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Climate 1,3 Sciences Sciences Dynamics , J. Fohringer Chemistry of the Past Solid Earth Techniques Geoscientific Methods and and Physics 1,6 Atmospheric Atmospheric Data Systems Geoscientific Earth System Earth System Measurement Instrumentation Hydrology and Ocean Science Annales Annales Biogeosciences The Cryosphere Natural Hazards Hazards Natural ¨ oter and Earth System System and Earth , B. Khazai Model Development Geophysicae 1,3 , K. Schr 1,5 1,10

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1,3 , F. Elmer 1,4 Climate Sciences Sciences Dynamics Chemistry of the Past Solid Earth Techniques Geoscientific Methods and and Physics Advances in Advances , and J. Zschau Atmospheric Atmospheric Data Systems Geoscientific Geosciences Earth System Earth System Measurement Instrumentation Hydrology and 1,9 Ocean Science Biogeosciences , T. Comes The Cryosphere Natural Hazards Hazards Natural EGU Journal Logos (RGB) Logos Journal EGU and Earth System System and Earth , T. Kunz-Plapp 1,4 Model Development 1,2 ¨ uhr , C. Lucas 1,8 , B. M 1,2 ¨ unzberg Section Earthquake Risk and Early Warning, GFZ German Research Centre for This discussion paper is/has beenSystem under Sciences review (NHESS). for Please the refer journal to Natural the Hazards corresponding and final Earth paper in NHESS if available. Center for Disaster Management and Risk Reduction Technology (CEDIM), PotsdamInstitute and for Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute ofGeophysical Institute Technology (GPI), Karlsruhe InstituteInstitute of for Technology (KIT), Industrial Karlsruhe, Production Germany (IIP), Karlsruhe Institute of Technology (KIT),Scientific Karlsruhe, Infrastructure and Platforms, GFZ German Research Centre for Geosciences, Section Hydrology, GFZ German ResearchSection Centre Geoinformatics, for GFZ Geosciences, German Potsdam, Germany ResearchInstitute Centre for for Nuclear Geosciences, Potsdam, and Germany Energy Technologies (IKET), Karlsruhe Institute ofInstitute Technology (KIT), of Photogrammetry and Remote Sensing (IPF), Karlsruhe Institute of Technology 10 Geosciences, Potsdam, Germany Received: 8 January 2013 – Accepted: 11Correspondence March to: 2013 M. – Kunz Published: ([email protected]) 25 MarchPublished 2013 by Copernicus Publications on behalf of the European Geosciences Union. Nat. Hazards Earth Syst. Sci.www.nat-hazards-earth-syst-sci-discuss.net/1/625/2013/ Discuss., 1, 625–679, 2013 doi:10.5194/nhessd-1-625-2013 © Author(s) 2013. CC Attribution 3.0 License. M. Vannieuwenhuyse T. M Investigation of superstorm Sandy 2012a in multi-disciplinary approach M. Kunz 1 Karlsruhe, Germany 2 (KIT), Karlsruhe, Germany 3 4 Germany 5 Potsdam, Germany 6 7 8 Karlsruhe, Germany 9 (KIT), Karlsruhe, Germany Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent Jones ff C. Total ◦ ered several ff ort to obtain a comprehensive ff ), Sandy was the second most costliest TC 628 627 2006 , erent patterns of impact and loss. Sandy was an ff ected by power outages that lasted a few days to ff erent sectors of the economy is calculated with sim- ff ) with total economic losses in the order of 160 US$ ected large areas in the US. It is examined how Sandy ff Swiss Re 2006 , ected by hurricanes in the past but is densely populated and ff Daniels et al. ) shortly before landfall that further increased the strength of the storm. 2003 , Although much better data emerge weeks and months after such an event, the Though Sandy was not the most severe storm event in terms of wind speed and From a hydro-meteorological perspective, most unusual was the very large spatial It is shown that the impact of Sandy was driven by the superposition of di The distribution of losses to di and Risk Reduction Technology (CEDIM) made an e the days following the event,damage causing will be temperatures in to excess drop of97 billion down 100 US$ billion to US$ for with almost direct our 0 damageto estimates and ranging business between over interruption. 78 10 and Seven to2005 years 16 (e.g., billion after US$ the for indirect record(inflation-adjusted damage to impact due 2012; of Hurricanein Katrina the history in of the United States. Forensic Disaster Analysis (FDA) Task Force of the Center for Disaster Management precipitation, the impactpeople particularly at in the the Eastweeks US in Coast was some were regions. enormous. a Furthermore,days More from many shortage than places in at 20 fuel million the supply. This East situation Coast was su aggravated by cold air outbreak in et al. High wind speeds were associatedAtlantic with and New record England breaking Coast storm duringflooding. high surges Very unusual (astronomical) at was tide, the also leading the US. tomainly storm’s Mid- widespread track due from to the blocking south by tothat an the north, extended has high which rarely was pressure been system.very Thus, a Sandy vulnerable hit to a region suchthird hurricane an that unexpected made event. landfall in Since New recording, Jersey. Sandy was only the extraordinary event due to itsin multihazard the nature aftermath and that aggravated the the cascades direct of impacts adverse significantly. events extent of up totropospheric 1700 km, trough. This primarily interaction a led result to of a the rapid extra-tropical interaction transition of (e.g., the TC with an upper- countries (US) in the world with di in the early hours200 of fatalities and 30 widespread October. damage Along to its one path, the poorest the (Haiti) severe and storm one of caused the more richest than impact of Hurricane Katrina inin 2005, the Hurricane history Sandy is of the the second United costliest States. hurricane 1 Introduction Hurricane Sandy was the lastricane tropical season. cyclone From (TC) of 24Caribbean the to to 2012 the 30 Northern East Coast Atlantic October, of Hur- Sandy the United moved States, on where it an made landfall unusual in track New Jersey from the ple input-output models as wellestimated as about government 4.2 estimates. billion Direct US$for economic in the losses US. the are Indirect Caribbean16.3 economic billion and US$. losses between Modelling from 78 sector-specific powerterruption and outages dependencies, losses is between quantifies 97 billion 10.8 estimated US$ total and in 15.5 business billion the US$. in- Thus, order seven of years after the record time analysis of catastrophes. extremes (high wind speeds,fects. storm In particular surge, the heavy interactioncreated between precipitation) Sandy a and and huge an by extra-tropical storm cascading weathercompares that system ef- to a historic hurricaneperspective. events, both from a hydro-meteorological and impact At the end of OctoberAtlantic 2012, Ocean Hurricane and Sandy entered movedSandy from the caused the United more Caribbean States than Sea 200 notCuba, into fatalities far and and the from severe the losses New US. in York. This Jamaica, Along paper Bahamas, its demonstrates Haiti, track, the capability and potential for near-real Abstract 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ), www. exam- 4 ) aims at discusses 5 2011 , IRDR ) information service from www.irdrinternational.org ) initiative located in Beijing. gives an overview of the hazard 3 ects and consequences associated briefly summarizes the various find- ff ). The word “forensic” is applied in the 6 describes background, procedure, and ) has been implemented as a research 2 www.icsu.org www.eqecat.com 2012 2011 , 630 629 , Burton ects, for example, due to power outages. It is exam- ff Wenzel et al. ), the first one of 30 October 2012, 20 h after Sandy had crossed the US generate a portrait of thetics disaster and with tracking its the evolution; aims of revealing its main characteris- – Forensic disaster investigation ( The objective of CEDIM’s FDA approach are to build up the capability to rapidly: The paper is structured as follows. Section This paper draws on two reports that are available on CEDIM’s webpage ( the private sector. target by the Integrated Research on Disaster Risk (IRDR, these heterogeneous sources are theassessments starting in point near for real-time, comprehensiveThese i.e. science-based information less are than complemented 24 h bythat own after are models a currently for catastrophic near-real developed. event The timeforce occurred. loss forensic activities, estimates approach as incorporates well event-triggered asport task specific the research near towards real-time new approach. methodologies that can sup- is not the pretension in thesic FORIN Disaster concept, Analysis CEDIM is (FDA; developingsense the of strategy scrutinising of disasters Foren- closely anduse with of a multi-disciplinary the approach high byable potential making in of science, modern engineering, remote observational sensing and and analytical information methodologies technology. Results avail- from (www.gdacs.org), as well as the CatWatch ( an ICSU (International CouncilThe for Science, Forensic Investigations ofuncovering Disasters the root (FORIN) causes programme ofbeyond natural ( the disasters typical through sectoral in-depth case investigations studies. that For go disaster analysis in near-real time, which where on thequakes, globe. which Data allow bases theparameters, have rapid such been estimation as gust of developed wind the speed, forknown. precipitation potential storms, totals, Moreover, damage or several floods, ground once services motion or are themation; are roughly earth- triggering among accessible these with are highly the relevant disaster Joint infor- Research Center (JRC) with its GDACS service the new technique of crowd sourcing, in minutes to hours after an extreme event any- strategy of CEDIM’s near-real time FDA. Section dented opportunities for naturaltime. disaster The loss Internet, assessment for instance, and provides analysis information in from near various real- sources, including the impact specifically for theand US, associated with indirect a focus losses.ings, on The lists power some last outages, conclusions, Sect. their andare consequences discusses necessary future for perspectives implementing and near-real requirements time that FDA. 2 CEDIM Forensic disaster analysis Modern technologies, accessible databases and information services open unprece- scribes the dependencies between the various economic sectors. situation and discusses what madeines Sandy an the extraordinary impact event. While of Sect. Sandy during the early stages in the Caribbean, Sect. cedim.de East Coast, the second onetion 10 that days led later. The tohydro-meteorological paper the events, describes extraordinary and the event, examines multihazard highlights thelosses situa- impacts the including such interaction as of cascading social theined e and how TC economic Sandy with compares other tometeorological historic and hurricane events impact incomparison the perspective. US, with Direct both past from and the events indirect hydro- and losses by are application of estimated an by economic loss model that de- with Sandy immediatelyway after by its collecting occurrence. anddatabases and compiling This sources scattered via was the and done Internet,els distributed by for in application near-real information of time an own from analyses methodologies interdisciplinary available developed and in mod- recent years, and by expert knowledge. and holistic overview of the causes, hazardous e 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ect. ff ). The very 1 − r-Simpson Hurricane Scale ) making Sandy a category 2 ffi 1 r-Simpson Hurricane Scale on − ffi ). Shortly before and while making 1 − 632 631 ) and gusts around 110 kts (204 km h 1 ects, heavy precipitation, storm surge, and river floods are presented. ected large areas. The storm was associated with high impact weather − ff ff contribute to the development of a framework for future loss and risk reduction. reveal the short and long-term impacts onestimate regional potential and losses national and scale; analyze the critical causes of loss and risk; – – – Constant in intensity, Sandy passed the Bahamas on 26 October. The following day Some hours before entering the US mainland, the hurricane intensified again and In the next subsections, the storm track, the spatial-temporal evolution of Sandy and A coastline of more than 1000 km in length was hit by a significant storm surge An important component in the CEDIM FDA is the near-real time approach as: showed a minimum pressure of 939Even hPa, however, the kept its well-known center “Long away from Island the Express” coast). in 1938 only had a minimum pressure of More and more weatherwas forecast expected models to beganUS. make The to landfall TC predict after was a aDelaware/New expected Jersey scenario to leftward Atlantic arrive where coast. movement in Sandy at the the night 29/30 East Octobershowed Coast somewhere mean of along wind the the speedslandfall, of the 80 center kts pressurerecord (148 km for of h hurricanes making Sandy landfall was north of 940 hPa, Cape Hatteras which (Hurricane Gladys was in a 1977 new low pressure heavy rainfall, Sandy crossedimum the intensity. eastern At parts 06:0095 kts UTC of (176 km on Cuba, h 25 wherehurricane. it October, the reached TC its had max- 1 min sustainedthe winds hurricane of made a right turn towards the northeast and started to lose strength. Sandy was added to theSo list far of this 2012 year, tropical it storm wastial systems tropical stage, on storm huge 22 convective system October, cloud #18 15:00 structures inand UTC. begun the 515 to km North organize 250 Atlantic south km region. of northsified At of Kingston, the as Panama ini- Jamaica. a With category24 further 1 October, strengthening, just hurricane Sandy before according was crossingricane to the clas- approached island the of Cuba, Sa Jamaica. where Heading the further north, storm the centre hur- arrived 24 h later. Associated with region in response to heavy precipitation turned out toits be hazardous a e minor hazardous e 3.2 Storm track of Sandy location in New Jersey and New York. In contrast, fluvial flooding in the Mid-Atlantic with the highest and often record breaking water levels occurring north of the landfall on the early morningranging of from 30 October. 1 According tounusual to 5, coincidence the of Sandy Sa reinforcing was conditionsSandy over a the and category US, an e.g. 2 extra-tropical the weather interaction Hurricanein between system, the (154–177 US created km and h a a hugethat storm stretched that up made to landfall Canada. the Due Great to Lakes the and hugedamage even spatial and beyond losses extension in in and southern several highStates. and of intensity, south-eastern the Sandy densely caused populated massive New England and Mid-Atlantic 3.1 Overview of Sandy From 22–29 October 2012,into hurricane the Sandy Atlantic made Ocean its and way finally from entered the the Caribbean United Sea States near Atlantic City (NJ) (i) many pieces ofof and information interaction emerge with withinsurance potential industry, users the relief (e.g., agencies) first is emergency particular(iii) days services, high methodologies of tourism during and industry, the disasters; models in- initial of (ii)tions stage CEDIM can of for interest be a near-real tested disaster; time and lossour calibrated evolution understanding and and can of implica- thus disasters (iv) within contribute their to respective significantly socio-economic speed contexts. up 3 Hazard description 5 5 25 15 20 10 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1 er- ff , and 1 − erent types ff ected by flood- ected area dif- , left). However, ff ff 3 ected coastlines of Virginia, b). f). The most intense rainfall ff 4 4 Fig. + 634 633 c). North of the storm center, hurricane-force 4 (see time-series of 1 − ected by heavy rainfall between 100 and 200 mm (see Table . Selected recordings of peak wind gusts on 29 and 30 October ff 1 . Strongest winds occurred along and near the coastlines of Vir- − 1 d, the storm surge at the Chesapeake Bay Bridge Tunnel Gauge oc- for two exemplary locations in New York and Washington DC region. 4 4 , right). Wallops Island (Virginia) recorded a total of 214 mm within 48 h, at 3 At the US East Coast, the huge extent of the hurricane led to storm surges caused by The intrusion of cold air near the surface from the northwest led to heavy snowfall Many parts between the Atlantic coast and the Great Lakes experienced wind gusts In the US, the Sates of Pennsylvania, Maryland, New Jersey, Delaware and Vir- play of spatio-temporal factors.was most At extreme: New measurements York from City New and York City Long show Island, that the the shift storm in surge the wind winds had an eastcaused to the west extreme (landward) watersachusetts. direction; levels seen wind The along gusts impact the offered and coastlines up due magnitude from to New to of 130 km Jersey the h the to bathymetric storm Mas- and surge geographical in characteristics the and a a complex inter- Delaware, New Jersey, New York,shown Connecticut, in Rhode Fig. Island and Massachusetts. As In the Caribbean, Haiti, Jamaicaing and and the debris eastern flow partriver caused of flowing by Cuba through heavy were Port precipitation. a auhousings. For Prince example, the in Croix Haiti rose de to Mission threatening levels forstorm the winds that adjacent advanced from south to north along the a curred twelve hours (equivalent toels one in astronomical tide) New before York the (Battery highest water gauge, lev- Fig. in excess of 85 km h are shown in Table ginia, Delaware, New Jersey and partsgust of recording New was York. At 128 km JFK h Intl. airport in NYC, highest 3.3.2 Storm surge and river floods more/Washington Intl. Airport as well as at Dulles Intl.especially Airport. in the southern and centralTennessee, Appalachian Kentucky, Mountains. North In Carolina, mountainous areas Westblizzard-like of Virginia conditions and and snow Virginia amounts people of experienced up to 1 m. responsible for the wettest days that have ever been recorded in October at Balti- 200 mm of rainfallrainfall. In was Cuba, recorded, rainfall in while excesscentral the of provinces. 200 western mm Furthermore, were parts observedshowed precipitation only did values in signals not around some obtained easterly receive 250 mm and from significant in satellite the sensors vicinity of the Bahamas (Fig. 3.3.1 Heavy Precipitation and Storm ForceWhile Winds Sandy began toand build, 250 heavy mm precipitation led to with widespreadwell rainfall flooding as totals in in were the the very between south-western south 200 tip of of the Dominican Haiti. Republic Over as the eastern parts of Jamaica, more than Baltimore/Washington Intl. Airport itoccurred was in 165 the mm vicinity (Fig. of the Chesapeake Bay (Easton, MD, 319 mm). Sandy was rainfall had its peak maximum over the open waters ofginia the were Caribbean strongest Sea. a and Fig. Wind speed and extremecanes precipitation and are also the contributed primary significantlyhazards hazards to may associated trigger the with secondary overall impact hurri- hazardsent of with types Sandy. even of These stronger secondary primary impacts. hazards,of Among flooding floods the is di can the be most distinguished:onto relevant Storm the one. surges coastlines Two that by di strong arecipitation. winds, An caused overview and by of fluvial water the floods massesgiven temporal that driven in evolution of may Fig. winds, result pressure, from and heavy water pre- levels is tober. From 30 tonear 31 Lake October Erie. Sandy moved further northwards and3.3 finally dissipated Space-time evolution of Sandy 947 hPa. The storm center of Sandy crossed the coastline around 00:00 UTC on 30 Oc- 5 5 25 15 10 20 15 20 25 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | and from ff response. ff ). The com- the coast were response is de- e. This gauge is ff 1975 ff 4 , d), for example, the 4 ) were obtained and analyzed f), the flood wave started from 4 ect. ff http://waterwatch.usgs.gov/index.php ects did not happen to the full extent at other 636 635 ff Wood and Rodriguez-Iturbe ; ects. Specific flow characteristics at this gauge, ff 20 yr and more. = 2006 f) contributed to this e d). This is due to the propagation of the surge along the , 4 4 ). s, which was very close to the flow value that is exceeded http://water.weather.gov/ahps 3 2 yr was quantified. Other gauges showed slightly higher = 2012 , Apel et al. f), Tn ected coastal areas. 4 USGS ff a–c). This superposition of e 4 100 yr event, occurred at the tidal gauge at Battery on the southern tip of Manhat- > erent USGS (2012) discharge gauges ( ects of the storm tide maxima were pronouncedly lowered by the low (astronomical) By contrast, recurrence intervals of peak discharges at locations o Observed water levels at the tidal gauges from northern Virginia to Rhode Island In order to assess how the peak coastal water levels and river discharges recorded Fluvial river flooding due to high precipitation amounts was recorded at several At lower reaches of the rivers or in estuaries near the Atlantic, high water levels ff ff cacy river at Jugcorrespond to Bridge frequent near flood events. Frederick. Reports In of extensive general, inundation the of structures observed and peak flows rather levels, for example, Tn ofEast 4 yr Branch has been Brandy estimated Wine for the Creek peak below discharge Downington at the and gauge a Tn of 6 yr for the Mono- e tide, but still reached levels of Tn substantially lower. For exampletomac at river the (Fig. streamflow gauge Point of Rocks at the Po- tan, where water levels unprecedentedof in the the aforementioned record occurred reinforcingnamely due e the to confluence the concurrence ofmay Hudson also have and contributed East toSound River the and at record at water the the level. northern At Chesapeake end Kings Bay Point of Bridge in Upper the Tunnel Bay gauge Long Island (Fig. scribe the AMS data variability, ourfunction assessment approach was based ( on aposite composite function distribution resulted fromweights. weighting Note that the at distributionare the functions provisional given based data point and on of subject likelihood time to all revision. observations consideredexceeded in recurrence this paper intervalsTn (Tn) of 10 yr. The highest levels, corresponding to a during Sandy compare to thestatistics past, were their recurrence quantified. intervalsdi (Tn) For using this extreme value purpose,NOAA (2012) annual tidal gauges maximum ( seriesthose (AMS) statistically. As for several the probability distribution functions may satisfactorily de- situated remote from thewater coast levels during at this the eventpeake were mouthing Bay reached Bridge of a Tunnel (Fig. full the 36Chesapeake Potomac h Bay. river. after Further, the The the high maximum water tidetober levels at and remained the at 1 Chesa- a November.the The high Potomac level superposition River during basin with the (Fig. the 31 inland Oc- flood wave flowing o in the Potomac river at the gauge Point of Rocks (Fig. as well as the tributariesbasins cover of large the parts Delaware of riverFurther, the in federal two states the gauges of area at Pennsylvania, of Maryland thetermined Philadelphia. and Hudson by Delaware. These river the river interplay reported of flooding.logical diverse The catchment hydrological runo characteristics processes depending and on conditions.of geomorpho- In snowfall this in specific the event,the Appalachian the headwater occurrence Mountains regions resulted of inFurthermore, the a the river initial temporary flow systems storage conditions had and of been thus water much below attenuated in normal the flow. For runo instance, superposition of tidal currents, stormrainfall. surges For and the fluvial flooding tide associatednamics gauge with and heavy of storm Washington surge DC, is for obvious example, in the the time-series influence shown of in tidal Fig. dy- moon high (astronomical) tide occurredtober at (Fig. the same time aroundlocations 01:00 of UTC the on a 30 Oc- gauges that are spatially clustered in the Potomac and upper Susquehanna river basins a discharge of around 60 m 90 % of the time ( cannot be attributed to single trigger mechanisms. Rather they were caused by the direction, minimum sea level pressure accompanied by maximum gusts and the full 5 5 25 20 10 15 25 10 15 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | icted ffl ected by ff the East Coast ff ). At its eastern edge 5 ), the probability of landfall in New ). 5 ) shows the elongated cloud band of a cold 638 637 6 ect to hurricane Sandy, which was on the way from the ff http://typhoon.atmos.colostate.edu The date Sandy made landfall is well outside the peak hurricane season, especially A perfect timing just before landfall initiated the transition from a tropical into an Sandy interacted with a huge upper level trough that stretched across the central Unusually high sea surface temperatures, that were well above average along the Nearly all tropical cyclones in the North Atlantic turn onto an east-north-easterly somewhat capricious and dangerous (see Fig. as far north asculation New provided York. the Cold potential airpalachians, for advection where blizzard-like snow from weather accumulations Canada were conditions included nearly in 1 into m parts Sandy’s in of cir- some the areas. Ap- symmetric warm core, which is aintrusion characteristic of feature cold of TCs. air Withincolder began the and to next 36 evolve asymmetric, h, warm the whichter and is landfall, cold characteristic Sandy fronts, turned for whereas intotion. extra-tropical the With a cyclones. core both cold-core-low Shortly became tropical and and af- completed extra-tropical the characteristics extra-tropical during transi- landfall Sandy became along the Appalachianhigh Mountains level into clouds aloft the indicates theare north-eastern strong active south-westerly US. already flow between An and the thethe extended Great lifting Lakes forces trough, shield that and Sandy of the Atlantic grewrecord coast. breaking rapidly; While 1700 temporarily km. approaching Even the farfor from storm example, the caused had center, wave storm heights a force of horizontal winds up occurred, to extension which, 6.6 of mextra-tropical in cyclone: Lake On Michigan. 28 October, 18:00 UTC, Sandy still showed an approximately front ahead of the upper level trough; the frontal clouds reach from central Florida portion of the US andthe moved trough into an provided easterly additional directionair, forcing (see which and Fig. resulted extra in lifting afrom of further 28 the strengthening October warm of 2012 and the 17:45 moist storm UTC tropical system. (Fig. The satellite image Thus, the storm was forcedNew towards York. the west-northwest and targeted New Jersey and track before they gettravel towards anywhere Europe close as extra-tropical tocal cyclones. situation the However, over the the US North particular American meteorologi- onwards mainland. continent led and Afterwards, to the Atlantic a they Ocean significant fromally usually shift 28 well-pronounced of October upper hurricane air Sandy. ridge Byestablished (high the over pressure eastern end at Canada. of In higher October, cooperation levels antem, with in the unusu- a the north ridge troposphere) Atlantic had low a pressuresouthwest. blocking sys- The e usual right turn, referred to as recurvature, was not possible for Sandy. track of Sandy, helped tolong keep term average the sea intensity surface temperature overof was several 2–4 the K days. US. on The The 27 warm deviation October waterhurricanes, o from provided and the more intensified latent the heat, TC whichwhich is while is the destructive there source for was of those no energy systems. or for only little vertical wind shear, Jersey is only 1probability % is during 51 %. a ForSandy hurricane other (e.g. season, States Delaware, Virginia, at whereas, New the for York) the US example, probability East in is Coast between Florida that 1 have this and been 8 %. a ricane prone region. Inby contrast hurricanes to in this, themade the landfall past. in US Since New East Jersey. recording,State According Coast University; to Sandy has the was rarely Hurricane only Probability been Project the a (Colorado third hurricane that were only on a local level. 3.4 Extraordinary event and multihazard characteristics Sandy was a late and strong hurricane in the Caribbean, which, however, is a hur- roads, significant evacuations of people and/or transfer of property to higher elevations 5 5 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , ). ¨ uhr ), at M 1 2012 Daniell , OCHA ). The damage 8 ; 2012b ecting Haiti (see was undertaken using ff , 2 IFRC ). Thirdly, damage to medical ). In Cuba, eleven people died ). 2012 2012 , , ´ e Publique, Republique d’Haiti), which 2012b , ) and in the CATDAT database ( ECHO ECHO ; 639 640 ; IFRC 2011 b , ). Wind, heavy rainfall and subsequent overflow- , 2012 , ` 2012 ere de la Sant 2012a , , CDEMA ; ECHO Daniell et al. OCHA ered direct or indirect impacts ( c ; , ff ect on livelihoods ( ff ). The estimation of losses listed in Table 2012b 2012b shows the cumulated absolute number of cholera deaths over time 2 , , ). Secondly, after the passage of Hurricane Isaac in August 2012 and 7 ), there are some factors that aggravate Sandy’s impact in Haiti. Firstly, IFRC OCHA ected people. During the passage of Sandy on 25 October (see Fig. 2012b ff , 2012 , ). Loss functions were developed based on previous damage seen in previous According to this analysis, losses were greatest in Cuba with around 5.5 % of GDP Even if Sandy historically was not the deadliest hurricane a OCHA interrupted transportation, and poor sanitaryterborne conditions diseases have such increased as the cholera. risk After of the wa- cholera outbreak in October 2010 in the wind speeds. FloodingSantiago was de also Cuba provinces, widespread. the percentage In of some damaged buildings sections reached over of 80 % the Holguin and nutrition ( facilities (including 22 cholera treatment centers), problems in restocking because of 2012 impacts of hurricanes in Cuba,speed, Haiti and storm the intensity rest of andInternational the flooding Caribbean Federation of as as Red a well Cross function as andagencies. of Red wind the Crescent current Societies (IFRC) damage and reported national from the where over 226 000 houses werefollowed damaged the and 17 storm 000 destroyed track (Fig. closely, with over 20 % of houses losing roofs due to the high Hurricane Sandy inleast October 60 2012 communities, resulting 450 000 in to destruction 1.5 of million agricultural people crops are at in an at increased risk of mal- Direct losses intions the (see Caribbean Table haveanalysis been of extensive destroyed and on damagedas a buildings agriculture, in GDP infrastructure, addition education comparison to andGDP health for other as sectoral as na- a losses reported proportion such of from capital ( stock and Hurricane Sandy struck aquake in country 2010 that with 350 is( 000 people still still recovering living from in camps the for devastating internally earth- displaced persons represents a slight increasefor in cholera deaths disease and spread,after new compared cases. Hurricane to Comparing Sandy the the withbe weeks current the seen before, increase progression that both in the of cholera current theinitial increase deaths epidemic outbreak is phase since still and rather its small alsoalready outbreak, compared started smaller it to to than the can decline. the peaks peak during in the June 20124.2 when cholera had Economic impacts yet was declining inSandy. terms Figure of newand cases and the deaths percentage in ofber the 2010. weekly months In cholera before the deaths209 Hurricane first deaths since seven were the weeks reported after (Minist epidemic Sandy, outbreak approximately in 22 000 Octo- new cases and et al. aftermath of the major earthquake in January 2010, cholera is still prevalent in Haiti, damaged 27 000 houses and emergencystroyed shelters and of 5298 100 families. damaged. 50 With schoolsCuba were 100 and 000 de- ha 90 000 of ha destroyed ofcountries crops devastated the cropland by by the risk heavy strong of rainalso winds and medium-term food in flooding e insecurity in has Haiti, in severely both increased and is expected to have and 3 million people su ing of rivers in the West and Southwest of Haiti killed at least 54 people, destroyed or least 80 people weremore killed killed, in 15 the missing; Caribbean, with highest243 death 000 houses tolls and 2601 in schoolsas Haiti were flooding. damaged (54 615 or health destroyed or centers by wereSect. strong damaged 4.2). winds or Access as impaired in well of their anhampered functioning estimated ( (see number also of 1 to 1.5 million people to safe water was 4.1 Social Impacts Cuba and Haitiber were of the a hardest hit countries in the Caribbean in terms of num- 4 Impact of Sandy in the Caribbean 5 5 25 25 15 20 10 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ). b ). , Rose ). The 2007 , 2012a , 2012 , DOE Fahey Platz et al. ected people rely on electricity and ff ects ff 642 641 cult. The a ffi shows the three-week timeline of power outages 9 ), Fig. b , 2012a , ). A combined total of around 21.3 million people (8.7 million customers) DOE ), it can be seen that Sandy is among the three most fatal events of recorded 1997 3 , ected residents of some of the most populated cities in the US, including NYC (in Nevertheless, nearly two weeks without electricity, heat and other provisions, exceed The main reason for fires following hurricane is usually electrical system failures and Haiti has also seen major damage through the combination of river flooding and In addition, much damage to schools, agriculture (sugar cane, bananas) and the ff wiring issues caused simply by the wind speed being too high for the intended safety the limits of most citizens’posed capacities a to particularly manage severe hardship theirthere on everyday is the lives. only The sick, elderly, situation limited handicapped im- TCs, data and available an poor. on Since empirical the comparison consequencesare of is heightened power exposed di outages to causeduse risks by generators, from or fire other andhomes gasoline-, carbon for propane-, heating, monoxide or and poisoning charcoal observed as burning in people former devices comparable inside incidents their ( in comparison to otherto major restore hurricanes power in supply2005 to the and 95 US. Ike % in It of 2008 tookfor all customers. resulted utility Hurricanes Katrina), in companies Katrina, longer Texas outages (23 13 Rita dayslongest for and days customers stretch for Wilma in to Katrina), Louisiana in 95 % (18 Mississippi days restoration and since 2004 Florida was ( 23 days after Hurricane Katrina. from the peak outageimpact, (29 84 % October) of topeople the the (mostly in energy recovery New (19 York system and7 November). New had November, a Jersey) One been Nor’easter were week still storm restored. waiting beganing after for to However, additional electricity impact about power supply. the On Mid-Atlantic 3.37 outages million outages and to Northeast after 368 bring- Hurricane 000 people. Sandy, However, the despite duration widespread of power these outages was not unusually long particular the lower partof of Manhattan). Energy Using ( data available from the US Department et al. were left without powerber, from but also peak from outagesPower the outages of subsequent stretched Hurricane across Nor’easter 21 Sandy Storm statesa from on on western 7 29 Indiana to November and northern ( Maine, 30 and Octo- highly interconnected, the consequences of disruptions may propagate widely ( essential role in sustainingture socioeconomic (including systems. services), As they are a directly part vulnerable of to physical natural infrastruc- disasters. As they are New York City (NYC)mer of 2012, which and 22 Keller oftalities in them in NY were the Times, states on 17 NewTable Staten November York, 2012). Island New Jersey Comparing (Reuters, and the 16 Pennsylvaniahistory hurricane Nove- with of fa- historic hurricane events deaths. (see For New Jersey, it is5.2 the deadliest single Impacts TC on event ever. infrastructure: cascading e Energy systems are amongst the most important critical infrastructure due to their 5.1 Impacts on life In the Eastern US, 142Jersey people died and because Pennsylvania. of Of Sandy, the most of 64 fatalities them in in New the York, state New of New York, 43 occurred in to hurricanes in Cuba, as2008. over 8 billion US$ damage occurred through Hurricane Ike in pluvial flash flooding with over 6000 buildingsand destroyed the and Bahamas 21 also 000 buildings saw damaged, majorto damage around with 4 both % countries of having GDP. losses equivalent 5 Impacts of Sandy in the US Some of this waswindspeeds and related flooding to from theSongo-La storm Maya). vulnerability surge of (as building seen stock, in yet Guama) some andpower rainfall to systems (seen variable occurred. in However, this has not been the largest economic loss due as compared to the total building stock estimated from the changes from last census. 5 5 25 15 20 10 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , ). 2011 Cuomo , Trapasso Comes and Schult- Daniell et al. ; 2005 ected. New York City has , ff ) Models for fire following hurri- ). The losses provided by 2012 2012 , , ) could contribute to additional losses on top ). Damage before this was quoted as being 644 643 Daniell . ). This would make Sandy the second highest 11 5.4 4 Kleindorfer and Saad 5.4 DeStefano ). This is related to the massive exposure located in this losses that have grown considerably due to the increasing 2012 , ). Although there was no gas shortage through resource deple- indirect 2006 Cuomo ( , ected population. Threats from water and food shortages, food poi- ff 10 ) (Perrow, 1984). Indirect economic losses are caused by the disruption or ; Governor Andrew Cuomo). An estimated 305 000 houses were damaged 10 2012 ) were slightly higher totalling over 15 billion US$ for New York City (about 2 % of , ). Numerous other dwelling fires occurred in other states. In total, over 60 million Indirect losses are generally high in productive locations such as New York City. They New York direct losses have totalled around 32.8 billion US$ for repairs and restora- New Jersey has released losses to housing, transit systems, infrastructure, tourism In addition, indirect losses (see Sect. In tall apartment buildings and commercial skyscrapers, lack of elevator service 2012 Bayleyegn et al. mann ern societies on critical infrastructure ( 5.4 Estimation of indirect losses Besides direct costs dueresult to in damage important of physicalinterrelatedness infrastructure, of natural globalised disasters supply often networks and the growing dependence of mod- in damage. The other relativeshown components in of Fig. the loss estimate by the government are capital stock. In addition,stated over that 265 the 000 economic losses businesses dueindirect were to causes direct a from causes 5.7 have billion totalled US$ 13.3 ( billion US$, and or destroyed in New York state as of 26 November 2012, causing around 9.7 billion US$ region of the US with New York and New Jersey combining to have over 5 trillion US$ natural disaster event since the Tohoku earthquake in March 2011 ( are scrutinized in the following Sect. economic loss from a US hurricane in history and the highest worldwide loss from a 5.3 Estimation of direct losses Early estimates of directand economic AIR losses were from in risk thetotal. modelling order firms of 20–50 such billion US$ as but EQECAT turned out to betion lower (Fig. than the final tion, the lack oflong electricity queues and prevented rationing. filling stations from dispensing fuel, resulting in of the 97 billion US$ estimatedsome damage, extent and indirect in losses the maythus business already impact be a included in to reduction New Yorkfor of and the 20 tourism direct % estimate loss and in estimate a New (see Jersey, Table range of losses is proposed from 78–97 billion US$ ( the gross city proper product). and coastlines at 29.4 billion34 US$ % (Fig. from New York,remaining 30 states % using from the New EQECATsylvania estimate. Jersey, 20 Using would % this hit from total around Pennsylvania system,states, and losses leaving 19 billion in a 16 US$, % Penn- total from with ofYork 97 and an billion US$ New additional damage Jersey loss from 15 billion estimates this US$ have event, from given fitted the this other fact model that well. New soning from refrigeration notsystems/drinking working, disease water outbreaks supply from and( malfunctioning deficits sewage in health care can become serious issues cane are currently limited tooutbreaks, poorly TC validated wind probabilistic loads relations damage betweenfire before number protection fire, of standards. wind speeds, and the level of preventive poses a serious problemcombined for power the outage disabled andstresses or severe weather the elderly conditions who a due cannot to navigate winter stairs. storms, The further York during Hurricane Sandy, 111 houses2012 were destroyed and 20 damaged ( factor associated with this infrastructure. In a fire inUS$ Breezy damage Point, can be Queens, attributedshowed to in fire the New in large New York impact alone.were Historically, of Hurricane able fires Katrina to following spread hurricane,2011 uncontrolled where also through due caused poorer to many parts evacuations, electrical fires of fires the ( city. Hurricane Irene in 5 5 25 25 20 20 15 10 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , and ), es- ected ff 12 1994 , Messner et al. ). ; Tierney Zimmermann et al. ). Furthermore, the 2006 2003 , , ects throughout further ected 55 million people ). Such an approach is ff 2007 ff , ces of each state. Indirect ffi 2007 , erent sizes, development levels ff Chang et al. Okuyama ). In the aftermath of a natural hazard, the 646 645 Penning-Rowsell et al. 2001 Zimmerman and Restrepo , shows, for each industry sector, the share of the US ecting on average 26.5 % of the US manufacturing sec- 13 ff ected region is particularly important for the chemical industry, ected. Rinaldi et al. ff ff , the value of power outage disruption is 16.3 billion US$. ects is quantified by considering the inter-industrial linkages and ff ) or transportation can cause cascading e 5.2 ), the output loss resulting from the interruption of a specific sector as 5.5 1986 ( ected on Monday, 29 October, to 2 million on Wednesday, 7 November, losses ff ing) ) demonstrated the possibility to estimate costs based on GDP per capita and ). Particularly the interruption of the most essential infrastructure such as electric Leontief For the estimation of indirect losses, the focus was on the assessment of business As can be seen, the a A similar approach is used to assess the costs for the power outages that occurred in To estimate the indirect losses, two approaches were used: an estimation of the costs 2005 dependencies. interruptions in the manufacturebusiness sector interruption because occurred most of in the this economic sector losses ( due to ing net losses in terms of lost output would amount to 2.39 billion US$. Nevertheless, well as its indirect e facturing sector, accounting for 26.5whole % of US the economy. added Figure value value added created created in in this the sector northeastern in states. the the apparel and leatherwere and interrupted allied at products, least textileextent on mills, of the etc. output Since day lossesthe most of on industry businesses Sandy’s this lost landfall, day two thistor days for gives (corresponding a the a to US first the economy. idea sector’s Under of value the added the assumption in that 14 northeastern states), the result- 14 northeastern states, which were exposed to Sandy, play a key role for the manu- and economic power. Given those requirements,input-output a data methodology was available chosen from basedeconomic on the losses national are statistical usuallyregion o quantified with in the terms helpbased of on of a production input-output national losses account’s modelstion input-output in flows ( matrix the between which the a represents various monetaryto industry transac- sectors. Based on an inverse matrix according The rapid assessment of indirectand losses requires incomplete robust data. methods At thatprevious the work disastrous with same events, limited occurring time in the countries of methods di must allow for comparisons with 5.4.2 Estimation of Economic Losses due to Business Interruption (I–O Model- power outages. Using the actual statisticsdiscussed of in power Sect. outages as portrayed in Fig. 2007 power (cf. Sect. infrastructure systems ( 6.3 billion US$. With close( estimates of 5.6 billion US$the for number one of day, people a the aftermath of Sandy. The GDPYork per and capita New per Jersey day averaged ispeople from 160.89 a Pennsylvania, New US$ . Usingare a about linear 3.22 billion recovery US$ function for from the 20 first million day, and 17.7 billion US$ for the first ten days of The total costs (directa and comparison indirect) with of similarbeen blackouts past can compared, events. be The including roughlystance, losses events estimated of the that based previous costs on have powerparticularly of blackouts not throughout have the the been northeastern 2003 caused states Northeast by of the blackout, disasters. US, which For were a estimated in- to be about associated supply chain disruptions ( of the power outagesof based on the a indirect comparison basedvulnerability with previous on of events a industrial and sector-specific anmodel sectors (I–O estimation model due model) that approach. to takes business into interruption account5.4.1 using the an indirect input-output Estimation based on past events greatest share of indirect lossespecially results from due business to interruption ( the decline of production resulting from destroyed infrastructure and failure of physical or economic linkages ( 5 5 15 25 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , ected ff Alward ects at the ff Cutter et al. erent business ff ect resulting from ff cient update of informa- ffi erent industrial sectors, as ff ). The sector specific vulnerability ) with recovery functions that have ects, the losses would approximate ), as well as the vulnerability of the ff ects on di 2011 2007 ff 2002 , , , ected branches are required (as opposed ects on the regional economies, including ff ff ected. ff 648 647 Merz erent industrial sectors vary greatly with respect ff Okuyama Webb et al. ). To construct the scenarios in a systematic way, the indirect ) to calculate those indirect e 2011 ects can be determined with the help of vulnerability indicators de- , ff 1986 , ), the potential impacts of Sandy were estimated on di shows the industrial vulnerability against indirect disaster e ), which simulates the induced e 1986 14 , Leontief ected industry determines the capacity of businesses to cope with the impacts 1992 ff ). Additionally, due to their hierarchical structure, indicator approaches are suitable Comes et al. , erent scenarios of recovery and business interruption duration. The vulnerability of Vulnerabilities are a starting point to assess the indirect economic losses, particu- Figure In order to assess the sector specific vulnerability, an indicator-based approach was With a similar input-output approach, Moody Analytics calculated a net loss output The extent of business interruption, their e ff Leontief determining the actual losses refer to the extent and severitylarly of the the longer hazard term itself.) aspects.bined Here with input-output an approaches approach ( quantifying production losses is com- state level. It canvulnerable states be are seen Maine, Virginiathat on and these the North states Carolina. map are Itextent that the can of therefore most from losses be vulnerable the also assumed against dependsstates. northeastern business on (Note interruption. states, the that However, direct the the vulnerability damages most is caused by not the the storm only in dimension a of risk; further components ties ( delivery or distribution processes). Therefore each sector’sindirect specific disaster vulnerability against e scribing its degree ofand dependency its on connectedness capital, in supply onwas chains calculated labor, ( based critical on infrastructure2011, 17 systems indicators. including These were input-output basedEconomic tables on Analysis. and national-level data other from data obtained from the US Bureau of been determined according to a sector’s( vulnerability. Using a linear input-output model interruption scenarios of themediate US aftermath economy. of As the the event, are uncertainties,used, fundamental which particularly (i.e., have hard in been to the proven quantify), im- useful scenarios as were a means to account for the severe uncertain- tion as revisions of information only in the a of further branching (e.g.,specific higher production level sights). of For detail theincident, by generic the considerations assessments integration remained in of onoccurs the information the early about due sectoral phases to after level. theinterruption the Production of damage downtime critical of mainly infrastructure or production the equipment, disturbance the of supply obstruction chain of processes (e.g., workers, the 2003 for (near) real-time disaster assessments as they enable the e to a complete update). Moreover, newly available information can be added in terms of disasters and interruption andfore to influences restart indirect their losses. businesses Asto after a di their disaster, characteristics and (such there- trial as dependence sectors, labor on dependence critical etc.),strongly infrastructure the depends or on vulnerability further the of indus- type industrial of production industry systems a used for its transparency and operational representation of vulnerability ( industry sector. In order toysis take coupled these with factors an intodi assessment account, of a the simple industrial input-outputthe anal- vulnerability a was used which tested ard event and the recovery time ( 9.4 billion US$ for two days business interruption. of 10.5 billion US$ due toin Sandy. all These estimations sectors were of calculatedi.e. the for Bridgeport, economy, the and New disruptions for York City, the Newton. regions Jersey, These New worst estimations York impacted City, were Philadelphia, byet and completed hurricane al. Washing- using Sandy, the IMPLAN regional multiplier ( well as the costs generated depend on other factors, such as the duration of the haz- supply chain disruptions into othermodel sectors ( of the economy. Using a linear input-output on employment and finaltotal consumption. lost The output of resulting approximately indirect 19.9 billion losses US$. correspond to a the estimation of losses must also take into account the ripple e 5 5 15 20 10 25 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ). 17 due to erences ff show that ). Besides 5 4 . About 3 % of the overall disruption and the impact of disrup- illustrates the di erences may arise due to ff ). Depending on the recov- 16 that shows all geolocated tweets 2010 power outage , . The time series in Fig. 17 or hurricane 650 649 Cimellaro et al. The overall impact depends to a large degree on impact of power blackouts cult as usually no appropriate sensors are installed ffi erent curvatures ( ff ected by Sandy, the costs would approximate 9.4 billion US$ for the two hurricane, flood, damage, victims ff ected 30 % of the finance sector US-wide on the two days of the storm, ff transportation system. ces a ffi The tweets provide detailed and very local information about Sandy’s impact such Furthermore, the spatial and temporal distribution of tweets reporting on power out- To get up-to-date information on the characteristics and the impact of the hurricane, In this paper, wefects, presented and consequences a associated multidisciplinary with Hurricane analysis Sandy. This of examination was the done causes, hazardous ef- temporal distribution of tweets allowlarger for area. inferring An intensity example andtions of impact of the technical of or large the natural potential event hazards is of in given a tweets by Fig. for spatio-temporalthe investiga- number of tweets corresponds well with maximum wind speeds and storm6 surges. Conclusions outside of traditional river channels and water sources. ages may indicate areasAbsence and of time any periods tweets withpresumably or impaired breakdown a or or significant unimpaired impairment power decrease of supply. supply of networks. tweets, Additionally, respectively, the may spatial indicate and tion extraction. as “sandy floods #fdr 63rd“Some street”, may “Flooding not on have Pitney power Rd but isannouncements we just of all from have general phones” damage, a (see the storm examples examplesSect. drain”, in give ) or Table information and about power outages flooding (Sect. (see flooding ) from at eyewitnesses particular are locations often of the theof only Twitter-users. Reports information flooding source, of since in data acquisition urban areas is di during Hurricane Sandy with the keyword the impact of a catastrophe areand scarce. the For potential hurricane use Sandy, we of tested social the media. applicability data was collected from theDuring tweets Hurricane of Sandy, the 5 328 micro-blogging 029from tweets service 29 were Twitter (see collected October Fig. and tokeywords stored 2 like in near November real-time intweets our (154 890) database. can be These localized messages by geo-coordinates were and filtered be by used for further informa- in expected losses forvarying varying vulnerabilities industrial and exposure sectors levels). (again, di 5.5 Observing impacts using social media Using data from social mediation provided with by eyewitnesses data seems from promising; conventionalthe in sensors, local combina- situation it only provides seconds a afterof more an information event comprehensive especially occurs. picture This for of may regions be with an low important infrastructure, source where other information of and o the indirect costs onmated the partial economy disruption would losses approximate during 9.8of the billion US$. recovery total period Adding shutdown to the for theare esti- losses all estimated of manufacturing the between two sectors, 10.8 days the and total 15.5 billion business US$. interruption Figure losses ery scenario, the indirect costsrange estimated from by 1.4 the model to for 5.6 10 billion days US$. following Assuming the storm that the closure of the stock exchanges days of the storm. However, theconsidered, time needed which for extends the the industrial sector timeto to restart for recover their utilities must be activity to during recover.the this The recovery sectors. capacity period Following of highly this depends businesses to rationale, on meteorological the an ensembles) vulnerability ensemble of were of calculated,functions by recovery using with scenarios exponential (comparable di potential recovery the event (across all sectors),tions the of the the assumptions about theassuming disaster that recovery. the As disruptions mentioned ofthe in the 14 the overall states manufacturing a preceding sector chapter, lasted for two days in costs were split up into several sub-scenarios considering the 5 5 25 15 20 10 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ered ff points ff between ff erent – though charac- ff ected states of Pennsylvania, erent sectors of the economy ff ff 652 651 ered from the high direct losses to residential and industrial buildings but ff ected by hurricanes in the past but is densely populated and very vulnerable to ff Landfall on the US occurred between 30 and 31 October 2012. The second report Tracking power outages and estimating downtimes requires a combination of simple The key scientific question addressed in this paper is to what extent, with which The US su Along the track from the Caribbean up to the eastern US, one of the poorest (Haiti) Haiti, only slowly recovering from the 12 January 2010 earthquake that destroyed Hurricane Sandy was an extraordinary event for the US in particular due the simul- managed during Sandy. The distribution of losses to di later. A summary of directNew losses Jersey was and created New forunderestimated York. the the EQECAT losses a and by other athe published factor value of of earlier loss 2 information estimates, to and butis 3. uncertainty, which There they in is is hard high to obviously demand quantify. a Earlyis but information trade obviously also o user-dependent highly and uncertain.estimation quantification The a of determination high-profile topic. uncertainties of the in trade-o near-real time loss models and crowd-sourcing tools, which should be brought closer together than was the social media thatdisaster. generate Utilizing huge these amounts resources ofand in information event data combination from bases with the provides analytic a very framework tools onset for near-real and of time historic a analysisfrom loss and predictions. CEDIM was ready by 7 November 2012 and published on the web-site one day larly in transport-dependent industry sectors).on Cold weather people imposing who harsh conditions dependheavy storms on were electric additional aggravating heating factors.the The US and role presidential the of elections the uncontrolled remains proximity a of electric speculative Sandy fires issue to until fed researcheddatabases by in and detail. which modelsSandy can the be losses extracted and andthe impacts potential predicted of for in near-real a time catastrophic near-real analysis event has time. like changed Our significantly working with the hypothesis Internet is and that in cholera infected persons is observedno although major current cholera numbers seem outbreak to increase indicate will that follow. also from power outageshouseholds, ranging subsequent between supply problems several with days gas, and and two business weeks interruption (particu- for individual medical facilities and schools were destroyed or hampered in functioning. An increase from destruction of those shelters. In addition, many cholera treatment centers, other and one of the richestteristic – countries patterns (US) of were impact142 devastated and with in loss. di the Apart US) from andto fatalities direct 97 (about billion economic 80 in losses in (about the Haitithat 4.2 and aggravated US), billion the Cuba; US$ the direct in rich impacts the and significantly. Caribbean; poor 78 were struck121 by % of cascades Haiti’s of (nominal) GDP adverseGDP.Crop and events destruction killed triggered more than danger 100 of 000Many malnutrition people, for lost 450 of another 000 4.5 to % the 1 500 000 350 people. 000 people still living in temporary shelters and camps su further increased the strengthSignificant of storm the surges storm duecurred in to simultaneously terms high with of high wind wind astronomicalstorm speeds surges tides. speed towards along This and the the caused precipitation. US eastern total Mid-Atlantic record US and breaking coast New England oc- coastlines. adverse events in thetrack aftermath more that or aggravated lessan the from extended direct the high impacts south pressure significantly. system. tobeen The Thus, a the Sandy north hit was asuch mainly region an in the unexpected event. the result Since US recording, ofmade Sandy that a was landfall has only blocking in rarely the by New third hurricane1700 Jersey. km, to Most primarily have a unusual result was oftrough. the the This interaction very of interaction the led large hurricane to spatial with a an extent rapid upper-tropospheric extra-tropical of transition up shortly to before landfall and information from available databasesown and methodologies and sources simple via input-output models, the and Internet, by expert by knowledge. taneous application occurrence of of specifictrack, meteorological the features leading multihazard to nature an that unusual storm’s further amplified intensities, and the cascades of in an interdisciplinary approach by collecting and compiling scattered and distributed 5 5 25 15 20 10 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , , 660 647 http:// , 643 , 640 639 , 2012. ected in terms of hazard and ff 4418.pdf doi:10.5194/nhess-11-2235-2011 649 Report 636 654 653 ¨ oschl, G.: A probabilistic modelling system for assess- 646 647 ected areas and the extent and duration of power blackouts). http://www.governor.ny.gov/press/11262012-damageassessment ff 644 The Center for Disaster Management and Risk Reduction Technol- 672 , B. J., and Shirley, W. L.: Social vulnerability to environmental hazards, Soc. , ff 630 643 644 644 648 , , 640 643 ects) will be subject of our future investigations. 670 ff , database, Nat. Hazards Earth Syst. Sci., 11, 2235–2251, rep., General Sir John Monash661 Foundation, KIT, Karlsruhe, Germany, 2012. 2011. wake of Hurricane Sandy, Bloomberg and regional county executives to review damage assessment for the2012. State in the Sci. Q., 84, 242–261, 2003. Multi-Criteria Decision Support118, Under 2011. Severe Uncertainty, Decis. Support Syst., 52, 107– Chain Management, 10th Internationaland Modelling, Conference 2012. on Applied Mathematical Optimization reliefweb.int/sites/reliefweb.int/files/resources/Full 36–41, 2011. pendencies in extremeHazards, events: 41, power 337–358, outage 2007. consequences in thedisaster 1998 resilience, Ice Eng. Struct., Storm, 32, Nat. 3639–3649, 2010. ing flood risks, Nat. 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H., Merz, B., and Bl linking methods and modelsaddition, has that more more systematic potentialuncertainty utilization than estimation of in are direct historic currently and databases indirect exploited, might lossAcknowledgements. and predictions. hold in the key for Alward, G., Siverts, E., Taylor, C., and Winter., S.: Micro IMPLAN, User’s Guide, Version 91-F., An equally simple model allows(and defining potentially an industrial other vulnerability administrative parameter units)ing for that impacts states are immediately to indicates be wherecurrent expected aggravat- loss even if numbers. the The state estimatesrect is provided costs: less here beyond a output do, however, lossesshould not be all consider considered. indirect all Additionally, losses the indi- dynamic acrossprice evolution globalised e of supply the networks losses (including, e.g., ogy (CEDIM) isfunded by an Helmholtz Centre interdisciplinary PotsdamKarlsruhe – Institute research German of center Research Technology (KIT). 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F.: The impact of a Series of Hurricanes on Okuyama, Y.: Economic Modeling for Disaster Impact Analysis: Past, Present and Future, Eco- OCHA: The Caribbean: Hurricane Sandy Situation Report No. 2 (as of 19 November 2012), DOE: Hurricane Sandy Situation Reports 1–20, Merz, M.: Entwicklung einer indikatorbasierten Methodik zur Vulnerabilit Messner, F., Penning-Rowsell, E., Green, C. H., Meyer, V., Tunstall, S., and van der Veen, DOE: Hurricane Sandy Nor’easter Situation Report 1–11, Leontief, W.: Input-Output Economics, Oxford University Press, 1986. Jones, S., Harr, P.,Abraham, J., Bosart, L., Bowyer, P.,Evans, J., Hanley, D., Hanstrum, B., Hart, IFRC: Emergency appeal No MDRCU002, Glide No: TC-2012-000180CUB 5 November 2012, Kleindorfer, P. R. and Saad, G. H.: Managing Disruption Risks in supply Chains, Production M IRDR: Forensic Investigations of Disasters: The FORIN project. (IRDR FORIN Publication ECHO: Impact of SANDY in the Caribbean – ECHO sitrep 3, 6 November 2012, DeStefano, M.: Bloomberg: Sandy Losses Total $19 billion, Fahey, J.: Power outages after Hurricane Sandy weren’t unusually long after all, The Associated IFRC: Hurricane Sandy: the world’s newest silent disaster?, Daniels, R. J., Kettl, D. F., and Kunreuther, H.: On risk and disaster: Lessons from Hurricane 5 5 25 30 10 15 20 30 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | precipitation in mm 1 645 − 636 http://dspace.udel.edu:8080/dspace/bitstream/ http://www.nydailynews.com/new-york/queens/ 657 658 peak wind gusts in km h ¨ uhr, B., Kunz-Plapp, T., Markus, M., and Vervaeck, A.: http://wdr.water.usgs.gov/wy2011/pdfs/01638500.2011. 645 643 645 647 , 2012. , 1994. 630 635 , J., and Schuler, R.: Electricity Case: Economic Cost Estimation Factors for Economic Selected recordings of peak wind gusts and precipitationamounts during Sandy on 29 ff , 2012. Atlantic City Intl. Airport, NJBaltimore/Washington Intl. AirportNew York JFK Intl. Airport,New NY York La Guardia Intl.Philadelphia Airport, Intl. 77.8 NY Airport, NJWallops Island, VA 109.5 94.6Patuxent River, MD 127.8Newark Intl. Airport, NJTeterboro 94.6 Airport, NJ 114.8 85.2 90.7 109.5 31.5 0.0 87.0 109.5 58.9 133.9 0.5 125.9 90.7 13.7 116.5 88.4 13.0 70.6 24.4 120.6 77.8 105.4 55.9 111.8 1.5 84.8 102.1 0.0 25.7 123.2 18.8 Station 29 Oct 30 Oct 29 Oct 30 Oct frequency models, Water Resour. Res., 11, 839–843, 1975. Water-Data Report WDR-US-2011, from disaster: a comparison ofHazards, the 4, Loma 45–58, Prieta 2002. earthquake and Hurricane Andrew, Environ. Real-time forensic disaster analysis,Vienna, in: Austria, 2012. Geophys. Res. Abstr., 14, EGU2012, 22–27 April, Civil Infrastructure Systems, 2005. families-return-breezy-point-hurricane-sandy-fire-destroyed-111-houses-article-1. 1195241#ixzz2EGnAEtbc pdf cies to improve security, Int. J Crit. Infrastruct., 2, 215–230,mono 2006. Assessment of Terrorist Attacks, New York University, Wagner Graduate School, Institute of ingcle, Hurricane Sandy 1st destroys November 111 houses, 2012, NY Daily News arti- 19716/596/1/PP213.pdf ciation International, Niagara Falls, Ontario, Wood, E. F. and Rodriguez-Iturbe, I.: A Baysian approach to analyzing uncertainty among flood USGS: Water-resources data for the United States, Water Year 2011: U.S. Geological Survey Webb, G. R., Tierney, K. J., and Dalhamer, J. M.:Wenzel, F., Predicting Daniell, J. long-term E., business Khazai, B., recoverey M Table 1. and 30 October 2012. Data source: NOAA Global Summary of the day and Ogimet.com. Zimmerman, R. and Restrepo, C. E.: The nextZimmermann, step: R., quantifying Lave, L., infrastructure Restrepo, C., interdependen- Dooskin, N., Hartwell, R., Miller, J., Remington, W., Si- Trapasso, C.: Families return to Breezy Point after devastating fire dur- 5 15 10 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Govt Govt % of GDP Source 660 659 The New England Storm 1938 and Sandy 2012 are ∗ ). 2012 280 3.8 CEDIM 100 0.0057 Jamaican ( (million US$) > 85> 0.14 CEDIM Daniell New York New Jersey Pennsylvania CountryCuba Estimates loss Haiti BahamasUSA 3380Jamaica 300–400 66 78 000–97 000 3.7–4.9 5.5 0.5 CCRIF 0.4 CEDIM CEDIM Jamaican Dominican Republic Canada Bermuda minor Direct Economic Loss estimates for the Caribbean and the US. Number of storm fatalities in the states New York, New Jersey and Pennsylvania from 1*1345 New England, 19386 Norfolk/Long Sandy, Is., 2012 1821 60 Hurricane Five, 1894 64 17 Edna, 1954 10 Unnamed, 29 1806 Agnes, 1972 Unnamed, 1944 21 Sandy, 2012 Unnamed, 1878 6 9 37 8 Irene, 2011 Diane/Connie, 1955 10 Agnes, 1972 Floyd, 75–90 1999 Floyd, 50 1999 Gale 6 of 1878 6–13 10 Sandy, 2012 TC 13 Allison, 2001 7 Rank Storm Name, Year Deaths Storm Name, Year Deaths Storm Name, Year Deaths Table 3. historic hurricane/storm events.17 Sources: November Reuters, 2012, 16 and November 2012, Keller in NY Times, deemed to be equally rankedinclude as indirect the deaths fatality whereas numberscarbone the for monoxide the numbers poisoning, New debris for England removal, Hurricane etc. Storm Sandy 1938 include did indirect not deaths via Table 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | refers ∗ http://t.co/ http://t.co/10K0clpi ected the US; the ff ). 2012 , Daniell cially lost power at my home in Glenolden, PA ffi (in billion US$ 2012) 662 661 yes #sandy Beach area, boats jumbled in marinas #connected no visible major damage. Worriedpoplars. about All our of many you, tulip be safe. XO jhLGBp2m Conestoga River still has aing couple the of bank. feet before reach- ridge 09:19:52 Wakes up, sees power and can still on, and no damage, ff Hurricane (Year) Direct Economic Losses in the US Katrina (2005)Sandy (2012)Andrew (1992)Ike (2008)Wilma (2005) 127.8 78–97* 42.7 23.9 31.3 Kirk Moore 13:50:28 Severe damage on South Green Street Tuckerton Lis Kalogris 13:02:38 Here in the EOB Garden we have lost power but so far Bill Speakman 09:26:27 Flooding on Pitney Rd is just from a storm drain. The Examples of tweets sent during hurricane Sandy on 30 October 2012; Time is in UTC. Direct Economic Losses by historic hurricanes that have a Damage Lamar Li Power outage /infrastructure John Powell Preston Kilgore 00:02:46 06:20:27 I Some have o may not have power but we all have phones Topic User Time Message Flood ELCIRCUITOTV 03:34:20 #sandy floods #fdr 63rd street Table 5. to the loss estimation by CEDIM Database (CATDAT Table 4. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 28 28 664 663 . r-Simpson Hurricane Scale, minimum pressure and maximum 1 min sus- ffi Wind peak gusts on 30 October 2012 (GFS-model 6 h-forecast). Image credit: Track of Hurricane Sandy from 24 to 30 October 2012. Indicated are storm category Fig. 2. www.wettergefahren-fruehwarnung.de Fig. 1. according to the Sa tained wind speed (in knots). Data source: National Hurricane Center. Track of Hurricane Sandy from 24 to 30 Oct. 2012. Indicated are storm category according to the Wind peak gusts on 30 October 2012 (GFS-model 6h-forecast). Image credit: www.wettergefahren- Track of Hurricane Sandy from 24 to 30 Oct. 2012. Indicated are storm category according to the Wind peak gusts on 30 October 2012 (GFS-model 6h-forecast). Image credit: www.wettergefahren-

fruehwarnung.de. Fig. 2. Data source: National Hurricane Center Saffir-Simpson Hurricane Scale, minimum pressure and maximum 1-minute sustained wind speed (in knots). Fig. 1. Figures

fruehwarnung.de. Fig. 2.

Data source: National Hurricane Center Saffir-Simpson Hurricane Scale, minimum pressure and maximum 1-minute sustained wind speed (in knots).

Fig. 1. Figures Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ); New peak (c) (b) http://water.weather. , but for Washington DC. (h) Wind direction and localized twitter responses with (a) (h) ; same as http://waterwatch.usgs.gov/index.php (i) ; (h) localized twitter responses with the ects and twitter response associated with ff hurricane hurricane 30 29 666 665 Washington DC NOAA (2012; in New York; in New York; (i) same as (h), but for Washington D.C. (e) power outage and flooding power outage Evolution and magnitude of the hazardous effects and twitter response associated with the landfall of and keywords Fig. 4. hurricane Sandy in the U.S. From top to bottom:JFK Intl. (a) Wind Airport; direction storm and surge (b) at peak the gusts tidalD.C. with gauges sea-level NOAA of pressure (c) (2012; at New http://water.weather.gov/ahps); York, (f) (d) precipitation Chesapeake Bay at Inlet Baltimore/Washingtondischarge and Intl. of (e) Airport Washington the and Potomac River at Pointindex.php; (g) of worldwide Rocks twitter USGS response discharge with gauges the (2012; keyword http://waterwatch.usgs.gov/ precipitation at Baltimore/Washington Intl. Airport and discharge of the Potomac flooding (f) Chesapeake Bay Inlet and ); Rainfall totals (in mm) from 18–25 October 2012 (left) over the Caribbean and 24– Rainfall totals (in mm) from 18-25 Oct. 2012 (left) over the Caribbean and 24-31 October (right) over Evolution and magnitude of the hazardous e (d) worldwide twitter response with the keyword Fig. 3. the U.S. East Coast. Image credit: TRMM Tropical Rainfall Measuring Mission. gov/ahps (g) Fig. 4. River at Point of Rocks USGS discharge gauges (2012; the landfall of hurricane Sandy in the US. From top to bottom: the keywords gusts with sea-level pressureYork, at JFK Intl. Airport; storm surge at the tidal gauges of Fig. 3. 31 October (right) overMission. the US East Coast. Image credit: TRMM Tropical Rainfall Measuring Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . with (b) wetter3.de and 30 October, 06:00 UTC (a) 32 668 667 Weather charts for 28 October, 18:00 UTC Satellite image on 28 October 2012, 17:45 UTC Image Credit: NASA GOES Project. Fig. 6. 500 hPa Geopotential heightrelative (black topography (colors) lines), from the surface Global pressure Forecast System (white (GFS). lines) Image credit: and 1000/500 hPa Fig. 5. Satellite image on 28 October 2012, 17:45 UTC Image Credit: NASA GOES Project. Fig. 6. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , ` ere Daniell ´ e Publique, Republique d’Haiti and CATDAT 33 34 670 669 ` ere de la Sant during the four months before and the seven weeks (a) from 17 October 2010 to 12 December 2012. Data: Minist (b) ). 2012a , ´ Number of cumulated Cholera deaths per week and percentage of cholera as death cause out of all e Publique, Republique d’Haiti and Database. CATDAT Number of cumulated Cholera deaths per week and percentage of cholera as death Residential damage in Cuba as a percentage of housing stock using data from ( IFRC ; deaths per week in Haiti (a) duringfrom the 17 four Oct. months before 2010 and to the 12 sevenDatabase Dec.. weeks after 2012. Hurricane Data: Sandy, and Minist (b) Fig. 7. Fig. 8. 2012 Fig. 7. cause out of all deaths per week in Haiti after Hurricane Sandy, and de la Sant Timeline of restoration of power outage from Hurricane Sandy between 29 and 30 Oct. and the Residential damage in Cuba as a percentage of housing stock using data from (Daniell, 2012; IFRC, from 29 Oct. to 19 Nov.). reports obtained from the U.S. Department of Energy, Office of Electricity Delivery and Energy Reliability Fig. 9. Nor’easter on 7 Nov. for affected customers in the U.S. (Customer outages are compiled from specific situation 2012a). Fig. 8. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ce of ffi 35 34 672 671 ected customers in the US (Customer outages are ff ). A breakdown of direct losses in New York State (in million US$ and %) reported by 2012 Timeline of restoration of power outage from Hurricane Sandy between 29 and 30 Oct. and the , Residential damage in Cuba as a percentage of housing stock using data from (Daniell, 2012; IFRC, Timeline of restoration of power outage from Hurricane Sandy between 29 and 30 Octo- Nor’easter on 7 Nov. for affected customers inreports the U.S. obtained (Customer from outages are the compiled U.S. fromfrom specific Department 29 situation of Oct. Energy, to 19 Office Nov.). of Electricity Delivery and Energy Reliability Fig. 9. 2012a). Fig. 8. Cuomo Fig. 10. ( Fig. 9. ber and the Nor’easter oncompiled 7 from November for specific a situation reports obtained from the US. Department of Energy, O Electricity Delivery and Energy Reliability from 29 October to 19 November). Direct Economic Losses (in billion US$ and %) by U.S. State from Hurricane Sandy. A breakdown of direct losses in New York State (in million US$ and %) reported by (Cuomo, 2012). Fig. 11. Fig. 10. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 36 35 674 673 Economic loss from power outages. Direct Economic Losses (in billion US$ and %) by US State from Hurricane Sandy. Fig. 12. Fig. 11. Share of sectors’ value added in affected States compared to the US economy. Economic loss from power outages. Direct Economic Losses (in billion US$ and %) by U.S. State from Hurricane Sandy. A breakdown of direct losses in New York State (in million US$ and %) reported by (Cuomo, 2012).

Fig. 13. Fig. 12.

Fig. 11. Fig. 10. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 37 36 676 675 ected States compared to the US economy. ff ects of disasters on the industry). ff erent sectors (obtained through the value added). The relative vulnerability ff Share of sectors’ value added in a Industrial vulnerability of Eastern US against indirect disaster impacts (To obtain those Industrial vulnerability of Eastern U.S. against indirect disaster impacts (To obtain those results, the Fig. 14. results, the sector specificof vulnerability regions was of regionalised the di by considering the industrial density index scaled from 0state to against 1, indirect 0 e and 1 being respectively the least vulnerable and most vulnerable Fig. 13. Fig. 14. sector specific vulnerability was regionalised bysectors considering (obtained the through industrial the value density added). ofrespectively regions The the of relative least vulnerability vulnerable the index and different scaled most vulnerable from state 0 against to indirect 1, effects 0 of and disasters on 1 the being industry).

Share of sectors’ value added in affected States compared to the US economy. Economic loss from power outages.

Fig. 13. Fig. 12. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent industrial sectors, taking into ff 38 678 677 38 Transportation and power dependencies of di Assessment of indirect industrial losses due to Sandy. Transportation and power dependencies of different industrial sectors, taking into account inter-CI Assessment of indirect industrial losses due to Sandy. Fig. 16. Fig. 15. dependencies. Black bars indicate transport dependency, grey bars power dependency. Fig. 16. account inter-CI dependencies.dependency. Black bars indicate transport dependency, grey bars power Fig. 15.

Transportation and power dependencies of different industrial sectors, taking into account inter-CI Assessment of indirect industrial losses due to Sandy.

Fig. 16.

dependencies. Black bars indicate transport dependency, grey bars power dependency. Fig. 15. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | for a timeline of six ). 2 for a timeline of six days, from 28 hurricane 5 km × hurricane 39 679 ). 2 grid cell (approximately 5 ◦ 5 km × Density Map of the located tweets with the keyword Density Map of the located tweets with the keyword grid cell (approximately 5 ◦ days, from 28 October toto 2 the November number 2012 of (dates tweets indicated per in 0.05 the figures). The density refers Fig. 17. Fig. 17. October to 2 November 2012 (dates0.05 indicated in the Figures). The density refers to the number of tweets per