The need for assessing the risk of extreme weather events
is ever increasing. In addition to quantification of risk today, the role of
aggravating factors such as high population growth and changing climate
conditions matters, too. We present the open-source software CLIMADA (CLIMate ADAptation),
which integrates hazard, exposure, and vulnerability to compute the necessary
metrics to assess risk and to quantify socio-economic impact. The software
design is modular and object oriented, offering a simple collaborative
framework and a parallelization strategy which allows for scalable
computations on clusters. CLIMADA supports multi-hazard calculations and
provides an event-based probabilistic approach that is globally consistent
for a wide range of resolutions, suitable for whole-country to detailed
local studies. This paper uses the platform to estimate and contextualize
the damage of hurricane Irma in the Caribbean in 2017. Most of the affected
islands are non-sovereign countries and also rely on overseas support in
case disaster strikes. The risk assessment performed for this region, based
on remotely available data available shortly before or hours after landfall
of Irma, proves to be close to reported damage and hence demonstrates a
method to provide readily available impact estimates and associated
uncertainties in real time.
Introduction
Improving the resilience of our societies in the face of volatile weather is
an urgent priority today and will increase in importance in the years to
come. This is due not only to changing climate conditions, but also to
rising population and economic growth. Given that the increased exposure has
been a significant driver to higher damages in the last century
(Bouwer, 2011), the climate of the past is by no
means a sufficient basis for decisions going forward. In 2017, the natural-catastrophe-related economic losses amounted to around USD 330 billion,
0.44 % of global gross domestic product (GDP) and almost double the previous
10-year average. A new annual record was set for the highest insured losses
– more than two-fifths of the economic losses – mainly due to payouts
related to three major hurricanes in the US (Harvey, Irma, and Maria);
wildfire outbreaks in California; and many thunderstorms, windstorms, and
other severe weather events around the world (Bevere et
al., 2018). In order to foster the use of the continuously increasing
weather and climate information to undertake preemptive (and precautionary)
action we present here the global and multi-hazard decision support tool for
CLIMate ADAptation, CLIMADA.
While measures exist to adapt to an ever changing environment, decision
makers on all levels – from multinational organizations, sovereign and sub-sovereign states, cities, companies, and down to the local community – need the
facts to identify the most cost-effective instruments; they need to know the
potential weather and climate-related damages over the coming decades to
identify measures to mitigate these risks – and to decide whether the
benefits will outweigh the costs. For this purpose, CLIMADA was developed,
which supports the appraisal of risk management options and adaptation
measures by estimating the expected socioeconomic impact of weather and
climate as a measure of risk today, the incremental increase from economic
growth, and the further incremental increase due to climate change. Starting
from a comprehensive mapping of hazards and exposures, using
state-of-the-art probabilistic risk modelling techniques, it integrates
different economic development and climate impact scenarios combined with a
cost-benefit approach to assess a comprehensive portfolio of adaptation
measures. Adaptation measures include, for example, building defences,
improved spatial planning, ecosystem-based approaches, building regulations
and risk transfer (insurance) against some of the more extreme weather
events. In this context, CLIMADA implements the Economics of Climate
Adaptation methodology, which establishes an economic framework to fully
integrate risk and reward perspectives of different stakeholders
(Bresch, 2016; Bresch and
ECA working group, 2009; Bresch and Schraft, 2011; Souvignet et al., 2016).
The first step in the cost-benefit analysis is to determine the current
risk. CLIMADA does this by modelling socioeconomic impacts of weather
extremes following an event-based probabilistic approach. Whilst other
multi-hazard impact modelling platforms exist, like HAZUS
(Schneider et al., 2006), CAPRA (Cardona et
al., 2012), and RISKSCAPE (King and Bell, 2006), CLIMADA has a
global scope and is open source, hosted on GitHub (https://github.com/CLIMADA-project/climada_python, last access: 17 July 2019) under the
GNU GPL license (GNU Operating System, 2007). We present here
the new generation of the platform, with improved performance, scalability,
maintainability, and a streamlined user interface. Written from scratch in
Python and based on an object-oriented design, the architecture defines
classes which enable risk assessment computations for independent research
areas to be developed separately yet with high reusability of common
functionalities. With this design we aim to foster usage of the platform in
interdisciplinary studies and international collaboration.
In this study, we describe the impact modelling procedure of CLIMADA and
demonstrate its capabilities through a risk analysis of tropical cyclones in
several overseas territories of the Caribbean. The methodology for the
cost-benefit estimation of adaptation measures is going to be presented in a
further paper. Even if the previous version of CLIMADA has been used in
several impact modelling analyses in the past (e.g.
Geiger et al.,
2018; Gettelman et al., 2017; Welker et
al., 2016), this is the first formal description and detailed exemplary
validation of the methodological approach. Following the introduction,
Sect. 2 describes the concept and design of the
tool. Section 3 specifies the impact modelling
implementation for tropical cyclones through the reproduction of hurricane
Irma in the affected overseas territories, compares it against the reported
economic damage, and provides a risk analysis. The paper concludes with a
discussion of the results obtained with CLIMADA.
In essence, CLIMADA implements the concept of risk as in
IPCC (2014). CLIMADA combines hazard
(e.g. a tropical cyclone wind footprint, leftmost inset), exposure (e.g. an
asset distribution, centre bottom inset), and vulnerability (functional
relationship between hazard intensity and impact, centre at the top) to
calculate risk. Severity, measured, for example, as direct economic impact, is
rendered by the red dots on the right panel. Please refer to the link
provided for an animated version.
Framework concept and designConcept
CLIMADA implements a fully probabilistic risk assessment model. According to
the IPCC (IPCC, 2014), natural
risks emerge through the interplay of climate and weather-related hazards;
the exposure of goods or people to this hazard; and the specific
vulnerability of exposed people, infrastructure, and environment. The unit
chosen to measure risk has to be the most relevant one in a specific
decision problem, not necessarily monetary units. Wildfire hazard might be
measured by burned area, exposure by population, or replacement value of
homes and hence risk might be expressed as number of affected people in the
context of evacuation or repair cost of buildings in the context of
property insurance. For example, see the definitions box of
Zscheischler et al. (2018) for a complete
description of the weather and climate risk elements used here.
Risk has been defined by the International Organization for Standardization
as the “effect of uncertainty on objectives” (Lark, 2015) and
similarly by the IPCC (2012) as the potential
for consequences when something of value is at stake and the outcome is
uncertain, recognizing the diversity of values. Risk can then be quantified
as the combination of the probability of a consequence and its magnitude:
risk=probability×severity.
In the simplest case, × stands for a multiplication; more generally, it represents a convolution of the respective distributions
of probability and severity. We approximate the severity as follows:
severity=Fhazard intensity, exposure, vulnerability=exposure⋅fimphazard intensity,
where fimp is the impact function which parameterizes to what extent an
exposure will be affected by a specific hazard. While “vulnerability
function” is broadly used in the modeller community, we refer to it as
“impact function” to explicitly include the option of opportunities (i.e.
negative damages). An opportunity can arise, for example, for specific bird
species populations in a warmer environment (e.g.
Gregory et al., 2009). Using this approach,
CLIMADA constitutes a platform to analyse risks of different hazard types in
a globally consistent fashion at different resolution levels, at scales from
multiple kilometres down to metres, depending on the purpose.
Figure 1 shows the main components of CLIMADA and
demonstrates one possible output.
Implementation
The component diagram in Fig. 2 shows the
architecture of CLIMADA which distinguishes three main packages, hazard, entity, and
engine, as described in the following.
Simplified risk assessment architecture of CLIMADA. See
Sect. 3 for more details on BlackMarble and
TropCyclone. For more information on CLIMADA's components see https://climada-python.readthedocs.io/en/stable/ (last access: 17 July 2019).
Hazard
A hazard describes weather events such as storms, floods, droughts, or
heat waves both in terms of probability of occurrence as well as physical
intensity. They are defined by the base class Hazard (see
Fig. 2) which gathers the required attributes
that enable the impact computation (such as centroids, frequency per event, and intensity per event and
centroid) and common methods such as readers and visualization functions.
Each hazard class collects historical data and transforms them, if necessary,
in order to construct a coherent event database. Stochastic events are
generated taking into account the frequency and main intensity
characteristics (such as local water depth for floods or gust speed for
storms) of historical events, producing an ensemble of probabilistic events
for each historical event. CLIMADA provides therefore an event-based
probabilistic approach which does not depend on a hypothesis of a priori
general probability distribution choices. The source of the historical data
(e.g. inventories or satellite images) and the methodologies used to compute
the hazard attributes and its stochastic events depend on each hazard type
and are defined in its corresponding Hazard-derived class (e.g. TropCyclone for tropical
cyclones, explained later in Sect. 3.2.2). This
procedure provides a solid and homogeneous methodology to compute impacts
worldwide. In the case where the risk analysis comprises a specific region where good
quality data or models describing the hazard intensity and frequency are
available, these can be directly ingested by the platform through the reader
functions, skipping the hazard modelling part (in total or partially), and
allowing us to easily and seamlessly combine CLIMADA with external sources.
Hence the impact model can be used for a wide variety of applications, e.g.
deterministically to assess the impact of a single (past or future) event or
to quantify risk based on a (large) set of probabilistic events. Note that
since the Hazard class is not an abstract class, any hazard that is not defined in
CLIMADA can still be used by providing the Hazard attributes.
Entity
The entity package of CLIMADA contains the socioeconomic aspects of a risk
assessment: exposures and impact functions. Default values of those can be
obtained using the container class Entity.
The exposure is quantified with a value, but this is not necessarily needed to be
a monetary asset. It can describe the geographical distribution of people,
livelihoods, and assets or infrastructure, generally speaking of all items
potentially exposed to hazards, including ecosystems and their services.
This information is provided by the Exposures class, where also optional attributes
related to insurability, such as deductible and coverage, are defined.
Similarly to the Hazard class, Exposures provides the values needed for the impact
computation – coord for coordinates and value defined for each coordinate – and common
functionalities of a container, reader, and visualization class (see
Fig. 2). It can be also directly used to compute
the impact, or the user might use the different exposure models and data
collections of CLIMADA, which are defined in the Exposures-derived classes. The Black
Marble model explained in Sect. 3.2.1 below
consists of an approximation of the economic exposure downscaling
macroeconomic parameters, which is suited for economic analyses on a
worldwide scale or at country or regional levels. Smaller-scale studies
(cities) use normally georeferenced archives. Extensive GIS (geographic information system) data can be
ingested with the reader methods.
Impact functions (fimp in Eq. 2) are defined
for different exposure and hazard types. They approximate the loss
probability function by relating the hazard intensity to the exposure's mean
damage ratio (MDR) in method calc_mdr of class ImpactFunc (see Fig. 2). Impact function models can be defined using derived classes of
ImpactFunc. ImpactFuncSet is the container of the ImpactFunc instances that are used in an assessment, which
represent the different exposures and hazards.
Engine
Finally, the engine package contains the end products of the interactions of the
classes defined in hazard and entity. The Impact class (see Fig. 2)
is used to compute the impact of a hazard on its corresponding exposures and
impact functions using the calc method and storing all the resulting risk
assessment metrics. The hazard defined at its centroids is first mapped to
the exposure coordinates. Then, the damage ratio derived by the impact
functions is translated into direct impact by multiplying it by the exposed
value as follows (based on Eq. 2):
xij=valjfimp(hij|γj),
where xij and hij are, respectively, the impact and hazard
intensity due to event i at location j, valj the value of
exposure at location j, γj are the parameters of exposure j
that characterize its vulnerability, and fimp the impact function. The
impact is obtained for every exposure j and every event i affecting it
(historical and stochastic). Based on this impact for event and the
frequency of each event, almost any risk metric can be calculated. Following
the formalism of Cardona et al. (2012) we approximate the following.
Expected annual impact (EAI) at exposure j contained in attribute
eai_exp:EAIj=∑ı¯=1NhistEX|Eı¯,jFEı¯=∑ı¯=1Nhist∑ı^xı^jFEı^=∑i=1NevxijF(Ei),where X is the impact random variable, E its expectation, Ei is an
event, and F its (annual) frequency. Nhist is the number of historical
events, ı¯ represents an historical event, ı^ represents
all the ensemble members of event ı¯, and Nev represents the total number of
events. Independence of events is assumed.
Average annual impact (AAI) is contained in attribute aai_agg and is the addition
of EAI over all exposures:AAI=∑j=1NexpEAIj,where Nexp is the number of exposures.
Probable maximum impact (PMI): PMI represents the impact that is exceeded at
a fixed low annual frequency (typically 1/1500 to 1/250). It is obtained
from the impact exceedance curve computed with method calc_freq_curve. Taking the total
probability theorem into account, this curve is approximated by
discretization of relationνx=1Tx=PX>x=∑ı¯=1NhistPX>x|Eı¯F(Eı¯)=∑ı¯=1Nhist∫0∞PX>x|hph|Eı¯dhF(Eı¯),where v(x) is the exceedance frequency of impact x,
T(x) its equivalent return period, and h the hazard
intensity. p(h|Eı¯) is the probability density function of
h given that the historical event Eı¯ took place and is
computed using the event's ensemble members. The probability of exceeding an
impact value given intensity h, PX>x|h, is computed using the exposure values and their impact
functions (Eq. 3). We assume that the impacts of
an event at different exposures are independent. Following these
definitions, a stakeholder interested in impacts with 300 years return
period can define the PMI as x such that vx=1300 yr-1.
If one aims to compare the risk of two sets of exposures, it is helpful to
retain the per event information.
Impact at event (at_event attribute of Impact): contains the per event impact over all
locationsxi=∑j=1Nexpxij.Using this formalism, metrics EAI and AAI can be expressed as follows,8EAIj=xj=∑i=1NevxijFEi,9AAI=∑i=1NevxiFEi.
If we now consider two sets of exposures (e.g. in two Caribbean islands),
risk aggregation, as often applied in an insurance context, becomes
straightforward. Let us assume in country A that damages are only considered
above a threshold TA and up to a cover limit CA, and likewise
TB and CB for country B. We calculate xiA
and xiB from ground up and can now apply the non-linear coverage conditions as
follows:
x^iA=min(maxxiA-TA,0,CA),x^iB=min(maxxiB-TB,0,CB).
Hence the combined covered impact is
x^i=x^iA+x^iB and now risk
measures as defined above easily apply to the resulting x^i. This
formalism allows for an elegant way to handle non-linear risk transfer
options on a portfolio of exposure sets.
Case study: hurricane Irma hitting the Antilles non-self-governing
territoriesCase study area
Nineteen countries in the Caribbean region are not independent sovereign
states; they retain constitutional relationships with their original
metropolitan powers through different systems (Clegg, 2015).
Natural catastrophes like hurricane Irma in September 2017 allow for the
comparison of the reactions of their respective mainland governments
(see Aballain, 2018). Irma made seven landfalls, four of which
occurred as category 5 in the Saffir–Simpson scale across the northern
Caribbean islands. It held a 60 h period of sustained category 5 intensity,
which is the second longest such period on record, behind the 1932 Cuba
Hurricane (Cangialosi et al., 2018). Irma's track over
our study area is depicted in Fig. 3.
The Caribbean island groups we analyse in this paper are specified in
Table 1. Whilst Antigua and Barbuda and St.
Kitts and Nevis are sovereign countries, the other islands are either
overseas collectivities of France (St. Barthélemy and Saint Martin),
constituent territories of the Netherlands (Sint Maarten), special
municipalities of the Netherlands (St. Eustatius and Saba), British overseas
territories (Anguilla, British Virgin Islands, and Turks and Caicos Islands),
or unincorporated and organized territories of the United States (United
States Virgin Islands). As such, the corresponding mainland is the one
responsible for security and defence.
Group of islands studied. Source is, in order prioritized, the
World Bank, the UN data, the Central Bureau voor de Statistiek, and Comptes
Economiques Rapides de l'Outre-mer and L'Institut d'Émission des
Départements d'Outre-mer. The areas are obtained from Wikipedia.
IslandMetropolitanAreaPopulationGDPGNI per capitagrouppower(km2)(current USD)(current USD)AnguillaUK9114 764a337 201 995a22 525aAntigua and BarbudaSovereign440100 963a1 460 144 703a13 973aBritish Virgin IslandsUK15330 661a971 237 110a28 897aSaba and St. EustatiusNetherlands13 and 211947a and 3193a48 000 000a and 100 000 000a>12 235aSt. BarthélemyFrance259625b414 710 000c>12 235aSt. Kitts and NevisSovereign26154 821a909 854 630a16 050aSt. MaartenNetherlands3739 969a1 081 577 185a26 208aSt. MartinFrance5331 949a614 258 169d>12 235aTurks and Caicos IslandsUK61634 900a917 550 492a28 767aUS Virgin IslandsUSA346107 510a3 765 000 000b>12 235a
The
superscripts indicate the year of the data: a 2016, b 2015, c 2014, and d 2010.
In the past the considered islands have developed their economy mainly
through the primary sector but a discernible trend towards an expanding
service sector started in the latter half of the 20th century, especially
towards tourism (it accounts for 80 % of the economy of Sint Maarten).
Some of the islands also developed their economies based on offshore
finance, like the British Virgin Islands, Nevis, Anguilla, or the Turks and
Caicos Islands.
Data and methodsEconomic model using Black Marble
In order to approximate the spatial distribution and the amount of exposed
economic value, we downscale the national gross domestic product (GDP) using
the nighttime lights of NASA's Black Marble 2016 annual composite of the
Visible Infrared Imaging Radiometer Suite (VIIRS) day–night band (DNB) at
500 m resolution (Román et al.,
2018). This has been implemented in the BlackMarble class of
Fig. 2. Data derived from nighttime satellite
imagery have helped develop various globally consistent proxy measures of
human well-being at the gridded, sub-national, and national level such as
socioeconomic variables, energy use, urban built-in expansion, and carbon
emissions (see review of Ghosh et al., 2013).
Henderson et al. (2012) relate growth of GDP to growth of
night lights intensity, while later Bickenbach et al. (2016) argue that the result of Henderson et al. (2012) is stable at country level but unstable
at lower levels. We consider the nominal GDP values at current US dollar at
year 2016, before Irma's intervention, from the World Bank and the UN data.
For the overseas collectivities of France, we infer the values from local
reports, as specified in Table 1. As observed by
Bickenbach et al. (2016), the GDP values for Saba and
St. Eustatius result in an overestimation if retrieved from the downscaling of
Netherlands' GDP through night light. In this case, we consider the GDP
values reported by the Central Bureau voor de Statistiek.
According to Zhao et al. (2017), a linear
relation between nighttime lights and GDP would result in
over-distributions in suburban areas, under-distributions in urban areas,
and very large under-distributions in urban core areas, where saturated
pixels exist. To overcome this problem, we use a square transformation to
correct urban and suburban GDP distribution. In addition, we multiply the
total GDP by the income group level defined by the World Bank, which is
based on the gross national income (GNI), as in
Gettelman et al. (2017). The economic
value exposed in each pixel vali is then
vali=GDP(inc_grp+1)DNi2∑jDNj2,
where inc_grp is the income group level (4 in all the
islands) and DNi∈[0,255] the digital number of the light in
pixel i.
In Table 2 we show the exposure values per
island. The GAR 2015 models (de Bono and Chatenoux, 2014) are also
presented since they are used in Cardona et al. (2017) to
compute Irma's damage with CAPRA. In GAR 2015 statistical data such as
socioeconomic information, building type, and capital stock are transposed
onto a grid of 5 km size using GIS data such as gridded population dataset
(LandScan) and VIIRS DNB data.
Exposed economic values and Irma damage (discussed further below
in Sect. 3.3) per island group in current millions
of USD.
IslandExposed valueExposed valueDamageDamageDamageDamageDamagegroupCLIMADAGAR 2015CLIMADAEM-DATNHCCAPRAothersAnguilla1686865792200>190555188aAntigua and Barbuda73016257538250150–300374–British Virgin Islands4856384914263000–24661650aSaba and St. Eustatius740–93––––St. Barthélemy2074–771–>480––St. Kitts and Nevis4549411226120–46520bSt. Maarten5408–21822500500–1049aSt. Martin3071–128241001000––Turks and Caicos Islands45881049687500>500284290aUS Virgin Islands18 82553442038––13385500c
The superscripts indicate the source of the data: a ECLAC (Economic Commission for Latin America and the Caribbean). b Estimate of damage in public sector (based on an interview of St. Kitts
and Nevis prime minister Timothy Harris; http://www.thestkittsnevisobserver.com/local-news/hurricane-irma-leaves-st-kitts-and-nevis-with-initial-53-2-million-in-damages/, last access: 17 July 2019). c Amount claimed to be needed for recovery (based on an interview
of Virgin Islands governor Kenneth Mapp;
https://eu.usatoday.com/story/news/2017/11/09/no-electricity-homes-ruins-reporter-goes-home-and-finds-misery-hope-and-resilience-u-s-virgin-island/826573001/, last access: 17 July 2019).
Modelling tropical cyclone damage
The TropCyclone class handles the tropical cyclone model in CLIMADA. It computes the hazard properties from input tropical cyclone tracks, which are managed by
the class TCTracks (see Fig. 2). TCTracks ingests the tracks of
the International Best Track Archive for Climate Stewardship (IBTrACS)
archive (Knapp et al., 2010). We use the
latest version, 04, of the archive. Since not all the track records contain
all the necessary information, especially before 1980, we use the selection
and processing of tracks described by
Geiger et al. (2018),
which does not consider tracks before 1950. Synthetic tracks are obtained
from historical ones by a direct random-walk process, starting at slightly
perturbed initial locations of the tracks
(Kleppek et al., 2008). Moreover, in
order to take the decay of wind intensities after landfall into account, we
statistically build an exponential decay coefficient of the wind speed (and
corresponding increasing pressure) and apply it to the synthetic tracks
after landfall. Thus, 693 tracks have crossed the surrounding area of study (a
square of approximately 3000 km side length containing the islands) between
1950 and 2017, and we generate an ensemble of 50 samples for each historical
event, obtaining a catalogue of 34 650 tropical cyclones. Assuming
stationarity to estimate current risk, the annual frequency assigned to each
event is the same as observed in the historic dataset. The event frequency
is then 15012017-1950+1 yr-1=13400 yr-1.
A tropical cyclone track contains the following information about the eye:
time, location, radius of maximum winds, and central and environmental
pressure. From these properties the 1 min sustained peak gusts are computed
as the sum of a static circular wind field (following
Holland, 2008) and the translational wind speed that
arises from the storm movement. We incorporate the decline of the
translational component from the cyclone centre by multiplying it by an
attenuation factor. See
Geiger et al. (2018) for
more details about the implementation and its limitations.
Finally, the event intensities are translated into damage using the impact
function of Emanuel (2011). In this function the
property damage starts above a threshold vthresh equal to 25.7 m s-1
and increases as the cubic power of the wind speed as follows:
fij=vij31+vij3,vij=max(vij-vthresh,0)vhalf-vthresh,
where vhalf is the wind gust where half of the property is damaged and
vij is the maximum wind gust at centroid i due to event j.
Following the findings of Sealy and Strobl (2017) for the
Bahamas, we consider vhalf=74.7 m s-1.
Model evaluation: direct economic damage of hurricane Irma
Fig. 3a and c shows the exposed
economic value of the islands computed with the Black Marble model (Sect. 3.2.1), Irma's track, and the contour curves of its
maximum 1 min sustained wind in knots computed with the tropical cyclone
model (Sect. 3.2.2). Fig. 3b and d shows the
computed damage generated by the storm using Eq. (3). The aggregated values of damage are shown in
Table 2. Economic damage estimations can vary
significantly between data sources. In Table 2 we
compare our results against the following sources: the Emergency Events
Database (EM-DAT)
EM-DAT: the Emergency Events Database –
Université catholique de Louvain (UCL) – CRED (Centre for Research on the Epidemiology of Disasters), Debarati Guha-Sapir –
https://www.emdat.be/ (last access: 17 July 2019), Brussels, Belgium.
, the National Hurricane
Center's (NHC) Tropical Cyclone Report (Cangialosi et
al., 2018), and the Economic Commission for Latin America and the Caribbean
(ECLAC) report (ECLAC, 2018). Moreover, we consider the results of
CAPRA in Cardona et al. (2017).
(a, c) Rendered exposed value, contour lines of Irma
maximum 1 min sustained winds (in knots) and Irma track as modelled by
CLIMADA. (b, d) Economic damage generated by Irma computed by
CLIMADA.
Economic damage computed by CLIMADA as percentage of local
exposure (per pixel). (a) Lesser Antilles; (b) Turks and
Caicos Islands.
In all the cases the order of magnitude of the damages computed by CLIMADA
lies within the ranges provided by various sources. Whilst one might argue
that Anguilla reaches a higher damage because of its presumably inflated GDP
from offshore finances, this is not the case for other tax havens, such as the
Turks and Caicos Islands, the British Virgin Islands, or the US Virgin
Islands. The amount claimed by the latter one represents the money needed
for recovery, which can exceed the replacement cost to previous standards
computed by the models. We note that in this case changing the polynomial
transformation of the night lights does not change considerably the
aggregated amount of damage per island. This is due to the small scale of
the islands compared to the wind fields scope. Changing the islands' income
group level does lead to less realistic damages.
Risk assessment
Risk emerges from the interaction between natural hazard and its exposure.
From the tropical cyclone ensembles built according to
Sect. 3.2.2, hazard return period maps can be
calculated. Figure 5 represents the maximum 1 min
sustained wind speeds in knots for return periods of 25, 50, 100, and 250 years. All
the island groups studied are approximately equally exposed to the same
level of intensity, which reaches the category 5 every 250 years.
Wind fields in knots for return periods (RPs) of 25, 50, 100, and
250 years in the study region.
Combining the hazard with the exposure value and location as in
Eq. (6), we obtain the damage levels of
Fig. 6 for return periods of up to 3400 years.
The curves grow initially fast, reaching the tens of millions of US dollar
damages with a return period of 10 years in all the islands. The whole
region being susceptible to frequent and intense tropical cyclones, every
mainland and sovereign territory should count with damages exceeding USD 1 billion every 100 years. The dots in the figure represent the damage
caused by Irma, which exemplifies an event with return period of 20–30 years
for the US Virgin Islands, St. Kitts and Nevis, Antigua and Barbuda, and
Saba and St. Eustatius. We notice that the assumption of independent events
in Eq. (6) does not allow us to study compound events.
Fig. 6 provides, therefore, the probability of
exceedance of an impact caused by a single tropical cyclone; the occurrence
of correlated events is not fully addressed.
The quality of the probability distribution obtained by the ensembles is
assessed in the probability–probability plot of Fig. 7. There we compare the cumulative probability distribution of the
damages of the historical events (693 events) – empirical distribution in
Fig. 7 – with the cumulative probability
distribution obtained from the full set of events (34 650 events) – model
distribution in Fig. 7 – evaluated at every
historical event. Whilst the zero damaging historical events (P(X≤0)≈0.94) are slightly underestimated by the model, the highly damaging
events reach a better fitting. In general, the historical damages lie close
to the identity line.
Impact exceedance frequency curves for the different
island groups and their accumulated value per mainland power. The dots
represent the damage reached by hurricane Irma.
Quality metrics of the impact distribution. (a)
shows the probability-probability plot. (b) shows the mean
value and the 95 % confidence interval (CI) when sampling on the hurricane
tracks and (c) represents the 95 % CI when sampling on impact
functions with a maximal 10 % shape modification, allowing to assess the
uncertainty introduced by the models.
Furthermore, we analyse the amount of uncertainty intrinsic to the
probabilistic hazard model and the vulnerability model. Performing a Monte
Carlo-based simulation with 100 repetitions of the synthetic track
generation, we compute their corresponding total damage and represent the
95 % confidence interval (CI) in Fig. 7b. We compute the same number of scenarios
with a fixed hazard and modify 10 % of the shape of the impact function by
uniformly sampling from its parameters of Eq. (12):
vhalf+εhalf and vthresh+εthresh, where
ε∈[-0.1v,0.1v]. The 95 % CI of the last distribution is
shown in Fig. 7c. The highest uncertainty range is obtained for
extremely rare events (once every 3400 years), which produce a total
damage in the region between USD 12.5 and 21 billion. It is clear from
the comparison of both figures that the uncertainty induced by the
socioeconomic model is greater than the one produced by the tropical cyclone
model, which is in line with
Gettelman et al. (2017). Other sources
of uncertainty can originate from modelling tropical cyclone damages based
only on their wind gusts. Even if the model works fine for regular
hurricanes like Irma (Sect. 3.3), rainfall and
storm surges can account for high damages also in less windy storms (e.g.
hurricane Sandy in 2012) that are not completely captured.
Average annual impact and exceedance impact level for several
yearly return periods in millions of USD.
Island groupAverage annual impactRP 25RP 100RP 250RP 1000RP 3000Anguilla18±4155±41435±89571±104732±117814±118Antigua and Barbuda83±18714±1892018±3872596±4433208±4843546±512British Virgin Islands50±11416±1101268±2701647±3212118±3212366±345Saba and St. Eustatius8±271±20204±40266±46334±50371±51St. Barthélemy22±5194±55547±113733±128905±1421015±142St. Kitts and Nevis50±10430±1111238±2391590±2691975±3102189±327St. Maarten57±13502±1381411±2901878±3382335±3702614±386St. Martin33±7285±78800±1631060±1921322±2051472±221Turks and Caicos Islands33±7236±63841±1811229±2451703±3041991±314US Virgin Islands198±411792±4254425±8175466±8996489±10917143±1194British117±28952±2452434±5103211±6074145±6804721±697Dutch66±16586±1651614±3322136±3762651±4102966±467French56±13495±1431352±2761790±3172181±3472462±390
Performing a last Monte Carlo simulation which combines both hazard and
impact function sampling, we obtain the results of
Table 3. The average annual impact reported (see
Eq. 5) indicates that the money
every island should yearly put aside to cover for all the coming hurricane
damages ranges from USD 8±2 million for Saba and St. Eustatius to USD 198±41 million for the US Virgin Islands. The metrics per mainland power are
also shown. After Irma's crisis the Foreign Affairs Committee of the House
of Commons recognized the vulnerability to natural disasters of their
overseas territories and the need to address it (Foreign
Affairs Committee, House of Commons, 2018). The results per mainland
presented here account for the probability of several overseas territories
being hit by the same event, and the accumulated damage that might arise.
These metrics can therefore help mainlands to define natural disaster
response strategies able to cope with all expected impacts, as well as
long-term cost-effective resilience measures.
Other risk assessment studies have been performed on the Caribbean before.
Bertinelli et al. (2016) study the hurricane damage
over the whole Caribbean and obtain USD 30 billion damage for a
500-year event. They compute a local wind damage index using night light
intensities and aggregate by country to compute the translated monetary
value using the GDP – night light relation of Chen and
Nordhaus (2011). Given that their study region includes bigger islands, such
as the Cayman Islands, Jamaica, and Puerto Rico, which amounts to a total GDP
28 times bigger than in our study area, their result is in agreement with
the USD 1.25 billion we obtain for a 500-year event in the whole
region. Comparing at the island level, the Caribbean
Catastrophe Risk Insurance Facility (2010) (CCRIF) estimates an expected annual damage of USD 15 million in Anguilla, which lies in our range estimate of USD 18±4 million. However, their study includes both damage from wind
and flooding of tropical cyclones.
Discussion and conclusions
CLIMADA offers a risk assessment framework to perform multi-hazard analyses
at global and local scales. We are building a catalogue of event-based
probabilistic hazard models for weather and climate disasters such as
drought, flood or windstorm, and their corresponding socioeconomic exposure
on a global scale at medium (10 km) to high (500 m) resolution. These models
allow one to perform risk analyses globally in a consistent way and with the
minimum necessary information. The modularity of the object-oriented tool
allows one to easily integrate third-party models through reading functions and
complementary Python libraries which support a vast variety of file formats
(e.g. for shape and grid files). Thanks to the description of the most
computationally cost-intensive algorithms in matrix from, CLIMADA can compute
impacts and risk metrics on a laptop efficiently even at high geographic
resolution for hundreds of thousands of events. Moreover, its parallelized
components allow one to scale the computations on a cluster, reducing
significantly the execution time of the most time-consuming computations,
such as the synthetic events generation (which does – nota bene – not need
to be repeated for each risk assessment). The performance characteristics
are indispensable for decision making, where different scenarios need to be
tested and compared as fast as possible to obtain a better understanding of
the inherent uncertainty.
In this paper we have presented the application of CLIMADA to assess the
economic impact of hurricane Irma to small Caribbean islands, where most of
them rely on overseas security support because of their non-sovereign
status. The computed damages agree well with reported ones, within the
uncertainties inherent to the losses reported. To put these impacts into
context, we performed a probabilistic tropical cyclone risk analysis in the
same region and estimated the intrinsic model uncertainties originating from
the hazard and vulnerability used. The only data used were satellite
nightlight intensities, two macroeconomic indicators (GDP and income group
level) of the islands, and the historical hurricane tracks of the IBTrACS
repository. This has allowed us to perform an analysis at 500 m spatial
resolution. The corresponding amount of exposure coordinates is 9311 and we
have generated a catalogue of 34 560 hurricane events. On a laptop with 4
virtual CPUs, it took less than 2 h to generate the full probabilistic
hazard set and only a few seconds to calculate the resulting impact and risk
metrics.
Future work includes the modelling of tropical cyclone damages not only
based on their wind intensity, but also based on coupled rainfall amount and
surge height, components that might become increasingly dominate because
of climate change (Garner et al., 2017). Other hazard types, exposures, and impact functions are being
developed in CLIMADA under a cooperative model using unit testing and a
continuous integration platform. The presented work constitutes the base for
the risk assessment functionality that will allow us to perform adaptation
options appraisal under different future scenarios.
Code and data availability
CLIMADA is openly available at GitHub (https://github.com/CLIMADA-project/climada_python, Bresch and Aznar-Siguna, 2019a) under
the GNU GPL license (GNU Operating System, 2007). The
documentation is hosted on Read the Docs (https://climada-python.readthedocs.io/en/stable/, Aznar-Siguna and Bresch, 2019) and includes a link to
the interactive tutorial of CLIMADA. v1.0.0 was used for this publication,
which is permanently available at the ETH Data Archive: 10.5905/ethz-1007-187 (Bresch and Aznar-Siguan, 2019b). The script reproducing the main
results of the paper as well as all the figures is available under https://github.com/CLIMADA-project/climada_papers (Bresch and Aznar-Siguna, 2019c).
Author contributions
DNB conceptualized CLIMADA and oversaw its implementation in
Python, based on the previous MATLAB implementation by himself. GAS designed and executed the Python implementation of the
software, defined the case study, and performed the analysis. GAS prepared the article in discussion with contributions by DNB.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
David N. Bresch developed the first version of CLIMADA as a basis for
teaching a master course on uncertainty and risk at ETH back in 2010. He is
thankful for many students' feedback and improvement suggestions, without
which the model would not be as comprehensive and stable a tool as it is by
now.
Review statement
This paper was edited by James Annan and reviewed by Mauricio Pohl and one anonymous referee.
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