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At the end of October 2012, Hurricane Sandy moved from the Caribbean Sea into the Atlantic Ocean and entered the United States not far from New York. Along its track, Sandy caused more than 200 fatalities and severe losses in Jamaica, The Bahamas, Haiti, Cuba, and the US. This paper demonstrates the capability and potential for near-real-time analysis of catastrophes. It is shown that the impact of Sandy was driven by the superposition of different extremes (high wind speeds, storm surge, heavy precipitation) and by cascading effects. In particular the interaction between Sandy and an extra-tropical weather system created a huge storm that affected large areas in the US. It is examined how Sandy compares to historic hurricane events, both from a hydro-meteorological and impact perspective. The distribution of losses to different sectors of the economy is calculated with simple input-output models as well as government estimates. Direct economic losses are estimated about USD 4.2 billion in the Caribbean and between USD 78 and 97 billion in the US. Indirect economic losses from power outages is estimated in the order of USD 16.3 billion. Modelling sector-specific dependencies quantifies total business interruption losses between USD 10.8 and 15.5 billion. Thus, seven years after the record impact of Hurricane Katrina in 2005, Hurricane Sandy is the second costliest hurricane in the history of the United States.
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Nat. Hazards Earth Syst. Sci., 13, 2579–2598, 2013
www.nat-hazards-earth-syst-sci.net/13/2579/2013/
doi:10.5194/nhess-13-2579-2013
© Author(s) 2013. CC Attribution 3.0 License.
Natural Hazards
and Earth System
Sciences
Open Access
Investigation of superstorm Sandy 2012 in a multi-disciplinary
approach
M. Kunz
1,2
, B. Mühr
1,2
, T. Kunz-Plapp
1,3
, J. E. Daniell
1,3
, B. Khazai
1,3
, F. Wenzel
1,3
, M. Vannieuwenhuyse
1,4
,
T. Comes
1,4
, F. Elmer
1,5
, K. Schröter
1,6
, J. Fohringer
1,7
, T. Münzberg
1,8
, C. Lucas
1,9
, and J. Zschau
1,10
1
Center for Disaster Management and Risk Reduction Technology (CEDIM), Potsdam and Karlsruhe, Germany
2
Institute for Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
3
Geophysical Institute (GPI), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
4
Institute for Industrial Production (IIP), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
5
Scientific Infrastructure and Platforms, GFZ German Research Centre for Geosciences, Potsdam, Germany
6
Section Hydrology, GFZ German Research Centre for Geosciences, Potsdam, Germany
7
Section Geoinformatics, GFZ German Research Centre for Geosciences, Potsdam, Germany
8
Institute for Nuclear and Energy Technologies (IKET), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
9
Institute of Photogrammetry and Remote Sensing (IPF), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
10
Section Earthquake Risk and Early Warning, GFZ German Research Centre for Geosciences, Potsdam, Germany
Correspondence to: M. Kunz (michael.kunz@kit.edu)
Received: 8 January 2013 – Published in Nat. Hazards Earth Syst. Sci. Discuss.: 25 March 2013
Revised: 3 July 2013 – Accepted: 6 September 2013 – Published: 18 October 2013
Abstract. At the end of October 2012, Hurricane Sandy
moved from the Caribbean Sea into the Atlantic Ocean and
entered the United States not far from New York. Along
its track, Sandy caused more than 200 fatalities and severe
losses in Jamaica, The Bahamas, Haiti, Cuba, and the US.
This paper demonstrates the capability and potential for near-
real-time analysis of catastrophes.
It is shown that the impact of Sandy was driven by the su-
perposition of different extremes (high wind speeds, storm
surge, heavy precipitation) and by cascading effects. In par-
ticular the interaction between Sandy and an extra-tropical
weather system created a huge storm that affected large ar-
eas in the US. It is examined how Sandy compares to historic
hurricane events, both from a hydro-meteorological and im-
pact perspective.
The distribution of losses to different sectors of the econ-
omy is calculated with simple input-output models as well as
government estimates. Direct economic losses are estimated
about USD 4.2 billion in the Caribbean and between USD 78
and 97 billion in the US. Indirect economic losses from
power outages is estimated in the order of USD 16.3 billion.
Modelling sector-specific dependencies quantifies total busi-
ness interruption losses between USD 10.8 and 15.5 billion.
Thus, seven years after the record impact of Hurricane Kat-
rina in 2005, Hurricane Sandy is the second costliest hurri-
cane in the history of the United States.
1 Introduction
Hurricane Sandy was the last tropical cyclone (TC) of the
2012 Northern Atlantic Hurricane season. From 24 to 30 Oc-
tober, Sandy moved on an unusual track from the Caribbean
to the East Coast of the United States, where it made land-
fall in New Jersey in the early hours of 30 October. Along
its path, the severe storm caused more than 200 fatalities and
widespread damage to one the poorest (Haiti) and one of the
richest countries (US) in the world with different patterns of
impact and loss. Sandy was an extraordinary event due to its
multihazard nature and the cascades of adverse events in the
aftermath that aggravated the direct impacts significantly.
From a hydro-meteorological perspective, the most un-
usual aspect was the very large spatial extent of up to
1700km, primarily a result of the interaction of the TC
with an upper-tropospheric trough. This interaction led to
a rapid extra-tropical transition (e.g., Jones et al., 2003)
Published by Copernicus Publications on behalf of the European Geosciences Union.
2580 M. Kunz et al.: Hurricane Sandy 2012
shortly before landfall that further increased the strength of
the storm. High wind speeds were associated with record-
breaking storm surges on the US. Mid-Atlantic and New
England Coast during high (astronomical) tide, leading to
widespread flooding. Very unusual was also the storm’s track
from the south to the north, which was mainly due to block-
ing by an extended high pressure system. Thus, Sandy hit a
region that has rarely been affected by hurricanes in the past
but is densely populated and very vulnerable to such an un-
expected event. Since recording, Sandy was only the third
hurricane that made landfall in New Jersey.
Though Sandy was not the most severe storm event in
terms of wind speed and precipitation, the impact, particu-
larly in the US, was enormous. More than 20 million peo-
ple on the East Coast were affected by power outages that
lasted a few days to weeks in some regions. Furthermore,
many places on the East Coast suffered several days from
shortages in fuel supply. This situation was aggravated by
a cold air outbreak in the days following the event, causing
temperatures to drop down to almost 0
C. Total damage will
be in excess of USD 100 billion, with our estimates ranging
between USD 78 and 97 billion for direct damage and over
USD 10 to 16 billion for indirect damage due to business in-
terruption. Seven years after the record impact of Hurricane
Katrina in 2005 (e.g., Daniels et al., 2006) with total eco-
nomic losses in the order of USD 160 (inflation-adjusted to
2012; Swiss Re, 2006), Sandy was the second most costliest
TC in the history of the United States.
The new Forensic Disaster Analysis (FDA) Task Force
of the Center for Disaster Management and Risk Reduction
Technology (CEDIM) intends to improve our understanding
of the temporal evolution and the impact of natural disas-
ters. The main research strategy is to consider not only the
natural hazard components, but also the related complex in-
teractions and cascading effects in and between the natural,
social, economic and infrastructure system. This is imple-
mented in an interdisciplinary way by collecting and com-
piling scattered and distributed information from available
databases and sources via the Internet, by application of our
own methodologies and models for near-real-time analyses
developed in recent years, and by expert knowledge. Al-
though much better data emerge weeks and months after such
an event, the CEDIM FDA concept attempts to obtain and
provide information within the first few hours to days after a
disaster. Time critically is considered important as potential
user interest (e.g., relief organizations, insurance industry,
tourist agencies) peaks in the initial stage of a disaster. Also,
many pieces of information emerge within the first days that
may later be obscured by a flood of information. Initial hy-
potheses on loss evolution and its implications can be tested
in the following days and, thus, may enhanceour understand-
ing of the impact and evolution of natural disasters within
their respective socio-economic context.
This paper draws on two reports that are available
on CEDIM’s webpage (www.cedim.de), the first one of
30 October 2012, 20h after Sandy had crossed the US East
Coast, and the second one 10 days later. The paper de-
scribes the multihazard situation that led to the extraordinary
event, highlights the interaction of the TC with other hydro-
meteorological events, and examines impacts such as social
and economic losses including cascading effects, for exam-
ple, due to power outages. It is examined how Sandy com-
pares to historic hurricane events in the US, both from the
hydro-meteorological and impact perspective. Direct and in-
direct losses are estimated by comparison with past events
and by application of an economic loss model that describes
the dependencies between the various economic sectors.
The paper is structured as follows. Section 2 describes
background, procedure, and strategy of CEDIM’s near-real-
time FDA. Section 3 gives an overview of the hazard situa-
tion and discusses what made Sandy an extraordinary event.
While Sect. 4 examines the impact of Sandy during the early
stages in the Caribbean, Sect. 5 discusses the impact specifi-
cally for the US, with a focus on power outages, their conse-
quences and associated indirect losses. Finally, Sect. 6 briefly
summarizes the various findings, lists some conclusions, and
discusses future perspectives and requirements that are nec-
essary for implementing near-real-time FDA.
2 CEDIM forensic disaster analysis
Modern technologies, accessible databases and information
services open unprecedented opportunities for natural disas-
ter loss assessment and analysis in near-real time. The Inter-
net, for instance, provides information from various sources,
including the new technique of crowd sourcing, in min-
utes to hours after an extreme event anywhere on the globe.
Databases have been developed for storms, floods, or earth-
quakes, which allow the rapid estimation of the potential
damage once the triggering parameters, such as gust wind
speed, precipitation totals, or ground motion are roughly
known. Moreover, several services are accessible with highly
relevant disaster information; among these are the Joint Re-
search Center (JRC) with its GDACS service (www.gdacs.
org), as well as the CatWatch (www.eqecat.com) information
service from the private sector.
Forensic disaster investigation (Burton, 2011) has been
implemented as a research target by the Integrated Re-
search on Disaster Risk (IRDR, www.irdrinternational.org),
an ICSU (International Council for Science, www.icsu.org)
initiative located in Beijing. The Forensic Investigations of
Disasters (FORIN) programme (IRDR, 2011) aims at uncov-
ering the root causes of natural disasters through in-depth
investigations that go beyond the typical sectoral case stud-
ies. For disaster analysis in near-real time, which is not
the pretension in the FORIN concept, CEDIM is develop-
ing the strategy of Forensic Disaster Analysis (FDA; Wenzel
et al., 2012). The word “forensic” is applied in the sense of
scrutinising disasters closely and with a multi-disciplinary
Nat. Hazards Earth Syst. Sci., 13, 2579–2598, 2013 www.nat-hazards-earth-syst-sci.net/13/2579/2013/
M. Kunz et al.: Hurricane Sandy 2012 2581
approach by making use of the high potential of modern
observational and analytical methodologies available in sci-
ence, engineering, remote sensing and information technol-
ogy. Results from these heterogeneous sources are the start-
ing point for comprehensive science-based assessments in
near-real time, i.e. less than 24h after a catastrophic event
occurred. This information are complemented by our own
models for near-real time-loss estimates that are currently
being developed. The forensic approach incorporates event-
triggered task force activities, as well as specific research to-
wards new methodologies that can support the near-real-time
approach.
The objective of CEDIM’s FDA approach are to build up
the capability to rapidly:
generate a portrait of the disaster with the aims of re-
vealing its main characteristics and tracking its evolu-
tion;
reveal the short- and long-term impacts on regionaland
national scale;
estimate potential losses and analyze the critical causes
of loss and risk;
contribute to the development of a framework for fu-
ture loss and risk reduction.
An important component in the CEDIM FDA is the near-
real-time approach as: (i) many pieces of information emerge
within the first days of disasters; (ii) interest of and interac-
tion with potential users (e.g., emergency services, tourism
industry, insurance industry, relief agencies) is particular
high during the initial stage of a disaster; (iii) methodolo-
gies and models of CEDIM for near-real-time loss evolution
and implications can be tested and calibrated and can thus
(iv) contribute to significantly speed up our understanding of
disasters within their respective socio-economic contexts.
3 Hazard description
3.1 Overview of Sandy
From 22–29 October 2012, Hurricane Sandy made its way
from the Caribbean Sea into the Atlantic Ocean and finally
entered the United States near Atlantic City (NJ) on the early
morning of 30 October. According to the Saffir–Simpson
Hurricane Scale ranging from 1 to 5, Sandy was a category 2
Hurricane (154–177 kmh
1
). The very unusual coincidence
of reinforcing conditions over the US, e.g. the interaction be-
tween Sandy and an extra-tropical weather system, created a
huge storm that made landfall in the US and affected large ar-
eas. The storm was associated with high impact weather that
stretched up to the Great Lakes and even beyond in southern
and southeastern Canada. Due to the huge spatial extension
and high intensity, Sandy caused massive damage and losses
in several of the densely populated New England and Mid-
Atlantic states.
A coastline of more than 1000km in length was hit by
a significant storm surge with the highest and often record-
breaking water levels occurring north of the landfall location
in New Jersey and New York. In contrast, fluvial flooding
in the Mid-Atlantic region in response to heavy precipitation
turned out to be a minor hazardous effect.
In the next subsections, the storm track, the spatial-
temporal evolution of Sandy and its hazardous effects, heavy
precipitation, storm surge, and river floods are presented.
3.2 Storm track of Sandy
Sandy was added to the list of 2012 tropical storm systems
on 22 October, 15:00 UTC. So far that year, it was tropical
storm system #18 in the North Atlantic region. At the ini-
tial stage, huge convective cloud structures begun to organize
250km north of Panama and 515 km south of Kingston, Ja-
maica. With further strengthening, Sandy was classified as a
category 1 hurricane according to the Saffir–Simpson Hur-
ricane Scale on 24 October, just before crossing the island
of Jamaica. Heading further north, the hurricane approached
Cuba, where the storm center arrived 24h later. Associated
with heavy rainfall, Sandy crossed the eastern parts of Cuba,
where it reached its maximum intensity. At 06:00 UTC on
25 October, the TC had 1 min sustained winds of 95kts
(176kmh
1
) and gusts around 110 kts (204kmh
1
) making
Sandy a category 2 hurricane.
Constant in intensity, Sandy passed The Bahamas on
26 October. The following day the hurricane made a right
turn towards the northeast and started to lose strength. More
and more weather forecast models began to predict a scenario
where Sandy was expected to make landfall after a leftward
movement on the East Coast of the US. The TC was expected
to arrive in the night 29/30 October somewhere along the
Delaware/New Jersey Atlantic coast.
Some hours before entering the US mainland, the hur-
ricane intensified again and showed mean wind speeds of
80kts (148kmh
1
). Shortly before and while making land-
fall, the center pressure of Sandy was 940hPa, which was a
new low pressure record for hurricanes making landfall north
of Cape Hatteras (Hurricane Gladys in 1977 showed a mini-
mum pressure of 939hPa, however, kept its center away from
the coast). Even the well-known “Long Island Express” in
1938 only had a minimum pressure of 947 hPa. The storm
center of Sandy crossed the coastline around 00:00 UTC on
30 October. From 30 to 31 October, Sandy moved further
northwards and finally dissipated near Lake Erie.
www.nat-hazards-earth-syst-sci.net/13/2579/2013/ Nat. Hazards Earth Syst. Sci., 13, 2579–2598, 2013
2582 M. Kunz et al.: Hurricane Sandy 2012
Table 1. Selected recordings of peak wind gusts and precipitation amounts during Sandy on 29 and 30 October 2012. Data source: NOAA
Global Summary of the day and Ogimet.com.
Station 29 Oct 30 Oct 29 Oct 30 Oct
peak wind gusts in kmh
1
precipitation in mm
Atlantic City Intl. Airport, NJ 94.6 90.7 58.9 88.4
Baltimore/Washington Intl. Airport 77.8 94.6 31.5 133.9
New York JFK Intl. Airport, NY 127.8 109.5 0.5 13.0
New York La Guardia Intl. Airport, NY 109.5 114.8 0.0 13.7
Philadelphia Intl. Airport, NJ 85.2 87.0 24.4 55.9
Wallops Island, VA 109.5 70.6 111.8 102.1
Patuxent River, MD 90.7 77.8 84.8 123.2
Newark Intl. Airport, NJ 125.9 120.6 1.5 25.7
Teterboro Airport, NJ 116.5 105.4 0.0 18.8
Figures
Fig. 1. Track of Hurricane Sandy from 24 to 30 Oct. 2012. Indicated are storm category according to the
Saffir-Simpson Hurricane Scale, minimum pressure and maximum 1-minute sustained wind speed (in knots).
Data source: National Hurricane Center
Fig. 2. Wind peak gusts on 30 October 2012 (GFS-model 6h-forecast). Image credit: www.wettergefahren-
fruehwarnung.de.
29
Fig. 1. Track of Hurricane Sandy from 24 to 30 October 2012. In-
dicated are storm category according to the Saffir–Simpson Hurri-
cane Scale, minimum pressure and maximum 1min sustained wind
speed (in knots). Data source: National Hurricane Center.
3.3 Space-time evolution of Sandy
Wind speed and extreme precipitation are the primary haz-
ards associated with hurricanes and also contributed signifi-
cantly to the overall impact of Sandy. These primary hazards
may trigger secondary hazards with even stronger impacts.
Among the different types of secondary hazards, flooding is
the most relevant one. Two different types of floods can be
distinguished: storm surges that are caused by water masses
driven onto the coastlines by strong winds, and fluvial floods
that may result from heavy precipitation. An overview of the
temporal evolution of winds, pressure, and water levels is
given in Fig. 4 for two exemplary locations in New York and
Washington DC region.
Figures
Fig. 1. Track of Hurricane Sandy from 24 to 30 Oct. 2012. Indicated are storm category according to the
Saffir-Simpson Hurricane Scale, minimum pressure and maximum 1-minute sustained wind speed (in knots).
Data source: National Hurricane Center
Fig. 2. Wind peak gusts on 30 October 2012 (GFS-model 6h-forecast). Image credit: www.wettergefahren-
fruehwarnung.de.
29
Fig. 2. Wind peak gusts on 30 October 2012 (GFS-model 6 h-
forecast). Image credit: www.wettergefahren-fruehwarnung.de.
3.3.1 Heavy precipitation and storm force winds
While Sandy began to build, heavy precipitation with rainfall
totals between 200 and 250 mm led to widespread flooding in
the very south of the Dominican Republic as well as in the
southwestern tip of Haiti. Over the eastern parts of Jamaica,
more than 200 mm of rainfall was recorded, while the west-
ern parts did not receive significant rainfall. In Cuba, rain-
fall in excess of 200mm were observed only in some east-
erly and central provinces. Furthermore, precipitation sig-
nals obtained from satellite sensors showed values around
250mm in the vicinity of The Bahamas (Fig. 3, left). How-
ever, rainfall had its peak maximum over the open waters of
the Caribbean Sea.
In the US, the states of Pennsylvania, Maryland, New Jer-
sey, Delaware and Virginia were affected strongest by heavy
rainfall between 100 and 200 mm (see Table 1 and Fig. 3,
right). Wallops Island (Virginia) recorded a total of 214mm
Nat. Hazards Earth Syst. Sci., 13, 2579–2598, 2013 www.nat-hazards-earth-syst-sci.net/13/2579/2013/
M. Kunz et al.: Hurricane Sandy 2012 2583
Fig. 3. Rainfall totals (in mm) from 18-25 Oct. 2012 (left) over the Caribbean and 24-31 October (right) over
the U.S. East Coast. Image credit: TRMM Tropical Rainfall Measuring Mission.
30
Fig. 3. Rainfall totals (inmm) from 18–25 October 2012 (left) over the Caribbean and 24–31 October (right) over the US East Coast. Image
credit: TRMM Tropical Rainfall Measuring Mission.
within 48h, while at Baltimore/Washington Intl. Airport it
was 165mm (Fig. 4f). The most intense rainfall occurred in
the vicinity of the Chesapeake Bay (Easton, MD, 319 mm).
Sandy was responsible for the wettest days that have ever
been recorded in October at Baltimore/Washington Intl. Air-
port as well as at Dulles Intl. Airport.
The intrusion of cold air near the surface from the north-
west led to heavy snowfall, especially in the southern and
central Appalachian Mountains. In mountainous areas of
Tennessee, Kentucky, North Carolina, West Virginia and Vir-
ginia people experienced blizzard-like conditions and snow
amounts of up to 1m.
Many parts between theAtlantic coast and the Great Lakes
experienced wind gusts in excess of 85 kmh
1
. Selected
recordings of peak wind gusts on 29 and 30 October are
shown in Table 1. The strongest winds occurred along and
near the coastlines of Virginia, Delaware, New Jersey and
parts of New York. At JFK Intl. airport in NYC, the highest
gust recording was 128kmh
1
(see time series of Fig. 4b).
3.3.2 Storm surge and river floods
In the Caribbean, Haiti, Jamaica and the eastern part of Cuba
were affected by flooding and debris flow caused by heavy
precipitation. For example, the Croix de Mission River flow-
ing through Port au Prince in Haiti rose to threatening levels
for the adjacent housings.
On the US East Coast, the huge extent of the hurricane
led to storm surges caused by storm winds that advanced
from south to north along the affected coastlines of Virginia,
Delaware,New Jersey, NewYork, Connecticut, Rhode Island
and Massachusetts. As shown in Fig. 4d, the storm surge at
the Chesapeake Bay Bridge Tunnel Gauge occurred twelve
hours (equivalent to oneastronomical tide) before the highest
water levels in New York (Battery gauge, Fig. 4c). North of
the storm center, hurricane-force winds had an east to west
(landward) direction (wind gusts of up to 130kmh
1
) and
caused the extreme water levels seen along the coastlines
from New Jersey to Massachusetts. The impact and magni-
tude of the storm surge in the affected area differed due to the
bathymetric and geographical characteristics and a complex
interplay of spatio-temporal factors. In New York City and
on Long Island, the storm surge was most extreme: measure-
ments from New York City show that the shift in the wind
direction, minimum sea level pressure accompanied by max-
imum gusts, and the full moon high (astronomical) tide oc-
curred at the same time: around 01:00 UTC on 30 October
(Fig. 4a–c). This superposition of effects did not happen to
the full extent at other locations of the affected coastal areas.
Fluvial river flooding due to high precipitation amounts
was recorded at several gauges that are spatially clustered in
the Potomac and upper Susquehanna river basins as well as
the tributaries of the Delaware River in the area of Philadel-
phia. These river basins cover large parts of the federal
states of Pennsylvania, Maryland and Delaware. Further, two
gauges at the Hudson River reported flooding. The runoff re-
sponse is determined by the interplay of diverse hydrological
processes depending on geomorphological catchment char-
acteristics and conditions. In this specific event, the occur-
rence of snowfall in the Appalachian Mountains resulted in
a temporary storage of water in the headwater regions of
the river systems and thus attenuated the runoff response.
Furthermore, the initial flow conditions had been much be-
low normal flow. For instance, in the Potomac River at the
gauge Point of Rocks (Fig. 4f), the flood wave started from
www.nat-hazards-earth-syst-sci.net/13/2579/2013/ Nat. Hazards Earth Syst. Sci., 13, 2579–2598, 2013
2584 M. Kunz et al.: Hurricane Sandy 2012
Fig. 4. Evolution and magnitude of the hazardous effects and twitter response associated with the landfall of
hurricane Sandy in the U.S. From top to bottom: (a) Wind direction and (b) peak gusts with sea-level pressure at
JFK Intl. Airport; storm surge at the tidal gauges of (c) New York, (d) Chesapeake Bay Inlet and (e) Washington
D.C. NOAA (2012; http://water.weather.gov/ahps); (f) precipitation at Baltimore/Washington Intl. Airport and
discharge of the Potomac River at Point of Rocks USGS discharge gauges (2012; http://waterwatch.usgs.gov/
index.php; (g) worldwide twitter response with the keyword hurricane; (h) localized twitter responses with the
keywords flooding and power outage in New York; (i) same as (h), but for Washington D.C.
31
Fig. 4. Evolution and magnitude of the hazardous effects and twitter response associated with the landfall of Hurricane Sandy in the
US. From top to bottom: (a) wind direction and (b) peak gusts with sea-level pressure at JFK Intl. Airport; storm surge at the tidal
gauges of (c) New York, (d) Chesapeake Bay Inlet and (e) Washington DC NOAA (2012; http://water.weather.gov/ahps); (f) precip-
itation at Baltimore/Washington Intl. Airport and discharge of the Potomac River at Point of Rocks USGS discharge gauges (2012;
http://waterwatch.usgs.gov/index.php; (g) worldwide twitter response with the keyword hurricane; (h) localized twitter responses with the
keywords flooding and power outage in New York; (i) same as (h), but for Washington DC.
Nat. Hazards Earth Syst. Sci., 13, 2579–2598, 2013 www.nat-hazards-earth-syst-sci.net/13/2579/2013/
M. Kunz et al.: Hurricane Sandy 2012 2585
a discharge of around 60m
3
s, which was very close to the
flow value that exceeded 90% of the time (USGS, 2012).
At lower reaches of the rivers or in estuaries near the At-
lantic, high water levels cannot be attributed to single trigger
mechanisms. Rather they were caused by the superposition
of tidal currents, storm surges and fluvial flooding associ-
ated with heavy rainfall. For the tide gauge of Washington
DC, for example, the influence of tidal dynamics and storm
surge is obvious in the time series shown in Fig. 4e. This
gauge is situated remote from the coast at the mouthing of
the Potomac River. The maximum water levels during this
event were reached a full36h after the hightide at the Chesa-
peake Bay Bridge Tunnel (Fig. 4d). This is due to the propa-
gation of the surge along the Chesapeake Bay. Further, the
water levels remained at a high level during the 31 Octo-
ber and 1 November. The superposition with the inland flood
wave flowing off from the Potomac River basin (Fig. 4f) con-
tributed to this effect.
In order to assess how the peak coastal water levels and
river discharges recorded during Sandy compare to the past,
their recurrence intervals (Tn) using extreme value statis-
tics were quantified. For this purpose, annual maximum se-
ries (AMS) for the different USGS (2012) discharge gauges
(http://waterwatch.usgs.gov/index.php and NOAA (2012)
tidal gauges (http://water.weather.gov/ahps) were obtained
and analyzed statistically. As several probability distribution
functions may satisfactorily describe the AMS data variabil-
ity, our assessment was based on a composite distribution
function approach (Apel et al., 2006; Wood and Rodriguez-
Iturbe, 1975). The composite function resulted from weight-
ing the distribution functions based on likelihood weights.
Note that at the given point of time all observations consid-
ered in this paper are provisional data and subject to revision.
Observed water levels at the tidal gauges from northern
Virginia to Rhode Island exceeded recurrence intervals (Tn)
of 10yr. The highest levels, corresponding to a Tn> 100yr
event, occurred at the tidal gauge at Battery on the south-
ern tip of Manhattan, where water levels unprecedented in
the record occurred due to the concurrence of the aforemen-
tioned reinforcing effects. Specific flow characteristics at this
gauge, namely the confluence of Hudson and East rivers at
the northern end of Upper Bay may also have contributed
to the record water level. At Kings Point in the Long Is-
land Sound and at the Chesapeake Bay Bridge Tunnel gauge
(Fig.
4d), for example, the effects of the storm tide maxima
were pronouncedly lowered by the low (astronomical) tide,
but still reached levels of Tn= 20yr and more.
By contrast, recurrence intervals of peak discharges at lo-
cations off the coast were substantially lower. For example at
the streamflow gauge Point of Rocks at the Potomac River
(Fig.
4f), Tn= 2yr was quantified. Other gauges showed
slightly higher levels, for example, Tn of 4 yr has been es-
timated for the peak discharge at the gauge East Branch
Brandy Wine Creek below Downington and a Tn of 6yr for
the Monocacy River at Jug Bridge near Frederick. In general,
Fig. 5. Weather charts for 28 Oct., 18:00 UTC (a) and 30 Oct., 06:00 UTC (b) with 500 hPa Geopotential
height (black lines), surface pressure (white lines) and 1000/500 hPa relative topography (colors) from the
Global Forecast System (GFS). Image credit: wetter3.de.
32
Fig. 5. Weather charts for 28 October, 18:00 UTC (a) and 30 Octo-
ber, 06:00UTC (b) with 500 hPa geopotential height (black lines),
surface pressure (white lines) and 1000/500hPa relative topogra-
phy (colors) from the Global Forecast System (GFS). Image credit:
wetter3.de.
the observed peak flows rather correspond to frequent flood
events. Reports of extensive inundation of structures and
roads, significant evacuations of people and/or transfer of
property to higher elevations 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 hurricane-prone region. In contrast to
this, the US East Coast has rarely been afflicted by hurri-
canes in the past. Since recording, Sandy was only the third
hurricane that made landfall in New Jersey. According to
the Hurricane Probability Project (Colorado State University;
http://typhoon.atmos.colostate.edu), the probability of land-
fall in New Jersey is only 1% during a hurricane season,
whereas, for example, in Florida this probability is 51%. For
other states on the US East Coast that have been affected by
Sandy (e.g. Delaware, Virginia, New York), the probability
is between 1 and 8%.
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2586 M. Kunz et al.: Hurricane Sandy 2012
Fig. 6. Satellite image on 28 October 2012, 17:45 UTC Image Credit: NASA GOES Project.
33
Fig. 6. Satellite image on 28 October 2012, 17:45UTC. Image
Credit: NASA GOES Project.
Unusually high sea surface temperatures, which were well
above average along the track of Sandy, helped to keep the
intensity over several days. The deviation from the long term
average sea surface temperature was 2–4K on 27 October
off the East Coast of the US. The warm water provided more
latent heat, which is the source of energy for hurricanes, and
intensified the TC while there was no or only little vertical
wind shear, which is destructive for those systems.
Nearly all tropical cyclones in the North Atlantic turn onto
an east-northeasterly track before they get anywhere close to
the US. mainland. Afterwards, they usually travel towards
Europe as extra-tropical cyclones. However, the particular
meteorological situation over the North American continent
and the Atlantic Ocean from 28 October onwards led to a
significant shift of Hurricane Sandy. By the end of October,
an unusually well-pronounced upper air ridge (high pressure
at higher levels in the troposphere) established over eastern
Canada. In cooperation with a North Atlantic low pressure
system, the ridge had a blocking effect to Hurricane Sandy,
which was on the way from the southwest. The usual right
turn, referred to as recurvature, was not possible for Sandy.
Thus, the storm was forced towards the west-northwest and
targeted New Jersey and New York.
Sandy interacted with a huge upper level trough that
stretched across the central portion of the US and moved
into an easterly direction (see Fig. 5). At its eastern edge
the trough provided additional forcing and extra lifting of
the warm and moist tropical air, which resulted in a fur-
ther strengthening of the storm system. The satellite image
from 28 October 2012, 17:45 UTC (Fig. 6) shows the elon-
gated cloud band of a cold front ahead of the upper level
trough; the frontal clouds reach from central Florida along
the Appalachian Mountains into the north-eastern US. An ex-
tended shield of high-level clouds aloft indicates the strong
south-westerly flow and the lifting forces that are already ac-
tive between the Great Lakes and the Atlantic coast. While
approaching the trough, Sandy grew rapidly; temporarily the
storm had a horizontal extension of record breaking 1700km.
Even far from the center, storm force winds occurred, which,
for example, caused wave heights of up to 6.6m in Lake
Michigan.
A perfect timing just before landfall initiated the transition
from a tropical into an extra-tropical cyclone: On 28 October,
18:00UTC, Sandy still showed an approximately symmetric
warm core, which is a characteristic feature of TCs. Within
the next 36 h, the intrusion of cold air began to evolve warm
and cold fronts, whereas the core became colder and asym-
metric, which is characteristic for extra-tropical cyclones.
Shortly after landfall, Sandy turned into a cold-core low and
completed the extra-tropical transition. With both tropical
and extra-tropical characteristics during landfall, Sandy be-
came somewhat capricious and dangerous (see Fig. 5).
The date Sandy made landfall is well outside the peak
hurricane season, especially as far north as New York. Cold
air advection from Canada included into Sandy’s circulation
provided the potential for blizzard-like weather conditions in
parts of the Appalachians, where snow accumulations were
nearly 1m in some areas.
4 Impact of Sandy in the Caribbean
4.1 Social impacts
Cuba and Haiti were the hardest hit countries in the
Caribbean in terms of number of affected people. During
the passage of Sandy on 25 October (see Fig. 1), at least
80 people were killed in the Caribbean, with highest death
tolls in Haiti (54 or more killed, 15 missing; OCHA, 2012b,
c; ECHO, 2012). In Cuba, eleven people died and 3 mil-
lion people suffered direct or indirect impacts (IFRC, 2012b;
OCHA, 2012a). Overall, 243 000 houses and 2601 schools
were damaged or destroyed by strong winds as well as flood-
ing; 615 health centers were damaged or impaired in their
functioning (see also Sect. 4.2). Access of an estimated
number of 1 to 1.5 million people to safe water was ham-
pered (IFRC, 2012b; ECHO, 2012). Wind, heavy rainfall
and subsequent overflowing of rivers in the west and south-
west of Haiti killed at least 54 people, destroyed or dam-
aged 27000 houses and emergency shelters of 5298 fam-
ilies; 50 schools were destroyed and 100 damaged. With
100000ha of destroyed crops by the strong winds in Cuba
and 90 000 ha of devastated cropland by heavy rain and
flooding in Haiti, in both countries the risk of food insecu-
rity has severely increased and is expected to also have a
medium-term effect on livelihoods (IFRC, 2012b).
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M. Kunz et al.: Hurricane Sandy 2012 2587
Fig. 7. Number of cumulated Cholera deaths per week and percentage of cholera as death cause out of all
deaths per week in Haiti (a) during the four months before and the seven weeks after Hurricane Sandy, and (b)
from 17 Oct. 2010 to 12 Dec.. 2012. Data: Minist
`
ere de la Sant
´
e Publique, Republique d’Haiti and CATDAT
Database
34
Fig. 7. Number of cumulated cholera deaths per week and percent-
age of cholera as death cause out of all deaths per week in Haiti (a)
during the four months before and the seven weeks after Hurricane
Sandy, and (b) from 17 October 2010 to 12 December 2012. Data:
Ministère de la Santé Publique, Republique d’Haiti and CATDAT
Database
Even if Sandy historically was not the deadliest hurricane
affecting Haiti (see Mühr et al., 2012), there are some fac-
tors that aggravate Sandy’s impact in Haiti. Firstly, Hurri-
cane Sandy struck a country that is still recovering from
the devastating earthquake in 2010 with 350000 people still
living in camps for internally displaced persons (OCHA,
2012c). Secondly, after the passage of Hurricane Isaac in Au-
gust 2012 and Hurricane Sandy in October 2012 resulting in
destruction of agricultural crops in at least 60 communities,
450000 to 1.5 million people are at an increased risk of mal-
nutrition (OCHA, 2012c, d; CDEMA, 2012; ECHO, 2012).
Thirdly, damage to medical facilities (including 22 cholera
treatment centers), problems in restocking because of inter-
rupted transportation, and poor sanitary conditions have in-
creased the risk of waterborne diseases such as cholera. Af-
ter the cholera outbreak in October 2010 in the aftermath of
the major earthquake in January 2010, cholera is still preva-
lent in Haiti, yet was declining in terms of new cases and
deaths in the months before Hurricane Sandy. Figure 7 shows
the cumulated absolute number of cholera deaths over time
Fig. 8. Residential damage in Cuba as a percentage of housing stock using data from (Daniell, 2012; IFRC,
2012a).
Fig. 9. Timeline of restoration of power outage from Hurricane Sandy between 29 and 30 Oct. and the
Nor’easter on 7 Nov. for affected customers in the U.S. (Customer outages are compiled from specific situation
reports obtained from the U.S. Department of Energy, Office of Electricity Delivery and Energy Reliability
from 29 Oct. to 19 Nov.).
35
Fig. 8. Residential damage in Cuba as a percentage of housing
stock using data from (Daniell, 2012; IFRC, 2012a).
and the percentage of weekly cholera deaths since the epi-
demic outbreak in October 2010. In the first seven weeks af-
ter Sandy, approximately 22000 new cases and 209 deaths
were reported (Ministère de la Santé Publique, Republique
d’Haiti), which represents a slight increase in disease spread,
compared to the weeks before, both for cholera deaths and
new cases. Comparing the current increase in cholera deaths
after Hurricane Sandy with the progression of the epidemic
since its outbreak, it can be seen that the current increase
is still rather small compared to the peaks during the initial
outbreak phase and also smaller than the peak in June 2012
when cholera had already started to decline.
4.2 Economic impacts
Direct losses in the Caribbean have been extensive on a
GDP comparison for nations (see Table 2). The estimation
of losses listed in Table 2 was undertaken using analysis of
destroyed and damaged buildings in addition to other sec-
toral losses such as agriculture, infrastructure, education and
health as a proportion of capital stock and GDP as reported
from Daniell et al. (2011) and in the CATDAT database
(Daniell, 2012). Loss functions were developed based on
previous damage seen in previous impacts of hurricanes in
Cuba, Haiti and the rest of the Caribbean as a function of
wind speed, storm intensity and flooding as well as the cur-
rent damage reported from the International Federation of
Red Cross and Red Crescent Societies (IFRC) and national
agencies.
According to this analysis, losses were greatest in Cuba
with around 5.5% of GDP where over 226 000 houses were
damaged and 17 000 destroyed (Fig. 8). The damage fol-
lowed the storm track closely, with over 20% of houses los-
ing roofs due to the high wind speeds. Flooding was also
widespread. In some sections of the Holguin and Santiago
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2588 M. Kunz et al.: Hurricane Sandy 2012
Table 2. Direct economic loss estimates for the Caribbean and the
US.
Country Estimates loss
(million US$)
% of GDP
Source
Cuba 3380 5.5 CEDIM
Haiti >280 3.8 CEDIM
The
Bahamas
300–400 3.7–4.9 CCRIF
USA 78000–97 000 0.5 CEDIM
Jamaica 66 0.4 Jamaican
Govt
Dominican
Republic
85 0.14 CEDIM
Canada >100 0.0057 Jamaican
Govt
Bermuda minor
de Cuba provinces, the percentage of damaged buildings
reached over 80% as compared to the total building stock
estimated from the changes from last census. Some of this
was related to the vulnerability of building stock, yet some
to variable wind speeds and flooding from storm surge (as
seen in Guama) and rainfall (seen in Songo-La Maya).
In addition, much damage to schools, agriculture (sugar
cane, bananas) and the power systems occurred. However,
this has not been the largest economic loss due to hurricanes
in Cuba, as over USD 8 billion damage occurred through
Hurricane Ike in 2008.
Haiti has also seen major damage through the combina-
tion of river flooding and pluvial flash flooding with over
6000 buildings destroyed and 21 000 buildings damaged, and
The Bahamas also saw major damage with both countries
having losses equivalent to around 4% of GDP.
5 Impacts of Sandy in the US
5.1 Impacts on life
In the eastern US, 142 people died because of Sandy, most
of them in New York, New Jersey and Pennsylvania. Of the
64 fatalities in the state of New York, 43 occurred in New
York City (NYC) 22 of which were on Staten Island (Reuters,
16 November 2012, and Keller in NY Times, 17 Novem-
ber 2012). Comparing the hurricane fatalities in the states
New York, New Jersey and Pennsylvania with historic events
(see Table 3), it can be seen that Sandy is among the three
most fatal events of recorded history of hurricane deaths. For
New Jersey, it is the deadliest single TC event ever.
5.2 Impacts on infrastructure: cascading effects
Energy systems are amongst the most important criti-
cal infrastructure due to their essential role in sustaining
Fig. 8. Residential damage in Cuba as a percentage of housing stock using data from (Daniell, 2012; IFRC,
2012a).
Fig. 9. Timeline of restoration of power outage from Hurricane Sandy between 29 and 30 Oct. and the
Nor’easter on 7 Nov. for affected customers in the U.S. (Customer outages are compiled from specific situation
reports obtained from the U.S. Department of Energy, Office of Electricity Delivery and Energy Reliability
from 29 Oct. to 19 Nov.).
35
Fig. 9. Timeline of restoration of power outage from Hurricane
Sandy between 29 and 30 October and the nor’easter on 7 Novem-
ber for affected customers in the US (Customer outages are com-
piled from specific situation reports obtained from the US Depart-
ment of Energy, Office of Electricity Delivery and Energy Reliabil-
ity from 29 October to 19 November).
socioeconomic systems. As a part of physical infrastructure
(including services), they are directly vulnerable to natu-
ral disasters. As they are highly interconnected, the conse-
quences of disruptions may propagate widely (Rose et al.,
1997). A combined total of around 21.3 million people
(8.7 million customers) were left without power from peak
outages of Hurricane Sandy on 29 and 30 October, but also
from the subsequent nor’easter storm on 7 November (DOE,
2012a, b). Power outages stretched across 21 states from
western Indiana to northern Maine, and affected residents of
some of the most populated cities in the US, including NYC
(in particular the lower part of Manhattan). Using data avail-
able from the US Department of Energy (DOE, 2012a, b),
Fig. 9 shows the three-week timeline of power outages from
the peak outage (29 October) to the recovery (19 November).
One week after impact, 84% of the energy system had been
restored. However, about 3.37 million people (mostly in New
York and New Jersey) were still waiting for electricity sup-
ply. On 7 November, a nor’easter storm began to impact the
Mid-Atlantic and Northeast bringing additional power out-
ages to 368000 people. However, despite widespread power
outages after Hurricane Sandy, the duration of these outages
was not unusually long in comparison to other major hurri-
canes in the US. It took utility companies 13 days to restore
power supply to 95% of customers. Hurricanes Katrina, Rita
and Wilma in 2005 and Ike in 2008 all resulted in longer
outages for customers in Louisiana (18 days for Katrina),
Texas (23 days for Katrina), Mississippi and Florida (Fahey,
2012). The longest stretch to 95 % restoration since2004 was
23 days after Hurricane Katrina.
Nevertheless, nearly two weeks without electricity, heat
and other provisions exceed the limits of most citizens’
capacities to manage their everyday lives. The situation
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M. Kunz et al.: Hurricane Sandy 2012 2589
Table 3. Number of storm fatalities in the states New York, New Jersey and Pennsylvania from historic hurricane/storm events. Sources:
Reuters, 16 November 2012, Keller in NY Times, 17 November 2012, and Daniell (2012).
New York New Jersey Pennsylvania
Rank Storm Name, Year Deaths Storm Name, Year Deaths Storm Name, Year Deaths
1* Sandy, 2012 64 Sandy, 2012 37 Diane/Connie, 1955 75–90
1 New England, 1938 60 Unnamed, 1806 21 Agnes, 1972 50
3 Edna, 1954 29 Irene, 2011 10 Sandy, 2012 13
4 Norfolk/Long Is., 1821 17 Unnamed, 1944 9 Floyd, 1999 6–13
5 Hurricane Five, 1894 10 Unnamed, 1878 8 Gale of 1878 10
6 Agnes, 1972 6 Floyd, 1999 6 TC Allison, 2001 7
* The New England Storm 1938 and Sandy 2012 are deemed to be equally ranked as the fatality numbers for the New England Storm 1938 did not
include indirect deaths whereas the numbers for Hurricane Sandy include indirect deaths via carbon monoxide poisoning, debris removal, etc.
Fig. 10. A breakdown of direct losses in New York State (in million US$ and %) reported by (Cuomo, 2012).
Fig. 11. Direct Economic Losses (in billion US$ and %) by U.S. State from Hurricane Sandy.
36
Fig. 10. A breakdown of direct losses in New York State (in million
US$ and %) reported by (Cuomo, 2012).
imposed a particularly severe hardship on the sick, elderly,
handicapped and poor. Since there is only limited data avail-
able on the consequences of power outages caused by TCs,
an empirical comparison is difficult. The affected people rely
on electricity and are heightened exposed to risks from fire
and carbon monoxide poisoning as people use generators, or
other gasoline-, propane-, or charcoal burning devices inside
their homes for heating, and observed in former comparable
incidents (Platz et al., 2007).
The main reason for fires following hurricane is usually
electrical system failures and wiring issues caused simply by
the wind speed being too high for the intended safety factor
associated with this infrastructure. In a fire in Breezy Point,
Queens, in New York during Hurricane Sandy, 111 houses
were destroyed and 20 damaged (Trapasso, 2012). Numer-
ous other dwelling fires occurred in other states. In total, over
USD 60 million damage can be attributed to fire in New York
alone. Historically, Hurricane Katrina showed the large im-
pact of fires following hurricane, where due to evacuations,
fires were able to spread uncontrolled through poorer parts of
the city. Hurricane Irene in 2011 also caused many electrical
fires (Daniell, 2012). Models for fire following hurricane are
currently limited to poorly validated probabilistic relations
between number of outbreaks, TC wind loads damage before
Fig. 10. A breakdown of direct losses in New York State (in million US$ and %) reported by (Cuomo, 2012).
Fig. 11. Direct Economic Losses (in billion US$ and %) by U.S. State from Hurricane Sandy.
36
Fig.11. Direct economic losses (in billion US$ and%) by US states
from Hurricane Sandy.
fire, wind speeds, and the level of preventive fire protection
standards.
In tall apartment buildings and commercial skyscrapers,
lack of elevator service poses a serious problem for the dis-
abled or elderly who cannot navigate stairs. The combined
power outage and severe weather conditions due to winter
storms, further stresses the affected population. Threats from
water and food shortages, food poisoning from refrigeration
not working, disease outbreaks from malfunctioning sewage
systems/drinking water supply and deficits in health care can
become serious issues (Bayleyegn et al., 2006). Although
there was no gas shortage through resource depletion, the
lack of electricity prevented filling stations from dispensing
fuel, resulting in long queues and rationing.
5.3 Estimation of direct losses
Early estimates of direct economic losses from risk mod-
elling firms such as EQECAT and AIR were in the order of
USD 20–50 billion but turned out to be lower than the final
total.
New York direct losses have totalled around USD 32.8 bil-
lion for repairs and restoration (Fig. 10; Governor Andrew
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2590 M. Kunz et al.: Hurricane Sandy 2012
Table 4. Direct economic losses by historic hurricanes that have
affected the US.
Hurricane (Year) Direct Economic Losses in the US
(in billion US$ 2012)
Katrina (2005) 127.8
Sandy (2012) 78–97*
Andrew (1992) 42.7
Ike (2008) 31.3
Wilma (2005) 23.9
* refers to the loss estimation by CEDIM (CATDAT Database Daniell,
2012).
Cuomo). An estimated 305 000 houses were damaged or de-
stroyed in New York state as of 26 November 2012, causing
around USD 9.7 billion in damage. The other relative com-
ponents of the loss estimate by the government are shown
in Fig. 10 (Cuomo, 2012). This is related to the massive ex-
posure located in this region of the US with New York and
New Jersey combining to have over USD 5 trillion capital
stock. In addition, over 265000 businesses were affected.
New York City has stated that the economic losses due to
direct causes have totalled USD 13.3 billion, and indirect
causes from USD 5.7 billion (DeStefano, 2012). The losses
provided by Cuomo (2012) were slightly higher totalling
over USD 15 billion for New York City (about 2% of the
gross city proper product).
New Jersey has released losses to housing, transit sys-
tems, infrastructure, tourism and coastlines at USD 29.4 bil-
lion (Fig. 11). Damage before this was quoted as being 34 %
from New York, 30% from New Jersey, 20% from Pennsyl-
vania and 16 % from remaining states using the EQECAT es-
timate. Using this total system, losses in Pennsylvania would
hit around USD 19 billion, with an additional USD 15 billion
from other states, leaving a total of USD 97 billion damage
from this event, given the fact that New York and New Jersey
loss estimates have fitted this model well.
In addition, indirect losses (see Sect. 5.4) could contribute
to additional losses on top of the USD 97 billion estimated
damage, and indirect losses may already be included to some
extent in the business impact in New York and tourism es-
timate in New Jersey, thus a reduction of 20 % and a range
of losses is proposed from USD 78–97 billion for the direct
loss estimate (see Table 4). This would make Sandy the sec-
ond highest economic loss from a US hurricane in history
and the highest worldwide loss from a natural disaster event
since the Tohoku earthquake in March 2011 (Daniell et al.,
2011).
Indirect losses are generally high in productive locations
such as New York City. They are scrutinized in the following
Sect. 5.4.
Fig. 12. Economic loss from power outages.
Fig. 13. Share of sectors’ value added in affected States compared to the US economy.
37
Fig. 12. Economic loss from power outages.
5.4 Estimation of indirect losses
Besides direct costs due to damage of physical infrastructure,
natural disasters often result in important indirect losses that
have grown considerably due to the increasing interrelated-
ness of globalized supply networks and the growing depen-
dence of modern societies on critical infrastructure (Klein-
dorfer and Saad, 2005; Comes and Schultmann, 2012; Per-
row, 1984). Indirect economic losses are caused by the dis-
ruption or failure of physical or economic linkages (Penning-
Rowsell et al., 2003; Messner et al., 2007). Particularly the
interruption of the most essential infrastructure such as elec-
tric power (cf. Sect. 5.2) or transportation can cause cascad-
ing effects throughout further infrastructure systems (Rinaldi
et al., 2001). In the aftermath of a natural hazard, the great-
est share of indirect losses results from business interruption
(Tierney, 1994), especially due to the decline of production
resulting from destroyed infrastructure and associated supply
chain disruptions (Zimmerman and Restrepo, 2006).
To estimate the indirect losses, two approaches were used:
an estimation of the costs of the power outages based on a
comparison with previous events and an estimation of the in-
direct based on a sector-specificmodel that takes into account
the indirect vulnerability of industrial sectors due to busi-
ness interruption using an input-output model (I-O model)
approach.
5.4.1 Estimation based on past events
The total costs (direct and indirect) of blackouts can be
roughly estimated based on a comparison with similar past
events. The losses of previous power blackouts have been
compared, including events that were not caused by disas-
ters. For instance, the costs of the 2003 Northeast black-
out, which affected 55 million people, particularly through-
out the northeastern states of the US, were estimated to be
about USD 6.3 billion. With close estimates of USD 5.6 bil-
lion for one day, Zimmermann et al. (2005) demonstrated the
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M. Kunz et al.: Hurricane Sandy 2012 2591
possibility to estimate costs based on GDP per capita and the
number of people affected.
A similar approach is used to assess the costs for the power
outages that occurred in the aftermath of Sandy. The GDP per
capita per day averaged from Pennsylvania, New York and
New Jersey is USD 160.89. Using a linear recovery func-
tion from 20 million people affected on Monday, 29 Oc-
tober, to 2 million on Wednesday, 7 November, losses are
about USD 3.22 billion for the first day, and USD 17.7 bil-
lion for the first ten days of power outages. Using the current
statistics of power outages as portrayed in Fig. 12 and dis-
cussed in Sect. 5.2, the value of power outage disruption is
USD 16.3 billion.
5.4.2 Estimation of economic losses due to business
interruption (I-O modeling)
The rapid assessment of indirect losses requires robust meth-
ods that work with limited and incomplete data. At the same
time the methods must allow for comparisons with previ-
ous disastrous events, occurring in countries of different
sizes, development levels and economic power. Given those
requirements, a methodology was chosen based on input-
output data available from the national statistical offices of
each state. Indirect economic losses are usually quantified in
terms of production losses in the affectedregion with the help
of input-output models (Okuyama, 2007). Such an approach
is based on a national account’s input-output matrix which
represents monetary transaction flows between the various
industry sectors. Based on an inverse matrix according to
Leontief (1986), the output loss resulting from the interrup-
tion of a specific sector as well as its indirect effects is quan-
tified by considering the inter-industrial linkages and depen-
dencies.
For the estimation of indirect losses, the focus was on the
assessment of business interruptions in the manufacture sec-
tor because most of the economic losses due to business in-
terruption occurred in this sector (Chang et al., 2007). Fur-
thermore, the 14 northeastern states which were exposed to
Sandy play a key role for the manufacturing sector, account-
ing for 26.5 % of the added value created in this sector in
the whole US economy. Figure 13 shows, for each industry
sector, the share of the US value added created in the north-
eastern states.
As can be seen, the affected region is particularly impor-
tant for the chemical industry, the apparel and leather and
allied products, textile mills, etc. Since most businesses were
interrupted at least on the day of Sandy’s landfall, this gives a
first idea of the extent of output losses on this day for the US
economy. Under the assumption that the industry lost two
days affecting on average 26.5% of the US manufacturing
sector (corresponding to the sector’s value added in 14 north-
eastern states), the resulting net losses in terms of lost output
would amount to USD 2.39 billion. Nevertheless, the esti-
mation of losses must also take into account the ripple effect
resulting from supply chain disruptions into other sectors of
the economy. Using a linear input-output model (Leontief,
1986) to calculate those indirect effects, the losses would ap-
proximate USD 9.4 billion for two days’ business interrup-
tion.
Using a linear I-O model (Leontief, 1986), the potential
impacts of Sandy on different business interruption scenar-
ios of the US economy were estimated. The model describes
inter-industry relationships within the economy, where the
output from one sector is defined by the production that may
become input for another sector. In our approach, the in-
put of the model is accounts data (year 2010; annually pub-
lished by the Bureau for Economic Analysis) describing the
monetary interactions between the various sectors on the US
national level. Based on these industrial interrelations, it is
possible to quantify the total loss caused by the decrease
or interruption of a sector’s production including its indi-
rect repercussions on the entire industrial production chain.
To estimate the extent of the production losses, a produc-
tion loss ratio is determined for each sector, which depends
on affected geographic area, intensity and duration of the
interruption. For the purposes of our near-real-time analy-
sis, we primarily assumed that the direct damage in the af-
termath of Sandy affected all industry sectors equally for
all of the 14 affected coastal states (Connecticut, Delaware,
Maine, Maryland, Massachusetts, New Hampshire, New Jer-
sey, New York, North Carolina, Ohio, Pennsylvania, Rhode
Island, North Dakota and Vermont). Afterwards, we deter-
mined for each sector which proportion of the national pro-
duction originates from each of the 14 states (through per-
centage of value added originating in affected states) and was
affected by disruptions or interruptions of a certain intensity.
In an additional worst case scenario, we assumed that all
production activities of the manufacture sector were entirely
interrupted during two days for all of the 14 states. Under
this assumption, and using the linear I-O model (Leontief,
1986) to calculate the indirect effects, the losses were esti-
mated to be approximately USD 9.4 billion for two days of
business interruption. Of course, this estimation can only be
considered as an upper limit.
With a similar input-output approach, Moody Analytics
calculated a net loss output of USD10.5 billion due to Sandy.
These estimations were calculated for the disruptions in all
sectors of the economy, and for the regions worst impacted
by Hurricane Sandy, i.e. Bridgeport, New York City, New
Jersey, New York City, Philadelphia, and Washington. These
estimations were completed using the IMPLAN regional
multiplier (Alward et al., 1992), which simulates the induced
effects on the regional economies, including on employment
and final consumption. The resulting indirect losses corre-
spond to a total lost output of approximately USD 19.9 bil-
lion.
The extent of business interruption, their effects on differ-
ent industrial sectors, as well as the costs generated depend
on other factors, such as the duration of the hazard event and
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2592 M. Kunz et al.: Hurricane Sandy 2012
Fig. 12. Economic loss from power outages.
Fig. 13. Share of sectors’ value added in affected States compared to the US economy.
37
Fig. 13. Share of sectors’ value added in affected states compared to the US economy.
the recovery time (Webb et al., 2002), as well as the vul-
nerability of the industry sector. In order to take these fac-
tors into account, a simple input-output analysis coupled with
an assessment of the industrial vulnerability was used which
tested different scenarios of recovery and business interrup-
tion duration. The vulnerability of the affected industry de-
termines the capacity of businesses to cope with the impacts
of disasters and interruption and to restart their businesses
after a disaster, and therefore influences indirect losses. As
different industrial sectors vary greatly with respect to their
characteristics (such as dependence on critical infrastructure
or further industrial sectors, labor dependence etc.), the vul-
nerability of industrial production systems strongly depends
on the type of industry affected.
In order to assess the sector-specific vulnerability, an
indicator-based approach was used for its transparency and
operational representation of vulnerability (Cutter et al.,
2003). Additionally, due to their hierarchical structure, in-
dicator approaches are suitable for (near) real-time disaster
assessments as they enable the efficient update of informa-
tion as revisions of information only in the affected branches
are required (as opposed to a complete update). Moreover,
newly available information can be added in terms of further
branching (e.g., higher level of detail by the integration of in-
formation about specific production sights). For the generic
assessments in the early phases after the incident, consider-
ations remained on the sectoral level. Production downtime
mainly occurs due to the damage of production equipment,
the obstruction of workers, the interruption of critical infras-
tructure or the disturbance of supply chain processes (e.g.,
delivery or distribution processes). Therefore each sector’s
specific vulnerability against indirect disaster effects can be
determined with the help of vulnerability indicators describ-
ing its degree of dependency on capital, on labor, critical in-
frastructure systems and its connectedness in supply chains
(Merz, 2011). The sector specific vulnerability was calcu-
lated based on 17 indicators. These were based on national-
level data from 2011, including input-output tables and other
data obtained from the US Bureau of Economic Analysis.
Figure 14 shows the industrial vulnerability against indi-
rect disaster effects at the state level. It can be seen on the
map that from the northeastern states, the most vulnerable
states are Maine, Virginia and North Carolina. It can there-
fore be assumed that these states are the most vulnerable
against business interruption. However, the extent of losses
also depends on the direct damages caused by the storm in
affected states. (Note that vulnerability is not the only di-
mension of risk; further components determining the actual
losses refer to the extent and severity of the hazard itself.)
Nat. Hazards Earth Syst. Sci., 13, 2579–2598, 2013 www.nat-hazards-earth-syst-sci.net/13/2579/2013/
M. Kunz et al.: Hurricane Sandy 2012 2593
Fig. 14. Industrial vulnerability of Eastern U.S. against indirect disaster impacts (To obtain those results, the
sector specific vulnerability was regionalised by considering the industrial density of regions of the different
sectors (obtained through the value added). The relative vulnerability index scaled from 0 to 1, 0 and 1 being
respectively the least vulnerable and most vulnerable state against indirect effects of disasters on the industry).
38
Fig. 14. Industrial vulnerability of the eastern US against indirect
disaster impacts (to obtain those results, the sector specific vulner-
ability was regionalized by considering the industrial density of re-
gions of the different sectors (obtained through the value added).
The relative vulnerability index scaled from 0 to 1, 0 and 1 being
respectively the least vulnerable and most vulnerable state against
indirect effects of disasters on the industry).
Vulnerabilities are a starting point to assess the indi-
rect economic losses, particularly the longer term aspects.
Here an approach quantifying production losses is combined
with input-output approaches (Okuyama, 2007) with recov-
ery functions that have been determined according to a sec-
tor’s vulnerability. Using a linear input-output model (Leon-
tief, 1986), the potential impacts of Sandy were estimated on
different business interruption scenarios of the US economy.
As the uncertainties, particularly in the immediate aftermath
of the event, are fundamental (i.e., hard to quantify), scenar-
ios were used, which have been proven useful as a means
to account for the severe uncertainties (Comes et al., 2011).
To construct the scenarios in a systematic way, the indirect
costs were split up into several sub-scenarios considering the
overall disruption due to the event (across all sectors), the
impact of power blackouts and the impact of disruptions of
the transportation system. The overall impact depends to a
large degree on the assumptions about the disaster recov-
ery. As mentioned in the preceding chapter, assuming that
the disruptions of the overall manufacturing sector lasted for
two days in the 14 states affected by Sandy, the costs would
approximate USD 9.4 billion for the two days of the storm.
However, the time needed for the industrial sector to recover
must be considered, which extends the time for utilities to
recover. The capacity of businesses to restart their activity
during this recovery period highly depends on the vulnera-
bility of the sectors. Following this rationale, an ensemble
of recovery scenarios (comparable to meteorological ensem-
bles) were calculated by using exponential potential recovery
functions with different curvatures (Cimellaro et al., 2010).
Depending on the recovery scenario, the indirect costs es-
timated by the model for 10 days following the storm range
from USD 1.4 to 5.6 billion. Assuming that the closure of the
stock exchanges and offices affected 30% of the finance sec-
tor US-wide on the two days of the storm, the indirect costs
on the economy would approximate USD 9.8 billion. Adding
the estimated partial disruption losses during the recovery pe-
riod to the losses of the two days of total shutdown for all
manufacturing sectors, the total business interruption losses
are estimated between USD 10.8 and 15.5 billion. Figure 16
illustrates the differences in expected losses for varying in-
dustrial sectors (again, differences may arise due to varying
vulnerabilities and exposure levels).
5.5 Observing impacts using social media
Using data from social media provided by eyewitnesses
seems promising; in combination with data from conven-
tional sensors, it provides a more comprehensive picture of
the local situation only seconds after an event occurs. This
may be an important source of information especially for re-
gions with low infrastructure, where other information of the
impact of a catastrophe are scarce. For Hurricane Sandy, we
tested the applicability and the potential use of social media.
To get up-to-date information on the characteristics and
the impact of the hurricane, data was collected from the
tweets of the micro-blogging service Twitter (see Fig.
17).
During Hurricane Sandy, 5328029 tweets were collected
and stored in near-real time from 29 October to 2 Novem-
ber in our database. These messages were filtered by key-
words like hurricane, flood, damage, victims or power out-
age. About 3 % of the tweets (154890) can be localized by
geo-coordinates and be used for further information extrac-
tion.
The tweets provide detailed and very local information
about Sandy’s impact such as “sandy floods#fdr 63rd street”,
“Flooding on Pitney Rd is just from a storm drain”, or “Some
may not have power but we all have phones” (see examples in
Table
5). Besides announcements of general damage, the ex-
amples give information about flooding (see Sect. 3.3.2) and
power outages (Sect. 5.2) at particular locations of the Twit-
ter users. Reports of flooding from eyewitnesses are often the
only information source, since data acquisition of flooding
in urban areas is difficult as usually no appropriate sensors
are installed outside of traditional river channels and water
sources.
Furthermore, the spatial and temporal distribution of
tweets reporting on power outages may indicate areas and
time periods with impaired or unimpaired power supply.
www.nat-hazards-earth-syst-sci.net/13/2579/2013/ Nat. Hazards Earth Syst. Sci., 13, 2579–2598, 2013
2594 M. Kunz et al.: Hurricane Sandy 2012
Fig. 15. Transportation and power dependencies of different industrial sectors, taking into account inter-CI
dependencies. Black bars indicate transport dependency, grey bars power dependency.
Fig. 16. Assessment of indirect industrial losses due to Sandy.
39
Fig. 15. Transportation and power dependencies of differentindustrial sectors, taking into account inter-CI dependencies. Black bars indicate
transport dependency, grey bars power dependency.
Fig. 15. Transportation and power dependencies of different industrial sectors, taking into account inter-CI
dependencies. Black bars indicate transport dependency, grey bars power dependency.
Fig. 16. Assessment of indirect industrial losses due to Sandy.
39
Fig. 16. Assessment of indirect industrial losses due to Sandy.
Nat. Hazards Earth Syst. Sci., 13, 2579–2598, 2013 www.nat-hazards-earth-syst-sci.net/13/2579/2013/
M. Kunz et al.: Hurricane Sandy 2012 2595
Fig. 17. Density map of the located tweets with the keyword hurricane for a timeline of six days, from 28 October to 2 November 2012
(dates indicated in the figures). The density refers to the number of tweets per 0.05
grid cell (approximately 5× 5km
2
).
Table 5. Examples of tweets sent during Hurricane Sandy on 30 Oct. 2012; time is in UTC.
Topic User Time Message
Damage Lamar Liffridge 09:19:52 Wakes up, sees power and can still on, and no damage, yes #sandy
Kirk Moore 13:50:28 Severe damage on South Green Street Tuckerton Beach area, boats jumbled
in marinas http://t.co/jhLGBp2m
Flood ELCIRCUITOTV 03:34:20 #sandy floods #fdr 63rd street http://t.co/10K0clpi
Bill Speakman 09:26:27 Flooding on Pitney Rd is just from a storm drain. The Conestoga River still
has a couple of feet before reaching the bank.
Power outage/ John Powell 00:02:46 I have officially lost power at my home in Glenolden, PA
infrastructure Preston Kilgore 06:20:27 Some may not have power but we all have phones #connected
Lis Kalogris 13:02:38 Here in the EOB Garden we have lost power but so far no visible major
damage. Worried about our many tulip poplars. All of you, be safe. XO
Absence of any tweets or a significant decrease of tweets,
respectively, may indicate presumably breakdown or impair-
ment of supply networks. Additionally, the spatial and tem-
poral distribution of tweets allow for inferring intensity and
impact of the event in a larger area. An example of the large
potential of tweets for spatio-temporal investigations of tech-
nical or natural hazards is given by Fig. 17 that shows all
geolocated tweets during Hurricane Sandy with the keyword
hurricane. The time series in Fig. 4 show that the number
of tweets corresponds well with maximum wind speeds and
storm surges.
6 Conclusions
In this paper, we presented a multidisciplinary analysis of the
causes, hazardous effects, and consequences associated with
Hurricane Sandy. This examination was done in an interdis-
ciplinary approach by collecting and compiling scattered and
distributed information from available databases and sources
via the Internet, by application of own methodologies and
simple input-output models, and by expert knowledge.
Hurricane Sandy was an extraordinary event for the US in
particular due the simultaneous occurrence of specific mete-
orological features leading to an unusual storm’s track, the
multihazard nature that further amplified intensities, and the
cascades of adverse events in the aftermath that aggravated
the direct impacts significantly. The track more or less from
the south to the north was mainly the result of a block-
ing by an extended high pressure system. Thus, Sandy hit
www.nat-hazards-earth-syst-sci.net/13/2579/2013/ Nat. Hazards Earth Syst. Sci., 13, 2579–2598, 2013
2596 M. Kunz et al.: Hurricane Sandy 2012
a region in the US that has rarely been affected by hurri-
canes in the past but is densely populated and very vulner-
able to such an unexpected event. Since recording, Sandy
was only the third hurricane to have made landfall in New
Jersey. Most unusual was the very large spatial extent of up
to 1700 km, primarily a result of the interaction of the hur-
ricane with an upper-tropospheric trough. This interaction
led to a rapid extra-tropical transition shortly before landfall
and further increased the strength of the storm in terms of
wind speed and precipitation. Significant storm surges due to
high wind speeds towards the eastern US coast occurred si-
multaneously with high astronomical tides. This caused total
record-breaking storm surges along the US Mid-Atlantic and
New England coastlines.
Along the track from the Caribbean up to the eastern US,
one of the poorest (Haiti) and one of the richest countries
(US) were devastated with different – though characteristic –
patterns of impact and loss. Apart from fatalities (about 80 in
Haiti and Cuba; 142 in the US) and direct economic losses
(about USD 4.2 billion in the Caribbean; 78 to 97 billion in
the US), the rich and poor were struck by cascades of adverse
events that aggravated the direct impacts significantly.
Haiti, only slowly recovering from the 12 January 2010
earthquake that destroyed 121% of Haiti’s (nominal) GDP
and killed more than 100000 people, lost another 4.5%
GDP. Crop destruction triggered danger of malnutrition for
450000 to 1500000 people. Many of the 350000 people
still living in temporary shelters and camps suffered from
destruction of those shelters. In addition, many cholera treat-
ment centers, other medical facilities and schools were de-
stroyed or hampered in functioning. An increase in cholera
infected persons is observed although current numbers seem
to indicate that no major cholera outbreak increase will fol-
low.
The US suffered from the high direct losses to residential
and industrial buildings but also from power outages ranging
between several days and two weeks for individual house-
holds, subsequent supply problems with gas, and business in-
terruption (particularly in transport-dependent industry sec-
tors). Cold weather imposing harsh conditions on people who
depend on electric heating and the uncontrolled electric fires
fed by heavy storms were additional aggravating factors. The
role of the proximity of Sandy to the US presidential elec-
tions remains a speculative issue until researched in detail.
The key scientific question addressed in this paper is to
what extent, with which databases and which models can the
losses and impacts of a catastrophic event like Sandy be ex-
tracted and predicted several hours after the event. Our work-
ing hypothesis is that the potential for near-real-time analysis
has changed significantly with the Internet and the social me-
dia that generate huge amounts of information from the very
onset of a disaster. Utilizing these resources in combination
with analytic tools developed by CEDIM and historic loss
and event databases provides a framework for near-real-time
analysis and predictions.
Whereas the first CEDIM report was published only sev-
eral hours after Sandy made landfall on the US east coast,
the second report with a focus on damage estimates for the
affected states of Pennsylvania, New Jersey and New York
and comparisons to past events was published by 7 Novem-
ber 2012. Risk modelers such as EQECAT and other pub-
lished earlier estimates, but they underestimated the losses
by a factor of 2 to 3. There is obviously a trade off between
the value of loss information and uncertainty, which is hard to
quantify. Early information is in high demand but also highly
uncertain. The determination of the trade-off points is obvi-
ously user-dependent and quantification of uncertainties in
near-real-time loss estimation a high-profile topic. Overall,
our work on Sandy shows that the forensic disaster analysis
is possible and useful directly after the event occurred. With
our new approach of FDA we fill the gap between the first
and rough damage estimations and events descriptions per-
formed by insurance companies and the FORIN concept of
IRDR.
Tracking power outages and estimating downtimes re-
quires a combination of simple models and crowd-sourcing
tools, which should be brought closer together thanwas man-
aged during Sandy. The distribution of losses to differentsec-
tors of the economy is done with a simple input-output model
and, given the data sparseness at an early stage of the analy-
sis, is a valid methodology. In this manner, fast assessment of
indirect losses due to business disruptions could be achieved.
First estimates were already released prior to Sandy’s land-
fall. Due to the ease of adaptation of all models, the anal-
yses were refined in the aftermath of the event as more in-
formation was published (e.g., about the most severely af-
fected areas and the extent and duration of power blackouts).
An equally simple model allows defining an industrial vul-
nerability parameter for states (and potentially other admin-
istrative units) that immediately indicates where aggravat-
ing impacts are to be expected even if the state is less af-
fected in terms of hazard and current loss numbers. The es-
timates provided here do, however, not consider all indirect
costs: beyond output losses all indirect losses across global-
ized supply networks should be considered. Additionally, the
dynamic evolutionof the losses (including, e.g., price effects)
will be subject of our future investigations.
Although our working hypothesis was essentially con-
firmed, it has been learnt that linking methods and models
has more potential than are currently exploited, and in ad-
dition, that more systematic utilization of historic databases
might hold the key for uncertainty estimation in direct and
indirect loss predictions.
Acknowledgements. The Center for Disaster Management and Risk
Reduction Technology (CEDIM) is an interdisciplinary research
center in the field of disaster management funded by Helmholtz
Centre Potsdam – German Research Centre for Geoscience (GFZ),
and Karlsruhe Institute of Technology (KIT). We acknowledge
support by Deutsche Forschungsgemeinschaft and Open Access
Nat. Hazards Earth Syst. Sci., 13, 2579–2598, 2013 www.nat-hazards-earth-syst-sci.net/13/2579/2013/
M. Kunz et al.: Hurricane Sandy 2012 2597
Publishing Fund of KIT. The authors thank the helpful comments
of the anonymous reviewers.
The service charges for this open access publication
have been covered by a Research Centre of the
Helmholtz Association.
Edited by: A. Mugnai
Reviewed by: three anonymous referees
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... The Center for Disaster Management and Risk Reduction Technology (CEDIM, www.cedim.kit.edu, last access: 9 May 2022), an interdisciplinary research center in the field of disasters, risks, and security at Karlsruhe Institute of Technology (KIT), Germany, has been conducting Forensic Disaster Analyses (FDA) in near-real time since 2011 (e.g., Kunz et al., 2013; 30 Merz et al., 2014;Piper et al., 2016;Wilhelm et al., 2021). The approach of forensically investigating disasters stems from the interdisciplinary research program Integrated Research on Disaster Risk (IRDR) and their program Forensic Investigation of Disasters (FORIN; Burton, 2010). ...
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The July 2021 flood in central Europe was one of the five costliest natural disasters in Europe in the last half century with estimated total damage of EUR 32 billion. This study investigates the complex interactions between meteorological, hydrological, and hydro-morphological processes and mechanisms that led to the exceptional flood. Furthermore, we present our estimates of the impacts in terms of inundation areas, traffic disruptions, and economic losses. The estimation of inundation areas as well as the derived damage assessments were carried out during or directly after the flood, and show the potential of near-real-time forensic disaster analyses for crisis management, emergency personnel on-site, and the provision of relief supplies. The superposition of several factors resulted in widespread extreme precipitation totals and water levels well beyond a 100-year event: slow propagation of the low pressure system Bernd, convection embedded in a mesoscale precipitation field, unusually moist air masses associated with a significant positive anomaly in sea surface temperature over the Baltic Sea, wet soils, and steep terrain. Various hydro-morphodynamic processes as well as changes in valley morphology observed during the event exacerbated the impact of the flood. Relevant effects included, among many others, the occurrence of extreme landscape erosion, rapidly evolving erosion and scour processes in the channel network and urban space, recruitment of debris from the natural and urban landscape, deposition and clogging of bottlenecks in the channel network with eventual collapse. This study is part one of a two-paper series. The second part puts the July 2021 flood into a historical context and into the context of climate change.
... The Center for Disaster Management and Risk Reduction Technology (CEDIM) developed a near-real-time forensic disaster analysis methodology. They collect and compile scattered and distributed information from available databases and sources via the Internet and expert knowledge (Kunz et al., 2013). The Forensic Investigations of Disasters (FORIN) project from the Integrated Research on Disaster Risk (IRDR) proposes an approach that aims to uncover the root causes of disasters through in-depth investigations that go beyond the regular reports and case studies conducted post-disaster events (Oliver-Smith et al., 2016). ...
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This contributing paper explores the opportunities offered by digital media mining to complement impact databases. Impact data on past disasters caused by natural hazards (in short, impact data) are of paramount importance for several applications. These include advocacy for investments in disaster risk reduction (DRR) and providing and evidence base for new policies. It is challenging, however, to create, sustain, and increase the adoption of an impact database with sufficient quality for different applications in humanitarian response and DRR. Online newspapers, both national and local ones, tend to cover small disasters more than some institutional databases that focus only on disasters above a particular threshold impact. Mining data from these sources offer a means to complement existing databases. This paper indicates that leading openly available databases have their different strengths and weaknesses. Mining digital newspapers helps shed light on data discrepancies given by different database. In the case studies, the enriched impact database was used to validate a hydrological model, particularly in defining triggers and improving monitoring. The study focused on sudden-onset disasters and further research will be needed to understand how mining can be used for slow-onset disasters such as droughts. Big data and modern information processing systems can also further improve operational excellence in humanitarian applications.
... These interdependencies then play the role of a risk diffusion factor. According to the concept of the cascading effect [25][26][27][28], some areas come to be impacted by the disaster, even if they were not located in the same area [29][30][31][32]. Therefore, some damages are not caused by direct impacts but indirect impacts. ...
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... The Center for Disaster Management and Risk Reduction Technology (CEDIM) developed a near-real-time forensic disaster analysis methodology. They collect and compile scattered and distributed information from available databases and sources via the Internet and expert knowledge (Kunz et al., 2013). The Forensic Investigations of Disasters (FORIN) project from the Integrated Research on Disaster Risk (IRDR) proposes an approach that aims to uncover the root causes of disasters through in-depth investigations that go beyond the regular reports and case studies conducted post-disaster events (Oliver-Smith et al., 2016). ...
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Chapter for UN Global Assessment Report on Disaster Risk Reduction 2022 (GAR 2022). See https://www.preventionweb.net/publication/enriching-impact-data-mining-digital-media
... Extreme weather events, such as hurricanes, typhoons, and winter storms, have shown an increasing trend in recent years (Hoffman and Bryan 2009;Kenward and Raja 2014;Kunz et al. 2013). These extreme weather events have caused devastating and widespread outages within the power infrastructure, which is one of the most critical infrastructure systems for the public's daily life (Dueñas-Osorio and Vemuru 2009;Karagiannis et al. 2019). ...
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Extreme weather events with an increased frequency have caused widespread damages to overhead power distribution systems (OPDS), an essential lifeline infrastructure, resulting in enormous societal and economic losses for communities. Cascading failure is a critical issue within OPDSs and starts with the failure of a system component, such as a pole, leading to large power outages. Therefore, the resilient assessment of OPDSs under extreme weather events could help evaluate system vulnerability and the performance of recovery strategies. Traditionally, the assessment is performed using an unweighted network based on topology, but this lacks the inclusion of OPDS structural properties. Therefore, a resilient assessment framework for OPDS subject to hurricane winds is proposed in this study with the integration of structural properties to consider the impact of system safety. Instead of the traditional unweighted network, a fragility-weighted topological network is formed to evaluate the performance of an OPDS against cascading failure. The system is found to be more vulnerable under an intentional failure scenario based on a comparison of performance under different attack scenarios. In addition, the impact of electricity load redistribution within the system can be obtained by performing dynamic analysis. Finally, different restoration strategies are included in the framework for comparison. Postdisruption restoration plans can be optimized in terms of recovery speed to benefit utility managers and decision makers from the improved resilience.
... Recently, research on coastal impacts caused by extreme events, such as hurricanes, has increased in several countries including the United States and Europe (e.g., Beven et al., 2008;Kunz et al., 2013;Van Verseveld et al., 2015;Spencer et al., 2015). Among these, Ballesteros et al. (2018) proposed a methodology, framed within the source-pathwayreceptor-consequence model (SPRC), which enables the identification of the main factors inducing coastal erosion at different timescales and their associated impact on the beaches on the Mediterranean coast. ...
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In many parts, coastal erosion is severe due to human-induced coastal zone development and storm impacts, in addition to climate change. In this study, the beach erosion risk was defined, followed by a quantitative assessment of potential beach erosion risk based on three components associated with the watershed, coastal zone development, and episodic storms. On an embayed beach, the background erosion due to development in the watershed affects sediment supply from rivers to the beach, while alongshore redistribution of sediment transport caused by construction of a harbor induces shoreline reshaping, for which the parabolic-type equilibrium bay shape model is adopted. To evaluate beach erosion during storms, the return period (frequency) of a storm occurrence was evaluated from long-term beach survey data conducted four times per year. Beach erosion risk was defined, and assessment was carried out for each component, from which the results were combined to construct a combined potential erosion risk curve to be used in the environmental impact assessment. Finally, the proposed method was applied to Bongpo–Cheonjin Beach in Gangwon-do, South Korea, with the support of a series of aerial photographs taken from 1972 to 2017 and beach survey data obtained from the period commencing in 2010. The satisfactory outcomes derived from this study are expected to benefit eroding beaches elsewhere.
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Climate change negatively affects the environment and human life in many respects. The evidence for climate change is strong and convincing enough. Rising sea levels, melting glaciers, severe changes in annual precipitation patterns, and detectable increases in global warming are the main indications. Research findings reveal that since the middle of the 20th century, human effects have been the main cause of changes. The disproportionate use of fossil fuels has resulted in a rapid increase in greenhouse gases in the atmosphere. If the greenhouse gases are not reduced, the increases in the average temperature of the world can reach 6.4 °C by 2100, and the related disaster hazards will gradually increase. Iran is among the countries that experience the effects of climate change on the environment and society with significant increases. While less than 100 disasters were reported annually between 1900-1980, the number of disasters reached 300-400 annually between 2000-2019. Studies show that climate change is the main reason for an effective increase in floods, storms, droughts, subsidences, and forest fires in Iran. With approximately 687 million tons of CO2 production, Iran is the first country responsible for climate change in the Middle East and the sixth country in the world. If precautions are not taken, climate-related hazards and disasters will be more severely affected in the next ten years.
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Climate change adversely affects the environment and human life in many respects. The evidence for climate change on earth is quite solid and convincing. Observable rises in sea levels, melting of a glacier’s ice, the regression of the glacial areas, critical changes in annual precipitation patterns, and global warming increases are specific evidence of climate change. Scientific studies and experimental findings indicate the role of human effects as a primary reason for the changes since the middle of the 20th century. The disproportionate use of fossil fuels resulted in the rapid increase of greenhouse gases in the atmosphere. Without a significant decrease in greenhouse gas levels, predictions demonstrate that the World’s average temperature increase may reach 6.4 °C by 2100, and the associated disaster hazards will gradually increase. Iran is among the countries that experience significant increases in climate change effects on the environment and society. Between 1900-and 1980, the number of reported disasters per year was below 100, whereas the annual number of disasters reached 400 between 2000 and 2019. Studies show that climate change is the main reason for disasters such as floods, storms, droughts, land subsidence, and wildfires in Iran. With approximately 687 million tons of CO2 production, Iran ranks first country responsible for climate change in the Middle East and the sixth country in the World. It is noted that without necessary precautions, the increase in climate-related disasters will be much higher in the next ten years.
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A growing number of cities are preparing for climate change by developing adaptation plans, but little is known about how these plans and their implementation affect the vulnerability of groups experiencing various forms of underlying social inequity. This review synthesizes research exploring the justice and equity issues inherent in climate change adaptation planning to lay the foundation for critical assessment of climate action plans from an equity perspective. The findings presented illuminate the ways in which inequity in adaptation planning favours certain privileged groups while simultaneously denying representation and resources to marginalized communities. The review reveals the specific ways inequity is experienced by disadvantaged groups in the context of climate change and begins to unpack the relationship between social inequity, vulnerability, and adaptation planning. This information provides the necessary background for future research that examines whether, and to what extent, urban adaptation plans prioritize social vulnerability relative to economic and environmental imperatives.
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The Center for Disaster Management and Risk Reduction Technology (CEDIM, www.cedim.de) - an interdisciplinary research center founded by the German Research Centre for Geoscience (GFZ) and Karlsruhe Institute of Technology (KIT) - has embarked on a new style of disaster research known as Forensic Disaster Analysis. The notion has been coined by the Integrated Research on Disaster Risk initiative (IRDR, www.irdrinternational.org) launched by ICSU in 2010. It has been defined as an approach to studying natural disasters that aims at uncovering the root causes of disasters through in-depth investigations that go beyond the reconnaissance reports and case studies typically conducted after disasters. In adopting this comprehensive understanding of disasters CEDIM adds a real-time component to the assessment and evaluation process. By comprehensive we mean that most if not all relevant aspects of disasters are considered and jointly analysed. This includes the impact (human, economy, and infrastructure), comparisons with recent historic events, social vulnerability, reconstruction and long-term impacts on livelihood issues. The forensic disaster analysis research mode is thus best characterized as "event-based research" through systematic investigation of critical issues arising after a disaster across various inter-related areas. The forensic approach requires (a) availability of global data bases regarding previous earthquake losses, socio-economic parameters, building stock information, etc.; (b) leveraging platforms such as the EERI clearing house, relief-web, and the many sources of local and international sources where information is organized; and (c) rapid access to critical information (e.g., crowd sourcing techniques) to improve our understanding of the complex dynamics of disasters. The main scientific questions being addressed are: What are critical factors that control loss of life, of infrastructure, and for economy? What are the critical interactions between hazard - socio-economic systems - technological systems? What were the protective measures and to what extent did they work? Can we predict pattern of losses and socio-economic implications for future extreme events from simple parameters: hazard parameters, historic evidence, socio-economic conditions? Can we predict implications for reconstruction from simple parameters: hazard parameters, historic evidence, socio-economic conditions? The M7.2 Van Earthquake (Eastern Turkey) of 23 Oct. 2011 serves as an example for a forensic approach.
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A significant number of tropical cyclones move into the midlatitudes and transform into extratropical cyclones. This process is generally referred to as extratropical transition (ET). During ET a cyclone frequently produces intense rainfall and strong winds and has increased forward motion, so that such systems pose a serious threat to land and maritime activities. Changes in the structure of a system as it evolves from a tropical to an extratropical cyclone during ET necessitate changes in forecast strategies. In this paper a brief climatology of ET is given and the challenges associated with forecasting extratropical transition are described in terms of the forecast variables (track, intensity, surface winds, precipitation) and their impacts (flooding, bush fires, ocean response). The problems associated with the numerical prediction of ET are discussed. A comprehensive review of the current understanding of the processes involved in ET is presented. Classifications of extratropical transition are described and potential vorticity thinking is presented as an aid to understanding ET. Further sections discuss the interaction between a tropical cyclone and the midlatitude environment, the role of latent heat release, convection and the underlying surface in ET, the structural changes due to frontogenesis, the mechanisms responsible for precipitation, and the energy budget during ET. Finally, a summary of the future directions for research into ET is given.
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The global CATDAT damaging earthquakes and secondary effects (tsunami, fire, landslides, liquefaction and fault rupture) database was developed to validate, remove discrepancies, and expand greatly upon existing global databases; and to better understand the trends in vulnerability, exposure, and possible future impacts of such historic earthquakes. Lack of consistency and errors in other earthquake loss databases frequently cited and used in analyses was a major shortcoming in the view of the authors which needed to be improved upon. Over 17 000 sources of information have been utilised, primarily in the last few years, to present data from over 12 200 damaging earthquakes historically, with over 7000 earthquakes since 1900 examined and validated before insertion into the database. Each validated earthquake includes seismological information, building damage, ranges of social losses to account for varying sources (deaths, injuries, homeless, and affected), and economic losses (direct, indirect, aid, and insured). Globally, a slightly increasing trend in economic damage due to earthquakes is not consistent with the greatly increasing exposure. The 1923 Great Kanto ($214 billion USD damage; 2011 HNDECI-adjusted dollars) compared to the 2011 Tohoku (>$300 billion USD at time of writing), 2008 Sichuan and 1995 Kobe earthquakes show the increasing concern for economic loss in urban areas as the trend should be expected to increase. Many economic and social loss values not reported in existing databases have been collected. Historical GDP (Gross Domestic Product), exchange rate, wage information, population, HDI (Human Development Index), and insurance information have been collected globally to form comparisons. This catalogue is the largest known cross-checked global historic damaging earthquake database and should have far-reaching consequences for earthquake loss estimation, socio-economic analysis, and the global reinsurance field.
Chapter
Input–output analysis is a practical extension of the classical theory of general interdependence which views the whole economy of a region, a country and even of the entire world as a single system and sets out to describe and to interpret its operation in terms of directly observable basic structural relationships.
This paper examines long-term recovery outcomes of businesses impacted by major natural disasters. Data were collected via two large-scale mail surveys—one administered to Santa Cruz County, California businesses 8 years after the Loma Prieta earthquake and the other administered to businesses in South Dade County, Florida, 6 years after Hurricane Andrew. Based on the results of OLS regression models, we argue that long-term recovery experiences of businesses are affected by various factors, including the economic sector in which a business operates, its age and financial condition, and the scope of its primary market; direct and indirect disaster impacts, including physical damage, forced closure, and disruption of operations; and owner perceptions of the broader economic climate. Previous disaster experience, level of disaster preparedness, and use of external sources of aid were not found to significantly affect the long-term economic viability of businesses in the two study communities.
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The statistical uncertainty resulting from the lack of knowledge of which model represents a given stochastic process is analyzed. This analysis of model uncertainty leads to a composite Bayesian distribution. The composite Bayesian distribution is a linear model of the individual Bayesian probability distributions of the individual models, weighted by the posterior probability that a particular model is the true model. The composite Bayesian probability model accounts for all sources of statistical uncertainty, both parameter uncertainty and model uncertainty. This model is the one that should be used in applied problems of decision analysis, for it best represents the knowledge, or lack of it, to the decision maker about future events of the process.
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This paper develops a methodology to estimate the regional economic impacts of electricity lifeline disruptions caused by a catastrophic earthquake. The methodology is based on specially designed input-output and linear programming models. A simulation of a major earthquake in the New Madrid Seismic Zone near Memphis, Tennessee, indicates the potential production loss over the recovery period could amount to as much as 7 percent of gross regional product. Reallocation of scarce electricity across sectors could reduce the impacts substantially. Additionally, an improved restoration pattern of electricity transmission substations across subareas could reduce losses even more.
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