Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 2 20433, USA; Fax: 202-522-2625;20433, Fax: USA; [email protected]

Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 2 20433, USA; Fax: 202-522-2625;20433, Fax: USA; Pubrights@Worldbank.Org

Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 2 20433, USA; fax: 202-522-2625;20433, fax: USA; [email protected]. e-mail: World The Publications, Bank Group,WorldBank 1818NW, H Street DC Washington, to should addressed be rights, subsidiary including licenses, and queries on rights Any work given. is attribution to this full long as as noncommercial purposes work for reproduced, whole be may in this or part, of in its knowledge, dissemination TheBecause workencourages Bank is subjectcopyright. this Worldto in material The Permissions and Rights or endorsement of territory of the boundaries. any such status or acceptance work do not of implythe The judgment any legal on part concerning the WorldBank this in shown on map other colors, information any and denominations, boundaries, work. The this included in data the of theaccuracy The guarantee not does WorldBank The the they represent.governments Executive or Directors, of Board Worldits Bank, of views the reflect work do not this in necessarily conclusionsinterpretations, and expressed findings, contributions. of The Theexternal workis a the with This productstaff of Bank World www.worldbank.org Internet: Telephone: 202-473-1000 20433 DC Washington 1818 NW HStreet The WorldBank Association Development Development and © 2016 /International for Reconstruction Bank International 3

Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 4 ACKNOWLEDGEMENTS ment of the Global Facility for Disaster Reduction and Recovery (GFDRR). Recovery and Reduction forment Disaster Facility of Global the manage under the Developing Countries in Management Risk Disaster Mainstreaming thefrom Japan-Worldfunding the for acknowledges Program Bank gratefully The team Leitmann. Joaquin theand peer reviewers Josef to Toro,Abicalil Thadeu thankful is The team Adaptation. Change of Climate Department Economic Development of Sustainable (SDS) its and Secretary the through Catarina information Santa and Stateprovidedthe by of benefited from data greatly The report study. of preparation to the this were crucial comments that and guidance provided special GPSURR) Specialist, NielsConsultancy). DRM Holm-Nielsen (Lead Technical Solutions Ambiental (Flood Modeling and Ltd. GPSURR), Consultant, (DRFI Gaspari Maria GPSURR), Antonios Pomonis Consultant, GPSURR), (DRFI (Consultant, Saraiva Mario GPSURR), Consultant, (GIS Dalotto Specialist Sánchez Roque Alberto GPSURR), Consultant, (DRM Schadeck Rafael GPSURR), Consultant, Moura De (DRFI Senra Fernanda GPSURR), Specialist, (Senior DRM Ishizawa Oscar GPSURR), Specialist, (DRM Gunasekera Rashmin comprising and GPSURR), led by Specialist, produced by a team Frederico was (DRM Ferreira report Pedroso This 5 -

Acknowledgements Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 6 CONTENTS Annex Annex REFERENCES 1: VULNERABILITY II:Part SANTA DECISION-MAKING AND CATARINA IMPLICATIONS –POLICY PROFILE RISK DISASTER ASSESSMENT 2.2 Exposure in Santa Catarina DRM Planning for Base 2. Knowledge and Vulnerability Models in Santa Catarina1. Patterns Hazards of Natural Historical I:Part IN INFORMED SANTA CATARINA DRM PLANNING Executive Abbreviations Summary 10 Acknowledgements and Acronyms 8 Santa Catarina1. State Potential Implications Planning Applications Policy 2.3 CAT Model Potential and Applications Policy 2.1 Modeling Potential and Hazard Flood in Past Recent the Capacity Response 1.2 Disaster Financial 1.1 Recovering State-level Losses and Damage Disaster 48 4 50 27 45 44 35 22 21 17 14 14 12 7

Contents Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 8 AND ACRONYMS AND ABBREVIATIONS OEP MDR IBGE EP DTM DSM DRM CAT BUC AMAX AEP AAL

Catastrophe

Mean Damage Ratio Damage Mean Aggregate Exceedance Probability Exceedance Aggregate Average Loss Annual Annual Daily Maximum Daily Annual Brazilian Institute of Geography and Statistics ( Statistics of and Geography Institute Brazilian Basic UnitBasic Cost Occurrence Exceedance Probability Exceedance Occurrence Exceedance Probability Exceedance Digital Surface Model Surface Digital Disaster Risk Management Risk Disaster Digital TerrainDigital Model Instituto Brasileiro de Geografia e Estatística e Geografia de Brasileiro Instituto ) 9

Abbreviations and Acronyms Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 10 SUMMARY EXECUTIVE ensure replicability in other Brazilian states or municipalities. or municipalities. states other Brazilian in replicability ensure to topographic information and hydro-meteorological data on accessible commonly as well as census on national the based heavily approachFinally, was proposed the methodological Catarina. applications Santa ofto body in a wide professions institutions direct and tential po andhas To ters. afirst-of-its-kind is of team’sin the Brazil best study this the knowledge, to disas improve state’s the possible and toimplications decision natural making resilience a policy draw potential to of number the team allowed the andstudy The depth of novelty follows: comprehensive and as modeling CAT led to asuccessful that of activities endeavored anumber has team the process, methodological awell-established Following model. (CAT) to produce Catastrophe astate-level aim ultimate government the with developed World by the Catarina’s and jointly Santa designed state was and study Bank anovel context, respectively. this In processes, decision-making operations and daily their in information and practices to government include state its institutions the DRM and consequentlyand empower risks exposure asset flood by identifying Catarina Santa in knowledge (DRM) management risk thedisaster body of further to seeks report This • • • • financial exposure to natural hazards. hazards. natural to exposure financial and to improve of its asset state’s the [AAL]) Average understanding Loss Annual [OEP], Probability [EP], Probability and Exceedance Exceedance Occurrence [AEP], Probability Exceedance (for Aggregate metrics produce example, general CATcontextthe Model. of specific the in attempt modelling imprecisemathematical implythose would rather in a define to were used not common assumptions considered scenarios as Climate models. surface and terrain digital and information able hydro-meteorological historical A CAT model was derived using exposure, vulnerability, and flood models to to models flood and vulnerability, exposure, using derived was model CAT A avail using produced was periods return different for model flood statewide A mation of building asset values and vulnerability to flooding events. to flooding vulnerability and values asset mation of building application. ogy technol were information compiled which asingle in databases, state-level and Residential and nonresidential models were developed to allow both the esti the both allow to developed were models nonresidential and Residential national- using generated were plans information geo-spatial of set robust A 11 - - - - -

Executive Summary Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience01 Planningpart 12 Omar Cardona - Sasakawa Award Winner Award -Sasakawa Cardona Omar “Disaster of Events the are materialization generate economic loses and social impacts” social and loses economic generate Natural which Risks, ultimately Hazards CATARINA SANTA IN PLANNING DRM INFORMED S anta C atarina private for zones, developments example. safe in or state to promote the investment interventions across and for DRM to areas prioritize theinformation from benefitin provided order can institutions such, State or national As on. so and estate real production public plants, infrastructure, road networks, like types, of different of assets georeferenced combination datasets in with used be can Flood maps Depth (meters) Depth 0- 0,2 0- -0,4 0,2 -0,6 0,4 -0,8 0,6 -1 0,8 +1

flood area

map -1

95.734 km in 100 year s

return 2 16% 1.000.523 rural Population Population 5.247.913 urban 84%

period 6.248.436* 13

PART I: INFORMED DRM PLANNING IN SANTA CATARINA Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning C 14 T 1. H floods V ata floods V able floods sep rina Hurricane200 Drought 2004-2005 i s 1. M tori ale doi ale I 2016), and 186,000 left without electricity for weeks (BBC 2008). Again in September2016), in (BBC 2008). for weeks without Again electricity 186,000 left and homes, 27,400their homeless, 7,154 left homes were completely (CEPED destroyed UFSC over 1.5 135 people. least At million over 78,700 people were killed, forced to evacuate and about 60 towns affected 2008 presentedin events table 1, the in Among flooding the impacts. andeconomicthetheir social of scope events better to these data on cific the Septembereventsin in 2011. Itajaí flood the and Valley Catarina, Table 1 spe presents 2014, in regions mountainous and west Hurricane the in thunderstorms 2004 and the hail the 2014, in in Itapocu event, flooding drought Valley 2004–2005 November the 2008, in and floods thelandslides as such eventsmagnitude greater of reflected which of records, the in number were observed peaks annual considered, period significant 20-year the Over noticed. was state the southern of regions the in and western incidence the of in higher events aslightly data, Onrecords. average, 2,704 official year.wererecords 135 persubmitted historical From of (value State GDP adjusted per year to 2014), through by reported municipalities as to amounted R$17.6 Catarina Santa in disasters or billion 0.4% from natural losses and From season. 1995 rainy the is to February), which (December summer to 2014, damage the during occur most floods most common and the events are droughts and Floods far. so Brazil recorded in only hurricane the Catarina, byHurricane affected also erosions.was Thestate coastal and tornadoes, mass windstorms, movements, hail, floods, flash floods, events: adverse droughts, ural diversity nat of great bya affected is Thestate of 6million. excess apopulation in tains km area of 95,346 an covers Southernin Brazil, Catarina, Thestate Santa of te 1.1 R ta c ajor mber 2 ta al p ocu 2 jaí 2008 P ecove N attern 01 01 4 4 1 atural affected Municipalities r ing 4 14 s

58 of 73 S D tate N i s 16 atural a 3 s -

ter level an Damage E d Losses U$ 1 U$ 1 H D vent $ U $ 337 U$ 5 0 azard 16 i s 0,9 M s a ,2 M 44 s s , 5 M te

I in ,1 U I $ 1 I M r s

D the

I .4 in 45 amage S ,7 R M anta I e c affecte Po

ent an C 47 pu 12 .963 3.26 d la atarina H 935. L 1. tion 1. d 21 235.590 o i 52 51 s e ss tory 8.23 72 s 01

of DISPLA homeless and - S 18 7. anta 94 .7 CE 56 21 22 D .1 C 35 01 .338 atarina Damage Housin - 2 and con and 1. 16 34 g 7 .1 66.6 26 73. 111 5 - - - 3 2 1 H F house destroyedunits Re igure 84% floods 62% Floods i co s World Bank Estimates Based on Official Data on Official Based Estimates World Bank events. drought excluding reported, disasters natural with comparison in is percentage This tory rd 1. F s the magnitude of the 2008 events, this year has the highest flood-related loss of all years. flood-relatedall loss of highest the has year this events, of 2008 the magnitude the Given basis. toon a related yearly economic floods 2presents the losses Figure million. R$489 The municipalities informed flood-related losses of R$9.8 billion, with an annual average of average annual informed flood-relatedR$9.8 an of losses The billion, with municipalities

of and 43 percent of registered events as illustrated in figure 1. figure in illustrated percent 43 as events ofand registered about representing terms percent53 in most significant, monetary the are losses and ages eventsbetween into Taking and consideration2014, 1995 only flooding dam property thestate. in 2015),hazard and persistentis a relevant flooding that suggest 2011), al. et. events2014in recent flood the and 2014) (Smithsonian and2015 (Floodlist 2016). 1980 Between 2011, and 11 have there been major of(Garcia episodes flooding R$112 and were ofhouses damaged, (CEPED public were registered million losses UFSC 2011when people flooded, 6 489,703 died, was state the 43,066 people affected, were

S looding anta 16% others 38% others C I atarina mpa c t s 2

in C ompari total (R$) (R$) Dama house damage 81% Floods 79% floods g e andl s on

to oss 19% others O 21% others ther e s D i s a s ter E vent 1 infrastructure damage h (R$) o 92% floods 47% Floods u s s

in e dam

the a R g e unit e 8% others 53% others c ent s

15 -

PART I: INFORMED DRM PLANNING IN SANTA CATARINA Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 1.000 2.000 3.000 4.000 5.000 16 3 F igure 0 World Bank Estimates Based on Official Data on Official Based Estimates World Bank 489 Average E 2. M of state. portion the eastern the incidence in ahigher with 1, occur events map shown in these concludedas it that was distribution of records the geographical on Based in oneeventsoccasion. at least flooding to related considered, 95 losses reported percent of municipalities interval 20-year the In region. the throughout economic disruptions causing thus weeks, for several operational domestic product (World 2013). Bank, the not region,in was port thelargest Theport of Itajaí, event of represented asingle cost percent approximately gross of 2.6 state the associated the 2008, in Forinstance, significant. are measured, not easily although economic the impacts, and the Government to losses financial high caused have flooding, 2008 the as such events, Large 102 95 c ap onomi 1. 96 26 D 97 82 amage c L o 187 98 e ss

and s 20 99

due

L 135 00 o

to ss e F 467 s 01

lood due 188 02

to E 112

less than e2Ocurrences than less vent 03 H ydrologi 11 a16 Ocurrences 8 a10 ocurrences 161 2 a4Ocurrences 5 a7Ocurrences 04 s 3 120 05 c al 15 06

D 115 07 i s a s 4.905 ter 08

E 183 09 vent 390 10 s 870 11 17 12 561 13 1.121 14 GDP and the financial costs due to natural disasters R$ 17,6 bi in 20 years $ $ $ $ $ $ $ $ $ Impacts Economic 0,4 214.217 R$ million (2013) GDP natural disasters (1995 -2014) due to of GDP GDP of % 17

PART I: INFORMED DRM PLANNING IN SANTA CATARINA Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 18 4 and2011 2009 the between when compared to numbers This observed, (figure 4). was allocation of final the 2012 between ashare 2015, and as performance, disbursement the disbursed funds the in Regarding adecline F 100.000.000 120.000.000 140.000.000 160.000.000 180.000.000 20.000.000 40.000.000 60.000.000 80.000.000 igure World Bank Estimates Based on Official Data on Official Based Estimates World Bank 3. S 0 sources of funding (figure (figure 3). of funding sources ante ex toward shift a suggesting allocation, of final the ashare absolute as in and both terms significantly, increased response for disaster of funds However, allocation initial over the time, further investigation. needs and study of beyond scope this the goes open question that an is to enable disbursements capacity lacktechnical of expected an commitments or negotiating in difficulties reflects this state’s to refers the bottleneck commitment capacity. main the Whether that numbers suggest (R$593 amount revised the execution, million). the Moreover,than to budgetary regard with of R$189 much allocation lower was initial million the because credits, extraordinary through were mobilized resources of response the share alarge period this it during possible is that to see disbursed, funds response (seethe3). disaster figure low million of Besides levels mately R$400 From to 2015, 2009 to amounted approxi response to related disaster of disbursement funds out. carried was analysis gap funding and a the state level developed at was indicators fiscal disaster-related set a of gaps. issue, this funding ficient investigate To response to avoiddisaster suf was in place capacity or whether follows not is response state’s the financial naturally that aquestion Catarina, of state Santa by the experienced losses and damage Given relevant the 1.2 F tate Initial allocationas%ofthefinal final allocation Initial allocation 2009 inancial - level 1% D D i s i 2010 a s a s ter s te 28% r R R e e spon s pon 2011 se se C

3% in apacity S anta 2012

C in 5% atarina

the R 2013 ecent ( R 15% $ P c a urrent s t 2014

72% value 2015 s ) 4 93% 100% 0 % 10% 20% 30% 40% 50% 60% 70% 80% 90% - - 100.000.000 120.000.000 140.000.000 160.000.000 180.000.000 facilitated our analyses. 6 5 F and processes. instruments disaster-specific moreand and flexible of agile lack the be evidence) might due of to lack spectrum (from observation aspeculative 2009–2011 for this arguments were. period Further the in those were as state severe not the 2012 as since struck that that events fact by the the explained be could 20.000.000 40.000.000 60.000.000 80.000.000 igure From 2009, compulsory transfers dedicated to disasters were created; therefore, data availability has increased and and increased has availability data therefore, created; were disasters to dedicated transfers From compulsory 2009, Data on Official Based Estimates World Bank 4. S 4. 0 only for 2009-2014 the was done which analysis this out carry to difficult rather availability, was data it of fiscal light In Catarina. Santa in issue a recurrent are gaps funding response disaster extent to what investigate to and losses damages public disaster with funds response disaster compareis stepto The next budget. on state the of disasters of impacts the analysis in-depth for further abasis as proxies and as taken section be should this in variables presented fiscal the data, fiscal of available the nature by the employed. was imposed limitations that Therefore,were the given notthe identified methodology by response Moreover, projects to related disaster containing lines it more budget possible is that general the state included budget. in is often funds execution Therefore,the of such sponse programs. re monitoring execution, and of disaster responsible the institution the for planning, the becomes then which secretariat, state-level corresponding to the funds response the transfers responsible the ministry line that is However, sectors state. many in practice the Brazilian each to allocated Government Federal by of the executed Brazil) (directly funds response disaster of the share the state) notto it the not possible considered is was as being to disaggregate transfers government of national (instead by through the directly executed response Disaster by institutions. state-level executed spending only includes the to this noteIt important that is tate disbursement asa%ofthefinalallocation Disbursement final allocation 2009 - level 92% D i s 2010 a s ter 72% 6 period. period. R e spon 2011 se 71%

in S anta 2012 C 54% atarina 2013 5 64% 2014 64% 2015 35% 1 0 % 10% 20% 30% 40% 50% 60% 70% 80% 90% 00% 19 -

PART I: INFORMED DRM PLANNING IN SANTA CATARINA Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 2009 2010 2011 2012 2013 2014 20 7 World Bank Estimates Based on Official Data on Official Based Estimates World Bank 0 negative long-term impacts. impacts. long-term negative major potentially with are significant, response disaster in gaps financial the shows that clearly information available despite —the —and its limitations estimates preliminary as gaps presentedabove taken should be funding However, floods. the of 2008 the while impacts of fiscal the picture a clearer to get 2009 sible and of 2008 to combine years the from 2009, only notavailable pos was it was data fiscal Unfortunately, detailed because much needs. below response of the was disbursement about R$120 2009 in million response disaster to amounted over the losses R$1 late and 2008, in billion damage saster the di public that 2009. of considering In words, other year fiscal the took during place the that most response of in Novembermeaning floods 2008, experienced state the that numbers 2009, do to for not consider in response it show of important is resources alack the though addition,In even the recentpast. in significant have been gaps sponse funding re into disaster the government state-level liabilities, translate losses and damage total percent30 thewhich only in of scenario an under optimistic even 5, show figure in As F igure Public DamageandLosses(optimisticscenario) 24 25 5. D 46 50 55 i s 100 a s ter 98 110 110 R 116 e spon se R$ Millions(current)

200 in S anta 214 C potential fundinggap atarina 300 , P 294 otential F 400 unding G ap s 7 500 482 - - - K 2. nowledge the proposed methodology used in the study, which is described as follows: study, as the in described is used proposed which the methodology 6illustrates Figure planning. for DRM base model to deliver knowledge astate-level risk CAT a with flood Catarina (andSanta in its effects) hazards of natural patterns historical of the investigation combines an study this mind, in framework aboveWith the DRM andprotection, (d) (e) financial paredness, recovery.resilient of (a) action: (b) identification, pillars risk onreduction, following risk the based (c) pre strategy of update aDRM and for design the approach paramount is a forward-looking adopting planning, for valuable DRM extremely above are discussed records historical the andwhile context this improveIn strategy. roomits DRM to significantly still is country, the there in abenchmark fact, been, in has and practices DRM in progress shown significant has state the over years, the While limited. still is capacity response thestate’s time, financial thesame and, at significant are losses and damage historical the indicated by majorplay a as floods events which role.such ofeconomic The effects among events, to disaster recurrent exposed is Catarina presented, previously Santa As B a • • • • • • draulic modeling techniques. modeling draulic and andhy hydrology data hydro-meteorological fromhistorical benefiting Step 4. A state-level flood model (fordifferent recurrence periods) was produced se models so AEP, so produced.models be could OEP, metrics related and curves, EP estimated. be events could of flood consequently, and, occurrence from the economic impacts damage physical so costs. building and patterns, construction area, of built input estimation to 2010 produce main the an the as Census National for study. the base information the to build layers) for a number of variables (geo-spatial to generate plans information rina level. state at the patterns loss disaster to identify analyzed was tem on Disasters Sys Information Integrated the event) through available of disaster the acteristics Step 6. A CAT model was generated by combining the vulnerability and flood and generatedStep vulnerability bythe A CAT 6. model combining was flood and models derived model from exposure the was Step 5. A vulnerability Step 3.were and Residential created nonresidential databases keeping exposure Stepfor 2. the Geographic data was collected and state analyzed of Santa Cata char the informing form a submit must municipality (each data 1. Disaster Step

for P DRM lanning

in S anta C atarina 21 - - - - -

PART I: INFORMED DRM PLANNING IN SANTA CATARINA Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 22 F da vulnerability modeling igure ca t t abase astrophe modeling 6. C 6. total runoff, improving other hydrological methodologies such as the rational method. rational the as such methodologies hydrological other improving runoff, total a moreaccurate more to introducing obtain unit shapes flexible as hydrograph well as computation. in Moreover, advances baseflow and the of capable is predicting method the betterments into technique updated account taking method, rainfall-runoff the pinning under improved the thedescriptionhas processes of hydrological process analytical This consideredandis appropriate usage for is suitableBrazil. global for method Agency. This Environment British by the used as models hydrological Hydrograph approach for building Flood Revitalized the well-established on model—based runoff arainfall developed using approach to model river asophisticated was flows stage, modeling hydrological the During follows: as stages main four through built was model hazard flood ferent the tract). (state, levels summary, municipality, In census and dif is to at maps, model produce flood hazard objective of the flood context, the this In andeconomic impacts. producesocial can that periods for return different (and its municipalities) Catarina Santa in flood-prone display areas will study particular this in which model, CATis a to develophazard model to a build 6, onethesteps necessary of presented figure in As 2.1 AT M da ta F

sources • • • • loo odeling drainage paths, and microdrainage basins) creating a knowledge base and input datasets input and datasets base aknowledge creating basins) microdrainage and paths, drainage (1991–2010), tributaries, river rivers, main representing features fluvial flood records, historic Thefirst stepwas to gather,refine, and analyze anddata available: example,records for rainfall and ‘where to put it’ geographically speaking ‘where speaking and to put it’ geographically ‘how out water’ much to figure of techniques avariety involves using modeling The second step was to develop a hydrological model. In essence, hydrological hydrological essence, In model. hydrological a develop to was step second The as validating those against past-recorded events. against those validating as surface flow involves the which modeling over floodplain els, The final (fourth) step was focused on generating state-level flood maps as well state-levelas maps flood on generating wasfocused step (fourth) final The mod Thethirdstep comprisedtwo-dimensional theusing modeling hydraulic d H aza M rd ethodologi damage andlossreport ibge st M flood hazardmodeling a exposure modeling o te gov. d eling c

census 2010 an al DSM &DtM DSM d A P pproa otential c h P olicy themat geographic informat A pplication geographic da ic gisla s yers ta ion system footprints analy sis - - - F T igure Rainfall (mm) otal 4.000 2.000 3.000 1.000 total rainfall R 0 7. F convolution in the routing model and, finally, the baseflow is added to the direct runoff to obtain total runoff. total to obtain runoff direct to added the is baseflow convolution the finally, model routing and, the in hydrograph unit the routed then is outlet catchment to using the runoff direct The runoff. into direct turned When simulating a flood event, the loss model is used to estimate the fraction of the total rainfall volume volume rainfall of total the fraction the to estimate used model loss event, is the aflood simulating When unoff 0 Initial soilmoisture the state of the soil at the start of the flood event based eventon of flood long-term the based antecedent rainfall. series of start at of soil the state the the to determine used was data on daily model based components, accounting moisture asoil main to the addition In model parameters. and input model components required the the variables with together components.between connections the shows 7 main three Figure of consists model The hydrological flow). subsurface andshallow flowdelayed of subsurface deep sum the flow from stream (portion and of baseflow storm water flows theover surface) when excess runoffoccurs (flowthat direct of water theinteractionintobetween account takes The methodology 0.5 lood Initial baseflow 1.0

1.5 hydrograph 2.0 M bf c 2.5 in 0 3.0 odel i 3.5 4.0 4.5 S 5.0 ummary net rainfall net 5.5

for 6.0 6.5

7.0 a tot 1000 1000 loss model 7.5 al rainf 8.0 c 8.5 ma 9.0 baseflow model x year 9.5 Time Intervals (hours) 10 all (BR,BL) 10.5 s 11

return total runoff total ROUTING MODEL 11.5 net rainf 12 12.5 T 13 p 13.5

period 14 all 14.5 15 15.5 16 16.5 17 direct runoff direct 17.5 tot 18 18.5 al flo 19 19.5 20 w 20.5 21 21.5 22 22.5 23 baseflow 23.5 10 20 30 40 0

23 flow (m³/s)

PART I: INFORMED DRM PLANNING IN SANTA CATARINA Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 24 recovery. and resilient (d) protection, (d) financial preparedness, 1 1 M in ap Global Facility for Disaster Reduction and Recovery pillars include (a) risk identification, (b) risk reduction, (c)reduction, (b) risk identification, (a) include risk pillars Recovery and Reduction Disaster for Facility Global 1,000 1,000 2. 2. I tajai and agreed with many counterparts (for United academia). governments, example, and Nations, national counterparts many with agreed and framework DRM the address to better decision makers and policy allow will study from this results the that Hence, it expected is activities. of planning avariety in useful be will that information detailed very with to enough provide makers policy high products is original out, resolution the of carried the of analysis the illustrative are report presented this maps in the tion, while addi modeled. In as flood period return conditions (bottom) under a1,000-year and regular in network drainage basin Itajai-acu 2shows the map exercise, modeling flood products from the of final example an As Y ear - açu s (

top B a s ) ) in F

lood D rainage s

N etwor k

W ithout 1 promoted by the World Bank across the globe promoted World by the globe the across Bank

F lood s ( bottom ) ) and S ubje c t

to -

M M ap ap 4. S 4. 3. M Depth (m) Depth years 20 1in Fluvial associated with relevant floods, especially in the coastal region. coastal the in especially floods, relevant with associated of probability percent, 20 are annual an frequent even with events, shows that Catarina Furthermore, from a macro perspective at the state level, combinedmap for state the at flood Santa the perspective from amacro Furthermore, low: 0 low: 5 HIGH: anta

uni c C ipality atarina

of S G tate a spar O F ne lood

in F M ive ap Y

ear with s F R lood eturn R eturn P eriod P

eriod of 20 Y 20 0 ear 0,5 s 1 2 Km 25

PART I: INFORMED DRM PLANNING IN SANTA CATARINA Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 26 nent focused on residential and nonresidential buildings. buildings. nonresidential and onnent residential focused compo study, modeling exposure the particular this in and, of asset the characteristics on physical It the hence is focused its construction. in used on material predominant the based occurs, aflood if buildings of exposed the or totality or the to replace repair costs how much investigates model it only exposure The damaged. be will unit the place, takes ofeventset concepts).an aflood-prone in if located notimply that does area Abuilding (see box 1for acomplete vulnerability and of concepts exposure the between distinction a to make it important is similarities, Given technical the buildings. nonresidential and forvalue residential constructed the to determine aims ultimately module exposure The uses. others among infrastructure, and public privatesectors, arenas, and different production in of state the resilience tool for overall become akey the improving can study from this on. so and Hence, results the productionworks, public plants, infrastructure, as road net such types, of different of assets georeferenced combination datasets in with used be can maps flood the More for example. generally,scales, parcels, at larger exposed hazards promote and or to encourage private development investment of in natural instead prone areas state the across investments for DRM toareas prioritize institutions or by state national used be could maps flood the resolution, of in level thematic contained information the this At e 2.2 occurrence of a particular natural phenomena in a given period of time. phenomena of agivenperiod time. in natural of aparticular occurrence happen due to the can that losses economic social and Likely risk. Disaster hazard. by anatural affected givenis be to a Susceptibility. location which to The degree phenomenon. of extent natural possibleits location and geographical condition element of an given geographical intrinsic An Exposure. productivity, and others. among services, processes, affect negatively phenomenon consequently of anatural will occurrence by the affected be can conditions that physical Preexisting vulnerability. or infrastructure Asset of time. length termined a de and within location geographical in a specific effects negative phenomena have potential Natural once triggered, hazard. Natural B xpo ox 1: C s u r e

an on d c V eptual ulne r F DRM ability M ramewor o d el s k

- - - details including building aspects of the household residence. The data characterizes weighting (or ponderation) areas. ponderation) (or weighting characterizes data The residence. household of the aspects building including details 2 F igure (ibge 2010) weighting area census track & We used the sample data derived from the sample questionnaire (Questionário da Amostra) as it contains more it contains as Amostra) da (Questionário questionnaire sample the from derived data sample We the used 9. E data struction patterns and standards for the state was calculated, according to the BUC to the according calculated, was for state the standards and patterns struction con (BUC) cost unitthemain the Then,of basic of Technical Standards. Association Brazilian to the according buildings for residential standards and patterns construction mentioned, previously 2010 from the data As Census National concentrations of exposure. to represent dataset available best the deemed mapping and aerial against checked was This database. use land local from the extracted was area urban the receptor database model; to the create for exposure the required was buildings on residential A database referenced to cross its replacement (see9). cost was figure pattern each and estimated was pattern construction each of vulnerability the Thenphysical estimated. was Catarina Santa of tract census each in volume built area nonresidential floor the and of data, lack this To scarce. was for state overcome the on buildings nonresidential data otherOn hand, the of unit. built cost and standards, patterns, of construction estimation accurate relatively household each of for residency,allowing thecharacteristics information on significant 2010 from the Sample data (b) provides and exposure. nonresidential Census National Two model: (a) exposure exposure the outputs residential were when sought building main R xpo e s id sure ential DSM DTM

and E xpo rebuild cost grid, andlinearly distribute split theregioninto1km residential buildings patterns andstandards for identify construction calculate basicunitcost(BUC) calculate basicunitcost(BUC) create thereceptordatabase V ulnerability s residential exposure u r e M odeling 2 M built volume andthefloorarea subtract theresidential tract level assemble thedata at census floor areaineach censustract derive thebuilt volume and diference betweenDSMandDTM analysis ofthespotheight residential structure susceptibility ofnon- investigation intothe tract ofSanta Catarina floor areaineach census residential built volume and derive theestimated non- ethodologi non-residential exposure c al A pproa 2 was used to identify to identify used was c h maps & exposure modeling exposure 27 -

PART I: INFORMED DRM PLANNING IN SANTA CATARINA Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 28 amounts. 3 We used the National Costs and Indexes of Construction Research System composition sheets to calculate these these calculate to sheets composition System Research of Construction Indexes and Costs National We the used To increase the precision of the flood analysis, the entire region was split into grids of 1 km splitthe into entire of 1 was regionTogrids precisionanalysis, the of flood the increase R$62,217.41, ofbuilt was wood, of R$840.78 acost representing forcost a house, predominantly construction one (d) The and total bathroom, one garage. ground area of (a)floor findings: bedrooms,average following two m2, the (b) 74.0 (c) for allowed Such analysis areas. rural in particularly state, the found in wooden houses of composition most common the characteristics with aunitary we prepared Brazil), of (notthe pattern Industry by ofUnion specified construction Construction such of To patterns. meter square per construction value the determine standard included the in households) not percent 28 are imately of of and all made wood, were predominantly state (approxCatarina Santa across of number houses asignificant foundIt that was area. weighting each within area urban the across equally were distributed costs rebuild the System Information processing, Geographic Through of Brazil. monthly Union by Industry the issued is of that Construction index buildings. It is clear that the replacement or repair in any given damaged building was directly con directly was building given damaged any replacement in the or that repair It clear is buildings. for value residential constructed the to determine was step next the established, was area built Once the was area obtained 198,749,558 floor residential total the tracts, census for all process the m Repeating tract. - RBFA by (m2): census area built Residential - Dom (units): and tract; census per number of units housing - Where: equation: following the using were obtained ings build of residential areas the unit, housing number Using of the each rooms in Census. 2010 the in available variables from the National extracted standards, construction and thematerials to according households the were classified described, previously As inundation. to potential exposed title of each share the to establish map exposure the against analyzed andspatially exported was map flood the 10,000-year flooding, to potential exposed To cells. the among were distributed proportion derive costs the rebuild ‘receptor’the of each R NMRooms (pcs) =AverageNMRooms number of rooms household per area; by weighting e s id ential V alue

o f B uilt RBFA = Dom x NMRooms x17 =DomRBFA xNMRooms A r ea

3 per m per 2 . 2 and and 2 - - - . M T able ap 5. S 2. R 2. anta is shown in map 5. map shown in is at R$1,243.36estimated m per then was ofmeter amount per R$247square value value The total billion. average the we obtained design, productthe environment of built BUC the the standard and for each was 198,749,558 Catarina Santa for area obtained floor residential The total m (seeprojects). table BUC 2for relative costs the standard for different construction the to the nected elative Residential exposure, in R$ millions R$ in exposure, Residential C residential singlefamilynormalstandard atarina residential singlefamilyhighstandard BU residential singlefamilylowstandard 0m to 100m to 0m 200m to 100m 300m to 200M 390m to 300m 490m to 390M residential multifamilylowstandard residential multifamilylowstandard residential multifamilylowstandard C ’ s for R e D sidential ifferent popular/social housing mean residentialBUC A 2 S and the value of the total asset exposure per municipality municipality per exposure asset of total value the the and et ss tandard wooden houses E xpo P sure roje type c t s 1,453.79 840.78 1,533.79 1,293.32 1,159.13 1,395.14 1,899.37 1,553.53 1,306.4 R$/m 2 6 2 . Through . Through 29

PART I: INFORMED DRM PLANNING IN SANTA CATARINA Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning all cases. Impact is highly is Impact the on cases. dependent all 30 *Percentage of the total monetary value of replacement/reconstruction. of monetary total the value*Percentage of replacement/reconstruction. produced. It allows exposure exposure allows It produced. The impact of a hazard will not be equal in equal be not will ahazard of impact The The vulnerability of various various of vulnerability The portfolios to be parameter to portfolios 12% characteristics of the asset at any given given at any asset the of characteristics Mean Damage Ratio (MDR) to to (MDR) Ratio Damage Mean flood intensity (depth) was (depth) intensity flood 21% Apartments Houses Wooden Built Value* A series of curves relating relating curves of A series ised to reflect character reflect to ised types are more vulnerable to flooding. flooding. to vulnerable more are types Catarina was researched. researched. was Catarina location. Some property construction construction property Some location. istics of insured assets. assets. insured of istics property types in Santa in Santa types property flow direction flow - -

loss 0.2 0.6 0.8 0.4 0 1 0.0 Vulnerability of various of Vulnerability 67% external water level external Houses brick Vulnerability Exposure and One story 1.0 2.0 12% 31% Built Residencial - area Apartments Houses Wooden types of building of types water (metres) depth 3.0 two stories two 4.0 5.0 three stories three 6.0 57% 7.0 Houses brick extent to which this was possible) and then related to the most appropriate vulnerability curves. curves. possible) most appropriate was to related the then and this vulnerability toextent which the (to theinterior of characteristics or um, lowanalysis (seean table 3).on based decisionwas This medi high, as defined was of buildings nonresidential susceptibility With information, the such (a) recorded: was (b) door material, interior (c) quality, and number of stories (1, or 3+). 2, information following the building, For nonresidential repeated. each was process the and Subsequently, for analysis. the selected anew, chosen View was location was Street nearby identified Google uponbuilding entering first the nonresidential location,each In flood. od peri return the by at 10,000-year ofleast model deemed to footprint flooding at be risk and extentthethe flood of within that were areas within located all chosen were The buildings negligible. would very be results on modeling loss the impact the because and investigation further evenassumptionthe warrant from to split ratio enough significantly notdid vary results established the out as carried was sampling no additional were100 taken, observations the After halved. was theweresamplewhenstable size obtained The results assessment. initial an to establish satisfactory deemed sample was survey the state, the within stock building vant rele proportion in total to the sample size 100 represent properties small arelatively Although a50/50 was there types. that split of susceptibility intended to to support provide was orsis data disprove heroic quantitative the assumption analy This state. the representative across cities three were at chosen within random areas case, each In areas. rural and View®, suburban, Street Google urban, encompassed which through undertaken of buildings, survey 100 involved manual This a state. the within thestructures nonresidential of intothe susceptibility investigation an was step The next population. contain Catarina percent 91 thetracts Santa of census The 10,029analyzed were spot (concerning not height 1,786 data available or because tracts) tracts). census IBGE file of shape (concerning 412census boundaries of aproblem area weighting the in eitherbecause analyzed could not be tracts census The remaining tracts. census existing possible 10,029 in was which (82 Catarina, of percent) Santa of 12,227 tracts the census the all applied across above was described out.theveloped tested Thenmethodology and were de hypotheses 143 various tracts), and where methodology containing the census areas of (7 Florianopolis weighting out apart in carried was study afeasibility Initially Catarina. Santa of tract census each in volume built area nonresidential floor and estimated 2010 census thefrom volume built area floor residential the data) and to derive (estimated the subtract and level tract at census data the assemble then spot and cell-level heights the using tract IBGE]). census each in the and to derive Thevolumearea approach built floor was Estatística, e de Geografia Brasileiro [Instituto Statistics of and Geography Institute (Brazilian 2011 between captured 2013, and 2010Catarina conjunction August in the with Census of Santa Model imageries Terrain Surface Digital Digital the Model and level, using tract census the at performed was the and volumearea built ofnonresidential floor estimation The N on r e s id ential E xpo s u r e 31 - - - - -

PART I: INFORMED DRM PLANNING IN SANTA CATARINA Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 32 M T able SC FLUvial 100yr Depth 100yr FLUvial SC exposure NON-RESI and Resi ap 6. S 6. 3. S low: 0m low: R$ 40M + 40M R$ HIGH: 10m R$ 0-10M R$ R$ 10 -20M R$ R$ 20 -30M 20 R$ R$ 30 -40M 30 R$ anta asset value at flood risk in thestate. in risk at flood value asset the of estimate an generates maps, hazard flood the combined with Catarina, Santa in nonresidential) (residential and 6presents how map modeled exposure the For instance, step. assessment vulnerability the with proceed study the process, modeling hazard the through mapped flood-prone and models nonresidential areas and residential With both table 3wereshown in possible. of interior. the (b) and (a) quality outcomes From the the criteria, door these the type on based selection a weremake criteriato assessment available The made. reliable only was of interior materials susceptibility the describing assessment By an referencing, u sc C eptibility atarina metal/glass door+highqualityinterior metal/glass door+lowqualityinterior wooden door+highqualityinterior wooden door+lowqualityinterior A ’ s A e ss et ss door materialandinteriorquality ment ss E xpo

for sure N

onre in F lood sidential - prone B A uilding medium high medium low suceptibilit rea s

s y tionship between event intensity and damage is described in the vulnerability curve. vulnerability the in described is damage event and intensity between tionship the In rela words, other intensity levels. hazard at different type to agivenproperty occur may that losses and resultant amountthedamage of indicates This reinstatement cost. of total the apercentage as to aproperty occur could that of ratio damage overall the describe that functions werevulnerability produced. Then generatedwe curves vulnerability appropriateand damage determined was in Brazil types property various of vulnerability the extensive research, Through among others. debris influence, and release, energy of impact, angle anticipatethe cannot flood models as estimation damage approach physical toward for ageneralized allow limitations tools and modeling asset).the Finally, mathematical of value total to the building—relative multistory set ahigh much than extent greater toa building a affect single storey low-set will event a flood (fortoexample, flooding more are vulnerable types given location. Additionally,any construction some property at asset the of dependentcharacteristics thehighly is physical on impact The cases. all in would not equal be of ahazard impact the that assumed it was acommon practice, As interruption. as business such factors through quences of flooding economic or wider the conse of inhabitants, the welfare the buildings, contents within did It considernotthe ofhazards. flood because damage their with associated losses financial the and of buildings vulnerability solely the structural on focused study This depth of floods. case, event’s flood intensity, particular this in giventhe asset the or to replace repair cost mean likely result—the produces anew and stages, modeling exposure and hazard the from combinesflood The information model of element the or component the of of fragility set elements. the understandings lished on estab based damage of level physical estimated into an to translated be of hazard the theintensity event. allows when It disaster subjected to a damage mentsphysical to suffer element expression of tendency of the an an or is context of aset ele this in Vulnerability economic and resilience. social therefore increase and risks disaster to manage measures hard to adopt and provide soft means the will events of natural afunction as of damage level the as crucial Such is astep analysis. of vulnerability the part of is events adverse type and typology of building the afunction as proportion the of damage determining risk, at assets and areas the sufficient to indicate are models exposure and hazard the While for lower designed be income. can programs period) specific so events to (low recurrent return exposed households are where areas critical and identifying risk to flood exposed settlements profiling in useful be module can vestment decisions. From development exposure the asocial viewpoint, in with cost-benefit perform interventionssuch and considerations before proceeding of beneficiaries target theabout informed better be can makers prone policy to floods, areas the in located assets the of the andfeatures thevalue knowing vestment. Thus, by in reduction infrastructure risk disaster of allocating charge in ample, to institutions forex is relevant, maps flood to the of buildings on exposure information the Adding 33 ------

PART I: INFORMED DRM PLANNING IN SANTA CATARINA Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 34 and financial loss associated with different flood events.differentflood with associated loss financial and risk the to calculate information vital us gave models of previous the each context, this In inputs for modeling. CAT as the them used —and models vulnerability and exposure, ard, haz study, the of — stage products we combined the individual final this In Profiling. Risk Disaster Catarina theSanta completing in process important an was of buildings stage CAT modeling risk The 1. refer to annex information detailed and curves vulnerability the tioned. For all men previously were produced as Catarina Santa in buildings of residential ceptibility and level of sus characteristics, patterns, construction for different the curves Damage parameters: building combinations of following the various covering curves ofset 47 damage provides a which method, Australia GeoScience the used study this assessment). summary, In and parameters vulnerability the on for 16 details incrementsintensityfull flood 1 for annex (see allow study this in used to related The water depth. models directly are Flood measures intensity appliedbeen calculations: forMDRequation to has replacement The following repair value. of cost the ratio the of average calculates The MDR to aflood’s(MDR) intensity measure. ratio damage the mean that relate component curves series of of a consists vulnerability The cat M 2.3 • • unknown and non-elevated,• Elevation: elevated, • Height (stories): 1, 3, low, 2, high and medium, adobe (clay),• Construction: wood steel, and concrete, pole/beam, else industrial, residential, • Occupancy: a BUC produced. was of buildings, patterns of construction the analysis an through and geolocations model produced this In is. words, other value financial their what and cated unknown and high-susceptibility, The exposure model data provides information on where the buildings are lo are buildings the where on information provides data model exposure The susceptible, low-susceptibility, susceptible, not interior): (of Susceptibility MDR = o d el

an Cost ReplacementValueorSumInsured d P otential Average CostofRepai P olicy A pplication r s - - - - the sake of interpreting the results from any given CAT model. givenCAT from any results the of interpreting sake the model. adopted Boxcommonly 2presents the for risk CAT terminologies the through wasobtained This Catrina. Santa in losses financial tion on howeventscause flood would not did spite have aprojecIn presented, previously we developed still and of models the year. Its EP is therefore Its is year. 100 EP percent. every or exceeded to equaled be expected is loss period one-year return The over per year, years. loss averaged many representsexpected the It of distribution. EP value aloss It mean the is calculation. losses al of annu the sum average represents an (AAL) Average Loss - Annual event or (OEP) asingle of(AEP) agiven amount. excess in losses annual OEP. either aggregate of having It likelihood shows the or AEP based on be may This exceeded. being loss givenfinancial any of probability the communicates (EP) Probability curve - Exceedance thereforeis value. little loss of financial exact of an probability the or over Calculating time. risks between comparisons for or to provide agiven range, benchmarks within losses expected calculate probabilities, exhaustion or attachment to identify beneficial as is it favored approachCATis for This itself. loss modeling, exact not and the OEP exceeded, refer to and being aloss Note AEP that included for are information. thus and analysis the in were calculated but of arereinsurance loss moreexcess CAT relevant for figures These threshold. acertain exceed will one event any in year most costly the (OEP) Probability that possibility the is Exceedance - Occurrence probability. therefore have alower annual will and often less exceeded) are events occur (and flood Bigger ratios. loss gross assessing used when be should figures These threshold. certain a combine to exceed will ayear within events of all cost total the that probability represents the (AEP) Probability Exceedance Aggregate B • • and event’s profile. This relationship is graphically represented by the vulnerability curves. vulnerability represented event’sthe by and graphically is relationship This profile. conditions of repair/replacement) cost the is what thehazard given location at aspecific The vulnerability modelThevulnerability helps understand howlikely itis for to be buildings damaged (and including physical characteristics of terrain. the characteristics physical including The modelhazard providesinformation onthe core components of flood events ox 2. T 2. erminologie s U sed

for

the C AT M odel R e sult s - 35 -

PART I: INFORMED DRM PLANNING IN SANTA CATARINA Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 36 F igure 10. S data were disaggregated into individual events and related back to the exposure database, and and database, exposure to the back related and events into individual were disaggregated data annual Last, data. hazard of modelto underlying to relate weather the the years 10,000 synthetic the forof each periods return intoannual event data annual transform to was step The next rainfall. to the structure to non-gauge sites by spatial interpolating toweather apply amore realistic of year second,acorrelation model to the generate asynthetic gauges; between distance with reduces correlationdescribe a to covariance how model onefitted was first relation were the models created; values). AMAX corTwo time between dependency is, thesame at (that pairs gauge at rainfall/flow the of significant probability describes generated which was matrix’ Then a uted variables. ‘co-variance values distrib new,into normally AMAX transform was to events synthetic in step preparing first The but not may precisely. predicted be statistically analyzed be may that ordistribution pattern random a probability arestochastic—having events synthetic These were prepared. Catarina Santa event in forevents data flood of realistic years generated. Then synthetic are events 10,000 weather synthetic input from which for creation of the acorrelation matrix primary the is and intois formedfiles series of a location. This gauge valid at each year for each were calculated values (AMAX) maximum daily supplied, records annual the rainfall historic the such, using As process. description modeling of CAT the to provide not asystematic reader does the aim as it the of process, overview general report provides a only This task. this to accomplish are required complex steps rather is a loss one.Several possible the to simulate financial damage differentlevels of with events, a set robust of simulated generating of The process model CAT process. the 10 model. of CAT Figure modules the summarizes as consider all them would some products, experts individual here as models we considered previous the Although 2013). Association event (Lloyd’s catastrophic fromMarket a resulting loss financial the calculate and tensity, of amount event damage the location of to determine an and in for magnitude, the accounts simulation Each events. of aset generates simulated that automated an system is study, activity of scope this modeling CAT the the Within ummary Local intensitycalculat Flood HazardModel what exposure model event Generat exposure da D itis&where iagram

ta of ion

a C ion AT M odel building vulnerabilitymodel cat damage estima loss calculat astrophe model loss estima tion tion ion - - - return period could generate losses of R$2.3 billion or billion of higher. generate R$2.3 could losses period return of generate R$1.8 losses will or billion more. givenyear any Events a20-year with within events all there that is a percentresults 10 these chance Therefore,on threshold. based a certain exceed will givenyear ina events all cost of thethat total 13 figure theshow in probability results AEP The T F return period igure able granular levels,e.g.CREST link tosp historical da vulnerability curves aggrega 10,000 4. S 4. 1,000 2,000 5,000 11. S harvest anclean (quality assess) 100 200 250 500 aal thetic event hazard data. This produced the key results of this study, tableare this in presented4. of which results thekey produced This data. event hazard thetic syn completethe of realistic years against 10,000 run was database exposure Hence, final the set. event full 10,000-year the using assessment loss probabilistic full and a scenarios flood historic three from data the using assessment loss ahistoric were run: ready, model analyses was Once the two process. 11 the Figure mapping return. illustrates hazard the referenced against water depth 50 10 20 ummary at ummary te uptoless iall 645,100,611 1,484,396,030 1,822,799,614 2,150,395,407 2,340,205,404 2,504,873,263 2,563,276,409 2,712,547,009 2,829,424,099 2,961,037,662 3,130,487,859 3,187,533,953 oep inR$ ta y v records

of ariable

of K

ey the O A S utput to c aeps inR$ 645,100,611 1,829,110,336 2,369,292,826 3,041,295,048 3,041,295,048 3,972,822,872 4,105,352,668 4,482,340,660 4,798,717,501 5,041,813,079 5,248,577,898 5,335,498,814 ha relat s

s from ti c ionship betweengauges sp (e.g. toelements) E genera S at vent footprints est anta ial- export ablish temporal te event S C et atarina 0.30 0.85 1.11 1.42 1.65 1.85 1.92 2.09 2.24 2.35 2.45 2.49 aep as%ofgrossdomesticproduct G eneration C AT M P ro odel link intensity(i.e.depth) c e ss genera

to floodseverity synthetic events model te realistic Run 37 -

PART I: INFORMED DRM PLANNING IN SANTA CATARINA Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 38 F F 1,000,000,000 1,500,000,000 1,000,000,000 2,500,000,000 3,000,000,000 3,500,000,000 4,000,000,000 4,500,000,000 5,000,000,000 igure losses igure 1,400,000,000 1,600,000,000 1,800,000,000 2,000,000,000 2,200,000,000 2,400,000,000 2,600,000,000 2,800,000,000 3,000,000,000 13. OEP AEP 12. example, to insurance and reinsurance companies. reinsurance and insurance to example, for useful, be can agivenyear, that in loss ametric of largest probability shows the the The OEP estimated. also OEP was the assessment of risk the AEP, the part Besides as future. the in gaps funding is not to enough avoidcapacity response additional response financial state’s the scenario, current the in into state-government liabilities, translate losses associated the only 10 if even spite In limitations, percent ofor of modeling such private) assets. of exposed the Moreover, (public ownership not model does damages. the segregate estimated the with ciated asso education,(for industry), effects but and housing, not for does account indirect example, sectors many in disasters of effects direct the encompasses which to buildings, damage ated with of over associ losses R$1 model losses considers to only the note the It billion. important that is more concluded model CAT that frequent from generate the It could significant events also is 0 for for 0 D D ifferent ifferent 200 R R 200 eturn eturn P P eriod eriod 400 400 s s return period return period 600 600 800 800 1,000 1,000 - - T able 3. R Potential modeling limitations might be related to lack of information on risk mitigation mitigation of on to information related risk lack be might Potential limitations modeling attempt. modelling CAT to lead a misleading or can erroneous, limited which model was orexposure (ii) features, the geographical not specific able was capture flood model to assessment whether of allow for(i)thebetter a to endeavor field surveys modeling with to need complement the toany highlight we context, would like this In state. at the second the as city the history, model CAT ranked the disaster ameaningful with cities Despite it the of not among is Palhoça. being for municipality the counter-intuitive AAL However, the noting it worth is infrastructure. quently, and assets more with exposed conse and, the state in theamongpopulous most municipalities also are These losses. and damages disaster of significant ahistory with municipalities the such, table 3lists As R$10 have AAL’s which Catarina’s of Santa municipalities than greater million. ranking the state. across distributed Table 3 a provides ofis spatially how picture risk flood eral on model CAT it the possible is based to have agen that means level, which nicipality out mu at the were carried analysis similar Moreover, curves, EP state-level the besides for adequate state’s is the that profile. risk of fund of sources aportfolio on it, establish based and, gaps funding future potential of analysis out scenario-based presented, previously it possible is to carry curve EP the on Based hazards. natural against protection for financial strategy state-level the update to abaseline as used be also study, can of context the model this CAT results In the or billion of more. generate R$2.3 losses will givenyear event any in thereis a 0.01% results these the costly Therefore, on most that threshold. based chance certain a exceed will anygivenevent year in thecostly most that shows The result OEP an k ing

of M uni c ipalitie jaraguá dosul jaraguá navegantes guaramirim Municipality blumenau rio dosul joinville tubarão palhoça tijucas gaspar s itajaí

with Avarage annual Loss annual Avarage H 11 10 9º 8º 7º 6º 5º 4º 3º 2º 1º º º ighe s t AAL R$ 100,115,000 R$ 29,454,000 R$ 24,355,000 R$ 39,102,000 R$ 42,905,000 R$ 13,056,000R$ R$ 19,365,000 R$ 34,267,000 R$ 27,568,000R$ R$ 20,507,000 R$ 13,357,000 in R $ 39 - - -

PART I: INFORMED DRM PLANNING IN SANTA CATARINA Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 40 tool to allow future independent analyses by any givenend by any user. independent analyses future tool to allow Risk Profiling the as developmentby as thewell the of Smart study designing when first considered features only possible are due to geo-spatial other analyses Such and hazards. natural against resilience its overall as well as to improve state the capacity its response allow (to to private transfer or the sector) risk pooling of mechanisms risk adopting possibility the governments subnational to consider and for national both instrument an ments, but also reduction criteria to considered invest another be risk when prioritizing as not only useful is knowledge foreach municipality profile risk of disaster the Finally, disaggregation the ablind spot unknown. previously identify therefore and feature such able to capture model was the and loses prone is city to disaster knowledge the on eventhave howimpliedof lack in might specific on this data damage However, thelack of official damages. led to surely significant which rains, hit by heavy severely region of entire was metropolitan Florianópolis, the with together of Palhoça, 1995 the in noted be It that should municipality also the purposes. for modeling used bases data the andin not accounted times in recent installed defenses flood and works loss annual av expect er ag ed e = R$ Mi ll 64 io n 5 - tutions to better address risk mitigation and disaster preparedness in areas exposed to natural hazards. to natural exposed areas in preparedness disaster and mitigation risk address totutions better insti state guide information to useful are event and recurrence depth flood the as strategies paredness inputs developing in pre as used be also products can reduction Geo-spatial investments. risk disaster to receive for low-riskpublicpossible areas or to identify private areas high-risk or investments select maps produced, flood map (see 3) is it on localized the For based example, decision-making. localized as well as microplanning and for macro both Complementarily, allows of study the of level detail the F will ensure continuity and sustainability of the numerous initiatives led by different institutions in Brazil. institutions led by different of numerous the initiatives sustainability and continuity ensure will Hence, which developed, a levels. common subnational forplatform and was integration national at both products to arepository be for but DRM also nature, of this studies in addressed ones usually the than queries/analyses foradditional allowing at aimed not (see 8).only needs initiative figure an vidual Such indi their to according users byperformed be to queries end specific allows that Profiling) Risk (Smart application into integrated a geo-spatial were study fully addition,In products generated by the this igure 8. S nap s hot SMART RISK PROFILING RISK SMART

the S mart

R i sk

P rofiling

T ool 41 - - -

PART I: INFORMED DRM PLANNING IN SANTA CATARINA Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 42 by type by Infrastructure R$ 3,28 BI R$ 63% damages 9% per sector per public distribution of damages of distribution Looking Back Looking R$ 5,23 BI R$ R$ 12,41R$ BI Losses reported reported Losses Facilities R$ 193,3 MI R$ 4% damages housing R$ 1,75R$ BI 33% 91% private 13,5 millions 110 thousand and 99,294and damaged. 11,200 destroyed were houses * municipalities of % GDP as reported Losses Annual Average losses reported greater with Municipalities deaths sick. and persons, displaced homeless, the including directly affected, Population or the National Protection Bureau and Civil Defense - SEDEC. 6,464 records were employed, of which 2,704 employed, were records - and Defense Civil SEDEC. Bureau 6,464 informed economic losses. or Protection the National National Secretariat of Protection and Civil Defense. Data from disaster records reported by municipalities to the to state by municipalities Data and Defense. Defence Civil agency Civil reported records SecretariatNational of Protection disaster from Damage Housing fatalities, damages, and economic economic and fatalities, damages, affected Population Impacts of natural disasters shocks 1995 2014*shocks - displaced from their homes. their from displaced 746,600 needed people shelter or have been 746 thousand amounts fixed for 2014. for fixed amounts by municipalities Real in records. 2704 reported Materials and losses Damage 17,64R$ billions $ $ $ $ Losses Economic DISPLACED and homeless joinville GASPAR Blumenau ITAJAí Alto Bela vista Bela Alto vargem Abdon Batista Abdon Celso Ramos Celso R$ 345 MI R$ 345 r$ 1,8r$ BI 1,8r$ BI r$ 1,4r$ BI 7,4% 10,8% 9,3% 10,1% 43

looking Back | Looking to the future Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience02 Planningpart 44 “… the absence of decisions once disaster risks risks once disaster of decisions absence the “… are identified resonates as not complying with notwith complying as resonates identified are “The understanding of Disaster Risks is Risks Disaster of understanding “The the basic principle from the first step.” first the from principle basic the the first step towards providing sound sound providing towards step first the Understanding Risk Brazil 2012 solutions to disaster events…”solutions to disaster IMPLICATIONS DECISION-MAKING AND POLICY PROFILE RISK DISASTER CATARINA SANTA p 1. otential ernment and its institutions: ernment its institutions: and gov state Catarina Santa to the highlighted be comprehensive can study list) from the applications (not potential Hence, following the a decision informed making. to DRM could lead that information on DRM strategic decisions wouldfrom benefitadditional investment and planning as such complex activities and backdrop, macro state this In improvedward DRM. to contribute institutions Catarina’s to to Santa various knowledge at providing aimed study the envisaged, initially As from as products. specific its as well Profile Risk Disaster Catarina Santa application the and potential of findings the summarizes section This • • • • • • • • • will provide real/measurable benefits; benefits; provide real/measurable will defenses flood on where engineered spending areas for identifying instrument an reduction as decisions). risk used be itmented can (structural For instance, wereimple be to on flood reduction investmentstrategies if return maximum stakeholders; many flood; impending of an pre to and warned exposed are they infrastructure; other critical and to roads risk the of assessments provide points, and detailed suitable refuge identify ation plans, hazards; flood significant experience may that areas not are developed within types use land vulnerable highly that ing settlements; established at risks as reduce flood as well flood-prone in areas tling reduction; risk damage; areduction flood in at achieving options aimed Inform the relocation of sensitive land uses away from the floodplain; away from the relocation of uses the sensitive land Inform present that locations identify to used be can provided data loss and Hazard across response disaster the coordinate to used be can maps hazard Flooding risks the on sensitized and educated be can zones hazard within Inhabitants evacu inform can it as planning disaster in important are maps hazard Flood for maps are toland useful Hazard use planning enable zonation of land ensur avoid peopleset to making decision inform to used be can products data The Use floodmaps and probabilistic loss toestimates infuture directactivity flood management flood nonstructural and structural future on Recommendations S anta C atarina S tate P lanning I mpli c ation s 45 ------

PART II: SANTA CATARINA DISASTER RISK PROFILE - POLICY AND DECISION-MAKING IMPLICATIONS Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 46 • • • • • • • • • comprehensively address disaster risks in its strategic planning process; process; planning its strategic in risks comprehensively disaster address to state Brazilian first the as Catarina Santa therefore place and study from the flood recovery; post quicker with along reduced be can losses for direct potential interiors the use theof overall andlower susceptibility waterproofing, property-level flood protection, with study this in detected areas risk the ing target ofBy flooding. impacts to the buildings and of communities resilience risks; associated the and improve which education onhazards for flood strategies, areas target to as ways identify used be can Thetools risk. a reduction flood in flood defense; a way of as of reservoirs management the decisions regarding informed better downstream; water arrives as consequence of eventual the standing underthe inform can This upstream. emergence of the flooding detect and extents flood resolution meaningful time to or capture rapid satellites revisit populations; vulnerable of evacuation the and locations, items relocation of to expensive safer barriers, the erection of as temporary such implementation mitigation ofmeasures flood more for the as time well responders emergency as forewarning response, and preparation allow disaster morefortime thus and happen they before floods level; state at the practices DRM as well as teristics catchmentcharac the of improveunderstanding scientific ultimately the will which captured be can series data rivers a to detailed flowgauges ing or level provide habitats; as valuable well its as risks; therefore disaster reducing state, natural torivers their restoring and include of reforestation areas deforested the can management of water into release Upland to rivers. the delay catchment aim which strategies, State planning activities and investments decisions can incorporate the results results the incorporate can decisions investments and activities planning State the improve to aims which work, direct also could study this of results The in result can which toolschanges, as be to used societal drive also Outputs can Use the floodmap and lossestimate productsasan information source to guide high from Radar Aperture Short as such data sensing remote of use Consider to predict technologies flood-forecasting consider to used be can data The whereimplement to add flood-monitoringBy of way A technologies. defining depos sediment control to restoration mangrove for suitable locations Identify management catchment effective develop to nature with working of way A - - - - - • • • • higher and therefore ex ante decisions could be made to reduce the impacts. impacts. to reduce the made be decisions ante therefore could ex and higher is anygiven year in of flooding likelihood when the periods identify could oscillations of study climatic afurther longer the Coupled this, term. in with profile risk the alter will which (or rainfall, in increases decreases) potentially and level, storm more sea surges, in frequent to tidal lead rises could change and coast; the along locations risk additional identify could storm surges of the impacts study investigating a Hence, be significant. might flooding al tid of meteorological impacts the that could mean land coastal wideand flat the range, tidal alimited has state the Although areas. Catarina’sta coastal plans; macro-drainage Systems and Urban Drainage Sustainable use theof through improved be flooding to mitigate can how site drainage work enabled be by could exploring additional created, model is Once apluvial into approach. consideration an such taking detected be to could loss exposure additional projects. Significant future in studied be could this that advised is events; 2008 the in served ob as to landslides linked intensities are rainfall high slopeand as instability, It is also recommended that climate change be considered. Over time, climate climate time, Over considered. be change climate that recommended also It is developmentSan high in the consideredgiven be also should risk flood Tidal it and study this in considered been not has flooding water) (surface Pluvial flooding between linkage the explore further to recommended is workFuture 47 - - -

PART II: SANTA CATARINA DISASTER RISK PROFILE - POLICY AND DECISION-MAKING IMPLICATIONS Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning

48 losses (r$) 1.400.000.000 1.600.000.000 1.800.000.000 2.000.000.000 2.200.000.000 2.400.000.000 2.600.000.000 2.800.000.000 3.000.000.000 gross loss ratios. Bigger flood events occur (and are exceeded) less often and will therefore have alow therefore will and often less exceeded) are (and occur events flood Bigger ratios. loss gross Looking forward, how much do floods The Aggregate Exceedance Probability (AEP) represents the probability that the total cost of all events events all of cost total that the probability the represents (AEP) Probability Exceedance Aggregate The within a year will combine to exceed a certain threshold. These figures should be used when assessing assessing when used be should figures These acertain threshold. exceed to combine will ayear within Looking to the future er annual probability. Note that AEP refers to a loss being exceeded, and not the exact loss itself. loss exact the not and exceeded, being aloss to refers that AEP Note probability. annual er cost atcost different return periods? 0 200 400 return period return 600 800 1000 - A Catastrophe (CAT) model is an automated model that generates a set of simulated events. Each simulation simulation Each events. of simulated a set model that generates automated an model is A Catastrophe (CAT) carries estimations of the magnitude, intensity, and location of an event to determine the amount of dam of amount the determine to event an of location and intensity, magnitude, the of estimations carries age and calculate the probable loss as a result of an extreme event. (Lloyd’s Market Association 2013). Association Market event. (Lloyd’s aresult extreme as an of loss probable the calculate and age > 50,000,000 25,000,000 -50,000,000 10,000,000 -25,000,000 7,500, 000-10,000,000 5,000,000 -7,500,000 2,500,000 -5,000,000 1,000,000 -2,500,000 500,000 -1,000,000 150,000 -500,000 0 -150,000 Floods and Financial Loss in Santa catarina Santa in 49 -

looking Back | Looking to the future Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 50 REFERENCES 180951697/?no-ist, 5/19/2016. accessed gins, http://www.smithsonianmag.com/smart-news/deadly-flooding-hits-brazil-build-world-cup- Cat_Modelling_Guidance.aspx from-http://www.lmalloyds.com/Web/market_places/finance/ available Modellers, Catastrophe & UK Hydrology,CentreEcology for method, Wallingford, rainfall-runoff FSR/FEH Revitalised 5/19/2016. , accessed Resultados_Gerais_da_Amostra/Microdados/ 2003 IBGE, Amostra. da 19, Sciences, Behavioral and p.487-493 Social Procedia 2008, November in –Brazil Catarina Santa in –considerations about occurring events the agement planning 5/19/2016cessed doSul Grande Catarina Santa em Naturais Desastres 5/19/2016accessed World (2013) Bank (2014)Smithsonian (2013)Lloyd’s (LMA) Association Market No.1, Report Supplementary Handbook Estimation Flood T.R. (2007) Kjeldson, Resultados da População. Gerais 2010 Características – Demográfico Censo IBGE. (2011) I.M. W.F.F. Roseghini, Aschidamini, and C.M., Garcia, Rio and Catarina Santa in Affected 200,000 –Over (2015) Floods Floodlist Brazil de Decorrentes ePrejuízos (2016) Materiais UFSC Danos dos CEPED Relatório 84 to rise Brazil in deaths BBC (2008) Flood , http://floodlist.com/america/brazil-floods-santa-catarina-rio-grande-do-sul, ac . Disponível em ftp://ftp.ibge.gov.br/Censos/Censo_Demografico_2010/ em Disponível . . Avaliação de Perdas e Danos: Inundações Bruscas em Santa Catarina. em Santa Bruscas Inundações de Perdas eDanos: . Avaliação Deadly flooding hits Brazil two days before days two World hits Cupbe Brazil flooding Deadly , World Bank – Brasilia, Brazil , World –Brasilia, Bank Catastrophe Modelling, Guidance for Non- for Guidance Modelling, Catastrophe , http://news.bbc.co.uk/1/hi/7747456.stm, Environmental man Environmental The The 51 - - -

REFERENCES Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience01 Planningannex 52 ASSESSMENT VULNERABILITY T able 1.1. V ulnerability Depth (m) Depth 0.2 0.8 0.6 0.3 2.5 0.4 1.2 0.7 1.8 1.6 1.3 4.5 0.9 0.5 0.1 3.5 1.4 1.7 1.9 1.5 1.1 12 10 15 9 8 7 6 5 4 3 2 1 0

R eturn

V alue Hazard Intensity Hazard s

for

E 26 20 22 30 28 23 24 27 29 12 32 13 33 25 21 10 16 18 31 14 17 19 11 15 9 8 7 6 5 4 3 2 1 0 lement s S oftware Elements’ MDR Value’ MDR Elements’ n.a 12 12 13 13 10 10 16 16 16 14 14 11 11 15 15 9 9 8 8 7 7 6 6 5 5 4 4 3 3 2 2 1 1 53

annex Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 54 T F igure able unknown elevation,susceptible concrete, 1-storey,industrial, unknown elevation,non-susceptible concrete, 1-storey,industrial, adobe, 1-storey unknown elevation,susceptible concrete, 1-storey,residential, unknown elevation,non-susceptibl concrete, 1-storey,residential, original ambiental definition original 1.2. D 1.2. 1.1. C efined ompari R s on elation

all s

hip D amage B e etween C 1_TA 1_rp1q 1_r1n 1_R 1_r1B 1_R 1_R 1_r1A 1_PA 1_PA 2_TA 1_I 1_I code type Census Brazil urve n n n n n S N S B A N u s

sc for eptibility

D ifferent

and

house (cortiço) house -popularstandard house -normalstandard apartments -lowstandard apartments -highstandard apartments -normalstandard house -lowstandard house -highstandard house (straworothermaterial) house (straworothermaterial) house (cortiço) Industrial Industrial definition type Census Brazil B B uilding razilian

H eight C en su s

M s ap non-elevated, unknownsusceptibilit wood, 1-storey,residential, adjusted Average of3-storeyGARCurves Average of2-storeyGARCurves Average of1-storeyGARCurves adjusted adjusted w1rnufor3-storeyheight unknown elevation,susceptible concrete, 3-storey,residential, unknown elevation,non-susceptibl concrete, 3-storey,residential, unknown elevation,susceptible concrete, 3-storey,industrial, unknown elevation,susceptible concrete, 2-storey,residential, unknown elevation,non-susceptibl concrete, 2-storey,residential, unknown elevation,susceptible concrete, 2-storey,industrial, unknown elevation,non-susceptibl concrete, 2-storey,industrial, A1 A1 UUU for3-storeyheight UUU for3-storeyheight e e e y 2_CM 1_CM 3_SN 2_SN 1_sn 3_cm 3_rp1q 3_r1n 2_rp1q 2_r1n 3_R 3_r1B 3_R 3_R 3_r1A 2_R 2_r1B 2_R 2_R 2_r1A 3_TA 3_PA 3_I 2_I 2_I n n n n n n n n n N S S B A N B A N House (WooD) House (WooD) unknown 3-storey unknown 2-storey unknown 1-storey house (wood) house -popularstandard house -normalstandard house -popularstandard house -normalstandard apartments -lowstandard apartments -highstandard apartments -normalstandard apartments -lowstandard apartments -highstandard apartments -normalstandard house -lowstandard house -highstandard house -lowstandard house -highstandard House (cortiço) house (straworothermaterial) Industrial Industrial Industrial 55

annex Santa Catarina: Disaster Risk Profiling for Improved Natural Hazards Resilience Planning 56