Climate change and critical infrastructure : storms.
Date: 2019-02-11
Institutions: Joint Research Centre
Subjects: adaptation to climate change, bad weather, climate change policy, energy grid, industrial infrastructure, natural disaster, natural hazard, research report, risk management

Climate change and critical 
infrastructure – storms 

Georgios Marios Karagiannis 

Monica Cardarilli 

Zehra Irem Turksezer 

Jonathan Spinoni 

Lorenzo Mentaschi 

Luc Feyen 

Elisabeth Krausmann 

2019 

EUR 29411 EN 

 
 
 
 
This publication is a Science for Policy report by the Joint Research Centre (JRC), the European Commission’s 
science and knowledge service. It aims to provide evidence-based scientific support to the European 
policymaking process. The scientific output expressed does not imply a policy position of the European 
Commission. Neither the European Commission nor any person acting on behalf of the Commission is 
responsible for the use that might be made of this publication. 

Contact information 
Name: Elisabeth Krausmann 
Address: Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra (VA), Italy 
Email: elisabeth.krausmann@ec.europa.eu 

JRC Science Hub 
https://ec.europa.eu/jrc 

JRC113721 

EUR 29411 EN 

PDF 

ISBN 978-92-79-96403-9 

ISSN 1831-9424 

doi:10.2760/986436 

Luxembourg: Publications Office of the European Union, 2019 

© European Union, 2019 

The reuse of the document is authorised, provided the source is acknowledged and the original meaning or 
message of the texts are not distorted. The European Commission shall not be held liable for any consequences 
stemming from the reuse. 

How to cite this report: Karagiannis, G.M., Cardarilli, M., Turksezer, Z.I., Spinoni, J., Mentaschi, L., Feyen, L. 
and Krausmann, E., Climate change and critical infrastructure – storms, EUR 29411 EN, Publications Office of 
the European Union, Luxembourg, 2019, doi:10.2760/986436. 

All images © European Union 2019, except: 
Cover page, Fré Sonneveld, 2014. Source: https://unsplash.com/@fresonneveld 
Page 11, Figure 5, FEMA, 2017. Source: https://www.fema.gov/media-library/assets/images/141939 

Climate change and critical infrastructure – storms 
This study attempts to elucidate the vulnerability of critical energy infrastructure to storms. First, it gauges how 
certain characteristics of storms affect the resilience of the power grid. Then, it presents a methodology for 
assessing the change in the level of risk to critical infrastructure due to the impact of climate change on the 
frequency and severity of storms. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Executive summary ............................................................................................... 2 

1  Introduction ...................................................................................................... 3 

2  Power grid resilience to storms – incident analysis ................................................. 5 

2.1  Research design and data collection ............................................................... 5 

2.2  Damage types and storm impact on power grid resilience ................................. 6 

2.3  Equipment damage and hazard mitigation ...................................................... 8 

2.4  Early warning, emergency response and recovery .......................................... 12 

3  Effects of climate change on the level of risk from an economic perspective ............ 15 

3.1  Methodology ............................................................................................. 15 

3.1.1  Probability of occurrence ..................................................................... 15 

3.1.2  Loss estimation ................................................................................. 15 

3.1.2.1  Losses to critical energy infrastructure ............................................ 16 

3.1.2.2  Impact on the local economy ......................................................... 18 

3.1.3  Change in risk level ............................................................................ 20 

3.2  Demonstration of the methodology .............................................................. 21 

3.2.1  Case study site .................................................................................. 21 

3.2.2  Hazard analysis ................................................................................. 21 

3.2.3  Loss estimation ................................................................................. 22 

3.2.3.1  Data and assumptions .................................................................. 22 

3.2.3.2  Results ....................................................................................... 24 

3.2.4  Risk analysis ..................................................................................... 28 

3.3  Discussion ................................................................................................ 29 

4  Recommendations for disaster risk management ................................................. 31 

References ......................................................................................................... 34 

List of abbreviations and definitions ....................................................................... 38 

List of figures ...................................................................................................... 39 

List of tables ....................................................................................................... 40 

Annex ................................................................................................................ 41 

i 

Executive summary 

Infrastructure systems are the backbone of modern economies, and critical infrastructure 
resilience  is  essential  to  sustainable  development.  Natural  hazards  can  affect  the 
electricity supply and result in power outages which can trigger accidents, bring economic 
activity  to  a  halt  and  hinder  emergency  response  until  electricity  supply  is  restored  to 
critical services.  

This  study  attempts  to  elucidate  the  vulnerability  of  critical  energy  infrastructure  to 
storms and outlines how certain characteristics of this hazard affect the resilience of the 
power  grid  based  on  forensic  analysis.  Wind  loading  and  debris  impact  are  the  main 
causes  of  storm  damage.  Tall,  slender  structures,  such  as  transmission  towers, 
distribution  poles  and  wind  turbines  are  most  affected.  Transmission  and  distribution 
assets can also be damaged by the impact of flying debris. Moreover, freezing rain forms 
glaze ice which accumulates on power lines and increases their catenary load. The added 
weight  can  cause  the  line  to  break  or  distribution  poles  and  transmission  towers  to 
collapse. Substations were also found to be affected by storms, particularly by inundation 
and  airborne  debris.  However,  damage  from  flying  debris  was  less  compared  to  that 
sustained  by  transmission  and  distribution  lines.  Storms  in  coastal  areas  may  affect 
transmission and distribution networks by increasing the amount of saltwater deposits on 
electrical  equipment.  Given  adequate  preparedness,  early  warning  can  help  expedite 
recovery by allowing TSOs and DSOs to activate disaster response plans, including surge 
mechanisms and mutual aid agreements, before the storm hits. 

We  also  present  a  methodology  to  investigate  the  impact  of  climate  change  on  the  risk 
posed  by  storms  to  the  power  grid.  Our  approach  combines  a  future  projection  of  the 
recurrence  interval  of  selected  storm  scenarios  and  the  assessment  of  the  estimated 
economic losses incurred by critical infrastructure and those resulting from the disruption 
of daily economic activity. A case study was conducted to demonstrate the methodology 
in a large urban area in Western Europe. We derived the projected peak wind gust of the 
10-,  50-  and  100-year  storm  scenarios  for  five  time  periods.  For  each  recurrence 
interval,  the  cost  to  repair  the  damage  to  overhead  lines  and  the  economic losses  from 
the  interruption  of  the  daily  economic  activity  amount  each  to  about  half  of  the  total 
losses. The proportion of the repair cost increases by approximately 10% for the 50-year 
and  the  100-year  storms  compared  to  the  10-year  scenario.  This  increase  causes  the 
total expected losses from the 50-year and the 100-year storms to rise as well. 

The  duration  of  the  power  outage  has  a  major  impact  on  the  estimated  losses  for  all 
scenarios across all time periods.  In this case  study, the increase  of the duration of the 
power outage from 3 days to 10 days increases the total expected losses 3.5 times. With 
longer-term  power  outages,  the  economic  losses  caused  by  interruption  of  the  daily 
economic activity progressively become the main determinant of the total impact. 

The scope of this study is limited to demonstrating the feasibility of the methodology and 
inductively  drawing  preliminary  conclusions  regarding  the  impact  of  storms  on  critical 
infrastructure given climate change conditions. It is not intended to supplement, replace 
or  challenge  existing  risk  assessment  and  management  plans  prepared  by  Member 
States. 

The following recommendations emerged from the findings of this study: 

—  Consider  increasing  transmission  tower  design  requirements  for  resistance  to  wind 

loading in standards and regulations. 

—  Consider the risk from climate change in investment analyses. 

—  Consider events with recurrence intervals longer than 100 years in hazard mitigation 

and emergency planning. 

—  Standardize mutual aid resources. 

—  Plan for surge capabilities and external contractors. 

2 

1  Introduction 

Infrastructure systems are the backbone of modern economies, and critical infrastructure 
resilience  is  essential  to  the  societal  well-being  and  sustainable  development.  The  risk 
environment  facing  critical  infrastructure  in  Europe  is  complex  and  in  constant  change. 
The Overview of Natural and Man-made Disaster Risks the European Union may face (1) 
outlines  numerous  natural,  technological  and  human-caused  hazards  and  threats. 
Meteorological  hazards,  such  as  floods,  storms  and  wildfires  (2),  are  among  those  most 
frequently  identified  by  Member  States  in  their  National  Risk  Assessments.  In  a  recent 
study, Forzieri et al. (2016) predict a likely increase in the frequency of extreme weather 
events across  Europe.  The exposure of  coastal and floodplain areas  to extreme weather 
is  expected  to  increase.  Neumann  et  al.  (2015)  estimate  that  between  82.9  and  85.7 
million people will be living in low-elevation coastal zones (3) and the 100-year floodplain 
in Europe by 2030. 

Because  of  the  high  population  density  and  important  economic  activity,  infrastructures 
are also concentrated in coastal and floodplain areas. Therefore, the  exposure of critical 
infrastructures  to  meteorological  hazards  is  likely  to  increase  disproportionately  with 
climate change. Forzieri et al. (2018) estimate that damage to critical infrastructure due 
to climate change in Europe could increase to ten times present values by the turn of the 
century,  but  southern  and  southeastern  European  countries  will  be  most  affected.  The 
impact  of  storm  surge,  riverine  and  flash  flooding,  and  windstorms  on  critical 
infrastructure  is  expected  to  increase  across  Europe.  The  losses  are  likely  to  be  highest 
for the chemical, manufacturing, transportation and energy sectors. 

Several  authors  (Forzieri  et  al.,  2018;  Neumann  et  al.,  2015;  Mechler  et  al.,  2014) 
highlight  the  need  for  further  research  in  the  quantification  of  vulnerability  of  critical 
infrastructures  to  meteorological  hazards.  This  study  focuses  on  the  energy  sector, 
mainly  because  of  its  ubiquity  in  everyday  life  and  the  dependence  of  all  other  critical 
infrastructures  on  a  reliable  supply  of  electric  power  (Karagiannis  et  al.,  2017a).  A 
previous  study  (Karagiannis  et  al.,  2017b)  presented  a  methodology  to  investigate  the 
impact  of  climate  change  on  the  risk  posed  by  floods  to  critical  infrastructure  and 
suggested the approach be expanded to other natural hazards as well. 

This  study  attempts  to  elucidate  the  vulnerability  of  critical  energy  infrastructure  to 
storms, with emphasis placed on the power grid. First, we discuss the impact of storms 
on the power grid and outline how certain characteristics of this type of hazard affect the 
resilience of the power grid using forensic analysis. Second, we present a methodology to 
investigate the impact of  climate change  on the  risk posed by  storms  to the power grid 
infrastructure.  Our  approach  combines  a  future  projection  of  the  recurrence  interval  of 
selected storm scenarios and the assessment of the estimated losses incurred to critical 
infrastructure  and  those  resulting  from  the  disruption  of  daily  economic  activity.  A  case 
study was conducted to demonstrate the methodology in a large urban area in Western 
Europe. The scope of this study aims at demonstrating the feasibility of the methodology 
and inductively drawing preliminary conclusions regarding the impact of storms on critical 
infrastructure given climate change conditions. It is not intended to supplement, replace 
or  challenge  existing  risk  assessment  and  management  plans  prepared  by  Member 
States. 

This  is  a  two-part  study.  Chapter  2  is  the  first  part,  which  is  intended  to  improve  the 
understanding  of  power  grid’s  resilience  to  storms.  It  is  based  on  the  analysis  of  past 
storm  occurrences  and  their  impact  on  the  power  grid.  Its  purpose  is  to  inform  policy-

(1)  Commission  Staff  Working  Document:  Overview  of  Natural  and  Man-made  Disaster  Risks  the  European 
Union  may  face,  Brussels,  23.5.2017,  SWD(2017)  176  final,  http://ec.europa.eu/echo/sites/echo-
site/files/swd_2017_176_overview_of_risks_2.pdf (accessed October 29, 2018) 

(2)  NFPA  1600  Standard  on  Disaster/Emergency  Management  and  Business  Continuity/Continuity  of 
Operations  Programs,  2016,  https://www.nfpa.org/codes-and-standards/all-codes-and-standards/list-of-
codes-and-standards/detail?code=1600 (accessed October 30, 2018) 

(3)  Defined  as  the  contiguous  and  hydrologically  connected  zone  of  land  along  the  coast  and  below  10  m  of 

elevation (Neumann et al., 2015). 

3 

                                           
making and strategic and disaster risk management planning in the European Union and 
Member  States.  Additionally,  however,  this  analysis  serves  as  a  foundation  of  the  risk 
assessment  methodology  that  follows.  Specifically,  Chapter  3  is  the  second  part  of  this 
study  and  outlines  a  methodology  for  the  estimation  of  the  change  in  the  risk  level  of 
failure or disruption of the power grid resulting from the impact of climate change on the 
frequency  and  severity  of  storms.  Risk  is  quantified  in  economic  terms  to  support  cost-
benefit analyses. 

4 

2  Power grid resilience to storms – incident analysis 

Widespread  power  outages  are  a  common  occurrence  during  storms.  This  chapter 
examines storm damage to  the power grid with a focus on restoration and resilience.  It 
endeavors to elucidate how the impact of storms affects the recovery of the power grid. 
It  is  intended  to  inform  policy-making  and  disaster  risk  management  planning  in  the 
European Union Member States. 

2.1  Research design and data collection 

The  research design for this part of the study  was predominantly purposeful. It  focused 
on disruptions of the power grid caused by storms and explored the relationship between 
recovery  time  and  hazard  characteristics.  This  part  was  data-driven,  using  an  inductive 
approach. The findings in this chapter are based on the analysis of lessons identified 
from 52 storms, including tropical and extratropical cyclones, as well as ice, snow and 
wind storms (Figure 1) affecting 14 countries in 4 continents (the full list of the events is 
included in the Annex). Whenever a storm affected more than one countries, a separate 
record  was  made  for  each  affected  country,  producing  a  total  of  58  cases  (4).  Cyclones 
made up approximately 2/3 of our sample, followed by wind storms. 

Figure 1. Breakdown of storms reviewed in this study, by type 

Source: JRC 

Storms  were included in the analysis if they caused damage to  power grid components. 
This  study  was  based  exclusively  on  publicly  available  information.  Sources 
included the scientific literature, technical reports, disaster response operations situation 
reports and press articles. The quality, detail and granularity of data describing the storm 
intensity,  damage,  power  outages  and  impact  on  the  population  were  a  particular 
challenge. Most of the information in the dataset was categorical, but numerical data was 
considered whenever feasible and available. The level of detail varied across sources. The 
information  was  sufficient  to  establish  the  type  of  damage  and  the  failure  mechanism. 
The  affected  population  was  typically  reported  as  the  proportion  of  customers  or 
households where the hazard impacts were observed. 

Nevertheless,  several  limitations  undermined  data  collection  and  analysis.  The  intensity 
of the wind storms and cyclones was either reported in terms of the mean wind speed or 

(4)  The terms “records” and “cases” are used here interchangeably. 

5 

 
                                           
peak  wind  gust  for  wind  storms,  but  reporting  was  inconsistent  in  terms  of  the  metric 
used.  Precipitation  height  was  reported  as  a  proxy  of  snow  and  ice  storm  intensity.  In 
addition,  the  exposure  of  power  grid  components  to  varying  storm  intensity  levels  (i.e. 
wind speed or precipitation height) was difficult to establish. Spatial distributions of wind 
speed and/or precipitation were generally neither comprehensive nor readily available.  

Inconsistencies in the description of the impact,  location and duration  of power  outages 
caused by storms were another challenge. The impact of a power  outage  was described 
in  several  different  units  of  measurement,  including  people,  customers,  households, 
houses, homes, businesses and properties suffering an outage. The impact was described 
with  numbers  in  55  out  of  58  records  (including  relative  descriptions  in  14  cases),  with 
categorical  descriptions  in  2  cases,  and  was  unknown  in  1  case.  Furthermore,  several 
different  thresholds  were  used  to  report  recovery  time.  Information  on  the  duration  of 
the  outage  was  available  in  only  34  cases.  Each  source  used  different  definitions  and 
indicators  of  recovery.  Most  sources  reported  the  time  needed  after  the  onset  of  the 
outage  to  restore  power  to  varying  numbers  or  percentages  of  affected  units  (e.g. 
customers, households etc.). 

Information  on  the  type  of  damage  and  the  types  and  numbers  of  affected  assets  were 
reported  in  38  out  of  58  cases,  of  which  only  30  included  numbers  of  damaged 
equipment and assets per category. However, it was often unclear whether damage was 
caused  by  storm-related  flooding,  wind  loading,  debris  impact,  or  a  combination  of  the 
above.  In  addition,  the  localization  of  damage  or  outages  was  also  unclear,  and  was 
described as a general area, not associated with any clear geographical or administrative 
boundaries,  or  for  the  entire  event.  Similar  challenges  were  noted  in  a  previous  study 
(Karagiannis  et  al.,  2017a),  but  the  quality  of reported  information  on  storms  seems  to 
be  lower  than  for  earthquakes  or  floods.  Clearly,  the  granularity  of  reported 
information needs to be improved if statistical correlations between wind speed 
and the duration of a power outage are to be made at a larger scale. 

In  addition,  there  are  inconsistencies  in  recovery  time  reporting.  First,  not  all  sources 
reported  recovery  time.  Second,  different  definitions  and  indicators  of  recovery  were 
used  by  each  reporting  source.  Some  sources  defined  recovery  as  the  restoration  of 
service  to  the  population  affected  by  a  power  outage,  while  others  as  the  repair  of  the 
power grid or subsystems to its pre-disaster state. Last, most sources reported the time 
to achieve a different fraction of complete recovery, such as a percentage of the affected 
population with restored service, or a percentage of power generation. 

Two  power  grid  recovery  thresholds  were  used  in  this  study.  The  first  threshold  is  the 
restoration  of  power  supply  to  customers.  Both  domestic  and  industrial  customers  were 
considered,  based  on  available  information.  This  threshold  includes  efforts  directed  at 
temporary repairs or workarounds, as well as the use of backup generators. The progress 
of  recovery  in  this  case  is  usually  reported  in  terms  of  the  percentage  or  number  of 
customers  with  power  supply,  or  the  quantity  of  power  supplied,  expressed  in  power 
units or as a percentage of pre-earthquake supply. The second threshold is the complete 
repair  of  the  network,  so  that  temporary  solutions,  including  generators,  are  no  longer 
required. 

2.2  Damage types and storm impact on power grid resilience 

Power outages occurred during all but one of the storms reviewed in this study. The one 
exception  was  a  wind  storm  which  occurred  in  September  1996  in  France  (Abi-Samra, 
2010). The high winds collapsed 22 transmission towers and 18 wood poles. Despite the 
collapse  of  a  regional  transmission  system,  equipment  located  outside  the  2-km-wide 
storm  track  was  undamaged  and  no  customers  were  affected.  One  reason  for  the 
limited impact was the narrow storm corridor. 

Storms  of  all  kinds  can  bring  heavy  rains,  strong  winds,  hail  and  lightning, and 
may  also  spawn  tornadoes  (Bullock,  Haddow  &  Coppola,  2012).  The  main  causes  of 
storm-related  damage  to  electricity  network  assets  are  inundation,  wind 

6 

loading  and  debris  impact.  Inundation  resulting  from  heavy  rains  associated  with 
storms and  storm  surge  can be detrimental to the power grid. Because  water is a good 
conductor of electricity, electrical equipment is highly sensitive to even minute quantities 
of moisture and dirt. Some  components, such as transformers and electrified substation 
equipment, may even suffer catastrophic and even explosive failures if wet. The effect of 
flood-related  damage  has  been  addressed  in  Karagiannis  et  al.  (2017b);  therefore,  this 
report focuses on wind damage, which has also  been demonstrated to create significant 
damage  to  the  electricity  infrastructure  system  (Reed  et  al.,  2010).  Nevertheless, 
inundation damage is taken into consideration in the analysis when relevant. 

As  expected,  the  areas  on  or  adjacent  to  the  storm’s  track  were  affected  the  most.  All 
other  things  being  equal,  the  number  of  people  affected  by  these  disruptions  increased 
with the peak wind speed and the population density in the affected area. However, the 
data  available  to  us  was  insufficient  to  derive  a  statistical  correlation  between  the  peak 
wind  speed,  the  population  density  in  the  affected  area  and  the  number  of  people 
affected by the outage. 

Despite the fragmented nature of available data, this analysis helped elucidate important 
aspects of the power grid’s resilience to storms. Table 1 below outlines the main findings. 
Transmission  and  distribution  towers  and  lines  appear  to  be  the  most 
vulnerable components of the networks. Utility poles (5) suffer bending failures and 
can  be  damaged  by  the  impact  of  flying  debris,  which  may  also  sever  overhead  lines. 
Inundation  is  more  severe  of  a  threat  for  substations.  Early  warning  is  possible  and, 
together with disaster preparedness, goes a long way in expediting recovery. 

Recovery  time  ranges  between  hours  and  months,  depending  on  the  extent  of 
the damage and the effectiveness of disaster response. Recovery here is construed 
as the restoration of power supply to all customers who are able to receive it. Given the 
inconsistencies  in  reporting  discussed  in  the  previous  section  and  the  inherent 
uncertainty  governing  temporary  repairs  and  recovery  operations,  recovery  time  is 
reported here as a range instead of a precise value. It is driven by the sheer number of 
damaged  items  and  the  time  it  takes  to  repair  or  replace  them.  The  following  sections 
discuss these aspects in further detail. 

Table 1. Overview of damage types and storm impacts on the resilience of the power grid 

Damage types 

Bending failure due to wind pressure 

Impact of flying debris 

Inundation (substations mostly) 

Contributing 
factors 

Early warning possible 

Disaster preparedness 

Most vulnerable 
components 

Utility poles and overhead lines 

Substations (including transformers) 

Recovery time 
driven by  

Recovery time 
range 

Number of items in need of repair or replacement 

Access to conduct repairs 

A few hours to six months; most commonly, up to one month. 

Recovery time was longer for hurricanes than for other storms. 

(5)  The  term  “utility  poles”  is  used  in  this  report  to  refer  collectively  to  transmission  and  distribution  cable-

bearing structures. 

7 

 
                                           
2.3  Equipment damage and hazard mitigation 

Wind  loads  increase  with  wind  speed  (6),  which  fluctuates  with  time  and  increases  with 
height.  Close  to  the  ground,  the  wind  is  slowed  down  by  friction,  while  at  higher 
elevations, the wind is faster. In structural and environmental engineering, the variation 
of  the  mean  wind  speed  variation  within  the  atmospheric  boundary  layer  is  described 
using  power  law  profiles  (Chen  &  Richard  Liew,  2003).  Several  building  codes  consider 
that  wind  speed  achieves  a  practical  maximum  speed  a  few  hundred  meters  above 
ground level (Edgar & Sordo, 2017). The friction increases with terrain roughness, which 
depends  on  the  size  and  number  of  the  surface  features  on  the  ground  over  which  the 
wind  blows  (Figure  2).  Therefore,  the  wind  forces  exerted  on  a  structure  also  increase 
with height. 

Figure 2. Wind profiles on different terrain types 

Source: Adapted from Bendjebbas et al. (2016) 

Wind  forces  act  inward  on  the  windward  side  of  a  structure  and  outward  on  the  other 
sides (Figure 3). The response of a structure exposed to wind, i.e. the effect of the wind 
on  it,  depends  on  the  structure’s  size,  shape  and  dynamic  properties.  Higher  wind 
pressures  are  exerted  on  tall,  slender  structures  presenting  a  wide  profile  against 
incoming winds, resulting in higher bending moments and base shear internal forces. 

Impact by debris was the second major cause of damage identified in this study. Falling 
trees or large branches were the most frequently cited examples of this type of effect. 

Because  of  their  high  exposure,  transmission  and  distribution  towers  and  lines  are 
affected  the  most.  Transmission  towers  are  tall,  three-dimensional  steel  trusses, 
designed to carry and support transmission cables. Several standards apply to the design 
of towers to resist wind loads (Table 2). Because transmission towers transport electricity 
over large distances, they are typically located in the open. They can also be exposed to 
high wind forces due to their height and the absence of surrounding features that could 
mitigate the impact of the wind forces. In addition, these structures may be subjected to 
higher  wind  pressures  when  the  cross-arms  (the  members  protruding  to  the  side  which 
support transmission cables) are facing the wind at a right angle. 

Transmission  tower  failures  were  recorded  in  nearly  all  incidents  reviewed  in  this 
study.  Wind  loading  produced  two  distinct  mechanisms  of  damage.  The  first  was 

(6)  Specifically, the wind pressure is proportional to the square of the wind speed. 

8 

 
                                           
bending  failure  of  the  tower  under  the  wind’s  distributed  horizontal  load.  This 
mechanism  typically  causes  the  affected  towers  to  twist  downward.  Although  the 
photographic  evidence  available  to  us  was  in  no  way  exhaustive,  the  point  of  inflection 
appears to be located in the middle third of the structure’s height, above the juncture of 
the  legs.  In  most  cases,  the  horizontal  conductor  support  cross-arms  appear  to  remain 
unaffected,  but  the  tower  twists  until  its  top  comes  to  rest  on  the  ground.  The  second 
mechanism  was  failure  of  the  tower  foundation,  causing  the  structure  to  tilt.  This 
failure mode is typical in hurricanes, when wind pressures are combined with heavy rain 
which  reduces  the  cohesion  and  resistance  of  the  soil  under  the  tower,  causing  the 
foundations to fail. 

Figure 3. Wind pressure on structure surfaces (we1 and we2 denote respectively the positive 
pressure exerted on the windward side and the negative pressure exerted on the leeward side of 
the structure) 

Source: Eurocode 1, Part 1-4 (European Committee for Standardization, 2010) 

Table 2. Standards applying to the design of transmission towers under wind action 

Organization 

Standard/Regulation 

American Society of 
Civil Engineers 

Guidelines for electrical transmission line structural 
loading, ASCE Manuals and Reports on Engineering 
Practice No. 74 

Year 

1991 

International 
Electrotechnical 
Commission 

Mexican Federal 
Electricity Commission 

(IEC Standard 60826) Design criteria of overhead 
transmission lines. 

2003 

Civil works manual. Chapter of wind design 

2008 

American Society of 
Civil Engineers 

(ASCE/SEI 7–10) Minimum design loads for 
buildings and other structures.  

European Committee for 
Standardization 

(EN 1991-1-4:2005+A1:2010) Eurocode 1: Actions 
on structures – Part 1-4: General actions – Wind 
actions 

2010 

2010 

Standards Australia, 
Standards New Zealand 

(AS/NZS 1170.2) Structural design actions. Part 2: 
Wind actions 

2011 

9 

 
In addition to direct damage from wind loads, severe damage to transmission towers and 
lines  resulted  from  flying  debris.  Smaller  debris,  such  as  tree  branches,  may  dangle 
and/or shear aerial cables at several points along a line. Larger debris, such as trees, can 
knock  down  transmission  towers  and  distribution  poles,  and  damage  heavy  equipment 
upon  impact.  The  numbers  of  transmission  tower  failures  because  of  wind  loading  vs. 
those attributed to debris impact were only available for a fraction of the events reviewed 
in this study. However, wind loading appears to be the most frequent failure mechanism. 
For instance, when cyclones Lothar and Martin hit Europe, most of the damage sustained 
by the French transmission grid resulted from wind loads rather than from debris impact, 
as  illustrated  in  Figure  4.  It  also  appears  that  63  and  90  kV  towers  were  much  more 
vulnerable to debris damage than 225 and 400 kV towers. In this case, falling trees were 
the main cause of debris damage (OSCE, 2016). 

Furthermore, Figure  4 shows that  in absolute terms  the number  of damaged 63 and 90 
kV  towers  is  more  than  twice  the  number  of  damaged  225  and  400  kV  towers. 
Nevertheless,  this  information  alone  does  not  support  any  conclusions  about  the 
vulnerability  of  any  type  of  tower  over  another  to  storm  damage.  Although  it  can  be 
argued that there are more 63 and 90 kV towers than 225 and 400 kV towers in any one 
area,  the  reported  information  does  not  include  the  number  of  towers  exposed  to 
equivalent wind speeds but undamaged. 

The  number  of  exposed  (i.e.  the  sum  of  out-of-order  and  undamaged)  63  and  90  kV 
circuits is more than four times higher than the number of exposed 225 kV circuits and 
about  9  times  higher  than  the  number  of  exposed  400  kV  circuits.  However, 
approximately 8% of exposed circuits of each voltage category went offline. 

Figure 4. Damage to the French transmission grid resulting from cyclones Lothar and Martin: 
towers destroyed by wind and debris (left axis), and circuits out of order (right axis) 

Source: OSCE, 2016 

Wind  turbines  also  suffered  damage  from  wind  loading.  These  structures  are  made  of  a 
tall and relatively slender column supporting a heavy load at the top. Therefore, despite 

10 

 
 
their  aerodynamic  shape  which  somewhat  reduces  wind  pressures,  a  relatively  small 
displacement of the large mass at the top generates a disproportionately greater bending 
moment  at  the  base.  The  result  is  a  failure  mechanism  similar  to  that  of  transmission 
towers. 

Wooden  and  concrete  distribution  poles  were  also  affected.  Distribution  pole 
failures  were  more  frequent  than  losses  of  transmission  towers  and  occurred  during 
every  storm  in  this  study.  The  mechanisms  of  damage  were  similar  to  those  of 
transmission  towers.  When  subjected  to  direct  wind  loading,  wooden  poles  may  snap 
(Figure  5)  and  concrete  poles  may  fail  in  bending.  Although  the  numbers  of  failures  of 
distribution  poles  and  transmission  towers  were  only  available  for  a  fraction  of  the 
incidents  reviewed  in  this  study,  the  distribution  system  suffered  disproportionately  in 
terms of absolute numbers. For instance, Hurricane Katrina damaged 3,000 miles (4,828 
km) of transmission lines and 28,500 miles (45,866 km) of Entergy’s infrastructure in the 
United  States  (Entergy  Corporation,  2005).  Although  the  fraction  of  this  damage 
attributed  to  flooding,  wind  forces  or  debris  impact  was  unclear,  transmission  and 
distribution  substations  typically  take  a  higher  toll  during  floods  (Karagiannis  et  al., 
2017a). The higher impact of storms to distribution poles and lines is arguably attributed 
to  their  higher  number  in  the  affected  area,  as  there  are  more  distribution  lines  than 
transmission lines, and the former tend to be more concentrated in urban areas. 

Figure 5. Snapped utility poles in St. Thomas, U.S. Virgin Islands, after Hurricane Irma 

Source: FEMA, 2017 (7) 

In  addition  to  the  effects  of  wind  loads  and  debris  impact,  a  particular  kind  of 
transmission  and  distribution  line  damage  occurs  during  ice  or  snow  storms.  Freezing 
rain  forms  glaze  ice  which  accumulates  on  power  lines  and  increases  their 
catenary  load.  The  added  weight  can  cause  the  line  to  break  or  distribution  poles  and 

(7)  Federal  Emergency  Management  Agency,  https://www.fema.gov/media-library/assets/images/141939 

(accessed December 5, 2018) 

11 

 
                                           
transmission  towers  to  collapse.  Furthermore,  glaze  ice  may  accumulate  on  trees, 
causing  large  branches  to  fall  tearing  down  power  lines.  For  instance,  these  types  of 
effects were quite pronounced during the winter storm which hit the Northeastern United 
States  (New  Hampshire,  New  York,  Vermont,  Massachusetts,  Connecticut,  Pennsylvania 
and Maine) in December 2008, leaving 1,365,500 customers without power. In the State 
of New York alone, National Grid replaced 350 utility poles and 235 km of cables (Nelson 
et al., 2009). 

Substations were also affected by storms in this study, albeit to a lesser extent 
compared to transmission and distribution lines. Inundation and airborne debris 
were  the  leading  mechanism  of  damage.  Because  electrical  equipment  is  often 
sensitive  to  even  minute  amounts  of  moisture  and  dirt,  flooding  can  take  substations 
offline  for  days  or  weeks  (Karagiannis  et  al.,  2017a).  Flying  debris  may  also  affect 
substation  equipment,  such  as  transformer  retaining  structures.  Records  of  substation 
damage  were  found  in  15  (out  of  52)  storms  reviewed  in  this  study.  Of  those,  12  were 
related to hurricanes and 3 to other types of storms. Hurricane records were unclear as 
to  whether  damage  was  due  to  flooding,  wind  loading  or  airborne  debris.  Other  than 
inundation, substations went offline  when they were disconnected from the grid 
because power lines were severed due to tower failures or debris. 

Storms  in  coastal  areas  may  affect  transmission  and  distribution  networks  by 
increasing  the  amount  of  saltwater  deposits  on  electrical  equipment.  As 
discussed  in  Karagiannis  et  al.  (2017a),  this  may  cause  catastrophic  failure  of  some 
electrified devices. Non electrified equipment may also be affected, because the saltwater 
deposits must be thoroughly cleaned before the equipment gets back online. 

Several mitigation strategies have been used to  increase the grid’s resilience to storms. 
One example is the use of alternative tower designs which limit the structure’s response 
to  wind  loads.  For  instance,  according  to  a  report  by  the  Puerto  Rico  Energy  Resiliency 
Working  Group  (2017),  monopole  towers  fared  better  than  the  lattice  structures  when 
Hurricane Maria hit Puerto Rico in 2017. Although not specified in the report, it is safe to 
presume  that  the  reason  is  that  their  narrower  profile  reduces  wind  pressures.  Another 
structural  mitigation  method  is  strengthening  the  foundations  of  transmission  towers  to 
reduce  their  vulnerability  to  toppling.  This  measure  may  also  help  to  mitigate  against 
flood and earthquake hazards (Karagiannis et al., 2017a). 

However,  structural  mitigation  measures  may  have  unwanted  side-effects.  For 
example, one flood mitigation strategy involves elevating vulnerable equipment, such as 
switchgear  and  relays,  above  the  expected  water  level  (Karagiannis  et  al.,  2017a). 
However, this strategy may expose substation equipment to debris impact. For instance, 
airborne  debris  was  the  primary  cause  of  heavy  damage  caused  by  Hurricane  Rita  to 
elevated substation equipment in Texas (Reed et al., 2010). 

2.4  Early warning, emergency response and recovery 

Early  warning  can  play  a  significant  role  in  electricity  grid  resilience  during  storms. 
Modern weather  forecasting  systems can provide notification of a forming storm system 
or worsening weather conditions with a lead time of a few days, depending on local and 
regional conditions. Uncertainty is inherent in all  hydrometeorological predictions, but is 
seldom communicated with weather forecasts. The lack of information on the uncertainty 
of  weather  predictions  can  undermine  decision-making  in  disasters  (National  Research 
Council, 2006). 

Although there is little that can be done to protect power grid assets from high winds in 
the  hours  or  days  before  a  storm  hits,  early  warning  is  a  significant  component  of 
electricity  resilience  in two  ways.  First,  effective  early  warnings can  help  TSOs  and 
DSOs mitigate the effects of the inundation caused by the intense rainfall which 
often  comes  with  storms,  by  preemptively  shutting  down  substations  which 
may be flooded. This measure prevents catastrophic damage which would otherwise be 
caused  when  electrified  equipment  came  in  contact  with  water  and  reduces  the  asset 

12 

recovery time to that needed for cleanup and repair (Karagiannis et al., 2017a). Second, 
early warning gives TSOs and DSOs time to activate their emergency operations 
plans. 

The  impact  of  storms  on  critical  power  infrastructure  follows  a  pattern  of  widespread 
moderate to catastrophic damage to the most  vulnerable components  along the storm’s 
path. Power  grid recovery  in the aftermath of storms  is therefore driven  by the 
need to repair or replace large numbers of utility towers and poles, and replace 
large  lengths  of  electric  cables.  The  success  of  emergency  response  for  TSOs  and 
DSOs  is  judged  by  how  fast  power  is  brought  back  online  to  the  maximum  number  of 
users  possible.  The  need  to  rapidly  conduct  a  vast  number  of  repairs  often  exceeds  the 
capacities  of  any  single  TSO/DSO  and  generates  a  demand  for  rapid  repair  capability, 
which is highly dependent on disaster preparedness. 

Rapidly increasing electricity infrastructure repair capabilities in the aftermath of a major 
storm requires mobilizing manpower, equipment and spare parts. TSOs/DSOs often enter 
into  mutual  aid  agreements  with  neighboring  jurisdictions  to  acquire  repair  crews  and 
equipment  in  the  event  of  an  emergency.  Additional  equipment  and  spare  parts  are 
usually  acquired  through  mutual  aid  agreements,  from  corporate  suppliers,  or  both. 
Response-generated  demands  include  shelter,  food  and  water  for  both  mutual  aid  and 
home-based  repair  crews,  transportation 
fuel  and 
consumables.  For  example,  the  rapid  restoration  of  power  to  every  customer  of 
Mississippi  Power  who  could  receive  it  in  the  aftermath  of  Hurricane  Katrina  is  largely 
attributed to the company’s efficient disaster logistics (Ball, 2006). 

for  people  and  equipment, 

Lessons identified after the response to a severe winter  storm that hit Slovenia in 2014 
emphasize  the  need  for  disaster  preparedness  in  a  European  context.  Over  the 
course  of  five  days,  from  January  30  to  February  3,  2014,  between  40  and  200  mm  of 
precipitation  fell  on  most  of  the  country.  In  the  western  and  southwestern  part  of  the 
country,  rainfall  reached  300  mm  (Markosek,  2015).  Freezing  rain  caused  extensive 
damage  to  the  country’s  transmission  and  distribution  infrastructure.  Electro  Ljubljana 
rapidly  reinforced  their  capabilities  through  mutual  aid  agreements  with  foreign 
distribution  companies.  The  response  effort  was  carried  out  by  1,500  people  from  civil 
protection, fire/rescue, the military, voluntary organizations, construction companies and 
electricity  companies.  The  use  of  temporary  transmission  towers  was  particularly 
effective,  as  it  helped  to  rapidly  reconnect  transmission  substations  to  the  grid  (OSCE, 
2014). 

However,  several  problems  hampered  the  response  and  slowed  down  recovery  efforts. 
Most were due to the lack of interoperability with non-traditional responders and mutual 
aid  resources.  First,  record  keeping  during  the  operation  was  a  challenge,  because 
different  companies  used  different  systems.  Second,  local  surge  arrangements  lacked 
efficiency.  Several  workers  were  engaged  without  written  agreements,  either  because 
printing was impossible due to the power outage, or because of the perceived urgency to 
make  the  resources  available  in  the  field.  Contractors  selected  through  a  tender  were 
sometimes  unfamiliar  with  the  area.  Many  volunteers  were  former  employees  of  the 
company,  by  then  retired.  Despite  their  technical  competencies,  they  could  not  be 
insured  and  were  thus  assigned  less  difficult  tasks.  Third,  language  barriers  hampered 
communications  with  mutual  aid  crews.  In  addition,  local  staff  were  tied  liaising  with 
mutual aid crews which were unfamiliar with the area and local practices, which reduced 
the number of available local resources. Last, transportation was difficult because of road 
closures, and cellular service was interrupted as soon as base stations ran out of backup 
power.  Until  power  was  at  least  partially  restored,  the  operation  had  to  rely  only  on 
satellite communications (OSCE, 2014). 

Access is also a major determinant of the power grid recovery in the  aftermath 
of storms. Transmission towers are often located in remote areas serviced by dirt roads 
which are easily blocked by landslides and debris such as fallen trees. For instance, most 
towers  damaged  by  Hurricane  Rita  were  located  in  marshes  and  were  thus  difficult  to 
access (Reed et al., 2010). Relocating transmission towers alongside main roads is likely 

13 

to increase the grid’s resilience. Although it is unknown whether this strategy will reduce 
the failure rate of towers, it is argued that it will make it easier for repair crews to access 
damage  locations  and  thus  speed  up  recovery.  Distribution  lines  are  usually  located 
alongside  urban  and  suburban  roads,  which  may  also  be  blocked  by  debris.  However, 
urban  and  suburban  roads  are  more  accessible  than  dirt  roads  in  mountainous  regions 
and are also usually easier to clear by heavy equipment. 

One  successful  response  strategy  involves  phasing  the  operation  to  protect 
repair  crews,  responders  and  equipment,  by  keeping  them  out  of  the  hazard 
zone. This means staging local and mutual aid resources at the edge of a hurricane zone 
or  sheltering  them  inside  strong  buildings  while  the  storm  passes.  Resources  are  then 
typically  deployed  after  the  storm  has  passed.  For  example,  Mississippi  Power  used  this 
strategy  to  streamline  its  response  to  Hurricane  Katrina  (Ball,  2006).  This  course  of 
action is also popular in search and rescue operations in the aftermath of hurricanes. 

Another successful strategy involved the use of temporary transmission towers. 
Called  Emergency  Restoration  Systems  (ERS)  or  simply  Restoration  Towers  in  some 
countries,  these  versatile  structures  are  relatively  easy  to  transport  and  can  be  erected 
with  minimal  equipment.  Although  a  transmission  tower  may  take  10  days  to  build,  a 
trained crew can erect an ERS tower within a day or two (Karagiannis, et al., 2017a) and 
allow  a  TSO  to  quickly  restore  a  line  until  a  permanent  structure  is  built.  Such  ERS 
towers  were  used  by  Electro  Ljubljana  in  the  aftermath  of  the  2014  ice  storm  and 
contributed a lot to the quick recovery. 

14 

 
 
3  Effects of climate change on the level of risk from an 

economic perspective 

A  pan-European  multi-hazard  analysis  by  Forzieri  et  al.  (2016)  indicates  that  the 
frequency  and  severity  of  windstorms  is  likely  to  increase  in  coastal  areas  in  Western, 
Eastern  and  Northern  Europe.  Because  of  the  high  population  density  and  intense 
economic activity of coastal areas, the level of risk is likely to increase substantially. This 
chapter demonstrates a methodology for the estimation of the change in the level of risk 
(quantified  in  economic  terms)  of  failure  or  disruption  of  the  power  grid  resulting  from 
the impact of climate change on the frequency and severity of storms. The assessment of 
the  level  of  risk  is  based  on  the  estimation  of  potential  losses  from  wind  loads  on  the 
transmission grid. A selected area in Europe is used as a case study. 

3.1  Methodology 

Disaster risk results from the probability of occurrence of a storm and the severity of the 
impact  of  the  event.  The  probability  may  be  expressed  either  qualitatively  or 
quantitatively,  depending  on  the  level  of  knowledge  about  each  hazard.  The  severity  is 
expressed  in  terms  of  the  expected  morbidity  and  mortality,  damage  to  property, 
disruption of infrastructure, and social consequences (Agius et al., 2017). The  scenario-
based approach is a popular disaster risk analysis methodology and is highly appropriate 
for the analysis of hazards when statistical information is available. 

Here, the change in the level of risk is estimated by the change in the probability and the 
impact severity of storms. Specifically, we analyze the change in the level of risk incurred 
by  the  increase  or  decrease  of  the  probability  of  occurrence  of  several  storm  scenarios 
brought  about  by  climate  change.  For  each  scenario,  the  level  of  risk  is  derived  as  the 
expected value of the economic losses resulting from the storm under review (Hickman & 
Zahn,  1966).  The  following  sections  discuss  how  we  determine  the  change  of  the 
probability of occurrence of each scenario and estimate the losses. 

3.1.1  Probability of occurrence 

The probability of occurrence of a storm is quantified by its recurrence interval, defined in 
this  case  as  the  average  number  of  years  between  storms  of  the  same  intensity  (8). 
Using  the  approach  discussed  in  Forzieri  et  al.  (2016;  2018),  we  derived  the  projected 
peak wind for a range of recurrence intervals over several future time slots. We use the 
ensemble mean values from 15 simulations combining different Global Circulation Models 
(GCMs) with different Regional Climate Models (RCMs) at a resolution of 0.11 degrees, as 
in the EURO-CORDEX (9) framework (Table 3). The return levels were computed using a 
peak-over-threshold  (98.5%)  generalized  Pareto  distribution  with  daily  maximum  wind 
speed as the input variable. 

3.1.2  Loss estimation 

Each  scenario  is  analyzed  to  determine  the  consequences  of  the  storm  on  the  affected 
communities.  The  potential  losses  are  a  function  of  the  intensity  of  the  hazard  and  the 
exposure of people and economic activities to that hazard. Loss estimations conducted in 
support  of  disaster  risk  assessments  and  hazard  mitigation  plans  consider  the  expected 
losses to people, buildings, infrastructure  and  other  community assets (Coppola, 2015). 
Here, loss estimation includes the economic losses  from the damage incurred to electric 
utilities and the impact to the local economy from the power outage. The total losses due 
to each scenario are calculated as the sum of the costs to repair the damage incurred to 

(8)  The actual number of years between storms of any given  intensity varies a lot. A common misconception 
about the 100-year storm is that it is likely to happen only once in 100 years. In reality, a 100-year storm 
is the storm which has a 1% annual exceedance probability, that is, a 1% chance of occurring every year. 
In other words, it is possible for the 100-year storm to occur two or more times per year. 

(9)  Coordinated  Downscaling  Experiment  -  European  Domain,  https://www.euro-cordex.net/  (accessed 

December 4, 2018) 

15 

                                           
electric  utilities  and  the  impact  to  the  local  economy  resulting  from  the  storm  and  the 
outage. The process is illustrated in Figure 6 and detailed in the following sections. 

Table 3. List of EURO-CORDEX simulations used in this study 

Simulation  Global Circulation Model  Regional Climate Model 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

ICHEC-EC-EARTH 

CNRM-CERFACS-CNRM-CM5 

DMI-HIRHAM5 

KNMI-RACMO22E 

CLMcom-CCLM4-8-17 

SMHI-RCA4 

CLMcom-CCLM4-8-17 

SMHI-RCA4 

CLMcom-CCLM4-8-17 

MPI-M-MPI-ESM-LR 

MPICSC-REMO2009 

SMHI-RCA4 

KNMI-RACMO22E 

MOHC-HadGEM2-ES 

CLMcom-CCLM4-8-17 

SMHI-RCA4 

NCC-NorESM1-M 

DMI-HIRHAM5 

IPSL-IPSL-CM5A-LR 

IPSL-INERIS-WRF331F 

SMHI-RCA4 

Source: EURO-CORDEX (10) 

The underlying assumption  is that the power outage associated with the storm 
does not cause additional casualties, injuries, or loss of property. This is likely to 
be  an  underestimation,  because  a  prolonged  power  outage  is  expected  to  undermine 
disaster  response  capabilities,  disrupt  healthcare  facilities,  render  heating  and  air-
conditioning systems inoperable, generate traffic jams and contribute to traffic accidents 
(Petermann  et  al.,  2011;  Karagiannis  et  al.,  2017a).  However,  existing  methodologies 
cannot grasp the secondary effects of power outages, as these depend to a large extent 
on  additional  parameters,  such  as  local  climate  and  weather,  and  disaster  response 
capabilities. 

3.1.2.1  Losses to critical energy infrastructure 

The  direct  losses  to  critical  energy  infrastructure  are  estimated  as  the  cost  of  repairing 
the  damaged  power  grid  assets.  The  repair  costs  are  calculated  from  the 
replacement  value  of  the  assets  and  the  potential  for  asset  failure.  The  latter  is 
estimated  from  the  exposure  of  assets  to  the  projected  peak  wind  speed  using 
appropriate fragility functions (Veeramany et al., 2015). 

(10)  Coordinated  Regional  Climate  Downscaling  Experiment,  http://www.cordex.org/  (accessed  December  4, 

2018). 

16 

                                           
Figure 6. Loss estimation approach 

First, we determine the exposure of power grid components to the storm. We combined 
geospatial  data  layers  of  electricity  network  assets  with  storm  scenarios  (developed  as 
discussed  in  section  3.1.1)  to  derive  the  peak  wind  speed  that  power  grid  components 
are exposed to. The storm scenarios yielded the projected peak wind speed values on the 
vertices  of  a  square  grid  with  a  .11  degree  (approximately  12  km)  edge.  The  projected 
peak wind speed was assumed to expand over the influence zone of each vertex, derived 
using Voronoi/Thiessen polygons (Sen, 2016; Longley et al., 2015). 

Subsequently,  fragility  functions  are  used  to  derive  the  cost  of  repairs  of  damaged 
components  from  the  peak  wind  speed  data.  Fragility  functions  express  the  conditional 
probability a damage state will be reached  or  exceeded as a function of the intensity of 
the hazard (Porter, 2015). Several fragility functions have been developed for towers and 
transmission lines (Dunn et al., 2018; Fu et al., 2017; Espinoza et al., 2016), which have 
been  shown  by  the  analysis  of  previous  storms  discussed  in  Chapter  2  to  suffer  more 
damage from windstorms than any other type of power grid asset. In their general form, 
storm fragility functions relate the probability of damage to the peak wind speed: 

𝐹𝑑(𝑥) = 𝑃[𝐷 ≥ 𝑑|𝑋 = 𝑥] 

(1) 

where: 

—  D = 0, 1... : damage state of a particular asset 

—  d: a particular damage state 

—  X: value of the hazard, i.e. the peak wind speed 

17 

 
 
 
—  x: a particular value of X 

The  wind  speed  at  the  height  of  transmission  lines  is  estimated  from  simulation  values 
using the logarithmic law discussed in Troen & Lundtang Petersen (1989): 

𝑢2 = 𝑢1

ln

ln

𝑧2
𝑧0
𝑧1
𝑧0

(2) 

where: 

—  z2: height at which the wind speed is sought (m) 

—  z1: reference height at which the wind speed is known (m) 

—  u2: wind speed at height z2 (m/s) 

—  u1: wind speed at reference height z1 (m/s) 

—  z0: surface roughness, taken from Table 4 (m) 

Table 4. Roughness length values used in this study and corresponding landscape types 

Roughness 

length (m) 

Landscape type 

0.0002 

Water surface. 

0.03 

0.4 

Open  agricultural  area  without  fences  or  hedgerows  and  very  scattered 
buildings. Only softly rounded hills. 

Villages,  small  towns,  agricultural  land  with  many  or  tall  sheltering 
hedgerows, forests and very rough and uneven terrain. 

0.8 

Larger cities with tall buildings. 

Source: Troen & Lundtang Petersen, 1989. 

The  failure  of  a  line  is  considered  to  be  independent  from  tower  failure,  so  different 
fragility functions are used. In addition, the outage of a transmission tower is considered 
to be independent of the condition of adjacent towers. The total damage is derived from 
the projected peak wind speed and replacement value of each affected facility or asset: 

𝑇𝑜𝑡𝑎𝑙 𝑅𝑒𝑝𝑎𝑖𝑟 𝐶𝑜𝑠𝑡 (𝑇𝑅𝐶) = ∑ ∑ ∑ 𝑓𝑑𝑗(𝑥𝑖𝑗) ∙ 𝑅𝑉𝑖𝑗

(3) 

𝑗=1

𝑖=1

𝑑=0

𝑚

𝑛

𝐷

where: 

—  i = 1, 2, …, n : facilities or assets belonging to each category 

—  j = 1, 2, …, m : categories of facilities or assets 

—  RVij: replacement value of each facility or asset i (of each category j) 

—  xij: peak wind speed at each facility i (of each category j) 

—  fdj(xij): fragility function of facility or asset category. 

3.1.2.2  Impact on the local economy 

The  effect  of  the  power  outage  on  the  local  economy  is  approximated  by  the  economic 
activity that is interrupted, on a per capita basis. When  all circuits supplying power to a 
transmission substation  fail because  of  wind shear and/or wind-borne  debris, customers 

18 

 
 
 
 
connected  to  that  substation  will  lose  power,  unless  the  TSO  can  reroute  power  from 
another  location.  Most  transmission  systems  use  three-wire,  three-phase  circuits,  which 
are  arranged  in  corridors  consisting  on  series  of  transmission  towers.  In  transmission 
grids designed for robustness and resilience, there may be more than one circuits in each 
corridor and each substation may be supplied by more than one corridor. In this study, a 
substation was assumed to lose power when all lines supplying it power were severed. A 
transmission  line  was  assumed  to  be  severed  when  there  was  at  least  one  failure  of  a 
tower or a wire between two substations. Therefore, the probability of a substation losing 
power is determined based on the probability of failure of its power supply: 

𝑚

𝑚

𝑛

𝐹𝑜𝑢𝑡 = ∏ 𝐹𝐶

= ∏ [1 − ∏(1 − 𝐹𝑇)
]

𝐶=1

𝐶=1

𝑇=1

(4) 

where: 

—  Fout: probability of outage of a given substation 

—  FC: probability of failure of corridor C 

—  n: number of line segments in corridor C 

—  m: number of corridors supplying the substation 

—  FT: probability of failure of a line segment T (a line may include one or more circuits, 

and each circuit is made of three wires) 

The  area  affected  by  the  loss  of  power  to  a  transmission  substation is  approximated  by 
its influence zone, which is derived using Voronoi/Thiessen polygons (Sen, 2016; Longley 
et al., 2015). The combination of the affected area with a population density map yields 
the number of people affected by the outage. 

The  business  losses  from  the  outage  are  approximated  by  the  daily  economic  activity 
that  is  interrupted  (Zimmerman  et  al.,  2005).  The  outage  stops  all  business  in  the 
affected area until power is back  online.  Costs are  estimated on a per capita basis. The 
business  losses  from  power  loss  at  a  single  substation  are  estimated  by  the  following 
equation: 

𝐵𝑢𝑠𝑖𝑛𝑒𝑠𝑠 𝐶𝑜𝑠𝑡 (𝐵𝐶) =

𝐺𝐷𝑃 ∙ 𝑡𝑜𝑢𝑡 ∙ 𝑃𝑜𝑢𝑡 ∙ 𝐹𝑜𝑢𝑡
365 ∙ 𝑃𝑡𝑜𝑡

(5) 

where: 

—  GDP: the Gross Domestic Product of the jurisdiction under review 

—  tout: the estimated duration of the outage (in days). 

—  Ptot: the jurisdiction’s entire population. 

—  Pout: the population affected by the outage. 

Then,  the  total  business  cost  is  the  sum  of  the  future  business  losses  from  the  loss  of 
power at each individual substation: 

𝑇𝑜𝑡𝑎𝑙 𝐵𝑢𝑠𝑖𝑛𝑒𝑠𝑠 𝐶𝑜𝑠𝑡 (𝑇𝐵𝐶) = ∑ 𝐵𝐶𝑖

𝑘

(6) 

This  formulation  is  based  on  three  underlying  assumptions.  The  first  is  that  the  power 
outage  lasts  longer  than  the  storm,  therefore  the  first  term  in  the  numerator  of  the 
Business Cost (BC) formulation is dominated by the duration of the outage and not of the 
storm. This is a realistic hypothesis, because the repair of damaged electric utility assets 
only starts after the storm has passed. The second assumption is that the duration of the 

19 

 
 
 
 
 
 
outage  is  the  same  throughout  the  affected  area.  This  could  be  an  overestimation, 
because power is restored progressively as repairs are made (Karagiannis et al., 2017a). 

The  third  assumption  is  that  the  local  economic  activity  is  homogeneously  distributed 
throughout the storm affected area. This could be an underestimation or overestimation 
of  the  business  cost,  depending  on  the  locus  of  economic  activities.  For  instance,  if 
business  is  more  concentrated  near  the  coast,  then  the  estimated  business  cost  will  be 
lower than the actual one. On the other hand, if local businesses are located  away from 
the coast, then the estimated cost would constitute an overestimation. 

3.1.3  Change in risk level 

The combined impact is calculated as the sum of the  total repair cost and the impact to 
the  local  economy  resulting  from  the  outage.  Then,  the  level  of  risk  is  derived  as  the 
present  value  of  the  expected  future  losses  incurred  from  each  scenario  (Hickman  & 
Zahn, 1966): 

𝑁

𝑅𝑖𝑠𝑘  = 𝐸 [∑

𝑡=1

𝑇𝑅𝐶𝑡
(1 + 𝑖)𝑁

𝑁

+ ∑

𝑡=1

𝑇𝐵𝐶𝑡
(1 + 𝑖)𝑁

] 

(7) 

where: 

—  TRCt: the total repair cost in year t, which is calculated using equation (3) 

—  TBCt: the expected business losses in year t, which are calculated using equation (5) 

—  i: the social discount factor  

—  N: the number of years in the future 

We analyze the change in the level of risk (expressed in terms of the expected economic 
losses)  caused  by  the  change  (increase  or  decrease)  of  the  probability  of  occurrence  of 
several storm scenarios because of climate change. 

In valuation analysis, the concept of time value of money reflects the notion that  money 
available today is worth more than in the future. Therefore, a discount factor is used to 
convert future cash flows in their equivalent present values. In this case, cash flows are 
the expected total repair cost and business losses over an extended period of time in the 
future.  From  the  point  of  view  of  a  private  entity,  such  as  a  TSO  or  DSO,  the  discount 
rate is the interest it has to pay. On the other hand, governments use what is called the 
social  discount  rate,  which  reflects  the  relationship  between  the  interest  rate  faced  by 
consumers and producers (Stiglitz & Rosenbauer, 2015). The choice of the social discount 
rate  is  a  contested  political  issue  and  differs  widely  among  countries.  For  instance,  the 
European Commission advocates a social discount rate of 3-5% for major projects funded 
under the Cohesion Policy 2014-2020 programming period (DG REGIO, 2015). However, 
Member States use different social discount rates for different projects (Evans, 2006), or 
may have not regulated a social discount rate at all. 

Another  set  of  difficulties  arise  with  the  change  in  the  characteristics  of  the  study  area 
over an extended period of time. First, the GDP of any country is expected to change in 
response  to  the  country’s  economic  environment,  resources  and  other  factors.  All  other 
things being equal, an increase in the GDP would bring about a proportionate increase of 
the  business  cost  estimated  from  Equations  5  and  6,  but  also  arguably  increase  the 
affected country’s resilience. Shocks such as economic crises are notorious for being able 
to  dramatically  shrink  the  economy  of  the  affected  country  to  a  fraction  of  its  pre-crisis 
capability.  However,  economic  outlooks  are  notoriously  uncertain  for  periods  of  time 
longer  than  one  or  two  years  (Silver,  2012),  let  alone  a  few  decades,  as  is  the  case  in 
this study. 

Second, population density is dynamic and depends on the population and land use. The 
United  Nations  World  Population  Prospects  (2017)  project  that  Europe’s  population  will 

20 

 
steadily  decline  by  approximately  12%  until  2100.  Nevertheless,  this  trend  is  not 
homogeneous  across  the  continent  and  the  population  of  some  EU  Member  States  is 
expected to increase. In addition, landscape and land use cannot be expected to remain 
constant  over  several  decades,  yet  are  impossible  to  predict  with  any  reasonable 
accuracy for the needs of our study. Different combinations of changes in population and 
land use would have wide ranging results in the business cost estimated from Equations 
5 and 6. All other things beings equal, a homogeneous increase in the population density 
of the affected country would have no effect on the business cost, because the increases 
in  the  population  of  the  affected  country  and  the  area  suffering  a  power  outage  would 
cancel each other out. However, if the change was heterogeneous across the country, for 
instance, if the population increased more in urban areas and less in rural areas, then the 
business cost would change accordingly. 

Last, critical infrastructure itself is aging. If no maintenance is undertaken, the condition 
of  infrastructure  assets  and  equipment  may  have  been  seriously  degraded  within  the 
timeframe of this study. A degradation of the serviceability would only naturally increase 
the  vulnerability  to  natural  hazards.  On  the  other  hand,  ongoing  maintenance  can  slow 
down the aging process and new projects could add to the power grid’s resilience through 
distributed  generation,  increased  centrality  and  hazard  mitigation  measures.  However, 
whether  any  such  actions,  or  a  combination  thereof,  will  be  undertaken  or  not,  and  by 
when, is impossible to foresee with any accuracy. 

3.2  Demonstration of the methodology 

A case study, based on a coastal metropolitan area in Europe, was used to demonstrate 
how  the  methodology  outlined  in  section  3.1  would  be  implemented.  The  following 
sections  outline  the  implementation.  Section  3.2.1  presents  the  case  study  site.  In  the 
interest  of  avoiding  the  unintentional  disclosure  of  any  potential  vulnerabilities,  every 
effort has been made to maintain the anonymity of the case study site, even at the cost 
of the accuracy of the results. Section 3.2.2 discusses the change in the projected peak 
wind speed for several return periods given two climate change scenarios. Section 3.2.3 
describes  the  impact  severity  of  the  storm  scenarios,  which  was  approximated  by  the 
damage to transmission grid assets and the interruption of the daily economic activity in 
the  area  because  of  the  storm  and  the  power  outage.  The  change  in  the level  of  risk  is 
outlined in Section 3.2.4. 

3.2.1  Case study site 

Galorndon  is  an  anonymized  sub-regional  administrative  division  with  a  Chief  Elected 
Official  –  Council  type  of  government.  It  has  a  population  of  approximately  1,500,000 
and a surface area of about 10,000 km2. Galorndon is located on Europe’s Atlantic Coast, 
with  two  navigable  rivers  converging  near  its  major  urban  center,  Galorndon  Core.  The 
topography  of  Galorndon  is  relatively  flat,  with  forests  in  the  south  and  agricultural  or 
rural areas throughout its territory. The mean temperature ranges from 6.6oC in January 
to  21.4oC  in  August.  The  average  annual  rainfall  is  944  mm,  with  a  peak  in  November 
(110 mm). Electricity in the country Galorndon is managed in accordance with European 
and national law. The choice of the site was driven by the availability of the data needed 
for  the  loss  estimation discussed  in  section  3.1.2,  notably  geospatial information  on  the 
transmission grid and the characteristics of components such as lines and substations. 

3.2.2  Hazard analysis 

Using  the  approach  discussed  in  Forzieri  et  al.  (2016;  2018),  we  derived  the  projected 
peak wind speed of the 10-, 50- and 100-year storm scenarios for five time periods, i.e. 
from  1981  to  2010,  from  2011  to  2040,  from  2021  to  2050,  from  2031  to  2060,  and 
from 2041 to 2070. In conjunction with the IPCC Fifth Assessment Report, we used two 
Representative  Concentration  Pathways  (RCP)  emission  scenarios.  The  RCP8.5  scenario 
represents very high greenhouse gas emission, which continues to rise even after 2100, 

21 

whereas the RCP4.5 scenario calls for the stabilization of radiative climate forcing at the 
end of the century (Moss et al., 2008). 

Under both RCPs, the intensity of today’s storms for a given return period would change 
in  the  future.  The  estimated  peak  wind  velocities  are  generally  greater  for  RCP8.5  than 
for  RCP4.5.  However,  the  variance  of  the  projected  velocities  is  generally  more  limited 
under  the  RCP8.5.  Because  of  the  relevance  of  this  study  to  civil  protection  and  critical 
infrastructure  protection  policies,  we  demonstrate  the  risk  analysis  using  the  RCP8.5, 
which is representative of the 90th percentile of the baseline CO2 emissions range (Moss 
et al., 2008), as a reasonable worst-case scenario. Nevertheless, the methodology can be 
used for any RCP. 

The  annual  probability  of  occurrence  of  each  event  is  calculated  from  the  return  period 
assuming  a  Poisson  process.  For  each  scenario,  we  used  a  logarithmic  law  (Equation  2) 
to estimate the wind speed based on local factors, such as the above ground level (AGL) 
height  of  the  affected  transmission  towers  and  land  use.  Table  5  below  shows  one 
example. 

Table 5. Estimation of the wind speed [m/s] for a transmission tower with a height of 18m AGL, 
located in a residential area with a surface roughness of 0.4m 

Scenario 

50-year storm 

100-year storm 

0
1
0
2
-
1
8
9
1

0
4
0
2
-
1
1
0
2

0
5
0
2
-
1
2
0
2

0
6
0
2
-
1
3
0
2

0
7
0
2
-
1
4
0
2

0
1
0
2
-
1
8
9
1

0
4
0
2
-
1
1
0
2

0
5
0
2
-
1
2
0
2

0
6
0
2
-
1
3
0
2

0
7
0
2
-
1
4
0
2

28.72  28.88  28.74  28.69  28.72  29.61  29.73  29.58  29.53  29.6 

29.72  29.81  29.73  29.63  29.54  32.51  32.70  32.54  32.48  32.51 

Time 
slot 

Area 
wind 
speed 

Local 
wind 
speed 

3.2.3  Loss estimation 

3.2.3.1  Data and assumptions 

The transmission grid layers were obtained from the open data platform of the country’s 
TSO.  In  this  study,  we  used  the  layers  of  substations  and  transmission  lines.  There  are 
41 transmission substations (maximum voltages of 400 kV down to 63 kV) and over 227 
km  of  transmission lines  (400  kV,  225  kV,  90  kV  and  63  kV).  Figure  7  below  illustrates 
the  transmission  network  in  and  around  Galorndon.  The  jurisdiction’s  layers  were  taken 
from the country’s open data portal and the transmission grids layers from the open data 
portal  of  the  country’s  TSO.  There  are  41  substations  and  over  227  km  of  lines.  We 
assumed no significant upgrade in or degradation of the transmission grid infrastructure 
until 2070. 

As  discussed  in  Chapter  2,  tall,  slender  structures,  such  as  transmission  towers, 
distribution  poles  and  wind  turbines  are  most  affected  by  storms.  Transmission  and 
distribution assets can also be damaged by the impact of flying debris. Substations were 
also found to be affected by storms, particularly by inundation and airborne debris, albeit 
to a lesser extent compared to transmission and distribution lines. Nevertheless, fragility 
functions  have  been  developed  for  transmission  towers    and  lines  only  (among  others, 
Dunn  et  al.,  2018;  Panteli  &  Mancarella,  2015;  Prahl  et  al.,  2015;  Prahl  et  al.,  2016; 
Winkler  et  al.,  2010).  Winkler  et  al.,  (2010)  use  building  fragility  functions  to  estimate 

22 

 
 
 
 
 
 
 
 
 
 
the  potential  losses  to  transmission  substations.  Here,  we  used  the  analytical  fragility 
functions  for  transmission  towers  and  overhead  lines  developed  by  Panteli  &  Mancarella 
(2017). This family of fragility functions, which includes separate functions for overhead 
lines and towers, was considered a better fit for the needs of this particular study than a 
single fragility or damage function for both types of assets across a corridor. Panteli et al. 
(2017)  highlight  the  relative  lack  of  empirical  fragility  functions  and  the  high  costs 
associated with the development of experimental fragility curves for transmission towers 
and  overhead  lines.  The  replacement  value  of  transmission  lines  was  considered  as  € 
900,000 per km (ICF Consulting, 2002). 

Figure 7. Map of the transmission grid on and around Galorndon (white: substations and overhead 
lines; black: underground lines) 

Source: JRC. Background: Bing®. 

In this particular case, the projected peak wind speeds were quite low, consequently the 
probability of damage to individual transmission towers was consistently null according to 
the base case fragility function proposed by Panteli et al. (2017). Therefore, the analysis 
shows  no  damage  to  transmission  towers.  This  outcome  however  is  in  contrast  to  the 
incident  analysis  in  Chapter  2,  which  indicates  that  transmission  towers  are  affected  in 
nearly every storm in that dataset. 

In  addition,  as  discussed  in  section  3.1.2.2,  a  substation  was  assumed  to  lose  power 
when all lines supplying it  with power were severed. When a substation loses power, all 
customers  connected  to  that  substation  will  lose  power,  unless  the  TSO  can  reroute 
power  from  another  location.  The  area  affected  by  the  loss  of  power  to  a  transmission 

23 

 
substation is approximated by its influence zone, which is derived using Voronoi/Thiessen 
polygons.  Therefore,  the  probability  of  any  location  experiencing  a  blackout  is  equal  to 
the  probability  of  loss  of  power  to  the  substation.  However,  transmission  networks  are 
built so that each higher-voltage substation supplies power to one or more lower-voltage 
substations  and  so  on.  For  example,  each  400  kV  substation  will  be  supplying  power  to 
several 225 kV substations, each 225 kV substation will be supplying power to several 90 
or  63  kV  substations  etc.,  until  power  reaches  the  sub-transmission  and  distribution 
network. In other words, each customer will be connected to at least one substation from 
each voltage category. Therefore, if we considered the area affected by the loss of power 
to more than one category of substations, we would be counting each location more than 
once  and  would  be  estimating  the  expected  business  losses  based  on  a  largely 
overestimated  population.  To  avoid  multiple  counting,  only  225  kV  substations  were 
included  in  the  analysis.  This  voltage  category  was  selected  as  a  good  compromise 
between  granularity  of  coverage  and  the  need  to  minimize  the  impact  of  the  grid’s 
centrality on the analysis. 

Population  density  was  determined  based  on  national  census  data  for  the  area  under 
consideration.  Several  assumptions  were  made  in  the  interest  of  anonymity,  including 
homogeneous  population  density  throughout  the  case  study  area  and  no  significant 
population  changes  since  the  last  population  census.  With  a  view  to  maintaining  the 
anonymity of the case  study area, we used the same GDP throughout the study period, 
assuming no significant change in the national economy of the case study site until 2070. 
The social discount factor was not included in the calculations for the same reason. 

3.2.3.2  Results 

One of the first outcomes of this approach is the probability of loss of power to individual 
substations.  As  an  example,  Figure  8  illustrates  the  probability  of  loss  of  power  to  each 
225 kV substation in the study area in the 100-year storm for each time slot due to line 
severing.  The  substations  have  been  designated  as  A  to  N.  Substation  K  is  by  far  most 
likely to lose power throughout all 5 time slots, because it is connected to less corridors 
which are exposed to higher wind speeds. By contrast, the probability of loss of supply to 
substations  A,  D  L  is  negligible,  and  very  limited  for  substations  C,  G  and  J.  The  lower 
level of risk is due to a combination of two factors. First, these substations are connected 
to  more  corridors,  which  makes  their  power  supply  more  resilient.  Second,  they  are 
exposed to milder winds, which reduces the likelihood of damage to any line or circuit. 

Of particular interest is the lack  of significant change  of the probability of loss of power 
supply to the substations across the five time periods. This is attributable to the limited 
variation of the intensity of the 100-year storm for short- and medium-term projections. 
The reason of this relative homoscedasticity is that we use the ensemble mean from 15 
simulations,  some  of  which  may  project  an  increase  in  return  levels,  while  others  may 
project a decrease. 

The expected economic losses resulting from direct damage to the transmission grid and 
the  interruption  of  daily  economic  activity  are  illustrated  in  Figure  9.  Here,  it  was 
assumed that 400 kV and 225 kV corridors included twin circuits, whereas 90 kV and 63 
kV corridors each included a single circuit. This configuration is used here as an example 
of a typical case in that area. However, the number of circuits for each individual corridor 
can  be  easily  modified  according  to  any  grid  architecture.  In  addition,  in  this  case,  the 
power outage was assumed to last for three days on average throughout the area. 

For  each  recurrence  interval,  the  cost  to  repair  the  damage  to  overhead  lines  and  the 
economic  losses  from  the  interruption  of  the  daily  economic  activity  amount  each  for 
about  half  of  the  total  losses.  Nevertheless,  the  repair  cost  fraction  increases  (by 
approximately  10%  of  the  total  cost)  for  the  50-year  and  the  100-year  storms 
compared  to  the  10-year  scenario.  Because  the  projected  peak  wind  speed  values 
were relatively low, the risk due to damage to transmission towers was negligible in this 
case. Furthermore, the expected economic losses from each scenario follow a discernible 
pattern across all time slots. Specifically, there is an initial increase in the total economic 

24 

losses  during  the  period  2011-2040,  which  is  followed  by  a  progressive  decrease  until 
2070. The change is more pronounced for the 10-year scenario, but remains statistically 
insignificant overall. 

Figure 8. Probability of loss of power to 225 kV substations under the 100-year storm 

Figure 9. Expected economic losses from the 10-year, 50-year and 100-year storm across five 
sequential and partially overlapping time periods 

25 

0.000.050.100.150.200.250.300.350.40ABCDEFGHIJKLProbabilitySubstation1981-20102011-20402021-20502031-20602041-2070 
Moreover, the total expected losses increase (by approximately 10% of the total) 
for the 50-year and the 100-year storms compared to the 10-year scenario. This 
change  seems  to  be  driven  by  the  repair  cost  increase.  In  other  words,  the  damage  to 
the transmission grid becomes more critical for the 50-year and the 100-year scenarios, 
compared to the 10-year storm. Nevertheless, there is no statistically significant increase 
of the economic impact from the 50-year to the 100-year storm. Nevertheless, this result 
is  conditional  on  the  assumption  of  a  constant  power  outage  duration  across  all 
scenarios. Although, ceteris paribus, a higher storm wind speed would arguably result in 
more severe and widespread damage in the affected area, the incident analysis discussed 
in Chapter 2 does not provide sufficient information to correlate the return period of the 
storm with the duration of the power outage. 

The  duration  of  the  power  outage  is  indeed  an  uncertain  determinant  of  the  expected 
losses resulting from the disruption of the daily economic activity. On the other hand, the 
Total  Repair  Cost  was  assumed  to  be  independent  of  the  duration  of  the  outage  for  the 
range of recovery times outlined in Table 1. So far, the duration of the power outage was 
assumed to be three days, a minimum emergency planning threshold (McEntire, 2018). 
As discussed in Chapter 2 and Karagiannis et al. (2017a), the duration of a power outage 
in  any  location  depends  on  the  duration  of  the  storm  (because  repairs  start  when  it  is 
safe  for  crews  to  operate),  the  extent  of  the  damage,  the  repair  capabilities  of  the 
TSO/DSO  and  the  prioritization  of  the  repairs.  Furthermore,  power  is  restored 
progressively  as  repairs  are  made.  Figure  10  illustrates  the  sensitivity  of  the  Total 
Business Cost of the 100-year scenario to the duration of the outage. As the duration of 
the power outage increases from 3 to 10 days, the estimated business cost increases as 
well and progressively becomes the main determinant of the  total impact. Whereas at 3 
days,  the  Total  Business  Cost  is  about  half  the  Total  Cost,  at  10  days  it  is  more  than 
double  the  Total  Repair  Cost.  Investments  in  emergency  repair  capabilities  can  help 
expedite  the  power  grid’s  recovery  and  limit  the  duration  of  the  outage.  A  sensitivity 
analysis  such  as  the  one  discussed  here  can  be  useful  in  estimating  whether  the 
cost  of  developing  and  maintaining  these  capabilities  is  justified  by  the 
mitigation  of  the  impact  resulting  from  the  disruption  of  the  daily  economic 
activity. 

26 

 
 
 
 
Figure 10. Total business cost for different power outage scenarios (note the y-axis scale change 
for 7 and 10-day outage) 

27 

 
 
3.2.4  Risk analysis 

The level of risk is estimated based on the expected economic losses from the storm and 
the power outage it could trigger. Figure 11 presents the level of risk (i.e. the product of 
the  occurrence  probability  and  the  estimated  economic  losses)  for  each  scenario.  Here, 
the  business  cost  estimation  is  based  on  a  3-day  power  outage.  The  10-year  scenario 
comes  with  the  highest  level  of  risk  and  the  100-year  scenario  is  associated  with  the 
lowest  level  of  risk.  Specifically,  the  risk  of  the  50-year  storm  is  twice  that  of  the  100-
year storm, and the risk from the 10-year storm is more than three times that of the 50-
year  scenario.  Nevertheless,  the  level  of  risk  (expressed  in  terms  of  economic  losses) 
does not appear to change substantially over time. 

Figure 11. Change in the level of storm risk to critical energy infrastructure in the case study area 

The  risk  analysis  presents  two  counter-intuitive  outcomes.  First,  the  risk  from  the  10-
year  storm  appears  to  be  much  higher  than  the  100-year  scenario.  Yet  the  intensity  of 
most natural hazards (including meteorological hazards, such as storms) increases as the 
return  period  increases.  In  addition,  conventional  wisdom  suggests  that  more 
devastating storms are a rarer occurrence. However, risk is a function of the probability 
of  occurrence  and  the  estimated  severity.  In  this  case,  the  expected  losses  for  the  50-
year  and  the  100-year  storms  are  10%  greater  than  those  for  the  10-year  storm. 
Therefore,  the  level  of  risk  (expressed  as  economic  losses)  is  driven  by  the 
probability of each scenario. In other words, the 10-year storm has a higher risk 
because it is expected to occur more frequently. 

As discussed in the previous sections, this outcome is due to several reasons. One is the 
relatively low values of projected wind speeds in the case study area, possibly because of 
the coarse spatial resolution. With these projections, the estimated probability of damage 

28 

 
 
to transmission lines was limited and no damage was expected to occur to transmission 
towers at all. In addition, the  increase of the projected wind speed  from the 10-year to 
the  50-year  to  the  100-year  scenario  is  relatively  limited  and  does  not  result  in  a 
significant  increase  of  the  probability  of  damage  to  transmission  lines,  and  therefore  of 
the  probability  of  loss  of  power  to  substations.  Therefore,  the  increase  of  the  total 
expected losses from the 10-year to the 50-year and the 100-year storm is about 10%. 

Another reason is that the increase of the estimated business losses is limited because of 
the assumption of a constant duration resulting from the power outage. One could argue 
that  the  100-year  storm  would  be  more  intense  and  therefore  produce  more  damage 
than  the  10-year  storm.  For  a  constant  repair  capability  level,  more  damage  would 
arguably prolong the recovery period and thus lead to a longer power outage. However, 
the relationship between the level of damage and the duration of the power outage may 
not  be  linear,  and  the  available  data  are  insufficient  to  produce  a  mathematical 
correlation. Furthermore, the projected peak wind speed values are greater for the 100-
year storm than for the 50-year and the 10-year storms, but the difference is relatively 
limited. 

The second outcome of the risk analysis which merits discussion is that  the level of risk 
does not appear to change significantly over time. Although the difference in the level of 
risk  among  the  10-year,  the  50-year  and  the  100-year  scenarios  is  dominated  by  the 
scenario probabilities, the change over time is driven by the estimated impact, which was 
shown  above  to  have  statistically  insignificant  changes.  In  this  particular  case,  this 
means that climate change  would not result in a substantial change of the level 
of  risk  posed  from  storms  to  the  power  grid  in  Galorndon.  At  first  glance,  this 
finding  could  be  interpreted  as  an  overall  limited  change  of  the  risk  of  economic  losses 
from storms to the power grid under climate change. However, we argue against such a 
generalization.  This  finding  is  probably  due  to  the  use  of  the  ensemble  mean  from  15 
simulations,  which  may  project  an  increase  or  decrease  in  return  levels.  More 
importantly,  Forzieri  et  al.  (2018)  underline  that  it  is  the  Mediterranean,  not  Europe’s 
Atlantic  coast,  which  is  likely  to  be  most  exposed  to  storms  in  light  of  climate  change. 
Therefore,  the  analysis  of  the  risk  of  an  area  with  higher  exposure  to  wind  loads  and 
perhaps  a  less  resilient  power  grid  could  arguably  yield  a  much  greater  change  in  the 
level of risk. 

The findings of the risk analysis demonstrate a favorable case for Galorndon. Specifically, 
the  highest  level  of  risk  of  economic  losses  stems  from  the  10-year  storm.  Therefore, 
prevention  and  preparedness  efforts  should  focus  on  this  scenario.  The  overall  low 
level  of  probability  of  failure  of  individual  assets  suggests  that  investing  in 
emergency  repair  and  recovery  capabilities  should  be  economically  justified  in 
this case, as the initial investment and maintenance cost will be spread over most, if not 
all, assets and over a large time frame. 

3.3  Discussion 

The purpose of this study has been to elucidate the vulnerability of critical infrastructure 
to  storms,  especially  in  light  of  climate  change.  We  have  focused  on  critical  energy 
infrastructure  as  a  first  step.  In  what  follows,  we  first  discuss  the  implications  of  this 
study for disaster risk management, then we outline the limitations of the case study. 

As  any  modeling  effort,  our  results  may  be  suffering  from  the  epistemic  uncertainty 
related  to  our  understanding  of  climate  change  and  the  impact  of  natural  hazards  on 
infrastructure. In this case, the estimated impact of climate change on the level of risk is 
critically  dependent  on  the  potential  uncertainties  in  the  data  and  the  climate  models 
used. Due to the coarse spatial resolution and the intrinsic features of climate models, in 
particular the Global Circulation Models, extreme values may be reduced and simulations 
may yield lower than expected peak wind speeds for single events. Moreover, the fragility 
functions  for  storms  are  less  mature  compared  to  those  for  other  natural  hazards,  such 
as earthquakes or floods. 

29 

Second,  Voronoi/Thiessen  polygons  effectively  approximate  the  influence  zone  of  each 
substation, but they  come with two disadvantages. First, the influence zone determined 
by the Thiessen polygon may not correspond to the actual area receiving power from the 
substation. In other words, the distribution network may be built in such a way that each 
client  does  not  get  power  from  their  nearest  substation.  Second,  the  use  of  Thiessen 
polygons  assumes  that  each  client  may  receive  electricity  from  only  one  substation.  If 
that substation is shut down, then this client loses power until the substation is brought 
back  online.  However,  each  client  may  be  connected  to  two  or  more  substations,  and 
redundancies are often built into the transmission grid (and more often in the distribution 
grid), which allow TSOs to switch to a different source when a substation is shut down. 

Third,  as  discussed  in  a  previous  study  (Karagiannis  et  al.,  2017b)  the  methodology 
presented  in  Chapter  3  does  not  take  into  account  the  interdependencies  between  the 
power grid and other critical infrastructure systems, and may underestimate the severity 
of  the  consequences.  The  impact  of  dependencies  and  interdependencies  on  the 
resilience  of  critical infrastructure  systems is notorious (Rinaldi et al., 2001; Pescaroli & 
Alexander, 2016). In addition, electricity is recognized as the critical infrastructure upon 
which all others rely. 

Last, this approach does not consider the  inherent redundancy of the transmission grid. 
When  a  line  or  substation  is  out  of  service,  the  TSO  can  reroute  power  through  other 
circuits. In fact, the  ENTSO-E  Continental Europe Operation Handbook (11) requires that 
the  loss  of  any  single  element  (such  as  line,  generating  unit  or  transformer)  shall  not 
cause a cascading failure outside the border of the affected TSO (N-1 principle). Although 
the  assumption  of  a  power  outage  resulting  from  the  loss  of  a  substation  is  supported 
from empirical data to a certain extent, as discussed in Chapter 2 and Karagiannis et al. 
(2017a),  the  results  may  be  an  overestimation  of  the  probability  and  consequences  of 
the power outage. 

Opportunities for further research include the extension of the approach to other critical 
infrastructure  systems,  provided  that  appropriate  fragility  functions  are  available.  In 
addition, the methodology could arguably be improved if it were combined with network 
analysis to help determine the cascading effects of the loss of one substation to the grid. 
Another  possible  improvement  would  be  the  consideration  of  population,  land-use  and 
economy  dynamics  in  the  estimation  of  the  impact  from  the  interruption  of  the  daily 
economic activity. Last, the findings of Chapter 2 can be combined with information from 
previous  studies  (among  others,  Karagiannis,  et  al.,  2017a;  Petermann  et  al.,  2011)  to 
identify cost-effective and sustainable, structural and organizational protection measures 
for critical infrastructure against selected natural hazards. 

(11)  European  Network  of  Transmission  System  Operates  for  Electricity,  Continental  Europe  Operation 
https://docstore.entsoe.eu/publications/system-operations-reports/operation-

Handbook, 
handbook/Pages/default.aspx (accessed November 29, 2018) 

30 

 
 
                                           
4  Recommendations for disaster risk management 

The  findings  of  this  study  reinforce  and  confirm  several  recommendations  discussed  in 
Karagiannis  et  al.  (2017a),  outlined  in  Table  6.  The  reader  is  referred  to  that  previous 
study  for  a  detailed  discussion  of  these  recommendations.  The  evidence  discussed  here 
justifies  these  Recommendations  based  on  the  same  rationale  as  in  Karagiannis  et  al. 
(2017a).  One  addition  could  be  made  to  Recommendation  6  pursuant  to  this  is  that 
Emergency  Restoration  Systems  (ERS)  should  be  part  of  the  emergency  response 
equipment  cache  of  any  TSO  or  DSO  whose  assets  are  located  in  high-risk  areas  for 
storm loading. 

Table 6. Recommendations from Karagiannis et al. (2017a) supported by the findings of Chapter 2 
of this report 

Recommendation 3 

Transition  from  hardening  system  assets  and  facilities  to 
building resilience into the grid. 

Recommendation 5 

TSOs/DSOs should develop, implement and exercise emergency 
operations plans. These plans should be updated when gaps are 
identified, e.g. in case of climate change. 

Recommendation 6 

Stockpile  spare  items  to  expedite  the  repair  or  replacement  of 
key assets and equipment. 

Recommendation 7 

Ensure  interoperability  among  neighboring  TSOs,  TSOs  and 
DSOs,  and  between  TSOs/DSOs  and  emergency  management 
organizations. 

Source: Karagiannis et al., 2017a. 

Besides  reinforcing several recommendations from a previous study, the findings of this 
study  support  four  additional  recommendations  about  disaster  management  for  critical 
energy infrastructure. 

Recommendation  1:  Consider  increasing  transmission  tower  design  requirements  for 
resistance to wind loading in standards and regulations. 

Chapter  2  illustrated  that  utility  poles  (i.e.  transmission  towers  and  distribution  poles) 
and overhead lines are the most vulnerable component of the power grid to wind storms, 
suffering  failures  from  horizontal  wind  loading.  In  addition,  Forzieri  et  al.  (2018) 
estimated that the peak wind speed is likely to increase throughout Europe in the future 
and the change is likely to be more pronounced in coastal areas. Chapter 3 showed that, 
for  such  an  area,  the  increase  in  the  peak  wind  speed  can  also  increase  the  risk  from 
wind  storms,  resulting  from  direct  damage  to  the  transmission  grid  and  the  ensuing 
power outage. Reinforcing steel lattice tower structures located in coastal and other high-
risk  areas  to  resist  wind  loads  should  decrease  the  risk  associated  with  wind  storms. 
Increasing the requirements stipulated in relevant regulations and standards, such as IEC 
Standard  60826  and  Eurocode  1  (Part  1-4),  could  help  harmonize  design  specifications 
across  Europe.  In  addition,  structural  mitigation  measures,  such  as  strengthening  the 
foundations of transmission towers to prevent  toppling, could also help mitigate against 
flood and earthquake forces. A benefit-cost analysis should be used to analyze the cost-
effectiveness of this approach. The risk assessment methodology introduced in Chapter 3 
could be used to support the calculation of the benefit-cost ratio. 

Recommendation 2: Consider the risk from climate change in investment analyses. 

This study and other publications (Forzieri et al., 2018; Karagiannis et al., 2017b) have 
demonstrated that climate change will change (increase or decrease) the risk posed from 
meteorological  hazards  to  critical  infrastructure  in  Europe.  Risk  assessments  and  cost-
benefit  analyses  are  often  part  of  the  decision-making  process  when  new  infrastructure 
investments are contemplated. If these studies do not consider climate change, they will 

31 

likely  be  based  on  under-  or  over-estimated  assumptions  about  the  frequency  and 
intensity of meteorological hazards critical infrastructure assets may be exposed to. The 
methodology  presented  in  Chapter  3  can  be  used  to  support  such  analyses  for  storms, 
whereas  the  approach  described  in  Karagiannis  et  al.  (2017b)  can  be  used  for  similar 
analyses for floods. 

Recommendation 3: Consider events with recurrence intervals longer than 100 years in 
hazard mitigation and emergency planning. 

Although  disaster  risk  assessments  will  typically  analyze  several  storm  scenarios,  the 
100-year event is considered the standard reference in hazard mitigation and emergency 
planning for many meteorological hazards. The analysis of the 10-year, 50-year and 100-
year  storm  scenarios  in  this  study  showed  that  the  level  of  risk  would  not  change 
substantially,  largely  because  the  projected  peak  wind  speed  values  would  remain 
relatively  stable.  Therefore,  a  prudent  disaster  risk  management  approach  would  be  to 
consider  events  with  recurrence  intervals  longer  than  100  years  when  designing  hazard 
mitigation  measures.  It  is  also  considered  a  wise  emergency  management  practice  to 
develop  emergency  operations  plans  based  on  the  worst-case  scenario  according  to 
historical data (Perry & Lindell, 2007). 

Recommendation 4: Standardize mutual aid resources 

The  lack  of  direct  interoperability  with  mutual  aid  resources  and  non-traditional 
responders is a notorious disaster response challenge (US Fire Administration, 2015). As 
natural  disasters  overwhelm  emergency  response  and  recovery  capabilities,  TSOs/DSOs 
turn  to  neighboring  utilities 
involve  many  diverse 
organizations  and  require  interoperability  among  TSOs,  DSOs,  regulatory  agencies  and 
civil  protection.  As  discussed  in  chapter  2  and  Karagiannis  et  al.  (2017a),  mutual  aid 
responses  come  with  a  wide  range  of  challenges,  including  barriers  to  access, 
coordination and quality. Mutual aid agreements should address several issues, including: 

for  help.  These  operations 

● 

Initiation and termination of international assistance. 

●  Transportation and entry of repair tools, spare parts and telecommunications and 

information technology equipment. 

●  Qualification  and  credentialing  of  personnel.  Engineering  and 

technician 
professions  are  generally  regulated  in  most  EU  Member  States,  and  do  not  fall 
under  the  automatic  recognition  scheme  of  Directive  2005/36/EC  (12).  Directive 
2006/123/EC  (13)  allows  professionals,  including  those  exercising  regulated 
professions  such  as  engineers  and  technicians,  to  provide  services  across  EU 
Member  States.  Nevertheless,  issues  such  as  liability  and  malpractice  insurance 
may not be addressed.  

●  Coordination with mutual aid crews and civil protection agencies. 

●  Telecommunications, including the use of radios. 

One possible solution could be the standardization of repair crews and similar resources 
under  the  Union  Civil  Protection  Mechanism.  Resource  typing  has  proven  to  facilitate 
efficient  and  effective  deployment  of  resources  in  the  emergency  management  and 
fire/rescue communities (FEMA, 2017; Mutual Aid System Task Force, Fairfax, VA, 2006). 
In  the  framework  of  the  Union  Civil  Protection  Mechanism,  Modules  are  self-sufficient, 
standardized  task  forces,  capable  of  being  deployed  overseas  at  a  short  notice  to 
augment the response capabilities of a disaster-affected country (14). Several other types 

(12)  Directive  2005/36/EC  of  the  European  Parliament  and  of  the  Council  of  7  September  2005  on  the 

recognition of professional qualifications (OJ L 255, 30.9.2005, p. 22-142) 

(13)  Directive 2006/123/EC of the European Parliament and of the Council of 12 December 2006 on services in 

the internal market (OJ L 376, 27.12.2006, p. 36-68) 

(14)  Commission  Implementing  Decision  of  16  October  2014  laying  down  rules  for  the  implementation  of 
Decision  No  1313/2013/EU  of  the  European  Parliament  and  of  the  Council  on  a  Union  Civil  Protection 
Mechanism and repealing Commission Decisions 2004/277/EC, Euratom and 2007/606/EC, Euratom (OJ L 
320, 6.11.2014, p. 1-45) 

32 

                                           
of resources, including experts and equipment, can be requested via DG ECHO’s Common 
Emergency Communication and Information System  (CECIS). The system has proven to 
be versatile enough to  manage various types  of international assistance, including large 
power  generators  (DG  ECHO,  2012).  Therefore,  mutual  aid  resources  for  electric  power 
companies  could  be  standardized  under  the  UCPM.  Analyzing  this  policy,  including  its 
effects, costs and feasibility, should be a joint endeavor of ENTSO-E and DG ECHO, while 
the JRC could have a supporting role. 

Recommendation 5: Plan for surge capabilities and external contractors. 

When disaster strikes, electric power companies often rely on contractors to augment 
their emergency repair and restoration capability on short notice. TSOs and DSOs should 
prepare for these operations before disaster strikes. Planning and preparedness should 
address resource management, documentation, finance, administration, insurance and 
other topics. Power companies should consider entering into standby contracts with local 
and regional contractors to minimize delays and administrative challenges. Staff need to 
be trained according to their duties and responsibilities. Last, these procedures need to 
be exercised together with external contractors, with a view to improving coordination 
and communications, and clarifying roles and responsibilities. 

33 

 
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37 

 
 
List of abbreviations and definitions 

AGL 

DG 

DSO 

ECHO 

Above Ground Level 

Directorate-General 

Distribution System Operator 

Directorate-General  for  European  Civil  Protection  and  Humanitarian  Aid 
Operations 

ENTSO-E 

European Network of Transmission System Operators for Electricity 

FEMA 

GCM 

GDP  

IAFC 

IEC 

NPP 

Federal Emergency Management Agency 

Global Circulation Model 

Gross Domestic Product 

International Association of Fire Chiefs 

International Electrotechnical Commission 

Nuclear Power Plant 

OSCE 

Organization for Security and Cooperation in Europe 

RCM 

RCP 

TSO 

Regional Climate Model 

Representative Concentration Pathways 

Transmission System Operator 

UCPM 

Union Civil Protection Mechanism 

38 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
List of figures 

Figure 1. Breakdown of storms reviewed in this study, by type .................................. 5 

Figure 2. Wind profiles on different terrain types ...................................................... 8 

Figure 3. Wind pressure on structure surfaces (we1 and we2 denote respectively the 
positive pressure exerted on the windward side and the negative pressure exerted on the 
leeward side of the structure) ................................................................................. 9 

Figure 4. Damage to the French transmission grid resulting from cyclones Lothar and 
Martin: towers destroyed by wind and debris (left axis), and circuits out of order (right 
axis) ...................................................................................................................10 

Figure 5. Snapped utility poles in St. Thomas, U.S. Virgin Islands, after Hurricane Irma
 ..........................................................................................................................11 

Figure 6. Loss estimation approach .......................................................................17 

Figure 7. Map of the transmission grid on and around Galorndon (white: substations and 
overhead lines; black: underground lines) ...............................................................23 

Figure 8. Probability of loss of power to 225 kV substations under the 100-year storm 25 

Figure 9. Expected economic losses from the 10-year, 50-year and 100-year storm 
across five sequential and partially overlapping time periods ......................................25 

Figure 10. Total business cost for different power outage scenarios (note the y-axis 
scale change for 7 and 10-day outage)....................................................................27 

Figure 11. Change in the level of storm risk to critical electric infrastructure in the case 
study area ...........................................................................................................28 

39 

 
List of tables 

Table 1. Overview of damage types and storm impacts on the resilience of the power 
grid ..................................................................................................................... 7 

Table 2. Standards applying to the design of transmission towers under wind action ..... 9 

Table 3. Recommendations from Karagiannis et al. (2017a) supported by the findings of 
Chapter 2 of this report .........................................................................................31 

Table 4. List of EURO-CORDEX simulations used in this study ...................................16 

Table 5. Roughness length values used in this study and corresponding landscape types
 ..........................................................................................................................18 

Table 6. Estimation of the wind speed [m/s] for a transmission tower with a height of 
18m AGL, located in a residential area with a surface roughness of 0.4m ....................22 

Table 7. Storms reviewed in this study, by continent ...............................................41 

40 

 
Annex 

This  annex  includes  a  list  of  the  storms  reviewed  in  this  study,  listed  in  Table  7  by 
continent. 

Table 7. Storms reviewed in this study, by continent 

Date 

Country/Region 

Storm 
type/name 

Peak wind 
speed 
(km/h) 

Europe 

1 September 1996 

25 December 1999 

28 December 1999 

France 

France 

France 

7-9 January 2005 

Estonia 

7-9 January 2005 

Sweden 

Wind storm 

116-179 

Cyclone Lothar 

150-250 

Cyclone Martin 

Cyclone Gudrun 
(Erwin) 

Cyclone Gudrun 
(Erwin) 

26 November 2005 

Western Europe 

Snow storm 

17 January 2007 

UK 

Cyclone Kyrill 

18 January 2007 

Germany 

Cyclone Kyrill 

18 January 2007 

Czech Republic 

Cyclone Kyrill 

23-27 January 2009 

France 

Cyclone Klaus 

26 February-7 March 
2010 

Portugal 

Cyclone Xynthia 

26 December 2011 

Finland 

Tapani Storm 

108 

(Cyclone Dagmar) 

18-24 January 2013 

Portugal 

Cyclone Gong 

1 October 2013 

Finland 

Cyclone Eino 

13 December 2013 

Sweden 

Cyclone Ivar 

13 December 2013 

Estonia 

Cyclone Ivar 

1 January 2018 

3 January 2018 

France 

France 

Storm Carmen 

Storm Eleanor 

18 January 2018 

Germany 

Cyclone Friederike 

140 

120 

115 

130 

140 

203 

41 

200 

135 

165 

65 

160 

202 

200 

198 

228 

 
 
North America 

21 September 1989 

4-10 January 1998 

US 

US 

Hurricane Hugo 

260 

Ice storm 

4-10 January 1998 

Canada 

Ice storm 

25 September 1998 

30 January 2002 

22 July 2003 

29 August 2005 

24 September 2005  

22-25 October 2005 

14 December 2006 

1-4 December 2007 

2 September 2008 

13 September 2008 

11-12 December 2008 

27-28 January 2009 

25 July 2010 

26 August 2011 

28 August 2012 

29-30 October 2012 

22 December 2013 

US 

US 

US 

US 

US 

US 

US 

US 

US 

US 

US 

US 

US 

US 

US 

US 

US 

Hurricane Georges 

175 

Snow storm 

Wind storm 

Hurricane Katrina 

Hurricane Rita 

Hurricane Wilma 

Wind storm 

Wind Storm 

Hurricane Gustav 

Hurricane Ike 

Winter storm 

Ice storm 

144 

201 

290 

190 

183 

235 

340 

177 

Wind storm 

96-112 

Hurricane Irene 

Hurricane Isaac 

Hurricane Sandy 

Ice storm 

22 December 2013 

Canada 

Ice storm 

29 August 2015 

17 November 2015 

25 February 2017 

25 August 2017 

10 September 2017 

20 September 2017 

US 

US 

US 

US 

US 

US 

Wind storm 

Wind storm 

Wind storm 

Hurricane Harvey 

Hurricane Irma 

Hurricane Maria 

42 

230 

128 

155 

145 

122 

96.5 

120 

215 

250 

 
 
 
 
 
 
 
Oceania 

10 January 1997 

New Zealand 

Cyclone Drena 

28-29 February 2004 

New Zealand 

Cyclone Ivy 

26 January 2011 

New Zealand 

Cyclone Wilma 

2 February 2011 

New Zealand and 
Australia 

Cyclone Yasi 

11-15 December 2012 

Samoa 

Cyclone Evan 

7-17 March 2014 

New Zealand 

Cyclone Lusi 

2-14 March 2015 

New Zealand 

Cyclone Pam 

28 March 2017 

New Zealand and 
Australia 

Cyclone Debbie 

9-14 April 2017 

New Zealand 

Cyclone Cook  

8 November 2017 

New Zealand 

Storm with rain 
and snow 

5 January 2018 

New Zealand 

Wind storm 

24 January 2008 

China 

Winter storm 

Asia 

130 

124 

260 

290 

210 

130 

320 

263 

200 

154 

128 

43 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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