslence that s the foundaton of the presented research [4]. Reslence n the context of dsaster management s defned as: the ablty of a system and ts component parts to antcpate, absorb, accommodate or recover from the effects of a hazardous event n a tmely and effcent manner, ncludng through ensurng the preservaton, restoraton or mprovement of ts essental basc structures and functons, [2]. Whle dsaster rsk management focuses on the reducton of pre-hazard vulnerabltes, dsaster reslence s acheved by ntroducng adaptaton optons that enable the communty to adapt to the mpacts of the hazard and enhance the ablty of the physcal, socal, economc sectors to functon n the event of a dsaster. These adaptaton optons help the system components to cope wth and recover from hazard mpacts n order to return to a pre-dsaster level of performance as rapdly as possble. Adaptaton optons can be grouped nto four categores: () robustness that s the strength or the ablty of the system to resst hazard-nduced stresses (e.g. flood protecton measures); () redundancy that s the ablty of a system to provde unnterrupted servces n the event of a dsrupton (eg. a twnned ppelne); () resourcefulness that s the utlzaton of materals (monetary, technologcal, nformatonal, and human resources) to establsh, prortze and acheve goals (e.g. moblzaton of dsaster management funds); and (v) rapdty that s the capacty to return the system to a pre-hazard level of functonng as quckly as possble [5]. Evdently, reslence s a proactve means of dsaster management makng t more desrable for mplementaton [4]. It s apparent that the need for the ntegraton of dsaster reslence management nto plannng, desgn and operatonal polces s strong. Suffcent lterature s avalable on the conceptualzaton of dsaster reslence [5,6]. More recently, however, researchers have found mert n defnng reslence quanttatvely [5,7]. Most of the proposed approaches are estmatng the reslence as a tme-ndependent measure and do not provde much nsght about the recovery capablty of the system over tme. The tme-ndependent statc reslence s merely an abstract attrbute of the system and do not completely descrbe the state of the system under dsturbance. Thus, the tme-ndependent statc reslence measures are practcally neffectve for plannng and developng approprate system recovery strateges from a dsaster. The frst sgnfcant attempt to quantfy reslence as a functon of tme and space s made by Smonovc and Peck [4] and snce then has emerged as a crtcal characterstc of complex dynamc systems n a range of dscplnes ecology, engneerng, health scences, socal

3 S. P. Smonovc, Int. J. of Safety and Securty Eng., Vol. 6, No. 2 (2016) 87 scences and economcs. Implementaton of dynamc measure through smulaton n tme and space enhances the understandng of the system capablty to recover from a dsastrous event. Smulaton s a natural systems modellng approach that can be used n the analyss of dynamc systems. A reslence metrc of Smonovc and Peck [4] allows for prortzaton of regons and systems (and ther components) that requres adaptaton upgrades. It also allows for the comparson of adaptaton optons that mprove communty reslence and the functonng of crtcal facltes n the event of a dsaster. The followng secton of the paper presents a new reslence measure and ts adaptaton for addressng spatal and temporal changes of complex systems subject to dsasters. In the thrd secton, the use of the proposed measure s llustrated through a presentaton of three examples. The paper ends wth a short dscusson and set of conclusons. 2 SPACE-TIME DYNAMIC RESILIENCE MEASURE The quanttatve reslence measure, frst ntroduced by Smonovc and Peck [4] followng Cutter et al. [6], has two qualtes: nherent (functons well durng non-dsaster perods); and adaptve (flexblty n response durng dsastrous events) and can be appled to physcal envronment (bult and natural), socal systems, governance network (nsttutons and organzatons), and economc systems (metabolc flows). An orgnal space-tme dynamc reslence measure (STDRM) of Smonovc and Peck [4] s desgned to capture the relatonshps between the man components of reslence; one that s theoretcally grounded n systems approach, open to emprcal testng, and one that can be appled to address realworld problems n varous communtes. 2.1 Mathematcal defnton of STDRM STDRM s based on two basc concepts: level of system performance and system adaptve capacty. They together defne reslence. The level of system performance ntegrates varous mpacts () of system dsturbance (dsastrous event). The followng mpacts (unts of reslence (r )) can be consdered: physcal, health, economc, socal and organzatonal, but the general measure s not lmted to them. Measure of system performance P (t, s) for each mpact () s expressed n the mpact unts (physcal mpact may nclude for example length [km] of road beng nundated; health mpact may be measured usng an ntegral ndex lke dsablty adjusted lfe year or somethng smpler lke number of hosptal beds; and so on). Ths approach s based on the noton that an mpact, P (t, s), whch vares wth tme and locaton n space, defnes a partcular reslence component of a system under consderaton, see Fg. 1 adapted from Smonovc and Peck [4]. The area between the ntal performance lne P 0 (t, s) and performance lne P (t, s) represents the loss of system performance, and the area under the performance lne P (t, s) represent the system reslence (r (t, s)). In Fg. 1, t 0 denotes the begnnng of the dsturbance, t 1 the end, and t r the end of the recovery perod. In mathematcal form the loss of reslence for mpacts () represents the area under the performance graph between the begnnng of the system dsrupton event at tme (t 0 ) and the end of the dsrupton recovery process at tme t r. Changes n system performance can be represented mathematcally as: t ( )= 0 ( ) where t [ t0, t r ] r ts, P P t, s dt t0 (1)

4 88 S. P. Smonovc, Int. J. of Safety and Securty Eng., Vol. 6, No. 2 (2016) Fgure 1: System performance. Fgure 2: System reslence. When performance does not deterorate due to dsrupton P 0 (t, s) = P (t, s), the loss of reslence s 0 (.e. the system s n the same state as at the begnnng of dsrupton). When all of system performance s lost, P (t, s) = 0, the loss of reslence s at the maxmum value. The system reslence, r (t, s) s calculated as follows: r r (, ts) ( t, s)= 1 P ( t t ) o o (2) As llustrated n Fg. 1, performance of a system whch s subjected to a dsaster event drops below the ntal value and tme s requred to recover the loss of system performance. Dsturbance to a system causes a drop n system reslence from value of 1 at t 0 to some value r (t 1, s) at tme t 1, see Fg. 2. Recovery usually requres longer tme than the duraton of dsturbance. Ideally, reslence value should return to a value of 1 at the end of the recovery perod, t r (dashed lne n Fg. 2); and the faster the recovery, the better. The system reslence (over all mpacts ()) s calculated usng:

5 S. P. Smonovc, Int. J. of Safety and Securty Eng., Vol. 6, No. 2 (2016) 89 M = 1 Rts (, )= r ( ts, ) 1 M (3) where M s total number of mpacts. The calculaton of STDRM for each mpact () s done at each locaton (s) by solvng the followng dfferental equaton: r () t = () () t AC t P t (4) where AC represents adaptve capacty wth respect to mpact. The STDRM ntegrates reslence types, dmensons and propertes by solvng for each pont n space (s): Rt () t 2.2 Computatonal mplementaton of STDRM = AC () t P () t (5) The mplementaton of the presented framework s proceedng by usng system dynamcs (SD) smulaton approach together wth spatal analyss software. SD s a logcal problem-solvng technque, whch combnes tradtonal management of complex systems and feedback theory wth computer smulaton for the purpose of ganng a better understandng of real-world system behavour. SD smulaton s an approprate approach for capturng the temporal dynamcs of dsaster reslence, but s not orgnally ntended for spatal modellng. The followng approach s developed for addressng a set of techncal challenges nvolved n lnkng together spatal and temporal smulaton. The man lnk s establshed through an ndependent couplng program (CP). The mplementaton ncludes the functonalty of: Vensm [8] for SD temporal smulaton; ArcGIS [9] for spatal GIS analyses; and CP desgned to provde the brdge between the frst two usng Python [10]. In ths way, the STDRM s able to smulate the entre set of dsaster mpacts under consderaton (physcal, economc, socal, health and organzatonal). 3 ILLUSTRATIVE EXAMPLES Three examples are selected to brefly present the utlty of STDRM n dsaster management. 3.1 Example 1: space-tme dynamc reslence of a communty to floodng There are two hosptals (heren referred to as HA and HB) whch servce a cty (see Fg. 3). Ths cty area covers raster cells (18,352 cells). The populaton wthn each raster cell s known and vares between [0, 6] people. Each of the hosptals, HA and HB, provdes health servces to a porton of the cty populaton: Servce Area of Hosptal A (SA-HA); Servce Area of Hosptal B (SA HB). Populaton of both servce areas uses a road network to access each hosptal locaton. A flood s ntroduced as a shock to the health system (shaded area n Fg. 3). Ths dsturbance affects the performance of health system n the cty by mpactng access of people affected by the dsaster to hosptal servces. As the road network becomes nundated, the SAs for each hosptal are adjusted to reflect the shortest

6 90 S. P. Smonovc, Int. J. of Safety and Securty Eng., Vol. 6, No. 2 (2016) Fgure 3: Locaton of hosptals. Fgure 4: Reslence of hosptal A. travel dstance to servce. Thus, a locaton ntally servced by HA may at some pont durng the smulaton become servced by HB and the populaton servced by each hosptal at any gven tme wll change dependng on the avalablty of the road network. These SAs are used to determne a servce populaton for each hosptal. Ths value, n turn, s transferred to the SD smulaton model and used n fnal calculaton of reslence. The result s a seres of maps that show changes n areas and populaton served by each hosptal and the correspondng reslence value (Fg. 4 bottom wndow) and set of temporal graphs that show changes n servceable populaton, patents affected by the dsaster, and reslence over tme (Fg. 4 upper wndow). 3.2 Example 2: space-tme dynamc reslence of a sngle-purpose reservor to water scarcty In ths example, the space-tme dynamc reslence of a sngle-purpose reservor (desgned for rrgaton) has been quantfed usng the approach presented n the paper. The reservor s subject to changng nflow and rrgaton demand that wll affect the reslence of ts water supply. Clmate change and other factors lke growng needs for food producton are sources of water scarcty n many places around the world. Insght n the reservor reslence can provde support for makng nformed decsons n these crcumstances. The smple sngle-purpose reservor smulaton model conssts of the contnuty equaton and a set of operatonal constrants. The contnuty equaton s expressed as: St = St 1 + It IRt Ot SPt (6)

7 S. P. Smonovc, Int. J. of Safety and Securty Eng., Vol. 6, No. 2 (2016) 91 where S t s the storage durng the tme perod t; S t-1 s the storage n the reservor durng prevous tme perod; I t s the nflow durng the tme perod t; IR t s the total rrgaton release from the reservor durng the tme perod t; O t are the losses from the reservor (evaporaton and other leakage losses); and SP t s the spll from the reservor durng the tme perod t. The system constrants, reservor operatng rules and the release decsons are captured usng IF-THEN-ELSE statements n the smulaton model. If the storage s greater than the rrgaton demand, then the actual demand s released; else the avalable storage s released. The system performance for the reservor rrgaton (SP,t ) s expressed as the rato of actual release made for rrgaton and the demand durng the tme t: IRt SP t, = (7) demand IR where IR t demand s the rrgaton demand durng the tme perod t. Ths performance measure s used for quantfyng the space-tme dynamc reslence. Analyss of the nflow data shows that the reservor s hghly ntermttent n nature and receves nflow only durng the monsoon season. Inflow durng the non-monsoon season s neglgble. The reservor supples rrgaton water to the command area at the downstream through the lft rrgaton scheme. The llustratve model s set to nclude 100 ndvdual felds to be rrgated by the reservor. The releases are made sequentally startng from feld 1, whch s closest to the reservor fnshng wth fled 100 beng furthest away from the reservor. In event of water scarcty, all the felds may not be rrgated to ther full demand. The system performance s estmated ndvdually for each feld usng Eqn (7). The computatonal procedure ntegrates (a) SD reservor and reslence smulaton models and (b) spatal rrgaton release dstrbuton model. Integrated system provdes for spacetme dynamc reslence calculaton of sngle-purpose reservor operatons. t Fgure 5: Temporal varaton of reslence to rrgaton water scarcty. Fgure 6: Spatal varaton of reslence to rrgaton water scarcty.

8 92 S. P. Smonovc, Int. J. of Safety and Securty Eng., Vol. 6, No. 2 (2016) The dynamc reslence of all 100 felds s shown n Fg. 5. It s observed that ntally all felds receve suffcent amount of water resultng n hgh reslence for most of the tme. Once the demand exceeds the supply, falure state, the reslence of ndvdual felds starts to vary sgnfcantly due to partal satsfacton of the demand, or no satsfacton. Durng the falure perods, due to lower amount of water beng avalable, not all the felds receve the full demand. Only the closest few felds receve the full demand, some felds receve partal demand and most of the felds do not receve any water. Hence, there s varaton n reslence value wth tme and n space. The spatal dynamc reslence of all 100 ndvdual felds s shown n Fg. 6 for four selected tme perods. Fgure 6a shows reslence of all felds at early part of the smulaton perod, day 30. Durng the ntal perod, rrgaton demand of all ndvdual felds s fully met and therefore a hgh reslence value. However, over of tme, due to lower nflow n to the reservor, the rrgaton demand can be satsfed only for some felds. Due to the smple assumptons that the water from the reservor s delvered n accordance wth the dstance (frst the closets and so on) some felds closer to the reservor get ther demand fully satsfed whereas felds further away do not. Fgure 6b shows the spatal dstrbuton of reslence at day 710. The felds closer to the reservor always receve suffcent amount of water and therefore show hgh reslence. Most of the felds further away fal to receve the rrgaton releases and hence ther reslence drops to zero. Fgure 6c shows day 715 where some of the felds already recovered from falure and ther reslence ncreased. Fgure 6d shows day 780 when almost all felds are recevng the water for rrgaton and ther reslence s on the rse. As t can be seen from Fg. 6, the value of reslence of ndvdual felds vares sgnfcantly. 3.3 Example 3 space-tme dynamc reslence of complex nfrastructure networks The nfrastructure network model s based on network theory, where two basc components, nodes and edges, buld up the model of a system. A network s represented by set of nodes G, set of junctons and end ponts N, and set of undrected segments E. For llustratve purposes, the smple nfrastructure system s consdered as shown n Fg. 7. The system contans streets (grey layer n Fg. 7), power grd (red layer n Fg. 7), water supply network (blue layer n Fg. 7), and nformaton nfrastructure layer (green layer n Fg. 7). Fgure 7: Schematc of nterdependent nfrastructure system model.

9 S. P. Smonovc, Int. J. of Safety and Securty Eng., Vol. 6, No. 2 (2016) 93 Snce varous hazards may affect the nfrastructure components, ther locaton n space s of hgh mportance. In an nfrastructure network model, the locaton of nodes s modelled n accordance wth ther geographcal locaton, defned n a two-dmensonal Eucldean coordnate system. Each node n the nfrastructure network has three coordnates (j, x, y), where j denotes the type of nfrastructure, and x and y denote the geographcal locaton of the node. Snce network nfrastructure elements exhbt a hgh level of nterdependences the model ncludes: node dependences; node/edge path dependences; node/edge cluster dependences; and geographc dependences. The reslence model of complex nfrastructure system consders magntude of nterrupted servces and the duraton of nterrupton. Each element of nfrastructure system can be n one of two states: functonng (1) and not functonng (0). Followng the mathematcal descrpton of the nterconnected nfrastructure network system and orgnal defnton of reslence [4], the sngle layer (j) reslence for multple dsturbances (j) s calculated (usng three dmensons: robustness, resourcefulness and rapdty) as: r = r + r z1, z2 zd j; z1, z2 zd f; z1, z2 zd f PA RR = z jz, R 1 1, z2 zd Rap 1 R R fz, 1 Rob fz, 1, z2 zd Rap and for multlayer nfrastructure system as: () t + z, z z j, z R d Rap 1 fz, 1 fz, 1 ( R ( t) R ( t)) Res fz, 1, z2 zd R ap Rob R (8) r = r + r z1, z2 zd z1, z2 zd z1, z2 zd PA RR = z z z z d R 1 1, 2 Rap R z1 Rob () t 1 2 d Rap + z1, z2 zd 1 R 1 R Rap z, z z z R 1 z1 z1 ( R ( t) R ( t)) Res z1, z2 z d Rap Rob (9) The notaton used n Eqns (8) and (9) s shown n Fg. 8 for the case wth two dsturbances. A smple numercal example ncludng a multlayer nfrastructure network wth 16 street crossngs and end ponts, 54 street segments, 16 water nfrastructure elements (pumps, Fgure 8: Infrastructure network performance.

10 94 S. P. Smonovc, Int. J. of Safety and Securty Eng., Vol. 6, No. 2 (2016) Fgure 9: Case study results. storage facltes, etc), 16 water ppes, 36 electrc sources, 16 electrc transmsson lnes, 5 nternet provder nodes and 8 nternet cable connectons s consdered. The network s subjected to floodng. The smulaton results shown n Fg. 9 compare the network reslence for each layer and complex multlayer system for fve adaptaton strateges: () RS-FF - frst repar the frst falure (blue lne n Fg. 9); () RS-FL - frst repar the last falure (red lne n Fg. 9); () RS-IE- frst repar the crtcal components (grey lne n Fg. 9); (v) RS-ED frst repar

11 S. P. Smonovc, Int. J. of Safety and Securty Eng., Vol. 6, No. 2 (2016) 95 the obvous dependent elements (orange lne n Fg. 9); and (v) RS-EP frst repar the non-obvous dependent elements (dark blue lne n Fg. 9). 4 DISCUSSION AND CONCLUSIONS The paper presents an orgnal framework for the quantfcaton of reslence through spatal SD smulaton, STDRM. The quanttatve reslence measure can combne varous mpacts (economc, socal, health, physcal etc.) caused by natural dsasters. The framework s desgned to provde for: () better understandng of factors contrbutng to system reslence; and () comparson of adaptaton optons usng reslence as a decson-makng crteron. The developed measure defnes reslence as a functon of tme and locaton n space. Three llustratve examples demonstrate the utlty of the proposed measure. REFERENCES [1] World Bank, Dsaster Rsk Management, avalable at [2] IPCC, Summary for polcymakers. Managng the Rsks of Extreme Events and Dsasters to Advance Clmate Change Adaptaton, eds. C.B. Feld, V. Barros, T.F. Stocker, D. Qn, D.J. Dokken, K.L. Eb, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tgnor & P.M. Mdgley, Cambrdge Unversty Press: Cambrdge, pp. 1 19, [3] Smonovc, S.P., Systems Approach to Management of Natural Dsasters: Methods and Applcatons, Wley: Hoboken, [4] Smonovc, S.P. & Peck, A., Dynamc reslence to clmate change caused natural dsasters n coastal megactes quantfcaton framework. Brtsh Journal of Envronment & Clmate Change, 3(3), pp , [5] Bruneau, M., Chang, S.E., Eguch, R.T., Lee, G.C., O Rourke, T.D., Renhorn, A.M., Shnozuka, M., Terney, K., Wallace, W.A. & von Wnterfeldt, D., A framework to quanttatvely assess and enhance the sesmc reslence of communtes. Earthquake Spectra, 19(4), pp , [6] Cutter, S.L., Barnes, L., Berry, M., Burton, C., Evans, E. & Tate, E., A place-based model for understandng communty reslence to natural dsasters. Global Envronmental Change, 18, pp , [7] Ayyub, B.M., Practcal reslence metrcs for plannng, desgn, and decson makng. ASCE-ASME Journal of Rsk and Uncertanty n Engneerng Systems, Part A: Cvl Engneerng, 1(3), , [8] Ventana systems, Vensm Reference Manual, avalable at [9] ESRI, ArcGIS, avalable at [10] Python org, Python 3.0, avalable at