Carlo Giupponi, Vahid
Mojtahed, Animesh K. Gain
and Stefano Balbi
Integrated Assessment of
Natural Hazards and Climate
Change Adaptation:
I. The KULTURisk
Methodological Framework
ISSN: 1827-3580W o r k i n g P a p e r s D e p a r t m e n t o f E c o n o m i c s C a ’ F o s c a r i U n i v e r s i t y o f V e n i c e N o . 0 6 / W P / 2 0 1 3
ISSN 1827-3580
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Department of Economics Ca’ Foscari University of Venice Cannaregio 873, Fondamenta San Giobbe 30121 Venice Italy
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Integrated Assessment of Natural Haza rds and Climate Change Adaptation:
I. The KULTURisk Methodological Framew ork Carlo Giupponi† Vahid Mojtahed† Animesh K. Gain† Stefano Balbi ‡† † University of Venice
‡ Basque Centre for Climate Change (BC3)
First Draft: March 2013
Abstract
A conceptual framework integrating different disciplines has been developed to comprehensively evaluate the benefits of risk prevention. Three main innovations are proposed with regards to the state of the art: (1) to include the social capacities of reducing risk, (2) to go beyond the estimation of direct tangible costs, and (3) to provide an operational solution to assess risks, impacts and the benefits of plausible risk reduction measures. The traditional risk metric in the physical sciences is the expected damage (direct tangible costs), which is defined as a function of hazard, vulnerability (physical) and exposure. The last element, exposure, provides the information to convert results into monetary terms. In the development of the KULTURisk Framework (KR-FWK), we considered several different pre-existing proposals, and we designed a new one as a conceptual model and also a flow-chart for the elaboration of information. The proposed KR-FWK is thus expected to provide: 1) an operational basis for multidisciplinary integration; 2) a flexible reference to deal with heterogeneous case studies and potentially various types of hazards; and 3) a means to support the assessment of alternative risk prevention measures including consideration of social and cultural dimensions. The project case studies of the process are expected to provide a quite diversified set of situations, allowing to consolidate the framework itself and to develop ad hoc tailored solutions for most common implementation cases.
Keywords
Natural disasters, Integrated Risk Assessment, Climate change adaptation
JEL Codes
Q51, Q54, D81
Address for correspondence: Carlo Giupponi
Department of Economics Ca’ Foscari University of Venice Cannaregio 873, Fondamenta S.Giobbe 30121 Venezia - Italy Phone: (++39) 041 234 9126 Fax: (++39) 041 234 9176 e-mail: cgiupponi@unive.it
This Working Paper is published under the auspices of the Department of Economics of the Ca’ Foscari University of Venice. Opinions expressed herein are those of the authors and not those of the Department. The Working Paper series is designed to divulge preliminary or incomplete work, circulated to favour discussion and comments. Citation of this paper should consider its provisional character.
1
Introduction
Several l egal document s incl udi ng the E uropean Flood Direct ive (2007/60/EC) call for the development of “flood risk maps showing the potential adverse consequence” of floods with different return times. Thos e m aps, t ogether with the results of other anal ys es and i n parti cul ar economic valuati on s , are then used as planning instruments to s upport decisi ons . The need thus em erges for methods and tools t o ass ess the advers e cons equences of t he flood risk i n an i nt egrat ed, com prehensi ve, and coherent m anner. Such effort i s in ess ence m ultidisci plinar y, incl uding contri buti ons rangi ng from h ydrology, and environmental sci ences t o economi c and s oci al sci ences. However, when dis cus sing about nat ural dis ast ers, t he not ions of vulnerabilit y and ri sk and the approaches for their ass essm ent have found di ffer ent and oft en contrasti ng solutions by various schools of thought in the recent years . Therefore , a strai ghtforward sol ution for dis cipli nar y int egration does
not exist and conceptual di screpanci es and terminologi cal
inconsis tencies em ergi ng from the vario us res earch comm unit ies have to be solved preliminaril y (Thom all a et al ., 2006; M ercer, 2010; R enaud and P erez, 2010). This is not an unusual si tuation , whi ch makes it diffi cult to col laborate wit hin an int erdi s cipli nar y environm ent, and thus limits the number of avail abl e operational soluti ons t o cope wi th soci etal needs and b y law obl igati ons of com pet ent authoriti es .
The defi niti on of ris k its elf and it s m easurem ent are still open issue s in the sci entific lit erature. Man y di sci plines dealing wit h ris k have different vi ews about its definition and t hus t he components that have t o be consi dered t ogether wit h t he process of its cal culat ion.
Prel iminar y anal ys es conducted in preparation of t he development of this paper brought to identi f y a seri es of evidences that were kept in the background duri ng al l the activiti es:
a) subst ant ial di screpanci es are evident in the ris k literat ure, fragm ented into m any disci plinar y s tream s;
b) at l east t wo disti nct research st reams are of great est i nt erest for our work: Dis ast er Risk Reduction (DRR), and Cli mat e C hange Adapt ation (CCA);
c) the ambiti on of tr yi ng to uni f y t he t erm inologies in us e is out of scope and KULTURi sk does not have the role for having an adequat e im pact , at the int ernational level, but can i nstead contri but e si gni ficantl y b y providi ng communi cation i nt erfaces and operational solutions ;
d) moreover, defi nitions are evolving withi n each comm unit y;
e) risk ass ess ment is usual l y focus ed on damages, i .e. direct tangibl e cost s, but t he y are (also b y law) onl y li mited m easures of ris k; other direct , i ndi rect, and int angibl e costs should be consi dered, whenever possibl e;
f) in general, soci al and non -ph ysical aspe cts are cruci al for a com prehensive ass es sment of t he ri sk;
One well est ablis hed approach for t he cal cul ation of risk in the ph ysi cal/ environm ental (P/E) s ciences within DRR res earch comm unit y refers risk t o the expect ed damage s (more preci sel y ‘di rect t a ngi bl e costs’), which are calculated as a function of hazard, P/E vulnerability , and exposure (C ri cht on, 1999):
R = f (H, V, E) [1]
The fi rst element is charact eriz ed b y probabilit y di stribut ions or referred to specific return t imes , and t ogethe r with t he s econd it is us ual l y expressed as a dim ensionles s index, whi le the l att er, exposure, provi des the unit of m easurem ent of risk, that is m one y .
This fram ework is s trai ght forward and widel y adopt ed, but it finds i ts limitat ions m ainl y i n the narrow considerati on of the com pl exit y of the various dim ensions of risk and in part icular of the social ones. In order to fi t within the formula report ed above, al l the ris k dim ens ions have to be ext rem el y simpl ifi ed and aggregat ed in order to produce t wo dimensionl ess i ndi ces of hazard and vulnerabil it y . This can be quite chall enging when t he att enti on is driven t o the soci al di mens ions of vulnerabilit y as in Cutter (1996 ), where it is disti nct from bioph ys i cal vulnerabilit y, but l at er aggregat ed i nt o a si ngl e notion of “pl ace vulnerability”.
While the DR R com munit y dri ves more emphas is on t he concept of ri sk, other res earch streams and in parti cular the one focused on clim at e change adapt ation (CCA), under the auspices of the Int e rgovernm ental Panel on Cli mat e C hange ( IPCC), were more focus ed on the as ses sm ent of vul nerabili t y. In t he DRR st udi es, vul nerabilit y i s indeed considered, but it is m ainl y regarded as an input for the quant ifi cat ion of ri sk. Inst ead, CCA res earch consi der s vulnerabilit y as an output deriving from soci al condi tions and process es , and i n parti cul ar b y the combination of the st atus of the adapti ve capacit y of t he social -ecologi cal s yst em and the pot enti al i mpact s deriving i n turn from t he combinati on of local sensitivi t y and the exposure to a speci fic hazard (Kl ein, 2004; IPCC -AR4 2007).
As a consequence , whil e DRR focuses on the knowl edge of hazard b y means of ris k anal ys is, CC A is focus ed more on the import ance of understandi ng the behavi our of and the conse quences for t he - local - comm uniti es involved b y m eans of vul nerabili t y ass es sm ent.
The t wo main res earch st reams have been increas ingl y i ntegrat ed since the clim at e change dimension has gained ever-great er att ent ion in t he considerati on of nat ural disas t ers, whil e clim at e vari abil it y and ext rem e events have been brought t o the core of both clim at e change sci ence and the politi cal agenda. The process of i nt egration between DR R and CCA on one hand and bet ween ph ysi cal / envi ronm ental and soci al sci ences on
the other hand i s s till in progress. Effects are cl earl y vi sibl e when considering t he s equence of IPCC publ ications in recent years . In t he fram ework adopted b y the IPCC -AR4 (2007), briefl y des cribed above, the concept of risk is missing , whil e potent ial im pacts (of clim at e change) we re included and vulnerabilit y result ed as an output . Recentl y, with the publ icati on of IPCC -SREX (2012), a s ubst ant ial move from the CCA communit y towards t he concepts and defi nitions consoli dat ed in t he DRR coul d be obs erved. Dis ast er risk is explicit l y i ncl uded in t he concept ual fram ework (s ee Fi gure 1), b ut the caus al chai n of rel ations bet ween clim ati c events and the concept s of vulnerabilit y and exposure is not clearl y defined, t hus lim iting t he possi bilit y to derive uncont roversi al operational assessm ent m ethods.
Figure 1: Managing the risk of extreme events and disasters to advance climate change adaptation, from IPCC- SREX (2012).
Man y aut hors and research proj ects have provi ded their own view on the subj ect m att er. Worth t o be mentioned here is the approach for
Probabi listi c Risk Ass essm ent proposed b y the CAPR A Platform1
(Cardona et al . , 2010) as an operati onal combination of multipl e disciplinar y models and cost benefit anal ys is for deci sion s upport. An alt ernat ive and rather complex concept ual framework can be found in the
MOVE P roj ect2 (2010) i n whi ch ri sk i s det ermined b y the i nteracti ons
bet ween the environment al dim ension in terms of haz ards and the soci et y with its s peci fi c feat ures i n t erms of s usceptibi lit y, fragili t y, resili ence, et c. A more mechanisti c approach based on s ys t em d ynamics a nd the notion of socio -ecos yst em m odelling can be found i n Turner et al. (2003), wit h focus on the anal ys is of vulnerabilit y i n relati on again to the not ions of resili ence, exposure, s ensit ivit y, etc., but wi thout explici t considerati on of risk.
In general , it is cl ear from t he anal ys is of the lit erat ure that there i s no practical solution for int egrating and s ynthesizing the m ain references without facing the need to decide among cont rasting definiti ons. Moreover, it should be noti ced that often t he propos ed fram eworks
1 See http://www.ecapra.org/. 2 See http://www.move-fp7.eu/.
provide onl y a pictorial repres entat ion of relat ionships among di fferent concept s without providi ng an y i denti fi cation of caus al or functional rel ati ons hips , which are instead t he basis for an y att empt to devel op operational al gorithms for r isk ass essm ent . Even in t he cas es i n which some sort of i ndex is propos ed (e.g. a vul nerabilit y index), t he procedure for the management of inform ation are oft en rather nai ve, wit h a preval ence of addit i ve procedures appli ed to di mensionl ess indicat ors , without t he adequate considerati on of fundament al iss ues, such as norm ali sat ion effects , internal compensati on, wei ghting, independence of vari abl es, et c.
Furthermore, risk as sess ment is usuall y focus ed on the val uation of the pot enti al cons equences , but ver y often thes e are limit ed to the expect ed dam ages in t erms of direct and t angi bl e expect ed costs. Yet ver y littl e att enti on i s gi ven to indi rect and intangible cos ts, whi ch are proven to be a quite rel evant component of the pot ential cons equences of a n atural disast er (Cochrane 2004, Oku yam a and Sahin 2009 ). Given the interconnecti vit y of the econom y at m ult iple scal es , it is very i mport ant to eval uate t he indi rect ris ks of flood dam ages outsi de of the dis ast er area t o achi eve a comprehensive ri sk ass ess ment. S i milarl y, the intangible dam age t o hum an health is a maj or com ponent of an y risk ass ess ment t hat oft en is descri bed ver y bri efl y and i n a ver y co arse manner.
Given t he current st atus of theoreti cal anal ys es and operational s oluti ons
bri efl y int roduced above, the KU LTURisk Proj ect3, an EU funded
research aim ed at devel oping a culture of risk prevention b y evaluati ng the benefits of di fferent ri sk prevention initiat ives, has approached the devel opm ent of a novel m ethodol ogy focused on di fferent t ypes of water -rel at ed cat ast rophes, such as i nundat ions, urban fl ash floods, and rainfall
tri ggered debri s flows4.
A methodologi cal fram ework and a n operat ional approach named Soci o Economi c R egional Risk Ass essm ent (S ERRA), devel oped up on the well -est abli shed R egional Risk As sessm ent m ethodol ogy (RR A ; Landis 2005) have been developed during the fi rst half of the 3 - year p roject, t o be implemented in a s eries of European proj ect cas e st udi es and els ewhere . The m ain purpos e is to provide an i nnovativ e appro ach for natural disast er ass essm ent s and clim at e change adaptation b y devel oping upon
pre-existing RR A5 approaches and b y: (i) includi ng consideration of the
soci al capacit ies of reduci ng ri sk, and ( ii ) defining an economic meas ure of ris k that goes be yond t he di rect t angi ble cos ts.
Accordi ng t o those purpos es , the KULT URisk Fram ework was desi gned to provi de: (i ) an operati onal basi s for m ultidis ci plinar y integrat ion; (ii ) a fl exible reference to deal wit h heterogeneous cas e s tudi es and pot enti all y vari ous t ypes of hazards; and (iii) means to s upport t he
3KULTURisk: Knowledge-based approach to develop a cULTUre of Risk prevention. FP7-ENV-2010
Project 265280 (http://www.kulturisk.eu/)
4 The present paper is the result of author’s elaborations on the contents of Project Deliverable 1.6. 5 For details about the KULTURisk approach to RRA, see Project deliverables 1.2 and 1.7 at
ass ess ment of al ternative ri sk reduction m easures including considerati on of soci al and cult ural dim ensions .
The m ain fo cus of the social dimensi on of ri sk is on t he role of ‘Adaptive’ and ‘Coping’ capacities of societies, which can prepare them for a bett er response to nat ural di sast ers as t he y face and adapt t o clim at e rel at ed risks and the unknowns of climate change. The economi c dimension is dealt with the estimation and monetization of a ‘Total Cost Matrix’ (TCM), made by the combination of the estim ated direct and indi rect and t angibl e and i ntangi ble costs .
As a whol e, SERR A is thus desi gned to com bine the noti on of p h ys i cal and envi ronment al ri sk together wi th s oci al vulnerabilit y and economical val ue fact ors to hel p poli c y/ deci sion m akers to perform a meti cul ous cost -benefit anal ys is (C BA) of different scenarios of ris k m iti gation at an aggregat ed or di saggregat ed sp at ial level. The framework and the methodol ogy were not desi gned upon C BA as the onl y poss ible soluti on gi ven that our specifi c aim was to provide operat ional s olutions for supporti ng decision s wit h focus on cli mat e change adapt ati on. Alt ernati ve methods, such as cost -effecti venes s or mul ti -crit eria anal ys es are als o considered and sol utions for im plem ent ati on are provided i n the foll owi ng s ecti ons.
In S ection 1, we s how t he concept ual discrepancies and t erminologi cal inconsis tencies em ergi ng from the vario us res earch comm unit ies deali ng with ris k and vul nerabilit y, and exami ne som e of t he main existi ng fram eworks, whi ch have inspi red our work. Two mai n innovati ons are propos ed wit h regard to t he st at e of t he art of di saster ris k asses sm ents: (1) to define a m easure of risk that goes be yond the di rect t angible costs , and (2) to incl ude the s oci al capaci ties of reducing ris k. Bot h thes e el ements of novelt y are t reated in S ection 2, where we present and describe the KU LTURisk Fram ework. In Secti on 3 and 4, we int roduce the approaches proposed for economi c valuation and for t he as sessm ent of the soci al dim ens ion. In Secti on 5, we descri be operational sol utions of the fram ework, whereas in S ect ion 6, we provi de t wo exampl es of applicati on cont ext s , whi ch sta y at t he extremes of the range of possi biliti es em erging from t reat ing risk in a s patiall y dis aggregated or aggregat ed wa y and as a di scret e or continuous relationship with t he
var ying levels of hazard. The choice between aggregated or
disaggre gated ris k asses sm ents m ainl y depends on t he availabi lit y of dat a. In S ection 7, we dis cus s the uncert aint y of disast er risk estim ati on. Finall y, we concl ude b y providing som e final rem arks .
2
Methodological framework for integrated risk assessment
A long pr ocess of collaborat ion and recursi ve exchange of i nterm edi at e draft s withi n the KULTUR isk cons ort ium brought us to a com prehensi ve gl ossar y of t he adopted termi nologi es, report ed in Tabl e 1 . The m ain sources of references are the IPCC -SREX (2012) and UNIS D R H yogo Fram ework (2009). The final choice of definit ions i s the responsibilit y of the aut hors of t his work, whi ch was based on the foll owing m ain cri teri a:
1. internal consis tenc y withi n the conceptual framework;
2. consist enc y wit h t he m ai n references of t he D RR and CCA literatures, havi ng i dentifi ed t he H yogo Fram ework and and the IP CC SREX R eport as the m ain ones ;
3. minimizing the changes compared to t he consoli dated approaches adopt ed within t he consortium.
Therefore, the KU LTURis k fram ework i mplem ented in SE R RA has the ambit ion t o offer an effecti ve i nterface and a common ground for teams working across divers e di sci plines , with the common aim of supporting decisi ons for ris k mi ti gat ion acti ons .
Before entering int o the det ails of the KU LTURisk Fram ework , it is important to consi der the decisi on -m aki ng cont ext in whi ch t he fram ework s houl d be utilized. In order t o impl em ent a decis ion-making process i n the fiel d of risk m anagem ent and as sessm ent of miti gation measures, a c ycl e of deci sion -m aki ng st eps can be identi fi ed, as propos ed b y UKC IP (2003). We disti ngui sh ei ght steps that we explain in the following paragraph (Fi gure 2).
Figure 2: Cyclic Decision-Making Flowchart for CCA, source: UKCIP(2003), redrawn. The firs t st ep is, obviousl y, st ating our goal , whi ch is devel oping a cult ure of wat er -rel at ed ri sk preventi on b y eval uating different risk reduction m easures. In t he s econd st ep, we need t o identif y t he receptors with respect to whi ch we would like t o assess the ris k and set forth the decisi on -making cri teria. The decision -making crit eri a can be for inst ance reduci ng the dam ages equal to 20% from flood or landsli des gi ven a certai n a m ount of budget or t he ease and cost of im pl em ent ation. In the t hird st ep, we assess risk wit h respect to identi fi ed recept ors,
haz ard features, and vulnerabilit y of the recept ors. In the next step, we point out our options for reducing t he ri sk. In t he fi ft h step, we evaluat e the m erit of our opti ons b y appl yi ng them to s everal cas e st udi es. From this point on, one can identi f y ot her steps rel at ed to m aking decision and post decisi on -making actions. In this work we focus our attent ion o n steps 2 to 5.
Herein, we first des cribe the com prehensi ve fram ework with reference t o the glos sar y report ed in Table 1. Init ial l y the KULTUR isk Fram ework was developed around t hree m ain subsections i denti fied as three m ain areas of expertis e withi n the consorti um, whi ch ar e des cribed in the foll owi ng: (1) the regional ris k ass essment, (2) the valuati on of pot enti al consequences , and (3) the soci al capacit ies/vul nerabiliti es . Aft er a l ong process of int eracti ons l eading t o a s equence of framework draft s, the sol ution was fo und in developing a com prehensive fram ework upon t he consolidat ed form alization of ri sk being a functi on of hazar d,
expos ur e, and vulnerabilit y . Therefore, t he final propos al complies with
the m ost consoli dat ed approach for risk ass ess ment, whil e providi ng an ori ginal and innovat ive soluti on operat i onal impl em ent ation. The mai n
concept s, and thus inform ation bas es, are consi dered for the
quanti ficati on of t he three com ponents and then impl ement ed i nto al gorit hms for the as sess ment of the fourt h, i.e. risk. Overal l formula [1]
holds in the various proces ses propos ed in SERR A6 (e.g. risk being
necess ari l y null, when haz ard is z ero) , even i f not necess aril y t he al gorit hm was forced to produce t wo i ndependent and di mens ionl es s indexes (H and V) t o be us ed i n a multi plicati ve combinati on wit h one monetar y index of exposure.
Table 1: The comparison between KULTURisk Framework and other frameworks terminologies.
K U L T U R is k F ra m e w o rk U N IS D R T e rm in o lo g y 2 0 0 9 IP C C - S R E X 2 0 1 2 A d a p tiv e C a p a c ity (IP C C - S R E X , 2 0 1 2 ) IP C C : T h e co m b i n a ti o n o f th e stre n g th s, a ttr i b u te s , a n d re so u rce s a va i l a b l e to a n i n d i v i d u a l , co m m u n i ty , so ci e ty , o r o rg a n i za t i o n (e x- a n te h a za rd ) th a t ca n b e u se d to p re p a re fo r a n d u n d e rta ke a ct i o n s to re d u ce a d ve rse i m p a ct s, m o d e ra te h a rm , o r e xp l o i t b e n e fi ci a l o p p o rtu n i ti e s . U N IS D R : N /A A tte n u a tio n C o n si d e rs m a n u fa ctu re d b a rri e r s to th e h a za rd , st ru c tu ra l a n d e xp l i ci t,
w h i ch m a y a f fe c t e xp o su re . N /A C o p in g C a p a c ity (IP C C - S R E X , 2 0 1 2 ) IP C C : T h e a b i l i ty o f p e o p l e , o r g a n i za ti o n s , a n d sy s te m s, u si n g a va i l a b l e s ki l l s , re so u r ce s, a n d o p p o rtu n i ti e s , to a d d re ss , m a n ag e , a nd o ve rco m e (e x - p o st h a za rd ) a d v e rse co n d i t i o n s. U N IS D R : T h e a b i l i t y o f p e o p l e , o rg a n i za ti o n s , a n d sys te m s , u s i n g a va i l a b l e sk i l l s a n d re so u r ce s , to fa ce a n d m a n a g e a d ve rse co n d i ti o n s, e m e rg e n ci e s , o r d i s a ste r s. D ire c t C o s ts T h e co st s d u e to th e d a m a g e s p ro vo ke d b y th e h a za rd a n d w h i ch o ccu r d u ri n g th e p h ys i ca l e ve n t (M e r z e t a l ., 2 0 1 0 ). N /A E x p o su re (IP C C - S R E X , 2 0 1 2 ) IP C C : T h e p re se n ce o f p e o p l e ; l i ve l i h o o d s;
e n vi ro n m e n ta l se rv i ce s a n d re so u rce s ; i n fra st ru c tu re ; o r e co n o m i c , s o ci a l , o r cu l tu ra l a sse t s i n p l a ce s th a t co u l d b e a d ve rse l y a ffe c te d . U N IS D R : P e o p l e , p ro p e rty , s ys te m s , o r o th e r e l e m e n ts p re se n t i n h a za rd zo n e s th a t a re th e re b y su b j e c t to p o te n t i a l l o s se s . H a za rd (IP C C - S R E X , 2 0 1 2 ) IP C C : T h e p o te n ti a l o ccu r re n c e o f a n a tu ra l o r h u m a n - i n d u ce d p h ysi ca l e ve n t th a t m a y ca u se l o s s o f l i fe , i n j u r y , o r o th e r h e a l th i m p a c ts , a s w e l l a s d a m a g e a n d l o ss to p ro p e rty , i n fra st ru c tu re , l i ve l i h o o d s, se rv i ce p ro v i s i o n , a n d e n vi ro n m e n ta l re so u r ce s . U N IS D R : A d a ng e ro u s p h e no me n o n , su b sta n ce , h u m a n a cti v i t y o r co n d i ti o n th a t m a y ca u se l o s s o f l i fe , i n j u ry o r o th e r h e a l th i m p a cts , p ro p e r ty d a m a g e , l o ss o f l i ve l i h o o d s a n d se rv i c e s, so c i a l a n d e co n o m i c d i sru p ti o n , o r e n vi ro n m e n ta l d a m a g e . In d ire c t C o s ts T h o se i n d u ce d b y th e h aza rd b u t o ccu r ri n g , i n sp a ce o r ti m e , o u tsi d e th e p h ysi ca l e ve n t (M e r z e t a l . , 2 0 1 0 ) N /A In ta n g ib le C o s ts : V a l u e s l o s t d u e to a d i sa s t e r, w h i ch ca n n o t, o r a re d i ff i cu l t /co n t ro v e rsi a l to , b e m o ne ti ze d b e ca u se th e y a re n o n -m a rke t va l u e s (M e r z e t a l ., 2 0 1 0 ). N /A P a th w a y T h e g e om o rp h o l o g i ca l ch a ra c t e ri s ti c s o f th e re g i o n u n d e r a sse s sm e n t, w h i ch a ffe c t th e w a y h a za rd s p ro p a g a te a n d th e re fo re e xp o su re (e .g . d i g i ta l e l e va ti o n m o d e l ). I t i n cl u d e s n a tu ra l b a rri e r s to th e h a za rd . N /A P o te n tia l C o n s eq u e nc e s A re e xp re sse d i n th e fo rm o f th e to ta l co s t m a t ri x . N /A R e c e p to r A p h ysi ca l e n ti ty , w i th a sp e ci fi e d g e o g ra p h i ca l e xte n t , w h i ch i s ch a ra c te ri ze d b y p a rt i cu l a r fe a tu re s (e .g . h u m a n b e i n g s, p ro te cte d a re a s, ci ti e s, e t c. ). N /A R e s ilie n c e N o t a p p l i e d i n o u r fra m e w o rk b u t ca n b e i n te rp re te d a s o p p o si te to th e d e fi n i t i o n o f vu l n e ra b i l i t y. IP C C : T h e ab i l i t y o f a s ys te m a n d i ts co m p o n e n t p a rts to a n ti ci p a te , a b so rb , a cco m m o d a te , o r re co ve r f ro m th e e ffe ct s o f a h a za rd o u s e ve n t i n a ti m e l y a n d e f fi ci e n t m a n n e r, i n cl u d i n g th ro u g h e n su ri n g th e p re se r va ti o n , re s to ra t i o n , o r i m p ro ve m e n t o f i t s e sse n t i a l b a si c st ru c tu re s a n d f u n cti o n s . U N IS D R : T h e ab i l i t y o f a s ys te m , co m m u n i t y o r so c i e t y e xp o se d to h a za rd s t o re si s t, a b so rb , a cco m m o d a te to a n d re co ve r f r o m th e e ffe ct s o f a h a za rd i n a ti m e l y a n d e ff i c i e n t m a n n e r, i n cl u d i n g th ro u g h th e p re se rva ti o n a n d r e sto ra t i o n o f i ts e s se n ti a l b a si c s tru ctu re s a n d fu n ct i o n s R is k T h e co m b i n a ti o n o f th e p ro b a b i l i ty o f a ce rta i n h a za rd to o ccu r a n d o f i ts co n se q u e n ce s. IP C C : T h e l i ke l i h o o d o ve r a sp e ci fi e d t i m e p e ri o d o f se ve re a l te ra t i o n s i n th e no rm a l fu n ct i o n i n g o f a co m m u n i t y o r a so c i e ty d u e to h a za rd o u s p h ys i ca l e ve n ts i n te ra c ti n g w i th vu l n e ra b l e so c i a l co n d i t i o n s, l e a d i n g to w i d e sp re a d a d ve rse h u m a n , m a te ri a l , e co n o m i c , o r e n vi ro n m e n ta l e ffe c t s th a t r e q u i re i m m e d i a te e m e rg e n cy re sp o n se to sa ti sf y c ri ti ca l h u m a n n e e d s a n d th a t m a y re q u i re e x t e rn a l su p p o rt fo r re co ve ry . U N IS D R : T h e co m b i n a ti o n o f t h e p ro b a b i l i t y o f a n e ve n t a n d i ts n e g a ti ve co n s e q u e n ce s. R is k p e rc e p tio n / A w a re n e s s T h e o ve ra l l vi e w o f r i s k a s p e r ce i ve d b y a p e rso n o r g ro u p i n c l u d i n g fe e l i n g , j u d g m e n t, a n d cu l tu re ( A R M O N IA p ro j e ct , 2 0 0 7 ). IP C C : N /A U N IS D R : T h e e xte n t o f co m m o n kn o w l e d ge a b o u t d i sa s te r ri s ks , th e fa c t o rs th a t l e a d to d i sa s te r s a n d th e a c ti o n s th a t ca n b e ta ke n i n d i v i d u a l l y a n d co l l e ct i ve l y to re d u ce e xp o su re a n d su sce p t i b i l i ty to h a za rd s , w h i l e i n c re a s i n g th e a d a p ti ve ca p a c i t y
S u s c ep tib il ity S u sce p ti b i l i t y b ri n g s i n a p h ysi ca l /e n vi ro n m e n ta l a s se s sm e n t o f th e re ce p to rs , i .e . th e l i ke l i h o o d th a t re ce p to r s co u l d p o te n ti a l l y b e h a rm e d b y a n y h a za rd g i ve n th e i r st ru c tu ra l fa cto rs , typ o l o g y o f te r r a i n a n d ch a ra c te ri s ti cs ( i n p h y si ca l a n d n o n -m o n e ta ry te r-m s ) N /A T a n g ib le C o s ts T h e co st s, w h i ch ca n b e e a s i l y sp e c i f i e d i n m o n e ta ry te rm s b e ca u se th e y re fe r to a sse t s , w h i ch a re tra d e d i n a m a rke t (M e rz e t a l . , 2 0 1 0 ). N /A V a lu e fa c to r T h e e n vi ro n m e n ta l va l u e o f th e e xp o se d so ci a l , e co n o m i ca l , a n d re ce p to r s. N /A V u ln e ra b ili ty Is co n s i s te d o f su s ce p ti b i l i ty a s th e P /E co m p o n e n t a nd ad a p ti ve & co p i n g ca p a ci ti e s a s th e so ci a l co m p o n e n t. IP C C : T h e p ro p e n si t y o r p re d i sp o s i t i o n to b e
a d ve rse l y a ffe cte d .
U N IS D R : T h e ch a ra cte ri s ti cs a n d ci r cu m s ta n ce s
o f a co m m u n i t y, s ys te m , o r a s se t th a t m a ke i t su s ce p ti b l e to th e d a m a g i ng e ffe ct s o f a h a za rd .
In order t o have an adequat e concret eness in t he i denti fication of the KU LTURisk Fram ework , we m ade a speci fic reference t o fl ood ri sk, bearing i n mind t hat subs equent proj ect acti viti es s houl d ass ess t he pot enti als for consi deri ng other risks.
Figure 3: The KULTURisk Framework with the identification of the main sources of data for the quantification of nodes.
As shown in Fi gure 3, the proposed approach for int egrated assessment consists of four pi llars nam el y hazard, vulnerabili ty, exposure, and ri sk , where the out com e of the first t hree affects t he l att er. In the case of a flood event , the h azard out com e is a map of int ensit y (expressed in
terms of depth, pers istence, or velocit y) of the flood, provi ded b y the h ydrologi cal anal ys is and modelli ng i.e. flood frequency anal ys is, geomorphologi cal charact eristi cs of t he region under ass ess ment (pathwa y), and manufactured barri ers agai nst t he haz ard (att enuati on) el ements of the as s ess ed area. Considering different return times and measures of i nt ensit y, mul tipl e haz ard m aps can be produced, following the specifi c requirem ents of the l egi slati on. Additi onall y, multipl e recept ors have to be consi dered . For exam pl e, t he Flood Di rective identified four cat egori es of recept ors : peopl e, economi c activities, cult ural goods, and t he envi ronm ent com ponent (EC, 2007).
In t he propos ed framework, E xposu re i dentifi es the pres ence of peopl e and as set s and as much as poss ibl e t he s oci al , envi ronm ent al, and economical value of them . Vu lnerabil i ty i s defined as another map resulti ng from the combinati on of P/E and soci al component s. The P/ E com ponent is capt ured b y the li kel ihood that receptors l ocat ed i n t he area considered coul d pot ent iall y be harmed ( susceptibili ty of receptors ). The social one is t he ex -ante prepared nes s of s ociet y given thei r risk perception of awarenes s to com bat haz ard and reduce its advers e impact or thei r ex -post s ki lls to overcome t he hazard dam ages and return to initi al st ate (repres ente d b y adapti ve and coping capaciti es ). A list of soci al i ndi cators t hat can prox y adapt ive and copi ng capaciti es i s propos ed in the following s ection. T he above-descri bed el em ents hel p us to cal culat e the expect ed dam ages rel at ed t o the ri sks associated to different haz ardous scenarios. W e decompos e risk i nto fou r com ponent s that together the y m ake the Total Cost Matrix (TCM) of i ndirect/direct tangibl e/intangibl e costs. The di rect costs are corres ponding to all the tangibl e/intangibl e cost s in the geographi cal l ocati on and duri ng the haz ardous event. All the cost s outsi de the tim e fram e or the geographical location of hazardous event are represent ed b y indirect costs. The das hed line i n ri sk component of Fi gure 3 i s pointing to t he above -mentioned distincti on. Haz ard, vulnerabili t y, and exposure are fores een as ma ps, therefore, the y are s pati all y expli cit, and the y will be int egrat ed in a G IS context. For instance in a grid cell of GIS maps of a cert ain s ize, we can explici tl y s how the expect ed depth of i nundati on and the presence of buildi ngs and peopl e and the li kel ihood of t hem t o be dam aged or harm ed. In m an y cas es, we expect that soci al dat a rel at ed t o adaptive and coping capacit ies can be m anaged in a spati all y dist ribut ed fas hion. T ypi call y b y al locat ing cens us and ot her inform ation t o administ rative sub -unit s, but we can imagine cas es in whi ch onl y aggregat ed inform ation could be avail abl e and therefore, the as sessm ent of ris k miti gation meas ures could onl y be possi ble i n a non -spati al aggregat ed manner. In those cases , the benefits ex pect ed from m easures, de ri ving from the com paris on of ex -post sit uati on with the Bas eline Scenario, could onl y be expressed as a l umped sum (in cas e of full m onetizati on) or as a series of delt as describing the changes of the indicat ors considered. The adoption of monetar y units for risk facilitat es the aggregation of the values obt ained whenever needed, for example t o sum up cost s referred to t he vari ous receptors.
3
The evaluation of benefits of risk reduction
Theeval uationoft hepotentialbenefi tsofan yriskpreventionm eas urerequirest heval uat ionof avoided costs due t o its im plem ent ati on. Furt hermore, the avoided damages m ust be confronted with the costs of t he m easure implementation its elf. Thus, havi ng identi fi ed a m easure or set of measures of pot enti al int erest , the fi rst st ep requi red i s to defi ne t he cost of a h ydrologi cal disast er without an y preventive m easure (bas eline scenario). A s econd step is to es timate the expect ed costs of the sam e hydrol ogi cal dis ast er with t he ris k pre vent ion m easure i n pl ace (al ternati ve scenario), including the cost of t he pre vent ion measure it sel f. The benefits are then the di ff erence between t he costs in the bas eline and in the alt ernati ve s cenari os. Nevert hel ess, what are the cost s of a hydrol ogi cal dis ast er? Tradi tional ri sk ass es sments h ave been primaril y dealing with di rect tangibl e costs in a ver y det ail ed fashi on, h owever there exist a whol e set of negl ect ed cos ts that should be considered in view of pr ovidi ng a com prehensi ve quant ifi cat ion of ri sk. For this reason the concept of T otal Cos ts Matrix (T CM) as shown in Fi gure s 3 and 4 has been propos ed .
Figure 4: The Total Cost Matrix, adapted from: Penning-Rowsell et al. (2003), Jonkman et al. (2008), and Merz et al. (2010).
One m a y argue that most of the t imes a det ail ed estim ati on of di rect tangibl e cost s is sufficient to com pare and justif y the choice of alt ernat ive ri sk reduction measures (RR Ms), in parti cul ar when st ruct ural RRMs are combi ned (e.g. di kes, dams, embankm ent s, et c.). Whether this
still holds when it com es t o evaluati ng the benefi ts of non -st ruct ural measures and of preparedness is an open iss ue, since, for i nstance, the importance of i nt angibl e and indi rect cos ts and benefits mi ght subst ant ial l y i ncreas e.
For exam ple, an earl y warni ng s yst em mi ght onl y parti all y reduce the amount of di rect tan gible costs (e.g. you can move your car but not your hous e), but it can:
a) save the li ves of ma ny people (direct intangibl e costs) ;
b) change beh avi our of peopl e by avoidi ng l ong lasti ng
trauma s( indire ct intangibl es costs) ;
c) pr event eva cua tion co s ts (i n d ir e ct tangible costs ) .
In order to go be yond the direct tangibl e costs, the following methodol ogi cal and operational requi rem ents emerge:
a) a m ore com prehensi ve and l ess reducti onist noti on of ri sk , but
with a dis aggregat ed structure as shown by TCM ;
b) a funct ional d es cri ption of the expected cons equences of the
haz ard cons idered according t o the quadrants of t he TCM;
c) the consi der ation of other t ypes of impacts, for whi ch the iss ue of
expressi on in m onet ar y t erms em erges;
d) the impl em ent ati on of methods for economi c valuation of non
market goods, in order to provi de monetar y values to int angibles , whenever pos sibl e and desi red for the s upport t o decisions bas ed upon th e im pl ement ation of Cost -Benefit Anal ysi s (C BA) m et hods;
e) the considerat ion of alt ernat ive eval uation met hods i n thos e cases
in which C BA is not possi ble or des ired, such as Cost - Effecti veness or Multi -Cri teri a Anal ys es (CEA and MCA, respectivel y) .
Depending on t he evaluati on m ethods adopted, the benefits of risk preventi on or miti gation measures will be express ed as t he di fference bet ween the pot ent ial cons equences det ermined b y t he bas eli ne scenario and t he alt ernative scenari o wit h new ris k prevention m easures in monetar y t erms (C BA cas e), or as the com bination of monet ar y estim ati ons of some com ponents (t ypi call y the expect ed costs of the measures consi dered, and direct and indi rect t angibl e costs ), with ot her perform ance indicat ors or indices (in case of CEA, MC A or ot her methods ).
4
The social dimension: adaptive and coping capacities for
risk prevention
One of the main innovations of the proposed fram ework is t he inclusion of soci al capaciti es (adaptive and coping capaciti es ) in the process of measuring ris k b y m eans of the TCM, which is a disaggregated wa y t o struct ure the pot enti al consequences. This is also an att em pt to capture and m ake t he concepts of soci al vulnerabilit y operat ional and as far as possi ble quantifiable .
The m ain chall enges f or the anal ys is of soci al capaciti es in a ris k ass ess ment context can be ident ifi ed wit h respect t o :
a) tailori ng t he s et of i ndi cat ors to the cont ext ;
b) defini ng em pi rical functions for the esti mation of indi cat ors
and aggregating them.
Soci al s ci ent ists us uall y investi gat e these capaciti es at the case stud y level b y m eans of questi onnai res and int eracti ng wit h local st akeholders, mainl y using a s emi -quant itative research approach (e.g. Steinfuhrer et al.2009). Indeed, the vari abl es m easuri ng thos e capaciti es should be chosen according to the cont ext of appl ication. However, as shown in Cutter et al. (2003), a minimal set of i ndi cators bas ed on secondar y data can be s el ect ed i n order to approxi mat e the m agnit ude of soci al vulnerabilit y.
We propos e a list of vari abl es and indicat ors , as shown in Table 2, whi ch ma y compose a mi nimal s et of dat a t o approximat e t hos e capaciti es. Furthermore, we declare our ass umpt ions about thei r cont ri butions to t he TCM. Som e of t he indi cat ors ma y affect bot h adaptive and coping capacit ies such as i ncome l evel. A s ociet y with hi gher incom e level could have had a hi gher adapti ve capacit y b y incorporating earl y warning s yst ems at the com munit y l evel, or at the indi vidual l evel by t aki ng precautionar y actions such as fortif ying thei r resi denti al buildi ng.
Equall y, hi gher i ncome can affect coping capacit y when the
communities’ or individuals’ ability in coping with flood is increased. Therefore, a careful scrutin y i s necessa r y for empi ri call y t esti ng the si gni fi cance of each indi cat or on adapti ve or copi ng capaci ties , while avoiding doubl e counting and i nt ernal correl ati ons . Whil e most of the indi cat ors can be derived from secondary dat a or from the census and regional accoun ts, s ome vari abl es m i ght be diffi cult t o deri ve without ad -hoc activi ties. This is parti cul arl y evi dent for trust or ri sk perception, whi ch is an important component of t he proj ect and of t he fram ework. Depending on t he geographi cal s cale , l evel of detail , avail abl e tim e , and financi al resources, proxies coul d alwa ys be consi dered as s ubstitut es to the proposed vari abl es.
Table 2: Adaptive and Coping Capacity Indicators
A S S U M E D re la tio n s h ip w ith c o s ts : V a ria b le A d a p ti v e C a p a c it y C o p in g C a p a c it y In d ic a to rs / P ro x ie s D ir e c t T a n g ib le D ir e c t In ta n g ib le In d ir e c t T a n g ib le In d ir e c t In ta n g ib le A g e X P e rce n t o f p o p u l a ti o n u n d e r fi ve ye a r s o l d ; P e rce n t o f p o p u l a ti o n o ve r 6 5 y e a rs; M e d i a n a g e ; + + G e n d e r X P e rce n t o f fe m a l e s; P e rce n t o f fe m a l e h e a d e d h o u se h o l d s; + + F a m i l y st ru c tu re X P e rce n t o f s i n g l e p a re n t s h o u se h o l d s, P e r ce n t o f h o u se h o l d s w i th m o re th a n 4 i n d i vi d u a l s, + + D i sa b l e d X X P e rce n t re s i d e n ts i n n u r si n g h o m e s, P e rce n t i l l o r d i sa b l e d re si d e n t s, + +
In co m e l e ve l X X P e r ca p i ta i n co m e ; M e d i a n m o n e ta ry va l u e o f o w n e r -o ccu p i e d h o u si n g ; M e d i a n re n t fo r re n te r - o c cu p i e d h o u si n g u n i t s; C re d i t ra ti n g o f i n h a b i ta n ts + + S o ci a l d i sp a r i t y X X G i n i i n d e x o f i n co m e ; P e rce n t o f h o u se h o l d s e a rn i n g m o re th a n X ; P e rce n t o f h o u s e h o l d s e a rn i n g l e s th a n Y ; D e p e n d e n ts o n so ci a l se r vi c e s + + E d u ca ti o n X N e g a ti ve o f p e r ce n t o f p o p u l a ti o n 2 5 ye a r s o r o l d e r w i th n o h i g h sch o o l d i p l o m a ; P e rce n t o f p o p u l a ti o n w i th h i g h e r e d u ca ti o n ; - + E m p l o ym e n t X P e rce n ta g e o f l a b o r fo rce u n e m p l o ye d ; T yp e o f e m p l o ym e n t ( fu l l ti m e , p a rt t i m e , se l f e m p l o ye d , e t c. ) + + S a fe ty n e tw o rk X N e g a ti ve o f q u a l i t y o f re l a ti o n sh i p s w i th i n th e co m m u n i ty ; P e r ce n t o f i so l a t e d p o p u l a ti o n ; P e rce n t p o p u l a ti o n ch a n g e ; Ne g a ti ve o f p e rce n t w i th 1 st to 2 n d l e ve l co n n e c ti o n s to c i v i l p ro te ct i o n ; + + T ru st X E xp e rt s e l i ci ta ti o n / m e a su re o f t ru s t + - R i s k p e r ce p ti o n X X E xp e rt s e l i ci ta ti o n / m e a su re o f p e rce p t i o n R i s k g o ve rn a n ce X X P e r ca p i ta n u m b e r o f co m m un i ty h o sp i ta l s; P e r ca p i ta n u m b e r o f p h ysi ci a n s ; L o ca l g o v. d e b t to re ve n u e ra ti o ; A c ce s s to p l a ce s (n u m b e r) o f sa fe t y d u r i n g th e e ve n t; N u m b e r o f re d cro ss vo l u n te e rs; H o u rs sp e n t o n tra i n i n g a n d m a n e u ve r - - - - E a rl y w a rn i n g ca p a ci ty X X N u m b e r o f e a rl y w a rn i n g s y ste m s i n p l a ce fo r t yp o l o g y o f h a za rd ; - - - - R i s k sp re a d i n g X X % o f h a za rd i n su re d h o u se h o l d s; % o f h a za rd i n su re d e co n o m i c a ct i v i t i e s ; - E co n o m i c d i ve r si fi ca t i o n X X N o rm a l i ze d H e r fi n d a h l i n d e x o f se c to ri a l ( i .e . co a rse : p ri m a r y, se co n d a ry , te r ti a r y ) c o n tri b u t i o n to G D P an d o r to e m p l o ym e n t; + + In te r co n n e cti v i t y o f e co n o m y X X N e t t ra d e i n g o o d s a n d se r v i c e s (e xp o rt s + ) ; P e r ce n t o f re si d e n t th a t tra ve l to w o r k o u t si d e th e m o d e l e d a re a ; - + + N e w co m e rs X P e rce n t re n te r - o c cu p i e d h o u si n g u n i ts; P e r ce n t o f re ce n t re s i d e n ts /i m m i g ra n ts ; P e rce n t o f p e o p l e l i vi n g i n i n fo rm a l h o u se s; - + +
5
Towards an operational KULTURisk Framework
Given the ambiti on to deal with het erogeneous iss ues and applicati on contexts, the propos ed fram ework i s necessaril y generi c , sim plifi ed in i ts overall conceptuali s ation, but st ill rat her compl ex in its practi cal implementation, and tai lored to speci fi c cas es . Practi cal appli cat ions shoul d be devel oped upon the simpler concept ual m odel provided b y the
fram ework pres ent ed in Fi gure 3, and proceed b y provi ding
quanti ficati on of the nodes accordi ng to the specifi c obj ectives and conditions (e.g. dat a avail abili t y) of each implementation.
For i nst ance, simpl er versio ns can consider aggregated costs and/or indi cat ors of soci al capaciti es. In addition, t he m ethodology of aggregation and t reatment of uncert ai nt y can var y according to the dat a avail abili t y, et c. Therefore, t he KULTUR isk Fram ework i ndeed needs t o be tail ored to deal with di fferent cont exts of anal ysi s.
The proj ect case st udi es provide a qui te di vers ifi ed s et of situations, allowing t o consoli dat e the fram ework itself and to develop ad hoc tailored sol utions for most common implem ent ation cas es. The proces s of t ailoring has vari ous degrees of freedom, whi ch are sum marized, in the following:
1. Identi fi cation of appli cat ion cont ext: scenarios and measures
(baseline vs. al ternat ives).
2. Dat a avail abilit y.
3. Indi cator sel ection.
5. Wei ghti ng.
6. Aggregation.
7. Uncert aint y.
The fi rst st ep t owards the im plement ation of the framework is t he
identification of the appli cati on context . Thi s is a st rat egi c choi ce,
whi ch depends not onl y on the anal yz ed s yst em but also on the applicati on p urpos e thus affecting the det ail l evel of anal ys is and t he evaluati on m et hod t o put the KULTUR isk Framework into operati on . Fundam ent al elem ents of s uch int roductor y st ep are t he definition of the norm ati ve fram e (for inst ance, i n Europe, the Flood Di recti ve of EC , (2007 ) in its national or regi onal impl em ent ations ), the id enti ficat i on of inform ation sources and m anagem ent s ys tems, the am bitions and preferences in terms of economi c val uat ion m ethods .
Data avai labili ty an d indicator sel ection for the t raditi onal ass ess ment
of risk grounded on t angi bl e cost s , focus on histori cal river flow, precipit ation, Di git al Elevation Model (DEM ), l and us e m aps, m aps of infrastructures et c., whi ch are usuall y avail abl e from regi onal or nati onal aut horiti es and/or ri ver basi n di strict s . More chall enging t ask for risk ass ess ment is the i dentifi cation of inform ation sources for ass es sing soci al capaciti es . The list of indi cators summarized in Table 2 can be considered as a reference basi s, usuall y from the nat ional cen s us.
Normali zation is t he procedure of t rans formi ng indi cat or values of
different metri cs int o a dim ensionless number, wit h the ai m t o allow for val uati on com paris on, and aggregation of indicators with different units of m easure. Norm ali sati on issues em erg e for t he t wo components of the risk ass essm ent formula [1] that are t o be provided as di mens ionl es s indi ces (H and V), but the y ma y b e also needed in E (exposure), whenever full monet isati on is not performed, and in general in m ost of the appl icati ons of MCA methods. There exist a number of di fferent norm aliz ation functi ons , and som e are m enti oned beneat h :
1. Ranking
2. Standardiz ation (z -s core)
3. Val ue functions
4. Min-m ax norm aliz ati on
5. Dist ance to a reference m easures
6. Cat egori cal s cales
The t ype o f norm al ization function depends on t he indicators under considerati on and on the preferences of t he experts and decis ion makers invol ved in the eval uati on process . The simpl est norm aliz ati on method consists o f ranking each indi cator value. The m ain advantages of ranking approach are its simpli cit y and the independence from outli ers. Dis advantages are t he loss of informat ion on abs olut e l evels and t he imposs ibilit y to draw an y conclus ion about di fference s i n perform ance.
One of the mos t com m onl y used norm aliz ation procedure is
Standardizati on (z-score) i n which all indicators can be convert ed into a
comm on scal e with an average of zero and st andard devi ation of one. The
least accept able (bes t and worst) values and to normaliz e t he m easured val ue bet ween the two threshold s. Value functi on is one of the most
widel y us ed normalizati on procedure s , using m athem ati cal
represent ati ons of hum an judgments, whi ch offer the possibilit y of treating people’s values and judgments explicitly, logically, and s yst em aticall y (Bei nat, 1997). Dist ance t o a r eference measure t akes the rat io of the indicat or for a generic value with respect t o the reference val ue. The reference coul d be a t arget to reach for example i n terms of requi red effectiveness of t he m easure . In det ermi ning cat egorical scale , first, we s el ect the cat egori es. The y can be numeri cal, such as one, two, or three stars, or qualitative, such as ‘fully achieved’, ‘partly achieved’, or ‘not achi eved’. We, them, assi gn to e ach cat egor y a s core, whi ch is , to a cert ain ext ent, arbi trar y.
Wei ghtin g is the procedure t o express the relative rel evance of
indivi dual indi cators in composit e i ndicators/i ndexes . Wei ghts are ess ent iall y value judg m ents , thus essentiall y subjecti ve, and have the propert y to m ake the obj ectives underl yi ng the cons truction of a com posit e expli cit . Depending on the subjecti ve judgm ent, different wei ghts m a y be assi gned to di fferent i ndi cators and there is no uni forml y agreed m et hodology to wei ght indivi dual indi cat ors before aggregating them int o a compos ite indi cator or index. Therefore, wei ghts usuall y have an im port ant i mpact on the com posite indicat or value and thi s i s wh y wei ghting models need t o be made explicit a nd t ransparent through invol ving the rel evant st akeholders. To construct a composit e indi cat or val ue and/or index, t he wei ghti ng of i ndi cators are carri ed out refl ecting stakeholders’ views. Commonly used weighting procedures are the foll owi ng:
a) Statist ical wei ghting met hods:
o Equal weights
o Principal Component Analysis o Factor Analysis
o Multiple Regression Models
b) Part ici pator y wei ght i ng m ethods
o Expert judgment o Public opinion
o Pair-wise comparison o Conjoint analysis
In t he indi cat or -bas ed as sessm ent , the out com e ( i.e. the i ndex) is t he result of aggregati on , i.e. a – oft en hierarchi cal – com bination of several indi cators that need to be aggregat ed in each node i n whi ch the y converge, or even t o produce an overal l index of risk . Aggregation of indi cat ors is obvi ous l y not a t ri vial task since t he chos en (am ong m an y) methodol ogy has m eaningful impacts on the com put ati on of the final index; furt herm ore, the choi ce of the aggregation m ethod t ypi cal l y
invol ves trade -offs bet ween loss of inform ation, comput ational
com plexit y, adherence to decision m akers’ preference s , t ransparenc y of procedure, et c. Am ong t he avail abl e aggregation operat ors are the foll owi ng:
a) Averagi ng operat ors o Quasi arithmetic means
o Order Weighted Average (OWA)
b) The ‘AND’ and ‘OR’ operators
c) Non -addi tive m easures (NAM).
Averaging operators are still t he most commonl y us ed in practice, gi ven
the sim pli cit y of their comput ati on, imm edi ac y , and t ransparenc y of the aggregation process . Nevert heless, averaging operat ors are t ypicall y com pens ator y (i .e. a bad score in one criterion can be offs et b y a good score in another one) and more import antl y the y are not abl e to consider an y int eraction among the crit eri a. Quasi-ari thmeti c means i ncl udes not onl y the s impl e arithmetic m ean, but al so geom etri c and harmoni c means. OWA is s till bas ed on wei ghted s ums, but t he crit eria are ordered b y magnit ude, and wei ghts can be m odel l ed to express vague quan ti fiers . ‘AND’ operators are a family of operators that express logical conjunction (pessim istic behavio u r assi gnin g t he l owest value of the cri teri a to the aggregation ), whereas ‘ OR’ operators cons ider logical disjuncti on (opt imi stic behavio u r). N on-Additive Measur es (NAM) approaches such as Choquet Int egral have been int roduced t o overcome the mai n drawbacks of t he averagi ng operat ors.
6
Uncertainty
The sources of un certaint y in our estim ation var y from t h os e rel at ed to the envi ronm ent al hazard (probabil it y of flood) to th os e regarding the models us ed in t he ass ess ment of ri sk (h ydrologi cal , economical, and soci al ones ), the param et ers of t he es timati on (value fact ors , soci al indi cat ors), et c.
In general , we can divide the uncertai nties into two mai n t ypes here: al eat or y and epist em ic, as reported in Table 3 . Epi st emi c uncertaint y is due to incom plet e knowl edge about a s ystem and i nform ati on gathering could reduce i t, whereas al eatory uncert aint y is due to natural phenom ena and its reducti on mi ght be i mpossi bl e. For i nst ance, we can reduce the uncertai nt y regardi ng the values of t he param eters of our estim ati on b y acqui ring m ore i nform ati on b y s everal m eans incl udi ng running regressi ons on the his t ori cal or avail abl e data.
However, t he uncert aint y wit h res pect t o the frequenc y of flood or t he effects of clim at e change i s of al eator y t ype. Since clim at e change is i n dist ant fut ure, in the pres ent tim e, we do not have suffi ci ent knowledge about i t. M oreover, flood frequenc y is an extrem e event and we cannot know whi ch extrem e value probabi lit y distri buti on (Gam ma, Gumbel ,
Weibull , GEV7, et c) s hould be used. Therefore, in m an y cas es we cannot
reduce the uncert ai nt y si nce it is not possibl e t o i ncrease our precision. One of t he m ain m otivation for the devel opm ent of the KULTURisk Fram ework is reduci ng the epist emi c uncert aint y in risk assessm ent , b y providi ng a sci ent ificall y sound and as far as possi ble quantitative
approach for accounting for the social and economic dimensions, and in parti cul ar t he capaci ties t o adapt and their res ponse t o a s pecifi c natural haz ard in a spatio -temporal cont ext. In parti cular, the m odelling of vulnerabilit y is an i mportant com ponent of a receptor that i nfluences the rel ati ons hip between the source of hazard and t he fi nal advers e effect. In case of risk as sess ment wit hout s oci o -economi c dim ension, we represent the al eatory uncertaint y in the flood frequenc y b y means of a probabilit y dist ributi on. The product of t his proba bi lit y dist ri bution and the pot enti al cons equences coming from different flood s cenari os gives us the expect ed damage. The reason t o do s o is becaus e the absolut e uncert aint y predi ction for a given scenario is not ver y us eful . Absol ute uncert aint y is l ess i nform ative for a decision maker in compari son to a rel ati ve uncert aint y predi ction t hat t akes into account di fferent s cenari os of flood and pot enti al cons equences.
The other source of uncert aint y is negl igence of appropri at e t emporal scal e. For ins tance, for estim ating the dam ages to agri cult ural products one has t o consider the m ont h and s easonal effect t o reduce t he uncert aint y of est im ations. Simil arl y, for s pat ial s cale, we need t o l ook for a s cale that mi ni mizes t he uncert ai nt y. At a too coars e s cale, either some recept ors are not seen (e.g. vehicl es ) or receptors becom e too homogenous and the risk is underestimated. In addition, receptors’ charact eri sti cs (t ype, locat ion, m at eri al , struct ure, et c.) mi ght change i n time due t o change in l and -use or urb an expansion and since risk ass ess ments are very expensive and time consumi ng and not perform ed ever y year, we need to take i nto account the changes i n the l and us e in a
6-10 years peri od b y means of stochast ic spatial mappi ng8. For ins tance,
if we group all industri al or comm erci al buildi ngs int o one cat egor y, we simpl y overlook t he di fferences am ong them i n t erm s of t hei r vulnerabilit y to fl ood. A chemi cal pl ant can be at a di fferent ris k due to
flood com pared to a warehouse.
Table 3: Sources of Uncertainty
Source of uncertainty Type of uncertainty Description
Flood Frequency Aleatory Uncertainty about future hydrologic events, including future stream-flow and rainfall. In the case of discharge-probability analysis, this includes uncertainty regarding the choice of a statistical distribution and uncertainty regarding values of parameters of the distribution.
Model Uncertainty Epistemic Lack of complete knowledge regarding the form of a hydrologic, hydraulic, or economic function to use in a particular application.
Spatial Uncertainty Epistemic Delineating the sub-basin. Changes in urban, ecosystem.
Temporal Uncertainty
Epistemic Uncertainty regarding time of exposure of agricultural crops, people, etc.
Parameters Uncertainty
Epistemic Uncertainty in a parameter due to limited understanding of the relationship or due to lack of accuracy with which parameters can be estimated for a selected hydrologic, hydraulic, or economic function.
Risk mitigation policies
Epistemic Uncertainty about risk reduction measures and their performance (reliability).
8 The European Flood Directive requires the state to perform flood risk analysis every 6 years. This
requires a probabilistic spatial model that envisages changes in land use for the uncertainty in the estimation.
7
Two examples of operational solutions
When considering t he avai lable solut ions to the aforem entioned is sues, it is pos sibl e to envis age a conti nuum of cases accordi ng to the s pati al represent ati on of ri sk and its probabil i stic descript ion (i.e. rel at ed t o singl e or m ultipl e haz ard scenarios or to a cont inuous haz ard -risk functi on).
Two exam pl es are propos ed bel ow (Fi gure 6 ) wi th reference to the t wo cases whi ch st a y at t he extrem es of t hi s continuum:
1. Cas e (A): risk i s spatiall y represent ed and refers to a singl e hazard
scenario;
2. Cas e (B): risk i s aggregat ed at t he spatial l evel and refers to a
continuous hazard -ri sk functi on.
Figure 6: Proposed Solution for Implementation.
In C ase (A), which is expected to be a frequent case according to the existing legisl ations, spat ial l y di saggregated ris k maps can be produced for each of the four cost quadrants (direct t angibl e, di rect intangible, indi rect t angibl e, indirect int angibl e) fol lowing RRA. The int egration of the soci al capacit y can be performed in analogy of RRA b y overl a yi ng the capacit y maps (both coping and adaptive capacit y) wit h ris k m aps using spati al Mult i -Criteri a Anal ysi s (MC A) im pl ement ed t hrough rout ines of overl a yi ng G IS m ap. The results of s uch process are obviousl y s patial in nat ure, and the y al low for explori ng the geographi cal dis tri butions of cost s and i dentif yi ng hot spots, i.e. areas at risk and with rel ati vel y hi gh expect ed costs of different kinds (see exampl e in Fi gure 7) . Aggregation and wei ghti ng of the spati al indi cat ors are important iss ues for developing s patiall y di saggregat ed risk maps. For ex ampl e, Birkmann et al. (2010) proposed equal wei ghti ng and addi tive aggregati on for developing spati all y expl i cit vulnerabilit y map, but sol utions m ust be refi ned on a case -b y-case basis with the invol vem ent of rel evant st akehol ders.
Figure 7: Cartographic model for the calculation of spatially explicit risk for people. In Case (B), ri sk functions are estim ated for each of t he four cost quadrants (direct t angibl e, di rect int angibl e, i ndi rect t angi bl e, and indi rect i ntangible), includi ng vari ables of adapti ve and coping capacit ies. For exam ple, indicat ors of ri sk percept ion, risk governance, and EWS (s ee Tabl e 2 ) could be included i n the mortal it y functions, expanding the work of J onkman et al. (2008).
Bui lding robust functions for s ensitive i ndicators such as t hos e rel at ed t o injuries and casualti es is , indeed , anot her challengi ng is sue. Where dat a about int ang ibl e costs are not avail able or cannot be produced, a Ba yesi an Network (BN) approach can s upport empi ri cal economet ri cs in the development of those functions, besides making use of experts’ opini ons . BN are known to facilit ate the expli cit mode lli ng of uncert ainti es i n a probabilist ic fram ework based on ac ycli c graphs . BN provides a det ail ed evaluati on of t he j oint influence of di fferent input paramet ers on the ris k allowi ng a t raceabl e and concis e repres ent ation of the caus al rel ationships between the considered variabl es. The expect ed results could ent ail a monetiz ed and probabilisti c quanti ficat ion of ri sk, although there rem ai n issues regardi ng dat a gatheri ng requi rem ents and ethi cs (e.g. when controvers ial i ntangibl es are monetiz ed). A preliminar y implementation of t he KULTURi sk Framework in a BN cont ext was devel oped b y Mojt ahed et al . (See Fi gure 8).
Figure 8: Bayesian Network for damages to people (Mojtahed et al., 2012).
8
Final Remarks
As m entioned in the int roduction, there is a need for a hol isti c and sci enti fi call y sound approach t oward fl ood -rel at ed risk ass essm ent. In this work we focus ed on develop ing a fram ework based on integrati ng different component s of ri sk from a multidis cipli nar y perspective , with focus on the soci al and econom ic di m ensions of ris k . W e propos e that the estim ati on of ris k should not onl y be bas ed on di rect t angibl e cost s but also should go be yond to cont ain indi rect and intangi bl e costs . Moreover, we add s oci al i ndi cators, whi ch have been oft en neglect ed i n the l iterature of risk ass es sment, t o s hape our fram ework, which will be applied to s everal case studi es in the fut ure.
An effective (more success ful wit h lower cost of impl em entation) ri sk reduction poli c y t hat is mainl y based on devel oping a cult ure for risk abat ement requi res more emphasi s on s oci al capacit ies of i ndivi duals or soci et y (whet her be coping or adapti ve) i n order to abat e all dam ages as summ ariz ed in t he Total Cost M atrix of t he KULTURis k Fram ework . The ke y feature of the Framework is i ntegrat ing th e mult i disciplinar y nat ure of flood -rel ated risk ass essm ent. From a h ydrological poi nt of view, the eval uat ion of the potenti al benefits of actions to cope with h ydrologi cal ris k is maint ai ned. In additi on, we go be yond the traditional approaches to assess risk b y looking into soci al vul nerabili t y besi des the mainstream, whi ch was mai nl y considering P/E vulnerabi lit y. Our other distincti on from the mainstream risk assess ment is b y enhanci ng cost estim ati on well beyond the t angi bles and di rect dam ages . For this
